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International Conference on Data engineering

作者： Xiaoou Ding Yingze Li Hongzhi Wang Chen Wang Yida Liu Jianmin Wang School of Computer Science and Technology Harbin Institute of Technology National Engineering Research Center for Big Data Software EIRI Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods.

关键词： Data integrity Time series analysis Semantics Maintenance engineering Data engineering Cognition Cleaning

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Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover

Efficient Relaxed Functional Dependency Discovery with Minim...

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International Conference on Data engineering

作者： Xiaoou Ding Yida Liu Hongzhi Wang Chen Wang Yichen Song Donghua Yang Jianmin Wang School of Computer Science and Technology Harbin Institute of Technology National Engineering Research Center for Big Data Software EIRI Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2 m-l ) to O(1.5 m-1 ). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks.

关键词： Data integrity Soft sensors Transforms Data engineering Cleaning Data governance Data mining

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Cost Minimization in Serverless Computing with Energy Harvesting SECs

Cost Minimization in Serverless Computing with Energy Harves...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Li, Yunqi Liu, Jing Jiang, Bin Yang, Changlin Wang, Qingtian Zhongyuan University of Technology School of Computer Science China State Information Center Beijing China China Scholarship Council China Sun Yat-sen University School of Software Engineering China China Telecom Research Institute Beijing China

ISBN: (纸本)9798350321524

With an increasing number of Mobile Users (MUs), Multi-access edge computing (MEC) has become a bottleneck in resource limitation. Serverless edge computing (SEC) is a promising approach to effectively alleviate the shortage of MEC. However, existing research on SEC focus on the operating mode of the SEC server, they ignore the interaction between SEC and MU. To this end, we propose a Stackelberg game approach to maximize the utility of each MU. We present the model of the Stackelberg game and propose an iterative algorithm as the solution. We also consider the impact of the function resource pool and using renewable energy on SEC. In particular, when a function that required by a MU is not stored in this SEC, it downloads the function from could with extra cost. Meanwhile, the SEC has a lower cost by using harvested energy rather than purchasing from the grid. Simulation results show that the proposed scheme is efficient in terms of SEC's profit and MU's demand. Moreover, both MUs and SECs gain benefits from renewable energy. © 2023 IEEE.

关键词： Energy harvesting

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Blockchain and Deep Learning Development of Smart Charging of Electric Vehicles to Meet the Demand Side Management 2

Blockchain and Deep Learning Development of Smart Charging o...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Shirley, C.P. Evangelin Sonia, S.V. Sathya, V. Manikandan, N. Vidhyalakshmi, M.K. Reddy, Ch. Venkata Krishna Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Department of Software Engineering Periyaar Maniammai Institute of Science and Technology Tamil Nadu Thanjavur India Kalasalingam Business School Kalasalingam Academy of Research and Education Krishnankoil Department of Business Administration Tamil Nadu Srivilliputhur India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India

ISBN: (纸本)9781665491990

The use of Electrical Vehicles (EVs) is increasing rapidly nowadays. This results in the efficient utilization of electrical energy. This leads to making the grid more economically reliable. Thus the smart charging of electric vehicles is done through blockchain technology with deep learning to meet the demand side management. This helps in the determination of the charging system with higher accuracy the systems. This indudes real-time implementation with higher precision control. This helps to obtain the exact optimal solution for dynamic functions. The deep earning helps to obtain real-time implementation in the system. This also helps to obtain the optimum charging cost consumed at a specific period. The charging station energy management system is implemented based on blockchain technology. This helps to meet the demand side management in the system. They are accompanied by centralized and decentralized energy models in the architecture. This also helps in the reduction of peer-to-peer transactions proceeding from the electricity grid to electric vehicles. © 2023 IEEE.

关键词： Energy management systems

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Evolutionary Multitasking Bi-Directional Particle Swarm Optimization for High-Dimensional Feature Selection

Evolutionary Multitasking Bi-Directional Particle Swarm Opti...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Yang, Jia-Quan Du, Ke-Jing Chen, Chun-Hua Wang, Hua Zhang, Jun Zhan, Zhi-Hui School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC8001 Australia School of Software Engineering South China University of Technology Guangzhou51006 China Hanyang University Ansan15588 Korea Republic of

ISBN: (纸本)9798350314588

Feature selection is an important data processing technique, aiming to reduce the redundant and irrelevant features of data. However, as the number of features increases, feature selection algorithms based on particle swarm optimization (PSO) face the challenges of low search efficiency and huge computational consumption due to the enormous search space. A recently proposed bi-directional feature fixation (BDFF) framework for PSO has shown its effectiveness in solving high-dimensional feature selection problems, but it may mislead the particles to search in the wrong direction and requires a long time to find a small feature subset. Utilizing the prior knowledge of feature selection is expected to further enhance the performance of BDFF. Therefore, this paper first designs two tasks to introduce the prior knowledge of feature selection into BDFF while retaining its global search ability. Then, the multitasking bi-directional PSO (MBDPSO) is proposed by combining BDFF and the evolutionary multitasking optimization (EMTO) technique, which can help transfer knowledge between the two tasks effectively. Experimental results on 10 public classification datasets demonstrate that the proposed MBDPSO has an excellent performance on high-dimensional feature selection problems. © 2023 IEEE.

关键词： Feature Selection

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An Investigation of Blockchain-Based Sharding 7th

An Investigation of Blockchain-Based Sharding

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

作者： Xiao, Jiahong Liang, Wei Cai, Jiahong Zhu, Hangyu Li, Xiong Xie, Songyou 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 Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9783031281235

Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical features of blockchain have also made it a hot issue for global research. With the wide application of blockchain technology in various industries, some defects are gradually exposed, and more prominently, the blockchain system is unable to meet the current demand of explosive growth of data volume and frequent data interaction. As one of the key technologies to solve this problem, sharding technology is gaining attention. This article introduces common blockchain scaling schemes and focuses on an overview of blockchain sharding. Sharding technology is introduced from two perspectives of intra-slice consensus and inter-slice consensus. The current mainstream slicing technology is summarized according to three different slicing methods: network sharding, transaction sharding, and state sharding. Finally, the challenges faced by current blockchain sharding technology are analyzed and the full text is summarized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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QoS Prediction Model of Cloud Services Based on Deep Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 564-566页

作者： WenJun Huang PeiYun Zhang YuTong Chen MengChu Zhou Yusuf Al-Turki Abdullah Abusorrah Nanjing University of Information Science&Technology School of Computer and SoftwareNanjingChina IEEE New Jersey Institute of Technology Helen and John C.Hartmann Department of Electrical and Computer EngineeringNewarkUSA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

关键词： services Services individual

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction

A Comparison of Deep Learning and Machine Learning Methods f...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： N. Srinivasa Gupta B. Valarmathi Ishaan Hiwanj Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Predicting water quality is essential to preserving human health and environmental sustain ability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72 %, 83 %, 69%, 63 %, 66%, 71 %, 63 %, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models - Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest - evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights.

关键词： Support vector machines Adaptation models Accuracy Scalability Water quality Boosting Reliability Random forests Long short term memory Regression tree analysis

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Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors

arXiv

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arXiv 2024年

作者： Li, Guanghua Lu, Wensheng Zhang, Wei Lian, Defu Lu, Kezhong Mao, Rui Shu, Kai Liao, Hao College of Computer Science and Software Engineering Shenzhen University China School of Computer Science and Technology University of Science and Technology of China China Illinois Institute of Technology United States

The proliferation of fake news has had far-reaching implications on politics, the economy, and society at large. While Fake news detection methods have been employed to mitigate this issue, they primarily depend on two essential elements: the quality and relevance of the evidence, and the effectiveness of the verdict prediction mechanism. Traditional methods, which often source information from static repositories like Wikipedia, are limited by outdated or incomplete data, particularly for emerging or rare claims. Large Language Models (LLMs), known for their remarkable reasoning and generative capabilities, introduce a new frontier for fake news detection. However, like traditional methods, LLM-based solutions also grapple with the limitations of stale and long-tail knowledge. Additionally, retrieval-enhanced LLMs frequently struggle with issues such as low-quality evidence retrieval and context length constraints. To address these challenges, we introduce a novel, retrieval-augmented LLMs framework-the first of its kind to automatically and strategically extract key evidence from web sources for claim verification. Employing a multi-round retrieval strategy, our framework ensures the acquisition of sufficient, relevant evidence, thereby enhancing performance. Comprehensive experiments across three real-world datasets validate the framework's superiority over existing methods. Importantly, our model not only delivers accurate verdicts but also offers human-readable explanations to improve result interpretability. © 2024, CC BY-NC-ND.

关键词： Fake detection

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What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs? 23

What Makes Good In-Context Demonstrations for Code Intellige...

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Proceedings of the 38th IEEE/ACM International Conference on Automated software engineering

作者： Shuzheng Gao Xin-Cheng Wen Cuiyun Gao Wenxuan Wang Hongyu Zhang Michael R. Lyu School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong China School of Big Data and Software Engineering Chongqing University China

ISBN: (纸本)9798350329964

Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models have shown the ability of in-context learning (ICL). ICL employs task instructions and a few examples as demonstrations, and then inputs the demonstrations to the language models for making predictions. This new learning paradigm is training-free and has shown impressive performance in various natural language processing and code intelligence tasks. However, the performance of ICL heavily relies on the quality of demonstrations, e.g., the selected examples. It is important to systematically investigate how to construct a good demonstration for code-related tasks. In this paper, we empirically explore the impact of three key factors on the performance of ICL in code intelligence tasks: the selection, order, and number of demonstration examples. We conduct extensive experiments on three code intelligence tasks including code summarization, bug fixing, and program synthesis. Our experimental results demonstrate that all the above three factors dramatically impact the performance of ICL in code intelligence tasks. Additionally, we summarize our findings and provide takeaway suggestions on how to construct effective demonstrations, taking into account these three perspectives. We also show that a carefully-designed demonstration based on our findings can lead to substantial improvements over widely-used demonstration construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%, 175.96%, and 50.81% on code summarization, bug fixing, and program synthesis, respectively.

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