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8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Xianyao Lu, Huajun Qiu, Shuailong Liu, Lijin Wu, Zhongqiang School of Information Science and Technology Hainan Normal University Hainan Haikou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400710094

This study introduces the research methods for detecting changes in the mangroves of Dongzhaigang, Hainan from 2019 to 2023. Mangrove ecosystems play a crucial role in providing habitats and have ecological and economic value, but in recent years, they have faced degradation and management issues. In order to effectively monitor mangrove changes, researchers have utilized remote sensing techniques and machine learning methods for mangrove cover information extraction and classification. The paper describes the methods of estimating mangrove cover using remote sensing satellite imagery and spectral indices and explores the applications of clustering, decision trees, support vector machines, and deep learning in mangrove change detection. The research results indicate that the support vector machine algorithm performs well in classifying mangrove and non-mangrove areas. This study provides a basis for the management and decision-making of Dongzhaigang Mangrove Nature Reserve in Hainan Province and highlights the future research potential in the application of multi-source and multi-resolution data and deep learning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Research progress of quantum artificial intelligence in smart city

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Intelligent and Converged Networks 2024年第2期5卷 116-133页

作者： Sumin Wang Ning Wang Tongyu Ji Yiyun Shi Chao Wang the Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai UniversityShanghai 200444China School of Information Engineering Ganzhou Key Laboratory of Cloud Computing and Big Data ResearchGannan University of Science and TechnologyGanzhou 341000China

The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence(AI).As Moore’s Law approaches its limit,it is imperative to break the constraints that are holding back artificial *** computing and artificial intelligence have been advancing along the highway of human civilization for many years,emerging as new engines driving economic and social *** article delves into the integration of quantum computing and artificial intelligence in both research and *** introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum *** discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives:quantum supervised learning,quantum unsupervised learning,quantum reinforcement learning,and quantum deep *** an effort to address the limitations of smart cities,this article explores the formidable potential of quantum artificial intelligence in the realm of smart *** does so by examining aspects such as intelligent transportation,urban operation assurance,urban planning,and information communication,showcasing a plethora of practical achievements in the *** the foreseeable future,Quantum Artificial Intelligence(QAI)is poised to bring about revolutionary development to smart *** urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers,constructing an efficient,stable,and adaptive hybrid computing architecture that integrates quantum and classical computing,preparing quantum data as needed,and advancing controllable qubit hardware equipment to meet actual *** ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities,robustness,excellent generalization capabilities,and interpretability.

关键词： quantum artificial intelligence smart city D-Wave quantum computer

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Real-Time Diagnosis of Configuration Errors for Software of AI Server Infrastructure

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IEEE Transactions on Dependable and Secure computing 2023年 1-12页

作者： Xu, Guangquan Ding, Xinru Xu, Sihan Jia, Yan Liu, Shaoying Feng, Shicheng Zheng, Xi Big Data School Qingdao Huanghai University Qingdao China College of Intelligence and Computing Tianjin University Tianjin China College of Cyber Science Nankai University Tianjin China Graduate School of Advanced Science and Engineering Hiroshima University Higashihiroshima Japan Department of Computing Macquarie University Sydney NSW Australia

Artificial intelligence (AI) server infrastructure has been built to support AI applications and handle data-intensive workloads. AI server infrastructure is the essential building blocks, and errors in AI server infrastructure may lead to fatal consequences to any AI applications built upon it. Compared to traditional software, software for AI server infrastructure is more configurable, and thus more likely to have configuration errors that might prevent correct software behaviors. Previous work on misconfiguration diagnosis requires sufficient execution history or manual intervention, and can hardly diagnose potential misconfigurations which are not triggered at launching. In this paper, we propose a real-time method to address these issues. Specifically, we combine program analysis and real-time log parsing to diagnose configuration errors. It maps each configuration option to the log code by applying program slicing only once, and parses real-time logs during the operation of the AI server without manual intervention. We evaluate the effectiveness of our approach on the core components of Hadoop, an exemplar AI Server Infrastructure Software. The results show that our method mapped more than 80% of the configuration options to log outputs, identified 90% of the configuration read sites as the slicing seeds, and successfully diagnosed about 10% configuration errors that can not be addressed by previous studies. IEEE

关键词： Real time systems

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FedRKG: A Privacy-Preserving Federated Recommendation Framework via Knowledge Graph Enhancement 18th

FedRKG: A Privacy-Preserving Federated Recommendation Framew...

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18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Yao, Dezhong Liu, Tongtong Cao, Qi Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9789819998951

Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. However, privacy concerns prevent the global sharing of the entire user-item graph. To address this limitation, some methods create pseudo-interacted items or users in the graph to compensate for missing information for each client. Unfortunately, these methods introduce random noise and raise privacy concerns. In this paper, we propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information, enabling higher-order user-item interactions. On the client side, a relation-aware GNN model leverages diverse KG relationships. To protect local interaction items and obscure gradients, we employ pseudo-labeling and Local Differential Privacy (LDP). Extensive experiments conducted on three real-world datasets demonstrate the competitive performance of our approach compared to centralized algorithms while ensuring privacy preservation. Moreover, FedRKG achieves an average accuracy improvement of 4% compared to existing federated learning baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recommender systems

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Research on performance optimization of virtual data space across WAN

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Frontiers of Computer Science 2024年第6期18卷 167-187页

作者： Jiantong HUO Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China

For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** aggregate the widely dispersed storage resources of national supercomputing centers in China,we have previously proposed a global virtual data space named GVDS in the project of“High Performance computing Virtual data Space”,a part of the National Key Research and Development Program of *** GVDS enables large-scale applications of the high-performance computing to run efficiently across ***,the applications running on the GVDS are often data-intensive,requiring large amounts of data from multiple supercomputing centers across *** this regard,the GVDS suffers from performance bottlenecks in data migration and access across *** solve the above-mentioned problem,this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation *** a WAN environment,the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources,guaranteeing lower average migration latency when multiple data migration tasks are running in *** addition,it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests(the IO proxy is a module of GVDS),so as to improve the application’s performance across *** experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.

关键词： storage aggregation across WANs large-scale applications GVDS data migration allocation of IO proxy resource

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

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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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Generated and Pseudo Content guided Prototype Refinement for Few-shot Point Cloud Segmentation 38

Generated and Pseudo Content guided Prototype Refinement for...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wei, Lili Lang, Congyan Chen, Ziyi Wang, Tao Li, Yidong Liu, Jun School of Computer Science & Technology Beijing Jiaotong University China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education China School of Computing and Communications Lancaster University United Kingdom

Few-shot 3D point cloud semantic segmentation aims to segment query point clouds with only a few annotated support point clouds. Existing prototype-based methods learn prototypes from the 3D support set to guide the segmentation of query point clouds. However, they encounter the challenge of low prototype quality due to constrained semantic information in the 3D support set and class information bias between support and query sets. To address these issues, in this paper, we propose a novel framework called Generated and Pseudo Content guided Prototype Refinement (GPCPR), which explicitly leverages LLM-generated content and reliable query context to enhance prototype quality. GPCPR achieves prototype refinement through two core components: LLM-driven Generated Content-guided Prototype Refinement (GCPR) and Pseudo Query Context-guided Prototype Refinement (PCPR). Specifically, GCPR integrates diverse and differentiated class descriptions generated by large language models to enrich prototypes with comprehensive semantic knowledge. PCPR further aggregates reliable class-specific pseudo-query context to mitigate class information bias and generate more suitable query-specific prototypes. Furthermore, we introduce a dual-distillation regularization term, enabling knowledge transfer between early-stage entities (prototypes or pseudo predictions) and their deeper counterparts to enhance refinement. Extensive experiments demonstrate the superiority of our method, surpassing the state-of-the-art methods by up to 12.10% and 13.75% mIoU on S3DIS and ScanNet, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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Cascade width learning based fatigue detection method

Cascade width learning based fatigue detection method

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2024 International Conference on Optics, Electronics, and Communication Engineering, OECE 2024

作者： Wang, Dong Wu, Pianhui Big Data and Computing Institute Guangdong Baiyun University Guangdong Guangzhou510450 China School of Mechatornic Engineering Guangdong Polytechnic Normal University Guangdong Guangzhou510450 China

ISBN: (纸本)9781510685437

The fatigue detection method based on neural network has been widely applied in the field of land transportation. The accuracy varies by different methods and solutions. Disadvantages of current widely applied methods for fatigue detection have been exposed by rational review, e.g., cost-effective, potable implementation issues. Cascade width learning based detection method with outstanding features is a sound solution to overcome shortcomings of other traditional detection methods. Therefore, it is of great significance and value to research this method and present its academic value and applicable values. This paper introduces a cascade width learning based fatigue detection method. The paper looks forward to development of cascade width learning based fatigue detection method, both academic and applicable values of proposed method have been presented by rational evaluation based on rational discussion on experimental results, the results prove that proposed method has achieved 94.9% accuracy in 52.43ms, which suggests more accuracy and fast speed than other common methods including: combinations of LBP and SVM, ASM and Fuzzy, CNN and PERCLO. © 2024 SPIE.

关键词： Eye Material fatigue Machine learning Mouth Head Neural networks Education and training Signal detection Video Visualization

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Unidirectional Generative Summary Model with Bidirectional Coding 5

Unidirectional Generative Summary Model with Bidirectional C...

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5th International Conference on Machine Learning, big data and Business Intelligence, MLBDBI 2023

作者： Yang, Chen Juanatas, Roben A. National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China

ISBN: (纸本)9798350359930

Due to the rapid growth of big data and artificial intelligence technology, the conventional automatic summarizing study has been shifting from abstracted summarization to generated summarization with the goal of offering higher quality, natural, and fluent text summary. The study of generative summaries has made extensive use of deep learning technology in recent years. One of the most popular models in this field is the seq2seq model, which is based on the attention mechanism and has produced impressive results in the summary generation job. Unfortunately, the majority of neural network-based generative abstract models now in use are one-way language models that lack abstract fluency, ambiguity processing, and context awareness. This research suggests a unidirectional generative summary model with bidirectional coding in light of this. The model mixes the two by having the input section have bidirectional language model coding capabilities and the output part retain unidirectional structure. The usefulness and development of the suggested strategy are demonstrated by experiments conducted on the CNewSum Chinese data set. © 2023 IEEE.

关键词： Deep learning

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A Short-term Traffic Flow Forecasting Method Based on SVM-PSO-BP Combined Model 5

A Short-term Traffic Flow Forecasting Method Based on SVM-PS...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Ding, ChunLing Juanatas, Roben A. College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China

ISBN: (纸本)9798350357950

The accuracy of traffic flow forecasting has a significant impact on intelligent traffic control. In recent years, scholars pay more and more attention to traffic flow forecasting based on combined models. Considering the strengths and weaknesses of the nonlinear regression SVM and BP neural network, this paper puts forward a short-term traffic flow forecasting method that use the combination of SVM and BP models. To get better forecasting performance of the BP neural network model, the article uses PSO(Particle Swarm Optimization) algorithm to optimize some parameters of the BP model, finally construct the SVM-PSO-BP combined model. Using RMSE(Root Mean Square Error) and MAE (Mean Absolute Error) as indicators to evaluate the model performance, and traffic flow data collected from British highways as the dataset, the models are respectively trained and used for prediction on weekdays and weekends. Through experiments, it is found that the proposed SVM-PSO-BP outperforms a single prediction model in both evaluation indicators, and can better reflect the changes and trends of traffic flow. © 2023 IEEE.

关键词： back propagation neural network short-term traffic flow prediction Support Vector Machine SVM-PSO-BP model

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