• 6-10-HIRP OPEN 2016


    1 HIRPO20160601: Large Scale Heterogeneous Data Processing

    Subject: resource scheduling
     
    It is also likely that the computing environment is heterogeneous. The cloud
    consists of generations of servers with different capacities and performance;
    therefore, various configurations of machines will be available. For example,
    some machines are more suitable to store large data whereas others run
    faster computations.
    The key question is how to schedule jobs on machines so that each receives
    its “fair” share of resources to make progress while providing good
    performance.

    2

    HIRPO20160602: Research on Techniques for Financial Anti-Fraud System

    3、

    HIRPO20160603: Research on Anomaly Detection for Multiple Dimensional Data

    Subject: data anomaly detection

    requires some statistical or machine
    learning methods to automatically detect anomaly in the data to reduce the
    human efforts.
    This is not the only application domain that could benefit for such mechanism.
    Examples of such application domains are: banking – detecting payments
    behavior which deviate from normal customer(s) patterns which can indicate
    frauds or money laundry schemas; hardware failure – detecting that physical
    machines in a data center will crash by observing that certain metrics are indicators of near failures (e.g. the heat of the HDD is increasing continuously
    over 1 hour might show that a HDD crash is expected).

    Finding patterns in multiple dimensional data that do not conform to expected
    behavior in real time or near real time, especially for the high-dimensional
    data.


    4、
    HIRPO20160608: Deep Learning based Robotic Perception

    Strategic cooperation: Give regular academic and technical reports.
    Efficient object detection and recognition: Exploit structural properties of neural
    networks and develop an efficient deep learning based object detection and
    recognition algorithm without compromising speed and accuracy.

    Robot self-learning: Explore unsupervised or weakly-supervised learning
    algorithms to improve the intelligence level of robotic perception. For example,
    solve the unknown categories recognition task, which is commonly
    encountered in robots scenarios. Or, the robot can learn to guide itself around
    the house.

    5

    HIRPO20160609: Deep Learning based Human Visual Characteristics Research

    1) Provide the functional modules of face detection techniques and correlation
    filter tracking techniques for the human following feature in the robot demo;
    2) Establish the technology accumulations, research capabilities and algorithm
    systems on deep learning, including face detection, face recognition, human
    detection, human identification, human tracking, human behavior recognition,
    age estimation, facial expression recognition and clothing assessment.

    6

    HIRPO20160610: Deep Learning based Scene Understanding

    1) Research on semantic segmentation: investigate pixel-wise semantic
    segmentation of an image, facilitating the object detection, semantic mapping
    and high-level scene understanding process;
    2) Research on instance semantic segmentation: not only give pixel-wise
    semantic segmentation of an image, but also differentiate between objects of
    the same category, i.e., instance semantic segmentation. It could be used in
    fine-grained scene understanding and interaction in the future;
    3) Research on VQA application scenarios: estimate objects, object
    attributes and object relationships of the scene based on visual analysis and
    answer questions about the scene. Exploit and design application scenarios of
    VQA systems in household environment.


    7 、

    HIRPO20160611: Manufacture Quality Risk Analysis &
    Prediction based on Test Data

    Subject: predictive analysis

    Through real time data analysis of product test data, incoming material’s test
    data, equipment status data, and test software information, to predict the risks
    of potential quality fluctuations in advance;
    When the quality problem of the production process occurs, it automatically
    identifies the key factors which impacted the abnormal fluctuations.

    8、
    HIRPO20160612: Behavior Analytics for Personalized

    Mobile Services Research on methodologies of understanding human behavior:
    investigate the possible data source and how to mine those data. Focus on
    one or more behaviors.
    Research on applications of the behavioral understanding: investigate
    how to utilize the behavioral understanding to provide personalized mobile
    services. Focus on one or more examples of services.
    Prototype of such a system: Huawei will provide vUIC platform if necessary
    and do prototyping on top of that to extend the MBB network intelligence to
    UE.

    #####

    II



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  • 原文地址:https://www.cnblogs.com/jungel24/p/5611064.html
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