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Designing planar cubic B-spline curves with monotonic curvature for curve interpolation

Computational Visual Media 2020年第3期6卷 349-354页

作者： Aizeng Wang Chuan He Fei Hou Zhanchuan Cai Gang Zhao School of Mechanical Engineering&Automation Beihang UniversityBeijing 100191China State Key Laboratory of Virtual Reality Technology&Systems Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

Monotonic curvature plays an important role in industrial design and styling of curves with aesthetic shapes,e.g.,in automobile and aircraft design[1].Used in conventional parametric CAD/CAM systems,general B-splines are not adequate for aesthetic *** for the straight line and circle,monotonic curvature distribution,associated with pleasing shape,is very difficult to achieve.

关键词： curvature monotonic curves

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Evaluating Hashing Algorithms in the Age of ASIC Resistance 3

Evaluating Hashing Algorithms in the Age of ASIC Resistance

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3rd International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2023

作者： Kuznetsov, Oleksandr Kuznetsova, Yelyzaveta Smirnov, Oleksii Kostenko, Oleksii Zvieriev, Volodymyr Department of Political Sciences Communication and International Relations University of Macerata Via Crescimbeni 30/32 Macerata62100 Italy Department of Information and Communication Systems Security School of Computer Sciences V. N. Karazin Kharkiv National University 4 Svobody Sq. Kharkiv61022 Ukraine Department of cyber security and software Central Ukrainian National Technical University 8 University Ave Kropyvnytskyi25006 Ukraine Scientific Laboratory of the Theory of Digital Transformation Law of the Scientific Center for Digital Transformation Law of the State Scientific Institution "Institute of Information Security Law of the National Academy of Legal Sciences of Ukraine" P.Orlyk str. 3 Kyiv01024 Ukraine Department of Software Engineering and Cybersecurity State University of Trade and Economics Kyoto 19 Kyiv02156 Ukraine

In the intricate matrix of cryptographic hashing, two contrasting paradigms vie for precedence: computational swiftness and resilience against specialized hardware, or ASICs. This study undertakes a meticulous exploration into these dueling priorities, juxtaposing conventional stalwarts like SHA-2 and KECCAK against the newer, ASIC-resistant X series. Leveraging detailed performance metrics and visual analytics, we discern the manifest advantage of SHA-2 and KECCAK in terms of computational alacrity. However, as the paper delves deeper, a compelling narrative unfolds. The deliberate design intricacies of the X series, despite their computational latency, emerge as robust bulwarks against potential centralization threats posed by ASIC miners. These algorithms embody a strategic deceleration, ensuring that the cryptocurrency terrain remains hospitable to a broad diaspora of miners. Through this multifaceted lens, we argue that in the cryptographic domain, raw speed is but one facet of a larger, more nuanced picture. The underlying ethos of decentralization and democratic access must guide our trajectory, even if it comes at the expense of pure efficiency. In essence, this paper endeavors to navigate the delicate balance between rapid computation and the overarching principles of an inclusive digital realm. © 2023 CEUR-WS. All rights reserved.

关键词： Computational efficiency

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Bionic Soft Fingers with Hybrid Variable Stiffness Mechanisms for Multimode Grasping

Bionic Soft Fingers with Hybrid Variable Stiffness Mechanism...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xiangbo Wang Tianran Zhang Hongze Yu Zhenwei Wen Lide Fang Huaping Liu Fuchun Sun Lixue Tang Bin Fang College of Quality and Technical Supervision Hebei University Baoding China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China School of Mechanical Engineering & Automation Beihang University Beijing China International School Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Tsinghua University Beijing China School of Biomedical Engineering Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a novel Bionic Soft Finger (BSF) that aims to overcome the limitations of conventional rigid manipulators in terms of adaptability and safety, as well as the challenges faced by soft hands regarding carrying capacity and stability. The BSF design uses a hybrid variable stiffness mechanism combining memory alloy actuators with particle jamming to achieve the desired bending angle and actuator stiffness. Our innovative approach utilizes a bionic finger design that incorporates a memory alloy skeleton and a water-cooled recirculation system, leading to a substantial reduction in the time required for each operation. Through the integration of particle jamming, we have enhanced the overall stiffness and performance of the manipulator, enabling load capacities of up to 3N per finger and more than twice the stiffness of a normal condition. Additionally, our design enables multimode grasping and incorporates a liquid metal strain sensor (METT) for real-time monitoring of finger bending angles. Comparative analyses demonstrate that our design exhibits superior stiffness and enables five-mode grasping in comparison to pneumatic actuators. We believe that bionic soft fingers present a promising solution for enhancing adaptability, safety, and performance in human-robot interaction applications.

关键词： Metals Grasping Bending Manipulators Stability analysis Skeleton Real-time systems

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Goal-Aware RSS for Complex Scenarios via Program Logic

Goal-Aware RSS for Complex Scenarios via Program Logic

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IEEE Symposium on Intelligent Vehicle

作者： Ichiro Hasuo Clovis Eberhart James Haydon Jérémy Dubut Rose Bohrer Tsutomu Kobayashi Sasinee Pruekprasert Xiao-Yi Zhang Erik André Pallas Akihisa Yamada Kohei Suenaga Fuyuki Ishikawa Kenji Kamijo Yoshiyuki Shinya Takamasa Suetomi National Institute of Informatics (NII) Tokyo Japan SOKENDAI (The Graduate University for Advanced Studies) Japan Japanese-French Laboratory for Informatics (IRL 3527) Tokyo Japan Dept. Computer Science Worcester Polytechnic Institute Worcester MA USA Inst. Software & Systems Engineering University of Augsburg Augsburg Germany Cyber Physical Security Research Center AIST Tokyo Japan Graduate School of Informatics Kyoto University Kyoto Japan Mazda Motor Corporation Fuchu Japan

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement—in addition to collision avoidance as in the original RSS—requires complex planning over long sequences of manoeuvres. To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic dFHL that accommodates continuous dynamics and safety conditions. Our framework presents a dFHL-based workflow for deriving goal-aware RSS rules; we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement.

关键词： Intelligent vehicles computer architecture software Cognition Safety Planning Complexity theory

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A Low Self-Noise Fiber Optic Gyroscope Based on Dual-polarization Configuration for Geophysical Rotation Sensing

A Low Self-Noise Fiber Optic Gyroscope Based on Dual-polariz...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Yanjun Chen Yuwen Cao Wenbo Wang Yan He Tong Zhou Zhengbin Lil State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing China School of Software and Microelectronics Peking University Beijing China

A fiber optic gyroscope based on dual-polarization configuration is designed and realized. The 24-hour stability test shows aself-noise level of $3\times 10^{-9}$ rad $/\mathrm{s}/\surd{\text{Hz}}$ within the freq... 详细信息

ISBN: (纸本)9781957171005

关键词： Vibrations Sensitivity Earthquakes Asia Frequency response Gyroscopes Sensors

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Vertical Federated Learning: Challenges, Methodologies and Experiments

arXiv

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arXiv 2022年

作者： Wei, Kang Li, Jun Ma, Chuan Ding, Ming Wei, Sha Wu, Fan Chen, Guihai Ranbaduge, Thilina School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Ministry of Education China Data61 CSIRO SydneyNSW2015 Australia Beijing100191 China The Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Recently, federated learning (FL) has emerged as a promising distributed machine learning (ML) technology, owing to the advancing computational and sensing capacities of end-user devices, as well as the increasing concerns on users' privacy. As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different types of clients. These sub-models are trained locally by vertically partitioned data with distinct attributes. Therefore, the design of VFL is fundamentally different from that of conventional FL, raising new and unique research issues. In this paper, we aim to discuss key challenges in VFL with effective solutions, and conduct experiments on real-life datasets to shed light on these issues. Specifically, we first propose a general framework on VFL, and highlight the key differences between VFL and conventional FL. Then, we discuss research challenges rooted in VFL systems from four aspects, i.e., security and privacy risks, expensive computation and communication costs, possible structural damage caused by model splitting, and system heterogeneity. Afterwards, we develop solutions to addressing the aforementioned challenges, and conduct extensive experiments to showcase the effectiveness of the proposed solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： Federated learning

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Implicit Multi-Scale Swin Transformer Network for Image Denoising

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Qi Ding, Yuwei Zhang, Weiqi Zhu, Yian Zhang, Bob Lin, Jerry Chun-Wei Harbin Institute of Technology at Weihai School of Economics and Management Weihai China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Northwestern Polytechnical University School of Computer Science Xi’an China Northwestern Polytechnical University Yangtze River Delta Research Institute Taicang China University of Macau PAMI Research Group Department of Computer and Information Science Taipa China Department of Distributed Systems and IT Devices Electrical Engineering and Mathematical Sciences Silesian university of technology Poland

Image denoising has been used in various edge computing scenarios such as consumer electronics to improve the image quality and user experience. Existing image denoising methods based on Convolutional Neural Networks (CNNs) and vision Transformers achieve good performance by empirically utilizing residual neural network (ResNet) as basic component. However, ResNet lacks interpretability in network design and also long-term memory of features, potentially restricting the performance of denoising networks. In this paper, we propose an Implicit Multi-scale Swin Transformer Network (IMSNet) for image denoising, which introduces the implicit Euler scheme from the feature memory perspective. Specifically, densely connected implicit feature extraction blocks (IFEBs) are designed to learn the residual mapping between noisy and clean images. The IFEB reformulates the initial skip connection of ResNet based on the implicit Euler discretization, providing both network interpretability and long-term memory. In IFEB, the multi-scale swin Transformer block (MSB) is designed as the implicit layer to capture spatial details and non-local contextual information at different scales. Additionally, a cross-layer feature fusion block (CLFF) is proposed to further improve feature reuse capabilities. Compared to existing denoising networks, the extensive experiments demonstrate the superior performance of our IMSNet in various image denoising tasks, and the flexibility in the practical applications with proposed light model on resource-restricted platforms such as consumer electronic devices. © 1975-2011 IEEE.

关键词： Image denoising

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When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey

arXiv

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arXiv 2025年

作者： Niu, Feifei Li, Chuanyi Liu, Kui Xia, Xin Lo, David State Key Laboratory for Novel Software Technology Software Institute Nanjing University Nanjing China Software Engineering Application Technology Lab Huawei Hangzhou China College of Computer Science and Technology Zhejiang University China School of Computing and Information Systems Singapore Management University Singapore

Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. This exploration aims to guide further advancements in the field, fostering a deeper understanding and refining practices for effective bug localization. Our study suggests that the integration of DL in IRBL enhances the model’s capacity to extract semantic and syntactic information from both bug reports and source code, addressing issues such as lexical gaps, neglect of code structure information, and cold-start problems. Future research avenues for IRBL encompass exploring diversity in programming languages, adopting fine-grained granularity, and focusing on real-world applications. Most importantly, although some studies have started using large language models for IRBL, there is still a need for more in-depth exploration and thorough investigation in this area. © 2025, CC BY.

关键词： Online searching

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EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

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Computational Intelligence and Neuroscience 2022年第0期2022卷 6486570页

作者： Ahmad, Ijaz Wang, Xin Zhu, Mingxing Wang, Cheng Pi, Yao Khan, Javed Ali Khan, Siyab Samuel, Oluwarotimi Williams Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Biomedical Engineering Sun Yat-Sen University Guangzhou China Department of Software Engineering University of Science and Technology Khyber Pakhtunkhwa Bannu Pakistan Institute of Computer Science and Information Technology The University of Agriculture Khyber Pakhtunkhwa Peshawar Pakistan

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL m

关键词： Electroencephalography

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A Multilevel Intelligent Assistant for Multilevel Social Network Analysis 10

A Multilevel Intelligent Assistant for Multilevel Social Net...

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10th IEEE International Conference on Intelligent systems, IS 2020

作者： Tianxing, Man Zhukova, Nataly Tsochev, Georgi ITMO University St. Petersburg Russia Laboratory of Computer-Information Systems and Software Engineering St. Petersburg Institute for Informatics and Automation Russian Academy of Sciences St. Petersburg Russia Bulgarian Academy of Sciences Laboratory of Telematics Sofia Bulgaria

ISBN: (纸本)9781728154565

In recent years, social network data analysis is an emerging field. Multilevel social network analysis could help researchers to figure out comprehensive influencing factors. But the raw data extracted from life is complicated and inoperable so that the entire analysis process is multi-step and changeable. The characteristics of social network datasets at different levels are diverse. There is no general data analysis algorithms for each level. The characteristics of the data and the requirements of the task are important basis for choosing suitable analysis methods. But it is hard for non-expert researchers. This paper proposes an intelligent multilevel assistant for multilevel social network analysis and implements it based on ontology technology. Algorithms at different levels process the corresponding data forms until the appropriate output model is generated. The use of ontology technology makes this framework extensible and understandable. Such a framework is significant for non-experts. © 2020 IEEE.

关键词： Ontology

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