Consensus decision-making uses crowd responses (usually from non-experts) to questions to reach a consensus answer based on human-machine collaboration. The crucial point is dynamic, which should not only enable rapid self-iteration toward the correct answer through crowd workers' responses but also adaptively suggest the next most valuable question(s) to accelerate the integration of the answer. However, existing methods reach consensus using either offline data or fixed question search structures, thereby largely sidestepping this dynamic nature. In response, we propose a bilevel optimization-based human-machine collaboration (BiO-HMC), which explores an inner & outer-level optimization to enable effective answer integration and efficient question selection. The resulting optimization problem is intractable because there is no closed-form expression in the inner-level optimization. We employ a gradient-based method and guarantee the method's theoretical convergence. Experimental results on synthetic and real-world datasets demonstrate the effectiveness and efficiency of the BiO-HMC model, i.e., achieving the highest confidence in the correct answer with the lowest labor cost.

The prevailing techniques in textual sentiment analysis include convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and attention mechanisms. However, the existing sentiment analysis methods typically focus on relationships among neighboring words, neglecting long-range dependencies. Moreover, they fail to reduce redundant information in semantic features, ultimately affecting the effectiveness of sentiment analysis. To tackle these challenges, this article introduces a novel text sentiment analysis model consisting of three key modules: word embedding, a scalable multichannel sentiment feature extractor, and a sentiment classifier. The scalable multichannel sentiment feature extractor employs a scalable N-gram dilated CNN (SN-DCNN) to extract multiscale high-level semantic by capturing both neighboring and nonneighboring word associations. To further enhance the model’s ability to capture semantic features, a BiLSTM network is introduced to capture long-range dependencies within sentences. Subsequently, a structured attention (SA) mechanism is incorporated after the BiLSTM to capture multiscale semantic information, thereby enabling the mapping of individual long-range dependencies to distinct semantic levels. In addition, a semantics-driven channel attention mechanism (SDCAM) is introduced to assign weights to multichannel structures, aiming to reduce the redundancy in the semantic features. Furthermore, by integrating multihead self-attention (MHSA), the model can effectively concentrate on key sentiment-related features, resulting in a comprehensive enhancement of its semantic representation capability. The sentiment classifier utilizes an adaptive loss function based on cross-entropy to correct the probability distribution of sentiment classes. This function enables the model to dynamically adjust its learning capacity for each class, leading to significant performance improvements, especially in datasets with imbalanced class distributions. Experimental results show that the proposed model improves accuracy by 0.94%, 0.96%, 0.36%, and 0.86% on the NLPCC2017-ECGC, ChnSentiCorp, COLD, and IMDB datasets, respectively, when using word-level embeddings. With character-level embeddings, the model improves accuracy by 0.76% and 1.23% on the ChnSentiCorp and COLD datasets, respectively, compared to the state-of-the-art methods.

Voiceprint verification has been widely adopted in daily applications due to its convenience and efficiency. However, the latent representations extracted during speech verification often encode sensitive personal information, such as emotion and gender, which raises significant privacy concerns. Existing methods mainly focus on protecting predefined sensitive attributes, but in many cases, the attributes that attackers are interested in are unknown to defenders. To address this limitation, we propose the Speech Information Bottleneck Network (SIBNet), a framework for representation level privacy enhancement based on information theoretic approaches. SIBNet differs from existing speech attribute privacy mechanisms in three key respects: (1) We formulate biometric privacy protection as an Information Bottleneck (IB) optimization problem that balances identity utility and sensitive attribute suppression. (2) SIBNet leverages a variational distribution parameterized by neural networks to approximate mutual information, enabling the derivation of lower bounds for effective model optimization. (3) An additive angular margin penalty is introduced into the speaker classification loss to promote discriminative embeddings and preserve identity utility. Experiments on public speech datasets demonstrate that SIBNet significantly outperforms state of the art methods in both biometric recognition accuracy and suppression of sensitive soft biometric attributes.

Since similar users frequently exhibit similar Quality of Service (QoS) when accessing similar services, effectively capturing neighborhood information hidden in QoS data becomes critical for latent factorization of tensor (LFT)-based QoS estimators. Current LFT models either calculate the complete set of neighborhoods or do not consider neighborhoods, resulting in a rapid rise in model complexity and poor estimation accuracy. Moreover, not every neighbor in the neighborhood set is beneficial to the user/service entity. To address these limitations, this study proposes a sampling-neighborhood-regularized latent factorization of tensor (SNLFT) model with three key ideas: 1) extracting primal latent factors (LFs), which are obtained to express related entities on the basis of high-dimensional and incomplete QoS data; 2) constructing the sampling-neighborhood set, which is acquired using the Gibbs sampling to reflect the similarities between the primal LF vectors of entities over time; 3) developing a sampling-neighborhood-regularized LFT model, where all the sampling neighborhoods of entities and L2-norm of desirable LFs are employed to regularize the objective function. Extensive experiments on eight dynamic QoS datasets demonstrate that SNLFT significantly outperforms state-of-the-art models in both estimation accuracy and computational efficiency.

The traditional nonnegative latent factorization of tensors (NLFTs) models can effectively represent high-dimensional and incomplete (HDI) tensors, but they currently face two main problems: 1) existing models are linear and cannot capture the nonlinear features of HDI tensors and 2) they rely on random initialization of nonnegative parameter and constraint-combined training schemes. To address these issues, this article proposes a neural NLFTs model with acceleration and unconstraint. The main ideas are given as follows: 1) utilizing a neural network (NN) structure and a nonlinear activation function to capture the nonlinear features within the HDI tensor accurately; 2) constructing a nonnegative mapping domain that transfers nonnegativity constraints from latent factors (LFs) to output decision parameters via a single-element-dependent mapping function, enabling an unconstrained optimization framework; and 3) utilizing the highly compatible momentum-incorporated stochastic gradient descent (SGD) algorithm as the backward propagation (BP) learning scheme of the model, which not only ensures training effectiveness and scalability but also accelerates convergence. Empirical studies on ten HDI tensors demonstrate that the proposed model achieves impressive estimation accuracy and per-iteration time cost compared to state-of-the-art models.

Recommender systems play a crucial role in personalizing user experiences by analyzing vast amounts of user interaction records to suggest relevant items. However, the use of sensitive user information raises significant privacy concerns, particularly in light of regulations such as the General Data Protection Regulation. In response to these challenges, this paper introduces Rapid Collaborative Machine Unlearning (RaCoMU), a novel framework designed to efficiently handle user data deletion requests while preserving recommendation quality. RaCoMU employs a two-step data partitioning strategy that consolidates user data within individual subsets, enhancing collaborative information utilization. Additionally, a collaboration-based attention aggregation method is proposed, which weights contributions of the submodels based on user similarities. Our extensive experiments on real-world datasets across three recommendation models demonstrate that the proposed RaCoMU achieves superior unlearning efficiency and significantly outperforms existing frameworks in model effectiveness. The findings underscore the potential of RaCoMU to balance user privacy and recommendation performance effectively.

This study introduces MarineGym, a high-performance reinforcement learning platform tailored for underwater robotics. It aims to address the limitations of existing underwater simulation environments in terms of reinforcement learning compatibility, training efficiency, and standardized benchmarking. MarineGym integrates a proposed GPU-accelerated hydrodynamic plugin based on Isaac Sim, achieving a rollout speed of 250,000 frames per second on a single NVIDIA RTX 3060 GPU. It also provides five models of unmanned underwater vehicles, multiple propulsion systems, and a set of predefined tasks covering core underwater control challenges. Additionally, the domain randomization toolkit allows flexible adjustments of the simulation and task parameters during training to improve the Sim2Real transfer. Further benchmark experiments demonstrate that MarineGym improves training efficiency over existing platforms and supports robust policy adaptation under various perturbations in the marine environment. We expect this platform to drive further advancements in RL research for underwater robotics. For more details about MarineGym and its applications, please visit our project page: https://marine-gym.com/.

Abstract Traffic flow prediction plays a crucial role in intelligent transportation systems as it enables effective control and management of urban traffic. However, existing methods that based on Graph Convolutional Networks (GCNs) primarily utilize local neighborhood information for message passing, resulting in limited perception of global structures. Additionally, it is also a challenge to extract spatial-temporal similarity features due to the constraints of graph structures. To address these issues, we propose a novel traffic flow prediction model based on Dynamic Spatial-Temporal Similarity Pyramid Network (DSTSPYN). Our model employs a spatial-temporal pyramid architecture, which dynamically adjusts the weights of central, edge, and global spatial-temporal features using an enhanced attention mechanism. Furthermore, it captures dynamic temporal dependencies at different scales through pyramid gated convolution. Meanwhile, the spatial similarity features of different time steps can be extracted through the spatial-temporal global similarity (STGS) module. We evaluate our model on four public transportation datasets and demonstrate that the DSTSPYN model outperforms several baseline methods in terms of prediction accuracy. It effectively captures the dynamic spatial-temporal correlations of the road network and edge node features, making it well-suited for long-term traffic flow prediction.

Workflow decision making is critical to performing many practical applications of scientific principles and data. Scheduling in edge-cloud environments can address the high complexity of workflow applications, while decreasing the data transmission delay between the cloud and end devices. However, due to the heterogeneous resources in edge-cloud environments and the complicated data dependencies between the tasks in a workflow, significant challenges for workflow scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations. Existing studies are mainly done subject to rigorous conditions without fluctuations, ignoring the fact that workflow scheduling is typically present in uncertain environments. In this study, we focus on reducing the execution cost of multiple workflow applications mainly caused by data transmission and task computation, while satisfying the required deadline constraints. Triangular fuzzy numbers are employed to represent the computing performance of servers and transmission bandwidth in fuzzy edge-cloud environments. A cost-driven scheduling strategy for multiple Poisson-arrived workflow applications using partial critical paths is proposed. It firstly merges cut edges through preprocess to reduce the workflow scale, then uniformly schedules all tasks on each partial critical path to avoid data transmission between dependent tasks and reduce the data transmission cost. The experimental results show that our strategy can obtain the optimal feasible scheduling scheme and have better robustness and real-time performance with different deadline constraints, compared with other benchmark strategies. Note to Practitioners—Vehicle identification is one of the workflow decision making systems in transportation environments, whose core technology is Deep Neural Networks (DNN). Traffic cameras with limited process capacity periodically record the images of on-road vehicles, and usually fail to complete the applications within their deadlines. Workflow decision making is one of the key issues to performance DNNs in vehicle identification applications. The uncertain environments have a great impact on the system latency for such problems, which can easily lead to the misjudgement of the optimal scheduling. In addition, it is difficult to select an optimal layers-servers solution from the numerous combinations. Therefore, we can employ the scheduling strategy (i.e., SWPCP) to make intelligent and faster workflow decisions for vehicle identification applications, which can reduce the execution cost mainly caused by layer computation and data transmission between layers within their deadlines, even in uncertain edge-cloud environments. Complex DNN layers (tasks) in vehicle identification applications can be scheduled to the cloud for execution, while simple ones are processed on the edge. The cloud and edge platforms collaborate with each other and execute the DNN layers with low system cost and latency.

Soft-biometric privacy-enhancement methods are widely used in face recognition systems to prevent attackers from inferring soft-biometric attributes (e.g., gender, age, and race). However, existing methods typically focus on either representation-level or image-level privacy protection. In this paper, we propose a novel Face Conditional Privacy Funnel Network (Face-CPFNet), a dual-level privacy-enhancement framework with three key innovations. First, it introduces a dual-level privacy protection system where attackers receive reconstructed face images by combining the random guess of the sensitive attribute's potential label with the face representation provided by the user. Second, we propose the Face-CPF optimization problem, based on the Conditional Privacy Funnel (CPF) and an additional information leakage constraint. A deep variational approximation approach with parameterized deep neural networks is used to solve this problem and develop the Face-CPFNet model. Third, it enables supervised disentangled representation learning for reconstructing face images with variations in generative factors, and further enhances privacy protection by introducing an information leakage constraint when the sensitive attribute is a discrete binary random variable. Experimental results on benchmark datasets indicate that Face-CPFNet strikes a more effective balance between face verification accuracy and soft-biometric privacy compared with existing SBPE and CPF models.

Blockchain-based IoT applications in agriculture have drawn extensive attention in recent years, allowing the implementation of smart agriculture solutions. By transmitting collected relevant data to a control center through the blockchain, corresponding regulation can be realized in agricultural production management systems. However, existing efforts for directly adopting the technique to data transmission are obstructed by several issues. The traceability of agricultural data stored in the blockchain leads to the exposure of the identity of the data collecting devices. And the tracing difficulty of completely invisible data for identity protection also exists in the smart agricultural system. To tackle these limitations, we propose a novel blockchain-based smart agricultural IoT system framework for regulating the agricultural production environment through trusted data. First, the elliptic curve integrated encryption scheme (ECIES) and the group signature scheme are integrated to guarantee the traceability and identity protection of the data and equipment, respectively. Then, to enhance the security of session key transmission in the ECIES scheme, we further design an on-chain-off-chain key agreement protocol (ECIES-OOKA). In addition, we propose a novel group manager selection method based on probabilistic linguistic term sets (PLTSs) for the group signature implementation. Finally, a practical example is provided to demonstrate the group manager selection process and verify the feasibility of the proposed method. The security and performance analysis for the system framework are also presented.

Soft clustering algorithms based on fuzzy C-means (FCM) have been extensively applied to complex data analysis. However, existing FCM variants still encounter key limitations: a large number of iterations due to slow convergence on highdimensional data, equal weights of all samples which increases sensitivity to noise, and strong dependence on empirically chosen fuzzy parameters, often resulting in suboptimal solutions. In order to address these challenges, in this work, we propose an adaptive FCM clustering algorithm based on anisotropic Gaussian kernel function (AGK-FCM for short), which facilitates the process of feature selection for multidimensional data with fewer iterations after feature reduction based on updating the kernel width vector. In order to enhance the classification accuracy, we assign adaptive weights to each sample and adjust these weights to mitigate the impact of outliers on classification accuracy. Unlike traditional fuzzy clustering algorithms, we employ the PSO-TVAC algorithm to determine the global optimal parameters, thus reducing the computational resources and enhancing the algorithm's efficiency. The experimental results based on 16 publicly available datasets validate that the proposed algorithm achieves higher classification accuracy with minimal number of iterations.

In the intelligent transportation system, the collected traffic data are usually incomplete. The low-rank completion models are effective in processing missing traffic data (MTD) imputation. However, the existing low-rank completion models encounter the following challenges: 1) Incorporating spatio-temporal information of traffic data often leads to significant time overhead; 2) To reduce the time overhead, some models based on the low-rank completion only embed the temporal information and discard the spatial information, thereby resulting in low imputation accuracy; 3) Missing traffic data carries complex spatio-temporal information, requiring multi-faceted analysis to extract valuable insights. To address these issues, we propose an efficient fourth-order dimension preserved tensor completion (FDPTC) with temporal constraint model. It works based on our proposed fourth-order dimension preserved (FDP) tensor decomposition model to capture the spatio-temporal information of traffic data from a high-dimensional perspective by extending dimensions. Additionally, we embed a temporal constraint into FDP tensor decomposition model to ensure consistency of MTD and introduce a non-negative constraint to accelerate convergence speed. By constructing this model, we successfully avoid direct operations on the extra information matrix/tensor, thereby optimizing efficiency. Experimental results on four real traffic datasets demonstrate that our proposed model achieves significantly higher imputation accuracy at an affordable computational burden compared with state-of-the-art models.

Traffic data prediction is a crucial component of Intelligent Transport Systems (ITS) as it contributes significantly to real-time navigation and congestion management. However, due to incomplete deployment of sensor and instability of data transmission, the traffic data that we collect is usually a high-dimensional and incomplete (HDI) matrix or tensor. Currently, there are two challenges with traffic data prediction as follows: a) Most of the existing models are designed for a full set of data, but traffic data are unavoidably missing. b) Most of the existing models often suffer from excessive complexity due to long sequences. To address these issues, we propose a novel3D Convolution-IncorporatedDimensionPreserved Decomposition (3DCIDP) model for traffic data prediction with three main fold ideas: a) enhancing the low-rank property of traffic data to accurately capture its structure, b) learning the constraints of historical sequences to predict sequences through representation modelling and c) capturing spatio-temporal interaction information in traffic data through multidimensional interaction features. To evaluate the performance of the proposed 3DCIDP, we conduct extensive experiments using five publicly available datasets with three different missing rates. When the proposed 3DCIDP is compared to state-of-the-art models, experimental results show that the Root Mean Square Error (RMSE) is reduced by an average of 4.22% and the training time is reduced by an average of 89.76% on the large-scale traffic datasets.

Blockchain-enabled security mechanisms play a crucial role in Maritime Intelligent Transportation Systems (MITSs). However, the growing dependence on network communication and data exchange raises serious privacy concerns. While numerous authentication schemes exist for MITSs to prevent unauthorized access, most exhibit limited scalability, high computational costs, and vulnerability to physical attacks, hindering their practical implementation. Still, the transmission of authentication information relies on open networks, exposing the system to diverse network attacks. To cope with these issues, a blockchain-enabled security architecture with Physically Unclonable Functions (PUFs) and consensus-based intrusion detection system (BPSAC) is proposed to protect privacy and ensure resilience against various network attacks. The architecture comprises a security authentication mechanism and a novel consensus-based intrusion detection system (C-IDS). Specifically, the security authentication mechanism mitigates performance overhead and privacy concerns by introducing PUFs. Subsequently, C-IDS is designed for intrusion detection without sacrificing the security of the model inference process by introducing a Differential Channel and Spatial Attention (DCSA) module, and an enhanced Practical Byzantine Fault Tolerance (PBFT) consensus algorithm named MI-PBFT. Experimental results demonstrate that the proposed security authentication mechanism reduces the computational overhead by at least 20% and the communication overhead by 5% compared to recent works. The proposed detection model achieves an attack detection accuracy of over 99% on the UNSW-NB15, KDDCup99, and NSL-KDD datasets. Furthermore, as the number of consensus nodes increases, MI-PBFT consensus algorithm reduces the transaction confirmation delay by 32% and improves the transactions per second by 80% compared to PBFT.

Intelligent Transportation Systems (ITSs) are designed to alleviate traffic congestion and provide convenience for travelers or decision-makers. However, the challenge lies in obtaining complete and accurate traffic data because of various factors. Therefore, many models have been studied for traffic data imputation. Most studies overlook the extreme case where the missing rate of observed traffic data exceeds 90%, causing the existing traffic data imputation models to struggle in achieving satisfactory results. To address this extreme case, this paper proposes a novel Latent Factor Analysis model with Temporal Regularized Constraint (LFA-TRC) for handling road traffic data imputation. The proposed model is built on three main parts: a) Enhancing the stability of the training process through spatio-temporal linear biases, b) Capturing information in the traffic data time series by the first-order temporal difference constraint, and c) Accelerating convergence during the training process by incorporating generalized momentum. Experimental results on seven real-world traffic datasets demonstrate that the proposed LFA-TRC model outperforms eight state-of-the-art models in traffic data imputation and reduces the Mean Absolute Percentage Error (MAPE) by an average of 20.81%.

High-dimensional and incomplete (HDI) data are involved frequently in big data-related industrial applications. Latent factor (LF) analysis aims at extracting the knowledge of great value from such extremely sparse HDI data efficiently. Non-negative LF models based on the single LF-dependent, non-negative, and multiplicative update rules exactly are the representative of LF analysis. However, these models face low generalization dilemma due to incompatible with general unconstrained optimization techniques. To address this issue, this article proposes a novel momentum-accelerated and biased unconstrained non-negative latent factor (MBUNLF) model, which matches with unconstrained optimization techniques. The proposed MBUNLF model is built on three main ideas: (a) Improving the generalization through a non-negative mapping function; (b) Capturing information among different entities through linear biases; (c) Accelerating convergence during the training process through generalized momentum method. Empirical studies on six datasets from industrial applications indicate that the proposed MBUNLF model outperforms nine state-of-the-art models when processing HDI data, reducing the root mean square error by 19.47% on average. It demonstrates the validity of the MBUNLF model in extracting non-negative LFs from HDI data.

High-Dimensional and Incomplete (HDI) tensors contain a wealth of knowledge and patterns, which are typically utilized to characterize complex relationships between entities in a variety of industrial applications. Currently, the neural network-based tensor factorization model has shown superiority when handling the missing data in HDI tensors. However, it only uses the outer product of latent factors (LFs) of entities and neglects the interactions between the LF. In addition, the simple linear operation does not consider the nonlinear structure of the HDI tensor. To overcome the aforementioned issues, an Attention-mechanism-based Neural Latent-Factorization-of-Tensors (ANLFT) model is provided in this article. It encompasses three primary ideas: (a) incorporating the theory of neural networks with the latent factorization of tensor to construct the nonlinear structure in the HDI tensor effectively; (b) adopting the attention mechanism to depict the interactions between LF; (c) using the position-transitional particle swarm optimization backward propagation learning ( \(\rm{P}^{2}\) BP) scheme to train the ANLFT model efficiently. The experimental results on eight HDI datasets show that the ANLFT model can obtain higher estimation performance gain than state-of-the-art models. The convergence performance of the proposed model is also competitive with that of state-of-the-art models.

The selection and utilization of different color spaces significantly impact the recognition performance of deep learning models in downstream tasks. Existing studies typically leverage image information from various color spaces through model integration or channel concatenation. However, these methods result in excessive model size and suboptimal utilization of image information. In this study, we propose the structure-preserved self-attention network (SPSANet) model for efficient fusion of image information from different color spaces. This model incorporates a novel structure-preserved self-attention (SPSA) module that employs a single-head pixel-wise attention mechanism, as opposed to the conventional multihead self-attention (MHSA) approach. Specifically, feature maps from all color space grouping paths are utilized for similarity matching, enabling the model to focus on critical pixel locations across different color spaces. This design mitigates the dependence of the SPSANet model on the choice of color space while enhancing the advantages of integrating multiple color spaces. The SPSANet model also employs channel shuffle operations to facilitate limited interaction between information flows from different color space paths. Experimental results demonstrate that the SPSANet model, utilizing eight common color spaces-RGB, Luv, XYZ, Lab, HSV, YCrCb, YUV, and HLS-achieves superior recognition performance with reduced parameters and computational cost.

Solid-state drives (SSDs) can be classified into two types: with and without built-in cache. In terms of performance, SSDs featuring cache exhibit a substantial performance advantage over their non-cache counterparts. The main focus of this paper is the management of built-in cache in SSDs. From a large number of previous studies, we observe that cache hit ratios remain relatively modest for the majority of workloads. First, based on this observation, we adopt an aggressive eviction policy, diverging from traditional eviction algorithms that follow an on-demand eviction policy. Next, considering the temporal locality and parallelism of cached data, we introduce multi-level linked lists to organize cached data. In this way, a smaller computational load can be used to increase the probability of triggering advanced commands. Finally, drawing inspiration from congestion control algorithm in computer networking, we design a cache hit ratio-aware unit. This unit can employ varying degrees of aggressive eviction policies based on its own state. The aim is to maximize the execution of advanced commands while limiting the impact of the aggressive eviction policy on the hit ratio. Our experimental simulations on real workloads show a substantial improvement in average response time compared to LRU, VBBMS, and Req-block, with our method achieving reductions of 19.7%, 19.4%, and 29.9%, respectively.

Predicting student performance (PSP) is critical to intelligent tutoring systems in online education services. Accurate predictions enable data-driven decision making and facilitate the implementation of timely educational interventions. While previous approaches, such as cognitive diagnosis models and data mining techniques, have demonstrated effectiveness with small-scale and static datasets, they face significant challenges in large-scale online learning environments. In these settings, vast volumes of learning responses are continuously generated as students engage with exercises. Those responses are characterized by high dimensionality and incompleteness (HDI) and often manifest as streaming data, which limits the applicability of most existing prediction methods, therefore posing a new challenge to traditional PSP tasks. To remedy the void of PSP in the HDI and incremental scenario, we propose an incremental nonlinear co-latent factor analysis (IN-CoLFA) model, which enhances latent factor analysis – an element-wise learning framework – by incorporating a co-factorization technique and integrating a neural network-inspired structure. To facilitate incremental learning, we develop momentum-accelerated stochastic gradient-based algorithms, which enable the model to perform offline training on historical data and continuously refine its predictions as new student performance becomes available. Experiments on several real-world datasets demonstrate the efficacy and efficiency of our approach in both stationary and incremental PSP tasks under HDI conditions.

Time-varying quality-of-service (QoS) data are usually utilized for Web service evaluation and selection. To accurately estimate the unknown information in time-varying QoS data, it is crucial to capture the temporal patterns hidden in the known data. The Non-negative Latent Factorization of Tensors (NLFT) model has performed well in describing the temporal patterns in time-varying QoS data. However, it assigns a single bias to each dimension of the target QoS tensor, making it suffer from estimation accuracy loss when describing the fluctuations of time-varying QoS data. To address this vital issue, this paper proposes an Adaptively Bias-extended NLFT (ABNT) model based on the fuzzy logic with two-fold ideas: a) extending the linear biases on each dimension of tensor for describing the complex fluctuations of QoS data precisely, b) building a fuzzy logic-incorporated particle swarm optimization algorithm to establish a self-adaptation mechanism for the count of extended linear biases and regularization coefficients. Detailed algorithms and analyses are provided for the proposed ABNT model. Empirical studies on two practical time-varying QoS datasets indicate that the estimation accuracy of the ABNT model outperforms that of state-of-the-art QoS data estimation models (with an average 23.94% improvement in MAE).

high-dimensional and incomplete (HDI) matrices are commonly encountered in a variety of big data-related industrial applications, which describe complex interactions between entities. The complete interaction relationship in the HDI matrix is essential to deal with various problems such as pattern recognition in industrial applications. Therefore, estimating the missing data in the HDI matrix is crucial. latent factor analysis (LFA) models have achieved advanced results in solving such problems. However, the existing LFA models cannot model the nonlinear structure hidden in the HDI matrix. neural networks (NNs) can handle the nonlinearity in the HDI data, but their high estimation accuracy relies on high computation cost and storage burden. To address the aforementioned problems, this article proposes a novel NNLFA model. It contains the following primary ideas: 1) it can model the nonlinear structure of the HDI matrix efficiently through NNs and 2) it incorporates the NNs into the LFA model to improve estimation accuracy while maintaining high computational and storage efficiency. To validate the superiority of the NNLFA model, experiments with six state-of-the-art models are conducted on six practical industrial application datasets. The experimental results indicate that the NNLFA model enhances estimation accuracy by up to 33.3%. In addition, NNLFA model shows strong competitiveness in terms of both time and storage efficiency when compared to baseline models.

Within the domain of intelligent education, also known as smart education, the recommender system propelled by deep learning strives to attain exemplary model performance. However, deep learning models invariably involve the processing of voluminous user privacy data during training phases, and they subjugate themselves to substantial risks of privacy breaches concerning both students and educators. Traditional approaches involving retraining the entire dataset and classical privacy protection methods such as differential privacy and homomorphic encryption struggle to balance model performance and training time expenditure. This presents significant challenges for individuals and enterprises in managing privacy concerns. While balancing personal and corporate interests in privacy protection, Machine Unlearning reveals its potential as a productive strategy to navigate these challenges. This study compares the time cost and performance of model retraining using Machine Unlearning with those of retraining using conventional approaches. The experiment results show that the use of Machine Unlearning algorithms not only effectively protects privacy but also significantly reduces the time required for model retraining. Furthermore, the performance of models employing Machine Unlearning is essentially congruent with that of retraining a model with an entire dataset.

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. Data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models.

In vehicular networks, some edge servers may not function properly due to the time-varying load condition and the uneven computing resource distribution, resulting in a low quality of caching services. To overcome this challenge, we develop a Vehicular dew computing (VDC) architecture for the first time by combining dew computing with vehicular networks, which can achieve wireless communication between vehicles in a resource-constrained environment. Consequently, it is crucial to develop an adaptive caching scheme that empowers vehicles to form efficient cooperation in VDC. In this paper, we propose an intelligent caching scheme based on VDC architecture, which includes two parts. First, to meet the dynamic nature of VDC, a spatiotemporal vehicle clustering algorithm is proposed to establish adaptive cooperation to assist content caching for vehicles. Second, the multi-armed bandit algorithm is employed to select suitable content for caching in vehicles based on real-time file popularity, and a model is established to dynamically update each vehicle’s request preferences. Extensive experiments are conducted to demonstrate that the proposed scheme has excellent performance in terms of cluster head stability and cache hit rate.

Deep Neural Networks (DNNs) have been extensively employed for automatic face recognition, enabling the extraction of compact and discriminative representations from facial images. However, these representations typically encode a multitude of information ranging from individual identities to sensitive soft-biometric attributes such as gender, race, or age. This raises concerns regarding the privacy disclosure of soft-biometric as these attributes should be protected. To address this issue, we propose a novel Face Information Bottleneck Network (FIBNet), which is a representation-level privacy-enhancing framework based on the Information Bottleneck (IB) principle. The proposed FIBNet differs significantly from previous representation-level privacy-enhancing techniques in three key aspects. First, it generates a privacy-enhanced face representation, providing novel insights through an information-theoretic privacy framework. Second, we formulate the privacy protection of soft-biometric attributes as an IB optimization problem by striking a tradeoff between preserving a controlled amount of identity information within face representations and suppressing soft-biometric attribute information. Last, the proposed approach protects soft-biometric privacy from adversaries interested in specific sensitive attributes that are unknown to the biometric system designers or users. Detailed experimental results obtained on widely recognized facial recognition datasets demonstrate that the proposed FIBNet significantly outperforms the state-of-the-art methods in terms of both biometric performance for face verification and its soft-biometric attribute suppression efficiency. These notable results verify FIBNet as a novel and effective approach for ensuring representation-level soft-biometric privacy.

Time-varying quality-of-service (QoS) data is often used to measure the performance of Web services. It is vital to capture the temporal pattern hidden in such time-varying QoS data for estimating unknown ones. A Nonnegative Latent Factorization of Tensors (NLFT) model is highly effective and efficient in describing temporal patterns. However, an NLFT model assigns a single bias into each order of the target QoS data tensor, making it unable to accurately describe the fluctuations of time-varying QoS data, thus impairing its estimation performance. To address this issue, this paper proposes an Extended linear Biases Nonnegative Latent factorization of tensor (EBNL) model with two-fold ideas: a) incorporating multiple linear biases into the model for describing QoS fluctuations precisely, and b) adopting a particle swarm optimization (PSO) algorithm to make the scale of linear biases self-adaptive. Experiments on two time-varying QoS data generated by real applications indicate that compared with several state-of-the-art QoS estimators, the proposed EBNL model achieves higher estimation accuracy for unknown QoS data.

Network intrusion detection systems (NIDS) play a crucial role in maintaining network security . However, current NIDS techniques tend to neglect the topological structures of network traffic to varying degrees. This fundamental oversight leads to challenges in handling class-imbalanced and highly dynamic network traffic. In this paper, we propose a novel dynamic multi-scale topological representation (DMTR) method for improving network intrusion detection performance. Our DMTR method achieves the perception of multi-scale topology and exhibits strong robustness. It provides accurate and stable representations even in the presence of data distribution shifts and class imbalance problems . The multi-scale topology is obtained through multiple topology lenses, which reveal topological structures from different dimensional aspects. Furthermore, to address the limitations of existing detection models based on static network traffic, the DMTR method also achieves dynamic topological representation through our proposed group shuffle operation (GSO) strategy. When new traffic data arrives, the topological representation is updated by preserving a portion of the original information without reprocessing all data. Experiments on four publicly available network traffic datasets demonstrate the feasibility and effectiveness of the proposed DMTR method in handling class imbalanced and highly dynamic network traffic.

Intelligent Transportation Systems (ITSs) are vital for alleviating traffic congestion and improving traffic efficiency. Due to the delay of network transmission and failure of detectors, massive missing traffic data are often produced in ITSs, which evidently decreases the accuracy of decision-making in road traffic management. Hence, how establishing a precise and efficient estimation of missing traffic data becomes a hot yet thorny issue. Low-rank matrix completion (LR-MC) model has proven to be highly effective to address this issue owing to its fine representativeness of such high-dimensional and incomplete data. However, the existing LR-MC models mostly fail to model the inherently temporal and spatial correlations hidden in traffic network structure, resulting in low estimation accuracy. To improve it, this paper proposes a Hessian regularization spatio-temporal low rank (HRST-LR) algorithm with three main-fold ideas: a) imposing low-rank property into the global features of a traffic matrix for precisely learning its structure, b) capturing the temporal evolvement via a second-order difference of time-series constraint, and c) modeling the similar space of road segments through a Hessian regularization spatial constraint, thus exploring the local correlation between road segments for representing the spatial patterns in the traffic data. Experimental results on four traffic data sets prove that HRST-LR outperforms several state-of-the-art methods in the missing traffic data estimation with the root mean squared error improvements often higher than 14% when the missing rate is 90%. Hence, the HRST-LR algorithm is highly valuable for traffic data imputation with the need of performing spatio-temporal low-rank analysis.

The rapid development of 6G can help to bring autonomous driving closed to the reality. Drivers and passengers will have more time for work and leisure spending in the vehicles, further generating a lot of data requirements. However, edge resources from small base stations are insufficient to match the wide variety of services of the future vehicular networks. Besides, due to the high-speed nature of the vehicles, users have to switch the connections among different base stations, whereas such way will cause external latency during the data request. Therefore, it is vital to enable the local cache of vehicle users to realize the reliable autonomous driving. In this paper, we consider caching the contents in the local cache, small base station, and edge server. In practice, the request preference of some single users may be different from a whole region. To maximize the efficiency of content cache, we design a strategy that uses reinforcement learning algorithm to optimize cache schemes on different devices. The experimental results demonstrate that our strategy can enhance the cache hit ratio by 10%-20% compared with the well-known counterparts.

Software-defined vehicular network (SDVN) is a promising networking paradigm that can provide intelligent information exchanges by separating network management and data transmission. Although the transmission quality of vehicles can be greatly improved by deploying softwarized networking schemes, critical networking issues such as the timeliness of data packets remain due to the dynamic nature of vehicular networks. It is vital to design efficient networking schemes by deeply considering the characteristics of the network, transportation system, and users, to improve overall network performance. To this end, this paper proposes asocial computing inspired predictiverouting scheme (SPIDER) for SDVNs that has a comprehensive consideration to enable low-latency reliable data exchange under dynamic vehicular networks. As for the link lifetime grounded on the vehicular historical data, we introduce the context feature mining and one-shot prediction method to predict vehicle movements with considering the energy saving. We also involve social computing techniques to find the relay nodes with good data spreading abilities. The extensive experiments prove our proposed scheme outperforms four existing schemes.

Evaluating startup companies is an important management process for technology business incubators and it is also a typical multicriteria decision-making (MCDM) problem. There exist various methods that have proposed to solve MCDM problems, but these methods heavily depend on the exact criteria weight values. The decision results of these methods are unstable. Moreover, they cannot provide the improvement suggestions for the nonoptimal startup companies. To overcome these two drawbacks, we propose a novel hesitant fuzzy linguistic decision-making method to solve the problem of evaluating startup companies. To this end, a novel semantic comparison method based on the experts' psychology and the ratio of score value to deviation degree is proposed to compare the hesitant fuzzy linguistic term sets. Then, a novel definition of hesitant fuzzy linguistic information envelopment efficiency (HFLIEE) is proposed, based on which, a novel hesitant fuzzy linguistic information envelopment analysis (HFLIEA) model and a novel preference model are proposed. By solving these models, all the alternatives can be ranked and nonoptimal alternatives can be improved. Finally, the numerical analysis is given to illustrate the applicability of the proposed models and the robustness analyses of the proposed models are provided. At the same time, they are compared with the previous hesitant fuzzy linguistic decision-making methods.

Smart agriculture can enhance agricultural production efficiency, improve the ecological environment, and realize the sustainable development of agriculture. Many countries and companies are working hard to develop or introduce smart agricultural solutions. Because of the shackles of traditional agricultural management methods and fierce competition with a variety of different solutions, it is a difficult task for enterprises to select and implement smart agricultural solutions smoothly. Hence, enterprises must assess alternative solutions and select a feasible solution in advance. This study drew a novel assessment and selection for smart agriculture solutions using an information error-based Pythagorean fuzzy cloud algorithm. First, an evaluation index system built on smart agriculture solutions was constructed from four aspects. Then, a new concept of Pythagorean fuzzy clouds was defined to express the evaluation information for each indicator. Simultaneously, the Pythagorean fuzzy cloud weighted Bonferroni mean (PFCWBM) operator was developed to aggregate the assessment information of multiple indicators. Next, an assessment and selection decision framework for smart agriculture solutions based on the PFCWBM operator was presented. In addition, an example was given to illustrate the effectiveness of the proposed algorithm. Finally, a discussion was conducted to verify the superiority of our approach. The results showed that our algorithm can characterize and evaluate complex information and has high sensitivity and environmental adaptability.

An opportunistic link between two mobile devices or nodes can be constructed when they are within each other’s communication range. Typically, cyber–physical environments consist of a number of mobile devices that are potentially able to establish opportunistic contacts and serve mobile applications in a cost-effective way. Opportunistic mobile service computing is a promising paradigm capable of utilizing the pervasive mobile computational resources around the users. Mobile users are thus allowed to exploit nearby mobile services to boost their computing capabilities without investment in their resource pool. Nevertheless, various challenges, especially its quality-of-service and reliability-aware scheduling, are yet to be addressed. Existing studies and related scheduling strategies consider mobile users to be fully stable and available. In this article, we propose a novel method for reliability-aware and deadline-constrained service composition over opportunistic networks. We leverage the Krill–Herd-based algorithm to yield a deadline-constrained, reliability-aware, and well-executable service composition schedule based on the estimation of completion time and reliability of schedule candidates. We carry out extensive case studies based on some well-known mobile service composition templates and a real-world opportunistic contact data set. The comparison results suggest that the proposed approach outperforms existing ones in terms of success rate and completion time of composed services.Note to Practitioners—Recently, the rapid development of mobile devices and mobile communication leads to the prosperity of mobile service computing. Services running on mobile devices within a limited range are allowed to be composed to coordinate through wireless communication technologies and perform complex tasks and business processes. Despite its great potential, mobile service compositions remains a challenge since the mobility of users and devices imposes high unpredictability on the execution of tasks. A careful investigation into existing methods has found their various limitations, e.g., assuming time-invariant availability of mobile services. This article presents a novel reliability-aware and deadline-constrained service composition method for mobile opportunistic networks. Instead of assuming time-invariant availability of mobile nodes, the proposed method is capable of estimating service availability at run-time and leveraging a Krill–Herd-based algorithm to yield the deadline-constrained, reliability-aware, and well-executable service composition schedules. Case studies based on well-known service composition templates and real-world data sets suggest that it outperforms traditional ones in terms of success and completion time of composed services. It can thus aid the design and optimization of composite services as well as their smooth execution in a mobile environment. It can help practitioners better manage the reliability and performance of real-world applications built upon mobile services.

Summary Collaborative cloud applications have become the dominant application mode in the big data era. These applications usually generate plenty of cooperative files, which share their ownerships with all collaborative participants. Data deduplication is a promising solution to improve the storage efficiency and save the user expenditure. However, it remains an open issue on how to securely prove the shared ownerships for the shared files and address the attacks on account of using data deduplication. To tackle the above issue, in this paper, we introduce a novel concept of the Proof of Shared oWnership (PoSW) and construct a secure multi‐server‐aided PoSW (ms‐PoSW) scheme for securing client‐side deduplication for the shared files, which is based on the convergent encryption, secret sharing, and bloom filter. In the ms‐PoSW scheme, we employ a sharing convergent key to avoid the single point of failure, introduce the secret sharing algorithm to implement the shared ownership, and construct a novel interaction protocol between the shared owners and the cloud server to prove the shared ownership. Furthermore, a hybrid PoSW scheme is constructed to address the secure proof of hybrid cloud architectures. Finally, security analysis and performance evaluation show the security and efficiency of the proposed schemes.

The linguistic intuitionistic fuzzy sets (LIFSs) and linguistic Pythagorean fuzzy sets (LPFSs) are two linguistic orthopair fuzzy sets whose membership grades are pairs of linguistic terms from the predefined linguistic term sets (LTSs). One linguistic term indicates the membership degree (MD), while the other one gives the nonmembership degree (NMD). In each LIFS, the sum of the subscripts of MD and NMD is less than the cardinality of LTS. In the LPFSs, the sum of the squares of the subscripts of MD and NMD is less than the square of the cardinality of LTS. In this paper, we propose a general form of these two linguistic orthopair fuzzy sets, which can be named linguistic q-rung orthopair fuzzy sets. We devise the operational laws, based on which, the linguistic q-rung orthopair fuzzy weighted averaging (LqROFWA) operator and linguistic q-rung orthopair fuzzy weighted geometric (LqROFWG) operator are developed to aggregate the linguistic q-rung orthopair fuzzy numbers (LqROFNs). Then, the novel interactional operational laws that consider the interactions between the MD and NMD from different LqROFNs are given. The partitioned geometric Heronian mean (PGHM) operator can effectively solve the decision-making problems in which the attributes grouped into the same clusters have interrelationships and the attributes belonging to different clusters have no interrelationship. Based on these novel operational laws and PGHM operator, the linguistic q-rung orthopair fuzzy interactional PGHM (LqROFIPGHM) operator and linguistic q-rung orthopair fuzzy interactional weighted PGHM (LqROFIWPGHM) operator are proposed and their properties are discussed. Based on the LqROFIWPGHM operator, an efficient multiattribute group decision-making model is given to deal with the linguistic q-rung orthopair fuzzy information. Finally, the superiorities of the interactional operational laws and LqROFIWPGHM operator are tested using some illustrative examples.

The probabilistic hesitant fuzzy preference relations (PHFPRs) provide the decision makers with an efficient means to express the preference information on pairwise comparisons over alternatives. In this paper, we propose an automatic consistency improving model for PHFPRs. First, we propose a novel normalization algorithm to normalize probabilistic hesitant fuzzy elements (PHFEs) by using probability splitting idea and develop novel operational laws. Then, we define the consistency index to compute the degree of deviation between the PHFPRs and their multiplicative consistent PHFPRs. We also develop a novel consistency threshold estimation method for obtaining the threshold of consistency index and then put forward an automatic consistency improving algorithm for repairing inconsistent PHFPRs. Moreover, two probabilistic hesitant fuzzy aggregation operators are put forward to aggregate preference values in acceptably multiplicative consistent PHFPRs for obtaining the ranking orders of alternatives. Finally, an illustrative example is given to show the implementation process of our proposed automatic consistency improving model and also we compare our proposed model with the existing studies.

Evaluating the Internet-of-Things (IoT) platforms is a crucial step in the development and deployment process of IoT applications. It is still an open issue. In this article, evaluating the IoT platforms is formulated to be a multicriteria decision-making (MCDM) problem since it involves multiple considerations and a novel integrated MCDM method is put forward for handling this problem. To this end, an evaluation criteria system is established to characterize these considerations for evaluating IoT platforms, and then the concept of probabilistic linguistic term sets (PLTSs) is introduced to express the group preference information of IoT platforms with respect to the criteria. Then, a novel probabilistic linguistic best-worst (PLBW) method based on the score value is put forward for calculating and analyzing the importance degrees of criteria. Based on the PLBW method, two-tuple distance measure, and two-level possibility degree, a novel integrated probabilistic linguistic MCDM model based on the TODIM method is proposed to rank IoT platforms. Finally, a practical case is provided to show the procedure of evaluating IoT platforms and the comparative analysis is performed to verify the advantages of the probabilistic linguistic TODIM method.

The ever-growing demand for electrical energy of sensing devices in the Internet of Things (IoT) has led to generating large amounts of electricity consumption data. Electricity service providers often use wireless sensor networks to collect sensing devices' electricity consumption data for statistical analysis, so as to provide sensing devices with improved electrical services. As an important data mining technique, while data clustering excels in dealing with such massive data, it imposes the risk of privacy disclosure in the process of data clustering. In an effort of solving this problem, Blum et al. proposed a differential privacy k-means algorithm, effectively preventing privacy disclosure. However, the availability of data clustering results is reduced due to the data distortion in Blum's algorithm. In this paper, we propose a privacy and availability data clustering (PADC) scheme based on k -means algorithm and differential privacy, which enhances the selection of the initial center points and the distance calculation method from other points to center point. Moreover, PADC attempts to reduce the outlier effect through detecting outliers during the clustering process. Security analysis indicates that our scheme satisfies the goal of differential privacy and prevents privacy information disclosure. Meanwhile, performance evaluation shows that our scheme, at the same privacy level, improves the availability of clustering results compared to the existing differential privacy k-means algorithms, suggesting that our proposed PADC scheme outperforms others for intelligent electrical service in IoT.

Edge computing is an emerging technology which places computing at the edge of the network to provide an ultra-low latency. Computation offloading, a paradigm that migrates computing from mobile devices to remote servers, can now use the power of edge computing by offloading computation to cloudlets in edge-clouds. However, the task scheduling of computation offloading in edge-clouds faces a two-fold challenge. First, as cloudlets are geographically distributed, it is difficult for each cloudlet to perform load balancing without centralized control. Second, as tasks of computation offloading have a wide variety of types, to guarantee the user quality of experience (QoE) in terms of task types is challenging. In this paper, we present Petrel, a distributed and application-aware task scheduling framework for edge-clouds. Petrel implements a sample-based load balancing technology and further adopts adaptive scheduling policies according to task types. This application-aware scheduling not only provides QoE guarantee but also improves the overall scheduling performance. Trace-driven simulations show that Petrel achieves a significant improvement over existing scheduling strategies.

As a novel and powerful tool, the notion of probabilistic linguistic term sets (PLTSs) can efficiently model this kind of qualitative assessment information utilizing several possible linguistic terms associated with probabilities or weights over alternatives. Considering that there are no investigation and research on the correlation coefficient and clustering analysis for the concept of PLTSs. Therefore, some correlation coefficient formulas are put forward to measure the relationship between two PLTSs and then they are utilized to develop two novel clustering algorithms to group PLTSs in this paper. We first define some correlation coefficient formulas and their weighted forms to measure the relationship between PLTSs. Then, we extend a fuzzy clustering algorithm for PLTSs and also propose a novel orthogonal clustering algorithm for PLTSs. Finally, we provide a practical example, which performs cluster analysis on the levels of general higher education in different regions of China, to test and verify the usability of our proposed clustering algorithm.

Wireless sensor networks consist of a large number of distributed sensor devices, which are connected and coordinated through multi‐hop routing. Due to the existence of correlated information and redundancy in measuring data, data messages can be combined and merged by performing data aggregation function in the routing process. To reduce energy consumption is a major optimisation objective of data aggregation approaches, which can be achieved by decreasing the compulsory communication load of routing. From the theoretical level, an energy model is proposed to validate the benefits of data aggregation on energy consumption. The key parameters which may impact the aggregation performance are further discussed. Finally, the corresponding simulations are implemented in order to verify the analysis conclusions from the theoretical model, and the comparison results can effectively reflect the advantages of data aggregation in different scenarios.