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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： data integrity

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A Survey on Privacy-Preserving data Publishing Methods and Models in Relational Electronic Health Records 1

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3rd International Conference on Sustainable Advanced Computing, ICSAC 2021

作者： Jayapradha, J. Prakash, M. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu603203 India

ISBN: (数字)9789811690129

ISBN: (纸本)9789811690112

The collections of various digital information from corporations, public sectors, private sectors, and individuals have formed remarkable opportunities for analysis and decision-making. The electronic health record is highly constructive for various study purposes;however, it should be carried in a way that preserves the identity and privacy of the patients. Due to the digital transformation, the flow of data increases that lead to the need for big data. Protecting privacy cannot be straightforward as there are several privacy threats such as membership disclosure, identity disclosure, and attribute disclosure. The privacy methods such as generalization, suppression, pseudonymization, bucketization, slicing are illustrated with examples. The study focused on various privacy models such as k-anonymity, l-diversity, t-closeness, differential privacy, and cryptographic technique. The models are discussed with the sample dataset and workflow. The paper has discussed the security and privacy concerns on data publishing, the need for big data scope, an overview of threats, samples, privacy methods, models, and challenges in privacy-preserving data publishing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Records management

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CLNX: Bridging Code and Natural Language for C/C++ Vulnerability-Contributing Commits Identification

arXiv

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arXiv 2024年

作者： Qin, Zeqing Wu, Yiwei Han, Lansheng School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China

Large Language Models (LLMs) have shown great promise in vulnerability identification. As C/C++ comprises half of the Open-Source Software (OSS) vulnerabilities over the past decade and updates in OSS mainly occur through commits, enhancing LLMs' ability to identify C/C++ Vulnerability-Contributing Commits (VCCs) is essential. However, current studies primarily focus on further pre-training LLMs on massive code datasets, which is resource-intensive and poses efficiency challenges. In this paper, we enhance the ability of BERT-based LLMs to identify C/C++ VCCs in a lightweight manner. We propose CodeLinguaNexus (CLNX) as a bridge facilitating communication between C/C++ programs and LLMs. Based on commits, CLNX efficiently converts the source code into a more natural representation while preserving key details. Specifically, CLNX first applies structure-level naturalization to decompose complex programs, followed by token-level naturalization to interpret complex symbols. We evaluate CLNX on public datasets of 25,872 C/C++ functions with their commits. The results show that CLNX significantly enhances the performance of LLMs on identifying C/C++ VCCs. Moreover, CLNX-equipped CodeBERT achieves new state-of-the-art and identifies 38 OSS vulnerabilities in the real *** Codes 68M25 Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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Two-Time-Scale Resource Allocation for Joint VR and Tactile Applications 9

Two-Time-Scale Resource Allocation for Joint VR and Tactile ...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Zhang, Tanni Chen, Youjia Guo, Boyang Ye, Yuchuan Cheng, Peng Ding, Ming Hu, Jinsong Fuzhou University College of Physics and Information Engineering Fuzhou China La Trobe University The Department of Computer Science and Information Technology Melbourne Australia Csiro Data61 SydneyNSW Australia

ISBN: (纸本)9798350317251

Multiple joint virtual reality (VR) and tactile applications, e.g. tele-operation, bring many challenges to wireless networks. Instead of considering the quality of service (QoS) of VR and tactile traffic independently, in this work, the integrated just-noticeable difference (JND) is introduced as a key metric to represent the joint Visuo-Tactile perceptual resolution. More importantly, considering their different time characteristics, a two-time-scale resource allocation scheme is proposed relying on the puncturing scheme for joint enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (URLLC) traffic. Moreover, both mobile edge computing (MEC) computing offload and K-Repetition schemes are involved to ensure the delay and reliability constraints. simulations show that our algorithm outperforms the traditional heuristic algorithm, i.e. better VR QoS is provided while the communication requirements for tactile is satisfied. © 2023 IEEE.

关键词： Virtual reality

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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration 2

Exploring the Effectiveness of Deep Learning in Audio Compre...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 IEEE.

关键词： Audio streaming

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Improving Event Coreference Resolution Using Prompt Learning and U-shaped Module

Improving Event Coreference Resolution Using Prompt Learning...

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Computer science and Blockchain (CCSB), International Conference on

作者： Mengyao Zhang Jun Long Wenti Huang School of Software Xinjiang University Urumqi China Institute of Big Data Central South University Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Event Coreference Resolution (ECR) focuses on clustering event mentions that allude to identical actual events. Previous research primarily focuses on encoding event mentions without incorporating human interpretations, making it difficult to integrate human-summarized rules for guiding the ECR task. Furthermore, the semantic relations between event mentions have not been thoroughly explored, which impacts coreference judgment. To address these issues, we first utilize the powerful Longformer to acquire event representations at the document level. In addition, we introduce a prompt learning template to better emphasize event types and parameters, enhancing the interaction between event modeling and coreference judgment. Ultimately, we create matching vectors for event mentions using tensor matching and incorporate a U-shaped module to effectively identify the semantic relationships among these mentions, which enhances coreference discrimination. DPTU surpasses other ECR models on the KBP2017 dataset in cause of some experiments.

关键词： Computer science Tensors Computational modeling Semantics Vectors Encoding Blockchains

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Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion

arXiv

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arXiv 2024年

作者： Zhu, Jianqing Huang, Huang Lin, Zhihang Liang, Juhao Tang, Zhengyang Almubarak, Khalid Alharthik, Abdulmohsen An, Bang He, Juncai Wu, Xiangbo Yu, Fei Chen, Junying Ma, Zhuoheng Du, Yuhao Zhang, He Alghamdi, Emad A. Zhang, Lian Sun, Ruoyu Li, Haizhou Wang, Benyou Xu, Jinchao King Abdullah University of Science and Technology Thuwal Saudi Arabia Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China Shenzhen Research Institue of Big Data Shenzhen China The Chinese University of Hong Kong Shenzhen China King Abdulaziz University Jeddah Saudi Arabia

This paper addresses the critical need for democratizing large language models (LLM) in the Arab world, a region that has seen slower progress in developing models comparable to state-of-the-art offerings like GPT-4 or Chat- GPT 3.5, due to a predominant focus on mainstream languages (e.g., English and Chinese). One practical objective for an Arabic LLM is to utilize an Arabic-specific vocabulary for the tokenizer that could speed up decoding. However, using a different vocabulary often leads to a degradation of learned knowledge since many words are initially out-of-vocabulary (OOV) when training starts. Inspired by the vocabulary learning during Second Language (Arabic) Acquisition for humans, the released AraLLaMA employs progressive vocabulary expansion, which is implemented by a modified BPE algorithm that progressively extends the Arabic subwords in its dynamic vocabulary during training, thereby balancing the OOV ratio at every stage. The ablation study demonstrated the effectiveness of Progressive Vocabulary Expansion. Moreover, AraLLaMA achieves decent performance comparable to the best Arabic LLMs across a variety of Arabic benchmarks. Models, training data, benchmarks, and codes will be all open-sourced. Copyright © 2024, The Authors. All rights reserved.

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Exploring The Role of Web Crawler and Anti-Crawler technology in big data Era 11

Exploring The Role of Web Crawler and Anti-Crawler Technolog...

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11th International Conference of Information and Communication technology, ICTech 2022

作者： Zhou, Fan Wang, Yang Urban Vocational College of Sichuan Department of Information Technology Sichuan Chengdu610101 China Luzhou Vocational and Technical College School of Artificial Intelligence and Big Data Sichuan Luzhou646000 China

ISBN: (数字)9781665496940

ISBN: (纸本)9781665496940

In the era of big data, with lower costs and higher efficiency, web crawlers access resources and information from the Internet, bringing a lot of convenience to businesses and individuals. Nevertheless, there are two sides to everything, as malicious crawlers bring incalculable threats and losses to websites. In order to prevent web crawlers from being abused or even developing into malicious crawlers, web sites usually perform anti-crawler based on techniques such as ip access frequency, browsing page speed, account login, input captcha, js encryption, ajax obfuscation, etc. Anti-crawlers cannot completely block crawlers with a particular technique, but only find ways to increase the cost of crawling for attackers, forcing the catching party to make the right choice after weighing the cost-benefit. © 2022 IEEE.

关键词： big data

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CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning

arXiv

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arXiv 2024年

作者： Sun, Jinze Sheng, Yongpan He, Lirong Qin, Yongbin Liu, Ming Jia, Tao College of Computer and Information Science Southwest University Chongqing400715 China School of Big Data & Software Engineering Chongqing University Chongqing401331 China School of Information Science and Engineering Chongqing Jiaotong University Chongqing400074 China College of Computer Science and Technology The State Key Laboratory of Public Big Data Guizhou University China Deakin University VIC3125 Australia

Temporal knowledge graph reasoning (TKGR) is increasingly gaining attention for its ability to extrapolate new events from historical data, thereby enriching the inherently incomplete temporal knowledge graphs. Existing graph-based representation learning frameworks have made significant strides in developing evolving representations for both entities and relational embeddings. Despite these achievements, there’s a notable tendency in these models to inadvertently learn biased data representations and mine spurious correlations, consequently failing to discern the causal relationships between events. This often leads to incorrect predictions based on these false correlations. To address this, we propose an innovative Causal Enhanced Graph Representation Learning framework for TKGR (named CEGRL-TKGR). This framework introduces causal structures in graph-based representation learning to unveil the essential causal relationships between events, ultimately enhancing the performance of the TKGR task. Specifically, we first disentangle the evolutionary representations of entities and relations in a temporal knowledge graph sequence into two distinct components, namely causal representations and confounding representations. Then, drawing on causal intervention theory, we advocate the utilization of causal representations for predictions, aiming to mitigate the effects of erroneous correlations caused by confounding features, thus achieving more robust and accurate predictions. Finally, extensive experimental results on six benchmark datasets demonstrate the superior performance of our model in the link prediction task. © 2024, CC BY.

关键词： Spatio-temporal data

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Advancing Early Detection: CNN for Automated Blood Cancer Diagnosis and Anomaly Detection 2

Advancing Early Detection: CNN for Automated Blood Cancer Di...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Raji, V. Seetharaman, Anburaman Kasi, Baskar Govindaraju, Sakthi Paarkavy, B. S K P Engineering College Department of Computer Science and Engineering Tiruvannamalai India Sri Venkateswaraa College of Technology Department of Artificial Intelligence and Data Science Chennai India Galgotias University School of Computer Science and Engineering Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India Saveetha Engineering College Department of AI&DS Chennai India

ISBN: (纸本)9798350368109

The evaluation and diagnosis of diseases related to blood cancer can be intricate and time-consuming. This complexity is compounded by the reliance on manual analyses employing techniques that consume a significant amount of time. Numerous methods have been created over the past ten years for the finding, analysis, and categorization of human blood cancer. The lack of methods or models that can automatically analyze human blood cells to detect the presence of cancer, however, is still a significant issue. The development of such a model holds the potential to enhance the early detection and prevention of these diseases, thereby facilitating more prompt medical diagnoses. In our ongoing research, we present our current endeavors in the creation of a Deeper with Convolutions Neural Network (DCNN) Learning Model capable of recognizing anomalies in blood cells. We employ a Hybrid Ensemble DCNN Learning technique for the identification of blood cancer. With accuracy rates surpassing 99%, this method is one of the most sophisticated deep learning models. © 2024 IEEE.

关键词： Contrastive Learning

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