Big Program 2015 - Association for Computing...
28
OCWiC 2015 Program Sawmill Creek Resort | 400 Sawmill Creek Drive W | Huron, OH 44839
Transcript of Big Program 2015 - Association for Computing...
OCWiC 2015 -‐ Program
Sawmill Creek Resort | 400 Sawmill Creek Drive W | Huron, OH 44839
1
This page is intentionally left blank
2
Be inspired, dream big! Welcome to OCWiC 2015!
This will be the 6th biennial meeting of The Ohio Conference for Women in Computing. It will be our biggest event thus far, and I am glad to see such an enthusiastic response and such diverse participation. I am grateful for everyone’s contribution and I want to thank all members of the organizing committee for their efforts. A special “thank you!” goes to our sponsorship chair, Valerie Cross. This conference would not have been possible without the contribution of our
sponsors. I also want to show appreciation to the OCWiC Advisory Committee, ACM-‐W, and Jodi Tims for all their guidance and support. Their efforts helped make this conference not only possible but also an outstanding event.
I hope you enjoy the conference and have a fantastic experience! Alina Lazar, General Chair
This is my first time serving as Program Chair for OCWiC, and I must acknowledge that I am impressed by the large number of submissions received, as well as by their high quality. We received over 40 proposals, making it a real challenge to pack so many presentations into two half days. Very impressive also is the diversity of topics covered by the proposals, making a rich program, and demonstrating that women contribute to many computing areas. This year’s program has something for everyone: exciting workshops with hands-‐on
activities; panels debating various issues in computing; interesting research showcased in presentations and posters. In addition, our invited speakers give us a glimpse into various computing industries, offer us tips on how to attract more female students into computing, and discuss the newest challenges in artificial intelligence.
The success of this meeting is ensured by many people’s hard work. I would like to thank our keynote and invited speakers, our panelists and workshop organizers, as well as those presenting posters and papers. Thank you to the organizing committee, in particular to Rachelle Hippler who made arrangements for this new venue and helped with the schedule, and to Valerie Cross’ outstanding effort in finding sponsors. Jodi Tims, Bettina Bair, and Ellen Walker, former organizers, kindly shared their expertise and gave quick and valuable feedback. Many thanks to Bonita Sharif, who handled the registrations, and to Angela Guercio, who maintained our website. A big thank you goes to Cindy Marling, who organized the poster session, and to Ashley Kline-‐Tozzi, who organized the resume review session. I am also thankful to our reviewers – your work and expertise helped with the difficult job of selecting the program. Finally, thank you to our sponsors for their generous financial and human resource contribution, and for investing in the future generation.
Our meeting attracts not only faculty and graduate students, but also undergraduates and several high -‐school students together with industry representatives. With such a diverse group there is much to learn from each other. Welcome all and make the most of this meeting: make sure you make new connections and friends, allow yourself to be inspired by new role models, and dream big!
Cheers to an enriching experience! Sofia Visa, Program Chair
3
OCWiC 2015 Organization
OCWiC Organizing Committee Alina Lazar, Youngstown State University, General Chair Sofia Visa, College of Wooster, Program Chair Valerie Cross, Miami University, Sponsorship Chair Rachelle K. Hippler, Bowling Green State Univ., Hospitality Chair Bettina Bair, The Ohio State University, Publicity Chair Angela Guercio, Kent State University at Stark, Website Chair Bonita Sharif, Youngstown State University, Registration Chair Cindy Marling, Ohio University, Poster Chair Ashley Kline-‐Tozzi, Cardinal Health, Resume Review Chair
OCWiC Advisory Committee Bettina Bair, The Ohio State University Valerie Cross, Miami University Rachelle Kristof Hippler, Bowling Green State University Jodi Tims, Baldwin-‐Wallace University Denise Vinton, Eaton Ellen Walker, Hiram College Shannon Whalen, The University of Akron Stu Zweben, The Ohio State University, Emeritus
Presentation Reviewers Bettina Bair, The Ohio State University Denise Byrnes, College of Wooster Valerie Cross, Miami University Rachelle Kristof Hippler, Bowling Green State University Zhong-‐Hui Duan, University of Akron Angela Guercio, Kent State University at Stark Janyl Jumadinova, Allegheny College
Cindy Marling, Ohio University Louis Oliphant, Hiram College Bonita Sharif, Youngstown State University Dana Simian, Lucian Blaga University, Romania Jodi Tims, Baldwin-‐Wallace University Ellen Walker, Hiram College Shannon Whalen, The University of Akron
Poster Contest Judges Neetu Agarwal, Microsoft Maha Allouzi, Kent State University Mary Jean Blink, TutorGen, Inc. Sarah Chapman, Red Fox Road Michelle Cheatham, Wright State University Barb Kruetzkamp, General Electric Olga Mendoza-‐Schrock, Wright State University
Karen Meyer, Wright State University Louis Oliphant, Hiram College Meral Ozsoyoglu, Case Western Reserve University Sue Penko, Baldwin Wallace University Annu Prabhakar, University of Cincinnati Julie Swango, OneDrive Inc. Kera Watkins, Wright State University
Resume Reviewers Sarah Chapman, Red Fox Road Cindy Heckman, Raytheon Barbara L. Kruetzkamp, General Electric Pamela Mater, General Electric Amy Mauger, Cardinal Health
Olga Mendoza-‐Schrock, Wright State University Lynn Miller, Raytheon Marie Smith, Eaton Catherine L. Smith, Marathon Petroleum Denise Vinton, Eaton
Student Volunteers Pratistha Bhandari, College of Wooster Katie Crosby, Bowling Green State University Jenna Crosby, Bowling Green State University
Allyson Sherrard, Bowling Green State University Jenna Wise, Youngstown State University Jessica Whitely, Youngstown State University
Sponsoring Organizations Platinum Sponsors Bowling Green State University Case Western Reserve University – Dept. of Electrical Engineering and Comp. Science Cardinal Health Eaton General Electric Kent State University – School of Digital Science Marathon The Ohio State University Ohio University – Russ College of Engineering and Technology Wright State University
Gold Sponsors ACM-‐W (Women in Computing) Microsoft Miami University – College of Engineering and Computing OEC University of Dayton
Silver Sponsors JP Morgan The University of Akron Miami University – Department of Computer Science and Software Engineering
Bronze Sponsors Carnegie Mellon University College of Wooster Raytheon The Ohio State University -‐ College of Engineering, Office of Research, and Advanced Computing Center for the Arts and Design Oracle Youngstown State University Friend Sponsors AK Steel Hyland Software HMB
4
Table of Contents
Preface ............................................................................................................................................................................... 2
OCWiC 2015 Organization ........................................................................................................................................... 3
Table of Contents ............................................................................................................................................................ 4
Conference Schedule ...................................................................................................................................................... 6
Venue -‐ map ...................................................................................................................................................................... 8
Invited speakers .......................................................................................................................................................... 10 Keynote speaker, sponsored by ACM DSP: Thinking About Thinking: A Talk in Four Parts; Lynn Andrea Stein
Invited speaker: Strategically Recruiting Women into Undergraduate Computer Science Programs; Gretchen Achenbach
Industry speakers: GE Aviation – How the Full Flight Data Team Leads the Industry using Big Data, Cloud and PredixTM; Kristen Hausfeld Application Development in the Business World; Amy Mauger, Ashley Kline-‐Tozzi
Research talks 1 .......................................................................................................................................................... 11 SmartHealth Technology for Type 1 Diabetes Management; Cindy Marling
EEG-‐Based Driver Drowsiness Detection; Youxuan Lucy Jiang, Marvin Andujar, Juan Gilbert
Biomedical Informatics Research and Applications: The Intersection of Computer Science and Healthcare; Andrea Peabody
Research talks 2 ........................................................................................................................................................... 13 Method Stereotypes as Patterns of Design in OO Software and their Applications; Natalia Dragan
An Eye-‐tracking Experiment Studying Problem Solving Behavior; Jessica Whitely, Jenna Wise, Alina Lazar, Bonita Sharif
Recommender Systems as Persuasion Technology: An E-‐Commerce Perspective; Melinda McGucken
Research talks 3 ........................................................................................................................................................... 15 Cracking Binary Analysis; Michelle Cheatham
SCALE: Student Centered Adaptive Learning Engine; Mary Jean Blink, Ted Carmichael, John C. Stamper
Dynamic Privacy Management in Services-‐based Interactions; Nariman Ammar
Research talks 4 ........................................................................................................................................................... 17 Performance versus quality of responses in online systems; Jaimie Kelley
Interning at Multiple ABB Locations in Ohio and Germany; Rachel Turner
Taking on an Internship; Ana Morales
Poster session .............................................................................................................................................................. 19 Web Service Privacy, Compatibility and k-‐Anonymity; Nariman Ammar
Dynamic Selfish Routing; Christine Antonsen
5
Prestissimo; Elizabeth Bentivegna, Cole Peppis, Ben Kuperman
Server-‐based Code Review and Analysis for Software Development Teams; Pratistha Bhandari
I AM AI -‐ Interactive Actor Modeling for Introducing Artificial Intelligence: A Senior Capstone Project; Alexandra Coman, Victoria Kerr, Thomas Bowersock, Yuki Matoba, Andrew Warren
What will you achieve with your computing skills?; Jenna Crosby, Jessica Carroll
Alexander Polynomial Program; Kiera Dobb
Hackathons: A benefit to student programmers; Samantha Glass
Legacy Facades: An approach to retrofit Data-‐Parallel platforms for legacy softwares; Puja Gupta, Christopher Stewart
EEG-‐Based Driver Drowsiness Detection; Youxuan Lucy Jiang, Marvin Andujar, Juan Gilbert
The Use and Misuse of Disposable Email; Samantha Mater, Krista Lafentres, Stephen Checkoway, Cynthia Taylor
Towards the Quantified Self: Diabetes Management; Hannah Quillin
A Frequency-‐ and Clustering-‐based Methodology for Finding Transcription Factor Binding Sites; Laith Sersain, Carlos Gonzalez, Sofia Visa
An Overview of Competitive Facility Location Games with Facilities as Players; Amanda Strominger, Alexa Sharp
Workshop 1: LEGO Mindstorms EV3 Robotics; Janyl Jumadinova ............................................................. 23
Workshop 2: Eye tracking; Bonita Sharif, Jenna Wise, Jessica Whitely .................................................... 23
Workshop 3: Open-‐Source Jeopardy; Jaimie Kelley ........................................................................................ 23
Workshop 4: Building self-‐confidence S.O.U.L.; Heather Petersen ............................................................ 24
Career perspectives panel ........................................................................................................................................ 24 Career Discussions; Mary Jean Blink
Social Networking with Style; Sarah Chapman,
Success IT Careers for Women; Angela McCutcheon
Programming panel .................................................................................................................................................... 25 Programmers in Groups: Male Bonding and Women in CS Classes; Andrea DeMott
Programming -‐ not its stereotypes; Kirsten Signar
Academic panel, Denise Byrnes ............................................................................................................................. 25
Industry panel, Marie Smith .................................................................................................................................... 26
6
Conferen
ce Schedule
Friday, Feb
ruary 2
0, 2015
Time
Eagle
Haw
k
Osprey
Other room
s
1:00 – 3:45
Registration -‐ Tall Pines room
3:30 – 3:40 Welcom
e & Introductions: A. Lazar, S. Visa
3:40 -‐4:40 Industry speakers: K. H
ausfeld; Amy M
auger and Ashley Kline-‐Tozzi
4:45-‐5:45 Academ
ic panel, Chair: D. Byrnes Michelle Cheatham
, Janyl Jumadinova, M
eral Ozsoyoglu, Zhongmei Yao
5:45-‐6:00
Coffee break -‐ Hiaw
atha room
6:00-‐7:00 Research talks 1, Chair: C. M
arling
1) SmartH
ealth Technology for Type 1 Diabetes M
anagement, C. M
arling (30 min.)
2) EEG-‐Based Driver Drowsiness
Detection, Y. L. Jiang, M. Andujar and J.
Gilbert (15 min)
3) Biomedical Inform
atics Research and Applications: The Intersection of Com
puter Science and Healthcare, A.
Peabody (15 min.)
Research talks 2, Chair: B. Sharif
1) Method Stereotypes as Patterns of
Design in OO Software and their
Applications, N. Dragan (30 m
in.)
2) An Eye-‐tracking Experiment Studying
Problem Solving Behavior, J. W
hitely, J. Wise, A. Lazar and B. Sharif (15 m
in.)
3) Recommender System
s as Persuasion Technology: An E-‐Com
merce Perspective,
M. McGucken (15 m
in.)
Research talks 3, Chair: J. Tims
1) Cracking Binary Analysis, M.
Cheatham (30 m
in.)
2) SCALE: Student Centered Adaptive Learning Engine, M
. J. Blink, T. Carm
ichael and J. Stamper (15 m
in.)
3) Dynamic Privacy M
anagement in
Services-‐based Interactions, N.
Ammar (15 m
in.)
7:15 -‐8:15
Dinner – South Hall room
in the Convention Center
8:30 -‐9:30 Workshop 1: LEGO M
indstorms Robots, J.
Jumadinova
Workshop 2: Eye tracking, B. Sharif
Workshop 3: Jeopardy, J. Kelly
Resume review
, Chair: A. Kline-‐Tozzi – Call of the W
ild (Wolf, Coyote, and Elk room
s)
9:30 – 10:00
Display posters up on the walls
Pontiac and Hiaw
atha rooms
Academic tables – Falls Overlook
10:00 – 12:00
Party: Rock &
Roll Express and photo booth – South H
all room in the Convention Center
7
Saturday, Feb
ruary 2
1, 2015
Time
Eagle room
Haw
k room
Osprey room
Other room
s
7:00 – 7:45
Breakfast – Salmon Run Restaurant
8:00 – 8:20
Poster preview, Chair: C. M
arling -‐ Pontiac and Hiaw
atha rooms
8:20 – 9:15
Poster session, Chair: C. Marling -‐ Pontiac and
Hiaw
atha rooms
9:20 – 10:20 Keynote speaker, sponsored by ACM
DSP: L. A. Stein
10:20 – 10:45
Coffee break -‐ H
iawatha room
10:45 –11:45
Workshop 4: Building self-‐confidence
SOUL, H. Peterson
Research talks 4, Chair: B. Bair
1) Performance versus quality of responses
in online systems, J. Kelley (20 m
in.)
2) Interning at Multiple ABB Locations in
Ohio and Germany, R. Turner (20 m
in.) -‐ not registered
3) Taking on an Internship, A. Morales (20
min.)
Invited speaker: Recruiting wom
en in CS, G. Achenbach
11:45 –12:45
Industry panel, Chair: M. Sm
ith
Neetu Agarw
al, Kathy Golden, Ashley Kline-‐Tozzi, Kristen H
ausfeld, Cathy Sm
ith
Career perspectives panel: M.J. Blink, S.
Chapman, A. M
cCutcheon Program
ming panel: A. DeM
ott , K. Signar
12:45 –2:00
Lunch – Pontiac and Hiaw
atha rooms
R. Hippler presents her research (20 m
in.) J. Tim
s talks about ACM-‐W (5 m
in.) Poster com
petition awards
Wrap up session
2:00 –3:00 Industry Tables
Industry tables – Falls Overlook
8
Venue - map
Lodge First Floor Call of the Wild: Wolf, Coyote, Elk
Lodge Second Floor Birds of Prey: Eagle, Hawk, Osprey Iroquois Nation: Pontiac, Hiawatha
Wilderness Hall Convention Center South Hall: Hawthorne, Sycamore, Red Cedar, Golden Oak
9
This page is intentionally left blank
10
Keynote speaker, sponsored by ACM DSP: Thinking About Thinking: A Talk in Four Parts Lynn Andrea Stein, Franklin W. Olin College of Engineering, [email protected]
In this talk, we will explore some principles that underlie intelligent systems, including our own cognition. The field of Artificial Intelligence has evolved dramatically in the half-‐century since it was founded; yet some ideas show up again and again. We will explore a few of these ideas — such as prediction and filtering — from their origins (older than computer science itself) to their more recent incarnations in smart cars, social networking, and other “post-‐AI” systems. The talk will conclude with a model of the ways that these principles affect our day-‐to-‐day lives.
Lynn Andrea Stein is a founding faculty member at Olin College of Engineering, Professor of Computer and Cognitive Science, and Associate Dean and Director of the Collaboratory. Stein's research, at Olin since 2000 and over the prior decade on the MIT faculty, spans the fields of artificial intelligence, programming languages, and human-‐computer interaction. She is co-‐author of foundational documents of the semantic web and the "mother" of a humanoid robot and an intelligent room. Stein has been innovating in computing and engineering curricula for more than three decades, with an emphasis on hands-‐on pedagogies, interactive technologies, and student engagement. Stein runs workshops to
stimulate curricular creativity, empower student-‐motivating pedagogic experimentation, and catalyze departmental and institutional change. She consults with a wide range of US and international institutions, serves on curricular advisory boards, speaks frequently at educational conferences, and embeds in sites to cause trouble and create constructive change.
Invited speaker: Strategically Recruiting Women into Undergraduate Computer Science Programs; Gretchen Achenbach, National Center for Women and Information Technology, [email protected]
Increasing the number of women who enroll in undergraduate computer science courses requires that we actively recruit. This session will focus on strategies that are most likely to yield high returns on your efforts. We will discuss how to identify your target audience, and how to craft messages about computing that emphasize interest, relevance, and belonging. We will explore strategies for reaching promising students, including methods that capitalize on existing programs and partners. Finally, we will discuss the need to track the success of your efforts, in order to refine your strategies for attracting more women to computing.
Dr. Achenbach is a research scientist at the National Center for Women and Information Technology (NCWIT), a research associate in the Department of Engineering and Society at the University of Virginia, and has a Ph.D. in Psychology from the University of Wisconsin-‐Madison.
11
Invited industry speakers: Kristen Hausfeld, Amy Mauger, Ashley Kline-‐Tozzi
GE Aviation – How the Comercial Engine Operation data Team Leads the Industry using Big Data, Cloud and PredixTM; Kristen Hausfeld, GE Aviation, [email protected]
General Electric Aviation uses customer engine data to prevent engine issues and better design future engines. GE Aviation is on the leading edge when it comes to Big Data and Cloud technologies along with their own big data analytics platform PredixTM, to streamline acquiring data from customers, decoding, storing and providing it to Engineers.
Application Development in the Business World; Amy Mauger, Cardinal Health, [email protected]; Ashley Kline-‐Tozzi, Cardinal Health, [email protected]
There are countless career paths in the computer science and related fields. At Cardinal Health, there are network and server administrators, technicians, software and Web application developers, user experience and designers, business analysts, project managers and more. Many jobs have overlapping or intersecting points. In addition, not every aspect of a job is “technical” in the traditional sense. Amy and Ashley will discuss their professional backgrounds, how their jobs are different from one another, and give examples as to how their roles have transformed with the needs of the business.
Research talks 1, Chair: Cindy Marling, Ohio University, [email protected]
SmartHealth Technology for Type 1 Diabetes Management; Cindy Marling, Ohio University, [email protected]
Abstract Patients with type 1 diabetes rely on exogenous supplies of insulin for survival. To avoid serious disease complications, they must painstakingly control their blood glucose levels. They rely on insulin pump and continuous glucose monitoring technology that inundates them and their professional caregivers with data. Intelligent decision support technology that renders this data actionable is the focus of the 4 Diabetes Support System project at Ohio University’s SmartHealth Lab. Introduction The mission of the SmartHealth Lab at Ohio University is to promote interdisciplinary research at the intersection of artificial intelligence (AI) and medicine. The 4 Diabetes Support System (4DSS) project aims to leverage AI technologies to aid in type 1 diabetes (T1D) management. T1D cannot be cured, but it can be treated and managed to improve health outcomes and reduce costly complications. Three clinical research studies, involving over 50 T1D patients, have been conducted to develop and evaluate software tools for T1D management. These tools are designed to assist clinicians managing T1D patients, and, pending FDA approval, could provide direct patient assistance via smart phones and medical devices [1].
T1D is treated with insulin and managed through blood glucose control. Good blood glucose control can prevent or delay devastating complications, such as heart disease, kidney failure, and blindness. Achieving good blood glucose control is difficult. Medical devices used to treat and manage T1D produce voluminous data but do not interpret it or make it actionable. Physicians must manually review large quantities of data, look for problems, and adjust therapy to correct them.
12
SmartHealth Technology The 4 Diabetes Support System (4DSS) project has three interrelated research and development thrusts. The first is case-‐based decision support to: (a) automatically detect problems in blood glucose control; (b) propose solutions to detected problems; and (c) remember which solutions are effective or not for individual patients. Problems and associated solutions, based on experiences of clinicians and T1D patients, are stored in a case base. When a patient experiences a problem, therapeutic adjustments that have helped prior patients in similar situations may be retrieved and adapted to individual needs.
The second thrust is glycemic variability measurement. Excessive glycemic variability is linked to hypoglycemia unawareness, a danger for T1D patients. Its automated detection would enable routine clinical screening to identify at-‐risk patients. Machine learning models are built to capture physician perception of blood glucose fluctuation. The best model to date provides a metric, which could be used as a clinical screen as well as for automated problem detection in the 4DSS.
The final thrust is blood glucose prediction. Patients do not always know when problems are impending; problems occurring while patients are asleep are especially dangerous. Undetected nocturnal hypoglycemia may result in the “dead in bed” syndrome. Anticipating that a sleeping patient is about to become hypoglycemic allows time to awaken the patient and intervene. A time series forecasting approach is used to build machine learning models based on insulin, life event and blood glucose data. This has standalone applicability for enhancing patient safety and may enable earlier problem detection in the 4DSS. A five-‐minute video, AI 4 Diabetes Support, is available at http://www.aaaivideos.org/2012/ai_4_diabetes/. Acknowledgements This work is conducted in collaboration with Frank Schwartz, MD, Razvan Bunescu, PhD, and Jay Shubrook, DO. This material is based upon work supported by the National Science Foundation under Grant Number 1117489. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation. Additional support comes from Medtronic and from Ohio University. Bibliography [1] C. Marling, M. Wiley, R. Bunescu, J. Shubrook, and F. Schwartz. Emerging applications for intelligent diabetes management. AI Magazine, 33(2):67–78, 2012.
EEG-‐Based Driver Drowsiness Detection; Youxuan Lucy Jiang, Miami University, [email protected]; Marvin Andujar, University of Florida, [email protected]; Juan Gilbert, University of Florida, [email protected]
Introduction Drowsy driving is one of the major threats to traffic safety. The National Highway Traffic Safety Administration estimated that more than 100,000 crashes were caused by drowsy driving each year, responsible for 40,000 injuries and 1,500 deaths [2]. Drowsy driving was reportedly involved in 41% of drivers’ driving experiences, which lead to 16.5% of fatal crashes and 13.1% crashes causing hospitalization of at least one person [3]. There is an evident requirement for improvement of technologies to detect and preclude driver drowsiness and prevent drowsy driving crashes.
Because many drivers are not aware of getting drowsy when driving, measuring changes in physiological signals, such as brain wave, heart rate, and eye blinking, has been considered as one of the most accurate techniques for drowsiness detection [4]. The Emotiv EPOC is a wireless EEG data acquisition and processing device that has been used in human-‐computer interaction (HCI) research to measure users’ brain signals and study users’ states, which shows its adaptability and accuracy among different task assignments [1, 5].
In our work, we are investigating the feasibility of using Emotiv as an in-‐vehicle interface to detect driver drowsiness in HCI studies. Our goal is to determine if there are patterns in the EEG data that indicate theta waves whose frequencies are between 4 and 8Hz, which refer to the first stage of sleep. With this, we can study the best way of alarming drowsy drivers without scaring them. Emotiv Setup The Emotiv EPOC has 14 electrodes to obtain the EEG signal, which are based on the international 10-‐20 locations. The international 10-‐20 system is the standard naming and positioning for the EEG measurements of any BCI device. It connects wirelessly via Bluetooth and a USB dongle to a computer. Because Emotiv is an inexpensive, portable, and safe device, it can be integrated into automotive technologies for non-‐invasive drowsiness detection. Matlab Setup The EEG signals from Emotiv were recorded using the Control Panel and the Test bench software. Matlab software is used to detect drowsiness by extracting EEG spectrum bands within certain frequency range. There are five common frequency bands (Delta, Theta, Alpha, Beta, and Gama) in EEG signals; each of them represents a single stage of cognitive status. In this study, we detect driver drowsiness by extracting theta waves between 4 and 8Hz, which indicates the first stage of sleep. An interactive Matlab toolbox called EEGLab was used to filter invalid data, calculate frequency, extract theta rhythms, and highlight those frequency ranges in output figures. Discussion As the first attempt to integrate Emotiv as an in-‐vehicle interface into automotive technologies, this study will determine if Emotiv has promise as a feasible method for drowsiness detection and warning for in-‐vehicle tasks. We are planning to conduct experiments using a driving simulation environment equipped with Emotiv to see if there are patterns
13
correlated with theta waves in the data. Future work can expand this study to investigate the best warning signal to preclude driver drowsiness, so that drivers can dynamically adapt themselves in time to ensure driving safety. References [1] Campbell, Andrew, Tanzeem Choudhury, Shaohan Hu, Hong Lu, Matthew K. Mukerjee, Mashfiqui Rabbi, and Rajeev DS Raizada. "NeuroPhone: brain-‐mobile phone interface using a wireless EEG headset." In Proceedings of the second ACM SIGCOMM workshop on Networking, systems, and applications on mobile handhelds, pp. 3-‐8. ACM, 2010. [2] Research on Drowsy Driving. http://www.nhtsa.gov/Driving+Safety/Distracted+Driving+at+Distraction.gov/Research+on+Drowsy+Driving [3] Tefft, Brian C. Asleep at the wheel: The prevalence and impact of drowsy driving. (2010). [4] Ueno, Hiroshi, Masayuki Kaneda, and Masataka Tsukino. "Development of drowsiness detection system." In Vehicle Navigation and Information Systems Conference, 1994. Proceedings., 1994, pp. 15-‐20. IEEE, 1994. [5] Vi, Chi, and Sriram Subramanian. "Detecting error-‐related negativity for interaction design." In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 493-‐502. ACM, 2012.
Biomedical Informatics Research and Applications: The Intersection of Computer Science and Healthcare; Andrea Peabody, The Ohio State University, [email protected]
Overview As a Graduate Student and Graduate Research Assistant at the Ohio State University and OSU Wexner Medical Center, I have collaboratively worked on several distinctly different projects providing computer science domain knowledge and technical abilities in complement to human sciences researchers. Biomedical Informatics is defined as the systematic application of information and computer science and technology to the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, motivated by efforts to improve human health. The 2012 US Bureau of Labor statistics estimated a staffing shortage of biomedical informaticians between 67% and 97%, with projected job growth of 24% in the past two years, and salary ranges of ~$85,000 and ~$190,000. Presenting a variety of the computer-‐science based aspects of biomedical informatics within both a world-‐renowned hospital system and an academic research department provides students with an in-‐depth understanding of the research being performed.
Research talks 2, Chair: Bonita Sharif, Youngstown State University, [email protected]
Method Stereotypes as Patterns of Design in OO Software and their Applications; Natalia Dragan, Kent State University, [email protected]
A developer emboldened with the knowledge of design patterns along with other well-‐known OO abstractions can construct well-‐designed OO software much more easily. The work presented here is focused on understanding OO design abstraction at different levels: method, class and system. Method stereotypes, which represent patterns of design at the method level, are used to characterize OO software. Moreover, applying their knowledge helps in improving existing approaches of feature location and re-‐documentation during the maintenance and evolution of software. The notion of stereotypes for OO modeling was first introduced by Wirfs-‐Brock. Initially, the main purpose was to support the classification of objects with respect to their roles and responsibilities in a software system. With the introduction of the Unified Modeling Language (UML) in the late 1990s, stereotypes became a powerful semantic extension mechanism, helping to increase the comprehensibility of UML diagrams. A new technique proposed generates the knowledge of method, class and system stereotypes from an existing object-‐oriented software system. The comprehension and understanding of a software system as a whole and its main blocks (methods and classes), and their main responsibilities is a significant activity during the maintenance and evolution of software and is essential for many reverse engineering and design recovery research avenues. An empirical study of twenty-‐eight open source systems formed the basis for a set of emergent stereotypes of the software abstractions at the at the method, class and system levels. We present a mechanism to automatically reverse engineer method and class stereotypes from existing systems along with a means to re-‐document methods and classes with their corresponding stereotypes. The distribution of method stereotypes forms the basis for the automatic identification of class stereotypes. Entire systems are also characterized by the method stereotypes distribution. This work is further extended to the characterization of changes in software during its evolution. Automatically classifying commits is done to assist developers gaining a high-‐level perspective of the design over a system’s evolution. Additionally, applications where method stereotypes improve existing approaches are presented. Adding stereotype information to each method enhances the corpus used in the information retrieval, Latent Semantic Indexing (LSI), for feature location. Experimental comparisons of using LSI for feature location with, and without, stereotype information are conducted
14
on a set of open-‐source systems. The results show that the added information improves the recall and precision in the context of feature location. Moreover, the use of stereotype information decreases the total effort that a developer would need to expend to locate relevant methods of the feature. Recently, method and class stereotypes have been used to automatically generate comments for methods and classes in C++ and Java programs. Stereotypes represent abstractive summaries for methods and classes and provide information that do not appear in the source code. Studies performed showed that the summaries generated provide valuable information for developers. An Eye-‐tracking Experiment Studying Problem Solving Behavior; Jessica Whitely, Youngstown State University, [email protected]; Jenna Wise, Youngstown State University, [email protected]; Alina Lazar, Youngstown State University, [email protected]; Bonita Sharif, Youngstown State University, [email protected]
Abstract How do students learn? What makes a novice different from an expert? Do the answers to the above questions depend on the task being performed? All these questions have deep and important implications in the study of understanding students’ behavior in learning specific topics. In this research, we try to study how students solve algorithm-‐related problems after they were enrolled in an algorithms course as part of their undergraduate study. In order to do this, we designed a set of tasks related to problem-‐solving, all derived from the material students learned throughout the course. We record students’ eye movements as they perform the tasks. An eye tracker is used to unobtrusively capture eye movements. We determine if it is possible to predict the level of student understanding of the material from eye movements. Results on task accuracy, task speed, as well as eye gaze information are reported. The results from the study will help better understand how students go about solving algorithm related problems. An eye tracker is a combination of hardware and software that allows us to track a subject’s eye while they are performing a task. Eye trackers work by monitoring where the eye is located, at any given time, thereby giving researchers information about where participants are looking and how long they spend at any given location. Eye tracking has only recently been used in the field of software engineering to study how developers work. We use this methodology to study problem solving behavior in an algorithms class. Problem To study problem solving behavior in an algorithms course. Experiment Design The focus of our study will be gathering and analyzing eye-‐tracking data from students in the Data Structures and Algorithms course held during the Fall 2014 semester at Youngstown State University. The participants will be set up in front of an eye tracker and shown various tasks based on concepts learned throughout the course. For example, they could be given a sequence of letters and asked to perform a heap sort, they would then be asked to put the letters in the order they would appear after the heap construction phase. Participants will also be required to fill out a survey prior to participating in the study. The questionnaire will ask students to rate their overall experience with programming and the level of comfort they have with the course material. They will also be required to fill out the NASA TLX survey between tasks. This survey will allow us to understand the cognitive workload of each participant. Between tasks they will also be asked to rate their confidence and their perceived level of difficulty of the task. Analysis We will present accuracy, time, gaze information, and NASA TLX scores for each task along with a discussion and analysis of the numbers. Through analysis of the collected data, we hope to determine if these metrics objectively evaluate student understanding in the course. Recommender Systems as Persuasion Technology: An E-‐Commerce Perspective; Melinda McGucken, Hiram College, [email protected]
This presentation aims to determine the ways recommender systems leverage psychological principles of persuasion in e-‐commerce applications. To accomplish this goal, distinct recommender systems are identified and their primary mode of persuasion according to compliance researcher Cialdini delineated. Cialdini's principles include reciprocity, commitment and consistency, social proof, liking, authority, and scarcity. Persuasion technology is a field within computer science that focuses on how technology is used in persuasion. The mechanism for this persuasion is described by psychology researchers, and can be applied to recommender systems as persuasive technology. While recommender systems are a popular persuasive technology with diverse applications, the focus of this paper is on e-‐commerce. This is an interdisciplinary study bringing together the disciplines computer science and psychology to examine recommender systems specifically. Analysis suggests that diverse recommender systems leverage each of Cialdini's six principles. The popularity of recommender systems with users is primarily attributed to their persuasive power according to Cialdini, with usefulness being a secondary factor. Research has been conducted that supports the popularity and perceived usefulness of recommender systems designed to leverage persuasion principles over ones specifically designed to return unique results. This finding has implications for the design and utilization of recommender systems in e-‐commerce applications.
15
Research talks 3, Chair: Jodi Tims, Baldwin Wallace University, [email protected]
Cracking Binary Analysis; Michelle Cheatham, Wright State University, [email protected]
Binary analysis refers to understanding the function of a program based on its executable file (i.e. assembly code, not source code). In particular, the goal is to do things like understand the function of a virus that has infected a computer in an organization or extract the intellectual property from a program. Like many foundational skills, binary analysis can be done for both “good” and “evil” purposes. For instance, creating a “no CD” crack for a video game, defeating copy protections, figuring out whether a binary file is malware or not, determining what a piece of malware is doing, or evaluating how long a set of software protections holds up against attack all fall under the general category of binary analysis. Binary analysis is very difficult. Working at the assembly code level is hard in the best of cases, and on top of that, in a cyber security context the person who developed the program may have put roadblocks in place to prevent it from being analyzed. For instance, code is often deliberately obfuscated, the malicious section may be a very small part of the overall program, and there are often anti-‐-‐-‐debugging protections put in place. Another source of difficulty is that reversers need to have a wide computer science background, including an understanding of assembly language, OS calls, computer organization, compilers, and programming language fundamentals. Reversers also need knowledge of RE-‐specific things like how to find the original entry point of a program, how to recognize self-‐modifying code, and how to find and subvert anti-‐debugging traps. New reversers typically require a year of training to be productive. One area of research related to binary analysis is to improve the tools available to reversers to mitigate some of the difficulties discussed above. Binary analysis tools currently operate at a very low level of abstraction – they reflect the organization of the computer rather than the way people think about the problem. Additionally, the tools are not well integrated, so doing even a simple analysis task often involves downloading and using three or four different applications. Tools that raise the level of abstraction at which reversers work are sorely needed. One exciting research thread in this area is to create applications that leverage rule-‐-‐-‐based, machine learning, and data mining techniques to aid non-‐-‐-‐experts in analyzing anomalous sections of executables. An example in this direction is Function Insight, a tool developed by Dr. Cheatham that facilitates run trace analysis at the functional level. Function Insight is a plug-‐-‐-‐in framework that allows people to develop their own “interestingness” metrics and see the results, enabling reversers to quickly focus their attention on key sections of a binary program (which may be tens of thousands of lines of assembly instructions). For instance, one possible interestingness metric is to use Sequential Pattern Mining on a run trace to determine which function calls normally lead to certain other function calls. Cases in which these patterns are violated could then be given a higher interest value to catch the analyst’s attention. This technique could be applied either within a single run trace or to compare two different traces. The higher the support and confidence levels of the violated rule, the more suspicious it is when the rule is broken. This approach has the potential to avoid many of the false positives flagged by current methods, which are often due to normal variations of function addresses, parameter values, and register values present between execution runs. There are two distinct groups working on binary analysis: hackers and academics. These two groups tend to have very different approaches. The first is more intuitive, results-‐-‐-‐driven, and considers their work a craft or art; the second tends to be more science-‐-‐-‐focused and seeks to build up new knowledge from fundamental principles in a logical and rigorous way. These philosophical differences can be seen clearly at conferences put on by the two groups. Much of the work presented at “black t-‐-‐-‐shirt” conferences like BlackHat, DefCon, and RECon is very applied. The general focus is on specific binary analysis efforts, capturing more information about what the executable is doing, and sorting/visualizing that information. Conversely, a lot of academic work related to binary analysis is focused on static rather than dynamic methods, even though most practitioners in the field tend to lean more heavily on dynamic analysis, particularly for confusing cases. Static analysis is appealing to academics because it borrows heavily from traditional computer science subfields such as compilers and formal methods. This talk will argue that binary analysis as a field needs to move towards science and away from “black art”, but in a way that is actually useful for reversers working in the field. Academic researchers need to partner with industry on application-‐-‐-‐driven binary analysis research. In addition, there is a need to change the way binary analysis is taught. Courses need to introduce the relevant computer science fundamentals early and focus on the underlying principles, rather than on particular tools. A good mix of application and theory will result in students who are prepared to analyze malware that has not been conceived of yet. Prior to joining the faculty at Wright State University, Dr. Michelle Cheatham worked in industry for ten years, including at the Air Force Research Lab and Riverside Research, here in Dayton. Her focus in this work was on cyber security, most recently on binary analysis. Dr. Cheatham is now working with other faculty members at Wright State to develop and teach courses for the online Cyber Security certificate program. In this 20-‐-‐-‐30 minute talk, she will demonstrate a realistic, yet very basic, software “cracking” activity, discuss her thoughts on research related to binary analysis, and express her view on the relationship between binary analysis educators and practitioners. Attendees will get a high level overview of this exciting and fast-‐-‐-‐growing subfield of computer science.
16
SCALE: Student Centered Adaptive Learning Engine; Mary Jean Blink, TutorGen, Inc, [email protected]; Ted Carmichael, TutorGen, Inc, [email protected]; John C. Stamper, Carnegie Mellon University, [email protected]
Abstract TutorGen was awarded a National Science Foundation Small Business Technology Transfer (STTR) research grant for our new software engine called SCALE -‐ Student Centered Adaptive Learning Engine. This innovative technology connects to new and existing computer-‐ based training, providing automatic adaptive capabilities to improve learning outcomes by applying various machine learning techniques. SCALE allows for the creation of intelligent tutoring systems by using existing data collected from educational systems to create the initial adaptivity and then improves over time as more data is collected. In this way SCALE allows for the creation of true ITS capability at a drastically reduced cost TutorGen Company Overview TutorGen is an educational technology company striving to improve education from the bottom up, using data-‐driven analytics. As a Carnegie Mellon University startup, we work at the intersection of educational systems, big data, and computer science, which are all growing fields. TutorGen is uniquely positioned to develop our product, called SCALE, by connecting existing expertise and research with the innovative vision to expand the capabilities of intelligent tutoring systems to reach a variety of markets using a human-‐centered, data-‐driven approach. SCALE will enhance student learning and support teacher and administrator assessment and management of student learning. Intelligent Tutoring Systems (ITS) Background For over two decades, education software providers have struggled to deliver on the promise of computer-‐ based training that is responsive to the needs of the individual. ITSs have been shown to be extremely effective [8, 9]. Due to the high development costs, truly adaptive learning systems have not seen widespread use. Studies have shown it takes between 100-‐1000 hours to create a single hour of content for an ITS [10], and most of this time is spent on creating domain-‐specific expert models. Historically, research and development of intelligent tutors have relied on subject area experts to provide the background knowledge and to develop student and problem models. Both cognitive tutors and constraint-‐based tutors rely on “student models” that experts create [11]. This is a time consuming process, and requires experts to not only understand the subject material, but the underlying processes used to give help and feedback. We believe that development of intelligent tutors can be enhanced by using data collected from students solving problems. Vast amounts of data are already collected from computer based educational software. The largest repositories of this type of data was created and is managed by the Pittsburgh Science of Learning Center (PSLC) DataShop, enabling retrieval and analysis for research purposes [12]. Data-‐driven methods applied to such large data repositories can enable the rapid creation of new intelligent tutoring systems making them accessible for many more students [7]. TutorGen Research TutorGen addresses the challenges of developing ITSs by providing an automated and transparent approach to adaptive learning. Applying artificial intelligence and educational data mining research, SCALE uses data-‐driven, human-‐centered methodologies to create personalized and adaptive instruction that emerges from the bottom up, drastically reducing development costs, improving the speed of creation, and making quality instruction readily available. SCALE is middleware that can be connected to existing computer-‐based training systems making them adaptive. This National Science Foundation (NSF) funded project (Mary Jean Blink, Principle Investigator; John Stamper CMU Co-‐PI) represents a breakthrough in developing adaptive educational systems by using student data collected from educational software systems to automatically generate intelligent tutoring capabilities. This work addresses the need to make adaptive learning more widely available, and provides tools to assess student performance in order to make interventions as early as possible. SCALE generates student models that build and organize, and improve over time. The system will include visualization tools for educators and developers to assess and possibly improve the models found. SCALE also tracks student progress tracing selected concepts or skills (knowledge tracing) in data collected from existing systems allowing for easy assessment at any point in time. The system will dynamically select the next problem for a student to maximize student learning and minimize time needed to master a set of skills. For complex multistep problems, SCALE can also provide context specific, just in time (JIT) hints to help students as they learn. Unlike a pure machine learning solution, a key differentiator of SCALE is that it is able to report to developers and educators exactly why the system behaves as it does. This allows for human input to refine models allowing for maximum improvements over time. Further, SCALE-‐enabled tutoring systems will improve over time with additional data and/or with the help of human input, using the SCALE ‘human-‐centered, data driven’ approach to discover or improve the underlying models that drive learning. Previous work in the automatic discovery of student models [4] and automated hint generation [1,5] lays the foundation of SCALE. The Knowledge Tracing and problem selection mechanisms use past research on knowledge component (KC) modeling like that used in DataShop [3]. The hint and feedback mechanism utilize past research with the Hint Factory [1], which is a novel method of automatically generating context specific, just-‐in-‐time (JIT) hints for students solving multi-‐step problems [1]. Acknowledgement This work was supported by NSF Grant IIP-‐1346448. References 1. Barnes, T., Stamper, J. (2008). Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data. In Procs of the 9th International Conference on ITS, pp. 373-‐382. Berlin, Germany: Springer. 2. Koedinger, K., McLaughlin, E., Stamper, J., Automated Student Model Improvement. In Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012). Chania, Greece. Jun 19-‐21, 2012. pp.17-‐24.
17
3. Stamper, J., Koedinger, K.R., Baker, R., Skogsholm, A., Leber, B., Demi, S., Yu, S., Spencer, D. (2011) Managing the Educational Dataset Lifecycle with DataShop. In Kay, J., Bull, S. and Biswas, G. eds. Proceeding of the 15th International Conference on Artificial Intelligence in Education (AIED2011) 4. Stamper, J., Koedinger, K.R. (2011) Human-‐machine Student Model Discovery and Improvement Using DataShop. In Kay, J., Bull, S. and Biswas, G. eds. Proceeding of the 15th International Conference on Artificial Intelligence in Education (AIED2011). 5. Stamper, J. (2006). Automating the Generation of Production Rules for Intelligent Tutoring Systems. In Proceedings of the 9th Intl. Conference on Interactive Computer Aided Learning (ICL2006). Kassel University Press. 6. Blink, M.J., Stamper, J., and Carmichael, T. (2014) SCALE: Student Centered Adaptive Learning Engine. In S. Trausan-‐Matu et al. (Eds.) Proceedings of the 12th International Conference on Intelligent Tutoring Systems (ITS 2014), pp. 654-‐655, 2014. Springer. 7. Stamper, J. Barnes, T. (2009) An Unsupervised, Frequency-‐based Metric for Selecting Hints in an MDP-‐based Tutor. 2nd Intl. Conf. on Educational Data Mining (EDM 2009), Cordoba, Spain, pp. 180-‐189. 8. Conati, C., Gertner, A., and VanLehn, K. (2002). Using Bayesian Networks to Manage Uncertainty in Student Modeling. In User Model. User-‐Adapt. Interact, volume 12 (4). 9. Heffernan, N. and Koedinger, K. (2002). An Intelligent Tutoring System Incorporating a Model of an Experienced Human Tutor. In Intelligent Tutoring Systems, pages 596–608. 10. Murray, T. (1999). Authoring intelligent tutoring systems: An analysis of the state of the art. Intl. J. Artificial Intelligence in Education, pp10: 98-‐129. 11. Mitrovic, A., Koedinger, K. & Martin, B. (2003). A comparative analysis of cognitive tutoring and constraint-‐ based modeling. User Modeling. pp 313-‐322. 12. Stamper, J., Barnes, T., and Croy, M. (2011) Experimental Evaluation of Automatic Hint Generation for a Logic Tutor. In Kay, J., Bull, S. and Biswas, G. eds. Proceeding of the 15th International Conference on Artificial Intelligence in Education (AIED2011). pp. 345-‐352. Berlin Germany:Springer. Dynamic Privacy Management in Services-‐based Interactions; Nariman Ammar, Wayne state University, [email protected]
Technology advancements have enabled the distribution and sharing of patient personal health data over several data sources. Each data source is potentially managed by a different organization, which may choose to expose its data as a Web service. Using such Web services, dynamic composition of atomic data type properties coupled with the context in which the data is accessed may breach sensitive data that does not comply with the users preference at the time of data collection. Thus, providing uniform access policies to such data can lead to privacy problems. Some fairly recent research has focused on providing solutions for dynamic privacy policy management. This talk presents an approach that advances these techniques, and fills some gaps in the existing works. In particular, dynamically incorporating user access context into the privacy policy decision, and its enforcement. I present an implementation of the proposed framework, three evaluation studies on the feasibility of the approach, and a research road map.
Research talks 4, Chair: Bettina Bair, The Ohio State University, [email protected]
Performance versus quality of responses in online systems; Jaimie Kelley, The Ohio State University, [email protected]
When users search Google or browse titles on Netflix, they expect to find what they seek with fast response times[2]. Online systems such as these can be made up of many components in the cloud, with varying access times that can impact performance and answer quality. I research the trade-‐offs between timeouts, which can guarantee fast answers, and full data, which guarantees correct answers in well-‐developed systems. My research enables measuring answer quality online, and can be used to better determine when to use load shedding for low priority requests or to modify service settings online in the cloud.
Online services such as search engines, recommendation engines, and question answering systems tend to be large cloud-‐based services made of many interacting components. For many reasons, including load imbalance, big data, and hardware faults, some components will return their results before others, and some may never return any results. Timeouts are widely used in order to keep the performance in these services high[7]. However, timeouts also can lead to eliding data critical to computing the results for which users ask. A reduction of answer quality can lead to profit loss, especially in cases where a user’s bad experience leads them to use other services instead, and when paid advertisements are elided or shown in
18
the wrong order[4]. A second run of a query, with full available data, is necessary to compute answer quality. Current methods of
measuring answer quality require offline testbeds which are inefficient or costly[5]. A small number of offline machines will compute results much slower than online, but with high accuracy, while a number of offline machines that nears the resources used online could double operating costs. Online services which extend timeouts carry high performance penalties; instead, we have implemented a framework that can speed up execution of certain targeted components of a service[6].
Ubora is a transparent framework that can be used to calculate answer quality online with minimal overhead. It uses a model of record, cache, and replay that allows a selected query to execute normally, yet have its full intermediate results cached for one set of replicated components. These results are stored in cache for short periods of time on instances where space is available, and when the query is replayed online, accesses to these targeted components are redirected to use the cached data[6].
We tested Ubora over multiple online services in our previous work, including a Lucene search engine, EasyRec recommendation engine[3], and the OpenEphyra question answering system[1]. We tested Ubora against a method of measuring answer quality which used extended timeouts, as well as a version of Ubora that naively propagated state through the service and a version of Ubora that did not use data regarding service load to determine when to schedule query replays.
Most recently we have begun using Ubora to modulate OpenEphyra during online Jeopardy tournaments in computer science outreach. Unlike IBM Watson, which was designed to compete against Jeopardy champions, OpenEphyra was originally designed to answer TREC questions with unstructured text data[8]. Ubora determines online when OpenEphyra needs to allocate more cache to compete successfully against human participants, and can scale back when the competition is becoming too one-‐sided. [1] The ephyra question answering system. http://www.ephyra.info/. [2] Netflix prize. http://www.netflixprize.com/index. [3] Easyrec-‐open source recommendation engine. http://easyrec.org/, 2014. [4] B. Forrest. Bing and google agree: Slow pages lose users. radar.oreilly.com, 2009. [5] J. Kelley, C. Stewart, S. Elnikety, and Y. He. Cache provisioning for interactive nlp services. In Workshop on Large-‐Scale Distributed Systems and Middleware, 2013. [6] J.Kelley, C. Stewart, S. Elnikety, Y. He, and D. Tiwari. Ubora: Measuring and Managing Answer Quality for Online Data-‐Intensive Services. Currently under submission at ASPLOS. [7] S. Ren, Y. He, S. Elnikety, and K. McKinley. Exploiting processor heterogeneity in interactive services. In IEEE ICAC, 2013. [8] D. Ferrucci, E. Brown, J. Chu-‐Carroll, J. Fan, D. Gondek, A. Kalyanpur, A. Lally, J. Murdock, E. Hyberg, J. Prager, N. Schlaerfer, and C. Welty. The AI Behind Watson-‐-‐-‐The Technical Article. In The AI Magazine, 2010.
Interning at Multiple ABB Locations in Ohio and Germany; Rachel Turner, Youngstown State University, [email protected]
Over the past year, I have completed two internships with the power and automation company ABB. From January 2014 until May 2014 I participated in a co-‐op at ABB in Wickliffe Ohio, for Research and Development Software Development. I discovered the ABB co-‐op through an internship fair at my university. After being invited to ABB Wickliffe interview day I was offered a co-‐op with the software development department. This was my first taste of a job in Software Development and I fell in love with it. I was able to see firsthand the development process of software from development to release. I was given to opportunity to add new features into a product as well as fix bugs. It is very rewarding to know that some ABB customer can now use those features that I contributed to. This co-‐op gave me experience with working on large projects with a project manager and other developers to produce exactly what the customer expects in the new software update. This has allowed me to gain and sharpen my office environment skills as well as decide if this is a career path I want to pursue after graduation.
The internship in Wickliffe Ohio led me to search for more ABB internships on the ABB Careers website. During this search, I found a listing for an available internship position at ABB Central Research Center (CRC) in Ladenburg Germany. ABB has only seven Central Research Centers in the world so I jumped on the chance to apply right away. Since I was already an intern in ABB Wickliffe it was easy for me to contact the potential employers. It also helped that my universities Professional Practice Program gave me many tips on how to make a European CV for my application. After applying I heard back from my supervisor a week later to set up a phone interview and amazingly an hour after the interview I was offered a position to intern at ABB CRC from May 2014 until January 2015. I accepted this opportunity in a heartbeat. It has delayed my graduation but there is nothing to compare with an opportunity like this.
Working at ABB in Germany has been a blast. I have learned so much about the research side of large software companies as well as experienced an international experience of a lifetime. Living and working in Germany has shown me how to function and participate in German culture. It has also given me a taste of many other cultures as well. The work environment at ABB CRC is very diverse with students and employers from all over the world. I had never been outside of the US prior to moving to Germany and there was a bit of a culture shock but I think that having this international experience will really increase my success at a future career especially with a worldwide company.
19
The work at ABB CRC is very research based. From the beginning of my internship I was given a single project to create a demo of a prospective product that ABB CRC would like to sell. My job was to discuss with my employer and the employees in sales to develop a demo that had the look, feel, and functionality of a product that ABB would like to sell. I got to experience the development of this demo from start to finish presenting the development and improvement to my employers along the way. In the end of this internship I will have a working demo that my employer can present to other ABB developers as well as customers as a potential product of ABB.
Working at ABB in Wickliffe OH and CRC Germany has given me valuable experience with software development and research in a company. I have gained so much from these opportunities and would like to encourage other people interested in Computer Science to search for and pursue fantastic opportunities like this while they still have the chance.
In my talk, I will talk about my experiences, lessons learned, resources students have access to, and also talk about what a typical day is like in the life of a student intern both locally and
Taking on an Internship; Ana Morales, Ohio University, [email protected]
There I was, sitting across from my manager, Pam Cunningham, for the very first time. I was extremely nervous but I was prepared and confident. I was willing to do anything they needed me to do. So I was just waiting for her to tell me what I was going to be doing for the summer. That’s when Pam said, “Ana, what do you want to work on? What is your passion?” I thought I was prepared for any question, but I realized that never asked myself what I wanted to do because I did not expect to be given that privilege. I looked at Pam and after a couple minutes of a very awkward silence I finally gave her a response. I have programmed in C++, C, Java, and Python for class and I have done JavaScript, HTML and CSS and for my student job at OIT Web Services. But if I said that just programming in those languages was my passion, I would be lying. Even though these are all great languages, Computer Science is a huge field and there is so much for me to learn still. In all honesty, my passion is learning because I just started growing as a professional. So for the summer, I wanted to learn/do something completely different. Pam just looked at me and started laughing, then she said “If you want something different, why don’t you try working with mainframes and learn COBOL, SQL, and JCL?” I smiled and said, “Challenge accepted.”
From competing in an overnight hackathon, to doing a 5-‐hour speed networking session with 30+ managers, to going to the Columbus Zoo, and more. My internship at Nationwide as an Application Developer was one of the best experiences of my life. On the very first day all the interns were given a book called Strength’s Finder 2.0 which allows you to take a test and gives you your top 5 strengths. This book helped me realized that we devote more time to fixing our shortcomings than to developing our strengths and if we did the opposite, we would excel more in what we do. Nationwide, uses these results to place you in a team where your strengths will be at use because they believe that if you are good at your job, you will enjoy work and produce better results. In this short talk, I will tell about my internship experiences and encourage others to take on internships of their own.
Poster session, Chair: Cindy Marling, Ohio University, [email protected]
Web Service Privacy, Compatibility and k-‐Anonymity; Nariman Ammar, Wayne State University, [email protected]
To guarantee privacy in service oriented environments, it is essential to know if there is compatibility between a client's privacy requirements and the Web service privacy policies before the client invokes a Web service operation. The client can then use the results of such a comparison to decide whether to invoke the operation. Privacy frameworks must be as comprehensive as possible, taking into account multiple dimensions of privacy. It is therefore crucial to take into account the k-‐Anonymity of Web service operations as part of an overall privacy framework. k-‐Anonymity values allow one to know the extent to which the operation invocation can be inferred if one knows that a downstream operation was invoked. One must calculate k-‐Anonymity, then integrate it into an existing privacy framework. In this paper, we focus on privacy at Web service operation level by presenting an approach that integrates k-‐Anonymity into an existing privacy framework via Web Services Conversation Language (WSCL) definitions.
Dynamic Selfish Routing; Christine Antonsen, Oberlin College, [email protected]
Selfish routing over static flows has been a popular research area in the algorithmic game theory community for many years. However, static flows are not a very realistic model in the traffic community as constraints can change over time and load cannot instantaneously traverse an edge. Dynamic flows take into account these attributes, so this field of research is growing
20
considerably. I explore the different models and notions of equilibrium that are developing, the problems encountered with these choices of models, and design my own small models and examples of dynamic routing games. I hope to excite others about the progress, and areas that still need to be worked on, in this expanding field of research.
Prestissimo; Elizabeth Bentivegna, Oberlin College, [email protected]; Cole Peppis, Oberlin College, [email protected]; Ben Kuperman, Oberlin College, [email protected]
Prestissimo was founded in 2012 in response to the difficulty students found using PRESTO, Oberlin’s user-‐unfriendly course selection website. An entirely student-‐run project, Prestissimo aimed to give the Oberlin community a better tool for viewing class information and choosing courses. Now over two years old, the site continues to grow as new students take the reins and add new functionality to meet Oberlin’s needs.
Our goal in taking over the project was to improve all aspects of the user experience. A large focus was creating a more aesthetically-‐pleasing and logically-‐formatted user interface (UI). We also implemented several new features to expand the Prestissimo experience, and cleaned up a lot of the source code created by past developers.
Server-‐based Code Review and Analysis for Software Development Teams; Pratistha Bhandari, College of Wooster, [email protected]
I worked as a summer research intern for Prenkte Romich Company and Saltillo through the College of Wooster’s AMRE program last summer. My teammate and I worked to build a centralized framework for code review, static analysis, and unit testing for Linux and Windows operating system. The resulting framework consists of Gerrit code review, SonarQube analysis, and Jenkins continuous integration. This framework shifts all code analysis to the server, which eliminates the need to run static analysis on developer machines. Gerrit lets developers participate in code reviews from any internet-‐connected location, and solves the issue of scheduling conflicts between developers. In addition, SonarQube tracks how full-‐project metrics have changed over time and between software versions. The Jenkins server works to continuously integrate changes into the product through regular software builds, and immediately reports build success or failure to developers. Through the use of these three components, the amount of incorrect code submitted is minimized, debugging is reduced, and code quality is improved.
This system presents significant advantages for the software engineers at PRC and Saltillo. Server-‐side analysis frees up resources on developer machines and allows full-‐project metrics to be evaluated to identify problem areas and trends over time. Continuous integration services provided by Jenkins encourage consistently working builds and frequent code integration, decreasing the time needed to release new software versions. With the addition of Gerrit code review in conjunction with the static analysis and continuous integration servers, new code can be consistently and thoroughly evaluated before it enters the code base.
I AM AI -‐ Interactive Actor Modeling for Introducing Artificial Intelligence: A Senior Capstone Project; Alexandra Coman, Ohio Northern University, a-‐[email protected]; Victoria Kerr, Ohio Northern University; Thomas Bowersock, Ohio Northern University; Yuki Matoba, Ohio Northern University; Andrew Warren, Ohio Northern University
Artificial Intelligence (AI) techniques are being used to enhance various aspects of interactive storytelling. One particularly challenging endeavor is that of creating story characters that are convincing and engaging to interact with.
Our Senior Capstone project is a multi-‐layered exploration of such techniques. Its surface layer will consist of implementations of several AI techniques for modeling the behavior and inner structure of interactive characters. A deeper layer will reveal to scrutiny and exploration the underlying AI “clockwork” that makes these characters “tick”. It will do so in a manner accessible to a non-‐expert audience.
We are using Unreal Engine to implement a character-‐driven game-‐like environment populated by multiple non-‐player characters (NPCs). The behavior of each character will be modeled using a different AI technique, such as Finite-‐State Machines, Reinforcement Learning, and Case-‐based Reasoning. These characters will allow the player to (a) interact with them, and (b) learn about the AI technique governing them by accessing visualizations of the mechanisms underlying their behavior.
The system is being designed so as to be attractive to a large and varied target audience, consisting of students within and outside the Computer Science field who are new to AI. While it is likely that many members of our target audience are regular players of computer games and would find such an environment immediately recognizable, we strongly wish to avoid alienating potential users who do not identify as avid “gamers”. Therefore, we are opting for a non-‐combat-‐centric game-‐like environment.
The educational purpose of our project is twofold: (1) the student members of our team are learning about AI techniques both through research of relevant literature and through hands-‐on implementation experience, and (2) the developed product will be an educational tool meant to spark interest in AI.
21
What will you achieve with your computing skills?; Jenna Crosby, Bowling Green State University, [email protected]; Jessica Carroll, Bowling Green State University, [email protected]
This poster presentation seeks to inspire viewers to see themselves as part of this vital community of creators of technology. By knowing the history and envisioning the future, viewers will be challenged to apply their skills. Our message is simple. Computing is a giant field. There is not only a place for you, but a true need for you.
Alexander Polynomial Program; Kiera Dobbs, College of Wooster, [email protected]
We introduce Knot Theory and a knot/link invariant called the Alexander Polynomial. The recursive nature of the Skein Relation used to calculate the Alexander Polynomial calls for a computer program to automate the process, which we create in C++. Then we describe how to represent knots with braids. The braid word and braid moves provide a method of numerically representing a knot and simplifying it for the automated Alexander Polynomial calculations. We outline how to recognize possible braid moves and the steps for polynomial construction in the program. An explanation is given to show that every braid word has the possibility to reduce to a collection of required base cases for the Alexander Polynomial calculation, as long as we modify the correct braid word element. We follow this with a proposition that we can simplify or rearrange any braid word to one of three forms, which allows us to select the correct element. Currently, this algorithm successfully computes the polynomial of knots through seven crossings and links of two components through five crossings.
Hackathons: A benefit to student programmers; Samantha Glass, Kent State Stark Computer Club, [email protected]
Hackathons are an interesting way for new programmers to explore the technology world, and is a beneficial experience. Hackathons encourages students to work as a team to create a product, starting from the ground up. Students go from brainstorming, to designing, to building a project from scratch. An event like a Hackathon put students in real world coding situations, and gives students a chance to use creativity and ingenuity to solve problems. Hackathons have a large array of uses, and are sponsored by colleges and businesses alike. Students and beginner coders should consider looking into and participating in these events to get valuable experience and exposure to coding outside of the classroom and academia.
Legacy Facades: An approach to retrofit Data-‐Parallel platforms for legacy softwares; Puja Gupta, The Ohio State University, [email protected]; Christopher Stewart, The Ohio State University, [email protected]‐state.edu
Legacy software created when clock rates doubled every 18 months should be revamped now that clock rates are stagnant. Unfortunately, in many cases, their creators moved to new projects and left behind valuable but outdated software. It is challenging to update legacy software because modern parallel platforms use new programming languages, data structures and operating environments. This paper presents {Legacy Facades}, a temporary operating environment capable of executing legacy software. With legacy facades, data-‐parallel platforms can execute legacy software without recompiling. In our approach, each map or reduce task is preceded by facade creation, a process that converts data stored in networked key-‐value stores to local files and operating environments. We show that workable facades can be learned without supervision for many applications. We've implemented legacy facades for several workloads. Our early results suggest that linear scaling is possible. EEG-‐Based Driver Drowsiness Detection; Youxuan Lucy Jiang, Miami University, [email protected]; Marvin Andujar, University of Florida, [email protected]; Juan Gilbert, University of Florida, [email protected]
Detect and prevent drowsy driving is one of the major tasks to reduce vehicle crashes and improve driving safety. However, current technologies have unsuccessfully provided barrier-‐free access for in-‐vehicle systems to detect driver drowsiness and warn drivers when they are getting drowsy. The presented work proposes using Emotiv to estimate driver drowsiness. Emotiv is an electroencephalogram (EEG) based Brain-‐Computer Interface (BCI) that provides portable and non-‐invasive method for brain signal measurement in vehicle. Our goal is to study the feasibility of implement EEG BCI devices into automotive technologies for drowsiness detection. This study also allows us to explore algorithms for signal processing and classification to study sleep wave detection using Emotiv as an in-‐vehicle interface in driving tasks.
The Use and Misuse of Disposable Email; Samantha Mater, Oberlin College, [email protected]; Krista Lafentres, Oberlin College, [email protected]; Stephen Checkoway, Johns Hopkins University, [email protected]; Cynthia Taylor, Oberlin College, [email protected]
22
Numerous online activities require users to provide an email address. However, there are many services which, while useful, may not be trusted with a user's primary email address. From viewing and posting on forums, to online shopping or downloading software, it is often impossible for users to enjoy the convenience of online services without exposing themselves to spam, phishing attempts, and more. Disposable email services provide users with temporary email addresses that can help mitigate the risk associated with providing a primary email address. Because disposable email addresses are not associated with passwords, all mail sent to any given disposable address is publicly available. Using four popular disposable email services (Dispostable, Mailinator, myTrashMail, and TempEMail), we were able to construct a dataset of 856,886 emails. We then analyzed these emails in order to answer the following questions: How are disposable email addresses most commonly used? What potentially dangerous personal information is exposed in these emails? Are disposable email addresses being used for cybercrime? Our preliminary results show evidence of numerous dubious activities and many pieces of personal information, from full names to home addresses, being exposed.
Towards the Quantified Self: Diabetes Management; Hannah Quillin, Ohio University, [email protected]
The NSF’s Research Experience for Undergraduates (REU) program offers undergraduate students the opportunity to work and learn in a research environment. I have been working in the SmartHealth Lab at Ohio University on software to help patients with type 1 diabetes. I have been researching different commercial physiological sensor bands and developing visualization software to display the output of the sensors. Patients with diabetes will wear the selected sensor bands, and the sensor data will be used to improve the blood glucose prediction models being developed in the SmartHealth Lab.
The sensor band selection criteria are: accuracy, relevance to diabetes management, access to raw data signals, and patient comfort. Many commercial devices operate under a proprietary system which obscures the raw data, which limits their utility. Also, if the device isn’t comfortable, the patients won’t wear it. The Basis band selected contains sensors for heart rate, galvanic skin response, skin temperature, and ambient temperature, which, in combination, give insight into a person’s daily activity.
The visualization software uses the Python Language, with the open-‐source tools matplotlib and wxpython, and takes advantage of the database already in use by the lab. The aim is to create a program that is easy-‐to-‐use, cross-‐platform, and robust, which allows doctors and AI researchers to view all of a patient’s sensor data in a comprehensive and meaningful way. Data from the CGM, sensor band, and patient-‐entered life events are all displayed graphically.
A Frequency-‐ and Clustering-‐based Methodology for Finding Transcription Factor Binding Sites; Laith Sersain, College of Wooster, [email protected]; Carlos Gonzalez, College of Wooster, [email protected]; Sofia Visa, College of Wooster, [email protected]
Here we develop a frequency-‐ and clustering-‐based algorithm for finding transcription factor binding sites in the Solanum Lycopersicum genome. The new methodology is implemented in programs (C++) and scripts (Python, MATLAB), and is used to identify several likely transcription factors.
An Overview of Competitive Facility Location Games with Facilities as Players; Amanda Strominger, Oberlin College, [email protected]; Alexa Sharp, Oberlin College, [email protected]
The problem of Facility Location is well studied with many interesting applications. In the optimization version, we ask how to place facilities such that total cost is minimized. Each facility is associated with a cost of being opened and each client facility pair has a cost of connecting, which is typically thought of as distance. There are many versions of this problem, all with a variety of approximation algorithms. It can be used to place hospitals such that the greatest distance to any person is minimized, or it can be used to place coffee shops such that the total distance to all customers is minimized.
However, there are many other factors to consider in real world scenarios. In particular, approximation algorithms assume that there is a central authority with the ability to place facilities, which is not always the case. Are facilities competing with each other for clients? Given anti-‐monopoly laws, the answer is almost certainly yes. Could clients for some reason go to a further facility? Perhaps they place some other value on different facilities. Could clients choose a facility based on a probability distribution? This is just a small sample of the possible questions that could arise.
There are many natural game theoretic approaches that might be taken to this problem. We considered games in which facilities are the players. Players are permitted to open some subset of facilities and each player seeks to maximize their utility, which typically correlates with serving more clients. We investigated many questions related to this game. Is there a Nash Equilibrium? If so, what are the prices of anarchy and stability? How does this impact the clients (are some clients really far away from all facilities)? If we change how clients choose facilities, how do the answers to these questions change? Our poster addresses these and other related questions.
23
Workshop 1: LEGO Mindstorms EV3 Robotics; Janyl Jumadinova, Allegheny College, [email protected]
Learn mechanical design and programming LEGO robots in Java using LeJOS firmware. Working in teams, the students will use LEGO elements, motors and sensors to build different robots and program them in Java to make the robots move, react and make sounds to solve various challenges.
Workshop 2: Eye tracking; Bonita Sharif, Youngstown State University, [email protected]; Jenna Wise, Youngstown State University, [email protected]; Jessica Whitely, Youngstown State University, [email protected]
An eye tracker is a combination of hardware and software that allows us to track a person’s eyes while they are performing a task. Eye trackers work by monitoring where the eye is located, at any given time, thereby giving researchers information about where participants are looking and how long they spend at any given location. Eye tracking has been used a lot in marketing and website analysis but only recently has it been used in the field of software engineering to study how developers work. Join us to see how this technology works.
Workshop 3: Open-‐Source Jeopardy; Jaimie Kelley, The Ohio State University, [email protected]
IBM Watson showed the audience of Jeopardy how technology could trump even Jeopardy champions at question answering. However, Watson had over 2,000 cores and 16 terabytes of storage at its disposal, as well as many talented engineers to tailor its design. Before these engineers started building Watson, they tested OpenEphyra, an open-‐source system originally developed to answer questions for TREC.[1][4]
OpenEphyra is slower and not as accurate as IBM Watson, so one doesn’t have to be a Jeopardy champion to win against the computer.[4] My research tries to keep OpenEphyra equal with the level of Jeopardy-‐playing ability found in its competitors, so the game remains fun and it is never certain who will win.
I have established a cloud-‐based online service around OpenEphyra.[2] The activity that I would like to lead at OCWiC allows groups from the audience to test their knowledge against this question-‐answering service in rounds of Jeopardy. I have multiple sets Jeopardy questions which automatically will send the questions to this service as they are revealed to the audience. My setup for the OpenEphyra online service does not cache answers to questions seen previously, so each question sent must be answered with fresh analysis as it is received. Instead of answering questions within a few seconds as IBM Watson tries to do, my setup establishes a 10 second window for answers.[3][4] Just like the human participants, OpenEphyra must indicate readiness to answer.
A simple hand raise or buzzer system may be used to determine who was fastest to know the answer, and therefore gets the chance to answer first. If that contestant is incorrect, the next may answer, until there are no more suggestions.
The way I would choose to run this activity is flexible, and based on the number of people who are interested in participating. Depending on the number of people interested in this activity, I would be willing to run mini-‐games for individual contestants that changed over time, split the audience into teams, or select volunteers from the audience to participate. When I have enacted this activity previously, the audience was divided into teams and anyone on their team might answer the question.
I made a few changes to the open-‐source OpenEphyra code to allow it to work as a service in the cloud. I wrote a knowledge miner subclass to take input from Lucene indices, and enabled it to use cached data as well as access disks. I filled its disks and indices with literature, news articles, movie reviews, Wikipedia, and other text-‐based documents. I also designed and wrote a framework that tracks the answer quality of online services, so that my OpenEphyra service can modify its layout in the cloud in response to how well it and its fellow participants are competing.[3] This element, from my own research, ensures that it is never certain who will win when playing Jeopardy against the service, and that the game is always fair and fun. [1] The ephyra question answering system. http://www.ephyra.info/. [2] J. Kelley, C. Stewart, S. Elnikety, and Y. He. Cache provisioning for interactive nlp services. In Workshop on Large-‐Scale Distributed Systems and Middleware, 2013. [3] J.Kelley, C. Stewart, S. Elnikety, Y. He, and D. Tiwari. Ubora: Measuring and Managing Answer Quality for Online Data-‐Intensive Services. Currently under submission at ASPLOS.
24
[4] D. Ferrucci, E. Brown, J. Chu-‐Carroll, J. Fan, D. Gondek, A. Kalyanpur, A. Lally, J. Murdock, E. Hyberg, J. Prager, N. Schlaerfer, and C. Welty. The AI Behind Watson-‐-‐-‐The Technical Article. In The AI Magazine, 2010. Workshop 4: Building self-‐confidence S.O.U.L.; Heather Petersen, Mount Vernon Nazarene University, [email protected]
This outline and proposal reflects the framework of a one-‐hour OCWIC workshop for female higher education students majoring in Computer Science. I am interested in empowering attendees by discussing conflict resolution, positive self-‐image, confidence and implementing multiple exercises under the acronym S.O.U.L. (Self, Others, Understand, Learn). I’m continuing to build out this framework and am excited about the possibility of sharing what I know with conference attendees.
I approached my big box retail manager in 1998 at 25 years old. I said I wanted to work in the technology department. He only half-‐jokingly said, “You can’t…you’re a girl”. I convinced him otherwise and in addition to a highly successful career in the department, I was the only female technology associate in the entire Pacific Northwest region of the company at the time. I felt so proud to break down gender assumptions and surprise customers with my expertise.
Today, girls have so much more opportunity, but I think they still face longstanding, inaccurate gender assumptions. Girls can be successful in any field they choose, and female S.T.E.M. scholars (Science, Technology, Engineering, Mathematics) should find inspiration in their own ambition, strength and intelligence. My workshop incorporates self-‐reflective exercises, creativity and tools for success within a warm, mentor-‐relationship narrative.
I have seasoned experience in the technology field and feel empowered by the work I continue to do today. I work in Information Technology at a small Ohio university. I have been mentoring girls within a higher education environment for the last 5 years. I have a complete understanding of the female college student and where they are in life. They are redefining themselves in a new context: away from their parents, away from everything familiar. The importance and impact of mentoring college age girls can never be underestimated. Mentoring is one of my biggest passions in life. I have actively participated in the process of female students finding their strength and recognizing their ability. What a privilege!
Career perspectives panel: Mary Jean Blink, Sarah Chapman, Andrea McCutcheon
Career Discussions; Mary Jean Blink, TutorGen, Inc., [email protected] Synopsis of Talk -‐ Drawing from personal experiences on the following:
• Discuss the variety of career opportunities within the technology field What are the benefits and drawbacks to working for different sized companies, working for start-‐ups, independently owned, or large public companies, and working as a consultant or an employee. Also consider differences when working for a technology company or any business that utilizes technology. What are the different areas within I/T – job categories (i.e., Network Administration and Security, Software Development, Hardware Design, Project Management, Data Analytics, Information Systems Audit. What are the differences in opportunities for specialists vs. generalists?
• Challenges and recommendations to be a successful technology professionals Due to the continuing changes in the technology field, a successful I/T career requires you to be a life-‐long learner. It is important to identify how you learn best. There are so many resources available today, but you must design and execute your own personal learning plan. It is also critical to build a professional network. Don’t wait until you are a senior in college to start this process. And it does not end. Events such as OCWiC conference are an excellent way to get connected to others in your field. Find local groups and actively participate to build the group as a whole as well as your professional contacts. The talk with specifics on how to do this with examples of how a professional network can help. Think beyond your own backyard. There are many career opportunities in geographically varied areas that may provide you with diverse experiences that help to expand your career opportunities. I spent time in Denver and the Silicon Valley that provided me with experience and perspective that I would not likely have gained otherwise. Finally, present some challenges and benefits of working in a predominately male career field. Social Networking with Style; Sarah Chapman, Red Fox Road, [email protected] As women in technology it's important to make sure you have every tool to market yourself and get the right connections. Social media savvy can be your secret weapon that takes you from good to great!
Do you know what to do you do when someone wants to connect on social media? Can you be found easily? Does your content reflect Your Brand? In this hands-‐on session you'll learn how to build your Professional Brand by leveraging the power of social media. From endorsements to tweet-‐speak; blogging to cross-‐generational connecting, you’ll learn how to
25
increase your network through multiple online platforms. You’ll also learn how to share only what you want your connections to see, so you can rest assured the photo of you and your friends won’t end up in the wrong newsfeed. Who would benefit: Everyone seeking to reinforce their professional brand through a strong network of online connections. Learning Objectives: After completing this course, you will be able to -‐ Manage your privacy settings to be able to show unique content to distinct contact types -‐ Understand when and how to use hashtags -‐ Articulate and understand your own needs in social media so you can use the platforms that will give you the best results. Success IT Careers for Women; Angela McCutcheon, Owens Community College, [email protected]
This session provides a success story of a female IT professional, who spent her career in a variety of IT positions while attending college part time and eventually achieving a Ph.D. in Instructional Technology. Her employment history includes roles as a computer programmer, system administrator, computer teacher, director of microcomputer training, director of electronic thesis and dissertation, technology supervisor, and chair of information systems & office administration. This presentation will include the short story of her employment and educational history, her freelance IT businesses, and how the field of IT paid her bills while she raised a family. Ways to keep technology skills relevant in the forever-‐changing field of computer technology will be discussed.
Programming panel: Andrea DeMott, Kirsten Signar
Programmers in Groups: Male Bonding and Women in CS Classes; Andrea DeMott, Ohio University, [email protected]
The goal of this session will be to analyze how the classroom 'culture' of computing in higher education may be constructed differently by women than men. I will propose steps we might be able to take, not to attract young women to computing fields, which is an approach that already receives plenty of attention, but instead, to keep them committed to their majors once they choose them. The feeling of 'belonging', of having made the right career choice, is a much different need for women than for men, I propose, and it may be one cause of the gender gap in our field. I will present observations from my own experience teaching programming courses and from a little superficial research into anthropology and psychology. I believe it would be helpful to discuss differing classroom behaviors, to try to understand the (possible) causes, and to try to empower female students (who enter classes that may be characterized by male bonding) to know what to expect, and how it may affect them."
Programming -‐ not its stereotypes; Kirsten Signar, [email protected]
Programming is a beautiful, challenging, and enriching experience, which can only be enjoyed as such when the programmer is engaged and enthusiastic. Certainly, many obstacles can stand between people and the computing experience, some of which include insecurity, gender discrimination, and inexperience. However, by recognizing these factors within ourselves, we are more prepared to tackle them. In doing so, we can see that our suppositions and stereotypical assumptions about the field are wrong, and this realization may relieve us of some obstacles. The goal here is to inform fellow women computer scientists that some things which they may experience are actually common, and that these assumptions and/or fears may or may not mean something about how likely they are to succeed at their goals. Commonly, such fears are based on insecurities and ignorance, which this talk will aim to improve.
Academic panel, Chair: Denise Byrnes, College of Wooster, [email protected]
Michelle Cheatham, Wright State University, [email protected] Janyl Jumadinova, Allegheny College, [email protected] Meral Ozsoyoglu, Case Western Reserve University, [email protected] Zhongmei Yao, University of Dayton, [email protected] In addition to questions form audience, the panelist will be discussing the following.
26
1) Introduce yourself and briefly describe your current position. Why did you choose to pursue an advanced degree? 2) How important was mentoring in your success in graduate school? Do you serve as a mentor in your current position and if so, what form does this mentoring take? 3) How do you balance the demands of your career with your personal life? Industry panel, Chair: Marie Smith, Eaton, [email protected]
Neetu Agarwal, Microsoft, [email protected] Kathy Golden, OE Connection, [email protected] Ashley Kline-‐Tozzi, Cardinal Health, [email protected] Kristen Hausfeld, GE, [email protected] Cathy Smith, Marathon Petroleum, [email protected] In addition to questions form audience, the panelist will be discussing the following. 1) What do you see as the future technology trends in your industry and what skills should a student develop to contribute to these future trends? 2) Given your role and industry, what groups do you interact with on a daily/regular basis and what does a typical day look like for you in your industry? 3) Regarding the overall strategic and operational direction for your company’s technology functions: what are the greatest changes seen in strategic development in IT over the last 5-‐10 years? What areas of technology have the most focus on improvement and/or advancement?
27
NOTES OCWiC 2015
