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检索条件"机构=Dept. of Electrical and Computer Engineering and Computer Science University of Cincinnati"

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An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural Network Technique for Event Detection in Video Sequences

An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural N...

引用

Cognitive, Green and Ubiquitous Computing (IC-CGU), International Conference on

作者： Kumud Sachdeva Jasminder Kaur Sandhu Rakesh Sahu Dept. of Computer Science Engineering Chandigarh University Gharuan Punjab India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

Classifying and detecting difficult video events based on visual modalities remains an uncertain problem. Conventional video presentation approaches ineffectively extract each modality, hindering video event detection (VED) rates. The research methodology involves pre-processing steps: uploading video data samples, extracting video frames, converting to grayscale (rgb2gray), enhancing, and calculating smooth frames from the UCF -101 dataset. This pre-processing phase aims to deliver high-quality frames without data loss. Next, the feature extraction process employs the HoG method to extract feature vectors from refined video frames, facilitating the training process and efficiently reducing dimensions. An adaptive, nature-inspired PBC method is then implemented to select reliable and optimized feature sets from the extracted ones. These selected optimized feature vectors are input into different events of the deep neural network (DNN) classifier. Finally, reliable feature sets are identified through feature matching, and experimental outcomes demonstrate a significant 21.3% enhancement in VED and classification, assessed through accuracy rate, specificity (SP), sensitivity (SN), etc. Compared with traditional approaches using manually designed feature sets, the proposed approach proves more effective. Simulation outcomes on publicly available VED databases consistently outperform state-of-the-art video representation methods such as EFS-linear multi-support vector machine (MSVM), convolutional neural network (CNN), etc.

关键词： Training Visualization Adaptive systems Sensitivity Event detection Video sequences Feature extraction

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Survey on Online Learning Applications in Multi-UAV Networks

Survey on Online Learning Applications in Multi-UAV Networks

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Jaemin Kim Hyosu Kim Jeongyeup Paek Yongseok Son Hyung Tae Lee Sungrae Cho Dept. of Computer Science and Engineering Chung-Ang University Seoul Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

This paper provides a comprehensive review of the application of online learning techniques in multi-UAV systems, highlighting their role in enhancing autonomy and efficiency in dynamic environments. Key areas of focus include adaptive path planning, collision avoidance, and swarm behavior control, along with the associated technical challenges and future research directions.

关键词： Surveys Reviews Scalability Reinforcement learning Robustness Real-time systems Path planning Complexity theory Particle swarm optimization Testing

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Improving IJTAG Test Efficiency and Security

Improving IJTAG Test Efficiency and Security

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2022 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2022

作者： Wang, Sying-Jyan Shih, Yen-Chang Li, Katherine Shu-Min Lin, Chen-Yeh Chong, Song-Kong National Chung Hsing University Dept. Computer Science Engineering Taichung Taiwan National Sun Yat-sen University Dept. Computer Science Engineering Kaohsiung Taiwan Cybersecurity Technology Institute Institute for Information Industry Taipei Taiwan

ISBN: (纸本)9781665409216

The IEEE Std. 1687 (IJTAG) provides a more efficient and flexible mechanism to access embedded instruments in complex system-on-chips (SoC). Embedded instruments are mainly used for testing, debugging, diagnosing, and post-silicon validation. In this paper, we propose a comprehensive protection method against data flow attacks in IJTAG networks. The network structure is arranged according to the access times of each instrument in to optimize the test efficiency and prevent third-party intellectual property attacks. Furthermore, we provide a method to group instruments to reduce hardware overhead and maintain the security. Experimental results show that the proposed methods achieve minimal overall access time (OAT) and segment insertion bit programming overhead. © 2022 IEEE.

关键词： System-on-chip

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Key Vertex Pairs Extraction for Graph Classification Based on Probability Distribution Learning

Key Vertex Pairs Extraction for Graph Classification Based o...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Jianming Huang Hiroyuki Kasai Dept. of Computer Science and Communication Engineering WASEDA University Tokyo Japan

Graph classification is a hot topic of machine learning for graph-structured data, and it is also a very potential and valuable research. However, the difficulty of graph classification is challenging and special, which is quite different from the normal classification problems. One of the most difficult points of graph classification is that the numbers of vertex neighbors in graphs are usually variable, which makes the number of weights uncertain and ambiguous. Recent work such like the graph attention network apply the transformer on the graph neural network. However, the learned attentions cannot strictly reveal the importance of each part of graph, which makes the model less explainable. Moreover, for small datasets, it performs less effectively because of the excessive parameters. In order to overcome these difficulties, we propose a lightweight model with an edge weighting function based on the probability distributions of node pair features learned by the Gaussian mixture model. Although the proposed framework is simple, the experimental results shows its effectiveness on small datasets.

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New Loss Function for Learning Dielectric Thickness Distributions and Generative Modeling of Breakdown Lifetime

New Loss Function for Learning Dielectric Thickness Distribu...

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2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Yan, Weiman Wu, Ernest Rosenbaum, Elyse University of Illinois Urbana-Champaign Dept. of Electrical and Computer Engineering Urbana IL United States Ibm Research Division Essex Junction VT United States

ISBN: (纸本)9798331504779

This work introduces a new loss function for modeling dielectric lifetime distributions with thickness nonuniformity. It is applicable to both maximum likelihood estimation and to a previously introduced machine learning (ML) framework, providing better agreement between measurement data and generated distributions. The ML method is used to model die and wafer level lifetime distributions and thickness variation. An analytic expression relating dielectric thickness to leakage current or vice versa can be extracted. © 2025 IEEE.

关键词： lifetime distribution machine learning TDDB Time-dependent dielectric breakdown

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“RNN-Driven Prognosis of Heart Disease Based on Health Parameters”

“RNN-Driven Prognosis of Heart Disease Based on Health Para...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Parvez Rahi Inderjeet Singh Ajay Pal Singh Alok Kumar Dept. of Computer Science & Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Cardiovascular diseases (CVD) are the leading cause of death globally and are estimated to affect 17. 9 million deaths annually. Early diagnosis is very important if one is to prevent adverse consequences. However, traditional diagnostic methods can take time to complete and miss subtle trends in patient information. This research presents an innovative approach utilizing a Recurrent Neural Network (RNN) model, optimized through Stochastic Gradient Descent (SGD), for the prediction of seven distinct types of heart disease: CAD, Heart Failure, AFib, Hypertensive Heart Disease, Congenital Heart Disease, Cardiomyopathy, and PAD. Their architecture is specifically suited for direct modeling of sequential patterns of the health related data that in turn helps to detect temporal patterns and changes in them. This model use ReLU activation function in their hidden layers, dropout in preventing overfitting and batch normalization for stable learning. Moreover, the feature augmentation is used in the model making it use various psychological and physiological parameters that help in making comprehensive predictions on the risks of heart disease. After training the RNN with a Kaggle dataset and properly tuning it, the model obtained an accuracy level of 91%, which means that is highly useful in identifying risk factors of cardiovascular diseases and could be used for early diagnosis and risk factors identification.

关键词： Heart Training Solid modeling Recurrent neural networks Stochastic processes Psychology Predictive models Cardiovascular diseases Batch normalization Tuning

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Physical Layer Authentication via Underwater Acoustic Multipath Channel Crafting

Physical Layer Authentication via Underwater Acoustic Multip...

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OCEANS

作者： Davide Eccher Paolo Casari Dept. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

With the increasing number of actors in the under-water environment and the development of new applications, such as large-scale monitoring and autonomous underwater vehicle control, securing underwater communications is becoming a primary necessity. Security was not prioritized in the past due to the constraints of underwater acoustic communications, which cannot sustain the overhead of typical cryptographic techniques. In this paper, we propose a method to authenticate a network device by exploiting the physical properties of the acoustic channel. In particular, our method hinges on the uniqueness and quasi-reciprocity of the channel, from which the authenticator (Alice) node can extract several parameters such as the number of multi path channel components, their delay and amplitude. These values are similar on both ends of a link between Alice and a legitimate transmitter (Bob), and can be used as a seed to craft a new artificial channel, that is then applied to transmissions from Bob to Alice. With this procedure, Alice can distinguish Bob from an impersonating attacker (Eve), given a previous message exchange history. Eve can try to bypass the protocol by estimating the channel parameters and by trying to replicate Bob's signal by crafting a similar channel. In our tests, we observe that the estimation error for Eve, caused by her wrong channel estimates, becomes significant even for short distances betwen Eve and Bob. This error results in a discrepancy between the signal generated by Eve and the one expected by Alice, and reveals Eve as an attacker.

关键词： Estimation error Protocols Transmitters Channel estimation Authentication Delays History Cryptography Underwater acoustics Multipath channels

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AuthiFi: An Advanced Blockchain e-KYC Solution with Enhanced Wallet Security Using Shamir’s Secret Sharing

AuthiFi: An Advanced Blockchain e-KYC Solution with Enhanced...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Sagar Karmoker Md. Adib Khan Mohammad Rifat Ahmmad Rashid Rashedul Amin Tuhin Nur Adnan Chowdhury Anik Mobinul Ahasan Tanjid Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Implemented conventional KYC methods in Bangladesh have several drawbacks such as data leakage, unproductiveness, and information opacity that affect the identity confirmation process of users across the organizations. Blockchain technology provides an efficient and secure means of developing supply chain transparency. The research outlines the design of an e-KYC system using blockchain, with wallet sharing service provided through sharding and enhanced identity validation through machine learning. Some questions answered by the system include private key management, transaction, and gas fees for user sovereignty, security, and accurate data in transactions. All users benefit from ease in sharding of wallets, while machine learning ensures that the chances of fraud are minimized, and accuracy enhanced. Specifically, this approach improves security, transparency, and operation, which can dramatically transform the process of identity verification in the financial sector of Bangladesh making the system more reliable.

关键词： Sharding Accuracy Supply chains Refining Redundancy Machine learning Transforms Blockchains Reliability Research and development

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A Comparative Exploration of Machine Learning and Graph Deep Learning Models for Discriminatory Speech Detection

A Comparative Exploration of Machine Learning and Graph Deep...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： J Kavya K N Pragathy Reddy Phanindra Maddipatla Anjali Kesanapalli Arti Arya Dept. of Computer Science Engineering PES University Bengaluru Karnataka India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

In an era marked by digital expansion, discriminatory speech remains a widespread concern, exerting significant societal implications. This study presents an overview of discriminatory speech detection techniques and explores the complexities involved in identifying discriminatory speech by utilizing a variety of datasets from various fields. The importance of preprocessing methods and feature engineering is next examined, opening the door for more in-dept. study. Furthermore, the paper delves into traditional Machine Learning (ML) algorithms and state-of-the-art Deep Learning (DL) models along with a crucial component of using graph-based structures with Natural Language Processing (NLP) techniques and the use of graphs to extract contextual information. Using both graphs and NLP algorithms enhances the identification process by uncovering contextual connections within textual data.

关键词： Deep learning Voice activity detection Machine learning algorithms Computational modeling Feature extraction Natural language processing Complexity theory

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PassChain: A Blockchain-Based Decentralized Password Manager

PassChain: A Blockchain-Based Decentralized Password Manager

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Nur Adnan Chowdhury Anik Md. Sabbir Hossain Habibur Rahman Ummay Khadiza Rumpa Joy Datta Rifat Nawaz Sagar Karmoker Md. Adib Khan Mahamudul Hasan Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

A Nobel approach to the password management system is introduced in this paper, which is through a decentralized system named blockchain. Our goal is to secure people’s passwords by providing them with a secure and distributed password management system named "PassChain." In today’s digital world, a secure password management system is crucial, but it is still challenging. The traditional system lacks security and is a centralized system, so trust issues also arise. Our proposed blockchain-based solution offers full control over users’ passwords. Because of the decentralized method, they are not stored on a single server. Decentralized servers are also more secure than databases. This approach will ensure user security and privacy and will also reduce dependency on third-party services that are prone to vulnerabilities.

关键词： Privacy Databases Passwords User interfaces Blockchains Browsers Security Servers Best practices

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