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arXiv 2025年

作者： Jian, Yugen Luo, Ke Chen, Jincai Fong, Xuanyao Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Computer of Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan430074 China

Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3DMR lies in performing sequential and correct writing of bits into the multi-layer recording medium. In this work, we have proposed a hierarchical recording architecture based on layered heat-assisted writing with a multi-head array. The feasibility of the architecture is validated in a dual-layer 3DMR system with FePt-based thin films via micromagnetic simulation. Our results reveal the magnetization reversal mechanism of the grains, ultimately attaining appreciable switching probability and medium signal-to-noise ratio (SNR) for each layer. In particular, an optimal head-to-head distance is identified as the one that maximizes the medium SNR. Optimizing the system’s noise resistance will improve the overall SNR and allow for a smaller optimal head-to-head distance, which can pave the way for scaling 3DMR to more recording layers. Copyright © 2025, The Authors. All rights reserved.

关键词： Hard disk storage

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Revisiting Common Randomness, No-signaling and information Structure in Decentralized Control

arXiv

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arXiv 2024年

作者： Dhingra, Apurva Kulkarni, Ankur A. Center of Machine Intelligence and Data Science CMInDS Indian Institute of Technology Bombay Mumbai400076 India Systems and Control Engineering Indian Institute of Technology Bombay Mumbai400076 India Center of excellence in Quantum Information Computing Science and Technology QuICST Indian Institute of Technology Bombay Mumbai400076 India

This work revisits the no-signaling condition for decentralized information structures. We produce examples to show that within the no-signaling polytope exist strategies that cannot be achieved by passive common randomness but instead require agents to either share their observations with a mediator or communicate directly with each other. This poses a question mark on whether the no-signaling condition truly captures the decentralized information structure in the strictest *** Codes 93C41, 93E20, 81Q93 © 2024, CC BY.

关键词： Random processes

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction 1

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Sriramkumar, R. Dayananda Sagar University AI & ML Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bagalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids. © 2025 IEEE.

关键词： Smart power grids

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Perceptual Video Summarization Using Keyframes Extraction Technique

Perceptual Video Summarization Using Keyframes Extraction Te...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Rajitha Jasmine R Padmaja Nimmagadda K. Sudhakar Benitha Christinal J P. Rajasekar Syed Musthafa A Department of Information Technology R. M. K. Engineering College Kavaraipettai India Department of Electronics and Communication Mohan Babu University Tirupati India Department of Computer Science Madanapalle Institute of Technology Madanapalle India Department of Information Technology Panimalar Engineering College Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chengalpattu India Department of Information Technology M. Kumarasamy College of Engineering Karur India

The growth of video content in recent years is a challenging problem due to increased memory storage and time consuming for analyzing content of the video. Therefore, there is a need to reduce the content for human usage. This paper presents a keyframes technique that uses co-occurrence matrix and permutation computation for perceptual video summarization (PVS). The Human Visual System (HVS) needs to incorporate the PVS. It helps to allow for the importance of perceptually significant contents. The proposed method uses different kinds of videos to evaluate the effectiveness of the work. The subjective evaluation scores have evaluated the proposed work.

关键词： Color Visual systems Feature extraction

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Correction: Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

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Automated Software engineering 2024年第1期32卷 1-11页

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

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Enhancing Cybersecurity with AI: Anomaly Detection in Network Traffic

Enhancing Cybersecurity with AI: Anomaly Detection in Networ...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： V. Kavitha S. Malathi Vishnu Priya P M Saranya Ekambaram B. Charith M. Deivakani Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Katankullathur Tamil Nadu India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer-Science Kristu Jayanti College Autonomous Bengaluru Karnataka India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Management & Commerce International Institute of Business Studies Bengaluru Karnataka India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Traditional symbol processing styles have the limitation to do so in the various and dynamic nature of ultramodern data transfers. The reason is that communication systems become more and more serious. This offers a potentially useful answer in that much of the functionality of machine literacy is afforded by data driven adaption and can offer the eventuality to ameliorate signal processing performance. The aim of this composition is to carry out a thorough investigation of several classes of machine literacy practices and their function in central fields of study like modulation recognition, noise reduction, channel estimation, and signal discovery. The study shows how machine literacy ways can provide much increase in the effectiveness, delicateness and robustness of signal processing tasks in communication systems. By doing detailed analysis and comparison you witness through this is fulfilled. Machine literacy will allow the unborn communication systems to adapt to advanced situations of robotization and smartness, which makes the progress possible in the similar fields, for instance, the Internet of Things (IoT), wireless deliverances, and other.

关键词： Wireless communication Symbols Telecommunication traffic Robustness Real-time systems Internet of Things Artificial intelligence Computer security Anomaly detection Robots

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UAV-Assisted data Synchronization for Digital-Twin-Enabled Vehicular Networks

UAV-Assisted Data Synchronization for Digital-Twin-Enabled V...

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IEEE International Conference on Communications in China (ICCC)

作者： Guoquan Wu Jiangtian Nie Jianhang Tang Yang Zhang Jiang Xiao Zehui Xiong School of Information Science and Engineering Yanshan University China School of Computer Science and Engineering Nanyang Technological University Singapore State Key Laboratory of Public Big Data Guizhou University China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Computer Science and Technology Huazhong University of Science and Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Digital twin (DT) is becoming a promising solution for vehicular networks to improve the interoperability of distributed autonomous driving systems. Mobile edge computing (MEC) has been introduced to provide low-latency services for DT-enabled vehicular networks at the edge of the network. However, it is hard to obtain the dynamic network topology for moving vehicles by the ground-based MEC system, which may deteriorate the service quality for DT synchronization. In this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted synchronization framework for DT-enabled vehicular networks. With the proposed framework, an intelligent resource allocation algorithm is developed to improve UAV resource utilization and maximize the synchronization completion ratio. By leveraging an advantage actor-critic (A2C) algorithm, the synchronization decisions are obtained with low time complexity. Experiment results demonstrate that the proposed algorithm can reduce the synchronization latency and improve the synchronization completion ratio effectively.

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Embedding VLAD in Transformer for Video Question Answering

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Jisuanji Xuebao/Chinese Journal of Computers 2023年第4期46卷 671-689页

作者： Guo, Dan Yao, Shen-Tao Wang, Hui Wang, Meng School of Computer and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230094 China Key Laboratory of Knowledge Engineering with Big Data（Hefei University of Technology） Ministry of Education Hefei230601 China Intelligent Interconnected Systems Laboratory of Anhui Province（Hefei University of Technology） Hefei230601 China

Video question answering（VideoQA）is a typical cross-modal understanding task. Its challenge lies in how to learn appropriate multimodal representation and cross-modal correlation for answer inference. Most existing video question answering methods focus on the latter，e. g.，relationship learning between each video frame or clip and word. In this work， we devote to advanced feature embedding of both video and query. We develop a clustering-based VLAD technique for VideoQA. The novelty of our work is the joint exploitation of temporal aggregation and correlation in multimodality. We propose an end-to-end trainable Transformed VLAD embedding network， named TVLAD-Net. TVLAD-Net constructs a differentiable aggregation network module（i. e.， convolutional Residual-less VLAD Block）to generate compact VLAD descriptors（transforming N frames，clips or words to compact K descriptors while K © 2023 science Press. All rights reserved.

关键词： Semantics

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When is the estimated propensity score better? High-dimensional analysis and bias correction

arXiv

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arXiv 2023年

作者： Su, Fangzhou Mou, Wenlong Ding, Peng Wainwright, Martin J. Department of Electrical Engineering and Computer Sciences United States Department of Statistics UC Berkeley United States Department of Mathematics Lab for Information and Decision Systems and Statistics and Data Science Center Massachusetts Institute of Technology United States

Anecdotally, using an estimated propensity score is superior to the true propensity score in estimating the average treatment effect based on observational data. However, this claim comes with several qualifications: it holds only if propensity score model is correctly specified and the number of covariates d is small relative to the sample size n. We revisit this phenomenon by studying the inverse propensity score weighting (IPW) estimator based on a logistic model with a diverging number of covariates. We first show that the IPW estimator based on the estimated propensity score is consistent and asymptotically normal with smaller variance than the oracle IPW estimator (using the true propensity score) if and only if n & d2. We then propose a debiased IPW estimator that achieves the same guarantees in the regime n & d3/2. Our proofs rely on a novel non-asymptotic decomposition of the IPW error along with careful control of the higher order terms. Copyright © 2023, The Authors. All rights reserved.

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A Wavelet-Based Real-Time Fire Detection with Multi-Modeling Framework

SSRN

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SSRN 2023年

作者： Baek, Jaeseung Alhindi, Taha J. Jeong, Young-Seon Jeong, Myong K. Seo, Seongho Kang, Jongseok Shim, We Heo, Yoseob Department of Industrial and Systems Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States Division of Data Analysis Korea Institute of Science and Technology Information Dongdaemun-gu Seoul02456 Korea Republic of

This paper presents a wavelet-based real-time automated fire detection algorithm that considers the multi-resolution property of the wavelet transforms. Unlike conventional fire detection algorithms, which fail to capture temporal dependency within the fire sensor signals, the proposed wavelet-based features characterize the temporal dynamics of sensor signals generated from various types of fire. Then, a real-time fire detection algorithm with a multi-modeling framework is developed to effectively utilize the selected features and construct multiple fire detectors that are sensitive in monitoring various kinds of fires without prior knowledge. In addition, we develop a novel multi-sensor fusion system which incorporates various chemical sensors and collects accurate and reliable fire dataset from different real-life fire scenarios to validate the performance of the proposed and existing fire detection algorithms. The experimental results with real-life and public fire data show that the proposed algorithm outperforms others with early detection time and reasonable false alarm rate. © 2023, The Authors. All rights reserved.

关键词： Wavelet transforms

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