Existing video-based automatic depression assessment (ADA) approaches frequently achieve video-level depression assessment by aggregating features or predictions of individual frames or equal-length segments within the given video. While their performances have been largely enhanced by recent advanced deep learning models, they typically fail to explicitly consider the varied importance of depression-related behavioural cues across different video segments, i.e., segments within one video may contain behaviours reflecting varying levels of depression. Underestimating segment-level variations can obscure the detection of facial behaviour cues associated with depression, thereby undermining the accuracy and interpretability of video-based depression detection systems. In this paper, we propose a novel video-based ADA approach that specifically identifies and differentiates video segments that exhibit depression-related facial behaviours across varying temporal durations, providing clear insights into how each segment contributes to the video-level depression prediction. To achieve this, a novel weakly supervised strategy is proposed to compare segment-level behaviours with video-level depression label, enabling the model to assign depression-relevant scores to multiple temporal scale video segments and attend selectively to those most indicative of depressive states. Extensive experiments on the AVEC 2013 and AVEC 2014 face video depression datasets demonstrate the effectiveness of our approach.

Amid recent advances for multivariate time series forecasting, self-supervised learning has emerged as a promising paradigm for deriving transferable knowledge from multi-domain data. Despite its effectiveness, existing approaches exhibit two critical limitations: (1) Underestimating the significance of multivariate dependencies in learning generalizable representations and (2) Failing to reconcile the complementary strengths of autoregressive and one-shot generative paradigms. In this work, we propose TimeCAP, a novel channel-aware pre-training framework that internalizes latent causal relationships among variables inherent in multi-domain data, and effectively transfers the acquired knowledge to downstream applications. Technically, we present a flexible channel-grouping learning approach, complemented by an adaptive meta-routing mechanism, enabling TimeCAP to parallel recognize intra-group local patterns while maintaining global coherence. Intra- and inter-group multivariate dependencies are captured through the self- and cross-attention with channel-aware mask, which strictly confine interactions among time-aligned, fine-grained multivariate tokens. To seamlessly unify two advanced generative paradigms, we propose a novel dynamic dual-head decoding and optimization strategy, empowering TimeCAP to leverage critical dependencies in the output series while avoiding cumulative errors over time. In the few-shot evaluation, TimeCAP achieves average MSE and MAE reductions of 11.8% and 6% over leading baselines, while also outperforming state-of-the-art models in full-shot and zero-shot settings by large margins.

Maximum utilisation of minimal amount of resources is pivotal for achieving a sustainable operation in large-scale Cloud Data Centres. Prediction driven resource provisioning in Cloud Data Centres is a potential approach to execute Cloud workloads in a sustianable way. Traditional prediction models often struggle to deliver accurate predictions under dynamic and heterogeneous cloud workloads, as capturing long-range dependencies and sudden workload spikes is often challenging in Cloud environments. In addition, recent time series models such as the Adversarial Error Correction Generative Adversarial Network (AEC-GAN) characterise shortcomings when applied to cloud workload datasets, particularly whilst managing volatility and learning irregular patterns. To address such challenges, this paper proposes a novel prediction model using Data Augment Aware Transformer Rectification-based Generative Adversarial Networks (TR-GAN), which incorporates a continuous and conditional learning Transformer block in the GAN's generator module to serve both as a data distribution moderator and as a data augmentation generator, ultimately to deliver accurate predictions. TR-GAN is the first GAN-based model tailored for long-term cloud workload forecasting that explicitly couples data augmentation with sequence rectification. Unlike discriminative forecasters, Its generative formulation allows it to model the intrinsic variability and uncertainty of cloud workloads,generate context-aware synthetic data to improve generalization under sparse or irregular patterns, and iteratively refine predictions through an adversarial learning process, thereby reducing error accumulation in long-horizon forecasts. The prediction performance of the proposed model is evaluated with two widely-used cloud workload datasets, namely the Google clusters and Alibaba traces. Experimental results demonstrate that the proposed TR-GAN model can deliver a prediction improvement of around 15% than notable state-of-the-art models, including Informer, Autoformer and AEC-GAN, for long forecasting horizons.

In dyadic human-human interactions, individuals may express multiple different facial reactions in response to the same/similar behaviours expressed by their conversational partners depending on their personalised behaviour patterns. As a result, frequently-employed reconstruction loss-based strategies lead the training of previous automatic facial reaction generation (FRG) models to not only suffer from the 'one-to-many mapping' problem, but also fail to comprehensively consider the quality of the generated facial reactions. Besides, none of them considered such personalised behaviour patterns in generating facial reactions. In this paper, we propose the first adversarial FRG model training strategy which jointly learns appropriateness and realism discriminators to provide comprehensive task-specific supervision for training the target facial reaction generators, and reformulates the 'one-to-many (facial reactions) mapping' training problem as a 'one-to-one (distribution) mapping' training task, i.e., the FRG model is trained to output a distribution representing multiple appropriate/plausible facial reaction from each input human behaviour. In addition, our approach also serves as the first offline FRG approach that considers personalised behaviour patterns in generating of target individuals' facial reactions. Experiments show that our PerReactor not only largely outperformed all existing offline solutions for generating more appropriate, diverse and realistic facial reactions, but also is the first approach that can effectively generate personalised appropriate facial reactions.

Identifying influential spreaders in complex networks is a crucial problem. This topic has garnered significant interest in network science research, and it is of considerable significance in traffic accident prediction, infectious disease prevention, and targeted advertising. The goal of it is to select a set of seed nodes that maximize the spread of influence. Traditional approaches, such as k-core decomposition, identify seed nodes based on their connectivity. However, these methods often select highly overlapping nodes, thereby limiting their overall influence. Alternative methods leverage Neighbourhood Coreness centrality to mitigate this issue by considering the$k$-shell indices of a node's neighbours, but they still fail to account for highly connected peripheral nodes or the influence of selected nodes on their neighbours. To address these limitations, we propose an Improved Neighborhood Coreness-based Heuristic Algorithm for Influence Maximization in Complex Networks (INC-HAIM). It iteratively selects uncovered nodes with the highest NC centrality and updates the coverage status and the centrality of neighbouring nodes and edge nodes, effectively reducing the impact of edge contributions. Experimental evaluations under the Independent Cascade (IC) model across multiple datasets demonstrate that as network size and propagation probability increase, INCHAIM consistently achieves superior influence spread compared to existing heuristic and centrality-based approaches.

MicroRNAs (miRNAs) play a critical role in modulating drug responses, highlighting the importance of accurately identifying miRNA-drug associations. However, existing computational methods often suffer from limited representation capability and high computational complexity, frequently relying solely on either global or local feature extraction. To address these limitations, we propose MHTMG, a novel predictive framework that integrates a Multi-Head Attention mechanism with a Parameterized Transformation Matrix (MHA-PTM) and GraphSAGE. The MHA-PTM component introduces a learnable transformation matrix that acts as a proxy token to refine the query, key, and value representations, thereby replacing traditional pairwise comparisons with a two-stage attention mechanism. This design substantially reduces computational overhead while preserving the model's capacity to capture global dependencies. Concurrently, a GraphSAGE encoder is utilized to extract local topological features from the miRNA-drug bipartite graph. By iteratively aggregating information from neighboring nodes, GraphSAGE enhances graph representations using the graph's intrinsic structural information. To fully exploit both global semantic features and local structural cues, the embeddings produced by MHA-PTM and GraphSAGE are concatenated and subsequently fed into a multi-layer perceptron (MLP) for final association prediction. In addition, the superior performance of MHTMG in representative case studies highlights its practical utility and reliability for elucidating miRNA-drug interactions.

Knowledge Tracing (KT) aims to model students' evolving knowledge states and predict their future performance. Traditional sequential models often struggle with long-range dependencies due to inefficient memory retention and high computational costs, limiting their effectiveness in capturing students' continuous learning trajectories. Meanwhile, graph-based KT models effectively represent knowledge concept relationships but are not designed for sequential modeling. To address these challenges, we propose DualFlowKT, a novel model that employs a dual-path architecture, integrating Graph Neural Networks (GNNs) for structured knowledge modeling and the Mamba state space model for efficient long-sequence learning. Specifically, the graph-based path extracts relational dependencies among knowledge concepts, while the sequential path leverages Mamba's selective state-space modeling to effectively capture long-range learning patterns with enhanced memory efficiency and reduced computational overhead. To further enhance input representations, we introduce a Feature Enhancement Module (FEM) that extracts critical learning features such as concept continuity, learning rate, and adaptive difficulty. Additionally, a gated fusion mechanism dynamically integrates knowledge structure representations from GNNs with sequential dependencies captured by Mamba, allowing the model to adaptively balance structural and temporal learning information. Extensive experiments on multiple KT benchmark datasets demonstrate that DualFlowKT outperforms existing methods, particularly in handling long-sequence interactions and complex knowledge structures. Furthermore, the model provides deeper insights into students' evolving knowledge states, supporting personalized education systems.

As heterogeneous graph learning models have been widely applied, the risk of privacy leakage has become a growing concern. To address this issue, traditional federated learning frameworks are primarily employed in current methods to protect the privacy of heterogeneous graphs. However, these methods still encounter challenges, including vulnerabilities to membership inference attacks (MIAs) and a negative impact on data utility due to the addition of fixed noise. To overcome these limitations, we propose the privacy-preserving decentralized federated heterogeneous graph neural network (PDFHGN). In this model, a two-stage differential privacy protection approach based on edge embeddings and model gradients is employed to defend against both active and passive MIAs, thereby alleviating privacy concerns among clients. Additionally, an adaptive gradient clipping threshold is implemented to dynamically adjust the noise perturbation ranges, ensuring minimal impact on model utility. Furthermore, by leveraging the flexibility of the decentralized federated learning framework, the model maintains robustness across various types of client network topologies. Experimental validation on four real-world datasets demonstrates that our model strikes a favorable privacy-utility tradeoff while remaining adaptable to multiple client network structures.

Time series forecasting is a pivotal task across diverse fields such as finance, energy, and meteorology, providing critical insights for informed decision-making and resource management. The growing complexity and scale of these tasks demand high-performance computing capabilities, which are crucial for efficiently processing vast amounts of temporal data and achieving real-time forecasting. Despite considerable progress, existing deep learning models face challenges in capturing complex temporal dependencies, lack explicit mechanisms to enhance temporal learning, and are encumbered by the computational overhead and limited adaptability of layer normalization in Transformer architectures. To address these limitations, we propose a novel GPDT model that integrates an auto-regressive pretraining framework, promoting temporal consistency and enabling the effective capture of both shortand long-term temporal dependencies. This pretraining strategy establishes a robust temporal inductive bias, which is further refined through task-specific fine-tuning to optimize forecasting performance. Additionally, the static layer normalization in the standard Transformer encoder is replaced with an adaptive dynamic tanh mechanism, which not only reduces computational costs but also improves the model's adaptability to evolving temporal patterns. Moreover, GPDT further leverages instance normalization, channel independence, and patch embedding to enhance both efficiency and accuracy. Comprehensive evaluation across a range of forecasting scenarios validates the model's superior performance and strong generalization capability.

Time series forecasting plays a crucial role in cloud computing resource management. However, existing models often struggle to balance prediction accuracy with computational efficiency when handling complex cloud resource data. This study, for the first time, applies the recently proposed TiDE (Time-series Dense Encoder) model to the Azure cloud dataset and introduces a novel forecasting framework based on classical time series decomposition techniques. The proposed architecture decomposes the original time series into seasonal, trend, and residual components, each of which is modeled specifically using dedicated TiDE models. By leveraging our sequence-decoupling strategy, we achieve substantial gains in forecasting accuracy while markedly reducing computational overhead. Experimental results show that the TiDE-enhanced framework outperforms state-of-the-art models in both accuracy and robustness. In particular, on high-dimensional, multi-channel Azure cloud workload prediction tasks, our approach demonstrates exceptional generalization and strong practical deployment potential.

Knowledge tracing (KT) is an essential technique for predicting students' future performance based on the analysis of their previous learning activities. The exploration of question relevance has been shown to have a significant impact on predicting student performance. However, existing studies rely on basic attention mechanisms when investigating relevance, without fully utilizing the relationships between questions, concepts, and interactions. Additionally, current methods often neglect the number of repetitions on specific concepts when modeling forgetting behavior. This paper introduces a new knowledge tracing model that integrates multiple features into the attention mechanism to track and predict students' mastery of questions more accurately. Specifically, we explore question correlation from two perspectives: co-occurrence and answer consistency. Then, a forgetting feature is introduced to simulate the natural process of students gradually forgetting previously learned knowledge over time, considering the effects of repeated learning of the same concepts and the intervals between learning sessions. Experimental results demonstrate that our model surpasses existing popular and state-of-the-art knowledge tracing models on multiple metrics and robustness, effectively enhancing the quality of educational instruction and aids in the realization of personalized learning.

Large Language Models (LLMs) have demonstrated exceptional performance across diverse tasks, yet their training remains highly resource intensive and susceptible to critical challenges such as training instability. A predominant source of this instability stems from gradient and loss spikes, which disrupt the learning process, often leading to costly interventions like checkpoint recovery and experiment restarts, further amplifying inefficiencies. This paper presents a comprehensive investigation into gradient spikes observed during LLM training, revealing their prevalence across multiple architectures and datasets. Our analysis shows that these spikes can be up to 1000× larger than typical gradients, substantially deteriorating model performance. To address this issue, we propose Spike-Aware Adam with Momentum Reset (SPAM), a novel optimizer designed to counteract gradient spikes through momentum reset and spike-aware gradient clipping. Extensive experiments, including both pre-training and fine-tuning, demonstrate that SPAM consistently surpasses Adam and its variants across a range of model scales. Additionally, SPAM facilitates memory-efficient training by enabling sparse momentum, where only a subset of momentum terms are maintained and updated. When operating under memory constraints, SPAM outperforms state-of-the-art memory-efficient optimizers such as GaLore and Adam-Mini. Our work underscores the importance of mitigating gradient spikes in LLM training and introduces an effective optimization strategy that enhances both training stability and resource efficiency at scale. Code is submitted.

Earth observation foundation models have shown strong generalization across multiple Earth observation tasks, but their robustness under real-world perturbations remains underexplored. To bridge this gap, we introduce REOBench, the first comprehensive benchmark for evaluating the robustness of Earth observation foundation models across six tasks and twelve types of image corruptions, including both appearance-based and geometric perturbations. To ensure realistic and fine-grained evaluation, our benchmark focuses on high-resolution optical remote sensing images, which are widely used in critical applications such as urban planning and disaster response. We conduct a systematic evaluation of a broad range of models trained using masked image modeling, contrastive learning, and vision-language pre-training paradigms. Our results reveal that (1) existing Earth observation foundation models experience significant performance degradation when exposed to input corruptions. (2) The severity of degradation varies across tasks, model architectures, backbone sizes, and types of corruption, with performance drop varying from less than 1% to over 25%. (3) Vision-language models show enhanced robustness, particularly in multimodal tasks. REOBench underscores the vulnerability of current Earth observation foundation models to real-world corruptions and provides actionable insights for developing more robust and reliable models.

Abstract Siamese Neural Networks (SNNs) have shown promise in addressing a variety of tasks, even with limited data availability. However, their adoption is hindered by the lack of transparency in their decision-making processes. A key challenge in explaining SNNs lies in the absence of an inverse mapping between high-dimensional input feature vectors and the low-dimensional embedding space. Therefore, computing direct distances between input features becomes meaningless. Existing autoencoder-based explanation methods face several limitations. These include poor image reconstruction quality due to insufficient data and the omission of final distance layer of the SNN during the explanation process. While the Siamese Network Explainer (SINEX) can explain audio and grayscale images, it does not support RGB images. To overcome these challenges, we propose a method called Features Distance-based eXplanation (FDbX). This approach identifies salient features using ridge regression, trained on perturbed SLIC-segmented images. To enhance the selection of important features, we incorporate Bayesian analysis, which assigns importance scores to features. To provide a comprehensive explanation of the decision route, we construct a mathematical model that represents important features and their Hamming distances as a bipartite graph. In this graph, nodes represent features and edges denote distances between feature pairs. The resulting explanation heatmaps highlight critical image segments, offering more intuitive and visually informative explanations than existing methods. We evaluate stability and faithfulness of our method using stability indices such as $$R^2$$ and mean squared error. To the best of our knowledge, this is the first work to introduce Variable and Coefficient Stability Indices for image datasets.

In order to achieve energy consumption optimization and decarbonization in the Industrial Internet of Things (IIoT), this article establishes a novel framework for optimal scheduling of park-level integrated energy systems (PIESs). This framework incorporates carbon capture technologies alongside a multienergy joint supply subsystem model featuring hydrogen storage, complemented by a structured stepped carbon trading mechanism. Furthermore, a novel optimized federation model is developed to balance low carbon emissions and economic performance by federating the carbon capture system into the tiered carbon trading mechanism. The primary objective of this model in the context of the IIoT is to minimize energy procurement costs, wind abandonment costs, and carbon trading expenses while maximizing revenues from carbon dioxide sales. The CPLEX optimization tool is utilized to address this complex challenge. Finally, several scenarios are simulated to demonstrate the effectiveness of the optimized PIESs in reducing the operational costs and minimizing carbon footprint.

Received Signal Strength Indicator (RSSI)-based indoor localization offers a cost-effective solution for autonomous mobile robot navigation in 3D indoor environments, including cross-floor and multi-building structures. However, localization accuracy is fundamentally constrained by the low sampling density and unstable measurement of RSSI data. So far, existing methods neglect cross-environment RSSI coherence (e.g., repeated signal patterns in geometrically similar areas), resulting in unreliable fingerprint databases. What’s more, most approaches fail to model the spatial hierarchy of buildings, floors, and coordinates, which leads to lower accuracy in indoor positioning model predictions. To address these issues, we propose EP-3DLoc, a novel 3D indoor localization framework that combines an Environment-Invariant feature-based Data Completion (EIC) method with a Position-Related feature-based Localization (PRL) method. The EIC enhances data quality by filling in sparse RSSI data using environment-invariant features, which are recurring RSSI patterns found in similar environmental structures. The PRL module combines multi-scale RSSI signal processing (raw data and image-like data) with a multi-task network that analyzes location relationships, enhancing localization accuracy in 3D environments. Experimental results on public datasets (TUT2018, UTSIndoorLoc, and UJIIndoorLoc) have demonstrated that EP-3DLoc achieves state-of-the-art performance on indoor localization in multi-building environments. Further testing on the self-constructed dataset HZAUIndoorLoc have revealed that EP-3DLoc not only outperforms existing methods in localization accuracy but also maintains low energy consumption and strong resistance to interference. The dataset HZAUIndoorLoc is available at https://github.com/Hanzoe/HZAUIndoorLoc-Dataset.

The rapid development of the Internet-of-Things (IoT) has transformed human interaction with the world. The Social Internet-of-Things (SIoT) integrates social, emotional, and behavioral aspects into traditional IoT, creating an intelligent network that connects various smart devices. By combining Edge Computing with Artificial Intelligence (AI), data can be processed and analyzed directly on edge devices, improving processing efficiency. However, limited edge resources hinder local AI training, requiring cloud-based training of high-precision models, which are then deployed on edge devices for inference. In time series forecasting, Multilayer Perceptrons (MLPs) are widely used for their computational efficiency but often overlook correlations between time series. Some models adopt channel mixing mechanisms to improve modeling capability but increase computational complexity. To address this problem, we propose the Core Fusion Time Series Dense Encoder (CFTD) model, which incorporates a Core Extract-Distribute (COED) module for efficient channel fusion. Extensive experiments on real-world datasets demonstrate that this novel CFTD model achieves excellent predictive performance compared to the state-of-the-art models.

The Agricultural Digital Twin Network (ADTN) represents one of the key enabling technologies of Agriculture 4.0. However, the unstructured nature of agricultural scenarios poses significant challenges to the geometric fidelity and scalability of ADTNs, as the morphological diversity and entity uniqueness of crops further reduce the rendering accuracy of virtual entities and increase modeling costs. Existing research lacks low-cost solutions for digital twin generation in unstructured agricultural environments, as implicit-based models struggle to capture global invariant features critical to crop structure and texture. To address these challenges, a crop-based ADTN (CBDTN) framework is designed, which incorporates low-cost image acquisition terminals, edge devices, and cloud platforms, thereby optimizing resource utilization through implicit modeling and minimizing costs in resource constrained scenarios. Furthermore, to improve the accuracy of virtual entity generation, a texture prior feature-enabled neural radiance field (TPFNeRF) is proposed, inspired by the human visual system. It emulates the processing mechanism of the visual cortex to extract consistent prior features from multi-view images, thereby providing robust rendering guidance that enhances perceptual fidelity in 3D reconstruction tasks. The experimental results show that the CBDTN with TPFNeRF exhibits superior performance in generating objects with complex textures and structures. In synthetic dataset, it significantly improves reconstruction quality, achieving 6.52 dB PSNR and 4.3% SSIM improvements over the baseline, and delivers overall performance superior to recent methods such as GFB-NeRF and DiSRNeRF. In real-world scenarios, TPFNeRF effectively reconstructs object geometry and color from fixed viewpoints using only 5 MB of model weights, significantly reducing CBDTN deployment costs.

Blockchain with the Internet of Things (BIoT) denotes that the blockchain system is used for managing node identities in centerless IoT. Large-scale BIoT (L-BIoT) means many blockchains are used for managing node identities from different IoTs. The verifiable query scheme for BIoT was widely discussed in real-time IoT system. However, the query scheme will require cross-chain operations when BIoT expands into L-BIoT. Cross-chain verifiable query schemes for BIoT exist two issues. 1) Multiround Consensus: Cross-chain query requires the relay chain and related chains to participate in consensus, and each interaction requires initiating one consensus round. 2) High-Overhead Verification: Each data item returned by various blockchains requires individual integrity verification. We propose CVCQ, an off-chain committee (OC)-based verifiable cross-chain query scheme for L-BIoT. CVCQ achieves only one-round consensus by shifting the on-chain data query and consensus to an OC. Meanwhile, the aggregation accumulator and proofs are utilized to verify data integrity, ensuring the low-overhead verification. We proved that CVCQ reduces complexity of consensus from$O(m^{\lambda +1}+m\cdot N^{\lambda })$to$O(m^{\lambda })$, and decreases the computational complexity of verification from$O(m\log n)$to$O(1)$, when cross-chain data query involving m regional IoTs with N nodes and n data. The experiment results show that CVCQ outperforms the existing schemes by achieving higher on-chain throughput and lower latency.

Industrial Cyber-Physical Systems (ICPSs) have been motivating various Industry 4.0 endeavours, particularly with the integration of fog computing. Cloud-fog data caching paradigms, as supportive elements of ICPSs, have been adopted to cache user data, catering to diverse ICPS requirements such as data sensitivity and reduced access latency. In this hierarchical caching context, ensuring Cloud-Fog Data Integrity (CFDI) is crucial for maintaining the consistent functionality of ICPSs. Existing solutions primarily focus on examining the integrity of data cached solely on either cloud or fog nodes. However, cloud-cached data and fog-cached data are tightly coupled and should be considered simultaneously when checking data integrity. In this work, we introduce an over-the-air CFDI verification scheme, namely AirDIV, with a high accuracy and security guarantee. Instead of aggregating integrity proofs after proof transmission, AirDIV completes proof aggregation and transmission over the air for efficiency improvement. To enhance practicability, we derive adjustable parameters and formulate an optimization problem to minimize over-the-air aggregation errors. Furthermore, with an effective proof generation method, AirDIV can defend against two common attacks, i.e., replay and forge attacks. We provide a theoretical analysis of AirDIV’s correctness, accuracy and security, while conducting extensive experiments on both simulated and real platforms to validate its efficiency.

Abstract Physics Informed Machine Learning has emerged as a popular approach for modeling and simulation in digital twins, enabling the generation of accurate models of processes and behaviors in real-world systems. However, existing methods either rely on simple loss regularizations that offer limited physics integration or employ highly specialized architectures that are difficult to generalize across diverse physical systems. This paper presents a generic approach based on a novel physics-encoded residual neural network (PERNN) architecture that seamlessly combines data-driven and physics-based analytical models to overcome these limitations. Our method integrates differentiable physics blocks–implementing mathematical operators from physics-based models–with feed-forward learning blocks, while intermediate residual blocks ensure stable gradient flow during training. Consequently, the model naturally adheres to the underlying physical principles even when prior physics knowledge is incomplete, thereby improving generalizability with low data requirements and reduced model complexity. We investigate our approach in two application domains. The first is a steering model for autonomous vehicles in a simulation environment, and the second is a digital twin for climate modeling using an ordinary differential equation (ODE)-based model of Net Ecosystem Exchange (NEE) to enable gap-filling in flux tower data. In both cases, our method outperforms conventional neural network approaches as well as state-of-the-art Physics Informed Machine Learning methods.

Abstract Events are usually embedded in latent topics and the extraction of these latent topics are enabled by event detection algorithms. Unsupervised algorithms like Clustering algorithms are very useful for detecting events but with requirements which may not be relevant or easy to determine when using unstructured textual social media data. For instance, some algorithms are required to be used on specific data shapes, but determining the shape of an unstructured data may not be practical aside from the high level of noise in the data. Many of the existing algorithms work well with structured data, however, some of these algorithms can be adapted to unstructured data with the caveat that cluster formations may not contain consistent contextual information. We propose a novel Multi-Cycle Recursive Clustering Algorithm (MCRCA), able to sequentially eliminate noise, resulting in high homogeneous cluster formations. MCRCA does not require the initial specification of clusters numbers as the estimated number of clusters can be deduced at convergence. Our algorithm out-performs the classical LDA and K-Means algorithms in forming highly homogeneous clusters, context-wise.

Edge intelligence is an emerging distributed computing paradigm that has been driven by the rapid proliferation of Internet of Things (IoT) devices, along with the advancements in edge computing and artificial intelligence. With latency-sensitive data commonly cached across multiple Edge Servers (ESs), efficient Edge Data Integrity Verification (EDIV) has become increasingly critical. Traditional ‘challenge-response’ EDIV methods incur substantial computation and communication costs by indiscriminately verifying all ESs, even though not all ESs may be simultaneously corrupted. A recent Federated Learning (FL)-based framework partially addressed this inefficiency by identifying potentially corrupted ESs early, considering only homogeneous ES activity data. However, due to heterogeneous activity data across diverse ESs, this approach suffers from reduced detection accuracy of potentially corrupted ESs, slower FL convergence, and unclear guidance for subsequent verification rounds, thus limiting the overall reduction in EDIV computation and communication costs. To that end, we proposeFederated learning withAdaptiveRegularizer-basedEdgeDataIntegrityVerification (FedAR-EDIV), which is an effective FL-based framework integrating an adaptive objective regularization strategy specifically designed to handle heterogeneous data distributions. FedAR-EDIV efficiently identifies potentially corrupted ESs during the FL process, achieves faster convergence, and significantly reduces computation and communication costs in the final EDIV procedure. It achieves up to 16× communication speedup and 9.1× computation cost reduction compared to baseline EDIV methods, and reaches FL-based detection accuracy exceeding 99.78% under heterogeneous conditions using KDD99 activity data. Additionally, FedAR-EDIV incorporates a dynamic reputation mechanism after each EDIV round to strategically guide subsequent verification rounds, ensuring fewer checks for trustworthy ESs and greater scrutiny for suspicious ones, thus further minimizing EDIV-related costs. We provide a theoretical analysis that demonstrates the convergence of FedAR-EDIV during FL training, as well as correctness, efficiency, and security during the EDIV process. Extensive experiments conducted on two different heterogeneous activity datasets validated that FedAR-EDIV substantially outperforms baseline methods in terms of corrupted ES detection accuracy, FL convergence speed, and overall EDIV computation and communication costs.

Cloud Data Centers are typically equipped with various types and performance levels of GPUs, making it crucial to effectively leverage these heterogeneous resources when deploying deep learning tasks. However, differences in GPU performance pose complex resource management challenges. Accurate workload forecasting can help address these challenges. Many existing prediction models employ Multi-Layer Perceptrons (MLPs) due to their simplicity, computational efficiency, and general applicability. Nevertheless, most MLP-based methods tend to prioritize low-frequency features, neglecting high-frequency features that are essential for capturing fine-grained patterns in resource utilization data. Our analysis reveals that this bias primarily stems from the dominance of low-frequency features in the dataset, causing the model's attention to disproportionately focus on them. To tackle this issue, we propose a frequency domain patch-based MLP model called FP-MLP, which partitions the frequency series into small patches, thereby balancing the representation of high and low-frequency features across different bands. This approach enables the model to fully capture hidden features in high-frequency data, thus improving predictive accuracy. Extensive experiments demonstrate that the FP-MLP model consistently outperforms various state-of-the-art baseline models with stable performance. For instance, on the Alibaba dataset, compared to the best baseline model, the FP-MLP model reduces the Mean Squared Error (MSE) by 8% and the Mean Absolute Error (MAE) by 17%; meanwhile, it also achieves competitive results on the Google.

The rapid growth of Cloud Computing, Artificial Intelligence, and Big Data cloud workloads, intensifying resource contention, operational costs, and carbon emissions due to underutilized data centers. Accurate workload prediction is thus for proactive resource management and improved utilization of Cloud data centers. However, traditional statistical and machine learning methods struggle with the dynamic, high-dimensional, and heterogeneous nature of cloud workloads. This paper proposes STiFF, a novel prediction framework that, for the first time in workload forecasting, transforms time series data into graph-based image representations to capture spatiotemporal dependencies. STiFF integrates three key modules: (1) a Two-Dimensional Moving Average Decomposition (2D-MAD) for trend smoothing, (2) a Global-Local Feature Extraction (GLE) module combining CNNs and Transformers for hierarchical pattern learning, and (3) a Multi-modal Feature Fusion (MFF) module leveraging attention mechanisms and partial prior knowledge. Extensive experiments on four real-world datasets demonstrate that STiFF achieves an average error reduction of 62.14%, with a maximum of 90.84%, and outperforms state-of-the-art methods in 84.375% of the evaluated cases.

Many Next-Generation consumer electronic devices would be distributed hybrid electronic systems, such as UAVs (Unmanned Aerial Vehicles) and smart electronic cars. The safety and risk control are the key issues for the sustainability of such consumer electronic systems. The modeling of hybrid electronic systems is difficult to be abstracted by traditional Petri Nets. This also makes the reachable marking graph unable to be applied to Petri Nets of the hybrid electronic systems. This paper proposes a novel Petri Net to model and analyze the hybrid electronic systems. We name it a Semi-continuous Colored Petri Net (SCPN) that inherits the excellent modeling capabilities and analysis methods of Petri Nets, and can formally depict hybrid quantities. In addition, we propose the construction algorithm for an SCPN reachable marking graph and prove its finiteness. Finally, we model and analyze an Adaptive Cruise Control (ACC) system of smart electronic cars as an example to prove the validity of SCPN. We use the proposed SCPN to model and analyze the running process of an ACC system under the continuous deceleration scenario of the front vehicle. The application study shows that the ACC system has logic flaws under the constant headway strategy when the front vehicle continues to decelerate. Based on this analysis, improvements to the SCPN of the ACC system are made, effectively enhancing its safety and logical correctness.

The rapid growth of Internet of Vehicle (IoV) devices and Artificial Intelligence (AI) applications has accelerated the adoption of Cloud and Edge Computing. The advent of sixth-generation mobile communication technology (6G) further facilitates the deployment of Cloud-Edge collaborative computing in large-scale Intelligent Transportation Systems (ITS). Effective ITS must efficiently handle both latency-sensitive tasks (e.g., obstacle detection, traffic signal recognition) and computationally intensive tasks (e.g., path optimization, traffic flow prediction). However, existing Cloud-Edge collaborative frameworks struggle to accurately classify diverse workloads and provide efficient low-latency processing, leading to energy inefficiencies and task failures. To address these challenges, this paper introduces a Deep Learning-based Cloud-Edge Collaboration Framework (CECF) designed to optimize energy conservation in Cloud and Edge environments. CECF employs a DNN-based classifier to categorize workloads for processing in the Cloud or Edge. The classified tasks are managed by a dedicated Cloud scheduler (DSGA) and an Edge scheduler (EA-DFPSO), respectively. To enhance scheduling efficiency for highly variable Cloud tasks, DSGA incorporates a novel self-adaptive mutation algorithm and a random point fixed distance crossover method. Extensive evaluations using real-world workload traces demonstrate that CECF achieves up to a 8.5% improvement in system reliability and reduces energy consumption by 35.88% compared to baseline approaches.

Vehicular Ad Hoc Networks (VANETs) are essential components of Intelligent Transportation Systems (ITS) and Vehicular Social Networks (VSN). As the number of connected vehicles continues to grow, the importance of ensuring security and privacy within VANETs becomes paramount. Robust authentication protocols are essential to safeguard vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, given the vulnerability of the open wireless communication medium in VANETs to interception and manipulation. While researchers are actively developing authentication and privacy schemes to secure message exchanges, existing solutions have not fully satisfied all the security and privacy requirements, thereby fueling ongoing research in this field. A significant challenge in this context is balancing the conflicting goals of privacy—protecting sensitive information from unauthorized access—and traceability—ensuring accountability by identifying malicious actors. This paper presents a comprehensive overview of VANETs, covering their key components, prevalent attack types, and the security and privacy prerequisites essential for effective authentication and privacy mechanisms. Specifically, the paper delves into the realm of identity-based authentication schemes within VANETs, subjecting them to rigorous evaluation based on security, privacy, and scalability. The primary objective is to empower the research community to design and implement efficient and secure authentication schemes, minimizing communication and computational overhead while ensuring resilience against a multitude of potential attacks.

Person re-identification (Re-ID) is a critical technology in security systems and video surveillance. However, most of the existing methods focused on precise Re-ID, which not only neglect the transmission overheads, computing energy consumption and carbon emissions, but are unsustainable. Furthermore, the personal semantics is usually blurred and distorted in real-world scenarios due to the bird's eye view (BEV) of cameras. Crossillumination and face-coverings also weakened the key personal semantics. Such deficiencies have resulted in a substantial amount of carbon emissions and poor Re-ID performance. To reduce the video transmission overheads, computing energy consumption and carbon emissions yet guaranteeing the accuracy of Re-ID, this paper proposes a novel semantic communication-based lowcarbon sustainable framework (SC-LCSF) for Re-ID. SC-LCSF adopts the semantic encoder based on an enhanced semanticsaware attention mechanism (ESA-SE) to extract the personal semantics. Only semantic information is transmitted at the semantic layer, which is then decoded into personal IDs by the multi-granularity semantic decoder (MG-SD). Two widely used public datasets, Market-1501 and CUHK03, and a newly curated real-world dataset, HZAU-SCUEC01, are used to train SC-LCSF and to evaluate its performance. Experimental results show that compared to the state-of-the-art (SOTA) methods, SC-LCSF achieves the best Rank-1 and mAP accuracy on all the datasets. Furthermore, SC-LCSF has a significant performance enhancement in low-carbon sustainable computing – the transmission data amount, CPU power consumption, CPU temperature, GPU power consumption, GPU temperature and Re-ID delay have a reduction of 96.8%, 39.6%, 27.9%, 40.9%, 29.7% and 76.6%, respectively.

A causal relationship forms when one event triggers another's change or occurrence. Causality helps to understand connections among events, explain phenomena, and facilitate better decision-making. In IoT systems, massive consumption of energy may lead to specific types of air pollution. There are causal relationships among air pollutants. Analyzing their interactions allows for targeted adjustments in energy use, like shifting to cleaner energy and cutting high-emission sources. This reduces air pollution and boosts energy sustainability, aiding sustainable development. This paper introduces a distributed data-driven machine learning method for high-level causal analysis (DMHC), which extracts general and high-level Complex Event Processing (CEP) rules from unlabeled data. CEP rules can capture the interactions among events and represent the causal relationships among them. DMHC deploys a two-layer LSTM attention mechanism model and decision tree algorithm to filter and label data, extracting general CEP rules. Afterward, it proceeds to generate event logs based on general rules with heuristic mining (HM), extracting high-level CEP rules that pertain to causal relationships. These high-level rules complement the extracted general rules and reflect the causal relationships among the general rules. The proposed high-level methodology is validated using a real air quality dataset.

We present SemiCRL, a novel framework for volumetric medical image segmentation that formulates an innovative contrastive learning methodology in a semi-supervised learning setting. We leverage the pseudo-labels generated in semi-supervised learning to guide the selection of negative samples for our contrastive learning, aiming to alleviate the class collision issue and learn enhanced class-discriminative latent representations. However, to address the inaccuracies in pseudo-labels, which stem from the empirical distribution misalignment between labeled and unlabeled data, we introduce a pseudo-label refinement strategy based on class prototypes computed from learned latent representations. Furthermore, our contrastive learning utilizes class prototypes as powerful reference points to enforce the alignment of latent-space distribution of labeled and unlabeled data, thus fostering knowledge transfer from labeled to unlabeled data, which in turn enhances the generation of accurate pseudo-labels in semi-supervised learning. Experiments on two public medical image datasets demonstrate our proposed method outperforms existing state-of-the-art semi-supervised approaches.

Community detection reveals the unique characteristics and relationships of in-network members, differentiated from out-of-community members and plays a pivotal role in network analysis. In recent years, deep learning techniques have made great strides in the application of community detection, especially on label sampling models, which train graph convolutional networks by constructing a balanced training set through structural centre localization and neighbourhood node expansion. However, such algorithms are limited on centre selection in community structure. To address this, this paper introduces a novel community detection algorithm based on peak density adaptive iterative segmentation, or LDACN in short. First, labels are assigned by adaptively selecting the centre node of the community structure, which results in a more even distribution of labels in the network. Subsequently, more accurate community segmentation is achieved by taking advantage of GCN's combined ability in capturing the connectivity relationships between the nodes and their intrinsic characteristics. Experimental results on synthetic and real-world network datasets show that our algorithm improves the effectiveness of community segmentation compared to the state-of-the-art algorithms.

Influence maximization plays a pivotal role in areas such as cybersecurity, public opinion management, and viral marketing. However, conventional methods for influence maximization grapple with challenges such as seed node clustering, information loss during influence propagation, and biases in overlapping influence consideration. In response to these issues, we introduce Fast Unfold Diffusion (FUD)- an influence maximization algorithm capitalizing on the uniform distribution characteristics in node space and the non-overlapping local features of nodes. This approach aims to facilitate swift and extensive global diffusion of node influence. The performance of our algorithm was validated through the execution of two hundred diffusion simulations, employing the Independent Cascade (IC) diffusion model across six real-world datasets. Compared to the latest algorithms, our algorithm offers substantial benefits in expanding the range of influence diffusion, the speed of influence diffusion and the stability of the algorithm. This not only effectively mitigates the problems found in traditional methodologies but also ensures a significant enhancement in the efficiency of influence maximization.

Human facial reactions play crucial roles in dyadic human-human interactions, where individuals (i.e., listeners) with varying cognitive process styles may display different but appropriate facial reactions in response to an identical behaviour expressed by their conversational partners. While several existing facial reaction generation approaches are capable of generating multiple appropriate facial reactions (AFRs) in response to each given human behaviour, they fail to take human's personalised cognitive process in AFRs generation. In this paper, we propose the first online personalised multiple appropriate facial reaction generation (MAFRG) approach which learns a unique personalised cognitive style from the target human listener's previous facial behaviours and represents it as a set of network weight shifts. These personalised weight shifts are then applied to edit the weights of a pre-trained generic MAFRG model, allowing the obtained personalised model to naturally mimic the target human listener's cognitive process in its reasoning for multiple AFRs generations. Experimental results show that our approach not only largely outperformed all existing approaches in generating more appropriate and diverse generic AFRs, but also serves as the first reliable personalised MAFRG solution. Our code is made available at https://github.com/xk0720/PerFRDiff.

SUMMARY The growing number of services processed and stored in the cloud has led to difficulties in managing and discovering the required services efficiently. Multilevel index model is an efficient method to manage and retrieve services in service repositories. When adding a new service to a multilevel index model, a key needs to be selected for the service, but existing key selection methods cannot adapt to the situation that hot services change over time. To address this problem, this article proposes an adaptive key selection method to improve the efficiency of service retrieval. However, the service addition operation of the adaptive key selection method is inefficient in the multilevel index model. For this reason, this article improves the multilevel index model by introducing local equivalence partition. This indexing model improves the service addition efficiency of the adaptive key selection method without affecting the service retrieval efficiency. It is experimentally demonstrated that the retrieval and addition efficiencies of the adaptive key selection method are close to the ideal state optimum under the multilevel index model with local equivalence partitioning.

Bitcoin transaction analysis is valuable for examining Bitcoin events. However, most of the existing methods are inadequate for dealing with transactions involving multiple entities. Furthermore, existing Bitcoin transaction analysis methods neglect to evaluate the influence of different entities on a Bitcoin event. This article aims to overcome such limitations by introducing a novel method for multi-entity Bitcoin transaction analysis along with proposing a method for multi-entity influence assessment based on the Bitcoin transaction network (BTN) model. To overcome the loss of tracking information, a Bitcoin gene operation named compound dyeing is devised and incorporated into the BTN simulation. After obtaining the simulation results, a method for multi-entity transaction behavior analysis is presented to identify and visualize the interactions among entities precisely and effectively. Furthermore, four influence indices with suitable visualization methods are proposed based on the features of the BTN to measure the business and trading influences of different entities. A real-world case study, the Mt.Gox coin loss event, is analyzed to demonstrate the effectiveness and efficiency of the proposed methods.

Blockchain technology finds widespread application in the management of Internet of Things (IoT) devices. In response to the challenges posed by performance scalability and the convergence of multiple ledgers stemming from an expanding network, this study introduces the concept of Co-Sharding . Within this framework, the ledger maintained by sub-chains overseeing IoT operations in distinct geographic regions is conceptualized as a shard within the Large-scale Internet of Things (LIoT) ledger. Meanwhile, elected nodes within each region assume responsibility for maintaining a coordinating shard, facilitating cross-regional communication and data interaction. Furthermore, our work presents a multi-objective optimization algorithm grounded in the multi-shard paradigm to enact a scheduling strategy that spans various regions. We undertake a series of pertinent experiments and conduct a comparative analysis of scheduling algorithms within the context of a real-world cross-regional agricultural IoT system, utilizing actual operational data. The comparative results demonstrate that, in comparison to intra-sub-region scheduling, the Co-Sharding approach enhances machine utilization rates by approximately 30% and reduces scheduling time by around 18% when confronted with a task count of 12. In terms of performance, Co-Sharding also exhibits the capability to reduce the storage requirements of lightweight nodes within each region by approximately 39% while concurrently improving throughput by approximately 1.5 times when contrasted with a single-chain architecture.

Building upon pre-trained ViT models, many advanced methods have achieved significant success in COVID-19 classification. Many scholars pursue better performance by increasing model complexity and parameters. While these methods can enhance performance, they also require extensive computational resources and extended training times. Additionally, the persistent challenge of overfitting, due to limited COVID-19 dataset sizes, remains a hurdle. To address these challenges, we proposed a novel method to optimize pre-trained transformer models for efficient COVID-19 classification with stochastic configuration networks (SCNs), referred to as OPT-CO. We proposed two optimization methods: sequential optimization (SeOp) and parallel optimization (PaOp), by incorporating optimizers in a sequential and parallel manner, respectively. Our method can enhance model performance without necessitating a significant parameter expansion. Additionally, we introduced OPT-CO-SCN to avoid overfitting problems through the adoption of random projection for head augmentation. The experiments were carried out to evaluate the performance of our proposed model based on two publicly available datasets. Based on the evaluation results, our method achieved superior, performance surpassing other state-of-the-art methods.

Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many works try to develop technologies to prevent frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this article proposes a novel fraud detection method that integrates machine learning and process mining models to monitor real-time user behaviors. First, we establish a process model concerning the business-to-customer (B2C) e-commerce platform, by incorporating the detection of user behaviors. Second, a method for analyzing abnormalities that can extract important features from event logs is presented. Then, we feed the extracted features to a support vector machine (SVM)-based classification model that can detect fraud behaviors. We demonstrate the effectiveness of our method in capturing dynamic fraudulent behaviors in e-commerce systems through the experiments.

Electronic control units (ECUs) connected by a controller area network (CAN) are used to perform various functions in modern vehicles. In the latest autonomous vehicles, redundant ECUs and a backup bus (different from CAN) are always equipped to prevent a single point of failure or network attack. However, due to the lack of protection measures of CAN bus, attackers can remotely intrude into the vehicle. Many schemes have proposed to use encryption to solve the security problem of CAN bus. Considering the current ECU storage space is limited, it is impossible to store all ECUs’ keys. When a single point of failure or network attack against an ECU occurs, it is necessary for the backup ECU to process the messages related to the failed ECU. How to ensure that the backup ECU can decrypt the encrypted messages and at the same time securely isolates the backbone network from the backup network is an urgent issue to be solved. In order to solve the problem of forwarding and processing such messages under encryption conditions, we propose an efficient re-encryption scheme based on proxy re-encryption. The scheme is also suitable for cross-bus communication without backup networks. Burrows-Abadi-Needham (BAN) logic, random oracle model and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool are utilized to prove that the scheme is secure. The scheme is simulated based on the MIRACL cryptography library on the computer and Raspberry Pi. The simulation results demonstrate that the proposed scheme is secure compared with the existing scheme.

This study proposes a distributed and extensible cross-region vehicle authentication scheme with the reputation for improving the security and efficiency of cross-region vehicle authentication. The existing authentication schemes demonstrate the following drawbacks: 1) Each vehicle is preloaded with the same system private key, which may be leaked so that the entire system would be destroyed; 2) Other schemes rely on trusted authority to aid in selecting some cluster head nodes; 3) The existing cross-region authentication schemes are not flexible and scalable since they depend on the infrastructure fixed on the roadside. With the proposed scheme, each vehicle stores a long-term private key that is different from those of other vehicles, thereby avoiding a system crash when destroying a vehicle. When the cross-region vehicle enters a new region, it can verify the reputation value of the surrounding vehicles to select the edge computing vehicle. The formal security proof shows that the proposed scheme has adequate security under the real-or-random model. The performance evaluation of our scheme with several related schemes reveals that it generates relatively low computation and communication overhead, is more robust, and achieves minimum packet loss ratio and delay.

Automated and accurate segmentation of medical images is important for facilitating clinical diagnosis and treatment. Currently, state-of-the-art(SOTA) diffusion based medical image segmentation methods are hampered by inherent randomness when generating diffusion model outcomes. Multiple generations are required to mitigate this randomness, which present a challenge for diffusion models. Due to the extended inference time required by diffusion models for multistep iterations, the process of obtaining final segmentation results by multiple generations is prolonged. As a result, this hampers the application of diffusion models in the medical field and limits the research potential of these models. In this paper, we present a medical image segmentation framework that concurrently predicts labels and noise. By leveraging label constraints within the diffusion model, we effectively suppress randomness, enabling the generation of segmentation maps with reduced errors in the initial stages and thereby suppress randomness. The performance of the proposed method is assessed using the ISIC2016 and Brats2018 datasets. Our approach necessitates just a single generation to produce effective segmentation results without the need for multiple steps to mitigate randomness and outperforms compared SOTA methods.

In a cloud-edge-end cooperation environment, edge and core cloud services are complementary and synergistic, jointly processing a large amount of private data uploaded by users. To prevent the leakage of private data, users must ensure that services are secure and trusted through remote attestation. Traditional one-to-one remote attestation schemes are typically used to test the cloud services. However, as the cloud platform scales and the number of edge and core cloud services grows rapidly, the traditional attestation method has problems, such as poor scalability and low attestation efficiency. Thus far, there has been a lack of feasible methods for users to verify multiple related services in a cloud-edge-end cooperation environment quickly. This paper presents a scalable layered cooperative service attestation (SLCSA) scheme, the first secure and scalable protocol for the efficient attestation of multiple cooperative services. The SLCSA scheme is based on a Boneh–Lynn–Shacham (BLS) multi-signature to improve the scalability of the scheme while enabling users to conduct the batch verification of services. We also analyze the security of the proposed scheme. To evaluate the proposed scheme, we implement it using Intel SGX, which can provide basic hardware-assisted attestation and a trusted execution environment for services. The experimental results show that the SLCSA scheme is practical and efficient in a cloud-edge-end cooperative environment.

The Software Defined Networking (SDN) paradigm is rapidly emerging as a viable alternative in the realm of Next Generation Internet of Things (NG-IoT) due to the inherent challenge posed by traditional computer networks. SDN is revolutionizing traditional network architecture by significantly reducing the reliance on hardware. One of the key challenges of implementing SDN in NG-IoT is the extensive communication that occurs between the switches and the controllers due to the bursty nature of the traffic. Therefore, it is crucial to explore solutions aimed at minimizing this control overhead caused by excessive communication. To alleviate the burden on SDN controllers caused by switches, we propose a new method, named LSDA, which can effectively reduce the flow of control information. To promote fairness within the network, packets heading towards the same controller are aggregated, thereby eliminating the need for complex disassembly in the case of different destination controllers. The simulation results demonstrate that LSDA can effectively reduce the packet loss ratio while simultaneously improving network delay and jitter.

Abstract Edge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for future cyber‐physical‐social systems (CPSS), which provides the ability to analyze data at edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyper‐connected smart sensing devices. As a part of future CPSS, online social networks are large‐scale complex networks that consist of a large number of network nodes and links. The dynamic discovery of communities, especially overlapping communities, is important to understand the evolution of online social networks. However, traditional community discovery algorithms cannot effectively discover overlapping communities in social networks. In order to address this challenge, an edge intelligence‐enabled dynamic overlapping community discovery and evolution prediction model (EIDEP) is proposed in this article. This model encompasses a label propagation algorithm based extension (LPAE) algorithm, which is able to efficiently discover the user community structures in online social networks. Based on the LPAE community discovery algorithm, a user interest behavior based evolution prediction (UIBEP) algorithm is incorporated in our EIDEP model in order to realize a fast yet accurate community evolution for online social networks, by considering the interest similarity of unlinked nodes in a given community. The performance of our proposed LPAE and UIBEP models is validated and evaluated against notable state‐of‐the‐art community discovery algorithms, through extensive experiments conducted based on a Twitter dataset.

Although proxy-assisted revocable attribute-based encryption provides fine-grained privacy protection and reduces the decryption cost of data users, it inherits several disadvantages in outsourcing decryption. The decryption capability in outsourcing decryption is divided into two parts: the outsourcing transformation key and the user decryption key. The proxy server utilizes transformation keys to transform the ciphertext which can only be decrypted by the users who generated the transformation key. When multiple users with the same attributes request outsourcing transformation, the proxy server must do numerous transformation operations, increasing the computing overhead of the proxy server and the user request response time. To address the aforementioned issues, we propose a multi-proxy assisted revocable attribute group-based encryption (MP-RAGBE) scheme. We form a user group out of users who have the same attributes and send the transformation keys directly to the proxy server. Users in the same group can decrypt the transformed ciphertext, and only a small number of transformation keys need to be updated when revocation occurs. The security and experimental analyses show that the proposed scheme meets the defined security requirements while significantly reducing user request response time, the overhead of transformation key generation, transmission, and ciphertext transformation.

Recently, the fog computing concept has been introduced into vehicular ad-hoc networks (VANETs) to formulate fog-based VANETs. Since the communication channels between vehicles and fog nodes are open and insecure, it is necessary to construct an authenticated key agreement (AKA) scheme for securing the channels. The existing AKA schemes have two main deficiencies. One is that the computational and communication overhead are not low enough to satisfy the requirements of delay-sensitive applications. The other is that the multi-trusted-authority (multi-TA) model has not been considered. To solve the deficiencies, we propose a lightweight and conditional privacy-preserving AKA scheme, where the main steps are designed with symmetric cryptography methods. The design can reduce the computational and communication overhead of the AKA process. Additionally, we consider the multi-TA model in the AKA process to solve the single-point-of-failure issue. By integrating Cuckoo filter into the multi-TA model, the secrecy of real identities of legal vehicles is guaranteed and the identity revocation function for illegal vehicles is supported in the AKA process. The security proof and analysis show that our proposed scheme satisfies the essential security and privacy requirements of VANETs. The performance analysis shows that our proposed scheme outperforms other related and represented schemes.

Autonomous Vehicles (AVs) are a highly discussed topic owing to their great performance and convenience. However, some requirements limit their wider deployment. Specifically, AV should be controlled by remote users during emergencies. It may lead to AV’s system facing the risk of being intruded on by a malicious party, resulting in unreasonable decisions. We, therefore, design the Internet of autonomous vehicle (IoAV) model to mitigate the problems arising from these limitations. To promote a secure remote control of the AV, a reliable authentication scheme, which can be used in IoAV, must be performed. Our proposed chaotic map-based authenticated key agreement (CMAKA) method provides secure remote control features for AVs. In this method, users, data centers, and AV establish a secure communication channel through the negotiation of three independent session keys. Furthermore, a physical unclonable function (PUF) is employed to produce a trusted private key during the authentication. The security of our scheme is evaluated using game hopping through the widespread Real-or-Random (ROR) model. Compared with other existing three-factor authentication schemes, the performance of our protocol is higher in both security requirements and total cost.

Cognitive radio (CR) provides an effective solution to meet the huge bandwidth requirements in intelligent transportation systems (ITS), which enables secondary users (SUs) to access the idle spectrum of the primary users (PUs). However, the high mobility of users and real-time service requirements result in the additional transmission collisions and interference, which degrades the spectrum access rate and the quality of service (QoS) of users in ITS. This paper proposes a spectrum access algorithm (Feilin) based on federated deep reinforcement learning (FDRL) to improve spectrum access rate, which maximizes the QoS reward function with considering the hybrid benefits of delay, transmission power and utility of SUs. To guarantee the utility of SUs, the warranty contract is designed for SUs to obtain compensation for data transmission failure, which promotes SUs to compete for more spectrum resources. To meet the real-time requirements and improve QoS in ITS, a spectrum access model called FDQN-W is proposed based on federated deep Q-network (DQN), which adopts the asynchronous federated weighted learning algorithm (AFWLA) to share and update the weights of DQN in multiple agents to decrease time cost and accelerate the convergence. Detailed simulation results show that, in the multiuser scenario, compared with the existing methods, the proposed algorithm Feilin increases the spectrum access success rate by 15.1%, and reduces the collision rate with SUs and the collision rate with PUs by 46.4% and 6.8%, respectively.

The way to use limited bandwidth resources to achieve high-quality video services in vehicular networks is an important research topic. A large number of studies have shown that the fast-growing field of software defined networking (SDN) can provide solutions to the problems encountered by traditional IP networks when implementing video multicasting. However, there is no research addressing the security issues for this scenario. In this paper, we explore the application scenarios of video multicast in software defined vehicular networks (SDVN), and propose a secure and effective access control protocol to solve multicast security issues. This protocol realizes the authentication of multicast video requesting vehicles and RSUs. According to the authentication results, the SDN controller constructs multicast paths that only reach legitimate RSUs and vehicles, and only the vehicle passing the authentication can obtain video decryption keys. The protocol resists common attacks and satisfies the security requirements in vehicular networks. In addition, the scheme supports batch verification, which reduces the time cost of authentication, and adopts broadcast encryption technology to effectively reduce the communication load. Compared with related schemes, our protocol performs better in terms of computation and communication cost, packet loss rate, and time delay.

E-commerce systems have become tremendously popular and important for modern business processes in the world of the digital economy. E-commerce business processes rely on the distributed and concurrent interaction process among Web applications of participants, such as clients, merchants, third-party payment platforms (TPPs), and bank systems. Such complex business interactions bridge the gap of trustiness among participants and introduce new security challenges in the form of logical vulnerabilities, which are prevalent in the business process at the application level. The most pressing challenge is to guarantee security throughout the checkout process at the conceptual design phase such that the logic errors can be detected before the actual implementation. Maintenance and repair of implemented e-commerce systems can be extremely costly. To this end, this article proposes a novel modeling and analyzing methodology for multiparticipants and multisessions e-commerce interaction processes based on colored Petri nets (CPNs). First, we define a novel model that can efficiently depict the key properties of e-commerce business interaction processes. Second, several modeling principles are formulated based on the design specification of e-commerce systems. Finally, the concept of Transaction-Logical Consistency is defined to analyze and verify the logical vulnerabilities of e-commerce systems. Through a discussed case study, we demonstrate the feasibility and applicability of the proposed methodology and its efficiency in detecting problems those can potentially lead to logical vulnerabilities.

Existing Cloud systems involve large volumes of data streams being sent to a centralised data centre for monitoring, storage and analytics. However, migrating all the data to the cloud is often not feasible due to cost, privacy, and performance concerns. However, Machine Learning (ML) algorithms typically require significant computational resources, hence cannot be directly deployed on resource-constrained edge devices for learning and analytics. Edge-enhanced compressive offloading becomes a sustainable solution that allows data to be compressed at the edge and offloaded to the cloud for further analysis, reducing bandwidth consumption and communication latency. The design and implementation of a learning method for discovering compression techniques that offer the best QoS for an application is described. The approach uses a novel modularisation approach that maps features to models and classifies them for a range of Quality of Service (QoS) features. An automated QoS-aware orchestrator has been designed to select the best autoencoder model in real-time for compressive offloading in edge-enhanced clouds based on changing QoS requirements. The orchestrator has been designed to have diagnostic capabilities to search appropriate parameters that give the best compression. A key novelty of this work is harnessing the capabilities of autoencoders for edge-enhanced compressive offloading based on portable encodings, latent space splitting and fine-tuning network weights. Considering how the combination of features lead to different QoS models, the system is capable of processing a large number of user requests in a given time. The proposed hyperparameter search strategy (over the neural architectural space) reduces the computational cost of search through the entire space by up to 89%. When deployed on an edge-enhanced cloud using an Azure IoT testbed, the approach saves up to 70% data transfer costs and takes 32% less time for job completion. It eliminates the additional computational cost of decompression, thereby reducing the processing cost by up to 30%.

Pneumoconiosis staging has been a challenging task for deep neural networks due to the stage ambiguity in early pneumoconiosis. In this article, we propose a deep log-normal label distribution learning method named DLN-LDL for pneumo-coniosis staging by exploring the intrinsic stage distribution pat-terns of pneumoconiosis. DLN-LDL effectively prevents the deep network from overfitting features in ambiguous chest radiographs that are irrelevant to the stage to which they belong by replacing the one-hot labels with log-normally distributed vectors. The experiments on our collected pneumoconiosis dataset confirm that the proposed DLN-LDL algorithm outperforms other classical methods in terms of Accuracy, Precision, Sensitivity, Specificity, F1-score and Area Under the Curve.

Abstract There has been much interest in link prediction research with significant studies on how to predict missing links or future links in a network based on observed information. However, the key solution to tackle the link prediction problem is how to measure the similarity between the nodes in a network with higher accuracy. Several techniques have been proposed that utilize the similarity between nodes to estimate their proximity in the network. In this paper, an efficient link prediction algorithm that predicts relationships between links using the network structure is proposed. This algorithm uses common neighbors in addition to the degree distribution of the nodes to estimate the possibility of the presence of a link between two nodes in a network based on local information. Extensive experiments are carried out and compared with 12 standard similarity‐based methods using seven real‐world datasets. The experimental results show that our method has higher prediction accuracy compared with most of the local information based methods like the Common Neighbor and Preferential Attachment. It is also competitive with the quasi‐local indicators such as LP and global indicators like Katz, with a lower computational complexity than the two.

A substantial amount of research has been carried out in Explainable Artificial Intelligence (XAI) models, especially in those which explain the deep architectures of neural networks. A number of XAI approaches have been proposed to achieve trust in Artificial Intelligence (AI) models as well as provide explainability of specific decisions made within these models. Among these approaches, global interpretation methods have emerged as the prominent methods of explainability because they have the strength to explain every feature and the structure of the model. This survey attempts to provide a comprehensive review of global interpretation methods that completely explain the behaviour of the AI models. We present a taxonomy of the available global interpretations models and systematically highlight the critical features and algorithms that differentiate them from local as well as hybrid models of explainability. Through examples and case studies from the literature, we evaluate the strengths and weaknesses of the global interpretation models and assess challenges when these methods are put into practice. We conclude the paper by providing the future directions of research in how the existing challenges in global interpretation methods could be addressed and what values and opportunities could be realized by the resolution of these challenges.

Vehicular ad hoc networks (VANETs) play an ever-increasing important role in improving traffic management and enhancing driving safety. However, vehicular communication using a wireless channel faces security and privacy challenges. The conditional privacy-preserving authentication (CPPA) scheme is suitable for solving the above challenges, but the existing identity-based CPPA schemes suffer from inborn key escrow issues. Motivated by this, we propose a lightweight CPPA scheme based on elliptic curve cryptography to solve the above issues, in which the pseudonym and public/private key pair of the vehicle is generated by itself, so that the proposed scheme avoids the key escrow issue. Furthermore, to achieve efficient vehicular communication, a CPPA scheme is proposed using a fog computing model that supports mobility, low latency, and location awareness. The pseudonym of the vehicle is generated by two hash chains in the proposed scheme, so that the storage overhead can be reduced efficiently under the condition that backward security is guaranteed. Security analysis shows that the scheme is secure under the random oracle and satisfies the security requirements of VANETs. Performance evaluation demonstrates that the proposed scheme outperforms related schemes in terms of computational and communication overhead.

Cross-cloud data migration is one of the prevailing challenges faced by mobile users, which is an essential process when users change their mobile phones to a different provider. However, due to the insufficient local storage and computational capabilities of the smart phones, it is often very difficult for users to backup all data from the original cloud servers to their mobile phones in order to further upload the downloaded data to the new cloud provider. To solve this problem, we propose an efficient data migration model between cloud providers and construct a mutual authentication and key agreement scheme based on elliptic curve certificate-free cryptography for peer-to-peer cloud. The proposed scheme helps to develop trust between different cloud providers and lays a foundation for the realization of cross-cloud data migration. Mathematical verification and security correctness of our scheme is evaluated against notable existing schemes of data migration, which demonstrate that our proposed scheme exhibits a better performance than other state-of-the-art scheme in terms of the achieved reduction in both the computational and communication cost.

With the rapid development of the industrial Internet of Things (IIoT) and cloud computing, an increasing number of companies outsource their data to cloud servers to save costs. To protect data privacy, sensitive industrial data must be encrypted before being outsourced to cloud servers. A multiuser searchable encryption (MUSE) scheme was introduced to ensure high efficiency of encrypted data retrieval. In an IIoT system with numerous users, the existing MUSE schemes suffer from certain key exposure problems owing to the limited key protection of smart devices and frequent queries by users. In this article, we propose a parallel key-insulated MUSE scheme for IIoT. This scheme utilizes broadcast encryption technology to implement MUSE. In addition, our scheme introduces a key-insulated primitive to improve the tolerance to key exposure. The security of our scheme is proved in the random oracle model. The experimental results show that our scheme achieves high computational efficiency.

With the access of massive mobile devices, spectrum resources are becoming increasingly scarce. How to effectively and securely utilize the limited spectrum resources has become a fundamental challenge for future mobile communication systems. Focusing on intelligent sensing and sharing, this article proposes a blockchain-based two-stage secure spectrum intelligent sensing and sharing auction mechanism (BISA), which selects appropriate base stations to form a consortium blockchain to guarantee secure and efficient spectrum auction with low complexity. In the first stage, a reverse-auction-based incentive mechanism is presented to provide bidding strategies for the primary users (PUs) and secondary users (SUs) selecting the PU that maximizes the utility. In the second stage, a unit-utility-based auction algorithm is proposed to achieve a stable match between PUs and SUs. PUs will select the SU with the maximum unit utility to complete the auction. Then, the transaction records are formed into blocks and uploaded to guarantee the security of transactions. Simulation results show that, compared with the existing methods, the proposed BISA increases the total utility and throughput of SUs by 216.4% and 189.3%, respectively.

Despite the substantial progress made by deep networks in the field of medical image segmentation, they generally require sufficient pixel-level annotated data for training. The scale of training data remains to be the main bottleneck to obtain a better deep segmentation model. Semi-supervised learning is an effective approach that alleviates the dependence on labeled data. However, most existing semi-supervised image segmentation methods usually do not generate high-quality pseudo labels to expand training dataset. In this paper, we propose a deep semi-supervised approach for liver CT image segmentation by expanding pseudo-labeling algorithm under the very low annotated-data paradigm. Specifically, the output features of labeled images from the pretrained network combine with corresponding pixel-level annotations to produce class representations according to the mean operation. Then pseudo labels of unlabeled images are generated by calculating the distances between unlabeled feature vectors and each class representation. To further improve the quality of pseudo labels, we adopt a series of operations to optimize pseudo labels. A more accurate segmentation network is obtained by expanding the training dataset and adjusting the contributions between supervised and unsupervised loss. Besides, the novel random patch based on prior locations is introduced for unlabeled images in the training procedure. Extensive experiments show our method has achieved more competitive results compared with other semi-supervised methods when fewer labeled slices of LiTS dataset are available.

Pneumoconiosis staging has been a very challenging task, both for certified radiologists and computer-aided detection algorithms. Although deep learning has shown proven advantages in the detection of pneumoconiosis, it remains challenging in pneumoconiosis staging due to the stage ambiguity of pneumoconiosis and noisy samples caused by misdiagnosis when they are used in training deep learning models. In this article, we propose a fully deep learning pneumoconiosis staging paradigm that comprises a segmentation procedure and a staging procedure. The segmentation procedure extracts lung fields in chest radiographs through an Asymmetric Encoder-Decoder Network (AED-Net) that can mitigate the domain shift between multiple datasets. The staging procedure classifies the lung fields into four stages through our proposed deep log-normal label distribution learning and focal staging loss. The two cascaded procedures can effectively solve the problem of model overfitting caused by stage ambiguity and noisy labels of pneumoconiosis. Besides, we collect a clinical chest radiograph dataset of pneumoconiosis from the certified radiologist's diagnostic reports. The experimental results on this novel pneumoconiosis dataset confirm that the proposed deep pneumoconiosis staging paradigm achieves an Accuracy of 90.4%, a Precision of 84.8%, a Sensitivity of 78.4%, a Specificity of 95.6%, an F1-score of 80.9% and an Area Under the Curve (AUC) of 96%. In particular, we achieve 68.4% Precision, 76.5% Sensitivity, 95% Specificity, 72.2% F1-score and 89% AUC on the early pneumoconiosis 'stage-1'.

Conditional privacy preservation and message authentication serve as the primary research issues in terms of security in vehicular networks. With the arrival of 5G era, the downloading speed of network services and the message transmission speed have significantly improved. Consequently, the content exchanged by users in vehicular networks is not limited to traffic information, and vehicles moving at high speeds can share a wide variety of contents. However, sharing content reliably and efficiently remains challenging owing to the fast-moving character of vehicles. To solve this problem, we propose a reliable and efficient content sharing scheme in 5G-enabled vehicular networks. The vehicles with content downloading requests quickly filter the adjacent vehicles to choose capable and suitable proxy vehicles and request them for content services. Thus, the purpose of obtaining a good hit ratio, saving network traffic, reducing time delay, and easing congestion during peak hours can be achieved. The security analysis indicates that the proposed scheme meets the security requirements of vehicular networks. Our cryptographic operations are based on the elliptic curve, and finally, the proposed scheme also displays favorable performance compared to other related schemes.

Vehicular ad hoc networks (VANETs) are vulnerable to many kinds of security attacks, so it is necessary to design an authenticated key agreement (AKA) scheme for securing communication channels in VANETs. Existing AKA schemes in VANETs have not provided an efficient and secure method to secure V2V and V2I communications simultaneously while meeting the necessary security and privacy requirements. Further, few key updating mechanisms, which are secure, conditional privacy-preserving, practical, and lightweight, exist in current VANETs AKA schemes. In this paper, we propose a proven secure AKA scheme for securing V2V and V2I communications in VANETs, which can be divided into two parts. The first part is a three-party authentication process in which vehicles, road side unit (RSU), and trust authority (TA) authenticate each other. The second part is the key agreement process, which is used in the key generation and updating processes. For this phase, we design a tree-based key agreement algorithm that considers two scenarios, i.e., the joining of an authenticated vehicle and the leaving of the vehicle. The formal security proof and the security analysis show that our proposed scheme satisfies session key security and the necessary security requirements in VANETs, respectively. The performance analysis demonstrates that our proposed scheme has an advantage over several representative AKA schemes in VANETs.

With the development of IoT, more and more data is offloaded from the cloud to the edge for computing, eventually forming a collaborative computing model at the edge. However, in this model, the problem of secure data transmission has not been solved. In this model, data is transmitted and forwarded in multiple messaging systems, and existing security schemes cannot achieve end-to-end security in a multi-hop, broadcast transmission model. Therefore, in this paper, we propose a new security scheme based on proxy re-encryption and broadcast encryption techniques. Moreover, the performance and security of the scheme are further enhanced by using online-offline techniques and a trusted execution environment when integrating the scheme with edge collaboration. Finally, this paper proves the security of the scheme in theory, compares the functionality of the scheme, analyzes the theoretical performance of the scheme, and finally measures the actual performance of the scheme in the edge collaboration system.

The cloud computing paradigm provides numerous tempting advantages, enabling users to store and share their data conveniently. However, users are naturally resistant to directly outsourcing their data to the cloud since the data often contain sensitive information. Although several fine-grained access control schemes for cloud-data sharing have been proposed, most of them focus on the access control of the encrypted data (e.g., restricting the decryption capabilities of the receivers). Distinct from the existing work, this article aims to address this challenging problem by developing a more practical bidirectional fine-grained access control scheme that can restrict the capabilities of both senders and receivers. To this end, we systematically investigate the access control for cloud data sharing. Inspired by the access control encryption (ACE), we propose a novel data sharing framework that combines the cloud side and the edge side. The edge server is located in the middle of all the communications, checking and preventing illegal communications according to the predefined access policy. Next, we develop an efficient access control algorithm by exploiting the attribute-based encryption and proxy re-encryption for the proposed framework. The experimental results show that our scheme exhibits superior performance in the encryption and decryption compared to the prior work.

Recommendation systems are gaining popularity with the proliferation of the Internet of People (IoP). The popularity and use of online social networks facilitate integrating these social relationships with recommender systems under a single framework of IoP. This article proposes a new approach for item recommendation based on the diffusion method that combines user relationships in social networks with user–item relationships derived from the IoP. Especially, a resource redistribution process is explored in the user–object network that gives mass diffusion a higher recommendation accuracy and heat conduct a greater diversity by considering the social degree of users whilst calculating the user degree in the network. A tuning parameter is introduced to adjust the weight of resources that the objects finally receives from users based on their social relationships. Finally, extensive experiments conducted on the real-world datasets which contain friendship relationships, demonstrate the efficiencies of our proposed method in achieving notable performance improvements in terms of the recommendation accuracy, service diversity, and practical dependability.

Energy costs have dramatically increased in data center networks as an increasing number of large-scale Internet applications are used. In software-defined vehicular networks (SDVN), the communication delay between two vehicles and between vehicles and the controller will dramatically climb up as the number of vehicles increases. This requires more controllers to provide communication service to minimize the latency. More controllers lead to high energy costs. Therefore, the number of controllers and their placement, the so-called controller placement problem (CPP), should be addressed. The appropriate placement of controllers can decrease the energy cost, enabling green communication in SDVN. Although CPP has been studied for static networks, it has not been effectively solved in highly dynamic and complex networks. In this article, a novel cost-effective CPP scheme for SDVN is proposed. First, our proposed minimum controller selection mechanism (MOSA) can reduce the number of controllers and guarantee the coverage of the area. Besides, an improved multiobjective artificial bee colony algorithm (IMABC) is proposed based on the original artificial bee colony algorithm. The IMABC can judge which controller should be switched on for data transmission based on real-time traffic flow. A route computation mechanism is proposed to evaluate the performance of our CPP scheme. The experimental results confirm that compared to other existing CPP schemes, our scheme can achieve a higher packet delivery ratio while greatly reducing energy consumption and latency.

The unprecedented development of the Internet of Things (IoT), artificial intelligence (AI), and Big Data has stimulated a boom of social networks such as Twitter, WeChat, Facebook, etc., generating a huge amount of social data that are worth further analysis. Social computing has an important focus on mining the deep relationships between social organizations, networks, and media. The increasing volumes and complexities make big social data mining more and more difficult. Hybrid Human–Artificial Intelligence (H-AI) is an approach combining both human intelligence and AI, so as to handle demanding problems in a harmonious way. By adopting H-AI in social computing, it would provide more possibilities for social data analysis, relationship discovery, outlier detection, and prediction, and is proving to be an emerging and promising direction for AI and big data research.

Existing security and identity-based vehicular communication protocols used in Vehicular Ad-hoc Networks (VANETs) to achieve conditional privacy-preserving mostly rely on an ideal hardware device called tamper-proof device (TPD) equipped in vehicles. Achieving fast authentication during the message verification process is usually challenging in such strategies and further they suffer performance constraints from resulting overheads. To address such challenges, this paper proposes a novel Chinese remainder theorem (CRT)-based conditional privacy-preserving authentication scheme for securing vehicular authentication. The proposed protocol only requires realistic TPDs, and eliminates the need for pre-loading the master key onto the vehicle's TPDs. Chinese remainder theorem can dynamically assist the trusted authorities (TAs) whilst generating and broadcasting new group keys to the vehicles in the network. The proposed scheme solves the leakage problem during side channel attacks, and ensures higher level of security for the entire system. In addition, the proposed scheme avoids using the bilinear pairing operation and map-to-point hash operation during the authentication process, which helps achieving faster verification even under increasing number of signature. Moreover, the security analysis shows that our proposed scheme is secure under the random oracle model and the performance analysis shows that our proposed scheme is efficient in reducing computation and communication overheads.

With the rising popularity of the Internet and communication technology, vehicles can analyze and judge the real-time data collected by various cloud service providers (CSPs) in a vehicular network. However, in a vehicular network environment, real-time data are transmitted via wireless channels, which can lead to security and privacy issues. To avoid illegal access by adversaries, vehicle authentication and key agreement mechanism has been considered as one of the promising security measures in vehicular network environments. Besides, most of the solutions focus on authentication between one vehicle and one CSP. In such strategies, the implementation of efficient authentication for multiple vehicles and CSPs simultaneously is usually challenging. Further, they are also subjected to performance limitations due to the overhead incurred. To solve these issues, we propose a many-to-many authentication and key agreement scheme for secure authentication between multiple vehicles and CSPs. The proposed scheme can prevent unauthorized access and provide SK-security even if temporary information is leaked. To improve the service, the CSP only needs to broadcast an anonymous message periodically instead of having to generate a unique anonymous message for each of vehicles. Similarly, when a vehicle wants to request the services of m CSPs, it only needs to send one request message instead of m. Therefore, the proposed scheme not only implements many-to-many communication but also significantly reduces the computation and communication overhead. Moreover, a thorough security analysis shows that the proposed scheme provides better security compared to other related schemes.

Link prediction in online social networks intends to predict users who are yet to establish their network of friends, with the motivation of offering friend recommendation based on the current network structure and the attributes of nodes. However, many existing link prediction methods do not consider important information such as community characteristics, text information, and growth mechanism. In this paper, we propose an intelligent data management mechanism based on relationship strength according to the characteristics of social networks for achieving a reliable prediction in online social networks. Secondly, by considering the network structure attributes and interest preference of users as important factors affecting the link prediction process in online social networks, we propose further improvements in the prediction process by designing a friend recommendation model with a novel incorporation of the relationship information and interest preference characteristics of users into the community detection algorithm. Finally, extensive experiments conducted on a Twitter dataset demonstrate the effectiveness of our proposed models in both dynamic community detection and link prediction.

Due to the defects caused by limited energy, storage capacity, and computing ability, the increasing amount of sensing data has become a challenge in wireless sensor networks (WSNs). To decrease the additional power consumption and extend the lifetime of a WSN, a multistage hierarchical clustering deredundancy algorithm is proposed. In the first stage, a dual‐metric distance is employed, and redundant nodes are preliminarily identified by the improved k‐means algorithm to obtain clusters of similar nodes. Then, a Gaussian hybrid clustering classification algorithm is presented to implement data similarity clustering for edge sensing data in the second stage. In the third stage, the clustered sensing data is randomly weighted to deduplicate the spatial correlation data. Detailed experimental results show that, compared with the existing schemes, the proposed deredundancy algorithm can achieve better performance in terms of redundant data ratio, energy consumption, and network lifetime.

In the field of social computing, influence-based propagation only studies the maximized propagation of a single piece of information. However, in the actual network environment, there are more than one piece of competing information spreading in the network, and the information will influence each other in the process of spreading. This paper focuses on the problem of competitive propagation of multiple similar information, which considers the influence of communities on information propagation, and establishes overlapping interest communities based on label propagation. Based on users' interests and preferences, the influence probability between nodes of different types of information is calculated, and combining the characteristics of the community structure, the influence calculation method of nodes is proposed. Specifically, aiming at the shortcomings of strong randomness in existing overlapping community detection methods that are based on label propagation, this paper proposes the User Interest Overlapping Community Detection Algorithm based on Label Propagation (UICDLP). Furthermore, when the seed node set of competition information is known, this paper proposes the Influence Maximization Algorithm of Node Avoidance (IMNA). Finally, the experimental results verified that the proposed algorithms are effective and feasible.

Recently, the automatic diagnosis of Turner syndrome (TS) has been paid more attention. However, existing methods relied on handcrafted image features. Therefore, we propose a TS classification method using unsupervised feature learning. Specifically, first, the TS facial images are preprocessed including aligning faces, facial area recognition and processing of image intensities. Second, pre-trained convolution filters are obtained by K-means based on image patches from TS facial images, which are used in a convolutional neural network (CNN); then, multiple recursive neural networks are applied to process the feature maps from the CNN to generate image features. Finally, with the extracted features, support vector machine is trained to classify TS facial images. The results demonstrate the proposed method is more effective for the classification of TS facial images, which achieves the highest accuracy of 84.95%.

Summary Elevator scheduling algorithms generally denote methods used to calculate how to use the elevator. These algorithms can distribute elevators to various floors of a building, thereby achieving efficient transportation. From the perspective of the elevator scheduling problem, we address the load‐balancing problem for software‐defined networking (SDN) architecture and propose a load‐balancing method based on the elevator scheduling algorithm, LBBESA. We take advantage of the flexibility of the SDN architecture, obtain the real‐time load of the server through real‐time statistical analyses of the SDN switch port traffic by the controller, and combine this with the idea of regional elevator allocation to coordinate the connection of the client's requests and realize the load balancing of each server in the cluster. Simulation experiments show that, compared with the round‐robin algorithm, LBBESA is more effective in the load balancing of the server pool and can improve the throughput of the server pool to a certain extent. In addition, our scheme is easy to implement and has high scalability.

In recent years, research on the security of Industry 4.0 and the Internet of Things (IoT) has attracted a close attention from industry, government, and the scientific community. Smart vehicular networks, as a type of industrial IoT, inevitably exchange large amounts of security and privacy-sensitive data, which make them attractive targets for attackers. For protecting network security and privacy, we have proposed an extensible and effective anonymous batch authentication scheme. In contrast to traditional pseudonym authentication schemes, the same system private key need not to be preloaded in our scheme, effectively avoiding a system failure when destroying a vehicle. Besides, the certificate revocation list (CRL) size is merely related to the number of vehicles that have been revoked, regardless of the number of pseudonym certificates for revoked vehicles. Moreover, this scheme maintains the effectiveness of the traditional scheme, effectively reduces the scale of the CRL, and employs an identity revocation scheme that supports rapid distribution. The scheme supports conditional privacy protection, namely, only the trusted authority (TA) can uniquely trace and revoke vehicles. For illegal vehicles, the TA releases the two hashed seeds to facilitate traceability by all entities in its domain. Furthermore, security analysis indicates that our solution is secure under the random oracle model and fulfills a series of security requirements of vehicular networks. Compared to existing authentication schemes, performance evaluations show that the scheme offers relatively good performance in terms of time consumption.

With the advancements in social media and rising demand for real traffic information, the data shared in vehicular ad hoc networks (VANETs) indicate that the size and amount of requested data will continue increasing. Vehicles in the same area often have similar data downloading requests. If we ignore the common requests, the resource allocation efficiency of the VANET system will be quite low. Motivated by this fact, we propose an efficient and privacy-preserving data downloading scheme for VANETs, based on the edge computing concept. In the proposed scheme, a roadside unit (RSU) can find the popular data by analyzing the encrypted requests sent from nearby vehicles without having to sacrifice the privacy of their download requests. Further, the RSU caches the popular data in nearby qualified vehicles called edge computing vehicles (ECVs). If a vehicle wishes to download the popular data, it can download it directly from the nearby ECVs. This method increases the downloading efficiency of the system. The security analysis results show that the proposed scheme can resist multiple security attacks. The performance analysis results demonstrate that our scheme has reasonable computation and communication overhead. Finally, the OMNeT++ simulation results indicate that our scheme has good network performance.

Diagnosing infants who are small for gestational age (SGA) at early stages could help physicians to introduce interventions for SGA infants earlier. Machine learning (ML) is envisioned as a tool to identify SGA infants. However, ML has not been widely studied in this field. To develop effective SGA prediction models, we conducted four groups of experiments that considered basic ML methods, imbalanced data, feature selection and the time characteristics of variables, respectively. Infants with SGA data collected from 2010 to 2013 with gestational weeks between 24 and 42 were detected. Support vector machine (SVM), random forest (RF), logistic regression (LR) and Sparse LR models were trained on 10-fold cross validation. Precision and the area under the curve (AUC) of the receiver operator characteristic curve were evaluated. For each group, the performance of SVM and Sparse LR was similarly well. LR without any sparsity penalties performed worst, possibly caused by the overfitting problem. With the combination of handling imbalanced data and feature selection, the RF ensemble classifier performed best, which even obtained the highest AUC value (0.8547) with the help of expert knowledge. In other cases, RF performed worse than Sparse LR and SVM, possibly because of fully grown trees.

Online social networks (OSNs) have emerged as a major platform for sharing information through social relationships and are one of the major sources of big data. Social networks can even accommodate sharing of live streaming data among the connected users. However, social information on social networks is often locally exploited rather than capturing the changes in the entire network over time. Obtaining user's influence statistics is limited only in their local vicinity, which may not facilitate capturing the changes in the user and post influences across the entire network, thereby resulting in lower accuracy while measuring user's topical influence. Moreover, low-influence users always exist in the network publishing low-quality posts. With the objectives of accurately capturing highly influential users and posts, this article proposes a novel dynamic social sensing model, named dynamic PageRank (DPRank) model, to evaluate the dynamic topical influence of the users of social information on social networks during the social information evolution. We deploy our proposed model to real-world Twitter data sets, which demonstrates the effectiveness of our proposed model against notable existing methods while identifying the true influence of users and posts in a dynamically evolving social network.

With an increasing number of cloud service providers (CSPs), research works on multi-cloud environments to provide solutions to avoid vendor lock-in and deal with the single-point failure problem have expanded considerably. However, a few schemes focus on the conditional privacy protection authentication of vehicular networks under a multi-cloud environment. In this regard, we propose a robust and extensible authentication scheme for vehicular networks to fulfil the ever-growing diversified service demands from users. According to our solution, the vehicles need to register with the trusted authority (TA) only once to achieve a fast and efficient authentication with CSPs. Additionally, as long as the new CSP is successfully registered in TA, it can participate in vehicular service. A cloud broker, which is managed by the TA, is responsible for connecting all the cloud services; consequently, the complexity involved in the selection of CSPs is hidden from the users' view. A detailed security analysis establishes that our scheme can fulfil conditional privacy protection and achieve the security objectives of vehicular networks. Our scheme is based on elliptic curve cryptography and does not employ the complex bilinear pairing operation. An evaluation of performance of the proposed scheme indicates that it is suitable for applications involving vehicular networks.

The papers in this special section focus on new methodologies and applications in artificial intelligence (AI) and machine learning (ML). With the recent development of machine learning (ML), artificial intelligence (AI) and cyber technologies in the field of industrial informatics, it is important to migrate the traditional businesses and services in the physical world to the digital cyber-enabled world. Cyber intelligence technologies, such as the fifth-generation (5G) mobile communication network, big data, Internet of Things (IoT), cloud computing, cognitive computing, ubiquitous computing and blockchains, enable goods, houses, services, information, and capitalization to be shared through the Internet of the Appendix]. Industrial applications, including various mechanical systems, utilities, supply chains, energy systems, power grids, infrastructures, manufactures, traffics, healthcare, and environmental issues, are partially operated or managed remotely with the influence of AI and cyber intelligence technology.

Cloud datacenters are turning out to be massive energy consumers and environment polluters, which necessitate the need for promoting sustainable computing approaches for achieving environment-friendly datacentre execution. Direct causes of excess energy consumption of the datacentre include running servers at low level of workloads and over-provisioning of server resources to the arriving workloads during execution. To this end, predicting the future workload demands and their respective behaviors at the datacenters are being the focus of recent researches in the context of sustainable datacenters. But prediction analytics of cloud workloads suffer various limitations imposed by the dynamic and unclear characteristics of Cloud workloads. This paper proposes a novel forecasting model named K-means based Rand Variable Learning Rate Backpropagation Neural Network (K-RVLBPNN) for predicting the future workload arrival trend, by exploiting the latency sensitivity characteristics of Cloud workloads, based on a combination of improved K-means clustering algorithm and Backpropagation Neural Network (BPNN) algorithm. Experiments conducted on real-world Cloud datasets shows that the proposed model shows better prediction accuracy, outperforming the traditional Hidden Markov Model, Naïve Bayes Classifier, and our earlier RVLBPNN model, respectively.

Summary There are many different index structures for service repositories, such as sequential index, inverted index, and multilevel indices that include three deployments. Different service sets maybe have different characteristics that may affect performance from different aspects. For a given service set, which index structure is the most optimal one? To address these issues, this paper analyses five indexing models and proposes expectation of traversed service count to estimate performance of service retrieval. Based on these expectation formulas, an optimal deployment method can be identified to maximize efficiency of service retrieval. Our experiments first validate correctness of the proposed formulas and then validate the effective of the optimal method.

Traditional public key infrastructure-based authentication schemes provide vehicular networks with identity authentication and conditional privacy protection, which are not sufficient for assessing the credibility of messages. Additionally, although the new generation of cellular networks (5G) can dramatically improve the transmission efficiency of the messages, many existing authentication schemes are based on complex bilinear pairing operations, and the calculation time is too long to be suitable for delay-sensitive 5G-enabled vehicular networks. To address these issues, we propose a reputation system-based lightweight message authentication framework and protocol for 5G-enabled vehicular networks. The trusted authority (TA) is in charge of reputation management. A vehicle with a reputation score below the given threshold cannot obtain a credit reference from the TA for participating in the communication; therefore, the number of untrusted messages in vehicular networks is reduced from the source. Security analysis shows that our scheme is secure against an adaptively chosen-message attack, and also satisfies a series of requirements of vehicular networks. The scheme is based on the elliptic curve cryptosystem and supports batch authentication; therefore, it shows better performance in terms of time consumption when compared with related schemes.

Internet of People (IoP), which focuses on personal information collection by a wide range of the mobile applications, is the next frontier for Internet of Things. Nowadays, people become more and more dependent on the Internet, increasingly receiving and sending information on social networks (e.g., Twitter, etc.); thus social networks play a decisive role in IoP. Therefore, community discovery has emerged as one of the most challenging problems in social networks analysis. To this end, many algorithms have been proposed to detect communities in static networks. However, microblogging social networks are extremely dynamic in both content distribution and topological structure. In this paper, we propose a model for efficient evolutionary user interest community discovery which employs a nature-inspired genetic algorithm to improve the quality of community discovery. Specifically, a preprocessing method based on hypertext induced topic search improves the quality of initial users and posts, and a label propagation method is used to restrict the conditions of the mutation process to further improve the efficiency and effectiveness of user interest community detection. Finally, the experiments on the real datasets validate the effectiveness of the proposed model.

Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation.

Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effective resource discovery services in edge environments, by achieving a proximity-preserving representation of the network resources. Most of the existing techniques of network embedding fail to capture accurate proximity information among the network nodes and further lack exploiting information beyond the second-order neighbourhood. This paper leverages artificial intelligence for network representation and proposes a deep learning model, named inductive content augmented network embedding (ICANE), which integrates the network structure and resource content attributes into a feature vector. Secondly, a hierarchical aggregation approach is introduced to explicitly learn the network representation through sampling the nodes and aggregating features from the higher-order neighbourhood. A semantic proximity search model is then designed to generate the top-k ranking of relevant nodes using the learned network representation. Experiments conducted on real-world datasets demonstrate the superiority of the proposed model over the existing popular methods in terms of resource discovery and the query resolving performance.

The interlinked processing units in modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoC) architecture is becoming a de facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS; therefore, Voltage Frequency Island (VFI) has been recently adopted for effective energy management mechanism in the large-scale multiprocessor chip designs. In this article, we investigated energy-efficient and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI-based NoC-MPSoCs (VFI-NoC-HMPSoC) with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we proposed a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI-based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent--inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We introduced a notion of Energy Gradient (EG) that guides EGD in its search for island voltage settings and minimize the total energy consumption. We conducted the experiments on eight real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of ∼24% and ∼30% over CA-TMES-Search and CA-TMES-Quick, respectively.

With the development of the Internet of Things (IoT), various large-scale real-time data processing applications for handling real-time sensor data are becoming one of the important applications in cloud computing. The academia, the industry, and even the government institutions have already begun to pay close attention to how to efficiently process large amounts of sensor data in real-time using cloud computing technology. Although cloud computing technology has attracted much attention with high-performance, there are strong needs for improving data processing efficiency of large-scale real-time data for IoT-based applications. In addition to this, currently the IoT paradigm is facing increasing difficulty to handle the data generated from IoT applications. As a result of this, it is challenging to ensure low latency and network bandwidth consumption, optimal utilization of computational recourses, scalability, security, and energy efficiency of IoT devices while moving all data to the cloud. Therefore, this centralized computing model is starting to shift to a decentralized model termed as edge computing, that allows data to be handled from the cloud to local devices such as smartphones, smart gateways or routers, local PCs or sensor nodes on a smaller scale in real-time.

In wireless sensor networks, data aggregation allows in-network processing, which leads to reduced packet transmissions and reduced redundancy, and thus is helpful to prolong the overall lifetime of wireless sensor networks. In current studies, Elliptic Curve ElGamal homomorphic encryption algorithm has been widely used to protect end-to-end data confidentiality. However, these works suffer from the expensive mapping function during decryption. If the aggregated results are huge, the base station has no way to gain the original data due to the hardness of the elliptic curve discrete logarithm problem. Therefore, these schemes are unsuitable for the large-scale WSNs. In this paper, we propose a secure energy-saving data aggregation scheme designed for the large-scale WSNs. We employ Okamoto-Uchiyama homomorphic encryption algorithm to protect end-to-end data confidentiality, use MAC to achieve in-network false data filtering, and utilize the homomorphic MAC algorithm to achieve end-to-end data integrity. Two popular IEEE 802.15.4-compliant wireless sensor network platforms, Tmote Sky and iMote 2 have been used to evaluate the efficiency and feasibility of our scheme. The results demonstrate that our scheme achieved better performance in reducing energy consumption. Moreover, system delay, especially decryption delay at the base station, has been reduced when compared to other state-of-art methods.

Given the recent proliferation in the number of smart devices connected to the Internet, the era of Internet of Things (IoT) is challenged with massive amounts of data generation. Fog Computing is gaining popularity and is being increasingly deployed in various latency-sensitive application domains including industrial IoT. However, efficient discovery of services is one of the prevailing issues in the fog nodes of industrial IoT, which restrain their efficiencies in availing appropriate services to the clients. To address this issue, this paper proposes a novel efficient multilevel index model based on equivalence relation, named the distributed multilevel (DM)-index model, for service maintenance and retrieval in the fog layer of industrial IoT to eliminate redundancy, narrow the search space, reduce both the number of traversed services and retrieval time, ultimately to improve the service discovery efficiency. The efficiency of the proposed index model has been verified theoretically and evaluated experimentally, which demonstrates that the proposed model is effective in achieving much better service discovery and retrieval performance than the sequential and inverted index models.

Software-defined networking (SDN) has become a popular paradigm for managing large-scale networks including cloud servers and data centers because of its advantages of centralized management and programmability. The issues of scalability and reliability that a single centralized controller suffers makes distributed controller architectures emerge. One key limitation of distributed controllers is the statically configured switch-controller mapping, easily causing uneven load distribution among controllers. Previous works have proposed load-balancing methods with switch migration to address this issue. However, the higher-load controller is always directly considered as the overloaded controller that need to shift its load to other controllers, even if it has no response time delay. The pursuit of absolute load-balancing effect can also result in frequent network delays and service interruptions. Additionally, if there are several overloaded controllers, just one controller with the maximum load can be addressed within a single load-balancing operation, reducing load-balancing efficiency. To address these problems, we propose SMCLBRT, a load-balancing strategy of multiple SDN controllers based on response time, considering the changing features of real-time response times versus controller loads. By selecting the appropriate response time threshold and dealing with multiple overloading controllers simultaneously, it can well solve load-balancing problem in SDN control plane with multiple overloaded controllers. Simulation experiments exhibit the effectiveness of our scheme.

With the increasing popularity and rapid development of Online Social Networks (OSNs), OSNs not only bring fundamental changes to information and communication technologies, but also make an extensive and profound impact on all aspects of our social life. Efficient content discovery is a fundamental challenge for large-scale distributed OSNs. However, the similarity between social networks and online social networks leads us to believe that the existing social theories are useful for improving the performance of social content discovery in online social networks. In this article, we propose an interest-aware social-like peer-to-peer (IASLP) model for social content discovery in OSNs by mimicking ten different social theories and strategies. In the IASLP network, network nodes with similar interests can meet, help each other, and co-operate autonomously to identify useful contents. The presented model has been evaluated and simulated in a dynamic environment with an evolving network. The experimental results show that the recall of IASLP is 20% higher than the existing method SESD while the overhead is 10% lower. The IASLP can generate higher flexibility and adaptability and achieve better performance than the existing methods.

Summary Urban traffic condition usually serves as basic information for some intelligent urban applications, for example, intelligent transportation system. The traditional acquisition of such information is often costly because of the dependencies on infrastructures, such as cameras and loop detectors. Crowdsensing, as a new economic paradigm, can be utilized together with vehicular networks to efficiently gather vehicle‐sensed data for estimating the traffic condition. However, it has the problem of being lack of data uploading efficiency and data usage effectiveness. In this paper, we take into account the topology of the road net to deal with these problems. Specifically, we divide the road net intoroad sectionsandjunction areas. Based on this division, we introduce a two‐phased data collection and processing scheme named road topology‐based scheme. It leverages the correlations among adjacent roads. In a junction area, data collected by vehicles are first processed and integrated by a sponsor vehicle to locally calculate traffic condition. Both the selection of the sponsor and the calculation of road condition utilize the road correlation. The sponsor then uploads the local data to a server. By employing the inherent relations among roads, the server processes data and estimates traffic condition for the road sections without vehicular data in a global vision. We conduct experiments based on real vehicle trace data. The results indicate that our design can commendably handle the problems of efficiency and effectiveness in traffic condition evaluation using the vehicular crowdsensing data. Copyright © 2016 John Wiley & Sons, Ltd.

Online social networks (OSNs) have attracted millions of users worldwide over the last decade. In response to a series of urgent issues faced by existing OSNs, such as information overload, single-point failure, and the privacy issue, this paper introduces a self-organized decentralized OSN (SDOSN) over a social overlay resembling real-life social graph. The social overlay considers social relationship and semantic content of users and focuses on the key OSNs functionality of efficient information dissemination and service discovery. Then a swarm intelligence search method is proposed to facilitate adaptive learning and effective service discovery in decentralized environments. Our evaluation, performed in simulation over a real-world dataset, shows that the proposed approach achieves better performance comparing with the state-of-the-art methods on different network structures.

The field of Big Data is particularly challenging in practical aspects of data collection, data analysis and data storage and has massive consequential implications for the future. The scope and scale of Big Data frequently necessitate the processing power attributed to High Performance Computing (HPC) and the scalability of Cloud Computing for processing and storage. Source topics for Big Data are continually expanding and include diverse topic areas of finance, medical, particle physics and social interaction and everything in between. Effective Big Data analysis encompasses the need to minimise data sets by identifying, analysing and storing only significant data; recognising and separating the relevant and irrelevant data provide many research opportunities in itself. The outcomes of Big Data analysis may explain past events and trends, suggest controls that are necessary for the present or predict a future world in terms of preparation, planning or positioning. This special issue will further publicise and promote this immensely import field of research with goals of creating a public record of achievements so far and providing inspiration for even greater developments in the coming years. Computational costs associated with Cloud Computing can be significant. In the paper ‘Online optimization scheduling for scientific workflows with deadline constraint on hybrid clouds’ 1, Bing Lin, Wenzhong Guo and Xiuyan Lin examine scheduling strategies and propose ‘hierarchical iterative application partition’ (HIAP) as an algorithm to increase the number of workflows completed within a given timeframe, thereby reducing overall costs. Ensuring the integrity of transmitted data is of paramount importance for data analysis, the paper ‘A MapReduce based Parallel K-Means Clustering for Large Scale CIM Data Verification’ 2 discusses the topic and presents a parallel K-means clustering algorithm for large scale Common Information Model (CIM) data verification. The paper concludes that time saving is achievable using parallel K-means while generating a high level of precision in data verification. ‘Bursty Event Detection from Microblog: A Distributed and Incremental Approach’ 3 is a practical example of the use of Big Data analysis. The researchers propose a method of bursty event detection, BEE+, as a means of tracking ‘topic drift’ from a microblog dataset of over 6 million posts. The amount of data collected and necessary storage rate are frequent considered to be problems for Big Data systems. ‘Performance Evaluation of a Distributed Storage Service in Community Network Clouds’ 4 compares the write and read capability of Tahoe-LAFS storage system is when deployed on community clouds and commercial systems. The paper concludes that write speeds are comparable, while read speeds were better in the commercial system. The information content of Big Data can have a significant commercial value. The cost to analyse data can be very high while the act of data collection can be both expensive and time consuming; loss of data to a competitor or invalidation because of falsification could result in commercial collapse of a business. ‘Secure Cryptographic Functions via Virtualization-based Outsourced Computing’ 5 considers the use of cryptography to protect data and, more fundamentally, suggests a method for the protection of the cryptographic system and process. In ‘Towards an Autonomous Decentralised Orchestration System’ 6, the authors propose distributed execution engines which exploit the benefits of parallel computation in the workflow to improve overall execution time. The paper provides an evaluation of the system and demonstrates the scalability benefit of the decentralised system. The limitations of Cloud related simulation tools are the subject of ‘Multi-layered simulations at the heart of workflow enactment on Clouds’ 7. The authors suggest that a multistage approach is advantageous in resolving some of the issues of scalability and scope without adversely affecting the performance of the workflow execution simulation. The continuing expansion of the Internet has presented ever greater challenges to crawler services used to collect information for indexing. ‘A Task Scheduling Strategy based on Weighted Round-Robin for Distributed Crawler’ 8 presents an implementation of a multithread distributed crawler which is scalable and fault tolerant. The paper includes experiments which indicate that the system exhibits good load balancing performance. The ability to adapt to changes of tenant requirements and cloud services is investigated in ‘Cross-Clouds Services Autonomic Management Approach based on Self-Organising Multi-Agent Technology’ 9. The research proposes a method where cloud services are managed by a series of autonomous agents which interact with each other to obtain macro-level service aggregation. The efficiency and usability of the proposed approach are confirmed with experimental results using public data sets. ‘Bilinear-map Accumulator based Verifiable Intersection Operations on Encrypted Data in Cloud’ 10 investigates the problem of conducting set-intersection operations on the ciphertext sets in the Cloud without the capability of decryption. The research opposed a model, called VIOEDC, to address this problem. The correctness and the security properties of the model have been approved in this paper. [Correction added on 07 June 2016, after first online publication: this paragraph has been added.] The papers presented in this special issue show that the subject arena of Big Data and Smart Computing continues to offer many diverse opportunities for research and investigation. It is anticipated that this special issue papers will provide a foundation for further work in the future by the authors and by many other researchers.

Given the wide range deployment of disconnected delay-tolerant social Internet of Things (SIoT), efficient resource discovery remains a fundamental challenge for large-scale SIoT. The existing search mechanisms over the SIoT do not consider preference similarity and are designed in Cartesian coordinates without sufficient consideration of real-world network deployment environments. In this paper, we propose a novel resource discovery mechanism in a 3-D Cartesian coordinate system with the aim of enhancing the search efficiency over the SIoT. Our scheme is based on both of preference and movement pattern similarity to achieve higher search efficiency and to reduce the system overheads of SIoT. Simulation experiments have been conducted to evaluate this new scheme in a large-scale SIoT environment. The simulation results show that our proposed scheme outperforms the state-of-the-art resource discovery schemes in terms of search efficiency and average delay.

Internet of Things (IoT) connects billions of devices in an Internet-like structure. Each device encapsulated as a real-world service which provides functionality and exchanges information with other devices. This large-scale information exchange results in new interactions between things and people. Unlike traditional web services, internet of services is highly dynamic and continuously changing due to constant degrade, vanish and possibly reappear of the devices, this opens a new challenge in the process of resource discovery and selection. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase in number of service consumers and consequent diversity of quality of service (QoS) available. Increase in both sides’ leads to the diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. This paper proposed an IoT service ranking and selection algorithm by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. One of the applications of IoT sensory data that attracts many researchers is transportation especially emergency and accident services which is used as a case study in this paper. Experimental results from real-world services showed that the proposed method achieved significant improvement in the accuracy and performance in the selection process.

Abstract Cloud computing, as an emerging computing paradigm, greatly facilitates resource sharing and enables providing computing power as services over the Internet. However, it also brings new challenges for security and access control, especially in infrastructure as a service clouds. The introduction of virtualization layer increases new security risks, which should be restricted and confined by more stringent access control techniques. In this paper, we propose a flexible and fine‐grained access control framework, named IaaS‐oriented Hybrid Access Control (iHAC), which combines the advantages of both the role‐based access control and type enforcement model. We consider access control issues from the perspective of virtual machines. A permission transition model is designed to dynamically assign permissions to virtual machines. A Virtual Machine Monitor (VMM)‐based access control mechanism is presented to confine the virtual machine's behaviors in a fine‐grained manner. A VMM‐enabled network access control approach is proposed to regulate the communication among virtual machines. iHAC is successfully implemented in the Internet based Virtual Computing Infrastructure (iVIC) platform, and several experiments are conducted to evaluate its effectiveness and efficiency. The results show that iHAC can make correct access control decisions with low performance overhead. Copyright © 2015 John Wiley & Sons, Ltd.

The new development of embedded systems has the potential to revolutionize our lives and will have a significant impact on future Internet of Thing (IoT) systems if required services can be automatically discovered and accessed at runtime in Machine-to-Machine (M2M) communication networks. It is a crucial task for devices to perform timely service discovery in a dynamic environment of IoTs. In this article, we propose a Socioecological Service Discovery (SESD) model for advanced service discovery in M2M communication networks. In the SESD network, each device can perform advanced service search to dynamically resolve complex enquires and autonomously support and co-operate with each other to quickly discover and self-configure any services available in M2M communication networks to deliver a real-time capability. The proposed model has been systematically evaluated and simulated in a dynamic M2M environment. The experiment results show that SESD can self-adapt and self-organize themselves in real time to generate higher flexibility and adaptability and achieve a better performance than the existing methods in terms of the number of discovered service and a better efficiency in terms of the number of discovered services per message.

With the energy and power costs increasing alongside the growth of the IT infrastructures, achieving workload concentration and high availability in cloud computing environments is becoming more and more complex. Virtual machine (VM) migration has become an important approach to address this issue, particularly; live migration of the VMs across the physical servers facilitates dynamic workload scheduling of the cloud services as per the energy management requirements, and also reduces the downtime by allowing the migration of the running instances. However, migration is a complex process affected by several factors such as bandwidth availability, application workload and operating system configurations, which in turn increases the complications in predicting the migration time in order to negotiate the service-level agreements in a real datacenter. In this paper, we propose an adaptive approach named improved MIGration (iMIG), in which we characterize some of the key metrics of the live migration performance, and conduct several experiments to study the impacts of the investigated metrics on the Kernel-based VM (KVM) functionalities, as well as the energy consumed by both the destination and the source hosts. Our results reveal the importance of the configured parameters: speed limit, TCP buffer size and max downtime, along with the VM properties and also their corresponding impacts on the migration process. Improper setting of these parameters may either incur migration failures or causes excess energy consumption. We witness a few bugs in the existing Quick EMUlator (QEMU)/KVM parameter computation framework, which is one of most widely used KVM frameworks based on QEMU. Based on our observations, we develop an analytical model aimed at better predictions of both the migration time and the downtime, during the process of VM deployment. Finally, we implement a suite of profiling tools in the adaptive mechanism based on the qemu-kvm-0.12.5 version, and our experiment results prove the efficiency of our approach in improving the live migration performance. In comparison with the default migration approach, our approach achieves a 40% reduction in the migration latency and a 45% reduction in the energy consumption.

To improve the video semantic analysis for video surveillance, a new video semantic analysis method based on the kernel discriminative sparse representation (KSVD) and weighted K nearest neighbors (KNN) is proposed in this paper. A discriminative model is built by introducing a kernel discriminative function to the KSVD dictionary optimization algorithm, mapping the sparse representation features into a high-dimensional space. The optimal dictionary is then generated and applied to compute the sparse representations of video features. For video semantic analysis, a weighted KNN algorithm based on the optimal sparse representation is proposed. In the algorithm, a kernel function is introduced to establish discrimination about sparse representation features and the classification vote result is weighted, the purpose of which is to improve the accuracy and rationality for video semantic analysis. The experimental results show that the proposed method significantly improves the discrimination of sparse representation features when compared with the traditional KSVD-based support vector machine method. The method can effectively detect the concept and event, which can be potentially useful for improving the video surveillance.

Summary The evolution of communication protocols, sensory hardware, mobile and pervasive devices, alongside social and cyber‐physical networks, has made the Internet of things (IoT) an interesting concept with inherent complexities as it is realised. Such complexities range from addressing mechanisms to information management and from communication protocols to presentation and interaction within the IoT. Although existing Internet and communication models can be extended to provide the basis for realising IoT, they may not be sufficiently capable to handle the new paradigms that IoT introduces, such as social communities, smart spaces, privacy and personalisation of devices and information, modelling and reasoning. With interaction models in IoT moving from the orthodox service consumption model, towards an interactive conversational model, nature‐inspired computational models appear to be candidate representations. Specifically, this research contests that the reactive and interactive nature of IoT makes chemical reaction‐inspired approaches particularly well suited to such requirements. This paper presents a chemical reaction‐inspired computational model using the concepts of graphs and reflection, which attempts to address the complexities associated with the visualisation, modelling, interaction, analysis and abstraction of information in the IoT. Copyright © 2013 John Wiley & Sons, Ltd.

Summary The networked intrusion detection system virtual appliance (NIDS‐VA), also known as virtualized NIDS, plays an important role in the protection and safeguard of IaaS cloud environments. However, it is nontrivial to guarantee both of the performance of NIDS‐VA and the resource efficiency of cloud applications because both are sharing computing resources in the same cloud environment. To overcome this challenge and trade‐off, we propose a novel system, named CloudMon, which enables dynamic resource provision and live placement for NIDS‐VAs in IaaS cloud environments. CloudMon provides two techniques to maintain high resource efficiency of IaaS cloud environments without degrading the performance of NIDS‐VAs and other virtual machines (VMs). The first technique is a virtual machine monitor based resource provision mechanism, which can minimize the resource usage of a NIDS‐VA with given performance guarantee. It uses a fuzzy model to characterize the complex relationship between performance and resource demands of a NIDS‐VA and develops an online fuzzy controller to adaptively control the resource allocation for NIDS‐VAs under varying network traffic. The second one is a global resource scheduling approach for optimizing the resource efficiency of the entire cloud environments. It leverages VM migration to dynamically place NIDS‐VAs and VMs. An online VM mapping algorithm is designed to maximize the resource utilization of the entire cloud environment. Our virtual machine monitor based resource provision mechanism has been evaluated by conducting comprehensive experiments based on Xen hypervisor and Snort NIDS in a real cloud environment. The results show that the proposed mechanism can allocate resources for a NIDS‐VA on demand while still satisfying its performance requirements. We also verify the effectiveness of our global resource scheduling approach by comparing it with two classic vector packing algorithms, and the results show that our approach improved the resource utilization of cloud environments and reduced the number of in‐use NIDS‐VAs and physical hosts. Copyright © 2013 John Wiley & Sons, Ltd.

With the globe facing various scales of natural disasters then and there, disaster recovery is one among the hottest research areas and the rescue and recovery services can be highly benefitted with the advancements of information and communications technology (ICT). Enhanced rescue effect can be achieved through the dynamic networking of people, systems and procedures. A seamless integration of these elements along with the service-oriented systems can satisfy the mission objectives with the maximum effect. In disaster management systems, services from multiple sources are usually integrated and composed into a usable format in order to effectively drive the decision-making process. Therefore, a novel service indexing method is required to effectively discover desirable services from the large-scale disaster service repositories, comprising a huge number of services. With this in mind, this paper presents a novel multilevel indexing algorithm based on the equivalence theory in order to achieve effective service discovery in large-scale disaster service repositories. The performance and efficiency of the proposed model have been evaluated by both theoretical analysis and practical experiments. The experimental results proved that the proposed algorithm is more efficient for service discovery and composition than existing inverted index methods.

With the rapid increase of growth and development in cloud computing it is clear to see that it is becoming a common trend within the industry leading to the adoption by businesses and Information Technology (IT) users alike. Coupled with this trend comes a vast amount of academic research into the cloud computing industry covering most aspects but still leaving some untouched. There is, however, a gap in the academic research for this specific area with current studies focusing on very specific performance issues without considering a general overview of performance. This paper demonstrates the importance of understanding the need to benchmark cloud platforms in order to gain a performance overview of the platforms which one may be integrating into a production environment. This paper investigates the performance of two open source cloud platforms, Open Stack and Cloud Stack. The performance testing is strictly focused on the platforms themselves other than the underlying elements. Therefore, the platforms have both been tested on a mutual hyper visor and mutual available resources. The experiment results demonstrate the advantages of performing cloud management platform benchmarking.

Live virtual machine migration is a technique often used to migrate an entire OS with running applications in a non-disruptive fashion. Prior works concerned with one-to-one live migration with many techniques have been proposed such as pre-copy, post-copy and log/replay. In contrast, we propose VMScatter, a one-to-many migration method to migrate virtual machines from one to many other hosts simultaneously. First, by merging the identical pages within or across virtual machines, VMScatter multicasts only a single copy of these pages to associated target hosts for avoiding redundant transmission. This is impactful practically when the same OS and similar applications running in the virtual machines where there are plenty of identical pages. Second, we introduce a novel grouping algorithm to decide the placement of virtual machines, distinguished from the previous schedule algorithms which focus on the workload for load balance or power saving, we also focus on network traffic, which is a critical metric in data-intensive data centers. Third, we schedule the multicast sequence of packets to reduce the network overhead introduced by joining or quitting the multicast groups of target hosts. Compared to traditional live migration technique in QEMU/KVM, VMScatter reduces 74.2% of the total transferred data, 69.1% of the total migration time and achieves the network traffic reduction from 50.1% to 70.3%.

Software aging has been cited in many scenarios including Operating System, Web Servers, Real-time Systems. However, few studies have been conducted in long running virtualized environments where more and more software is being delivered as a service. Furthermore, state-of-the-art methods lack the ability to deal with miscellaneous upper applications and underlying systems transparently in virtualized scenarios. In this paper, we detect aging phenomenon by conducting experiments in physical and virtual machines and identify the differences between the two, and propose a feature code-based methodology for failure prediction through system call, then implement a prototype in virtual machine manager layer to predict failure time and rejuvenate transparently, which is suitable in virtualized scenarios. The evaluation shows the prediction deviation against reality is less than 10%.

Virtualisation is a prevalent technology in current computing. Among the many aspects of virtualisation, it can be employed to reduce hardware costs by server consolidation, implement "green computing" by reducing power consumption and as an underpinning process for cloud computing enabling the creation of a range of virtual networks and virtual supercomputers. This paper presents performance measurements for a cloning system known as iVIC that has been developed in Beihang University, China. In an extension to earlier work, it focuses on the factors the limit the number of clones that can be successfully started. IVIC creates clusters of virtual computers that can communicate with each other through virtual switch mechanisms. The virtual switches can also allow communication between the clone environment and the physical world. Testing has been undertaken to identify the limiting factors for creating and starting numbers of clone machines, measure the power consumption of the physical system and the computational performance capability of the clones.

In this paper three current massively multiplayer online game peer to peer protocols are simulated and analysed. The results of the simulations suggest that improvement is still needed in order to lower the bandwidth usage of the protocols. Areas for improvement for the protocols are suggested and their business viability is discussed. It is also discovered that a complete solution that is suitable for the market is not yet available. Ideas for future research into peer to peer protocols and network middleware are put forward.

Wireless networks are an integral part of life for many residential properties. The use of laptops and smartphones has lead to a large increase in the number of wireless networks. The research within this project revealed how residential users were securing their networks, this research mirrored a previous investigation into wireless security from 2009, as well as a comparison with other authors work dating back to 2006 and 2007. These findings were then analyzed and reasons behind the positive trend in encryption utilization were examined.

With the development of the theory and technology of wireless sensor networks (WSN), location-based applications, such as location-based access control, pose new challenges. In order to improve the accuracy of node localization, the energy consumption must be considered in conjunction with security. Improving the accuracy of node localization as much as possible under the premise of ensuring the security of the localization process forms the basis of location-based applications in wireless sensor networks. In this paper, a secure localization method of nodes based on the congruity of time is proposed. This method does not need to meet time synchronization conditions between user nodes and base stations. It calculates the congruity of time according to the communication delay between nodes, and then estimates the location of the user node. It can ensure the accuracy of localization and the security of localization processes.

Modern distributed applications are embedding an increasing degree of dynamism, from dynamic supply-chain management, enterprise federations, and virtual collaborations to dynamic resource acquisitions and service interactions across organizations. Such dynamism leads to new challenges in security and dependability. Collaborating services in a system with a Service-Oriented Architecture (SOA) may belong to different security realms but often need to be engaged dynamically at runtime. If their security realms do not have a direct cross-realm authentication relationship, it is technically difficult to enable any secure collaboration between the services. A potential solution to this would be to locate intermediate realms at runtime, which serve as an authentication path between the two separate realms. However, the process of generating an authentication path for two distributed services can be highly complicated. It could involve a large number of extra operations for credential conversion and require a long chain of invocations to intermediate services. In this paper, we address this problem by designing and implementing a new cross-realm authentication protocol for dynamic service interactions, based on the notion of service-oriented multiparty business sessions. Our protocol requires neither credential conversion nor establishment of any authentication path between the participating services in a business session. The correctness of the protocol is formally analyzed and proven, and an empirical study is performed using two production-quality Grid systems, Globus 4 and CROWN. The experimental results indicate that the proposed protocol and its implementation have a sound level of scalability and impose only a limited degree of performance overhead, which is for example comparable with those security-related overheads in Globus 4.

Sensor networks have the potential to revolutionise the capture, processing and communication of critical data for use of disaster rescue and relief. In order to provide a dependable rescue capability through dynamically integrating newly developed and legacy sensor systems with other systems and computing, new methodologies are required for the dependable integration of services in heterogeneous environments. In this study, the authors present a new architectural model which can proactively self-adapt to changes and evolution occurring in the provision of search and rescue capabilities in a dynamic environment. This performance and reliability of the approach has been evaluated using simulations in a dynamic environment and demonstrated through developing and testing a demonstration system for a scenario of disaster area monitoring.

Efficient resource search in large-scale unstructured peer-to-peer (P2P) systems remains a fundamental challenge. In order to improve search performance, interest-based search is a good way to tackle the challenge. However, the existing interest-based search algorithms pay attention to user interest model and search history, but ignore some influence factors of improving performance. In this paper, through analysis of node's multi-dimensional quality-of-service (QoS) for search, it is found that node state's information is essential for improving search performance. So a State-Based Search (SBS) strategy is presented by improving Sripanidkulchai's interest-based search model, where Node's state evaluation is based on node's QoS fuzzification. The SBS enhances the methods of shortcuts adding, ranking and selecting, and state's life-cycle is predicted by grey theory. The experimental results show that the SBS can improve performance by reducing search response time and achieving load balance.

The vision of service-oriented computing is one of loosely coupled services that create agile applications to encapsulate business objectives and processes. The potential of services to form complex systems of systems, with emergent behaviour, necessitates the need to understand how we can we develop sufficient confidence in their qualities. In this paper we argue that successful development and evolution of service oriented computing is dependent on making informed decisions at the architectural level. This poses a number of challenges concerning how to evaluate such system architectures and the measurements and metrics that are important in their assessment. This paper describes research-in-progress into the development of a scenario-based architectural evaluation framework that allows architectural-level reasoning across systems of systems in the context of Network Enabled Capability: a UK Ministry endeavour designed to achieve enhanced [military] effect through the physical networking and coherent integration of existing and future resources.

Network enabled capability (NEC) is the U.K. Ministry of Defencepsilas response to the quickly changing conflict environment in which its forces must operate. In NEC, systems need to be integrated in context, to assist in human activity and provide dependable inter-operation. In order to provide reliable and sustainable military capability, fast paced changes must be conducted without halting the operation of a capability. In this paper we present the concept of evolutionary service-oriented architecture for delivering sustainable capability. The reliability of the architecture is evaluated by simulations using a computer-based model. The simulation results indicate that the evolutionary service-oriented architecture can provide higher reliability and sustainability in the provision of capability in a dynamic environment.

Abstract In unstructured peer‐to‐peer (P2P) networks, two autonomous peer nodes can be connected if users in those nodes are interested in each other's data. Owing to the similarity between P2P networks and social networks, where peer nodes can be regarded as people and connections can be regarded as relationships, social strategies are useful for improving the performance of resource discovery by self‐organizing autonomous peers on unstructured P2P networks. In this paper, we present an efficient social‐like peer‐to‐peer (ESLP) method for resource discovery by mimicking different human behaviours in social networks. ESLP has been simulated in a dynamic environment with a growing number of peer nodes. From the simulation results and analysis, ESLP achieved better performance than current methods. Copyright © 2008 John Wiley & Sons, Ltd.

Similarly to social networks where people are connected by their social relationships, two autonomous peer nodes can be connected in unstructured peer-to-peer (P2P) networks if users in those nodes are interested in each other's data. The similarity between P2P networks and social networks, where peer nodes are people and connections are relationships, leads us to believe that human strategies in social networks are useful for improving the performance of resource discovery by self-organising autonomous peers on unstructured P2P networks. In this paper, we present an efficient social-like peer-to-peer (ESLP) model for resource discovery by mimicking different human behaviours in social networks.

Service oriented architecture (SOA) is becoming established in computing as a means to integrate processing and data across organisations. This paper proposes that system-level integration can benefit from service oriented architectural descriptions and loose coupling between the problem domain requirements and different system solutions. The problem domain is exemplified as military capability, from the UK Ministry of Defence (MoD), in particular, network enabled capability (NEC). Representations of military capability in the problem domain can be described in terms of processes. The processes are sequences of functions that can be described as services. Then different types of system solutions can implement the described services. Firstly, the paper presents an overview of conceptual SOA and in the context of military capability compares three levels of service integration: business services, systems services and computing services. Secondly, the paper presents a framework for evaluating the performance and effectiveness of service integration to compare different solutions in delivering military capability.

For resource discovery in social networks, people can directly contact some acquaintances that have knowledge about the resources they are looking for. However, in current peer-to-peer networks, peer nodes lack capabilities similar to social networks, making it difficult to route queries efficiently. In this paper, we present a social-like system (Social-P2P) for resource discovery by mimicking human behaviours in social networks. Different from most informed search algorithms, peer nodes learn knowledge from the results of previous searches and no additional overhead is required to obtain extra information from neighbouring nodes. Unlike community-based P2P information sharing systems, we do not intend to create and maintain peer groups or communities consciously. Peer nodes with the same interests will be highly connected to each other spontaneously. Social-P2P has been simulated in a dynamic environment. From the simulation results and analysis, Social-P2P achieved better performance than current methods