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6th Workshop on Speech and Language Processing for Assistive Technologies, SLPAT 2015

作者： Kacorri, Hernisa Huenerfauth, Matt Center CUNY Doctoral Program in Computer Science United States Rochester Institute of Technology Golisano College of Computing and Information Sciences United States

ISBN: (纸本)9781941643792

Advancing the automatic synthesis of linguistically accurate and natural-looking American Sign Language (ASL) animations from an easy-To-update script would increase information accessibility for many people who are deaf by facilitating more ASL content to websites and media. We are investigating the production of ASL grammatical facial expressions and head movements coordinated with the manual signs that are crucial for the interpretation of signed sentences. It would be useful for researchers to have an automatic scoring algorithm that could be used to rate the similarity of two animation sequences of ASL facial movements (or an animation sequence and a motioncapture recording of a human signer). We present a novel, sign-language specific similarity scoring algorithm, based on Dynamic Time Warping (DTW), for facial expression performances and the results of a user-study in which the predictions of this algorithm were compared to the judgments of ASL signers. We found that our algorithm had significant correlations with participants comprehension scores for the animations and the degree to which they reported noticing specific facial expressions. © SLPAT 2015 - 6th Workshop on Speech and Language Processing for Assistive Technologies, Proceedings.

关键词： Animation

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Performance prediction for Grid workflow activities based on features-ranked RBF network

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High Technology Letters 2009年第2期15卷 203-207页

作者：王洁 Duan Rubing Farrukh Nadeem Institute of Computing Technology Chinese Academy of Sciences Beijing 100050 P.R. China Institute of Computer Science University of Innsbruck Innsbruck Austria Graduate University of Chinese Academy of Sciences Beijing 100049 P.R. China

Accurate performance prediction of Grid workflow activities can help Grid schedulers map activitiesto appropriate Grid *** paper describes an approach based on features-ranked RBF neural networkto predict the performance of Grid workflow *** results for two kinds of real worldGrid workflow activities are presented to show effectiveness of our approach.

关键词： performance prediction radial basis function （RBF） neural network features rank Grid workflow activities

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An implemented VLSI architecture of inverse quantizer for AVS HDTV video decoder

An implemented VLSI architecture of inverse quantizer for AV...

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ASICON 2005: 2005 6th International Conference on ASIC

作者： Sheng, Bin Gao, Wen Wu, Di Department of Computer Science and Technology Harbin Institute of Technology China Institute of Computing Technology Chinese Academy of Sciences China Graduate School of Chinese Academy of Sciences China

ISBN: (纸本)0780392108

AVS is Chinese new audio and video coding standard. A pipeline-based architecture of inverse quantizer for AVS video standard is proposed in this paper. Due to using one-pass processing for Run Length Decoding, Inverse Scan and Inverse Quantization, the architecture can save many buffers, which are used to store intermediate results during multi-pass processing. Furthermore, the processing speed is up to one coefficient per clock cycle. This architecture has been described in Verilog HDL, simulated with VCS digital simulator, and synthesized using 0.18μm CMOS cells library by Synopsys Design Compiler. The circuit totally costs about 13.7k logic gates when running at 200MHz. Simulation results show that the architecture can support real-time inverse quantization for HDTV (1280×720, 60fps) video. This architecture has been implemented in a single chip HDTV decoder for AVS video and audio. © 2005 IEEE.

关键词： VLSI circuits

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An implemented architecture of deblocking filter for H.264/AVC

An implemented architecture of deblocking filter for H.264/A...

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2004 International Conference on Image Processing, ICIP 2004

ISBN: (纸本)0780385543

H.264/AVC is a new international standard for the compression of natural video images, in which a deblocking filter has been adopted to remove blocking artifacts. In this paper, we propose an efficient processing order for the deblocking filter, and present the VLSI architecture according to the order. Making good use of data dependence between neighboring 4×4 blocks, our design reduces the requirement of on-chip SRAM bandwidth and increases the throughput of the filter processing. The architecture has been described in Verilog HDL, simulated with VCS and synthesized using 0.25μm CMOS cells library by Synopsys Design Compiler. The circuit costs about 24k logic gates (not including a 32×64 SRAM and two 32×96 SRAMs) when the working frequency is set to 100MHz. This design can support real-time deblocking of HDTV (1280×720, 60fps) H.264/AVC video. This architecture is valuable for the hardware design of H.264/AVC CODEC. ©2004 IEEE.

关键词： Optical filters

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The minimal buffering requirements of congestion controlled interactive multimedia applications 8th

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8th International Workshop on Interactive Distributed Multimedia Systems, IDMS 2001

作者： Li, Kang Krasic, Charles Walpole, Jonathan Shor, Molly H. Pu, Calton Oregon Graduate Institute Department of Computer Science and Engineering United States Oregon State University Electrical and Computer Engineering Department United States Georgia Institute of Technology College of Computing United States

ISBN: (纸本)3540425306

This paper uses analysis and experiments to study the minimal buffering requirements of congestion controlled multimedia applications. Applications in the Internet must use congestion control protocols, which vary transmission rates according to network conditions. To produce a smooth perceptual quality, multimedia applications use buffering and rate adaptations to compensate these rate oscillations. While several adaptation policies are available, they require different amounts of buffering at end-hosts. We study the relationship between buffering requirements and adaptation policies. In particular, we focus on a widely pursued policy that adapts an application.s sending rate exactly to the average available bandwidth to maximize throughput. Under this adaptation policy, at least a minimal amount of buffering is required to smooth the rate oscillation inherent in congestion control, and we view this minimal buffering requirement as a cost of maximizing throughput. We derive the minimal buffering requirement for this policy assuming that applications use an additive-increase-and-multiplicative-decrease (AIMD) algorithm for congestion control. The result shows the relationship between parameters of AIMD algorithms and the delay cost. We show that the buffering requirement is proportional to the parameters of the AIMD algorithm and quadratic to the application.s sending rate and round-trip-time. We verify this relationship through experiments. Our results indicate that adaptation policies that maximize throughput are not suitable for interactive applications with high bit rates or long round-trip-times. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Throughput

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Zipf's Law in MOOC Learning Behavior 2

Zipf's Law in MOOC Learning Behavior

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Chang Men Xiu Li Zhihui Du Jason Liu Manli Li Xiaolei Zhang Graduate School at Shenzhen Tsinghua University Department of Computer Science and Technology Tsinghua University School of Computing and Information Sciences Florida International University Institute of Education Tsinghua University

ISBN: (纸本)9781509036202

Learners participating in Massive Open Online Courses(MOOC) have a wide range of backgrounds and *** MOOC learners sign up the courses to take a brief look;only a few go through the entire content,and even fewer are able to eventually obtain a *** discovered this phenomenon after having examined 76 courses on the xuetang X *** specifically,we found that in many courses the learning coverage—one of the metrics used to estimate the learners' active engagement with the online courses—observes a Zipf *** apply the maximum likelihood estimation method to fit the Zipf's law and test our hypothesis using a chi-square *** result from our study is expected to bring insight to the unique learning behavior on MOOC and thus help improve the effectiveness of MOOC learning platforms and the design of courses.

关键词： MOOC learning coverage maximum likelihood estimation Zipf distribution

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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Case-based cybersecurity incident resolution 31

Case-based cybersecurity incident resolution

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31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019

作者： Colome, Marcelo Nunes, Raul Ceretta De Lima Silva, Luis Alvaro Graduate Program in Computer Science - PPGCC Applied Computing Department Federal University of Santa Maria Av. Roraima Campus UFSM Camobi Santa Maria RS Brazil

ISBN: (纸本)1891706489

Intelligent computing techniques have a paramount importance to the treatment of cybersecurity incidents. In such Artificial Intelligence (AI) context, while most of the algorithms explored in the cybersecurity domain aim to present solutions to intrusion detection problems, these algorithms seldom approach the correction procedures that are explored in the resolution of cybersecurity incident problems that already took place. In practice, knowledge regarding cybersecurity resolution data and procedures is being under-used in the development of intelligent cybersecurity systems, sometimes even lost and not used at all. In this context, this work proposes to integrate Case-Based Reasoning techniques and IODEF standard in order to retain concrete problem-solving experiences of cybersecurity incident resolution to be reused in the resolution of new incidents. Experimental results so far obtained with a Case-based Cybersecurity Incident Resolution System (CbCSecIRS) implemented show that information security knowledge can be retained in a reusable memory, so improving the resolution of new cybersecurity problems. © 2019 Knowledge Systems institute graduate School. All rights reserved.

关键词： Intrusion detection

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Evaluation of an agent-based reinforcement learning framework for model-based testing 7

Evaluation of an agent-based reinforcement learning framewor...

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7th International Congress on Ambient Intelligence, Software Engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Ramírez-Méndez, Jose Quesada-López, Christian University of Costa Rica Graduate Program in Computing and Informatics San Pedro Costa Rica University of Costa Rica Department of Computer Science and Informatics San Pedro Costa Rica

ISBN: (纸本)9798350364538

This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework's performance was assessed on three key metrics: effectiveness and efficiency in achieving model coverage objectives, the quantity and uniqueness of generated test cases, and code coverage. The results show improved metrics for the framework compared to traditional testers in model coverage evaluation and test case metrics. Specifically, the framework achieved higher effectiveness and efficiency, generating a higher average number of test cases with a substantial proportion of unique cases, indicating more diverse and thorough testing. Additionally, the framework, along with random and greedy testers, achieved over 70% coverage across branch, method, and line code metrics. The framework showed slightly higher values in method and line coverage compared to other testers. The evaluation highlights the use of agent-based reinforcement learning to support model-based testing in planning test case generation, exploring models using multiple strategies, and learning guided by testing metrics. Future work will focus on further refining the framework learning model, including error coverage evaluation, and testing its applicability across different software systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Sparse bilinear preserving projections

Sparse bilinear preserving projections

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1st Asian Conference on Pattern Recognition, ACPR 2011

作者： Lai, Zhihui Chen, Qingcai Jin, Zhong Bio-Computing Research Center Shenzhen Graduate School Harbin Institute of Technology Shenzhen China School of Computer Science Nanjing University of Science and Technology Nanjing Jiangsu China

ISBN: (纸本)9781457701221

The techniques of linear dimensionality reduction have been attracted widely attention in the fields of computer vision and pattern recognition. In this paper, we propose a novel framework called Sparse Bilinear Preserving Projections (SBPP) for image feature extraction. We generalized the image-based bilinear preserving projections into sparse case for feature extraction. Different from the popular bilinear linear projection techniques, the projections of SBPP are sparse, i.e. most elements in the projections are zeros. In the proposed framework, we use the local neighborhood graph to model the manifold structure of the data set at first, and then spectral analysis and L1-norm regression by using the Elastic Net are combined together to iteratively learn the sparse bilinear projections, which optimal preserve the local geometric structure of the image manifold. Experiments on some databases show that SBPP is competitive to some state-of-the-art techniques. © 2011 IEEE.

关键词： Feature extraction

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