REU 2017

1. MTuThF: 8 hours, 9am-12pm and 1-6pm in the office or in a lab.
2. Wednesday: 4 hours in the office or in a lab, and 4 hours for exploration.
3. You are required to sign up a time sheet posted at the office every day.

Schedule

NOTE: We encourage students to consider submitting their final project reports as papers (full papers, or short papers) to venues such as: the annual National Workshops for REU Research in Networking and Systems (REUNS), IEEE CCNC, IEEE ICNC, IEEE NCA and others with July/August 2017 deadlines.

MU campus-wide summer activities included in and paid for by our REU program (Contact: Katie Hays, haysk@missouri.edu)

Intern/mentor BBQ (student & mentor)

Evening Seminars

MU Summer Forum participation

Farewell picnic (student & 3 guests)

Newsletters, copies & office supplies

Computer networks basics

• Watch Computer Networks, U. of Washington
• Watch Computer Networks Lectures

Research advice

• Watch Advice to young scientists

Software-Defined Networking related materials

• Watch Software Defined Networking
• GENI https://www.geni.net
• Open Networking Foundation https://www.opennetworking.org
• http://faculty.missouri.edu/calyamp/publications/geni-cclabs-gree14.pdf
• http://faculty.missouri.edu/calyamp/publications/vdc-analyst-comnet14.pdf
• http://faculty.missouri.edu/calyamp/publications/sciencedmz-refarch-icnc14.pdf
• http://faculty.missouri.edu/calyamp/publications/openFlow-vdc-lb-im13.pdf

Reading paper

• How to read a paper
• How to read a research paper

Writing paper

• Use IEEE publication Word template or LaTeX style files to typeset your report.

Giving presentation

• How to give a talk
• The Slide-Structure of a Technical 20-min Presentation
• MS project presentation examples: Sandeep, Sean

Computer Science Ethics

• Code of Ethics, ACM
• Computer and Information Ethics, Stanford Encyclopedia of Philosophy
• Social and ethical issues in computer science

IRB training (http://research.missouri.edu/irb/CITI_Instructions) to receive IRB certification

• Responsible Conduct of Research courses
• Human Subject Research courses

OKR

• How OKRs work in Google

Latex resources

• MikTex as the LaTeX compiler, http://miktex.org/
• Texmaker as the Editor Environment, http://www.xm1math.net/texmaker/
• Tutorial: https://www.latex-tutorial.com/tutorials

An REU project example: Software Defined Networking on an Android Ad Hoc Network, by Brandon Guttersohn, 2012.

• Weekly/daily research journal
• Project Website
• A conference paper: SDNAN: Software-defined networking in ad hoc networks of smartphones, IEEE Communications and Networking Conference (CCNC), pp.861-862, 11-14 Jan. 2013.
• More about me

• Visual Computing at the Network Edge for Disaster Incident Response: Project Overview, Poster
  Advisor: Dr. Prasad Calyam
  Gradutate Student Mentors: Jon Patman, Dmitrii Chemadonav
  External Collaborator: Dr. Flavio Esposito, St. Louis University
  Research Students: Andrew Crutcher, Caleb Koch, Kyle Coleman
  
  Abstract:

  Recent research studies have stimulated the development of various computation offloading frameworks for bringing cloud services closer to the user via edge networks. Specifically, an edge device needs to offload computationally intensive tasks because of energy and processing constraints. This need presents the problem of (1) determining how to partition a particular task and (2) identifying which edge nodes receive the task. We address the problem of task partitioning by accounting for its size and the available network resources. We then incorporate machine learning (ML) techniques to select appropriate servers for offloading. In particular, we use linear regression to develop a “hyperprofile” for each edge node based on a given set of device and server features. This hyperprofile provides a cost estimate of offloading to that particular server. We employ a k–nearest neighbor search on the set of hyperprofiles to identify candidate edge nodes to which partitions of the task are offloaded. We evaluate our approach via a set of simulations and show that we can offload various tasks to nodes under a variety of circumstances. These experiments are ongoing and conclusive results will be presented at the forum.



• Virtual Reality for Online Social Training of Children with Austism: Project Overview, Poster
  Advisor: Dr. Henry He
  Graduate Student Mentors: Guanghan Ning, Sai Shreya Nuguri
  Research Students: Adam Starr, Chiara Zizza, Devin Hudson
  
  Abstract:

  Autism Spectrum Disorder (ASD) is characterized by atypical behavioral patterns, social communication impairments, cognitive problems, and learning disabilities. Traditional treatments require trained therapists; however, in rural areas, many individuals with ASD lack such support due to high costs and limited access to trained staff. Virtual learning environments such as iSocial have shown promise in providing treatment remotely. In this work-in-progress paper, we outline vSocial, a virtual reality learning environment (VRLE) for youth with ASD that connects remote users with special education teachers over high-speed networks. We have implemented multiple vSocial learning modules in High Fidelity, a social VR platform. VR can offer immersive lessons which are more easily applied to the real world than non-VR lessons. With our evaluation, we are able to show that vSocial is a functional VRLE with reasonable bandwidth usage and viable cost.



• Stroke Patient Daily Activity Observation System: Project Overview, Poster
  Advisor: Dr. Skubic
  Graduate Student Mentor: Mengxuan (Mary) Ma
  Clinical Collaborator: Dr. Rachel Proffitt, Occupational Therapy
  Research Students: Jaired Collins, Joseph Warrens
  
  Abstract:

  Stroke is a leading cause of long-term adult disability. Stroke patients can recover through rehabilitation programs prescribed by occupational therapists (OT); however, an individualized rehabilitation program can reduce recovery times compared to traditional ones. In this paper, we describe a robust interface to extract depth and skeleton data, which allows personalized therapist care and facilitates rapid healing. To this end, we present a daily observation system (DAOS) that uses a Kinect v2 sensor to retrieve motion data. The DAOS is placed in an imperfect data collection environment, the kitchen. The depth information allows us to perform action recognition to track what movements are problem areas, while skeleton data is used to calculate mean velocities of joints, max extensions, symmetry of hand movements, and other useful metrics for a therapist to utilize. Action recognition is accurate through reasonable data sets. Combining an action and its opposite improves recognition rates. Assessment values such as max extension and joint velocities are verified to be accurate within a small margin of error with a Vicon system. Through action recognition and accurate assessment, we present a novel system that can assist therapists and their ability to provide quality care to stroke victims.



• Healthy Coping in Diabetes: Project Overview, Poster
  Advisors: Dr. Yi Shang, Dr. Min Soon Kim
  Graduate Student Mentors: Jayanth Kanugo, Nishant Chettri, Peng Sun
  Research Students: Amy Cheng, Vaishnavi Raghavaraju
  
  Abstract:

  Patient self-management of diabetes is crucial to reducing its chronic progression and serious future health complications. Due to the physical and emotional complexities of diabetes, adherence to self-care activities can quickly decline due to disheartening progress. While there exists technology that caters to diabetic self-management, the current state of the art mostly assists patients through mobile applications. For the elderly, these mobile apps are shown to be marginally effective and even frustrating to use. Therefore, this project proposes Healthy Coping in Diabetes, a Google Home application that can act as an innovative intervention strategy to assist elderly patients with self-management of Type II Diabetes. Our application emphasizes the use of a voice interface rather than a strictly tactile interface in order to provide older patients with the opportunity to engage in practical application of clinical care management more easily. We perform a feature-based comparison of our application against currently available mobile apps to determine the relative fulfillment of official AADE self-management components. Finally, we use the Usability Metrics to receive feedback and further improve the application capabilities.

• Day 1: Briefing and lunch at Flat Branch
  
• Data Center Tour
  
• EEE Summer School, June 8
  
• VR Photos, June 14
  
• Group photo
  
• Power Plant Tour, 21st July
  

Sponsored by the National Science Foundation (Award CNS-1359125)

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