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Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

Computers, Materials & Continua 2023年第4期75卷 2101-2117页

作者： Yanping Chen Xuyang Bai Xiaomin Jin Zhongmin Wang Fengwei Wang Li Ling School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anShaanxi710121China ZTE Corporation Shenzhen51805China

Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost.

关键词： Edge computing task scheduling blockchain task caching industrial security

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A knowledge-enhanced interest segment division attention network for click-through rate prediction

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Neural Computing and Applications 2024年第34期36卷 21817-21837页

作者： Liu, Zhanghui Chen, Shijie Chen, Yuzhong Su, Jieyang Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users’ potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users’ potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Dual-Branch Attention Transformer for Visual Commonsense Reasoning 6

Dual-Branch Attention Transformer for Visual Commonsense Rea...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Ma, Xuerui Bai, Zongwen Zhou, Meili Gao, Yiqun School of Physics and Electronic Information Yan'an University Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an China

ISBN: (纸本)9798331541750

Visual Commonsense Reasoning (VCR) is a challenging task that requires a model to select the correct answer in the context of a given image and question, while also providing a reasonable explanation to justify the chosen answer. In this paper, we propose a novel dual-branch attention network model that effectively models dense intra-modal and inter-modal interactions. Our model utilizes a self-attention mechanism to attend to visual and textual information independently, while employing a bidirectional guided attention mechanism to facilitate effective interaction between different modalities, thus correlating visual information with textual information. By stacking multiple dual-branch attention modules (DBAs), our model is capable of deeply exploring data features and extracting more powerful and informative prior features. By achieving a common focus on visual and textual information, we enhance the subtlety of our understanding and significantly improve our ability to perform visual reasoning. Our comprehensive and qualitative assessments on the public VCR dataset illustrate that our methodology surpasses most models, providing a new solution for Visual Commonsense Reasoning tasks. © 2024 IEEE.

关键词： dual-branch attention multimodel Visual Commonsense Reasoning

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Trajectory optimization for UAV-enabled relaying with reinforcement learning

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Digital Communications and Networks 2025年第1期11卷 200-209页

作者： Chiya Zhang Xinjie Li Chunlong He Xingquan Li Dongping Lin School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhenChina Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhenChina Peng Cheng Laboratory(PCL) ShenzhenChina National Mobile Communications Research Laboratory Southeast UniversityNanjingChina Shenzhen Institute of Information Technology ShenzhenChina Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen UniversityShenzhenChina

In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the *** solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based ***,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL *** simulation results show that both approaches can solve the proposed optimization problem and obtain good ***,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.

关键词： Unmanned aerial vehicle Emergency communications Trajectory optimization Convex optimization Reinforcement learning

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Spatial-Temporal Graph Deviation Neural Networks for Anomaly Detection in Time Series

Spatial-Temporal Graph Deviation Neural Networks for Anomaly...

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2023 International Conference on Electronic, Electrical and Information Technology, EEIT 2023

作者： Guo, Ziyu Kong, Weiyang Liu, Yubao Sun Yat-Sen University Guang Zhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Anomaly detection in time series data (e.g., sensor data) is becoming a fundamental research problem that has various applications. Due to the complex inter-sensor relationships, it is challenging to detect anomalous events such as system faults and attacks hidden the high-dimensional time series. Recent advancements in deep learning approaches such as Graph Neural Networks (GNN) have greatly improved anomaly detection performance in time series data. However, existing methods usually use separate components to capture spatial and temporal dependence relationship and ignore the heterogeneities in spatial-temporal data which motivates us to capture them together. In this paper, we propose a novel approach STGDNN (short for Spatial-Temporal Graph Deviation Neural Networks) that learns the spatial-temporal dependence relationship together in structure learning of graph neural networks. In addition, we use the learned spatial-temporal graph structure and attention weights to explain the detected anomalies. The experiments on three real world datasets show our superiority in detection accuracy, anomaly diagnosis, and model interpretation compared with state-of-the-art methods. © Published under licence by IOP Publishing Ltd.

关键词： Anomaly detection

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Improved Fault Classification in Photovoltaic Panels Using kNN Machine Learning

Improved Fault Classification in Photovoltaic Panels Using k...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2024

作者： Khlifi, Abdelilah Khlifi, Yamina Embedded System and Data Processing Laboratory Mohammed First University ENSAO Oujda Energy Morocco

ISBN: (纸本)9783031883033

Photovoltaic (PV) panels can experience various defects due to operational conditions, environmental factors, or human errors, leading to performance degradation and general risks such as system failures, inefficiencies, and potential fire hazards. This study presents a defect classification method using the k-nearest neighbors (kNN) algorithm, optimized with current-voltage curves. This method identifies three specific faults: Partial shading, shunted modules, and ground faults. To enhance classification, preprocessing techniques, notably normalization, are applied. The performance of the kNN algorithm is compared using datasets of different sizes (500 and 2000 samples) and varying test sizes (20%, 30%, 40%), with both normalized and non-normalized data. Results show that normalization significantly improves the mean score and reduces RMSE, with a statistically significant difference, demonstrating the effectiveness of normalization in fault classification. This study is essential for advancing the detection and classification of defects in photovoltaic (PV) panels. By enhancing these capabilities, the research aims to significantly optimize system performance and safety, thereby mitigating potential risks and improving the overall reliability and efficiency of PV installations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Accuracy ANOVA classification data kNN Normalization RMSE

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Cross-Modal Hash Retrieval with Category Semantics 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Xu, Mengying Lai, Hanjiang Yin, Jian Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (数字)9783031533051

ISBN: (纸本)9783031533044

With the multi-medal resource springing up, the field of cross-modal research has witnessed rapid advancement. Among these, Cross-Modal Hash retrieval has attracted attention due to its time-saving capabilities during the retrieval process. However, most existing cross-modal hash retrieval methods focus on capturing the relationship between modalities and often train in a one-to-one correspondence manner. Consequently, they often neglect the comprehensive distribution of the entire dataset. In response, we propose a novel approach that introduces semantic relationships between categories to capture class relationships and enable the measurement of the full distribution. First, the original image and text are encoded to obtain respective representations. Then, these are fused to generate a joint representation, which is passed through a classification head to obtain category semantics. The semantic information is used to allocate hash centers from a Hadamard matrix. Meanwhile, the image and text representations are fed into a hash layer to produce hash codes. We train the network with a modal matching loss for alignment, as well as center and class loss to capture the full data distribution. Extensive evaluations on two benchmark databases demonstrate that our approach achieves competitive performance compared to state-of-the-art methods. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Multimodal Sentiment Analysis of Graphic Texts Based on Multicategorical Relative Fusion 6

Multimodal Sentiment Analysis of Graphic Texts Based on Mult...

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Zhai, Sheping Gao, Mingyang Yang, Rui Kang, Xinnian Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Unlike the traditional unimodal sentiment analysis, multimodal sentiment analysis can jointly process features between different modalities. Existing multimodal sentiment analysis methods simply extract features from text and images ignoring the influence of emoticons that may be contained in text and scene information in images on sentiment polarity judgment. At the same time, the unimodal feature fusion stage does not fully fuse the features of each modality resulting in low accuracy of sentiment analysis. Therefore, this paper proposes a multimodal sentiment analysis model based on multicategorical relative fusion of images and texts. which separates text content and emoji in the word embedding stage in the feature extraction stage, the traditional emoji sentiment dictionary is queried to obtain their sentiment scores and attention mechanism is introduced to extract scene features and object features of images respectively;in the feature fusion stage, the emoji sentiment scores. mixed image. The final multimodal features are obtained by fusion the emoji sentiment scores. mixed image features and text features with layer normalization. The experiments on the dataset Yelp show that the improvement of the model in the feature extraction and feature fusion stages effectively improves the accuracy and F1 value of multimodal sentiment analysis in which the accuracy increases by 4.7 percentage points on the New York dataset and the F1 value reaches 85.7% on the San Francisco dataset. © 2023 ACM.

关键词： Sentiment analysis

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Improved Control Technique Based on Neural Network for AC-Chopper of Railway Substations 2

Improved Control Technique Based on Neural Network for AC-Ch...

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2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2022

作者： Mir, Ismail Anas, Benslimane Ayat, Yassine Mohammed, Benzaouia Mohamed First University Energy Embedded System and Data Processing Laboratory Ensao Oujda Morocco Embedded System and Data Processing Oujda Energy Embedded System and Data Processing Energy Morocco

ISBN: (纸本)9781665422093

Improving the efficiency of the unbalance compen-sators in high-speed railway (HSR), mainly depends on the development of an intelligent control to limit the unbalance factors imposed by the Moroccan energy provider (ONEE). In this paper, a neural network model (ANN) to control the AC-chopper converter has been proposed. The aim is to achieve a stable operation under real conditions of variation of power consumption by substation. The efficiency of this control mode compared to a control method based on look-up table (LUT) with PID corrector is presented. The different stages of the system as well as the control strategy have been modeled and implemented in MATLAB/Simulink. © 2022 IEEE.

关键词： Neural network models

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Research on resource scheduling method based on Kubernetes 8

Research on resource scheduling method based on Kubernetes

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8th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2025

作者： Song, Haoyang Zhou, Liming Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9798331517915

With the continuous development of cloud computing, the demand for cloud services in all walks of life is increasing. The scale of cloud computing services based on container technology is constantly growing, and Kubernetes has been widely used as a mainstream tool for container orchestration. It supports functions such as automatic deployment and service discovery, dramatically simplifies the container deployment process, and improves the availability and reliability of applications in cloud environments. However, the default scheduling strategy of Kubernetes adopts the greedy idea of first-come-first-served when facing multi-pod tasks, and schedules Pods to working nodes one by one. This scheduling scheme will lead to unbalanced cluster load and low resource utilization. To solve this problem, this paper introduces the selection, crossover, and mutation ideas in genetic algorithms into the artificial fish school algorithm, improves the foraging behavior in the artificial fish school algorithm, and constructs a custom scheduling strategy based on the enhanced artificial fish school algorithm. Experiments have shown that compared with the default scheduling strategy of Kubernetes, the customized scheduling strategy in this paper can effectively improve the load balancing of the cluster and improve the utilization of cluster hardware resources in the scenario of multi-pod scheduling. © 2025 IEEE.

关键词： Genetic algorithms

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