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Helix Scan:A Scan Design for Diagnosis

Tsinghua Science and technology 2007年第S1期12卷 83-88页

作者：王飞胡瑜李晓维 Graduate School of Chinese Academy of Sciences Key Laboratory of Computer System and Architecture Institute of Computing TechnologyChinese Academy of Sciences

Scan design is a widely used design-for-testability technique to improve test quality and efficiency. For the scan-designed circuit, test and diagnosis of the scan chain and the circuit is an important process for silicon debug and yield learning. However, conventional scan designs and diagnosis methods abort the subsequent diagnosis process after diagnosing the scan chain if the scan chain is faulty. In this work, we propose a design-for-diagnosis scan strategy called helix scan and a diagnosis algorithm to address this issue. Unlike previous proposed methods, helix scan has the capability to carry on the diagnosis process without losing information when the scan chain is faulty. What is more, it simplifies scan chain diagnosis and achieves high diagnostic resolution as well as accuracy. Experimental results demonstrate the effectiveness of our design.

关键词： test diagnosis scan chain diagnosis design for diagnosis (DFD)

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Correcting Pronoun Homophones with Subtle Semantics in Chinese Speech Recognition 30

Correcting Pronoun Homophones with Subtle Semantics in Chine...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Zhaobo Gan, Rui Yuan, Pingpeng Jin, Hai National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9782493814104

Speech recognition is becoming prevalent in daily life. However, due to the similar semantic context of the entities and the overlap of Chinese pronunciation, the pronoun homophone, especially "他/她/它 (he/she/it)", (their pronunciation is "Tā") is usually recognized incorrectly. It poses a challenge to automatically correct them during the post-processing of Chinese speech recognition. In this paper, we propose three models to address the common confusion issues in this domain, tailored to various application scenarios. We implement the language model, the LSTM model with semantic features, and the rule-based assisted Ngram model, enabling our models to adapt to a wide range of requirements, from high-precision to low-resource offline devices. The extensive experiments show that our models achieve the highest recognition rate for "Tā" correction with improvements from 70% in the popular voice input methods up to 90%. Further ablation analysis underscores the effectiveness of our models in enhancing recognition accuracy. Therefore, our models improve the overall experience of Chinese speech recognition of "Tā" and reduce the burden of manual transcription corrections. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Speech recognition

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Robust Learning to Rank Based on Portfolio Theory and AMOSA Algorithm

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IEEE Transactions on systems, Man, and Cybernetics: systems 2017年第6期47卷 1007-1018页

作者： Li, Jinzhong Liu, Guanjun Yan, Chungang Jiang, Changjun Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Watershed Ecology and Geographical Environment Monitoring NASG Department of Computer Science and Technology Jinggangshan University Ji'an343009 China

Effectiveness is the most important factor considered in the ranking models yielded by algorithms of learning to rank (LTR). Most of the related ranking models only focus on improving the average effectiveness but ignore robustness. When a ranking model ignores robustness, the effectiveness for many queries is possibly very poor although the average effectiveness for all queries is relatively high. Therefore, Wang et al. first consider robustness in their ranking models. However, the robustness formula defined by Wang et al. cannot characterize those queries whose effectiveness are hurt seriously in comparison with the baseline model. In order to overcome this shortcoming, we propose a novel formula of characterizing robustness based on portfolio theory, and construct a multiobjective optimization model of the robust LTR in which the formula is used. Based on this model, we propose an approach of risk-sensitive and robust LTR, named as R2Rank, which is based on the framework of archived multiobjective simulated annealing algorithm and the idea of preference ranking organization method for enrichment evaluation. The experimental results show that the ranking models produced by our proposed R2Rank approach are better in both effectiveness and robustness than those produced by three state-of-the-art LTR approaches. © 2016 IEEE.

关键词： Simulated annealing

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SIS model of epidemic spreading on dynamical networks with community

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中国高等学校学术文摘·计算机科学 2009年第3期3卷 361-365页

作者： Chengyi XIA Shiwen SUN Feng RAO Junqing SUN Jinsong WANG Zengqiang CHEN Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China Key Laboratory of Computer Vision and System of Ministry of Education Tianjin University of Technology Tianjin 300191 Department of Automation Nankai University Tianjin 300071 China

We present a new epidemic Susceptible-Infected-Susceptible (SIS) model to investigate the spreading behav-ior on networks with dynamical topology and community structure. Individuals in the model are mobile agents who are allowed to perform the inter-community (i.e., long-range) motion with the probability p. The mean-field theory is uti-lized to derive the critical threshold (λ_C) of epidemic spread-ing inside separate communities and the influence of the long-range motion on the epidemic spreading. The results indicate that λ_C is only related with the population density within the community, and the long-range motion will make the original disease-free community become the endemic state. Large-scale numerical simulations also demonstrate the theoretical approximations based on our new epidemic model. The current model and analysis will help us to fur-ther understand the propagation behavior of real epidemics taking place on social networks.

关键词： Susceptible-Infected-Susceptible (SIS) model disease propagation complex network dynamical topology community behavior

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Observable liveness of Petri nets with controllable and observable transitions

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Science China(Information Sciences) 2017年第11期60卷 256-264页

作者： Guanjun LIU Changjun JIANG Department of Computer Science Tongji University Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University E-commerce Transactions and Information Services Collaborative Innovation Center of Shanghai University Knowledge Service Platform Tongji University

Observability and/or controllability are paid much attention to by researchers not only from the control science field[1–3]but also from the computer science field[4–9].Recently,Desel and Kilinc[4]proposed the notion of observable liveness for Petri nets with(un-)observable and(un-)controllable *** purpose of defining observable liveness is to represent that"a user can always enforce the occurrence of any observable transition only by stimulating the net by choosing appropriate enabled controllable transition"[4].An important assumption of

关键词： net cot PN Observable liveness of Petri nets with controllable and observable transitions

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Human behavior recognition based on axonometric projections and PHOG feature

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Journal of Computational Information systems 2014年第8期10卷 3455-3463页

作者： Shen, Xiaoxia Zhang, Hua Gao, Zan Xue, Yanbin Xu, Guangping Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China

Human behavior recognition has become a hot research topic in computer vision. In this paper, we propose an effective method to recognize human behaviors from sequences of depth maps, which provide additional body shape and motion information for behavior recognition. In our approach, we construct a novel difference motion history image, and propose axonometric projection to capture the target motion process, after that, pyramid histogram of orientated gradients is extracted for each view to describe the target motion. Large scale experimental results on challenging and public MSR-action3D behavior dataset demonstrate that the performances of our difference motion history image and our descriptors are satisfying, what's more, our proposed axonometric projection approach further improves the performance, which significantly outperforms the state-of-the-art methods. © 2014 Binary Information Press.

关键词： Image enhancement

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Data Secure Transmission Model Based on Compressed Sensing and Digital Watermarking technology

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Wuhan University Journal of Natural Sciences 2014年第6期19卷 505-511页

作者： WANG Chundong BAI Yi MO Xiuliang Key Laboratory of Computer Vision and System Ministry of Education/Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology

Since the Internet of Things（IoT） secret information is easy to leak in data transfer,a data secure transmission model based on compressed sensing（CS） and digital watermarking technology is proposed here. Firstly, for node coding end, the digital watermarking technology is used to embed secret information in the conventional data carrier. Secondly, these data are reused to build the target transfer data by the CS algorithm which are called observed signals. Thirdly, these signals are transmitted to the base station through the wireless channel. After obtaining these observed signals, the decoder reconstructs the data carrier containing privacy information. Finally, the privacy information is obtained by digital watermark extraction algorithm to achieve the secret transmission of signals. By adopting the watermarking and compression sensing to hide secret information in the end of node code, the algorithm complexity and energy consumption are reduced. Meanwhile, the security of secret information is *** simulation results show that the method is able to accurately reconstruct the original signal and the energy consumption of the sensor node is also reduced significantly in consideration of the packet loss.

关键词： the Internet of Things（IoT） data security com pressed sensing（CS） digital watermarking information privacy

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DGAN: Flight data anomaly detection based on dual-view graph attention network

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IEEE Sensors Journal 2025年第11期25卷 20956-20969页

作者： Yan, Wenlong Li, Xu Zheng, Linjiang Shang, Jiaxing Wu, Lin Liu, Hongtao Li, Xiuyi Qian, Yu Chongqing University College of Computer Science Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China Zhuhai Xiangyi Aviation Technology Co. Ltd Zhuhai519030 China Shanghai Jiaotong University School of Aeronautics and Astronautics Minhang District Shanghai200240 China Civil Aviation Flight University of China School of Flight Technology Guanghan618307 China

A large number of flight data is collected by various sensors during flight, including altitude, speed, pitch, etc. Flight unsafe events, such as hard landings, can be reflected in flight anomaly data. Thus, flight data anomaly detection is crucial for aviation safety, enabling the identification of deviations from normal operations. However, due to the complexity and high dimensionality of the flight data, existing methods often fail to adequately extract temporal and variable information. As a result, these methods exhibit poor detection performance, especially when the anomaly data is scarce. To address these issues, we propose a novel Dual-view Graph Attention Network (DGAN) model for flight data anomaly detection. Specifically, two parallel Graph Attention (GAT) layers are employed to create embeddings that are rich in temporal correlations and variable dependencies. And TCN-based autoencoder module is proposed for data augmentation on these embeddings to extract two views consistent with the underlying data patterns. Furthermore, DGAN constructs pairs of positive and negative samples both within a single view and between different views. A dual-view contrastive training strategy is proposed to construct contrastive loss, which can learn a good representation of flight sequence and ensure effective anomaly detection even in cases of scarce anomaly data. We conduct experiments on a dataset comprising 44,808 real-world flight samples from the Airbus A321. The results demonstrate that the performance of DGAN exceeds state-of-the-art (SOTA) models. Moreover, we examine the effectiveness of DGAN through further visualizations and case studies. © 2001-2012 IEEE.

关键词： Training aircraft

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Predictability assessment of marine heatwaves in the Northeast Pacific based on SEAS5

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Weather and Climate Extremes 2025年 48卷

作者： Liu, Zhouhong Wang, Boni Shan, Haixia State Key Laboratory of Climate System Prediction and Risk Management Nanjing University of Information Science & Technology Nanjing 210044 China International Geophysical Fluid Research Center Nanjing University of Information Science & Technology Nanjing 210044 China Changwang School of Honors Nanjing University of Information Science & Technology Nanjing 210044 China School of Marine Sciences Nanjing University of Information Science & Technology Nanjing 210044 China Ocean College Zhejiang University Zhoushan 316021 China Southern Ocean Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai 519000 China Jiangsu Meteorological Service Center Nanjing 210008 China

Marine Heatwaves (MHWs), extreme ocean warming events, have attracted global attention. This research utilizes forecast data from SEAS5 (Seasonal Forecasting system 5) and OISST (Optimum Interpolation Sea Surface Temperature), applying a range of evaluation metrics from both deterministic and probabilistic forecasting viewpoints. It assesses the forecasting performance of the SEAS5 in the Northeast Pacific (NEP) over the period from 1994 to 2021, examining both spatial and temporal dimensions. The midwest of the NEP exhibits subpar performance in terms of both deterministic and probabilistic predictions when compared to other regions. Furthermore, the SEAS5's forecast skill for MHWs is significantly influenced by the El Niño-Southern Oscillation (ENSO) and seasonal variations. This study establishes probability thresholds for MHWs' occurrences to assess MHWs' predictability using SEAS5, demonstrating that forecasting effectiveness across NEP subregions strongly depends on probability thresholds. To evaluate model performance in terms of reliability and resolution, the research concentrates on the Brier Score decomposition, revealing that the southeastern NEP region exhibits superior reliability and resolution. Additionally, the study focuses on not only the comprehensive efficacy of SEAS5 forecast on the NEP as a whole, but also on the specific performance across different regions. The proposed reliability categorization of MHWs indicates that the majority of regions within the NEP fall into Category 3 and above (at least marginally useful) across all lead times. The SEAS5 has shown high predictability in forecasting the occurrences of MHWs in the NEP, exhibiting diverse forecasting accuracy for MHWs across various maritime regions. © 2025 The Authors

关键词： Deterministic evaluation Marine heatwaves Northeast Pacific Probabilistic evaluation Reliability

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On complementary effect of blended behavioral analysis for identity theft detection in mobile social networks 1

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13th International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2017

作者： Wang, Cheng Luo, Jing Yang, Bo Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Shanghai China

ISBN: (数字)9789811088902

ISBN: (纸本)9789811088896

User behavioral analysis is expected to act as a promising technique for identity theft detection in the Internet. The performance of this paradigm extremely depends on a good individual-level user behavioral model. Such a good model for a specific behavior is often hard to obtain due to the insufficiency of data for this behavior. The insufficiency of specific data is mainly led by the prevalent sparsity of users’ collectable behavioral footprints. This work aims to address whether it is feasible to effectively detect identify thefts by jointly using multiple unreliable behavioral models from sparse individual-level records. We focus on this issue in mobile social networks (MSNs) with multiple dimensions of collectable but sparse data of user behavior, i.e., making check-ins, posing tips and forming friendships. Based on these sparse data, we build user spatial distribution model, user post interest model and user social preference model, respectively. Here, as the arguments, we validate that there is indeed a complementary effect in multi-dimensional blended behavioral analysis for identity theft detection in MSNs. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Social networking (online)

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