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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Talluri Upender C. Sathiyamoorthy Udayakumar K D. Kirubakaran Purushothaman V Kiruba Thangam Raja Department of CSE CMR College of Engineering and Technology Medchal Hyderabad Telangana Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore Tamil Nadu India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues.

关键词： Deep learning Technological innovation Ethics Recurrent neural networks Autonomous systems Finance Medical services Safety Internet of Things Artificial intelligence

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Comparative Analysis of Cryptocurrency Trading Platforms Using the Analytic Hierarchy Process 3

Comparative Analysis of Cryptocurrency Trading Platforms Usi...

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3rd International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2023

作者： Kuznetsov, Oleksandr Kryvinska, Natalia Ilchenko, Oleksii Smirnova, Tetiana Ulianovska, Yuliia Department of Political Sciences Communication and International Relations University of Macerata Via Crescimbeni 30/32 Macerata62100 Italy Department of Information and Communication Systems Security School of Computer Sciences V. N. Karazin Kharkiv National University 4 Svobody Sq. Kharkiv61022 Ukraine Department Department of Information Systems University of Comenius Bratislava Slovakia Oracle Slovensko spol. s r.o. Bratislava Slovakia Department of cyber security and software Central Ukrainian National Technical University 8 University Ave Kropyvnytskyi25006 Ukraine Department of Computer Science and Software Engineering University of Customs and Finance av. Volodymira Vernadskogo 2/4 Dnipro49000 Ukraine

In the rapidly evolving world of cryptocurrency trading, selecting an optimal trading platform is paramount for both novice and seasoned investors. This study aims to provide a comprehensive evaluation of leading cryptocurrency trading platforms using the Analytic Hierarchy Process (AHP). The AHP, a structured technique for organizing and analyzing complex decisions, was employed to rank platforms based on multiple criteria, including UI/UX, security protocols, asset diversity, and more. Initial findings, considering equal importance for all criteria, highlighted Binance as the most favorable platform. However, when varying the importance of criteria based on empirical data, the distinctions between platforms became less pronounced, yet Binance retained its top position. This research not only offers valuable insights for potential investors but also emphasizes the significance of considering multiple criteria in decision-making processes. Furthermore, the study underscores the versatility of the AHP in evaluating multifaceted scenarios, making it a valuable tool for future research in diverse domains. © 2023 CEUR-WS. All rights reserved.

关键词： Analytic hierarchy process

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A Performance Study of Membership Inference Attacks on Different Machine Learning Algorithms

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Journal of Information Hiding and Privacy Protection 2021年第4期3卷 193-200页

作者： Jumana Alsubhi Abdulrahman Gharawi Mohammad Alahmadi Department of Computer Science University of GeorgiaAthensGA30602USA Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia

Nowadays,machine learning(ML)algorithms cannot succeed without the availability of an enormous amount of training *** data could contain sensitive information,which needs to be *** inference attacks attempt to find out whether a target data point is used to train a certain ML model,which results in security and privacy *** leakage of membership information can vary from one machine-learning algorithm to *** this paper,we conduct an empirical study to explore the performance of membership inference attacks against three different machine learning algorithms,namely,K-nearest neighbors,random forest,support vector machine,and logistic regression using three *** experiments revealed the best machine learning model that can be more immune to privacy ***,we examined the effects of such attacks when varying the dataset *** on our observations for the experimental results,we propose a defense mechanism that is less prone to privacy attacks and demonstrate its effectiveness through an empirical evaluation.

关键词： Membership inference attack data privacy machine learning security

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Training Enhancement of Deep Learning Models for Massive MIMO CSI Feedback with Small Datasets

arXiv

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arXiv 2022年

作者： Liu, Zhenyu Ding, Zhi The School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications China The Department of Electrical and Computer Engineering University of California Davis United States

Accurate downlink channel state information (CSI) is vital to achieving high spectrum efficiency in massive MIMO systems. Existing works on the deep learning (DL) model for CSI feedback have shown efficient compression and recovery in frequency division duplex (FDD) systems. However, practical DL networks require sizeable wireless CSI datasets during training to achieve high model accuracy. To address this labor-intensive problem, this work develops an efficient training enhancement solution of DL-based feedback architecture based on a modest dataset by exploiting the complex CSI features, and augmenting CSI dataset based on domain knowledge. We first propose a spherical CSI feedback network, SPTM2-ISTANet+, which employs the spherical normalization framework to mitigate the effect of path loss variation. We exploit the trainable measurement matrix and residual recovery structure to improve the encoding efficiency and recovery accuracy. For limited CSI measurements, we propose a model-driven lightweight and universal augmentation strategy based on decoupling CSI magnitude and phase information, applying the circular shift in angular-delay domain, and randomizing the CSI phase to approximate phase distribution. Test results demonstrate the efficacy and efficiency of the proposed training strategy and feedback architecture for accurate CSI feedback under limited measurements. Copyright © 2022, The Authors. All rights reserved.

关键词： Channel state information

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Recurrent Learning on $\text{PM}_{2.5}$ Prediction Based on Clustered Airbox Dataset: Extended Abstract

Recurrent Learning on $\text{PM}_{2.5}$ Prediction Based on ...

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International Conference on Data engineering

作者： Chia-Yu Lo Wen-Hsing Huang Ming-Feng Ho Min-Te Sun Ling-Jyh Chen Kazuya Sakai Wei-Shinn Ku Taiwan Semiconductor Manufacturing Company Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Electrical Engineering and Computer Science Tokyo Metropolitan University Tokyo Japan Department of Computer Science and Software Engineering Auburn University Auburn AL USA

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408844

By predicting the air pollutant concentration, people can take precautions to avoid overexposure to air pollutants. Consequently, accurate $\mathbf{PM}_{2.5}$ prediction becomes more important. In this paper, we propose a $\mathbf{PM}_{2.5}$ prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. Our $\mathbf{PM}_{2.5}$ prediction system is composed of four parts: data collection, data preprocessing, prediction model construction, and Line platform. To assess the performance of the model prediction, the daily average error and the hourly average accuracy for the duration of a week are calculated. The experimental results show that LSTM based on K- means has the best performance among all methods. Therefore, LSTM based on K-means is chosen to provide real-time $\mathbf{PM}_{2.5}$ prediction through the Linebot.

关键词： Atmospheric modeling Conferences Neural networks Data preprocessing Predictive models Data collection Air pollution

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AFF-Dehazing: Attention-based feature fusion network for low-light image Dehazing

AFF-Dehazing: Attention-based feature fusion network for low...

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作者： Zhou, Yu Chen, Zhihua Sheng, Bin Li, Ping Kim, Jinman Wu, Enhua Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong School of Information Technologies The University of Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

Images captured in haze conditions, especially at nighttime with low light, often suffer from degraded visibility, contrasts, and vividness, which makes it difficult to carry out the following vision tasks. In this article, we propose an attention-based feature fusion network (AFF-Dehazing) for low-light image dehazing. Our method decomposes the low-light image dehazing into two task-independent streams containing four modules: image dehazing module, low-light feature extractor module, feature fusion module, and image restoration module. The basic block of these modules is the proposed attention-based residual dense block. Since the dual-branch are used, AFF-Dehazing can avoid learning the mixed degradation all-in-one and enhance the details of low-light haze images. Extensive experiments show that our method surpasses previous state-of-the-art image dehazing methods and low-light enhancement methods by a very large margin both quantitatively and qualitatively. © 2021 John Wiley & Sons, Ltd.

关键词： Image reconstruction

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State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects

arXiv

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arXiv 2025年

作者： Goyal, Harshika Wajid, Mohammad Saif Wajid, Mohd Anas Khanday, Akib Mohi Ud Din Neshat, Mehdi Gandomi, Amir Indian Institute of Technology Kharagpur India Department of Computer Science and Intelligent Systems Tecnológico de Monterrey Monterrey Mexico Institute for the Future of Education TEC de Monterrey Monterrey Mexico Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Faculty of Engineering and Information Technology University of Technology Sydney Ultimo SydneyNSW2007 Australia Obuda University Budapest1034 Hungary School of Computer and Mathematical Sciences University of Adelaide Adelaide5005 Australia

The rapid expansion and advancement of deepfake technology, which is specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across a multitude of fields, including but not limited to the realms of the entertainment industry, forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methodologies, especially through the implementation of techniques such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformer Neural Networks, the outcomes achieved in the generation of deepfakes have been nothing short of astounding and transformative. Furthermore, the recent advancements in diffusion models have served to invigorate and catalyze ongoing research efforts in this fascinating domain. At the same time, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in both deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations that are shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Besides, task definitions are standardized to ensure clarity, while datasets and metrics are diligently introduced, paving the way for a

关键词： Generative adversarial networks

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AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

arXiv

引用

arXiv 2024年

作者： Sun, Yiwen Ye, Furong Zhang, Xianyin Huang, Shiyu Zhang, Bingzhen Wei, Ke Cai, Shaowei School of Data Science Fudan University Shanghai China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China 4paradigm Inc Beijing China Department of Industrial Engineering Tsinghua University Beijing China

Conflict-Driven Clause Learning (CDCL) is the mainstream framework for solving the Satisfiability problem (SAT), and CDCL solvers typically rely on various heuristics, which have a significant impact on their performance. Modern CDCL solvers, such as MiniSat and Kissat, commonly incorporate several heuristics and select one to use according to simple rules, requiring significant time and expert effort to fine-tune in practice. The pervasion of Large Language Models (LLMs) provides a potential solution to address this issue. However, generating a CDCL solver from scratch is not effective due to the complexity and context volume of SAT solvers. Instead, we propose AutoSAT, a framework that automatically optimizes heuristics in a pre-defined modular search space based on existing CDCL solvers. Unlike existing automated algorithm design approaches focusing on hyperparameter tuning and operator selection, AutoSAT can generate new efficient heuristics. In this first attempt at optimizing SAT solvers using LLMs, several strategies including the greedy hill climber and (1 + 1) Evolutionary Algorithm are employed to guide LLMs to search for better heuristics. Experimental results demonstrate that LLMs can generally enhance the performance of CDCL solvers. A realization of AutoSAT outperforms MiniSat on 9 out of 12 datasets and even surpasses the state-of-the-art hybrid solver Kissat on 4 datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Fault tolerance

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A Framework for Systematic Classification of Assets for Security Testing

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computers, Materials & Continua 2021年第1期66卷 631-645页

作者： Sadeeq Jan Omer Bin Tauqeer Fazal Qudus Khan George Tsaramirsis Awais Ahmad Iftikhar Ahmad Imran Maqsood Niamat Ullah National Center for Cyber Security Department of CS&ITUniversity of Engineering&TechnologyPeshawarPakistan Department of Information Technology FCITKing Abdulaziz UniversityJeddahSaudi Arabia Dipartimento di Informatica(DI) UniversitàDegli Studi di Milano StataleVia Celoria 18MilanoItaly Department of Computer Science&IT University of Engineering&TechnologyPeshawarPakistan Department of Software Engineering University of Engineering&TechnologyMardanPakistan University of Buner BunerPakistan

Over the last decade,a significant increase has been observed in the use of web-based Information systems that process sensitive information,e.g.,personal,financial,*** this increased use,the security of such systems became a crucial aspect to ensure safety,integrity and authenticity of the *** achieve the objectives of data safety,security testing is ***,with growth and diversity of information systems,it is challenging to apply security testing for each and every ***,it is important to classify the assets based on their required level of security using an appropriate *** this paper,we propose an asset security classification technique to classify the System Under Test(SUT)based on various factors such as system exposure,data criticality and security *** perform an extensive evaluation of our technique on a sample of 451 information ***,we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of *** technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.

关键词： Security security testing privacy asset classification

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Quality Evaluation in Guavas using Deep Learning Architectures: An Experimental Review 4

Quality Evaluation in Guavas using Deep Learning Architectur...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Choudhury, Tanupriya Mahdi, Hussain Falih Agarwal, Archit Chakraborty, Arnav Arunachalaeshwaran, V R Sarkar, Tanmay Tomar, Ravi Uttarakhand Dehradun2480007 India University of Diyala Department of Computer and Software Engineering Baqubah Iraq School of Computing National University of Singapore Kent Ridge119007 Singapore School of Computer Science Lambton College ONL4Z3E9 Canada Malda Polytechnic Department of Food Processing West Bengal Malda732102 India

ISBN: (纸本)9781665468350

Determining the quality of fruit can be very challenging without the proper understanding. There are many factors that can result in determining the quality and therefore the actual taste of any given fruit. In this paper we attempt to determine the quality of the guava fruit on what are considered the three most important aspects of flavour by experts, those being, Colour, Shape and texture. All three of these aspects will be determined on a scale of 1-5. In order to produce these results, we are using an exceptional deep learning architecture and a primary dataset curated by a panel of experts who photographed the fruits in six-hour intervals for many days. We have compared the results over multiple deep learning architectures to find the most accurate results. This paper shows the importance of deep learning as a potential future tool for assessing the quality of fruits. © 2022 IEEE.

关键词： Fruits

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