The Mamba architecture has been widely applied to various low-level vision tasks due to its exceptional adaptability and strong performance. Although the Mamba architecture has been adopted for spectral reconstruction, it still faces the following two challenges: (1) Single spatial perception limits the ability to fully understand and analyze hyperspectral images; (2) Single-scale feature extraction struggles to capture the complex structures and fine details present in hyperspectral images. To address these issues, we propose a multi-scale, multi-perceptual Mamba architecture for the spectral reconstruction task, called M3SR. Specifically, we design a multi-perceptual fusion block to enhance the ability of the model to comprehensively understand and analyze the input features. By integrating the multi-perceptual fusion block into a U-Net structure, M3SR can effectively extract and fuse global, intermediate, and local features, thereby enabling accurate reconstruction of hyperspectral images at multiple scales. Extensive quantitative and qualitative experiments demonstrate that the proposed M3SR outperforms existing state-of-the-art methods while incurring a lower computational cost.

Online platforms such as Amazon and Netflix serve users across multiple countries and regions, underscoring the importance of multi-market recommendation (MMR). Most MMR methods adopt a pre-training and fine-tuning paradigm, in which a unified model is first trained on centralized, global data and subsequently adapted to specific markets. However, this approach ignores the privacy of market data. While traditional federated learning preserves privacy, it typically aims to obtain a global model by aggregating model parameters and does not account for significant market heterogeneity. Additionally, because ID spaces are disjoint across markets, embedding-based aggregation strategies become ineffective. To overcome these challenges, we propose a federated collaborative signal quantization (FedMM) method for multi-market click-through rate (CTR) prediction. Our core idea leverages a discrete codebook mechanism to achieve privacy-preserving transmission and align disjoint ID spaces. We further employ a hierarchical codebook structure to capture cross-market shared patterns and market-specific characteristics. Specifically, we deploy a residual quantized variational autoencoder (RQ-VAE) with a dual-layer codebook mechanism for each market to quantize collaborative embeddings. The first layer utilizes a global federated codebook, updated via aggregation to capture universally shared collaborative patterns, while the second layer maintains a local codebook to learn market-specific semantics. Finally, the learned discrete codes, which integrate both general and specific collaborative signals, are incorporated into downstream CTR models to enhance prediction accuracy across all markets. Extensive experiments on benchmark datasets demonstrate that FedMM significantly improves recommendation performance with privacy guarantees.

Multi-scenario multi-task recommendation (MSMTR) systems must address recommendation demands across diverse scenarios while simultaneously optimizing multiple objectives, such as click-through rate and conversion rate. Existing MSMTR models typically consist of four information units: scenario-shared, scenario-specific, task-shared, and task-specific networks. These units interact to generate four types of relationship information flows, directed from scenario-shared or scenario-specific networks to task-shared or task-specific networks. However, these models face two main limitations: 1) They often rely on complex architectures, such as mixture-of-experts (MoE) networks, which increase the complexity of information fusion, model size, and training cost. 2) They extract all available information flows without filtering out irrelevant or even harmful content, introducing potential noise. Regarding these challenges, we propose a lightweight Automated Information Flow Selection (AutoIFS) framework for MSMTR. To tackle the first issue, AutoIFS incorporates low-rank adaptation (LoRA) to decouple the four information units, enabling more flexible and efficient information fusion with minimal parameter overhead. To address the second issue, AutoIFS introduces an information flow selection network that automatically filters out invalid scenario-task information flows based on model performance feedback. It employs a simple yet effective pruning function to eliminate useless information flows, thereby enhancing the impact of key relationships and improving model performance. Finally, we evaluate AutoIFS and confirm its effectiveness through extensive experiments on two public benchmark datasets and an online A/B test.

Abstract Different from most conventional recommendation problems, sequential recommendation (SR) focuses on learning users’ preferences by exploiting the internal order and dependency among the interacted items, which has received significant attention from both researchers and practitioners. In recent years, we have witnessed great progress and achievements in this field, necessitating a new survey. In this survey, we study the SR problem from a new perspective (i.e., the construction of an item’s properties), and summarize the most recent techniques used in sequential recommendation such as multi-modal SR, generative SR, LLM-powered SR, ultra-long SR, and data-augmented SR. Moreover, we introduce some frontier research topics in SR, e.g., open-domain SR, data-centric SR, cloud-edge collaborative SR, continuous SR, SR for good, and explainable SR. We believe that our survey could be served as a valuable roadmap for readers in this field.

In recent years, more and more researchers and practitioners have focused on multi-domain CTR prediction and achieved great success. Though users’ behaviors often exhibit sequentiality, little effort has been made on multi-domain sequential recommendation (MDSR). Most existing works on MDSR sort the interactions from all domains in chronological order and then predict the next interacted items in each domain. However, they neglect separate interaction sequences in each domain. Therefore, they cannot exploit the commonalities and differences among different domains well. Cross-domain sequential recommendation (CDSR) models are usually designed for performance improvement in one target domain rather than in each domain. Although extending a CDSR model to an MDSR one directly or indirectly is feasible, it will result in high time complexity. Meanwhile, they often ignore data imbalance across different domains, which might cause negative transfer. As a response, we propose a novel MDSR solution called multi-sequence multi-task learning (MML). Our MML consists of three modules, including hybrid-domain sequential preference learning (HSPL), intra-domain sequential preference learning (ISPL) and multi-task learning & prediction (MLP). Specifically, HSPL aims to learn hybrid-domain sequential preferences. Meanwhile, we construct augmented sequences and leverage contrastive learning to learn more unbiased hybrid-domain sequential preferences for alleviating negative transfer. ISPL is designed to capture intra-domain sequential preferences. In the MLP module, three specific tasks and a behavior regularizer are leveraged to ensure that each module can learn the corresponding preferences sufficiently and enhance knowledge transfer among different domains. We conduct extensive experiments on some public datasets using different backbone models and show that our MML is able to achieve significantly better performance than the state-of-the-art methods in two or more domains in most cases. Meanwhile, our MML can achieve the same time complexity as the MDSR models only using hybrid interaction sequences. The source code can be found at https://github.com/plw2019/MML .

competitive swarm optimizer (CSO) is considered as a prominent paradigm for solving large-scale multiobjective optimization problems (LMOPs). However, the pairwise random competition (PRC) mechanism used in most existing CSOs may limit their performance in solving LMOPs due to the following reasons. First, when the winner particle obtained by PRC is of poor quality, it may limit the learning effect of its corresponding loser particle. Second, due to the stochastic nature of PRC, the evolutionary direction of the loser particles may be drastically perturbed over the iterations, thus slowing down their convergence speed. To alleviate the above issues, this article proposes a constrained learning (CL)-based CSO for tackling LMOPs, called CL-CSO. First, CL-CSO adopts a set of reference vectors to divide the original objective space into several subregions. Second, CL-CSO designs a CL-based strategy, including the intra-subregion learning and cross-subregion learning strategy, which let the loser particles only learn from the winner particles in their intra-subregions or neighboring subregions, respectively. Moreover, CL-CSO designs a Gaussian model assisted evolutionary strategy to help the evolution of winner particles, aiming to further improve the diversity and quality of winner particles. This way, the learning effect of particles and the overall convergence speed can be significantly enhanced. Compared to several competitive algorithms for tackling LMOPs, experimental results show that CL-CSO performs well in solving two well-known benchmark LMOPs (containing 2-3 objectives and 500-5000 decision variables), as well as real-world instance selection problems.

Band selection (BS) is a key method in Hyperspectral image (HSI) classification that helps to reduce the computational burden and improve the class separability. However, with the emerging of unmanned aerial vehicle (UAV)-borne HSI datasets, their attributes such as high spatial and spectral resolution as well as large-scale samples pose serious challenges to the existing BS methods, making them inefficient. In addition, the efficient utilization of the prior knowledge from the data collected by fixed UAV-borne sensors in different regions is often easily overlooked. In view of these issues, this paper proposes a neural network-assisted particle swarm optimization (PSO) algorithm for cross-domain BS of UAV-borne HSIs. First, a knowledge learning strategy is designed for the source domain, which applies a neural network model to learn the useful prior knowledge in labeled source domain data. Then, a network-assisted PSO algorithm is introduced to search for the optimal subset of bands in the target domain under the guidance of the valid prior knowledge captured from the source domain by the network model. Moreover, a similarity-based grouping strategy is designed to group similar bands and then select bands from each group with the aims of reducing the redundant information in the subset of bands. Finally, experimental results on three common UAV-borne HSI datasets show that our proposed method can efficiently handle UAV-borne HSI data with large samples, as it is able to find a subset of bands with higher quality compared to several state-of-the-art BS methods.

While previous works on Multi-Behavior Sequential Recommendation (MBSR) have made great efforts to exploit the behavior types, they often fail to fully leverage the behavior and attribute information to learn different aspects of user preferences. Firstly, for predicting user preferences under target behaviors, there is considerable room for improvement by capturing both intra- and inter-behavior item transition relationships at the sequence level, as well as extracting the inter-behavior common and different aspects of a user’s preferences at each timestep based on the contextual sequential information. Secondly, in MBSR, the effect of behavior types on item attributes has not been revealed, which is very important in real-world applications since a behavior such as view representing a user’s weak preferences could not explicitly indicate the user’s interests on an attribute. To address the above two issues, we propose a novel solution called Behavior-aware Attribute-infused Sequential Encoders (BASE) . Specifically, a module named tri-path preference learning is designed to capture user multi-aspect interests via a sequence encoder, a behavior encoder and an attribute encoder. Besides, a behavior-aware dual-granularity contrastive learning module constructs sample pairs in item and attribute granularities to enhance the model’s ability to utilize potential item and attribute information. Extensive empirical studies on three public datasets show that our BASE significantly outperforms various state-of-the-art methods from five different categories. The datasets and our implementation codes are released at https://github.com/Erin-Gr/BASE .

Multi-Behavior Recommendation (MBR) aims to improve the performance w.r.t. the target behavior (i.e., purchase) by leveraging auxiliary behaviors (e.g., click, favorite). However, in real-world scenarios, a recommendation method often needs to process different types of behaviors and generate personalized lists for each task (i.e., each behavior type). Such a new recommendation problem is referred to as Multi-Behavior Multi-Task Recommendation (MMR). So far, the most powerful MBR methods usually model multi-behavior interactions using a cascading graph paradigm. Although significant progress has been made in optimizing the performance of the target behavior, it often neglects the performance of auxiliary behaviors. To compensate for the deficiencies of the cascading paradigm, we propose a novel solution for MMR, i.e., Behavior-Informed Graph Embedding Learning (BiGEL). Specifically, we first obtain a set of behavior-aware embeddings by using a cascading graph paradigm. Subsequently, we introduce three key modules to improve the performance of the model. The Cascading Gated Feedback (CGF) module enables a feedback-driven optimization process by integrating feedback from the target behavior to refine the auxiliary behaviors preferences. The Global Context Enhancement (GCE) module integrates the global context to maintain the user’s overall preferences, preventing the loss of key preferences due to individual behavior graph modeling. Finally, the Contrastive Preference Alignment (CPA) module addresses the potential changes in user preferences during the cascading process by aligning the preferences of the target behaviors with the global preferences through contrastive learning. Extensive experiments on two real-world datasets demonstrate the effectiveness of our BiGEL compared with 12 very competitive methods. The datasets, scripts, and source code are available at https://github.com/Wenhaocarry/BiGEL .

Most existing GNN-based recommendation methods focus on exploiting a user–item heterogeneous graph, which, however, will cause efficiency and effectiveness challenges, in a federated learning setting considering user privacy. We find that a user–user or item–item homogeneous graph is often privacy-insensitive and can significantly enhance the efficiency and effectiveness of federated graph embedding learning. Hence, we propose a novel framework called Federated Homogeneous Graph Neural Network (FedHoG) , which can provide privacy-preserving recommendations with high-quality and communication-efficient graph learning. We first design a privacy-preserving homogeneous graph construction method, which enables the server to construct an item–item graph and a user–user graph without leaking user privacy. Then, we develop a federated homogeneous graph learning method that enables balanced GNN model training among the server and clients. We also propose a lightweight homogeneous graph convolution method to achieve better graph embedding learning. Finally, extensive experiments on three public datasets show the advantages of our FedHoG in performance and efficiency. The datasets, source codes, and scripts are available at https://github.com/XZHhong/FedHoG .

Multi-behavior sequential recommendation (MBSR), which captures sequential patterns and behavioral heterogeneity to model users’ multifaceted preferences, has shown promising results. Despite their effectiveness, existing methods often suffer from performance degradation due to inherent data sparsity in real-world scenarios. Current data augmentation methods in recommendation systems predominantly focus on single-behavior modeling, failing to account for the diversity of user preference expressions across different types of behaviors. Moreover, conventional augmentation strategies risk introducing noise or irrelevant patterns during sample generation, potentially distorting the next-item prediction task. To address these challenges, we propose a novel and generic framework called multi-behavior data augmentation for sequential recommendation (MBASR). Specifically, we propose five distinct behavior-aware data augmentation operations, which are designed based on interactions both within and across subsequences, to generate diverse and enriched training samples. Each augmentation operation leverages correlations between behaviors or similarities among users, ensuring that the enhanced data remains aligned with users’ natural behavior patterns. Furthermore, we introduce a combined augmentation method, merging two data augmentation operations to achieve better results. In addition, we introduce two position-based sampling strategy that can effectively reduce the perturbation brought by the augmentation operations to the original data. Notably, as a data-centric solution, our MBASR can be seamlessly integrated into various MBSR models without modifying their underlying structures. Comprehensive evaluations on four real-world datasets validate the efficacy of our MBASR, demonstrating significant performance improvement across mainstream MBSR models. The source code, scripts and datasets are available at https://github.com/XiaoQi-C/MBASR

In cloud computing, static resource configurations create a trade-off: tenants overprovision to avoid resource starvation, causing inefficiency and cost, while providers suffer low utilization despite high allocations. To improve efficiency, providers often use overcommitted environments where multiple workloads share hosts, but this leads to interference and potential Quality-of-Service (QoS) violations. This paper introduces a realtime dynamic control framework that mitigates interference by adaptively pinning workloads to CPU groups. Using deep reinforcement learning (DRL) with the Proximal Policy Optimization (PPO) algorithm, an intelligent agent continuously adjusts CPU pinning based on real-time feedback to maintain Service- Level-Agreement (SLA) compliance. Experiments under two optimization objectives—overall-performance-first and priorityperformance- first—show that the proposed approach improves overall QoS by$\approx$25% compared with static pinning. When prioritization is enabled, high-priority workloads gain significant performance improvements while lower-priority ones remain within SLA limits. These results demonstrate that a DRL-based CPU-pinning strategy effectively manages resource contention in overcommitted clouds, enhancing utilization while upholding tenant SLAs.

Sequential recommendation (SR) aims to capture users' dynamic preferences based on their historical interactions and provide personalized next-item prediction. Multi-behavior SR (MBSR) further considers behavior types of user-item interactions, which can reveal diverse user interests and alleviate the data sparsity issue w.r.t. the target purchase behaviors. Most existing MBSR approaches ignore the importance of target items closely related to user interests. Moreover, they often suffer from the problem of limited vector representation capability. To tackle the above two challenges, we propose a novel solution called target item-oriented conditional diffusion differential Transformer (ICDDT). Specifically, our ICDDT introduces distribution representations via the diffusion model, allowing effective utilization of target item information during training to better capture user preferences. Firstly, our ICDDT achieves a more appropriate behavior-aware step selection in the diffusion phase by distinguishing the sampling distributions of diffusion steps w.r.t. behavior types. Secondly, our ICDDT introduces three conditions of interaction sequences, target behaviors and diffusion steps into the reverse phase to guide the training of the differential Transformer-based approximator, generating denoised target item representations as user personalized interests. Finally, our ICDDT sets an inference step truncation factor to fit the diffusion step sampling distributions and accelerate the inference process. We conduct extensive experiments on two real-world datasets, where the results show that our ICDDT significantly outperforms all baselines on all metrics. The datasets, source codes and scripts are available at https://github.com/Erin-Gr/ICDDT.

Can Multimodal Large Language Models (MLLMs) develop an intuitive number sense similar to humans? Targeting this problem, we introduce Visual Number Benchmark (VisNumBench) to evaluate the number sense abilities of MLLMs across a wide range of visual numerical tasks. VisNumBench consists of about 1,900 multiple-choice question-answer pairs derived from both synthetic and real-world visual data, covering seven visual numerical attributes and four types of visual numerical estimation tasks. Our experiments on VisNumBench led to the following key findings: (i) The 17 MLLMs we tested, including open-source models such as Qwen2.5-VL and InternVL2.5, as well as proprietary models like GPT-4o and Gemini 2.0 Flash, perform significantly below human levels in number sense-related tasks. (ii) Multimodal mathematical models and multimodal chain-of-thought (CoT) models did not exhibit significant improvements in number sense abilities. (iii) Stronger MLLMs with larger parameter sizes and broader general abilities demonstrate modest gains in number sense abilities. We believe VisNumBench will serve as a valuable resource for the research community, encouraging further advancements in enhancing MLLMs' number sense abilities. Code and dataset are available at https://wwwtttjjj.github.io/VisNumBench/.

The automated construction of educational knowledge graphs from programming tutorial websites faces significant challenges due to the heterogeneous nature of web content structures and the complex relationships between code snippets and their contextual information. While existing approaches rely heavily on either manual rule crafting or direct large language model (LLM) processing, both methods struggle with cross-platform scalability and extraction accuracy. We present a novel distribution-guided extraction approach that systematically analyzes HTML structural patterns through strategic sampling before deploying LLM-based extraction scripts. This approach significantly reduces manual engineering effort while maintaining high accuracy across diverse platforms. Our method achieves a substantial improvement in code snippet extraction accuracy (from 0.15–0.68 to 0.54–0.91) across major programming tutorial websites including w3schools, MDN, and runoob. The extracted knowledge is integrated into a comprehensive educational graph that powers a question-answering system, achieving faithfulness scores of 0.93 and answer correctness of 0.90. These results demonstrate the effectiveness of combining statistical pattern analysis with LLM-driven extraction for building robust, cross-platform educational knowledge systems.

Multi-scenario recommendation (MSR) is a popular training paradigm in industrial platforms for uniformly integrating information from multiple scenarios and serving them simultaneously. A key challenge in MSR research is accurately identifying the commonalities and distinctive information between scenarios. Currently, most existing MSR methods focus on implicitly extracting this information from the architectural level. However, this continues to increase the complexity and training overhead of MSR. Furthermore, the custom components responsible for extracting implicit information in each MSR method are too dependent on the specific MSR architecture and are not easily reused in other methods. Given these challenges, we first show in a motivating experiment that it may be beneficial to explicitly select a reasonable set of shared instances that can affect parameter optimization in all scenarios during the training of MSR, i.e., to explicitly obtain the critical information required for MSR from the data level. Then, this paper proposes SSIM with an adaptive selection network. Specifically, SSIM can be integrated with existing MSR methods in a lightweight way to adaptively select an informative and shareable subset of instances from each scenario to improve recommendations. In particular, the selected multi-scenario shared subset has extraordinary reusability and can be easily saved to benefit model training of various future MSR models. Finally, we evaluate SSIM and demonstrate its effectiveness through experiments on two public multi-scenario benchmarks and an online A/B test.

Sequential recommendation (SR) aims to infer users' future interests and suggest the next items for them. Most SR methods learn one single vector to represent a user's recent interests, i.e., a user representation. Despite their great success, most of them do not explicitly consider the phenomenon of users' interest drift when learning user representations. Moreover, interest drift presents two critical challenges for these SR methods: (1) how to explore the potential distributions of the users' varying interest drift levels; and (2) how to capture the interest drift-aware collaborative knowledge among the users. In this paper, we delve into the issue of interest drift in SR and propose a novel and generic framework, i.e., Interest Drift-driven User Representation Learning (IDURL), to enhance SR methods to tackle the above two challenges. Specifically, our IDURL contains an interest drift quantization (IDQ) module to enable a quantitative measurement of the interest drift. Moreover, a drift representation generation module models the users' latent varying levels of interest drift, and an interest drift-guided representation disentanglement module optimizes the distributions of the interest drift levels under the guidance of IDQ. Furthermore, an interest drift-aware representation alignment module helps to capture the interest drift-aware collaborative knowledge among users. Finally, the users' overall interest representations are obtained to calculate the preference scores on the candidate items. Extensive experiments on four public datasets show the effectiveness of our IDURL. The source code of our IDURL is available at: https://github.com/xiaolLIN/IDURL.

Multi-scenario recommendation (MSR) has become a core component of various online platforms, but its increasing model size has also brought attention to its efficiency optimization. An important effort is to find effective and efficient feature embedding layers for MSR, and existing work focuses on scenario-level feature selection, i.e., all instance embeddings in the same scenario get the same filtering results on the feature set, and the filtering results are different for different scenarios. However, this ignores the information redundancy of the dimension set and the individuality of different instances in the same scenario. To address these limitations, we propose a multi-scenario instance embedding learning (MultiEmb) framework that implements exclusive feature-dimension redundant information removal for different instances within a scenario to obtain the optimal individual embeddings. The core of our MultiEmb is to introduce an instance embedding selection network to effectively complete the above challenging tasks, in which a set of feature selection and dimension selection adaptive components are equipped for each scenario, and their combination completes the optimal embedding selection for each instance. Finally, we evaluate MultiEmb through extensive experiments on two public multi-scenario benchmarks and demonstrate its effectiveness, compatibility, transferability, etc.

Cross-domain sequential recommendation (CDSR) is proposed to alleviate the data sparsity issue while capturing users' sequential preferences. However, most existing methods do not explore the item transition patterns across different domains and can also not be applied to a multi-domain scenario.Moreover, previous methods rely on overlapping users as bridges to transfer knowledge, which struggles to capture the complex associations across domains without sufficient overlapping users. In this paper, we introduce item attributes into CDSR, and propose a heterogeneous graph transfer learning method to address these issues.Specifically, we construct a cross-domain heterogeneous graph to allow the association of user, item, and category nodes from different domains,and enhance the flexibility of the model by enabling message propagation between more nodes through edge expansion based on the semantic similarity and co-occurrence probability.In addition, we devise meta-paths from different perspectives for nodes at item, user and category levels to guide information aggregation, which can transfer knowledge across domains and reduce the reliance on the number of overlapping users.We further design attention modules to capture users' dynamic preferences from the item sequences they have interacted with in each domain, and explore the transition patterns within category sequences which reflect users' coarse-grained preferences.Finally, we perform knowledge transfer across different domains, and predict the most likely items that users will interact with in each domain. Extensive empirical studies on three real-world datasets indicate that our HGTL significantly outperforms the state-of-the-art baselines in all cases.

Feature selection (FS) is a very important technique for hyperspectral image (HSI) classification, as successfully selecting informative features can significantly increase the learning performance while reducing the computational cost. However, most of the existing FS methods tend to treat the HSI as a whole for FS, which does not fully consider the unique characteristics of HSIs and disregards the fact that different feature classes possess varying preferences for features. Thus, this paper proposes a superpixel segmentation based evolutionary multitasking algorithm for FS of HSIs, called SS-EMT. First, the superpixel segmentation method is used to partition the original HSI into several superpixel blocks, which can preserve well the information of different classes of the original image. Second, in order to explore each superpixel block efficiently, an evolutionary multitasking algorithm using particle swarm optimization is designed, which treats each superpixel block as a subtask and then optimizes these subtasks collaboratively by transferring useful knowledge among related subtasks. In addition, a new individual evaluation mechanism is devised to obtain multiple high-quality feature subsets with different numbers of features simultaneously in a single run, thus reducing the computational cost. Finally, extensive experimental results on four common HSI datasets under three classifiers validate that our proposed method outperforms several state-of-the-art FS methods.

Cross-user federated recommendation (CUFR) is a promising solution for providing personalized services without collecting users’ raw data. However, most previous CUFR works mainly focus on providing accurate and privacy-preserving personalized recommendations, but overlook the fact that users can opt out at any time during the training process. In response, we study an emerging and new problem of efficiently training an unlearned model to forget the data of the clients who leave a federated system. It is challenging to simply apply or slightly modify existing machine unlearning or federated unlearning methods to CUFR because of the unique collaboration effect in recommender systems. Although a recent gradient calibration-based method (i.e., FRU) shows promising in training an unlearned model, there are still some limitations: (i) there is a potential possibility that some clients run out of the storage space, (ii) all the remaining clients need to participate in computing the new gradients, (iii) it masks the uniqueness of the local gradients, and (iv) the errors of the calibrated gradients will increase gradually with more iterations. In this article, we propose a novel CUFR unlearning (CUFRU) method. Specifically, we design a gradient transfer station (GTS) module for storing the historical gradients while enabling clients to dynamically participate in the computation of the calibrated gradients with the new gradients based on their online status. Moreover, we design a novel iteration-aware gradient calibration mechanism to strike a balance between the weights of the historical and new gradients at the different stages of the unlearning process, alleviating the calibration errors. Finally, we conduct extensive experiments on three real-world datasets to show that our CUFRU can more efficiently train an unlearned model with the competitive recommendation performance.

Cross-domain recommendation (CDR) aims to alleviate the data sparsity problem by leveraging the benefits of modeling two domains. However, existing research often focuses on the recommendation performance while ignores the privacy leakage issue. We find that an attacker can infer user attribute information from the knowledge (e.g., user preferences) transferred between the source and target domains. For example, in our experiments, the average inference accuracies of attack models on gender and age attributes are 0.8323 and 0.3897. The best-performing attack model achieves accuracies of 0.8847 and 0.4634, exceeding a random inference by 25.10% and 64.04%. We can see that the leakage of user attribute information may significantly exceed what would be expected from random inference. In this paper, we propose a novel recommendation framework named CVGAE (short for camouflaged variational graph autoencoder), which effectively models user behaviors and mitigates the risk of user attribute information leakage at the same time. Specifically, our CVGAE combines the strengths of VAEs in capturing latent features and variability with the ability of GCNs in exploiting high-order relational information. Moreover, to ensure against attribute inference attacks without sacrificing the recommendation performance, we design a user attribute protection module that fuses user attribute-camouflaged information with knowledge transfer during cross-domain processes. We then conduct extensive experiments on three real-world datasets, and find our CVGAE is able to achieve strong privacy protection while making little sacrifices in recommendation accuracy.

Most federated learning-based recommender systems allow clients to access a well-trained high-quality model locally, which provides adversaries with the opportunity to infringe the legitimate copyright of the model. In response, we study an emerging and important problem, i.e., copyright protection of a federated recommendation model, which has not yet been addressed in the community of federated learning or recommender systems. We propose the first backdoor-based ownership verification scheme for federated recommendation (OVFR), which enables the server to claim its ownership for a given suspicious recommendation model. First, we propose to generate a trigger set tailored to recommendation scenarios. In particular, we generate some fake users and items, and then construct a set of fake users with fake interaction records as a trigger set. Moreover, we ensure that the distribution of the popularity of the fake items follows a long-tailed distribution for the effectiveness of the incorporated watermarking. To provide robustness assurance, we propose two different hybrid strategies to make the embeddings of the fake items similar to those of the real items. Second, we focus on effectively learning from a trigger set for recommendation scenarios. In particular, we design an MSE loss function and a contrastive loss function for incorporating the backdoor-based watermarking into the item embeddings, since the item embeddings are often more valuable and easier to be accessed than other parameters of a federated recommendation model. We then design a contrastive loss function to reduce the risk of the fake items being detected. Extensive experiments on three public datasets show the effectiveness of our OVFR in terms of ownership verification, model performance, and robustness.

Different from the data sparsity that traditional recommendations suffer from, context-aware recommender systems (CARS) face specific sparsity challenges related to contextual features, i.e., feature sparsity and interaction sparsity. How knowledge graphs address these challenges remains under-discussed. To bridge this gap, in this article, we first propose a novel pairwise intent graph containing nodes of users, items, entities, and enhanced intents to integrate knowledge graphs into CARS efficiently. Enhanced intent nodes are generated through the specific fusion of relational sub-intent and contextual sub-intent, and they are derived from semantic information and contextual information, respectively. We develop a pairwise intent graph embedding learning (PING) framework based on it. Specifically, our PING uses a pairwise intent joint graph convolution module to obtain refined embedding of all the features, where each enhanced intent node acts as a hub to effectively propagate information among different features and between all the features and knowledge graphs. Then, a recommendation module with refined embeddings is used to replace the randomly initialized embeddings of downstream recommendation models to improve model performance. Extensive experiments on three public datasets and some real-world scenarios verify the effectiveness and compatibility of our PING.

Cross-domain sequential recommendation aims to alleviate the sparsity problem while capturing users’ sequential preferences. However, most existing methods learn the user preferences in each domain separately, and then perform knowledge transfer between them to associate two separated domains, which neglects the item transition patterns across sequences from different domains. Moreover, the sparsity problem still exists since some items in the target and source domains are interacted with only a limited number of times. To address these issues, in this paper we propose a generic framework named multi-view graph contrastive learning (MGCL). Specifically, we tackle the problem from the perspective of an intra-domain item representation view and an inter-domain user preference view. From the former view, we adopt the contrastive mechanism to jointly learn the dynamic sequential information in a user sequence graph and the static collaborative information in the cross-domain global graph, while the latter view is to capture the complementary information of the user’s preferences from different domains. Considering that there are multiple domains in real-world scenarios, we further extend MGCL to MGCL+ for multi-domain sequential recommendation and design multi-domain adaptive gated networks to alleviate the negative transfer problem. Extensive empirical studies on three real-world datasets demonstrate that our MGCL and MGCL+ significantly outperforms the state-of-the-art methods.

Since the advent of cloud computing, Elastic Computing (EC) has become the standard architecture for resource allocation and scheduling. EC typically allocates computing resources based on predefined specifications, such as virtual machine or container flavors. However, these flavors are often constrained by fixed CPU-to-memory ratios, which frequently fail to match the actual resource needs of applications. As a result, cloud providers experience high resource allocation rates nearing saturation ($> $80%) but with low utilization ($< $25%). This study introduces Flexible Computing (FC), a novel approach to resource allocation and scheduling. Unlike EC, FC allocates resources based on an application resource usage profile, derived from the historical resource consumption of workloads, rather than relying on fixed specifications. Additionally, FC incorporates a real-time performance degradation detection mechanism to address performance issues caused by the noisy-neighbor effect when colocated workloads interfere with each other. FC dynamically adjusts resource allocation according to actual usage, ensuring that application performance meets Service Level Agreements (SLAs), while preventing resource waste and performance degradation from improper resource over-commitment. Large-scale experimental validations conducted on the FC architecture within Huawei Cloud data centers demonstrate that, compared to EC, FC can reduce computing resource consumption by over 33% while managing the same workloads. Furthermore, FC's real-time performance degradation detection model achieves a prediction error of less than 5% across various testing environments, highlighting its commercial viability.

With the development of unmanned aerial vehicle (UAV) technology, UAV-enabled vehicular edge computing (VEC) has emerged as a powerful computational paradigm that improves edge resource efficiency. In particular, supporting stateful services, which require persistent context across offloading sessions, introduces new challenges. In the VEC systems, computation offloading, UAV trajectory planning, and service migration must be jointly optimized to maintain quality of service (QoS). However, existing works rarely consider this joint optimization, especially under high mobility scenarios. To fill this gap, this paper first considers the joint computation offloading, UAV trajectory, and service migration problem in the stateful service-oriented UAV-enabled VEC system and then formulates it as a dynamic multi-objective optimization problem, with the purpose of minimizing UAV flight cost, vehicle energy consumption, service migration time, and age of information (AoI). To effectively address the formulated problem, a novel joint Trajectory, Offloading, and Migration optimization approach (TOM) based on a dynamic multifactorial evolutionary algorithm is proposed. In particular, a service migration strategy is designed in TOM to efficiently migrate services in a parallel manner. In addition, an environmental adaptation strategy is triggered to cope with rapid dynamic changes in the environment. Extensive simulations on real-world datasets show that our proposed method outperforms several state-of-the-art peer methods.

The vehicle routing problem with simultaneous pickup-delivery and time windows (VRPPDTW) is applicable to a wide range of practical scenarios within the domains of transportation and logistics. When addressing this complex problem, conventional heuristic algorithms have exhibited limitations in effectiveness, primarily due to the need for expert knowledge in heuristic design, which often prioritizes immediate improvements and leads to convergence towards local optima. To overcome this challenge, we propose a deep Q-network-based iterated local search algorithm (DQN-ILS). This algorithm employs a deep Q-learning network to automatically learn a policy for selecting heuristics to improve the current solution. Simultaneously, an adaptive perturbation strategy is designed to avoid becoming trapped in local optima. In this way, our algorithm can better balance the trade-off between exploration and exploitation when solving VRPPDTW. Experimental studies conducted on a real test suite of large-scale VRPPDTW instances validate the superiority of the proposed DQN-ILS over several recently proposed methods.

The advantages of resource elasticity and proactive data backup in cloud databases have attracted a large number of users to consider deploying their IT systems in the cloud. Factors such as performance, reliability, security, and ease of use are the most important considerations for users when choosing to migrate to the cloud. This paper mainly discusses the necessity of Quality of Service (QoS) awareness for cloud applications in the current public cloud environment, as well as the technological trends and challenges associated with it.

In cloud computing, tenants tend to select predefined flavors with substantial resources that go beyond their needs, in order to avoid resource starvation. As a result, cloud service providers experience a high allocation ratio with a low utilization ratio of resources. To improve the resource utilization, cloud service providers offer lower-priced resources by building an overcommitted environment to reuse resources. However, the colocation interference of workloads in the overcommitted environment may cause performance anomalies and degrade quality of service (QoS). In this paper, we propose a real-time mechanism to pin a group of workloads to a certain subset of CPUs to increase resource utilization and minimize interference. To be specific, we leverage deep reinforcement learning (DRL) to automatically adjust the CPU pinning configuration in the overcommitted environment according to the performance of workloads.

Sequential recommendation (SR) and multi-behavior sequential recommendation (MBSR) both come from real-world scenarios. Compared with SR, MBSR takes into account the dependencies of different behaviors. We find that most existing works on MBSR are studied in the context of e-commerce scenarios. In terms of the data format of the behavior types, we observe that the conventional label-formatted data carries limited information and is inadequate for scenarios like social media. With this observation, we introducebehavior set and extend MBSR to behavior set-informed sequential recommendation (BSSR). In BSSR, behavior dependencies become more complex and personalized, and user interest arousal may lack explicit contextual associations. To delve into the dynamics inhered within a behavior set and adaptively tailor recommendation lists upon its variability, we propose a novel solution called Explicit and Implicit modeling via Dual-Path Transformer (EIDP) for BSSR. Our EIDP adopts a dual-path architecture, distinguishing between explicit modeling path (EMP) and implicit modeling path (IMP) based on whether to directly incorporate the behavior representations. EMP features the personalized behavior set-wise transition pattern extractor (PBS-TPE) as its core component. It couples behavioral representations with both the items and positions to explore intra-behavior dynamics within a behavior set at a fine granularity. IMP utilizes light multi-head self-attention blocks (L-MSAB) as encoders under specific behavior types. The obtained multi-view representations are then aggregated by cross-behavior attention fusion (CBAF), using the behavior set of the next time step as a guidance to extract collaborative semantics at the behavioral level. Extensive experiments on two real-world datasets demonstrate the effectiveness of our EIDP. We release the implementation code at: https://github.com/OshiNoCSMA/EIDP.

Sequential recommendation has been widely used to predict users’ potential preferences by learning their dynamic user interests, for which most previous methods focus on capturing item-level dependencies. Despite the great success, they often overlook the stage-level interest dependencies. In real-world scenarios, user interests tend to be staged, e.g., following an item purchase, a user’s interests may undergo a transition into the subsequent phase. And there are intricate dependencies across different stages. Meanwhile, users’ behaviors are usually heterogeneous, including auxiliary behaviors (e.g., examinations) and target behaviors (e.g., purchases), which imply more fine-grained user interests. However, existing methods have limitations in explicitly modeling the relationships between the different types of behaviors. To address the above issues, we propose a novel framework, i.e., dynamic stage-aware user interest learning (DSUIL), for heterogeneous sequential recommendation, which is the first solution to model user interests in a cross-stage manner. Specifically, our DSUIL consists of four modules: (1) a dynamic graph construction module transforms a heterogeneous sequence into several subgraphs to model user interests in a stage-wise manner; (2) a dynamic graph convolution module dynamically learns item representations in each subgraph; (3) a behavior-aware subgraph representation learning module learns the heterogeneous dependencies between behaviors and aggregates item representations to represent the staged user interests; and (4) an interest evolving pattern extractor learns the users’ overall interests for the item prediction. Extensive experimental results on two public datasets show that our DSUIL performs significantly better than the state-of-the-art methods.

Multi-behavior recommender systems (MBRS) have been commonly deployed on real-world industrial platforms for their superior advantages in understanding user preferences and mitigating data sparsity. However, the cascade graph modeling paradigm adopted in mainstream MBRS usually assumes that users will refer to all types of behavioral knowledge they have when making decisions about target behaviors, i.e., use all types of behavioral interactions indiscriminately when modeling and predicting target behaviors for each user. We call this a full decision chain constraint and argue that it may be too strict by ignoring that different types of behavioral knowledge have varying importance for different users. In this paper, we propose a novel automated decision chain selection (AutoDCS) framework to relax this constraint, which can consider each user's unique decision dependencies and select a reasonable set of behavioral knowledge to activate for the prediction of target behavior. Specifically, AutoDCS first integrates some existing MBRS methods in a base cascade module to obtain a set of behavior-aware embeddings. Then, a bilateral matching gating mechanism is used to select an exclusive set of behaviors for the current user-item pair to form a decision chain, and the corresponding behavior-augmented embeddings are selectively activated. Subsequently, AutoDCS combines the behavior-augmented and original behavior-aware embeddings to predict the target behavior. Finally, we evaluate AutoDCS and demonstrate its effectiveness through experiments over four public multi-behavior benchmarks.

Side-information integrated sequential recommendation incorporates supplementary information to alleviate the issue of data sparsity. The state-of-the-art works mainly leverage some side information to improve the attention calculation to learn user representation more accurately. However, there are still some limitations to be addressed in this topic. Most of them merely learn the user representation at the item level and overlook the association of the item sequence and the side-information sequences when calculating the attentions, which results in the incomprehensive learning of user representation. Some of them learn the user representations at both the item and side-information levels, but they still face the problem of insufficient optimization of multiple user representations. To address these limitations, we propose a novel model, i.e., Multi-Sequence Sequential Recommender (MSSR), which learns the user's multiple representations from diverse sequences. Specifically, we design a multi-sequence integrated attention layer to learn more attentive pairs than the existing works and adaptively fuse these pairs to learn user representation. Moreover, our user representation alignment module constructs the self-supervised signals to optimize the representations. Subsequently, they are further refined by our side information predictor during training. For item prediction, our MSSR extra considers the side information of the candidate item, enabling a comprehensive measurement of the user's preferences. Extensive experiments on four public datasets show that our MSSR outperforms eleven state-of-the-art baselines. Visualization and case study also demonstrate the rationality and interpretability of our MSSR.

Multi-scenario recommender systems (MSRSs) have been increasingly used in real-world industrial platforms for their excellent advantages in mitigating data sparsity and reducing maintenance costs. However, conventional MSRSs usually use all relevant features indiscriminately and ignore that different kinds of features have varying importance under different scenarios, which may cause confusion and performance degradation. In addition, existing feature selection methods for deep recommender systems may lack the exploration of scenario relations. In this paper, we propose a novel automated multi-scenario feature selection (MultiFS) framework to bridge this gap, which is able to consider scenario relations and utilize a hierarchical gating mechanism to select features for each scenario. Specifically, MultiFS first efficiently obtains feature importance across all the scenarios through a scenario-shared gate. Then, some scenario-specific gate aims to identify feature importance to individual scenarios from a subset of the former with lower importance. Subsequently, MultiFS imposes constraints on the two gates to make the learning mechanism more feasible and combines the two to select exclusive features for different scenarios. We evaluate MultiFS and demonstrate its ability to enhance the multi-scenario model performance through experiments over two public multi-scenario benchmarks.

Edge Computing is promising for latency-sensitive applications. However, current edge resource scheduling is inefficient. Deep Learning as a Service (DLaaS) provides deep learning methods to optimize the resource scheduling problem, but faces great challenges of security and reliability. On one hand, DLaaS training agents and raw data are exposed to various adversarial attacks. On the other hand, dishonest DLaaS trainers can generate poisoned models to attack the DLaaS system. In this article, we proposeSAPE, aSecure and decentralized DLAaSPlatform inEdge computing. SAPE allows users to submit their tasks, which will be scheduled to the appropriate edge clusters to minimize the task execution time. We formulate the resource scheduling problem and develop the federated deep reinforcement learning (DRL) method to optimize the problem and resist the adversarial attacks of DLaaS. We utilize blockchain and propose a consortium-based verification scheme to improve the reliability of the federated training process, protecting the DLaaS models from being poisoned and compromised. We conduct experiments to evaluate the latency and security performance of SAPE and the federated DRL scheduling policy. The results show that SAPE outperforms the traditional schemes when defending against adversarial attacks towards the DLaaS platform in edge computing.

The competitive swarm optimizer (CSO) classifies swarm particles into loser and winner particles and then uses the winner particles to efficiently guide the search of the loser particles. This approach has very promising performance in solving large-scale multiobjective optimization problems (LMOPs). However, most studies of CSOs ignore the evolution of the winner particles, although their quality is very important for the final optimization performance. Aiming to fill this research gap, this article proposes a new neural net-enhanced CSO for solving LMOPs, called NN-CSO, which not only guides the loser particles via the original CSO strategy, but also applies our trained neural network (NN) model to evolve winner particles. First, the swarm particles are classified into winner and loser particles by the pairwise competition. Then, the loser particles and winner particles are, respectively, treated as the input and desired output to train the NN model, which tries to learn promising evolutionary dynamics by driving the loser particles toward the winners. Finally, when model training is complete, the winner particles are evolved by the well-trained NN model, while the loser particles are still guided by the winner particles to maintain the search pattern of CSOs. To evaluate the performance of our designed NN-CSO, several LMOPs with up to ten objectives and 1000 decision variables are adopted, and the experimental results show that our designed NN model can significantly improve the performance of CSOs and shows some advantages over several state-of-the-art large-scale multiobjective evolutionary algorithms as well as over model-based evolutionary algorithms.

Recommendation models are deployed in a variety of commercial applications to provide personalized services for users. However, most of them rely on the users’ original rating records that are often collected by a centralized server for model training, which may cause privacy issues. Recently, some centralized federated recommendation models are proposed for the protection of users’ privacy, which however requires a server for coordination in the whole process of model training. As a response, we propose a novel privacy-aware decentralized federated recommendation (DFedRec) model, which is lossless compared with the traditional model in recommendation performance and is thus more accurate than other models in this line. Specifically, we design a privacy-aware structured client-level graph for the sharing of the model parameters in the process of model training, which is a one-stone-two-bird strategy, i.e., it protects users’ privacy via some randomly sampled fake entries and reduces the communication cost by sharing the model parameters only with the related neighboring users. With the help of the privacy-aware structured client-level graph, we propose two novel collaborative training mechanisms in the setting without a server, including a batch algorithm DFedRec(b) and a stochastic one DFedRec(s), where the former requires the anonymity mechanism while the latter does not. They are both equivalent to probabilistic matrix factorization trained in a centralized server and are thus lossless. We then provide formal analysis of privacy guarantee of our methods and conduct extensive empirical studies on three public datasets with explicit feedback, which show the effectiveness of our DFedRec, i.e., it is privacy aware, communication efficient, and lossless.

Dynamic pickup and delivery problems (DPDPs) with various constraints, such as docks, time windows, capacity, and last-in-first-out loading, have posed significant challenges for existing vehicle routing algorithms, as most of them only optimize a single weighted objective function, which makes it difficult to maintain the solutions’ diversity and may easily become stuck in local optima. To alleviate this issue, this paper introduces a decomposition-based multiobjective evolutionary algorithm with tabu search for solving the above DPDPs. First, our algorithm leverages multiobjectivization and reformulates the DPDP as a multiobjective optimization problem (MOP), which is further decomposed into multiple subproblems. Then, these subproblems are approached simultaneously and collaboratively by using a crossover process to enhance the diversity of the solutions, followed by using an efficient tabu search to speed up the convergence. In this way, our algorithm can better balance the trade-off between exploration and exploitation for solving this MOP, and then one promising solution can be selected from the population to complete some pickup and delivery tasks in an interval of the DPDP. Simulation results on 64 test problems from a practical scenario of Huawei demonstrate that the proposed algorithm outperforms other competitive algorithms for tackling DPDPs. Additionally, more experiments are conducted on 20 large-scale distribution problems within JD Logistics to validate the generalization capability of our algorithm.

Static analysis tools for capturing bugs and vulnerabilities in software programs are widely employed in practice, as they have the unique advantages of high coverage and independence from the execution environment. However, existing tools for analyzing large codebases often produce a great deal of false warnings over genuine bug reports. As a result, developers are required to manually inspect and confirm each warning, a challenging, time-consuming, and automation-essential task. This article advocates a fast, general, and easily extensible approach called Llm4sa that automatically inspects a sheer volume of static warnings by harnessing (some of) the powers of Large Language Models (LLMs). Our key insight is that LLMs have advanced program understanding capabilities, enabling them to effectively act as human experts in conducting manual inspections on bug warnings with their relevant code snippets. In this spirit, we propose a static analysis to effectively extract the relevant code snippets via program dependence traversal guided by the bug warning reports themselves. Then, by formulating customized questions that are enriched with domain knowledge and representative cases to query LLMs, Llm4sa can remove a great deal of false warnings and facilitate bug discovery significantly. Our experiments demonstrate that Llm4sa is practical in automatically inspecting thousands of static warnings from Juliet benchmark programs and 11 real-world C/C++ projects, showcasing a high precision (81.13%) and a recall rate (94.64%) for a total of 9,547 bug warnings. Our research introduces new opportunities and methodologies for using the LLMs to reduce human labor costs, improve the precision of static analyzers, and ensure software trustworthiness

A recommendation system is of vital importance in delivering personalization services, which often brings continuous dual improvement in user experience and organization revenue. However, the data of one single organization may not be enough to build an accurate recommendation model for inactive or new cold-start users. Moreover, due to the recent regulatory restrictions on user privacy and data security, as well as the commercial conflicts, the raw data in different organizations cannot be merged to alleviate the scarcity issue in training a model. In order to learn users’ preferences from such cross-silo data of different organizations and then provide recommendations to the cold-start users, we propose a novel federated learning framework, i.e., federated cross-organization recommendation ecosystem (FedCORE). Specifically, we first focus on the ecosystem problem of cross-organization federated recommendation, including cooperation patterns and privacy protection. For the former, we propose a privacy-aware collaborative training and inference algorithm. For the latter, we define four levels of privacy leakage and propose some methods for protecting the privacy. We then conduct extensive experiments on three real-world datasets and two seminal recommendation models to study the impact of cooperation in our proposed ecosystem and the effectiveness of privacy protection.

Recently, federated recommendation has become a research hotspot mainly because of users’ awareness of privacy in data. As a recent and important recommendation problem, in heterogeneous one-class collaborative filtering (HOCCF), each user may involve of two different types of implicit feedback, that is, examinations and purchases. So far, privacy-preserving HOCCF has received relatively little attention. Existing federated recommendation works often overlook the fact that some privacy sensitive behaviors such as purchases should be collected to ensure the basic business imperatives in e-commerce for example. Hence, the user privacy constraints can and should be relaxed while deploying a recommendation system in real scenarios. In this article, we study the federated multi-behavior recommendation problem under the assumption that purchase behaviors can be collected. Moreover, there are two additional challenges that need to be addressed when deploying federated recommendation. One is the low storage capacity for users’ devices to store all the item vectors, and the other is the low computational power for users to participate in federated learning. To release the potential of privacy-preserving HOCCF, we propose a novel framework, named discrete federated multi-behavior recommendation (DFMR), which allows the collection of the business necessary behaviors (i.e., purchases) by the server. As to reduce the storage overhead, we use discrete hashing techniques, which can compress the parameters down to 1.56% of the real-valued parameters. To further improve the computation-efficiency, we design a memorization strategy in the cache updating module to accelerate the training process. Extensive experiments on four public datasets show the superiority of our DFMR in terms of both accuracy and efficiency.

As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical domains. While there are techniques for verifying DNNs with formal guarantees, they are limited in scalability and accuracy. In this article, we present a novel counterexample-guided abstraction refinement (CEGAR) approach for scalable and exact verification of DNNs. Specifically, we propose a novel abstraction to break down the size of DNNs by over-approximation. The result of verifying the abstract DNN is conclusive if no spurious counterexample is reported. To eliminate each spurious counterexample introduced by abstraction, we propose a novel counterexample-guided refinement that refines the abstract DNN to exclude the spurious counterexample while still over-approximating the original one, leading to a sound, complete yet efficient CEGAR approach. Our approach is orthogonal to and can be integrated with many existing verification techniques. For demonstration, we implement our approach using two promising tools, Marabou and Planet , as the underlying verification engines, and evaluate on widely used benchmarks for three datasets ACAS , Xu , MNIST , and CIFAR-10 . The results show that our approach can boost their performance by solving more problems in the same time limit, reducing on average 13.4%–86.3% verification time of Marabou on almost all the verification tasks, and reducing on average 8.3%–78.0% verification time of Planet on all the verification tasks. Compared to the most relevant CEGAR-based approach, our approach is 11.6–26.6 times faster.

Service providers tend to deploy application services to several different virtual machines (VMs) to improve the scalability and manageability of the cloud data center (CDC). Therefore, high frequency communication traffic is always involved among those VMs that are deployed the same application service. In order to reduce the communication cost (CC) of CDC, all VMs running the same service should be redeployed in the same subnet as much as possible by using live migration technology, because CC between VMs in different subnets is much higher than that within the same subnet. On the other hand, the migration time (MT) to complete all migration tasks is also crucial for providers and customers, because a prolonged MT will lead to the increased maintenance cost and the deterioration of quality of service (QoS). To address the aforementioned issues, this paper proposes an efficient service-aware VM scheduling approach (ES-VSA) based on a multi-objective evolutionary algorithm to minimize CC and MT, simultaneously. Finally, experiments are conducted on four different scenarios, and the simulation results demonstrate that our proposed algorithm is superior to several state-of-the-art algorithms in terms of both CC and MT.

Cooperative multi-agent reinforcement learning (Co-MARL) commonly employs different parameter sharing mechanisms, such as full and partial sharing. However, imprudent application of these mechanisms can potentially constrain policy diversity and limit cooperation flexibility. Recent methods that group agents into distinct sharing categories often exhibit poor performance due to challenges in precisely differentiating agents and neglecting the issue of promoting cooperation among these categories. To address these issues, we introduce a dynamic selective parameter sharing mechanism embedded with multi-level reasoning abstractions (DSPS-MA). Our approach uses self-comparison sequences to infer agents’ abstract concepts, defining the differences between agents and allowing them to dynamically select partners to share parameters based on these abstract concepts. We also design an intrinsic reward to offer comprehensive collaboration guidance for agents, and introduce a policy cosine similarity regularization term to ensure sufficient policy diversity. Empirical evaluations demonstrate that our approach yields higher returns and faster convergence than state-of-the-art methods.

In recent years, sequential recommendation (SR) has gained significant attention in the recommender systems community. However, most previous works only consider the (user, item, timestep) interaction sequences, which limits the recommendation performance. To overcome this limitation, some studies have utilized user reviews to enrich the understanding of user preferences. However, existing review-based sequential recommendation (RBSR) methods only use either a user’s review on items or an item’s reviews by users, overlooking their complementary nature. In addition, most existing RBSR methods use a simple dot-product operation between the embeddings of a user and the candidate items for scoring, which may not adequately capture the complex relationships among the item sequence, review sequence and candidate items. To release the potential of RBSR, we propose a novel model called cascaded cross attention (CCA), which utilizes aggregated reviews to compensate for the information that is lacking in individual reviews. Moreover, we propose a cascaded cross-attention layer to better capture the dependency intra a sequence and the relationships between a sequence and the candidate items. Extensive experimental results on three public datasets demonstrate that our CCA outperforms the state-of-the-art methods. Additionally, the case study and visualization results showcase high interpretability of our CCA.

Cross-domain sequential recommendation is an important development direction of recommender systems. It combines the characteristics of sequential recommender systems and cross-domain recommender systems, which can capture the dynamic preferences of users and alleviate the problem of cold-start users. However, in recent years, people pay more and more attention to their privacy. How to protect the users’ privacy has become an urgent problem to be solved. In this paper, we propose a novel privacy-preserving cross-domain sequential recommender system (PriCDSR), which can provide users with recommendation services while preserving their privacy at the same time. Specifically, we define a new differential privacy on the data, taking into account both the ID information and the order information. Then, we design a random mechanism that satisfies this differential privacy and provide its theoretical proof. Our PriCDSR is a non-invasive method that can adopt any cross-domain sequential recommender system as a base model without any modification to it. To the best of our knowledge, our PriCDSR is the first work to investigate privacy issues in cross-domain sequential recommender systems. We conduct experiments on three domains, and the results demonstrate that our PriCDSR, despite introducing noise, still outperforms recommender systems that only use data from a single domain.

Variational autoencoder (VAE) is known as a classic and effective method in modeling users’ homogeneous behaviors in recommender systems. In recent years, graph neural networks (GNNs) have achieved promising performance in learning users’ preferences by modeling complex relationships between users and items. However, most VAE- and GNN-based methods are for single-behavior recommendation, rather than the more prevalent counterpart in real-world applications, i.e., multi-behavior recommendation. This motivates us to leverage VAE and GNNs to address the more important and challenging problem of multi-behavior recommendation. Traditional multi-behavior recommendation models have not captured the complex transition relationships across different types of behaviors well. Moreover, they often ignore the varying semantic strength of different types of behaviors. To tackle the above two challenges, we propose a novel multi-behavior recommendation framework named VCGAE (short for Variational Collective Graph AutoEncoder), which inherits the advantages of VAE and GNNs to fully exploit the multi-behavior data. Specifically, we design a behavior transition network that considers different transition relationships across different types of behaviors in order to learn users’ personal preferences. In addition, we introduce a behavior contrastive regularization module that extracts different correlations between users’ auxiliary behaviors and target behavior. By doing so, we gain a deeper understanding of the semantic strength of different behaviors, thereby improving the overall recommendation performance. Extensive experiments on three real-world datasets clearly demonstrate the effectiveness of our VCGAE compared with the state-of-the-art methods.

Although debiasing in multimedia recommendation has shown promising results, most existing work relies on the ability of the model itself to fully disentangle the biased and unbiased information and considers arbitrarily removing all the biases. However, in many business scenarios, it is usually possible to extract a subset of features associated with the biases by means of expert knowledge, i.e., the confounding proxy features. Therefore, in this paper, we propose a novel debiasing framework with confounding proxy priors for the accuracy-bias tradeoff learning in the multimedia recommendation, or CP2Rec for short, in which these confounding proxy features driven by the expert experience are integrated into the model as prior knowledge corresponding to the biases. Specifically, guided by these priors, we use a bias disentangling module with some orthogonal constraints to force the model to avoid encoding biased information in the feature embeddings. We then introduce an auxiliary unbiased loss to synergize with the original biased loss in an accuracy-bias tradeoff module, aiming at recovering the beneficial bias information from the above-purified feature embeddings to achieve a more reasonable accuracy-bias tradeoff recommendation. Finally, we conduct extensive experiments on a public dataset and a product dataset to verify the effectiveness of CR2Rec. In addition, CR2Rec is also deployed on a large-scale financial multimedia recommendation platform in China and achieves a sustained performance gain.

Although knowledge graph has shown their effectiveness in mitigating data sparsity in many recommendation tasks, they remain underutilized in context-aware recommender systems (CARS) with the specific sparsity challenges associated with the contextual features, i.e., feature sparsity and interaction sparsity. To bridge this gap, in this paper, we propose a novel pairwise intent graph embedding learning (PING) framework to efficiently integrate knowledge graphs into CARS. Specifically, our PING contains three modules: 1) a graph construction module is used to obtain a pairwise intent graph (PIG) containing nodes for users, items, entities, and enhanced intent, where enhanced intent nodes are generated by applying user intent fusion (UIF) on relational intent and contextual intent, and two sub-intents are derived from the semantic information and contextual information, respectively; 2) a pairwise intent joint graph convolution module is used to obtain the refined embeddings of all the features by executing a customized convolution strategy on PIG, where each enhanced intent node acts as a hub to efficiently propagate information among different features and between all the features and knowledge graph; 3) a recommendation module with the refined embeddings is used to replace the randomly initialized embeddings of downstream recommendation models to improve model performance. Finally, we conduct extensive experiments on three public datasets to verify the effectiveness and compatibility of our PING.

A practical recommender system should be able to handle heterogeneous behavioral feedback as inputs and has multi-task outputs ability. Although the heterogeneous one-class collaborative filtering (HOCCF) and multi-task learning (MTL) methods has been well studied, there is still a lack of targeted manner in their combined fields, i.e., Multi-behavior Multi-task Recommendation (MMR). To fill the gap, we propose a novel recommendation framework called Behavior-aware Variational AutoEncoder (BVAE), which meliorates the parameter sharing and loss minimization method with the VAE structure to address the MMR problem. Specifically, our BVAE includes behavior-aware semi-encoders and decoders, and a target feature fusion network with a global feature filtering network, while using standard deviation to weigh loss. These modules generate the behavior-aware recommended item list via constructing better semantic feature vectors for users, i.e., from dual perspectives of behavioral preference and global interaction. In addition, we optimize our BVAE in terms of adaptability and robustness, i.e., it is concise and flexible to consume any amount of behaviors with different distributions. Extensive empirical studies on two real and widely used datasets confirm the validity of our design and show that our BVAE can outperform the state-of-the-art related baseline methods under multiple evaluation metrics. The processed datasets, source code, and scripts necessary to reproduce the results can be available at https://github.com/WitnessForest/BVAE.

Sequential recommendation methods play an irreplaceable role in recommender systems which can capture the users’ dynamic preferences from the behavior sequences. Despite their success, these works usually suffer from the sparsity problem commonly existed in real applications. Cross-domain sequential recommendation aims to alleviate this problem by introducing relatively richer source-domain data. However, most existing methods capture the users’ preferences independently of each domain, which may neglect the item transition patterns across sequences from different domains, i.e., a user’s interaction in one domain may influence his/her next interaction in other domains. Moreover, the data sparsity problem still exists since some items in the target and source domains are interacted with only a limited number of times. To address these issues, in this paper we propose a generic framework named multi-view graph contrastive learning (MGCL). Specifically, we adopt the contrastive mechanism in an intra-domain item representation view and an inter-domain user preference view. The former is to jointly learn the dynamic sequential information in the user sequence graph and the static collaborative information in the cross-domain global graph, while the latter is to capture the complementary information of the user’s preferences from different domains. Extensive empirical studies on three real-world datasets demonstrate that our MGCL significantly outperforms the state-of-the-art methods.

Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce. The success of these web applications largely depends on the ability of the employed machine learning model to accurately distinguish influential features from all the predetermined features in tabular data. Intuitively, in practical business scenarios, different instances should correspond to different sets of influential features, and the set of influential features of the same instance may vary in different scenarios. However, most existing methods focus on global feature selection assuming that all instances have the same set of influential features, and few methods considering instance-wise feature selection ignore the variability of influential features in different scenarios. In this paper, we first introduce a new perspective based on the influence function for instance-wise feature selection, and give some corresponding theoretical insights, the core of which is to use the influence function as an indicator to measure the importance of an instance-wise feature. We then propose a new solution for discovering instance-wise influential features in tabular data (DIWIFT), where a self-attention network is used as a feature selection model and the value of the corresponding influence function is used as an optimization objective to guide the model. Benefiting from the advantage of the influence function, i.e., its computation does not depend on a specific architecture and can also take into account the data distribution in different scenarios, our DIWIFT has better flexibility and robustness. Finally, we conduct extensive experiments on both synthetic and real-world datasets to validate the effectiveness of our DIWIFT.

Previous works on generative adversarial networks (GANs) mainly focus on how to synthesize high-fidelity images. In this paper, we present a framework to leverage the knowledge learned by GANs for semantic face manipulation. In particular, we propose to control the semantics of synthesized faces by adapting the latent codes with an attribute prediction model. Moreover, in order to achieve a more accurate estimation of different facial attributes, we propose to pretrain the attribute prediction model by inverting the synthesized face images back to the GAN latent space. As a result, our method explicitly considers the semantics encoded in the latent space of a pretrained GAN and is able to faithfully edit various attributes like eyeglasses, smiling, bald, age, mustache and gender for high-resolution face images. Extensive experiments show that our method has superior performance compared to state of the art for both face attribute prediction and semantic face manipulation.

Decomposition methods have been widely employed in evolutionary algorithms for tackling multiobjective optimization problems (MOPs) due to their good mathematical explanation and promising performance. However, most decomposition methods only use a single ideal or nadir point to guide the evolution, which are not so effective for solving MOPs with extremely convex/concave Pareto fronts (PFs). To solve this problem, this article proposes an effective method to adapt decomposed directions (ADDs) for solving MOPs. Instead of using one single ideal or nadir point, each weight vector has one exclusive ideal point in our method for decomposition, in which the decomposed directions are adapted during the search process. In this way, the adapted decomposed directions can evenly and entirely cover the PF of the target MOP. The effectiveness of our method is analyzed theoretically and verified experimentally when embedding it into three representative multiobjective evolutionary algorithms (MOEAs), which can significantly improve their performance. When compared to seven competitive MOEAs, the experiments also validate the advantages of our method for solving 39 artificial MOPs with various PFs and one real-world MOP.

Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-${F}$method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this article devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer (CSO) to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a CSO is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on 18 high-dimensional datasets.

Heterogeneous sequential recommendation that models sequences of items associated with more than one type of feedback such as examinations and purchases is an emerging topic in the research community, which is also an important problem in many real-world applications. Though there are some methods proposed to exploit different types of feedback in item sequences such as RLBL, RIB, and BINN, they are based on RNN and may not be very competitive in capturing users’ complex and dynamic preferences. And most existing advanced sequential recommendation methods such as the CNN- and attention-based methods are often designed for making use of item sequences with one single type of feedback, which thus can not be applied to the studied problem directly. As a response, we propose a novel feedback-aware local and global (FLAG) preference learning model for heterogeneous sequential recommendation. Our FLAG contains four modules, including (i) a local preference learning module for capturing a user’s short-term interest, which adopts a novel feedback-aware self-attention block to distinguish different types of feedback; (ii) a global preference learning module for modeling a user’s global preference; (iii) a local intention learning module, which takes a user’s real feedback in the next step, i.e., the user’s intention at the current step, as the query vector in a self-attention block to figure out the items that match the user’s intention well; and (iv) a prediction module for preference integration and final prediction. We then conduct extensive experiments on three public datasets and find that our FLAG significantly outperforms 13 very competitive baselines in terms of two commonly used ranking-oriented metrics in most cases. We also include ablation studies and sensitivity analysis of our FLAG to have more in-depth insights.

The bias problems in recommender systems are an important challenge. In this paper, we focus on solving the bias problems via uniform data. Previous works have shown that simple modeling with a uniform data can alleviate the bias problems and improve the performance. However, the uniform data is usually few and expensive to collect in a real product. In order to use the valuable uniform data more effectively, we propose a novel and general knowledge distillation framework for counterfactual recommendation with four specific methods, including label-based distillation, feature-based distillation, sample-based distillation and model structure-based distillation. Moreover, we discuss the relation between the proposed framework and the previous works. We then conduct extensive experiments on both public and product datasets to verify the effectiveness of the proposed four methods. In addition, we explore and analyze the performance trends of the proposed methods on some key factors, and the changes in the distribution of the recommendation lists. Finally, we emphasize that counterfactual modeling with uniform data is a rich research area, and list some interesting and promising research topics worthy of further exploration. Note that the source codes are available athttps://github.com/dgliu/TKDE_KDCRec.

In this paper, we consider the lifelong age progression and regression task, which requires to synthesize a persons appearance across a wide range of ages. We propose a simple yet effective learning framework to achieve this by exploiting the prior knowledge of faces captured by well-trained generative adversarial networks (GANs). Specifically, we first utilize a pretrained GAN to synthesize face images with different ages, with which we then learn to model the conditional aging process in the GAN latent space. Moreover, we also introduce a cycle consistency loss in the GAN latent space to preserve a persons identity. As a result, our model can reliably predict a person's appearance for different ages by modifying both shape and texture of the head. Both qualitative and quantitative experimental results demonstrate the superiority of our method over concurrent works. Furthermore, we demonstrate that our approach can also achieve high-quality age transformation for painting portraits and cartoon characters without additional age annotations.

With the implementation of privacy protection laws such as GDPR, it is increasingly difficult for organizations to legally collect users’ data. However, a typical machine learning-based recommendation algorithm requires the data to learn users’ preferences. Some recent works thus turn to develop federated learning-based recommendation algorithms, but most of them either cannot protect the users’ privacy well, or sacrifice the model accuracy. In this article, we propose a lossless and generic federated recommendation framework via fake marks and secret sharing (FMSS). Our FMSS can not only protect the two types of users’ privacy, i.e., rating values and rating behaviors, without sacrificing the recommendation performance, but can also be applied to most recommendation algorithms for rating prediction, item ranking, and sequential recommendation. Specifically, we extend existing fake items to fake marks, and combine it with secret sharing to perturb the data uploaded by the clients to a server. We then apply our FMSS to six representative recommendation algorithms, i.e., MF-MPC and NeuMF for rating prediction, eALS and VAE-CF for item ranking, and Fossil and GRU4Rec for sequential recommendation. The experimental results demonstrate that our FMSS is a lossless and generic framework, which is able to federate a series of different recommendation algorithms in a lossless and privacy-aware manner.

Debiased recommendation with a randomized dataset has shown very promising results in mitigating system-induced biases. However, it still lacks more theoretical insights or an ideal optimization objective function compared with the other more well-studied routes without a randomized dataset. To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to system-induced biases. First, we formulate a new ideal optimization objective function with a randomized dataset. Second, according to the prior constraints that an adopted loss function may satisfy, we derive two different upper bounds of the objective function: a generalization error bound with triangle inequality and a generalization error bound with separability. Third, we show that most existing related methods can be regarded as the insufficient optimization of these two upper bounds. Fourth, we propose a novel method called debiasing approximate upper bound ( DUB ) with a randomized dataset, which achieves a more sufficient optimization of these upper bounds. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of our DUB.

How to effectively mitigate the bias of feedback in recommender systems is an important research topic. In this article, we first describe the generation process of the biased and unbiased feedback in recommender systems via two respective causal diagrams, where the difference between them can be regarded as the source of system-induced biases. We then define this difference as a confounding bias and propose a new perspective on debiased representation learning to alleviate it. Specifically, for the case with biased feedback alone, we derive the conditions that need to be satisfied to obtain a debiased representation from the causal diagrams. Then, we propose a novel framework called debiased information bottleneck (DIB) to optimize these conditions and then find a tractable solution for it. The proposed framework constrains the model to learn a biased embedding vector with independent biased and unbiased components in the training phase, and uses only the unbiased component in the test phase to deliver more accurate recommendations. We further propose a variant of DIB by relaxing the independence between the biased and unbiased components. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of the proposed framework.

The success of deep neural networks and their potential use in many safety-critical applications has motivated research on formal verification of deep neural networks. A fundamental primitive enabling the formal analysis of neural networks is the output range analysis. Existing approaches on output range analysis either focus on some simple activation functions, such as$\text{relu,}$or compute a relaxed result for some activation functions, such as exponential linear unit$\text{({elu}}$). In this article, we propose an approach to compute the output range for feed-forward deep neural networks via linear programming. The key idea is to encode the activation functions, such as$\text{{elu}}$and$\text{sigmoid}$, as linear constraints in term of the line between the left and right end-points of the input range and the tangent lines on some special points in the input range. A strategy to partition the network to get a tighter range is presented. The experimental results show that our approach gets a tighter result than RobustVerifier on$\text{{elu}}$networks and$\text{sigmoid}$networks. Moreover, our approach performs better than (the linear encodings implemented in) Crown on$\text{{elu}}$networks with$\alpha =0.5, 1.0$and$\text{sigmoid}$networks, and better than CNN-Cert and DeepCert on$\text{{elu}}$networks with$\alpha = 0.5$or 1.0. For$\text{{elu}}$networks with$\alpha = 2.0$, our approach can achieve results that are closed to Crown, CNN-Cert, and DeepCert. Finally, we also found that the network partition helps to achieve a tighter result as well as to improve the efficiency for$\text{{elu}}$networks.

Recently, a number of resource allocation strategies have been proposed for evolutionary algorithms to efficiently tackle multiobjective optimization problems (MOPs). However, these methods mainly allocate computational resources based on the convergence improvement under the decomposition-based framework, which may become ineffective with the increased number of optimization objectives. To address this problem, this article suggests an immune-inspired resource allocation strategy, which breaks through the decomposition-based framework and can better balance convergence and diversity for many-objective optimization. In our method, the diversity distances of solutions are defined by the Euclidean distances of their projected points on the unit hyperplane. Then, based on the diversity distances, resource allocation is realized by using an immune cloning operator to encourage exploring sparse regions of the search space. Moreover, to provide high-quality solutions in coordination with this immune cloning operator, a novel archive update mechanism is designed. When compared to most well-known resource allocation strategies, our method is advantageous for many-objective optimization. The experimental results also validate the superiority of our method over several state-of-the-art evolutionary algorithms for solving two sets of complicated MOPs having 5 to 15 objectives.

Recently, sequential recommendation has become a research hotspot while multi-behavior sequential recommendation (MBSR) that exploits users' heterogeneous interactions in sequences has received relatively little attention. Existing works often overlook the complementary effect of different perspectives when addressing the MBSR problem. In addition, there are two specific challenges remained to be addressed. One is the heterogeneity of a user's intention and the context information, the other one is the sparsity of the interactions of target behavior. To release the potential of multi-behavior interaction sequences, we propose a novel framework named NextIP that adopts a dual-task learning strategy to convert the problem to two specific tasks, i.e., next-item prediction and purchase prediction. For next-item prediction, we design a target-behavior aware context aggregator (TBCG), which utilizes the next behavior to guide all kinds of behavior-specific item sub-sequences to jointly predict the next item. For purchase prediction, we design a behavior-aware self-attention (BSA) mechanism to extract a user's behavior-specific interests and treat them as negative samples to learn the user's purchase preferences. Extensive experimental results on two public datasets show that our NextIP performs significantly better than the state-of-the-art methods.

Existing legal judgment prediction methods usually only consider one single case fact description as input, which may not fully utilize the information in the data such as case relations and frequency. In this paper, we propose a new perspective that introduces some contrastive case relations to construct case triples as input, and a corresponding judgment prediction framework with case triples modeling (CTM). Our CTM can more effectively utilize beneficial information to refine the encoding and decoding processes through three customized modules, including the case triple module, the relational attention module, and the category decoder module. Finally, we conduct extensive experiments on two public datasets to verify the effectiveness of our CTM, including overall evaluation, compatibility analysis, ablation studies, analysis of gain source and visualization of case representations.

Autoencoder-based methods have achieved significant performance on item recommendation. However, they may not perform well on tail items due to the ignorance of the items’ popularity bias. As a response, in this paper, we focus on tail items and propose a novel adversarial learning method for tail recommendation (ALTRec). In our ALTRec, the generator (i.e., AutoRec) not only reconstructs the input well, but also minimizes the (any two-user) similarity difference between the input stage and the output stage to keep users’ interaction relationships unchanged. And the discriminator maps the inputs and outputs of the generator to a same semantic space for scoring the similarity and maximizes the similarity difference as the target, and will identify some unsatisfactory predictions, especially on tail items. In order to preserve the similarity, the generator will pay more attention to the tail items compared with the previous autoencoder-based methods. An ablation study validates the effectiveness of preserving the two-user similarity, as well as the adversarial learning strategy in our ALTRec. Extensive experiments on three real-world datasets show that our ALTRec significantly boosts the performance on tail items compared with several state-of-the-art methods.

Deep reinforcement learning (DRL) has been studied in a variety of challenging decision-making tasks, e.g., autonomous driving. \textcolor{black}{However, DRL typically suffers from the action shaking problem, which means that agents can select actions with big difference even though states only slightly differ.} One of the crucial reasons for this issue is the inappropriate design of the reward in DRL. In this paper, to address this issue, we propose a novel way to incorporate the smoothness of actions in the reward. Specifically, we introduce sub-rewards and add multiple constraints related to these sub-rewards. In addition, we propose a multi-constraint proximal policy optimization (MCPPO) method to solve the multi-constraint DRL problem. Extensive simulation results show that the proposed MCPPO method has better action smoothness compared with the traditional proportional-integral-differential (PID) and mainstream DRL algorithms. The video is available at https://youtu.be/F2jpaSm7YOg.

Recommendation methods based on deep learning frameworks have drastically increased over recent years, covering virtually all the sub-topics in recommender systems. Among these topics, one-class collaborative filtering (OCCF) as a fundamental problem has been studied most extensively. However, most of existing deep learning-based OCCF methods are essentially focused on either defining new prediction rules by replacing conventional shallow and linear inner products with a variety of neural architectures, or learning more expressive user and item factors with neural networks, which may still suffer from the inferior recommendation performance due to the underlying preference assumptions typically defined on single items. In this paper, we propose to address the limitation and justify the capacity of deep learning-based recommendation methods by adapting the setwise preference to the underlying assumption during the model learning process. Specifically, we propose a new setwise preference assumption under the neural recommendation frameworks and devise a general solution named DeepSet, which aims to enhance the learning abilities of neural collaborative filtering methods by activating the setwise preference at different neural layers, namely 1) the feature input layer, 2) the feature output layer, and 3) the prediction layer. Extensive experiments on four commonly used datasets show that our solution can effectively boost the performance of existing deep learning based methods without introducing any new model parameters.

Collaborative ranking with implicit feedback such as users' clicks is an important recommendation problem in various real-world applications. Most existing approaches are developed based on some pointwise or pairwise preference assumptions, although the listwise assumption is widely accepted as a better alternative due to its consistency with the final delivery result. In this paper, we first identify two fundamental limitations of the most current collaborative listwise approaches, in which their modeling is based on the Plackett-Luce probability. They are too strict and too weak relative preference comparison between the items with the same feedback and between the items with different feedback, respectively. As a response, we propose a novel and improved listwise approach called SQL-Rank++, which is able to learn the user preferences more accurately by leveraging some specifically constructed auxiliary lists, including some positive lists and some negative lists. Specifically, the positive lists have as much semantic consistency as the original list as possible, while the negative lists are the opposite. To construct these auxiliary lists, we design a self-based sampling strategy and a user similarity-based one. Finally, we have four variants of our SQL-Rank++ with different combinations of the auxiliary lists. We then conduct extensive experiments on four public datasets, and find that our SQL-Rank++ achieves very promising performance in comparison with several pointwise, pairwise and listwise approaches. We also study the influence of the two sampling strategies and the key components in our SQL-Rank++.

The common item-based collaborative filtering framework becomes a typical recommendation method when equipped with a certain item-to-item similarity measurement. On one hand, we realize that a well-designed similarity measurement is the key to providing satisfactory recommendation services. On the other hand, similarity measurements designed for sequential recommendation are rarely studied by the recommender systems community. Hence in this paper, we focus on devising a novel similarity measurement called position-aware similarity (PAS) for sequential recommendation. The proposed PAS is, to our knowledge, the first count-based similarity measurement that concurrently captures the sequential patterns from the historical user behavior data and from the item position information within the input sequences. We conduct extensive empirical studies on four public datasets, in which our proposed PAS-based method exhibits competitive performance even compared to the state-of-the-art sequential recommendation methods, including a very recent similarity-based method and two GNN-based methods.

In recent years, multi-access edge computing (MEC) is emerging to provide computation and storage resources to the Internet of things (IoT) devices to assist them in high-performance demanding tasks. Real-time task requests from the IoT devices often have strict delay constraints. However, in practice, the delay requirements of task requests often fail to be satisfied because of the inappropriate computation processing method and inefficient resource allocation in MEC networks. In this article, we present a novel framework for MEC networks with unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) to facilitate computation offloading with delay constraints. In addition, we propose a novel constrained reinforcement learning method with a dynamic balance mechanism named$Q_{c}-DQN$. Finally, we conduct extensive simulations to verify the effectiveness of our proposed method. Compared to the benchmark schemes, our scheme not only improves the overall network performance and reduces the task completion time, but also meets the delay constraints.

In recent years, multi-access edge computing (MEC) is emerging to provide computation and storage capabilities to the Internet of things (IoT) devices to improve the quality of service (QoS) of IoT applications. In addition, intelligent reflecting surface (IRS) techniques have attracted great interests from both academia and industry to improve the communication efficiency. Although existing works leverage the IRS technique in MEC networks, they mainly focus on the single-IRS single-area scenario. However, in practice, multi-IRS will be deployed in multi-area scenarios in future networks. Consequently, considering the single-IRS single-area scenario will have inferior performance. In this paper, to address the aforementioned issue, we propose an efficient resource provisioning scheme for multi-IRS multi-area scenarios in MEC networks. We first model the problem as a cooperative multi-agent reinforcement learning process, where each agent manages one area and all agents share the network bandwidth and computation resources. Then, we propose a multi-agent actor-critic method with an attention mechanism for resource management with latency guarantee. Finally, we conduct extensive simulations to verify the effectiveness of the proposed scheme. Our scheme can reduce the required computation resources by up to 11.84% when compared with the benchmark works. It is also shown that our proposed scheme can improve the efficiency of resource allocation and scale well with the increasing demand from IoT devices.

Most methods for context-aware recommendation focus on improving the feature interaction layer, but overlook the embedding layer. However, an embedding layer with random initialization often suffers in practice from the sparsity of the contextual features, as well as the interactions between the users (or items) and context. In this paper, we propose a novel user-event graph embedding learning (UEG-EL) framework to address these two sparsity challenges. Specifically, our UEG-EL contains three modules: 1) a graph construction module is used to obtain a user-event graph containing nodes for users, intents and items, where the intent nodes are generated by applying intent node attention (INA) on nodes of the contextual features; 2) a user-event collaborative graph convolution module is designed to obtain the refined embeddings of all features by executing a new convolution strategy on the user-event graph, where each intent node acts as a hub to efficiently propagate the information among different features; 3) a recommendation module is equipped to integrate some existing context-aware recommendation model, where the feature embeddings are directly initialized with the obtained refined embeddings. Moreover, we identify a unique challenge of the basic framework, that is, the contextual features associated with too many instances may suffer from noise when aggregating the information. We thus further propose a simple but effective variant, i.e., UEG-EL-V, in order to prune the information propagation of the contextual features. Finally, we conduct extensive experiments on three public datasets to verify the effectiveness and compatibility of our UEG-EL and its variant.

Heterogeneous sequential recommendation (HSR) is a very important recommendation problem, which aims to predict a user’s next interacted item under a target behavior type (e.g., purchase in e-commerce sites) based on his/her historical interactions with different behaviors. Though existing sequential methods have achieved advanced performance by considering the varied impacts of interactions with sequential information, a large body of them still have two major shortcomings. Firstly, they usually model different behaviors separately without considering the correlations between them. The transitions from item to item under diverse behaviors indicate some users’ potential behavior manner. Secondly, though the behavior information contains a user’s fine-grained interests, the insufficient consideration of the local context information limits them from well understanding user intentions. Utilizing the adjacent interactions to better understand a user’s behavior could improve the certainty of prediction. To address these two issues, we propose a novel solution utilizing global and personalized graphs for HSR (GPG4HSR) to learn behavior transitions and user intentions. Specifically, our GPG4HSR consists of two graphs, i.e., a global graph to capture the transitions between different behaviors, and a personalized graph to model items with behaviors by further considering the distinct user intentions of the adjacent contextually relevant nodes. Extensive experiments on four public datasets with the state-of-the-art baselines demonstrate the effectiveness and general applicability of our method GPG4HSR.

Neural network-based models for collaborative filtering have received widespread attention, among which variational autoencoder (VAE) has shown unique advantages in the task of item recommendation. However, most existing VAE-based models only focus on one type of user feedback, leading to their performance bottlenecks. To overcome this limitation, we propose a novel VAE-based recommendation model called VAE++, which can effectively utilize heterogeneous feedback to boost recommendation performance. Specifically, it combines three different types of signals, i.e., purchase feedback, examination feedback and their mixed feedback, via two well-designed modules, i.e., a target representation enhancement module and a target representation refinement module. The former exploits the mixed feedback to improve the learning of purchase representations, while the latter leverages the examination feedback to further refine them. In particular, purchase and examination preferences are jointly decoded in one decoder to ensure the high quality of the reconstructed samples. Extensive experiments on three public datasets show that our VAE++ achieves the best results compared with several state-of-the-art methods.

Modeling other agents' behaviors plays an important role in decision models for interactions among multiple agents. To optimize its own decisions, a subject agent needs to model what other agents act simultaneously in an uncertain environment. However, modeling insufficiency occurs when the agents are competitive and the subject agent cannot get full knowledge about other agents. Even when the agents are collaborative, they may not share their true behaviors due to their privacy concerns. Most of the recent research still assumes that the agents have common knowledge about their environments and a subject agent has the true behavior of other agents in its mind. Consequently, the resulting techniques are not applicable in many practical problem domains. In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model before their interactions. The challenges lie in generating and measuring new behaviors of other agents. Starting with prior knowledge about other agents' behaviors, we use a linear reduction technique to extract representative behavioral features from the known behaviors. We subsequently generate their new behaviors by expanding the features and propose two diversity measurements to select top- K $K$ behaviors. We demonstrate the performance of the new techniques in two well-studied problem domains. The top- K $K$ behavior selection embarks the study of unknown behaviors in multiagent decision making and inspires investigation of diversifying agents' behaviors in competitive agent interactions. This study will contribute to intelligent systems dealing with unknown unknowns in an open artificial intelligence world.

In the last few decades, ciphertext-policy attribute-based encryption (CP-ABE) technology has attracted great interest, since it can provide fine-grained, flexible, and access control for sensitive data to implement a high secure and efficient data-sharing mechanism. In this article, based on the linear secret sharing scheme (LSSS), an efficient scheme is proposed to realize a collaborative decryption function. For any user group, when the user’s attribute set cannot access the ciphertext alone, the private key of other users in the same group can be used for collaborative decryption with the permission of the data owner. Our scheme uses the LSSS matrix that can significantly reduce the computation and storage overhead when comparing with the existing schemes. Then, a multiauthorization model is created based on the Bohen–Lynn–Shacham technology in order to solve the key-management issue. Finally, we implemented the specific functions of the framework through JAVA, and built a private chain to verify the feasibility of data transfer between users.

Generally, decomposition-based evolutionary algorithms in many-objective optimization (MaOEA/Ds) have widely used reference vectors (RVs) to provide search directions and maintain diversity. However, their performance is highly affected by the matching degree on the shapes of the RVs and the Pareto front (PF). To address this problem, this article proposes a self-guided RV (SRV) strategy for MaOEA/Ds, aiming to extract RVs from the population using a modified k -means clustering method. To give a promising clustering result, an angle-based density measurement strategy is used to initialize the centroids, which are then adjusted to obtain the final clusters, aiming to properly reflect the population's distribution. Afterward, these centroids are extracted to obtain adaptive RVs for self-guiding the search process. To verify the effectiveness of this SRV strategy, it is embedded into three well-known MaOEA/Ds that originally use the fixed RVs. Moreover, a new strategy of embedding SRV into MaOEA/Ds is discussed when the RVs are adjusted at each generation. The simulation results validate the superiority of our SRV strategy, when tackling numerous many-objective optimization problems with regular and irregular PFs.

Fuzzy clustering techniques, especially fuzzy C-means (FCM) and its weighted variants, are typical partitive clustering models that are widely used for revealing possible hidden structures in data. Although they can quantitatively depict the overlapping areas with a partition matrix, their performances deteriorate when dealing with high-dimensional data because the distance computations may be negatively impacted by the irrelevant features, and then the concentration effect may arise. Moreover, they are sensitive to noisy environments. To tackle these obstacles, a robust jointly sparse fuzzy clustering method (RJSFC) is proposed in this study. The representative prototypes, sparse membership grades, and an orthogonal projection matrix are simultaneously learnt when optimizing RJSFC. The obtained low-dimensional embeddings can preserve the local neighborhood structure, and the clustering is conducted in the transformed lower dimensional space rather than the original space, which improves the capability of fuzzy clustering for dealing with high-dimensional scenarios. Furthermore,${L_{2,1}}$-norm is exploited as the basic metric for both loss and regularization parts in RJSFC, the robustness of the model and the interpretability of the extracted features are enhanced. The notions of fuzzy clustering, neighborhood structure preservation, and feature extraction are seamlessly integrated into a unified model. The limitation of the previous two-stage clustering framework when dealing with high-dimensional data entailing dimensionality reduction and clustering procedures separately can be effectively addressed. Extensive experimental results on various well-known datasets demonstrate the usefulness of RJSFC when comparing with some state-of-the-art methods.

With the marriage of federated machine learning and recommender systems for privacy-aware preference modeling and personalization, there comes a new research branch called federated recommender systems aiming to build a recommendation model in a distributed way, i.e., each user is represented as a distributed client where his/her original rating data are not shared with the server or the other clients. Notice that, besides the sensitive information of a specific rating score assigned to a certain item by a user, the information of a user's rated set of items shall also be well protected. Some very recent works propose to randomly sample some unrated items for each user and then assign some virtual ratings, so that the server can not identify the scores and the set of rated items easily during the server-client interactions. However, the virtual ratings assigned to the randomly sampled items will inevitably introduce some noise to the model training process, which will then cause loss in recommendation performance. In this paper, we propose a novel lossless federated recommendation method (FedRec++) by allocating some denoising clients (i.e., users) to eliminate the noise in a privacy-aware manner. We further analyse our FedRec++ in terms of security and losslessness, and discuss its generality in the context of existing works. Extensive empirical studies clearly show the effectiveness of our FedRec++ in providing accurate and privacy-aware recommendation without much additional communication cost.

This paper proposes an improved weighted optimization-based framework (iWOF) for solving large-scale multiobjective optimization problems (LSMOPs). Compared to the original framework, there are two main contributions in our work. Firstly, a novel evolutionary search strategy involving two different search operators with different search characteristics, i.e., a particle swarm optimization (PSO) operator and differential evolution (DE) operator, is designed in iWOF, which aims to provide a robust search ability on finding optimal solutions in a huge decision space. Secondly, different from the stage in the original framework that divides the whole evolutionary process into two independent stages, the evolutionary process in the proposed iWOF is simplified to only one stage, which effectively reduces the number of predefined parameters. Besides that, the evolving numbers of weight optimization and original optimization in iWOF are adjusted adaptively according to the evolutionary stage. The experimental results on three different groups of benchmark LSMOPs validate the superiority of the proposed iWOF over WOF and other several state-of-the-art multiobjective evolutionary algorithms.

Tile-based approach is widely adopted in adaptive 360\textdegree~video streaming systems. Existing QoE-driven streaming approaches usually obtain the tile selection and adjust the bitrate based on the viewport prediction with a fixed tiling, which fail to consider the unstable prediction performance. However, varying the tiling of the video can produce different number of tiles with different sizes, and thus can have distinct impacts on error tolerance for viewport prediction and on decoding complexity for resource-constrained mobile client. In this work, we introduce adaptive tiling into the conventional bitrate adaptation for mobile 360degree~video streaming. We first analyze the impacts of tilings on tile selection and decoding time, which verify the benefit of tiling adaptation in various practical aspects. We then formulate the QoE optimization problem for adaptive tiling and bitrate streaming and discuss the design details of our adaptation algorithm, which can adapt to the performance of viewport prediction and the decoding capabilities of mobile clients in addition to the conventional influencing factors. Finally, the superiority of our proposed approach compared with the state-of-the-art methods is evaluated through extensive trace-driven simulations.

In a recommender system, a user’s interaction is often biased by the items’ displaying positions and popularity, as well as the user’s self-selection. Most existing recommendation models are built using such a biased user-system interaction data. In this paper, we first additionally introduce a specially collected unbiased data and then propose a novel transfer learning solution, i.e., transfer via joint reconstruction (TJR), to achieve knowledge transfer and sharing between the biased data and unbiased data. Specifically, in our TJR, we refine the prediction via the latent features containing bias information in order to obtain a more accurate and unbiased prediction. Moreover, we integrate the two data by reconstructing their interaction in a joint learning manner. We then adopt three representative methods as the backbone models of our TJR and conduct extensive empirical studies on two public datasets, showcasing the effectiveness of our transfer learning solution over some very competitive baselines.

With the implementation of privacy protection laws such as GDPR, it is increasingly difficult for organizations to legally collect user data. However, a typical recommendation algorithm based on machine learning requires user data to learn user preferences. In order to protect user privacy, a lot of recent works turn to develop federated learning-based recommendation algorithms. However, some of these works can only protect the users’ rating values, some can only protect the users’ rating behavior (i.e., the engaged items), and only a few works can protect the both types of privacy at the same time. Moreover, most of them can only be applied to a specific algorithm or a class of similar algorithms. In this paper, we propose a generic cross-user federated recommendation framework called FR-FMSS. Our FR-FMSS can not only protect the two types of user privacy, but can also be applied to most recommendation algorithms for rating prediction, item ranking, and sequential recommendation. Specifically, we use fake marks and secret sharing to modify the data uploaded by the clients to the server, which protects user privacy without loss of model accuracy. We take three representative recommendation algorithms, i.e., MF-MPC, eALS, and Fossil, as examples to show how to apply our FR-FMSS to a specific algorithm.

How to effectively mitigate the bias of feedback in recommender systems is an important research topic. In this paper, we first describe the generation process of the biased and unbiased feedback in recommender systems via two respective causal diagrams, where the difference between them can be regarded as the source of bias. We then define this difference as a confounding bias, which can be regarded as a collection of some specific biases that have previously been studied. For the case with biased feedback alone, we derive the conditions that need to be satisfied to obtain a debiased representation from the causal diagrams. Based on information theory, we propose a novel method called debiased information bottleneck (DIB) to optimize these conditions and then find a tractable solution for it. In particular, the proposed method constrains the model to learn a biased embedding vector with independent biased and unbiased components in the training phase, and uses only the unbiased component in the test phase to deliver more accurate recommendations. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of the proposed method and discuss its properties.

With the exponential growth in the number of Internet-of-Things (IoT) devices, the cloud-centric computing paradigm can hardly meet the increasingly high requirements for low latency, high bandwidth, ease of availability, and more intelligent services. Therefore, a distributed and decentralized computing architecture is imperative, where edge-centric computing, such as fog computing and mist computing, has been recently proposed. Edge-centric computing resources can be managed locally and personally rather than being administered by a remote centralized third party. However, security and privacy issues are the main challenges due to the absence of trust between the IoT devices and edge computing nodes (ECNs). A blockchain, as a decentralized, trustless, and immutable public ledger, can well solve the trust-absence issue. In this article, we first elaborate on the security and privacy issues of edge-computing-enabled IoT, and then present the key characteristics of blockchains, which make blockchains well suited for the edge-centric IoT scenarios. Furthermore, we propose a general framework for blockchain-based edge-computing-enabled IoT scenarios that specifies the step-by-step procedure of a single transaction between an IoT end and an ECN. In addition, we design a smart contract within a private blockchain network that exploits the state-of-the-art machine learning algorithm, asynchronous advantage actor-critic (A3C), to allocate the edge computing resources, which exemplifies how artificial intelligence (AI) can be combined with blockchains. We further discuss the benefits of the convergence of AI and blockchains. Finally, simulation results are presented.

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT makes it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to the existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Noncryptographic device verification is needed to ensure trustworthy IoT. In this article, we propose an enhanced deep learning framework for IoT device identification using physical-layer signals. Specifically, we enable our framework to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability. We have evaluated the effectiveness of the proposed framework using real data from automatic dependent surveillance-broadcast (ADS-B), an application of IoT in aviation. The proposed framework has the potential to be applied to the accurate identification of IoT devices in a variety of IoT applications and services.

Edge computing, especially multiaccess edge computing, is seen as a promising technology to improve the Quality of user Experience (QoE) of many artificial intelligence (AI) applications in the evolution toward Internet-of-Things (IoT) infrastructure. However, the management and deployment of massive edge data centers bring new challenges for the current network. In this article, we propose a new edge-based IoT platform for AI (EBI-PAI), based on software-defined network (SDN) and serverless technology. EBI-PAI provides a unified service calling interface and schedules the resources automatically to satisfy the QoE requirements of users. To optimize performances during incremental deployment, we formulate the deployment problem, prove its complexity, and design heuristic algorithms to solve it. We implement EBI-PAI based on an opensource serverless project and deploy it in real networks. To evaluate EBI-PAI, we conduct comprehensive simulations based on the generated and real-world network topology, and real-world base station data set. The simulation results show that EBI-PAI can greatly improve QoE with the same budget and save the budget to achieve similar QoE. We finally carry out a case study with real user demands, and it further validates the simulation results.

Abstract With the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data-driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs)—a general framework for multiagent sequential decision making under uncertainty. We propose a data-driven framework to solve the I-DIDs model and focus on learning the behavior of other agents in problem domains. The challenge is on learning a complete policy tree that will be embedded in the I-DIDs models due to limited data. We propose two new methods to develop complete policy trees for the other agents in the I-DIDs. The first method uses a simple clustering process, while the second one employs sophisticated statistical checks. We analyze the proposed algorithms in a theoretical way and experiment them over two problem domains.

Verifiable pattern matching enables users to obtain authenticated query results over outsourced data on an untrusted remote server. It is a fundamental problem in many security-critical big data applications, including big database search, human genome data search, text search, etc., especially when these applications are outsourced to third-party clouds. However, the state-of-the-art schemes do not yet support efficient data updates. In this work, we propose the first dynamic verifiable pattern matching scheme to support efficient data updates. The proposed scheme is built on two ideas: one is to embed unique randomness to decouple the character and its index in the outsourced data, enabling efficient data updates; the other is to reduce the verifiable pattern matching problem to a discrete set membership testing problem, which relies on the decoupling introduced in the first idea. Based on these two ideas, the proposed scheme first employs the suffix array index structure to search pattern matching queries. The scheme then authenticates the outsourced text using a newly designed authenticated data structure based on the RSA accumulator, which guarantees the verifiability of pattern matching query results. Data update is naturally supported using the RSA accumulator working on discrete sets. Based on the proposed design, we have prototyped a proof-of-concept for the proposed scheme and have conducted an extensive experimental evaluation. In addition to supporting efficient data update, our experimental results show that the proposed scheme incurs reduced verification cost in comparison with the baseline state-of-the-art scheme.

Protection on end users’ data stored in Cloud servers becomes an important issue in today’s Cloud environments. In this paper, we present a novel data protection method combining Selective Encryption (SE) concept with fragmentation and dispersion on storage. Our method is based on the invertible Discrete Wavelet Transform (DWT) to divide agnostic data into three fragments with three different levels of protection. Then, these three fragments can be dispersed over different storage areas with different levels of trustworthiness to protect end users’ data by resisting possible leaks in Clouds. Thus, our method optimizes the storage cost by saving expensive, private, and secure storage spaces and utilizing cheap but low trustworthy storage space. We have intensive security analysis performed to verify the high protection level of our method. Additionally, the efficiency is proved by implementation of deploying tasks between CPU and General Purpose Graphic Processing Unit (GPGPU) in an optimized manner.

Traditional reproduction operators in many-objective evolutionary algorithms (MaOEAs) seem to not be so effective to tackle many-objective optimization problems (MaOPs). This is mainly because the population size cannot be set to an arbitrarily large value if the computational efficiency is of concern. In such a case, the distance between the parents becomes remarkably large and, consequently, it is not easy to reproduce a superior offspring in high-dimensional objective space. To alleviate this problem, an elite gene-guided (EGG) reproduction operator is proposed to tackle MaOPs in this article. In this operator, an elite gene pool is built by collecting the knee points from the current population. Then, the offspring is produced by exchanging the genes with this elite gene pool under an exchange rate, aiming to reserve more promising genes into the next generation. In order to provide new genes for the population, other genes will be disturbed under a disturbance rate. The settings and functional analysis of the exchange rate and disturbance rate are studied using several experiments. The proposed EGG operator is easy to implement and can be embedded to any MaOEA. As examples, we show the embedding of the proposed EGG operator into four competitive MaOEAs, that is, MOEA/D, NSGA-III, θ -DEA, and SPEA2-SDE provide some advantages over simulated binary crossover, differential evolution, and an evolutionary path-based reproduction operator on solving a number of benchmark problems with 3 to 15 objectives.

Compared with conventional authentication schemes, which utilize cryptographic mechanisms and operate at an upper layer, authentication at the physical (PHY) layer possesses many advantages, such as, higher information-theoretic security by introducing uncertainty to an adversary, better efficiency, and better compatibility by avoiding any operations at the upper layer. An effective PHY-layer authentication scheme should consider covertness, security, and robustness together. However, in published literature, these three properties have been separately analyzed. This paper proposes a generic security model for PHY-layer authentication, and we design a new systematic metric, which is referred to as the probability of security authentication (PSA). According to the proposed generic security model, we can not only systematically analyze the effect of the parameter of a certain PHY-layer authentication scheme on the final performance, but also fairly compare the performance of different PHY-layer authentication schemes under the same channel conditions at the legitimate and adversary receivers. For performance analysis, the probability of detection (PD) and the probability of false alarm (PFA) of two PHY-layer authentication schemes over fading channels are defined, and their closed-form expressions are well derived. On the basis of the proposed generic security model, a systematic performance comparison between different PHY-layer schemes is provided.

Recommender systems have been a valuable component in various online services such as e-commerce and entertainment. To provide an accurate top-N recommendation list of items for each target user, we have to answer a very basic question of how to model users’ feedback effectively. In this article, we focus on studying users’ explicit feedback, which is usually assumed to contain more preference information than the counterpart, i.e., implicit feedback. In particular, we follow two very recent transfer to rank algorithms by converting the original feedback to three different but related views of examinations, scores, and purchases, and then propose a novel solution called holistic transfer to rank (HoToR), which is able to address the uncertainty challenge and the inconvenience challenge in the existing works. More specifically, we take the rating scores as a weighting strategy to alleviate the uncertainty of the examinations, and we design a holistic one-stage solution to address the inconvenience of the two/three-stage training and prediction procedures in previous works. We then conduct extensive empirical studies in a direct comparison with the two closely related transfer learning algorithms and some very competitive factorization- and neighborhood-based methods on three public datasets and find that our HoToR performs significantly better than the other methods in terms of several ranking-oriented evaluation metrics.

Currently, video content accounts for the majority of network traffic. With increased live streaming, rigorous requirements have been introduced for better Quality of Experience (QoE). It is challenging to meet satisfactory QoE in live streaming, where the aim is to achieve a balance between 1) enhancing the video quality and stability and 2) reducing the rebuffering time and end-to-end delay, under different scenarios with various network conditions and user preferences, where the fluctuation in the network throughput degrades the QoE severely. In this paper, we propose an approach to improve the QoE for live video streaming based on Deep Reinforcement Learning (DRL). The new approach jointly adjusts the streaming parameters, including the video bitrate and target buffer size. With the basic DRL framework, TCLiVi can automatically generate the inference model based on the playback information, to achieve the joint optimization of the video quality, stability, rebuffering time and latency parameters. We evaluate our framework on real-world data in different live streaming broadcast scenarios, such as a talent show and a sports competition under different network conditions. We compare TCLiVi with other algorithms, such as the Double DQN, MPC and Buffer-based algorithms. The simulation results show that TCLiVi significantly improves the video quality and decreases the rebuffering time, consequently increasing the QoE score by 40.84% in average. We also show that TCLiVi is self-adaptive in different scenarios.

In this paper, we study a recent and important recommendation problem called heterogeneous one-class collaborative filtering (HOCCF), where we have two different types of one-class feedback, i.e., a set of browses and a set of purchases, for preference learning. Previous methods exploit the browses and purchases by extending some existing methods on modeling homogeneous one-class feedback in an integrative or sequential manner. However, an integrative method may be of high complexity in model training due to the expanded prediction rule, and a sequential method is of inconvenience in deployment because more than one parametric models have to be maintained. In this paper, we convert the HOCCF problem to an adaptive transfer learning task, where we first model browses via a factorization model from a perspective of the role of browser, and then adaptively refine the learned model parameters via purchases from a perspective of the dependent role of purchaser. Based on this conversion, we design a novel solution called role-based adaptive factorization (ROAF), and then derive two specific variants with pairwise preference learning and pointwise preference learning. Finally, we conduct extensive empirical studies on two large datasets, and find that our ROAF is a very promising solution in terms of recommendation accuracy, besides its convenience of one single parametric model in deployment.

In this paper, we study an important recommendation problem with heterogeneous feedback of users' grade scores such as 5-star grade scores and like/dislike binary ratings assigned to items. As a response, we address the problem from a transfer learning perspective, i.e., taking the grade scores as the target data and the binary ratings as the auxiliary data, in order to share knowledge between two different types of data more sufficiently. Technically, besides the observed explicit feedback of grade scores and binary ratings, we propose to exploit the implicit preference context beneath the feedback, which is incorporated into the prediction process of users' grade scores to items. Finally, we develop a novel and generic transfer learning solution, i.e., preference-aware transfer (PAT), which embodies several recent algorithms as parts of our solution and special cases. To verify our novel solution, we then conduct extensive empirical studies on two large and public datasets and find that our PAT performs significantly better than the state-of-the-art methods.

Sequential recommendation has been a hot research topic because of its practicability and high accuracy by capturing the sequential information. As deep learning (DL) based methods being widely adopted to model the local and dynamic preferences beneath users’ behavior sequences, the modeling of users’ global and static preferences tends to be underestimated that usually, only some simple and crude users’ latent representations are introduced. Moreover, most existing methods hold an assumption that users’ intention can be fully captured by considering the historical behaviors, while neglect the possible uncertainty of users’ intention in reality, which may be influenced by the appearance of the candidate items to be recommended. In this paper, we thus focus on these two issues, i.e., the imperfect modeling of users’ global preferences in most DL-based sequential recommendation methods and the uncertainty of users’ intention brought by the candidate items, and propose a novel solution named fusing item similarity models with self-attention networks (FISSA) for sequential recommendation. Specifically, we treat the state-of-the-art self-attentive sequential recommendation (SASRec) model as the local representation learning module to capture the dynamic preferences beneath users’ behavior sequences in our FISSA, and further propose a global representation learning module to improve the modeling of users’ global preferences and a gating module that balances the local and global representations by taking the information of the candidate items into account. The global representation learning module can be seen as a location-based attention layer, which is effective to fit in well with the parallelization training process of the self-attention framework. The gating module calculates the weight by modeling the relationship among the candidate item, the recently interacted item and the global preference of each user using an MLP layer. Extensive empirical studies on five commonly used datasets show that our FISSA significantly outperforms eight state-of-the-art baselines in terms of two commonly used metrics.

Recommender systems are feedback loop systems, which often face bias problems such as popularity bias, previous model bias and position bias. In this paper, we focus on solving the bias problems in a recommender system via a uniform data. Through empirical studies in online and offline settings, we observe that simple modeling with a uniform data can alleviate the bias problems and improve the performance. However, the uniform data is always few and expensive to collect in a real product. In order to use the valuable uniform data more effectively, we propose a general knowledge distillation framework for counterfactual recommendation that enables uniform data modeling through four approaches: (1) label-based distillation focuses on using the imputed labels as a carrier to provide useful de-biasing guidance; (2) feature-based distillation aims to filter out the representative causal and stable features; (3) sample-based distillation considers mutual learning and alignment of the information of the uniform and non-uniform data; and (4) model structure-based distillation constrains the training of the models from the perspective of embedded representation. We conduct extensive experiments on both public and product datasets, demonstrating that the proposed four methods achieve better performance over the baseline models in terms of AUC and NLL. Moreover, we discuss the relation between the proposed methods and the previous works. We emphasize that counterfactual modeling with uniform data is a rich research area, and list some interesting and promising research topics worthy of further exploration. Note that the source codes are available at \urlhttps://github.com/dgliu/SIGIR20_KDCRec.

With the development of Internet of Things (IoT), edge computing becomes more and more prevalent currently. However, edge computing needs to deploy a large number of edge servers to reduce the communication latency, which will bring additional costs to the system. Although there exist some idle computing resources at the edge, the owners distrust each other and lack the incentives to contribute to the system. In this article, we propose a new edge computing platform decentralized and trusted platform for edge computing (DeTEC), which provides a unified interface to users, resolves the user's requests to the most appropriate edge server through domain name server, and returns the computational results to the IoT user. To build a trustworthy system, DeTEC integrates the blockchain technology with edge computing, such that the contributions of each participant could be accounted and rewarded. We formulate the task allocation problem, taking both node capacity and reward fairness into consideration, and solve it through a heuristic algorithm. Finally, to guarantee the trustworthiness of computational results, we utilize a police patrol model and try to optimize the system overall reward. We implement DeTEC based on an open source project and conduct comprehensive experiments to test its performance. The results show that our DeTEC system works well in the IoT scenario.

Developing a dialogue response generation system is one of important topics in natural language processing, but many obstacles are yet to be overcome before autogenerated dialogues with a human-like quality can become possible. A good evaluation method will help narrow the gap between machines and humans in dialogue generation. Unfortunately, the existing automatic evaluation methods are biased and correlate very poorly with human judgments of response quality. Such methods are incapable of assessing whether a dialogue response generation system can produce high-quality, knowledge-related and informative dialogues. In response to this challenge, we design an information-oriented framework to simulate human subjective evaluation. Using this framework, we implement a learning-based metric to evaluate the quality of a dialogue. An experimental validation demonstrates our proposed metric's effectiveness in dialogue selection and model evaluation on a Twitter dataset (in English) and a Weibo dataset (in Chinese). In addition, the metric is more relevant than the existing methods of dialogue evaluation to human subjective judgment.

In this paper, we study top-N recommendation by exploiting users' explicit feedback such as 5-star numerical ratings, which has been overlooked to some extent in the past decade. As a response, we design a novel and generic transfer learning based recommendation framework coarse-to-fine transfer to rank (CoFiToR), which is a significant extension of a very recent work called transfer to rank (ToR). The key idea of our solution is modeling users' behaviors by simulating users' shopping processes. Therefore, we convert the studied ranking problem to three subtasks corresponding to three specific questions, including (i) whether an item will be examined by a user, (ii) how an item will be scored by a user, and (iii) whether an item will finally be purchased by a user. Based on this new conversion, we then develop a three-staged solution that progressively models users' preferences from a coarse granularity to a fine granularity. In each stage, we adopt an appropriate recommendation algorithm with pointwise or pairwise preference assumption to answer each question in order to seek an effective and efficient overall solution. Empirical studies on two large and public datasets showcase the merits of our solution in comparison with the state-of-the-art methods.

Online social media today has a large number of users and has become a huge platform to collect and share the social data generated by the end users. In addition, based on the development of social applications, the social sensing system has been greatly developed to generate, transmit, and store, which is helping the prosperous of the social computing systems. However, the violation of the security of end users' social data stored and shared through the social computing system becomes a serious and urgent issue. The data protection on social media platforms is very different compared with the scenario of the traditional encryption algorithms, and most of the existing schemes are not suitable for data protection in the current social sensing and data-sharing system. In this article, we present a novel design based on the agnostic selective encryption concept to efficiently protect the social data based on the embedded block coding with optimized truncation system. By selectively encrypting only a small portion of the bitstreams in the middle layer of this coding system, a high level of protection and efficiency can both be achieved. We also experiment with our method on four common social data formats, and the security analysis tests are performed to verify the high protection level of our method.

The robust controllability (RC) of a complex system tries to select a set of dominating entities for the functional control of this entire system without uncertain disturbances, and the research on RC will help to understand the system's underlying functions. In this article, we introduce the control cost in signed networks and present a cost-aware robust control (CRC) problem in this scenario. The aim of CRC is to minimize the cost to control a set of dominating nodes and transform a set of unbalanced links into balanced ones, such that the signed network can be robustly controlled without uncertain unbalanced factors (like nodes and links). To solve this problem, we first model CRC as a constrained combination optimization problem, and then present a memetic algorithm with some problem-specific knowledge (like the neighbors of nodes, the constraints of CRC, and the fast computation of the cost under each optimization) to solve this problem on signed networks. The extensive experiments on both real social and biological networks assess that our algorithm outperforms several state-of-the-art RC algorithms.

With the explosive growth of web resources, an increasingly important task in recommender systems is to provide high-quality personalized services by learning users’ preferences from historically observed information. As an effective preference learning technology, collaborative filtering has been widely extended to model the one-class or implicit feedback data, which is known as one-class collaborative filtering (OCCF). For a long time, pairwise ranking-oriented learning scheme has been viewed as a superior solution than the pointwise scheme for OCCF due to its higher accuracy in most cases. However, we argue that with appropriate model design, pointwise preference learning can achieve comparable or even better performance than the counterpart, i.e., pairwise preference learning. In particular, we propose a new preference assumption, i.e., pointwise preference on user/item-set. Based on this new assumption, we develop a novel, simple, and flexible solution called collaborative filtering via pointwise preference learning on user/item-set (CoFi-points). Furthermore, we derive two specific algorithms of CoFi-points with respect to the involved user-set and item-set, i.e., CoFi-points(u) and CoFi-points(i), referring to preference assumptions defined on user-set and item-set, respectively. Finally, we conduct extensive empirical studies on four real-world datasets with the state-of-the-art methods, and find that our solution can achieve very promising performance with respect to several ranking-oriented evaluation metrics.

With the rapid increase of the data size, it has increasing demands for selecting features by exploiting both labeled and unlabeled data. In this paper, we propose a novel semi-supervised embedded feature selection method. The new method extends the least square regression model by rescaling the regression coefficients in the least square regression with a set of scale factors, which is used for evaluating the importance of features. An iterative algorithm is proposed to optimize the new model. It has been proved that solving the new model is equivalent to solving a sparse model with a flexible and adaptable ℓ2;pnorm regularization. Moreover, the optimal solution of scale factors provides a theoretical explanation for why we can use {||w1||2, . . .,||wd||2} to evaluate the importance of features. Experimental results on eight benchmark data sets show the superior performance of the proposed method.

Recommender systems play an important role in providing personalized services for users in the context of information overload. Generally, users’ feedback toward items often contain the most significant information reflecting their preferences, which enables accurate personalized recommendation. In real applications, users’ feedback are usually heterogeneous (rather than homogeneous) such as purchases and examinations in e-commerce, which reflects users’ preferences in different degrees. Effective modeling of such heterogeneous one-class feedback is challenging compared with that of homogeneous feedback of ratings. As a response, heterogeneous one-class collaborative filtering (HOCCF) is proposed, which often converts the heterogeneous feedback into two parts (i.e., target feedback and auxiliary feedback), aiming to care more about the target feedback (e.g., purchases ) with the assistance of the auxiliary feedback (e.g., examinations ). In this survey, we provide an overview of the representative HOCCF methods from the perspective of factorization-based methods, transfer learning-based methods, and deep learning-based methods. First, we review the factorization-based methods according to different strategies. Second, we describe the transfer learning-based methods with different knowledge sharing manners. Third, we discuss the deep learning-based methods according to the neural architectures. Moreover, we include some important example applications, describe the empirical studies, and discuss some promising future directions.

Exploiting temporal effect has empirically been recognized as a promising way to improve recommendation performance in recent years. In real-world applications, one-class data in the form of (user, item, timestamp) are usually more accessible and abundant than numerical ratings. In this article, we focus on exploiting such one-class data in order to provide personalized next-item recommendation services. Specifically, we base our work on the framework of time-aware item-based collaborative filtering and propose a simple yet effective similarity measurement called bidirectional item similarity (BIS) that is able to capture sequential patterns even from noisy data. Furthermore, we extend BIS via some factorization techniques and obtain an adaptive version, i.e., adaptive BIS (ABIS), in order to better fit the behavioral data. We also design a compound weighting function that leverages the complementarity between two well-known time-aware weighting functions. With the proposed similarity measurements and weighting function, we obtain two novel collaborative filtering methods that are able to achieve significantly better performance than the state-of-the-art methods, showcasing their effectiveness for next-item recommendation.

This paper proposes a novel multiobjective particle swarm optimization algorithm with dynamic resource allocation, showing promising performance especially for tackling some complicated multiobjective optimization problems. With the decomposition approach, each particle is assigned to optimize one subproblem with a novel velocity update strategy to speed up the convergence. Moreover, a dynamic resource allocation strategy is designed based on the relative improvement of subproblems, which can reasonably allocate computational resource to the particles that are able to search superior solutions. By this way, the proposed algorithm not only has strong exploratory capability, but also can converge quickly to the true Pareto-optimal front. The experimental results fully demonstrate the superiority of our proposed algorithm over four state-of-the-art multiobjective optimization algorithms, when tackling thirty-five test problems.

Enumerating maximal cliques from an uncertain graph is a fundamental problem in uncertain graph analysis. Given an uncertain graph G, a set of nodes C in G is a maximal (k, τ)-clique if (1) |C|>k and C is a clique with probability at least τ, and (2) C is a maximal node set meeting (1). The state-of-the-art algorithm for enumerating all maximal (k, τ)-cliques is very costly when handling large uncertain graphs, as its time complexity is proportional to 2^n where n is the number of nodes in the uncertain graph. To overcome this issue, we propose two new core-based pruning algorithms to reduce the uncertain graph size without missing any maximal (k, τ)-clique. We also develop a novel cut-based optimization technique to further improve the pruning performance of the core-based pruning algorithms. Based on these pruning techniques, we propose an improved algorithm to enumerate all maximal (k, τ)-cliques, and a new algorithm with several novel upper-bounding techniques to compute one of maximum (k, τ)-cliques from the pruned uncertain graph. The results of extensive experiments on six real-world datasets demonstrate the efficiency and effectiveness of the proposed algorithms.

Multiview representations reveal the latent information of the data from different perspectives, consistency and complementarity. Unlike most multiview learning approaches, which focus only one perspective, in this paper, we propose a novel unsupervised multiview learning algorithm, called comprehensive latent factor learning (CLFL), which jointly exploits both consistent and complementary information among multiple views. CLFL adopts a non-negative matrix factorization based formulation to learn the latent factors. It learns the weights of different views automatically which makes the representation more accurate. Experiment results on a synthetic and several real datasets demonstrate the effectiveness of our approach.

The initialization of neural networks with random weights (NNRW) has a significant impact on model performance. However, there is no suitable way to solve this problem so far. In this paper, the relationship between meta features of a dataset and the initialization of NNRW is studied. Specifically, we construct seven regression datasets with known attributes' distributions, then initialize NNRW with different distributions and trained them based on the datasets to get the corresponding models respectively. The relationship between the attributes' distributions of the datasets and the initialization of NNRW is analyzed by the performance of the models. Several interesting phenomena are observed: firstly, initializing NNRW with the Gaussian distribution can help the model to have a faster convergence rate than ones with the Gamma and Uniform distribution. Secondly, if one or more attributes in a dataset that follow the Gamma distribution, using Gamma distribution to initialize NNRW may result in a slower convergence rate and easy overfitting. Thirdly, initializing NNRW with a specific distribution with smaller variances can always achieve faster convergence rate and better generalization performance than the one with larger variances. The above experimental results are not sensitive to the activation function and the type of NNRW. Some theoretical analyses about the above observations are also given in the study.

In this paper, we propose a novel preference assumption for modeling users' one-class feedback such as "thumb up" in an important recommendation problem called one-class collaborative filtering (OCCF). Specifically, we address a fundamental limitation of a recent symmetric pairwise preference assumption and propose a novel and first asymmetric one, which is able to make the preferences of different users more comparable. With the proposed asymmetric pairwise preference assumption, we further design a novel recommendation algorithm called asymmetric Bayesian personalized ranking (ABPR). Extensive empirical studies on two large and public datasets show that our ABPR performs significantly better than several state-of-the-art recommendation methods with either pointwise preference assumption or pairwise preference assumption.

With wide adoption of Internet of Things (IoT) across the world, the IoT devices are facing more and more intensive computation task nowadays. However, the IoT devices are usually limited by their computing capability and battery lifetime. Mobile edge computing provides new opportunities for developments of IoT, since edge computing servers which are close to devices can provide more powerful computing resources. The IoT devices can offload the intensive computing tasks to edge computing servers, while saving their own computing resources and reducing energy consumption. However, the benefits come at the cost of higher latency, mainly due to additional transmission time, and it may be unacceptable for many IoT applications. In this paper, we try to find a tradeoff between the energy consumption and latency, in order to satisfy user demands of various IoT applications. We formalize the problem into a constrained multiobjective optimization problem and find the optimal solutions by a modified fast and elitist nondominated sorting genetic algorithm (NSGA-II). To improve the performance of the algorithm, we propose a novel problem-specific encoding scheme and genetic operators in the proposed modified NSGA-II. We also conduct extensive simulation experiments to evaluate the proposed algorithm and its sensitivity under certain major parameters. The experimental results show that the proposed algorithm can find a large number of optimal solutions to adjust the corresponding offloading decision according to the real-world situation.

Complex systems are often characterized by complex networks with links and entities. However, in many complex systems such as protein-protein interaction networks, recommender systems, and online communities, their links are hard to reveal directly, but they can be inaccurately observed by multiple data collection platforms or by a data collection platform at different times. Then, the links of the systems are inferred by the integration of the collected observations. As those data collection platforms are usually distributed over a large area and in different fields, their observations are unreliable and sensitive to the potential structures of the systems. In this paper, we consider the link inference problem in network data with community structures, in which the reliability of data collection platforms is unknown a priori and the link errors and reliability of platforms' observations are heterogeneous to the underlying community structures of the systems. We propose an expectation maximization algorithm for link inference in a network system with community structures (EMLIC). The EMLIC algorithm is also used to infer the link errors and reliability of platforms' observations in different communities. Experimental results on both synthetic data and eight real-world network data demonstrate that our algorithm is able to achieve lower link errors than the existing reliable link inference algorithms when the network data have community structures.

Heterogeneous one-class collaborative filtering is an emerging and important problem in recommender systems, where two different types of one-class feedback, i.e., purchases and browses, are available as input data. The associated challenges include ambiguity of browses, scarcity of purchases, and heterogeneity arising from different feedback. In this article, we propose to model purchases and browses from a new perspective, i.e., users’ roles of mixer, browser and purchaser. Specifically, we design a novel transfer learning solution termed role-based transfer to rank (RoToR), which contains two variants, i.e., integrative RoToR and sequential RoToR. In integrative RoToR, we leverage browses into the preference learning task of purchases, in which we take each user as a sophisticated customer (i.e., mixer ) that is able to take different types of feedback into consideration. In sequential RoToR, we aim to simplify the integrative one by decomposing it into two dependent phases according to a typical shopping process. Furthermore, we instantiate both variants using different preference learning paradigms such as pointwise preference learning and pairwise preference learning. Finally, we conduct extensive empirical studies with various baseline methods on three large public datasets and find that our RoToR can perform significantly more accurate than the state-of-the-art methods.

Dialogue response generation system is one of the hot topics in natural language processing, but it is still a long way to go before it can generate human-like dialogues. A good evaluation method will help narrow the gap between the machine and human in dialogue generation. Unfortunately, current evaluation methods cannot measure whether the dialogue response generation system is able to produce high-quality, knowledge-related, and informative dialogues. Aiming to identify and measure the existence of information in dialogues, we propose a novel automatic evaluation metric. By learning from the knowledge representation method in knowledge base, we define the heuristic rules to extract the information triples from dialogue pairs. And we design an information matching method to measure the probability of the existence of information in a dialogue. In experiments, our proposed metric demonstrates its effectiveness in dialogue selection and model evaluation on the Reddit dataset (English) and the Weibo dataset (Chinese).

Radio frequency identification (RFID) technology, with its advantages such as battery-free tags, low cost, and scalability, has been playing an important role in many application domains, such as large-scale storage systems, supermarkets, construction sites, etc. Many of those application scenarios also require indoor positioning technologies, for example, warehouse goods positioning, item positioning in production assembly lines, and worker positioning in construction sites. However,indoor positioning using RFID faces accuracy degradation in dynamic environments, especially when tracking randomly moving targets. In this paper, we proposePSOTrack, a continuous RFID-based tracking system for random moving targets in unconstrained indoor environments. InPSOTrack, a data preprocessed, and an optimized particle swarm optimization algorithm is applied to determine the initial position, after that a dynamic correction method for trajectory prediction is proposed for continuous tracking. Results show that the proposed algorithm effectively improves the positioning accuracy and is able to achieve 1 m localization accuracy in dynamic indoor environments, which makes it a promising technology to support future pervasive RFID-based tracking applications.

We are on the threshold of the explosive growth in the global Internet-of-Things (IoT) market. Comparing with the legacy human-centric applications, machine type communication scenarios exhibit totally different characteristics, such as low throughput, delay insensitivity, occasional transmission, and deep coverage. Meanwhile, it also requires the terminal devices to be cheap enough and sustain long battery life. These demands hasten the prosperity of low power wide area (LPWA) technologies. Narrowband IoT (NB-IoT) is the newest Long Term Evolution (LTE) specification ratified by the third generation partner project as one of the LPWA solutions to achieve the objectives of super coverage, low power, low cost, and massive connection. Working in the licensed frequency band, it is designed to reuse and coexist with the existing LTE cellular networks, which endows it with outstanding advantages in decreasing network installation cost and minimizing product time-to-market. In this backdrop, it has been extensively regarded as one of the most promising technologies toward the IoT landscape. However, as a new LTE standard, there are still a lot of challenges that need to overcome. This paper surveys its evolutions, technologies, and issues, spanning from performance analysis, design optimization, combination with other leading technologies, to implementation and application. The goal is to deliver a holistic understanding for the emerging wireless communication system, in helping to spur further research in accelerating the broad use of NB-IoT.

In multiagent systems, social norms serves as an important technique in regulating agents’ behaviors to ensure effective coordination among agents without a centralized controlling mechanism. In such a distributed environment, it is important to investigate how a desirable social norm can be synthesized in a bottom-up manner among agents through repeated local interactions and learning techniques. In this article, we propose two novel learning strategies under the collective learning framework, collective learning EV-l and collective learning EV-g , to efficiently facilitate the emergence of social norms. Extensive simulations results show that both learning strategies can support the emergence of desirable social norms more efficiently and be applicable in a wider range of multiagent interaction scenarios compared with previous work. The influence of different topologies is investigated, which shows that the performance of all strategies is robust across different network topologies. The influences of a number of key factors (neighborhood size, actions space, population size, fixed agents and isolated subpopulations) on norm emergence performance are investigated as well.

Understanding users’ social relationships plays an important role in many disciplines, including marketing, management science, and so on and is the fundamental context information required in many context-aware applications. However, despite significant research progress in social learning, sensing and reconstructing the off-line social structures in an accurate and nonobtrusive way is still a challenging open problem. In this paper, we propose SODAR, an off-line SOcial colocation Detection And network Reconstruction system, a social learning system that exploits wireless probes emitted by the smartphones carried by users to learn and infer their social relationships and reconstruct their off-line social structures. The probe capturing and filtering mechanisms collects high-quality wireless probe information, and the passive localization techniques are used to process the data and detect colocation events, which are used for the novel social representation learning. The learned social representation vector for each user contains rich social information and can be used to determine the social distances for each pair of users. With projection and clustering techniques, the off-line social structures can be visualized and reconstructed. We implemented the system and deployed the system to different indoor spaces covering more than 1000 m2. The evaluation results show that the SODAR system is able to reliably learn social representations for each user and effectively reconstruct the off-line social structures.

Delivering high-definition (HD) videos to a large number of Internet users is a challenging research problem due to its heavy bandwidth consumption and inelastic quality-of-service (QoS) requirement. Different from the traditional content delivery networks and overlay peer-to-peer networks, crowdsourcing-based platforms, e.g., Thunder Crystal, deliver HD videos by renting agents' bandwidth and storage resources. Cash will be rewarded to agents based on agents' upload traffic. Online video providers, i.e., Tencent Video and YouKu, pay Thunder Crystal for its video distribution service. Therefore, a critical problem for Thunder Crystal is to evaluate the performance it can achieve, which determines Thunder Crystal's competitiveness and its bargaining power with online video providers. Although previously studied proportional video replication and random request scheduling strategy are implemented by Thunder Crystal, the system performance cannot be evaluated by simply using existing models, because of its novel business model. To address this problem, this paper proposes a theoretical framework that can analyze the performance for synchronized streaming, video-on-demand (VoD) streaming, and video downloading, which are all supported by Thunder Crystal. A differentiated bandwidth allocation is designed to boost Thunder Crystal's streaming performance by assigning downloading users more fluctuating bandwidth, which only slightly degrades downloading performance. Finally, simulation is conducted to validate the accuracy of our theoretical results.

Verifiable pattern matching is the problem of finding a given pattern verifiably from the outsourced textual data, which is resident in an untrusted remote server. This problem has drawn much attention due to a large number of applications. The state-of-the-art method for this problem suffers from low efficiency. To enable fast verifiable pattern matching, we propose a novel scheme in this paper. Our scheme is based on an ordered set accumulator data structure and a newly developed verifiable suffix array structure, which only involves fast cryptographic hash computations. Our scheme also supports fast multiple-occurrence pattern matching. A striking feature of our proposed scheme is that our scheme works even with no secret keys, which ensures public verifiability. We conduct extensive experiments to evaluate the proposed scheme using Java. The results show that our scheme is orders of magnitude faster than the state-of-the-art work. Specifically, our scheme with public verifiability only costs a preprocessing time of 47 s (merely one-time off-line cost during outsourcing), a search time of 30 μs, a verification time of 149 μs, and a proof size of 2760 bytes for a verifiable pattern matching query with pattern length 200 on 10-million long textual data which consists of sequences of two-byte, Unicode characters in Java.

While location is one of the most important context information in mobile and pervasive computing, large-scale deployment of indoor localization system remains elusive. In this work, we propose MPiLoc, a multi-floor indoor localization system that utilizes data contributed by smartphone users through participatory sensing for automatic floor plan and radio map construction. Our system does not require manual calibration, prior knowledge, or infrastructure support. The key novelty of MPiLoc is that it clusters and merges walking trajectories annotated with sensor and signal strengths to derive a map of walking paths annotated with radio signal strengths in multi-floor indoor environments. We evaluate MPiLoc over five different indoor areas. Evaluation shows that our system can derive indoor maps for various indoor environments in multi-floor settings and achieve an average localization error of 1.82 m.

The FP-tree based edge weight computation (EWC for short) with MapReduce has demonstrated its remarkable performance for extracting weighted graphs from big data for data analysis. However, our investigation finds that existing algorithm includes unnecessary scan on the datasets as well as unnecessary information for the FP-tree construction, which prolong the runtime execution. In addition, applying inappropriate Reducers-to-cores mapping strategy may make it exhaust the resources and fail to complete the job execution. This paper designs, implements and evaluates an optimized FP-tree based graph EWC algorithm with MapReduce on Multi-core Clusters. First, we design a more compact FP-tree based EWC with 2-phase MapReduce, reducing one phase scan of the dataset. Second, we propose a reduced FP-tree data structure to reduce the FP-tree construction cost. Third, we examine two strategies for mapping Reducers to cores for EWC on each multi-core computer: {\em one-Reducer-one-core} and {\em one-Reducer-multiple-cores}. Finally, an empirical comparison performance study has been carried out on the optimized EWC algorithm against the existing one over a massive application dataset generated by a real social network. The results demonstrate that the optimized FP-tree based EWC algorithm obtains about 39\% to 55\% percentage improvement in execution time, and in the meantime achieves better scale-out and scale-up speedup. This paper's findings can also be applied to improve the scalability and efficiency of the parallel and distributed execution of applications involving large scale all-pairs set intersection computation over multi-core MapReduce clusters.

Self interacting proteins (SIPs) play an important role in various aspects of the structural and functional organization of the cell. Detecting SIPs is one of the most important issues in current molecular biology. Although a large number of SIPs data has been generated by experimental methods, wet laboratory approaches are both time-consuming and costly. In addition, they yield high false negative and positive rates. Thus, there is a great need for in silico methods to predict SIPs accurately and efficiently. In this study, a new sequence-based method is proposed to predict SIPs. The evolutionary information contained in Position-Specific Scoring Matrix (PSSM) is extracted from of protein with known sequence. Then, features are fed to an ensemble classifier to distinguish the self-interacting and non-self-interacting proteins. When performed on Saccharomyces cerevisiae and Human SIPs data sets, the proposed method can achieve high accuracies of 86.86 and 91.30 percent, respectively. Our method also shows a good performance when compared with the SVM classifier and previous methods. Consequently, the proposed method can be considered to be a novel promising tool to predict SIPs.

This paper proposes a velocity learning method based on the directional Gaussian model to detect speeding events in surveillance scenarios. The proposed method is of an uncalibrated type, but yet has considered the influences of projective transformation on estimating the motion velocities in an image plane, which is convenient and feasible to use in real application. We have analyzed the theory that the velocity in the image plane varies due to the changes of the direction of the moving object with constant velocity in a real world plane. With the support of this theory, we propose to learn the velocities calculated on a certain position in different direction bins to tolerate the effect of projective transformation on velocity modeling. To facilitate the whole framework, two key issues have to be addressed. First, we have designed an improved Fisher model to optimize the direction bins, which reflect the major moving directions in a scenario. Second, we have adopted a weighted sampling strategy and surface fitting to solve the lack of sample problem during the learning process. Experiments conducted on real surveillance videos show that the proposed method can obtain competitive results compared with the state-of-the-art methods.

The growth of mobile cloud computing (MCC) is challenged by the need to adapt to the resources and environment that are available to mobile clients while addressing the dynamic changes in network bandwidth. Big data can be handled via MCC. In this paper, we propose a model of computation partitioning for stateful data in the dynamic environment that will improve the performance. First, we constructed a model of stateful data streaming and investigated the method of computation partitioning in a dynamic environment. We developed a definition of direction and calculation of the segmentation scheme, including single-frame data flow, task scheduling, and executing efficiency. We also defined the problem for a multiframe data flow calculation segmentation decision that is optimized for dynamic conditions and provided an analysis. Second, we proposed a computation partitioning method for single-frame data flow. We determined the data parameters of the application model, the computation partitioning scheme, and the task and work order data stream model. We followed the scheduling method to provide the optimal calculation for data frame execution time after computation partitioning and the best computation partitioning method. Third, we explored a calculation segmentation method for single-frame data flow based on multiframe data using multiframe data optimization adjustment and prediction of future changes in network bandwidth. We were able to demonstrate that the calculation method for multiframe data in a changing network bandwidth environment is more efficient than the calculation method with the limitation of calculations for single-frame data. Finally, our research verified the effectiveness of single-frame data in the application of the data stream and analyzed the performance of the method to optimize the adjustment of multiframe data. We used a MCC platform prototype system for face recognition to verify the effectiveness of the method.

Intelligent recommendation has been well recognized as one of the major approaches to address the information overload problem in the big data era. A typical intelligent recommendation engine usually consists of three major components, that is, data as the main input, algorithms for preference learning, and system for user interaction and high-performance computation. We observe that the data (e.g., users’ behavior) are usually in different forms, such as examinations (e.g., browse and collection) and ratings, where the former are often much more abundant than the latter. Although the data are in different representations, they are both related to users’ true preferences and are also deemed complementary to each other for preference learning. However, very few ranking or recommendation algorithms have been developed to exploit such two types of user behavior. In this article, we focus on jointly modeling the examination behavior and rating behavior and develop a novel and efficient ranking-oriented recommendation algorithm accordingly. First, we formally define a new recommendation problem termed behavior ranking , which aims to build a ranking-oriented model by exploiting both the examination behavior and rating behavior. Second, we develop a simple and generic transfer to rank (ToR) algorithm for behavior ranking, which transfers knowledge of candidate items from a global preference learning task to a local preference learning task. Compared with the previous work on integrating heterogeneous user behavior, our ToR algorithm is the first ranking-oriented solution, which can effectively generate recommendations in a more direct manner than those regression-oriented methods. Extensive empirical studies show that our ToR algorithm performs significantly more accurately than the state-of-the-art methods in most cases. Furthermore, our ToR algorithm is very efficient in terms of the time complexity, which is similar to those for homogeneous user behavior alone.

Among various ranging techniques, Radio Signal Strength (RSS) based approaches attract intensive research interests because of its low cost and wide applicability. RSS-based ranging is prone to be affected by the multipath phenomenon which allows the radio signals to reach the destination through multiple propagation paths. To address this issue, previous works try to profile the environment and refer this profile during run-time. In a practical dynamic environment, however, the profile frequently changes and the painful retraining is needed. Ratherthan such static ways of profiling the environments, in this paper, we tryto accommodate the environmental dynamics automatically in real-time. The key observation is that given a pair of nodes, the RSS at different spectrum channels will be different. This difference carries the valuable phase information of the radio signals. By analyzing these RSS values, we are able to identify the amplitude of signals solely from the Line-of-Sight (LOS) path. This LOS amplitude is a simple function of the path length (the physical distance). We find that the analysis is a typical non-linear curvature fitting problem that has no general routing algorithms. We prove that, this problem format is ill-conditioned which has no stable and trustable solutions. To deal with this issue, we further explore the practical considerations for the problem and modify it to a greatly improved conditioning shape. We solve the problem by numerical iterations and implement these ideas in a real-time indoor tracking system called MuD. MuD employs only three TelosB nodes as anchors. The experiment results show that in a dynamic environment where five people move around, the averaged localization error is about 1 meter. Compared with the traditional RSS-based approaches in dynamic environments, the accuracy improves up to 10 times.

Passive indoor localization for smartphones requires no explicit cooperation of the smartphone and enables a new spectrum of applications such as passive user tracking, mobility monitoring, social pattern analysis, etc. However, existing passive localization methods either achieve coarse-grained localization accuracy or require expensive infrastructure support. In this paper, we present Pallas, a self-bootstrapping system for fine-grained passive indoor localization using non-intrusive WiFi monitors. Pallas uses off-the-shelf access point hardware to opportunistically capture WiFi packets to infer the location of smartphones in the indoor environment. The key novelty of Pallas lies in that the passive fingerprint database for localization is automatically constructed and updated without any active participation of WiFi devices or manual calibration. To achieve this, Pallas first identifies passive landmarks that are present in WiFi RSS traces. Given the knowledge of the indoor floor plan and the location of WiFi monitors, Pallas statistically maps the collected RSS traces to specific indoor pathways. With sufficient mapping opportunistically detected, Pallas is able to bootstrap a fine-grained passive fingerprint database and build Gaussian processes for localization automatically without requiring any additional calibration effort.

The similarity measurements of business processes have important applications in business process management, such as process model search indexing, facilitate reuse, processes merge, etc. Existing researches are mostly based on the syntactic or semantic of text labels of activities or tasks. This paper presents a method for similarity measurement based on the internal structure of business processes without considering the text labels of process activities. Petri nets are used to define the process models. The nodes (transitions and places) are mapped by an iterative mapping strategy to identify the correspondence of two Petri nets. After getting a stable best mapping of places and transitions, we can compute the similarity mesurement of two processes. Experiments on the real data sets show that our algorithm is reliable and effective to the actual demand.

In smart city, all kinds of users' data are stored in electronic devices to make everything intelligent. A smartphone is the most widely used electronic device and it is the pivot of all smart systems. However, current smartphones are not competent to manage users' sensitive data, and they are facing the privacy leakage caused by data over-collection. Data over-collection, which means smartphones apps collect users' data more than its original function while within the permission scope, is rapidly becoming one of the most serious potential security hazards in smart city. In this paper, we study the current state of data over-collection and study some most frequent data over-collected cases. We present a mobile-cloud framework, which is an active approach to eradicate the data over-collection. By putting all users' data into a cloud, the security of users' data can be greatly improved. We have done extensive experiments and the experimental results have demonstrated the effectiveness of our approach.

Users’ online behaviors such as ratings and examination of items are recognized as one of the most valuable sources of information for learning users’ preferences in order to make personalized recommendations. But most previous works focus on modeling only one type of users’ behaviors such as numerical ratings or browsing records, which are referred to as explicit feedback and implicit feedback, respectively. In this article, we study a Semisupervised Collaborative Recommendation (SSCR) problem with labeled feedback (for explicit feedback) and unlabeled feedback (for implicit feedback), in analogy to the well-known Semisupervised Learning (SSL) setting with labeled instances and unlabeled instances. SSCR is associated with two fundamental challenges, that is, heterogeneity of two types of users’ feedback and uncertainty of the unlabeled feedback. As a response, we design a novel Self-Transfer Learning (sTL) algorithm to iteratively identify and integrate likely positive unlabeled feedback, which is inspired by the general forward/backward process in machine learning. The merit of sTL is its ability to learn users’ preferences from heterogeneous behaviors in a joint and selective manner. We conduct extensive empirical studies of sTL and several very competitive baselines on three large datasets. The experimental results show that our sTL is significantly better than the state-of-the-art methods.

The dramatically growing demand of Cyber Physical and Social Computing (CPSC) has enabled a variety of novel channels to reach services in the financial industry. Combining cloud systems with multimedia big data is a novel approach for Financial Service Institutions (FSIs) to diversify service offerings in an efficient manner. However, the security issue is still a great issue in which the service availability often conflicts with the security constraints when the service media channels are varied. This paper focuses on this problem and proposes a novel approach using the Semantic-Based Access Control (SBAC) techniques for acquiring secure financial services on multimedia big data in cloud computing. The proposed approach is entitled IntercroSsed Secure Big Multimedia Model (2SBM), which is designed to secure accesses between various media through the multiple cloud platforms. The main algorithms supporting the proposed model include the Ontology-Based Access Recognition (OBAR) Algorithm and the Semantic Information Matching (SIM) Algorithm . We implement an experimental evaluation to prove the correctness and adoptability of our proposed scheme.

Automated weighted graph construction from massive data is essential to weighted graph theory based data mining processes, where the edge weight computation is time consuming or even fails to complete on a single machine when necessary resources are exhausted. In addition, existing work lacks of the measurement on the accuracy of the edge weights, which represents the graph accuracy and affects the following data mining results. This paper describes the classification, implementation and evaluation of edge weight computation algorithms with MapReduce Framework, which is a powerful parallel and distributed processing model. First, a classification of the edge weight computation algorithms is developed and how they can be applied on MapReduce is also discussed. Then we propose comprehensive measurements on the edge weight accuracy in terms of the number of edges, strength distribution, community structure, Hop-plot and effective diameters. Finally, a performance study has been conducted to evaluate these algorithms in terms of memory and disk usage, execution time and accuracy using a real massive social network application dataset. The results are presented and discussed. Our comparison results can help find out the most effective parallel and distributed edge weight computation algorithm for constructing a weighted graph for a given massive dataset.

Most recently, gesture recognition has increasingly attracted intense academic and industrial interest due to its various applications in daily life, such as home automation, mobile games. Present approaches for gesture recognition, mainly including vision-based, sensor-based and RF-based, all have certain limitations which hinder their practical use in some scenarios. For example, the vision-based approaches fail to work well in poor light conditions and the sensor-based ones require users to wear devices. To address these, we propose WiG in this paper, a device-free gesture recognition system based solely on Commercial Off-The-Shelf (COTS) WiFi infrastructures and devices. Compared with existing Radio Frequency (RF)-based systems, WiG stands out for its systematic simplicity, extremely low cost and high practicability. We implemented WiG in indoor environment and conducted experiments to evaluate its performance in two typical scenarios. The results demonstrate that WiG can achieve an average recognition accuracy of 92% in line-of-sight scenario and average accuracy of 88% in the none-line-of sight scenario.

Crowdsourcing-based video service systems, e.g., Thunder Crystal, are novel content distribution platforms composed by a large number of agent devices, acting like miniservers. Most agents are normal Internet users who would like to earn rewarded cash by uploading content through their devices. Compared with CDN, the bandwidth cost is much cheaper; while compared with Peer-to-Peer(P2P), the bandwidth supply is more stable. In this work, we create a stochastic model to analyze the live and VoD streaming performance in such crowdsourcingbased video service systems. Simulation is conducted to validate the accuracy of our analytical results.

Most previous works on coordination in cooperative multiagent systems study the problem of how two (or more) players can coordinate on Pareto-optimal Nash equilibrium(s) through fixed and repeated interactions in the context of cooperative games. However, in practical complex environments, the interactions between agents can be sparse, and each agent's interacting partners may change frequently and randomly. To this end, we investigate the multiagent coordination problems in cooperative environments under a social learning framework. We consider a large population of agents where each agent interacts with another agent randomly chosen from the population in each round. Each agent learns its policy through repeated interactions with the rest of the agents via social learning. It is not clear a priori if all agents can learn a consistent optimal coordination policy in such a situation. We distinguish two different types of learners depending on the amount of information each agent can perceive: individual action learner and joint action learner . The learning performance of both types of learners is evaluated under a number of challenging deterministic and stochastic cooperative games, and the influence of the information sharing degree on the learning performance also is investigated—a key difference from the learning framework involving repeated interactions among fixed agents.

Green cloud is an emerging new technology in the computing world in which memory is a critical component. Phase-change memory (PCM) is one of the most promising alternative techniques to the dynamic random access memory (DRAM) that faces the scalability wall. Recent research has been focusing on the multi-level cell (MLC) of PCM. By precisely arranging multiple levels of resistance inside a PCM cell, more than one bit of data can be stored in one single PCM cell. However, the MLC PCM suffers from the degradation of performance compared to the single-level cell(SLC) PCM, due to the longer memory access time. In this paper, we present a genetic-based optimization algorithm for chip multiprocessor (CMP) equipped with PCM memory in green clouds. The proposed genetic-based algorithm not only schedules and assigns tasks to cores in the CMP system, but also provides a PCM MLC configuration that balances the PCM memory performance as well as the efficiency. The experimental results show that our genetic-based algorithm can significantly reduce the maximum memory usage by 76.8 percent comparing with the uniform SLC configuration, and improve the efficiency of memory usage by 127 percent comparing with the uniform 4 bits/cell MLC configuration. Moreover, the performance of the system is also improved by 24.5 percent comparing with the uniform 4 bits/cell MLC configuration in terms of total execution time.

Learning users' preferences is critical to enable personalized recommendation services in various online applications such as e-commerce, entertainment and many others. In this paper, we study on how to learn users' preferences from abundant online activities, e.g., browsing and examination, which are usually called implicit feedbacks since they cannot be interpreted as users' likes or dislikes on the corresponding products directly. Pairwise preference learning algorithms are the state-of-the-art methods for this important problem, but they have two major limitations of low accuracy and low efficiency caused by noise in observed feedbacks and non-optimal learning steps in update rules. As a response, we propose a novel adaptive pairwise preference learning algorithm, which addresses the above two limitations in a single algorithm with a concise and general learning scheme. Specifically, in the proposed learning scheme, we design an adaptive utility function and an adaptive learning step for the aforementioned two problems, respectively. Empirical studies show that our algorithm achieves significantly better results than the state-of-the-art method on two real-world data sets.

In this paper, a SOM (self organizing map)-based approach to task assignment of multi-robots in 3-D dynamic environments is proposed. This approach intends to mimic the operating mechanism of biological neural systems, and integrates the advantages and characteristics of biological neural systems. It is capable of dynamically planning the paths of multi-robots in 3-D environments under uncertain situations, such as when some robots are added in or broken down or when more than one robot is needed for some special task locations. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.

Parallel coordinates have been widely used to analyze high-dimensional data. Numerous methods have been designed to provide overview patterns in parallel coordinate plots. However, detailed information is also important in data analysis. When several lines overlap or are close to one another, distinguishing detailed information of polyline crossings is difficult. In this paper, we present a novel approach to address the problem of polyline crossing ambiguity by using data labels. We place different labels along various polylines to give cues for differentiation of lines. We bend the lines and optimize the arrangement of curved lines to provide space for clear visible labels. An energy system that models attractive and repulsive forces of lines is used to guide the search for optimized line arrangement. The experiments on several real datasets demonstrate the effectiveness of our approach.

In traditional radio-based localization methods, the target object has to carry a transmitter (e.g., active RFID), a receiver (e.g., 802.11 × detector), or a transceiver (e.g., sensor node). However, in some applications, such as safe guard systems, it is not possible to meet this precondition. In this paper, we propose a model of signal dynamics to allow the tracking of a transceiver-free object. Based on radio signal strength indicator (RSSI), which is readily available in wireless communication, three centralized tracking algorithms, and one distributed tracking algorithm are proposed to eliminate noise behaviors and improve accuracy. The midpoint and intersection algorithms can be applied to track a single object without calibration, while the best-cover algorithm has higher tracking accuracy but requires calibration. The probabilistic cover algorithm is based on distributed dynamic clustering. It can dramatically improve the localization accuracy when multiple objects are present. Our experimental test-bed is a grid sensor array based on MICA2 sensor nodes. The experimental results show that the localization accuracy for single object can reach about 0.8 m and for multiple objects is about 1 m.

Chip multiprocessor (CMP) techniques have been implemented in embedded systems due to tremendous computation requirements. Three-dimension (3D) CMP architecture has been studied recently for integrating more functionalities and providing higher performance. The high temperature on chip is a critical issue for the 3D architecture. In this article, we propose an online thermal prediction model for 3D chips. Using this model, we propose novel task scheduling algorithms based on rotation scheduling to reduce the peak temperature on chip. We consider data dependencies, especially inter-iteration dependencies that are not well considered in most of the current thermal-aware task scheduling algorithms. Our simulation results show that our algorithms can efficiently reduce the peak temperature up to 8.1 ˆ C.

Users often use tags to annotate and categorize web content. A folksonomy is a system of classification derived from the practice and method of collaboratively creating and managing tags. The most significant feature of a folksonomy is that it directly reflects the vocabulary of users. This feature is very useful in tag-based content searching and user browsing. Based on mutual-overlapping measurement of tag's instance sets, an ontology learning algorithm to construct concept hierarchy from folksonomy is proposed. A case study of datasets from a famous Chinese e-business website taobao is carried out. The precision, valid, recall and F-measure rates of the constructed concept hierarchy are 54%, 84%, 100% and 70% respectively. The experimental results on real world datasets show that the proposed method is feasible.

In this paper, we not only extend the temporal hierarchical alternating least squares (HALS) to spatial domain, but also incorporate two necessary characteristics of material abundances, full additivity and sparsity, to unmix hyperspectral data. The new algorithm is abbreviated as HALSSC (HALS with Sparsity Constraint). Different from the other endmember extraction approaches, the proposed algorithm does not need the existence assumption of pure pixel of each endmember in the scene. Experimental results on highly mixed synthetic data and real hyperspectral data from Washington DC mall confirm the accuracy of the developed algorithm.

In cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the tasks execution order. Furthermore, a resource allocation mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose an adaptive resource allocation algorithm for the cloud system with preemptable tasks. Our algorithms adjust the resource allocation adaptively based on the updated of the actual task executions. And the experimental results show that our algorithms works significantly in the situation where resource contention is fierce.

Video scene classification and segmentation are fundamental steps for multimedia retrieval, indexing and browsing. In this paper, a robust scene classification and segmentation approach based on support vector machine (SVM) is presented, which extracts both audio and visual features and analyzes their inter-relations to identify and classify video scenes. Our system works on content from a diverse range of genres by allowing sets of features to be combined and compared automatically without the use of thresholds. With the temporal behaviors of different scene classes, SVM classifier can effectively classify presegmented video clips into one of the predefined scene classes. After identifying scene classes, the scene change boundary can be easily detected The experimental results show that the proposed system not only improves precision and recall, but also performs better than the other classification systems using the decision tree (DT), K nearest neighbor (K-NN) and neural network (NN).

Because of the surprisingly increasing volume and complicated nature of remote sensing images (RSIs), one of the main obstacles to realize efficient retrieval of the RSIs is the lack of effective sharing technologies and semantic description methods. In this letter, we design and implement a prototype grid system named RSIsGrid for semantic-based RSIs retrieval using ontology and grid technologies. In order to verify the semantic-based method, measures such as the recall, precision, and query time are used. Test results have indicated that the semantic-based method can promote the query performance of the RSIs.