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检索条件"机构=Hunan Key Laboratory for Service Computing"

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Research on global register allocation for code containing array-unit dual-usage register names

Research on global register allocation for code containing a...

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作者： Hu, Yonghua Zhang, Xin Wang, Shuying Liang, Wei Li, Kuan-Ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China

Array-unit dual-usage register is a kind of register resource that can be read or written as a whole or individually. It is mainly configured in processors with SIMD processing units and provides register-level speed data transfer between the scalar and vector processing units. To improve the efficiency of algorithms by using an array-unit dual-usage register, we investigate in this article the problem of adapting register allocation to code containing array-unit dual-usage register names. We propose a corresponding global register allocation method by combining the allocation of regular registers with array-unit dual-usage register, ensuring that the names of array-unit dual-usage register can be used in the input code of register allocation. Moreover, we present the processing framework of this method and the specific algorithms of some related vital aspects and demonstrate the working principles of the algorithms by an example. Experimental studies were conducted on a platform based on the FT-M7002 DSP core, and showing that our register allocation method can effectively handle codes containing array-unit dual-usage register names and support relevant application algorithms to improve their data transfer scheme. For some typical algorithms with input matrix, substantial performance improvements of twofold or higher are achieved. © 2022 John Wiley & Sons, Ltd.

关键词： Data transfer

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A Short Review of Truck and Drone Collaborative Delivery Problem 10

A Short Review of Truck and Drone Collaborative Delivery Pro...

引用

10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhu, Yena Liu, Min Jin, Dongyao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

ISBN: (纸本)9798350312461

With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. The advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. Therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented. © 2023 IEEE.

关键词： Reinforcement learning

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A Web service Recommendation Method Based on Adaptive Gate Network and xDeepFM 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Tao, Zhi Cao, Buqing Ye, Hongfan Kang, Guosheng Peng, Zhenlian Wen, Yiping School of Computer Science and Engineering and Hunan Key Laboratory of Service Computing and New Software Service Technology Hunan University of Science and Technology Hunan Xiangtan China School of Computer and Artificial Intelligence Huaihua University Huaihua China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

With the rapid development of service computing technology, more and more companies and organizations are encapsulating and publishing their operational data or resources to the Internet in the form of Web services, resulting in an exponential increase in the number of Web services. To automatically generate or recommend a group of Web services according to the user's natural language requirements description, to build a Mashup application to meet the user's requirement is a hot topic in service computing. Some researchers enhance the quality of Web service recommendation by using auxiliary information into the recommendation system. However, they mainly focus on adding external information (e.g., pre-training of external corpora) to enhance semantic features, while some internal statistical features of the corpus such as word distribution on labels and frequency are not fully exploited. Compared to other exterior knowledge, statistical features are naturally compatible with Web service recommendation tasks. To fully exploit the statistical features, this paper proposes a Web service recommendation method based on adaptive gate network and xDeepFM model. In this method, firstly, the description document of Web services is taken as the basic corpus, the semantic and statistical information in the corpus are mined by utilizing the adaptive gate network, and the statistical features are encoded by a variational encoder. Secondly, the similarity between Web services is derived from the semantic features, at the same time the popularity and co-occurrence of Web services are calculated. Thirdly, the xDeepFM model is used to mine the explicit and implicit higher-order interactions in the sparse matrix which consists of the above information to recommend Web services for Mashup application. Finally, a multiple sets of experiments based on the dataset crawled from the ProgrammableWeb have been conducted to evaluate the proposed method and the experimental result shows that t

关键词： Web services

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Towards Using Local Process Mining to Analyse Learning Behavior Pattern 17th

Towards Using Local Process Mining to Analyse Learning Behav...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Ouyang, Sipeng Wen, Yiping Liu, Jianxun Qi, Lianyong Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan411201 China Qufu Normal University Qufu273165 China

ISBN: (纸本)9789819923557

Learning behavior is the key factor to promote the occurrence of effective learning. Analyzing learning behavior pattern is helpful to understand the common learning behavior of different types of learners. This paper introduces the application of traditional process mining methods such as Sequence Miner, Heuristics Miner and Inductive Miner in the area of learning behavior analysis. The shortcomings of these methods are also analyzed. To overcome their shortcomings, a method of local process mining-based learning behavior pattern analysis is proposed in this paper. Comparative experiments based on real data sets are performed to verify its effectiveness. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Blockchain Scalability Technologies 7th

Blockchain Scalability Technologies

引用

7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Xu, Nengxiang Cai, Jiahong Gong, Yinyan Zhang, Huan Huang, Weihong Li, Kuan-ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9783031281235

As the underlying implementation technology of the current main-stream digital currency, blockchain can establish a trusted distributed system without relying on third-party trusted institutions or a privacy-protect system. The decentralized characteristics of blockchain have broad application scenarios, such as the Internet of Things, financial technology and other fields. However, the scalability of the current blockchain is seriously insufficient, such as limited throughput in performance, small storage capacity, and difficulty in scaling functions. This paper introduces the definition and technical classification of scalability, analyzes the current problems of scalability and briefly introduces the current main-stream expandable technologies such as sharding, on-chain and off-chain storage, off-chain payment channel and cross-chain technology from two aspects of performance and function, as well as the principles and ideas of these technologies. Finally, the research progress of current blockchain extension technology is summarized, and the problems faced by the current extension scheme are pointed out, which provides a direction for future research work. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Cryptography of Blockchain 7th

Cryptography of Blockchain

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Long, Ying Gong, Yinyan Huang, Weihong Cai, Jiahong Xu, Nengxiang Li, Kuan-ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9783031281235

With the development of digital currencies and 5G technology, blockchain has gained widespread attention and is being used in areas such as healthcare, industry and smart vehicles. Many security issues have also been exposed in the course of blockchain applications. Cryptography can ensure the security of data on the blockchain, the integrity and validity of data as well as the ability to authenticate users and anonymize them. This article therefore examines the cryptography underlying blockchain security issues, providing an overview of cryptographic homomorphic encryption, zero-knowledge proofs and secure multi-party computation commonly used in blockchains. At the same time, the development of quantum computing is bound to affect existing cryptographic systems, and blockchains applying these cryptographic systems are bound to be hit hard, so this article discusses four of the most promising post-quantum cryptography techniques available: hash-based public key cryptography, code-based public key cryptography, multivariate public key cryptography, and lattice-based public key cryptography. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Public key cryptography

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Explainable AI: A Way to Achieve Trustworthy AI 10

Explainable AI: A Way to Achieve Trustworthy AI

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10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024

作者： Li, Yanlu Xiao, Yufeng Gong, Yinyan Zhang, Ronglin Huo, Yingzi Wu, Yanbing Hunan Key Laboratory for Service Computing Novel Software Technology Xiangtan411201 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350389524

AI is black-box and non-explainable, in other words, due to the complexity of the decision-making process of AI, people are unable to know why and how AI makes the decision. For these reasons, people will question and worry about AI's decision-making. Against this background, researchers have proposed the concept of Explainable Artificial Intelligence (XAI). The aim of this paper is to help readers be able to better understand XAI through extensive surveys. In this paper, we discuss the concept of XAI, the motivation of the research, the current status of the research, and the difficulties in the research process. At the same time, we also discuss the specific methods to realize AI explainability by dividing AI explainability methods into two ways: transparent design and ex post facto interpretation. In addition, we select the three fields of healthcare, finance, and autonomous driving, discuss the application of XAI in these three fields, and select some representative results as examples through surveys. Finally, we summarize this article and present our outlook on the future development of XAI. © 2024 IEEE.

关键词： Machine learning

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Meter Location System Base on Jetson NX 1

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Yang, Chengjun zhou, Ling Yang, Ce Hechi University Guangxi Hechi China School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

Analog meter is still widely used due to their mechanical stability and electromagnetic impedance. Relying on humans to read mechanical meters in some industrial scenarios is time-consuming or dangerous, it is difficult for current meter reading robots to operate quickly and maintain high accuracy in edge computing devices. Computer vision-based meter reading systems can solve such dilemmas. We designed an SSD network-based meter image acquisition system that can run in real time in an NVIDIA Jetson NX development board. Moreover, the model can quickly classify meter types and locate meter coordinates in the presence of light changes, complex backgrounds, and camera angle deflection. Tested on NVIDIA Jetson NX using TensorRT acceleration, the inference speed and accuracy reached 9.238 FPS and 53.95 mAP, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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AMSC: Adaptive Multi-channel Graph Convolutional Network-Enhanced Web services Classification 23

AMSC: Adaptive Multi-channel Graph Convolutional Network-Enh...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Qing, Yueying Cao, Buqing Peng, Mi Zhang, Lulu Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China University of Massachusetts Boston Department of Computer Science Boston United States

ISBN: (纸本)9781665494571

With the development of service Oriented Architecture (SOA), the number of Web services on the Internet is also growing rapidly. Classifying Web services accurately and efficiently is helpful to improve the quality of services discovery and promote the efficiency of service composition. However, existing deep learning-based Web services classification methods, such as graph convolutional networks (GCNs), are incapable of adaptively learning the correlation between service topology structure and service node features concurrently, resulting in unsatisfactory classification performance. To address this problem, this paper proposes an adaptive multi-channel GCN-enhanced Web services classification method. In this method, we first extract specific and shared embedding, in the Web API node isomorphic network, from the node features, topology, and combination of Web service nodes. Then, an attention mechanism is used to learn the importance weight of each embedding. By doing this, we adaptively integrate these weights to ensure the consistency and difference of each learned embedding. Finally, experimental results on real datasets from Programmable Web show that compared with LINE, Node2vec, Deep-walk, GCN, and GAT, the method proposed in this paper has an average improvement in accuracy of 19.81%, 19.35%, 19.16%, 11.56%, and 7.75% respectively. © 2021 IEEE.

关键词： Correlation Adaptive systems Network topology Smart cities Quality of service Feature extraction Topology

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Bilinear Graph Neural Network-Enhanced Web services Classification 23

Bilinear Graph Neural Network-Enhanced Web Services Classifi...

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作者： Zhang, Lulu Cao, Buqing Peng, Mi Qing, Yueying Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China University of Massachusetts Boston Department of Computer Science Boston United States

ISBN: (纸本)9781665494571

With the growing number of Web services, classifying Web services accurately and efficiently has become a challenging problem. Effective service classification is conducive to improving the quality of service discovery and the efficiency of service composition. Existing graph neural network methods represent the target service node by a weighted sum of the features of the neighbor service nodes, and have achieved remarkable results in service classification. However, these methods ignore the impact of possible interactions between neighbor service nodes on the target service node. To address this problem, we propose a Web services classification method based on bilinear graph neural network. Our proposed method can model the bilinear interactions between neighbor service nodes and effectively improve the accuracy of service classification. First, our proposed model uses the Word2Vec model to extract the latent semantic vectors from service description documents. We then construct the service relationship network according to tags and shared annotation relationships of Web services. Next, a bilinear aggregator is used to model the pairwise interactions between neighbor service nodes, highlighting the local common attributes. Finally, the bilinear aggregator is combined with the weighted sum aggregator to construct a bilinear graph neural network, which can learn more comprehensive representations of services. The experimental results on the real dataset from ProgrammableWeb show that the classification accuracy of the proposed method is greatly improved compared with DeepWalk, Node2Vec, GCN, and GAT. © 2021 IEEE.

关键词： Smart cities Annotations Mashups Semantics Quality of service Feature extraction Graph neural networks

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