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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Neelam Singh Rohit Srivastava Hussain Falih Mahdi Subhangi Sati Purvika Joshi Ayan Sar Devansh Singh Tanupriya Choudhury Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science University of Petroleum and Energy Studies Bidholi Dehradun Uttarakhand India Department of Computer and Software Engineering University of Diyala Baquba Iraq Thapar University Patiala Punjab

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Diabetic Retinopathy (DR) is one of the most severe eye complications associated with diabetes, hence the need to diagnose and treat at the initial stages. The current approaches of DR detection that use ophthalmoscopic inspection of the optic fundus images are subjective and take an ample amount of time. This paper focuses on the application of Convolutional Neural Networks (CNNs) in automating DR detection as well as its classification process using large archive annotated datasets for training and testing. The structure of our CNN excels in capturing stipples of the retinal image and reaches a level higher than 90% accuracy while remaining sensitive and specific. By presenting feature maps and class activation maps, there is an improved credibility as the process the model goes through is explained. The analysis shows that using the CNN-based system can bring notable benefits in the form of early detection, a decrease in the amount of work performed by ophthalmologists, and enhancement of patient outcomes. Future work seeks to implement this model in clinical practice and work to overcome problems such as data privacy and real-time processing, thus demonstrating the revolutionary impact that deep learning holds for ophthalmology.

关键词： Deep learning Training Diabetic retinopathy Adaptation models Accuracy Retina Data models Convolutional neural networks Artificial intelligence Testing

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Detection of Deepfakes: Protecting Images and Vedios Against Deepfake

Detection of Deepfakes: Protecting Images and Vedios Against...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Ying Tian Wang Zhou Amin Ul Haq Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer and Software Engineering Xihua University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

In recent years, with the rapid development of deep learning and computer vision technology, the forgery technology of images and videos has become increasingly mature, posing new challenges to information security and social stability. Behind the re-evolution of deepfake lies the rampant proliferation of fake content, which is used for election tampering, identity fraud, fraud, spreading fake news, and so on. To address these challenges, researchers are constantly exploring and developing image-based deepfake detection techniques, which aim to effectively identify and prevent deepfake content in images and videos. This article will introduce the current development status of deepfake detection technology, present its principles and methods in detail, and look ahead to its future development directions.

关键词： Deep learning Deepfakes computer vision Voting Neural networks Information security Information processing Forgery Fraud computer security

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DRESSING UP LLM: EFFICIENT STYLIZED QUESTION-ANSWERING VIA STYLE SUBSPACE EDITING

arXiv

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arXiv 2025年

作者： Ma, Xinyu Xu, Yifeng Lin, Yang Wang, Tianlong Chu, Xu Gao, Xin Zhao, Junfeng Wang, Yasha School of Computer Science Peking University China Center on Frontiers of Computing Studies Peking University China National Research and Engineering Center of Software Engineering Peking University China

We introduce DRESS, a novel approach for generating stylized large language model (LLM) responses through representation editing. Existing methods like prompting and fine-tuning are either insufficient for complex style adaptation or computationally expensive, particularly in tasks like NPC creation or character role-playing. Our approach leverages the over-parameterized nature of LLMs to disentangle a style-relevant subspace within the model’s representation space to conduct representation editing, ensuring a minimal impact on the original semantics. By applying adaptive editing strengths, we dynamically adjust the steering vectors in the style subspace to maintain both stylistic fidelity and semantic integrity. We develop two stylized QA benchmark datasets to validate the effectiveness of DRESS, and the results demonstrate significant improvements compared to baseline methods such as prompting and ITI. In short, DRESS is a lightweight, train-free solution for enhancing LLMs with flexible and effective style control, making it particularly useful for developing stylized conversational agents. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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IoT and Man-in-the-Middle Attacks

arXiv

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arXiv 2023年

作者： Fereidouni, Hamidreza Fadeitcheva, Olga Zalai, Mehdi Department of Computer Science and Operations Research University of Montreal MontrealQC Canada Department of Computer Engineering and Software Engineering Polytechnique Montreal MontrealQC Canada

This paper provides an overview of the Internet of Things (IoT) and its significance. It discusses the concept of Man-in-the-Middle (MitM) attacks in detail, including their causes, potential solutions, and challenges in detecting and preventing such attacks. The paper also addresses the current issues related to IoT security and explores future methods and facilities for improving detection and prevention mechanisms against MitM. © 2023, CC BY.

关键词： Internet of things

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Common Problems in software Requirement engineering Process: An Overview of Pakistani software Industry

Common Problems in Software Requirement Engineering Process:...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Sadia Khalid Uzair Rasheed Mishal Muneer Wasi Haider Butt Rabia Mehmood Usman Qamar Department of Computer and Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Computer Science and IT University of Lahore Lahore Pakistan Department of Computer Science Lahore College for Women University Lahore Pakistan

Requirement engineering is a major phase of software development process. A project's success mainly depends on an efficient and effective requirement engineering process. Practices have been defined to ensure successful requirement engineering of software projects. Yet the professionals face numerous issues during this phase. This paper explores the software requirement engineering practices from in the software industry of Pakistan. It highlights the common problems faced by the software professionals, as well as commonly deployed solutions and practices.

关键词： Industries Organizations Market research software Requirements engineering Faces

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Challenges of Using Pre-trained Models: the Practitioners' Perspective

Challenges of Using Pre-trained Models: the Practitioners' P...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Xin Tan Taichuan Li Ruohe Chen Fang Liu Li Zhang State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Economics and Management Beihang University Beijing China

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

The challenges associated with using pre-trained models (PTMs) have not been specifically investigated, which hampers their effective utilization. To address this knowledge gap, we collected and analyzed a dataset of 5,896 PTM-related questions on Stack Overflow. We first analyze the popularity and difficulty trends of PTM-related questions. We find that PTM - related questions are becoming more and more popular over time. However, it is noteworthy that PTM-related questions not only have a lower response rate but also exhibit a longer response time compared to many well-researched topics in software engineering. This observation emphasizes the significant difficulty and complexity associated with the practical application of PTMs. To delve into the specific challenges, we manually annotate 430 PTM - related questions, categorizing them into a hierarchical taxonomy of 42 codes (i.e., leaf nodes) and three categories. This taxonomy encompasses many PTM prominent challenges such as fine-tuning, output understanding, and prompt customization, which reflects the gaps between current techniques and practical needs. We discuss the implications of our study for PTM practitioners, vendors, and educators, and suggest possible directions and solutions for future research.

关键词： Codes Systematics Taxonomy Market research software Complexity theory Time factors

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RAT: A Refactoring-Aware Tool for Tracking Code History

RAT: A Refactoring-Aware Tool for Tracking Code History

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International Conference on software engineering - Companion, ICSE, Companion

作者： Feifei Niu Junqian Shao Chaofan Xu Christoph Mayr-Dorn Wesley K. G. Assunção Liguo Huang Chuanyi Li Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science North Carolina State University North Carolina USA Department of Computer Science and Engineering Southern Methodist University Dallas Texash USA

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source code elements break such history. To this end, this paper presents RAT, a refactoring-aware tool for keeping track of code elements evolution across time, not just in terms of revisions but also in terms of refactoring. This is the first tool that enables fine-grained code element traceability of the whole repository. Empirical evaluation of leveraging our tool in three bug localization techniques relying on code history shows significant improvement in localization accuracy. Based on our findings, we believe that many of the state-of-the-art approaches using past source code data would benefit from our tool. Demo Tool: https://***/feifeiniu-se/RAT_Demo Demo Video: https://***/VI_xwUaIPp4

关键词： Location awareness software maintenance Codes Source coding computer bugs Maintenance engineering History

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FAS-reid: Fair Architecture Search for Person Re-IDentification

FAS-reid: Fair Architecture Search for Person Re-IDentificat...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Shengbo Chen Shijie Chen Zhou Lei School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

The development of deep learning has driven the development of ReID, and more and more excellent methods have been proposed, but most of these are artificially designed network backbones. Automation is a trend in the development of deep learning. This paper propose FAS-ReID(Fair Architecture Search for Person Re-IDentification), which can automatically design and generate a neural network backbone for ReID. This paper compare operation selection to competition, and inject noise to make the competition fairer, while making the architecture of automated search More adaptable to ReID We use TriHard loss to improve the feature extraction ability. Our experiments show that the backbone searched by FAS-ReID is better and reduces the search time. Meanwhile, as for the problem of performance collapse caused by skip-connection enrichment in the search process, FAS-Reid does not use the early stop strategy to avoid performance loss like other solutions, which is also the reason why our method is more reliable and robust.

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Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

Expediting In-Network Federated Learning by Voting-Based Con...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiaoxin Su Yipeng Zhou Laizhong Cui Song Guo College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology HKSAR

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with Compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic.

关键词： Training Data privacy Quantization (signal) Federated learning Computational modeling Memory management Collaboration

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Evaluation of the Energy Efficiency of software Calculations in the Design of Microcontroller Devices 17

Evaluation of the Energy Efficiency of Software Calculations...

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Topalov, Andrii Styslo, Taras Tkach, Vitalii Peschanenko, Volodymyr Styslo, Oksana Skrypyuk, Rostyslav Admiral Makarov National University of Shipbuilding Department of computerized control systems Mykolaiv Ukraine King Danylo University Department of IT Ivano-Frankivsk Ukraine Kherson State University Department of computer science and software engineering Ivano-Frankivsk Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of automation and computer-integrated technologies Ivano-Frankivsk Ukraine

ISBN: (纸本)9798331520564

The paper presents a study of the effectiveness of software from the point of view of minimizing the energy consumption of microprocessor devices. In this case, the programming of the microcontroller in various programming languages for arithmetic operations, as well as data output through the port, is considered. In the course of the tests, the peculiarities of the application of this or that program and their operation, taking into account the energy saving of the microcontroller, are noted. © 2024 IEEE.

关键词： Microcontrollers

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