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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Sajjad, Mohammed Hemavathi, D. School of Computing Srm Institute of Science and Technology-Kattankulathur Department of Data Science and Business Systems Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Object detection has a pivotal role in the field of security. Often, a security breach occurs under cover of night when the visibility is reduced. Manual supervision is relatively difficult as visibility is drastically reduced and human error increases to a large extent. In recent times, automation in security has gained a lot of traction. There are various inventions made in the field of automated surveillance. There are various models to detect objects in a frame with high accuracy accurately. With the help of pre-defined datasets and exclusive object detection models following the concepts of deep learning, an automated surveillance system can be implemented with ease. This study discusses the impact of automated surveillance, both night vision and daytime surveillance, the pros and cons, and the development of automated surveillance models that lead to accurate results. The study mainly focuses on deep learning concepts used to improve object detection. © 2022 IEEE.

关键词： Object detection

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Multi-Stage Transfer Learning Evolutionary Algorithm for Dynamic Multiobjective Optimization 13

Multi-Stage Transfer Learning Evolutionary Algorithm for Dyn...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Qianhui Zhu, Qingling Ji, Junkai College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

Recently, the application of transfer learning within dynamic multiobjective evolutionary algorithms (DMOEAs) has shown significant potential to solve dynamic multiobjective optimization problems (DMOPs). This approach utilizes the transfer learning technique which reuses information from previous environments to accelerate the search process in the new environment. However, the risk of negative transfer can lead to an erroneous search direction and inefficient use of computational resources. To address this issue, this paper proposes a novel multistage transfer learning DMOEA, named MSTL. The algorithm is designed to enhance the convergence and diversity of the population through the combination of the pre-transfer learning stage and the transfer learning and validation stage when environmental changes occur. In the pre-transfer learning stage, a suitable target domain, source domain, and validation classifier sample sets are generated guided by historical information. In the transfer learning and validation stage, the TrAdaboost technique to transfer knowledge from the target to the source domain, with the results verified by two validation classifiers to mitigate negative transfer. Experimental results demonstrate that our proposed algorithm outperforms four competing algorithms in terms of diversity and convergence on a widely recognized benchmark suite. © 2024 IEEE.

关键词： Dynamic multiobjective optimization evolutionary algorithm transfer learning

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Exploring the Efficacy and Limitations of Histogram-Based Fake Image Detection 28th

Exploring the Efficacy and Limitations of Histogram-Based Fa...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Hölscher, Dirk Reich, Christoph Gut, Frank Knahl, Martin Clarke, Nathan Institute for Data Science Cloud Computing and IT Security Furtwangen University Furtwangen78120 Germany Centre for Cyber Security Communications and Network Research Plymouth University Plymouth United Kingdom

Generative image models pose challenges to image authenticity and trustworthiness, blurring the line between real and fake content. This paper addresses these concerns by proposing a histogram-based approach using pre-trained models (vgg16, ResNet50, Xception) to train classification networks for distinguishing real from generated images. Leveraging histograms derived from images, the method aims to accurately classify images as authentic or synthetic. Through experiments, the paper examines the effectiveness of the approach in mitigating the risks associated with fake contents widespread dissemination. Results demonstrate promising advancements in detecting image manipulation and preserving the integrity of visual information amidst the spread of generative models. Using pre-trained models the paper shows high classification accuracy for detecting fake images. © 2024 The Authors.

关键词： Generative adversarial networks

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Fededrated Learning: Research, Challenges, and Future Directions 5

Fededrated Learning: Research, Challenges, and Future Direct...

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

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Yadav, Rishika Malik, Kiran Tripathi, Aanjey Mani Galgotias University School of Computing Science and Engineering UP Greater Noida India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation AP Guntur India Graphic Era Hills University Department of Computer Science and Engineering Uttarakhand Dehradun India Mriem Department of Computer Science and Engineering Rohtak India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Federated Learning, a disruptive and novel aspect of machine learning, is at the forefront of decentralized, privacy-conscious data processing. This in-depth review study navigates the complex environment of Federated Learning Ecosystems, attempting to extract the core of the topic while shining light on its research horizons, pressing difficulties, and promising approaches. Our trip begins with a look at the fundamental concepts of Federated Learning, emphasizing its importance in the landscape of distributed machine learning paradigms. We track its development via historical milestones and key contributions, offering context for the field's increasing research. This study focuses on the many features of Federated Learning research, including privacy-preserving techniques, communication protocols, aggregation approaches, and real-world applications. We aggregate major results and advances within these domains, highlighting notable authors and their contributions. However, the route to Federated Learning's full fulfillment is plagued with difficulties, including questions about privacy, communication efficiency, and scalability. In the future, we will enlighten the growing paths of Federated Learning research, forecasting trends, providing insights into overcoming present problems, and visualizing its integration into diverse fields. To summarize, this review article serves as a guidepost for scholars, practitioners, and enthusiasts wanting to understand the vast domain of Federated Learning. It captures the core of this dynamic discipline, offers strategic counsel for resolving its obstacles, and encourages optimism about its bright future within the domain of distributed machine learning paradigms. © 2024 IEEE.

关键词： data Analysis Distributed Environments Edge computing Federated Learning Machine Learning

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data-Driven Analysis of Climate Change Impacts on Public Health: A Systematic Review and Future Directions

Data-Driven Analysis of Climate Change Impacts on Public Hea...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Yarmohammadtoosky, Sahar Xie, Ying Kennesaw State University School of Data Science and Analytics MariettaGA United States Kennesaw State University College of Computing and Software Engineering MariettaGA United States

ISBN: (纸本)9798350362480

Climate change profoundly impacts human health, ecosystems, and food and water security. This review provides a comprehensive analysis of climate change's effects on human health using data science methods, including statistical and machine learning models, to assess current knowledge. It identifies critical gaps in data collection and methodological limitations across different regions and climates. Key recommendations include improving data resolution and duration to enhance model accuracy, as well as fostering collaboration between data scientists, public health experts, and climate specialists. The review underscores the need for innovative, data-driven models to better predict and address the health impacts of climate change. © 2024 IEEE.

关键词： Electronic health record

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Indoor Navigation Using Ultra Wide Band

Indoor Navigation Using Ultra Wide Band

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2023 Intelligent computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Divya, B. Veeramakali, T. Revathi, S. Harisharajan, R.R. Adhirooban, D. Ebishdon, G.V. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India School of Computing Srm Institute of Science and Technology Dept of Data Science and Business Systems Tamilnadu Kattankulathur India

ISBN: (纸本)9798350394580

Indoor positioning is a significant and intriguing topic in navigation systems that present numerous use cases for investigation. Researchers are exploring technologies capable of providing accurate locations in indoor spaces where GNSS positioning systems are ineffective. In the numerous solutions available for indoor location services, Ultra-wideband (UWB) technology was chosen for its ability to provide high-precision positioning information for multiple points in real-time. This paper presents a UWB indoor positioning system that utilizes time of flight (TOF) algorithm. At times, time difference of arrival (TDOA) algorithms compute range measurements, this facilitates the trans-receiver (TAG) plane position algorithm's location determination. UWB technology was chosen because it is the latest technology available on Android 12 and further also offers high accuracy, resistance to interference and minimal signal reflection from walls. UltraWide Band(UWB) localization encounters various obstacles, such as multipath distortions, additional signal delays, time discrepancies in clocks, signal interference, and the computational resources necessary to determine the user's location. © 2023 IEEE.

关键词： Indoor Positioning System(IPS) Time of Flight(TOF) Ultra Wide Band(UWB)

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McGrids: Monte Carlo-Driven Adaptive Grids for Iso-Surface Extraction

arXiv

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arXiv 2024年

作者： Ren, Daxuan Shi, Hezi Zheng, Jianmin Cai, Jianfei College of Computing and Data Science Nanyang Technological University Singapore Department of Data Science & AI Monash University Australia

Iso-surface extraction from an implicit field is a fundamental process in various applications of computer vision and graphics. When dealing with geometric shapes with complicated geometric details, many existing algorithms suffer from high computational costs and memory usage. This paper proposes McGrids, a novel approach to improve the efficiency of iso-surface extraction. The key idea is to construct adaptive grids for iso-surface extraction rather than using a simple uniform grid as prior art does. Specifically, we formulate the problem of constructing adaptive grids as a probability sampling problem, which is then solved by Monte Carlo process. We demonstrate McGrids’ capability with extensive experiments from both analytical SDFs computed from surface meshes and learned implicit fields from real multiview images. The experiment results show that our McGrids can significantly reduce the number of implicit field queries, resulting in significant memory reduction, while producing high-quality meshes with rich geometric details. © 2024, CC BY.

关键词： Mesh generation

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EQCNN: Enhanced Remote Sensing Imagery Classification with Circuit-Based Error-Corrected Quantum Convolutional Neural Networks 25

EQCNN: Enhanced Remote Sensing Imagery Classification with C...

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25th International Conference on Digital Image computing: Techniques and Applications, DICTA 2024

作者： Zaman, Muhammad Kehkashan, Tanzila Akhunzada, Adnan Alaidaros, Hashem Uddin, Mueen Azeem, Muhammad University of Lahore Faculty of Computer Science Punjab Sargodha40100 Pakistan Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia College of Computing & It University of Doha for Science & Technology Department of Data and Cybersecurity Doha Qatar Dar Al-Hekma University Department of Cybersecurity Jeddah Saudi Arabia

ISBN: (纸本)9798350379037

Quantum information processing is more complex than classical counterpart because of the No-Cloning theorem, decoherence, and issues detecting quantum states. Building a quantum computer without error detection and correction module is challenging. To overcome this challenge, qubit error correction codes are implemented. Inadequate error detection and correction codes have been the downfall of earlier attempts to encode quantum information, leading to disrupted qubit transmission. Qubit information is decohered by circuit noise. Unfortunately, the principal qubit error, phase flip error is not corrected by existing qubit error correction methods. In this study, we proposed Quantum Error Detection and Correction scheme (QEDC) to keep qubit in a coherent state. Used S gates, unitary gates, 10 CNOT gates, and Hadamard gates spanning on 4 physical and 1 logical qubits over the IBM Quantum Experience Machine. We found that the error probability is reduced to 7.91% owing to the efficiency of the quantum information encoding and phase flip control. Furthermore, we proposed Error Corrected Quantum Convolution Neural Network (EQCNN) for Image Classification. EQCNN combat and correct qubit errors that occur during the image processing. EQCNN outperformed over Earth Observatory (EO) dataset, EuroSAT and achieved an impressive results of 99.3% accuracy, 98.9% F1-score, 96.73% precision, and 98.4% recall. © 2024 IEEE.

关键词： Qubits

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Conformal prediction based on principal component analysis for high-dimensional outlier detection

Conformal prediction based on principal component analysis f...

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2024 International Conference on Cloud computing, Performance computing, and Deep Learning, CCPCDL 2024

作者： Qian, Xiaoyu Wu, Jinru Lin, Youwu School of Mathematics and Computing Science Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541002 China Guilin541002 China

ISBN: (纸本)9781510683044

Current outlier detection methods exhibit many limitations in high-dimensional settings. Traditional statistical approaches rely on strong assumptions and lack practicality and generality. Meanwhile, despite the better performance, machine learning methods suffer from low interpretability and reliability due to their complex mechanisms and the absence of confidence estimation. Although outlier detection methods based on principal component analysis (PCA) have shown some advantages by extracting important features from high-dimensional data, they have not entirely solved these problems. Conformal prediction, a finite-sample distribution-free uncertainty quantification method recently applied to outlier detection, produces a set-valued prediction with a Type-I error guarantee and false discovery rate control. This paper proposes a new distribution-free method for high-dimensional outlier detection, PCA-CP, combining principal component analysis and conformal prediction. PCA-CP overcomes the shortcomings of previous methods, demonstrating high generality and reliability while achieving high performance. Experiments on simulation and real data show that PCA-CP outperforms previous methods, achieving higher power and lower False Discovery Rate, thus proving its significant advantages. © 2024 SPIE.

关键词： Prediction models

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CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference

CoSafe: Evaluating Large Language Model Safety in Multi-Turn...

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

作者： Yu, Erxin Li, Jing Liao, Ming Wang, Siqi Gao, Zuchen Mi, Fei Hong, Lanqing Department of Computing The Hong Kong Polytechnic University Hong Kong Research Centre for Data Science & Artificial Intelligence Hong Kong Huawei Noah's Ark Lab Canada

ISBN: (纸本)9798891761643

As large language models (LLMs) constantly evolve, ensuring their safety remains a critical research issue. Previous red teaming approaches for LLM safety have primarily focused on single prompt attack or goal hijacking. To the best of our knowledge, we are the first to study LLM safety in multi-turn dialogue coreference. We created a dataset of 1, 400 questions across 14 categories, each featuring multi-turn coreference safety attacks. We then conducted detailed evaluations on five widely used open-source LLMs. The results indicated that under multi-turn coreference safety attacks, the highest attack successful rate was 56% with the LLaMA2-Chat-7b model, while the lowest was 13.9% with the Mistral-7B-Instruct model. These findings highlight the safety vulnerabilities in LLMs during dialogue coreference interactions. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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