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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kour, Kanwal Preet Singh, S. Kanwal Deep Bali, Malvinder Singh Ghantasala, G. S. Pradeep Rajesh Sharma, R. Ananthanagu, U. Manipal University School of Computer Science and Engineering Jaipur India Federation University Department of Computer Science Australia Manipal University Department of Data Science and Engineering Rajasthan Jaipur India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

In recent times, blockchain has evolved the security of traditional supply chain systems. Different issues of supply chain management like flexibility and reliability can be easily addressed using blockchain. Meat Packing Industry involves the complete process of processing, packaging and supplying the meat and its products throughout the world for human consumption. The animals used for the meat supply chain include pigs, sheep, cattle etc. Due to the enormous increase in the number of meat-eaters worldwide, its packing and timely supply are of utmost importance. The meat packing industry has revolutionized a lot by using the latest supply chain methods based on new technologies like blockchain. These include adulteration, long-term delays leading to spoilage of the product, mixing of other similar products which are lower in price etc. All these issues can be easily handled by using blockchain and IoT for meat supply chain management. Using these technologies, the challenges ranging from timely transportation of good quality meat to the best quality meat delivered to the customer can be easily addressed. In this paper, an effective blockchain and IoT-based model has been proposed which will counter the above challenges and improve the reliability and security factor. © 2024 IEEE.

关键词： Supply chain management

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OOCDB:A Comprehensive,Systematic,and Real-time Organs-on-a-chip database

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Genomics, Proteomics & Bioinformatics 2023年第2期21卷 243-258页

作者： Jian Li Weicheng Liang Zaozao Chen Xingyu Li Pan Gu Anna Liu Pin Chen Qiwei Li Xueyin Mei Jing Yang Jun Liu Lincao Jiang Zhongze Gu School of Life Science and Technology Southeast UniversityNanjing 210096China School of Biological Science&Medical Engineering Southeast UniversityNanjing 210096China School of Computer Data&Information SciencesUniversity of Wisconsin-MadisonMadisonWI 53706USA

Organs-on-a-chip is a microfluidic microphysiological system that uses microfluidic technology to analyze the structure and function of living human cells at the tissue and organ levels in ***-on-a-chip technology,as opposed to traditional two-dimensional cell culture and animal models,can more closely simulate pathologic and toxicologic interactions between different organs or tissues and reflect the collaborative response of multiple organs to *** the fact that many organs-on-a-chip-related data have been published,none of the current databases have all of the following functions:searching,downloading,as well as analyzing data and results from the literature on ***,we created an organs-on-a-chip database(OOCDB)as a platform to integrate information about organs-on-a-chip from various sources,including literature,patents,raw data from microarray and transcriptome sequencing,several open-access datasets of organs-on-a-chip and organoids,and data generated in our *** contains dozens of sub-databases and analysis tools,and each sub-database contains various data associated with organs-on-a-chip,with the goal of providing researchers with a comprehensive,systematic,and convenient search ***,it offers a variety of other functions,such as mathematical modeling,three-dimensional modeling,and citation mapping,to meet the needs of organs-on-a-chip.

关键词： Organ Mathematical model Citation map Organs-on-a-chip

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A Pre-selected Local Weighted Method for Improving the Performance of LMKNN Classifier 6

A Pre-selected Local Weighted Method for Improving the Perfo...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Zhang, Yuzhi Guan, Ziyan Xiong, Chen School of Aeronautic Science and Engineering Beihang University Beijing China School of Big Data & Software Engineering Chongqing University Chongqing China School of Computer Science Xi'an Jiaotong University City College Xi'an China

ISBN: (纸本)9798350374315

The K-nearest neighbor (KNN) algorithm and its variant Local Mean K-nearest neighbor (LMKNN) have been widely used in the field of data mining due to their simplicity and intuition. However, these methods have limitations when dealing with unbalanced datasets and edge samples, such as sensitivity to the choice of nearest neighbor size and degradation of classification performance for outlier points. To address these issues, this paper proposes a pre-selected locally weighted mean K-nearest neighbor (PLWMKNN) algorithm, which aims to improve the performance of LMKNN through an advanced pre-selection mechanism and a weighted average method. PLWMKNN firstly pre-selects favorable classes as the nearest neighbors based on the distance weights, and then computes the weighted center vectors of each class to classify them by the minimum distance principle. Through experimental validation on four real datasets, PLWMKNN shows significant performance improvement over traditional KNN and LMKNN for unbalanced datasets. Our results demonstrate the effectiveness of the PLWMKNN algorithm in improving classification accuracy and robustness, especially when dealing with extremely unbalanced datasets. © 2024 IEEE.

关键词： Nearest neighbor search

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ILRM: Imitation Learning Based Resource Management for Integrated CPU-GPU Edge Systems with Renewable Energy Sources

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2392-2397页

作者： Hou, Xiangpeng Zhou, Junlong Li, Liying Zhao, Mingzhou Cong, Peijin Wu, Zebin Hu, Shiyan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Hong Kong Department of Data and Systems Engineering Hong Kong Hong Kong

This paper focuses on integrated CPU-GPU edge systems with renewable energy sources and studies the resource management problem to minimize the energy consumption of real-time tasks while ensuring temperature and reliability *** propose an imitation learning (IL)-based resource management scheme, ILRM, implemented in two phases: offline Oracle generation and online imitation learning. In the offline phase, we design a fast-converging heuristic to generate near optimal solutions (i.e., Oracles) for training an online prediction model. In the online phase, we realize imitation learning using the trained model that predicts the resource configuration policies for the incoming task sets to be scheduled. A data aggregation method is also developed to enhance the robustness of the prediction model. We validate ILRM through extensive experiments on both simulated and real integrated CPU-GPU edge platforms. © 1982-2012 IEEE.

关键词： Graphics processing units Reliability Central Processing Unit Renewable energy sources Real-time systems Integrated circuit modeling Imitation learning Generators Energy consumption Edge computing

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VaeDiff-DocRE: End-to-end data Augmentation Framework for Document-level Relation Extraction 31

VaeDiff-DocRE: End-to-end Data Augmentation Framework for Do...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Tran, Khai Phan Hua, Wen Li, Xue School of Electrical Engineering and Computer Science The University of Queensland Australia Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761964

Document-level Relation Extraction (DocRE) aims to identify relationships between entity pairs within a document. However, most existing methods assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets. To tackle this challenge, we propose a novel data augmentation approach using generative models to enhance data from the embedding space. Our method leverages the Variational Autoencoder (VAE) architecture to capture all relation-wise distributions formed by entity pair representations and augment data for underrepresented relations. To better capture the multi-label nature of DocRE, we parameterize the VAE's latent space with a Diffusion Model. Additionally, we introduce a hierarchical training framework to integrate the proposed VAE-based augmentation module into DocRE systems. Experiments on two benchmark datasets demonstrate that our method outperforms state-of-the-art models, effectively addressing the long-tail distribution problem in DocRE. Our code is released at: https://***/khaitran22/VaeDiff-DocRE. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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CMNEE: A Large-Scale Document-Level Event Extraction dataset based on Open-Source Chinese Military News 30

CMNEE: A Large-Scale Document-Level Event Extraction Dataset...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhu, Mengna Xu, Zijie Zeng, Kaisheng Xiao, Kaiming Wang, Mao Ke, Wenjun Huang, Hongbin Laboratory for Big Data and Decision National University of Defense Technology China School of Computer Science and Engineering Southeast University China Computer Science and Technology Tsinghua University China

ISBN: (纸本)9782493814104

Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis and decision assistance. However, event extraction in the military field faces the data scarcity problem, which impedes the research of event extraction models in this domain. To alleviate this problem, we propose CMNEE, a large-scale, document-level open-source Chinese Military News Event Extraction dataset. It contains 17,000 documents and 29,223 events, which are all manually annotated based on a pre-defined schema for the military domain including 8 event types and 11 argument role types. We designed a two-stage, multi-turns annotation strategy to ensure the quality of CMNEE and reproduced several state-of-the-art event extraction models with a systematic evaluation. The experimental results on CMNEE fall shorter than those on other domain datasets obviously, which demonstrates that event extraction for military domain poses unique challenges and requires further research efforts. Our code and data can be obtained from https://***/Mzzzhu/CMNEE. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： data mining

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Optimized data Processing for Hard Drive Failure Prediction 5

Optimized Data Processing for Hard Drive Failure Prediction

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Sivakumar, M. Ragavendra, R. Satapathy, Sandeep Kumar Mishra, Shruti Mallick, Pradeep Kumar Vellore Institute of Technology School of Computer Science & Engineering Tamilnadu Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India Kiit University School of Computer Engineeing Odisha India

ISBN: (纸本)9798331522100

With the rise of big data, efficient data handling techniques have become increasingly crucial. Hard drive failure prediction is a critical domain where accurate and timely data handling is paramount. This research paper introduces a data analysis and pre-processing approach for hard drive failure prediction based on machine learning techniques. A large dataset encompassing hard drive operational data, metadata, and error rates was collected. Extensive pre-processing was performed, including data cleaning and transformation. Multiple Machine Learning algorithms, including random forests, were employed to train predictive models for hard drive failure. The results demonstrate the effectiveness of our approach in achieving high accuracy for hard drive failure prediction, highlighting the significance of our pre-processing methods. Additionally, this paper compares existing machine learning algorithms and explores the role of big data in enhancing hard drive failure prediction. The utilization of big data and advanced data handling techniques has led to improved accuracy and predictive capabilities, making it an indispensable factor in this field. © 2025 IEEE.

关键词： data curation

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FedTop:a constraint-loosed federated learning aggregation method against poisoning attack

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Frontiers of computer Science 2024年第5期18卷 233-235页

作者： Che WANG Zhenhao WU Jianbo GAO Jiashuo ZHANG Junjie XIA Feng GAO Zhi GUAN Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China China Unicom Beijing 100033China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China

Federated learning(FL)is a decentralized machine learning paradigm,which has significant advantages in protecting data privacy[1].However,FL is vulnerable to poisoning attacks that malicious participants perform attacks by injecting dirty data or abnormal model parameters during the local model training and aim to manipulate the performance of the global model[2].

关键词： constraint loose aggregation

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Multi-modal Self-perception Enhanced Large Language Model for 3D Region-of-Interest Captioning with Limited data

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IEEE Transactions on Multimedia 2025年 27卷 2935-2948页

作者： Shi, Lu Kan, Shichao Jin, Yi Zhang, Linna Cen, Yigang Bejing Jiaotong University and the Beijing Key Laboratory of Advanced Information Science and Network Technology Institute of Information Science Beijing100044 China Central South University School of Computer Science and Engineering Hunan Changsha0410083 China Beijing Jiaotong University Key Laboratory of Big Data and Artificial Intelligence in Transportation Ministry of Education and the School of Computer and lnformation Technology Beijing100044 China Guizhou University School of Mechanical Engineering Guiyang550025 China

3D Region-of-Interest (RoI) Captioning involves translating a model's understanding of specific objects within a complex 3D scene into descriptive captions. Recent advancements in Large Language Models (LLMs) have shown great potential in this area. Existing methods capture the visual information from RoIs as input tokens for LLMs. However, this approach may not provide enough detailed information for LLMs to generate accurate region-specific captions. In this paper, we introduce Self-RoI, a Large Language Model with multi-modal self-perception capabilities for 3D RoI captioning. To ensure LLMs receive more precise and sufficient information, Self-RoI incorporates Implicit Textual Info. Perception to construct a multi-modal vision-language information. This module utilizes a simple mapping network to generate textual information about basic properties of RoI from vision-following response of LLMs. This textual information is then integrated with the RoI's visual representation to form a comprehensive multi-modal instruction for LLMs. Given the limited availability of 3D RoI-captioning data, we propose a two-stage training strategy to optimize Self-RoI efficiently. In the first stage, we align 3D RoI vision and caption representations. In the second stage, we focus on 3D RoI vision-caption interaction, using a disparate contrastive embedding module to improve the reliability of the implicit textual information and employing language modeling loss to ensure accurate caption generation. Our experiments demonstrate that Self-RoI significantly outperforms previous 3D RoI captioning models. Moreover, the Implicit Textual Info. Perception can be integrated into other multi-modal LLMs for performance enhancement. We will make our code available for further research. © 1999-2012 IEEE.

关键词： Digital elevation model

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Enhancing Document-Level Relation Extraction through Entity-Pair-Level Interaction Modeling

Enhancing Document-Level Relation Extraction through Entity-...

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

作者： Liu, Wanlong Zeng, Dingyi Zhou, Li Xiao, Yichen Zhang, Malu Chen, Wenyu School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Data Science The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9798350368741

Document-level relation extraction aims at extracting relational facts between two entities in a document. Existing approaches mainly focus on target entities, utilizing techniques such as graph neural networks to enhance their representations. However, they ignore the rich semantic correlations among entity pairs which provide wider and multifaceted information at a higher level. In this paper, we propose the Relation-based Entity-pair-level Inference (REI) model, which facilitates information interaction at the entity-pair level, enhancing logical reasoning among entities and capturing semantic correlations among entity pairs. Our REI model comprises two modules: Relation-based Information Aggregation (RIA) and Entity-pair-level Information Interaction (EII). The RIA module builds and integrates relation representations to filter out distractions from unrelated entity pairs, while the EII module models entity-pair-level information interaction through multi-head attentions. Extensive experiments on the DocRED, DWIE, CDR, and GDA datasets demonstrate the superiority of the proposed REI model, outperforming previous state-of-the-art approaches. Furthermore, we provide detailed experimental analyses based on the performance gains and illustrate the interpretability. © 2025 IEEE.

关键词： Information Extraction Linguistic Inference Natural Language Processing Relation Extraction

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