Vehicular edge computing (VEC) has been recognized as a promising technique to process delay-sensitive vehicular applications. Nevertheless, in order to accommodate the rapid growth in the number of connected vehicles, it’s inevitable that there will be an increasing deployment of conventional infrastructure with limited communication ranges. This could potentially lead to escalating costs and impede the full realization of the VEC system. In this paper, a roadside intelligent reflecting surface (IRS) assisted VEC network is introduced, where the IRS is deployed outside the coverage of roadside units (RSUs) to extend the service range. Furthermore, the maximum total number of successful offloading tasks problem within the scheduling time problem is formulated, encompassing the optimization of offloading decisions, computation resource allocation and phase shift of IRS. To tackle the formulated challenging problem, we first decouple the original problem into two subproblems. Then, a heuristic algorithm is proposed, where a many-to-one matching algorithm is proposed to joint optimize offloading decision and the computation resource, and an iterative algorithm is utilized to optimize the phase shift coefficients of IRS. The simulation results validate the effectiveness of the proposed algorithm in comparison to other schemes, and the IRS can effectively maintain network performance even when there are intervals in RSU coverage areas.
During emergency scenarios, network access may be disrupted due to damaged Base Stations (BSs), and deploying Unmanned Aerial Vehicles (UAVs) as communication relays is common in rescue scenarios due to their convenience in ensuring network access. In such situations, the dissemination of rescue-related information messages is crucial, and broadcast messages are often prioritized. Thus, ensuring high broadcast throughput while guaranteeing accessibility for all victims and rescue teams is a significant challenge. Moreover, the broadcast burden is further aggravated by multiple rescue teams broadcasting messages simultaneously. To address this issue, Network Coding Based Cooperative (NCBC) broadcast scheme is a promising approach for enhancing broadcast throughput in emergency scenarios. By employing the NCBC broadcast scheme, we show that the broadcast throughput can be significantly improved. We propose a heuristic algorithm for generating optimal deployment of UAVs using network coding strategy and evaluate the broadcast throughput quantitatively. Simulation results show that our approach can ensure user accessibility and yield at least 26.69
In mobile edge computing (MEC), each user chooses and then offloads the task to an edge server, whereas data security is a concern in MEC due to the lack of trust between users and edge servers. Blockchain is introduced to provide a reliable environment for MEC. In blockchain-based MEC, edge servers are used as the nodes in both MEC and blockchain. After processing the users' tasks, the edge servers upload the results and other task-related information to the blockchain. The edge servers simultaneously execute two kind of tasks, i.e., the tasks offloaded by the users and the blockchain tasks. Therefore, the user offloading decision affects the processing latency of MEC tasks, and there is a trade-off between the resource allocation for MEC and blockchain tasks. However, most existing studies optimize the resource allocation for blockchain and MEC individually, which leads to the suboptimal performance of blockchain-based MEC. In this paper, we study the problem of user offloading decision and the computing resource allocation of edge servers for MEC and blockchain tasks, with the objective to minimize the total processing delay of MEC and blockchain tasks. We propose an algorithm for joint computing resource allocation for MEC and blockchain (JMB). Theoretical analysis proves that JMB is a 3.16-approximation algorithm. Simulation results show that JMB can effectively reduce the delay in blockchain-based MEC.
Measuring the similarity between two business process models holds significant importance across various applications. At present, there are many different similarity calculation methods, such as structural similarity based on the graph edit distance(GED), text similarity based on task node description, and behavioral similarity calculation based on path matching. However, existing similarity computation methods cannot produce reliable results since: (1) To apply GED, business process graphs will be simplified to homogeneous graph where the heterogeneity as well as the routing semantics of the business process is removed. (2) To derive comprehensive similarity evaluation, linear weighted sum of different similarity metrics is a common way, but the final result strongly depends on the weighting coefficients that are empirically assigned. In this paper, we fuse multidimensional metrics to compensate for the sole reliance on structural similarity based on GED. To address the limitations of comprehensive evaluation, we propose a novel multidimensional process similarity evaluation method based on the entropy weight method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. We also design a experimental method to verify the effectiveness of our method, leveraging an open source dataset. The experiment shows that our method can better represent the similarity of business process graphs than other methods.
PurposeThis paper aims to propose an incremental graph indexing method based on probabilistic inferences in Bayesian network (BN) for approximate nearest neighbor search (ANNS) that adds unindexed queries into the graph index incrementally.Design/methodology/approachThis paper first uses the attention mechanism based graph convolutional network to embed a social network into the low-dimensional vector space, which could improve the efficiency of graph index construction. To add the unindexed queries into the graph index incrementally, this study proposes to learn the rule-based BN from social interactions. Thus, the dependency relations of unindexed queries and their neighbors are represented, and the probabilistic inferences in BN are then performed.FindingsExperimental results demonstrate that the proposed method improves the search precision by at least 5% and search efficiency by 10% compared to the state-of-the-art methods.Originality/valueThis paper proposes a novel method to construct the incremental graph index based on probabilistic inferences in BN, such that both indexed and unindexed queries in ANNS could be addressed efficiently.
Writing high-quality unit tests plays a crucial role in discovering and diagnosing early-stage errors and preventing their further propagation throughout the development cycle. However, the low readability of existing automated test case tools hinders developers from directly using them. In addition, current approaches exhibit sensitivity to individual words in the input code, often producing completely different results for minor changes in the input code. To tackle these problems, we propose AssertGen, a powerful Java assertion generation model that maintains consistent output for minor variations in code snippets. Inspired by software mutation testing, we propose 11 heuristic strategies for code mutation, aiming to generate variant code that is human-readable but misleading to the model, by making minor changes to code text or structural information. Then, we use the variant code to attack the model to test the model’s robustness. We observe that the variant based on variable names (VM), the mutation based on method names (FM), and the mutation method False_Control_Flow, which adds additional control flow, have the greatest impact on the quality of generated assertions by the model. To enhance the robustness of AssertGen, we use multiple mutations to expand the original dataset, allowing the model to learn how to counter the instability caused by mutations during the training process. Experiment results show our assertion generation model achieves a BLEU score of 60.08 and a perfect prediction rate of 47.91
Internet of Things (IoT) is an evolving paradigm for building smart cross-industry. The data gathered from IoT devices may have anomalies or other errors for various reasons, such as malicious activities or sensor failures. Anomaly detection is thus in high need for guaranteeing trustworthy execution of IoT applications. Existing IoT anomaly detection methods are usually built upon unsupervised methods and thus can be inadequate when facing complex IoT data regularity. In this article, we propose a semi-supervised approach for detecting IoT time series anomalies based on Graph Structure Learning (GSL) using multi-layer perceptron Graph Convolutional Networks (GCN) and the Mean Teachers (MT) mechanism. The proposed model is capable of leveraging a small amount of labeled data (1
The rapid growth of malware and its variants has a significant detrimental effect on the security of the Internet infrastructure. In recent years, deep learning-based methods have demonstrated significant success in malware detection. Nonetheless, there are concerns regarding the requirement for substantial labeled data and the feature selection methods used in present approaches. In this paper, we propose a semi-supervised learning-based method for malware traffic classification, which exploits the raw bitmap representation of malware traffic. We employ stacked bi-LSTM to learn the feature representation of malware traffic and adopt semi-supervised learning (SSL) to enhance the model performance by leveraging unlabeled traffic. Pseudo-labeling and consistency regularization are used to produce pseudo-labels, which can compute unsupervised loss. The loss function consists of two terms: a supervised loss applied to labeled data and an unsupervised loss, which are combined together for model training. Experiments indicate that our method is capable of classifying malware traffic with satisfactory accuracy.
As code intelligence and collaborative computing advances, code representation models (CRMs) have demonstrated exceptional performance in tasks such as code prediction and collaborative code development by leveraging distributed computing resources and shared datasets. Nonetheless, CRMs are often considered unreliable due to their vulnerability to adversarial attacks, failing to make correct predictions when faced with inputs containing perturbations. Several adversarial attack methods have been proposed to evaluate the robustness of CRMs and ensure their reliable in application. However, these methods rely primarily on code’s textual features, without fully exploiting its crucial structural features. To address this limitation, we propose STRUCK, a novel adversarial attack method that thoroughly exploits code’s structural features. The key idea of STRUCK lies in integrating multiple global and local perturbation methods and effectively selecting them by leveraging the structural features of the input code during the generation of adversarial examples for CRMs. We conduct comprehensive evaluations of seven basic or advanced CRMs using two prevalent code classification tasks, demonstrating STRUCK’s effectiveness, efficiency, and imperceptibility. Finally, we show that STRUCK enables a more precise assessment of CRMs’ robustness and increases their resistance to structural attacks through adversarial training.
Aiming at the issues of low comprehensiveness and timeliness of data, difficulty in balancing data anonymity and traceability, and challenges of securely storing massive data in traditional traffic data sharing systems, this paper proposes a UAV-VANET integrated system (UVIS) based on consortium blockchain. The UAV integrated into the VANET can promptly provide drivers and traffic managers with comprehensive traffic information and images for traffic planning, thus enhancing transportation efficiency and safety. To achieve traceability of anonymous data sharing, we introduce a proxy re-encryption mechanism to realize precise data access control, which can not only protect data and identity privacy but also trace the true identity of malicious users. Additionally, it effectively prevents the collusion between proxies and data requesters from stealing unauthorized confidential information. To alleviate the pressure of traffic data storage, we adopt a storage method that combines blockchain and IPFS, ensuring secure storage of massive data. Security analysis shows that the UVIS has achieved secure sharing of traffic data. We analyze its efficiency theoretically, and demonstrate the practicality through experiments.
Gesture recognition plays a pivotal role in enabling natural and intuitive human-computer interaction (HCI), finding applications in diverse domains such as smart homes, robot control, and virtual reality. Thanks to advances in computer vision, the most popular method currently is to use the camera for gesture recognition. However, the camera struggles to function properly in poor lighting and inclement weather, and risks invading privacy. Due to the robust and non-invasive features of millimeter-wave radar, gesture recognition based on millimeter-wave radar has received extensive attention from researchers in recent years. In this paper, we propose a novel graph neural network named STAPointGNN for gesture recognition using millimeter-wave radar. In order to better extract features in the spatial and temporal dimensions of point clouds collected by millimeter-wave radar, we designed a spatial-temporal attention mechanism based on graph neural network. We also propose a novel point flow embedding method to capture the motion features of the point clouds in adjacent frames. To verify the superiority of our method, we conduct experiments on two public millimeter-wave radar gesture recognition datasets. The results show that our model outperforms existing mainstream algorithms.
Efficient image retrieval and recognition are pivotal for optimal mobile web vision services. Traditional web-based solutions offer limited accuracy, high overhead, and struggle with vast image volumes. Transferring images for real-time cloud recognition demands stable communication, and large-scale concurrent requests strain computational and network resources. This paper introduces a distributed recognition approach, leveraging cloud-edge-device collaboration through edge computing’s low latency and high bandwidth. We present a lightweight image saliency detection model tailored for mobile web, enhancing initial image feature extraction. Additionally, we introduce an edge-based, deep learning-driven method to amplify image retrieval speed and precision. We incorporate a location and popularity-based caching system to alleviate strains on cloud resources and network bandwidth during extensive image requests. Our real-world tests validate our approach: our saliency detection model outpaces the benchmark by reducing the model size by up to 94
Entity prediction and relation prediction are the two major tasks of temporal knowledge graph (TKG) reasoning. The key to answering queries about future events is to understand historical trends and extract the information most likely to affect the future, i.e., the TKG reasoning task is both influenced by the trends of time-evolving graphs and directly driven by the facts relevant to a specific query. Existing methods mostly build models separately for these two characteristics, namely evolution representation learning and query-specific methods, failing to integrate these two crucial factors that determine reasoning results into a single framework. In this paper, we propose a novel temporal hybrid reasoning network (tHR-NET), simultaneously considering the modeling of graph feature space evolution and the enhancement of query-related feature representations in TKG. Specifically, we introduce a global graph space evolution module to extract graph trends, which influence entity/relation representations at each timestamp through a temporal view projection. Additionally, we propose a query-specific increment module for targeted enhancement of entity and relation representations, capturing query-related factors over extended durations. Through extensive experiments on real datasets, tHR-NET demonstrates distinct advantages in parallel entity and relation prediction.
As the field of code intelligence continues to grow, Code representation learning has emerged as a research hot spot. Given that code structure can be naturally represented as graphs, Graph Neural Networks (GNNs) have proven highly effective for learning graph representations of source code. Pooling, as an essential operation for GNN-based models, is limited in its ability to leverage the rich hierarchical information presented in tree-like graph, especially Abstract Syntax Trees. In order to learn the graph representation of code more effectively, we propose a novel pooling method called TreePool. TreePool directly splits tree-like graphs using depth filtering based on the tree structure to form a sequence of pooled graphs sorted by descending size of subgraphs. Then local-local contrastive learning between these neighboring subgraphs is conducted to preserve the information of the graph before pooling. Through TreePool, multiple views of representation are learned and fused to obtain the final code graph representation. We conduct TreePool on a supervised framework and experimental results demonstrate that the average improvements on two real-world datasets in terms of accuracy are 1.1
The accurate segmentation of stroke lesion regions holds immense significance in shaping treatment strategies and rehabilitation protocols. Due to the large difference in the volume of stroke lesion areas and the great similarity between lesion areas and normal tissues, most of the existing methods for lesion segmentation cannot deal with these problems well. This paper proposes a novel network named MD-TransUNet for the segmentation of stroke lesions, whose framework is based on the UNet architecture. To fully obtain deep image features, it uses ResNet50 for downsampling. MD (multi-dilated) module is employed as the skip connection to gain more receptive fields. Different receptive fields can adapt to varying volumes of lesion areas. Then, a feature extraction module with multi-level attention mechanism is designed using ConvLSTM, non-local spatial attention, and channel attention modules to suppress useless information expression in skip connections and upsampling processes while focusing more on effective spatial and channel information in features. The experiments show that our proposed network gets superior performance than benchmark methods and indicates the generalization and effectiveness of the proposed model.
Quality of service (QoS) is the main factor in service selection and recommendation, and it is influenced by dynamic factors, such as network condition and user location, and static factors represented by the invocation sequence at a fixed time slice. In order to jointly consider these two factors, this work proposes a multi-dimensional sequential contrastive learning framework named MDSCL, which applies contrastive learning method to learn the sequence representations of both user and time dimensionalities. An overlap crop augmentation strategy is proposed to obtain positive examples for user sequences and time sequences, respectively. Besides, MDSCL includes an integrated feature extractor that combines WaveNet and BiLSTM to facilitate the long short-term feature capturing. Extensive experiments on WSDREAM have been conducted to verify the effectiveness of our approach.
In IoT systems, traditional encryption can be used to protect IoT applications and data at rest or in transit that transforms data in to ciphertext making it unreadable. However, it is very challenging to protect IoT systems against attacks targeting data and applications in use. Using homomorphic encryption, this work proposed a lightweight collaborative computing scheme to protect both applications and data in IoT environment that includes IoT devices, mobile apps, and cloud server. A novel key management system scheme proposed as a trusted third party to collaboratively generate and distribute keys by cloud servers and IoT devices, in which data is only visible to the data owner but keep encrypted to other parties. A SEAL-CKKS scheme and a K-means clustering algorithms were validated, and the experimental results demonstrated the effectiveness of proposed schemes, in which the K-means clustering algorithm in the plaintext state, the proposed scheme still maintains an accuracy up to 84.1%.
The data of applications in connected and autonomous vehicles are important, which is usually collected by service providers to improve their services, such as object detection model. But, wireless communication is susceptible to various kinds of attacks. Thus, the data of the application module needs to be securely shared to the corresponding service provider. However, current schemes are with limited performance while a service provider collects multiple application data at the same time. By adopting signcryption and chaotic map, an efficient authentication and key agreement scheme is proposed, while batch authentication is achieved for efficient message authentication of multiple applications, and the efficient revocation is realized based on Chinese remainder theorem under the assistance of trusted execution environment supported vehicle computing/communication unit. The formal security proof shows that the scheme is secure under the random oracle model, and the experiment results shows that the scheme is more efficient than related schemes and can meet the requirements of CAV.
In the cyber attack and defense process, the opponent’s strategy is often dynamic, random, and uncertain. Especially in an advanced persistent threat scenario, it is not easy to capture its behavior strategy when confronted with a long-term latent, highly dynamic and unpredictable opponent. FlipIt game can model the stealth interaction of advanced persistent threat. However, it is insufficient for traditional reinforcement learning approach to solve real-time and non-stationary game model. Therefore, how to model a non-stationary opponent implicitly and keep the defense agent’s advantage continuously is essential. In this paper, we propose an extended FlipIt game model incorporating opponent modeling. And then we propose an approach that combines deep reinforcement learning, opponent modeling, and dropout technology to perceive the behavior of a non-stationary opponent and defeat it. Instead of explicitly identifying the opponent’s intention, the defense agent observes the opponent’s last move actions from the game environment, stores the information in its knowledge, then perceives the opponent’s strategy and finally makes a decision to maximize its benefits. We show the excellent performance of our approach whether the opponent adopts traditional, random or composite strategies. The experimental results demonstrated that our approach can perceive the opponent quickly and maintain the superiority of suppressing the opponent.
Origin-Destination (OD) Matrix Prediction is an important part of public transportation service which aims to predict the number of passenger demands from one region to another and capture the passengers’ mobility patterns. This problem is challenging because it requires forecasting not only the number of demands within a region, but the origin and destination of each trip as well. To address this challenge, we propose an effective model, ODCRN (Origin-Destination Convolution Recurrent Network) which incorporates traffic context and bi-directional semantic information. First, we obtain the semantic embedded features of the region as the static traffic context by the Node2vec algorithm, and the traffic flow of the region is counted as the dynamic traffic context. Second, we construct two adjacency matrices which represent origin-destination and destination-origin travel demands within urban areas respectively based on the OD matrices of each time slot, and use the graph convolutional network to aggregate traffic context information of the semantic neighbors in both directions. Then, we use a unit constructed by GRU and the graph convolution network to capture the spatial-temporal correlations of the input data. Finally, we use those correlations and traffic contexts to predict the OD matrix for the next time slot. Our model is evaluated on TaxiNYC and TaxiCD datasets, and experimental results demonstrate the superiority of our ODCRN model against the state-of-the-art approaches.
