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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Zheng Xing, Weiwei Cheng, Yue Liu, Weibin Zou, Zhiyuan Wang, Zhi Chen, Genxiang Lu, Wei Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University Institute of Information Science Beijing China Beijing Information Science and Technology University Computer School Beijing China Beijing Jiaotong University School of Physical Sciences and Engineering Beijing China Minzu University of China College of Science Beijing China

ISBN: (纸本)9798331509712

Optical Communication Simulation Task Allocation (OCSTA) constitutes an interdisciplinary quandary. This paper proposes a swarm intelligence approach harnessing a Discrete Dung Beetle Optimizer (DDBO) algorithm with a globally equilibrated strategy to offer a resolution avenue for this intricate conundrum. Firstly, we provide a comprehensive exposition of the mathematical model underpinning the OCSTA, which employs the spatial positioning of the population to articulate diverse allocation solutions. Then, a weighted random selection technique is used to generate initial solutions. Thirdly, a predator avoidance strategy is introduced to facilitate updates in the dung beetle position. Finally, the global equilibrium mechanism with the swap operator is exploited to harmonize the exploratory and exploitative capabilities, thereby further augmenting the quality of the solutions. We conducted several simulation experiments1 across various distinct task load scenarios, and statistical tests are employed to evaluate the significant differences between the proposed algorithm and other state-of-the-art methods. The outcomes revealed that the DDBO solution yielded an improvement of approximately 15.8%, which underscores the competitiveness and robustness of solving the OCSTA. © 2024 IEEE.

关键词： Swarm intelligence

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Image Emotion Classification Network Based on Multilayer Attentional Interaction,Adaptive Feature Aggregation

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computers, Materials & Continua 2023年第5期75卷 4273-4291页

作者： Xiaorui Zhang Chunlin Yuan Wei Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Adani University AhmedabadGujaratIndia

The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the *** have shown that certain local regions are more likely to inspire an emotional response than the whole ***,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the ***,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature *** perform more accurate emotional region prediction,this study designs a multilayer attentional interaction *** module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention *** layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal *** complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation *** module uses global average pooling to compress spatial information and connect channel information from all ***,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each ***,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost *** reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small *** experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods

关键词： Attentionmechanism emotional region prediction image emotion classification transfer learning

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Streamlining software Release Process and Resource Management for Microservice-based Architecture on multi-cloud 25

Streamlining Software Release Process and Resource Managemen...

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25th International Multi Topic Conference, INMIC 2023

作者： Herath, Isuru Pathum Jayawardena, Samuditha Fadhil, Ahamed Kodagoda, Nuwan Arachchillage, Udara Srimath S.Samaratunge Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Sri Lanka

ISBN: (纸本)9798350317701

The software development process is more flexible with the concept of containerization in the microservice platform. This research is on three key components to resolve problems faced by the developers and DevOps teams in the IT industry. First, the development phase expects a fully automated software release process from to the deployment phase and then optimize processes tailored to Docker, and Kubernetes, in microservice-based applications. Then streamline the process and leverage the container orchestration technologies to monitor the main aspect of the development lifecycle through the multi-cloud deployment on demand of the growth of day-today releases on multi-regions. A centralized monitoring platform is developed to monitor the deployed applications and that provides comprehensive visibility regarding performance and health of microservices. At the stage of scalarization in microservices, Vertical Pod Autoscaling (VPA) and Horizontal Pod Autoscaling (HPA) are available approaches for resource allocation, and they require measuring the minimum and maximum resource limits. As a result, an intelligent resource allocation system is proposed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms to cater to dynamic resource allocation, optimizing scalability, and improving cost-efficiency. This research aims to achieve practical insights into the IT industry's automated deployment, managing, scaling, and monitoring of microservice-based applications through the mentioned components. © 2023 IEEE.

关键词： Long short-term memory

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Analysis and Research on BOX64's Core Technologies for Cross-Architecture software Migration

Analysis and Research on BOX64's Core Technologies for Cross...

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2023 International Conference on Frontiers of Robotics and software engineering, FRSE 2023

作者： Zhang, Jin Shan, Zehu Liu, Xiaodong Wang, Wenzhu Li, Zhuoheng Peng, Long Yu, Jie Hunan Normal University College of Computer and Communication Engineering Changsha University of Science and Technology College of Information Science and Engineering Changsha China Hunan Normal University College of Information Science and Engineering Changsha China National University of Defense Technology College of Computer Science Changsha China Haihe Lab of Itai Tianjin China Kylin Software Tianjin China

ISBN: (纸本)9798350301113

In the current context of increasingly diverse processor architectures and complex application requirements, how to seamlessly migrate software written for specific architectures (such as X86) to other architectures (such as ARM) has become a critical and urgent issue to resolve. This research primarily explores the key roles of WRAPPER and Dynarec technologies in this process. WRAPPER technology encapsulates the library function calls and system calls of X86_64 Linux programs, enabling them to run seamlessly and efficiently on the ARM64 Linux platform, thereby achieving cross-architecture software compatibility. Dynamic recompilation technology, on the other hand, can dynamically convert the binary code of the source architecture into the binary code of the target architecture during runtime, further enhancing program performance. Through in-depth theoretical analysis and empirical testing, the research team has found that there is still potential for optimization in these two technologies regarding library function calls and memory management. The team conducted a comprehensive theoretical analysis and evaluation of possible optimization strategies, expecting to further improve software performance on non-X86_64 architecture platforms through the implementation of these strategies. This has practical research value for the development and porting of software on non-X86_64 architecture platforms. © 2023 IEEE.

关键词： Binary codes

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Artificial Intelligence-Based Centralized Resource Management Application for Distributed Systems 13

Artificial Intelligence-Based Centralized Resource Managemen...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Hettiarachchi, Lasal Sandeepa Jayadeva, Senura Vihan Bandara, Rusiru Abhisheak Vikum Palliyaguruge, Dilmi Arachchillage, Udara Srimath S. Samaratunge Kasthurirathna, Dharshana Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Due to the decentralized nature and emergence of new practices, tools, and platforms, microservices have become one of the most widely spread software architectures in the modern software industry. Furthermore, the advancement of software packaging tools like Docker and orchestration platforms such as Kubernetes enable developers and operation engineers to deploy and manage microservice applications more effectively and efficiently. However, establishing and managing microservice applications are still cumbersome due to the infrastructure configuration and array of disjoint tools that fail to understand the application's dynamic behavior. As a result, developers need to configure multiple tools and platforms to automate the deployment and monitoring process to provide the optimal deployment strategy for microservices. Even though many tools are available in the industry, the fully automated product which comprises deployment, monitoring, resiliency evaluation and optimization were not developed yet. In response to this issue, we propose an artificial intelligence (AI)-based centralized resource management tool, that provides an automated low latency container management, cluster metrics gathering, resiliency evaluation and optimal deployment strategy behave in dynamic nature. © 2022 IEEE.

关键词： Machine learning

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A Self-play and Sentiment-Emphasized Comment Integration Framework Based on Deep Q-Learning in a Crowdsourcing Scenario : Extended Abstract 39

A Self-play and Sentiment-Emphasized Comment Integration Fra...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Rong, Huan Sheng, Victor S. Ma, Tinghuai Zhou, Yang Al-Rodhaan, Mznah Nanjing University of Information Science & Technology School of Artificial Intelligence Nanjing China Texas Tech University Department of Computer Science Lubbock United States Nanjing University of Information Science & Technology School of Computer & Software Nanjing China Auburn University Department of Computer Science & Software Engineering Auburn United States King Saud University College of Computer & Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350322279

Crowdsourcing is a sourcing model where individuals or organizations obtain goods and services from a large, relatively open and often rapidly evolving group of internet users. The most common way that crowdsourcing can facilitate machine learning is to annotate instances with labels [1]. However, the same instance may have inconsistent class labels, in the eyes of various annotators. Therefore, current efforts in crowdsourcing mainly focus on the truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, instances with the integrated labels could facilitate the training on machine learning models. The future direction of crowdsourcing is to apply more fine-grained truth inference methods to different application domains [2]. Consequently, we evolve toward another challenging problem of comment integration. That is, how can we integrate or summarize the core opinions of multiple product comments obtained from users, rather than the discrete labels. © 2023 IEEE.

关键词： Crowdsourcing

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DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay

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Journal of Internet Technology 2024年第1期25卷 79-86页

作者： Yang, Zhichao Kong, Yan Hsia, Chih-Hsien School of Computer and Software Nanjing University of Information Science and Technology China Department of Computer Science and Information Engineering National Ilan University Taiwan Department of Business Administration Chaoyang University of Technology Taiwan

In recent years, with the increasingly severe traffic environment, most cities are facing various traffic congestion problems, and the demand for intelligent regulation of traffic signals is also increasing. In this study, we propose a new intelligent traffic light control algorithm, dual experience replay light (DERLight), which innovatively and efficiently designs a dual experience replay training mechanism based on the classic deep Q network (DQN) framework and considers the dynamic epoch function. As results show that compared with some state-of-the-art algorithms, DERLight can shorten the average travel time of vehicles, increase the throughput at intersections, and also speed up the convergence of the network. In addition, the design of this algorithm framework is not only limited to the field of intelligent transportation, but also has transferability for some other fields. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Reinforcement learning

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Artificial Intelligence for Edge Service Optimization in Internet of Vehicles:A Survey

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Tsinghua science and Technology 2022年第2期27卷 270-287页

作者： Xiaolong Xu Haoyuan Li Weijie Xu Zhongjian Liu Liang Yao Fei Dai the School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology and EngineeringNanjing 210044China the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China the School of Big Data and Intelligence Engineering Southwest Forestry UniversityKunming 650233China

The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time ***,collected information is transmitted to a centralized resourceintensive cloud platform for service *** Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular ***,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time *** Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources *** extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite *** address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in ***,we establish the general condition and relative concepts about IoV,EC,and ***,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service ***,we discuss a number of open issues in optimizing edge services with AI.

关键词： edge service internet of vehicles artificial intel igence

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PLAME: Piecewise-Linear Approximate Measure for Additive Kernel SVM (Extended abstract) 40

PLAME: Piecewise-Linear Approximate Measure for Additive Ker...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Chan, Tsz Nam Li, Zhe Leong Hou, U. Cheng, Reynold Shenzhen University Department of Computer Science and Software Engineering China Alibaba Cloud University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China The University of Hong Kong Department of Computer Science and Musketeers Foundation Institute of Data Science Hong Kong

ISBN: (纸本)9798350317152

Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to largescale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for training additive kernel SVM models. Experimental results verify that this approach can achieve the best trade-off between accuracy, memory space, and training time compared with different types of state-of-the-art methods. © 2024 IEEE.

关键词： Additives

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