IS 709/809: Computational Methods for IS Research — Fall 2019

Times: Tuesday 7:10pm – 9:40pm

Location: Information Technology 233

Instructor: Nirmalya Roy

Instructor’s Office Location and Hours: ITE 421 Thursday 10:00 am – 12:00 pm, 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 robust and reliable 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 (graph theory, computational complexity, information and communication technology, operational managements, system performance evaluation 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
  • System Modeling and Performance Measurements
  • Information and Communication Technology
  • Smart Service Systems Applications

Course Prerequisites: IS 698 (Smart Home Health Analytics) or IS 733 (Data Mining) or consent of the instructor

Recommended Textbooks (Optional):

Week

Date

Topic

Handout/ Assignment

Due

Notes

1

9/3

  • Course overview, logistics, etc.
  • Introduction to Algorithm Analysis and System Modeling



2

9/10

  • Math Review for Computational Methods and Algorithm Analysis
  • Research Reflection (Smart Service Systems, CPS, FW discussions)


3

9/17

  • Computational Complexity
  • Research Reflection (Smart Service Systems + CPS + FW)



Class Cancelled

4

9/24

  • Computational Complexity
  • Research Reflection (Smart Service Systems + CPS + FW)

Homework 1

Research Reflection Schedule

  • Saydeh Karabatis: Smart ankle-foot Prosthesis
  • Redwan Walid: Behavior Monitoring and Depression Analytics
  • Rohan Putatunda: The Living Bridge
  • Mahmudur Rahman: CPS for Emerging Fitness Space
  • Indrajeet Ghosh: Motion Tracking Library for Sports Analytics
  • Argho Sarkar: Brain-Computer Interface for Restoration of Walking
  • Antonios Xenakis: Verified Human Interfaces, Control, and Learning for Semi-Autonomous Systems
  • Neil Kpamegan: Control and Moving Target Defense for Secure CPS

5

10/1

  • Computational Complexity (Contd.)
  • Research Reflection


HW 2

Research Reflection Schedule

  • Masnoon Nafees: Smart Factories for Human-Robot Workflow
  • Emon Dey: Building Energy-Efficient IoT Frameworks
  • Raja Yalamanchi: A Smart, Hyperlocal, Context-Aware Hazard Notification Service System
  • Xinbo Yang: Fitness app connects exercisers to experts
  • Teja Pasam: Mobile Assistive Robots for Elderly Care

6

10/8

  • Sorting Algorithm Analysis [Insertion sort, Selection sort, Shellsort, Mergesort, Quicksort, Decision trees, Counting Sort, External sorting (Multiway Merge) etc.]
  • Research Paper Presentations (Indrajeet Ghosh, Neil Kpamegan)



Indrajeet Ghosh

Neil Kpamegan

7

10/15

  • Research Paper Presentations



Saydeh Karabatis, Rohan Putatunda, Emon Dey,

Md Mahmudur Rahman,

Redwan Walid, Masnoon Nafees

8

10/22

  • Research Paper Presentations
  • Research Proposal



Argho Sarkar, Antonios Xenakis, Raja Yalamanchi, Xinbo Yang,

Teja Pasam

9

10/29

  • Sorting Algorithm Analysis (Contd.)



10

11/5

  • Introduction to Graph Algorithms, Topological Sort, Shortest Paths, Network Flow; Minimum Spanning Tree Applications (Prim’s and Kruskal’s Algorithms)



11

11/12

  • Introduction to Graph Algorithms (Contd.)
  • Smart Service or Cyber-Physical Systems Performance Evaluation: Queueing Theory, Erlang Concept, Basic Model & Notation, Little’s Theorem


Network Flow

MST (Textbook DSAA: Chapter 9, Section 9.3, 9.4, 9.5)

12

11/19

  • Poisson process & Exponential distribution, Markovian Property, Memorylessness, Stochastic Process, Markov Process, Birth & Death process, Markovian Systems
  • Research project progress pitch [5-minutes oral presentation]



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

11/26

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


Simple Queueing Model (Textbook FQT: Chapter 2, Sections 2.1 and 2.2)

Exam Review

14

12/3

  • Exam


HW 4


Research Paper Presentation Schedule:

(See above)

Research Papers:

In this course we will be discussing various papers recently published in ACM/IEEE UBICOMP (IMWUT) / ISWC 2019. Please check the following link.

http://ubicomp.org/ubicomp2019/program_papers.html


Oct 8 Presentations:

  • Indrajeet Ghosh: Bringing IoT to Sports Analytics
  • Neil Kpamegan: Drinks & Crowds: Characterizing Alcohol Consumption through Crowdsensing and Social Media


Oct 15 Presentations:

  • Saydeh Karabatis: Beyond Respiration: Contactless Sleep Sound-Activity Recognition Using RF Signals:
  • Rohan Putatunda: The Case for Smartwatch-based Diet Monitoring
  • Emon dey: A First Look at Deep Learning Apps on Smartphones
  • Md Mahmudur Rahman: Detecting Unseen Anomalies in Weight Training Exercises
  • Redwan Walid: Reducing Inefficiencies in Taxi Systems
  • Masnoon Nafees: Using Built-In Sensors to Predict and Utilize User Satisfaction for CPU Settings on Smartphones


Oct 22 Presentations:

  • ARGHO Sarkar: Swimming style recognition and lap counting using a smartwatch and deep learning
  • Antonios Xenakis: A Data-Driven Misbehavior Detection System for Connected Autonomous Vehicles
  • Raja A Yalamanchi: Understanding Cycling Trip Purpose and Route Choice Using GPS Traces and Open Data
  • Teja Pasam: Using Unobtrusive Wearable Sensors to Measure the Physiological Synchrony Between Presenters and Audience Members
  • Xinbo Yang: Integrating Cyber-Physical Systems in a Component-Based Approach for Smart Homes


Sample Research Project Reports:

David Welsh and Nirmalya Roy. Smartphone-based Mobile Gunshot Detection, In Proceedings of the 13th Workshop on Context and Activity Modeling and Recognition (CoMoRea’17), co-located with PerCom, March 2017. [pdf]

H M Sajjad Hossain, Md Abdullah Al Hafiz Khan, and Nirmalya Roy. SoccerMate: A Personal Soccer Attribute Profiler using Wearables, in Proceedings of the 1st IEEE PerCom International Workshop on Behavioral Implications of Contextual Analytics (BICA), co-located with PerCom, March 2017. [pdf]

Abu Zaher Md Faridee, Sreenivasan Ramasamy Ramamurthy, H M Sajjad Hossain, and Nirmalya Roy. HappyFeet: Recognizing and Assessing Dance on the Floor, in Proceedings of the ACM 19th International Workshop on Mobile Computing Systems and Applications (HotMobile), Feb. 2018 [pdf]

Varun Mandalapu, Lavanya Elluri and Nirmalya Roy. Developing Machine Learning based Predictive Models for Smart Policing, in Proceedings of the 1st IEEE International Students Workshop on Smart Computing (SmartStudents), co-located with SmartComp, pp. 1-6, Washington D.C., June 2019

Research Projects:

Team No

Team Members

Topic/Title

Devices/ Datasets

1

Neil Kpamegan

Drinking Habits: What kind of drinker are you?

smart phone

2

Rohan Putatunda

Urban Sound Classification: A Data Augmentation Approach with CNN

3

Saydeh Karabatis

An undignified end of life: Identifying major predictors of Dementia Alzheimer's disease

4

Masnoon Nafees

Are you doing your exercise regularly? Monitoring and Assessing the Gym Activity

MS band

5

Emon Dey

Deep IoT: Recognizing Human Activity on IoT Devices using Deep Models

Shimmer, Nvidia Jetson nano

6

Argho Sarkar & Mahmudur Rahman

Are you doing your exercise correctly? Detecting Abnormal Body Positions during Weight Training Exercise

MS band, UCI Machine Learning Repository

7

Antonios Xenakis & Redwan Walid

Is your better half prone to divorce? Predicting Divorce using Data Science

https://archive.ics.uci.edu/ml/datasets/Divorce+Predictors+data+set

8

Raja Anirudh Yalamanchi & Teja Pasam

Are you a good driver? Utilizing Smartwatch to Detect the Driving Patterns

MS band

9

Xinbo Yang

Keeping Track of Changing Body Weights

MS band

10