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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6952 条记录，以下是371-380 订阅

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Flexible-responsive data replication methodology for optimal performance in cloud computing

Flexible-responsive data replication methodology for optimal...

引用

international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Kolte, Snehal Pradhan, Madhavi Ajay Computer Engineering AISSMS COE SPPU Pune India

ISBN: (纸本)9781032669663

Cloud computing has gained significant attention from researchers due to its ability to offer reliability, fast data access and intense data availability through the use of replicas. However, managing replicas in large, dynamic, and virtualized data centers can be complex. In order to minimize response time and deliver worthwhile availability to achieve load balancing in cloud, a placement strategy has been newly put forward. This strategy takes into account two key factors: average response time, and storage capacity utilization. It is important to use replication wisely, as storage space at each site is limited and only important replicas should be kept it plays a vital role in the education field and in any business environment. Additionally, a new replica replacement approach has been developed based on the amount of copies, count replica accessed, file availability, based on replica last accessed, and the size of the replica. The effectiveness of this approach has been evaluated using the CloudSim simulator, and it has been shown effective in average response time, and storage capacity utilization. © 2024 The Author(s).

关键词： Scheduling algorithms

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User-Independent Motion and Location Analysis for Sussex-Huawei Locomotion data

User-Independent Motion and Location Analysis for Sussex-Hua...

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ACM international Joint conference on Pervasive and Ubiquitous computing / ACM international Symposium on Wearable computing (UbiComp/ISWC)

作者： Hwang, Sungjin Cho, Youngwug Kim, Kwanguk Hanyang Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9798400702006

Transportation mode detection (TMD) is a context-aware computing technology with significant potential in several applications. However, the development of TMD technologies for real-world scenarios remains challenging, including user-independent evaluations and multimodal analyses. In this study, our team (HYU-CSE) suggested a TMD model as part of the Sussex-Huawei Locomotion (SHL) recognition challenge, and we used the SHL motion and location data. The proposed TMD model was based on the DenseNet architecture, and post-processing using voting schemes was applied to refine the detection performance. The results suggested that the proposed method achieved 94.13% of an F1 score with userindependent analysis. We hope that our study will ultimately help in the design of better TMD applications.

关键词： Transportation mode detection Smartphone sensors Deep learning

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Exploration of Tourism Monitoring Index System in the Context of Big data 2

Exploration of Tourism Monitoring Index System in the Contex...

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2nd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Luo, Xi Chengdu Neusoft University School of Information and Business Management Sichuan Chengdu China

ISBN: (纸本)9798350339765

The monitoring and regulation of tourism industry benefits is an effective way to realize the conservation and presentation of heritage sites, and it is important to collaborate with big data (BD) information technology. This paper briefly analyzes the big data technology (BDT), tourism monitoring data collection and processing and TMIS design principles, and explores the TMIS in the context of BD by classifying, collecting and organizing tourism information, as well as the design of data storage and calling resource objects, realizing the basic information of scenic spots and the query of contemporary all-embracing data and cultural data in BD *** using the TMIS system, tourist satisfaction increased from 64% to 85%, staff satisfaction increased from 42% to 67%.The powerful data collection, processing, display and visualization functions of the BD platform are of great significance to the construction of the tourism monitoring index system (TMIS), providing favorable guarantees for the collection, calculation, processing, access and query of massive information data. © 2023 IEEE.

关键词： Big data

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AEWF: Autoencoder-based Website Fingerprinting in data-limited Scenarios

AEWF: Autoencoder-based Website Fingerprinting in Data-limit...

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IEEE international Performance, computing, and Communications conference (IPCCC)

作者： Lin, Jianjun Sun, Bo Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Natl Comp Network Emergency Response Tech Team Coordinat Ctr China CNCERT CC Beijing Peoples R China

ISBN: (纸本)9798350302936

With the assistance of deep learning (DL), website fingerprinting (WF) attacks, which enable a local passive attacker to ascertain the website visited by a user, even when utilizing an anonymous network such as Tor, have become increasingly effective. Nevertheless, the majority of these attacks necessitate a substantial amount of labeled data, posing a practical burden. Recent attempts at mitigating this issue involve the utilization of auxiliary data. However, these methods either require the auxiliary data to be labeled or fail to achieve satisfactory results. In this study, we propose a novel approach that leverages autoencoders to better exploit the unlabeled auxiliary data. Through the reconstruction task, the network learns to extract representative features, thereby enhancing the classifier's performance. We validate the efficacy of our method, which outperforms competing algorithms in the majority of our experimental setups, encompassing both closed-world (CW) and open-world (OW) scenarios.

关键词： anonymous network Tor website fingerprinting

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An Approach to Perform Sentiment Analysis using data Mining algorithms 2

An Approach to Perform Sentiment Analysis using Data Mining ...

引用

2nd international conference on Edge computing and Applications, ICECAA 2023

作者： Kaur, Milanjit Joshi, Kamini Goyal, Bhawna Dogra, Ayush Chandigarh University Department of Computer Science and Engineering Mohali India Chandigarh University Department of Ece Ucrd Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350347579

To perform the sentiment analysis as a basis for defining and extracting subjective information from sources or easily relating to the identification phase of the polarity of the text, the concept of Natural processing is used. Participatory approach is required to perform this analysis. It was also called opinion mining as it extracts a user's view or perspective. There are many attributes which pose a problem with knowledge. It is an arbitrary for choosing assets giving a wider range of values. In the current paper, various algorithms of classification are used and it is concluded that the best algorithm is random forest. The issue is that decision trees, especially if the tree is particularly deep, are vulnerable to being over fit. To minimize the bias and error of variance, classification along with random forest classification is used. Through practicing on different data sets, random forests minimize variance. In the proposed study, boosted methodology along with Random forest, instead of using only random forest is implemented due to which optimization of the Ant colony search alongside with the proposed classification to hit the classification for sentiment analysis of various reviews of films for research precision. © 2023 IEEE.

关键词： Sentiment analysis

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Design of 5G private network quality monitoring System based on edge computing 8

Design of 5G private network quality monitoring System based...

引用

8th international conference on Intelligent computing and Signal processing, ICSP 2023

作者： Wang, Zheng Wang, Xiaoni Beijing Information Science and Technology University School of Applied Science Beijing China

ISBN: (纸本)9798350302455

In order to ensure the continuous, secure, and reliable network connection and coverage of the private network environment, the quality assurance requirements of different network states in different 5G private network scenarios are addressed. This paper proposes a 5G private network quality monitoring system based on edge computing. By connecting the 5G acquisition module to the private network environment, the system uses edge computing technology to carry out preliminary cleaning, integration, encryption and other processing of the collected data, unload the computing tasks originally needed to be carried out in the cloud to the terminal, and realize data transmission between the terminal and the big data cloud platform based on MQTT protocol. Finally, the system provides real-time monitoring of the private network environment through visual display on the front-end interface. The experimental results show that the system remains stable after pressure testing and has good reliability, which can provide real-time security protection and traceability basis for the private network environment. © 2023 IEEE.

关键词： Big data

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VQA-Classification-Algorithm-Oriented Performance Benchmarks for Quantum computing 8

VQA-Classification-Algorithm-Oriented Performance Benchmarks...

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8th international conference on Cloud computing and Big data Analytics (ICCCBDA)

作者： Zhang, Meng Wang, Jing Yao, Fei Lai, Junsen China Acad Informat Commun Technol CAICT MIIT Res Inst Telecommun Transmiss RITT Beijing Peoples R China

ISBN: (纸本)9781665455336

Quantum computing, taking advantage of its parallel computing, is expected to provide exponential acceleration on some difficult problems. It is one of the important directions for the leapfrog development of computing power in the future. However, due to the limitation of the number of quantum bits, coherence time, fidelity and other factors, the computing power of quantum computers has not been fully utilized at the current stage. In recent years, researchers have proposed a variety of quantum computing performance benchmarks to evaluate and research the performance of quantum computers from the bit, circuit, system, application and other levels. However, the existing performance benchmarks cannot directly evaluate the ability of quantum computers to solve specific problems. Considering the above problems, this paper proposes a VQA-classification-algorithm-oriented performance benchmarks for quantum computing, which takes the classification effect under different data scales as one of the evaluation indicators. This benchmark can comprehensively evaluate the ability of quantum computers to solve data classification problems from the three dimensions of scale, speed, and quality. The benchmark was used to evaluate the superconducting quantum computer and simulator of the IBM and Huawei quantum platform, and its effect was verified experimentally.

关键词： quantum computing variational quantum algorithms performance benchmarks data classification

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Resource Allocation and Scheduling of Art data Recognition Tasks: Automatic Optimization of AI algorithms 14th

Resource Allocation and Scheduling of Art Data Recognition T...

引用

14th international conference on Frontier computing, FC 2024

作者： Wang, Jianling Dongying Vocational College Shandong Dongying257091 China

ISBN: (纸本)9789819627974

In this paper, we study how to optimize the resource allocation and scheduling in art data recognition task by AI algorithm to improve the recognition efficiency and accuracy. This study first analyzes the main challenges faced by current art data recognition, including data diversity, limited processing resources and complexity of recognition tasks. Then this paper introduces the existing resource allocation and scheduling strategies, and points out their limitations. Then, this paper proposes a new automatic optimization framework based on Deep Reinforcement Learning (DRL), which aims to dynamically adjust resource allocation and optimize task scheduling strategies, thus improving the overall recognition performance. In the experimental phase, the performance of DRL algorithm, SVM(Support Vector Machine) algorithm, decision tree and random forest algorithm in art art data recognition task is evaluated through four experiments. In the benchmark performance comparison experiment, the area under the ROC (Receiver Operating Characteristic) curve of DRL reaches 0.95. In the resource utilization efficiency experiment, the average Central processing Unit (CPU) usage of DRL is 45% and the memory usage is 30%. In dynamic load adaptability experiments, DRL shows shorter task response time and higher system throughput under different load conditions. In the final long-term stability and scalability experiments, DRL maintains 95% recognition accuracy after long-term operation, and achieves 25% processing speed improvement after increasing computing resources. From the above data conclusions, it can be concluded that DRL strategy performs significantly better than other recognition machine learning algorithms in art art data recognition tasks in terms of benchmark performance and resource utilization efficiency, and indirectly emphasizes the potential and advantages of DRL algorithm in processing complex recognition tasks. © The Author(s), under exclusive license to Springer N

关键词： Scheduling algorithms

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RCD-SGD: Resource-Constrained Distributed SGD in Heterogeneous Environment Via Submodular Partitioning

RCD-SGD: Resource-Constrained Distributed SGD in Heterogeneo...

引用

IEEE/CVF international conference on Computer Vision (ICCV)

作者： He, Haoze Dube, Parijat New York Univ New York NY 10003 USA IBM Res New York NY USA

ISBN: (纸本)9798350307443

The convergence of SGD based distributed training algorithms is tied to the data distribution across workers. Standard partitioning techniques try to achieve equal-sized partitions with per-class population distribution in proportion to the total dataset. Partitions having the same overall population size or even the same number of samples per class may still have Non-IID distribution in the feature space. In heterogeneous computing environments, when devices have different computing capabilities, even-sized partitions across devices can lead to the straggler problem in distributed SGD. We develop a framework for distributed SGD in heterogeneous environments based on a novel data partitioning algorithm involving submodular optimization. Our data partitioning algorithm explicitly accounts for resource heterogeneity across workers while achieving similar class-level feature distribution and maintaining class balance. Based on this algorithm, we develop a distributed SGD framework that can accelerate existing SOTA distributed training algorithms by up to 32%.

关键词： distributed SGD, submodular optimization, data partitioning, deep learning training

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Secure, Efficient, and Privacy-Protecting One-to-Many Cross-Chain Shared data Consistency Audit 21

Secure, Efficient, and Privacy-Protecting One-to-Many Cross-...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Dan Ding, Panpan Zhou, Yuqian Yang, Yuguang Li, Chenting Nanjing University of Aeronautics and Astronautics Nanjing211100 China Beijing University of Technology Beijing100022 China

ISBN: (纸本)9798350329223

In recent years, blockchain has been introduced into more and more research fields and is expected to realize data sharing among multiple parties. To solve this problem, researchers are working to realize cross-chain collaboration and interaction. There is a lack of research on the consistency of cross-chain shared data. Based on the supervision chain, this paper proposes a cross-chain shared data consistency audit scheme that is safe, efficient, and can protect data privacy. The proposed scheme includes a sender and multiple receivers. If all receivers correctly and completely store the data sent by the sender, their data is consistent and the same as the sender. The supervision chain verifies the data consistency of multiple receivers through batch processing to achieve efficient auditing. In addition, the scheme ensures data privacy in the cross-chain and audit processes through the RSA encryption algorithm and the intractability of the DL problem. Finally, the security analysis shows the correctness and security of the proposed scheme. Theoretical and experimental analysis shows the efficiency of the scheme. © 2023 IEEE.

关键词： Blockchain

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