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2023 International Conference on computer, Vision and Intelligent technology, ICCVIT 2023

作者： Liu, Jiayike Li, Qingbo Liu, Ailin Liu, Dong Liu, Yang Zhang, Tianqiao School of Information and Communication Guilin University of Electronic Technology Guilin541000 China School of Computer and Artificial Intelligence Xiangnan University Chenzhou423000 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Chenzhou423000 China School of Life and Environmental Sciences Guilin University of Electronic Technology Guilin541000 China

ISBN: (纸本)9798400708701

Optical coherence tomography is a non-destructive optical signal acquisition and processing method for imaging surface tissue structure. OCT has become an important and innovative corneal measurement and diagnostic technology. Propose a multi-layer detection method for ophthalmic OCT retinal images. Firstly, perform 2D Gabor filtering on the OCT image, then binarize the filtered image, and finally perform eight neighborhood contour localization. To provide reference for the quantitative analysis of the thickness of different physiological layers in the anterior segment in the clinical diagnosis and lesion assessment of ophthalmic diseases. © 2023 ACM.

关键词： Optical tomography

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CBi-GRU: Post-route timing path analysis method based on combined bidirectional GRU

CBi-GRU: Post-route timing path analysis method based on com...

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International Conference on Integrated Circuits and Microsystems (ICICM)

作者： Bin Wang Pengcheng Huang Huiqing You Yaohua Wang Key Laboratory of Advanced Microprocessor Chips and Systems Changsha China College of Computer Science National University of Defense Technology Changsha China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

As the level of circuit integration increases, the physical design becomes more and more complex, iterative static timing analysis(STA) is one of the most time-consuming task to achieve timing closure. After the route stage, fast and accurate timing analysis prediction can effectively reduce the number of static timing analysis in iterations, thereby improving design efficiency and reducing design cycle. In order to quickly predict the timing results after route, we propose a fast timing analysis predictor based on gate recurrent unit (GRU) neural network. This method extracts the features of combinations of cells and nets, assigns attention weights to different combinations, then predicts the hold slacks and setup slacks of each path in multi-corners. The experimental results in the sub-30nm show that the standard deviation of the prediction error ranges 5.41∼16.44ps, and the mean absolute error ranges 6.87∼18.55ps, and the prediction correlation is more than 0.96. Moreover, the speed of timing path prediction is more than 197 times faster than the analysis by STA tools, so that it can effectively and accurately accelerate the process of timing analysis and improve the efficiency of physical design.

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An Empirical Study on Edge-to-Cloud Continuum for Smart Applications: Performance, Design Patterns, and Key Factors

An Empirical Study on Edge-to-Cloud Continuum for Smart Appl...

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IEEE International Conference on Edge Computing (EDGE)

作者： Norman Chen Adel N. Toosi Bahman Javadi Daghash Alqahtani Mohammad S. Aslanpour Minxian Xu Department of Software Systems and Cybersecurity Monash University Clayton Australia School of Computer Data and Mathematical Sciences Western Sydney University Chinese Academy of Sciences Shenzhen Institute of Advanced Technology

ISBN: (数字)9798350368499

ISBN: (纸本)9798350368505

The rapid evolution of cloud-native technologies has facilitated seamless application deployment and execution across the entire edge-to-cloud continuum. This continuum offers a myriad of benefits, including reduced latency, optimized bandwidth utilization, enhanced data privacy, improved reliability, scalability, and flexibility. However, realizing a coherent edge-to-cloud continuum poses challenges especially in resource management, due to the heterogeneous and dynamic nature of computing resources such as resource scheduling and load balancing. This paper focuses on the Container-as-a-Service model enabling independent execution of functions/microservices anywhere on the continuum. We propose an architectural design for constructing a practical edge-to-cloud infrastructure and conduct comprehensive performance evaluations using a real edge-to-cloud testbed. Through an empirical study, we aim to identify key factors impacting application performance and resource management within the continuum, with a specific focus on AI-based IoT applications. Our experiments explore various design patterns including load balancing techniques, scheduling algorithms, invocation methods, gateway and data source location, and factors such as bandwidth and delay, providing practical insights for practitioners and researchers alike.

关键词： Performance evaluation Scheduling algorithms Soft sensors Scalability Bandwidth Logic gates Load management

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Identifying Sybil Attacks in Blockchain Networks through Behavioral Analysis and Zero Knowledge Proof implementations

Identifying Sybil Attacks in Blockchain Networks through Beh...

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Data, Computation and Communication (ICDCC), International Conference on

作者： R Giridhar Reddy Pushpinder Singh Pateja Adarsh Patel Gopinath Palaniappan Balaji Rajendran School of Computer Science and Engineering Vellore Institute of Technology Bhopal Bhopal India Centre for Development of Advanced Computing (C-DAC) Bangalore Resilient Information Systems Engineering Bengaluru India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Blockchain technology is significant because it makes data sharing between several parties safe, transparent, and effective. Multi-step transactions that require verification and traceability can benefit from Blockchain technology. However, the Blockchain technology too comes with its own vulnerabilities and often Blockchain networks are attacked by attacks like 51% Attack, Eclipse Attack, Sybil Attack, Time jacking Attack, Selfish Mining Attack, Finney Attack, Race Attack and so on. One among those attacks is the Sybil attacks, which are a big threat to the integrity of Blockchain networks since they assist malicious actors to create several identities, potentially overwhelming the system and defeating the very principle of consensus mechanisms. In this paper, we have captured an approach on designing a multi-layered mechanism for identifying Sybil attacks with the integration of behavioral analysis, Blockchain analysis techniques, Zero-Knowledge Proofs (ZKPs), and a robust security architecture for governance and validator selection. The broad idea is to cancel pseudo-anonymity in the Blockchain systems by detecting behavioral patterns, identifying exchange wallets, and mapping inter-wallet relationships. Integration of these approaches with ZKPs assists in improving identity verification while simultaneously maintaining user anonymity. The proposed architecture for security uses community-based governance and adaptive validator selection processes to strengthen the defense against Sybil attacks. Token concentration analysis traces down the distribution of stakes within the network in order to find potential risks due to centralization. Our findings thus conclude that integration of the security framework along with behavior analysis and ZKPs effectively reduces the proliferation of fraudulent identities in the Blockchain networks.

关键词： Focusing computer architecture Blockchains Fraud Consensus protocol Security Stakeholders Ransomware Data mining Zero knowledge proof

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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition systems

A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Kulwa, Frank Samuel, Oluwarotimi Williams Asogbon, Mojisola Grace Oyemakinde, Tolulope Tofunmi Obe, Olumide Olayinka Li, Guanglin Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Guangdong Shenzhen518055 China University of Derby School of Computing and Engineering DerbyDE22 3AW United Kingdom Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9781665493840

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario ( © 2023 IEEE.

关键词： Electrodes

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Merging Parameter Estimation and Classification Using LASSO 63

Merging Parameter Estimation and Classification Using LASSO

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Wang, Le Wang, Ying Qiu, Yu Li, Mian Hjalmarsson, Hakan School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Division of Decision and Control Systems Stockholm10044 Sweden UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China Saic Motor Rd Innovation Headquarters Shanghai201807 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China Centre for Advanced Bio Production Stockholm10044 Sweden

ISBN: (纸本)9798350316339

Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not a priori be evident which sensors provide useful information about the target signal, and various operating conditions often necessitate different models. In this paper, we provide a systematic method to construct a soft sensor that can deal with these issues. We propose a single estimation criterion, where the objectives are encoded in terms of model fit, model sparsity (reducing the number of different models), and model parameter coefficient sparsity (to exclude irrelevant sensors). The proposed method is tested on real-world scenarios involving prototype vehicles, demonstrating its effectiveness. © 2024 IEEE.

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Failed yet successful: Learning from discontinued civic tech initiatives

Failed yet successful: Learning from discontinued civic tech...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing systems, CHI EA 2023

作者： Hamm, Andrea Shibuya, Yuya Cerratto Pargman, Teresa Bendor, Roy Hansen, Nicolai Brodersen Raetzsch, Christoph Shoji, Masahiko Bieber, Christoph Hendawy, Mennatullah Klerks, Gwen Schouten, Ben Weizenbaum Institute for the Networked Society Germany The University of Tokyo Japan Dept. of Computer and Systems Sciences Stockholm University Sweden Industrial Design Engineering Delft University of Technology Netherlands Computer Science Aalborg University Denmark Aarhus University Denmark Musashi University Japan Digital Democratic Innovations Center for Advanced Internet Studies Germany Urban Planning and Design Ain Shams University Egypt Industrial Design Eindhoven University of Technology Netherlands Transdisciplinary Innovation University of Technology Sydney Australia

ISBN: (纸本)9781450394222

The design of civic tech is often confronted with impediments, barriers, and a lack of resources. These and other causes may lead to the discontinuation and even abandonment of initiatives. Since seemingly failed projects are much more difficult to publish as articles, this workshop will provide academics and practitioners with a rare opportunity to exchange experiences and insights on discontinued civic tech initiatives. The goal of the workshop is to develop a better understanding of why some civic tech initiatives fail and ask whether discontinued initiatives may still somehow contribute to social change and the growth of digital civics. A variety of sub-questions around discontinued civic tech will be addressed in the workshop, including matters of participation, citizen science, public management, power structures and biases, and communication. © 2023 Owner/Author.

关键词： E-learning

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scHi-CSim:a flexible simulator that generates high-fidelity single-cell Hi-C data for benchmarking

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Journal of Molecular Cell Biology 2023年第1期15卷 27-40页

作者： Shichen Fan Dachang Dang Yusen Ye Shao-Wu Zhang Lin Gao Shihua Zhang School of Computer Science and Technology Xidian UniversityXi’an 710071China School of Automation Northwestern Polytechnical UniversityXi’an 710072China NCMIS CEMSRCSDSAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeĳing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeĳing 100049China Center for Excellence in Animal Evolution and Genetics Chinese Academy of SciencesKunming 650223China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

Single-cell Hi-C technology provides an unprecedented opportunity to reveal chromatin structure in individual ***,high sequencing cost impedes the generation of biological Hi-C data with high sequencing depths and multiple replicates for downstream ***,we developed a single-cell Hi-C simulator(scHi-CSim)that generates high-fidelity data for ***-CSim merges neighboring cells to overcome the sparseness of data,samples interactions in distance-stratified chromosomes to maintain the heterogeneity of single cells,and estimates the empirical distribution of restriction fragments to generate simulated *** demonstrated that scHi-CSim can generate high-fidelity data by comparing the performance of single-cell clustering and detection of chromosomal high-order structures with raw ***,scHi-CSim is flexible to change sequencing depth and the number of simulated *** showed that increasing sequencing depth could improve the accuracy of detecting topologically associating *** also used scHi-CSim to generate a series of simulated datasets with different sequencing depths to benchmark scHi-C clustering methods.

关键词： single-cell Hi-C data simulator high-fidelity single-cell Hi-C clustering distance-stratified sampling benchmarking

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Synthesis of Narrowband Spectral Filters in Active Photonic Multi-Project Wafer Runs

Synthesis of Narrowband Spectral Filters in Active Photonic ...

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2022 IEEE Photonics Conference, IPC 2022

作者： Fatema, Saleha Cohen, Lucas M. Lingaraju, Navin B. Weiner, Andrew M. Purdue University School of Electrical and Computer Engineering West LafayetteIN47907 United States Sri International Advanced Technology and Systems Division 1100 Wilson Blvd. Suite 2800 ArlingtonVA22209 United States

ISBN: (数字)9781665434874

ISBN: (纸本)9781665434874

关键词： Active filters Photonics Narrowband

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PMR-CNN: Prototype Mixture R-CNN for Few-Shot Object Detection

PMR-CNN: Prototype Mixture R-CNN for Few-Shot Object Detecti...

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IEEE Symposium on Intelligent Vehicle

作者： Jiancong Zhou Jilin Mei Haoyu Li Yu Hu Zhejiang Lab The Research Center for Advanced Computing Systems Hangzhou China Hangzhou Institute for Advanced Study Hangzhou China The Research Center for Intelligent Computing Systems Institute of Computing Technology Beijing China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Few-shot object detection is a challenging task because of the limited annotation data. Under the limitation of few-shot samples, images from the same class may differ significantly in appearance and pose. Although the research has progressed considerably since adding the prototype vector to few-shot object detection, the previous paradigm is still constrained by several factors: (1) using a single prototype to represent the support image tends to cause semantic ambiguity; (2) the way of extracting prototypes is too simple, like global average pooling, which makes prototypes not representative enough. In this work, we design PMR-CNN to address the above limitations. PMR-CNN proposes a new method of prototype generation and enhances the representative information by using multiple prototypes to represent support images. For experiments, we not only evaluate our method on general image dataset MS COCO, but also evaluate on SiTi (a real-world autonomous driving dataset collected by us). Experiment on the few-shot object detection benchmark shows that we have a significant advantage over the previous methods. Code is available at: https://***/Chientsung-Chou/PMR-CNN.

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