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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： He, Jiaming Liu, Cheng Hou, Guanyu Jiang, Wenbo Li, Jiachen School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Computer Science and Cyber Security Oxford Brookes College Chengdu University of Technology China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798350368741

Retrieval-augmented generation (RAG) expands the capabilities of large language models (LLMs) in various applications by integrating relevant information retrieved from external data sources. However, the RAG systems are exposed to substantial privacy risks during the information retrieval process, leading to potential data leakage of private information. In this work, we present a Privacy-preserving Retrieval-augmented generation via Embedding Space Shifting (PRESS), systematically exploring how to protect privacy in RAG systems. Specifically, we first conduct proximal policy optimization (PPO) based training on pre-trained language models to generate target training samples. Then we employ a purposive shift fine-tuning on the text embedding model with the generated samples for guiding the RAG system to map potential privacy leaking queries to safe target in embedding space. Extensive experimental results on representative models and datasets demonstrate that our protection method achieves high defense performance with high efficiency while keeping the normal functionality of the RAG system. © 2025 IEEE.

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Ensuring Ethical, Transparent, and Auditable Use of Education data and Algorithms on AutoML 6

Ensuring Ethical, Transparent, and Auditable Use of Educatio...

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6th International Conference on Machine Learning and Natural Language Processing, MLNLP 2023

作者： Griep, Kylie Stone, Leah Edwards, Stephen Hill, Malik Cui, Jialin Liu, Chengyuan Jia, Qinjin Song, Yang Department of Computer Science University of North Carolina Wilmington United States Master of Data Science Program University of North Carolina Wilmington United States Department of Computer Science NC State University United States

ISBN: (纸本)9798400709241

Automated machine learning (AutoML) creates additional opportunities for less advanced users to build and test their own data mining models. Even though AutoML creates the models for the user, there is still technical knowledge and tools needed to evaluate those models, and due to the black-box nature of the machine learning models, problems can arise with regard to algorithmic biases and fairness. Such biases can escalate in future applications, necessitating a structured approach for fairness evaluation in AutoML. This involves defining fairness criteria, selecting appropriate metrics, assessing fairness across groups, and addressing biases. In the realm of educational data mining, where AutoML is prevalent, biases related to attributes like gender or race can lead to unethical outcomes. Since fairness metrics vary in definition and strength, and some may even contradict others, making fairness evaluation more complex. In this paper, ten fairness metrics were chosen, explored, and implemented on four AutoML tools, Vertex AI, AutoSklearn, AutoKeras, and PyCaret. We identified two open educational datasets and built both prediction and classification models on those AutoML frameworks. We report our work in evaluating different machine learning models created by AutoML and provide discussions about the challenges in evaluating fairness in those models and our effort to mitigate and resolve the problems of algorithmic bias in educational data mining. © 2023 ACM.

关键词： data mining

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Internet of Medical Things: A Revolution in Healthcare for Enhancing Cardiac Monitoring and Patient-Care Through Wearables 3

Internet of Medical Things: A Revolution in Healthcare for E...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Chhoton, Amit Chakraborty Sharma, Arvind K. Yogananda School of Artificial Intelligence Computers and Data Science Shoolini University Himachal Pradesh India Daffodil International University Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331518592

Wearables play an important role in parallel with IoT towards the era of healthcare and it has to be designed effectively providing workable solutions for all areas of other medical scopes. The fundamental objective of this technology kinship is to make smart healthcare services possible towards societal necessities. This paper conducts a systematic review on current medical applications with Wearable Internet of Things. The use of well-defined keywords for strict paper selection is a criterion in further comprehensive analysis with published scientific work. This paper synthesizes the current state of knowledge and Wearable Internet of Things applications to be used in early prognosis, diagnosis, treatments and patient monitoring more specifically in cardiovascular diseases. Of interest is the characteristics in terms of design and structure, as well as efficiency in reducing the current disparities in healthcare. Additionally, this paper presents potential lines of research and obstacles to be overcome before increasing implementation of Wearable Internet of Things devices in smart-healthcare. © 2024 IEEE.

关键词： Diagnosis

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Automatic Detection of Diabetic Retinopathy from Fundus Images using Machine Learning Based Approaches 25

Automatic Detection of Diabetic Retinopathy from Fundus Imag...

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25th International Multi Topic Conference, INMIC 2023

作者： Bangyal, Waqas Haider Shakir, Rabia Ashraf, Adnan Qayyum, Zia Ul Rehman, Najeeb Ur Kohsar University Department of Computer Science Murree Pakistan Fuuast Department of Computer Science Islamabad Pakistan School of Computer Science & Technoly Beijing China Allama Iqbal Open University Department of Computer Science Islamabad Pakistan University of Gujrat Department of Computer Science Gujrat Pakistan

ISBN: (纸本)9798350317701

Diabetes-related retinopathy (DR) is the primary cause of blindness in the modern world. It affects retinal blood vessels. It causes blindness over time with no initial symptoms. Early detection of DR helps prevent vision loss. The method for detecting DR is based on Machine Learning (ML) network algorithms that categorize patient fundus photos by DR severity. This research proposes accurate ML-based architectures. Cropping and scaling are used to preprocess Kaggle DR dataset photos. Unbalanced data affects our model's accuracy. 70:30 split evaluates prediction performance. 94% accurate decision model ML-based approaches have a more robust and generic method for quantitative DR image analysis. These results are useful for imbalanced large-scale datasets. Machine learning-based approaches have better results. © 2023 IEEE.

关键词： Decision trees

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Expediting Prediction Accuracy with Exploration and Incorporation of Virtual data

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SN computer science 2024年第5期5卷 1-19页

作者： Nayak, Sarat Chandra Dehuri, Satchidananda Cho, Sung-Bae Department of Computer Science and Engineering School of Technology GITAM (Deemed to be University) Hyderabad India Department of Computer Science Fakir Mohan University Odisha Balasore 756019 India Department of Computer Science Yonsei University 50 Yonsei-ro Sudaemoon-gu Seoul 03722 South Korea

Achieving high accuracy in time series forecasting (TSF) is challenging;particularly for series with inadequate training data or insignificant correlation among data. Modelling such time series with ANN based systems which are data driven and necessitate abundant training samples, leads to poor generalization ability thus, inferior accuracy. In such circumstances, the time series may be enriched with few more virtual data points (VDPs) explored from existing training samples keeping the corelation factor in mind. Incorporation of such VDPs intensify the volume of training data and helps the model in extracting the inherent nonlinearity efficiently. This article proposes an extreme learning with Rao algorithm based VDP (ELRA-VDP) generation and incorporation approach to expediate TSF accuracy of ANN model. Extreme learning method (ELM) considers random hidden node parameters and apply a generalized inverse function on hidden node outputs to determines the output node parameters. Inclusion of random hidden node parameters may reduce the performance of ELM therefore, we used Rao algorithm (RA) to optimize the initial parameters and construct an efficient learning scheme, i.e., ELRA. Given a training sample, the ELRA estimates the VDPs as well as forecasts the next data in an alternative manner. The parameter-free optimization mechanism of RA and lower complexity with faster learning ability of ELM makes ELRA computationally efficient. TSF problems are then conducted with and without VDPs. Simulation studies show that ELRA-VDP method yields better predictions than others with a lowest MAPE value of 0.1746 and quite beneficial for datasets having a lesser amount of data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Extreme learning machine Interpolation Rao algorithm Time series forecasting Virtual data position

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Constructing a Secure Charity NFT Auction Platform Using Fisco Bcos Blockchain for Enhancing Transparency and Traceability

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IEEE Access 2024年 12卷 36924-36941页

作者： Chen, Chin-Ling Zhan, Wan-Bing Tsaur, Woei-Jiunn Huang, Der-Chen Liu, Ling-Chun Changchun Sci-Tech University Changchun School of Information Engineering Jilin130600 China Chaoyang University of Technology Department of Computer Science and Information Engineering Taichung413310 Taiwan Xiamen University of Technology Xiamen School of Computer and Information Engineering Fujian361024 China National Taipei University Computer Center New Taipei City237303 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan National Chung Hsing University Department of Computer Science and Engineering Taichung402202 Taiwan

In the charity sector, fundraising and transparency have long been key issues. Charity NFT (Non-Fungible Token) auctions, an emerging charity fundraising model integrating blockchain and NFT concepts, bring opportunities and challenges. Blockchain provides distributed data integrity and transparency via cryptography-linked data blocks, while NFTs enable unique digital ownership representation. This study designs a charity NFT auction platform on the Fisco Bcos blockchain, using multi-signature algorithms to ensure NFT authenticity, ECDSA (Elliptic Curve Digital Signature Algorithm) signatures for transaction integrity and traceability, NFTs and virtual currencies to reduce costs, and IPFS (InterPlanetary File System) for storage. The implemented system achieves 2104 TPS throughput with 492-millisecond latency, increasing transaction processing with low latency. Overall, the platform aims to address charity issues like opaque fund flows, high costs, and fake initiatives through the strategic application of blockchain and NFT functionality. © 2013 IEEE.

关键词： Smart contract

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Deep Learning-based Hybrid Recommendation System for Safe Approach to Curated Cosmetics 3

Deep Learning-based Hybrid Recommendation System for Safe Ap...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rohitha Chowdary, G. Tharun, J. Sivaiah, G. Raveena, S. Surendran, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Insititute of Medical and Technical Science Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

In the rapidly evolving cosmetics market, it is imperative to prioritize customer safety while offering personalized recommendations. This study introduces a hybrid recommendation system for cosmetics, using deep learning to combine the advantages of collaborative filtering with content-based filtering. The system assesses user preferences, skin types, and ingredient sensitivities, recommending solutions that are both effective and safe. The system proposes an integration of user-based and item-based collaborative filtering methodologies with deep neural networks to effectively elucidate intricate links among individuals, items, and their characteristics. This hybrid technique facilitates the system's ability to propose goods that provide both aesthetic appeal and skin safety, therefore fostering a healthier and more confident lifestyle. The system has an impressive accuracy of 92.5% in recommending suitable cosmetic products for individual clients. This enhanced accuracy reduces the likelihood of negative reactions and elevates customer satisfaction via personalized suggestions. © 2025 IEEE.

关键词： Contrastive Learning

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BiLSTM for Resume Classification 22

BiLSTM for Resume Classification

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics, SAMI 2024

作者： Jalili, Amirreza Tabrizchi, Hamed Razmara, Jafar Mosavi, Amir College of Science University of Tehran School of Mathematics Statistics and Computer Science Department of Computer Science Tehran Iran University of Tabriz Faculty of Mathematics Statistics and Computer Science Department of Computer Science Tabriz Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

ISBN: (纸本)9798350317206

In the era of digital recruitment and increasing volumes of job applications, the effective categorization and classification of resumes have become essential for streamlining the hiring process. The purpose of this paper is to present a Bidirectional LSTM architecture method designed to enhance the accuracy and efficiency of resume screening and classification. Leveraging the power of the presented bidirectional LSTM architecture allows the network to effectively capture complex information and context. To enhance the model's performance, we also incorporate word embedding, further enriching textual data representation. We evaluate the proposed method using a comprehensive dataset of resumes across various industries and job roles, demonstrating its superior performance in terms of classification accuracy and speed compared to traditional methods. Furthermore, we discuss the potential applications of this method in recruitment automation and offer insights into its scalability and adaptability in real-world scenarios, providing a valuable tool for human resource (HR) professionals and recruitment agencies seeking to optimize their hiring processes. © 2024 IEEE.

关键词： data mining

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Analysis of MFCC and Chromagram Features in Lightweight Bidirectional LSTM and Attention Models for Music Genre Classification 21

Analysis of MFCC and Chromagram Features in Lightweight Bidi...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Huang, Renxiang Li, Xu Li, Jianping School of Artificial Intelligence and Big Data Sichuan University of Arts and Science China Khoury College of Computer Science Northeastern university BostonMA02115 United States School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798331519254

Music genre classification is essential for organizing music libraries and enhancing recommendation systems. This paper evaluates four lightweight models combining Mel Frequency Cepstral Coefficients (MFCCs) and Chromagram features with Bidirectional LSTM and Multi-head Attention architectures. Using the GTZAN dataset, we found that models with Multi-head Attention, outperformed with MFCCs features, achieving a test accuracy of 85.6%. MFCCs excel in capturing timbral characteristics critical for genre classification, while Chromagram features proved less effective. Although overfitting occurred, future studies can focus on incorporating methods like data augmentation and regularization to improve performance. These lightweight models offer a balance between accuracy and computational efficiency, making them suitable for real-time applications. © 2024 IEEE.

关键词： Real time systems

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Search-Based Automated program Repair: A Survey 24

Search-Based Automated Program Repair: A Survey

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Zhang, Shilong Li, Dongcheng Zhao, Man Li, Hui Wong, W. Eric School of Computer Science China University of Geosciences Wuhan China California State Polytechnic University - Humboldt Department of Computer Science Arcata United States University of Texas at Dallas Department of Computer Science Richardson United States

ISBN: (纸本)9798350365658

This paper presents a review of state-of-the-art search-based automated program repair techniques, which are crucial for efficiently and accurately fixing bugs and vulnerabilities in software engineering. Traditional manual repair methods are increasingly seen as inadequate due to their high labor costs and time consumption. The versatility and applicability of search-based techniques have sparked significant interest in the software engineering community, driving a surge in research focused on developing more effective and efficient automated repair methods. The paper begins by introducing the foundational concepts and background of traditional automated program repair. It then delves into a detailed discussion of various search-based techniques and algorithms proposed in recent years. The review summarizes commonly used search-based frameworks, critically analyzing their strengths and limitations. It also provides a comparative analysis of different techniques concerning target languages, datasets, and other relevant aspects. In addition to evaluating current methodologies, the paper identifies ongoing challenges in the field and suggests potential directions for future research. By offering a thorough and systematic overview, this review aims to provide valuable insights and guidance for advancing the development and implementation of search-based automated program repair techniques. © 2024 IEEE.

关键词： Software testing

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