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

共 6957 条记录，以下是4481-4490 订阅

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Dealloc FTL: Efficiently Managing Temporary Files on SSDs

Dealloc FTL: Efficiently Managing Temporary Files on SSDs

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international conference on Big data and Smart computing (BIGCOMP)

作者： Joontae Kim Sangwon Lee Computer Science and Engineering Sungkyunkwan Univ. Suwon Korea

The size of the data is growing exponentially. Accordingly, the size of the temporary files also increases proportionally. Temporary files have different characteristics from regular files. They are used temporarily and then become obsolete within a short time. However, most SSDs do not consider this characteristic of temporary files. This paper proposes a new FTL architecture considering the features of temporary files: Dealloc FTL. As a result of comparing the transient file processing performance through the experiment, it showed a performance improvement of about 2 to 8 times compared to the original FTL.

关键词： Computer architecture Big data applications Transient analysis

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HBC: Combining Lossy and Lossless Hybrid Bilayer Compression Framework on Time-Series data

HBC: Combining Lossy and Lossless Hybrid Bilayer Compression...

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IEEE international conference on Big data and Cloud computing (BdCloud)

作者： Wanying Lu Liang Liu Wenbin Zhai Haoyuan Chen Yulei Liu Nanjing University of Aeronautics and Astronautics China

The popularization and application of the Internet of Things (IoT) technology has brought massive time series data, which puts forward higher requirements for data compression technology. At present, most existing compression methods use only a single lossy or lossless compression algorithm to perform data compression. Furthermore, traditional lossy compression methods usually adopt a fixed error threshold. However, in practical applications, users have different accuracy requirements for time series data in different numerical ranges. In this paper, we design a Hybrid Bilayer Compression (HBC) framework, which consists of a data accuracy-aware lossy compression layer and a data feature-aware lossless compression layer. First, the original time series data is lossy compressed on the top layer of HBC, where the error threshold can be adaptively adjusted according to the user’s accuracy requirements. Then, based on the features of lossy-compressed data, we use supervised learning to select the optimal lossless compression algorithm from the algorithm pool to further compress the data. Experimental results show that compared with three state-of-the-art compression algorithms, HBC reduces the storage space by 52.1%, 91.66%, and 92.27%, respectively.

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Errors of Identifiers in Anonymous databases: Impact on data Quality 17th

Errors of Identifiers in Anonymous Databases: Impact on Data...

引用

17th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2022

作者： Pombinho, Paulo Cavique, Luís Correia, Luís LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Universidade Aberta Lisboa Portugal

ISBN: (纸本)9783031180491

data quality is essential for a correct understanding of the concepts they represent. data mining is especially relevant when data with inferior quality is used in algorithms that depend on correct data to create accurate models and predictions. In this work, we introduce the issue of errors of identifiers in an anonymous database. The work proposes a quality evaluation approach that considers individual attributes and a contextual analysis that allows additional quality evaluations. The proposed quality analysis model is a robust means of minimizing anonymization costs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Metadata

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Context-aware Outlier Detection for Sensor data Stream processing

Context-aware Outlier Detection for Sensor Data Stream Proce...

引用

IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Aboubakr Benabbas Marco Grawunder Daniela Nicklas Chair of Mobile Software Systems University of Bamberg Bamberg Germany Department of Computer Science University of Oldenburg Oldenburg Germany

Sensor data streaming platforms feed pervasive applications with data through continuous processing tasks. Outliers should be constantly removed from the data. The existence of ontologies and their semantics to express the different properties of sensing devices in semantic models can be leveraged towards solving the problem of outlier detection. We present an outlier detection data stream operator based on sensor ontology-based definitions. This outlier detection operator could be defined and adjusted to fit in different use cases with different requirements. We provide a proof of concept implementation of the operator using a data Stream Management System and evaluate the effect of increasing outlier rates in data sets on the overall performance of the outlier detection.

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Performance Analysis of Various Machine Learning Classification Models Using Twitter data: National Education Policy

Performance Analysis of Various Machine Learning Classificat...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： Khushi Agarwal S. Deepa R.V SivaBalan C. Balakrishnan Department Computer Science Christ University Bangalore India

With the exponential growth of social networking sites, people are using these platforms to express their sentiments on everyday issues. Collection and analysis of people's reactions to purchases of products, public services, etc. are important from a marketing and innovation perspective. Sentiment analysis also called opinion mining or emotion extraction is the classification of emotions in text. This technique has been widely used over the years to determine sentiment within given text data. Twitter is a social media platform primarily used by people to express their feelings about specific events. In this paper, collected tweets about National Education Policy which has been a hot topic for a while; and analyzed them using various machine learning algorithms such as Random Forest classifier, Logistic Regression, SVM, Decision Tree, XGBoost, Naive Bayes. This study shows that the Decision tree algorithm is performing best, compare to all the other algorithms.

关键词： Sentiment analysis Technological innovation Machine learning algorithms Social networking (online) Education Blogs Support vector machine classification

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Design and Implementation of Adaptive Scheduling Management for User-Mode Thread Pool

Design and Implementation of Adaptive Scheduling Management ...

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2022 international conference on Computer Graphics, Artificial Intelligence, and data processing, ICCAID 2022

作者： Cai, Bin Qiu, Bingcheng Yao, Jian Xiao, Xiaohong Liu, Lei Xie, Jiangang Zhang, Xu Wu, Ruilong Guangdong Guangzhou China Wuhan University Hubei Wuhan China Blue Dot Power Data Technology Co. Ltd. Beijing China China Hi-Tech Group Co. Ltd. Guangdong Guangzhou China

ISBN: (纸本)9781510663350

Progress in network and storage technology enables data center services to provide high-concurrency and low-latency processing responses. The user-mode thread pool is one of the technologies that can be used to improve the overall concurrent responsiveness of the service system while ensuring its stability of the system. In the system of multicore processor architecture, the scheduling management of the user-mode thread pool is much more complicated than that of the thread pool. First, the existing method cannot fully utilize the advantage of parallel execution of multicore processor architecture, and it is difficult to implement unified resource provisioning management among multiple user-mode thread pools. Second, the existing user-mode thread scheduling method cannot distinguish the performance requirements of different data processing tasks but treat all user-mode thread tasks in the same way. Last, the existing user-mode thread scheduling method controls the execution of all kernel threads, which is difficult to isolate host threads and other kernel threads in the system. It fails to reflect the performance requirements of different user-mode thread tasks, or to provide finer-grained execution timing control of the user-mode thread. This paper provides a two-level scheduling management method for user-mode thread pool, which can effectively integrate OS scheduling mechanism and user-mode thread scheduling mechanism, with a view to distinguishing the needs of different data processing tasks, enabling more targeted and fine-grained scheduling and execution control of user-mode threads, achieving isolation of host threads from non-host threads and differentiated scheduling control without affecting the scheduling mechanism of the OS itself, and enabling the global unified resource provisioning management of multi-user thread pools for multicore processor architecture. Generally, the new method can greatly improve the efficiency and performance of user thread pools

关键词： Network architecture

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Multi-sensor data Fusion and Deep Machine Learning Models-Based Mental Stress Detection System 1

引用

2nd international conference on Advances in data-driven computing and Intelligent Systems, ADCIS 2023

作者： Gedam, Shruti Paul, Sanchita Jharkhand Mesra Ranchi India

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

Stress is a concern in today’s paced society impacting individuals in various aspects of their lives including educational environments. It is crucial to identify and examine students’ stress levels as it offers insights into their well-being academic performance and excellence of life. The goal of this study is to develop a mental stress detection system that utilizes a combination of multiple sensor’s data fusion and deep machine learning (ML) models with the help of three physiological signals named electrocardiogram (ECG), galvanic skin response (GSR) and skin temperature (ST). data is gathered from 200 students with the help of some stressors using a novel Internet of Medical Things (IoMT) device developed using low-cost sensors. Then the data went through pre-processed methods before being analyzed using some techniques of deep ML models like multi-layer perceptron neural networks (MLPNN), recurrent neural networks (RNN), and long short-term memory (LSTM) networks to identify stress. The results show that the RNN and LSTM models regularly outperform the MLPNN model in recognizing mental stress. These models adequately depicted the dynamic character of stress reactions while efficiently capturing temporal dependencies. The accuracy of the RNN and LSTM models was 92.34% and 93.20%, respectively. These models’ improved performance demonstrates their potential in stress detection applications. The combination of multi-sensor data fusion with deep ML algorithms allows for the precise detection of stress-related physiological changes. This has major consequences for student well-being and academic success since it enables personalized stress management tactics and interventions. Student’s mental health and academic experience can be improved by proactive efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Time Series Forecasting of Ethereum Price by FB-Prophet

Time Series Forecasting of Ethereum Price by FB-Prophet

引用

international conference on Signal processing and Communication (ICSPC)

作者： P Yuvarani P Bharani B Dharun P Dinesh Dept. of Electronics and Communication Engineering M.Kumarasamy College of Engineering Karur India

Ethereum is one of a technology that allows us to create D-Applications and organizations, keep assets, transact, and communicate without being controlled by a central authority. Investing in cryptocurrencies is now a big business, with tremendous capital flow and billions of industries taking over what was formerly a small market. With this investment, it is critical to grasp the highs and lows of a certain cryptocurrency as well as the output provided by such decisions. Prediction of cryptocurrencies is concrete and needs a thorough comprehension of the daily movement of money. This paper comprises of cryptocurrency price prediction and analysis utilizing the FB-Prophet algorithm, using Ethereum as the cryptocurrency under consideration for analysis and prediction Ethereum. In this paper, we will anticipate the daily closing price series of the Ethereum cryptocurrency using pricing data from previous years (January 2020 to December 2021).

关键词： Time series analysis Signal processing algorithms Pricing Predictive models Signal processing Prediction algorithms data models

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Design of Healthcare Blockchain Systems to Support Accessible Edge computing

Design of Healthcare Blockchain Systems to Support Accessibl...

引用

Information Technology: IoT and Smart City (ITIoTSC), international conference on

作者： Yongqi Cai Jiyu Jiao Ziqiang Ma Jiali Chen Yajie Lan Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-Founded by Ningxia Municipality and Ministry of Education School of Electronic and Electrical Engineering Ningxia University Yinchuan China National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-Founded by Ningxia Municipality and Ministry of Education School of Information Engineering Ningxia University Yinchuan China

ISBN: (数字)9798350328349

ISBN: (纸本)9798350328356

Edge computing is an extension of IoT evolution and cloud computing technology development, which is closer to data sources and can provide low-latency and high-bandwidth service support to end users. In this paper, we propose a blockchain-enabled offloading method for edge computing to address the data silos and high latency of service delivery in the healthcare industry, using a cloud-edge-chain three-layer underlying architecture model to achieve a joint design of task offloading and blockchain updates to improve the quality of healthcare services, and blockchain technology to provide authentication for edge service nodes to improve system security, data traceability, and user identity management. The results show that compared with the traditional reinforcement learning Q-learning algorithm, the computational offloading method of the DRLO algorithm designed in this paper converges faster and can effectively reduce the total system processing latency under the same conditions, with a 26% reduction in system processing latency and a 9.2% increase in reward value. Meanwhile, with the change of local terminal computing resources, the algorithm proposed in this paper can still keep the total system latency at a low level.

关键词： Cloud computing Heuristic algorithms Computational modeling Soft sensors Medical services Computer architecture Blockchains

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Block-gram: Mining Knowledgeable Features for Smart Contract Vulnerability Detection 7th

Block-gram: Mining Knowledgeable Features for Smart Contrac...

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7th international conference on Smart computing and Communication, SmartCom 2022

作者： Li, Tao Wang, Haolong Fang, Yaozheng Jian, Zhaolong Wang, Zichun Xie, Xueshuo Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University Tianjin China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031281235

Effective vulnerability detection of large-scale smart contracts is critical because smart contract attacks frequently bring about tremendous economic loss. However, code analysis requiring traversal paths and learning methods requiring many features training is too time-consuming to detect large-scale on-chain contracts. This paper focuses on improving detection efficiency by reducing the dimension of the features, combined with expert knowledge. We propose a feature extraction method Block-gram to form low-dimensional knowledgeable features from the bytecode. We first separate the metadata and convert the runtime code to opcode sequence, dividing the opcode sequence into segments according to some instructions (jump, etc.). Then, we mine extensible Block-gram features for learning-based model training, consisting of 4-dimensional block features and 8-dimensional attribute features. We evaluate these knowledge-based features using seven state-of-the-art learning algorithms to show that the average detection latency speeds up 25 to 650 times, compared with the features extracted by N-gram. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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