6-10-HIRP OPEN 2016

Posted 2020-07-28 jungel24

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.

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