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B106B001

Multi-scale Geometrical Analysis and Sparse Representation

1 48 3

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1.1

1.2

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1.4

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2.6

3 (8)

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8.3

8.4

Jean-Luc Stark, . , , 2014.

CodeB106B002

Advanced System Software Theory and Technologies

1 Class Hours () 32 Credits 2

2Majors Concerned ()

Computer Science and Technology, Software Engineering

3Preparatory Courses

Introduction of Compilers, Software Engineering, Operating System

4Teaching Purpose ()

The goal of this course is to (1) design and implement of various types of system software, and (2) apply program analyses and optimizations in compiler and evaluate them. Students will learn about compilation techniques for obtaining high performance on modern computer architectures. The relationship between machine architecture and systems software will be our focus. Shared/distributed memory and client/server systems as well as several important analysis and optimization techniques will be presented in class lectures.

5Teaching Method()

In-class lecture, assignment and lab

6Main Contents and Requirement for Students ()

Topics include: CISC and RISC architectures, machine-dependent and machine-independent assembler, loaders and linkers, compiler-related techniques (control-flow analysis, data-flow analysis, program optimization, code generation, optimizations for OOL, JIT, garbage collection, data dependence analysis, type systems and so on), operating systems features, software engineering related system design.

Students are required to implement a compiler with optimization capabilities in the COOL framework.

7Referencing Textbooks and Required References for Students ()

Textbooks

Alfred V.Aho, Monica S.Lam, Ravi Sethi, and Jeffrey D.Ullman. Compilers: Principles, Techniques, and Tools, second edition (photocopy edition), Beijing: China Machine Press, 2011.

References

Keith D Cooper and Linda Torczon, Engineering a Compiler, Second Edition, Morgan Kaufmann, 2011.

Leland L. Beck. System Software: An Introduction to Systems Programming (3rd Edition), Addison-Wesley, 1996.

Required References for Students

Efficiently computing static single assignment form and the control dependence graph

http://dl.acm.org/citation.cfm?id=115320

Load-reuse analysis: design and implementation

http://www.cs.cmu.edu/afs/cs/academic/class/15745-s06/web/handouts/bodik-pldi99.pdf

Efficient representations and abstractions for quantifying and exploiting data reference locality

http://www.cs.cmu.edu/afs/cs/academic/class/15745-s06/web/handouts/chilimbi-pldi01.pdf

Optimal spilling for CISC machines with few registers

http://www.cs.cmu.edu/afs/cs/academic/class/15745-s06/web/handouts/appel-pldi00.pdf

A Graph-Free Approach to Data-Flow Analysis


Efcient Flow-Sensitive Interprocedural Data-Flow Analysis in the Presence of Pointers

http://www.cs.utexas.edu/users/lin/papers/cc06.pdf

A practical framework for demand-driven interprocedural data flow analysis

http://dl.acm.org/citation.cfm?id=267959.269970&coll=DL&dl=GUIDE&CFID=422404330&CFTOKEN=37999885

Iterative Data-ow Analysis, Revisited

http://pdf.aminer.org/000/279/837/iteration_revisited_examples_from_a_general_theory.pdf

Ownership Types for Safe Programming:Preventing Data Races and Deadlocks

http://web.eecs.umich.edu/~bchandra/publications/oopsla02.pdf

Ownership, encapsulation and the disjointness of type and effect


Generic ownership for generic Java


Ownership and immutability in generic Java


Refactoring android Java code for on-demand computation offloading

http://dl.acm.org/citation.cfm?doid=2384616.2384634

8Author () Zhao, Yang

9Teacher () Zhao, Yang

B106B003

Advanced System Software Theory and Technologies

32 2

:

/

1

1.1

1.2 CISC

1.3 RISC

2

2.1

2.2

2.3

2.4

3

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8 COOL

4

4.1

4.2

4.3

4.4

5

6

COOLCOOL

Alfred V.Aho, Monica S.Lam, Ravi Sethi, and Jeffrey D.Ullman. Compilers: Principles, Techniques, and Tools, 2, , 2011.

Keith D Cooper and Linda Torczon, Engineering a Compiler, Second Edition, Morgan Kaufmann, 2011.

Leland L. Beck. System Software: An Introduction to Systems Programming (3rd Edition), Addison-Wesley, 1996.

Efficiently computing static single assignment form and the control dependence graph


Load-reuse analysis: design and implementation

http://www.cs.cmu.edu/afs/cs/academic/class/15745-s06/web/handouts/bodik-pldi99.pdf

Efficient representations and abstractions for quantifying and exploiting data reference locality

http://www.cs.cmu.edu/afs/cs/academic/class/15745-s06/web/handouts/chilimbi-pldi01.pdf

Optimal spilling for CISC machines with few registers

http://www.cs.cmu.edu/afs/cs/academic/class/15745-s06/web/handouts/appel-pldi00.pdf

A Graph-Free Approach to Data-Flow Analysis


Efcient Flow-Sensitive Interprocedural Data-Flow Analysis in the Presence of Pointers

http://www.cs.utexas.edu/users/lin/papers/cc06.pdf

A practical framework for demand-driven interprocedural data flow analysis


Iterative Data-ow Analysis, Revisited

http://pdf.aminer.org/000/279/837/iteration_revisited_examples_from_a_general_theory.pdf

Ownership Types for Safe Programming:Preventing Data Races and Deadlocks

http://web.eecs.umich.edu/~bchandra/publications/oopsla02.pdf

Ownership, encapsulation and the disjointness of type and effect


Generic ownership for generic Java


Ownership and immutability in generic Java




B106B004

Computing Theory and Computational Intelligence

32 2

:

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1 (2)

1.1

1.2

1.3

2 (2)

2.1 *#

2.2

2.3

3 (6)

3.1

3.2

3.3 *

3.4

3.5

3.6

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3.8

3.9 #

3.10 *#

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5.5

5.6

5.7

5.8

5.9

5.10

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,,..,2008

B106C001

Advanced Autonomous Mobile Robot

322

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2

1

2 ROS

10

3

4

5

6

10

7

8

9

10

10

11

12

1

2 ,

1. SebstianThrun, Wolfram Burgard, Dieter Fox.Probabilistic Robotics. The MIT Press.

2. Ronald Siegwart et al. Introduction to Autonomous Mobile Robot. The MIT Press.

3. Gregory Dudek et al.Computational Principles of Mobile Robotics. Cambridge University Press.

1. http://www.sciencedirect.com

2. http://www.Ieeexplore.com

CodeB106C002

Services Computing and Business Process Management (II)





Software Engineering, Database


This course aims to let students gain an understanding of the paradigm of services computing, and related techniques and approaches, like SOA and loosely-couple, Web services, service composition, business process management, workflow, Petri nets, BPEL, process mining. Besides, students are expected to understand the mergence of services computing and business process management (BPM), and state-of-the-art techniques and future research directions.

5Teaching Method()

In-class lecture

6Introduction to the course ():

This course is an advanced course of software engineering. Topics include: the paradigm of services computing, SOA and loosely-couple, Web services, service composition, business process management, workflow, Petri nets, BPEL, formal verification for services and business processes, program analysis techniques for Web services and business processes, process mining, and so on.


1 INTRODUCTION (2 HRS)

1.1 PARADIGM OF SERVICES COMPUTING

1.2 BUSINESS PROCESS MANAGEMENT

1.3 RELEVANT CONFERENCES AND JOURNALS

2 SERVICES COMPUTING AND BUSINESS PROCESS MANAGEMENT (4 HRS)

2.1 CLOUD COMPUTING AND SAAS

2.2 SERVICE-ORIENTED ARCHITECTURE (SOA)

2.3 WEB SERVICES

2.4 WORKFLOW AND BUSINESS PROCESS

2.5 WHERE SERVICES COMPUTING AND BUSINESS PROCESS MEET

2.6 ABSTRACT AND EXECUTABLE PROCESSES

3 SERVICE COMPOSITION (4 HRS)

3.1 PROGRAMMING-IN-THE-LARGE

3.2 SERVICE COMPOSITION

3.3 SERVICE ORCHESTRATION AND SERVICE CHOREOGRAPH

3.4 BPEL and WS-CDL

4 FORMAL VERIFICATION(5 HRS)

4.1 FORMAL METHODS

4.2 PETRI NETS

4.3 ANALYSIS TECHNIQUES OF PETRI NETS

4.4 WORKFLOW NETS

4.5 WORKFLOW VERIFICATION AND ANALYSIS

5 PROGRAM ANALYSIS (5 HRS)

5.1 CONTROL FLOW GRAPH

5.2 CONTROL DEPENDENCE AND DATA DEPENDENCE

5.3 PROGRAM DEPENDENCE GRAPH (PDG)

5.4 PROCESS CONSISTENCY

5.5 PROCESS REFACTORING

6 PROCESS MINING (6 HRS)

6.1 DATA MINING AND PROCESS MINING

6.2 PROCESS DISCOVERY

6.3 CONFORMANCE CHECKING

6.4 LOG RECOVERY& REPAIR

7 DISCUSSION (6 HRS)

7.1 HOT TOPICS

7.2 FURTHER STUDY

7.3 HOW TO DO RESEARCH IN THIS FIELD

7.4 DISCUSSION ON SOME TOPICS


Textbooks

Thomas Erl. Service-Oriented Architecture: Concepts, Technology, and Design, Beijing: Science Press, 2012.

Dumas, Marlon, et al. Fundamentals of business process management. Berlin: Springer, 2013.

Required References for Students

Service-Oriented Computing: State-of-the-Art and Open Research Issues

https://doc.freeband.nl/dsweb/Get/Rendition-19063/UvT%20SOC%20research%20agenda.pdf

Service-oriented computing

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1607964

Petri nets: Properties, analysis and applications

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=24143&tag=1

The application of Petri nets to workflow management

http://www.worldscientific.com/doi/abs/10.1142/S0218126698000043

The Program Dependence Graph and Its Use in Optimization

http://dl.acm.org/citation.cfm?id=24039.24041&coll=DL&dl=ACM&CFID=445873799&CFTOKEN=50827646

Process Mining: Discovery, Conformance and Enhancement of Business Processes

http://www.processmining.org/book/start

9Author () Song, Wei

10Teacher () Song, Wei

B106C003

Advanced Software Engineering

32 2

3 :

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1 (3)

1.1

1.2

1.3

1.4

2 * (6)

2.1

2.2

2.3 *

2.4

3 *(8)

3.1

3.2

3.3

3.4

3.5

3.6

3.7

4 *# (9)

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

5 *#(6)

5.1

5.2

5.3

1 :Pressman,Roger S.7, :,2010.

2 Penny GrubbArmstrong A Takang2004.

B106C004

Multidimensional Signal Processing

32 2

:

1.

2.

1

1.1

1.2

1.3

2

2.1

2.2

2.3

3 Z

3.1

3.2 Z

3.3

3.4 Z

3.5 2D

4

4.1

4.2

4.3 2D

4.4

4.5

4.6

5

5.1 FIR

5.2 IIR

5.3

6

6.1

6.2

6.3

6.4

: 20+ 20%+ 60

John W. Woods, Multidimensional Signal Image and Video Processing and Coding

Second Edition. Elsevier, 2012.

Dudgeon D., Mersereau R., Merser R. Multidimensional Digital Signal Processing

Second Edition. Prentice Hall, 1995.

B106C005

Big Data Analysis

32 2

:

Map-reduce

1 2

2 Map/Reduce 4

3 2

4 4

5 2

6 4

7 4

Map/ReduceUCI(10)

7

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- ~(),Micheline Kamber(),(),(),() 201281

:.(),(),(). ; 1 (201361)

http://infolab.stanford.edu/~ullman/mmds.html

Effective Data Mining Technology. http://www.enablesoft.com/;

UCI. http://www.ics.uci.edu/~mlearn/MLRepository.html

B106C006

II

Machine Learning (II)

322

:

EM

1 1

1.1

1.2

2 2

2.1

2.2

2.3

2.3

2.4

3 2

3.1

3.2

3.3

3.4

4. 2

4.1

4.2

4.3

4.4

52

5.1

5.2

5.3

5.4

6. 3

6.1

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6.4

6.5

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7.1

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7.3

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7.5

8. 2

8.1

8.2 EM

8.3 EM

8.4 EM

9. K-3

9.1

9.2

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9.4 K-

10. 2

10.1 EM

10.2

10.3

10.4

11. 2

11.1

11.2

11.3 Bagging

11.4 Boosting

11.5

12. 4

12.1

12.2

12.3

12.4

12.5

12.6L-BFGS

12.7

12.8

13. 4

13.1

13.2

13.3

13.4

13.5

. . 2012.

Bishop C. M. Pattern recognition and machine learning. New York: springer, 2006.

Tom M. Mitchell, Machine Learning, McGraw Hill, 1997

1.http://cs229.stanford.edu/notes/cs229-notes1.pdf

2. Bishop Pattern recognition and machine learning4.1

3.

http://cs229.stanford.edu/notes/cs229-notes3.pdf

http://blog.pluskid.org/?page_id=683

http://ntu.csie.org/~piaip/svm/svm_tutorial.html

4.


5. EM


http://www.cs.columbia.edu/~mcollins/em.pdf

6.

http://cs229.stanford.edu/notes/cs229-notes7a.pdf

http://cs229.stanford.edu/notes/cs229-notes7b.pdf

http://home.in.tum.de/~xiaoh/pub/em.pdf

Bishop Pattern recognition and machine learning9

B106C007

Advanced Software Testing Theory and Technology

32 2

3 :

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1.4

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3 *(3)

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5 *#(6)

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5.5

6 (2)

6.1

6.2

7 *#(3)

7.1

7.2

7.3

8 (3)

8.1

8.2

8.3

1 Glenford J. Myers, Tom Badgett, Corey Sandler, The Art of Software Testing, Wiley, 2011. (ISBN-10:1118031962/ ISBN-13:978-1118031964, Edition 3)

2 Aditya P. Mathur , Foundations of Software Testing, Prentice Hall India, 2008. (ISBN: 8131716600)

/() William E. Perry. , , . :,2008.

B106C008

Theory of Pattern Recognition

32 2

:

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3.11 -SVCC SVC-SVR/-SVR #

3.12

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4.6 /bootstrap*

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4.8

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C.M.Bishop, Pattern Recognition and Machine Learning, Springer Press, 2007

R.O.Duda 2003

2010

N.Cristianini2004

-2009

John Shawe-Tayor, , , , 2006.

B. Scholkopf and A. J. Smola, Learning with Kernels, MIT Press, 2001

B106C009

New Generation Network Technology

32 2

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1

1 0.5

1.1

1.2 INTERNET*

1.3 IP

1.4

2 IPv6 0.52.1 IPv62.2 IPv62.3 ICMPv6#

3 IPv6 13.1 IPv63.2 IPv63.3 IPv63.4 IPv6 DNS

4 IPv6 1

4.1 IPv6*4.2 4.3

5 IPv6 25.1 IPv65.2 IPv6

5.3 IPv6*

5.4 IPv6

5.5 IPv6IPv4

5.6 IPv6

6 IP 2

6.1

6.2 IP

6.3 IP*

6.4 IP

6.5 IP

7 ATMIP 2

7.1

7.2 ATMIPARP

7.3 #

7.4 ATMIPv6

8 IP 1

8.1

8.2 IP

9 MPLS2

9.1 MPLS

9.2 IMPLS*

9.3 MPLS

9.4 MPLS

10 MPLS2

10.1 VPN

10.2 MPLSVPN

10.3 MPLSVPN

11 Ad Hoc 211.1 Ad Hoc11.2 Ad Hoc*11.3 Ad HocQoS#11.4 Ad HocTCP11.5 Ad Hoc

12 Ad Hoc 212.1 Ad Hoc

12.2 *

12.3

12.4

12.5

12.6

13 Ad HocIP 2 13.1 IP

13.2 IP

13.3 DDHCP#

13.4

14 1

14.1

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15 2

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15.3

15.4

15.5

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20 UWB 220.1 UWB

20.2 UWBMAC#20.3 UWB20.4

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1

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1.INTERNET2001

2. , , ,2005. , 4., , Ad Hoc --, ,2006

5.,IP,,1999.

6.Edgar H. Callaway, Jr. , Wireless Sensor Networks: Architectures and Protocols ,CRC Press LLC, 2004

7.Edited by C. S. Raghavendra, Wireless Sensor Networks, Kluwer Acamedic Press, 2004

B106C010

Image Analysis and Image Understanding

32 2

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1.1 HoughHough

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2.2Harris

2.3Harris

2.4 SUSAN*

3. (2)

3.1

3.2.H.S*3.3

3.4

4. (4)

4.1 *

4.2 *

4.3 *

4.3.1 Gabor

4.3.2 LBP

4.4 *

4.5

4.6

5. HOG (2)

5.1 HOG

5.2 HOG*

5.3 HOG

5.4

6. SIFTSURF (4)

6.1SIFT

6.1.1 SIFT*

6.1.2 PCA-SIFT

6.1.3 Affine SIFT

6.1.4 SIFT

6.2 SURF

7. (2)

7.1 GIST*

7.2

7.3

8. (4)

8.1*

8.2

8.3PDE

9. 4

9.1

9.2

9.3

10. 4

10.1 *

10.2

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1. , ,

2. Milan Sonka, Vaclav Hlavac, Roger Boyle(Image Processing, Analysis, and Machine Vision)2006ISBN7-115-11496-X/TP.3544

3. . 2008

4. 2010

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2. . .

3. . .

4. . .

5. . .

6. . , , 2012

7. Rafael C. Gonzalez, Richard E. Woods, Steven L. Digital image processing using MATLAB:MATLAB/().

8. 2003

9. Jean-Luc Stark, . , , 2014.

B106Z001

The Frontier of Intelligent Science & Technology

32 2

,

:

CVPRICCVECCVACPR35

3~5

6

3~5

8

3~5

6

3~5

6

3~5

6

CVPRAAAIICCVECCVACPR

B106Z002

Analysis of Mass Data

32 2

NoSQL

1. 2

2. 2

2.1

2.2

3. 4

3.12

3.22

4.4

4.1 Deep Learning 2

4.2 2

5. 8

5.1 2

5.2 2

5.3 2

5.4HadoopMapReduce 2

6. 4

6.1 2

6.2 SAP HANA 2

7. NoSQL 4

7.1 Key-Value 2

7.2 2

8. 2

[1] Ian H. Witten H., Alistair Moffat.,Timothy C. Bell (Author) C.. (2)., 2014-1-1

[2] Frank J. Ohlhorst...2013-9-1

[3]. ..2013-10-1

[4] Pramod J. SadalageMartin Fowler. NoSQL Distilled : a brief guide to the emerging world of polyglot persistence Addison-Wesley, 2013

B106Z003

The Novel Technology of Software Engineering

32 2

,

1.

2.

3.

123

B106Z004

The development of trusted software

32 2

,

4.

5.

Trusted

Trustworthy

High Confidence

Dependability

6.

a)

b)

c)

d)

123

B106Z005

The Forefront of Information Security Technology

32 2

,

7.

8.

9.

123

B106Z006

New Remote Sensing Information Processing Technology

322

:

,

1.

1.1

1.2

1.3

1.4

1.5

2.

2.1

2.1.1

2.1.2

2.2

2.2.1

2.2.2

2.2.3

2.2.4

2.2.5

2.2.6

2.2.7

2.2.8

2.3

2.3.1

2.3.2

2.4

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2.4.3

2.4.4

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2.4.6

3

3.1

3.1.1

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3.1.3

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3.3.1

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[1] , , , 2003.

[2] , , , 2004

[3] 2012

[4] 2014

CodeL106B001

Principles and Methods of Artificial Intelligence



Pattern Recognition and Intelligence System, Computer Science and Engineering, Automatic Control


Discrete mathematics, Probability theory, Linear algebra, Algorithm design and analysis


The objective of this class is to teach students modern AI. Students will learn about the basic techniques and tricks of the trade. We also aspire to excite students about the field of AI.

5Teaching Method()

Narrating the main contents of the textbook with aid of computer multimedia and having discussions on class. Assigning home works and final project and providing additional readings.

6Introduction to the course ()

This course introduces students to the basics of Artificial Intelligence, which includes machine learning, probabilistic reasoning, and robotics.


(1) Overview of AI

(2) Statistics, Uncertainty, and Bayes networks

(3) Machine Learning

(4) Logic and Planning

(5) Markov Decision Processes and Reinforcement Learning

(6) Hidden Markov Models and Filters

(7) Adversarial and Advanced Planning

(8) Image Processing and Computer Vision

(9) Robotics and robot motion planning

(10) Natural Language Processing and Information Retrieval


[1] Russell and Norvig. Artificial Intelligence: A Modern Approach. A comprehensive reference for all the AI topics that we will cover.

[2] Koller and Friedman. Probabilistic Graphical Models. Covers factor graphs and Bayesian networks.

[3] Sutton and Barto. Reinforcement Learning: An Introduction. Covers Markov decision processes and reinforcement learning.

[4] Hastie, Tibshirani, and Friedman. The elements of statistical learning. Covers machine learning.

[5] Tsang. Foundations of constraint satisfaction. Covers constraint satisfaction problems.

9Author () HU, Xuelei

10Teacher () HU, Xuelei

CodeL106C001

Data Mining and Big Data Analysis

1. Class Hours () 32 Credits 2

2. Applicable professional():

Software Engineering, Computer Science and Technology, Pattern Recognition and Artificial Intelligence

3. Preparatory courses:

Probability and Statistics, Linear Algebra, Distributed Computing

4. Purpose of teaching():

This course introduces data mining methods and basic algorithms, such as descriptive data mining, predictive data mining algorithms, calculate the classification, clustering algorithms, time series analysis, association rule mining, big data analysis system introduced key technologies, such as large data storage technology, big data preprocessing techniques, in-memory computing, distributed and parallel computing technologies. Allows students to master basic data mining methods and big data analysis methods to enhance the ability in practice.

5. Teaching methods():

This course is in the classroom-based, Hands combining manner.

6. The main content and teaching requirements for students():

Teaching content of classroom teaching and practice of teaching composition, in which the content of classroom instruction and 20 hours; practice teaching content, 12 hours:

Chapter 1, data mining and analysis of large data overview (2 hours)

1.1 Overview of Data Mining

1.2 Overview of big data analytics

Chapter 2 Concept Description: Characterization and Comparison (2 hours)

2.1 What is the concept description?

2.2 attribute-oriented induction

2.3 Mining class comparison: distinguish between different classes

Chapter 3 mining (2 hours) association rules

3.1 Meaning of mining association rules

3.2 Association Rule Mining Algorithm

Chapter 4 Classification and Prediction (3 hours)

4.1 classification, prediction

4.2 decision (judgment) tree induction

4.3 Bayesian classifier

4.4 Some other classification methods

4.5 forecast

4.6 assess the accuracy of classification

Chapter 5, cluster analysis (3 hours)

5.1 means clustering analysis

5.2 Cluster analysis of data types and conversion

5.3 division method

Chapter 6, large data storage and pretreatment technology (3 hours)

6.1 row and column storage

Distributed File System 6.2

6.3 ETL

Chapter 7 Memory Computing technology (2 hours)

7.1 Memory Computing Overview

7.2 Memory Computing Architecture

7.3 Memory Data Management

Chapter 8 of distributed parallel computing technology (3 hours)

8.1 Map Reduce computing paradigm

Teaching content:

Experiment using the UCI machine learning databases http://www.ics.uci.edu/ ~ 5 sets of data sources used learn / MLRepository.html provided for specific data sets, designed and implemented classification or clustering algorithm , to accomplish the task. (4 hours)

The second experiment, choose a typical data mining algorithms, computing paradigm based on Map Reduce parallel computing. (4 hours)

7. Textbooks, reference books and student reading references()

Textbooks:

1, Data Mining - Concepts and Techniques (plus) (third edition) - Han Wei (Author), Micheline Kamber (Author), Pei Jian (Author), Fan (translator), X. Meng (translator) Machinery Industry Publishing Press, August 1, 2012

2, application and practice of data mining: the era of big data analysis scenarios Tao Xiamen University Press, October 1, 2013

Reference:

1, Li Hang. Statistical learning methods. Tsinghua University Press, 2012.

2, Big Data: Technology Strategy Practice Zhoubao Yao (editors), Liu Wei (Editor), Fan Cheng workers (Editor) Electronic Industry Press; 1st edition (June 1, 2013)

References:

1, Data Mining: What Is Data Mining? http://www.anderson.ucla.edu/fac ... lace / datamining.htm;

2, Effective Data Mining Technology http://www.enablesoft.com/.;

3, UCI datasets. Http://www.ics.uci.edu/ ~ mlearn / MLRepository.html

8. Outline written by: Li, Tao

9. Instructor: Li, Tao

CodeL106C003

Formal Specification and Testing of Software

1Class Hours () 32 Credits2




Software Engineering, Discrete Mathematics, Program Design Formal Semantics


With the growing significance of computer systems within industry and wider society, techniques that assist in the production of reliable software are becoming increasingly important. The complexity of many computer systems requires the application of a battery of such techniques. Two of the most promising approaches are formal specification methods and software testing. While traditionally they have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This course introduces the state of the art regarding ways in which the presence of a formal specification can be used to assist testing.

5Teaching Method()

Lecture, Seminar


Software testing is an important and, traditionally, extremely expensive part of the software development process, with the importance and cost depending on the nature and criticality of the system. Studies have suggested that testing often forms more than 50% of the total development cost, and hence dominates the overall production cost. Where formal specifications exist, these may be used as the basis for automating parts of the testing process and this can lead to more efficient and effective testing. The formal specifications may also be used as the basis for generating a test oracle. For those systems having a persistent internal state, the formal specifications can provide support for finding appropriate testing paths through a finite state structure. This course explores the ways in which the presence of a formal specification can support testing.


This course will

introduce the basic concept of software testing;

survey a range of formal specification methods;

discuss the relationship between formal specification methods and software testing;

focus on using the following formal specification methods to support software testing:

Model-based formal specifications

Finite State-based Specifications

Process Algebra Specifications

Algebraic Specifications

A student who has mastered the material of this course can be expected to

understand the basic concept of software testing;

be able to apply appropriate specification methods to specify software systems;

understand how to generate tests from a specification written using a model based notation such as Z, VDM, or B; a finite state machine; a process algebra; or an algebraic specification language, respectively.


(1) Introduction to Software Testing[M], Paul Ammann

(2) Using Formal Specifications to Support Testing[J], Robert M. Hierons and some of its references

9Author () Liu, Dongmei

10Teacher () Liu, Dongmei

CodeL106C004

Pattern Recognition Technology

1Class Hours () 32 Credits 2


Control science and engineering, computer science and technology


probability theory, mathematical statistics, linear algebra, matrix analysis


Pattern recognition is an important professional course for computer science, automatic control, and systems engineering majors. In this course, students can not only master some basic methods for pattern recognition, but also know recent developments in this research field.

5Teaching Method()

Class teaching, large coursework


This course deals with the fundamentals of characterizing and recognizing patterns and features of interest. Pattern recognition techniques are concerned with the theory and algorithms of putting abstract objects, e.g., measurements made on physical objects, into categories. Typically the categories are assumed to be known in advance, although there are techniques to learn the categories (clustering). Methods of pattern recognition are useful in many applications such as information retrieval, data mining, document image analysis and recognition, computational linguistics, forensics, biometrics and bioinformatics.


1 Introduction

1.1 Basic concept in pattern recognition

1.2 Pattern recognition theory and methods

1.3 Pattern recognition system

1.4 Applications in pattern recognition

2 Bayes decision theory

2.1 Introduction

2.2 Minimum error rate based Bayes decision

2.3 Minimum risk based Bayes decision

2.4 Classifier design

2.5 Normal distribution based Bayes classifier

2.6 Parameter estimation and non-parametric estimation

3 Linear discriminant function and linear classifier

3.1 Introduction

3.2 Linear discriminant function

3.3 Generalized linear discriminant function

3.4 Linear classifier design

3.5 Other classifiers

4 Feature selection and feature extraction

4.1 Introduction

4.2 Feature selection

4.3 Feature extraction

4.4 Applications

5 Cluster analysis

5.1 Introduction

5.2 Similarity criteria (similarity measurement)

5.3 Cluster criterion function

5.4 Classical clustering algorithms

5.5 Fuzzy cluster

6 Fisher discriminant analysis

6.1 Introduction

6.2 Traditional Fisher discriminant analysis

6.3 Discriminant analysis for multiple classes

6.4 Discriminant analysis for small sample size

6.5 Two-dimensional discriminant analysis

7 Support vector machine

7.1 Statistical learning theory

7.2 Linear support vector machine

7.3 Nonlinear support vector machine

7.4 Applications

8 Nonlinear discriminant analysis

8.1 Introduction

8.2 Kernel principal component analysis

8.3 Kernel discriminant analysis

9 Applications and advances in pattern recognition

9.1 Applications in pattern recognition

9.2 Advances in pattern recognition


Pattern Classification (second edition)

Richard O.Duda,etc

Statistical Pattern Recognition (second edition) Andrew R.Webb

Pattern Recognition(3rd Edition)

S. Theodoridis,etc

9Author () Sun, Quansen

10Teacher () Ji,Zexuan; Liu,Yazhou

CodeL106C005

Software Evaluation and Copyright Protection



Software Engineering, Computer Science and Technology, Control Science and Engineering, Pattern Recognition and Intelligent Systems, and other related disciplines


Probability Theory and Mathematical Statistics, Program Design


This course is a professional course on assessment and learning software copyright protection of basic principles and methods of assessment and understanding of the latest developments of software copyright protection at home and abroad. In introducing the development of advanced assessment techniques and typical cases on copyright protection, copyright protection to enable students to master the concept, design ideas, methods and key technologies, training students to analyze and problem-solving skills in software evaluation and copyright protection, improve complex systems to protect students' practical ability.

5Teaching Method()

Classroom teaching, laboratory

6Main Contents and Requirement for Students () (Note: "*" indicates the focus, "#" indicates the difficulty, "" indicates involving frontier):

(1) Software copyright and methods class design and organization (12 hours)

1.1 systems, models and simulation *

1.2 Continuous systems and discrete event systems

1.3 Software Patent Organization and methods of the class

1.4 Experimental and Case

(2) Categories of copyright integrated system design and organization (12 hours)

2.1 principles, methods and testing

2.2 System Modeling Method *

2.3 system classes Patent Organization *

2.4 Experimental and Case

(3) Software copyright design and organization (8 hours)

3.1 Development Overview

3.2 The concept and meaning

3.3 Basic Principles

3.4 The main method #

3.5 IT typical system

3.6 Typical industrial automation systems

3.7 Communication systems and applications in the field of electronic information

In curricular teaching activities focus on the students understand the problem context, the theoretical foundation and technical means. In order to develop students' ability to research, this course requires every students who elective the course write a patent application document.

Lesson to teach and experiment-based, self-study, supplemented by measures to discuss membership, etc., thereby ensuring the quality of teaching


(1) Wu Yue concept: the invention and utility model patent application documents: written case analysis (third edition), Intellectual Property Press, 2011

8Author () Tao, Li

9Teacher () Tao, Li

CodeL106C006

The Architectures and Protocols of the Next-Generation Internet

1. Class Hours 32 Credits 2

2. Majors Concerned


3. Preparatory Courses

Computer Network

4. Teaching Purpose

This course is designed to help graduate students in computer science and networking related programs to look into today's Internet architecture and protocol limitations. With the realistic usage context of current network application and service, we help the students identify and analyze the major disadvantages and threats the current Internet is exposed to, and understand its underlying systematic and architectural causes. The course also surveys and introduces the state-of-the-art technology and projects in future Internet design. Students after this course are expected to be motivated and inspired by the new Internet design discussed in class, and apply the behind reform motivation and design philosophy into their research work and exploration procedure.

5. Teaching Method

This is a graduate-level course with a proper combination of in-class lectures, seminars and out-of-class readings. The instructor uses lectures to explain the design and architecture of the current Internet, analyze and emphasize its limitations as inefficiency, unreliability, vulnerability. Then the students are grouped to explore in each limitation aspect for proposed or possible solutions. Publications from the NSF FIND and FIA projects are carefully selected and assigned to students as out-of-class readings. Typical and signification new Internet architecture designs will be brought to class for discussion.

6. Introduction to the Course

The current Internet system has been continuously reviewed, renovated and patched to address the increasing demands on capacity, quality of service, speed, and reliability. However, its fundamental host-to-host level TCP/IP based architecture is born vulnerable to security attacks, and lack of performance reliability of Internet services. Pioneer researchers have proposed a lot of new and innovative designs, architectures, protocols and mechanisms to build the next-generation Internet. This course is designed to help graduate PhD students with related research interests to quickly explore and understand the current research work and achievements on the new Internet innovation procedure. The course contents covers for future Internet the technology evolution at the physical/link layers in media transmission, the new switching and routing paradigms at the device and system layers, the proposed alternatives to TCP/IP at the transport layer, and the revolutionary new approaches for providing and managing network services and resources.

7. Main Contents and Requirements

a) Internet architecture overview

b) Internet switching and routing

c) TCP/IP protocol suite

d) Internet applications and services

e) Internet attacks and defenses

f) Named Data Networking

g) Cloud-computing-centric architecture

h) Expressive Internet Architecture

i) Choice Network

j) Network with Extreme Mobility

8. Textbooks, References

Next-Generation Internet: Architectures and Protocols, by Byrav Ramamurthy, George N. Rouskas, and Krishna Moorthy Sivalingam, Cambridge University Press, ISBN: 0521113687

TCP/IP Protocol Suite, by Behrouz Forouzan, McGraw-Hill Science/Engineering/Math, ISBN: 0073376043

http://www.nets-fia.net/

http://www.nets-find.net/

9. AuthorWei, Songjie,

10.TeacherWei, Songjie,

CodeL106C007

Computer Vision

1Class Hours () 32 Credits2

2Majors Concerned () CSE, EE


A strong programming background is assumed, as well as familiarity with linear algebra (vector and matrix operations), and knowledge of basic probability theory and statistics. Matlab will be the primary programming language/environment used in the programming assignments. All students are expected to have passedArtificial Intelligence, orData Modeling and Analysis, or an equivalent, before attending this course.


Introduce the student to the concepts of computer vision: what is meant, how it is done, and its current limitations. In-depth exercises will help the student to become familiar with the algorithms that are being used.

5Teaching Method()

Course study, project and homework.


This course introduces students to basic concepts and techniques in computer vision. Students successfully completing this course will be able to apply a variety of computer techniques for the design of efficient algorithms for real-world applications, such as optical character recognition, face detection and recognition, motion estimation, human tracking, and gesture recognition. The topics covered include image filters, edge detection, feature extraction, object detection, object recognition, tracking, gesture recognition, image formation and camera models, and stereo vision. The course will be graded based on programming assignments, and a final programming project.


1) Hardware

a. cameras: still, motion, stereo

b. lenses: pinhole, simple, compound, panoramic

c. sensors: film, CCD, CMOS, image intensifiers

d. exposure control: mechanical & electronic shutters

e. analog-to-digital conversion, communication, computer video cards

2) Light

a. radiometry

b. luminance, illumination

c. tendue, brightness theorem

d. surfaces: lambertian, specular, BDRF

e. environmental influences on appearance: inter-reflection, chameleons

3) Color

a. the electromagnetic spectrum

b. the human visual system, color camera technologies

c. color temperature, perceived color constancy, whiteness correction

d. color representations, gamut, many-to-oneness of color perception

e. infrared, ultraviolet, multi-spectral imaging

4) Feature extraction

a. simple features, simple filters: edges, neighborhoods

b. many variations on the theme of edge detection

c. complex features: man-made (e.g., corners), nature-made (e.g., faces)

d. decomposition, Gabor filters, wavelets, scale-independence

e. transformations, compression, lossy-compression

5) Motion

a. motion tracking, optical flow

b. structure from motion

c. affine structure from motion

d. projective structure from motion

e. video compression

6) Binocular and multi-view stereo

a. recovery of scene depth from a stereo pair

b. the corresponding point problem

c. occlusions, windows, periodicities, other ambiguities

d. stereo from motion, holography, motion from stereo

e. stereo for people (teleoperation) vs. stereo for computers

7) Image understanding

a. segmentation of objects and regions

b. foreground-background, sky-ground, belonging-anomalous

c. people, vehicles, threats, targets

d. vision-based driving and navigation

e. scene enhancement for inspection and teleoperation


[1] Computer Vision: A Modern Approach, Forsyth & Ponce, ISBN: 0130851981

[2] Image Processing: Analysis and Machine Vision, Sonka, Hlavac, & Boyle, ISBN: 0-534-9539

[3] Computer Vision, Shapiro & Stockman, ISBN: 0130307963

9Author () Liu, Yazhou

10Teacher () Liu, Yazhou

CodeL106C008

Information Security and Applied Cryptography


2. Majors Concerned


3. Preparatory Courses

Discrete Mathematics

C++ Programming

Operating Systems

Computer Network

4. Teaching Purpose

This course is a comprehensive and rigorous introduction about information security and data cryptography, with a focus on services, mechanisms, and applications. Besides the indispensable discussion about security protocols and encryption algorithms, the course contents span confidentiality, integrity, authentication, and availability, and a good coverage of practical applications of these ideas in the areas of operational, physical, network, and operating system security. Students are guided to examine and understand the security strength of protocols and systems against arbitrary attackers and attack scenarios.

5. Teaching Method

The course is provides as a composition of lectures, seminars, and laboratory exercises. The instructor gives lectures about fundamental concepts, theories, algorithm, and protocols. Then each student is required for course reading and research on a specific system or application for information security and cryptography. Students will be guided to host seminars about their research topics. Laboratory excises are designed to provide students opportunities to implement and experience the techniques learned in class.

6. Introduction to the Course

The first part of the course discuss rules and mechanisms to keep information secure on both individual computer and computer network. Contents include system management, file management, network management, service management, secrecy management, user authentication, risk evaluation and attack counter-measures. The second part covers essential cryptography concepts and algorithms in its original mathematical sense, while keeps connections and emphasis of these theoretical contents with practice. We will introduce classical and main-stream cryptography algorithms for symmetric-encryption, public key cryptography, data integrity and authentication. Upon successful completion of this course, students build their knowledge on how to design and manage information security in cyberspace, and what are the choices of cryptographic services and mechanisms to achieve their goals.

7. Main Contents and Requirements

a) Computer security management

b) File management

c) User management and authentication

d) Network security management

e) Symmetric-key encryption

f) Public key cryptography

g) Data integrity and digital signatures

h) Internet security protocols

8. Textbooks, References

Introduction to Modern Cryptography, by Jonathan Katz , Yehuda Lindell, Chapman and Hall/CRC, ISBN: 1584885513

Elementary Information Security, by Richard Smith, Jones & Bartlett Learning, ISBN: 1449648207

9. AuthorWei, Songjie

10.TeacherWei, Songjie,

CodeL106C009

Pattern Recognition



Pattern Recognition and Intelligent System


LinearAlgebra, Probabilityand Statistics


This Selective Course is for ph.D/Master candidates whose major is Pattern Recognition and Intelligent System. After learning this course, the ph.D/Master candidates should master the principal and methods of pattern recognition, and know the advances in the theoretical study on the pattern recognition.

5Teaching Method()

Oralinstruction


The main content of this course is about the advances in the nonlinear cases of pattern recognition related research fields, including nonlinear principal component analysis, nonlinear Fisher discriminate analysis, support vector machine, feature fusion and classifier combination, and unsupervised learning.


(Note: symbol * for emphasis, # for difficulty and for leading advance, respectively)

Chapter 1 Nonlinear Principal Component Analysis (9 credit hours)

1.1 principal component analysis (PCA)

1.2 kernel-based learning theory *#

1.3 kernel principal component analysis (KPCA) *

1.4 the pre-image problem in kernel methods #

1.5 gernalized KPCA

1.6 nonlinear PCA

Chapter 2 Nonlinear Discriminant Aanalysis (12 credit hours)

2.1 linear Fisher discriminant syslysis

2.2 kernel Fisher discriminant syslysis (KFDA) *

2.3 algorithm of selecting the significant training samples #

2.4 KFDA for multi-class problem *

2.5 regularization and kernel optimization in KFDA #

2.6 reproducing kernels and KFDA #

2.7 relationship between KPCA and KFDA #

2.8 the unified framework of discriminant analysis learning methods

Chapter 3 Support Vector Machines (15 credit hours)

3.1 linear learning method

3.2 dual form *

3.3 the generalization theory in statistical learning #

3.4 convex optimization *

3.5 support vector classifier (SVC) for linear separable cases *#

3.6 soft margin SVC *#

3.7 kernel based SVC *#

3.8 linear regression and hard -band hyperplane *#

3.9 support vector regression (SVR) for linear regression *#

3.10 the kernel optimization problem in SVM #

3.11 -SVC, C-SVC, -SVR, -SVR #

3.12 SVC for multi-class problem

3.13 SMO algorithm and big data problem in SVM

Chapter 4 Feature Fusion and Classifier Combination (6 credit hours)

4.1 ugly duckling theorem #

4.2 bias/variance dilemma in classification *#

4.3 feature fusion strategy

4.4 feature fusion based on canonical correlation analysis *

4.5 theory of classification combination *

4.6 bootstrap, resampling *

4.7 Bagging/Boosting in classifier design*

4.8 cross validation

Chapter 5 Unsupervised Learning (6 credit hours)

5.1 unsupervised Bayesian learning *

5.2 C-Means clustering

5.3 Fuzzy C-Means clustering #

5.4 kernel based C-Means clustering

5.5 unsupervised manifold learning *


Referencing Textbooks:

[1]. C.M.Bishop, Pattern Recognition and Machine Learning, Springer Press, 2007

Required References:

[2]. R.O.Duda, P.E. Hart, D.G.. Stork, , Pattern Classification,

[3]. N.Cristianini, John Shawe-Tayor, Introduction to Support Vector Machine,

[4]. B. Scholkopf and A. J. Smola, Learning with Kernels, MIT Press, 2001

[5]. John Shawe-Tayor et al, Kernel Method for Pattern Analysis

9Author () Sun, Tingkai, Liu, Chuancai

10Teacher () Sun, Tingkai

S106B001

Computer Vision and Image Understanding

32 2

,

---,

*#

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1.1

1.2

1.3

1.4

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* 2.1

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5 6

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* 5.1

# 5.2

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7 4

7.1

7.2

7.3

8 4

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8.2

8.3

[1] DA Forsyth and J. Ponce, Computer Vision: A Modern Approach, Prentice Hall

[2] 2004

[3] 1998

[4] 2006.

[5]Jean-Luc Stark, . , , 2014.

CodeS106B002

Distributed System





Computer Networking


This course aims to let students gain an understanding of the principles and techniques behind the design of distributed systems, such as locking, concurrency, scheduling, and communication across the network. Besides, students who choose this course should be able to gain practical experience designing, implementing, and debugging real distributed systems.

5Teaching Method()

In-class lecture and lab


This course is an advanced course in computer networking and distributed systems. Topics include: communication, processes, naming, synchronization, consistency and replication, security, distributed object-based systems and so on.


Detailed schedule is:

1 INTRODUCTION (2 hrs)

1.1 DEFINITION OF A DISTRIBUTED SYSTEM

1.2 GOALS

1.3 HARDWARE CONCEPTS

1.4 SOFTWARE CONCEPTS

1.5 THE CLIENT-SERVER MODEL

2 COMMUNICATION (4 hrs)

2.1 LAYERED PROTOCOLS

2.2 REMOTE PROCEDURE CALL

2.3 REMOTE OBJECT INVOCATION

2.4 MESSAGE-ORIENTED COMMUNICATION

2.5 STREAM-ORIENTED COMMUNICATION

LAB 1 SIMPLE DISTRIBUTED COMPUTING

3 PROCESSES (6 hrs)

3.1 THREADS

3.2 CLIENTS

3.3 SERVERS

3.4 CODE MIGRATION *

3.5 LOAD BALANCE *

3.6 SOFTWARE AGENTS *

LAB 2 ROOM DISTRIBUTION BASED UPON JAVA RMI

4 NAMING (2 hrs)

4.1 NAMING ENTITIES

4.2 LOCATING MOBILE ENTITIES

4.3 REMOVING UNREFERENCED ENTITIES

5 SYNCHRONIZATION (5 hrs)

5.1 CLOCK SYNCHRONIZATION

5.2 LOGICAL CLOCKS

5.3 GLOBAL STATE

5.4 ELECTION ALGORITHMS

5.5 MUTUAL EXCLUSION

5.6 DISTRIBUTED TRANSACTIONS

LAB 3 PERSONAL CALENDAR BASED ON CORBA

6 CONSISTENCY AND REPLICATION (2 hrs)

6.1 INTRODUCTION

6.2 DATA-CENTRIC CONSISTENCY MODELS

6.3 CLIENT-CENTRIC CONSISTENCY MODELS

6.4 DISTRIBUTION PROTOCOLS

6.5 CONSISTENCY PROTOCOLS

6.6 EXAMPLES

7 FAULT TOLERANCE (4 hrs)

7.1 INTRODUCTION TO FAULT TOLERANCE

7.2 PROCESS RESILIENCE

7.3 RELIABLE CLIENT-SERVER COMMUNICATION

7.4 RELIABLE GROUP COMMUNICATION

7.5 DISTRIBUTED COMMIT

7.6 RECOVERY

LAB 4 THE SOLUTION FOR BYZANTINE GENERAL PROBLEM

8 DISTRIBUTED OBJECT-BASED SYSTEMS (5 hrs)

8.1 CORBA TECHNOLOGY

8.2 DISTRIBUTED COM

8.3 JAVA RMI

8.4 COMPARISON OF CORBA, DCOM, AND RMI

9 NEW TECHNOLOGY WEB SERVICES (4 hrs)

9.1 INTRODUCTION

9.2 GOALS OF WEB SERVICES

9.3 UDDI,WSDL,SOAP and XML

9.4 FAMOUS COMPANIES JOBS

9.5 MS .Net

9.6 WEB SERVICES AND GRID COMPUTING


Andrew S. Tanenbaum, Maarten van Steen. Distributed Systems: Principles and Paradigms, second edition (photocopy edition), Beijing: Tsinghua University Press, 2008.

George Coulouris, Jean Dollimore and Tim Kindberg. Distributed Systems: Concepts and Design, fifth edition (photocopy edition), Beijing: Mechanical Industry Press, 2012.

Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications

http://pdos.csail.mit.edu/papers/chord:sigcomm01/chord_sigcomm.pdf

Freenet: A Distributed Anonymous Information Storage and Retrieval System

http://homepage.cs.uiowa.edu/~ghosh/freenet.pdf

A Survey of Peer-to-Peer Security Issues

http://www.cs.rice.edu/~dwallach/pub/tokyo-p2p2002.pdf

MapReduce: simplified data processing on large clusters

http://www.cs.amherst.edu/~ccm/cs34/papers/p107-dean.pdf



A Secure Migration Process for Mobile Agents

http://iel.ucdavis.edu/publication/2011/SPE.pdf

XML-Based Agent Communication, Migration and Computation in Mobile Agent Systems

http://iel.ucdavis.edu/publication/2008/JSS8098.pdf

Epidemic algorithms for replicated database maintenance

http://bitsavers.informatik.uni-stuttgart.de/pdf/xerox/parc/techReports/CSL-89-1_Epidemic_Algorithms_for_Replicated_Database_Maintenance.pdf

Tutorial: Gossip Protocols for Large-Scale Distributed Systems

http://sbrc2010.inf.ufrgs.br/resources/presentations/tutorial/tutorial-montresor.pdf

Scalable Application Layer Multicast

http://pages.cs.wisc.edu/~suman/pubs/sigcomm02.pdf

Toward Automatic Context-based Attribute Assignment for Semantic File Systems.

http://www.pdl.cmu.edu/PDL-FTP/ABN/CMU-PDL-04-105.pdf

Why cant I nd my les?

http://www.pdl.cmu.edu/PDL-FTP/Storage/hotOS03.pdf



S106B003

Automated functional testing theory

32 2

:CC++Java

;

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1.2

1.3 *

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2.2

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3.3

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4.4

4.5 UI Automation

4.6 QTP

4.7 QTP3

4.8 QTP

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1.QTP.:,2013

2. []James WhittakerJason ArbonJeff CarolloGoogle. .2013

Selenium

S106B004

Pattern Recognition Technology

32 2

4

5

*#

1 (2)

1.1 *

1.2 *

1.3

1.4

2 (6)

2.1

2.2 Bayes*#

2.3 Bayes *#

2.4 *

2.5 Neyman-Pearsen

2.6

2.7

2.8

2.9 *

2.10Bayes*

2.11

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3.1

3.2 *

3.3 *#

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3.5

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4.1

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4.3 *

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4.6 *#

5 (4)

5.1

5.2

5.3 *#

5.4 *#

5.5 K-L*#

6 Fisher (4)

6.1

6.2 fisher*

6.3 *#

6.4 *#

6.5 *#

7 (4)

7.1

7.2 *#

7.3 *#

7.4

8 (4)

8.1

8.2 *#

8.3 *#

8.4

Pattern Classification (second edition) (Richard O.Duda,etc

Statistical Pattern Recognition (second edition) Andrew R.Webb

Pattern Recognition(3rd Edition) () S. Theodoridis,etc

CodeS106B005

The Formal Semantics of Program





Discrete MathematicsProgramming Language


In order to adapt to the rapid development of computer science and technology and meet the demand for systems development personnel, it is necessary for the Graduates of Computer Science and Technologyto have a comprehensive understanding of The Formal Semantics of Program while deeply studying your own professional knowledge. In this way, the students can master the two essential components of the Programming language: the formal syntax and formal semantics of the language.

The course is an integral part of the programtheory, itprecisely define and explain the semantics of computer programming languages by taking mathematics as a tool as well as using symbols and formulas. Through this course, students can lay a solid foundation on the formal semantics of computer language, master the basic theory, basic methods and important conclusions of formal semantics. Also, they can learn about the latest research developments and hot spots at home and abroad to be well prepared for the future research.

5Teaching Method()

The course bases on lecturing in the classroom, supplemented by discussions among the students, also 25 percent experimental contents included (Axiomatic semantics proof based on Hoare's axiomatic method).


semantic, true value and logic

1.1 proposition, sentence meaning and semantics

1.2 message types of the sentence meaning

1.3 study scope of the Formal Semantics of Program

1.4 statusof logic in the semantic study

alleged semantics

2.1 alleged semantic basis

2.2 storage semantics

2.3 environmental semantics

2.4 command semantics

2.5 examples ofalleged semantics

operational semantics

3.1 structured operation semantics andattributegrammar of program

3.2 machine calculation of application expression

axiomatic semantics

4.1 Hoare axiom system

4.2 Dijkstra's weakest precondition

4.3 Martin-Lof type theory

4.4 proof method of program correctness

semantics of concurrent programming languages

5.1 concurrent systems

5.2concurrent programming language and its

alleged semantics and axiomatic semantics

5.3 communication sequential process and its

operational semantics

instance applicationsof auxiliary formal

semantics description

axiomatic semantics proof experiment based on

Hoare's axiomatic method

7.1 programming of Hoare axiomatic rules

7.2 semantic nature of program to be proved: discretion of P(precondition) and

Q(postcondition).

7.3 derivation and validation logistic


Formal Syntax and Semantics of Programming Languages: a laboratory based approach. Kenneth Slonneger, Barry L. Kurtz. ISBN: 0-201-65697-3. Addison-Wesley Publishing Company. 1995.

8Author () Zhang, Kun

9Teacher () Zhang, Kun

S106B006

Principles and Methods of Artificial Intelligence

32 2

:

7

Stuart Russell, Peter Norvig2004

[1] 2010

[2] 2011

[3] 2004

[4] 2007

[1]Stuart Russell, Peter Norvig2004

[2] 2010

[3] 2011

S106B007

The Formal Semantics of Program

32 2

:

CC++Java

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1.2

1.3 *

1.4 *

2 (4)

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4 (8)

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4.2 Dijkstra*#

4.3 Martin-Lof*#

4.4 *#

5 (6)

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5.2 *

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6 (2)

7 Hoare(4)

7.1 Hoare

7.2 PQ

7.3 *#

1.(),..:,2006

2.,,..:,2000

3...:,1994

Glynn Winskel..:,2004

CodeS106B008

Distributed System





Computer Networking


This course aims to let students gain an understanding of the principles and techniques behind the design of distributed systems, such as locking, concurrency, scheduling, and communication across the network. Besides, students who choose this course should be able to gain practical experience designing, implementing, and debugging real distributed systems.

5Teaching Method()

In-class lecture and lab


This course is an advanced course in computer networking and distributed systems. Topics include: communication, processes, naming, synchronization, consistency and replication, security, distributed object-based systems and so on.


1 INTRODUCTION (2 hrs)

1.1 DEFINITION OF A DISTRIBUTED SYSTEM

1.2 GOALS

1.3 HARDWARE CONCEPTS

1.4 SOFTWARE CONCEPTS

1.5 THE CLIENT-SERVER MODEL

2 COMMUNICATION (4 hrs)

2.1 LAYERED PROTOCOLS

2.2 REMOTE PROCEDURE CALL

2.3 REMOTE OBJECT INVOCATION

2.4 MESSAGE-ORIENTED COMMUNICATION

2.5 STREAM-ORIENTED COMMUNICATION

LAB 1 SIMPLE DISTRIBUTED COMPUTING

3 PROCESSES (6 hrs)

3.1 THREADS

3.2 CLIENTS

3.3 SERVERS

3.4 CODE MIGRATION *

3.5 LOAD BALANCE *

3.6 SOFTWARE AGENTS *

LAB 2 ROOM DISTRIBUTION BASED UPON JAVA RMI

4 NAMING (2 hrs)

4.1 NAMING ENTITIES

4.2 LOCATING MOBILE ENTITIES

4.3 REMOVING UNREFERENCED ENTITIES

5 SYNCHRONIZATION (5 hrs)

5.1 CLOCK SYNCHRONIZATION

5.2 LOGICAL CLOCKS

5.3 GLOBAL STATE

5.4 ELECTION ALGORITHMS

5.5 MUTUAL EXCLUSION

5.6 DISTRIBUTED TRANSACTIONS

LAB 3 PERSONAL CALENDAR BASED ON CORBA

6 CONSISTENCY AND REPLICATION (2 hrs)

6.1 INTRODUCTION

6.2 DATA-CENTRIC CONSISTENCY MODELS

6.3 CLIENT-CENTRIC CONSISTENCY MODELS

6.4 DISTRIBUTION PROTOCOLS

6.5 CONSISTENCY PROTOCOLS

6.6 EXAMPLES

7 FAULT TOLERANCE (4 hrs)

7.1 INTRODUCTION TO FAULT TOLERANCE

7.2 PROCESS RESILIENCE

7.3 RELIABLE CLIENT-SERVER COMMUNICATION

7.4 RELIABLE GROUP COMMUNICATION

7.5 DISTRIBUTED COMMIT

7.6 RECOVERY

LAB 4 THE SOLUTION FOR BYZANTINE GENERAL PROBLEM

8 DISTRIBUTED OBJECT-BASED SYSTEMS (5 hrs)

8.1 CORBA TECHNOLOGY

8.2 DISTRIBUTED COM

8.3 JAVA RMI

8.4 COMPARISON OF CORBA, DCOM, AND RMI

9 NEW TECHNOLOGY WEB SERVICES (4 hrs)

9.1 INTRODUCTION

9.2 GOALS OF WEB SERVICES

9.3 UDDI,WSDL,SOAP and XML

9.4 FAMOUS COMPANIES JOBS

9.5 MS .Net

9.6 WEB SERVICES AND GRID COMPUTING


Textbooks :

Andrew S. Tanenbaum, Maarten van Steen. Distributed Systems: Principles and Paradigms, second edition (photocopy edition), Beijing: Tsinghua University Press, 2008.

ReferencesGeorge Coulouris, Jean Dollimore and Tim Kindberg. Distributed Systems: Concepts and Design, fifth edition (photocopy edition), Beijing: Mechanical Industry Press, 2012.

References for Students

Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications

http://pdos.csail.mit.edu/papers/chord:sigcomm01/chord_sigcomm.pdf

Freenet: A Distributed Anonymous Information Storage and Retrieval System

http://homepage.cs.uiowa.edu/~ghosh/freenet.pdf

A Survey of Peer-to-Peer Security Issues

http://www.cs.rice.edu/~dwallach/pub/tokyo-p2p2002.pdf

MapReduce: simplified data processing on large clusters

http://www.cs.amherst.edu/~ccm/cs34/papers/p107-dean.pdf



A Secure Migration Process for Mobile Agents

http://iel.ucdavis.edu/publication/2011/SPE.pdf

XML-Based Agent Communication, Migration and Computation in Mobile Agent Systems

http://iel.ucdavis.edu/publication/2008/JSS8098.pdf

Epidemic algorithms for replicated database maintenance

http://bitsavers.informatik.uni-stuttgart.de/pdf/xerox/parc/techReports/CSL-89-1_Epidemic_Algorithms_for_Replicated_Database_Maintenance.pdf

Tutorial: Gossip Protocols for Large-Scale Distributed Systems

http://sbrc2010.inf.ufrgs.br/resources/presentations/tutorial/tutorial-montresor.pdf

Scalable Application Layer Multicast

http://pages.cs.wisc.edu/~suman/pubs/sigcomm02.pdf

Toward Automatic Context-based Attribute Assignment for Semantic File Systems.

http://www.pdl.cmu.edu/PDL-FTP/ABN/CMU-PDL-04-105.pdf

Why cant I nd my les?

http://www.pdl.cmu.edu/PDL-FTP/Storage/hotOS03.pdf



S106B009

Information Security

32 2

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1.3

1.4

1.5

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Code S106C001

Bioinformatics

1Class Hours ()32 Credits2


Pattern Recognition and Intelligent System


Machine Learning, Pattern Recognition Technique


The main purposes of this course are to bring fundamental introduction and future directions of bioinformatics to students, broaden the students horizons, and build a solid foundation for future scientific research.

5Teaching Method()

Teaching methods of this course include teaching, self-learning, and reading report.


Bioinformatics is an emerging interdiscipline of biology, computer science, information science, statistics, and so on.

Prospective students should have basic knowledge on molecular biology, computer science, and mathematics. Particularly, students should learn machine learning and pattern recognition technique courses in advance.

After finishing this course, students will master basic tools and have the capability to perform related genomics and proteomics research.


Chapter 1 - Overview of bioinformatics (2)

1.1 The scope of bioinformatics

1.2 Bioinformatics and Internet

1.3 Useful bioinformatics sites on the WWW

Chapter 2 - Data acquisition (2)

2.1 Sequence DNA,RNA and proteins

2.2 Determination of protein structure

2.3 Gene and protein expression data

2.4 Protein interaction data

Chapter 3 - Databases - contents, structure and annotation (2)

3.1 File formats

3.2 Annotated sequence databases

3.3 Genome and organism-specific databases

3.4 Miscellaneous databases

Chapter 4 - Retrieval of biological data (2)

4.1 Data retrieval with Entrez and DEGET/LinkDB

4.2 Data retrieval with SRS (sequence retrieval system)

Chapter 5 - Searching sequence databases (4)

5.1 Sequence similarity searches

5.2 Amino acids substitution matrices

5.3 Databases searches: FASTA and BLAST

5.4 Sequence filters

5.5 Iterative databases searches and PSI-BLAST

Chapter 6 - Multiple sequence alignment (2)

6.1 Multiple sequence alignment and family relationship

6.2 Protein families and pattern databases

6.3 Protein domain families

Chapter 7 - Sequence annotation (2)7.1 Principles of genome annotation7.2 Annotation tools and resources

Chapter 8 - Structural bioinformatics (8)

8.1 Conceptual models of protein structure

8.2 The relationship of protein three-dimensional structure to protein function

8.3 The evolution of protein structure and function8.4 Obtaining, viewing and analyzing structural data 8.5 Structural alignment8.6 Classification of proteins of known three-dimensional structure: CATH and SCOP8.7 Introduction to protein structure prediction8.8 Structure prediction by comparative modeling8.9 Secondary structure prediction8.10 Advanced protein structure prediction and prediction strategies

Chapter 9 - Microarray data analysis(2)

9.1 Microarray data: analysis methods 9.2 Microarray data: tools and resources 9.3 Sequence sampling and SAGE

Chapter 10 - Seminar on Bioinformatics (8)

10.1 Literature review in Bioinformatics

10.2 Discussion on selective papers of structural bioinformatics


Referencing Textbook:

Charlie Hodgman, Andrew French, and David R.Westhead. Instant Notes in Bioinformatics, Taylor & Francis; 2 edition (September 24, 2009).

Required References

Five most recently published papers in bioinformatics.

9Author () Yu, Dong-Jun

10Teacher () Yu, Dong-Jun

S106C003

Applied Cryptography

32 2

:

1

1.1

1.2

1.3

2

2.1

2.1.1

2.1.2

2.2

2.2.1

2.2.2

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2.2.4

2.3

2.3.1

2.3.2

2.3.3

2.3.4

2.3.5

3

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3.1.2

3.1.3

3.2

3.2.1

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3.2.1 skey

3.3

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3.4

3.5

3.6

3.7

4

4.1

4.2

4.3

4.4

5

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5.1.1

5.1.2

5.1.3

5.2

5.2.1

5.2.2

5.2.3

6

6.1

6.2

6.3

6.3.1

6.3.2

6.3.3

7

7.1

7.2 Diffie-Hellman

7.3

7.4 RSA

7.5 DSA

7.6

Hash

(1)

(2)

(3) hash

Bruce Schneier. (C). 2000.

William Stallings.:(4). 2006

. . 2000

Code S106C004

Fundamentals of Image Analysis

1Class Hours ()32 Credits2


Computer Science and Technology, Pattern Recognition and Intelligent System,Biomedical Engineering


Experience with C/C++ or Matlab. Courses on Matrix Theory, Probability Theory


This is an advanced senior and graduate level elective course on Digital Image Processing and Analysis, which provides a comprehensive theory of various image processing tasks and the practical experience to simulate them. Upon the completion of this course, the students will have gained a hands-on experience about the below topics through extensive simulation assignments.

To study the image fundamentals and mathematical transforms necessary for image processing. To study the image enhancement techniques To study image restoration procedures. To study the image compression procedures. To study the image segmentation and representation techniques.

5Teaching Method()

Combined tutorial mode and Class Teaching

Group discussion: Small-group discussion consists of two to six students and is given a task to do or a topic to discuss. One student may be selected to summarize the group's discussion for the whole class.

Practice: Matlab Programming for Image Processing Algorithm Implementation;C++ Programming Practice in Image Processing

Seat work. Following a teacher-directed or group discussion, each student is assigned a project to consider or work to be completed that is related to what preceded it


Chapter 1: Digital Image Fundamentals 2h

What is an image?

Image Representation

Human visual system

Sampling and Fourier analysis

Chapter 2:Spatial Image enhancement Technique 4h

Basic grey level transformation

Histogram equalization

Image subtraction

Image averaging

Spatial filtering: Smoothing, sharpening filters Laplacian filters

Chapter 3:Basic transformations and Frequency domain filters 4h

Introduction to Fourier Transform and DFT Properties of 2D Fourier Transform FFT Separable Image Transforms -Walsh Hadamard Discrete Cosine Transform, Haar, Slant Karhunen Loeve transforms.

Frequency domain filters : Smoothing Sharpening filters Homomorphic filtering

Chapter 4:Image Restoration and Reconstruction 6h

Model of Image Degradation/restoration process

Noise models

Inverse filtering

Least mean square filtering

Constrained least mean square filtering

Blind image restoration

Pseudo inverse

Singular value decomposition

Chapter 5:Morphological Image Processing 4h

Erosion, dilation, opening, closing

Basic Morphological Algortihms: hole filling, connected components, thinning,

skeletons

Chapter 6: Color Image Processing 2h

Color Models

Color Transforms

Image Segmentation Based on color

Chapter 7: Image Compression 4h

Fundamentals

Basic Compression Methods

Chapter 8: Image Segmentation and measurements 6h

Edge detection Thresholding - Region Based segmentation

Boundary representation: chair codes- Polygonal approximation Boundary segments boundary descriptors: Simple descriptors-Fourier descriptors - Regional descriptors

Texture


Textbooks:

Digital Image Processing (3rd edition), Rafael C. Gonzalez and Richard E. Woods, Prentice Hall, 2007, ISBN 013168728X, http://www.imageprocessingplace.com

Digital Image Processing Using MATLAB, Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins, Prentice-Hall, 2003. ISBN 0130085197

Additional References:

Handbook of Image and Video Processing, Publisher: Academic Press; 2 edition (June 21, 2005), Alan C. Bovik (Author)

Computer Vision Research Groups: http://www-2.cs.cmu.edu/~cil/v-groups.html

ITK - Segmentation & Registration Toolkit

8Author () XiaoLiang, Wei, Zhihui

9Teacher () Xiao, Liang

CodeS106C005

Services Computing and Business Process Management (I)

1. Class Hours ()32 Credits2




Software Engineering, Database


This course aims to let students gain an understanding of the paradigm of services computing and business process management, as well as related techniques and approaches, like SOA and loosely-couple, Web services, service composition, workflow, BPEL, Petri nets, etc. Besides, students are expected to understand the mergence of services computing and business process management (BPM).

5Teaching Method()

In-class lecture


This course is an advanced course of software engineering. Topics include: the paradigm of services computing, SOA and loosely-couple, Web services, service composition, business process management, workflow, Petri nets, BPEL, formal verification for services and business processes, and so on.


1 INTRODUCTION (4 HRS)

1.1 PARADIGM OF SERVICES COMPUTING

1.2 BUSINESS PROCESS MANAGEMENT

1.3 RELEVANT CONFERENCES AND JOURNALS

2 SERVICES COMPUTING (5 HRS)

2.1 SERVICE-ORIENTED ARCHITECTURE (SOA)

2.2 WEB SERVICES

2.3 WSDL,UDDI, SOAP

2.4 RESTFUL SERVICES

3 SERVICE COMPOSITION (6 HRS)

3.1 PROGRAMMING-IN-THE-LARGE

3.2 SERVICE COMPOSITION

3.3 SERVICE ORCHESTRATION

3.4 SERVICE CHOREOGRAPH

3.5 BPEL

4 BUSINESS PROCESS MANAGEMENT (5 HRS)

4.1 WORKFLOW AND BUSINESS PROCESS

4.2 WHERE SERVICES COMPUTING AND BUSINESS PROCESS MEET

4.3 KEY ISSUES RELATED TO BUSINESS PROCESS MANAGEMENT (BPM)

4.4 ABSTRACT AND EXECUTABLE PROCESSES

5 FORMAL VERIFICATION(6 HRS)

5.1 FORMAL METHODS

5.2 PETRI NETS

5.3 ANALYSIS TECHNIQUES OF PETRI NETS

5.4 WORKFLOW NETS

5.5 WORKFLOW VERIFICATION AND ANALYSIS

6 DISCUSSION (6 HRS)

6.1 HOT TOPICS

6.2 FURTHER STUDY

6.3 DISCUSSION ON SOME TOPICS


Textbooks

Thomas Erl. Service-Oriented Architecture: Concepts, Technology, and Design, Beijing: Science Press, 2012.

Dumas, Marlon, et al. Fundamentals of business process management. Berlin: Springer, 2013.

Required References for Students:

Service-Oriented Computing: State-of-the-Art and Open Research Issues

https://doc.freeband.nl/dsweb/Get/Rendition-19063/UvT%20SOC%20research%20agenda.pdf

Service-oriented computing

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1607964

Petri nets: Properties, analysis and applications

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=24143&tag=1

The application of Petri nets to workflow management

http://www.worldscientific.com/doi/abs/10.1142/S0218126698000043

9Author () Song, Wei

10Teacher () Song, Wei

CodeS106C006

Machine Learning



Computer Science and Technology, Pattern Recognition and Intelligent system


Probability and Statistic, Linear Algebra, Optimization


The purpose of this course is to introduce the basic algorithms of machine learning, let students have strong fundamentals of machine learning and the ability of using machine learning algorithms to address real-world applications.

5Teaching Method()

This course is conducted in a combined manner including teaching, seminar, etc.


The main content of this course contains: 1) the fundamental algorithms of machine learning, such as linear regression, Perceptron model, logistic regression, support vector machines, nave Bayes, etc.; 2) the key concepts and principles in machine learning, such as assumption, learning, inferences, model selection, generalization, etc.; 3) real-world machine learning application such as text classification system.


1. Introduction

1.1 What is machine learning

1.2 The History of machine learning

1.3 Real-world applications of machine learning

2. Basic concepts in machine Learning

2.1 Supervised machine learning

2.2 Semi-supervised, unsupervised machine learning

2.3 Cost function and model learning

2.4 Evaluation and model selection

2.5 Over-fitting and Regularization

3. Linear regression

3.1 Regression and classification

3.2 Least mean square criterion

3.3 Maximum likelihood estimation

3.4 Application of linear regression

4. Logistic regression

4.1 Model assumptions

4.2 Model learning

4.3 Gradient descent, Newtons method, and Quasi-Newton

4.4 Softmaxregresson

5. Perceptron

5.1 The perceptron criterion

5.2 Parameter learning

5.3 Stochastic gradient descent

6. Support vector machines

6.1 Maximum margin criterion

6.2 Dualoptimzation

6.3 Soft-margin SVM

6.4 Kernel functions

6.5 How to use the LIBLINEAR toolkit

7. Nave Bayes

7.1 Generative model vs. Discriminative model

7.2 Multinomial event model

7.3 Parameter learning based on MLE

7.4 Bayes decision rule

7.5 Applications of Nave Bayes

8. Semi-supervised and unsupervised machine learning

8.1 Self-training

8.2 Co-training

8.3Transductive SVM

8.4 Semi-supervsed Nave Bayes based on EM algorithm

8.5 K-means clustering

8.6 Gaussian Mixture Model

9. Ensemble learning

9.1 The principle of ensemble learning

9.2 Ensemble rules

9.3 The Bagging algorithm

9.4 The Boosting algorithm

9.5 The other ensemble learning algorithms

10. Machine learning practice

10.1 Implementation of supervised machine learning algorithms

10.2 Design of a text classification system


Textbooks

Tom M. Mitchell, Machine Learning, McGraw Hill, 1997

Bishop C. M. Pattern recognition and machine learning. New York: springer, 2006.

Reading materials

1. Linear regression: http://cs229.stanford.edu/notes/cs229-notes1.pdf

2. Perceptron model: Bishop, Pattern recognition and machine learning, Section 4.1

3. Support vector machines:


http://blog.pluskid.org/?page_id=683

http://ntu.csie.org/~piaip/svm/svm_tutorial.html

4. Nave Bayes:


5. EM Algorithm:


http://www.cs.columbia.edu/~mcollins/em.pdf

6. Gaussian Mixture Model:

http://cs229.stanford.edu/notes/cs229-notes7a.pdf

http://cs229.stanford.edu/notes/cs229-notes7b.pdf

http://home.in.tum.de/~xiaoh/pub/em.pdf

Bishop, Pattern recognition and machine learning, Chapter 9

9Author () Xia, Rui ()

10Teacher () Xia, Rui ()

CodeS106C007

Trusted Computing Technologies

1.Class Hours ()32 Credits2




Operating System ;Computer Networks;Information Security and Cryptography


This course is designed to guide graduate students in computer science and technology program to understand the cutting-edge Trusted Computing technologies and application specifications. Students after the class should achieve cognition and familiarity of the followings:

The basic and advanced Trusted Platform Modules (TPM) capabilities

The good practice of TPMs and related technologies in academic researches and enterprise applications

The thought and experience in applying the necessary tools and information to design and build systems that take advantage of trusted computing

5Teaching Method()

This is a graduate-level course with a proper combination of in-class lectures, seminars and out-of-class readings and projects. The instructor uses lectures to explain the concepts and Specifications from TCG architectures. Then the students are grouped to survey and discuss in each TCG work group and solution. Publications from top-tier Trusted Computing conferences and journals such as IEEE ATC, TDSC are carefully selected and assigned to students as out-of-class readings. Typical and signification TPM implementations and applications are brought to class for discussion.

6Main Contents and Requirement for S

Documents

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