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International Conference on information Technology and Electrical Engineering (ICITEE)

作者： Muhammad Avied Bachmid Muhammad Yasir Anshari Haq Muhammad Rafly Mumtaz Aryo Pinandito Media Game and Mobile Laboratory Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia Faculty of Computer Science Universitas Brawijaya Malang Indonesia Media Game and Mobile Laboratory Department of Information System Faculty of Computer Science Universitas Brawijaya Malang Indonesia

ISBN: (数字)9798350375817

ISBN: (纸本)9798350375824

Prior study has developed the RouteSegmentation algorithm to identify the perimeter area surrounding a route. In this study, a comparative experiment was carried out to investigate the performance of the RouteSegmentation algorithm implementation in contrast to the RouteBoxer algorithm. Both algorithms play a crucial role in geographical information systems (GIS), particularly in enhancing navigational tools by identifying points of interest along specified routes. Through systematic experimentation using various perimeter distance values and types of route, the trade-offs between processing efficiency and the granularity of the area identified by these algorithms were evaluated. The results demonstrate that RouteSegmentation maintained relatively consistent performance across different perimeter distance values and statistically significantly faster processing time than RouteBoxer. Furthermore, while the RouteBoxer algorithm benefits from larger perimeter distance values by reducing processing time, it compromises the coverage results of the algorithm and potentially undermines its usefulness in practical scenarios. This study not only provides insights into the optimal use of perimeter distance values for each algorithm but also guides users in selecting the appropriate algorithm based on their specific application needs, balancing detailed geographical analysis and runtime efficiency.

关键词： Meters Visualization Systematics Runtime Navigation Stability analysis Performance analysis Reliability Thermal stability Geographic information systems

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Trace Semantics and Algebraic Laws for Total Store Order Memory Model

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Journal of computer science & Technology 2021年第6期36卷 1269-1290页

作者： Li-Li Xiao Hui-Biao Zhu Qi-Wen Xu Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China Department of Computer and Information Science Faculty of Science and TechnologyUniversity of Macao MacaoChina

Modern multiprocessors deploy a variety of weak memory models(WMMs).Total Store Order(TSO)is a widely-used weak memory model in SPARC implementations and x86 *** omits the store-load constraint by allowing each core to employ a write *** this paper,we apply Unifying Theories of Programming(abbreviated as UTP)in investigating the trace semantics for TSO,acting in the denotational semantics style.A trace is expressed as a sequence of snapshots,which records the changes in registers,write buffers and the shared *** the valid execution results containing reorderings can be described after kicking out those that do not satisfy program order and modification *** paper also presents a set of algebraic laws for *** study the concept of head normal form,and every program can be expressed in the head normal form of the guarded choice which is able to model the execution of a program with *** the linearizability of the TSO model is ***,we consider the linking between trace semantics and algebraic *** linking is achieved through deriving trace semantics from algebraic semantics,and the derivation strategy under the TSO model is provided.

关键词： weak memory model Total Store Order(TSO) trace semantics algebraic law Unifying Theories of Programming(UTP)

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Verifiable searchable symmetric encryption for conjunctive keyword queries in cloud storage

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Frontiers of computer science 2022年第6期16卷 103-121页

作者： Qingqing GAN Joseph K.LIU Xiaoming WANG Xingliang YUAN Shi-Feng SUN Daxin HUANG Cong ZUO Jianfeng WANG Guangzhou Key Laboratory of Multilingual Intelligent Processing School of Information Science and Technology/School of Cyber SecurityGuangdong University of Foreign StudiesGuangzhou510006China Department of Computer Science Jinan UniversityGuangzhou510632China Faculty of Information Technology Monash UniversityClayton3168Australia State Key Laboratory of Integrated Service Networks(ISN) Xidian UniversityXi’an710071China

Searchable symmetric encryption(SSE)has been introduced for secure outsourcing the encrypted database to cloud storage,while maintaining searchable *** various SSE schemes,most of them assume the server is honest but curious,while the server may be trustless in the real *** a malicious server not honestly performing the queries,verifiable SSE(VSSE)schemes are constructed to ensure the verifiability of the search ***,existing VSSE constructions only focus on single-keyword search or incur heavy computational cost during *** address this challenge,we present an efficient VSSE scheme,built on OXT protocol(Cash et al.,CRYPTO 2013),for conjunctive keyword queries with sublinear search *** proposed VSSE scheme is based on a privacy-preserving hash-based accumulator,by leveraging a well-established cryptographic primitive,Symmetric Hidden Vector Encryption(SHVE).Our VSSE scheme enables both correctness and completeness verifiability for the result without pairing operations,thus greatly reducing the computational cost in the verification ***,the proposed VSSE scheme can still provide a proof when the search result is ***,the security analysis and experimental evaluation are given to demonstrate the security and practicality of the proposed scheme.

关键词： searchable symmetric encryption verifiability conjunctive keyword queries hash-based accumulator cloud storage

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges 9

Unleashing the Potential of Decentralized Federated Learning...

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9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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Quantitative Scalability of Nodes and Geographical Coverage in LEACH Protocol 6

Quantitative Scalability of Nodes and Geographical Coverage ...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Mir, Foudil Meziane, Farid Bounceur, Ahcene Laouid, Abdelkader Faculty of Exact Sciences Bejaia06000 Algeria University of Derby Data Science Research Centre Derby United Kingdom Kfupm Information And Computer Science Department Dhahran Saudi Arabia University of El Oued Liap Laboratory El Oued39000 Algeria

ISBN: (纸本)9798350350265

Energy optimisation using clustering remains a constant challenge in wireless sensor networks (WSNs). The high volume of data generated within these networks leads to excessive energy consumption when transferring it to the processing center. Therefore, clustering protocols, in particularly hierarchical routing protocols dedicated to WSNs, must imperatively integrate energy efficiency mechanisms in order to extend the functional lifetime of the nodes and the network. In this paper, we focus on an in-depth analysis of scalability in the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. The LEACH protocol, renowned for its random and distributed approach to WSNs energy optimisation, forms the focus of our analysis and exploration. Our study focuses in particular on the quantitative scalability of nodes and the scalability of geographical coverage. The aim is to improve our understanding of the behaviour of the LEACH protocol in large-scale environments, particularly with regard to node and network lifetime and energy management parameters. © 2024 IEEE.

关键词： Scalability

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Deep Learning and Network Analysis:Classifying and Visualizing Geologic Hazard Reports

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Journal of Earth science 2024年第4期35卷 1289-1303页

作者： Wenjia Li Liang Wu Xinde Xu Zhong Xie Qinjun Qiu Hao Liu Zhen Huang Jianguo Chen School of Geography and Information Engineering China University of GeosciencesWuhan430078China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan430078China School of Computer Science China University of GeosciencesWuhan430078China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen518034China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China Wuhan Geomatics Institute Wuhan430074China Faculty of Earth Resources China University of GeosciencesWuhan430074China

If progress is to be made toward improving geohazard management and emergency decision-making,then lessons need to be learned from past geohazard information.A geologic hazard report provides a useful and reliable source of information about the occurrence of an event,along with detailed information about the condition or factors of the *** such reports,however,can be a challenging process because these texts are often presented in unstructured long text formats,and contain rich specialized and detailed *** text classification is commonly used to mine disaster text data in open domains(e.g.,news and microblogs).But it has limitations to performing contextual long-distance dependencies and is insensitive to discourse *** deficiencies are most obviously exposed in long text ***,this paper uses the bidirectional encoder representations from Transformers(BERT),to model long ***,utilizing a softmax layer to automatically extract text features and classify geohazards without manual *** latent Dirichlet allocation(LDA)model is used to examine the interdependencies that exist between causal variables to visualize *** proposed method is useful in enabling the machine-assisted interpretation of text-based ***,it can help users visualize causes,processes,and other geohazards and assist decision-makers in emergency responses.

关键词： geologic hazard network analysis latent dirichlet allocation text classification deep learning

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A batch microfabrication of a self-cleaning,ultradurable electrochemical sensor employing a BDD film for the online monitoring of free chlorine in tap water

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Microsystems & Nanoengineering 2022年第2期8卷 85-96页

作者： Jiawen Yin Wanlei Gao Weijian Yu Yihua Guan Zhenyu Wang Qinghui Jin Faculty of Electrical Engineering and Computer Science Ningbo University315211 NingboP.R.China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences200050 ShanghaiP.R.China

Free chlorine is one of the key water quality parameters in tap ***,a free chlorine sensor with the characteristics of batch processing,durability,antibiofouling/antiorganic passivation and in situ monitoring of free chlorine in tap water continues to be a challenging *** this paper,a novel silicon-based electrochemical sensor for free chlorine that can self-clean and be mass produced via microfabrication technique/MEMS(Micro-Electro-Mechanical System)is proposed.A liquid-conjugated Ag/AgCI reference electrode is fabricated,and electrochemically stable BDD/Pt is employed as the working/counter electrode to verify the effectiveness of the as-fabricated sensor for free chlorine *** sensor demonstrates an acceptable limit of detection(0.056 mg/L)and desirable linearity(R^(2)=0.998).Particularly,at a potential of+2.5 V,hydroxyl radicals are generated on the BBD electrode by electrolyzing water,which then remove the organic matter attached to the surface of the sensor though an electrochemical digestion *** performance of the fouled sensor recovers from 50.2 to 94.1%compared with the initial state after self-cleaning for 30 *** addition,by employing the MEMS technique,favorable response consistency and high reproducibility(RSD<4.05%)are observed,offering the opportunity to mass produce the proposed sensor in the future.A desirable linear dependency between the pH,temperature,and flow rate and the detection of free chlorine is observed,ensuring the accuracy of the sensor with any hydrologic *** interesting sensing and selfcleaning behavior of the as-proposed sensor indicate that this study of the mass production of free chlorine sensors by MEMS is successful in developing a competitive device for the online monitoring of free chlorine in tap water.

关键词： chlorine batch electrode

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Automatic depression recognition by intelligent speech signal processing:A systematic survey

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 701-711页

作者： Pingping Wu Ruihao Wang Han Lin Fanlong Zhang Juan Tu Miao Sun Jiangsu Key Laboratory of Public Project Audit School of Engineering AuditNanjing Audit UniversityNanjingChina School of Information Engineering Nanjing Audit UniversityNanjingChina Key Laboratory of Modern Acoustics(MOE) School of PhysicsNanjing UniversityNanjingChina Faculty of Electrical Engineering Mathematics&Computer ScienceDelft University of TechnologyDelftThe Netherlands

Depression has become one of the most common mental illnesses in the *** better prediction and diagnosis,methods of automatic depression recognition based on speech signal are constantly proposed and updated,with a transition from the early traditional methods based on hand‐crafted features to the application of architectures of deep *** paper systematically and precisely outlines the most prominent and up‐to‐date research of automatic depression recognition by intelligent speech signal processing so ***,methods for acoustic feature extraction,algorithms for classification and regression,as well as end to end deep models are investigated and ***,general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition.

关键词： acoustic signal processing deep learning feature extraction speech depression recognition

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Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection

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IEEE Transactions on Affective Computing 2024年第2期16卷 697-711页

作者： Liu, Xin Yuan, Kaishen Niu, Xuesong Shi, Jingang Yu, Zitong Yue, Huanjing Yang, Jingyu Tianjin University School of Electrical and Information Engineering Tianjin300072 China Lappeenranta-Lahti University of Technology LUT Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta53850 Finland Beijing Institute for General Artificial Intelligence Beijing100080 China Xi'an Jiaotong University School of Software Engineering Xi'an710049 China Great Bay University Dongguan523000 China

Facial Action Unit (AU) detection is a crucial task in affective computing and social robotics as it helps to identify emotions expressed through facial expressions. Anatomically, there are innumerable correlations between AUs, which contain rich information and are vital for AU detection. Previous methods used fixed AU correlations based on expert experience or statistical rules on specific benchmarks, but it is challenging to comprehensively reflect complex correlations between AUs via hand-crafted settings. There are alternative methods that employ a fully connected graph to learn these dependencies exhaustively. However, these approaches can result in a computational explosion and high dependency with a large dataset. To address these challenges, this paper proposes a novel self-adjusting AU-correlation learning (SACL) method with less computation for AU detection. This method adaptively learns and updates AU correlation graphs by efficiently leveraging the characteristics of different levels of AU motion and emotion representation information extracted in different stages of the network. Moreover, this paper explores the role of multi-scale learning in correlation information extraction, and design a simple yet effective multi-scale feature learning (MSFL) method to promote better performance in AU detection. By integrating AU correlation information with multi-scale features, the proposed method obtains a more robust feature representation for the final AU detection. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on widely used AU detection benchmark datasets, with only 28.7% and 12.0% of the parameters and FLOPs of the best method, respectively. The code for this method is available at https://***/linuxsino/Self-adjusting-AU. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Linguistic feature template integration for Chinese-Vietnamese neural machine translation

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Frontiers of computer science 2022年第4期16卷 223-225页

作者： Zhiqiang YU Yantuan XIAN Zhengtao YU Yuxin HUANG Junjun GUO Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China School of Mathematics and Computer Science Yunnan Minzu UniversityKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China

1 Introduction and main contributions Template-based approaches have achieved significant progress in low-resource neural machine translation(NMT)recently[1],such as the efficient works,NMT-GTM[2],SoftPrototype[3],***,most previous works only retrieve target sentence as template to generate translation,neglecting the utilization of linguistic feature that contained in the source sentence and template.

关键词： translation template such

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