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25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017

作者： Oliveira, Luiz Fernando R. França, Felipe M.G. Systems Engineering and Computer Science Program COPPE Universidade Federal Do Rio de Janeiro Brazil

ISBN: (纸本)9782875870391

The extreme learning machine (ELM) is known for being a fast learning neural model. This work presents a performance comparison between ELM and the WiSARD weightless neural network model, regarding training and testing times, and classification accuracy as well. The two models were implemented in the same programming language and experiments were carried out on the same hardware environment. By using a group of datasets from the public repositories UCI and Statlog, experimental results shows that the WiSARD presented training times approximately one order of magnitude smaller than ELM, while classification accuracy varied according the number of classes involved. However, while WiSARD's architecture setups were not exhaustively searched, architecture setups for ELM were kept the same as the ones found in the literature as the best for each given dataset. © ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.

关键词： Classification (of information)

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Metasurfaces for next-generation wireless communication systems

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National science Review 2023年第8期10卷 17-19页

作者： Younes Ra'di Nikita Nefedkin Petar Popovski Andrea Alù Department of Electrical Engineering and Computer Science Syracuse University Photonics Initiative Advanced Science Research Center City University of New York Department of Electronic Systems Aalborg University Physics Program Graduate Center City University of New York

The pace of development in the world of 5G communication systems has proven to be much more demanding than previous generations, with 5G-Advanced seemingly around the corner [1]. Extensive research is already underway to structure the next generation of wireless systems(i.e. 6G), which may potentially enable an unprecedented level of human–machine interaction [2].

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Towards a formal model to reason about context-aware exception handling

Towards a formal model to reason about context-aware excepti...

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2012 5th International Workshop on Exception Handling, WEH 2012

作者： Rocha, Lincoln S. Andrade, Rossana M. C. Group of Computer Networks Software Engineering and Systems Graduate Program in Computer Science Federal University of Ceará Fortaleza - CE Brazil

ISBN: (纸本)9781467317665

The context-awareness is a central aspect in the design of pervasive systems, characterizing their ability to adapt its structure and behavior. The context-aware exception handling (CAEH) is an existing approach employed to design exception handling in pervasive systems. In this approach, the context is used to define, detect, propagate, and handle exceptions. CAEH is a complex and error prone activity, needing designers' insights and domain expertise to identify and characterize contextual exceptions. However, despite the existence of formal methods to analyze the adaptive behavior of pervasive systems, such methods lack specific support to specify the CAEH behavior. In this paper, we propose a formal model to reason about the CAEH behavior. It comprises an extension of the Kripke Structure to model the context evolution of a pervasive system and a transformation function that derivates the CAEH control flow from that proposed structure. © 2012 IEEE.

关键词： Model checking

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A verification mechanism of feature models for mobile and context-aware software product lines

A verification mechanism of feature models for mobile and co...

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5th Brazilian Symposium on Components, Architectures and Reuse, SBCARS 2011

作者： Marinho, Fabiana G. Andrade, Rossana M.C. Werner, Cláudia Graduate Program in Computer Science Federal University of Ceará Fortaleza Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9780769546261

Software Product Lines (SPLs) have been used to develop mobile and context-aware applications, which provide services and data for their users from anywhere and at any time using context information. In SPLs, commonality and variability of a system family are identified and often documented in a feature model. However, the development of a feature model for mobile and context-aware SPLs is not trivial, since it should comprise system and context information. Furthermore, the consistency check of feature models in the considered domain is also complex and demands advanced skills of software engineers. This paper proposes a mechanism to formalize and verify the correctness and consistency of feature models for mobile and context-aware SPLs based on a profile enriched with OCL specifications. © 2011 IEEE.

关键词： Verification

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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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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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