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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Wakamatsu, Yuichi Kamiya, Naoki Zhou, Xiangrong Hara, Takeshi Fujita, Hiroshi Department of Media Informatics Graduate School of Information Science and Technology Aichi Prefectural University 1522-3 Ibaragabasama Nagakute-shi Aichi480-1198 Japan Department of Information Systems Graduate School of Information Science and Technology Aichi Prefectural University 1522-3 Ibaragabasama Nagakute-shi Aichi480-1198 Japan Department of Electrical Electronic and Computer Engineering Faculty of Engineering Gifu University 1-1 Yanagido Gifu-shi Gifu501-1193 Japan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Supervised learning for image segmentation requires annotated images. However, image annotation has the problem that it is time-consuming. This problem is particularly significant in the erector spinae muscle segmentation due to the large size of the muscle. Therefore, this study considers the relationship between the number of annotated images used for training and segmentation accuracy of the erector spinae muscle in torso CT images. We use Bayesian U-Net, which has shown high accuracy in thigh muscle segmentation, for the segmentation of the erector spinae muscle. In the network training, we limit the number of slices for each case and the number of cases to 100%, 50%, 25%, and 10%. In the experiment, we use 30 torso CT images, including 6 cases for the test dataset. Experimental results are evaluated by the mean Dice value of the test dataset. Using 100% of the slices per case, the segmentation accuracy with 100%, 50%, 25%, and 10% of the cases was 0.934, 0.927, 0.926, and 0.890, respectively. On the other hand, using 100% of the cases, the segmentation accuracy with 100%, 50%, 25%, and 10% of the slices per case was 0.934, 0.934, 0.933, and 0.931, respectively. Furthermore, the segmentation accuracy with 100% of the cases and 10% of the slices per case was higher than that of the previous method. We showed that it is feasible to achieve high segmentation accuracy with a limited number of annotated images by selecting several slices from a limited number of cases for training. © 2021 SPIE.

关键词： Image segmentation

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A General Framework of Reversible Data Hiding with Controlled Contrast Enhancement

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computers, Materials & Continua 2020年第1期62卷 157-177页

作者： Shaowei Weng Yiyun Liu Yunqing Shi Bo Ou Chunyu Zhang Cuiping Wang School of Information Engineering Guangdong University of TechnologyGuangzhou510006China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen UniversityShenzhen518060China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07103 USA College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China School of Information Engineering Xizang Minzu UniversityXianyang712082China School of Electronic Information Qingdao UniversityQingdao266071China

This paper proposes a two-step general framework for reversible data hiding(RDH)schemes with controllable contrast *** first step aims at preserving visual perception as much as possible on the basis of achieving high embedding capacity(EC),while the second step is used for increasing image *** the second step,some peak-pairs are utilized so that the histogram of pixel values is modified to perform histogram equalization(HE),which would lead to the image contrast ***,for HE,the utilization of some peak-pairs easily leads to over-enhanced image contrast when a large number of bits are ***,in our proposed framework,contrast over-enhancement is avoided by controlling the degree of contrast *** the second step can only provide a small amount of data due to controlled contrast enhancement,the first one helps to achieve a large amount of data without degrading visual *** RDH method which can achieve high EC while preserve good visual quality,can be selected for the first *** fact,Gao et al.’s method is a special case of our proposed *** addition,two simple and commonly-used RDH methods are also introduced to further demonstrate the generalization of our framework.

关键词： Reversible data hiding controlled contrast enhancement general framework PEE-based RDH method IT-based RDH method

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On the importance of AI research beyond disciplines

arXiv

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arXiv 2023年

作者： Dignum, Virginia Casey, Donal Cerratto-Pargman, Teresa Dignum, Frank Fantasia, Valentina Formark, Bodil Hammarfelt, Björn Holmberg, Gunnar Holzapfel, André Larsson, Stefan Lagerkvist, Amanda Lakemond, Nicolette Lindgren, Helena Lorig, Fabian Marusic, Ana Rahm, Lina Razmetaeva, Yulia Sikström, Sverker Tatar, Kıvanç Tucker, Jason Department of Computing Science Umeå University Sweden Department of Philosophy Lund University Sweden Department of Business Studies Uppsala University Sweden Dept. of Computer and Systems Sciences Stockholm University Sweden Umeå University Sweden Swedish School of Library and Information Science University of Borås Sweden Department of Management and Engineering Linköping University Sweden Division of Media Technology and Interaction Design KTH Sweden Department of Technology and Society Lund University Sweden Department of Informatics and Media Uppsala University Sweden Department of Computer Science and Media Technology Malmö University Sweden University of Split School of Medicine Split Croatia Department of Philosophy and History KTH Sweden Department of Law Uppsala University Sweden Department of Psychology Lund University Sweden Chalmers University of Technology Sweden Department of Global Political Studies Malmö University Sweden

As the impact of AI on various scientific fields is increasing, it is crucial to embrace interdisciplinary knowledge to understand the impact of technology on society. The goal is to foster a research environment beyond disciplines that values diversity and creates, critiques and develops new conceptual and theoretical frameworks. Even though research beyond disciplines is essential for understanding complex societal issues and creating positive impact it is notoriously difficult to evaluate and is often not recognized by current academic career progression. The motivation for this paper is to engage in broad discussion across disciplines and identify guiding principles fir AI research beyond disciplines in a structured and inclusive way, revealing new perspectives and contributing to societal and human wellbeing and sustainability. © 2023, CC BY.

关键词： Sustainable development

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Point-KAN: Leveraging Trustworthy AI for Reliable 3D Point Cloud Completion With Kolmogorov Arnold Networks for 6G-IoT Applications

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IEEE Internet of Things Journal 2025年

作者： Sangaiah, Arun Kumar Anandakrishnan, Jayakrishnan Kumar, Sujith Bian, Gui-Bin AlQahtani, Salman A. Draheim, Dirk National Yunlin University of Science and Technology International Graduate School of AI Douliu 64002 Taiwan National Institute of Technology Puducherry Karaikal 609609 India Chinese Academy of Sciences Institute of Automation National Key Laboratory for Multi-modal Artificial Intelligence Systems Beijing 100190 China King Saud University College of Computer and Information Sciences New Emerging Technologies and5G Network and Beyond Research Chair Department of Computer Engineering Riyadh Saudi Arabia Tallinn University of Technology Information Systems Group School of Information Technologies Tallinn 12618 Estonia

3D point clouds are data points defining the morphology of environments, and completion refers to the reconstruction of missing points. 6G Internet of Things (6G-IoT) connected with 3D mapping devices needs reliable, consistent, high-fidelity real-time point cloud completion for accurate environment registration. Trustworthy AI, modeled with dependable Deep Learning (DL), enables reliable and robust point completion with spatial-geometrical consistency for deployment with 6G-IoT devices. Although several DL-based completion techniques are integrated with 6G-IoT devices, they have reliability issues, limiting key trustworthy AI characteristics. This research focuses on the reliability and robustness aspects of trustworthy AI to propose Point-KAN, a dependable real-time 3D point cloud completion model for 6G IoT-connected 3D mapping devices. Point-KAN integrates multi-head attention and Kolmogorov-Arnold Networks (KAN) within the modules of Attention Enhanced-Embedded Feature Collector (AEFC) and KAN-Enhanced Feature Mapper (KEFM) for trustworthy point cloud completion. Empirical evaluations on the ShapeNet demonstrate the superiority of Point-KAN against state-of-the-art (SOTA). Results concrete Point-KAN’s evolution as a trustworthy AI framework ensures reliability and robustness for real-time deployment in 6G-IoT-connected devices, facilitating 3D environment mapping. © 2014 IEEE.

关键词： 3D Point Cloud Completion 6G IoT Kolmogorov-Arnold Networks (KANs) Trustworthy AI

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Incorporating simulated spatial context information improves the effectiveness of contrastive learning models

arXiv

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arXiv 2024年

作者： Zhu, Lizhen Wang, James Z. Lee, Wonseuk Wyble, Brad Data Science and Artificial Intelligence Area College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Human-Computer Interaction Area College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Department of Communication and Media School of Social Sciences and Humanities Loughborough University Leicestershire Loughborough United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States Department of Psychology The Pennsylvania State University University ParkPA United States

Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spatial context of the agent provides a similarity signal for self-supervised contrastive learning. We present a unique approach, termed Environmental Spatial Similarity (ESS), that complements existing contrastive learning methods. Using images from simulated, photorealistic environments as an experimental setting, we demonstrate that ESS outperforms traditional instance discrimination approaches. Moreover, sampling additional data from the same environment substantially improves accuracy and provides new augmentations. ESS allows remarkable proficiency in room classification and spatial prediction tasks, especially in unfamiliar environments. This learning paradigm has the potential to enable rapid visual learning in agents operating in new environments with unique visual characteristics. Potentially transformative applications span from robotics to space exploration. Our proof of concept demonstrates improved efficiency over methods that rely on extensive, disconnected datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： computer vision

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Wirelessly Enabled Beam Steering MSF Antenna for Biomedical Imaging Applications

Wirelessly Enabled Beam Steering MSF Antenna for Biomedical ...

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URSI International Symposium on Electromagnetic Theory (EMTS)

作者： Asif Bilal Abdul Quddious Amin Darvazehban Sasan Ahdi Rezaeieh Stavros Iezekiel Marco A. Antoniades EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Research Center for Excellence University of Cyprus Nicosia Cyprus Chair of RF and Photonics Engineering TU Dresden Dresden Germany School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia Department of Electrical Computer & Biomedical Engineering Toronto Metropolitan University Toronto ON Canada

ISBN: (数字)9798350399288

ISBN: (纸本)9798350399295

In this work, a wirelessly enabled beam steering metasurface (MSF) antenna for bio-imaging is proposed. The design consists of multiple layers, the top layer consists of an MSF layer, while the bottom layer consists of two layers, one that contains the feeding antennas and the second consists of the feeding network. In the feeding network of the MSF antenna, an RF energy harvesting (EH) system has been incorporated that allows dynamic switching to achieve the desired beam steering required for torso scanning. In the designed EH system, a receiving antenna and rectifying circuit harvests the low power received RF signal at three different frequencies in the 2.4 GHz ISM band. In order to avoid coupling between the EH system and MSF antenna, the former operates in the 2.4 GHz ISM band, while the latter operates in the 900 MHz ISM band. The proposed approach has potential to be used in biomedical imaging applications and to provide an alternative to complex biasing networks required to reconfigure devices.

关键词： Microwave antennas Torso Beam steering Antenna theory Switches Wireless power transfer Antenna feeds Metasurfaces Antennas Biomedical imaging

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Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information

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Cyber-Physical Systems 2025年

作者： Byeon, Haewon AlMahadin, Ghayth Bsoul, Qusay Quraishi, Aadam Soni, Mukesh Khan, Shahi Raza Shabaz, Mohammad Department of AI and Software Inje University Gimhae Korea Republic of Data Science Department College of Information Technology Mutah University Mutah Jordan Department of Cybersecurity College of Computer Sciences and Informatics Amman Arab University Amman Jordan M.D. Research Intervention Treatment Institute HoustonTX United States Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune Pune India MJMC Centre for Media and Mass Communication Jamia Hamdard Delhi India Model Institute of Engineering and Technology Jammu India

This paper proposes an improved cyber-physical false information detection model based on cross-modal ambiguous learning. This work introduced the IC2 LFD model with an emphasis on ‘text-image’ ambiguity, focusing on the main goal of intelligent multi-cross modal data fake information identification. The experiment revealed that the significance of text and images differs in intelligent multi-cross modal data fake information detection. The effectiveness of the model is verified on the twitter dataset, showing a 6% accuracy improvement compared to the baseline model and a 1.6% performance improvement over detection methods without dynamic weight allocation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adversarial machine learning

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3D object recognition system based on local shape descriptors and depth data analysis

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Recent Patents on computer Science 2019年第1期12卷 18-24页

作者： Chowdhary, Chiranji Lal Department Computer Applications and Creative Media School of Information Technology and Engineering Vellore Institute of Technology Vellore India

Background: A physical object, which is actually in 3D form, is captured by a sen-sor/camera (in case of computer vision) and seen by a human eye (in case of a human vision). When someone is observing something, many other things are also involved there which make it more challenging to recognize. After capturing such a thing by a camera or sensor, a digital image is formed which is nothing other than a bunch of pixels. It is becoming important to know that how a computer understands images. Objective: This paper is for highlighting novel techniques on 3D object recognition system with local shape descriptors and depth data analysis. Methods: The proposed work is applied to RGBD and COIL-100 datasets and this is of four-fold as preprocessing, feature generation, dimensionality reduction, and classification. The first stage of preprocessing is smoothing by 2D median filtering on the depth (Z-value) and registration by orientation correction on 3D object data. The next stage is of feature generation and having two phases of shape map generation with shape index map and SIFT/SURF descriptors. The dimensionality reduction is the third stage of this proposed work where linear discriminant analysis and principal component analysis are used. The final stage is fused on classification. Results: Here, calculation of the discriminative subspace for the training set, testing of object data and classification is done by comparing target and query data with different aspects for finding proper matching tasks. Conclusion: This concludes with new proposed approach of 3D Object Recognition. The local shape descriptors are used for 3D object recognition system to implement and test. This system is achieves 89.2% accuracy for Columbia object image library-100 images by using local shape descriptors. © 2019 Bentham Science Publishers.

关键词： Principal component analysis

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Detection of Tor Traffic using Deep Learning 17

Detection of Tor Traffic using Deep Learning

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17th IEEE/ACS International Conference on computer Systems and Applications, AICCSA 2020

作者： Sarkar, Debmalya Vinod, P. Yerima, Suleiman Y. Manipal Institute of Technology Mahe Department of Information and Communication Technology 576104 India Scms School of Engineering and Technology Department of Computer Science and Engineering Kerala India Cyber Technology Institute Faculty of Computing Engineering Media De Montfort University Leicester United Kingdom

ISBN: (纸本)9781728185774

Tor, originally known as The Onion Router, is a free software that allows users to communicate anonymously on the Internet. This makes Tor attractive to cyber criminals, and the anonymity provided can be misused by hackers to enable remote control of victim systems. Indeed, a large volume of Tor traffic is used for malicious purposes such as fast port scans, hacking attempts, ex-filtration of stolen credentials, etc. This makes Tor traffic detection an important component of intrusion detection and prevention systems. Hence, in this paper we present a deep neural network (DNN) based system for the detection and classification of encrypted Tor traffic. The system achieved 99.89% accuracy in the classification of Tor and non-Tor traffic on the UNB-CIC Tor network dataset. Experiments conducted for classifying Tor traffic types demonstrated an accuracy of 95.6%, which is 6.2% higher than previous work on the same dataset. Additionally, the robustness of the proposed DNN classifier is evaluated using adversarial samples generated from a Generative Adversarial Network (GAN). We observed that 100% of the adversarial examples were unidentified by the DNN classifiers. Further retraining of the DNN classifiers with adversarial examples eventually improved their robustness against the adversarial attack. © 2020 IEEE.

关键词： Deep neural networks

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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