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检索条件"任意字段=17th International Conference on Distributed Computing and Intelligent Technology"

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Performance Analysis of Cooperative HADF Relaying V2I Communication Systems for ND-symbol Burst Transmission using PSA-CE methods Over Quasi-Static Rayleigh Fading channels

Performance Analysis of Cooperative HADF Relaying V2I Commun...

引用

17th international IEEE conference on intelligent Transportation Systems & the Asia-Pacific Council on Systems Engineering conference 2014

作者： Min Baek Gilwon Seo Chungha Bong Kyunbyoung Ko the Convergence-Information Technology Research Center for Next-Generation Intelligent Transport System (C-ITRC for NGITS) the Communication and Signal Processing Lab.(CSPL)the Department of IT ConvergenceKorea National University of Transportation50 Daehak-roChungju-siChungbuk380-702 Korea the Department of Control and Instrumentation Engineering Korea National University of Transportation50 Daehak-roChungju-siChungbuk380-702 Korea

In this paper,we derive the average symbol error rate(SER) for the ND-symbol burst hybrid adaptive decodeor-amplify-forward(HADF) relaying vehicle-to-infrastructure(V2I) communication systems over quasi-static independent and non-identical distributed(INID) Rayleigh fading *** proposed analytical approach includes channel estimation(CE)errors generated by practical pilot symbol assisted-CE(PSACE) ***,based on the error-events at relaying on board equipments(OBEs) for ND-symbol burst data transmission,the conditional SER expression has been derived as an exact ***,the approximated average SER(ASER)expressions are presented as the closed-forms and their accuracy is confirmed by the comparison with simulation ***,we can conclude that our analytical expressions are tractable forms,and can be used as a tool to verify effects of an erroneous detection and hybrid transmission at each relaying OBE on the combined signal-to-noise ratio(SNR) and ASER for cooperative HADF relaying communication systems.

关键词： SNR ADF Performance Analysis of Cooperative HADF Relaying V2I Communication Systems for ND-symbol Burst Transmission using PSA-CE methods Over Quasi-Static Rayleigh Fading channels Rayleigh

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A Lightweight YOLOv5 Model Integrating GhostNet and Attention Mechanism

A Lightweight YOLOv5 Model Integrating GhostNet and Attentio...

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Computer Vision, Image and Deep Learning (CVIDL), international conference on

作者： Xinke Dou Ting Wang Shiliang Shao School of Information Engineering Shenyang University of Chemical Technology Shenyang China State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

Aiming at the problems of complex structure of neural network, large number of parameters and difficult deployment to terminal devices, this paper proposes an improved lightweight object detection model for the purpose of building a model with high classification accuracy and low complexity. First, the one-stage detector yolov5 is used as the base network. Considering the excessive redundant features of feature extraction, Ghost module is used to replace the traditional convolution, effectively allocate computing resources, to reduce the size of the model and improve the speed of model detection. When designing model bottleneck structure, Normalization-based Attention Module (NAM) is added to improve training efficiency and reduce model complexity. Experimental results show that compared with YOLOv5, the calculation speed of the proposed model is increased by 17% and the accuracy is reduced by less than 2%, which is of great significance in the environment of low-performance computing equipment.

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Modeling and Simulating Daily Power Budgets for Sustainable Data Centers

Modeling and Simulating Daily Power Budgets for Sustainable ...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Rumenigue Hohemberger Arthur Francisco Lorenzon Marcelo Caggiani Luizelli Fábio Diniz Rossi Science and Technology Farroupilha (IFFar) Federal Institute of Education Alegrete Brazil Federal University of Pampa (UNIPAMPA) Alegrete Brazil

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

A novel energy-efficient scenario that makes possible to maintain sustainable data centers to feed part of resources through renewable energy sources has emerged. As renewable energies are accumulated in the form of power budgets, data centers must adapt a slice of the processing resources required to meet applications at those limits. this work is modeling and simulating the computing capacity of a data center according to daily power budgets from different sources of renewable energy. the results showed that based on the daily energy harvest of today's renewable energy sources, intelligent resource orchestration could use such energy so that up to 40% of what is needed to maintain a quality-of-service data center comes from non-polluting sources.

关键词： Data centers Data models Servers Computational modeling Energy consumption Biomass

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Blockchain-enabled Edge computing Framework for Hierarchic Cluster-based Federated Learning

Blockchain-enabled Edge Computing Framework for Hierarchic C...

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Xiaoge Huang Yuhang Wu Zhi Chen Qianbin Chen Jie Zhang School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Communication and Information Engineering University of Sheffield United Kingdom

ISBN: (纸本)9781665450867

Federated learning implements decentralized machine learning tasks without exposing users' private data. However, in practical scenarios, intelligent devices data pertain to different fields are non-independent and identically distributed (non-IID), which leads to a decrease in the accuracy of the global model. In addition, if there are untrusted devices participated in federated learning, the global model accuracy will be decreased. To address the above-mentioned issues, in this paper, we propose a blockchain-enabled hierarchic cluster-based federated learning in edge computing framework to improve the accuracy of the global model and ensure the local model credibility. Firstly, we propose the hierarchic cluster-based federated learning (HCFL) algorithm, which realizes hierarchically aggregation based on user cosine similarity to improve global model accuracy. Moreover, blockchain technology is enabled in the proposed HCFL algorithm to verify the local model gradient from IDs before global aggregation. Moreover, incentive mechanism is proposed to dynamically adjust reward of IDs for promote IDs train trusted models. Finally, simulation results demonstrate the efficiency and performance of the blockchain-enabled hierarchic cluster-based federated learning framework.

关键词： Wireless communication Federated learning Computational modeling Simulation Heuristic algorithms Signal processing algorithms Clustering algorithms

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Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding : Extended Abstract

Spatiotemporal Activity Modeling via Hierarchical Cross-Moda...

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international conference on Data Engineering

作者： Yang Liu Xiang Ao Linfeng Dong Chao Zhang Jin Wang Qing He Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Zhejiang University Hangzhou China College of Computing Georgia Tech United States Megagon Labs Mountain View United States

With the ever-increasing urbanization process, modeling people’s spatiotemporal activities from their online traces has become a crucial task. State-of-the-art methods for this task rely on cross-modal embedding, which maps items from different modalities (e.g., location, time, text) into the same latent space. Despite their inspiring results, existing cross-modal embedding methods merely capture co-occurrences between items without modeling their high-order interactions. In this paper, we first construct the user interaction graph and the activity graph from raw data records and propose a hierarchical cross-modal embedding method that takes the high-order relationships into consideration. We introduce both inter-record and intra-record meta-graph structures, which enable learning distributed representations that preserve high-order proximities across graphs from different layers. Our empirical experiments on three real-world datasets demonstrate that our method not only outperforms state-of-the-art methods for spatiotemporal activity prediction but also captures cross-modal proximity at a finer granularity.

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DEEP FEATURE WEIGHTING IN NAIVE BAYES FOR CHINESE TEXT CLASSIFICATION

DEEP FEATURE WEIGHTING IN NAIVE BAYES FOR CHINESE TEXT CLASS...

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2016 4th IEEE international conference on Cloud computing and Intelligence Systems（IEEE CCIS2016）

作者： Qiaowei Jiang Wen Wang Xu Han Shasha Zhang Xinyan Wang Cong Wang School of Software Engineering Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications School of Automation Beijing University of Posts and Telecommunications Air Force General Hospital

ISBN: (纸本)9781509012572

Naive Bayes(NB) continues to be one of the popular methods for text categorization because of its simplicity, efficiency and efficacy. In all of the existing feature weighting approaches, the learned feature weights are only applied to the classification of formula of Naive Bayes. In this paper, we propose a high efficient method which is called deep feature weighting Naive Bayes(DFWNB) [1]. DFWNB addresses to incorporate the learned weights into both the formula of classification and its conditional probability estimates. this paper defines the weight of each feature by TF-IDF feature weighting method. In the field of data mining, there are numerous studies about English text categorization but the Chinese text classification has received less attention from researchers. thus, we apply the deep feature weighting naive Bayes to Chinese text classifiers and obtain a better performance than ordinary feature weight naive Bayes(OFWNB).

关键词： Naive Bayes Deep feature weighting Chinese text classification TF-IDF

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Detection and Evaluation of Cybersecurity threats in MANET Based on AI

Detection and Evaluation of Cybersecurity Threats in MANET B...

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Ubiquitous computing and intelligent Information Systems (ICUIS), international conference on

作者： R. Mohandas Karthik Kumar Vaigandla N. Sivapriya Kirubasankar K Department of Electronics & Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu Department of Electronics & Communication Engineering Balaji Institute of Technology and Science Warangal Telangana Department of Computer Applications Cauvery College for Women Trichy Tamil Nadu

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

this research focuses on the use of AI to enhance the security of Mobile Ad-hoc Networks (MANETs), which aredecentralized and mobile wireless networks characterized by their dynamic nature. the work aims to assess the risks associated with cyber activities and identify various sorts of cyber threats, such as distributed Denial of Service (DDoS) attacks, malware invasions, data leaks and unauthorized access attempts. this will be achieved by using Artificial Intelligence (AI)-powered algorithms and models. the objective is to enhance the accuracy in identifying and categorizing these threats, hence increasing the security and dependability of MANET networks. Anomaly detection is a supplementary protection mechanism that specifically targets the hardware and network traffic of MANETs. Implementing a monitoring strategy is necessary in order to detect any anomalous activity that might potentially indicate security vulnerabilities or attacks in the MANET setups. the primary objective is to identify and address the unique characteristics of timely detection, which may enhance the security capabilities of MANET against cyber attacks. Machine learning (ML) algorithms often demonstrate impressive performance in efficiently identifying and accurately classifying various cyber threats, including DDoS attacks, malware infiltrations, and attempted unauthorized access. this anomaly detection technology effectively identifies robot abnormalities and malicious actions in network data, while also proactively avoiding system vulnerabilities or threats. In addition, this research discovered very effective AI-based cybersecurity solutions for dynamic decentralized MANET systems. these systems were specifically designed for tasks such as street-view switching, route finding, self healing, and self configuration.

关键词： Privacy Accuracy Machine learning Denial-of-service attack Ad hoc networks Malware Reliability Object recognition Mobile computing Anomaly detection

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Uncertain Investment Models for an Insurer with Ruin Constraint

Uncertain Investment Models for an Insurer with Ruin Constra...

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2012 IEEE 19th international conference on Industrial Engineering and Engineering Management(IE&EM 2012)

作者： Qing-feng Song Kai Shi School of Science Tianjin Institute of Urban Construction School of Computer Science and Communication Engineering Tianjin University of Technology

this paper focuses on the optimal investment proportion problem of insurance premium for an insurer in uncertain environments. Two uncertain investment models with ruin constraint are investigated, namely, an investment model with ruin constraint and constant per unit time premium (CPRCIM) and an investment model with ruin constraint and variable premium (VPRCIM), where the individual claim amounts are assumed as uncertain variables and the claim numerical processes are characterized as uncertain renewal processes. the equivalent forms of the above investment models are investigated, particularly, the expressions are given for normal distributed uncertain investment interest rate and lognormal distributed uncertain individual claim amount.

关键词： Investment Ruin Insurance Uncertain variable

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Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning

Robust Categorical Data Clustering Guided by Multi-Granular ...

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international conference on distributed computing Systems

作者： Shenghong Cai Yiqun Zhang Xiaopeng Luo Yiu-Ming Cheung Hong Jia Peng Liu Guangdong University of Technology Guangzhou China Guangzhou Huali College Guangzhou China Hong Kong Baptist University Hong Kong SAR China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Data set composed of categorical features is very common in big data analysis tasks. Since categorical features are usually with a limited number of qualitative possible values, the nested granular cluster effect is prevalent in the implicit discrete distance space of categorical data. that is, data objects frequently overlap in space or subspace to form small compact clusters, and similar small clusters often form larger clusters. However, the distance space cannot be well-defined like the Euclidean distance due to the qualitative categorical data values, which brings great challenges to the cluster analysis of categorical data. In view of this, we design a Multi-Granular Competitive Penalization Learning (MGCPL) algorithm to allow potential clusters to interactively tune themselves and converge in stages with different numbers of naturally compact clusters. To leverage MGCPL, we also propose a Cluster Aggregation strategy based on MGCPL Encoding (CAME) to first encode the data objects according to the learned multi-granular distributions, and then perform final clustering on the embeddings. It turns out that the proposed MGCPL-guided Categorical Data Clustering (MCDC) approach is competent in automatically exploring the nested distribution of multi-granular clusters and highly robust to categorical data sets from various domains. Benefiting from its linear time complexity, MCDC is scalable to large-scale data sets and promising in pre-partitioning data sets or compute nodes for boosting distributed computing. Extensive experiments with statistical evidence demonstrate its superiority compared to state-of-the-art counterparts on various real public data sets.

关键词： Competitive learning distributed databases Clustering algorithms Euclidean distance Big Data Boosting Encoding

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Dynamic look-ahead time in book-ahead reservation in QoS-enabled networks

Dynamic look-ahead time in book-ahead reservation in QoS-ena...

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IEEE international conference on Networks

作者： I. Ahmad J. Kamruzzaman S. Aswathanarayaniah Gippsland School of Computing and Information Technology Monash University Australia

In recent time book-ahead (BA) reservation has drawn increasing attention as multimedia and distributed applications become functionally richer. Book-ahead reservation has proved itself as an effective technique for time sensitive applications that require high amount of bandwidth with guaranteed QoS. One of the major concerns in the implementation of BA reservation in a QoS-enabled network is the preemption of on-going instantaneous requests (IR) calls. Preemption is a threat to service continuity which is seen as an important issue from user's perceived QoS definition found in recent studies. Earlier models proposed to reduce IR preemption use constant look-ahead time. this paper proposes a novel model to calculate look-ahead time dynamically taking traffic parameters and network state into consideration. Simulation results show that the proposed model achieves lower preemption probability, higher throughput, lower wasted throughput and higher effective revenue.

关键词： intelligent networks Quality of service throughput Bandwidth Telecommunication traffic Traffic control Video on demand Costs Admission control Computer networks

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