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arXiv 2023年

作者： Zou, Yin-Tao Ding, Chengxiang School of Microelectronics & Data Science Anhui University of Technology Maanshan243002 China

We study the universal dynamical relaxation behaviors of a quantum XY chain following a quench, paying special attention to the case that the prequenched Hamiltonian, or the postquenched Hamiltonian, or both of them are at critical points of equilibrium quantum phase transitions. In such "critical quench", we find very interesting real-time dynamical scaling behaviors and the crossover phenomena between them. For a quench from a noncritical point to a critical point, we find that, compared to the noncritical quench, the universal power-law scaling behavior does not change;however, there may be a crossover between the exponential decaying behavior and the power-law scaling. For a quench from a critical point to a noncritical point, the power-law scaling behaviors t−3/2 and t−3/4 in the noncritical quenches may be changed to t−1 and t−1/2, respectively. If the prequenched Hamiltonian is set to be a point that is close to but not exactly at a critical point, we find interesting crossover phenomena between different power-law scaling behaviors. We also study the quench from the vicinity of a multicritical point, we find crossover behaviors that are induced by a different mechanism, and new crossover exponent is found. All the results are related to the gap-closing properties of the energy spectrum of the critical points. Copyright © 2023, The Authors. All rights reserved.

关键词： Hamiltonians

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An Efficient Partial Video Copy Detection for a Large-scale Video database

An Efficient Partial Video Copy Detection for a Large-scale ...

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International Conference on big data Computing and Communications (bigCOM)

作者： Zhan Luo Lan Zhang Jiewei Lai Xinming Wang Chen Tang School of Data Science University of Science and Technology of China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

Partial video copy detection (PVCD) aims to discover copy segments of query videos from a video database, which plays an important role in video copyright protection, filtering, tracking, etc. For a large-scale video database, PVCD can be divided into two stages: the first stage involves searching for video-level copies of the query video in the database, and the second stage is to further localize the copy segments within the video-level copies. Thus, two major challenges arise: (1) efficiently and effectively calculating the similarity between videos; (2) localizing mixed-duration video pairs. To address the above challenges, we propose an efficient PVCD approach for a large-scale video database, based on the Bag-of-Words (BoW) framework, which decouples video-level similarity and copy localization into cell-level. This approach consists of two modules. The first is an efficient video similarity measurement (VSM) module for the large-scale video database. VSM aggregates cell-level similarity into video-level similarity, and with a dual index, it greatly improves retrieval speed while accurately measuring spatiotemporal transformations. The second is a greedy pattern detection (GPD) module for video copy localization. GPD quickly and accurately detects similarity patterns through a greedy strategy on the similarity matrix formed by matching frames in each cell, then aggregates them into complete predicted copy segments. On the comprehensive dataset self-SVD, VSM significantly outperforms state-of-the-art methods by 7.28% in mAP, and the retrieval speed is increased by over 318 times. Additionally, for short videos at the scale of hundreds of millions, the response speed can theoretically reach seconds. On the copy localization dataset MIX, composed of mixed-duration videos, GPD also achieves the best performance.

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Robust Finite-Time Frequency Control of Grid Connected Microgrids for Real-Time Electricity Market Against Cyber-Attacks

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IEEE Transactions on Power Systems 2025年

作者： Rouhani, Seyed Hossein Mobayen, Saleh Su, Chun-Lien The Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung City807618 Taiwan Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou640301 Taiwan

In a deregulated environment, traditional frequency control objectives are combined with market-driven strategies to ensure reliable and efficient power grid operations in competitive electricity markets. Traditionally, price agreements are settled offline. However, the rise of private distributed generation companies and the fourth industrial revolution are transitioning the market to real-time bidding, characterized by extreme uncertainty and increased cyberattack risks. To address these challenges, this paper proposes a novel model-following finite-time robust tracking control strategy. The proposed method effectively handles extreme time-varying uncertainty, false data injection cyber-attacks, and delays, making it practical for real-time operations. Key features include robust control to mitigate uncertainties and cyberattacks and finite-time convergence with time delay compensation. Extensive testing on a typical microgrid within a real-time bidding market, even under false data injection attacks, demonstrates significant improvements in frequency stability and reliability. These improvements are evident under various conditions, including governor dead-band nonlinearity and time delays. Compared to alternative methods, the proposed method demonstrates superior robustness and faster convergence, validated by Opal RT hardware simulations. These results highlight the effectiveness of the proposed method in ensuring reliable operation of the microgrid. © 2025 IEEE. All rights reserved.

关键词： Cyber attacks

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Investigation of Traffic Radar Coverage Efficiency under Different Placement Strategies

Investigation of Traffic Radar Coverage Efficiency under Dif...

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International Conference on Internet of Things 2024, ICIoT 2024

作者： Yao, Shun Zhou, Jin Chieng, David Kwong, Chiew Foong Lee, Boon Giin Li, Jianqiang Chen, Yusen Next-Generation Internet of Everything Laboratory Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Computer Science University of Nottingham Ningbo China Ningbo China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Zhejiang Communication Investment Group Co. Ltd. Zhejiang China

This paper presents a comprehensive investigation of traffic radar coverage efficiency under different placement strategies in collaboration with Zhejiang Communications Investment Group Company Limited (CICO). The overall goal is to maximise coverage while minimising blind spots and the number of radars, given the constraints of the road. The NSGA-II multi-objective optimisation algorithm is found to be capable of generating a set of optimal solutions for the proposed radar placement configurations. When aiming for 100% coverage, the results indicate that a multi-antenna radar configuration requires the lowest radar density. Overall, this study provides valuable insights to transportation infrastructure owners in determining the best radar placement strategies for road traffic monitoring. © The Institution of Engineering & technology 2024.

关键词： Multiobjective optimization

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Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition 24

Generating Action-conditioned Prompts for Open-vocabulary Vi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Jia, Chengyou Luo, Minnan Chang, Xiaojun Dang, Zhuohang Han, Mingfei Wang, Mengmeng Dai, Guang Dang, Sizhe Wang, Jingdong School of Computer Science and Technology MOEKLINNS Lab Xi'an Jiaotong University Shaanxi Xi'an China University of Science and Technology of China Anhui Hefei China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China ReLER Lab AAII University of Technology Sydney SydneyNSW Australia Zhejiang University of Technology College of Computer Science and Technology China SGIT AI Lab State Grid Corporation of China Beijing China Baidu Inc Beijing China United Arab Emirates Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China SGIT AI Lab State Grid Corporation of China China School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798400706868

Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing methods typically adapt pretrained image-text models to the video domain, capitalizing on their inherent strengths in generalization. A common thread among such methods is the augmentation of visual embeddings with temporal information to improve the recognition of seen actions. Yet, they compromise with standard less-informative action descriptions, thus faltering when confronted with novel actions. Drawing inspiration from human cognitive processes, we argue that augmenting text embeddings with human prior knowledge is pivotal for open-vocabulary video action recognition. To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts. Specifically, we harness the knowledge in LLMs to produce a set of descriptive sentences that contain distinctive features for identifying given actions. Building upon this foundation, we further introduce a multi-modal action knowledge alignment mechanism to align concepts in video and textual knowledge encapsulated within the prompts. Extensive experiments on various video benchmarks, including zero-shot, few-shot, and base-to-novel generalization settings, demonstrate that our method not only sets new SOTA performance but also possesses excellent interpretability. © 2024 ACM.

关键词： Embeddings

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Random Security Generators with Enhanced Cryptography for Cybersecurity in Financial Supply Chains

Random Security Generators with Enhanced Cryptography for Cy...

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Advances in Computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Latha Thammareddi Venkateswara Rao Anumolu Kulasekhara Reddy Kotte Bhagath Chandra Chowdari Marella K Arun Kumar Jatin Bisht Project Management Operations India ERP Solutions-Information Technology India SLRI Solutions INC India Department of Financial Services Insights & Data India Big Data Architect India Department of Computer Science & Engineering Bipin Tripathi Kumaon Institute of Technology Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Blockchains, like Ethereum, depend on a distributed system of collaborative servers referred to as inspectors or mine workers to validate transactions and generate novel legal blocks. In today's electronic world, it is critical to secure financial supply chains (FSCs) from complex cyber-attacks. This study combines Quantized Random Security Generators (QRSGs) with Artificial Intelligence (AI) to improve cybersecurity in FSC. QRSGs use quantum physics to produce random outcomes, which is critical for establishing safe cryptography credentials and resolving the flaws of regular predictable RSGs. This study found that the QRSG-based approach outperformed traditional RSG methods. The QRSG demonstrated strong entropy and uncertainty, transferring both NIST SP800-22 and Diehard examinations at much greater levels. The platform's real-time safety features were improved by the AI element's outstanding performance measures in identifying security breaches. More rapid decoding and encryption times, reduced delay, and increased resilience to brute-force, quantum, and prediction assaults were all demonstrated by the coupled QRSG and AI methods. This study emphasizes how crucial it is to use AI and QRSGs to safeguard FSCs. The combined platform provides a complete option to reduce cyber-attacks by offering strong cryptography security and real-time attack identification. These results demonstrate how QRSG and AI innovations can completely transform cybersecurity in the banking industry, safeguarding vital financial systems and guaranteeing the privacy and reliability of financial payments.

关键词： Technological innovation Uncertainty Supply chains Transforms Real-time systems Generators Power capacitors Blockchains Artificial intelligence Cyberattack

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An Endless Outpainting Tool Based on Stable Diffusion

An Endless Outpainting Tool Based on Stable Diffusion

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Peter Garrity Leonidas Deligiannidis School of Computing and Data Science Wentworth Institute of Technology Boston MA USA

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

We developed a tool that utilizes the capabilities of machine learning to explore the possibilities of image outpainting. Our tool is built around an image generation model based on Stable Diffusion and provides an easy-to-use command-based interface. The command-line interface allows us to generate an endless set of images. It starts with a given image, and after the outpainting process, it feeds the generated image as input to the next iteration. To preserve the original image's dimensions, before feeding an image as input to the next iteration, we scale it down so that the generated images are the same dimensions as the original image. By using several iterations like this, we can repeatedly create new details around the edge of an input image.

关键词： Scientific computing Image synthesis Image edge detection Machine learning Feeds Computational intelligence

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Finding the Missing data: A BERT-inspired Approach Against Package Loss in Wireless Sensing

arXiv

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arXiv 2024年

作者： Zhao, Zijian Chen, Tingwei Meng, Fanyi Li, Hang Li, Xiaoyang Zhu, Guangxu Shenzhen Research Institute of Big Data China School of Computer Science and Engineering Sun Yat-sen University China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models. To overcome this challenge, we propose a deep learning model based on Bidirectional Encoder Representations from Transformers (BERT) for CSI recovery, named CSI-BERT. CSI-BERT can be trained in an self-supervised manner on the target dataset without the need for additional data. Furthermore, unlike traditional interpolation methods that focus on one subcarrier at a time, CSI-BERT captures the sequential relationships across different subcarriers. Experimental results demonstrate that CSIBERT achieves lower error rates and faster speed compared to traditional interpolation methods, even when facing with high loss rates. Moreover, by harnessing the recovered CSI obtained from CSI-BERT, other deep learning models like Residual Network and Recurrent Neural Network can achieve an average increase in accuracy of approximately 15% in Wi-Fi sensing tasks. The collected dataset WiGesture and code for our model are publicly available at https://***/RS2002/CSI-BERT. Copyright © 2024, The Authors. All rights reserved.

关键词： Wi-Fi

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Federated Opposite Learning Based Arithmetic Optimization Algorithm for Image Segmentation Using Multilevel Thresholding 26

Federated Opposite Learning Based Arithmetic Optimization Al...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Qingxin Qi, Qi Li, Ni Hainan University School of Computer Science and Technology Haikou570228 China Hainan Normal University School of Mathematics and Statistics Haikou571158 China Hainan Normal University Ministry of Education Key Laboratory of Data Science and Smart Education Haikou570228 China

ISBN: (纸本)9798350331684

Multilevel thresholding is a widely used method in image segmentation. However, the traditional methods are costly to obtain the optimal thresholds through exhaustive search. The Nature-inspired algorithm is a gradient-free optimizer that overcomes these shortcomings and generates the best thresholds with high quality and efficiency. For this purpose, this paper suggests an improved arithmetic optimization algorithm with federated opposite learning for multilevel thresholding image segmentation, namely FOL-AOA. In this method, the federated opposite learning strategy is incorporated to avoid the particles being trapped into local optimal and increase the population diversity. The cross-entropy is employed as the objective function minimized by FOL-AOA. To assess the performance of the proposed method, we considered the use of a variety of benchmark images under different threshold levels and compared them against five predecessor approaches. The obtained results manifest that FOL-AOA outperforms the comparison methods in terms of the fitness values as well as two image quality indicators such as PSNR and SSIM. © 2023 IEEE.

关键词： Image segmentation

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ScanTrimmer: an Online Dynamic Objects Removal Framework in Laser Scan for Robust Localization

ScanTrimmer: an Online Dynamic Objects Removal Framework in ...

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IEEE International Conference on Robotics and Biomimetics

作者： Jing Wang Yifan Duan Jianmin Ji School of Data Science University of Science and Technology of China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

The Simultaneous Localization and Mapping (SLAM) technique is often employed in robotic localization tasks. For lidar-based SLAM, point cloud registration (PCR) is one of the crucial factors for overall localization precision. However, the quality of point-to-point correspondence between two distinct point clouds obtained by PCR is environment dependent. Particularly in dynamic environments, the movement of objects can disrupt this correspondence, resulting in a degradation of localization accuracy. Therefore, previous SLAM methods often fail in environments with ample dynamic objects. Intuitively, we can remove dynamic points from the input laser scan to degenerate the influence of environment dynamicity. However, identifying dynamic objects precisely from point clouds which is challenging for the SLAM running in robotic systems with high real-time requirements. This paper proposes a new online lidar-based SLAM framework, i.e., ScanTrimmer, which significantly enhances the localization accuracy of the SLAM systems by detecting and deleting potential dynamic points through space-based comparison in point cloud distribution between scans taken at a specific interval. The experiments demonstrate that ScanTrimmer can improve the localization accuracy by 44.69% on the simulated dataset and 25.99% on the real-world dataset compared with the previous method.

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