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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Li, Zheyuan He, Lijun Wang, Juncheng Jia, Ziye Wang, Yanting Yuen, Chau School of Software Northwestern Polytechnical University China School of Information and Control Engineering China University of Mining and Technology China Hong Kong Baptist University Department of Computer Science Hong Kong Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology China School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350368369

The excessive proliferation of Low Earth Orbit (LEO) satellites inescapably bring the explosive growth of space data in LEO Satellite Networks (LSNs). Meanwhile, the stochastic arrivals of space data together with the time-varying satellite-ground links in LSNs pose significant challenges for offloading a large volume of space data from LSNs to ground stations. To circumvent these challenges, we systematically study the energy-constrained online data offloading problem to jointly optimize power allocation and task scheduling for LSNs. First, we leverage Lyapunov optimization to decouple our formulated long-term stochastic joint optimization problem into a set of per-time-slot subproblems. Then, each subproblem is decoupled into a task scheduling problem and a power allocation problem. Next, we derive the optimal solution to the power allocation problem and propose a multi-armed bandit based quasi-optimal solution to the task scheduling problem. Finally, extensive simulation results show that our proposed algorithm has superior performance over the state-of-the-art solutions. © 2025 IEEE.

关键词： Stochastic systems

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Video-based Stained Glass

Video-based Stained Glass

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作者： Kang, Dongwann Lee, Taemin Shin, Yong-Hyeon Seo, Sanghyun Department of Computer Science and Engineering Seoul National University of Science and Technology 232 Gongneung-ro Nowon-gu Seoul01811 Korea Republic of Department of Artificial Intelligience and Software Kangwon National University 346 Chungang-ro Kangwon-do Samcheok-si25913 Korea Republic of School of Art and Technology Chung-Ang University 4726 Seodong-daero Daedeok-myeon Anseong-si17546 Korea Republic of

This paper presents a method to generate stained-glass animation from video inputs. The method initially segments an input video volume into several regions considered as fragments of glass by mean-shift segmentation. However, the segmentation predominantly results in over-segmentation, causing several tiny segments in a highly textured area. In practice, assembling significantly tiny or large glass fragments is avoided to ensure architectural stability in stained glass manufacturing. Therefore, we use low-frequency components in the segmentation to prevent over-segmentation and subdivide segmented regions that are oversized. The subdividing must be coherent between adjacent frames to prevent temporal artefacts, such as flickering and the shower door effect. To temporally subdivide regions coherently, we obtain a panoramic image from the segmented regions in input frames, subdivide it using a weighted Voronoi diagram, and thereafter project the subdivided regions onto the input frames. To render stained glass fragment for each coherent region, we determine the optimal match glass fragment for the region from a dataset consisting of real stained-glass fragment images and transfer its color and texture to the region. Finally, applying lead came at the boundary of the regions in each frame yields temporally coherent stained-glass animation. © 2022 KSII.

关键词： Textures

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Economic and environmental analysis of a solar-powered ev charging system in india—a case study 2nd

Economic and environmental analysis of a solar-powered ev ch...

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2nd International Conference on Electrical and Electronics engineering, ICEEE 2021

作者： Shrivastava, Prashant Soon, Teykok Mekhilef, Saad Ahmad, Furkan Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Power Electronics and Renewable Energy Laboratory Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia School of Software and Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Indian Institute of Technology New Delhi Delhi110016 India

ISBN: (纸本)9789811607486

With the growing environmental concern, the demand for electric vehicle is increasing in India. However, the limited existing charging facilities slow down the rate of adoption in the market. Presently, the solar-based charging system is gaining popularity, due to its low greenhouse gas emission. In this study, a case study for different solar charging systems suited in Delhi is presented. The economic and environmental analysis of different solar-based EV charging systems utilized for charging three types of commercial EVs such as 2, 3, and 4-W is explained. The cost of energy required for 100 km drive is lower with vehicle integrating PV charging system;however, it can act as a supportive system for driving range improvement. The analysis shows that the economically solar rooftop charging system is suitable for all types of EVs. The CO2 emission produced by all types of solar-based EV charging systems is lower than coal-based charging systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Solar energy

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Comparison of Persian Handwritten Digit Recognition in Three Color Modalities Using Deep Neural Networks 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Zohrevand, Abbas Sattari, Mahdi Sadri, Javad Imani, Zahra Suen, Ching Y. Djeddi, Chawki Department of Computer Engineering Kosar University of Bojnord Bojnord Iran Department of Computer Engineering University of Tabriz Tabriz Iran Department of Computer Science and Software Engineering Faculty of Engineering and Computer Science Concordia University MontrealQC Canada Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran Department of Mathematics and Computer Science Larbi Tebessi University Tebessa Algeria

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

Most of the methods on handwritten digit recognition in the literature are focused and evaluated on black and white image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), we investigate to see whether color modalities of handwritten digits affect their recognition rate? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten digit databases that have all three color modalities of handwritten digits. To answer this question, we select 13,330 isolated digits from novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected dataset are divided into training, validation, and testing sets. Afterward, similar conventional CNN models are trained with the samples of our training set. While the experimental results on the testing set show that CNN on the black and white digit images has a better performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modality. Also, comparisons of training times in three color modalities show that recognition of handwritten digits in black and white using CNN is much more efficient. © 2020, Springer Nature Switzerland AG.

关键词： Color

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Digital Image Recovery Based on Lifting Wavelet Transform 1

Digital Image Recovery Based on Lifting Wavelet Transform

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1st International Conference on engineering and Technology, ICoEngTech 2021

作者： Taha, Taha Basheer Taha, Dujan B. Ngadiran, Ruzelita Ehkhan, Phaklen IT Department Faculty of Sciences TISHK International University Erbil Iraq Software Engineering Department College of Computer Science and Mathematics Mosul University Iraq Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia

Recently, with the wide distribution of digital media, the need for authenticating digital images was increased. Therefore, many image tamper detection and recovery algorithms were introduced in literature to detect malicious modifications and retrieve the original images. The process of detection and recovery, however, used to have complex operation which requires long processing time. In this paper, a simplified image recovery algorithm is presented by using lifting wavelet transform. In the proposed method, the approximation band is hidden inside the bits of the original image and to be retrieved without relying on source image. For images with hidden data, the average PSNR and SSIM values were 31.22 and 0.977 respectively, and images were successfully retrieved after block attack. © Published under licence by IOP Publishing Ltd.

关键词： Digital storage

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VML-MOC: Segmenting a multiply oriented and curved handwritten text lines dataset

arXiv

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arXiv 2021年

作者： Barakat, Berat Kurar Cohen, Rafi El-Sana, Jihad Rabaev, Irina Department of Computer Science Ben-Gurion University Israel Software Engineering Department Shamoon College of Engineering Israel

This paper publishes a natural and very complicated dataset of handwritten documents with multiply oriented and curved text lines, namely VML-MOC dataset. These text lines were written as remarks on the page margins by different writers over the years. They appear at different locations within the orientations that range between 0◦ and 180◦ or as curvilinear forms. We evaluate a multi-oriented Gaussian based method to segment these handwritten text lines that are skewed or curved in any orientation. It achieves a mean pixel Intersection over Union score of 80.96% on the test documents. The results are compared with the results of a single-oriented Gaussian based text line segmentation method. © 2021, CC BY.

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Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach

Towards Efficient Resume Understanding: A Multi-Granularity ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Feihu Jiang Chuan Qin Jingshuai Zhang Kaichun Yao Xi Chen Dazhong Shen Chen Zhu Hengshu Zhu Hui Xiong University of Science and Technology of China Career Science Lab BOSS Zhipin PBC School of Finance Tsinghua University Institute of Software Chinese Academy of Sciences Shanghai Artificial Intelligence Laboratory The Thrust of Artificial Intelligence The Hong Kong University of Science and Technology (GuangZhou) The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou HKUST Fok Ying Tung Research Institute

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In the contemporary era of widespread online recruitment, resume understanding has been widely acknowledged as a fundamental and crucial task, which aims to extract structured information from resume documents automatically. Compared to the traditional rule-based approaches, the utilization of recently proposed pre-trained document understanding models can greatly enhance the effectiveness of resume understanding. The present approaches have, however, disregarded the hierarchical relations within the structured information presented in resumes, and have difficulty parsing resumes in an efficient manner. To this end, in this paper, we propose a novel model, namely ERU, to achieve efficient resume understanding. Specifically, we first introduce a layout-aware multi-modal fusion transformer for encoding the segments in the resume with integrated textual, visual, and layout information. Then, we design three self-supervised tasks to pre-train this module via a large number of unlabeled resumes. Next, we fine-tune the model with a multi-granularity sequence labeling task to extract structured information from resumes. Finally, extensive experiments on a real-world dataset clearly demonstrate the effectiveness of ERU.

关键词： Visualization Resumes Layout Transformers Feature extraction Encoding Data mining Labeling Recruitment

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Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance

arXiv

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arXiv 2024年

作者： Dong, Li Peng, Yubo Jiang, Feibo Wang, Kezhi Yang, Kun School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China School of Intelligent Software and Engineering Nanjing University Suzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Department of Computer Science Brunel University London United Kingdom

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an Adaptive Client Training (ACT) strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC (ESC) mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Evolution of Quantum Computing: A Systematic Survey on the Use of Quantum Computing Tools

arXiv

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arXiv 2022年

作者： Upama, Paramita Basak Faruk, Jobair Hossain Nazim, Mohammad Masum, Mohammad Shahriar, Hossain Uddin, Gias Barzanjeh, Shabir Ahamed, Sheikh Iqbal Rahman, Akond Department of Computer Science and Engineering Eastern University Bangladesh Department of Software Engineering and Game Development Kennesaw State University United States Department Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Schulich School of Engineering University of Calgary Canada Department of Physics and Astronomy University of Calgary Canada Department of Mathematics Statistics and Computer Marquette University United States Department of Computer Science Tennessee Tech university United States

Quantum Computing (QC) refers to an emerging paradigm that inherits and builds with the concepts and phenomena of Quantum Mechanic (QM) with the significant potential to unlock a remarkable opportunity to solve complex and computationally intractable problems that scientists could not tackle previously. In recent years, tremendous efforts and progress in QC mark a significant milestone in solving real-world problems much more efficiently than classical computing technology. While considerable progress is being made to move quantum computing in recent years, significant research efforts need to be devoted to move this domain from an idea to a working paradigm. In this paper, we conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing and analyze them from an application and Quantum Computing perspective. We present quantum Computing Layers, Characteristics of Quantum computer platforms, Circuit Simulator, Open-source Tools Cirq, TensorFlow Quantum, ProjectQ that allow implementing quantum programs in Python using a powerful and intuitive syntax. Following that, we discuss the current essence, identify open challenges and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community. © 2022, CC BY.

关键词： Circuit simulation

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series 19

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xiong, Zhiying Fan, Qilin Wang, Kai Li, Xiuhua Zhang, Xu Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing China Chongqing University Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing China Harbin Institute of Technology School of Computer Science and Technology Weihai China Harbin Institute of Technology Research Institute of Cyberspace Security Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350346558

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods. © 2022 IEEE.

关键词： Anomaly detection

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