智慧城市生態圈的ai應用技術 - cinsa.org.cn · deputy director， state key lab of iot...

引領創新科技建設智慧澳門

智慧城市生態圈的AI應用技術賈維嘉，講座教授、實驗室副主任

Weijia Jia, Chair Professor

Deputy Director， State Key Lab of IoT for Smart City

University of Macau 1


➢ 智能搜索➢ 語義理解➢ 知識圖譜

➢ 對象關聯➢ 實體識別➢ 霧式管理

➢ 全效感知➢ 時空覆蓋➢ 三元協同

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Info/Data & Knowledge Flows

IoT/sensor networksApplications

候選子圖 Top-k結果圖知識圖譜用戶查詢圖

應用層

中間層

感知層


我們要做什麼？智慧城市物聯網生態圈！


1. Background

2. AI at Bottom: Sensors/IoT

3. AI at Middle: Fog/Edge Networks

4. AI at Upper: Knowledge Graph

5. Discussions： AI S&P

Contents

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o 2018年7月獲國家科技

部批准

o 2018年10月8日揭牌

背景

4引領創新科技建設智慧澳門

引領創新科技建設智慧澳門 5

智慧城市物聯網國家重點實驗室


1. 全球已啟動或在建的智慧城市達1000多個

• 正在以20%以上的速度增加

• 歐盟在2009年提出《歐洲2020戰略》

• 日本提出 “智慧日本”

• 新加坡推動政府服務的電子化

• 荷蘭提出 Amsmarterdam 城市能源地圖

2. 國內截止2015年底

• 85%以上的城市都在進行智慧城市建設

• 智慧城市試點已接近300個

• 規劃對智慧城市的投資總規模將逾5000億元

智慧城市國內外研究現狀



澳門的挑戰與機遇


安全防災

能源配置生態影響

交通擁塞


State Key Lab@University of Macau

Fundamental Research & Key

Technology 基础研究/关键技术

为支撑

Talent Training人才培养为平台

City Development 城市发展为主线

IOTSC 智慧城市物联网国家重点实验室

Applications应用支持为特征

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Research focuses

Smart Sensing 感

知技术

Data Analysis and Control

数据分析与控制技术

Advance Networking &

Fog Computing网络存储,传输与雾

计算

Platformof AI/Big

Data

Smart Grid

能源物联网

Smart

Transportation

基于物联网的智能交通

Public Safety Monitoring

and Disaster Prevention

城市公共安全监控和灾害防治

Big Data &

Intelligent Tech

大数据与智能技术

Intelligent Sensing

and Net Comm.

智能傳感與網路通信

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1. Background

2. Bottom: Sensors/IoT

3. Middle: Fog/Edge Networks

4. Upper: Knowledge Graph

5. Discussions： AI S&P

Contents

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Info/Data & Knowledge Flows

IoT/sensor networksApplications


應用層

中間層

感知層


智慧城市物聯網生態圈！

Our IoT devices

1. High frequency

radio, low power

consumption, wall

penetration

2. Real-time video

push and alarm to

user's mobile

phone

3. WeChat alarm and

personal service

inquiry and report

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Optimal patterns achieving 2D-coverage and k-connectivity: k = [1,…, 6]

Rs – Sensing RadiusRc – Connection Radius

2D Optimal Connectivity/Coverage

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1. Background




5. Discussions: AI S&P

Contents

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Key Tech: Fog/Edge Computing

PublicCloud A

Cloud/Servers

Fog/edge networks

App Environments

IoT & SensorNetworks

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EnterpriseCloud B

PrivateCloud C

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IoT/sensors

Info/Data Flows

Bottom layer

Middle layer

Upper layer

How the data/tasks are handled by the

fog nodes?15


Issues with Fog/Edge

• Resource allocations

• Power/energy awareness

• Real-time QoS

• Mobility service support

• Offloading

• Edge-to-edge data transfer

• Edge-to-edge AI services …

Container Migrations

Migrating data/tasks➔Allocating resources from fog node 𝐹1to 𝐹3 through containers ➔ QoS ↑ costs↓ .

Migration Modeling and Learning Algorithms for Containers in Fog Computing, IEEE Transactions on Service

Computing (TSC), 2018 17

• Containers are much lighter than VMs

• Compared with VM, application data and runtime libs are migrated without migrating the guest OS.

Containers: Why not VM?

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Delay

• Network delay:

• Computation delay:

• Total delay:


Parameter Attentions

• Resource utilization:

• Total power consumption:

• Container migration cost:


Nightmare: Dimensional Curse

• Delay of Ci at Fj

• Matrix of delays:

• Overall state space:

• To minimize• Delay 𝑑𝑡𝑜𝑡𝑎𝑙• Power 𝑝𝑡𝑜𝑡𝑎𝑙• Migration cost 𝑚𝑡𝑜𝑡𝑎𝑙 subject to resource

requirements:min𝐶 = 𝜔1𝑑𝑡𝑜𝑡𝑎𝑙 +𝜔2𝑝𝑡𝑜𝑡𝑎𝑙 +𝑚𝑡𝑜𝑡𝑎𝑙

𝑠. 𝑡. 0 ≤ σ1 𝐶𝑗.𝑙 𝑡 =𝐹𝑖

𝐶𝑗.𝑎 𝑡

𝐹𝑖.𝑐≤ 1 𝑖 = 1,2, … ,𝑚

• Difficulties: • Bin-pack (NP-hard) • High dimensional state

Objectives

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• RL Loop:

• State 𝑆𝜏, Action 𝐴𝜏, and Reward 𝑅𝜏:• 𝑆𝜏 = 𝑋 𝜏 , 𝐶. 𝑙 𝑡0 + 𝜏 𝑇 , 𝐶. 𝑎 𝑡0 + 𝜏 𝑇 ∈ 𝑺.• 𝐴𝜏 = 𝐶. 𝑙 𝑡0 + 𝜏 𝑇 ∈ 𝑨.

• 𝑅𝜏 = − 𝜔1𝑑𝑡𝑜𝑡𝑎𝑙 𝜏 + 𝜔2𝑝𝑡𝑜𝑡𝑎𝑙 𝜏 + 𝑚𝑡𝑜𝑡𝑎𝑙 𝜏

• 𝑄 𝑣𝑎𝑙𝑢𝑒: 𝑄(𝑆𝜏−1, 𝐴𝜏−1)

Reinforcement Learning (RL)

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1. Background



4. Upper: Knowledge Graph – The key


Contents

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AI framework

PublicCloud A

Cloud/Servers

Fog/edge networks

App Environments

IoT & SensorNetworks

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PrivateCloud C

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IoT/sensors

Info/Data Flows

Bottom layer

Middle layer

Upper layerAI Framework

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• 重點研究知識圖譜與數據相結合的學習與表示技術算法

• 探討城市大數據的多源獲取，提出多源數據的融合技術，利用遷移學習和聯邦學習對城市大數據進行挖掘和分析

• 提出將結構化信息和數據結合的聯合模型，以刻畫知識圖譜內外實體的有效聯繫

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城市大數據與智能技術


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研發具國際領先的智慧城市物聯網共性基礎科學及平臺方案；人-物&物-物之間的多關係提取與智能鏈接。

城市大數據與物聯網智能

代表性成果:

• 物聯網邊緣動態傳算：IEEE TPDS 2019，TSC2018

- 長尾任務動態調度

- 容器智能遷移

- 強化學習以降低解空間維度

• 語意實體多關係提取：AAAI 2019, ACL2019， EMNLP 2018, NAACL2019

- 實體多關係並行提取

- 優化膠囊網絡及设计新型對抗網路進行關係精煉提取

• 多反饋網絡實體信息抽象集成, IJCAI 2019，ACM Trans Data Mining/Discovery 2019

- 多反饋網絡建模- 關係網絡對演模型


1. Extracting Entities (E)

2. Extracting Relations (R)

3. Predicating E-R in KG

Knowledge Graph (KG) --Fundamental Challenges

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What is Knowledge Graph?• Freebase, Concept Graph, NELL, WordNet…

• Triplet: (head entity--A, relation--r, tail entity--B)

• ”The height of Liu is 174cm” ➔ (Liu, height, 174cm)


Entity Triple Extractions

• Entity linking forming KG• entity = node• relation = edge• fact = triple (A, r, B)

• Typical knowledge graphs• WordNet, Freebase, Nell• DBpedia, Wikidata• …

App Example: Time-Sync CommentsVideo-Tagging

• Time-sync comments are crowdsourced & interactive

• Content involving current video, feelings of users or replies to other comments

• Obtain more accurate and specific tags

(1) Semantic relevance

(2) Real-time

(3) Inter-dependence

(4) Noise

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Mining Tags as Relations

• 1. Graph Construction

• 2. Topic Partitioning & Radius

• 3. Weight Distribution and Tag Extraction

ICME, 2017, ICME 2019

Outcomes: {User A, “Messi”, User B} { Video V, “Messi”, [0:18, 0:24]}

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Fundamental challenges

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Two recent works on extracting relations from

Natural Language:

• Single Relation Extraction in Sentence

• – EMNLP-18

• Multiple Overlapping Relation Extractions

• – AAAI-19

2. Extracting Relations

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Problem and Challenges

• Multi-labeled Relation Extraction

ID Instances Relations

S1[Arthur Lee], the leader of Love, died on

Thursday in [Memphis].person/place_death

person/place_lived

S2[Arthur Lee] was born in [Memphis],

Tennessee, and lived there until 1952.person/place_birth

person/place_lived

S3

[Abraham Lincoln] was an American

statesman and lawyer who served as the

16th President of the [United States].

person/president_of

person/nationality

Overlapping

Relations

Discrete

Features


Extracting Overlapping Features

• Capsule Network

Capsule Network inImage Recognition

Capsule Network inRelation Extraction (RE)

Examples for Capsule Network in image recognition come from Aurelien Geron’s lecture.https://youtu.be/pPN8d0E3900

[Arthur Lee] was born in [Memphis], Tennessee, and lived there until 1952.

place_birth

place_lived


Attentive Capsule Network

• Attention-based Routing Algorithm

• Sliding-margin loss


Experiments• Experiments on two previous benchmarks (NYT-10 and

SemEval-2010 Task 8)





Fundamental challenges

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KGC– Predict Entity/Relations

• Triples in KGs.• (Isaac Newton, _birth_in, Lincolnshire)

• How to predict the missing part: (A,r, ?), (A, ?, B), (?,r, B)

John was bornin ?

John was born in Miami

KnowledgeGraph

VectorSpace


Translation-based Models

• Representation learning: Similar entities arerepresented as similar vectors

• Typical: TransE (Translation Embedding):

𝐴, 𝑟, 𝐵 ➔ Ԧ𝐴 + Ԧ𝑟 = 𝐵

• Optimization: minimizing Ԧ𝐴 + Ԧ𝑟 − 𝐵• E.g.: TransH, TransR, TransG, DKRL, TKRL,

SSP, …

TransT: Type-based Multiple Embedding Representations for Knowledge Graph Completion, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2017.


Contributions: Multiple Embeddings

• Assign one vector for each semantics

𝒑𝒕𝒓𝒖𝒆 𝑨, 𝒓, 𝑩 = σ𝑖=1𝑛𝐴 σ𝑗=1

𝑛𝐵 𝑤𝐴,𝑖𝑤𝐵,𝑗𝑝𝑡𝑟𝑢𝑒 𝑣𝐴,𝑖 , 𝑣𝑟 , 𝑣𝐵,𝑗

𝑝𝑡𝑟𝑢𝑒is defined as

𝑝𝑡𝑟𝑢𝑒 𝑣𝐴,𝑖 , 𝑣𝑟 , 𝑣𝐵,𝑗 = 𝑒− 𝑣𝐴,𝑖+𝑣𝑟−𝑣𝐵,𝑗


Relation Prediction Classifications

• Predicting the missing relation when given two entities. FB15K is the benchmark dataset.

• Triple classifications predict if a given triple is correct. FB15K is the benchmark dataset of this task.


1. Introduction


3. Middle : Fog computing



Contents

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From attacker’s view: What can attackers do in AI?

From AI’s view: How can we prevent attacks from AI?

ATTACKER

AI

privacysecurity

DATAMODEL

Security & Privacy

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DeepFakes

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SpamTracer: Manual Fake Review Detection for O2O Commercial Platforms by Using Geolocation Features, Inscrypt 2018.

Machine learning for detectingfake reviewers

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Bae H, Jang J, Jung D, et al. Security and Privacy Issues in Deep Learning, ACM Computing Surveys 2018

Potential Threats in AI

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Data

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S&P Info/Data & KnowledgeFlows

S&P 5G & IoT/sensor networksApplications


應用層

中間層

感知層


智慧城市物聯網生態圈


MOST China; FDCT Macau, SAR; NSFC

University of Macau

Shanghai Jiaotong University

Acknowledgements

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謝謝！Thanks！

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智慧城市生態圈的ai應用技術 - cinsa.org.cn · deputy director， state key lab of iot...

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