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2022 International Electrical engineering Congress, iEECON 2022

作者： Udomsom, Suruk Rattakorn, Punramon Mankong, Ukrit Umezawa, Toshimasa Matsumoto, Atsushi Kanno, Atsushi Yamamoto, Naokatsu Mendez-Astudillo, Manuel Atthi, Nithi Yamwong, Wittawat Intuyod, Kitti Pinlaor, Somchai Kawanishi, Tetsuya Imai, Kazuo Chiang Mai University Biomedical Engineering Institute Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand National Institute of Information and Communications Network System Research Institute Tokyo Japan Ansys Inc. Tokyo Japan Thai Microelectronics Center National Electronics and Computer Technology Center Chachoengsao Thailand Khon Kaen University Faculty of Medicine Department of Pathology Khon Kaen Thailand Khon Kaen University Faculty of Medicine Department of Parasitology Khon Kaen Thailand Waseda University Department of Electronic and Physical Systems Tokyo Japan Imai Engineering Tokyo Japan

ISBN: (纸本)9781665402064

The detection of S100 Calcium Binding Protein A6, a useful cholangiocarcinoma marker, has been demonstrated for the first time using silicon nitride photonic resonator sensor. The sensor was designed and fabricated by low-cost 500 nm technology. Label-free sensing results based on the reactions between anti-S100A6 antibody and S100A6 antigen, showed good sensitivity and specificity. This sensing technique has potential in low-cost and low-level detection of the biomarker, deserving further study. © 2022 IEEE.

关键词： Silicon nitride

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Low-Latency, Predictable, and High-Precision Networking for Cooperative Vehicle Infrastructure System

SSRN

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SSRN 2022年

作者： Lin, Chi Wu Yu, Chang Wang, Ning Institute of Intelligent System School of Software Dalian University of Technology Dalian China Department of Computer Science and Information Engineering Chung Hua University Hsinchu Taiwan Computer Science & Research Rowan University GlassboroNJ United States

Traditionally, infrastructural architects and road design have independently analyzed respective subsystems' needs in customary transportation. The two's inability to cooperate has culminated in possible safety issues, lousy road congestion, and unnecessary resource utilization. Moreover, challenges concerning sensor arrangement, data collecting, wireless communications, multi-source data fusion, analytical methodologies, and the development of decision-making processes might occur when cooperative vehicle infrastructure systems are used. In recent decades, Cooperative vehicle infrastructure systems(CVIS) have been suggested to assure intelligent transportation's effectiveness, effectiveness, and durability by integrating cars and infrastructures. A cooperative vehicle infrastructure system represents a novel business structure linked to businesses like autos, semiconductors, relevant information, and road infrastructure. informatics, machine intelligence, telecommunications, data fusion, and intelligent monitoring are all included in this domain. Recent generations of networking technologies are pushing the boundaries of what became achievable utilizing cooperative vehicle infrastructure systems. Especially cooperative vehicle infrastructure systems, such instance, factory automation, Mixed Reality, and Touchscreen Network need ultra-low delay evaluated in single-digit milliseconds and cannot accept any fluctuation. Higher financial literacy requires an increased cloudification of application scenarios that require precise service level requirements for cooperative vehicle infrastructure systems. Almost all of those apps are mission-critical and therefore can risk perhaps a data packet being interrupted. Consequently, emerging applications need network infrastructure capable of delivering high-precision and predictable levels of service and accompanying assurances bordering on predictability. Cooperative vehicle infrastructure systems will require reassessing the f

关键词： Autonomous vehicles

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The sensitivity of language models and humans to Winograd schema perturbations 58

The sensitivity of language models and humans to Winograd sc...

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Abdou, Mostafa Ravishankar, Vinit Barrett, Maria Belinkov, Yonatan Elliott, Desmond Søgaard, Anders Department of Computer Science University of Copenhagen Department of Informatics University of Oslo John A. Paulson School of Engineering and Applied Sciences Harvard University Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology

ISBN: (纸本)9781952148255

Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of commonsense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues. © 2020 Association for Computational Linguistics

关键词： Computational linguistics

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On the robustness of EEG tensor completion methods

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Science China(Technological Sciences) 2021年第9期64卷 1828-1842页

作者： DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi College of Artificial Intelligence Nankai UniversityTianjin 300350China Skolkowo Institute of Science and Technology Moscow 121205Russia College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China Department of Informatics Nicolaus Copernicus UniversityTorun 87-100Poland Systems Research Institute Polish Academy of SciencesWarsaw 01-447Poland College of Computer Science Nankai UniversityTianjin 300350China Instituto Argentino de Radioastronom´ıa IAR-CCT La Plata CONICET/CIC-PBA/UNLPVilla Elisa 1894Argentina Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKENWako-Shi 351-0106Japan Department of Psychiatry University of CambridgeCambridge CB28AHUK Data and Signal Processing Research Group University of Vic-Central University of CataloniaCatalonia 08500Spain

During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were ***, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or

关键词： electroencephalogram corrupted data missing data tensor completion tensor decomposition

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Graph Laplacian diffusion localization of connected and automated vehicles

arXiv

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arXiv 2021年

作者： Piperigkos, Nikos Lalos, Aris S. Berberidis, Kostas Computer Engineering and Informatics Department University of Patras Greece The Industrial Systems Institute ATHENA Research Center Patras Greece Industrial Systems Institute ATHENA Research Center Patras Greece Electrical and Computer Engineering Department University of Patras Greece

In this paper, we design distributed multi-modal localization approaches for Connected and Automated vehicles. We utilize information diffusion on graphs formed by moving vehicles, based on Adapt-then-Combine strategies combined with the Least-Mean-Squares and the Conjugate Gradient algorithms. We treat the vehicular network as an undirected graph, where vehicles communicate with each other by means of Vehicle-to-Vehicle communication protocols. Connected vehicles perform cooperative fusion of different measurement modalities, including location and range measurements, in order to estimate both their positions and the positions of all other networked vehicles, by interacting only with their local neighborhood. The trajectories of vehicles were generated either by a well-known kinematic model, or by using the CARLA autonomous driving simulator. The various proposed distributed and diffusion localization schemes significantly reduce the GPS error and do not only converge to the global solution, but they even outperformed it. Extensive simulation studies highlight the benefits of the various approaches, outperforming the accuracy of the state of the art approaches. The impact of the network connections and the network latency are also investigated. © 2021, CC BY.

关键词： Vehicles

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Chaos in chains: Exploring a novel supply chain model through bifurcation analysis, multi-stability and complete synchronization via backstepping control

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Partial Differential Equations in Applied Mathematics 2024年 11卷

作者： Johansyah, Muhamad Deni Vaidyanathan, Sundarapandian Hannachi, Fareh Sambas, Aceng Foster, Bob Aruna, Chittineni Ramesh, Repudi Rusyaman, Endang Faculty of Mathematics and Natural Sciences Universitas Padjadjaran School of Electrical and Communication Engineering Vel Tech University Department of Management Sciences University of Tebessa Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Faculty of Business and Economics Universitas Informatika dan Bisnis Indonesia Department of Computer Science and Engineering KKR & KSR Institute of Technology and Sciences

Chaotic systems are highly sensitive to initial conditions, meaning small changes can lead to significant outcomes. This sensitivity can be leveraged to enhance the flexibility and adaptability of supply chains, allowing them to respond quickly to changing market demands and conditions. In this research work, we obtain a New Chaotic Supply Chain Model (NCSCM) by introducing a sinusoidal model uncertainty in the third differential equation of the Hamidzadeh chaotic supply chain model (2023). The sinusoidal uncertainty in the model accounts for the volatility and fluctuations of the supply chain model. We investigate the dynamic behaviors of NCSCM by performing a detailed Bifurcation Diagram (BD) analysis and Lyapunov Exponents (LEs). Next, we carry out a 0−1 test for the NCSCM to confirm the chaotic attractor of the system. We also study the multi-stability properties of the NCSCM. Finally, with the help of backstepping control, we derive new results for the complete synchronization of the novel chaotic supply chain models. © 2024

关键词： Chaotic systems Dynamical systems Finance models Stability

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SLA-SCA: Secure and Lightweight Authentication and Key Agreement Mechanism for Smart City Applications

SLA-SCA: Secure and Lightweight Authentication and Key Agree...

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Communications, Computing, Cybersecurity, and informatics (CCCI), International Conference on

作者： Bhairvi Pant Mohammad S. Obaidat Shreya Singh Mudita Pant Mohammad Wazid Ashok Kumar Das Kuei-Fang Hsiao Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China School of Computing SRM University Kattankulathur TN India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Computer Science Department University of Texas-Permian Basin Odessa USA

The notion of a smart city is a modern concept characterized by the widespread integration of the Internet of Things (IoT) across various facets of urban life, including healthcare, traffic management, agriculture, and more. This establishes real-time communication and data exchange between all users, servers and devices through the Internet. As we begin to discuss communication between parties, one must also recognize the imperative need for this exchange to be secure and private, as such exchanges are extremely vulnerable to attacks such as man-in-the-middle and replay attacks and even situations like compromising of anonymity during exchanges. This paper focuses on finding an innovative solution to the same challenge. It proposes a mechanism to establish secure communication between the smart IoT devices and the connected cloud servers through a secure mutual authentication and key establishment process with a short name “SLA-SCA”. The security analysis of the proposed SLA-SCA is done, which proves its resilience against the various potential attacks of such a communication. The proposed SLA-SCA outperformed the other existing schemes regarding important security and functionality features.

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3D Skull Completion via Two-stage Conditional Diffusion-Based Signed Distance Fields

3D Skull Completion via Two-stage Conditional Diffusion-Base...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhenhong Liu Xudong Ru Xingce Wang Zhongke Wu Yi-Cheng Zhu Chong Zhang Alejandro F. Frangi School of Artificial Intelligence Beijing Normal University Beijing China Department of Neurology Peking Union Medical College Hospital Beijing China Bioimage Analysis Barcelona Spain Department of Computer Science Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences School of Engineering The University of Manchester UK Department of Cardiovascular Sciences and the Department of Electrical Engineering Medical Imaging Research Centre (MIRC) KU Leuven Leuven Belgium Alan Turing Institute London UK

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

A fast and fully automatic design of 3D cranial implants is highly desired in cranioplasty, and is key to the treatment of skull trauma. We have defined the repair of skull defects as a 3D shape completion task by proposing a two-stage diffusion model based on the representation of 3D shapes using signed distance function (SDF). Specifically, we design a diffusion model conditioned on partial shapes, we compress the 3D shape into a compact latent representation using the encoder in the vector quantized variational autoencoder (VQ-VAE) and learn the diffusion model based on this compressed discrete representation. Encoding the latent space with the autoencoder can achieve high-quality 3D cranial shape completion. In order to accurately capture local and fine-grained shape details, the training data is geometrically encoded from a compactly learned code-book. The two-stage diffusion generator with a coarse-to-fine approach possesses precise and expressive structural modeling capabilities to ensure the supplementation of detailed geometric information. Experimental results verified sufficient expressiveness of our model with generating high-fidelity results with fine-grained local details, outperforming the state-of-the-art methods.

关键词： Solid modeling Three-dimensional displays Shape Autoencoders Training data Implants Diffusion models Skull Vectors Cranial

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Automated Dominative Subspace Mining for Efficient Neural Architecture Search

arXiv

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arXiv 2022年

作者： Chen, Yaofo Guo, Yong Liao, Daihai Lv, Fanbing Song, Hengjie Kwok, James Tin-Yau Tan, Mingkui School of Software Engineering South China University of Technology Guangzhou510641 China Max Planck Institute for Informatics Max Planck Institute Saarbrücken66123 Germany Changsha Hisense Intelligent System Research Institute Co. Ltd Changsha410006 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology 999077 Hong Kong

Neural Architecture Search (NAS) aims to automatically find effective architectures within a predefined search space. However, the search space is often extremely large. As a result, directly searching in such a large search space is non-trivial and also very time-consuming. To address the above issues, in each search step, we seek to limit the search space to a small but effective subspace to boost both the search performance and search efficiency. To this end, we propose a novel Neural Architecture Search method via Dominative Subspace Mining (DSM-NAS) that finds promising architectures in automatically mined subspaces. Specifically, we first perform a global search, i.e., dominative subspace mining, to find a good subspace from a set of candidates. Then, we perform a local search within the mined subspace to find effective architectures. More critically, we further boost search performance by taking well-designed/searched architectures to initialize candidate subspaces. Experimental results demonstrate that DSM-NAS not only reduces the search cost but also discovers better architectures than state-of-the-art methods in various benchmark search spaces. Copyright © 2022, The Authors. All rights reserved.

关键词： Local search (optimization)

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FreeTumor: Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition

arXiv

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arXiv 2025年

作者： Wu, Linshan Zhuang, Jiaxin Zhou, Yanning He, Sunan Ma, Jiabo Luo, Luyang Wang, Xi Ni, Xuefeng Zhong, Xiaoling Wu, Mingxiang Zhao, Yinghua Duan, Xiaohui Vardhanabhuti, Varut Rajpurkar, Pranav Chen, Hao Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Tencent AI Lab Shenzhen China Department of Biomedical Informatics Harvard University Boston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Radiology Shenzhen People’s Hospital Shenzhen China Department of Radiology The Third Affiliated Hospital of Southern Medical University Guangzhou China Department of Radiology Sun Yat-Sen Memorial Hospital Sun Yat-Sen University Guangzhou China Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Division of Life Science The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology Hong Kong Shenzhen-Hong Kong Collaborative Innovation Research Institute The Hong Kong University of Science and Technology Shenzhen China

Tumor is a leading cause of death worldwide, with an estimated 10 million deaths attributed to tumor-related diseases every year. AI-driven tumor recognition unlocks new possibilities for more precise and intelligent tumor screening and diagnosis. However, the progress is heavily hampered by the scarcity of annotated datasets, which demands extensive annotation efforts by radiologists. To tackle this challenge, we introduce FreeTumor, an innovative Generative AI (GAI) framework to enable large-scale tumor synthesis for mitigating data scarcity. Specifically, FreeTumor effectively leverages a combination of limited labeled data and large-scale unlabeled data for tumor synthesis training. Unleashing the power of large-scale data, FreeTumor is capable of synthesizing a large number of realistic tumors on images for augmenting training datasets. To this end, we create the largest training dataset for tumor synthesis and recognition by curating 161,310 publicly available Computed Tomography (CT) volumes from 33 sources, with only 2.3% containing annotated tumors. To validate the fidelity of synthetic tumors, we engaged 13 board-certified radiologists in a Visual Turing Test to discern between synthetic and real tumors. Rigorous clinician evaluation validates the high quality of our synthetic tumors, as they achieved only 51.1% sensitivity and 60.8% accuracy in distinguishing our synthetic tumors from real ones. Through high-quality tumor synthesis, FreeTumor scales up the recognition training datasets by over 40 times, showcasing a notable superiority over state-of-the-art AI methods including various synthesis methods and foundation models. On average, FreeTumor improves the segmentation Dice scores by 6.7% and early tumor detection sensitivity by 16.4%. These findings indicate promising prospects of FreeTumor in clinical applications, potentially advancing tumor treatments and improving the survival rates of patients. Copyright © 2025, The Authors. All rights reserved.

关键词： computerized tomography

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