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Deep Semantic and Attentive Network for Unsupervised Video Summarization

ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1-21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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Formalizing the situation of a business process activity

Formalizing the situation of a business process activity

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2012 IEEE 16th International Conference on computer Supported Cooperative Work in Design, CSCWD 2012

作者： Mattos, Talita Da Cunha Santoro, Flávia Maria Revoredo, Kate Nunes, Vanessa Tavares Postgraduate Information Systems Program UNIRIO Rio de Janeiro Brazil COPPE/PESC - Systems Engineering and Computer Science Program UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9781467312127

Business processes are dynamic and constantly evolving. Contextual elements that had not yet been identified and represented can arise and influence the execution of each process instance in diverse manners. In this scenario, the identification of these elements is considered of great importance. This paper proposes a formalism for context associated with business processes that intends to identify the situation of an activity in order to subsidize decisions to be taken within the execution of a process. © 2012 IEEE.

关键词： Business Process Context

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Toward Parallelization Technique for Stream-based Lossless Data Compression

Toward Parallelization Technique for Stream-based Lossless D...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kato, Taiki Yamagiwa, Shinichi Wada, Koichi Master's Program in Computer Science Degree Program in Systems and Information Engineering University of Tsukuba Ibalaki Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Ibalaki Tsukuba Japan

ISBN: (纸本)9798350324457

IoT edge platform has become popular in various distributed environments. The edge devices need to communicate BigData among them or with the cloud servers by collaborating with AI technologies for finding events from the applications. Those devices exchange data streams from such as distributed sensors and remote image/video devices. We focus on an acceleration technique for the communication performance using a stream-based lossless data compression technology. This paper proposes a parallelization technique for the compression process in a software environment running on a multicore processor. The technique invokes concurrent compression processes assigned to multiple threads with splitting a data stream to chunks. The paper exposes three scheduling methods for assigning the chunks to the threads: in-order, hybrid and out-of-order. As an original data order of chunks must be obtained in decompression side, the proposed technique introduces packeting mechanisms in each chunk by adding headers to support the scheduling methods. Through experimental performance evaluations, we discuss the packeting overhead focusing on compression ratio and speedup by the parallelization with three scheduling methods. © 2023 IEEE.

关键词： Data Stream IoT Lossless Data Compression Multi-Threading Parallelization

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Performance Enhancement of Stream-Based Decompression Process by Notifying Compression Buffer Size 28

Performance Enhancement of Stream-Based Decompression Proces...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Kato, Taiki Yamagiwa, Shinichi Marumo, Koichi University of Tsukuba Master's Program in Computer Science Degree Program in Systems and Information Engineering Ibalaki Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Ibalaki Tsukuba Japan

ISBN: (纸本)9798350300482

This paper focuses on a performance enhancement of communication performance by compressing data stream. ASE coding is an effective lossless data compression method for data stream. The software implementation of the coding/decoding method inevitably meets a performance mismatch in memory and storage devices. In the compressor side, it is predictable to decide the size of an original data block and is available to process a flexible buffer memory. However, the decompressor is not able to predict the buffer size because the original data size is not obvious before the decompression. This causes a performance mismatch in the filesystem level. This paper proposes a novel method to address the performance mismatch by applying a notification mechanism of compression size from the compressor. This paper describes the mechanism focusing on the system call usage. Through experimental evaluations, we show the performance improvement of the decompression performance for handling data stream. © 2023 IEEE.

关键词： Virtual storage

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Optimal Placement of Software Monitors Aiding Systematic Testing

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IEEE Transactions on Software engineering 1975年第4期SE-1卷 403-411页

作者： Ramamoorthy, C.V. Kim, K.H. Chen, W.T. Computer Science Division Department of Electrical Engineering and Computer Sciences University of California Berkeley Calif 94720 United States Department of Electrical Engineering Systems and Computer Science Program University of Southern California Los Angeles Calif. United States

The usefulness of software monitors in testing large ‘programs is discussed. Several types of testing strategies based on the use of monitors are surveyed. Since there is a computational overhead involved in employing monitors, attempts are made to minimize the number of monitors employed. This optimization problem is formulated and analyzed in graph-theoretic terms. Implementation aspects are considered through both discussion and examples. Copyright © 1975 by The Institute of Electrical and Electronics Engineers, Inc.

关键词： Optimal placement overhead program testing software monitor test-input test-output test-path test-run

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On-line system identification using context discernment

On-line system identification using context discernment

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International Joint Conference on Neural Networks, IJCNN 2005

作者： Holmstrom, Lars Santiago, Roberto Lendaris, George G. NW Computational Intelligence Laboratory Portland State University Portland OR 97201 Systems Science Ph.D. Program Department of Systems Science and Electrical and Computer Engineering

ISBN: (纸本)0780390482

Mathematical models are often used in system identification applications. The dynamics of most systems, however, change over time and the sources of these changes cannot always be directly determined or measured. To maintain model accuracy, it is desirable to design system identifiers that can adapt to these dynamical shifts. We use reinforcement learning to train an agent to recognize dynamical changes in a modeled system and to estimate new parameter values for the model. The subsequent actions of this agent are characterized as "moving" the parameterized model on an optimal trajectory in model parameter space. It is found that this method is capable of quickly and accurately discerning the correct parameter values. © 2005 IEEE.

关键词： Identification (control systems)

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Improving a web usability inspection technique using qualitative and quantitative data from an observational study

Improving a web usability inspection technique using qualita...

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SBES 2009 - 23rd Brazilian Symposium on Software engineering

作者： Conte, Tayana Vaz, Verônica Massolar, Jobson Mendes, Emilia Travassos, Guilherme Horta Manaus Brazil Systems Engineering and Computer Science Program COPPE UFRJ Rio de Janeiro Brazil Computer Science Department University of Auckland Auckland New Zealand

ISBN: (纸本)9780769538440

We have proposed a checklist-based usability inspection technique (WDP - Web Design Perspectives-Based Usability Evaluation) specific for Web applications' usability evaluation and we are following an experimentation-based methodology to support its development and improvement. This paper describes an observational study conducted, aimed at eliciting how inspectors apply the WDP technique. We discuss the quantitative and qualitative results of the result and their impact on improving the WDP technique. We analyzed the qualitative data using the procedures from the Grounded Theory (GT) method. © 2009 IEEE.

关键词： Inspection

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Propositional dynamic logic with storing, recovering and parallel composition

Propositional dynamic logic with storing, recovering and par...

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作者： Benevides, Mario R.F. De Freitas, Renata Petrucio, Viana Computer Science Department Systems and Computer Engineering Program Federal University of Rio de Janeiro Brazil Institute of Mathematics Fluminense Federal University Brazil

This work extends Propositional Dynamic Logic (PDL) with parallel composition operator and four atomic programs which formalize the storing and recovering of elements in data structures. A generalization of Kripke semantics is proposed that instead of using set of possible states it uses structured sets of possible states. This new semantics allows for representing data structures and using the five new operator one is capable of reasoning about the manipulation of these data structures. The use of the new language (PRSPDL) is illustrated with some examples. We present sound and complete set of axiom schemata and inference rules to prove all the valid formulas for a restricted fragment called RSPDLo. © 2011 Elsevier B.V. All rights reserved.

关键词： Data structures

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