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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xiao, Feng Shen, Cong Chen, Yu Yang, Tian Chen, Shengyong Liao, Zhijun Tang, Jijun Tianjin University of Technology School of Computer Science and Engineering Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China School of Basic Medical Sciences Fujian Medical University Fujian Fuzhou China College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States

ISBN: (纸本)9781665401265

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score. © 2021 IEEE.

关键词： Diagnosis

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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DeepKAF: A Heterogeneous CBR Deep Learning Approach for NLP Prototyping

DeepKAF: A Heterogeneous CBR Deep Learning Approach for NLP ...

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2020 International Conference on INnovations in intelligent systems and Applications, INISTA 2020

作者： Amin, Kareem Kapetanakis, Stelios Polatidis, Nikolaos Althoff, Klaus-Dieter Dengel, Andreas Smart Data and Knowledge Services German Research Center for Artificial Intelligence Technische Universität Kaiserslautern Kaiserslautern Germany School of Computing Engineering and Mathematics University of Brighton Brighton United Kingdom Intelligent Information Systems Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (纸本)9781728167992

With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture. © 2020 IEEE.

关键词： Case based reasoning

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Research on robot path planning based on multi-strategy genetic ant colony optimization algorithm

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Information Sciences 2025年 718卷

作者： Zheng Li Mingyuan Du Jinlei Qin Xuechao Chen Department of Computer North China Electric Power University 071003 Baoding China Hebei Key Laboratory of Knowledge Computing for Energy & Power 071003 Baoding China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education 071003 Baoding China

The ant colony optimization (ACO) algorithm is commonly used for path planning but faces issues like slow convergence, a tendency to get trapped in local optima, and instability in performance. This paper presents the multi-strategy genetic ant colony optimization (MSGACO) algorithm, which enhances ACO through several innovations: adaptive pheromone distribution initialization, a three-level node selection mechanism, a direction heuristic function, a selective crossover mechanism, and an elitist selection strategy. The adaptive pheromone distribution initialization, combined with the three-level node selection mechanism and direction heuristic function, increases the selection probability for nodes nearer to the destination. Additionally, the selective crossover mechanism and elitist selection strategy optimize the searched paths, resulting in more high-quality solutions and allocating pheromones to them. The main parameters of these strategies are optimized, and their effectiveness is verified. To showcase the benefits of MSGACO, experiments are performed utilizing five exemplary environmental models, with comparisons made to existing methodologies. The results showed a reduction of approximately 60.7% in path length and 52% in convergence iterations compared to the traditional ant colony optimization algorithm.

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Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses

arXiv

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arXiv 2023年

作者： Kang, Jiawen He, Jiayi Du, Hongyang Xiong, Zehui Yang, Zhaohui Huang, Xumin Xie, Shengli The School of Automation Guangdong University of Technology Guangzhou510006 China The Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China Zhejiang Laboratory Hangzhou31121 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore

For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significantly eases communication resource pressure for vehicular metaverse applications to achieve this goal. SemCom enables high-quality and ultra-efficient vehicular communication, even with explosively increasing data traffic among vehicles. In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool. The global and local metaverse are brand-new concepts from the metaverse’s distribution standpoint. Considering the QoS of users, this article explores the potential security vulnerabilities of the proposed framework. To that purpose, this study highlights a specific security risk to the framework’s SemCom module and offers a viable defense solution, so encouraging community researchers to focus more on vehicular metaverse security. Finally, we provide an overview of the open issues of secure SemCom in the vehicular metaverses, notably pointing out potential future research directions. © 2023, CC BY.

关键词： Quality of service

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Role of strategy update rules in the spatial memory-based mixed strategy games

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The European Physical Journal B 2021年第1期94卷 1-11页

作者： Zhang, Fan Wang, Juan Gao, Hongyu Li, Xiaopeng Xia, Chengyi Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Tianjin University of Technology Tianjin People’s Republic of China School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin People’s Republic of China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin People’s Republic of China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin People’s Republic of China

based on a continuous mixed strategy game model considering the memory mechanism and the uncertainty of individual decision-making, we explore the influence of several different update rules on the spatial cooperation behavior. Here, we mainly investigate the role of the Moran-like process, Fermi rule and replicator dynamics in the evolution of cooperation. Meanwhile, we further compare the evolution of cooperation when the memory mechanism and individual uncertainty are taken into account. In particular, memory length M and strategy adjustment factor $$\delta $$ can promote the cooperation behavior in different ways. Extensive Monte Carlo simulations indicate that the Moran-like process can generally drive the expansion of cooperative clusters faster and finally achieve the highest frequency of cooperation although the Fermi update rule performs better under the same condition. Moreover, the replicator dynamics create the worst scenario as far as the evolution of cooperation is concerned. Therefore, the level of cooperation strongly depends on the strategy update rules; these findings may be helpful to understand and analyze the evolutionary process of cooperation in many real-world natural and social systems.

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MFE-Net: A Novel Multi-scale Feature Enhancement Network for SAR Ship Instance Segmentation

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IEEE Geoscience and Remote Sensing Letters 2025年

作者： Liu, Li Zhang, Shuo Hu, Mingtao North China Electric Power University Hebei Key Laboratory of Knowledge Computing for Energy and Power Department of Computer Baoding071000 China Ministry of Education Engineering Research Center of Intelligent Computing for Complex Energy Systems Department of Computer North China Electric Power University Baoding071000 China University of Chinese Academy of Sciences Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China

Deep learning has achieved significant progress in ship instance segmentation for synthetic aperture radar (SAR) im-ages. However, due to the challenges posed by inshore scenes, such as dense clustering, arbitrary arrangement orientation, and multi-scale targets, the performance of existing methods still falls short of expectations. To overcome the dilemma, we propose MFE-Net, a one-stage instance segmentation network based on oriented bounding box (OBB). First, we design an Orientation-aware Seg-mentation Head (OSeg Head) to enhance the network's perception of directional features while mitigating interference from complex background. Additionally, a Feature Aggregation Diffusion Pyra-mid (FADP) is proposed, consisting of the Multi-level Feature Alignment Module (MAM) and the Multi-scale Feature Enhance-ment Module (MFEM). The FADP aggregates global semantic and detailed information, and effectively enhances the feature repre-sentations of both deep and shallow layers through multi-scale fea-ture extraction. Experimental results on two public benchmark datasets (HRSID, SSDD) and a self-built dataset (HRSID_IS) demonstrate that MFE-Net achieves an ideal trade-off between ac-curacy and efficiency, respectively reaching 58.9%, 63.0%, and 46.9% AP with parameters of 12.36M. © 2004-2012 IEEE.

关键词： Deep learning

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NAS-Count: Counting-by-Density with Neural Architecture Search 16th

NAS-Count: Counting-by-Density with Neural Architecture Sear...

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16th European Conference on Computer Vision, ECCV 2020

作者： Hu, Yutao Jiang, Xiaolong Liu, Xuhui Zhang, Baochang Han, Jungong Cao, Xianbin Doermann, David School of Electronic and Information Engineering Beihang University Beijing China Key Laboratory of Advanced Technologies for Near Space Information Systems Ministry of Industry and Information Technology Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beijing China YouKu Cognitive and Intelligent Lab Alibaba Group Hangzhou China Beihang University Beijing China Computer Science Department Aberystwyth University AberystwythSY23 3FL United Kingdom Department of Computer Science and Engineering University at Buffalo New York United States

ISBN: (纸本)9783030585419

Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation problem, but at the expense of demanding design efforts. In this work, we automate the design of counting models with Neural Architecture Search (NAS) and introduce an end-to-end searched encoder-decoder architecture, Automatic Multi-Scale Network (AMSNet). Specifically, we utilize a counting-specific two-level search space. The encoder and decoder in AMSNet are composed of different cells discovered from micro-level search, while the multi-path architecture is explored through macro-level search. To solve the pixel-level isolation issue in MSE loss, AMSNet is optimized with an auto-searched Scale Pyramid Pooling Loss (SPPLoss) that supervises the multi-scale structural information. Extensive experiments on four datasets show AMSNet produces state-of-the-art results that outperform hand-designed models, fully demonstrating the efficacy of NAS-Count. © 2020, Springer Nature Switzerland AG.

关键词： Decoding

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DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping

DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NL...

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IEEE International Symposium on Innovations in intelligent systems and Applications (INISTA)

作者： Kareem Amin Stelios Kapetanakis Nikolaos Polatidis Klaus-Dieter Althoff Andreas Dengel Smart Data and Knowledge Services German Research Center for Artificial Intelligence Technische Universität Kaiserslautern Kaiserslautern Germany School of Computing Engineering and Mathematics University of Brighton Brighton United Kingdom Intelligent Information Systems Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

关键词： Machine learning Training Neural networks Natural language processing Cognition Buildings Task analysis

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The Lagrangian remainder of Taylor’s series, distinguishes O(f(x)) time complexities to polynomials or not

arXiv

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arXiv 2020年

作者： Bakas, Nikolaos P. Kosmatopoulos, Elias Chatzichristofis, Savvas A. Computation-based Science and Technology Research Center Cyprus Institute 20 Konstantinou Kavafi Street Aglantzia Nicosia2121 Cyprus Democritus University of Thrace Electrical and Computer engineering Intelligent Systems Lab Department of Computer Science Neapolis University Pafos 2 Danais Avenue Pafos8042 Cyprus

The purpose of this letter is to investigate the time complexity consequences of the truncated Taylor series, known as Taylor Polynomials [1–3]. In particular, it is demonstrated that the examination of the P = NP equality, is associated with the determination of whether the nth derivative of a particular solution is bounded or not. Accordingly, in some cases, this is not true, and hence in general. Copyright © 2020, The Authors. All rights reserved.

关键词： Taylor series

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