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检索条件"任意字段=International Conference on Information Technology - Coding and Computing"

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A technology Review of Zero Knowledge Proof Techniques 10

A Technology Review of Zero Knowledge Proof Techniques

引用

10th international conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Rostamkolaei Motlagh, Seyed Mohsen Pahl, Claus Barzegar, Hamid R. El Ioini, Nabil Free University of Bozen-Bolzano Bolzano39100 Italy University of Nottingham Semenyih43500 Malaysia

ISBN: (纸本)9789897587504

Distributed systems, particularly in IoT, require robust privacy-preserving authentication mechanisms to address increasing concerns about data security and integrity. Zero-Knowledge Proofs (ZKPs) have emerged as a promising solution to balance security, privacy, and efficiency. This paper reviews and compares state-of-the-art ZKP protocols, focusing on their suitability for decentralized, resource-constrained environments. We propose a comprehensive evaluation framework and apply it to zk-SNARK, zk-STARK, and Bulletproof protocols, analyzing metrics such as scalability, efficiency, and proof size. Our findings provide actionable insights into the trade-offs between these protocols, offering guidance for their application in IoT systems. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Cluster computing

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AI-driven Student Task Management Platform With Plagiarism Checker For Submissions 5

AI-driven Student Task Management Platform With Plagiarism C...

引用

5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Durge, Tejaswini Bansod, Aniket Deshmukh, Soham Boke, Sanika Shewale, Chaitali Wawage, Pawan Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798331522100

In current society, plagiarism of material from external sources is common and claiming it as one's own is considered a serious offense, particularly in academic settings. Plagiarism has become a significant issue among students at all levels-undergraduate, postgraduate and even doctoral. This paper addresses the specific challenge of plagiarism in computer programming assignments, where students frequently blend and modify original solutions to produce fraudulent work. To address this issue, a system has been proposed that enables teachers to efficiently check for plagiarism with a single click by uploading a folder of submissions. Although various software tools have been developed to detect plagiarism, there has been limited evaluation of their effectiveness in accurately identifying the true authorship of submitted work. Our approach leverages artificial intelligence algorithms to break down submitted articles into smaller sections and compare them against existing databases. This research aims to effectively identify and recognize instances of plagiarism while preventing potential copyright issues. Index Terms - Plagiarism detection, Text similarity, Machine learning, Cosine similarity, Semantic analysis © 2025 IEEE.

关键词： Students

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Lossy Compression of Point Clouds Using coding Rate Reduction Compressed Sparse Representation 5

Lossy Compression of Point Clouds Using Coding Rate Reductio...

引用

5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Li, Yinghao Wang, Yanjie Wang, Wenmeng Li, Yafei Yao, Mengyuan Li, Jingke School of Cyberspace Security Zhengzhou University Henan Zhengzhou China Henan Technical College of Construction Henan Zhengzhou China

ISBN: (纸本)9798331533694

Point cloud compression is a technique that aims to address the challenge of storing and transmitting large-scale 3D data by reducing the size of point cloud data while maintaining sufficient useful information. However, the large amount of data in point clouds and their irregular spatial arrangement complicates storage and transmission. To address this issue, a novel lossy point cloud compression method is proposed in this paper. Our strategy treats point cloud compression as an iterative optimization process, where each layer performs an optimization step to achieve the sparse compression goal. An autoencoder structure is used to enhance the point cloud compression task with coding rate reduction transformer and geometric attribute information. In the encoding phase, a novel self-encoder is used to learn the compressed and sparse representation and geometric features of the point cloud to be used as a compressed embedding of the point cloud. In the decoding stage, point-wise split deconvolution is designed for upsampling the point cloud in order to better reconstruct the point cloud. Experimental results show that the proposed method achieves better reconstruction quality and geometry at the same bit rate as other state-of-the-art compression methods. © 2025 IEEE.

关键词： Data compression

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GREEN information technology: A KNOWLEDGE MANAGEMENT APPROACH TO ORGANIZATIONAL SUSTAINABILITY 18

GREEN INFORMATION TECHNOLOGY: A KNOWLEDGE MANAGEMENT APPROAC...

引用

18th international conferences IADIS information Systems 2025 and 23rd international conference on e-Society 2025

作者： Smuts, Hanlie University of Pretoria Lynnwood Road Hatfield Pretoria0083 South Africa

ISBN: (纸本)9789898704658

In the ubiquitous computing environment within which organizations operate, they depend significantly on information technology (IT) to drive business value and economic success. However, organizational sustainability is more than just economics;it also includes environmental and social aspects that, in the context of IT, require organizational attention since the disposal of IT equipment, the way software solutions are developed, and the amount of data generated all impact organizational sustainability. The data centers required to process generated data are energy-intensive and contribute significantly to carbon emissions. This study aimed to consider key knowledge management (KM) strategies and tactics that support green IT initiatives to advance organizational sustainability. Five hundred and thirty-nine (539) responses collected through an online questionnaire were analyzed in two ways. First, three KM strategies, namely performance tracking and measurement, dark data management, and knowledge exchange, were identified through factor analysis. Second, the role of different stakeholders, namely KM practitioners, organizational learning practitioners, data analysts, and data scientists, was analyzed through an individual multiple-factor analysis, highlighting a significant difference between KM and learning practitioners on the one hand and data analysts and scientists on the other. The findings were visualized in a KM strategies and tactics work system to guide KM efforts in organizations seeking to reach their sustainability goals. © 2025 Proceedings of the international conferences IADIS information Systems 2025 and e-Society 2025. All rights reserved.

关键词： Green development

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Research on dynamic network security detection technology based on SDN 4

Research on dynamic network security detection technology ba...

引用

4th international conference on Network Communication and information Security, ICNCIS 2024

作者： Zhao, Kai Beijing Polytechnic Beijing100176 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Traditional network architecture can no longer meet the development needs of technologies such as cloud computing and big data. SDN network architecture has the characteristics of high openness and programmability, which can quickly respond to changes in business requirements. SDN decouples the control management function of the network from the data forwarding function, simplifies network configuration work based on logic centralized controllers and open programming interfaces, thereby achieving a flat management mode and flexible data forwarding function. The centralized control and programmability of SDN networks also pose significant security threats to SDN. Based on the CSE-CIC-IDS2018 dataset, this article tests the performance of various intelligent learning algorithms, analyzes data flows, distinguishes normal data from abnormal data, improves detection accuracy, and has practical significance for the security defense of SDN networks. © 2025 SPIE.

关键词： Network security Computer security Network architectures Control systems Machine learning Data transmission Defense and security Detection and tracking algorithms information security Security technologies

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Analysing Zero-Shot Readability-Controlled Sentence Simplification 31

Analysing Zero-Shot Readability-Controlled Sentence Simplifi...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Barayan, Abdullah Camacho-Collados, Jose Alva-Manchego, Fernando School of Computer Science and Informatics Cardiff University United Kingdom Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9798891761964

Readability-controlled text simplification (RCTS) rewrites texts to lower readability levels while preserving their meaning. RCTS models often depend on parallel corpora with readability annotations on both source and target sides. Such datasets are scarce and difficult to curate, especially at the sentence level. To reduce reliance on parallel data, we explore using instruction-tuned large language models for zero-shot RCTS. Through automatic and manual evaluations, we examine: (1) how different types of contextual information affect a model's ability to generate sentences with the desired readability, and (2) the trade-off between achieving target readability and preserving meaning. Results show that all tested models struggle to simplify sentences (especially to the lowest levels) due to models' limitations and characteristics of the source sentences that impede adequate rewriting. Our experiments also highlight the need for better automatic evaluation metrics tailored to RCTS, as standard ones often misinterpret common simplification operations, and inaccurately assess readability and meaning preservation. © 2025 Association for Computational Linguistics.

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HiRes: Hierarchical Feature Optimization and Rescorer for Automatic ICD coding

HiRes: Hierarchical Feature Optimization and Rescorer for Au...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhenpeng Liang Hongjiao Guan Wenpeng Lu Xueping Peng Bing Xu Muyun Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The international Classification of Diseases (ICD) coding assigns standardized codes to diseases. Automating this process enhances the efficiency and accuracy of clinical records processing. However, current methods struggle with noisy and lengthy clinical texts, making it difficult to ensure the reliability of feature extraction. Furthermore, they typically make separate binary predictions for each code, overlooking the dependencies between them. To address these issues, we propose a novel model called Hierarchical Feature Optimization and Rescorer (HiRes). We employ a cascaded convolution architecture to mitigate noise and enhance feature representation. Additionally, a masked autoencoder-based rescorer is introduced to capture ICD code interdependencies, refining initial predictions for improved accuracy. The model also considers the inconsistencies in code representations. Experiments on the MIMIC datasets demonstrate the effectiveness of our model.

关键词： Codes Accuracy Computational modeling Noise Refining Encoding Reliability Speech processing Optimization Diseases

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A Study of Anchored Instruction Model Design of College English Writing Course Based on Big Data Visualization Analysis 24

A Study of Anchored Instruction Model Design of College Engl...

引用

7th international conference on Educational technology Management, ICETM 2024

作者： Meng, Qingwei Xu, Changyu Shanghai Technical Institute of Electronics and Information Shanghai China

ISBN: (纸本)9798400717468

In the era of digital economy, the traditional writing teaching mode no longer meets the cultivation requirements of language service talents in the emerging language service industry. This study analyzes the application of anchored instruction in different fields, extracts clustering labels and key words on the basis of CiteSpace, accommodating anchored instruction to English writing teaching to assist students construct a real enterprise working context. Through the newly-built anchored instruction model, this study divides the anchored instruction targeting at the English writing teaching into 5 phases, inclusive of "setting anchor" phase, "dropping anchor" phase, "using anchor" phase,"unlocking anchor" phase and "evaluating anchor" phase. By wielding the big data analysis tools, the teacher enables to collect and analyse the data to better improve and modify the teaching plan. The newly-built anchored instruction model featuring the language technology service industry evaluates students’ professional core qualities required for professional development and provides foreign language service industry in China with high-quality foreign language talents. © 2024 Copyright held by the owner/author(s).

关键词： Students

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Tomato Leaf Disease Identification and Growth Dataset Augmentation Based on UAE-GAN 13

Tomato Leaf Disease Identification and Growth Dataset Augmen...

引用

13th international conference on Intelligent Control and information Processing, ICICIP 2025

作者： Li, Xin Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

To delve into the characterization of growth disorders in different crops, it is important to support the model with a large amount of image data that includes a variety of disease types and disease levels to capture the typical and subtle differences of various diseases on plant leaves. However, the actual process of gathering data is challenging, sample coverage is challenging to accomplish, data capture is impeded, and the quality of the data is subpar. This work aims to address the issue of data shortages by employing technical methods. In particular, we creatively investigated the UAE-GAN approach, which naturally combines CycleGAN, U-Net, Variational Autoencoder VAE, and Autoencoder to increase the data. Among these, U-Net can precisely extract the small details of disease locations in crop photos and provide a strong basis for further processing thanks to its special codec architectural benefits. The Variational Autoencoder (VAE) significantly enhances the diversity of data by mapping the image to the latent space and sampling based on a certain probability distribution, so producing new image samples that are distinct from the original image yet inherently connected. Learning the coding and decoding of the original image is the foundation of autoencoders. If a mild disruption is introduced into the coding process, it can achieve data augmentation in another dimension and create a sequence of new images with just little modifications to the original image. The aforementioned models are closely linked with CycleGAN to efficiently map and convert in a variety of picture domains and to fully leverage CycleGAN's remarkable unsupervised image conversion capabilities. The perception ability, feature capture ability, and information conversion ability of the fusion model for crop image data are significantly improved, and the key elements of each link in the data enhancement process are comprehensively considered to ensure that the generated new image data can not o

关键词： Fruits

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Optimizing Response Consistency of Large Language Models in Medical Education through Prompt Engineering 5

Optimizing Response Consistency of Large Language Models in ...

引用

5th international conference on Advanced Research in computing, ICARC 2025

作者： Weerathunge, Tharindu Jayalal, Shantha Wijayasiriwardhane, Keerthi University of Kelaniya Faculty of Science Department of Industrial Management Sri Lanka Waikato Institute of Technology Center for Information Technology 3240 New Zealand

ISBN: (纸本)9798331530983

This research focuses on optimizing the response consistency of Large Language Models (LLMs) in medical education through advanced prompt engineering techniques. LLMs often give different answers to the same question, making self-consistency a critical parameter for assessing their performance. Addressing this inconsistency is essential in high-stakes fields like healthcare, where reliable and accurate information is important. The study employed custom prompt engineering strategies, including zero-shot, few-shot, and Chain-of-Thought (CoT) prompting, to improve LLM output consistency and accuracy. We implemented a retrieval-augmented generation (RAG) framework to use external knowledge from trusted medical resources, keeping the responses accurate and contextually appropriate. Responses were scored on several dimensions: content relevance, completeness, and clinical correctness, and assessed for consistency by asking repeated queries. The results showed significant enhancement in the consistency and accuracy of the responses, proving the effectiveness of the presented method. This work outlines suggestions for the use of LLMs in a way that can be incorporated into medical education while considering the limitations. It underscores the need for further exploration of prompt engineering to improve LLM performance and establishes these tools as reliable resources for training healthcare professionals. © 2025 IEEE.

关键词： Medical education

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