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arXiv 2024年

作者： Li, Jinmin Dai, Tao Zha, Yaohua Luo, Yilu Lu, Longfei Chen, Bin Wang, Zhi Xia, Shu-Tao Zhang, Jingyun Tsinghua Shenzhen International Graduate School Tsinghua University China College of Computer Science and Software Engineering Shenzhen University China Research Center of Artificial Intelligence Peng Cheng Laboratory China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China WeChat Pay Lab33 Tencent China

Invertible Rescaling Networks (IRNs) and their variants have witnessed remarkable achievements in various image processing tasks like image rescaling. However, we observe that IRNs with deeper networks are difficult to train, thus hindering the representational ability of IRNs. To address this issue, we propose Invertible Residual Rescaling Models (IRRM) for image rescaling by learning a bijection between a high-resolution image and its low-resolution counterpart with a specific distribution. Specifically, we propose IRRM to build a deep network, which contains several Residual Downscaling Modules (RDMs) with long skip connections. Each RDM consists of several Invertible Residual Blocks (IRBs) with short connections. In this way, RDM allows rich low-frequency information to be bypassed by skip connections and forces models to focus on extracting high-frequency information from the image. Extensive experiments show that our IRRM performs significantly better than other state-of-the-art methods with much fewer parameters and complexity. Particularly, our IRRM has respectively PSNR gains of at least 0.3 dB over HCFlow and IRN in the ×4 rescaling while only using 60% parameters and 50% FLOPs. The code will be available at https://***/THUKingmin/IRRM. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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On the Performance of RIS-enabled NOMA for Aerial Networks

On the Performance of RIS-enabled NOMA for Aerial Networks

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IEEE Conference on Wireless Communications and Networking

作者： Lina Bariah Fouzi Boukhalfa Wael Jaafar Sami Muhaidat Halim Yanikomeroglu Technology Innovation Institute Masdar City Abu Dhabi UAE Department of Software and Information Technology Engineering École de Technologie Supérieure (ÉTS) Montreal QC Canada Department of Electrical Engineering and Computer Science Center for Cyber-Physical Systems Khalifa University Abu Dhabi UAE Department of Systems and Computer Engineering Carleton University ON Canada

In this paper, we investigate the performance of reconfigurable intelligent surface (RIS)-assisted aerial communications, where a ground base station (GBS) communicates with distant terrestrial and/or aerial users through the assistance of a RIS-equipped unmanned aerial vehicle (RIS-UAV). The GBS uses the non-orthogonal multiple access (NOMA) scheme to transmit its signal, which is directed to the users via the RIS-UAV. First, the end-to-end channel is characterized, by considering the shadowed Rician fading, then the outage probability performance metric is derived for the underlying system model. Through numerical results, we demonstrate the impact of several system parameters on the performance of NOMA users. Specifically, we found that RIS elements need to be carefully allocated among different NOMA users, according to their channel conditions, in order to achieve the needed quality of service.

关键词： Measurement NOMA Base stations Rician channels Quality of service Probability Power system reliability

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Optimal and Efficient Algorithms for Decentralized Online Convex Optimization

arXiv

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arXiv 2024年

作者： Wan, Yuanyu Wei, Tong Xue, Bo Song, Mingli Zhang, Lijun School of Software Technology Zhejiang University Ningbo China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering Southeast University Nanjing China Department of Computer Science City University of Hong Kong Hong Kong National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Artificial Intelligence Nanjing University Nanjing China

We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using only local computations and communications. Previous studies have established O(n5/4ρ−1/2√T) and O(n3/2ρ−1 log T) regret bounds for convex and strongly convex functions respectively, where n is the number of local learners, ρ √T) for convex functions and Ω(n) for strongly convex functions. To fill these gaps, in this paper, we first develop a novel D-OCO algorithm that can respectively reduce the regret bounds for convex and strongly convex functions to Õ(nρ−1/4√T) and Õ(nρ−1/2 log T). The primary technique is to design an online accelerated gossip strategy that enjoys a faster average consensus among local learners. Furthermore, by carefully exploiting spectral properties of a specific network topology, we enhance the lower bounds for convex and strongly convex functions to Ω(nρ−1/4√T) and Ω(nρ−1/2 log T), respectively. These results suggest that the regret of our algorithm is nearly optimal in terms of T, n, and ρ for both convex and strongly convex functions. Finally, we propose a projection-free variant of our algorithm to efficiently handle practical applications with complex constraints. Our analysis reveals that the projection-free variant can achieve O(nT 3/4) and O(nT 2/3(log_ T)1/3) regret bounds for convex and strongly convex functions with nearly optimal Õ(ρ−1/2√T) and Õ(ρ−1/2T 1/3(log T)2/3) communication rounds, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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Design of 5G Vehicle-road-cloud Collaborative Architecture for Vehicle Platooning

Design of 5G Vehicle-road-cloud Collaborative Architecture f...

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International Conference on Network and Information Systems for computers (ICNISC)

作者： Hongyan Shi Xinran Qu Jing Ma Jiajian Li School of Mechanical Engineering Dalian University of Technology DUT Dalian China Department of Computer Science & Software Engineering School of Engineering Computer & Mathematical Sciences Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9781665453523

This paper presents an architecture design of vehicle-road-cloud collaboration based on a 5g communication network, which supports vehicle platooning and applies it in the case of vehicle formation crossing the border. The architecture includes roadside, cloud systems, 5g network, and infrastructure deployment connecting vehicles, as well as specifications for interfaces of automated vehicles (network communication interface specifications and application interface specifications). In addition, solutions and simple tests are proposed for the cross-border problems in the case of vehicle platooning crossing the border, mainly including interconnection delay between PLMNs, and low delay communication between leader and team members' vehicles and roadside infrastructure.

关键词： computers 5G mobile communication Collaboration computer architecture Delays Communication networks Security

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A Comprehensive Review of Artificial Intelligence Applications in Major Retinal Conditions

arXiv

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arXiv 2023年

作者： Raja, Hina Hassan, Taimur Hassan, Bilal Akram, Muhammad Usman Raja, Hira Abd-Alrazaq, Alaa A. Yousefi, Siamak Werghi, Naoufel Department of Ophthalmology University of Tennessee Health Science Center Memphis United States Department of Electrical Computer and Biomedical Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Computer and Software Engineering National University of Sciences and Technology Pakistan Margalla Institute of Health Sciences Rawalpindi Pakistan AI Center for Precision Health Weill Cornell Medicine-Qatar Doha Qatar Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

This paper provides a systematic survey of retinal diseases that cause visual impairments or blindness, emphasizing the importance of early detection for effective treatment. It covers both clinical and automated approaches for detecting retinal disease, focusing on studies from the past decade. The survey evaluates various algorithms for identifying structural abnormalities and diagnosing retinal diseases, and it identifies future research directions based on a critical analysis of existing literature. This comprehensive study, which reviews both clinical and automated detection methods using different modalities, appears to be unique in its scope. Additionally, the survey serves as a helpful guide for researchers interested in digital retinopathy. © 2023, CC BY-NC-ND.

关键词： Optical tomography

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Investigating Novel Approaches to Defend software Supply Chain Attacks

Investigating Novel Approaches to Defend Software Supply Cha...

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IEEE International Conference on software Reliability engineering Workshops (ISSRE Wksp)

作者： Md Jobair Hossain Faruk Masrura Tasnim Hossain Shahriar Maria Valero Akond Rahman Fan Wu Department of Software Engineering and Game Development Kennesaw State University USA Institute for Cybersecurity Workforce Development Kennesaw State University USA Departrnent of Information Technology Kennesaw State University USA Department of Computer Science and Software Engineering (CSSE) Auburn University USA Department of Computer Science Tuskegee University USA

ISBN: (数字)9781665476799

ISBN: (纸本)9781665476805

software supply chain attacks occur during the processes of producing software is compromised, resulting in vulnerabilities that target downstream customers. While the number of successful exploits is limited, the impact of these attacks is significant. Despite increased awareness and research into software supply chain attacks, there is limited information available on mitigating or architecting for these risks, and existing information is focused on singular and independent elements of the supply chain. In this paper, we extensively review software supply chain security using software development tools and infrastructure. We investigate the path that attackers find is least resistant followed by adapting and finding the next best way to complete an attack. We also provide a thorough discussion on how common software supply chain attacks can be prevented, preventing malicious hackers from gaining access to an organization's development tools and infrastructure including the development environment. We considered various SSC attacks on stolen code-sign certificates by malicious attackers and prevented unnoticed malware from passing by security scanners. We are aiming to extend our research to contribute to preventing software supply chain attacks by proposing novel techniques and frameworks.

关键词： Resistance Codes computer hacking Conferences Supply chains Organizations software

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Career Aura-Smart Resume and Employment Recommender 3

Career Aura-Smart Resume and Employment Recommender

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Dissanayake, Kavindi Mendis, Senidu Subasinghe, Randula Geethanjana, Dineth Lunugalage, Dilani Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Computer Systems Engineering Department Malabe Sri Lanka Sri Lanka Institute of Information Technology Information Technology Department Malabe Sri Lanka Sri Lanka Institute of Information Technology Computer Science and Software Engineering Department Malabe Sri Lanka

ISBN: (纸本)9781665408622

Recruitment and Job seeking are two major factors that are directly proportional to each other. Due to the competitive nature of the present world, the process of acquiring the best resource effectively and efficiently has become a challenging aspect for the companies. As a result, modern job portals have become increasingly popular to address the challenges identified in the early recruitment and job search process. The purpose of this research is to introduce an optimal solution to address the ineffective areas identified in the job and recruitment domain which can further enhance the recruitment and job seeking decisions by utilizing deep learning and sentiment analytic approach along with descriptive analysis. The proposed system recommends the relevant job opportunities by omitting the irrelevant job advertisements for job hunters who are interested in the IT job domain while they input their resume to the system and additionally, they can improve their career decisions by adhering to the prediction schemes. Moreover, the system facilitates recruiters to headhunt top talents efficiently once they input job requirements to the system and candidate suggestions are not only made depending on their resume information but also analyzing their LinkedIn endorsements. © 2021 IEEE.

关键词： Deep learning

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A Survey on Federated Learning in Intelligent Transportation Systems

arXiv

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arXiv 2024年

作者： Zhang, Rongqing Wang, Hanqiu Li, Bing Cheng, Xiang Yang, Liuqing School of Software Engineering Tongji University Shanghai201804 China School of Electronics Peking University Beijing100871 China Guangzhou511458 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR5999077 Hong Kong

The development of Intelligent Transportation System (ITS) has brought about comprehensive urban traffic information that not only provides convenience to urban residents in their daily lives but also enhances the efficiency of urban road usage, leading to a more harmonious and sustainable urban life. Typical scenarios in ITS mainly include traffic flow prediction, traffic target recognition, and vehicular edge computing. However, most current ITS applications rely on a centralized training approach where users upload source data to a cloud server with high computing power for management and centralized training. This approach has limitations such as poor real-time performance, data silos, and difficulty in guaranteeing data privacy. To address these limitations, federated learning (FL) has been proposed as a promising solution. In this paper, we present a comprehensive review of the application of FL in ITS, with a particular focus on three key scenarios: traffic flow prediction, traffic target recognition, and vehicular edge computing. For each scenario, we provide an in-depth analysis of its key characteristics, current challenges, and specific manners in which FL is leveraged. Moreover, we discuss the benefits that FL can offer as a potential solution to the limitations of the centralized training approach currently used in ITS applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent systems

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Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

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computers, Materials & Continua 2021年第3期66卷 3161-3182页

作者： Ihsan Jasim Hussein M.A.Burhanuddin Mazin Abed Mohammed Mohamed Elhoseny Begonya Garcia-Zapirain Marwah Suliman Maashi Mashael S.Maashi BIOCORE Research Group Universiti Teknikal Malaysia MelakaMelaka76100Malaysia Director of UTeM International Centre BIOCORE Research GroupUniversiti Teknikal Malaysia MelakaMelaka76100Malaysia College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates eVIDA Laboratory University of DeustoBilbao48007Spain Medical Laboratory Science Department King Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of ***,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound *** main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer *** addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion *** study uses two databases in training and testing the proposed segmentation *** breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in *** of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound *** segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data *** the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

关键词： Viola-Jones model breast cancer segmentation ovarian tumor ovarian tumor segmentation breast cancer ultrasound images active contour cascade model

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