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1st International Conference on Advances in computing Research, ACR’23

作者： Alsobeh, Anas Shatnawi, Amani Program School of Computing Southern Illinois University CarbondaleIL United States Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan

ISBN: (纸本)9783031337420

Efficiency and convenience have increased as a result of the Internet of Things’ (IoT) expanding adoption across several enterprises. Nevertheless, the dynamic nature of IoT devices severely hinders real-time security breach prevention. Model checking, self-adaptation, and the Behavior-Interaction-Priority (BIP) component model are combined into an application layer to increase data-driven security for IoT systems. An integrated approach is proposed in this paper as a conceptual model for Internet of Things (IoT) security. This approach may have the potential to significantly improve the identification and mitigation of security concerns, including bogus data, malware attacks, and secure system transactions. The framework model is comprised of four primary components: BIP-based Secure Data Collection and Storage (BIP-SDCS), BIP-based Data Analytics (BIP-DA), BIP-based Secure Threat Detection and Response (BIP-STDR), BIP-based Continuous Monitoring (BIP-CM). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Model checking

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Advanced Security Model for Cloud Infrastructure Using Cryptography and Steganography

Advanced Security Model for Cloud Infrastructure Using Crypt...

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2024 International Conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Sujithra, T. Justus, Vivek Mahaboob, Mohamed Iqbal Durai, S. Manipal Academy of Higher Education Department of CSE Manipal Institute of Technology Manipal India SRM Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur603203 India Sri Sivasubramaniya Nadar College of Engineering Department of Information Technology Chennai603110 India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Dept. of CSE Chennai India

ISBN: (纸本)9798350390049

Cloud computing offers an infrastructure for individual to business with fluctuating workloads by enabling clients to use resources such as memory, software, hardware, bandwidth etc., on "pay on use"method. Security is a very big challenge when we are dealing with third party services/ cloud infrastructures. Improving the security of data stored on cloud infrastructure is the primary goal of this article. It is achieved by incorporating the features of cryptographic and steganographic techniques. The proposed system introduces a two-level security framework involving cryptographic and steganographic modules. The proposed cryptography module provided improved information security with reduced computational complexity by addressing the challenges of traditional encryption methods. Natural Language Processing (NLP) is used to classify data into segments requiring different levels of security. High-security segments are encrypted using higher end cryptography algorithms like AES, while lower-security segments use simpler methods like substitution techniques. It results in reduced computational complexity. The proposed system further secures the data with the help of steganography. It embedded the encrypted information over the images using the YUV color model, specifically manipulating the brightness component to conceal the data. This combined strategy ensures effective and safe cloud data storage by providing strong security with little computational overhead. The experimental results show that the proposed method outperforms the existing method in the context of better computational complexity, image quality index and embedded and extraction time in evaluating the stego image. © 2024 IEEE.

关键词： Cloud security

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An Audit on the Perspectives and Challenges of Hallucinations in NLP

An Audit on the Perspectives and Challenges of Hallucination...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Venkit, Pranav Narayanan Chakravorti, Tatiana Gupta, Vipul Biggs, Heidi Srinath, Mukund Goswami, Koustava Rajtmajer, Sarah Wilson, Shomir College of Information Sciences and Technology Pennsylvania State University United States Adobe Research United States School of Interactive Computing Georgia Institute of Technology United States Department of Computer Science & Engineering Pennsylvania State University United States

ISBN: (纸本)9798891761643

We audit how hallucination in large language models (LLMs) is characterized in peer-reviewed literature, using a critical examination of 103 publications across NLP research. Through the examination of the literature, we identify a lack of agreement with the term 'hallucination' in the field of NLP. Additionally, to compliment our audit, we conduct a survey with 171 practitioners from the field of NLP and AI to capture varying perspectives on hallucination. Our analysis calls for the necessity of explicit definitions and frameworks outlining hallucination within NLP, highlighting potential challenges, and our survey inputs provide a thematic understanding of the influence and ramifications of hallucination in society. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Deep Video Demoireing via Compact Invertible Dyadic Decomposition

Deep Video Demoireing via Compact Invertible Dyadic Decompos...

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IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Quan, Yuhui Huang, Haoran He, Shengfeng Xu, Ruotao South China University of Technology School of Computer Science and Engineering Guangzhou 510006 China Pazhou Lab Guangzhou 510335 China Singapore Management University School of Computing and Information Systems 188065 Singapore

ISBN: (纸本)9798350307184

Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, which thus are easily confused with moire patterns and can be significantly erased in the removal process. By interpreting video demoireing as a multi-frame decomposition problem, we propose a compact invertible dyadic network called CIDNet that progressively decouples latent frames and the moire patterns from an input video sequence. Using a dyadic cross-scale coupling structure with coupling layers tailored for multi-scale processing, CIDNet aims at disentangling the features of image patterns from that of moire patterns at different scales, while retaining all latent image features to facilitate reconstruction. In addition, a compressed form for the networks output is introduced to reduce computational complexity and alleviate overfitting. The experiments show that CIDNet outperforms existing methods and enjoys the advantages in model size and computational efficiency.

关键词： Deep Video Computational Efficiency Image Features Model Size Video Sequences Image Pattern Latent Features Compressed Format Complex Texture Multiscale Process Latent Image Deep Neural Network Attention Mechanism Video Frames Peak Signal-to-noise Ratio Multiscale Analysis Feature Alignment Adjacent Frames Supplement For Details Discrete Cosine Transform Frame Alignment U-shaped Structure Alignment Blocks Multi-scale Structure Reversible Loss Color Shift information Fidelity Latent Code Spatial Pyramid Pooling Spatial Pyramid

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Beamforming and Trajectory Design for Active IRS-Assisted UAV Relaying Systems 35

Beamforming and Trajectory Design for Active IRS-Assisted UA...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Huang, Qiulei Song, Zhaohui Xiong, Zehui Xu, Guanjun Zhao, Nan Niyato, Dusit Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Space Information Research Institute Hangzhou Dianzi University Hangzhou310018 China Singapore University of Technology and Design 487372 Singapore Nanyang Technological University College of Computing and Data Science 639798 Singapore

ISBN: (纸本)9798350362244

Intelligent reflecting surface (IRS) can reconfigure the channel conditions, while the passive beamforming gain is limited by the severe double path-loss effect. Fortunately, active IRS (AIRS) is emerging to tackle obstacles by simultaneously adjusting the phases and amplitudes. In this paper, we propose an AIRS-assisted unmanned aerial vehicle (UAV)-relaying scheme, where the AIRS is equipped on the UAV to reflect the signal from the ground base station (GBS) to users via non-orthogonal multiple access. We jointly adjust transmit beamforming, reflection matrix and UAV trajectory to maximize the average sum rate, which is non-convex. Thus, the problem is decomposed into three subproblems via block coordinate descent. The beamforming optimization is solved through semidefinite relaxation. Then, the reflection matrix of AIRS and UAV trajectory are jointly optimized via successive convex approximation. Finally, we design an iterative algorithm to effectively tackle the original problem. Simulation results are shown to verify the performance of the designed scheme. © 2024 IEEE.

关键词： Beamforming

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Lightweight Convolutional Network For Automated Photovoltaic Defect Detection 9

Lightweight Convolutional Network For Automated Photovoltaic...

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9th International Conference on information technology Trends, ITT 2023

作者： Zahid, Arsalan Hussain, Muhammad Hill, Richard Al-Aqrabi, Hussain University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom Higher Colleges of Technology Faculty of Computer Information Science Sharjah United Arab Emirates

ISBN: (纸本)9798350327502

As the World moves towards renewable energy, photovoltaic modules are a fundamental option due to their green nature. However, the manufacturing process of solar cells is complex and vulnerable to discrepancies which can impact the overall performance of the system. Although human-led inspection is seen as the de-facto quality inspection protocol, issues pertaining to bias, cost and time can make it an expensive process. To this effect, this paper focuses on the development of a custom convolutional architecture that is lightweight, hence deployable within manufacturing facilities to assist with defective solar cell inspection. In addition, to address the issue of data scarcity, representative data augmentations are producing tailored towards enhancing the model's generalizability. The high efficacy of the proposed CNN and proposed augmentations can be gauged by the fact that 98% F1 score was achieved overall. © 2023 IEEE.

关键词： Convolutional neural networks

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HD-KT: Advancing Robust Knowledge Tracing via Anomalous Learning Interaction Detection 24

HD-KT: Advancing Robust Knowledge Tracing via Anomalous Lear...

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33rd ACM Web Conference, WWW 2024

作者： Ma, Haiping Yang, Yong Qin, Chuan Yu, Xiaoshan Yang, Shangshang Zhang, Xingyi Zhu, Hengshu Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Anhui Hefei China Institutes of Physical Science and Information Technology Anhui University Anhui Hefei China Career Science Lab BOSS Zhipin PBC School of Finance Tsinghua University Beijing China School of Artificial Intelligence Anhui University Anhui Hefei China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Anhui Hefei China Career Science Lab BOSS Zhipin Beijing China

ISBN: (纸本)9798400701719

Knowledge tracing (KT) is a crucial task in online learning, aimed at tracing and predicting each student's knowledge states throughout their learning process. Over the past decade, it has garnered widespread attention due to it provides the potential for more tailored and adaptive online learning experiences. Although most current KT methodologies emphasize optimizing network structures to enhance predictive accuracy for future student performance, they often neglect anomalous interactions in students' learning processes, which may arise from low data quality (i.e., inferior question quality) and abnormal student behaviors (i.e., guessing and mistakes). To this end, in this paper, we propose a novel framework, termed HD-KT, designed to enhance the robustness of existing KT methodologies with Hybrid learning interactions Denoising approach. Specifically, we introduce two detectors for anomalous learning interactions, namely knowledge state-guided anomaly detector and student profile-guided anomaly detector. In the first detection module, we design a sequential autoencoder to identify anomalous learning interactions by detecting atypical student knowledge states. In the second module, we incorporate an attention mechanism by modeling a student's long-term profile to capture irregular interactions. Extensive experiments on four real-world benchmark datasets have decisively shown our HD-KT markedly boosts the robustness of numerous prevailing KT models, consequently increasing the accuracy of future student performance predictions. Additionally, our case studies highlight the versatility of HD-KT in addressing diverse downstream tasks, such as exercise quality analysis and learning behavior-based student clustering. © 2024 ACM.

关键词： Students

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Ransomware Detection Techniques Using Machine Learning Methods 1

Ransomware Detection Techniques Using Machine Learning Metho...

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1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Wadho, Shuaib Ahmed Yichiet, Aun Gan, Ming Lee Lee, Chen Kang Ali, Sijjad Akbar, Rehan Universiti of Tunku Abdul Rahman Faculty of Information and Comunication Technology Kampar Malaysia Shenzhen Universiy College of Computer Science and Software Engnierng China Florida International University School of Computing and Information Sciences Miami United States

ISBN: (纸本)9798350373042

Considering the rising frequency and refinement of ransomware attacks, there is a rising significance for dynamic and successful methods of detection and mitigation. Conventional mark-based approaches frequently demonstrate lacking in recognizing new and developing variations of ransomware. This paper investigates the use of machine learning methods for ransomware detection, expecting to improve the precision and flexibility of detection instruments. It presents a far-reaching examination of different machine learning methods and algorithms, assessing their viability in perceiving ransomware patterns. The discoveries offer important bits of knowledge into the development of cybersecurity arrangements that are stronger and proactive in tending to the dynamic landscape of ransomware threat. © 2024 IEEE.

关键词： Malware

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Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks

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SN Computer science 2024年第1期5卷 1-13页

作者： Almahadin, Ghayth Subburaj, Maheswari Hiari, Mohammad Sathasivam Singaram, Saranya Kolla, Bhanu Prakash Dadheech, Pankaj Vibhute, Amol D. Sengan, Sudhakar Department of Networks and Cybersecurity Faculty of Information Technology Al Ahliyya Amman University Country Amman Jordan School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India Department of Networks and Cybersecurity Information Technology Al Ahliyya Amman University Amman Jordan Department Computing Technology School of Computing SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Chennai 603203 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management and Gramothan (SKIT) Rajasthan Jaipur 302017 India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) MH Pune 411016 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Identifying suspicious activities or behaviors is essential in the domain of Anomaly Detection (AD). In crowded scenes, the presence of inter-object occlusions often complicates the detection of such behaviors. Therefore, developing a robust method capable of accurately detecting and locating anomalous activities within video sequences becomes crucial, especially in densely populated environments. This research initiative aims to address this challenge by proposing a novel approach focusing on AD behaviors in crowded settings. By leveraging a spatio-temporal method, the proposed approach harnesses the power of both spatial and temporal dimensions. This enables the method to effectively capture and analyze the intricate motion patterns and spatial information embedded within the continuous frames of video data. The objective is to create a comprehensive model that can efficiently detect and precisely locate anomalies within complex video sequences, specifically those featuring human crowds. The efficacy of the proposed model will be rigorously evaluated using a benchmark dataset encompassing diverse scenarios involving crowded environments. The dataset is designed to simulate real-world conditions where millions of video footage need to be continuously monitored in real time. The focus is on identifying anomalies, which might occur within short time frames, sometimes as brief as five minutes or even less. Given the challenges posed by the massive volume of data and the requirement for rapid AD, the research emphasizes the limitations of traditional Supervised Learning (SL) methods in this context. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection Autoencoders LSTM Machine learning Spatio-temporal features Suspicious activities

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Prediction of Level of Damage of Brain Due To Tumor Via Integrated CNN Technique 5

Prediction of Level of Damage of Brain Due To Tumor Via Inte...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Agrawal, Vivek Kaswan, Kuldeep Singh Kumar, Sanjay Galgotias College of Engineering and Technology Department of Information Technology U.P Greater Noida India Galgotias University School of Computing Science and Engineering U.P Greater Noida India Galgotia College of Engineering and Technology Department of Computer Science Engineering U.P Greater Noida India

ISBN: (纸本)9798350383522

Image transform is the individual of ultimate questioning and intelligent field at the period. Tumor is an uneven expansion of container cruel intellect. The lump may be classification as favorable (non-malignant) and diseased (malignant). Earlier stage of swelling is previously in time or order discovered concerning matter through test of figure by doctors and it is a opportunity absorbing task and occasionally gives inaccurate consequence. Today different register forms are secondhand in healing field that supports a fast and correct result. Magnetic Resonance Images (MRI) is ultimate usual image method for resolving within makeup of human bulk. The MRI is secondhand even in disease of most harsh affliction of healing wisdom like mind tumors. The intellect carcinoma discovery process comprise figure prepare methods includes four stages. Image pre-handle, concept separation, feature ancestry, and eventually categorization. There is various existent of methods are handy for intellect carcinoma separation and categorization to discover the intelligence swelling. There are many methods handy presents a study of existent methods for intellect swelling discovery and their benefits and restraints. To overcome these disadvantages, intend a Convolution Neural Network (CNN) located classifier. CNN located classifier used to equate the prepared and test dossier, from this take high-quality result. © 2024 IEEE.

关键词： Brain Tumor Detection CNN Image Preprocessing Roduction

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