Monthly Archives: November 2014

Code Commentary and Automatic Refactorings using Feedback from Multiple Compilers

Code Commentary and Automatic Refactorings using Feedback from Multiple Compilers

  • Nicklas Bo Jensen, Christian W. Probst, and Sven Karlsson. Code Commentary and Automatic Refactorings using Feedback from Multiple Compilers. In Proceedings of the Swedish Workshop on Multicore Computing (MCC), Lund, Sweden, 2014.
    [BibTeX] [Abstract] [Download PDF]

    Optimizing compilers are essential to the performance of parallel programs on multi-core systems. It is attractive to expose parallelism to the compiler letting it do the heavy lifting. Unfortunately, it is hard to write code that compilers are able to optimize aggressively and therefore tools exist that can guide programmers with refactorings allowing the compilers to optimize more aggressively. We target the problem with many false positives that these tools often generate, where the amount of feedback can be overwhelming for the programmer. Our approach is to use a filtering scheme based on feedback from multiple compilers and show how we are able to filter out 87.6% of the comments by only showing the most promising comments.

    @InProceedings{2014-11-JENSEN-2,
    author = {Nicklas Bo Jensen and Christian W. Probst and Sven Karlsson},
    title = {{Code Commentary and Automatic Refactorings using Feedback from Multiple Compilers}},
    booktitle = {{Proceedings of the Swedish Workshop on Multicore Computing (MCC)}},
    date = {2014-11-27/2014-11-28},
    address = {Lund, Sweden},
    url = {http://orbit.dtu.dk/en/publications/code-commentary-and-automatic-refactorings-using-feedback-from-multiple-compilers(47725fbb-1c72-47fa-a167-32ae319d5a0d).html},
    abstract = {Optimizing compilers are essential to the performance of parallel programs on multi-core systems. It is attractive to expose parallelism to the compiler letting it do the heavy lifting. Unfortunately, it is hard to write code that compilers are able to optimize aggressively and therefore tools exist that can guide programmers with refactorings allowing the compilers to optimize more aggressively. We target the problem with many false positives that these tools often generate, where the amount of feedback can be overwhelming for the programmer. Our approach is to use a filtering scheme based on feedback from multiple compilers and show how we are able to filter out 87.6% of the comments by only showing the most promising comments.},
    year = {2014}
    }

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Tracking Multiple High-Density Homogeneous Targets

Tracking Multiple High-Density Homogeneous Targets

  • Fabio Poiesi and Andrea Cavallaro. Tracking Multiple High-Density Homogeneous Targets. 2014. Online video
    [BibTeX] [Watch video]
    @Misc{2014-11-POIESI,
    author = {Fabio Poiesi and Andrea Cavallaro},
    title = {{Tracking Multiple High-Density Homogeneous Targets}},
    note = {Online video},
    date = {2014-11-23},
    video = {https://www.youtube.com/watch?v = zNuabwxDBHk},
    year = {2014}
    }

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Analogue Network Coding-aided Game Theoretic Medium Access Control Protocol for Energy-Efficient Data Dissemination

Analogue Network Coding-aided Game Theoretic Medium Access Control Protocol for Energy-Efficient Data Dissemination

  • Angelos Antonopoulos, Joao Bastos, and Christos Verikoukis. Analogue Network Coding-aided Game Theoretic Medium Access Control Protocol for Energy-Efficient Data Dissemination. IET Science, Measurement & Technology, 8(6):399-407, 2014. doi:10.1049/iet-smt.2013.0192
    [BibTeX] [Download PDF]
    @Article{2014-11-ANTONOPOULOS,
    author = {Angelos Antonopoulos and Joao Bastos and Christos Verikoukis},
    title = {{Analogue Network Coding-aided Game Theoretic Medium Access Control Protocol for Energy-Efficient Data Dissemination}},
    journal = {{IET Science, Measurement \& Technology}},
    date = {2014-11},
    volume = {8},
    number = {6},
    pages = {399-407},
    url = {http://digital-library.theiet.org/content/journals/iet-smt/8/6},
    year = {2014},
    doi = {10.1049/iet-smt.2013.0192}
    }

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DySectAPI: Scalable Prescriptive Debugging

DySectAPI: Scalable Prescriptive Debugging

  • Nicklas Bo Jensen, Sven Karlsson, Niklas Quarfot Nielsen, Gregory L. Lee, Dong H. Ahn, Matthew Legendre, and Martin Schulz. DySectAPI: Scalable Prescriptive Debugging. In Supercomputing, Portland, Oregon, USA, 2014.
    [BibTeX] [Abstract] [Download PDF]

    We present the DySectAPI, a tool that allow users to construct probe trees for automatic, event-driven debugging at scale. The traditional, interactive debugging model, whereby users manually step through and inspect their application, does not scale well even for current supercomputers. While lightweight debugging models scale well, they can currently only debug a subset of bug classes. DySectAPI fills the gap between these two approaches with a novel user-guided approach. Using both experimental results and analytical modeling we show how DySectAPI scales and can run with a low overhead on current systems.

    @InProceedings{2014-11-JENSEN-1,
    title = {{DySectAPI: Scalable Prescriptive Debugging}},
    author = {Nicklas Bo Jensen and Sven Karlsson and Niklas Quarfot Nielsen and Gregory L. Lee and Dong H. Ahn and Matthew Legendre and Martin Schulz},
    booktitle = {Supercomputing},
    address = {Portland, Oregon, USA},
    date = {2014-11-16/2014-11-21},
    url = {http://sc14.supercomputing.org/sites/all/themes/sc14/files/archive/tech_poster/tech_poster_pages/post237.html},
    year = 2014,
    abstract = {We present the DySectAPI, a tool that allow users to construct probe trees for automatic, event-driven debugging at scale. The traditional, interactive debugging model, whereby users manually step through and inspect their application, does not scale well even for current supercomputers. While lightweight debugging models scale well, they can currently only debug a subset of bug classes. DySectAPI fills the gap between these two approaches with a novel user-guided approach. Using both experimental results and analytical modeling we show how DySectAPI scales and can run with a low overhead on current systems.}
    }

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Measures of Effective Video Tracking

Measures of Effective Video Tracking

  • Tahir Nawaz, Fabio Poiesi, and Andra Cavallaro. Measures of Effective Video Tracking. 2014. Online video
    [BibTeX] [Watch video]
    @Misc{2014-11-NAWAZ,
    author = {Tahir Nawaz and Fabio Poiesi and Andra Cavallaro},
    title = {{Measures of Effective Video Tracking}},
    note = {Online video},
    date = {2014-11-18},
    video = {https://www.youtube.com/watch?v = pp8HQEQ-INU},
    year = {2014}
    }

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Testing Infrastructure for Operating System Kernel Development

Testing Infrastructure for Operating System Kernel Development

  • Maxwell Walter and Sven Karlsson. Testing Infrastructure for Operating System Kernel Development. In Proceedings of the Swedish Workshop on Multicore Computing (MCC), Lund, Sweden, 2014.
    [BibTeX] [Download PDF]
    @InProceedings{2014-11-WALTER,
    author = {Maxwell Walter and Sven Karlsson},
    title = {{Testing Infrastructure for Operating System Kernel Development}},
    booktitle = {{Proceedings of the Swedish Workshop on Multicore Computing (MCC)}},
    date = {2014-11-27/2014-11-28},
    address = {Lund, Sweden},
    url = {http://orbit.dtu.dk/fedora/objects/orbit:137313/datastreams/file_2596186f-b313-410f-8558-63b1c4a02261/content},
    year = {2014}
    }

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Towards Cognitive and Perceptive Video Systems

Towards Cognitive and Perceptive Video Systems

  • Toygar Akgün, Charles Attwood, Andrea Cavallaro, Christian Fabre, Fabio Poiesi, and Piotr Szczuko. Human Behavior Understanding in Networked Sensing, chapter Towards Cognitive and Perceptive Video Systems, pages 3-17. Springer, 2014. doi:10.1007/978-3-319-10807-0_1
    [BibTeX] [Abstract]

    In this chapter we cover research and development issues related to smart cameras. We discuss challenges, new technologies and algorithms, applications and the evaluation of today’s technologies. We will cover problems related to software, hardware, communication, embedded and distributed systems, multi-modal sensors, privacy and security. We also discuss future trends and market expectations from the customer’s point of view.

    @InBook{2014-11-AKGUN,
    author = {Toygar Akg\"{u}n and Charles Attwood and Andrea Cavallaro and Christian Fabre and Fabio Poiesi and Piotr Szczuko},
    chapter = {Towards Cognitive and Perceptive Video Systems},
    editor = {Paolo Spagnolo and Pier Luigi Mazzeo and Cosimo Distante},
    title = {{Human Behavior Understanding in Networked Sensing}},
    pages = {3-17},
    date = {2014-11-07},
    publisher = {Springer},
    doi = {10.1007/978-3-319-10807-0_1},
    abstract = {In this chapter we cover research and development issues related to smart cameras. We discuss challenges, new technologies and algorithms, applications and the evaluation of today's technologies. We will cover problems related to software, hardware, communication, embedded and distributed systems, multi-modal sensors, privacy and security. We also discuss future trends and market expectations from the customer's point of view.},
    year = {2014}
    }

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The costs of fusion in smart camera networks

The costs of fusion in smart camera networks

  • Sandeep Katragadda, Juan C. SanMiguel, and Andrea Cavallaro. The costs of fusion in smart camera networks. In Proceedings of the International Conference on Distributed Smart Cameras, Venice, Italy, 2014. doi:10.1145/2659021.2659032
    [BibTeX] [Abstract]

    The choice of the most suitable fusion scheme for smart camera networks depends on the application as well as on the available computational and communication resources. In this paper we discuss and compare the resource requirements of five fusion schemes, namely centralised fusion, flooding, consensus, token passing and dynamic clustering. The Extended Information Filter is applied to each fusion scheme to perform target tracking. Token passing and dynamic clustering involve negotiation among viewing nodes (cameras observing the same target) to decide which node should perform the fusion process whereas flooding and consensus do not include this negotiation. Negotiation helps limiting the number of participating cameras and reduces the required resources for the fusion process itself but requires additional communication. Consensus has the highest communication and computation costs but it is the only scheme that can be applied when not all viewing nodes are connected directly and routing tables are not available.

    @InProceedings{2014-11-KATRAGADDA,
    title = {{The costs of fusion in smart camera networks}},
    author = {Sandeep Katragadda and Juan C. SanMiguel and Andrea Cavallaro},
    booktitle = {{Proceedings of the International Conference on Distributed Smart Cameras}},
    address= {Venice, Italy},
    date = {2014-11-04/2014-11-07},
    year = {2014},
    doi = {10.1145/2659021.2659032},
    abstract = {The choice of the most suitable fusion scheme for smart camera networks depends on the application as well as on the available computational and communication resources. In this paper we discuss and compare the resource requirements of five fusion schemes, namely centralised fusion, flooding, consensus, token passing and dynamic clustering. The Extended Information Filter is applied to each fusion scheme to perform target tracking. Token passing and dynamic clustering involve negotiation among viewing nodes (cameras observing the same target) to decide which node should perform the fusion process whereas flooding and consensus do not include this negotiation. Negotiation helps limiting the number of participating cameras and reduces the required resources for the fusion process itself but requires additional communication. Consensus has the highest communication and computation costs but it is the only scheme that can be applied when not all viewing nodes are connected directly and routing tables are not available.}
    }

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Cost-Aware Coalitions for Collaborative Tracking in Resource-Constrained Camera Networks

Cost-Aware Coalitions for Collaborative Tracking in Resource-Constrained Camera Networks

  • Juan C. SanMiguel and Andrea Cavallaro. Cost-Aware Coalitions for Collaborative Tracking in Resource-Constrained Camera Networks. IEEE Sensors Journal, 15(5):2657-2668, 2014. doi:10.1109/JSEN.2014.2367015
    [BibTeX] [Abstract]

    We propose an approach to create camera coalitions in resource-constrained camera networks and demonstrate it for collaborative target tracking. We cast coalition formation as a decentralized resource allocation process where the best cameras among those viewing a target are assigned to a coalition based on marginal utility theory. A manager is dynamically selected to negotiate with cameras whether they will join the coalition and to coordinate the tracking task. This negotiation is based not only on the utility brought by each camera to the coalition, but also on the associated cost (i.e. additional processing and communication). Experimental results and comparisons using simulations and real data show that the proposed approach outperforms related state-of-the-art methods by improving tracking accuracy in cost-free settings. Moreover, under resource limitations, the proposed approach controls the tradeoff between accuracy and cost, and achieves energy savings with only a minor reduction in accuracy.

    @Article{2014-11-SANMIGUEL,
    title={{Cost-Aware Coalitions for Collaborative Tracking in Resource-Constrained Camera Networks}},
    author={Juan C. SanMiguel and Andrea Cavallaro},
    journal={{IEEE Sensors Journal}},
    volume={15},
    number={5},
    pages={2657-2668},
    date={2014-11-04},
    year={2014},
    doi={10.1109/JSEN.2014.2367015},
    abstract={We propose an approach to create camera coalitions in resource-constrained camera networks and demonstrate it for collaborative target tracking. We cast coalition formation as a decentralized resource allocation process where the best cameras among those viewing a target are assigned to a coalition based on marginal utility theory. A manager is dynamically selected to negotiate with cameras whether they will join the coalition and to coordinate the tracking task. This negotiation is based not only on the utility brought by each camera to the coalition, but also on the associated cost (i.e. additional processing and communication). Experimental results and comparisons using simulations and real data show that the proposed approach outperforms related state-of-the-art methods by improving tracking accuracy in cost-free settings. Moreover, under resource limitations, the proposed approach controls the tradeoff between accuracy and cost, and achieves energy savings with only a minor reduction in accuracy.}
    }

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Cost-Effective Features for Reidentification in Camera Networks

Cost-Effective Features for Reidentification in Camera Networks

  • Syed Fahad Tahir and Andrea Cavallaro. Cost-Effective Features for Reidentification in Camera Networks. IEEE Transactions on Circuits and Systems for Video Technology, 24(8):1362-1374, 2014. doi:10.1109/TCSVT.2014.2305511
    [BibTeX] [Abstract]

    Networks of smart cameras share large amounts of data to accomplish tasks such as reidentification. We propose a feature-selection method that minimizes the data needed to represent the appearance of objects by learning the most appropriate feature set for the task at hand (person reidentification). The computational cost for feature extraction and the cost for storing the feature descriptor are considered jointly with feature performance to select cost-effective good features. This selection allows us to improve intercamera reidentification while reducing the bandwidth that is necessary to share data across the camera network. We also rank the selected features in the order of effectiveness for the task to enable a further reduction of the feature set by dropping the least effective features when application constraints require this adaptation. We compare the proposed approach with state-of-the-art methods on the iLIDS and VIPeR datasets and show that the proposed approach considerably reduces network traffic due to intercamera feature sharing while keeping the reidentification performance at an equivalent or better level compared with the state of the art.

    @Article{2014-02-TAHIR,
    title={{Cost-Effective Features for Reidentification in Camera Networks}},
    author={Syed Fahad Tahir and Andrea Cavallaro},
    journal={{IEEE Transactions on Circuits and Systems for Video Technology}},
    volume={24},
    number={8},
    pages={1362-1374},
    date={2014-02-11},
    year={2014},
    doi={10.1109/TCSVT.2014.2305511},
    abstract={Networks of smart cameras share large amounts of data to accomplish tasks such as reidentification. We propose a feature-selection method that minimizes the data needed to represent the appearance of objects by learning the most appropriate feature set for the task at hand (person reidentification). The computational cost for feature extraction and the cost for storing the feature descriptor are considered jointly with feature performance to select cost-effective good features. This selection allows us to improve intercamera reidentification while reducing the bandwidth that is necessary to share data across the camera network. We also rank the selected features in the order of effectiveness for the task to enable a further reduction of the feature set by dropping the least effective features when application constraints require this adaptation. We compare the proposed approach with state-of-the-art methods on the iLIDS and VIPeR datasets and show that the proposed approach considerably reduces network traffic due to intercamera feature sharing while keeping the reidentification performance at an equivalent or better level compared with the state of the art.}
    }

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