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Software Services Engineering (SSE) IEEE International Conference on

作者： Jiaqi Zhang Yanchun Sun Yuqi Zhou Jiawei Wu Huizhen Jiang Gang Huang Key Laboratory of High Confidence Software Technologies Ministry of Education School of Computer Science Peking University Beijing P.R. China School of Software and Microelectronics Peking University Beijing P.R. China National Key Laboratory of Data Space Technology and System

ISBN: (数字)9798350368512

ISBN: (纸本)9798350368529

N owadays, software service design is increasingly oriented toward addressing human needs, aiming to extract users' needs and behavioral patterns from open-source data. GitHub's massive open-source repositories have emerged as a crucial data source for software service researchers seeking to extract valuable insights and develop software services tailored for developers. Both GitHub and researchers are making efforts to help researchers and developers better utilize GitHub data. In 2017, GitHub launched “topics”, enabling developers to assign keywords to repositories. This feature fosters linkages between repositories, aiding in their discovery by other developers. For software development, topics offer two significant values. First, topics provide researchers with new insights to better mine GitHub data and provide enhanced support for developers. Second, developers utilizing topics to annotate their repositories may enhance their visibility and engagement within the community, potentially bolstering their repository's popularity. Despite the increasing number of topics, no research has systematically analyzed their content and potential value. Therefore, we conduct the first empirical study on topics, providing valuable conclusions for future researchers and developers. We conduct a case study encompassing 900 repositories to analyze the information explicitly presented in the topic content, and three experiments to verify whether topics have the potential to be used as repository features and user features in GitHub-related studies. Furthermore, we delve into the correlation between topics and repository popularity, by analyzing the number of stars repositories received. Our findings cover the composition of topic content, the potential value of topics for GitHub-related research, and the impact of topics on repository popularity.

关键词： Couplings Correlation Soft sensors Stars Focusing Feature extraction Behavioral sciences

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A Self-Supervised Learning Approach for Zero-Day Attack Detection in Network Traffic Analysis

A Self-Supervised Learning Approach for Zero-Day Attack Dete...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Jian Xu Heyao Chen Jiaxin Huang Mengyuan Zhao Dongwen Luo Electrical and Electronics Engineering University of Southern California California USA Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China Information Studies Trine University Phoenix USA Information System Northeastern University Oakland USA Department of Microelectronics South China University of Technology Guangzhou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

This paper presents a self-monitoring approach for zero-day stop detection in network traffic analysis. The plan integrates self-monitoring with an integrated network to identify past attack patterns without relying on technical data show. A novel pre-training mechanism is developed to learn robust feature representations from unlabeled network traffic, enabling effective detection of zero-day attacks. The architecture employs a hierarchical feature extraction approach combined with an adaptive detection mechanism that continuously updates to address concept drift in network traffic patterns. The evaluation has been carried out by various benchmarks, including CICIDS2017 and TON-IoT, showing the effectiveness of the plan. The system achieves a detection rate of 97.8% for zero-day attacks while maintaining a false positive rate of 1.2%, outperforming existing methods. The self-monitoring function of learning reduces the dependence on registered data while maintaining high accuracy. The system demonstrates high performance in various tasks, achieving up to 100,000 flows per second with minimal workload. The evaluation results confirmed the good use in the global network environment, especially in situations where traditional educational monitoring is limited due to the lack of training materials.

关键词： Training Control engineering Adaptive systems Concept drift Network intrusion detection Telecommunication traffic Self-supervised learning Benchmark testing Feature extraction Monitoring

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Study of a Dynamically Reconfigurable Data Readout Scheme of a Pixel Readout ASIC for Hybrid X-ray Imaging Detectors

Study of a Dynamically Reconfigurable Data Readout Scheme of...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： S. Chen J. Cheng B. Wang W. Gao School of Computer Science and Technology Institute of Microelectronics Northwestern Polytechnical University Xi’an China Engineering Research Center of Embedded System Integration Ministry of Education Xi’an China

The application of hybrid pixel detectors with a dynamically selectable readout mode in X-ray imaging is highly desirable. The concept of a detector system that can switch readout mode under different conditions is demanded to realize fast and versatile imaging. This paper presents design techniques and preliminary results of a pixel readout ASIC prototype with a reconfigurable readout scheme in a 180 nm CMOS process for silicon pixel detectors. A bus-based reconfigurable data readout scheme is presented. Three readout modes are realized: full-frame readout, zero-suppression readout, and region-of-interest readout. In particular application scenarios, the readout mode will be switched based on the number of pixels with valid data to improve the frame rate. Assuming that all pixels with valid data are transmitted, the reconfigurable data readout method saves 40 % of the data transmission time than zero-suppression readout under 50 MHz.

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A Parallel and Pipelined Hardware Architecture for Fractional-Pixel Motion Estimation in AVS3

A Parallel and Pipelined Hardware Architecture for Fractiona...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Zeng, Yuning Zhu, Xizhong Xiang, Guoqing Hao, Zhijian Zhang, Peng Huang, Xiaofeng Yan, Wei Peking University School of Software and Microelectronics Beijing China Advanced Institute of Information Technology Peking University Beijing China Peking University Shenzhen Guraduate School School of Electronic and Computer Engineering Shenzhen China Fudan University State Key Laboratory of ASIC System Shanghai China Hangzhou Dianzi University School of Communication Engineering Hangzhou China

ISBN: (纸本)9781665441544

The latest generation of video coding standards has significantly improved video coding performance. The third generation of audio video coding standards (AVS3) is one of these latest standards. However, due to the more flexible block partition mechanism in AVS3, the computational overhead brought by fractional-pixel motion estimation (FME) increases significantly, making it more difficult to implement a real-time FME hardware. In this paper, a parallel and pipelined hardware architecture supporting block sizes from 4x4 to 64x64 is proposed for FME in AVS3, and it is specially designed for coding tree unit (CTU)-level pipelined architecture of AVS3 encoder. Three necessary modules are developed in this architecture. The first module concerns derivation of motion vector predictors (MVPs). The second module is used to derive fractional-pixel motion vectors (FMVs) of coding units (CUs) partitioned by Binary-tree (BT) and Quad-Tree (QT) mechanisms. And a motion vector substitution (MVS) module is proposed to substitute the FMVs of the remaining CUs with the FMVs derived in the second module. Experimental results show that the proposed methods suffer only 0.62% performance degradation in BD-Rate. Furthermore, the architecture is implemented on XILINX ALVEO U250 FPGA at 400 MHz and the presented FME hardware design can support the real-time encoding of 4K@30fps. © 2022 IEEE.

关键词： Pipelines

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A Completely Blind Video Quality Evaluator

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IEEE Signal Processing Letters 2022年 29卷 2228-2232页

作者： Qi Zheng Zhengzhong Tu Xiaoyang Zeng Alan C. Bovik Yibo Fan State Key Laboratory of ASIC & System College of Microelectronics Fudan University Shanghai China Laboratory for Image and Video Engineering (LIVE) Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA

Automatic video quality assessment of user-generated content (UGC) has gained increased interest recently, due to the ubiquity of shared video clips uploaded and circulated on social media platforms across the globe. Most existing video quality models developed for this vast content are trained on large numbers of samples labeled during large-scale subjective studies, which are often fail to exhibit adequate generalization abilities on unseen data. Thus, it is also desirable to develop opinion-unaware, “completely blind” video quality models, that are free of training, yet can compete with existing learning-based models. Here we propose such a model called VIQE (VIdeo Quality Evaluator), which we designed based on a comprehensive analysis of patch- and frame-wise video statistics, as well as of space-time statistical regularities of videos. The statistical features desired from the analysis capture complementary predictive aspects of perceptual quality, which are aggregated to obtain final video quality scores. Extensive experiments on recent large-scale video quality databases demonstrate that VIQE is even competitive with state-of-the-art opinion-aware models. The source code is being made available at https://***/uniqzheng/VIQE .

关键词： Video recording Quality assessment Feature extraction Computational modeling Streaming media Distortion Distortion measurement

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Conduction Mechanism and Influencing Factors of SiC MOSFET

Conduction Mechanism and Influencing Factors of SiC MOSFET

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2022 International Conference on Intelligent Equipment and Data Processing, IEDP 2022

作者： An, Yiping Wang, Yifan Wu, Yujin Yang, Jiazhen Electrical and Computer System Engineering Rensselaer Polytechnic Institute 1999 Burdett Avenue TroyNY12180 United States School of Microelectronics Fudan University 220 Handan Road Yangpu District Shanghai200082 China School of Advanced Technology Xi'An JIAOTONG LIVERPOOL University Renai Road 111 industrial park Jiangsu province Suzhou215000 China Nanjing University of Posts and Telecommunications 9 Wenyuan Road Qixia District Jiangsu Province Nanjing City210003 China

With the rapid development of the microelectronics industry, power semiconductor devices are getting more and more attention from the industry. At the same time, discussions on the application of power semiconductor materials have never stopped. Over the years, SiC material has stood out because of its superior physical characteristics. Compared with traditional Si MOSFET technology, SiC MOSFET technology has better physical characteristics in some aspects and has a broader market prospect. This paper introduces the conduction mechanism of SiC MOSFET and its performance factors, and the volume effect, bias temperature instability, and threshold voltage drift of SiC MOSFET are discussed. This paper is helpful in understanding the broad application prospects of SiC MOSFET technology and the difference and reform between SiC MOSFET and traditional Si MOSFET. © Published under licence by IOP Publishing Ltd.

关键词： Threshold voltage

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Low-Cost Lithography Hotspot Detection with Active Entropy Sampling and Model Calibration 21

Low-Cost Lithography Hotspot Detection with Active Entropy S...

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Proceedings of the 58th Annual ACM/IEEE Design Automation Conference

作者： Yifeng Xiao Miaodi Su Haoyu Yang Jianli Chen Jun Yu Bei Yu Department of Microelectronics Fudan University Shanghai China College of Mathematics and Computer Science Fuzhou University Fuzhou China Department of Computer Science and Engineering The Chinese University of Hong Kong NT Hong Kong China Department of Microelectronics Fudan University Shanghai China and State Key Lab of ASIC & System Fudan University Shanghai China State Key Lab of ASIC & System Fudan University Shanghai China

ISBN: (纸本)9781665432740

With feature size scaling and complexity increase of circuit designs, hotspot detection has become a significant challenge in the very-large-scale-integration (VLSI) industry. Traditional detection methods, such as pattern matching and machine learning, have been made a remarkable progress. However, the performance of classifiers relies heavily on reference layout libraries, leading to the high cost of lithography simulation. Querying and sampling qualified candidates from raw datasets make active learning-based strategies serve as an effective solution in this field, but existing relevant studies fail to take sufficient sampling criteria into account. In this paper, embedded in pattern sampling and hotspot detection framework, an entropy-based batch mode sampling strategy is proposed in terms of calibrated model uncertainty and data diversity to handle the hotspot detection problem. Redundant patterns can be effectively avoided, and the classifier can converge with high celerity. Experiment results show that our method outperforms previous works in both ICCAD2012 and ICCAD2016 Contest benchmarks, achieving satisfactory detection accuracy and significantly reduced lithography simulation overhead.

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Graphene-based dual-function acoustic transducers for machine learning-assisted human-robot interfaces

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InfoMat 2023年第2期5卷 66-79页

作者： Hao Sun Xin Gao Liang-Yan Guo Lu-Qi Tao Zi Hao Guo Yangshi Shao Tianrui Cui Yi Yang Xiong Pu Tian-Ling Ren Institute of Microelectronics and Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijingPeople's Republic of China CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijingPeople's Republic of China State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingPeople's Republic of China School of Computer Science&Technology Beijing Institute of TechnologyBeijingPeople's Republic of China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijingPeople's Republic of China

Human–robot interface(HRI)electronics are critical for realizing robotic ***,we report graphene-based dual-function acoustic transducers for machine learning-assisted human–robot interfaces(GHRI).The GHRI functions both an artificial ear through the triboelectric acoustic sensing mechanism and an artificial mouth through the thermoacoustic sound emission *** success of the integrated device is also attributed to the multifunctional laser-induced graphene,as either triboelectric materials,electrodes,or thermoacoustic *** systematically optimizing the structure parameters,the GHRI achieves high sensitivity(4500 mV Pa^(–1))and operating durability(1000000 cycles and 60 days),capable of recognizing speech identities,emotions,content,and other information in the human *** the assistance of machine learning,30 speech categories are trained by a convolutional neural network,and the accuracy reaches 99.66%and 96.63%in training datasets and test ***,GHRI is used for artificial intelligence communication based on recognized speech *** work shows broad prospects for the development of robotic intelligence.

关键词： human-robot interface laser-induced graphene machine learning thermoacoustic triboelectric nanogenerator

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Biofunctionalization-Optimized MoS2-Based Fet Biosensors for the Detection of Tau Protein

SSRN

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SSRN 2024年

作者： Wen, Xiaohong Zhao, Xuefeng Shan, Xinzhi Lu, Hongliang Gao, Xiumin Zhuang, Songlin Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology 516 Jungong Road Shanghai200093 China State Key Laboratory of High-Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Sciences Shanghai200050 China State Key Laboratory of ASIC System Shanghai Institute of Intelligent Electronics & Systems School of Microelectronics Fudan University Shanghai200433 China

Field effect transistor (FET)-based biosensors can accurately quantify changes in weak signals and show excellent performance advantages in the detection of trace biological disease markers. Biological functionalization (biofunctionalization) is an important factor in the fabrication of FET-based biosensors, which directly affects the performance of the biosensor. However, little attention from researchers has been paid to biofunctionalization, which results in uneven performance of biosensors. Herein, MoS2-based FET biosensors are designed and fabricated using four different biofunctionalization methods. The most suitable biofunctionalization method is obtained by comparing the sensing performances of different functionalized MoS2based FET biosensors. Moreover, the working mechanism of the optimized biofunctionalization method is explored. Besides, the biofunctionalization-optimized MoS2-based FET biosensor exhibits excellent sensing performance, achieving sensitive detection of Tau protein in the concentration range of 10-2~103 pg/mL. This study provides a universal and effective strategy for the biofunctionalization optimization of two-dimensional materials-based FET biosensors. © 2024, The Authors. All rights reserved.

关键词： Molybdenum compounds

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Biofunctionalization-Optimized Mos2-Based Fet Biosensors for the Detection of Tau Protein

SSRN

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SSRN 2024年

Field effect transistor (FET)-based biosensors can accurately quantify changes in weak signals and show excellent performance advantages in the detection of trace biological disease markers. Biological functionalization (biofunctionalization) is an important factor in the fabrication of FET-based biosensors, which directly affects the performance of the biosensor. However, little attention from researchers has been paid to biofunctionalization, which results in uneven performance of biosensors. Herein, MoS2-based FET biosensors are designed and fabricated using four different biofunctionalization methods. The most suitable biofunctionalization method is obtained by comparing the sensing performances of different functionalized MoS2-based FET biosensors. Moreover, the working mechanism of the optimized biofunctionalization method is explored. Besides, the biofunctionalization-optimized MoS2-based FET biosensor exhibits excellent sensing performance, achieving sensitive detection of Tau protein in the concentration range of 10-2~103 pg/mL. This study provides a universal and effective strategy for the biofunctionalization optimization of two-dimensional materials-based FET biosensors. © 2024, The Authors. All rights reserved.

关键词： Molybdenum compounds

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