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HIRP OPEN 2016 Big Data & Artificial Intelligence

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Call for Proposals

Big Data & Artificial Intelligence

HIRP OPEN 2016


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Copyright © Huawei Technologies Co., Ltd. 2015-2016. All rights reserved.

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Application Deadline: 09:00 A.M., 18th July, 2016 (Beijing Standard Time, GMT+8).

If you have any questions or suggestions about HIRP OPEN 2016, please send Email

([email protected]). We will reply as soon as possible.

mailto:[email protected]).


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Catalog

HIRPO20160601: Large Scale Heterogeneous Data Processing ............................................. 4

HIRPO20160602: Research on Techniques for Financial Anti-Fraud System ......................... 8

HIRPO20160603: Research on Anomaly Detection for Multiple Dimensional Data ............... 11

HIRPO20160604: Low Latency Storage for Stream Data ....................................................... 14

HIRPO20160605: Research on SDN&NFV Network Maintenance Dystem Architecture and

Technology .............................................................................................................................. 21

HIRPO20160606: Novel Algorithm Design and Use Cases for Data Stream Mining based on

StreamDM ................................................................................................................................ 25

HIRPO20160607: Communication Network Model Research based on AI Technique .......... 28

HIRPO20160608: Deep Learning based Robotic Perception ................................................. 30

HIRPO20160609: Deep Learning based Human Visual Characteristics Research ................ 34

HIRPO20160610: Deep Learning based Scene Understanding ............................................. 38

HIRPO20160611: Manufacture Quality Risk Analysis & Prediction based on Test Data ....... 42

HIRPO20160612: Behavior Analytics for Personalized Mobile Services ................................ 44


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HIRPO20160601: Large Scale Heterogeneous Data

Processing

1 Theme: Big Data & Artificial Intelligence

2 Subject: resource scheduling

3 Background

Big data analytics have become a necessity to the business worldwide.

Modern data centers host huge volumes of data, stored over large number of

nodes with multiple storage devices and process them using thousands of

cores. Cloud computing has been revolutionizing the IT industry by adding

flexibility to the way IT is consumed, enabling organizations to pay only for the

resources and services they use. In an effort to reduce IT capital and

operational expenditures, organizations of all sizes are using Clouds to provide

the resources required to run their applications. Organizations are using cloud

platforms to run scalable analyses on their data, gaining insight into the health

of their systems and the activities of their customers.

To improve performance and cost-effectiveness of a data analytics cluster in

the cloud, the data analytics system should account for heterogeneity of the

environment and workloads. Data analytics workloads have heterogeneous

resource demands because some workloads may be CPU intensive whereas

others are I/O-intensive. Some of them might be able to use special hardware

like GPUs to achieve dramatic performance gains.

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,


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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.

4 Scope

Possible research topics include:

Resource modeling, including:

o Support for describing the resource information for resource in

the heterogeneous environment;

o Support for describing the need for resource request in the

heterogeneous environment;

o Support the mapping between resource and resource request;

o Support the data locality information description;

o Support the environment topology information description.

Resource information collection

o Support for plug-n-play resource to be added in heterogeneous

environment, e.g. a new resource type could be automatically

recognized during resource information collection;

o Automatic collect resource information in a heterogeneous

environment;

o Automatic schema construction;

o Support large scale (10K nodes).


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Basic resource scheduling

o Support the cost properties association with resource;

o Support the performance/capability properties association with

resource;

o Support Cost Based Optimizer model for resource scheduling;

o Support fair based model for resource scheduling.

Intelligent resource scheduling

o Workload resource consumption and runtime predication in

heterogeneous environment, for instance, running workload in

bare metal vs VM, ARM vs x86, private cloud vs public cloud, or

with different size of allocation, performance and etc.;

o SLA expression to translate to workload resource and runtime

demand, uses business objective rather than static resource

requirement to express goal;

o Intelligent scheduling to choose the best allocation by balancing

multiple objectives, SLA (time, performance), cost etc in

heterogeneous environment.

5 Expected Outcome and Deliverables

Demo system of a resource management/scheduling system for

heterogeneous environment;

Simulation tool for performance evaluation;

One or more patents.


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6 Acceptance Criteria

The demo system should support complex resource type in a heterogeneous

environment and provide way to add new resource type in the system without

code changes;

The system design must support 10K nodes scale and could be proved via

simulation;

One or more patent ideas accepted by Huawei.

7 Phased Project Plan

Expected project Duration (year): 1 year.

Project Phase

Duration Content Objective Output

Phase 1 ~6 months

Technical analytics and Solution design and

Finish solution design.

Solution design documents.

Phase 2 ~6months Demo system implement

The enterprise Hadoop cluster can use public cloud computing resources smoothly.

Prototype demo

Click here to back to the Top Page


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HIRPO20160602: Research on Techniques for

Financial Anti-Fraud System


2 Subject: financial fraud detection technology

3 Background

Financial fraud is a broad term with various potential meanings, but for our

purposes it can be defined as the intentional use of illegal methods or practices

for the purpose of obtaining financial gain. Credit card fraud is one of popular

financial frauds, which has many types of credit card fraud, including never

received fraud, account take-over fraud, lost or stolen fraud, counterfeit fraud,

card not present. In addition, fraudsters are very inventive, fast moving people.

They are continually refining their methods, and as such there is a requirement

for detection methods to be able to evolve accordingly. Therefore, detecting

financial fraud is a difficult task.

Traditional anti-fraud methods are mostly based on rules defined by human

analyst via investigating cases using their intuition, experience, and domain

knowledge. But human rules require significant human efforts to identify the

fast moving patterns (concept drift) of fraudulent activity by modifying existing

rules or adding new rules. Thus, machine learning based method, which could

adaptively combat the concept drift, has become of importance whether in the

academic or business organizations currently.


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4 Scope

Problem to be resolved

Identify the fraudulent credit card transactions based on machine learning

based fraud detection methods. Two techniques should be studied:

P1: Anomaly detection technique to identify the abnormal transactions

based from different aspects, e.g. account, equipment, location,

behavior, relationship, preference;

P2: Predictive modeling techniques based on the labeled data to

identify the fraudulent transactions, including, but not limited to, neural

networks, logistic regression, deep learning, ensemble methods.

Additionally, these techniques should handle three characteristics of

the data: concept drift, class imbalance, and cost-sensitivity.


Design documents and prototype demo of the solution reaching the

acceptance criteria in session 6.


For P1 and P2, the scoring should be finished within 20

milliseconds(ms) for one transaction with 100 dimensions;

For P2, true positive rate>90%; false positive rate<5%; gross losses

rate<0.2%.



Project Phase Duration Content Objective Output


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Project Phase Duration Content Objective Output

Phase 1 ~4 months Finished tasks of P1

(1) The scoring should be finished within 20ms for one transaction with 100 dimensions

Algorithm design documents, prototype demo and the code

Phase 2 ~8 months Finished tasks of P2.

(1) The scoring should be finished within 20ms for one transaction with 100 dimensions

(2) True positive rate>90%; false positive rate<5%; gross losses rate<0.2%




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HIRPO20160603: Research on Anomaly Detection for

Multiple Dimensional Data


2 Subject: data anomaly detection

List of Abbreviations

DataCompass: Splunk-like platform for analyzing machine data

HDD: Hard Disk Drive

SVM: Support vector machine, a machine learning method

3 Background

Traditional tools are able to handle the 3V (variety, velocity, volume) of the

machine data, i.e. logs, configuration, message queues, thus Datacompass is

designed. One of important features is supporting interactive analyzing the

data, which denotes DataCompass has real-time or near real-time capability.

Datacompass will be applied to the scenario of the public cloud (Deutsche

Telecom) operation. Datacompass 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


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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).

From the point of view of the analytics tools, the current state of the art is that

anomaly detection methods, being able to support interactive analysis, only

focus on the low dimensional data. The methods which could handle high

dimensional methods, such as k-means, one class SVM, needs a large

computational cost and have NOT real-time or near real-time capability.

Therefore new mechanisms are needed to be able to provide detection of

anomalies and of abnormal patterns online.

4 Scope

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.

P1: Anomaly detection for 1-D time series.

P2: Anomaly detection for low dimensional data being less than 100

dimensions (without and with an order). Note that ordered multiple

dimensional data are a.k.a multiple time series.

P3: Anomaly detection for high dimension data being greater than 100

(without and with an order).

P4: Online anomaly detection for the data less than 20 dimensions.


Design documents and prototype demo of the solution reaching the

acceptance criteria in session 6.


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For P1~P3, the model building (learning) should be finished within 1s,

2.5s, and 5s, respectively;

For P1~P4, the scoring should be finished within 10 milliseconds(ms);

For P1~P4, for the data with known anomalies, true positive rate>90%,

false positive rate<5%.



Project Phase

Duration Content Objective Output

Phase 1

~4 months Finished tasks of P1 and P2.

(2) The model building should be finished with 1 and 2.5 seconds for P1 and P2, respectively.

(3) The scoring should be finished within 10 ms for one record

(4) true positive rate>90%; false positive rate<5%


Phase 2

~8 months Finished tasks of P3 and P4.

(1) The model building should be finished with 5 seconds for P3.

(2) The scoring should be finished within 10 ms for one record for both P3 and P4

(3) true positive rate>90%; false positive rate<5%




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HIRPO20160604: Low Latency Storage for Stream Data


2 Subject: research on hadoop HDFS and related

stream tools from big data ecosystem


HDFS: Stands for Hadoop Distributed File System and is the de facto storage

used for Big Data processing.

Kafka: Is the message broker tool most commonly used for streaming.

RamCloud: The message broker tool most commonly used for streaming.

RAMCloud: A storage system design for super-high-speed storage for

large-scale datacenter applications.

3 Background

An increasing number of Big Data applications and scenario require to deal

with increasing amounts of small data. This trend is easily observed in

domains like finance, whether forecast, IoT, insurance or social networks. In

many related applications and systems such small items are continuously

collected from the stream sources or are received from other stream

processing computation. Even if the stream engines running the applications

are processing such stream data on the fly, by passing it through the topology

of stream operators, there is an increasing need to be able to store such items

efficiently.


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Unlike traditional storage, the main challenge raised when aiming to store

stream data is the large number of small items. Additionally, as the nature of

the computation required for such data is time-critical, the data once stored

needs to be accessible with very high performances (i.e., low latencies). This

makes the existing storage options, such as the ones available in the Hadoop

ecosystem, mostly unfit for such scenarios as they cannot fully meet

out-of-the-box all performance requirements. HDFS, the default Big Data

storage, was not design as stream storage and cannot thus provide the

sub-second performance required by such applications. Using other solution

that target streaming will lead to hybrid architectures, which introduce extra

dependencies, increase the O&M complexity and can lead to less reliable

solutions. Moreover some of these stream data management solutions, such

as Kafka, are not proper storage systems (e.g., can only hold data temporarily)

and provide limited data access semantics (e.g., access data only based on its

offset) that drastically reduce the search performance.

All these issues point to the need that an own dedicated solution for low

latency stream storage is needed. Such a solution should provide on the one

hand traditional storage functionality and on the other hand stream-like

performance (i.e., low latency IO access to items/range of items). This shows

the necessity for an extensive research study to explore architectural options

to provide such storage for stream data, either as a standalone component or

as an extension of an existing Big Data solution (e.g., HDFS). The latter option

is preferred considering the benefits of having a unified storage system for all

types of Big Data.

4 Scope

The goal of the project is to provide a solution for a low latency storage for

stream data. The ideal solution would propose an extension to HDFS that


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would bring to the system high performance when storing stream data (e.g.,

billions of small items) and when accessing the data either via scans or

random access (e.g., millisecond or ideally nanosecond access time to retrieve

data). This is shown in Figure 1. To achieve these we expect that good

practice that exists in other systems that demonstrated good performances will

be ported to HDFS (alongside with novel techniques which might be needed),

such as: Kafka partitioning and ability to deal with billions of items, RamCloud

and DXRam techniques of managing data in distributed caches to enable ns

access time across large collections of items, etc. Additionally, as Big Data

processing is being migrated quite often to the cloud, it is necessary that the

solution is compatible with such infrastructures, is able to provide similar

performance as in Big Data clusters and ultimately can be used as a service.

The main research questions, issues and requirements to be addressed are:

How can HDFS be extended to support billions of small items;

How can it be tuned to enable high performance data access in large

collection of small items (e.g. ms or ns IO access for scan, range

queries and random access);

How can the performance be guaranteed at increasing scales

Can the stream storage (add-on) work as an application library to share

data across the nodes of the distributed application as well as

standalone or within HDFS and be accessible from other application

(e.g. RMC, REST, API…);

What are the best partition techniques, data placing and search

strategies for stream storage;

Can the solution work as a cloud service with same or similar

performance.


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Figure 1 Overview of the various features to be added to HDFS to properly support stream

storage


The expected outcomes for the project are:

1) Evaluation study (i.e., in the form of a scientific paper) of existing Big Data

tools and their performance ability to support stream storage and the

performance of IO operations. The goal is to identify the best practices and

techniques to tackle the requirements for stream storage;

2) Architecture option for extending HDFS for stream storage while providing

low latency IO for scans, range queries and random access. The

architecture should be scalable to a variable number of data nodes;

3) The system should be design to run at scale. At such it needs to support

multiple metadata nodes (i.e., namenodes for HDFS). Additionally with the


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increase of the data nodes, the system should allow an increase in the

number of supported objects (e.g., 2x data nodes means 2x more objects)

with the same performance targets;

4) A library or a software system that implements the architecture of point 2;

5) If point 2 cannot be meet, a new stand-alone solution should be provided

that can run both as standalone solution and as an application library for

storing stream data and a strong scientific motivation that argues the

limitation of HDFS to be extended to such scenarios.


The acceptance criteria with respect to the outcomes are:

1) Study meets the academic norms of paper/technical writing and

research analysis. The study analytics considers at least 4 existing

solutions (e.g., kafka, kudu, Hbase, DXRam, RamCloud, redis.io…) and

identifies limitations as well as best practices and techniques to be used

for stream storage;

2) The architecture is HDFS compatible and is able to support billion+

objects and enables at most millisecond access to elements for range

queries and random access. A description of the data partition

techniques and search strategies to be used, which are compatible with

HDFS. The solution can work with systems that have 1,2 or more

namenodes;

3) Demo system for the implementation of the architecture described in

point 2 which demonstrates the performance of storing: at least 1 billion

objects and enable ms access to random access and to range queries.

The system should demonstrate that it enables scalability by running

the demo on various setups (5, 10 and more nodes if available);


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4) Through and strong argumentation of why point 2 cannot be meet (why

HDFS cannot be extended to support stream storage). An alternative

design solution to point 2, that meets all the performance requirements

and can work both as a standalone and as application library;

5) The solution is demonstrated to work also on cloud platforms and as a

service (stream storage as a service).



Project

Phase Duration Content Objective Output

Phase 1 ~3

months

Evaluation of

existing tools

(Kafka, Kudu,

Redis,

RamCloud,

DXRam)

Identify limitations of

existing solutions

Identify best

architectural options

for stream storage

Report

Architecture

design

guidelines

Phase 2 ~2

months

Architecture

design of

stream storage

1. Identify

architecture options

for HDFS to support

both batch and stream

storage

2. If 1 cannot be

meet, provide

alternative solution

3. If 2, then identify

solutions for running

the solution both as a

stand-alone service

and as an application

library

System

architecture

Phase 3 ~7

months

The Storage for

Stream data

system

Implement the system

Implement JAVA

connectors/ APIs

The stream

storage

APIs


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Implement external

connectors (outside

application domains)

Connectors



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HIRPO20160605: Research on SDN&NFV Network

Maintenance Dystem Architecture and Technology


2 Subject: SDN big data fault analysis

3 Background

1) Trend

SDN & NFV network technique introduce a powerful combination of changes

to bring networks into new age, which realizes automatic deployment of

network business, efficient and reliable network operation, and lower the

CPEX. In the global view, SDN & NFV network will be widely applied in

commercial deployment from 2016 to 2020. Many carriers and internet

companies will deploy SDN & NFV networks in different scenarios by their own

plan, and the SDN &NFV network will enter the mature period.

2) Challenge values

For the automation SDN&NFV network, its maintenance system must be

automatic, visual and intelligent. The maintenance system have many

components such as data collection system, data storage & access system,

data visualization, data analysis, fault diagnosis, fault recovery, etc. The key

technique is as follow:

Standardization data collection, including measure data (KPI) and description

data (system log), which can realize efficient data collection, pre-processing,

data transformation.


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Intelligent fault diagnosis algorithms. The algorithms detect fault and send

alarm when single net unit faults, locate fault device from a network or a link,

check the correctness of configuration, forecast the possible fault in the

network or a device.

Intelligent network recovery system. The system combines expert experience

and machine learning method, which can recommend recovery solution when

network is abnormal.

Different from traditional network, SDN&NFV network is in evolution, many

potential problems are not exposed in commercial operation. The goal of this

project is to investigate and explore the possible network maintenance

technique based on big data analysis technique for future carrier network. The

research will have profound and valuable impact on the evolutionary network

technique both in industry and academic.

4 Scope

The scope of the project contains two key directions: SDN&NFV network

running data collection technique and data based fault diagnosis & recovery

technique. The content of the research includes, but is not limited to, the

following parts:

1) Data collection:

Definition of standard data format, including key performance index

(KPI), log, configuration;

Design and analysis of data collection system, including system

framework, collecting technique, data transformation technique;

Design and analysis of data storage system, including system

framework, access method, and data transfer technique.

2) Data based fault diagnosis & recovery:


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Data based fault diagnosis technique for network unit;

Data based fault diagnosis technique for network level;

Data based fault forecasting technique;

Fault correlation analysis technique, analyzing the correlation for

different types of fault;

Fault recovery intelligent recommendation technique, experience based

intelligent recommendation technique.


The deliveries of the project include, but are not limited to the following:

1) Research reports on SDN&NFV data formation standard definition, data

collection, data storage & access, fault diagnosis & recovery applications

traffic patterns, scenarios, new carrier network architecture, network security,

etc;

2) Possible prototype on SDN&NFV data collection system, data storage &

access system, or fault diagnosis & recovery algorithms;

3) Publications in peer-reviewed Journals or top ranked conferences, and/or

invention/ patents on SDN&NFV impact on carrier network, network related

technology innovation.


Sta

ge

Date Work description Output Evaluation Criteria

1 ~3

mont

hs

Specify

milestones.

Thesis proposal

cover the whole

research scope.

Routine technical

1, An determined

work plan about what

should we do in this

project and how to

guaranty the

successful of the

The documents

can be accepted

by Huawei’s

Review Group.


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& work progress

meeting.

collaboration

2, Thesis proposal

3, Research report on

one or more items

described in section 5

2 ~5

mont

hs

Continuing the

research work.

Academic paper’

writing.

Prototype design

and coding.

Routine technical

& work progress

meeting.


more items described

in section 5

2, Complete at least

one academic paper

3, Prototype design

document & source

code(partial )

The design

documents can be

accepted by

Huawei’s Review

Group.

3 ~4

mont

hs

Complete the

research work.

Academic paper

is accepted by

the Journals or

top ranked

conferences.

Implement

prototype for

demonstration

and verifying.

Routine technical

& work progress

meeting.


all items described in

section 5

2, Complete all

papers

3, Complete

prototype

1, Finish the

prototype

implementation,

complete the

prototype’s coding,

testing, verifying,

and related report

2, Hold an

associated

workshop or attend

a SDN&NFV

related summit, on

which make an

open speech or

demonstration.

3, The paper

published in

peer-reviewed

Journals or top

ranked

conferences



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HIRPO20160606: Novel Algorithm Design and Use

Cases for Data Stream Mining based on StreamDM


2 Subject: stream mining/real-time machine learning

3 Background

Data mining techniques consume a large amount of resources since they need

to do many iterations during the learning phase, while data stream mining

techniques only use one pass over data, and due to that are more challenging.

Currently, more and more companies use stream mining to process large

quantities of data in real-time, to build incremental model to help business

units. At the end of 2015, Huawei Noah’s Ark Lab released StreamDM-- a new

real-time machine learning library built on top of Spark Streaming, including

SGD Learner, Naïve Bayes, Hoeffding Tree, CluStream, StreamKM++ and

bagging. The motivation of StreamDM is to help industry and researchers to

have a fast solution for real time data mining cases, and we expect more

people to contribute to StreamDM, including algorithms and use cases.

4 Scope

1) We are seeking proposals of real business scenarios based on StreamDM.

These business scenarios should be challenging and interesting. They can be

deployed by universities or companies, and they will use StreamDM’s current

algorithms or new algorithms, implemented and contributed to StreamDM in

the future;


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2) We are seeking proposals to implement well known distributed stream

mining algorithms based on StreamDM. The algorithms can either be well

known algorithms or new algorithms. The algorithms can be, but not limited to,

classification, clustering, frequent item mining or regression algorithms. The

algorithms should be distributed and incremental, implemented and

contributed to StreamDM;

3) This project can accept only a few numbers of proposals, each one with the

same funding. Proposals including both 1) and 2) are extremely welcome, and

proposals with potential patents will be given extra amount of funding.


Proposals of real business scenarios should include sample data, documents

and application codes that can be contributed to StreamDM at github, and

performance comparisons with other Stream machine learning APIs.

Proposals of algorithm implementation should include documents, codes and

test codes which can be contributed to StreamDM at github, and performance

comparisons with similar algorithms implemented in other Stream machine

learning APIs.


Project proposal is accepted by the evaluation team, Huawei;

Project deliverables are accepted by the evaluation team, Huawei;

Documents and codes are merged to StreamDM at github.


1) Proposals of real business scenarios


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Phase1 (~3 months): Scenarios detailed description, solution of use cases;

Phase2 (~6 months): Codes, detailed documents;

Phase3 (~3 months): Performance test results and Pull request and merge to

StreamDM at github.

2) Proposals of algorithm implementation

Phase1 (~3 months): Algorithms design documents;

Phase2 (~6 months): Algorithms codes and test codes;

Phase3 (~3 months): Performance test results and Pull request and merge to

StreamDM at github.

Proposals of 1) or 2) have extra patent, the patent should be finished before

T+9.



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HIRPO20160607: Communication Network Model

Research based on AI Technique


2 Subject: architecture and resource management


AI: Artificial Intelligence

3 Background

With the development of machine learning, artificial intelligence becomes a hot

research area again. Another change in communication world is that the

communication object is from the relationship between humans to the

relationship between machines (M2M). The network and configuration will be

more and more sophisticated, so the AI based technology is a preferred

solution for network measurement and management. If this technology used,

analysis of communication network model is very important.

4 Scope

Survey on the use case for AI technology in wireless communication

networks;

Research on the communication network model using AI technology;

For the special use case, give the detailed algorithm design and analysis;

Verify the AI algorithm effect on the communication network.


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1 survey reports on key technology of artificial intelligence using wireless

communication;

1-2 research reports on key technology of artificial intelligence, including

candidate schemes of optimal technology used in wireless communication;

1 design/analysis reports and verification about key technology of artificial

intelligence using wireless communication, such as system architecture;

1-2 patents and 1 publication submission.


Survey Report: Comprehensive study of the subject;

Research Report/Design Report: Technical solution can be implemented.

Clear technological advancement can be proved. Clear advancement can be

proved;

Patent Proposal: Patent proposals are evaluated and accepted by the internal

Huawei patent evaluation;

Publication: Paper written and submitted to a prestigious conference.


Phase 1 (~2 months): Survey on key technology of artificial intelligence using

wireless communication, including industry and academia area;

Phase 2 (~7 months): Research on key technology of artificial intelligence

using wireless communication, including architecture design, model selection,

algorithm design and so on.

Phase 3 (~3 months): Verification of the proposed architecture and technology.



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HIRPO20160608: Deep Learning based Robotic

Perception


2 Subject: computer vision


GPU: Graphics Processing Unit

3 Background

The resurgence of neural networks, most prominently in the form of deep

learning (DL), has recently led to significant technological advances in image

understanding, speech recognition, and natural language processing. In

computer vision, supervised deep learning models of the Convolutional Neural

Network (CNN) family have led to significant error reduction on large-scale

classification tasks, due to their hierarchical nature and to the directness of

their feature and classifier learning. Since 2012, deep learning methods, in

particular those based on CNN, have greatly improved the performances of

traditional computer vision tasks including image classification, object

recognition, object detection, edge detection, face recognition, image

denosing/super-resolution, image quality assessment, tracking and event

recognition. The rapidly improved availability and accessibility of large-scale

Internet images/videos, in particular from mobile platforms, has greatly

facilitated the CNN training process with GPU-powered massive parallel

computing platforms, making training tens millions of parameters in CNN

practically a feasibility (training time ranges between a few hours to a couple of

weeks).


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Computer vision technologies have become increasingly mature, being used in

real products including self-driving, image search, smart-phone applications,

surveillance and security, robotics, and smart home applications. Internet

powerhouse companies have made a big effort in investing in developing deep

learning technologies with large support of human, machine, and data

resources.

It is evident that deep learning technologies have led to the recent

breakthroughs in both academia and industry, creating intelligent products that

greatly enhance the quality of human lives. Therefore, the emphasis on

advancing in deep learning and computer vision is a must. Not only will areas

like conventional mobile terminals and intelligent monitoring be enhanced with

the emerging deep learning technology, next-generation products, such as

household robots, and driverless cars are expected to function primarily based

on deep learning.

Object detection and image recognition are considered as central problems in

computer vision. They are the building blocks for other complex vision systems

that consist of a suite of individual modules to make a real product. Specifically,

visual object recognition goes beyond determining whether the image contains

instances of certain object categories. It also refers to attributes of objects

such as location, pose and so on, making it a challenging task in computer

vision. Progress in CNN-based methods sped the development of image

classification, object detection, and semantic segmentation. However, there

still exists gaps between what many of these methods can do and what is

required in real-world situations, in term of speed, performance, and demand

in power and memory. Thus, we are motivated to go deeper into the structure

of deep models and optimize the object detection and recognition algorithms.

In addition, we also hope to transfer knowledge from the seen to unseen object

classes to improve the adaptiveness of the future perception systems.


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4 Scope

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.


Establish the technology accumulations, research capabilities and algorithm

systems on deep learning based object detection and recognition.

1) Software and prototype deliverables: Efficient object detection and

recognition algorithm; Robot self-learning algorithm and application system;

2) Document deliverables: Research report for efficient object detection and

recognition algorithm; Research report for robot self-learning algorithm and

application system; Academic papers and patents;

3) Other work: Tele-conference each month for technology communication and

progress briefing; Technical report each quarter of a year.


1) Acceptance criteria: Achieve top performance on popular object detection

and recognition datasets, for example ImageNet and PASCAL VOC;


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2) Demo scenario: A typical apartment is shown in the following figure. Room

layout includes: living room, kitchen, and bedroom. There could be coffee table,

sofa, TV cabinets and other furniture in living room; table, stove in kitchen; bed

and other furniture in bedroom. There also may be objects such as plants, TV

in living room and small object such as beverage cups, mineral water on the

desk or table. In some cases there may be people moving in the house. The

whole area may be larger than 200m2.


Phase1 (~6 months): Project Objectives is to develop an efficient object

detection and recognition algorithm. Achieve top performance on popular

object detection and recognition datasets, for example ImageNet and PASCAL

VOC. Deliverables List is to deliver the codes, systems and instructions of the

efficient object detection and recognition algorithm. Scenario test in Huawei

based on designed demo scheme;

Phase2 (~6 months): Project Objectives is to develop robot self-learning

algorithm and application system. Deliverables List is to deliver the robot

self-learning algorithm and application system. Scenario test in Huawei based

on designed demo scheme.



34

HIRPO20160609: Deep Learning based Human Visual

Characteristics Research





3 Background

















weeks).


35






resources.








on deep learning.

Research on human visual characteristics includes face detection and

recognition, human detection, identification, tracking, behavior recognition, and

age estimation. Both in the future application of intelligent products and for

entertainment, the study of human visual characteristics shows greater value.

On one hand, the smart home application and a series of future scenarios, the

research on the human visual characteristics offers necessary technical

capabilities for human-computer interaction, intelligence services and other

high-level applications. On the other hand, the study of human visual

characteristics also supplies some entertainment, which can to some extent

attract users.


36

4 Scope

1) Body tracking: Establish the body's real-time tracking technology and

research capabilities to address the following problem for intelligence service

robots;

2) Face attributed recognition: through the analysis of human attributes in

images / videos such as age estimation, gender estimation, clothing with

attributes and expression, provide the necessary functions for high-level

application scenarios like Smart Home and intelligent robots;

3) Face detection/recognition: Establish the ability for face learning and

recognition in home environment;

4) Human behavior recognition: recognition of variety human behaviors which

provides the necessary basic skills for high level applications like

human-computer interaction and abnormal behavior of the warning.


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.


Support the human following feature in robot demo;

Work in Huawei team at least one day per month.


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Phase1 (~6 months): Project Objectives is to develop face

detection/recognition and body tracking algorithms in robot. Deliverables List is

to deliver the codes, systems and instructions of the developed algorithm.

Scenario test in Huawei based on designed demo scheme;

Phase2 (~6 months): Project Objectives is to develop face attribute recognition

and human behavior recognition algorithms in robots. Deliverables List is to

deliver the systems and instructions of the developed algorithm. Scenario test

in Huawei based on designed demo scheme.



38

HIRPO20160610: Deep Learning based Scene

Understanding





VQA: Visual Question Answering

3 Background

















39


weeks).






resources.








on deep learning.

Human can constantly observe the environment structure that surrounds

his/her. For example, when walking in the house, we recognize objects within it

and make corresponding reactions. Such capabilities help us accomplish

various tasks even in unfamiliar places. Building a system that can

automatically perform scene understanding, is a crucial prerequisite for a

variety of applications, including robot navigation, semantic mapping,

autonomous driving and human-machine interaction. Therefore, image

semantic segmentation, as the fundamental component of scene

understanding, is the key to many high-level semantic related applications. On

one hand, semantic segmentation produces highly compact representation of

images. Indexed with these representations, we can greatly improve the

efficiency of retrieving and processing. On the other hand, semantic


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segmentation lay down the foundation for other applications such as object

detection and scene captioning.

4 Scope

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.


Algorithm, system and technical reports for semantic image segmentation;

Algorithm, system and technical reports for instance semantic segmentation;

Design report of visual question answering application system;

Academic papers and patents;

Tele-conference each month for technology communication and progress

briefing;

Technical report each quarter in Huawei.


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Phase1 (~4 months): Develop a fast image semantic segmentation algorithm

with labelling no less than 20 classes of common household items. Achieve top

performance on popular semantic segmentation datasets;

Phase2 (~4 months): Develop instance level semantic segmentation algorithm

with labelling no less than 20 classes of common household items. Achieve top

performance on popular semantic segmentation datasets;

Phase3 (~4 months): Develop a visual question answering system application

system.



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HIRPO20160611: Manufacture Quality Risk Analysis &

Prediction based on Test Data


2 Subject: predictive analysis

3 Background

Production volume goes higher significantly and cycle time reduced much

shorter.

Currently the manufacture testing quality control system is designed to trouble

shooting and fast tracking based on problem. We hope to enhance the system

to be able to identify the potential risk in advance and eliminated it in time.

4 Scope

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.


Technical reports including business analysis and data mining model for test

process quality control system;

Predictive analysis system with source code and document.


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Provide sample model for 2~3 products;

The risk catch ratio higher than 70%;

The error warning ratio less than 30%.


Phase1 (~4 months): Business analysis; Data analysis, data cleaning, model

training, model optimization base on sample data;

Phase2 (~3 months): Using history data from real products to validate the

model;

Phase3 (~5 months): Deploy model to production environment; Training &

transfer to the development team and deploy to production.



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HIRPO20160612: Behavior Analytics for Personalized

Mobile Services


2 Subject: others


OTT: Over the Top

POC: Prove of Concept

UE: User Equipment

3 Background

Considering users’ everyday reliance on smart phones, there is an

ever-increasing requirement for personalized mobile services in many aspects

of human life, such as health, education, transportation, and shopping. Thanks

to the spurt of mobile big data, e.g., call logs and location footprint, as well as a

wealth of sensors in mobile phones, e.g., gyroscope, accelerometer and light,

such personalized mobile services have become possible. For example, a

data-driven approach can be used to predict the emotional state of human by

leveraging smart phone usage data and/or location footprint, and devise

emotion-aware recommendation for shopping.

The abundance of mobile data on UE is significantly more beneficial in

understanding human behavior than social network data that has been widely

studied. Mobile data reflects the real-world behavior of human, such as

mobility, call logs and location. This could be dramatically different from social


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network data that is simply the human actions in cyber-world, which are often

faked or contrary to the actual behavior.

Furthermore, mobile data is collected in a passive sensing fashion. It will not

impact human’s normal life activities. This is especially preferable over the

conventional survey-based studies in psychology. The survey-based studies

are usually expensive in terms of time and money, and not suitable for

long-term behavior analysis.

The unique advantages of mobile data are the foundation of successful

personalized mobile services. Mobile operators can utilize their available data

and/or cooperate with OTT providers to obtain additional UE sensor data in

order to provide personalized services and enhance user experience. This will

definitely reduce customer churn and improve customer loyalty. According to

the Harvard Business School, increasing customer retention rates by 5 percent

increases profits by 25 percent to 95 percent.

4 Scope

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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Technical reports of general requirements and main components of

personalized service platform;

Solution proposal for understanding human behavior;

Solution proposal for providing personalized service using the behavioral

understanding;

A working prototype of such a system.


A detailed report on both item 1, 2 and 3 in section 6. A working prototype for

prove of concept.


Phase1 (~2 months): Survey of existing personalized mobile services,

focusing on the data source, mining methodologies and applications;

Phase2 (~2 months): Solution proposal for understanding human behavior and

its potential personalized service;

Phase3 (~5 months): Collection of user data from mobile phones;

Phase4 (~3 months): A prototype for POC on such a system;

Huawei will be able to provide lab time on vUIC platform to facilitate the

prototype POC.
