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GIDCL: A Graph-Enhanced Interpretable data Cleaning Framework with Large Language Models

Proceedings of the ACM on Management of data 2024年第6期2卷 1-29页

作者： Mengyi Yan Yaoshu Wang Yue Wang Xiaoye Miao Jianxin Li School of Computer Science and Engineering Beihang University Beijing CN Shenzhen Institute of Computing Sciences Shenzhen University Shenzhen Guangdong CN Shenzhen Institute of Computing Sciences Shenzhen Guangdong CN Center for Data Science Zhejiang University Hangzhou Zhejiang CN

data quality is critical across many applications. The utility of data is undermined by various errors, making rigorous data cleaning a necessity. Traditional data cleaning systems depend heavily on predefined rules and constraints, which necessitate significant domain knowledge and manual effort. Moreover, while configuration-free approaches and deep learning methods have been explored, they struggle with complex error patterns, lacking interpretability, requiring extensive feature engineering or labeled data. This paper introduces GIDCL (Graph-enhanced Interpretable data Cleaning with Large language models), a pioneering framework that harnesses the capabilities of Large Language Models (LLMs) alongside Graph Neural Network (GNN) to address the challenges of traditional and machine learning-based data cleaning methods. By converting relational tables into graph structures, GIDCL utilizes GNN to effectively capture and leverage structural correlations among data, enhancing the model's ability to understand and rectify complex dependencies and errors. The framework's creator-critic workflow innovatively employs LLMs to automatically generate interpretable data cleaning rules and tailor feature engineering with minimal labeled data. This process includes the iterative refinement of error detection and correction models through few-shot learning, significantly reducing the need for extensive manual configuration. GIDCL not only improves the precision and efficiency of data cleaning but also enhances its interpretability, making it accessible and practical for non-expert users. Our extensive experiments demonstrate that GIDCL significantly outperforms existing methods, improving F1-scores by 10% on average while requiring only 20 labeled tuples.

关键词： data quality graph neural network interpretable large language models

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Efficient DNA Sequence Identification: A Classification Approach Integrating Gaussian RBM 1

Efficient DNA Sequence Identification: A Classification Appr...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Gulati, Chahat Barman, Biswajit Balde, Govind Hanmantrao Arul Mary Rexy, V. Sachdeva, Meena Patil, Harshal Birla Institute of Technology and Science BITS Department of Electronics and Communication Rajasthan Pilani India Asian International University Department of Biotechnology Imphal West Manipur India N.T.V.S'S G.T.Patil Arts Commerce and Science College Department of Zoology Nandurbar India Saveetha College of Liberal Arts and Sciences SIMATS Department of Commerce Chennai India JIMS Engineering Management and Technical Campus Department of Computer Science Greater Noida India Sri Balaji University Department of Data Science Maharashtra Pune India

ISBN: (纸本)9798350371802

Using machine learning techniques, this article examines cancer patients' DNA sequences. Cancer is certainly one of the most common and devastating diseases in the contemporary era, with a large number of new cases and deaths every year. Many people lose their lives each year to this terrible illness. Methods to combat this illness are the focus of intense investigation by scientists from a wide range of fields. Recently, machine learning has improved the analysis of cancer patients' DNA sequences. Sequencing is crucial for preprocessing, feature extraction, and model training. Among the preprocessing stages is cleaning up the sequence, which entails eliminating PCR repetitions, impurities, and vectors. Feature extraction has been carried out on each DNA sequence in order to retrieve statistical characteristics. It is of utmost importance to regulate features accurately when training a Gaussian RBM. A grade of 96.44% demonstrated a considerable improvement in accuracy, according to the results. © 2024 IEEE.

关键词： Polymerase chain reaction

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Towards Harmless Rawlsian Fairness Regardless of Demographic Prior

arXiv

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arXiv 2024年

作者： Wang, Xuanqian Li, Jing Tsang, Ivor W. Ong, Yew-Soon School of Computer Science and Engineering Beihang University China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the training set, namely harmless Rawlsian fairness. We ascertain that such a fairness requirement with no prior demographic information essential promotes training losses to exhibit a Dirac delta distribution. To this end, we propose a simple but effective method named VFair to minimize the variance of training losses inside the optimal set of empirical losses. This problem is then optimized by a tailored dynamic update approach that operates in both loss and gradient dimensions, directing the model towards relatively fairer solutions while preserving its intact utility. Our experimental findings indicate that regression tasks, which are relatively unexplored from literature, can achieve significant fairness improvement through VFair regardless of any prior, whereas classification tasks usually do not because of their quantized utility measurements. The implementation of our method is publicly available at https://***/wxqpxw/VFair. © 2024, CC BY.

关键词： Differential privacy

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Mandarin Speech Recognition Based on Chinese Syllable Similarity for Children 7

Mandarin Speech Recognition Based on Chinese Syllable Simila...

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7th International Conference on Signal and Image Processing, ICSIP 2022

作者： Xu, Anchong Hu, Wenxin Yang, Jing Zheng, Jun East China Normal University School of Data Science Engineering 3663 N. Zhongshan Rd. Shanghai China Institute of Brain and Education Innovation School of Data Science Engineering East China Normal University 3663 N. Zhongshan Rd. Shanghai China East China Normal University Computer Science and Technology 3663 N. Zhongshan Rd. Shanghai China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Deep learning has facilitated the advancement and wide application of speech recognition technology. While mainstream speech recognition models are usually trained with adult standard speech data, which do not take into account the particularities of special groups, which is not suitable for all groups. Compared with adult speech, child speech recognition is more challenging because there are greater differences in acoustic and linguistic features between speakers. Furthermore, children have their own unique pronunciation habits and pronunciation methods and it's hard for them to clear the difference between such as level and oblique tones, front nasal and back nasal. In addition, the lack of adequate and appropriate children's voice resources increases the challenge of designing robust end-to-end neural structures. Therefore, we propose a novel model for Chinese children's speech recognition, the Chinese children syllable similarity recognition model, which is used to help distinguish and identify children's ambiguous pronunciation, in order to achieve a better recognition effect. Since the speech recognition of young children is usually short words and phrase recognition, the existing recognition evaluation metrics are not suitable. In the paper, we design a sliding window matching judgment method, which greatly improves the accuracy of children's speech recognition and achieves very good recognition effect. Our model achieves a judgment accuracy of 91.7% on our dataset, which is higher than other models. © 2022 IEEE.

关键词： Speech recognition

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A Novel CAD Framework with Visual and Textual Interpretability: Multimodal Insights for Predicting Respiratory Diseases

A Novel CAD Framework with Visual and Textual Interpretabili...

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International Artificial Intelligence and data Processing Symposium (IDAP)

作者： Raza Mukhlis Saied Salem Hyunwook Kwon Jamil Hussain Ahmet Arif Aydin Mugahed A. Al-Antari Department of Artificial Intelligence and Data Science College of AI Convergence Sejong University Seoul Korea Department of Computer Engineering Faculty of Engineering Inonu University Malatya Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Generating textual interpretability using recent advancements in large language models (LLMs) is crucial for enhancing the efficiency of comprehensive computer-aided diagnosis (CAD) systems. This improves transparency between medical staff, intelligent CAD systems, and end-users by creating a trustworthy and effective intermediate medical diagnosis environment. In this paper, an innovative explainable throughout CAD system is introduced, designed to predict diseases from Chest X-rays (CXR) in a comprehensive scenario. The primary goal is to undertake multiple tasks that reduce the burden on medical staff and enrich CAD outcomes, including classification, visual explanations (heatmaps), and textual report generation. The proposed CAD system is developed through eight key steps: data Collection and Annotation, data Preparation, Text Vectorizations (Indexing), Visual Encoder, RAG-Fusion, Structural Prompt, XAI LLmTextual Reasoning (LLM Model), and Final Output (LLM textual report, image classification, and heatmap localization). The AI-based CAD system is trained and evaluated using the public benchmark MIMIC-CXR dataset with 14 different classes. The classification performance achieved an overall accuracy of $70 \%$, precision of $70 \%$, and F1-score of $0.60 \%$, while for text report generation, the system obtained an average BERTScore precision of 0.83, RougeL 0.16, and a Meteor score of 0.28. These promising results suggest the potential for further improvement of the CAD system and its applicability to real-world medical tasks.

关键词： Heating systems Location awareness Visualization Solid modeling Accuracy Pulmonary diseases data models Meteors Medical diagnostic imaging X-ray imaging

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Closer through commonality: Enhancing hypergraph contrastive learning with shared groups

Closer through commonality: Enhancing hypergraph contrastive...

引用

IEEE International Conference on Big data

作者： Daeyoung Roh Donghee Han Daehee Kim Keejun Han Mun Yi Graduate School of Data Science KAIST Republic of Korea School of Computer Engineering Hansung University Republic of Korea Department of Industrial and Systems Engineering KAIST Republic of Korea

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Hypergraphs provide a superior modeling frame-work for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional homogeneous graphs. However, there have been few studies on hypergraph-based contrastive learning, and existing graph-based contrastive learning methods have not been able to fully exploit the high-order correlation information in hypergraphs. Here, we propose a Hypergraph Fine-grained contrastive learning (HyFi) method designed to exploit the complex high-dimensional information inherent in hypergraphs. While avoiding traditional graph augmentation methods that corrupt the hypergraph topology, the proposed method provides a simple and efficient learning augmentation function by adding noise to node features. Furthermore, we expands beyond the traditional dichotomous relationship between positive and negative samples in contrastive learning by introducing a new relationship of weak positives. It demonstrates the importance of fine-graining positive samples in contrastive learning. Therefore, HyFi is able to produce high-quality embeddings, and outperforms both supervised and unsupervised baselines in average rank on node classification across 10 datasets. Our approach effectively exploits high-dimensional hypergraph information, shows significant improvement over existing graph-based contrastive learning methods, and is efficient in terms of training speed and GPU memory cost. The source code is available at https://***/Noverse0/***.

关键词： Training Costs Correlation Source coding Noise Memory management Graphics processing units Contrastive learning data models Topology

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A Comparative Study of Access Control Models for Ubiquitous Computing Systems 23

A Comparative Study of Access Control Models for Ubiquitous ...

引用

Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Sayali Renuse Parikshit N. Mahalle Gitanjali Rahul Shinde Nilesh P. Sable Department of Computer Engineering Vishwakarma Institute of Information Technology India Department of Artificial intelligence and Data science Vishwakarma Institute Of Information Technology India Computer Science Engineering Vishwakarma Institute of Information Technology India Information Technology Vishwakarma Institute of Information Technology India

ISBN: (纸本)9798400709418

The Internet of Things (IoT) and Machine-to-Machine (M2M) communication have connected devices, enabling major advances in various fields. The priority now is secure and efficient management of these interconnected systems. IoT ecosystems need access control to manage interactions. Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Policy-Based Access Control (PBAC), and Context-Based Access Control (CBAC) are examined in the context of Industrial IoT (IIoT) in this paper. Intelligent manufacturing and anticipatory maintenance are among the many opportunities created by IoT and IIoT rapid growth. However, these advances present new security and access control challenges. Access control is essential for managing user, device, and application authorization in complex, interconnected environments. Our goal is to analyze their suitability, pros, and cons in industrial settings where strict access control is needed to ensure system safety, confidentiality, and efficiency. The IIoT case study implementation of these access control mechanisms is the focus of this paper. We examine an IIoT scenario in which a manufacturing plant has a network of machinery, sensors, and control systems. The case study shows how RBAC, ABAC, PBAC, and CBAC apply pragmatically. We evaluate their ability to manage access to critical machinery, data, and devices. We analyze and compare these access control mechanisms within the IIoT framework to determine the best one for the industrial environment, taking into account scalability, security, real-time decision making, and industrial process complexity. This paper examines access control mechanisms and practical observations from a real-world IIoT case study to improve IIoT security discussions. The findings can help IIoT practitioners, researchers, and decision-makers choose access control solutions. Industrial operations can become safer and more efficient.

关键词： Access Control

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A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Chen, Jiajun Hu, Chunqiang Sheng, Weihong Li, Ruinian Zhao, Ruifeng Yu, Jiguo Chongqing University College of Computer Science Chongqing401331 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Bowling Green State University Computer Science Department United States Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Qilu University of Technology Big Data Institute Jinan250353 China

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user's personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model's feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation. © 2004-2012 IEEE.

关键词： Differential privacy

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Cybernated Watermarking Approach on Image with Cryptographic Encryption

Cybernated Watermarking Approach on Image with Cryptographic...

引用

computer, Communication and Control (IC4), International Conference on

作者： R. Sivasankari T Veeramakali S M Mustafa Nawaz P. Prakash P Umadevi A Alvin Ancy Dept. of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Dept of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Dept of Computer Science and Engineering Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Sri Sai Ram Engineering College Department of S & H KPR Institute of Engineering and Technology Coimbatore Dept of Electronics and Communication Engineering Anna University Chennai

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types; a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text.

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Toward Cost-effective Adaptive Random Testing: An Approximate Nearest Neighbor Approach

arXiv

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arXiv 2023年

作者： Huang, Rubing Cui, Chenhui Lian, Junlong Towey, Dave Sun, Weifeng Chen, Haibo The School of Computer Science and Engineering Macau University of Science and Technology Taipa999078 China The School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212013 China The School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo315100 China The School of Big Data and Software Engineering Chongqing University Chongqing401331 China

Adaptive Random Testing (ART) enhances the testing effectiveness (including fault-detection capability) of Random Testing (RT) by increasing the diversity of the random test cases throughout the input domain. Many ART algorithms have been investigated such as Fixed-Size-Candidate-Set ART (FSCS) and Restricted Random Testing (RRT), and have been widely used in many practical applications. Despite its popularity, ART suffers from the problem of high computational costs during test-case generation, especially as the number of test cases increases. Although several strategies have been proposed to enhance the ART testing efficiency, such as the forgetting strategy and the k-dimensional tree strategy, these algorithms still face some challenges, including: (1) Although these algorithms can reduce the computation time, their execution costs are still very high, especially when the number of test cases is large;and (2) To achieve low computational costs, they may sacrifice some fault-detection capability. In this paper, we propose an approach based on Approximate Nearest Neighbors (ANNs), called Locality-Sensitive Hashing ART (LSH-ART). When calculating distances among different test inputs, LSH-ART identifies the approximate (not necessarily exact) nearest neighbors for candidates in an efficient way. LSH-ART attempts to balance ART testing effectiveness and efficiency. © 2023, CC BY.

关键词： Nearest neighbor search

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