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Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation

IEEE Transactions on Cybernetics 2024年第8期54卷 4643-4651页

作者： Jaw, Da-Wei Huang, Shih-Chia Lu, Zhi-Hui Fung, Benjamin C. M. Kuo, Sy-Yen National Taiwan University Department of Electrical Engineering Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan Fudan University School of Computer Science The Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of Education Shanghai200433 China McGill University School of Information Studies MontrealQCH3A 1X1 Canada

Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with sufficient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low- and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low- and sufficient-luminance domains. © 2013 IEEE.

关键词： Feature extraction

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Detection and Identification of Lung Cancer Using an Improvised CNN Model: A Novel Approach to Assist Doctors in Diagnosing Lung Cancer

Detection and Identification of Lung Cancer Using an Improvi...

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International Conference on Business Intelligence and Information Technology, BIIT 2022

作者： Mohammed, Sabah Govathoti, Sudeepthi Satyanarayana, K.V. Joshua, Eali Stephen Neal Department of Computer Science Lakehead University 955 Oliver Road Thunder BayONP7B 5E1 Canada Department of CSE GITAM School of Technology GITAM Deemed-to-be-University Telangana Hyderabad India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam India Department of Computer Science Engineering GITAM School of Technology GITAM Deemed-to-be-University Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9789819934157

Conditions that affect the lungs and make breathing difficult are respiratory ailments. Lung cancer is a disease that affects people all over the globe. The greater the number of individuals aware of them, the better. The likelihood of surviving lung cancer jumps from 14 to 49% if the disease is detected early. Even though CT scans are far more accurate than X-rays, a comprehensive diagnosis requires several scans. The development and testing of a deep neural network for identifying lung cancer in CT images are carried out here. The CNN, DenseNet software was used to determine if the lung image was normal or malignant. This was accomplished via the use of adaptive boosting. There are 201 photographs of lungs in the research collection. Only 15% of the pictures are chosen for testing and classification, while the rest are discarded. According to the testing results, the proposed approach is 95.85% accurate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computerized tomography

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Zero-day Android botnet detection using neural networks

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Neural Computing and Applications 2025年第17期37卷 10795-10805页

作者： Seraj, Saeed Pimenidis, Elias Trovati, Marcello Polatidis, Nikolaos Department of Computer Science Royal Holloway University EghamTW20 0EX United Kingdom School of Computing and Creative Technologies University of the West of England BristolBS16 1QY United Kingdom Department of Computer Science Edge Hill University OrmskirkL39 4QP United Kingdom School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Android devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The widespread adoption of Android devices, coupled with the open-source architecture of the Android operating system, has rendered them a prime target for malicious actors. Among the most perilous threats are Android botnets, which enable malicious actors, often referred to as botmasters, to exert remote control for the execution of destructive attacks. Android botnets have huge potential to be an emerging threat to mobile device security. In this paper, we focus on detecting evolving Android botnets and introduce a new dataset of 3458 apps, represented by 455 permission-based features. We propose an improved multilayer perceptron neural network for zero-day botnet detection. Our methodology, in this way, achieves an accuracy of 98.5%, thus outperforming traditional classifiers. It has a lot of functionality and is based on the neural network approach, making it able to identify slight botnet behaviours in order to improve Android security. © The Author(s) 2024.

关键词： Bot (Internet)

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STARS Assisted Semi-Grant-Free NOMA Communications

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4904-4919页

作者： Xie, Jin Yue, Xinwei Zhao, Jingjing Zou, Yixuan Liu, Yuanwei Liu, Rongke Ding, Zhiguo Key Laboratory of Information and Communication Systems China Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput;2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios;and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced. © 2025 IEEE. All rights reserved.

关键词： Laplace transforms

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Study of City Crimes with Smart Solutions Integrating into Smart Cities, Internet of Things (IoT), and Cybersecurity Systems 2

Study of City Crimes with Smart Solutions Integrating into S...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Rahman, Al-Siyam Anubhove, Md. Sadik Tasrif Zeyad, Mohammad Ahmed, S.M. Masum Walid, Md. Abul Ala Chattogram Bangladesh Cybersecurity Faculty of Sciences Brussels Belgium Department of Urban Engineering Avenues Compiègne60203 France Via Roentgen 1 Milano20136 Italy Department of Computer Science and Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798350370188

The development of smart cities enhances the quality of life for many people by promoting digital interconnection, which encourages dependability and accessibility in cities. Cities have multiple security issues to deal with, as well as kidnapping, rape, child abuse, and violence, completely of which stand on the rise globally. This kind of crime constantly disturbs the victim not only physically and mentally but also forces them to attempt suicide, which can ruin the happiness of many families in a city. Conventional methods are insufficient to safeguard a city from crime and provide a secure way of life for everyone, which is depraved for society and their day-to-day activities. However, this paper, first analyzes the different types of criminal activities in Bangladesh, such as kidnapping, rape, murder, and child abuse, and here offer various technological solutions, to protect cities against the worst crimes, using technologies like cybersecurity and the Internet of Things (IoT) based technologies. With the help of various technological solutions, a city becomes safer while also achieving Sustainable Development Goals (SDG's) number 11, which promotes sustainable cities and communities. This paper clarifies how to reduce criminal activities by making use of a range of technologies, which benefit the country and improve people's quality of life. © 2024 IEEE.

关键词： Crime

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Two-timescale Optimization for E2E Network Slicing-aided Cloud-edge Collaborative Networks

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9777-9789页

作者： Wang, Yunfeng Zhao, Liqiang Chu, Xiaoli Song, Shenghui Deng, Yansha Nallanathan, Arumugam Zhou, Guorong Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510100 China University of Sheffield Department of Electronic and Electrical Engineering SheffieldS10 2TN United Kingdom Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong King's College London Department of Engineering LondonWC2R 2LS United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

To leverage the synergy between cloud computing (CC) and edge computing (EC) to support various services while reducing the CC/EC switching overhead, the two-timescale utility maximization problem for an end-to-end network slicing-aided cloud-edge collaborative network (E2E-CECN) is formulated covering both the high throughput and low delay service requirements. To solve the utility maximization problem while dynamically adjusting the weights of the E2E-CECN utility to accommodate the variation of users' service requests, we proposed a reward comparison double deep Q network algorithm to optimize the large timescale joint virtual base station activation and CC-EC scheduling, and a reward comparison deep deterministic policy gradient algorithm to optimize the small timescale allocation of backhaul link capacity (BLC), CC/EC capability and transmission power. Numerical results show that both the high-throughput and low-delay service requirements can be satisfied simultaneously under the reasonable BLC usage and power consumption. © 1967-2012 IEEE.

关键词： Cloud platforms

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Visual lifelog retrieval: humans and machines interpretation on first-person images

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Multimedia Tools and Applications 2023年第24期82卷 37757-37787页

作者： Yen, An-Zi Fu, Min-Huan Ang, Wei-Hong Chu, Tai-Te Tsai, Ssu-Hao Huang, Hen-Hsen Chen, Hsin-Hsi Department of Computer Science National Yang Ming Chiao Tung University 1001 Ta Hsueh Road Hsinchu30010 Taiwan Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Institute of Information Science Academia Sinica 128 Academia Road Taipei115 Taiwan

People usually forget the details of life experiences and encounter situations where they require to recall their past experiences. Therefore, lifelog retrieval turns out to be an emerging task in the AI community. Nowadays, people can record their life experiences by capturing images through wearable devices, writing blog posts, and so on. These personal big data stored in digital format can be considered lifelogs for retrieval. In this work, we focus on constructing a visual lifelog retrieval system that is able to efficiently find related images given the input textual queries. The core challenge of visual lifelog retrieval with textual queries comes from the semantic gap between visual and textual data. In this work, we propose LifeConcept, an interactive lifelog search system that is aimed at not only accelerating the retrieval process but also fetching more precise results. To reduce the semantic gap, we incorporate visual and textual concepts from images into our system utilizing pre-trained textual embeddings. Moreover, we propose a concept recommendation method enabling users to set up the related conditions for their requirements efficiently and search the desired images with appropriate query terms based on the suggestion. Experimental results show that textual concepts from images detected by CV models improve the retrieval results. We further employ annotators to label captions of images for investigating the difference between model-generated captions and human-labeled captions. The human-annotated dataset is released to facilitate future study of visual lifelog retrieval. Four research questions are discussed to explore the characteristic of models and humans interpreting the first-person images captured by wearable cameras. The impacts of model-generated captions and human-labeled captions in terms of visual lifelog retrieval are also included. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Na

关键词： Wearable technology

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Broadcasting in chains of rings 14

Broadcasting in chains of rings

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Harutyunyan, Hovhannes A. Hovhannisyan, Narek Maraachlian, Edward Concordia University Department of Computer Science and Software Engineering MontrealQCH3G 1M8 Canada

ISBN: (纸本)9798350335385

Broadcasting is an information dissemination problem in an interconnection network where one node, called the originator, must distribute a message to all other nodes by placing a series of calls along the links of the network. Every time the informed nodes aid the originator in distributing the message. Finding the broadcast time of any node in an arbitrary network is NP-complete. Polynomial time solutions are known only for a few classes of networks. In this paper, we consider chains of rings. We present a linear algorithm to find the broadcast time of arbitrary chains of rings and closed chains of rings. © 2023 IEEE.

关键词： Broadcasting

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Broadcasting in Split Graphs 13th

Broadcasting in Split Graphs

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13th International Symposium on Algorithms and Complexity, CIAC 2023

作者： Harutyunyan, Hovhannes A. Hovhannisyan, Narek Department of Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783031304477

Broadcasting is an information dissemination primitive where a message is passed from one node (called originator) to all other nodes in the network. With the increasing interest in interconnection networks, an extensive amount of research was dedicated to broadcasting. Two main research goals of this area are finding inexpensive network structures that maintain efficient broadcasting and finding the broadcast time for well-known and widely used network topologies. In the scope of this paper, we will mainly focus on determining the broadcast time and the optimal broadcasting scheme for graphs. Determination of the broadcast time of a node x in an arbitrary network G is known to be NP-hard. Polynomial time solutions are known only for a few classes of networks. There also exist various heuristic and approximation algorithms for different network topologies. In this paper, we will consider networks that can be represented as split graphs. We will present a polynomial time 2-approximation algorithm for the broadcast time problem and will introduce an algorithm for generating optimal or near-optimal broadcast schemes in split graphs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information dissemination

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