Multimedia content offers additional context for recommender systems to better understand user interests. Existing studies on multimodal recommendation primarily focus on constructing item-item semantic graphs. However, most of these methods capture only shallow semantic structures based on feature similarity and struggle to model more complex or cross-entity semantic relationships (e.g., user-item). Moreover, in these methods, collaborative signals often dominate and suppress semantic knowledge, which limits its role in representation learning. To address these issues, we propose SCALE, a novel framework that combines subspace-aware graph construction and contrastive alignment for multimodal recommendation with large language models. Specifically, we first use large language models and encoders to extract user and item features. Following the subspace clustering assumption, we apply the Orthogonal Matching Pursuit algorithm to mine complex semantic structures within the item-item, user-user, and user-item spaces, and integrate them into a unified semantic graph. We then perform graph convolution on both the semantic and interaction graphs, and aggregate the results for recommendation. Furthermore, contrastive losses are employed to enhance semantic fusion and alignment. Extensive experiments on five real-world datasets demonstrate that SCALE significantly outperforms state-of-the-art multimodal recommendation models, highlighting its effectiveness in modeling complex relationships and integrating semantic knowledge with collaborative signals.

Sequential recommendation has emerged as a fundamental task in various domains, aiming to predict a user's next interaction based on historical behavior. Recent advances in deep sequence models, particularly Transformer-based architectures and the more recent Mamba, have substantially pushed the boundaries of sequential modeling performance. However, existing methods still face two critical challenges. First, many current approaches overlook the hierarchical structures and high-order dependencies among items, typically restricting representation learning to conventional Euclidean spaces, which limits their capacity to capture complex relational information. Second, although Mamba excels at long-range dependency modeling, its reliance on static Feed-Forward Networks (FFNs) hinders its ability to dynamically adapt to evolving user preferences across diverse contexts. To address these limitations, we propose a Hyperbolic-Enhanced Mixture-of-Experts Mamba recommender (HM2Rec) for sequential recommendation. HM2Rec first encodes user-item relationships through hyperbolic graph convolution to exploit hierarchical structure more effectively. Then, a Variational Graph Auto-Encoder (VGAE) is employed to reconstruct node embeddings, improving structural robustness. To further enhance sequential modeling, we integrate Rotary Positional Encoding (RoPE) into Mamba to better capture relative position dependencies, and replace the FFN with Mixture-of-Expert (MOE) module, enabling dynamic and personalized expert selection for each token. Our extensive experiments on four widely-used public datasets demonstrate that HM2Rec outperforms several advanced baseline models.

Vision-Language Models (VLMs) have demonstrated remarkable capabilities in visual classification tasks. Existing methods for enhancing VLMs on this task often rely heavily on direct category-to-image matching, which limits generalization and results in suboptimal performance. In addition, these methods provide no understanding of why a specific category is chosen. To address these limitations, we introduce a new deliberative visual classification task that decomposes the classification process into multiple deliberative steps and leverages Large Language Models (LLMs) to perform explicit reasoning before the final decision. Specifically, we propose a Retrieval-driven Reasoning model (RdR) with two components, i.e., retrieval database construction and deliberative category prediction. The first component leverages LLMs to extract category-relevant descriptors and constructs a retrieval database for effective image–descriptor matching. The second component facilitates multiple deliberative steps and performs explicit reasoning based on the retrieved descriptors to augment the category prediction. Extensive experiments on multiple datasets demonstrate that RdR consistently outperforms strong baselines, highlighting its robustness and generalization ability.

Accurate conversion rate (CVR) prediction is critical for recommender systems to capture user conversion intent and increase platform revenues. Traditional CVR models commonly suffer from sample selection bias (SSB) and data sparsity (DS), which has led to the adoption of click-through & conversion rate (CTCVR) multi-task learning frameworks to alleviate these issues. However, existing methods implicitly mislabel some unclicked samples with genuine conversion potential as negatives, thereby exacerbating the false negative sample (FNS) problem. To address this, we propose IdeFN, a multi‑task CVR framework that identifies false negatives in the unclicked space to enable CVR prediction across the entire exposure space and leverages CTR as an auxiliary task for shared‑parameter learning. Specifically, IdeFN consists of two main components, i.e., relaxed partial optimal transport (RPOT) module and sample relabeling mechanism (SRM). The former estimates the soft matching strengths between unclicked samples and positive samples under a relaxed partial optimal transport formulation, establishing corresponding relationships between these samples. The latter adaptively re-labels the unclicked samples according to the derived matching strengths, without relying on static or heuristic thresholds, thus enhancing the reliability of the generated pseudo-labels. Experimental results demonstrate that IdeFN effectively mitigates the FNS problem, achieving substantial improvements in CVR prediction accuracy.

Contrastive learning has emerged as a promising paradigm by inherently generating self-supervised signals and uncovering latent patterns from interaction data to enhance recommendation performance. However, most current graph contrastive learning-based recommendation methods rely on random augmentation strategies,which may disrupt graph structural information and compromise model robustness. In addition, long-tail items suffer from insufficient exposure, making it difficult to learn high-quality feature rep- resentations, ultimately degrading recommendation effectiveness.To overcome these limitations, this paper presents DDGCL, a dual diffusion-based graph contrastive learning method. A contrastive view optimization module is designed, which employs singular value decomposition to perform low-rank approximation on the interaction graph, efficiently extracting global structural features while accelerating the diffusion process. The diffusion model then performs noise addition and denoising on this basis to generate contrastive views that preserve graph structural information. In addition, a method for embedding augmentation designed for long-tail items is proposed. This module utilizes a conditional diffusion model, where global graph information serves as conditional con- straints to guide the denoising process of long-tail items, thereby improving their representation learning. A comprehensive evaluation on multiple public benchmark datasets demonstrates that DDGCL significantly outperforms various baseline models, validating the effectiveness of the proposed approach.

Sequential recommendation aims to predict the next user interaction by modeling historical behavior sequences. Recently diffusion models (DMs) have emerged as a promising generative approach due to their robustness and capacity for uncertainty modeling. However, existing diffusion-based recommendation approaches still encounter two major challenges: sample drift during the noise injection process, which compromises the stability of generation; and limited adaptability to noisy data, which hampers the effectiveness of personalized recommendations. To address these issues, we propose GSDiffRec, a novel generative sequential recommendation approach that integrates two core modules: (i) Semantic-Targeted Guidance Module (STG) built upon an enhanced Transformer backbone equipped with shaped attention and convolutional components to improve representational efficiency and modeling capacity; and (ii) Geodesic Diffusion Module (GDM) enforcing manifold constraints through geodesic random walks, thereby preserving geometric consistency and enhancing denoising stability throughout the diffusion process. Extensive experiments on two public Amazon datasets demonstrate that GSDiffRec significantly outperforms a wide range of competitive baselines. Further ablation studies validate the complementary contributions and effectiveness of the GDM and STG modules.

Abnormal user detection has been a critical and widely studied research problem in social networks since these users can create significant risks to platform security and privacy leakage. Currently, graph-based models are commonly used for exploring the structured social network data and temporally dynamic user interactions, leading to significant advances in dynamic heterogeneous graph-based abnormal user detection. However, most existing approaches are correlation-driven and lack the ability to separate stable patterns from transient noise. Furthermore, these methods are highly dependent on inherent labels and fail to detect common few-shot anomalies in social networks. To address these issues, we propose CIFAD, a Causal-Invariant Few-shot Anomaly Detection method that improves few-shot anomaly detection with an active annotation strategy. Specifically, CIFAD first integrates a sparse lagged attention encoder to model multi-relational temporal interactions. Furthermore, it introduces causal-invariant subspace decomposition to disentangle stable causal signals from dynamic environmental noise and improve generalization. Finally, it designs an active annotation strategy based on influence functions and coverage optimization to maximize the utility of limited labels in a closed-loop process. Extensive experiments on multiple real-world social network datasets demonstrate that our method achieves higher accuracy than state-of-the-art methods, validating its robustness in abnormal user detection for social networks.

With the rapid growth of multi-modal content on the Web, robust vision-language models are essential for semantic understanding and classification of web images under diverse and dynamic contexts, supporting Web applications such as multimedia search and recommendation. Prompt learning has proven effective for enhancing vision-language models in semantic image classification tasks. However, previous methods often suffer from poor generalization: the learned prompts tend to overfit the base classes seen during training, leading to poor performance on unseen classes and under distribution shifts. This issue is especially challenging in Web-scale data, where new classes emerge and distributions shift dynamically. To address these limitations, we propose PLIKD, a novel prompt learning method that integrates instance-aware knowledge distillation for robust Web-scale semantic image classification. Specifically, PLIKD introduces an instance-aware knowledge extraction module, which leverages multi-modal large language models through a step-by-step strategy to extract external knowledge for each image instance. To incorporate this extracted knowledge, PLIKD further introduces an instance-aware knowledge distillation module, which consists of two key steps: (1) a dual-teacher strategy for robust and informative knowledge distillation, and (2) fine-grained cross-modal alignment via Smooth and Sparse Optimal Transport. Extensive experiments demonstrate that PLIKD significantly improves generalization to both seen and unseen classes, and remains robust under distribution shifts, outperforming existing state-of-the-art methods on Web-scale semantic image classification.

Multi-modal recommendation has become essential with the rapid expansion of online platforms such as e-commerce and video-sharing applications. In this work, we focus on the Multi-Modal New Item Recommendation (MMNIR) problem, where items with multi-modal content but newly introduced items lack interaction history. The MMNIR problem is particularly challenging in two aspects: (1) a large number of new items are created rapidly over time without any interaction data, (2) not all existing interactions are equally useful, and it is non-trivial to identify informative behaviors from users with similar preferences. However, previous methods fail to identify users with similar preferences and to capture informative behaviors from historical data. Furthermore, conventional models primarily rely on simple co-occurring signals, leading to spurious neighbors and neglecting the informative behaviors of truly similar users with consistent preferences. To fill this gap, we propose Joint Similar User Exploration and Informative Behavior Guidance (SuperG) for solving the MMNIR problem. SuperG first proposes a similar user exploration module to identify users with similar preferences to the target user. Then it incorporates an informative behavior mining module to retrieve informative behaviors from both the target user and similar users' histories to support new item recommendation. Finally, SuperG proposes a behavior-guided hybrid recommendation module to incorporate the retrieved behavioral signals to guide the recommendation of new items. Our empirical study on three real datasets demonstrates that SuperG outperforms the state-of-the-art models under the MMNIR setting.

Web-scale platforms and online services rely on log-based anomaly detection to safeguard availability, latency SLOs, and user experience. In real-world web interactions, system logs often exhibit irregular temporal intervals, bursty densities, and heterogeneous semantics, which pose significant challenges for log anomaly detection. Existing methods such as LSTM and Transformer assume a fixed input window, which conflicts with the inherently irregular nature of system logs. Moreover, most prior works build a single-view representation, overlooking the multi-relational nature of logs. To overcome these challenges, we propose DyLogNet, a dynamic multi-relational graph framework for log anomaly detection. Specifically, this framework constructs a density-aware dynamic graph with variable-length windows, and represents logs from three relational perspectives: temporal co-occurrence, semantic similarity, and anomaly tendency. Next, we design a cross-layer attention mechanism that integrates heterogeneous structures to highlight the most relevant relations and enhance event representations. Furthermore, a cross-snapshot memory injection module updates global memory through a recurrent unit and injects it into current graph representations via an affine transformation, enabling temporal continuity. Experiments on three public log datasets demonstrate that DyLogNet outperforms state-of-the-art methods, especially in few-shot scenarios.

With the rapid development of ubiquitous networks and unmanned devices, air quality monitoring data is increasingly collected via wireless networks from sensors at multiple monitoring stations. However, the complexity of these data introduces significant challenges. Existing spatiotemporal methods for air quality prediction often struggle with issues such as inadequate handling of spatial relationships, difficulties in modeling long-term temporal dependencies, and limited generalization capabilities. To address these challenges, this paper proposes a novel spatiotemporal modeling approach—Spatiotemporal λ-Bernstein Graph Convolutional Network with Transformer for Multi-Site Air Quality Prediction (ST-BernT). This method constructs a graph structure based on spatiotemporal correlations, integrating a Gaussian-weighted adjacency matrix derived from geographic distances with an adjacency matrix capturing the temporal correlations of pollutant concentration time series, thereby precisely modeling spatial dependencies. Subsequently, a dynamic filter adjusted by λ-Bernstein polynomials is proposed to adaptively process spatiotemporal data characterized by the coexistence of low- and high-frequency components on the graph. Furthermore, a hierarchical generative transformer (GPHT) is introduced to enhance the model’s ability to capture long-term temporal patterns, such as periodicity and seasonality, while supporting parallel prediction across multiple sites, significantly improving computational efficiency and accuracy. Experimental results demonstrate that ST-BernT exhibits strong accuracy, adaptability, and generalization capability in multi-site air quality prediction tasks, particularly showing enhanced robustness in large-scale long-term forecasting scenarios.

Hash-based collaborative filtering (Hash-CF) approaches recently employ efficient Hamming distance of learned binary representations to accelerate recommendations. Benefiting from its probabilistic nature, Variational Autoencoder (VAE) enables robust Hash-CF with stronger generalization ability. However, VAE-based Hash-CF still faces two challenging problems: 1) Traditional VAE urges the latent variables of different users (or items) to fit a unified and monotonous prior distribution, and lacks considerations for distinctive characteristics of users (or items). The obtained representations of users and items with slight individual differentiation may further weaken the performance of Hash-CF for subsequent personalized recommendations. 2) Hash-CF under the VAE framework requires discrete optimization on latent Bernoulli distributions, which are discrete and NP-hard to optimize. In this paper, we propose a Dual Discrete Collaborative Filtering (DDCF) approach, including a cluster-enhanced representation generation module and a CNF-enabled discrete optimization module. The former module mainly develops cluster-aware latent space to generate discriminative representations for users or items with significantly different characteristics. The latter module employs Continuous Normalizing Flow (CNF) to achieve discrete optimization on latent Bernoulli distributions steadily and effectively. Extensive experiments conducted on multiple real-world datasets demonstrate the superiority of our DDCF compared with the state-of-art methods in terms of effectiveness and efficiency.

In a collaborative large-small model (CLSM) system, application providers can deploy their small-scale intelligent services at the edge to serve the mobile users. Due to the personalized characteristics of edge small models, an application provider must properly accommodate its users for service and/or data provision. In general, a cost-effective mobile user allocation (MUA) solution must serve the maximum number of users with minimum edge resources by considering mobile users' preferences. However, existing studies of MUA assume that users' and edge servers' available resources are static and neglect the matching between mobile users' preferences and the edge small models' personalization. This is unrealistic in real-world CLSM systems where the mobility of users changes dynamically and the edge servers' available resources differ periodically. Therefore, users must be allocated on the fly to edge servers whose available resources also vary over time. Most existing MUA approaches are impractical in real-world CLSM systems because they are usually designed to address the MUA problem offline, and their performance is usually poor. In this paper, AirMUA, a novel online scheme, is designed, aiming to solve the dynamic MUA (DMUA) problem. AirMUA allocates users to edge servers as they join the system and utilizes system resources dynamically released by user departures. Comprehensive experiments and analyses are conducted to evaluate its performance systematically. Extensive experimental results show the high performance of AirMUA, which ensures its practicality in real-world CLSM systems.

Convolutional Neural Networks (CNN) have been widely adopted in multi-access edge computing system due to their powerful feature extraction and high inference accuracy. Harnessing the heterogeneous computational capacities of end de vices and edge servers, end-edge collaborative inference addresses resource constraints of end devices and latency challenges of real time inference. However, existing studies typically assume that all potential inference models are pre-employed on the server, which may not always align with real-world deployment scenarios. Moreover, the economic incentives of edge service providers are often overlooked in current distributed inference research. To address these issues, we propose an end-edge collaborative inference framework for CNN in multi-access edge computing system, named DisCNN. We first model the interaction between latency-sensitive end devices (EDs) and resource-constrained edge service providers as a Stackelberg game and prove the existence of a Subgame Perfect Equilibrium. Next, we derive the optimal response characteristics of the EDs and develop an efficient algorithm to allocate computation and communication resources to EDs while partitioning the CNN accordingly. To compute the optimal offloading set, we introduce a linear-time algorithm with a bounded approximation ratio. Then, to jointly optimize caching, resource allocation, and pricing, we design an efficient approximate algorithm to compute a near-optimal solution. Finally, extensive simulation results validate the effectiveness of the proposed framework.

Edge computing enables distributed machine learning models to be deployed and trained near the user space. However, the intricate nature of edge computing raises several challenges to distributed machine learning frameworks: 1) inferior convergence arising from non-independent and identically distributed (non-IID) edge data; 2) inefficient structural adaptation, where device dynamism complicates the adjustment of aggregation structure; and 3) reduced training efficiency, as resource heterogeneity and fluctuations create systemic stragglers. To address these issues, a distributed hierarchical model training framework has been proposed by considering the dynamic aggregation structure and frequency in this paper. This framework designs an Edge Aggregation Structure and Frequency method, namely EASF, for distributed model training in heterogeneous edge computing environments. First, a dynamic distributed aggregation structure method is formulated to consider various data distribution patterns. This method constructs and modifies the aggregation structure in a distributed manner to adapt to variations in working edge devices. Second, a self-adapted aggregation frequency method and a timeout abandonment mechanism are proposed to allow each node to update its aggregation frequency adaptively. Lastly, a theoretical analysis demonstrates the convergence property of the EASF method in dynamic environments. Extensive experiments have been conducted on a set of open testbeds. Results show that the EASF significantly improves the efficiency and accuracy of hierarchical model training in heterogeneous edge computing.

Content recommender systems (RSs), which recommend content (e.g., news, videos) to users, can inadvertently facilitate the spread of misinformation (e.g., fake news, inaccurate content) due to their ignorance of content credibility. The widespread misinformation can lead to serious consequences, including public confusion and social unrest. Despite the pressing need to mitigate misinformation in content RSs, only a few studies have attempted to tackle this. Moreover, they generally require retraining the entire model from scratch, which is time-consuming and impractical for real-world applications. To this end, we propose MisEraser , a novel misinformation unlearning framework that effectively mitigates misinformation in content RSs without retraining. Specifically, MisEraser consists of three key components: (1) A misinformation disentanglement network , which effectively separates misinformation-specific information from item content while preserving misinformation-free information to maintain recommendation accuracy; (2) A misinformation fusion network , which fuses misinformation-specific signals from both users’ interaction sequences and the entire misinformation item set to enhance the comprehensiveness of misinformation unlearning; and (3) A misinformation erasing network , which effectively removes the fused misinformation-specific signals from trained recommendation models, enabling them to generate misinformation-suppressed and more responsible recommendations. Extensive experiments demonstrate the effectiveness of MisEraser in mitigating misinformation while maintaining high recommendation accuracy and efficiency.

API recommendation is a crucial task in code intelligence, aiming to suggest suitable APIs for programming queries. Recent efforts have integrated Large Language Models (LLMs) into this task. However, these methods overlook the compatibility between recommended APIs and fail to fully utilize the factual knowledge of APIs. Moreover, these prompting-only methods are limited by the insufficient domain-specific knowledge of LLMs. In this article, we propose a novel fine-tuning method, KDRAR, designed to leverage knowledge-driven reasoning with LLMs for compatible and interpretable API recommendation. To fully utilize the factual knowledge, we introduce a dual matching strategy that leverages both function descriptions and keyword matching to retrieve candidate APIs. To handle compatibility, we translate compatibility information into descriptive knowledge, which is integrated into the recommendation process. Furthermore, we adopt a distilled fine-tuning strategy: a student LLM is trained via distillation from a teacher LLM to perform step-by-step reasoning for enhanced recommendation and explanation. By considering both function matching and compatibility information, the knowledge-driven reasoning not only improves API recommendation accuracy but also provides reasonable explanations for the recommendations. Experimental results show that our method significantly outperforms baseline methods on API recommendation tasks across multiple API domains.

This is an erratum for the article “Knowledge-Driven Reasoning for Compatible and Interpretable API Recommendation via Teacher LLM Distillation” published in ACM Trans. Inf. Syst. 44, 1, Article 27 (December 2025), 30 pages.

Traditional methods for ensuring security and privacy face challenges in safeguarding multimedia data within the IoT-edge continuum, as their significant computational demands render them unsuitable for IoT devices with limited resources. Next, we find that the federated learning techique can naturally adapt to edge frameworks and provide effective data security and privacy protection. In this article, we propose FLiForest, an innovative anomaly detection approach that integrates federated learning with the isolation forest algorithm, tailored for the IoT-edge continuum. Specifically, our method designs a three-stage process, including data collection and sampling, model training, and data testing, to joint-train an isolation forest among clients and edge servers. FLiForest facilitates decentralized model training across IoT devices, enhancing data privacy and reducing computational burden, without necessitating the exchange of multimedia data. Through extensive experiments on a variety of multimedia datasets, the efficacy of our method is benchmarked against the state-of-the-art anomaly detection methods, showcasing its superior detection accuracy and robustness in ensuring data privacy and security.

Deep Reinforcement Learning (DRL) has garnered significant attention as a promising approach for developing intelligent and adaptive recommender systems. This paradigm is especially well-suited for recommendation scenarios characterized by dynamic user environments, temporally delayed feedback signals, and continuously shifting user preferences. However, deploying DRL within recommendation scenarios introduces a range of intricate challenges. These include the design of meaningful and task-aligned reward functions, effective navigation of vast and complex action spaces, and the need to maintain sample efficiency in data-sparse environments. Ensuring robust and stable training dynamics adds further difficulty. This special issue brings together a diverse collection of cutting-edge research that addresses these pressing challenges, showcasing advances that move the field toward more adaptive, robust, and personalized recommendation systems grounded in reinforcement learning.

Federated learning (FL) enables edge devices to collaboratively train a global model without sharing raw data. In the edge environment, resource constraints hinder efficient training and aggregation of FL. Although prior studies have established model pruning as a practical strategy to reduce resource demand, the parameter symmetry problem (i.e., permutation-equivalent parameter orderings in neural networks) remains underexplored. Without addressing this symmetry, pruning on heterogeneous clients results in aggregation mismatches and degraded accuracy. In this paper, we propose Attention-guided One-shot Pruning for Federated Learning (FedAOP) to address these challenges. First, we design an attention module that integrates spatial and channel attention to highlight critical spatial responses and evaluate channel importance. Then, leveraging these importance scores, we propose an attentive pruning algorithm to generate client-specific models, thereby reducing resource consumption. Furthermore, we introduce an aggregation algorithm with attention matching, thereby mitigating the adverse effects of parameter symmetry under heterogeneous pruning. We implementFedAOPon a benchmark FL platform. The experimental results on benchmark datasets demonstrate thatFedAOPconsistently outperforms state-of-the-art baselines by up to 11.3% in accuracy while reducing the average model footprint by 32%.

Graph Neural Networks (GNNs) are widely applied on graph-level tasks, such as node classification, link prediction and graph generation. Existing GNNs mostly adopt a message-passing mechanism to aggregate node information with their neighbors, which often makes node information similar after rounds of aggregations and leads to oversmoothing. Although recent works have made improvements by combining different message aggregation methods or introducing semantic encodings as priors, these message-passing based GNNs still fail to combat oversmoothing after multiple iterations of node aggregation. Besides, the feature extraction ability of these methods is restricted because of the graph sparsity that hinders the aggregation of node information. To deal with the above two issues, we propose Neighborhood-based and Label-enhanced Graph Transformer (NLGT), a novel and effective framework for graph learning. Specifically, we present a label-enhanced feature fusion mechanism that integrate the shallow node features and label embeddings as enhanced features. Moreover, we design a neighborhood-based mask attention mechanism to alleviate the negative effects caused by the sparsity of the graph. In the predicting stage, we aggregate the prediction results from multiple sampled sub-graphs and apply voting mechanisms to enhance the accuracy and robustness of our framework. Finally, extensive experiments are conducted on four open benchmark datasets, which demonstrate the effectiveness and robustness of our proposed framework compared with existing state-of-the-art methods.

Graph Contrastive Learning (GCL), as a primary paradigm of graph self-supervised learning, spurs a fruitful line of research in tackling the data sparsity issue by maximizing the consistency of user/item embeddings between different augmented views with random perturbations. However, diversity, as a crucial metric for recommendation performance and user satisfaction, has received rather little attention. In fact, there exists a challenging dilemma in balancing accuracy and diversity. To address these issues, we propose a new Graph Contrastive Learning (DivGCL) model for diversifying recommendations. Inspired by the excellence of the determinant point process (DPP), DivGCL adopts a DPP likelihood-based loss function to achieve an ideal trade-off between diversity and accuracy, optimizing it jointly with the advanced Gaussian noise-augmented GCL objective. Extensive experiments on four popular datasets demonstrate that DivGCL surpasses existing approaches in balancing accuracy and diversity, with an improvement of 23.47% at T@20 (abbreviation for trade-off metric) on ML-1M.

Sequential recommendation aims to capture the temporal dependencies of items in a user's historical interactions and make recommendations based on this. Previous generative methods addressed the issue of data not directly reflecting user preference uncertainty by modeling the distribution of latent item representations. Diffusion model (DM)-based methods have achieved significant success due to their high-quality generation and stable training. However, they lack satisfactory user sequence representations to guide the generation process, impacting recommendation performance. Moreover, these methods overlook the drawback of slow inference speed, severely limiting their practical value. To obtain effective generative guidance signals and accelerate the recommendation process, we propose DAE4Rec. In this approach, a Graph Auto-Encoder (GAE) is used to obtain interpretable item node representations, revealing global transitions of items that previous methods struggled to uncover. Then, we use it to construct a generative guidance signal with lower coupling and variance for the diffusion model. Additionally, by employing a non-Markov chain derived from the forward diffusion process, it is the first to implement a 'skip-step' reverse process in diffusion model-based methods. And a creatively designed compensator is used to bridge the performance gap caused by 'skip-step'. Extensive experiments on three real-world datasets demonstrate that DAE4Rec outperforms other state-of-the-art generative sequential recommenders.

Time series forecasting has significant applications across various domains, including industry, agriculture, and finance. Transformer-based models have shown significant promise in enhancing time series forecasting over the past few years. However, existing methods struggle to simultaneously capture local details and global semantics under single-view architectures. They also find it difficult to dynamically adapt to time-varying and multi-scale temporal patterns while accurately modeling the complex, time-varying relationships between multiple variables. To address these challenges, we propose HRCformer, a novel Transformer-based framework that introduces two key innovations: the Hierarchical Recursive Interaction Convolution (HRIC) and the Triad Adaptive Recalibration Module (TARM). HRIC achieves joint modeling of fine-grained short-term fluctuations and high-order cross-period dependencies in time series by integrating Divide-and-Process Convolution for local processing with Recursive Channel Interaction Convolution for global processing. TARM further enhances dynamic modeling via Dynamic Variance Attention, which amplifies critical temporal deviations through 3D attention, and the Adaptive Multivariate Recalibration, which uses a two-layer fully connected network with nonlinear activation to learn the dynamic relationships between channels, suppresses noise, and emphasizes informative multivariate interactions. Comprehensive experiments conducted on seven real-world datasets highlight the superiority of HRCformer compared to prior state-of-the-art methods.

Nowadays, domain adaptation techniques have been widely investigated for knowledge sharing from labeled source domain to unlabeled target domain. However, target domain may include some data samples that belong to unknown categories in real-world scenarios. Moreover, the target domain cannot access the source data samples due to privacy-preserving restrictions. In this paper, we focus on the source-free open set domain adaptation problem which includes two main challenges, i.e., how to distinguish known and unknown target samples and how to exploit useful source information to provide trustworthy pseudo labels for known target samples. Existing approaches that directly apply conventional domain alignment methods could lead to sample mismatch and misclassification in this scenario. To overcome these issues, we propose a Distinguish Then Exploit model (DTE) with two components, i.e., weight barcode estimation and sparse label assignment. Weight barcode estimation first calculates the marginal probability of target samples via partially unbalanced optimal transport, then quantize barcode results to distinguish unknown target samples. Sparse label assignment utilizes sparse sample-label matching via proximal term to fully exploit useful source information. Our empirically study on several datasets shows that DTE outperforms the state-of-the-art models on tackling the source-free open set domain adaptation problem.

Document-level relation extraction (RE) aims to extract comprehensive correlations between entities and relations from documents. Most of existing works conduct transfer learning on pre-trained language models (PLMs), which allows for richer contextual representation to improve the performance. However, such PLMs-based methods suffer from incorporating structural knowledge, such as entity-entity interactions. Moreover, current works struggle to infer the implicit relations between entities across different sentences, which results in poor prediction. To deal with the above issues, we propose a novel and effective framework, named DocKS-RAG, which introduces extra structural knowledge and semantic information to further enhance the performance of document-level RE. Specifically, we construct a Document-level Knowledge Graph from the observable documentation data to better capture the structural information between entities and relations. Then, a Sentence-level Semantic Retrieval-Augmented Generation mechanism is designed to consider the similarity in different sentences by retrieving the relevant contextual semantic information. Furthermore, we present a hybrid-prompt tuning method on large language models (LLMs) for specific document-level RE tasks. Finally, extensive experiments conducted on two benchmark datasets demonstrate that our proposed framework enhances all the metrics compared with state-of-the-art methods.

Contrastive Learning (CL) has recently achieved significant progress in the field of recommender systems, as it leverages supervision signals from raw data to mitigate the issue of data sparsity. However, most existing methods rely on random or heuristic data augmentation strategies, which often disrupt the intrinsic structural relationships in the graph and introduce spurious connections, leading to suboptimal representations for recommendation tasks. To address these challenges, we propose a novel graph contrastive learning model based on Large Language Model (LLM), named LLMGCL, which leverages LLM for both embedding augmentation and graph augmentation, and incorporates inter-layer contrastive learning to improve recommendation performance. Specifically, the proposed method first designs an LLM-driven embedding augmentation strategy, where item embeddings pre-trained by LLM are aggregated to construct user embeddings, enabling contrastive views to retain richer semantic relationships. Furthermore, to enhance the structural integrity of the graph while minimizing noise, we develop two targeted graph augmentation strategies powered by LLM: one leverages LLM to identify high-confidence user-item interactions from historical behaviors to refine observed preferences, while the other infers potential but unobserved interactions to reinforce graph connectivity. Finally, we design an inter-layer contrastive learning module that aligns representations across different Graph Neural Network layers, effectively mitigating the over-smoothing effect and enhancing feature discrimination. These combined strategies significantly improve recommendation quality, as demonstrated by extensive experiments on public datasets.

The rapid growth of multimedia content has driven the development of recommender systems. Most previous work focuses on uncovering latent relationships among items to learn better representations. However, this approach does not sufficiently account for user affinities, potentially leading to an imbalance in the structure modeling of users and items. Moreover, the sparsity and imbalance of user-item interactions further hinder effective representation learning. To address these challenges, we propose a framework called BLAST, which balances structures and interactions via large language models and optimal transport for multimodal recommendation. Specifically, we utilize large language models to summarize side information and generate user profiles. Based on these profiles, we design an intra- and inter-entity structure balancing module to capture item-item and user-user relationships, integrating these affinities into the final representations. Furthermore, we impose constraints on negative sample selection, augment the training data with false negative items and the optimal transport algorithm, thereby leading to smoother interactions. We evaluate BLAST on three real-world datasets, and the results demonstrate that our method significantly outperforms state-of-the-art baselines, which validates the superiority and effectiveness of BLAST.

Recommender systems play a critical role in many applications by providing personalized recommendations based on user interactions. However, it remains a major challenge to capture complex sequential patterns and address noise in user interaction data. While advanced neural networks have enhanced sequential recommendation by modeling high-order item dependencies, they typically assume that the noisy interaction data as the user's preferred preferences. This assumption can lead to suboptimal recommendation results. We propose a Variational Graph Auto-Encoder driven Graph Enhancement (VGAE-GE) method for robust augmentation in sequential recommendation. Specifically, our method first constructs an item transition graph to capture higher-order interactions and employs a Variational Graph Auto-Encoder (VGAE) to generate latent variable distributions. By utilizing these latent variable distributions for graph reconstruction, we can improve the item representation. Next, we use a Graph Convolutional Network (GCN) to transform these latent variables into embeddings and infer more robust user representations from the updated item embeddings. Finally, we obtain the reconstructed user check-in data, and then use a Mamba-based recommender to make the recommendation process more efficient and the recommendation results more accurate. Extensive experiments on five public datasets demonstrate that our VGAE-GE model improves recommendation performance and robustness.

Sequential recommendation generates embedding representations from historical user-item interactions to recommend the next potential interaction item. Due to the complexity and variability of historical user-item interactions, extracting effective user features is quite challenging. Recent studies have employed sequential networks such as time series networks and Transformers to capture the intricate dependencies and temporal patterns in historical user-item interactions, extracting more effective user features. However, limited by the scarcity and suboptimal quality of data, these methods struggle to capture subtle differences in user sequences, which results in diminished recommendation accuracy. To address the above issue, we propose a contrastive learning framework with LLMs-based view augmentation (CLLMRec), which effectively mines differences in behavioral sequences through sample generation. Specifically, CLLMRec utilizes LLMs (Large Language Models) to augment views and expand user behavior sequence representations, providing high-quality positive and negative samples. Subsequently, CLLMRec employs the augmented views for effective contrastive learning, capturing subtle differences in behavioral sequences to suppress interference from irrelevant noise. Experimental results on three public datasets demonstrate that the proposed method outperforms state-of-the-art baseline models, and significantly enhances recommendation performance.

As machine learning models become widely deployed in data-driven applications, ensuring compliance with the 'right to be forgotten' as required by many privacy regulations is vital for safeguarding user privacy. To forget the given data, existing re-labeling based unlearning methods employ a single-step adjustment scheme that revises the decision boundaries in one re-labeling phase. However, such single-step approaches lead to coarse-grained changes in decision boundaries among the remaining classes and impose adverse effects on the model utility. To address these limitations, we propose 'Self-Unlearning with Layered Iteration (SULI),' a novel unlearning approach that introduces a layered iteration strategy to re-label the forgetting data iteratively and refine the decision boundaries progressively. We further develop a 'Selective Probability Adjustment (SPA)' technique, which uses a soft-label mechanism to promote smoother decision-boundary transitions. Comprehensive experiments on three benchmark datasets demonstrate that SULI achieves superior performance in effectiveness, efficiency, and privacy compared to the state-of-the-art baselines in both class-wise and instance-wise unlearning scenarios. The source code is released at https://github.com/Hongyi-Lyu-MQ/SULI.

Graph anomaly detection (GAD), with its ability to accurately identify anomalous patterns in graph data, plays a vital role in areas such as network security, social media platforms, and fraud detection. Graph autoencoder-based methods are widely used for GAD due to their efficiency and effectiveness in capturing complex patterns and learning meaningful representations. However, the above methods are constrained by hardware memory, hindering the detection for large-scale graph data. In this paper, we propose a Memory-Efficient framework for large-scale attributed Graph Anomaly Detection (MEGAD). Specifically, MEGAD first generates node embeddings and then refines them through a lightweight joint optimization model, ensuring minimal memory overhead. The optimized embeddings are subsequently fed into a detector to compute anomaly scores. Extensive experiments demonstrate that our framework achieves comparable accuracy to state-of-the-art methods across multiple datasets while significantly reducing memory consumption on large-scale graphs.

Recent advancements in generative models have positioned them as one of the principal tools for sequential recommendation due to their exceptional sample diversity and generalization capabilities. Among these, diffusion model-based sequential recommenders have achieved remarkable success. However, most existing approaches still face critical challenges, resulting in suboptimal generation quality: (1) They fail to leverage multimodal knowledge for constructing item representations with well-structured distributional characteristics and semantically enriched information; (2) They predominantly rely on discrete diffusion processes, leading to high error accumulation, reduced time efficiency, and constrained controllability in generative sampling. To mitigate these challenges, we propose LSGM4Rec, a novel framework that integrates Large Language Models (LLMs) with advanced multimodal encoding models to establish multimodal fusion embeddings for items. This design ensures distinct distributional characteristics while enabling the incorporation of semantically rich modal features into guidance condition. Furthermore, we pioneer the stochastic differential equations (SDEs) for recommendation, facilitating smooth transitions between data distributions and enabling optimal trade-off between sampling efficiency and generation quality. Extensive experiments on three datasets demonstrate that LSGM4Rec outperforms existing state-of-the-art sequential recommendation methods.

Test-time adaptation (TTA) offers the potential to enhance model generalizability without relying on training data or retraining processes. However, TTA faces challenges under covariate shift, where discrepancies between the distributions of training and testing phases hinder model performance. This limitation stems from the fact that existing methods usually rely heavily on training data and fail to establish a good connection between the model and the marginal distribution of test data, resulting in reduced generalization ability. To mitigate this issue, we introduce a novel self-supervised framework that integrates latent score matching and pseudo-label refinement into the TTA paradigm to enhance the model's perception of the test data distribution. Our approach, Joint Test-time Adaptation with Refined Pseudo-labels and Latent Score Matching, reinterprets a classifier as a score estimator and trains it using pseudo-label refinement. This enables the model to better align with the test distribution through latent score matching, while simultaneously preserving discriminative performance via pseudo-label refinement. Extensive experiments across diverse architectures and benchmarks demonstrate that TAPS consistently outperforms state-of-the-art methods in terms of generalization performance under various distribution shifts.

Semi-Unbalanced Optimal Transport (SemiUOT) shows great promise in matching two probability measures by relaxing one of the marginal constraints. Previous solvers often incorporate an entropy regularization term, which can result in inaccurate matching solutions. To address this issue, we focus on determining the marginal probability distribution of SemiUOT with KL divergence using the proposed Equivalent Transformation Mechanism (ETM) approach. Furthermore, we extend the ETM-based method into exploiting the marginal probability distribution of Unbalanced Optimal Transport (UOT) with KL divergence for validating its generalization. Once the marginal probabilities of UOT/SemiUOT are determined, they can be transformed into a classical Optimal Transport (OT) problem. Moreover, we propose a KKT-Multiplier regularization term combined with Multiplier Regularized Optimal Transport (MROT) to achieve more accurate matching results. We conduct several numerical experiments to demonstrate the effectiveness of our proposed methods in addressing UOT/SemiUOT problems.

Federated learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a shared model without exposing their raw data. However, existing FL research has primarily focused on optimizing learning performance based on the assumption of uniform client participation, with few studies delving into performance fairness under inconsistent client participation, particularly in model-heterogeneous FL environments. In view of this challenge, we propose PHP-FL, a novel model-heterogeneous FL method that explicitly addresses scenarios with varying client participation probabilities to enhance both model accuracy and performance fairness. Specifically, we introduce a Dual-End Aligned ensemble Learning (DEAL) module, where small auxiliary models on clients are used for dual-end knowledge alignment and local ensemble learning, effectively tackling model heterogeneity without a public dataset. Furthermore, to mitigate update conflicts caused by inconsistent participation probabilities, we propose an Importance-driven Selective Parameter Update (ISPU) module, which accurately updates critical local parameters based on training progress. Finally, we implement PHP-FL on a lightweight FL platform with heterogeneous clients across three different client participation patterns. Extensive experiments under heterogeneous settings and diverse client participation patterns demonstrate that PHP-FL achieves state-of-the-art performance in both accuracy and fairness. Our code is available at: https://github.com/Siyuan01/PHP-FL-main.

The long-tail problem in sequential recommender systems stems from imbalanced interaction data, resulting in suboptimal model performance for tail users and items. Recent studies have leveraged head data to enhance tail data for diminish the impact of the long-tail problem. However, these methods often adopt ad-hoc strategies to distinguish between head and tail data, which fails to capture the underlying distributional characteristics and structural properties of each category. Moreover, due to a substantial representational gap exists between head and tail data, head-to-tail enhancement strategies are susceptible to negative transfer, often leading to a decline in overall model performance. To address these issues, we propose a hierarchical partitioning and stepwise enhancement framework, called HPSERec, for long-tailed sequential recommendation. HPSERec partitions the item set into subsets based on a data imbalance metric, assigning an expert network to each subset to capture user-specific local features. Subsequently, we apply knowledge distillation to progressively improve long-tail interest representation, followed by a Sinkhorn optimal transport-based feedback module, which aligns user representations across expert levels through a globally optimal and softly matched mapping. Extensive experiments on three real-world datasets demonstrate that HPSERec consistently outperforms all baseline methods. The implementation code is available at https://anonymous.4open.science/r/HPSERec-2404.

Code summarization aims to generate concise natural language descriptions of source code, helping developers to acquaint with software systems and reduce maintenance costs. Existing code summarization approaches widely employ attention mechanisms to assess the relevance between nodes in the Abstract Syntax Tree (AST), which generates context vectors that reflect the semantics of the source code. However, these approaches solely relying on AST lack the extraction of features at other levels of granularity, such as code tokens and Control Flow Graph (CFG), which suffer from severe semantic gaps when capturing data and control dependencies. To address this issue, we design an enhanced semantic extractor with multi-granularity feature fusion (MGF-ESE) to improve the model capability in comprehending and processing the overall semantics of the code. Specifically, we present a novel AST generation method that, based on controlling the scale of nodes, introduces syntactic description nodes to raise the semantic density of AST feature. Then we perform both local and global encoding of CFG after embedding the statement nodes. Moreover, through a cross-attention mechanism, we fuse code tokens and CFG with AST to enhance the model's capacity to capture both syntactic and structural information from source code. Finally, extensive experiments on two open-source datasets show that MGF-ESE outperforms the state-of-the-arts with higher-quality code summaries on key metrics, including BLEU, METEOR, and ROUGE-L.

Next Point-of-Interest (POI) recommendation has become a crucial task in Location-Based Social Networks (LBSNs), which provide personalized recommendations by predicting the user's next check-in locations. Commonly used models including Recurrent Neural Networks (RNNs) and Graph Convolutional Networks (GCNs) have been widely explored. However, these models face significant challenges, including the difficulty of capturing the hierarchical and tree-like structure of POIs in Euclidean space and the sparsity problem inherent in POI recommendations. To address these challenges, we propose a Hyperbolic Variational Graph Auto-Encoder (HVGAE) for next POI recommendation. Specifically, we utilize a Hyperbolic Graph Convolutional Network (Hyperbolic GCN) to model hierarchical structures and tree-like relationships by converting node embeddings from euclidean space to hyperbolic space. Then we use Variational Graph Auto-Encoder (VGAE) to convert node embeddings to probabilistic distributions, enhancing the capture of deeper latent features and providing a more robust model structure. Furthermore, we combine the Mamba4Rec recommender and Rotary Position Embedding (RoPE) and propose Rotary Position Mamba (RPMamba) to effectively utilize POI embeddings rich in sequential information, which improves the accuracy of the next POI recommendation. Extensive experiments on three public datasets demonstrate the superior performance of the HVGAE model.

In recent years, the maturation of emerging technologies such as Virtual Reality, Digital Twins and Blockchain has accelerated the realization of the metaverse. As a virtual world independent of the real world, the metaverse will provide users with a variety of virtual activities which bring great convenience to society. In addition, the metaverse can facilitate digital twins, which offers transformative possibilities for the industry. Thus, the metaverse has attracted the attention of the industry, and a huge amount of capital is about to be invested. However, the development of the metaverse is still in its infancy and little research has been undertaken so far. We describe the development of the metaverse. Next, we introduce the architecture of the socially beneficial metaverse (SB-Metaverse) and we focus on the technologies that support the operation of SB-Metaverse. In addition, we also present the applications of SB-Metaverse. Finally, we discuss several challenges faced by SB-Metaverse which must be addressed in the future.

Abstract With the explosive development of the Internet of Things (IoT), it is convenient and important to collect health data from medical sensors and smart devices and construct medical knowledge graph. The knowledge graph contributes to investigating the connection between patient and disease, especially for epidemic surveillance. However, it is possible to cause the leakage of sensitive health information due to the untrusted data collector or various malicious attackers. In this paper, we attempt to utilise federated learning to construct a special knowledge graph, that is, individual‐symptom relationship diagram with local differential privacy (LDP‐ISRD), for epidemic risk surveillance, which presents the underlying infectious relationship among individuals. At first, we propose a federated learning‐based framework of LDP‐ISRD by utilising individuals' smart devices in IoT. Then, we leverage locations to determine the connection among individuals in terms of physical contact. Next, we propose a randomised algorithm PrivISRD to implement federated learning‐based LDP‐ISRD, which consists of symptom perturbation and aggregation. Finally, extensive experiments evaluate the impact of various parameters and results demonstrate that LDP‐ISRD has good performance.

In the trend of continuously advancing urban intelligent transport construction, traditional traffic signal control (TSC) struggles to make effective decisions with complex traffic conditions. Although multi-agent deep reinforcement learning (MARL) shows promise in optimizing traffic flow, most existing studies ignore the complex relationships between signal lights and fail to communicate with neighbors effectively. Moreover, the deterministic strategies generated by Q-learning-based methods struggle to be extended to large-scale urban road networks. Therefore, this paper proposes a multi-agent graph-based soft actor-critic (MAGSAC) approach for TSC, which combines graph neural networks with the Soft Actor-Critic (SAC) algorithm and extends it to multi-agent environments to address the TSC problem. Specifically, we employ graph-based networks and attention mechanism to expand the receptive domain of agents, enable environmental information to be shared among agents, and utilize the attention mechanism to filter out unimportant information. The algorithm adheres to the Centralized Training Decentralized Execution (CTDE) paradigm to minimize the non-stationarity of MARL. Finally, a rigorous experimental evaluation was conducted using the CityFlow simulator on both synthetic traffic grids and real-world urban road networks. Experimental results show that MAGSAC outperforms other TSC methods in performance metrics, including average queue length and waiting time, and achieves excellent performance under complex urban traffic conditions.

The convergence of Web 3.0 and Metaverse technologies with Connected Autonomous Vehicles (CAVs) is catalyzing a new era of intelligent vehicular systems, characterized by decentralization, immersive interaction, and enhanced autonomy. This paradigm is especially valuable in scenarios demanding secure peer-to-peer coordination, trustless automations, and seamless integration of physical and virtual environments under real-time constraints. Nonetheless, realizing such intelligent applications introduces critical challenges, including the development of robust decentralized governance and smart contracts, ensuring ultra-low-latency and high-throughput communications in edge computing contexts, achieving seamless digital–physical synchronization via high-fidelity digital twins or Metaverse representations, and guaranteeing scalability and privacy across distributed vehicle networks. This special issue brings together a collection of pioneering research that tackles these multifaceted challenges, showcasing innovations that advance the state of the art toward more secure, responsive, immersive, and decentralized CAV systems empowered by Web 3.0 and Metaverse technologies.

Social media platforms such as Weibo and TikTok have become more influential than traditional media. Sentiment in social media comments reflects users’ attitudes and impacts society, making sentiment analysis (SA) crucial. AI driven models, especially deep-learning models, have achieved excellent results in SA tasks. However, most existing models are not interpretable enough. First, deep learning models have numerous parameters, and their transparency is insufficient. People cannot easily understand how the models extract features from input data and make sentiment judgments. Second, most models lack intuitive explanations. They cannot clearly indicate which words or phrases are key for emotion prediction. Moreover, extracting sentiment factors from comments is challenging because a comment often contains multiple sentiment characteristics. To address these issues, we propose a dual-branch combination network (DCN) for SA of social media short comments, achieving both word-level and sentence-level interpretability. The network includes a key word feature extraction network (KWFEN) and a key word order feature extraction network (KWOFEN). KWFEN uses popular emotional words and SHAP for word-level interpretability. KWOFEN employs position embedding and an attention layer to visualize attention weights for sentence-level interpretability. We validated our method on the public dataset weibo2018 and TSATC. The results show that our method effectively extracts positive and negative sentiment factors, establishing a clear mapping between model inputs and outputs, demonstrating good interpretability performance.

With the widespread adoption of multimedia web APIs (API) in web and mobile applications, a substantial proliferation of these APIs is observed. These APIs have streamlined development processes, reducing both time and costs. Nevertheless, identifying the required APIs from the vast array of options has emerged as a significant challenge. Collaborative filtering (CF)-based recommendation technologies have demonstrated their efficiency in presenting developers with potentially useful APIs. However, these methods often suffer from popularity bias, i.e., popular APIs tend to dominate the recommendation lists. This imbalance in recommendation opportunities among APIs hinders the growth of the multimedia API ecosystem. To mitigate the popularity bias produced by CF-based API recommendation methods, this article introduces a novel debiasing strategy that combines a log postprocessing adjustment (LPA) with determinant point process (DPP). Specifically, the LPA is employed during the prediction phase to yield a more balanced set of candidate APIs. Then, DPP is utilized to generate recommendation lists that are not just relevant but also diverse in terms of API popularity. Experimental results reveal that our proposed method surpasses existing state-of-the-art approaches in multimedia API recommendation, excelling in both accuracy and the capability to mitigate popularity bias effectively.

Mobile edge computing (MEC) considerably enhances the capabilities and performance of connected autonomous vehicles (CAVs) by deploying edge servers (ESs) on roadside units (RSUs) near CAVs, thereby ensuring low-latency services. Given the constrained and costly nature of ES resources (computing, storage, and bandwidth), equitable ES utilization is critical for CAV operations. However, fairness considerations are often overlooked in current budgeted edge server placement (ESP) strategies, potentially worsening resource imbalances and compromising user experience. This paper investigates the fairness-aware budgeted edge server placement (FESP) problem within RSUs, proving its NP-hardness. To address FESP, we first propose FESP-O, an integer programming-based optimal approach for small-scale problems, followed by FESP-APX, an approximation approach for large-scale scenarios that provides near-optimal solutions. We analyze the time complexity and approximation ratio of our proposed algorithms and validate their efficacy through experiments on real-world datasets. Extensive experimental results demonstrate significant performance improvements over baseline and state-of-the-art methods, indicating practical suitability and efficiency.

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 224% compared with state-of-the-art distributed DNN inference schemes.

Recently, machine/deep learning techniques are achieving remarkable success in a variety of intelligent control and management systems, promising to change the future of artificial intelligence (AI) scenarios. However, they still suffer from some intractable difficulty or limitations for model training, such as the out-of-distribution (OOD) issue, in modern smart manufacturing or intelligent transportation systems (ITSs). In this study, we newly design and introduce a deep generative model framework, which seamlessly incorporates the information theoretic learning (ITL) and causal representation learning (CRL) in a dual-generative adversarial network (Dual-GAN) architecture, aiming to enhance the robust OOD generalization in modern machine learning (ML) paradigms. In particular, an ITL- and CRL-enhanced Dual-GAN (ITCRL-DGAN) model is presented, which includes an autoencoder with CRL (AE-CRL) structure to aid the dual-adversarial training with causality-inspired feature representations and a Dual-GAN structure to improve the data augmentation in both feature and data levels. Following a newly designed feature separation strategy, a causal graph is built and improved based on the information theory, which can enhance the causally related factors among the separated core features and further enrich the feature representation with the counterfactual features via interventions based on the refined causal relationships. The ITL is incorporated to improve the extraction of low-dimensional feature representations and learn the optimized causal representations based on the idea of "information flow." A dual-adversarial training mechanism is then developed, which not only enables the generator to expand the boundary of feature distribution in accordance with the optimized feature representation from AE-CRL, but also allows the discriminator to further verify and improve the quality of the augmented data for OOD generalization. Experiment and evaluation results based on an open-source dataset demonstrate the outstanding learning efficiency and classification performance of our proposed model for robust OOD generalization in modern smart applications compared with three baseline methods.

With the growing privacy concerns in recommender systems, recommendation unlearning is getting increasing attention. Existing studies predominantly use training data, i.e., model inputs, as unlearning target. However, attackers can extract private information from the model even if it has not been explicitly encountered during training. We name this unseen information as attribute and treat it as unlearning target. To protect the sensitive attribute of users, Attribute Unlearning (AU) aims to make target attributes indistinguishable. In this article, we focus on a strict but practical setting of AU, namely Post-Training Attribute Unlearning (PoT-AU), where unlearning can only be performed after the training of the recommendation model is completed. To address the PoT-AU problem in recommender systems, we propose a two-component loss function. The first component is distinguishability loss, where we design a distribution-based measurement to make attribute labels indistinguishable from attackers. We further extend this measurement to handle multi-class attribute cases with efficient computational overhead. The second component is regularization loss, where we explore a function-space measurement that effectively maintains recommendation performance compared to parameter-space regularization. We use stochastic gradient descent algorithm to optimize our proposed loss. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposed methods.

Personalized incentives are crucial for boosting user engagement and increasing platform revenues. Many studies have utilized uplift modeling to estimate the conditional average treatment effects (CATEs) of incentives and then allocate them under cost constraints. However, identifying which users should receive such incentives remains challenging, posing a selection bias problem. Traditional representation-based approaches mitigate bias by balancing treated and controlled distributions but overlook local similarity information. Recognizing that similar users should exhibit similar outcomes, it is vital to preserve both intra-similarity within treatment groups and inter-similarity between covariate and representation spaces. Moreover, existing methods primarily focus on CATE accuracy, neglecting the ranking ability vital for uplift modeling. We propose the Similarity Preserved Counterfactual Incentive Effect Estimation ( S-CIEE ) method, comprising three modules: (1) an Intra-Similarity Preservation Regularizer via Fused Gromov-Wasserstein Optimal Transport, (2) an Inter-Similarity Preservation Regularizer using a similarity constraint, and (3) a Rank-Aware Learning module for uplift ranking. Comprehensive experiments on one semi-synthetic and two real-world datasets show that S-CIEE improves both CATE accuracy and uplift modeling performance.

Embedding-based recommender systems rely on historical interactions to model users, which poses challenges for recommending to new users, known as the user cold-start problem. Some approaches incorporate social networks to deduce preferences based on the social circles of cold-start users to solve the problem of sparse features. However, such methods have difficulty distinguishing between superficial correlations and causal relationships in social behaviors, leading to inaccuracies in predicting user preferences. To address the aforementioned issues, we propose the Causal Contrastive Learning Recommendation (C2lRec) framework. Specifically, we causally model the inference of hidden preferences from the feature and historical behavior of warm users and predict user interactions based on such preferences. The counterfactual inference is subsequently performed to intervene and extract interactions from historical behaviors of warm users that influence their preferences, designating as primary causal variables. Additionally, we utilize the primary causal variables from users within the social circle of cold-start users to substitute the missing historical interactions of cold-start users and employ a similar causal modeling approach to uncover hidden preferences as we do with warm users. Finally, we realize causal contrastive learning to enhance the distribution of cold-start users. Extensive experiments conducted on three public datasets demonstrate that the recommendation performance of C2lRec exceeds that of state-of-the-art methods.

In mobile edge computing (MEC) environments, with the increase in the number of web services possessing the same or similar functions, the prediction of nonfunctional quality of service (QoS) indicators turns increasingly vital in satisfying the diverse needs of different users. Despite the significant achievements of QoS prediction approaches such as collaborative filtering (CF), they often fail to obtain informative representations due to complex user-service-time QoS data. Moreover, changing network conditions and overlooked temporal factors lead to reduced accuracy. In response to these challenges, this paper proposes a novel deep learning to hash method for time-aware QoS prediction built on a VQ-VAE (namedPred$_{QoS}$QoS). In the proposedPred$_{QoS}$QoS, inspired by advanced CF approaches, we make QoS predictions at a specific moment for the target user through its similar timeslots. Specifically, we train a codebook to discretize the continuous latent representation obtained by the encoder based on vector quantization (VQ), which is motivated by the generative vector-quantized variational autoencoder (VQ-VAE) model, allowing us to derive compact binary codes representing the QoS data. Then, the similarities between QoS data are determined, helping to make effective and efficient predictions. Furthermore, the time-awarePred$_{QoS}$QoSapproach incorporates a temporal factor by training on multiple QoS data (including the user, service, and time dimensions). As a result, the discrete hash codes (i.e., QoS data representations) derived from the encoder can fully uncover the time factor’s dynamic impact on the QoS data, thereby yielding significantly improved prediction performance. In summary, thePred$_{QoS}$QoSapproach learns compact hash codes for original QoS data while taking time into account, enabling accurate predictions to be produced through similar timeslots for the target users. Finally, comprehensive experiments carried out on the real-world WS-DREAM dataset affirm the exceptional performance of thePred$_{QoS}$QoS.

The usage of heterogeneous resources provisioned by edge nodes can be co-optimized through re-scheduling microservices. Current (re-)scheduling approaches typically treat the task of co-optimization as a single-objective optimization problem, which cannot address the issue of imbalanced usage of heterogeneous resources (e.g., CPU, memory, bandwidth) on a single edge node. More importantly, these approaches are inadequate in handling: (i) the adaptive co-optimization of heterogeneous resources, (ii) the fine-grained construction of micro service dependencies, and (iii) multi-step online mi croservice re-scheduling. To address these challenges, this paper proposes a Dependency-aware Online Microservice re-Scheduling (DOMS) approach. DOMS formulates microservice re-scheduling as a multi-knapsack optimization problem and solves it using a Double Dueling Deep Q-Network (D3QN) with prioritized experience replay. Specifically, an adaptive heterogeneous resources balancing detection algorithm is developed, incorporating a dynamic detection threshold mechanism. A fine-grained microservice performance metrics dependency graph is constructed by capturing causal relationships to represent sequential execution dependency. Based on this graph, a microservice multi-step scheduling partition algorithm is devised. Extensive experiments are conducted upon publicly-available datasets, and evaluation results demonstrate that DOMS outperforms the state-of-the-art techniques with improvements of at least 1.85%, 6.45%, 0.56%, and 3.18% in terms of latency, energy consumption, balance degree, and throughput. These results highlight the effectiveness and superiority of DOMS in maintaining a balanced usage of heterogeneous resources and improving network throughput, while satisfying latency and energy consumption constraints.

Software vulnerabilities pose serious threats to software security. When faced with multiple software vulnerabilities at the same time, it is urgent to determine whether the vulnerabilities are high-risk. Existing vulnerability assessment approaches only learn the mapping relationships between vulnerability descriptions and severity levels, while ignoring the sharing of the same or similar elements between vulnerabilities. Furthermore, solely focusing on vulnerability descriptions fails to accurately characterize the vulnerability behavior. In this paper, we propose a novel vulnerability knowledge graph (KG) to capture the relationships between vulnerabilities. To construct the vulnerability KG automatically, we propose to leverage vulnerability elements extracted from vulnerability descriptions to link different vulnerabilities. Based on the constructed KG, we further propose a novel KG-based vulnerability assessment (VA) approach KG4VA, which precisely finds the similar vulnerability for an encountered vulnerability description by analyzing and matching the elements entities based on the vulnerability KG. The experiment results show that KG4VA outperforms the baselines in almost all metrics (e.g., 3.27%-10.83% accuracy improvements). Moreover, our ablation experiments demonstrate that the vulnerability knowledge graph can indeed offer valuable information for vulnerability assessment.

With the rapid development of the mobile internet, there is an increasing demand for quick access to effective data. Consequently, more research is focusing on data processing and optimization of recommendation systems in edge computing environments. However, in traditional edge computing environments, recommendation systems typically depend on frequent data query interactions among all edge servers to obtain results, which increases time delays. This issue is further exacerbated by the need to process large amounts of data within edge storage systems. To address this challenge, we propose an efficient recommendation method based on data allocation. Specifically, during the data allocation process, we first extract similar features of users, ensuring that the characteristics of some user data align as closely as possible with the overall user feature distribution. Then, using an improved consistent hashing algorithm, we achieve a uniform data layout, allowing the recommendation system to efficiently and accurately provide recommendations by querying data from the nearest edge server to the user. Finally, extensive experiments on real datasets show that our method significantly reduces time delays and improves the accuracy of recommendation results.

Recently, patch-based transformer methods have demonstrated strong effectiveness in time series forecasting. However, the complexity of self-attention imposes demands on memory and compute resources. In addition, though patches can capture comprehensive temporal information while preserving locality, temporal information within patches remains important for time series prediction. The existing methods mainly focus on modeling long-term dependencies across patches, while paying little attention to the short-term dependencies within patches. In this paper, we propose the Global and Local Frequency-domain Network (GLFNet), a novel architecture that efficiently learns global time dependencies and local time relationships in the frequency domain. Specifically, we design a frequency filtering layer to learn the temporal interactions instead of self-attention. Then we devise a dual filtering block consisting of global filter block and local filter block which learns the global dependencies across patches and local dependencies within patches. Experiments on seven benchmark datasets demonstrate that our approach achieve superior performance with improved efficiency.

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods.

By distributing computing and storage resources to edge servers (ESs), edge computing enables service execution closer to requests, alleviating high latency caused by long-distance data transmission. The communication between ESs facilitates collaborative service placement (CSP) on multiple ESs, leading to latency reduction and thereby improving the quality of service. However, the discrepancies in service request distributions among ESs pose challenge to effective CSP, while limited prior knowledge further increases the difficulty in policy formation. Additionally, the delayed awareness of placement strategies among other ESs exacerbates the influence of non-Markovian nature. To overcome above challenges, a leader-follower based multi-agent reinforcement learning for CSP scheme, named LFMCSP, is proposed in this paper. Specifically, a CSP model is formulated, with ESs abstracted as intelligent agents within it. Then, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is utilized to explore potential patterns in complex requests by experience sharing. To achieve transparency of global states for all agents, the leader-follower architecture is integrated into MADDPG. Through rigorous experiments and comparisons with various baseline schemes, LFMCSP demonstrates effectiveness in reducing service execution delays.

Recently, hyperbolic spaces have proven beneficial for service recommendation due to their exponentially growing spatial properties conforming to power-law distributed user-item networks. Among them, the combination of hyperbolic space with graph convolution has achieved great success. However, hyperbolic convolutional models still perform multi-layer convolution in tangent space (Euclidean space), leading to the still inevitable problem of over-smoothing arising from multi-layer convolution. In addition, most of these models randomly draw negative samples from items that users have not interacted with, so that some of the samples obtained may not be well suited for model optimization. To tackle the above challenges, we propose a new Hyperbolic GCN model based on Contrastive Learning and Second-order Reachable sampling for collaborative filtering (HG-CLSR), which improve high quality of representations by exploring the distribution of users and items in hyperbolic space. Specifically, We first introduce a root alignment approach to encourage embeddings to align with the tangent space, thereby reducing distortions during the embedding mapping process in space. Then, we perform contrastive learning in hyperbolic space to motivate the spatial distribution of nodes to better fit the hyperbolic space. Moreover, we sample in the user’s second-order reachable item set, which ensures that the negative sample is more similar to the positive sample, so that the negative node can provide better information for guiding model optimization. Extensive experiments on three real-world datasets demonstrate that the HG-CLSR is significantly superior compared to existing hyperbolic models.

Estimating individual treatment effects (ITE) from observational data is challenging due to the absence of counterfactuals and the treatment selection bias. Prevalent ITE estimation methods tackle these challenges by aligning the treated and controlled distributions in the representational space. However, two critical issues have long been overlooked: (1)Mini-batch sampling sensitivity (MSS) issue, where representation distribution alignment at a mini-batch level is vulnerable to poor sampling cases, such as data imbalance and outliers; (2)Inconsistent representation learning (IRL) issue, where representation learning within a unified backbone network suffers from inconsistent gradient update directions due to the distribution skew between different treatment groups. To resolve these issues, we propose CE-RCFR, a Robust CounterFactual Regression framework for Consensus-Enabled causal effect estimation, including a relaxed distribution discrepancy regularizer (RDDR) module and a consensus-enabled aggregator (CEA) module. Specifically, for the robust representation alignment perspective, RDDR addresses the MSS issue by minimizing unbalanced optimal transport divergence between different treatment groups with a relaxed marginal constraint. For the accurate representation optimization perspective, CEA addresses the IRL issue by resolving the consistent gradient update directions on shared parameters within the backbone network. Extensive experiments demonstrate that CE-RCFR significantly outperforms the state-of-the-art methods in treatment effect estimations.

Federated learning addresses privacy concerns in multimedia recommender systems by enabling collaborative model training without exchanging raw data. However, existing federated recommendation models are mainly based on basic backbones like Matrix Factorization (MF), which are inadequate to capture complex implicit interactions between users and multimedia content. Graph Convolutional Networks (GCNs) offer a promising method by utilizing the information from high-order neighbors, but face challenges in federated settings due to problems such as over-smoothing, data heterogeneity, and elevated communication expenses. To resolve these problems, we propose a Cluster-driven Personalized Federated Recommender System with Interest-aware Graph Convolution Network (CPF-GCN) for multimedia recommendation. CPF-GCN comprises a local interest-aware GCN module that optimizes node representations through subgraph-enhanced adaptive graph convolution operations, mitigating the over-smoothing problem by adaptively extracting information from layers and selectively utilizing high-order connectivity based on user interests. Simultaneously, a cluster-driven aggregation approach at the server significantly reduces communication costs by selectively aggregating models from clusters. The aggregation produces a global model and cluster-level models, combining them with the user's local model allows us to tailor the recommendation model for the user, achieving personalized recommendations. Moreover, we propose an adversarial optimization technique to further augment the robustness of CPF-GCN. Experiments on three datasets demonstrate that CPF-GCN significantly outperforms the state-of-the-art models.

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://github.com/jyzgh/DapperFL.

Sequential recommendation models generate embeddings for items through the analysis of historical user-item interactions and utilize the acquired embeddings to predict user preferences. Despite being effective in revealing personalized preferences for users, these models heavily rely on user-item interactions. However, due to the lack of interaction information, new users face challenges when utilizing sequential recommendation models for predictions, which is recognized as the cold-start problem. Recent studies, while addressing this problem within specific structures, often neglect the compatibility with existing sequential recommendation models, making seamless integration into existing models unfeasible.To address this challenge, we propose CMCLRec, a Cross-Modal Contrastive Learning framework for user cold-start RECommendation. This approach aims to solve the user cold-start problem by customizing inputs for cold-start users that align with the requirements of sequential recommendation models in a cross-modal manner. Specifically, CMCLRec adopts cross-modal contrastive learning to construct a mapping from user features to user-item interactions based on warm user data. It then generates a simulated behavior sequence for each cold-start user in turn for recommendation purposes. In this way, CMCLRec is theoretically compatible with any extant sequential recommendation model. Comprehensive experiments conducted on real-world datasets substantiate that, compared with state-of-the-art baseline models, CMCLRec markedly enhances the performance of conventional sequential recommendation models, particularly for cold-start users.

In the rapidly evolving landscape of the Internet of Things (IoT), concerns about privacy and security have become significant as interconnected devices communicate and collaborate. Fingerprints, serving as unique biometric identifiers, play a crucial role in the authentication and identification processes within this interconnected and exchanged network. However, attention is often directed towards the disclosure of visible fingerprints, overlooking latent fingerprints. This is primarily due to the challenges involved in extracting latent fingerprints, especially those remaining on the adhesive side of tape. Traditional methods physically/chemically peel tape to extract these fingerprints, but cause irreversible damage to the tape, hindering accurate fingerprint extraction. In this context, our investigation reveals that Optical Coherence Tomography (OCT) technology allows for the extraction of high-quality OCT fingerprint images from the adhesive side of tape, yielding precise fingerprint recognition and gender classification results. Concretely, we build a novel type of robotic-arm spectral-domain OCT (SD-OCT), which is software-controlled for the movement of the sample arm, making sample scanning more flexible and efficient. Furthermore, we utilize a deep learning network to perform representation learning on OCT fingerprints for the purpose of gender classification. In the first branch, we input OCT fingerprints into an EfficientNet-B3 network to learn their spatial domain features. Simultaneously, in the second branch, we design a network that utilizes Discrete Cosine Transform (DCT) to extract frequency domain features from OCT fingerprints. Ultimately, we integrate the spatial and frequency domain features extracted from OCT fingerprint images to generate comprehensive features. Therefore, in this paper, we introduce a novel Gender Classification approach based on Spatio-Frequency Feature Fusion of OCT Fingerprint Images (named GenClassOCT-SF). The GenClassOCT-SF involves a robotic-arm SD-OCT system for superior-quality fingerprints acquisition and a deep learning network for spatial and frequency domain feature extraction. The fusion of these features enables highly accurate gender classification. Finally, we conduct gender classification experiments on the collected OCT fingerprint dataset to demonstrate the effectiveness of our proposed method.

Abstract In the past few years, latency‐sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self‐driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across‐domain business task computing on distributed edge servers. With this observation, a trade‐off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low‐latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low‐latency edge server. The experiments and comparison analysis verify the availability of our method.

Abstract Currently, the accurate prediction of social relationships can effectively reduce the decision‐making burden of users in various service platforms. However, in the big data environment, the users' data information used for the relationship prediction is highly fragmented distribution, so it is a non‐trivial challenge to integrate the users' sequence data information from different platforms while preventing sensitive information leakage. To this end, based on the microservice environment, we devise a cross‐platform social relationship prediction approach (CPSRP) to address the above problems. Briefly, the improved Simhash method aggregates similar users into the common bucket. Then the embedding technique converts the users' sparse data information into the low‐dimensional dense continuous feature vectors; the redefined Gated Recurrent Unit (r‐GRU) network and the Multilayer Perceptron (MLP) network are employed to extract the overall temporal sequence features of users. The relationship prediction is finally executed according to the users' sequential features. Extensive experiments are conducted on Epinions, and the experimental results further prove the benefits of our proposal in terms of relationship prediction while protecting users' sensitive information.

Scientific and therapeutic advances in perinatology and neonatology have improved the survival prospects of preterm and extremely-low-birth-weight infants. Infants’ cries are a valuable noninvasive tool for monitoring their neurologic health, especially if they are premature. Automatic acoustic analysis and data mining are employed in this study to determine the discriminative features of preterm and full-term infant cries. The use of machine learning for recognizing sounds in a newborn's cry language has received less attention than previous methods for analyzing the sounds. Moreover, to extract appropriate features from infant cries, adequate knowledge and appropriate signal descriptors are required. Accordingly, to analyze infant cry language, we propose an approach that uses fractal descriptors to extract discriminant features from spectrograms of windowed signals, followed by iterative neighborhood component analysis (iNCA) to select appropriate features. Additionally, the improved deep support vector machine (DeepSVM) is used to classify the infants’ crying types and their meanings. The proposed method is verified using a newborn sound dataset. According to the classification of five types of crying perception based on various characteristics, 98.34% of all crying perceptions have been recognized. Although there are many classes examined, the feature extraction method based on the fractal method and our optimal classification have a much higher diagnostic accuracy compared with similar methods for analyzing baby crying language. The proposed method can overcome many problems associated with analyzing babies’ crying sounds and understanding their language, such as uncertainty and unusual errors in classification.

As the growing trends in technology continue to drive massive transformation throughout the automotive sector, connected and autonomous transportation has become the future vision. Many researchers and practitioners wonder how connected, and autonomous vehicles will affect future transportation. This Special Issue explores some issues in the transition towards autonomous vehicles and their future trends and developments with artificial intelligence (AI) and robotics.

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This paper proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (namedMHDP$_{SVD\_{A}RIMA}$) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show thatMHDP$_{SVD\_{A}RIMA}$approach is effective and efficient in predicting missing healthcare data.

In the social production system, image data are rapidly generated from almost all fields such as factories, hospitals, and transportation, promoting higher requirements for image anomaly detection technologies, including low consumption, higher adaptability, and accuracy. However, existing anomaly detection methods are fragile to heterogeneous image data generated by complex social production systems and tend to require strong computing power and resource support. To address the above problems, a knowledge-driven anomaly detection framework is proposed, in which a local feature enhancement method is designed to strengthen the knowledge representation of the initial features extracted from images. The attention mechanism in deep learning is introduced to adjust the feature attention dynamically according to prior knowledge, which solves the problem of feature loss in the cascade training. To verify the effectiveness of the proposed framework, extensive experiments on social production datasets are conducted. The results demonstrate that our framework outperforms the selected methods on image datasets with different complexity and sample distributions.

Fake news is a prevalent issue in modern society, leading to misinformation, and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awareness and understanding of news, while also effectively mitigating the dissemination of fake news. However, news credibility assessment meets challenges when processing large-scale and constantly growing data, due to insufficient and unreliable labels and standards, and diversity and semantic ambiguity of news contents. Recently, machine learning models have been well developed to address these issues, but suffer from limited effectiveness. A unified framework is also required for them to represent various entities and relationships involved in news stories. This article proposes an entity ontology-based knowledge graph network (EKNet) to leverage knowledge graphs and entity frameworks for news credibility assessment. The model utilizes the information from knowledge graphs by combining entities and relationships from news and knowledge graphs. Experimental results show that the EKNet has advantages in evaluating news credibility over existing methods. Specifically, compared to several strong baselines, the model demonstrates a significant performance improvement in scores across various tasks. Which indicates that using the EKNet to address the challenges in news credibility assessment is highly effective and can conduct better performance for the problem of fake news in the social media environment.

Collaborative filtering (CF) is dedicated to learning the representations of users and items based on interactive data. Regrettably, the lack of fine-grained modeling of interactive motivation makes the model less interpretable. A feasible solution is to combine the disentangling idea with the graph neural network (GNN) and capture different types of interaction relationships by using a message propagation mechanism on the graph of user–item interaction. However, this process typically relies on the disentangling of users’ hidden intents, ignoring the significance of item features to user engagement. This fact leads to the inadequate interpretability of existing models. To make up for the deficiency, this article proposes a new feature-aware disentangled GNN (FDGNN) for the recommendation. By learning the relationship between user behavior and important features of items, the model aims to achieve better recommendation performance and model interpretability. In the end, we first realize the feature partition based on mutual information and then design an attention-based graph disentangling model to realize the fine-grained disentangling of user intents. In addition, to further ensure the independence of the disentangled intents, we augment the model with disagreement regularization. Through multilayer embedding propagation, FDGNN can display a capture CF effect in feature semantics. The interpretability and efficiency of our proposed approach are demonstrated by numerous pertinent experiments.

This special issue comprises 12 articles, showcasing the latest advances in computational social systems research.

As a distributed intelligent paradigm, fuzzy federated learning (FuzzyFL) can reduce the uncertainty and noise of biomedical data and is suited to enhance the accurate detection of adolescent idiopathic scoliosis (AIS). The advanced paradigm requires the hospitals to share the gradient of the fuzzy deep neural network (FDNN) rather than biomedical data. Not only that, the recent research works have been devoted to privacy-preserving FuzzyFL for secure AIS detection that adds differential privacy-based noise to the gradients against membership inference attack, attribute inference attack. However, a novel reconstruction attack called gradient leakage attack (GLA) on inferring biomedical data over the gradient brings the security challenges to FuzzyFL and, thus, has a negative influence on AIS detection. It is natural to ask a fundamental question: Can differentially private FuzzyFL for AIS detection over biomedical data defend GLA? In this article, we construct a privacy-preserving FuzzyFL framework calledPrivateFuzzyFLthat offers a great opportunity to present the systematic evaluation of the private FDNN threatened by the GLAs. In our experiments on a set of chest X-ray images and four FDNNs, we compare more than ten private fuzzy federated optimization algorithms in terms of the defense effect and the utility cost and derive that, first, the existing private FDNNs in FuzzyFL can offer a certain amount of privacy protection for biomedical data against the GLA; and second, the perturbation algorithm with better defense effect usually causes the worse AIS detection of the FDNN.

The provision of privacy-preserving recommendations for geological tourist attractions is an important research area. The historical check-in data collected from location-based social networks (LBSNs) can be utilized to mine their preferences, thereby facilitating the promotion of the geological tourism industry. However, such check-ins often contain sensitive user information that poses privacy leakage risks. To address this issue, some methods have been proposed to develop privacy-preserving point-of-interest (POI) recommendation systems. These methods commonly rely on either perturbation-based or federated learning techniques to protect users’ privacy. However, the former can hinder preference capture, while the latter remains vulnerable to privacy breaches during the parameter-sharing process. To overcome these challenges, we propose a novel privacy-preserving POI recommendation model that incorporates users’ privacy preferences based on a simplified graph convolutional neural network. Specifically, we employ a generative model to create a subset of POIs that reflect users’ preferences but do not reveal their private information, and then we design a simplified graph convolutional network to analyze the high-order connectivity between users and POIs that are privacy-preserving. The resulting model enables efficient POI recommendation under strict privacy protection, which is particularly relevant to geological tourism. Experimental results on two public datasets demonstrate the effectiveness of our proposed approach.

As an emerging concept, the metaverse incorporates a range of advanced technologies and offers a great opportunity to enhance the experiences of healthcare in clinical practice and human health. However, many cyber security issues often occur in the metaverse healthcare analytics such as DDoS attack, probe attack, and port scanning attack. Fortunately, 6G-enabled intrusion detection can detect anomalous activities with the help of an anomaly detection algorithm for metaverse healthcare analytics. Nevertheless, different from static data, data streams in metaverse healthcare have the intrinsic characteristics of infiniteness, correlation, and distribution change. Traditional static data anomaly detection algorithms do not consider these characteristics, which may result in low accuracy and efficiency. In this paper, aDataStreamAnomalyDetection (DS_AD) approach driven by 6G network is proposed for metaverse healthcare analytics, which incorporates a sliding window and model update into LSHiForest. DS_AD uses a change detection mechanism to optimize the model update. The core design utilizes hash functions to partition data spaces to find anomalies. To validate the feasibility of DS_AD, multiple groups of experiments are designed and executed on SMTP and HTTP datasets. Experimental results show that compared with baselines, our proposal performs favorably for data streams in terms of accuracy and efficiency.

With the advent of Internet of Things (IoT) applications, smart IoT devices are ubiquitous. Executing these devices on cloud data centers can lead to network congestion and transmission delay that causes failure instances in cloud architecture. Therefore, fog computing is derived to satisfy the low latency, location awareness, and mobility requirements of massive IoT applications near end users. Concerned with the high requirements of compute-intensive business applications, fog computing structure becomes complex due to limited computing capacity, and therefore these applications are offloaded to the cloud. Therefore, to overcome the offloading problem of workflow tasks in this work, a novel method named: Improved Chemical Reaction Optimization for Workflow Scheduling in a Hybrid Environment (ICRO-WSHE) has been proposed. The proposed algorithm aims at minimizing the execution cost of workflow applications under the defined deadline constraints. The algorithm is based on CRO, which has fewer parameters and the fastest convergence speed. However, the existing CRO has drawbacks in terms of getting stuck in the local optimum and having difficulty obtaining real optimal solutions. Therefore, the proposed mechanism modifies the existing CRO algorithm and includes the suitable features of PSO (Particle Swarm Optimization) and FQR (Fitness-based Quasi Reflection) methods to prevent the shortcomings of the CRO method. Further, a double-point synthesis operation is incorporated to increase the exploration rate and improve the working of the proposed algorithm. The result of the simulation experiment based on different types of workflows shows that the proposed method outperforms existing algorithms and proves to be a practical approach.

Nowadays, there exists a lot of cross-region data transmission demand on the cloud. It is promising to use serverless computing for data compressing to save the total data size. However, it is challenging to estimate the data transmission time and monetary cost with serverless compression. In addition, minimizing the data transmission cost is non-trivial due to the enormous parameter space. This paper focuses on this problem and makes the following contributions: 1) We propose empirical data transmission time and monetary cost models based on serverless compression. It can also predict compression information, e.g., ratio and speed using chunk sampling and machine learning techniques. 2) For single-task cloud data transmission, we propose two efficient parameter search methods based on Sequential Quadratic Programming (SQP) and Eliminate then Divide and Conquer (EDC) with proven error upper bounds. Besides, we propose a parameter fine-tuning strategy to deal with transmission bandwidth variance. 3) Furthermore, for multi-task scenarios, a parameter search method based on dynamic programming and numerical computation is proposed. We have implemented the system called Fluid-Shuttle, which includes straggler optimization, cache optimization, and the autoscaling decompression mechanism. Finally, we evaluate the performance of Fluid-Shuttle with various workloads and applications on the real-world AWS serverless computing platform. Experimental results show that the proposed approach can improve the parameter search efficiency by over$3\times $compared with the state-of-art methods and achieves better parameter quality. In addition, our approach achieves higher time efficiency and lower monetary cost compared with competing cloud data transmission approaches.

The last decade has witnessed a tremendous growth of service computing, while efficient service recommendation methods are desired to recommend high-quality services to users. It is well known that collaborative filtering is one of the most popular methods for service recommendation based on QoS, and many existing proposals focus on improving recommendation accuracy, i.e., recommending high-quality redundant services. Nevertheless, users may have different requirements on QoS, and hence diversified recommendation has been attracting increasing attention in recent years to fulfill users’ diverse demands and to explore potential services. Unfortunately, the recommendation performances relies on a large volume of data (e.g., QoS data), whereas the data may be distributed across multiple platforms. Therefore, to enable data sharing across the different platforms for diversified service recommendation, we propose aPrivacy-preserving Diversified Service Recommendation(PDSR) method. Specifically, we innovate in leveraging the Locality-Sensitive Hashing (LSH) mechanism such that privacy-preserved data sharing across different platforms is enabled to construct a service similarity graph. Based on the similarity graph, we propose a novel accuracy-diversity metric and design a 2-approximation algorithm to select$K$services to recommend by maximizing the accuracy-diversity measure. Extensive experiments on real datasets are conducted to verify the efficacy of our PDSR method.

Exploring user-item interaction cues is crucial for the performance of recommender systems. Explicit investigation of interaction cues is made possible by using graph-based models, where each user-item relationship is described by an edge, and the introduction of user-user social network. While existing graph-based recommendation methods use only a single-value edge to define the relationship between a pair of user and item, which limits the ability to represent complex user-item interactions. Furthermore, some social recommendation methods overlook the heterogeneous user behavior patterns in social and interaction relationships, resulting in the suboptimal performance of existing systems. In this paper, we propose a novel Social-aware Multi-dimensional Edge Feature-based Graph Representation Learning method, called SMEF. It represents all users and items as a graph and deep learns a multi-dimensional edge feature to explicitly describe the task-specific relationships of each user-item pair. Specifically, the proposed SMEF focuses on two distinct user behavior patterns toward social friends and interactive items, which explore the underlying heterogeneous relationship cues within them. This way, the learned multi-dimensional edge features encode user information from both social and interaction aspects. The proposed SMEF is a plug-and-play module that can be combined with different recommendation frameworks and Graph Neural Networks (GNNs) backbones to generate high quality user representations. The experimental results achieved on three publicly accessible datasets show that our SMEF-based method outperforms strong baselines.

With the exponential growth of mobile devices and the increasing demand for personalized and context-aware services, service-oriented applications play a crucial role in alleviating information overload and improving user experience. At the same time, Edge-Cloud Intelligence (ECI) provides a distributed computing paradigm that leverages the computational capabilities of edge servers to bring services closer to end-users, enabling low-latency and high-bandwidth data processing. The convergence of service applications and ECI has emerged as a promising way to enhance the efficiency and quality of service provisioning in today's dynamic and resource-constrained mobile environment. This call for papers invites researchers and practitioners to contribute to the exploration of the synergy between service applications and ECI infrastructure.

As an important financial investment commodity, the price fluctuations of gold significantly impact the global economy and the stability of the financial market. Therefore, it is of great significance to accurately predict the price of gold futures. This paper presents a novel gold futures price prediction model, PCA-AGRU, based on Principal Component Analysis (PCA) and Adaptive Gated Recurrent Unit (AGRU). PCA is utilized for the dimensionality reduction of data while extracting essential features. AGRU bases on Gated Recurrent Unit (GRU), embeds Self-Attention (SA), and adds an adaptive adjustment mechanism, making the model more effective in capturing long-term dependencies within time series data. This paper uses the international gold futures market data as the experimental dataset, and uses Maximal Information Coefficient (MIC) to analyze the correlation of the influencing factors. The PCA-AGRU model is compared with five prediction models of GRU, SA-GRU, AGRU, PCA-GRU, and PCA-SA-GRU. The experimental results show that the PCA-AGRU model performs best in forecasting gold futures prices.

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collaborative filtering, to disentangle the representations of users and items at the granularity of social users’ intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user’s intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores.

Anomaly detection plays an increasingly important role in various fields for critical tasks such as intrusion detection in cybersecurity, financial risk detection, and human health monitoring. A variety of anomaly detection methods have been proposed, and a category based on the isolation forest mechanism stands out due to its simplicity, effectiveness, and efficiency, e.g., iForest is often employed as a state-of-the-art detector for real deployment. While the majority of isolation forests use the binary structure, a framework LSHiForest has demonstrated that the multi-fork isolation tree structure can lead to better detection performance. However, there is no theoretical work answering the fundamentally and practically important question on the optimal tree structure for an isolation forest with respect to the branching factor. In this paper, we establish a theory on isolation efficiency to answer the question and determine the optimal branching factor for an isolation tree. Based on the theoretical underpinning, we design a practical optimal isolation forest OptIForest incorporating clustering based learning to hash which enables more information to be learned from data for better isolation quality. The rationale of our approach relies on a better bias-variance trade-off achieved by bias reduction in OptIForest. Extensive experiments on a series of benchmarking datasets for comparative and ablation studies demonstrate that our approach can efficiently and robustly achieve better detection performance in general than the state-of-the-arts including the deep learning based methods.

In an edge storage system, popular data can be stored on edge servers to enable low-latency data retrieval for nearby users. Suffering from constrained storage capacities, edge servers must process users' data requests collaboratively. For sourcing data, it is essential to find out which edge servers in the system have the requested data. In this paper, we make the first attempt to study this edge data query (EDQ) problem and present EDIndex, a distributed Edge Data Indexing system to enable fast data queries at the edge. First, we introduce a new index structure named Counting Bloom Filter (CBF) tree for facilitating edge data queries. Then, to improve query performance, we enhance EDIndex with a novel index structure named hierarchical Counting Bloom Filter (HCBF) tree. In EDIndex, each edge server maintains an HCBF tree that indexes the data stored on nearby edge servers to facilitate data sourcing between edge servers at the edge. The results of extensive experiments conducted on an edge storage system comprised of 90 edge servers demonstrate that EDIndex 1) takes up to 8.8x less time to answer edge data queries compared with state-of-the-art edge indexing systems; and 2) can be implemented in practice with a high query accuracy at low initialization and maintenance overheads.

Benefiting from the powerful data analysis and prediction capabilities of artificial intelligence (AI), the data on the edge is often transferred to the cloud center for centralized training to obtain an accurate model. To resist the risk of privacy leakage due to frequent data transmission between the edge and the cloud, federated learning (FL) is engaged in the edge paradigm, uploading the model updated on the edge server (ES) to the central server for aggregation, instead of transferring data directly. However, the adversarial ES can infer the update of other ESs from the aggregated model and the update may still expose some characteristics of data of other ESs. Besides, there is a certain probability that the entire aggregation is disrupted by the adversarial ESs through uploading a malicious update. In this article, a privacy-preserving FL scheme with robust aggregation in edge computing is proposed, named FL-RAEC. First, the hybrid privacy-preserving mechanism is constructed to preserve the integrity and privacy of the data uploaded by the ESs. For the robust model aggregation, a phased aggregation strategy is proposed. Specifically, anomaly detection based on autoencoder is performed while some ESs are selected for anonymous trust verification at the beginning. In the next stage, via multiple rounds of random verification, the trust score of each ES is assessed to identify the malicious participants. Eventually, FL-RAEC is evaluated in detail, depicting that FL-RAEC has strong robustness and high accuracy under different attacks.

With the construction and development of smart cities, accurate and real-time traffic prediction plays a vital role in urban traffic. However, traffic data has the characteristics of nonlinearity, nonstationary, and complex structure, so traffic prediction has always been a challenging problem. The traditional statistical model is good at dealing with linear data and poor at dealing with nonlinear data. Although the ability to capture nonlinear data has improved, the deep learning approach has difficulty in meeting the real-time requirements of traffic prediction. To solve the above challenges, we propose a novel approach based on the autoregressive integrated moving average model (ARIMA) model and combining empirical mode decomposition (EMD) and singular value decomposition (SVD) technology, i.e., ESARIMA. This method first uses EMD to stabilize the traffic data, then uses SVD to compress data and reduce the noise, so as to improve the efficiency and accuracy of ARIMA model in predicting traffic flow. Finally, we use real data sets to verify the feasibility of ESARIMA. The experimental results show that our method outperforms state-of-the-art baselines.

An emergency medical vehicle can save the patient’s life if it arrives at his location as quickly as possible. Unmanned aerial vehicles (UAVs) offer wide visibility and mobility, making them a viable choice for smart cities and intelligent transportation systems (ITSs) as edge devices for the Internet of Things (IoT). Based on population behavior and overcrowding, video surveillance through the Internet of multimedia things (IoMT) and public safety in smart cities can help determine the most efficient routes for emergency medical vehicles. This study investigates UAV overcrowding and abnormal population activity patterns, which affect the flow of emergency medical vehicles and traffic flow. Moreover, the purpose of this article is to analyze received video frames from UAVs in order to identify the most efficient route for emergency medical vehicles in smart cities to transfer patients in the event of abnormalities or overcrowding. In order to detect overcrowding on the streets, a hybrid Cascade-ResNet is utilized, which detects congestion based on many data points. Based on our proposed approach, we achieve a 2.5% improvement over similar methods because it is effective, flexible, and accurate. UAV video frames can be used to communicate with emergency response vehicles, to monitor traffic congestion, and to monitor other aspects of smart city life.

Digital-twin-enabled cloud-centric architecture is a promising evolution trend of sixth generation (6G) network, which brings new opportunities and challenges for mobile video streaming-related services requiring the exponentially increasing traffic demands. Device-to-Device (D2D) communication paradigm is an attractive technique to alleviate the problem. However, the previous research work on D2D built on individuals’ random mobility or position snapshot and cannot guarantee the stable communication flow. In this paper, we leverage the cybertwin as a centric controller and take advantages of crowdsourcing technology to attract mobile users to follow the specified path and share their network resources with other users. The design of the specified path is formulated as a problem of user recruitment optimization with cost constraint, which is a NP-Hard problem. Firstly, we investigate a special case of only one mobile user to offer the network resource and present a pseudo-polynomial time algorithm. Secondly, we present a graph-partition-based approach to solve the more complex case of multiple mobile users. Thirdly, we discuss the least expected budget to achieve the maximum utility in an ideal model. Fourthly, we perform extensive experiments to evaluate and compare the performance with the typical ones in simulated digital-twin-enabled 6G networks.

The ever-increasing web application programming interfaces (APIs) in various service-sharing communities (e.g., ProgrammableWeb.com and Mashape.com) have enabled software developers to quickly create their interested mashups conveniently and economically. However, the big volume of candidate web APIs and their differences often make it hard for software developers to discover a set of appropriate web APIs for mashup creation by considering API functions and API quality performances (e.g., popularity, compatibility, and diversity) simultaneously. These decrease the mashup development success rate and the mashup developers’ satisfaction significantly. In view of these challenges, a novel web APIs’ recommendation method named the popularity-aware and diverse method of web API compositions’ recommendation (PD-WACR) is proposed in this article. In concrete, we model web APIs’ functions, popularity, and compatibility with an API correlation graph. Afterward, correlation graph-based web APIs’ recommendation is performed with popularity and compatibility guarantee. Moreover, a top-$k$strategy is adopted in the recommendation process, so as to diversify the final recommended web APIs’ results. Finally, massive experiments are carried out on a real-world web API dataset crawled from ProgrammeableWeb.com. Experimental comparisons with related methods show the advantages and innovations of the proposed PD-WACR method.

Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users' interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest (POI) recommendation has become a hot research topic in augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the recurrent neural network-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The graph neural network-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose anInteraction-enhanced andTime-awareGraphConvolutionNetwork (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The enterprise management systems can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN brings better results compared to the existing methods.

The structural information detection of road conditions, which is adopted for improving driving comfort, patrol inspection, road maintenance, and accident rescue. In order to improve the trustworthiness of road condition detection, a real-time artificial intelligence road detection system based on binocular vision sensors is investigated in this article. The system is deployed on the low-power edge computing platform, which can upload the processing results to the cloud through the Internet-of-Things devices. The authors use binocular disparity information and image-based lightweight deep segmentation network to enhance the detection robustness and accuracy in the industrial Internet-of-Things application scenarios. Considering the small training dataset, a special data labeling regularization and training strategy have also been proposed for training this network. In addition, we employ multiframes feature matching and measurement data filtering to enhance the measurement accuracy. The experimental results demonstrate that our monocular–binocular fusion framework is robust and efficient.

In Industry 4.0, the development of intelligent supply chains is the key to improving enterprise efficiency and customer satisfaction. At present, the strategy with blockchain to optimize the supply chain ensures the security of data sharing and interaction between different suppliers. However, the quality of service (QoS) loss in time efficiency and supplier cooperation is still a severe problem in these strategies. Therefore, it is essential to conduct reputable supplier selection to reduce QoS loss under the premise of blockchain security. Meanwhile, choosing a reputable supplier as the primary peer in the blockchain consensus process can also effectively reduce transaction latency. To solve this problem, a reputation-aware supplier assessment system, named SAS, is proposed. Specifically, Canopy and K-medoids are utilized to classify suppliers preliminarily. Then backpropagation neural network is deployed to evaluate supplier reputation. The results show that SAS is effective in supplier reputation evaluation through extensive experiments.

The papers presented in this special issue focus on technological innovations in wearable, implantable, mobile, and remote healthcare that include Internet of Things (IoT), sensor informatics, and patient monitoring applications. These new technologies are used to track the key signs of people’s health to improve their lifestyle and health disorders. Innovations in IoT devices play a vital role in assisting patients in managing their health conditions. Thanks to the advent of modern communication technologies and Internet of Things (IoT) paradigms that have made the implementation of biomedical devices nearly universal. Now with the evolving industrial revolution, patients and healthcare providers are expecting something more. Practically speaking, healthcare wearables have experienced tremendous growth in the past few years, and it is expected to grow even more shortly, making it an ideal space for the biomedical informatics research community to solve complex healthcare problems and more informed decision making to improve human health.

As a fast-growing and promising technology, Internet of Things (IoT) significantly promotes the informationization and intelligentization of Maritime Transportation System (MTS). The massive data collected during the voyage is usually disposed of with the assistance of cloud or edge computing, which imposes serious cyber security threats. For multifarious cyber-attacks, Intrusion Detection System (IDS) is one of the efficient mechanisms to prevent IoT devices from network intrusion. However, most of the methods based on deep learning train their models in a centralized manner, which needs uploading all data to the central server for training, increasing the risk of privacy disclosure. In this paper, we consider the characteristics of IoT-based MTS and propose a CNN-MLP based model for intrusion detection which is trained through Federated Learning, named FedBatch. Federated Learning keeps the model training local and only updates the global model through the exchange of model parameters, preserving the privacy of local data on vessels. First, the characteristics of the communication between different vessels are discussed to model the federated learning process during the voyage. Then, the lightweight local model constructed by Convolutional Neural Network (CNN) and Multi-Layer Perception (MLP) is designed to save on computing and storage overhead. Moreover, to mitigate the straggler problem during the federated learning in MTS, we proposed an adaptive aggregation method, named Batch Federated Aggregation, which suppresses the oscillations of model parameters during federated learning. Finally, the simulation results on the NSL-KDD dataset demonstrate the effectiveness and efficiency of FedBatch.

This paper is on designing a compact data structure for multi-set membership testing that allows fast set querying. Multi-set membership testing is a fundamental operation for computing systems. Most existing schemes for multi-set membership testing are built upon Bloom filter and fall short in either storage space cost or query speed. To address this issue, we propose Noisy Bloom Filter (NBF), Error Corrected Noisy Bloom Filter (NBF-E), and Data-driven Noisy Bloom Filter (NBF-D) in this paper. We optimize their misclassification and false positive rates by theoretical analysis and present criteria for selection between NBF, NBF-E, and NBF-D. The key novelty of the three schemes is to store set ID information in a compact but noisy way that allows fast recording and querying and use a denoising method for querying. Especially, NBF-E incorporates asymmetric error-correcting coding techniques into NBF, and NBF-D encodes set ID based on their cardinality. To evaluate NBF, NBF-E, and NBF-D in comparison with the prior art, we conducted experiments using real-world network traces. The results show that NBF, NBF-E, and NBF-D significantly advance the state-of-the-art on multi-set membership testing.

Using Web APIs registered in service sharing communities for mobile APP development can not only reduce development period and cost, but also fully reuse state-of-the-art research outcomes in broad domain so as to ensure up-to-date APP development and applications. However, the big volume of available APIs in Web communities as well as their differences make it difficult for APIs selection considering compatibility, preferred partial APIs and expected APIs functions which are often of high variety. Accordingly, how to recommend a set of functional-satisfactory and compatibility-optimal APIs based on the APP developer's multiple function expectation and pre-chosen partial APIs is on demand as a significant challenge for successful APP development. To address this challenge, we first construct a Web APIs correlation graph that incorporates functional descriptions and compatibility information of Web APIs, and then propose a correlation graph-based approach for personalized and compatible Web APIs recommendation in mobile APP development. Finally, through extensive experiments on a real dataset crawled from Web APIs websites, we prove the feasibility of our proposed recommendation approach.

With the continuous increment of city volume and size, a number of traffic-related urban units (e.g., vehicles, roads, buildings, etc.) are emerging rapidly, which plays a heavy burden on the scientific traffic control of smart cities. In this situation, it is becoming a necessity to utilize the sensor data from massive cameras deployed at city crossings for accurate traffic flow prediction. However, the traffic sensor data are often distributed and stored by different organizations or parties with zero trust, which impedes the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it requires challenging efforts to balance the trade-off between data sharing and data privacy to enable cross-organization traffic data fusion and prediction. In light of this challenge, we put forward an accurate LSH (locality-sensitive hashing)-based traffic flow prediction approach with the ability to protect privacy. Finally, through a series of experiments deployed on a real-world traffic dataset, we demonstrate the feasibility of our proposal in terms of prediction accuracy and efficiency while guaranteeing sensor data privacy.

In the era of smart healthcare tremendous growth, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. Android system has been widely used in the field of IoMT, and as the main operating system. However, Android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the “sizes” of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection capability. We have carried out an experimental verification on three existing excellent methods (i.e., Drebin, HinDroid, and DroidEvolver) and obtain good promotion effects by our framework.

Abstract Deep reinforcement learning (DRL) has shown promising results in modeling dynamic user preferences in RS in recent literature. However, training a DRL agent in the sparse RS environment poses a significant challenge. This is because the agent must balance between exploring informative user-item interaction trajectories and using existing trajectories for policy learning, a known exploration and exploitation trade-off. This trade-off greatly affects the recommendation performance when the environment is sparse. In DRL-based RS, balancing exploration and exploitation is even more challenging as the agent needs to deeply explore informative trajectories and efficiently exploit them in the context of RS. To address this issue, we propose a novel intrinsically motivated reinforcement learning (IMRL) method that enhances the agent’s capability to explore informative interaction trajectories in the sparse environment. We further enrich these trajectories via an adaptive counterfactual augmentation strategy with a customised threshold to improve their efficiency in exploitation. Our approach is evaluated on six offline datasets and three online simulation platforms, demonstrating its superiority over existing state-of-the-art methods. The extensive experiments show that our IMRL method outperforms other methods in terms of recommendation performance in the sparse RS environment.

The Intelligent Meteorological System (IMS) with cloud computing (CC) provides users with various meteorological services, but the long-distance communication of CC often brings high latency, which makes the IMS perform poorly in the series of real-time services for meteorological emergency decision-making. Considering the shortcomings of CC, edge computing is adopted to the IMS to process most service requests. In the IMS with cloud-edge computing, the commonly used contents of services is cached on edge servers (ESs) to reduce resource scheduling, thus avoiding high time costs. Due to the storage and computing resource limitations of ESs, the massive types of services and the changing service requests, how to determine the caching contents is still a challenge. In this paper, a service caching scheme based on deep reinforcement learning, named SCDR, is proposed. Specifically, a service caching framework for IMS with cloud-edge computing is designed. Then, the distributed distributional deep deterministic policy gradient (D4PG) is leveraged to realize the optimization of the service caching strategy with the highest service coverage rate and low processing latency. Besides, the performance of the generated caching strategy is evaluated through simulation experiments.

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3142265]

With the ever-increasing popularity of microservice architecture, a considerable number of enterprises or organizations have encapsulated their complex business services into various lightweight functions as published them accessible APIs (Application Programming Interfaces). Through keyword search, a software developer could select a set of APIs from a massive number of candidates to implement the functions of a complex mashup, which reduces the development cost significantly. However, traditional keyword search methods for APIs often suffer from several critical issues such as functional compatibility and limited diversity in search results, which may lead to mashup creation failures and lower development productivity. To deal with these challenges, this paper designs DAWAR, a diversity-aware Web APIs recommendation approach that finds diversified and compatible APIs for mashup creation. Specifically, the APIs recommendation problem for mashup creating is modelled as a graph search problem that aims to find the minimal group Steiner trees in a correlation graph of APIs. DAWAR innovatively employs the determinantal point processes to diversify the recommended results. Empirical evaluation is performed on commonly-used real-world datasets, and the statistic results show that DAWAR is able to achieve significant improvements in terms of recommendation diversity, accuracy, and compatibility.

The widespread use of mobile devices propels the development of new-fashioned video applications like 3D (3-Dimensional) stereo video and mobile cloud game via web or App, exerting more pressure on current mobile access network. To address this challenge, we adopt the crowdsourcing paradigm to offer some incentive for guiding the movement of recruited crowdsourcing users and facilitate the optimization of the movement control decision. In this paper, based on a practical 4G (4th-Generation) network throughput measurement study, we formulate the movement control decision as a cost-constrained user recruitment optimization problem. Considering the intractable complexity of this problem, we focus first on a single crowdsourcing user case and propose a pseudo-polynomial time complexity optimal solution. Then, we apply this solution to solve the more general problem of multiple users and propose a graph-partition-based algorithm. Extensive experiments show that our solutions can improve the efficiency of real-time D2D communication for mobile videos.

Summary With the advent of Internet of Things (IoT) age, the variety and volume of web services have been increasing at a fast speed. This often leads to users' selections for web services more complicated. Under the circumstance, a variety of methods such as collaborative filtering are adopted to deal with this challenging situation. While traditional collaborative filtering method has some shortcomings, one of which is that only centralized user‐service data are considered while distributed quality data from multiple platform are ignored. Generally, service recommendation across different platforms often involves data communication among multiple platforms, during which user privacy may be disclosed and much computational time is required. Considering these challenges, a unique amplified locality‐sensitive hashing (LSH)‐based service recommendation method, that is, SRAmplified‐LSH, is proposed in the article. SRAmplified‐LSH can guarantee a good balance between accuracy and efficiency of recommendation and user privacy information. Finally, extensive experiments deployed on WS‐DREAM dataset validate the feasibility of our proposed method.

Advancements in digital technologies, such as the Internet of Things (IoT), fog/edge/cloud computing, and cyber-physical systems have revolutionized a broad spectrum of smart city applications. The significant contributions and rapid developments of advanced artificial intelligence-based technologies and approaches, like, machine learning and deep learning, which are applied for extracting accurate information from extensive data, perform a potential role in IoT applications. Moreover, blockchain technology's fast adoption also contributes a significant role in the development of the new digital smart city ecosystem. Thus, artificial intelligence and blockchain technology convergence revolutionize smart city infrastructures to establish sustainable ecosystems for IoT applications. Nevertheless, these advancements and technological improvements also provide both opportunities and challenges for developing sustainable IoT applications. This paper aims to examine the convergence of blockchain technology and artificial intelligence, a unique driver towards technological transformation in intelligent and sustainable IoT applications. We mainly discussed the advantages of blockchain technology that might promote the advancement and development of sustainable IoT applications. On the basis of the discussion, we introduced a smart and sustainable conceptual framework that leverages cloud computing, IoT devices, and artificial intelligence to process and obtain necessary information. The system provides digital analytics and saves results in decentralized cloud repositories through blockchain technology to promote various applications. Moreover, the layer-based architecture allows a sustainable incentive structure, which can possibly assist secure and protected smart city applications. We reviewed the enhanced solutions, summing up the key points that can be applied for generating various artificial intelligence and blockchain-based systems. Also, we discussed the issues that still remain open and our future research goals; that can introduce new ideas and future guidelines for sustainable IoT applications.

The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point-of-interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check-in. Faced with this challenge, we propose an attention-based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check-ins. Furthermore, since the neural network is similar to a “black box” in feature learning, the decision-making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check-in records, which can improve the interpretability of the model. Considering the time impact on users' check-in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check-in data.

The emerging technologies, such assmart sensors, 5G/6G wireless communication, artificial intelligence, etc., have been maturing the future Internet of Things (IoT) by connecting the massive number of devices, which are expected to consistently collect and transmit real-time data to support business intelligence in an efficient and privacy-preserving way. The IoT can afford businesses predictive maintenance, improve field service, asset tracking, and further enhance customer satisfaction and facility management in industrial sectors. However, the privacy concern in IoT is a big challenge in IoT applications and services. This work proposed a lightweight privacy-preserving scheme based on homomorphic encryption in the context of the IoT, in which we investigated and analyzed the privacy issues between the data owners, untrustworthy third-party cloud servers, and the data users. Meanwhile, computationally efficient homomorphic algorithms are proposed to guarantee the privacy protection for the data users. Experimental results demonstrate that the proposed scheme can effectively prevent privacy breaches in IoT.

Internet of Things (IoT) devices have become an integral part of our lives and are increasingly used in almost every field. Subsequently, there are a large number of latency-sensitive IoT applications (e.g., face recognition and autonomous driving) targeted for mobile edge computing environments. These IoT applications are often split into multiple collaborative tasks and offloaded onto containers or virtual machines (VMs) with certain failure rates and recovery rates. If these containers or VMs are not deployed in the same edge servers, the bandwidth resources of edge clouds must be consumed to transfer data. These factors increase the completion time of IoT applications to different degrees, and then affect their reliability level. Therefore, there exists equilibrium between the reliability level and bandwidth consumption. In this article, we investigate the equilibrium of minimizing the bandwidth consumption of IoT applications while maximizing the reliability level of these IoT applications during task offloading. We propose a multiobjective optimization problem, and transform it to a single-objective optimization problem. Furthermore, we introduce two efficient approaches to acquire two near-optimal solutions. The results of simulation experiments demonstrate that our proposed approaches can observably enhance the reliability level and reduce the bandwidth consumption of IoT applications compared with other related approaches. Meanwhile, we also make a comparative analysis of our proposed approaches.

In location-based social networks (LBSNs), extensive user check-in data incorporating user preferences for location is collected through Internet of Things devices, including cell phones and other sensing devices. However, directly acquiring the preferences of spars users remains an open challenge. This article offers a point-of-interest (POI) category recommendation model based on group preferences (PPCM). This model is proposed for three reasons: 1) because data influence the training of a deep learning model, the group influence of users is taken into account. To protect the privacy of users’ check-in records and classify similar users into the same group, locality-sensitive hashing (LSH) is used; 2) a successive POI category recommendation model should capture the long- and short-term dependence ability. The attention mechanism and a temporal sliding window are paired with the long short-term memory (LSTM). This paradigm is useful for efficiently mining users’ long-term dependencies and interests; and 3) although the overall users’ check-in data are vast, check-in location options are also massive for a single user. There is a scarcity of data that may be utilized to mine user interests. Thus, instead of using POI, we leverage the POI category to better mine the user’s interests. On real check-in data sets from New York City and Tokyo, the PPCM is compared to other models. The comparison results indicate that the PPCM has improved recommendation performance.

To securely transmit secret data between Internet of Things (IoT) nodes, it is required to the implement information hiding technique for secure communication in the IoT environment. The traditional information hiding approaches generally select a multimedia file, such as texts, images, and video clips as the cover, and then embed secret information into the cover by slight modification. However, it is not feasible to directly apply these approaches in the IoT environment for the following reasons. First, it is hard for some IoT nodes to effectively and efficiently process and transmit the complex multimedia data. Second, the modification trace left in the cover will cause the presence of hidden secret information to be easily exposed by steganalysis tools. To address the above issues, we propose a coverless information hiding scheme based on probability graph learning for secure communication in the IoT environment. Instead of modifying an existing multimedia cover, we conceal secret information in a generated sequence of IoT data to realize secure communication between different nodes in the IoT environment. According to the node-data interaction relationships, we first learn the transition probability graph (TPG) to describe the transition probabilities between IoT data elements. Then, guided by a given secret message that needs to be hidden, we sequentially select a set of highly correlated data elements from the TPG to generate the sequence. The experimental results and theoretical analysis demonstrate that the proposed information hiding scheme can achieve high hiding capacity with desirable imperceptibility and security performances in the IoT environment.

The ever-increasing popularity of web APIs allows app developers to leverage a set of existing web APIs to achieve their sophisticated objectives. The heavily fragmented distribution of web APIs makes it challenging for an app developer to find appropriate and compatible web APIs. Currently, app developers usually have to manually discover candidate web APIs, verify their compatibility and select appropriate and compatible ones. This process is cumbersome and requires detailed knowledge of web APIs which is often too demanding. It has become a major obstacle to further and broader applications of web APIs. To address this issue, we first propose a web API correlation graph built on extensive data about the compatibility between web APIs. Then, we propose WAR (WebAPIsRecommendation), the first data-driven approach for web APIs recommendation that integrates web API discovery, verification and selection operations based on keywords search over the web API correlation graph. WAR assists app developers without detailed knowledge of web APIs in searching for appropriate and compatible web APIs by typing a few keywords that represent the tasks required to achieve app developers’ objectives. WAR can significantly save app developers’ time and effort in searching for web APIs. We conducted large-scale experiments on 18,478 real-world web APIs and 6,146 real-world apps to demonstrate the usefulness and efficiency of WAR.

Scalable data processing platforms built on cloud computing becomes increasingly attractive as infrastructure for supporting big data applications. But privacy concerns are one of the major obstacles to making use of public cloud platforms. Multidimensional anonymisation, a global-recoding generalisation scheme for privacy-preserving data publishing, has been a recent focus due to its capability of balancing data obfuscation and usability. Existing multidimensional anonymisation methods suffer from scalability problems when handling big data due to the impractical serial I/O cost. Given the recursive feature of multidimensional anonymisation, parallelisation is an ideal solution to scalability issues. However, it is still a challenge to use existing distributed and parallel paradigms directly for recursive computation. In this paper, we propose a scalable approach for big data multidimensional anonymisation based on MapReduce, a state-of-the-art data processing paradigm. Our basic idea is to partition a data set recursively into smaller partitions using MapReduce until all partitions can fit in the memory of a computing node. A tree indexing structure is proposed to achieve recursive computation. Moreover, we show the applicability of our approach to differential privacy. Experimental results on real-life data demonstrate that our approach can significantly improve the scalability of multidimensional anonymisation over existing methods.

The ever-increasing popularity of recommendation systems allows users to find appropriate services without excessive effort. However, due to the unstable and complex network environment, the historical behavior data of users are quite sparse in most cases. The inherent drawbacks render preference prediction infeasible for cold-start users and have become a crucial issue to be resolved in recommendation systems. To deal with the problems, we first present a Trust-based Collaborative Filtering (TbCF) algorithm to perform basic rating prediction in a manner consistent with the existing CF methods. Then, we propose the Hybrid Collaborative Filtering Recommendation approach with User-Item-Trust Records ($\text {UIT}_{\text {hybrid}}$), a novel approach that incorporates user trust into the existing CF-based methods in a harmonious way to supplement rating information.$\text {UIT}_{\text {hybrid}}$employs multiple perspectives to extract proper services and achieves a good tradeoff between the robustness, accuracy, and diversity of the recommendation. We conduct extensive real-world experiments on the Epinions data set to demonstrate the feasibility and efficiency of$\text {UIT}_{\text {hybrid}}$.

The development of the Internet of vehicles (IoV) has spawned a series of driving assistance services (e.g., collision warning), which improves the safety and intelligence of transportation. In IoV, the driving assistance services need to be met in time due to the rapid speed of vehicles. By introducing edge computing into the IoV, the insufficiency of local computation resources in vehicles is improved, providing high quality services for users. Nevertheless, the resources provided by edge servers are often limited, which fail to meet all the needs of users in IoV simultaneously. Thereby, how to minimize the tasks processing latency of users in the case of limited edge server resources is still a challenge. To handle the above problem, a task offloading scheme fuzzy-task-offloading-and-resource-allocation (F-TORA) based on Takagi–Sugeno fuzzy neural network (T–S FNN) and game theory is designed. Primarily, the cloud server predicts the future traffic flow of each section through T–S FNN and transmits the prediction results to the roadside units (RSUs). Then, the RSU adjusts the current load based on the captured future traffic flow data. After the load balancing of each RSU, the optimal task offloading strategy is determined for the users by game theory. Following, the edge server acts as an agent to allocate computing resources for the offloaded tasks by$Q$-learning algorithm. Finally, the robust performance of the proposed method is validated by comparative experiments.

The development of Internet of Vehicles (IoV) has produced a considerable amount of real-time traffic data. These traffic data constitute a kind of digital twin that connects the physical vehicles and their virtual representation via 5G communications. Generally, through analyzing the digital twin traffic data, traffic administrators can optimize traffic scheduling and alleviate traffic jams. However, the exceptions of IoV sensors inevitably raise an issue of traffic data sparsity and consequently influence scientific traffic scheduling decisions. Inspired by this drawback, in this article, a digital twin-assisted real-time traffic data prediction method is proposed by analyzing the traffic flow and velocity data monitored by IoV sensors and transmitted through 5G. At last, we conduct a set of experiments based on a traffic dataset collected by Nanjing city of China. Reported results show the feasibility of our proposal in smart traffic flow and velocity prediction that call for a quick response and high accuracy.

With the emergence of various convolutional neural network (CNN)-based applications and the rapid growth of CNN model scale, the resource-constricted end devices can hardly deploy CNN-based applications. Current work optimizes the CNN model on edge servers and deploys the optimized model on devices in an edge computing environment. However, most of them only optimize the resource consumption within or across models solely, whereas neglecting the other side. In this article, we propose a novel CNN-based resource optimization approach (CroApp) that not only optimizes the resource consumption within the CNN model but also pays attention to resource optimization across the applications. Specifically, we adopt model compression as the “inner-model” optimization method, as well as computation sharing as the “intermodel” optimization method. First, during “inner-model” optimization, the CroApp prunes unnecessary parameters within the model on edge servers to reduce the scale of the model. Then, during “intermodel” optimization, the CroApp trains a set of shareable models based on the pruned model and sends these shareable models to end devices. Finally, the CroApp adaptively adjusts the shared models to reduce resource consumption. The experimental results show that the CroApp outperforms the state-of-the-art approaches in terms of resource reduction, scalability, and application performance.

With the advancements of the Health 2.0 technology, large-scale healthcare services are available online. Recommender systems for healthcare services have emerged for decision assistance. Most existing collaborative recommendation algorithms only mine global interactions while failing to capture the local different information of users or items. Besides, privacy concern is another significant problem to be considered in recommender systems for healthcare services. In this article, a privacy-aware factorization-based hybrid method is proposed for healthcare service recommendations. For better modeling of user preferences and service features, multiview embeddings of users and healthcare services are learned. Besides, we address the privacy problem by integrating local differential privacy and locality-sensitive hashing techniques into the recommendation model for privacy-aware neighbor searching. The final prediction is made based on a stochastic gradient descent learning-based hybrid collaborative model. Experiments demonstrate the effectiveness of the proposed method in both recommendation performance and privacy concerns.

Various cyber attacks often occur in logistics network of the Industry 4.0, which poses a threat to Internet security. Intrusion detection can intelligently detect anomalous activities and secure the Internet with the help of anomaly detection algorithms. Different from static data, intrusion detection data are a dynamic data form and have the following characteristics. First, it is multiaspect. Second, it contains point anomalies and group anomalies. Third, there are correlations between different attributes. Nevertheless, these properties pose a challenge on existing anomaly detection approaches. Thus, a novel anomaly detection approach MDS_AD is proposed in this article to deal with the challenges. It combines locality-sensitive hashing (LSH), isolation forest, and PCA techniques. MDS_AD has the following properties. 1) The introduced LSH can operate on multiaspect data. 2) MDS_AD can effectively catch group anomalies from the experimental results. 3) The PCA is utilized to reduce dimensionality for correlations between different attributes. 4) MDS_AD is a streaming approach, which can perform model update and process data in constant memory and time. To confirm the performance of MDS_AD, multiple experiments are designed and implemented on UNSW-NB15 dataset. Experimental results show that MDS_AD outperforms state-of-the-art baselines.

Through the collaboration of cloud and edge, cloud-edge computing allows the edge that approximates end-users undertakes those non-computationally intensive service processing of the cloud, reducing the communication overhead and satisfying the low latency requirement of Internet of Vehicle (IoV). With cloud-edge computing, the computing tasks in IoV is able to be delivered to the edge servers (ESs) instead of the cloud and rely on the deployed services of ESs for a series of processing. Due to the storage and computing resource limits of ESs, how to dynamically deploy partial services to the edge is still a puzzle. Moreover, the decision of service deployment often requires the transmission of local service requests from ESs to the cloud, which increases the risk of privacy leakage. In this article, a method for privacy-aware IoV service deployment with federated learning in cloud-edge computing, named PSDF, is proposed. Technically, federated learning secures the distributed training of deployment decision network on each ES by the exchange and aggregation of model weights, avoiding the original data transmission. Meanwhile, homomorphic encryption is adopted for the uploaded weights before the model aggregation on the cloud. Besides, a service deployment scheme based on deep deterministic policy gradient is proposed. Eventually, the performance of PSDF is evaluated by massive experiments.

In the edge computing-supported Internet of Vehicles (IoV), the edge servers (ESs) are distributed near the road side units (RSUs) to process the transmitted data for various IoV services in real time. Generally, due to the budget constraints, the ES scale is limited. If the ESs are not appropriately placed, it may cause the unbalanced load distribution of the ESs. Therefore, how to place a constant number of ESs with the goal of avoiding the risk of overload and improving the quality of services remains a challenge. To tackle this challenge, a quantified edge server placement strategy, named QESP, is fully investigated, to improve the coverage rate, the workload balance and reduce the average waiting time of services in the IoV. Technically, binary encoding and quantum encoding are fully leveraged to model the ES locations. Then, the niched pareto genetic algorithm II (NPGA-II) and the quantum rotation gate are adopted to obtain the appropriate solutions for the ES placement problem. Furthermore, an assessment function is devised to find the optimal scheme among the solutions obtained. Finally, the validity of QESP is evaluated by using real-world vehicular big data.

With the prosperity of Industry 4.0, numerous emerging industries continue to gain popularity and their market scales are expanding ceaselessly. The Internet of Vehicles (IoV), one of the thriving intelligent industries, enjoys bright development prospects. However, at the same time, the reliability and availability of IoV applications are confronted with two major bottlenecks of time delay and energy consumption. To make matters worse, massive heterogeneous and multi-dimensional multimedia data generated on the IoV present a huge obstacle to effective data analysis. Fortunately, the advent of edge computing technology enables tasks to be offloaded to edge servers, which significantly reduces total overhead of IoV systems. Deep reinforcement learning (DRL), equipped with its excellent perception and decision-making capability, is undoubtedly a dominant technology to solve task offloading problems. In this article, we first employ an optimized Fuzzy C-means algorithm to cluster vehicles and other edge devices according to their respective service quality requirements. Then, we employ an election algorithm to assist in maintaining the stability of the IoV. Last, we propose a task-offloading algorithm based on the Deep Q Network (DQN) to acquire an optimal task offloading scheme. Massive simulation experiments demonstrate the superiority of our method in minimizing time delay and energy consumption.

Multi-access Edge Computing (MEC), as an extension of cloud computing, provides storage resources at the network edge to enable low-latency data retrieval for users. Due to limited physical sizes and constrained storage resources, individual edge servers cannot store a large amount of data when operating independently. They often need to offload data to other edge servers to serve users collaboratively. Operated by different edge infrastructure providers, edge servers usually work in a distrusted environment. Incentive and trust are the two main challenges in facilitating collaborative edge storage. This article proposes CSEdge, a novel decentralized system that tackles these challenges to enable collaborative edge storage based on blockchain. On CSEdge, edge servers can submit data offloading requests for others to contend for. Winners are selected based on their reputations. They will store the offloaded data and receive rewards for successfully finishing data offloading tasks. Via a distributed consensus, their performance will be recorded on blockchain for future reputation evaluation. A prototype of CSEdge is built on Hyperledger Sawtooth and experimentally evaluated against a baseline system and two start-of-the-art systems in a simulated MEC environment. The results demonstrate that CSEdge can effectively and efficiently facilitate collaborative edge storage among edge servers.

Currently, the world is experiencing the rapid spread of Coronavirus Disease 2019 (COVID-19). Since the epidemic continues to take a devastating impact on the society, economy, and healthcare, the real-time detection of COVID-19 is essential for fast and cost-effective diagnosis services. Fortunately, deep learning (DL), as a promising technology, enables the COVID-19 diagnosis services on chest X-ray (CXR) images. The training task of DL model is generally implemented at the centralized cloud. However, due to the geo-distributed data sources and the transmission of large amounts of raw data to the centralized cloud, the transmission latency becomes a bottleneck of the COVID-19 diagnosis model training. In this paper, we propose aDistributedCOVID-19 detection model training method on CXR images with edge-cloud collaboration, named DisCOV. Specifically, to improve the training efficiency and guarantee the model accuracy, a distributed lightweight model-based training algorithm is designed with the cooperation of edge computing and cloud computing. In addition, a resource allocation algorithm is developed during the training to jointly minimize the time cost and energy consumption. Extensive experiments based on real-world CXR image datasets demonstrate that DisCOV is better performed and more promising than the existing baselines.

Computation offloading is an attractive and challenging problem in Edge Computing. Existing offloading approaches are mostly designed for the mobile devices in the service coverage of edge servers, which neglects the devices in the dummy area (i.e., the region where offloading service is unavailable). The mobile devices in the dummy area cannot offload tasks to edge servers, making it difficult to support large-scale tasks. In view of this challenge, we propose a relay-assisted offloading approach which considers the mobility of devices. It can leverage other devices in the service coverage of edge servers as relays to enable the device in the dummy area to complete offloading t asks. As a result, the simulation experiment based on real-world datasets proves the feasibility and efficiency of the proposed approach.

Distribution estimation of physical symptom is a powerful statistical technique to monitor and predict the situation of infectious diseases. With the widespread popularization of smart devices, edge computing is an efficient computing paradigm, which takes advantage of these devices to implement the safe and fast collection and distribution analysis of physical symptoms. However, the sharing of individuals' data, especially of sensitive information (e.g., age, symptom), inevitably raises the great privacy concerns of people due to the possible leakage and the illegal utilization. It is necessary and important to not only implement the highly intelligent epidemic analysis, but also guarantee the privacy of data. In this paper, we focus on locally private distribution estimation of physical symptom with attribute constraints in edge computing. At first, we present an edge-computing-based framework for distribution estimation of physical symptoms, in which each component undertakes the respective task. Then, we propose a naive privacy algorithm, i.e., NAIVEPRIVDISTEST, to directly perturb the values of quasi-identifying attributes and physical symptoms. Finally, extensive experiments demonstrate that NAIVEPRIVDISTEST has the accurate distribution estimation under the privacy constraints.

With the development of the vehicular network (VN), emerging driver assistance applications are adhibited in daily life. Commonly, edge computing is adopted to satisfy the timeliness requirements of these applications, as the vehicular devices are usually insufficient in computation resources. Nevertheless, the increasing volume of service requests (SRs) are potential to overload the edge servers (ESs), thus increasing the task execution time. Besides, the randomness and the diversity of the SRs also challenge the dynamic resource allocation for the users. To deal with these challenges, a task offloading and resource allocation scheme based on game theory and reinforcement learning (RL) named TORA is proposed. Specifically, game theory is leveraged to determine the optimal task offloading strategy for improving the quality of service (QoS). Meanwhile, RL is applied to implement the dynamic resource allocation of the ES. Finally, the robust performance of the proposed method is validated by comparative experiments.

With the powerful computing capability, cloud computing (CC) is employed to accommodate services execution for the Internet of vehicles (IoV), but the long-distance communication of CC often brings high latency. Considering the shortcomings of CC, edge computing (EC) shifts service execution to the edge, which decreases the execution delay. Due to the storage and computing resource limits of edge servers (ESs), how to dynamically deploy partial services to the edge is still a puzzle. Moreover, the single resource-constrained ES is more vulnerable to attacks, which increases the leakage risk of vehicle privacy. In this article, a method for intelligent IoV service deployment and execution with privacy preservation in cloud-edge computing, named GoDeep, is proposed. Specifically, homomorphic encryption is adopted to construct a privacy-aware mechanism for IoV service execution. Besides, a service deployment scheme based on deep deterministic policy gradient (DDPG) is proposed. Eventually, the performance of GoDeep is evaluated by the simulation experiments.

Summary With an increasing number of public WiFi hotspots have been constructed in metropolitan areas, citizens can leverage these WiFi hotspots to surf the Internet with their smart devices almost everywhere. Compared to cellular networks, most people prefer using WiFi because this can not only save money by reducing the cellular data traffic but also save energy to prolong the battery lifetime of mobile devices. Mobile offloading techniques give the opportunity for smart devices to offload cellular data traffic to WiFi networks whenever they are connected WiFi networks. Although offloading to WiFi networks can cause communication delay, many delay‐tolerant applications still prefer doing so in order to save the cellular data traffic. Existing offloading approaches only take the application delay tolerance into consideration and therefore easy to induce some tasks missing deadlines. In this paper, we propose a novel WiFi‐aware method for mobile data offloading through WiFi networks with deadline constraints. Specifically, it takes the WiFi distribution in city areas into consideration while making offloading decision. The simulation results on real‐world data have shown that our method improves the percentage of tasks that can meet their deadlines, and reduces the average task completion time by comparing with other outstanding methods.

With the continuous accumulation of users' check-in data, we can gradually capture users' behavior patterns and mine users' preferences. Based on this, the next point-of-interest (POI) recommendation has attracted considerable attention. Its main purpose is to simulate users' behavior habits of check-in behavior. Then, different types of context information are used to construct a personalized recommendation model. However, the users' check-in data are extremely sparse, which leads to low performance in personalized model training using recurrent neural network. Therefore, we propose a category-aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check-in data, capture long-range dependence between user check-ins and get better recommendation results of POI category. We combine the spatiotemporal information of check-in data and take the POI category as users' preference to train the model. Also, we develop an attention-based category-aware GRU (ATCA-GRU) model for the next POI category recommendation. The ATCA-GRU model can selectively utilize the attention mechanism to pay attention to the relevant historical check-in trajectories in the check-in sequence. We evaluate ATCA-GRU using a real-world data set, named Foursquare. The experimental results indicate that our ATCA-GRU model outperforms the existing similar methods for next POI recommendation.

Stroke is one of the leading causes of death and disability in the world. The rehabilitation of Patients' limb functions has great medical value, for example, the therapy of functional electrical stimulation (FES) systems, but suffers from effective rehabilitation evaluation. In this paper, six gestures of upper limb rehabilitation were monitored and collected using microelectromechanical systems sensors, where data stability was guaranteed using data preprocessing methods, that is, deweighting, interpolation, and feature extraction. A fully connected neural network has been proposed investigating the effects of different hidden layers, and determining its activation functions and optimizers. Experiments have depicted that a three-hidden-layer model with a softmax function and an adaptive gradient descent optimizer can reach an average gesture recognition rate of 97.19%. A stop mechanism has been used via recognition of dangerous gesture to ensure the safety of the system, and the lightweight cryptography has been used via hash to ensure the security of the system. Comparison to the classification models, for example, k-nearest neighbor, logistic regression, and other random gradient descent algorithms, was conducted to verify the outperformance in recognition of upper limb gesture data. This study also provides an approach to creating health profiles based on large-scale rehabilitation data and therefore consequent diagnosis of the effects of FES rehabilitation.

With the advent of the Internet of Things (IoT) and the increasing popularity of the intelligent transportation system, a large number of sensing devices are installed on the road for monitoring traffic dynamics in real time. These sensors can collect streaming traffic data distributed across different traffic sites, which constitute the main source of big traffic data. Analyzing and mining such big traffic data in massive IoT can help traffic administrations to make scientific and reasonable traffic scheduling decisions, so as to avoid prospective traffic congestions in the future. However, the above traffic decision making often requires frequent and massive data transmissions between distributed sensors and centralized cloud computing centers, which calls for lightweight data integrations and accurate data analyses based on large-scale traffic data. In view of this challenge, a big data-driven and nonparametric model aided by 6G is proposed in this article to extract similar traffic patterns over time for accurate and efficient short-term traffic flow prediction in massive IoT, which is mainly based on time-aware locality-sensitive hashing (LSH). We design a wide range of experiments based on a real-world big traffic data set to validate the feasibility of our proposal. Experimental reports demonstrate that the prediction accuracy and efficiency of our proposal are increased by 32.6% and 97.3%, respectively, compared with the other two competitive approaches.

Summary With the ever‐increasing popularity of web application programming interfaces (APIs) sharing communities, it is becoming a promising way for software developers to design and create their interesting Apps through composing a set of selected web APIs that can collectively fulfill the App functions expected by the App developer. However, the App developer's web APIs selection decision‐makings are often nontrivial due to the massive candidate APIs as well as their diverse functions. Furthermore, it is difficult to guarantee that the selected web APIs are compatible enough. Moreover, traditional web APIs recommendation approaches only return a recommended APIs list, which are often not sufficient to accommodate the App developer's undetermined and fuzzy personalized preferences. Considering the above challenges, a novel keywords‐driven web APIs recommendation approach called keywords‐driven and compatibility‐aware multiple API group recommendation is proposed in this article for green and compatible software, which cannot only satisfy the App developer's functional requirements, but also return a group of web APIs recommended lists. Each returned list includes a set of compatible web APIs. Finally, we design a series of experiments based on a real‐world web APIs dataset, that is, PW dataset crawled from www.programmableWeb.com. Experimental reports compared with other competitive approaches in existing literatures indicate the effectiveness and efficiency of our proposal in this work.

Summary Edge computing (EC) emerges as a novel computing paradigm to offload computing tasks from user equipments (UEs) to edge notes (ENs) in fifth‐generation networks, which definitely breaks the resource limitation of UEs to a certain degree. However, it is troublesome to guarantee the overall operating performance of ENs due to the uneven distributed resource demands of UEs, the resulting transmission delay and the data loss for computation offloading between the covered EN and the deployed destination EN. In view of this challenge, a blockchain‐based computation offloading method, named BCO, is proposed in this paper. Technically, since blockchain is a promising technique for the decentralized system, a blockchain‐based EC framework is designed to degrade the data loss possibility by integrating blockchain and EC. Then, the nondominated sorting genetic algorithm, the third version (NSGA‐III), is leveraged to acquire the balanced offloading strategies. Furthermore, by taking advantage of Simple Additive Weighting and Multiple Criteria Decision Making, the optimal offloading strategy is identified. Finally, systematic experiments and analyses on the comparative experiment are conducted to verify the efficiency of our proposed method BCO.

With the ever-increasing maturity and popularization of the Internet of Things (IoT), tremendous business applications developed by distinct enterprises or organizations have been encapsulated into lightweight web services that can easily be accessed or invoked remotely. However, the big volume of candidate web services places a heavy burden on the users' service selection decision-making process. Under the circumstance, a variety of intelligent recommendation solutions have been developed to reduce the high decision-making cost. Traditional resolutions usually challenge in two aspects. First, the recommendation parameters, i.e., the quality of services (QoS), usually relies on user/service location heavily; therefore, low-quality recommended results may be returned to users if user/service location information is overlooked. Second, historical QoS data often contain partial sensitive information of users; therefore, it becomes a necessity to protect the sensitive QoS data while making accurate recommendation decisions. To tackle the above challenges, we introduce the concepts of user/service location information and locality-sensitive hashing (LSH) in the domain and propose a location-aware recommendation approach with privacy-preservation capability. A wide range of experiments is set up based on the popular WS-DREAM data set, whose results prove the effectiveness and efficiency of our approach.

As one of the most successful recommendation techniques, neighborhood-based collaborative filtering (CF), which recommends appropriate items to a target user by identifying similar users or similar items, has been widely applied to various recommender systems. Although many neighbor-based CF methods have been put forward, there are still some open issues that have remained unsolved. First, the ever-increasing volume of user–item rating data decreases the recommendation efficiency significantly as a recommender system needs to analyze all the rating data when searching for similar neighbors or similar items. In this situation, users’ requirements on quick response may not be met. Second, in neighbor-based CF methods, more attention is paid to the recommendation accuracy while other key indicators of recommendation performances are often ignored, i.e., recommendation diversity (RD), which probably produces similar or redundant items in the recommended list and decreases users’ satisfaction. Considering these issues, a diversified and scalable recommendation method (called DR_LT) based on locality-sensitive hashing and cover tree is proposed in this article, where the item topic information is used to optimize the final recommended list. We show the effectiveness of our proposed method through a set of experiments on MovieLens data set that clearly shows the feasibility of our proposal in terms of item recommendation accuracy, diversity, and scalability.

With the development of industrial intelligence, the resource requests of various social media services in smart cities are expanding rapidly. For hosting services, the edge computing (EC) platform for its low-latency resource provisioning is fully explored. However, the mapping between edge servers (ESs) and services affects the service latency. Meanwhile, the real-time dynamic distribution of resource requirements also impairs the load balance. Therefore, how to optimize the load balance of ESs while meeting the latency-critical requests remains challenging. To deal with the above challenge, in this article, we propose a resource pre-allocation (RPA) method for the social media services with cognitive analytics. Technically, the deep spatiotemporal residual network (ST-ResNet) is employed to complete the cognitive analytics of resource requests. Then based on the analysis results, the optimal resource allocation (ORA) scheme is designed with multiobjective optimization. Finally, the performance of RPA is evaluated by a real-world resource request data set.

Real-time multimedia applications have gained immense popularity in the industrial Internet of Things (IIoT) paradigm. Due to the impact of the complex industrial environment, the transmission of video streaming is usually unstable. In the duration of a low bandwidth transmission, existing optimization methods often reduce the original resolution of some frames in a random way to avoid the video interruption. If the key frames with some important content are selected to be transmitted with a low resolution, it will greatly reduce the effect of industrial supervision. In view of this challenge, a real-time video streaming optimization method by reducing the number of video frames transmitted in the IIoT environment is proposed. Concretely, a deep learning-based object detection algorithm is recruited to effectively select the key frames in our method. The key frames with the original resolution will be transmitted along with audio data. As some nonkey frames are selectively discarded, it is helpful for smooth network transmitting with fewer bandwidth requirements. Moreover, we employ edge servers to run the object detection algorithm, and adjust video transmission flexibly. Extensive experiments are conducted to validate the effectiveness, and dependability of our method.

Nowadays, large number of cloud-based techniques have been used in industrial control systems (ICS), which also brings many security threats. The emergence of security-aware industrial control has paved the way of security-aware scheduling in cloud-based industrial applications. Actually, most cloud-based industrial applications are time sensitive, which need real-time processing. Edge cloud computing paradigm extends the computing ability of traditional cloud model with low-latency local resources. Thus, heterogeneous clouds that consist of both centralized resources and edge resources may be a promising resource model to provide both scalable and low-latency resources for cloud-based industrial applications. In view of these challenges, in this article, we propose a security-aware dynamic scheduling method for real-time resource allocation in ICS. First, a three-level security model is designed for both tasks and cloud resources in ICS, and a two-tier heterogeneous cloud architecture is introduced. Accordingly, a security-aware scheduling method based on distributed particle swarm optimization is presented for resource allocation with security concerns. To deal with the dynamics of edge resources and the mobility of mobile industrial applications, a dynamic scheduling mechanism based on dynamic workflow model is proposed for real-time optimization. Experimental results validate that the scheduling control policy proposed in this article can achieve a good balance between scheduling performance and security performance.

As one of the cyber–physical–social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial–temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users’ context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones.

The automotive industry, a key part of industrial Internet of Things, is now converging with cognitive computing (CC) and leading to industrial cognitive Internet of Vehicles (CIoV). As the major data source of industrial CIoV, social media has a significant impact on the quality of service (QoS) of the automotive industry. To provide vehicular social media services with low latency and high reliability, edge computing is adopted to complement cloud computing by offloading CC tasks to the edge of the network. Generally, task offloading is implemented based on the premise that edge servers (ESs) are appropriately quantified and located. However, the quantification of ESs is often offered according to empirical knowledge, lacking analysis on real condition of intelligent transportation system (ITS). To address the abovementioned problem, a collaborative method for the quantification and placement of ESs, named CQP, is developed for social media services in industrial CIoV. Technically, CQP begins with a population initializing strategy by Canopy and K-medoids clustering to estimate the approximate ES quantity. Then, nondominated sorting genetic algorithm III is adopted to achieve solutions with higher QoS. Finally, CQP is evaluated with a real-world ITS social media data set from China.

Internet of Vehicles (IoV) supports multiple traffic services by processing abundant data from sensors and video surveillance devices. With edge computing, video surveillance services can be certainly improved due to the handy resource provision for video storage and processing. Generally, to reduce the hardware and maintenance investment, it is a popular manner to deploy the limited amount of edge nodes along with the surveillance devices. However such edge node layout leads to the unstable service distribution and complicated data transmission across the surveillance devices and edge nodes, which consequently decreases the quality of the surveillance services. In addition, the service trustworthiness is suspected since the privacy information may be revealed to some extent during the data transmission. To combat these challenges, a trust-aware task offloading method (TOM) for video surveillance in edge computing enabled IoV is presented for minimizing the response time of the services, achieving the load balance of the edge nodes and realizing privacy protection. Technically, SPEA2 (improving the strength Pareto evolutionary algorithm) is employed to acquire balanced task offloading solutions. Then, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and MCDM (Multiple Criteria Decision Making) are exercised to ascertain the optimal solution. Finally, the experimental simulation demonstrates that TOM performs efficient and trust.

Intelligent vehicular networks converged with software-defined networking provides several flow-based surveillance services to mobile applications on vehicular nodes. But, as the scale of such networks grows exponentially, a substantial delay in processing tremendous flows emerges. The delay can be reduced by accelerating the packet classification methods, which are nowadays exploited in software-defined vehicular networks. Fast packet classification lets firewalls to inspect each incoming packet at wire speed. One of the well-known packet classification methods is the KD-tree algorithm. This paper presents an enhanced version of this algorithm that uses the geometric space to display different fields and increases search speed by recursive decomposition of the search space. Also, the enhanced KD-tree is integrated with a leaf-pushing technique, which enhances the performance of KD-tree search during classification. The proposed algorithm is implemented using a bloom filter data structure and a hash table. Experimental results show that the proposed leaf-pushed KD-tree algorithm improves packet classification speed up to 24 times in comparison with the conventional KD-tree. Moreover, the proposed algorithm can significantly reduce the classification time in comparison with state-of-the-art tree-based algorithms.

Currently, Edge computing (EC) paradigm is adopted to provision the low-latency resources for the massive real-time services in Internet of vehicles (IoV). To alleviate the QoE (Quality of Experience) degradation of the vehicular users due to the uncertainties (e.g., resource conflicts and communicating interruption), software-defined network (SDN) is involved in the EC-enabled IoV to manage the cooperative operation of distributed edge nodes (ENs). However, the increasing privacy leakage for the IoV service offloading causes the disclosure of the sensitive information, including driving location, personal information of the driver, etc. Moreover, the regulation of SDN is practically insufficient, as the general control is incompetent to maintain balanced operation with the premise of efficient service utility. In view of these challenges, a secure service offloading method, named SOME, is designed to promote IoV service utility and edge utility, meanwhile ensuring privacy security, in SDN-enabled EC. Specifically, an SDN-based framework for IoV service management is developed to address the inherent uncertainty of edge network by SDN controllers. Besides, the locality-sensitive-hash (LSH) is leveraged to realize utility- and privacy-aware service selection. Eventually, comparative experiments are implemented to verify the effectiveness of SOME.

Nowadays, the applications related to Internet of connected vehicles (IoCV) have been greatly promoted by the roadside units (RSUs). To improve the transmission efficiency by the RSUs, 5G is introduced to the IoCV scenario for offering sufficient communication bandwidth. Generally, the traditional offloading destinations of the computing tasks in IoCV are the distant cloud servers, which consequently increases the response time of the tasks. Edge servers, placed together with macro base stations (MABSs) in 5G and RSUs, offer alternatives to host the tasks. However, the complicated locations of MABSs and RSUs make it difficult to distinguish the offloading destinations of the computing tasks in IoCV. In view of this, an adaptive computation offloading method, named ACOM, is devised for edge computing in 5G-envisioned IoCV to optimize the task offloading delay and resource utilization of the edge system. More specifically, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is fully leveraged to generate the available solutions. Then, the optimal offloading solution is obtained by utility evaluation. Eventually, the experimental results demonstrate the effectiveness of ACOM.

Currently, the integration of the supply chain and blockchain is promising, as blockchain successfully eliminates the bullwhip effect in the supply chain. Generally, concurrent Practical Byzantine Fault Tolerance (PBFT) consensus method, named C-PBFT, is powerful to deal with the consensus inefficiencies, caused by the fast node expansion in the supply chain. However, due to the tremendous complicated transactions in the supply chain, it remains challenging to select the credible primary peers in the concurrent clusters. To address this challenge, the peers in the supply chain are classified into several clusters by analyzing the historic transactions in the ledger. Then, the primary peer for each cluster is identified by reputation assessment. Finally, the performance of C-PBFT is evaluated by conducting experiments in Fabric.

Using unsupervised learning methods for clinical diagnosis is very meaningful. In this study, we propose an unsupervised multi-view & multi-medoid variant-entropy-based fuzzy clustering (M 2 VEFC) method for epilepsy EEG signals detecting. Comparing with existing related studies, M 2 VEFC has four main merits and contributions: (1) Features in original EEG data are represented from different perspectives that can provide more pattern information for epilepsy signals detecting. (2) During multi-view modeling, multi-medoids are used to capture the structure of clusters in each view. Furthermore, we assume that the medoids in a cluster observed from different views should keep invariant, which is taken as one of the collaborative learning mechanisms in this study. (3) A variant entropy is designed as another collaborative learning mechanism in which view weight learning is controlled by a user-free parameter. The parameter is derived from the distribution of samples in each view such that the learned weights have more discrimination. (4) M 2 VEFC does not need original data as its input—it only needs a similarity matrix and feature statistical information. Therefore, the original data are not exposed to users and hence the privacy is protected. We use several different kinds of feature extraction techniques to extract several groups of features as multi-view data from original EEG data to test the proposed method M 2 VEFC. Experimental results indicate M 2 VEFC achieves a promising performance that is better than benchmarking models.

With the ever-increasing prosperity of web Application Programming Interface (API) sharing platforms, it is becoming an economic and efficient way for software developers to design their interested mashups through web API re-use. Generally, a software developer can browse, evaluate, and select his or her preferred web APIs from the API's sharing platforms to create various mashups with rich functionality. The big volume of candidate APIs places a heavy burden on software developers’ API selection decisions. This, in turn, calls for the support of intelligent API recommender systems. However, existing API recommender systems often face two challenges. First, they focus more on the functional accuracy of APIs while neglecting the APIs’ actual compatibility. This then creates incompatible mashups. Second, they often require software developers to input a set of keywords that can accurately describe the expected functions of the mashup to be developed. This second challenge tests partial developers who have little background knowledge in the fields. To tackle the above-mentioned challenges, in this article we propose a compatibility-aware and text description-driven web API recommendation approach (named WAR text ). WAR text guarantees the compatibility among the recommended APIs by utilizing the APIs’ composition records produced by historical mashup creations. Besides, WAR text entitles a software developer to type a simple text document that describes the expected mashup functions as input. Then through textual description mining, WAR text can precisely capture the developers’ functional requirements and then return a set of APIs with the highest compatibility. Finally, through a real-world mashup dataset ProgrammableWeb, we validate the feasibility of our novel approach.

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.

Internet of Vehicles (IoV) enables numerous in-vehicle applications for smart cities, driving increasing service demands for processing various contents (e.g., videos). Generally, for efficient service delivery, the contents from the service providers are processed on the edge servers (ESs), as edge computing offers vehicular applications low-latency services. However, due to the reusability of the same contents required by different distributed vehicular users, processing the copies of the same contents repeatedly in an edge server leads to a waste of resources (e.g., storage, computation, and bandwidth) in ESs. Therefore, it is a challenge to provide high-quality services while guaranteeing the resource efficiency with edge content caching. To address the challenge, an edge content caching method for smart cities with service requirement prediction, named E-Cache, is proposed. First, the future service requirements from the vehicles are predicted based on the deep spatiotemporal residual network (ST-ResNet). Then, preliminary content caching schemes are elaborated based on the predicted service requirements, which are further adjusted by a many-objective optimization aiming at minimizing the execution time and the energy consumption of the vehicular services. Eventually, experimental evaluations prove the efficiency and effectiveness of E-Cache with spatiotemporal traffic trajectory big data.

Anomaly detection is one of the most important data mining tasks in many real-life applications such as network intrusion detection for cybersecurity and medical diagnosis for healthcare. In the big data era, these applications demand fast and versatile anomaly detection capability to handle various types of increasingly huge-volume data. However, existing detection methods are either slow due to high computational complexity, or unable to deal with complicated anomalies like local anomalies. In this paper, we propose a novel anomaly detection method named OPHiForest with the use of the order preserving hashing based isolation forest. The core idea is to learn the information from data to construct better isolation forest structure than the state-of-the-art methods like iForest and LSHiForest, which can achieve robust detection of various anomaly types. We design a fast two-step learning process for the order preserving hashing scheme. This leads to stronger order preservation for better hashing, and therefore enhances anomaly detection robustness and accuracy. Extensive experiments on both synthetic and real-world data sets demonstrate that our method is highly robust and scalable.

The ever-increasing popularity of the recommender system provides a convenient way for users to find their interesting items among plenty of candidate services. However, on account of the enhancement of user privacy protection consciousness in recent years, users tend to conceal their evaluation information from the public. Thus, a large number of users with little explicit rating information are generated (i.e., cold-start users), which makes it challenging to implement high-quality recommendations. It has become a serious barrier to further and broader applications of the recommender system. In response to this issue, we take social network information into account and first propose TeCF (Trust-enhanced Collaborative Filtering). Our proposal integrates user-based, item-based, and trust-based collaborative filtering methods harmoniously and achieves a good trade-off between privacy preservation and service recommendation accuracy. A case study is conducted to validate the feasibility and comprehensiveness of our research.

The development of the Internet of Vehicles (IoV) enables numerous emerging in-vehicle applications to accommodate users with various contents, thus enhancing their traveling experiences. In IoV, content decoding tasks are typically offloaded to edge servers for implementation, as edge computing is an admirable paradigm to provide low-latency services. However, as different vehicular users may request the same contents, processing these contents repeatedly leads to the waste of storage, computation and bandwidth resources. Therefore, fine-grained computation offloading and content caching are demanded in IoV. In this paper, a joint optimization method for computation offloading and content caching based on traffic flow prediction, named COC, is proposed. Firstly, traffic flow covered by each edge server is predicted by a modified deep spatiotemporal residual network (ST-ResNet). Secondly, the non-dominated sorting genetic algorithm III (NSGA-III) is leveraged to realize the many-objective optimization to shorten the execution time and reduce the energy consumption of computation and transmission in IoV. Finally, evaluated by real-world big data from Nanjing China, COC shows a great reduction in execution time and energy consumption of transmission and computation compared to other methods.

With the advent of the Internet of Vehicles (IoV), drivers are now provided with diverse time-sensitive vehicular services that usually require a large scale of computation. As civilian vehicles are generally insufficient in computational resources, their service requests are offloaded to cloud data centers and edge computing devices (ECDs) with ample computational resources to enhance the quality of service (QoS). However, ECDs are often overloaded with excessive service requests. In addition, as the network conditions and service compositions are complicated and dynamic, the centralized control of ECDs is hard to achieve. To tackle these challenges, a dynamic task offloading method with minority game (MG) in cloud-edge computing, named DOM, is proposed in this paper. Technically, MG is an effective tool with a distributed mechanism which can minimize the dependency on centralized control in resource allocation. In the MG, reinforcement learning (RL) is applied to optimize the distributed decision-making of participants. Finally, with a real-world dataset of IoV services, the effectiveness and adaptability of DOM are evaluated.

Mobile edge computing (MEC) has recently become a promising paradigm to meet the increasing computing requirement of mobile devices, and hybrid energy supply has been considered as an effective approach for saving the energy consumption of the MEC system and making it environmentally friendly. In particular, the joint task scheduling and energy management (TSEM) scheme plays a crucial role in reaping the benefits of MEC with hybrid energy supply. In this article, we focus on jointly optimizing the TSEM decisions to maximize the utility of the MEC system which accounts for both the computation throughput and the fairness among different cells, by formulating a stochastic optimization problem subject to the constraints of queue stability and energy budget. We transform the formulated problem into a deterministic problem and then decouple it into four independent subproblems, which can be solved in a distributed manner without future system statistical information. An online TSEM algorithm is developed to derive the optimal solutions to these subproblems. Mathematical analysis shows that TSEM can achieve a close-to-optimal system utility and realize the utility-queue tradeoff. The experimental results validate the advantages of TSEM in improving the system utility and stabilizing the queue length.

In the participatory sensing framework, privacy protection of the Internet of Things (IoT) is very important. In this article, cryptography-based methods are utilized to protect participants' privacy information in unsecured network channels for dynamic and real-time sensing tasks. The edge computing paradigm is introduced in the traditional participatory sensing framework to reduce network latency. Then, the Rivest Cipher 4 stream cipher and logistic mapping are combined to deal with the problems of participants' limited resources and untruthful third-party platforms. Finally, the product algebra and logistic mapping are combined to deal with the problems of large numbers of participants' access and poor randomness of keystream. Through extensive performance evaluation and comparison experiments on the real-world data, the effectiveness and adaptation of the proposed privacy protection based on stream cipher are verified. It could effectively solve the problem of poor network latency and improve the privacy protection level of IoT.

Internet of Things (IoT) devices and the edge jointly broaden the IoT's sensing capability and the monitoring scope for various applications. Though accessing sensing data and making decisions through IoT smart devices turns out to be commonplace, it is challenging to guarantee user privacy and preserve the accuracy (integrity) of the collected data. The IoT smart devices frequently lose either IoT user's privacy or data integrity. This also makes it crucial to put a threshold on the cost of computation and load of the IoT devices, as gradually more IoT services demand access to the resources that devices offer. In this article, we propose BalancePIC, a scheme that attempts to preserve a balance in the three aspects (user privacy, data integrity in edge-assisted IoT devices, and the computational cost). It achieves the balance through a balanced truth discovery approach and a proposed enhanced technique for data privacy, which are used in IoT devices and edge server interactions. It authenticates the IoT user participation with privacy in the truth discovery process through a biometric-ECC-based authentication algorithm. The nature of the BalancePIC scheme is to straightforwardly provide the likelihood for a simple amendment on the cryptography technique and weight assignment. This lessens the overall computational cost for the IoT user devices but also restricts the communications between the user devices and the edge server, which is important for data integrity. We present an enhanced technique to preserve privacy by guarding the user from potential threats and suspicious data collection parties. To achieve this, BalancePIC takes steps to blur the original sensory data of the device by processing results in groups called zones. Simulation result analysis provides evidence for the balance preservation in the three aspects.

Currently, edge computing (EC), emerging as a burgeoning paradigm, is powerful in handling real-time resource provision for Internet of Things (IoT) applications. However, due to the spatial distribution of geographically sparse IoT devices and the resource limitations of EC units (ECUs), the resource utilization of corresponding edge servers is relatively insufficient and the execution performance is ineffective to some extent. A privacy leakage, including personal information, location, media data, etc., during the transmission process from IoT devices to edge servers severely restricts the application of ECUs in IoT. To address these challenges, a two-phase offloading optimization strategy is put forward for joint optimization of offloading utility and privacy in EC enabled IoT. Technically, a utility-aware task offloading method, named UTO, is devised first to obtain the goal of maximizing the resource utilization of ECUs and minimizing the implementation time cost. Then a joint optimization method, named JOM, for utility and privacy tradeoffs is designed to balance the privacy preservation and execution performance. Eventually, the experimental evaluations are designed to illustrate the efficiency and reliability of UTO and JOM.

Smart city is a fast-developing system enabled by Internet of Things (IoT) with massive collaborative services (e.g., intelligent transportation and collaborative diagnosis). Generally, the terminals in the smart city are provided with limited computing ability, thus incapable of processing the diversified and cross-application services. Faced with insufficient resource provisioning for the collaborative smart city services, edge computing is emerged as a novel paradigm to provide city terminals with more processing capacity. Nevertheless, as there is a tremendous threat of disclosing private information in the offloading of collaborative services, it is imperative to improve privacy security in the edge computing. With the intention of addressing the privacy disclosure, an intelligent offloading method (IOM) for smart city, realizing privacy preservation, improving offloading efficiency, and promoting edge utility, is proposed. Technically, the information entropy mechanism is employed to be integrated with edge computing to obtain the balance between privacy preservation and collaborative service performance. Eventually, the simulation analysis is implemented to verify the effectiveness of IOM.

Smart city is gradually forming a large scope of Internet of Things (IoT) networks with diffusely deployed IoT devices that produce quantities of services. Considering the large-scale and widely distributed features of IoT networks, edge computing is emerged as a powerful and suitable paradigm to provide computing abilities for the IoT devices at the edge of the networks. In edge computing, the IoT services could be placed on the edge computing units (ECUs) for execution, which provides low latency and eases the burden of bandwidth. However, it is still challenging to improve the overall ECU execution performance (i.e., the resource usage, the load balance levels, and the power consumption of ECUs) and meanwhile prevent privacy leakage of the IoT devices for service placement. To tackle this challenge, a trust-oriented IoT service placement method, abbreviated as TSP, is proposed for smart cities in edge computing. Technically, improving the strength Pareto evolutionary algorithm (SPEA2) is leveraged to acquire the balanced placement strategies for the tradeoffs among the execution performance metrics with privacy preservation. Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) and multicriteria decision-making (MCDM) techniques are employed to identify the optimal placement strategy among the obtained service placement strategies. Eventually, systematic experiments are conducted to verify the efficiency and reliability of TSP.

Currently, the generative adversarial network (GAN), with complex training processes in the physical machine (PM), has achieved great priority in image generation , audio conversion, image translation, etc. To improve the training efficiency of GAN, the edge computing paradigm is accepted as an alternative of the PMs to accommodate the training tasks, that is, the training tasks are migrated to the edge nodes (ENs) for hosting. However, it is still a key challenge to keep the overall network performance (i.e., load balance , transmission time) and privacy protection of training tasks at the same time. To address this challenge, a privacy-aware task offloading method, named POM, is developed accordingly in this paper. First, improving the strength pareto evolutionary algorithm (SPEA2) is fully investigated to obtain the offloading strategies for collaboratively improving the training performance and privacy preservation . Then, the most balanced offloading strategy is acquired for training GAN. Eventually, systematic experiments indicate that POM achieves an optimal performance efficiently among the other representative benchmark methods.

In this paper, we consider the issue of the secure transmissions for the cognitive radio-based Internet of Medical Things (IoMT) with wireless energy harvesting. In these systems, a primary transmitter (PT) will transmit its sensitive medical information to a primary receiver (PR) by a multi-antenna-based secondary transmitter (ST), where we consider that a potential eavesdropper may listen to the PT’s sensitive information. Meanwhile, the ST also transmits its own information concurrently by utilizing spectrum sharing. We aim to propose a novel scheme for jointly designing the optimal parameters, i.e., energy harvesting (EH) time ratio and secure beamforming vectors, for maximizing the primary secrecy transmission rate while guaranteeing secondary transmission requirement. For solving the nonconvex optimization problem, we transfer the problem into convex optimization form by adopting the semidefinite relaxation (SDR) method and Charnes–Cooper transformation technique. Then, the optimal secure beamforming vectors and energy harvesting duration can be obtained easily by utilizing the CVX tools. According to the simulation results of secrecy transmission rate, i.e., secrecy capacity, we can observe that the proposed protocol for the considered system model can effectively promote the primary secrecy transmission rate when compared with traditional zero-forcing (ZF) scheme, while ensuring the transmission rate of the secondary system.

Cloud computing has promoted the success of big data applications such as medical data analyses. With the abundant resources provisioned by cloud platforms, the quality of service (QoS) of services that process big data could be boosted significantly. However, due to unstable network or fake advertisement, the QoS published by service providers is not always trusted. Therefore, it becomes a necessity to evaluate the service quality in a trustable way, based on the services' historical QoS records. However, the evaluation efficiency would be low and cannot meet users' quick response requirement, if all the records of a service are recruited for quality evaluation. Moreover, it may lead to `Lagging Effect' or low evaluation accuracy, if all the records are treated equally, as the invocation contexts of different records are not exactly the same. In view of these challenges, a novel approach named Partial Historical Records-based service evaluation approach (Partial-HR) is put forward in this paper. In Partial-HR, each historical QoS record is weighted based on its service invocation context. Afterwards, only partial important records are employed for quality evaluation. Finally, a group of experiments are deployed to validate the feasibility of our proposal, in terms of evaluation accuracy and efficiency.

With the rapid development of Industry 5.0 and mobile devices, the research of mobile crowdsensing networks has become an important research focus. Task allocation is an important research content that can inspire crowd workers to participate in crowd tasks and provide truthful sensed data in mobile crowdsourcing systems. However, how to inspire crowd workers to participate in crowd tasks and provide truthful sensed data still has many challenges. In this article, based on the Markov model and collaborative filtering model, the similarities, trajectory prediction, dwell time, and trust degree are considered to propose the Markov and Collaborative filtering-based Task Recommendation (MCTR) model. Then, based on the Walrasian equilibrium, the optimum solution is researched to maximize the social welfare of mobile crowdsourcing systems. Finally, the comparison experiments are carried out to evaluate the performance of the proposed multiobjective optimization and the Markov-based task allocation with other methods. Through comparison experiments, the efficiency and adaptation of mobile crowdsourcing systems could be improved by the proposed task allocation.

The development of smart cities and deep learning technology is changing our physical world to a cyber world. As one of the main applications, the Internet of Vehicles has been developing rapidly. However, privacy leakage and delay problem for data collection remain as the key concerns behind the fast development of the cyber intelligence technologies. If the original data collected are directly uploaded to the cloud for processing, it will bring huge load pressure and delay to the network communication. Moreover, during this process, it will lead to the leakage of data privacy. To this end, in this article we design a data collection and preprocessing scheme based on deep learning, which adopts the semisupervised learning algorithm of data augmentation and label guessing. Data filtering is performed at the edge layer, and a large amount of similar data and irrelevant data are cleared. If the edge device cannot process some complex data independently, it will send the processed and reliable data to the cloud for further processing, which maximizes the protection of user privacy. Our method significantly reduces the amount of data uploaded to the cloud, and meanwhile protects the user's data privacy effectively.

Benefiting from the real-time processing ability of edge computing, computing tasks requested by smart devices in the Internet of Things are offloaded to edge computing devices (ECDs) for implementation. However, ECDs are often overloaded or underloaded with disproportionate resource requests. In addition, during the process of task offloading, the transmitted information is vulnerable, which can result in data incompleteness. In view of this challenge, a blockchain-enabled computation offloading method, named BeCome, is proposed in this article. Blockchain technology is employed in edge computing to ensure data integrity. Then, the nondominated sorting genetic algorithm III is adopted to generate strategies for balanced resource allocation. Furthermore, simple additive weighting and multicriteria decision making are utilized to identify the optimal offloading strategy. Finally, performance evaluations of BeCome are given through simulation experiments.

Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that. In practice, NMF not only neglects the manifold structure of data samples, but also overlooks the priori label information of different classes. In this paper, a novel matrix decomposition method called Hyper-graph regularized Constrained Non-negative Matrix Factorization (HCNMF) is proposed for selecting differentially expressed genes and tumor sample classification. The advantage of hyper-graph learning is to capture local spatial information in high dimensional data. This method incorporates a hyper-graph regularization constraint to consider the higher order data sample relationships. The application of hyper-graph theory can effectively find pathogenic genes in cancer datasets. Besides, the label information is further incorporated in the objective function to improve the discriminative ability of the decomposition matrix. Supervised learning with label information greatly improves the classification effect. We also provide the iterative update rules and convergence proofs for the optimization problems of HCNMF. Experiments under The Cancer Genome Atlas (TCGA) datasets confirm the superiority of HCNMF algorithm compared with other representative algorithms through a set of evaluations.

In this paper, we propose a fully automatic image colorization method for grayscale images using neural network and optimization. For a determined training set including the gray images and its corresponding color images, our method segments grayscale images into superpixels and then extracts features of particular points of interest in each superpixel. The obtained features and their RGB values are given as input for, the training colorization neural network of each pixel. To achieve a better image colorization effect in shorter running time, our method further propagates the resulting color points to neighboring pixels for improved colorization results. In the propagation of color, we present a cost function to formalize the premise that neighboring pixels should have the maximum positive similarity of intensities and colors; we then propose our solution to solving the optimization problem. At last, a guided image filter is employed to refine the colorized image. Experiments on a wide variety of images show that the proposed algorithms can achieve superior performance over the state-of-the-art algorithms.

Benefiting from the massive available data provided by Internet of multimedia things (IoMT), enormous intelligent services requiring information of various types to make decisions are emerging. Generally, the IoMT devices are equipped with limited computing power, interfering with the process of computation-intensive services. Currently, to satisfy a wide range of service requirements, the novel computing paradigms, i.e., cloud computing and edge computing, can potentially be integrated for service accommodation. Nevertheless, the private information (i.e., location, service type, etc.) in the services is prone to spilling out during service offloading in the cloud-edge computing. To avoid privacy leakage while improving service utility, including the service response time and energy consumption for service executions, a Locality-sensitive-hash (LSH)-based offloading method, named LOM, is devised. Specifically, LSH is leveraged to encrypt the feature information for the services offloaded to the edge servers with the intention of privacy preservation. Eventually, comparative experiments are conducted to verify the effectiveness of LOM with respect to promoting service utility.

In recent years, the number of web services grows explosively. With a large amount of information resources, it is difficult for users to quickly find the services they need. Thus, the design of an effective web service recommendation method has become the key factor to satisfy the requirements of users. However, traditional recommendation methods often tend to pay more attention to the accuracy of the results but ignore the diversity, which may lead to redundancy and overfitting, thus reducing the satisfaction of users. Considering these drawbacks, a novel method called DivMTID is proposed to improve the effectiveness by achieving accurate and diversified recommendations. First, we utilize users’ historical scores of web services to explore the users’ preferences. And we use the TF-IDF algorithm to calculate the weight vector of each web service. Second, we utilize cosine similarity to calculate the similarity between candidate web services and historical web services and we also forecast the ranking scores of candidate web services. At last, a diversification method is used to generate the top- K recommended list for users. And through a case study, we show that DivMTID is an effective, accurate, and diversified web service recommendation method.

Due to the high demands of mobile video streaming, wireless networks have witnessed great pressure on increasing the transmitting rate. Crowdsourcing sets the trend of ensuring direct communication among the participants, thus expanding the bandwidth of networks, shunting the traffic volume of the core networks and improving the video service quality for mobile users. However, irregularly responding to the requestors poses a threat to the battery life of the mobile devices, and decreasing the service time and incomes of the providers remains challenging. To address this challenge, we propose a multi-objective crowdsourcing method, named MCM, for mobile video streaming. Technically, DBSCAN (Density-based Spatial Clustering for Applications with Noise) and IDP (Improved Dynamic Programming) are utilized to generate the service strategies. Consequently, experimental evaluations are conducted to demonstrate the efficiency of MCM.

Nowadays, recommender systems have become one of the main tools and methods for users to search for their interested papers from massive candidates.Typically, through analyzing the typed keywords by a user, a recommender system can easily retrieve the papers that cover the keywords, in an efficient and economic manner.However, one paper often only contains partial keywords that the user is interested in; therefore, the recommender system needs to analyze a pre-built paper citation graph and then return a set of papers that collectively satisfy the user's requested keywords.While the existing paper citation graph does not consider the possible self-citations and potential correlations among the papers that are not connected in the paper citation graph but with close publication time.Considering the above drawbacks, in this paper, we propose a link prediction approach that combines time, keywords and authors information for constructing a new relation graph.Finally, a case study is employed to explain our approach step by step and demonstrate the feasibility of our proposal.

Abstract Cloudlet is a novel computing paradigm, introduced to the mobile cloud service framework, which moves the computing resources closer to the mobile users, aiming to alleviate the communication delay between the mobile devices and the cloud platform and optimize the energy consumption for mobile devices. Currently, the mobile applications, modeled by the workflows, tend to be complicated and computation‐intensive. Such workflows are required to be offloaded to the cloudlet or the remote cloud platform for execution. However, it is still a key challenge to determine the offloading resolvent for the deadline‐constrained workflows in the cloudlet‐based mobile cloud, since a cloudlet often has limited resources. In this paper, a multiobjective computation offloading method, named MCO, is proposed to address the above challenge. Technically, an energy consumption model for the mobile devices is established in the cloudlet‐based mobile cloud. Then, a corresponding computation offloading method, by improving Nondominated Sorting Genetic Algorithm II, is designed to achieve the goal of energy saving for all the mobile device while satisfying the deadline constraints of the workflows. Finally, extensive experimental evaluations are conducted to demonstrate the efficiency and effectiveness of our proposed method.

Summary The fine‐grained access control has been proved to be a reliable tool to ensure preserving of privacy of end users. In fog computing, one of the challenges is to understand users' attitudes and behaviors toward personalized control. However, few of studies have given a clear view on users' perception of the burden of interactivity when they set complex privacy settings. To this end, we conducted a user study including a lab study with 26 participants and an evaluation with 223 participants. From the lab study, we found that participants were satisfied with improved privacy settings but did not adapt well to complex personalized interfaces. We proposed effective methods to assist users to balance between the full control and the additional interaction burden, including sorting, recommendations, and establishing profiles. After this lab study, we organized a survey evaluation additionally to explore users' current usage of privacy features. Results from the evaluation showed that the principle reason that users failed to use privacy features was that they were not appropriately aware of these features. A key conclusion is that privacy settings should not only let users take over the control of smartphones but also inform them of the knowledge on privacy practices.

We are pleased to announce the publication of the special issue focusing on intrusion detection and prevention in cloud, fog, and Internet of Things (IoT).Internet of Things (IoT), cloud, and fog computing paradigms are as a whole provision a powerful large-scale computing infrastructure for many data and computation intensive applications.Specifically, the IoT technologies and deployment can widely perceive our physical world at a fine granularity and generate sensing data for further insight extraction.The fog computing facilities can provide computing power near the IoT devices where data are generated, aiming to achieve fast data processing for time critical applications or save the amount of data transmitted into cloud for storage or further processing.The cloud computing platforms can offer big data storage and large-scale processing services for cheap long-term storage or data intensive analytics with more advanced data mining models.Hence, it can be seen that the IoT/fog/cloud computing infrastructures can support the whole lifecycle of large-scale applications where big data collection, transmission, storage, processing, and mining can be seamlessly integrated.However, these state-of-the-art computing infrastructures still suffer from severe security and privacy threats because of their built-in properties such as the ubiquitous-access and multitenancy features of

The increasing number of web APIs registered in service sharing communities (e.g., http://ProgrammableWeb.com that provides a platform benefiting the social interactions between different software developers) has provided a promising way to quickly build various apps with diverse functions. Generally, an app developer can manually discover, select, and compose a set of appropriate web APIs to build a new app satisfying the developer's functional and nonfunctional business requirements, economically and conveniently. However, the above manual web APIs selection process is usually time-consuming and cumbersome as most app developers often do not have much background knowledge of candidate web APIs. Moreover, the manually selected web APIs cannot always guarantee to be integrated successfully as the compatibilities between different web APIs are often varied and not validated. In view of these challenges, we define a weighted APIs correlation graph (W-ACG) in this paper to model the APIs functions and compatibilities. Furthermore, we propose a novel web APIs recommendation approach named Keywords-based and Compatibility-aware APIs Recommendation (K-CAR) based on the defined W-ACG. Through analyzing the input keywords describing the functions expected by an app developer, K-CAR can return the app developer a set of optimal APIs that are not only functional-qualified but also compatibility-guaranteed. Extensive experiments are deployed on 18 478 real-world web APIs and 6146 real-world apps to evaluate the usefulness and efficiency of K-CAR.

Crowdsourcing is a booming technique that enables participants to exchange data directly, thus making it possible to answer latency-sensitive service requests and relieve the burden of core networks. With some incentives, providers compete to furnish service requests, thus pledging the quality of experience (QoE) for requestors. However, the decentralized communication in crowdsourcing increases the probability of information tapering. Furthermore, providers' arbitrary selection of the requests poses great threat to the efficient and profitable service provision for the requestors. To deal with these challenges, we propose a blockchain-powered crowdsourcing method, named BPCM, while considering the privacy preservation in mobile environment. Specifically, a mobile crowdsourcing framework based on blockchain is designed first to preserve the privacy of the participants and keep the integrity of the service request and provision. Then, density-based spatial clustering of applications with noise (DBSCAN) and improved dynamic programming (IDP) are adopted to cluster the requestors and generate service strategies, respectively. Furthermore, simple additive weighting (SAW) and multiple criteria decision making (MCDM) are utilized to select the optimal strategy that achieves the tradeoffs among maximizing the service time, increasing the profits, and reducing the energy consumption for the providers. Finally, comprehensive experiments are conducted to verify the accuracy and effectiveness of BPCM.

At present, to improve the accuracy and performance for personalized recommendation in mobile wireless networks, deep learning has been widely concerned and employed with social and mobile trajectory big data. However, it is still challenging to implement increasingly complex personalized recommendation applications over big data. In view of this challenge, a hybrid recommendation framework, i.e., deep CNN-assisted personalized recommendation, named DCAPR, is proposed for mobile users. Technically, DCAPR integrates multisource heterogeneous data through convolutional neural network, as well as inputs various features, including image features, text semantic features, and mobile social user trajectories, to construct a deep prediction model. Specifically, we acquire the location information and moving trajectory sequence in the mobile wireless network first. Then, the similarity of users is calculated according to the sequence of moving trajectories to pick the neighboring users. Furthermore, we recommend the potential visiting locations for mobile users through the deep learning CNN network with the social and mobile trajectory big data. Finally, a real-word large-scale dataset, collected from Gowalla, is leveraged to verify the accuracy and effectiveness of our proposed DCAPR model.

As one of the hot issues in the research on complicated network, collaborative network traffic analysis plays a core role in improving network performance, analyzing network behaviors and predicting abnormal traffic, especially from large-scale network data sets. Several approaches on flow-level traffic data analysis have been proposed about analyzing the structure and situation of the network. Different from the state-of-the-art, we first propose a new decomposition model based on the alternating direction method of multipliers for packet-level traffic data. In addition, we present the iterative scheme of the algorithm for network anomaly detection problem, which is termed NTA-ADMM. Based on this approach, we can carry out intrusion detection for packet-level network traffic data, no matter whether it is polluted by noise or not. Finally, we design a prototype system for network anomaly detection such as unauthorized access from a remote machine to a local machine (R2L) attack and so on. The experiments have shown that our approach is effective in revealing the patterns of network traffic data and detecting attacks from large-scale network traffic. Moreover, the experiments have demonstrated the robustness of the algorithm even when the network traffic is polluted by the large volume anomalies and noise.

Recommending appropriate product items to the target user is becoming the key to ensure continuous success of E-commerce. Today, many E-commerce systems adopt various recommendation techniques, e.g., Collaborative Filtering (abbreviated as CF)-based technique, to realize product item recommendation. Overall, the present CF recommendation can perform very well, if the target user owns similar friends (user-based CF), or the product items purchased and preferred by target user own one or more similar product items (item-based CF). While due to the sparsity of big rating data in E-commerce, similar friends and similar product items may be both absent from the user-product purchase network, which lead to a big challenge to recommend appropriate product items to the target user. Considering the challenge, we put forward a Structural Balance Theory-based Recommendation (i.e., SBT-Rec) approach. In the concrete, (I) user-based recommendation: we look for target user's “enemy” (i.e., the users having opposite preference with target user); afterwards, we determine target user's “possible friends”, according to “enemy's enemy is a friend” rule of Structural Balance Theory, and recommend the product items preferred by “possible friends” of target user to the target user. (II) likewise, for the product items purchased and preferred by target user, we determine their “possibly similar product items” based on Structural Balance Theory and recommend them to the target user. At last, the feasibility of SBT-Rec is validated, through a set of experiments deployed on MovieLens-1M dataset.

With the ever‐increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross‐platform data collaboration and service recommendation process. Locality‐Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy‐preserving distributed service recommendation. However, existing LSH‐based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH‐based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

With the advent of IoT (Internet of Things) age, considerable web services are emerging rapidly in service communities, which places a heavy burden on the target users' service selection decisions. In this situation, various techniques, e.g., collaborative filtering (i.e., CF) is introduced in service recommendation to alleviate the service selection burden. However, traditional CF-based service recommendation approaches often assume that the historical user-service quality data is centralized, while neglect the distributed recommendation situation. Generally, distributed service recommendation involves inevitable message communication among different parties and hence, brings challenging efficiency and privacy concerns. In view of this challenge, a novel privacy-preserving distributed service recommendation approach based on Locality-Sensitive Hashing (LSH), i.e., DistSRLSH is put forward in this paper. Through LSH, DistSRLSH can achieve a good tradeoff among service recommendation accuracy, privacy-preservation and efficiency in distributed environment. Finally, through a set of experiments deployed on WS-DREAM dataset, we validate the feasibility of our proposal in handling distributed service recommendation problems.

To maximize the economic benefits, a cloud service provider needs to recommend its services to as many users as possible based on the historical user-service quality data. However, when a cloud platform (e.g., Amazon) intends to make a service recommendation decision, considering only its own user-service quality data is insufficient, because a cloud user may invoke services from multiple distributed cloud platforms (e.g., Amazon and IBM). In this situation, it is promising for Amazon to collaborate with other cloud platforms (e.g., IBM) to utilize the integrated data for the service recommendation to improve the recommendation accuracy. However, two challenges are present in the above-mentioned collaboration process, where we attempt to use multi-source data for the service recommendation. First, protecting users' privacy is challenging when IBM releases its own data to Amazon. Second, the recommendation efficiency and scalability are often low when the user-service quality data of Amazon and IBM update frequently. Considering these challenges, a privacy-preserving and scalable service recommendation approach based on distributed locality-sensitive hashing, i.e., SerRecdistri-LSH, is proposed in this paper to handle the service recommendation in a distributed cloud environment. Extensive experiments on the WS-DREAM data set validate the feasibility of our approach in terms of service recommendation accuracy, scalability, and privacy preservation.

The QoS (quality of service) of a cloud service is not always trusted as advertised, due to the variable network environment or fake advertisement reasons. Therefore, to promote the trusted service selection, we should first evaluate the trust of each cloud service, based on its historical QoS records from past invocations. However, different from the traditional web service whose historical QoS record is a fixed value (e.g., a historical latency record of a web service is 2s), the historical QoS record of a cloud service is usually not fixed, but fluctuant during the long-running period of a single service invocation. For example, an virtual organization O rents cloud service WeatherInquiry between 8:00 am and 8:00 pm so that the employees of O can access WeatherInquiry. In this scenario, the latency of WeatherInquiry is fluctuant from the perspective of organization O, during the served 12 hours. In this situation, it is a challenge to evaluate the trust of service WeatherInquiry, based on WeatherInquiry's multiple historical QoS records that fluctuate continuously. In view of this challenge, we introduce a novel concept of flexible SLA, to better accommodate the fluctuant QoS of cloud service, and further put forward a trust evaluation method based on fluctuant QoS and flexible SLA, i.e., FL-FL (FLuctuant QoS-FLexible SLA-based trust evaluation method, FL-FL). Finally, a set of experiments are designed and deployed to validate the feasibility of our proposal, in terms of effectiveness and efficiency.

Due to the unstable network environment or fake advertisement reasons, the QoS (quality of service) data published by web service providers is not always trusted. Therefore, it is a promising way to evaluate the service quality, based on the historical QoS records generated from the past invocations of web services. However, for some web services with large numbers of historical QoS records, the evaluation efficiency is usually low and cannot meet the quick response requirements of subsequent service selection or service composition. Moreover, it will lead to the 'Lagging Effect' (i.e., The evaluation result cannot reflect the up-to-date quality change trend of a web service), if all the historical QoS records are treated equally in service evaluation. In view of these challenges, a novel service evaluation method named Partial-HR (Partial Historical Records-based service evaluation method) is put forward in this paper, by which only partial important historical QoS records are employed to evaluate the service quality. Finally, a set of experiments are designed and deployed to validate the feasibility of our proposal, in terms of evaluation accuracy and efficiency.

Abstract Scientific workflow is gaining ever‐increasing attention as it integrates a wide range of heterogeneous services to enable and accelerate the scientific discovery processes. However, the exception occurred in a scientific workflow execution often decreases the robustness of a scientific workflow system. For example, if a service engaged in a scientific workflow schedule becomes unavailable during its execution, an exception might be raised and the whole workflow execution may be interrupted unexpectedly. In this situation, it is a challenge to smooth the interrupted workflow execution. In view of this challenge, an exception handling method, named RelaxingMe (constraints Relaxing Method, RelaxingMe) is proposed in this paper. This method aims at relaxing the original QoS constraint values requested by the interrupted task node, in order to find a near‐to‐optimal candidate service to replace the unavailable one. At last, the feasibility of our method is evaluated by an experiment. Copyright © 2011 John Wiley & Sons, Ltd.

There exist so many web services that share same or similar functional properties, so it is often a challenging effort to select a credible and optimal web service based on their various history QoS records. In view of this challenge, in this paper, a novel QoS-aware web service selection method is put forward, based on credibility evaluation associated with negotiated QoS dimensions. More specifically, the historical empirical data, i.e., execution logs of a service, are used for evaluation purpose. At last, a case study is employed for illustration purpose, and the evaluations are presented to demonstrate the feasibility of our method.

Web service composition, i.e., WSC, has emerged as a promising way to integrate various distributed computing resources for complex application requirements. However, much computation time is needed to determine the optimal composite solution, which embarrasses the popularity of WSC in actual real time applications. In view of this challenge, in this paper, a heuristic service composition method, named LOEM (Local Optimization and Enumeration Method, LOEM), is proposed. It aims at filtering the candidates of each task to a small number of promising ones by local selection, and then enumerates all the composite solutions to pursue a near-to-optimal one. The experiment results demonstrate the feasibility of LOEM in dealing with the WSC problems.