IS 709/809: Computational Methods for IS Research — Spring 2016

Times: Wednesday 4:30pm – 7:00pm
Location: Sherman Hall 210
Instructor: Nirmalya Roy
Instructor’s Office Location and Hours: ITE 421 Monday 1:30 – 3:00pm, or by appointment
Instructor’s Email: nroy at umbc dot edu

Course Descriptions: Computational methods are inevitable tools for many facets of information systems research. These methodologies are used as fundamental tools and techniques in research and advanced practice in information systems, with particular focus on networking hardware and software technologies that deal with data and systems. Data becomes useful when it provides meaningful information through data analysis and mining, pattern recognition and learning, information extraction and visualization. System becomes useful when it meets the required end performance metrics through the governing policies and procedures and underlying models and simulations. Sophisticated data analysis and system performance measurements require a mixture of skills ranging from algorithmic foundation, data mining, machine learning, computational modeling, and information systems performance evaluation. This course covers the mixture of these skills with the goal of providing information science graduate and masters students with the ability to employ them in future research. The course is project-based, allowing students to understand the use of computational methods to pursue research objectives and interests.

Course Objectives: The purpose of this course is to provide a comprehensive foundation to apply computational research methods in solving problems in Information Systems. This course should enhance students’ reasoning, problem-solving and modeling abilities, particularly in dealing with algorithmic problems. More specifically, the course has the following objectives:
• Familiarize students with the concepts and applications of computational techniques (machine learning, data science, graph theory, computational complexity, information and communication technology, operational managements etc.) to solve computational problems.
• Teach students how to think and formalize problems algorithmically and experimentally.

We will not assume any background beyond high school level mathematics and familiarity with programming concepts. However, students are expected to spend time in learning the concepts in this course, many of which will be covered in details.

Course Topics:
• Algorithmic Complexity
• Information and Communication Technology
• System Modeling and Performance Measurements
• Data Science
• Machine Learning
• Applications

Course Overview: 
• Each student will be required to present 1 paper in the entire semester.
• There will be one mid-term exam, and no final exam.
• There will be a development project (programming/testbed development). The main idea is to use real devices to build real systems which help collect data and albeit information latter! Example is developing smart home activity recognition data collection toolkit, or developing a household appliance energy metering system, or sensor based stress monitoring system or a system which keeps an eye on your diet. We will be using off-the-shelf commercially available devices, smart phones, wireless sensors, smart plugs or any other devices as need be for this purpose. The main intuition behind this semester long development project is getting real data for your research project. Energy education related project equipment will be sponsored by Constellation Energy while all other equipment will be provided by the instructor.

• The next most important component of the course will be the final research project obviously based on the computational methods you learn in this class. You are expected to do these projects in groups of 2 (preferably). Think about the research project problem early, discuss your ideas with me, read papers in the area, formulate your solution, and finally get the data from your development project and implement, and present it to the class.

Course Prerequisites: IS 650 (Data Communication and Networks) or IS 733 (Data Mining) or consent of the instructor

Recommended Textbooks (Optional):

Course Requirements and Grading:

Course Participation & Class Presentation 10%
Homeworks, Quizzes & Programming Assignments  30%
1 Midterm Exam  30%
Research & Development Project  30%

Tentative Course Schedule:
(Subject to change as the semester progresses)

Week Date Topic Handout/Assignment Due Notes
 1  1/27 Course overview, logistics, etc. Course Syllabus Class Cancelled due to the Snow
 2  2/3 Introduction to Algorithm Analysis and System Modeling Introduction 
 3  2/10 Introduction to Machine Learning

Math Review for Computational Methods and Algorithm Analysis

Homework 1
Presentation Logistics
Introduction ML (Textbook IML: Chapter 1, Sections  1.1 and  1.2)

Math Review 1 (Textbook DSAA: Chapter 1, Section 1.2)
Math Review 2 (Textbook DSAA: Chapter 1, Section 1.2)

 4  2/17 Computational Complexity Homework 2

Quiz 1

Homework 1

Research
Paper Selection

Comp. Complexity (Textbook DSAA: Chapter 2, Section 2.1, 2.2, 2.3, 2.4)
 5  2/24 Sorting Algorithm Analysis [Insertion sort, Selection sort, Shellsort, Mergesort, Quicksort, Decision trees, Counting Sort, External sorting (Multiway Merge) etc.] Sorting Algo. Analysis (Textbook DSAA: Chapter 7, Section 7.1, 7.2, 7.4, 7.6, 7.7, 7.9, 7.10, 7.11)
 6  3/2 Research Paper Presentation

(Peter, Vishak, David, and Foteini)

Homework 2
 7  3/9 Research Paper Presentation

(Timothy, Mohammad, Arpita, and Navneet)

 8  3/16 Spring Break
 9  3/23 Introduction to Graph Algorithms, Topological Sort

Testbed development project progress pitch
[3-minutes oral presentation]

Homework 3 Testbed development project
3-minutes madness slide due by 3/22
Intro Graph Algorithms (Textbook DSAA: Chapter 9, Section 9.1, 9.2)
10 3/30 Shortest Paths; Network Flow; Minimum Spanning Tree Applications (Prim’s and Kruskal’s Algorithms) Shortest Path
Network Flow
MST (Textbook DSAA: Chapter 9, Section 9.3, 9.4, 9.5)
11 4/6 Machine Learning: Supervised Learning; Bayesian Learning Homework 4

Mining Association Rules,
Mining Sequential Patterns

Homework 3 (extended to 4/13) Supervised (Textbook IML: Chapter 2, Sections  2.1,  2.3, 2.4, 2.5, 2.7, and 2.8)
Bayesian (Textbook IML: Chapter 3, Sections  3.1,  3.2 and 3.6)
12 4/13 Introduction to TND, Statistics of Things Waiting in the Line (Queueing Theory), Characteristics of Queueing Process

Erlang Concept, Basic Model & Notation, Little’s Theorem

Poisson process & Exponential distribution, Markovian Property, Memorylessness, Stochastic Process, Markov Process

Homework 3 Intro Queueing Theory (Textbook FQT: Chapter 1, Sections  1.1, 1.2, 1.3, 1.4, 1.5, 1.7, 1.8, and 1.9)
13 4/20 Birth & Death process, Markovian Systems

Single Server system: M/M/1-Queue; steady state probabilities, M/M/1 performance measures

Exam Review

 Homework 5 Homework 4 Simple Queueing Model  (Textbook FQT: Chapter 2, Sections  2.1 and  2.2)
Exam Review
14 4/27 Exam
15 5/4 Final Research & Development Project Presentation Homework 5 Final Project Report Template

Sample IEEE Style File

Research Paper Presentation Schedule:

Peter Guerra: RunBuddy: a smartphone system for running rhythm monitoring; ACM Ubicomp 2015
Foteini  Cheirdari: MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones; ACM Ubicomp 2015

David Welsh: Monitoring building door events using barometer sensor in smartphones, ACM Ubicomp 2015

Arpita Roy: Unobtrusive Sensing Incremental Social Contexts using Fuzzy Class Incremental Learning, ICDM 2015

Timothy Michael Casey: Mindless computing: designing technologies to subtly influence behavior, ACM Ubicomp 2015
Vishak Iyer: Empath2: A Flexible Web and Cloud-based Home Health Care Monitoring System, ACM PETRA 2015
Navneet Kaur: Your reactions suggest you liked the movie: automatic content rating via reaction sensing, ACM UbiComp 2013

Mohammad Alodadi: Connecting Personal-scale Sensing and Networked Community Behavior to Infer Human Activities, ACM Ubicomp 2014

Supplementary Materials: Links to Research Papers and Possible Research Projects:

Good conferences and workshops in broad area of Information Systems (machine learning, data mining, pervasive and ubiquitous computing, networking, health IT, HCC ):

  • IEEE ICDM, ICDE, PerCom, ICML,
  • ACM KDD, SenSys, IPSN, Ubicomp, MobiSys, BuildSys, CHI

Research Papers:

Contactless Sleep Apnea Diagnosis on Smartphones; Rajalakshmi Nandakumar, Shyamnath Gollakota, Nathaniel Watson, ACM MobiSys 2015

Invisible Sensing of Vehicle Steering with Smartphones; Dongyao Chen, Kyong-Tak Cho, Sihui Han, Zhizhuo Jin, Kang G. Shin, ACM MobiSys 2015

Approval Voting and Incentives in Crowdsourcing; Nihar Shah, Dengyong Zhou, Yuval Peres; ICML 2015

Bayesian Active Transfer Learning in Smart Homes. Tom Diethe, Niall Twomey, Peter Flach; ICML workshop 2015

Unobtrusive Sensing Incremental Social Contexts using Fuzzy Class Incremental Learning, Zhenyu Chen, Yiqiang Chen, Xingyu Gao, Shuangquan Wang, Lisha Hu, Chenggang Clarence Yan, Nicholas D. Lane, Chunyan Miao, IEEE International Conference on Data Mining (ICDM’15)

Smart Devices are Different: Assessing and Mitigating Mobile Sensing Heterogeneities for Activity Recognition; Allan Stisen, Henrik Blunck, Sourav Bhattacharya, Thor Siiger Prentow, Mikkel Baun Kjærgaard, Anind Dey, Tobias Sonne, and Mads Moller Jensen, In Proc. 13th ACM Conference on Embedded Networked Sensor Systems (SenSys 2015), Seoul, Korea, 2015

Connecting Personal-scale Sensing and Networked Community Behavior to Infer Human Activities, Nicholas D. Lane, Li Pengyu, Lin Zhou, Feng Zhao, ACM Ubicomp 2014

SymDetector: detecting sound-related respiratory symptoms using smartphones, Xiao Sun, Zongqing Lu, Wenjie Hu, Guohong Cao; ACM Ubicomp 2015

Evaluating tooth brushing performance with smartphone sound data, Joseph Korpela, Ryosuke Miyaji, Takuya Maekawa, Kazunori Nozaki, Hiroo Tamagawa; ACM Ubicomp 2015

SleepTight: low-burden, self-monitoring technology for capturing and reflecting on sleep behaviors, Eun Kyoung Choe, Bongshin Lee, Matthew Kay, Wanda Pratt, Julie A. Kientz; ACM Ubicomp 2015

RunBuddy: a smartphone system for running rhythm monitoring; Tian Hao, Guoliang Xing, Gang Zhou; ACM Ubicomp 2015

MyoVibe: vibration based wearable muscle activation detection in high mobility exercises; Frank Mokaya, Roland Lucas, Hae Young Noh, Pei Zhang; ACM Ubicomp 2015

DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar; Tauhidur Rahman, Alexander T. Adams, Ruth Vinisha Ravichandran, Mi Zhang, Shwetak N. Patel, Julie A. Kientz, Tanzeem Choudhury; ACM Ubicomp 2015

DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning; Nicholas D. Lane, Petko Georgiev, Lorena Qendro; ACM Ubicomp 2015

Monitoring building door events using barometer sensor in smartphones, Muchen Wu, Parth H. Pathak, Prasant Mohapatra; ACM Ubicomp 2015

MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones, Mashfiqui Rabbi, Min Hane Aung, Mi Zhang, Tanzeem Choudhury; ACM Ubicomp 2015

Mindless computing: designing technologies to subtly influence behavior, Alexander T. Adams, Jean Costa, Malte F. Jung, Tanzeem Choudhury; ACM Ubicomp 2015

Personalization revisited: a reflective approach helps people better personalize health services and motivates them to increase physical activity; Min Kyung Lee, Junsung Kim, Jodi Forlizzi, Sara Kiesler; ACM Ubicomp 2015

Need accurate user behaviour?: pay attention to groups! Kasthuri Jayarajah, Youngki Lee, Archan Misra, Rajesh Krishna Balan; ACM Ubicomp 2015

Household occupancy monitoring using electricity meters, Wilhelm Kleiminger, Christian Beckel, Silvia Santini; ACM Ubicomp 2015

PerCCS: person-count from carbon dioxide using sparse non-negative matrix factorization; Chandrayee Basu, Christian Koehler, Kamalika Das, Anind K. Dey; ACM Ubicomp 2015

puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation; Nazir Saleheen, Amin Ahsan Ali, Syed Monowar Hossain, Hillol Sarker, Soujanya Chatterjee, Benjamin Marlin, Emre Ertin, Mustafa al’Absi, Santosh Kumar; ACM Ubicomp 2015

I did not smoke 100 cigarettes today!: avoiding false positives in real-world activity recognition; Le T. Nguyen, Ming Zeng, Patrick Tague, Joy Zhang; ACM Ubicomp 2015

Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis, Luca Canzian, Mirco Musolesi; ACM Ubicomp 2015

CityMomentum: An Online Approach for Crowd Behavior Prediction at a Citywide Level; Zipei Fan, Xuan Song, Ryosuke Shibasaki, Ryutaro Adachi; ACM Ubicomp 2015

Student Support Services: UMBC is committed to eliminating discriminatory obstacles that disadvantage students based on disability. Student Support Services (SSS) is the UMBC department designated to receive and maintain confidential files of disability-related documentation, certify eligibility for services, determine reasonable accommodations, develop with each student plans for the provision of such accommodations, and serve as a liaison between faculty members and students regarding disability-related issues.  If you have a disability and want to request accommodations, contact SSS in the Math/Psych Bldg., room 213 or at 410-455-2459.  SSS will require you to provide appropriate documentation of disability.  If you require accommodations for this class, make an appointment to meet with me to discuss your SSS-approved accommodations.

Academic Integrity: Cheating in any form, will be subject to discipline according to university regulations. Projects that contain plagiarized materials will receive an automatic letter grade of ‘F’. Multiple violations will be handled according to university regulation. Please refer to Academic Integrity for more information.