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CleanEr: Interactive, Query-Guided Error Mitigation for data Cleaning systems 40

CleanEr: Interactive, Query-Guided Error Mitigation for Data...

引用

40th ieee international conference on data Engineering, ICDE 2024

作者： Schreiber, Ran Amsterdamer, Yael Bar-Ilan University Israel

ISBN: (纸本)9798350317152

A key challenge in data cleaning is estimating which of the tuples in a given database are correct and which are not. However, the output of such systems typically includes both false positives and false negatives, i.e., incorrect tuples labeled as correct and vice versa. When queries are performed over the output of such cleaning systems, cleaning errors may have an intricate impact on the query results. We introduce CleanEr, a generic framework that is used on top of existing data cleaning systems and that assists users in identifying the impact of potential cleaning errors on query results, and in deciding accordingly whether and how to proceed with the cleaning. We introduce novel indicators reflecting the current uncertainty with respect to the tuples in the query result, as well as the effect of each relevant input tuple on this uncertainty. We design and implement efficient algorithms for computing these indicators in CleanEr. Based on these indicators, CleanEr helps the data analysts decide whether to trust the query output and guides them in further cleaning of relevant parts of the data through an interactive process. We propose to demonstrate CleanEr using NELL, a large database extracted from the Web. © 2024 ieee.

关键词： Cleaning

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At-Risk Students Identification based on Machine Learning Approach: A Case Study of Computer science Bachelor Student in Tunisia 20

At-Risk Students Identification based on Machine Learning Ap...

引用

20th ACS/ieee international conference on Computer systems and Applications (AICCSA)

作者： Khalifa, Amani BenSaid, Fatma Kacem, Yessine Hadj Jridi, Zouhaier Univ Gafsa Fac Sci Gafsa Gafsa Tunisia Univ Sfax REGIM Lab Res Grp Intelligent Machines Sfax Tunisia Univ Sfax Natl Engn Sch Sfax CES Lab Sfax Tunisia

ISBN: (纸本)9798350319439

Various data mining techniques, like prediction and clustering, can be applied on educational data in order to study the student's performance and behavior. Predicting academic results is one of the methods that aim at monitoring student progress and anticipating students that are at risk of failure in their academic career. In this paper, we propose a machine learning (ML) based Educational data Mining (EDM) approach, named ARSITUN, for the identification of at-risk students. Using ARSITUN, an early intervention can be performed for the detected students in order to lower the risk of their failure. The proposed approach was developed and tested using student's data that were collected from the Tunisian administration system for bachelors and masters called "Salima". We created a new dataset, named GCSD, that concerns 358 students from the faculty of sciences of Gafsa during the school years period 2014-2022. The experimental results showed that our EDM model reaches an accuracy of 90.44% for computer science bachelors' grade prediction (Tunisian case study).

关键词： Educational data mining At-risk student identification Machine learning Student's performance Bachelors grade prediction

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Urban Rail Transit Passenger Flow Prediction Based on data Mining 5

Urban Rail Transit Passenger Flow Prediction Based on Data M...

引用

5th international conference on Information science, Parallel and Distributed systems, ISPDS 2024

作者： Meng, Jie Yan, Hongshu Jiawang Ren, Xuebin Dalian Polytechnic University Dalian China

ISBN: (纸本)9798350352719

To improve the accuracy of urban rail transit passenger flow prediction, this paper proposes a prediction model for urban rail transit passenger flow based on data mining. Firstly, factor analysis (FA) is used to model and analyze historical data affecting passenger flow to determine common factors and calculate scores. These common factor scores are then used as inputs, combined with the PSO-BP network prediction model for passenger flow forecasting. The forecast results of this model are compared with the prediction results of the PSO-BP model, and it is found that the forecast results of this model are closer to the actual passenger flow. © 2024 ieee.

关键词： Prediction models

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Blockchain-based Federated Learning data Sharing Model 5

Blockchain-based Federated Learning Data Sharing Model

引用

5th international conference on Information science, Parallel and Distributed systems, ISPDS 2024

作者： Xie, Kang Feng, Yunxia Xu, Hongda Han, Zekun University of Science and Technology School of Information Science and Technology Qingdao Qingdao China

ISBN: (纸本)9798350352719

Federated learning has been widely researched and applied in various scenarios. In the context of mobile computing, federated learning protects users from exposing their private data while enabling collaborative training of global models for various practical applications. However, the security of federated learning has come under increasing scrutiny due to attacks from malicious clients or untrusted central servers on global models or user privacy data. To address these security concerns, this paper proposes a federated learning data sharing model based on blockchain and reputation values. This data sharing model utilizes IPFS for storing model parameters, with the model parameters stored in IPFS hash addresses to alleviate the storage pressure on the blockchain. Additionally, an access control mechanism is designed. A consensus mechanism based on reputation values is proposed, aiming to enhance model performance in the face of malicious attacks. © 2024 ieee.

关键词： Federated learning

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Building a Digital Twin for Mechanical systems using a Global Expansion Approach 3

Building a Digital Twin for Mechanical Systems using a Globa...

引用

3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Siripuram, Suryachandra Naidu Kammar, Ankit Gupta, Arjun Vardhan, Kanthala Maha Vishnu Baqersad, Javad Kettering Univ NVH 1700 Univ Ave Flint MI 48504 USA Kettering Univ Expt Mech Lab 1700 Univ Ave Flint MI 48504 USA

ISBN: (纸本)9798350372977;9798350372984

Digital twins of structures are virtual representation of mechanical systems that continuously update using sensor data. However, correlating the million degrees of freedom of the numerical models for the digital twin to the test data is challenging. Thus, proper expansion techniques are desirable. This paper provides analysis of a cantilever beam's dynamic response through the integration of experimental and numerical approaches. There are reduction/expansion approaches to match these degrees of freedom. However, the traditional approaches can only expand displacement, velocity, and acceleration. Sometimes, only strain data is available on the structure. Some researchers have discovered approaches for strain expansion and reduction. This paper describes a global expansion approach for expanding test data capable of expanding strain to displacement or displacement to strain. To show the merit of the approach, a digital twin of a beam is created and tested in the current work.

关键词： Digital Twin Global expansion approach Correlation Strain Displacement test data

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Deep Learning Enabled Secure IoT Module for Smart Agriculture in Diverse Environmental Conditions 14

Deep Learning Enabled Secure IoT Module for Smart Agricultur...

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Ansari, Shaharyar Alam Ali, Salman Luqman, Mohammad Ahmad, Shahnawaz School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science Aligrah Muslim University Aligarh India

ISBN: (纸本)9798331531935

The Internet of Things (IoT) stands as a revolutionary technology which impacts smart agriculture and different sectors beyond it. The research describes an intelligent IoT-enabled monitoring system which uses sensors to bring improved agricultural performance through environmental real-time observations combined with artificial intelligence decision systems. The proposed system completes its operation by executing sensor data acquisition followed by deep learning-based analysis and secure data transmission procedures. The initial stage of IoT-enabled operation utilizes environment-sensing devices which collect essential parameters for precision farming. A Recurrent Neural Network (RNN) processing system applies classification methods to collected data to generate useful insights. Secure cloud data transmission takes place through an encryption system which combines Substitution-Caesar cipher with Improved Elliptic Curve Cryptography (IECC). The system demonstrates 99.64% accuracy, and the proposed encryption method proves efficiency through its average execution times of encryption at 0.3904 ms and decryption at 0.1102 ms which exceeds current models in speed and security performance. © 2025 ieee.

关键词： Recurrent neural networks

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ST-GIN: An Uncertainty Quantification Approach in Traffic data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks 26

ST-GIN: An Uncertainty Quantification Approach in Traffic Da...

引用

ieee 26th international conference on Intelligent Transportation systems (ITSC)

作者： Wang, Zepu Zhuang, Dingyi Li, Yankai Zhao, Jinhua Sun, Peng Wang, Shenhao Hu, Yulin Univ Penn Dept Comp & Informat Sci Philadelphia PA USA MIT Dept Civil & Environm Engn Cambridge MA USA Duke Kunshan Univ Dept Nat & Appl Sci Suzhou Peoples R China Univ Florida Dept Urban & Reg Planning Gainesville FL USA Wuhan Univ Dept Informat & Commun Engn Wuhan Peoples R China

ISBN: (纸本)9798350399462

Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems. However, real-world transportation data, collected from loop detectors or similar sources, often contains missing values (MVs), which can adversely impact associated applications and research. Instead of discarding this incomplete data, researchers have sought to recover these missing values through numerical statistics, tensor decomposition, and deep learning techniques. In this paper, we propose an innovative deep learning approach for imputing missing data. A graph attention architecture is employed to capture the spatial correlations present in traffic data, while a bidirectional neural network is utilized to learn temporal information. Experimental results indicate that our proposed method outperforms all other benchmark techniques, thus demonstrating its effectiveness.

关键词： Recurrent neural networks

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Energy-Efficient GPU-intensive Workload Scheduling for data Centers 22

Energy-Efficient GPU-Intensive Workload Scheduling for Data ...

引用

22nd ieee international conference on Machine Learning and Applications, ICMLA 2023

作者： Smith, Matthew Zhao, Luke Cordova, Jonathan Jiang, Xunfei Ebrahimi, Mahdi California State University Department of Computer Science Northridge United States Brown University Department of Computer Science United States

ISBN: (纸本)9798350345346

Cooling costs count for a significant part of the total energy consumption in data centers, and previous re-searchers mainly focused on investigating thermal-ware workload distribution strategies for CPU-intensive workloads. This paper introduces a novel machine learning-based approach that aims at reducing energy consumption through thermal-aware workload distribution to build energy-efficient data centers for GPU-intensive workload. To achieve this goal, the study employs the GpuCloudSim Plus simulator, which effectively models the dis-tribution of GPU-intensive applications under diverse workloads and utilizations. The integration of machine learning models allows for accurate temperature predictions and comprehensive evaluation of the proposed algorithm's performance. We pro-posed a new workload scheduling algorithm, ThermalAwareGpu, to reduce the energy cost for GPU-intensive workload. We evaluated our algorithm by generating three common patterns of workloads, and saved up to 12.79 % of computing cost compared to the baseline algorithms. Our future work includes exploring the estimation of data center cooling energy and conducting in-depth comparisons of different workload balancing algorithms on various compute-intensive workloads. © 2023 ieee.

关键词： data centers energy-efficient GPU machine learning workload management

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Research on Supply Chain Traceability System Based on Semantic Multi-Blockchain and Pre-OnChain data Verification 16

Research on Supply Chain Traceability System Based on Semant...

引用

international conferences on Internet of Things (iThings) / Green Computing and Communications (GreenCom) / Cyber, Physical and Social Computing (CPSCom) / Smart data, Smartdata and Congress on Cybermatics (Cybermatics)

作者： Li, Lulu Qu, Huan Wang, Wei Wang, Junyu Wang, Zhihui Nong, Bin Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ State Key Lab ASIC & Syst Shanghai Peoples R China Zhuhai Fudan Innovat Inst Dept IoT Zhuhai Peoples R China

ISBN: (纸本)9798350309461

Blockchain-based supply chain traceability systems are characterized by decentralization, transparency, and immutability. However, if all nodes replicate the entire ledger, it would result in significant storage overhead. Some nodes might be forced to exit the system due to inadequate storage resources. Additionally, the chained structure of blockchain implies that query delays will significantly increase as the volume of data grows. Furthermore, the immutable nature of blockchain demands a higher level of accuracy in the original data within the traceability system, as once erroneous data is uploaded to the blockchain, it cannot be altered or removed. In this paper, First, we analyze the requirements of supply chain business scenarios and the characteristics of traceability data. Then, we propose a semantic multi-chain storage architecture, categorizing data from different business entities into respective semantic side-chains, thereby addressing issues such as insufficient node storage and on-chain data explosion. Second, We introduce a semantic aggregation storage optimization technique, consolidating data with identical semantic keys into a single block, thereby reducing the number of blocks accessed during queries and improving query efficiency. Lastly, for the first time, we introduce a pre-onchain data reliability verification mechanism based on data characteristics. The results indicate that the proposed solution can offer reduced on-chain storage space, faster query speeds, and enhanced reliability of the original data sources.

关键词： supply chain traceability multi-blockchain off-chain database data aggregation data reliability verification

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Epsilon non-Greedy: A Bandit Approach for Unbiased Recommendation via Uniform data 23

Epsilon non-Greedy: A Bandit Approach for Unbiased Recommend...

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23rd ieee international conference on data Mining (ieee ICDM)

作者： Sani, S. M. F. Hosseini, Seyed Abbas Rabiee, Hamid R. Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350381641

Often, recommendation systems employ continuous training, leading to a self-feedback loop bias in which the system becomes biased toward its previous recommendations. Recent studies have attempted to mitigate this bias by collecting small amounts of unbiased data. While these studies have successfully developed less biased models, they ignore the crucial fact that the recommendations generated by the model serve as the training data for subsequent training sessions. To address this issue, we propose a framework that learns an unbiased estimator using a small amount of uniformly collected data and focuses on generating improved training data for subsequent training iterations. To accomplish this, we view recommendation as a contextual multi-arm bandit problem and emphasize on exploring items that the model has a limited understanding of. We introduce a new offline sequential training schema that simulates real-world continuous training scenarios in recommendation systems, offering a more appropriate framework for studying self-feedback bias. We demonstrate the superiority of our model over state-ofthe-art debiasing methods by conducting extensive experiments using the proposed training schema.

关键词： Recommendation systems Self-feedback Loop Bias Contextual Multi-Arm Bandit Continuous Training

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