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- Type:
- Article
- Description/Abstract:
- Depression is a common illness that negatively affects feelings, thoughts and behaviors and can harm regular activities like sleeping. It is a leading cause of disability and many other diseases (Choudhury, et al 2013, Mathur et al, 2016, Watkins et al, 2013). According to WHO (World Health Organization) 1 statistics, more than 300 million people over the world are affected in depression and in each country at least 10% are provided treatment. Poor recognition and treatment of depression may aggravate heart failure symptoms, precipitate functional decline, disrupt social and occupational functioning, and lead to an increased risk of mortality (Cully, et al 2009). Early detection of depression is thus necessary. Unfortunately the rates of detecting and treating depression among those with medical illness are quite low (Egede, 2007). This research proposes a solution of using random forest classifier algorithm to detect and predict detection. A mobile application will be developed in order to collect user data and make prediction.
- Creator/Author:
- Halliday, Nnennaya
- Submitter:
- Jess Kropczynski
- Date Uploaded:
- 05/15/2020
- Date Modified:
- 05/15/2020
- Date Created:
- 2019-04-11
- License:
- All rights reserved
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- Type:
- Article
- Description/Abstract:
- Cloud computing has been one of the major disruptive technology of this century changing the entire face of IT infrastructure across all spectrum. This has led to tremendous development, improvement and cost efficient means of securing IT infrastructures. Virtualization is the backbone driving the numerous cloud solutions and also making them marketable in the pay-as-you-use mechanism for all kind of deployment. This research is focus on improving the security and performance of cloud storage, backup and disaster recovery by evaluating the possibility of eliminating the Recovery Point Objective (RPO) and Recovery Time Objective (RTO). A live synchronization between production and Disaster Recovery (DR) sites is presented. We considered the mechanism behind Virtual Machines (VM) and hypervisor interaction with physical memory on host computers and evaluated the ability of VM to read/write directly to a unified multiple storage locations. Dependencies, requirements and guidelines for implementing this solution would also be analyzed.
- Creator/Author:
- Li, Chengcheng and Efosa, Ogbomo
- Submitter:
- Jess Kropczynski
- Date Uploaded:
- 05/15/2020
- Date Modified:
- 05/15/2020
- Date Created:
- 2019-04-11
- License:
- All rights reserved
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- Type:
- Article
- Description/Abstract:
- Shooting crime is a serious public health problem in the US. The analysis of any historical crime data reveals that crime is non-randomly distributed in time and space. Based on this notion, hot spots policing has gained its momentum to effectively predict future crime locations. Recent studies; however, pointed out that traditional hot spots policing occasionally predict rare crimes such as homicides and shootings due to their less frequent recurring counts in a given place and time (specifically for shorter time periods such as weeks and months). Given this context, we developed a new shooting prediction system (SHOPS) to explore whether recent dynamic/mobility activity patterns of known violent individuals increase the prediction of short-term fatal and non-fatal shootings compared to the traditional hot spots policing. Findings suggest that SHOPS predicts fatal and non-fatal shooting locations more precisely by identifying fewer hotspot locations. Policy implications of the study were discussed in the conclusion section.
- Creator/Author:
- Varlioglu, M. Said and Ozer, Murat
- Submitter:
- Jess Kropczynski
- Date Uploaded:
- 05/15/2020
- Date Modified:
- 05/15/2020
- Date Created:
- 2019-04-11
- License:
- All rights reserved
-
- Type:
- Article
- Description/Abstract:
- The principles of minimalist design are evident in much of the technology we use today. This is especially the case with mobile applications. The most successful of which attempt to minimize the amount of user input needed to provide users with the information they are seeking. Although many mobile applications use data gathered by in-system activity such as a GPS to minimize input from users—some systems require user input, such is the case with roommate matching. This study utilizes the RoomUP mobile application as a testbed to define minimal criteria that can be used to gather user input and produce a compatible roommate match. Participatory design with prospective student users is used to reduce the number of variables and provide recommendations for a minimalistic user interface. The resulting prototype is then used to verify that it meets design goals and supports a satisfactory user experience.
- Creator/Author:
- Maddirala, Sumanth
- Submitter:
- Jess Kropczynski
- Date Uploaded:
- 05/15/2020
- Date Modified:
- 05/15/2020
- Date Created:
- 2019-04-11
- License:
- All rights reserved
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- Type:
- Article
- Description/Abstract:
- The healthcare industry is thriving and the abundant amount of data involved raises call for help relating to managing and maintaining them. It becomes a hassle to keep the data in it’s required place and to pull and retrieve whenever necessary. The search for a proper data mining technique to enhance the process is always appreciated and encouraged. Our era is controlled by the upcoming technologies that are fast paced and yield great results. There is always a scope for improvement and optimization. Every individual from every generation has been an avid user of mobile phone and its applications. Healthcare facilities have slowly begun to depend on applications and technologies associated and supported by mobile phones and other networking platforms in order to have everyone within the facility and also the patients who have ties to the facility have access to the information that they are entitled to have.
- Creator/Author:
- Kaushik, Sanjana
- Submitter:
- Jess Kropczynski
- Date Uploaded:
- 05/15/2020
- Date Modified:
- 05/15/2020
- Date Created:
- 2019-04-11
- License:
- All rights reserved
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- Type:
- Article
- Description/Abstract:
- With the prevalence of anxiety, depression, and stress among young adult populations, adaptive and innovative treatment options must be considered for the future. While there are various approaches to mental health treatment, art therapy is one traditional method that has been used to treat the symptoms of mental health disorders across various health contexts and populations. Some art therapists have even integrated information and communication technologies (ICTs) into their practices. With these factors in mind and considering the prominence of ICTs use among student populations, this study seeks to understand how the immersion and presence afforded by one such technology, virtual reality (VR), can impact the outcomes of art therapy practices. Through the use of an arts-based VR application, Tilt Brush, this study compares traditional art therapy methods as they are employed in and outside of VR. Through the comparison of self-reported measures, we can better understand the possibilities and effectiveness of art therapy practices delivered via Tilt Brush VR.
- Creator/Author:
- Schaaf, Andrea
- Submitter:
- Jess Kropczynski
- Date Uploaded:
- 05/15/2020
- Date Modified:
- 05/15/2020
- Date Created:
- 2019-04-11
- License:
- All rights reserved
-
- Type:
- Article
- Description/Abstract:
- Hypervisor-based hardware virtualization- also known as the first phase of virtualization uses Virtual Machines (VM) to provide better hardware resource utilization and application isolation. A VM provides some level of portability, but still requires a full operating system (OS) with all the binaries and libraries required to run the service it hosts. Therefore, moving an application from a development to a production environment for instance is no different than moving them between two Physical Machines (PM). Container-based virtualization-sometimes known as the next phase of virtualization addresses some of these limitations by providing virtualization at the OS level. Docker is an open source engine launched in 2013 by a company called Docker, Inc. Docker is used to manage the lifecycle of containers. Using containers, it is no longer necessary to dedicate an entire VM to an application in order to provide isolation, thus saving OS license costs. In this project we plan to formulate a generic model that can be used to fine tune a container-based setup for maximum performance benefit.
- Creator/Author:
- Chengcheng Li and Nitin Mathur
- Submitter:
- Jess Kropczynski
- Date Uploaded:
- 05/15/2020
- Date Modified:
- 05/15/2020
- Date Created:
- 2019-04-11
- License:
- All rights reserved
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- Type:
- Dataset
- Description/Abstract:
- Dataset Summary: This dataset studies the main challenges that students in these institutions faced during the transition from face-to-face (f2f) to remote mode of instruction and the resources that they used to minimize these adversities. In order learn about their experiences during this transition, I surveyed at the end of the Spring Semester students enrolled in two Political Science (POL) classes. The results showed that majority of students struggled with stress caused by moving away from campus and self-quarantine leading to deteriorating mental and physical health. Concerns about student health along with distraction at home were identified as top adversities for student well-being. Survey results also showed that educational resources can have varying impact on student learning in introductory and upper-level courses. For example, lecture notes, power point presentations and online videos can be better resources for remote instruction in an introductory class, while class meetings via video conferencing platforms can be the preferred resource of instruction in upper-level courses. Below is the questionnaire used for this study: Survey Questionnaire: Transition to Remote Instruction During COVID-19 Crisis: Qualtrics Link for POL1080: https://artsciuc.co1.qualtrics.com/jfe/form/SV_bd7cF1OF6eNeYBv Qualtrics Link for POL2074: https://artsciuc.co1.qualtrics.com/jfe/form/SV_3xegnXy4LFSC2t7 1. As you know, the University of Cincinnati has transitioned from face-to-face to remote instruction for Spring Semester since March 14, 2020 due to COVID-19. Once it was decided to switch to remote instruction, how did you expect that this decision would impact your performance in this class? I thought it would improve my performance I thought it would impair my performance I did not think that it would impact my performance I don’t know 2. Based on your experience with remote instruction, how do you think the new form of instruction impacted your performance in this class? I did better in this class after we switched to remote teaching I did worse in this class after we switched to remote teaching The switch to remote teaching had no impact on my performance I don’t know. 3. Do you agree or disagree with the following statement: “I felt that the instructor in this class provided timely instructions and information about the switch from face-to-face to remote form of content delivery in the class”? Completely agree Partially agree Partially disagree Completely disagree Not sure/ don’t know. 4. Do you agree or disagree with the following statement: “I felt that the instructor in this class cared about my performance in the class once we switched from face-to-face to remote form of content delivery in the class”? Completely agree Partially agree Partially disagree Completely disagree Not sure/ don’t know. 5. Which of the following course resources (if available) helped you ease the transition from face-to-face to remote instruction (check all that apply)? Online instructional videos created or made available by the instructor Instructor-led class meetings via a web-conferencing platform (e.g. Webex, Zoom, MS Teams, Skype) Meetings with the instructor via a web-conferencing platform (e.g. Webex, Zoom, MS teams, Skype) during their office hours Instructor’s lecture notes and presentation materials (e.g. Power Point Slides) Online quizzes or interactive questions administered via web platforms (e.g. Canvas, Blackboard, Echo 360 or others). Online forums made available for this course Assigned course readings Book publisher’s online resources (websites, book ancillaries, etc.) Supplemental assistance from teaching assistants (e.g. office hours, online sessions, etc.) Supplemental peer-led review sessions (e.g. Learning Assistant Sessions, Supplemental Instruction Sessions, etc.) Group activities with peers enrolled in the class (e.g. study sessions via conference platforms) Others (please list) _________. 6. Which one of the following course resources was most helpful to you in the transition from face-to-face to online mode of content delivery (select only one)? Online instructional videos created or made available by the instructor Instructor-led class meetings via a web-conferencing platform (e.g. Webex, Zoom, MS Teams, Skype) Meetings with the instructor via a web-conferencing platform (e.g. Webex, Zoom, MS teams, Skype) during their office hours Instructor’s lecture notes and presentation materials (e.g. Power Point Slides) Online quizzes or interactive questions administered via web platforms (e.g. Canvas, Blackboard, Echo 360 or others). Online/ web discussion forums made available for this course Assigned course readings Textbook publisher’s online resources (websites, book ancillaries, etc.) Supplemental assistance from teaching assistants (e.g. office hours, online sessions, etc.) Supplemental peer-led review sessions (e.g. Learning Assistant Sessions, Supplemental Instruction Sessions, etc.) Group activities with peers enrolled in the class (e.g. study sessions via web-conferencing platforms) Others (please list) _________. 7. Which of the following, do you think, impacted negatively your performance in this class during the transition from face-to-face to remote instruction (please select all relevant options)? I had to move away from campus in the middle of the semester My physical or mental health deteriorated after we switched to remote instruction I missed face-to-face interaction with the instructor, the TAs and the undergrad assistant (SI) I did not have stable and reliable Internet connection at home I had a lot of distraction at home I lost my job/ income due to the COVID-19 epidemic I had to take an additional job to support myself and/ or my family Self-quarantine and/ or social distancing caused me a lot of stress The news about the COVID-19 epidemic and concerns about my health and the health of my loved ones caused me a lot of stress Other (please list) ___________. 8. Which of the following, do you think, impacted negatively your performance in this class during the transition from face-to-face to remote instruction (please select only one options)? I had to move away from campus in the middle of the semester My physical or mental health deteriorated after we switched to remote instruction I missed face-to-face interaction with the instructor, the TAs and the undergrad assistant (SI) I did not have stable and reliable Internet connection at home I had a lot of distraction at home I lost my job/ income due to the COVID-19 epidemic I had to take an additional job to support myself and/ or my family Self-quarantine and/ or social distancing caused me a lot of stress The news about the COVID-19 epidemic and concerns about my health and the health of my loved ones caused me a lot of stress Other (please list): 9. Based on your experience with this course’s transition from face-to-face to remote instruction for Spring Semester 2020, what aspects of this transition had greatest values for you? Open ended question: 10. Based on your experience with this course’s transition from face-to-face to remote instruction for Spring Semester 2020, what changes would you recommend to ease this transition in the future? Open ended question: 11. What is your gender? Male Female Other/ prefer not to disclose 12. What is your major? Political Science International Affairs Interdisciplinary/ Cyber Strategy and Policy Interdisciplinary/ Law and Society Another major (please specify) 13. What is your class level? First year (freshman) Second year (sophomore) Third year (junior) Fourth year (senior) 14. What is your race or ethnicity? White Black or African American Asian American Indian or Alaska Native Native Hawaiian or Pacific Islander International student Other 15. What do you think your grade will be for this course? A or A- B+, B or B- C+, C or C- D+, D or D- F Nor sure/ don't know
- Creator/Author:
- Ivanov, Ivan
- Submitter:
- Ivan Ivanov
- Date Uploaded:
- 05/14/2020
- Date Modified:
- 05/14/2020
- Date Created:
- 2020-05-13
- License:
- All rights reserved
-
- Type:
- Document
- Description/Abstract:
- The first webinar for the 2019-2020 RDAP year occurred on Oct 30th from 3:30 to 4:30 EST was “Ask me Anything” town hall meeting “Thriving as a Data Information Professional”. A panel of experienced data informational professional shared experiences and expertise answered questions about daily work as a data information professional. Panelists were: Wendy Mann - Geroge Mason (director of digital scholarship center) Lynda Kellam - UNC Greensbro, now at Cornell Megan Sapp Nelson - Purdue (data information literacy) Jon Wheeler - New Mexico (data curation librarian) Christie Ann Wiley - U of IL (research data services librarian) Notes were taken by Wanda Marsolek No recording is available
- Creator/Author:
- Koshoffer, Amy; Sapp Nelson, Megan ; Mann, Wendy ; Marsolek, Wanda ; Wiley, Christie Ann; Kellam, Lynda , and Wheeler, Jon
- Submitter:
- Amy Koshoffer
- Date Uploaded:
- 05/07/2020
- Date Modified:
- 05/07/2020
- Date Created:
- 2019-10-03
- License:
- Attribution-NonCommercial-NoDerivs 4.0 International
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- Type:
- Student Work
- Description/Abstract:
- Hypothesis: Electroencephalography and Artificial Neural Networks can be combined to read in a user’s EEG-based brain activity and then to correctly classify that activity. Goal: This research project aims to combine EEG (electroencephalography) and ANNs (artificial neural networks) by reading in a user's EEG-based brain activity and using an ANN to correctly classify that activity. This specific application aims to classify EEG data of a user being presented with digits (0-9) and letters (A-J). Process: The project goals are accomplished by building an EEG headset capable of collecting data, generating a labeled dataset (EEG activity is the data, character being presented is the label), and creating ANNs to analyze the labeled dataset. Results: The EEG headset based on the UltraCortex III from OpenBCI was successfully built. A data collection protocol was created, programs were coded to facilitate this data collection, and the dataset was successfully generated (3160 samples total, 158 samples/character). Several ANNs were created, and these networks were capable of learning and of overfitting on the data, but the classification on test data did not reach accuracy levels beyond chance (more time needs to be devoted in trying different networks and manipulating the data). Future work: Tasks which are recommended for continuing this project include adjusting network parameters of existing CNNs, trying a wider variety of neural network architectures, trying data mining techniques, extracting more features in different ways from the existing data, collecting more digit and letter EEG data, altering data collection process.
- Creator/Author:
- Chapko, Anastasiya
- Submitter:
- Anastasiya Chapko
- Date Uploaded:
- 05/02/2020
- Date Modified:
- 05/05/2020
- Date Created:
- 2020-04-28
- License:
- Attribution-NonCommercial-NoDerivs 4.0 International