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Parole Interview Transcript/Decision - Fusl000123 (2018-05-23)

Parole Interview Transcripts and Decisions

No abstract provided.

Parole Interview Transcript/Decision - Fusl000124 (2017-01-10)

Parole Interview Transcripts and Decisions

No abstract provided.

Parole Interview Transcript/Decision - Fusl000125 (2017-07-26)

Parole Interview Transcripts and Decisions

No abstract provided.

Parole Interview Transcript/Decision - Fusl000130 (2021-02-23)

Parole Interview Transcripts and Decisions

No abstract provided.

Parole Interview Transcript/Decision - Fusl000133 (2021-03-24)

Parole Interview Transcripts and Decisions

No abstract provided.

Device Health Seamless Check By Pcm, Hp Inc

Defensive Publications Series

During notebook user daily working, user requires to execute specific check utilities to examine their system or device health. Usually, user always was the last one to aware their system or device got something wrong while they encountered “can’t power on” or “can’t recognize SSD” showing up. System or Device decay is unavoidable while user operates notebook, so it is very important to have alert before system or device really crash, so that user it capable to do some actions (ex. data backup) before notebook can’t work normally. It is annoying thing to do Data Recovery when notebook can’t power …

A Health Guard For Vr Gamer, Hp Inc

Defensive Publications Series

VR gaming is high concertation and immerse, so people is easy to forget his physical conditions. We usually got news that persons get sudden faint due to epilepsy or some dangerous physical condition while in metaverse. For this, we provide a health monitor and a method to help to protect user while he/she is in Metaverse. We detect local epileptic responses by measuring changes in the sympathetic nerve and body hormones and ions including adrenal corticosteroid, sodium ions, potassium ions and from the indicator to provide one algorithm to trigger the mitigate method to protect the user.

Pc System Alarm Mechanism, Hp Inc

Defensive Publications Series

PC systems are often placed unguarded and can easily become victims of theft. This invention utilizes the commonly seen features on PC systems (PC beep, G-sensor, and network function) to improve PC systems’ security and protection. PC systems are monitored remotely by IT center through network. When a PC system is physically removed from network, security will be informed, and the PC system will sound alarm. The implementation of this invention into current PC system design is inexpensive. Little HW modification is required. Previous anti-theft protection methods mostly only consider locking PC system to its original position. This invention informs …

Phishing Detection Using Natural Language Processing And Machine Learning, Apurv Mittal, Dr Daniel Engels, Harsha Kommanapalli, Ravi Sivaraman, Taifur Chowdhury

SMU Data Science Review

Phishing emails are a primary mode of entry for attackers into an organization. A successful phishing attempt leads to unauthorized access to sensitive information and systems. However, automatically identifying phishing emails is often difficult since many phishing emails have composite features such as body text and metadata that are nearly indistinguishable from valid emails. This paper presents a novel machine learning-based framework, the DARTH framework, that characterizes and combines multiple models, with one model for each composite feature, that enables the accurate identification of phishing emails. The framework analyses each composite feature independently utilizing a multi-faceted approach using Natural Language …

Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti

SMU Data Science Review

Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new …

Short Term Forecasting Of Solar Radiation, Ashwin Thota, Bradley Blanchard, Lijju Mathew, Paritosh Rai, Sid Swarupananda

SMU Data Science Review

This paper details how to predict solar radiation at a location for the next few hours using machine learning techniques like Facebook’s Prophet, and Amazon’s DeepAR+. Multiple techniques like AutoRegressive (ARIMA) and Exponential Smoothing (ES) have been used to forecast solar radiation, but they lack accuracy and are not scalable. Whereas Prophet, and Amazon’s DeepAR+ are scalable, accurate, and easily integrated into other machine learning techniques. This will be the first time where the combination of these techniques along with Linear Regression, Random Forest, XGBoost and Decision Tree will be leveraged to forecast solar radiation for the short term. Predicting …

Using Natural Language Processing To Increase Modularity And Interpretability Of Automated Essay Evaluation And Student Feedback, Chris Roche, Nathan Deinlein, Darryl Dawkins, Faizan Javed

SMU Data Science Review

For English teachers and students who are dissatisfied with the one-size-fits-all approach of current Automated Essay Scoring (AES) systems, this research uses Natural Language Processing (NLP) techniques that provide a focus on configurability and interpretability. Unlike traditional AES models which are designed to provide an overall score based on pre-trained criteria, this tool allows teachers to tailor feedback based upon specific focus areas. The tool implements a user-interface that serves as a customizable rubric. Students’ essays are inputted into the tool either by the student or by the teacher via the application’s user-interface. Based on the rubric settings, the tool …

Stock Forecasts With Lstm And Web Sentiment, Michael Burgess, Faizan Javed, Nnenna Okpara, Chance Robinson

SMU Data Science Review

Traditional time-series techniques, such as auto-regressive and moving average models, can have difficulties when applied to stock data due to the randomness inherent to the markets. In this study, Long Short-Term Memory Recurrent Neural Networks, or LSTMs, have been applied to pricing data along with sentiment scores derived from web sources such as Twitter and other financial media outlets. The project team utilized this approach to complement the technical indicators observed at the end of each trading day for three stocks from the NASDAQ stock exchange over a 12-year span. A common benchmark to assess model performance on time series …

Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan

SMU Data Science Review

Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While …

Classification Of Pixel Tracks To Improve Track Reconstruction From Proton-Proton Collisions, Kebur Fantahun, Jobin Joseph, Halle Purdom, Nibhrat Lohia

SMU Data Science Review

In this paper, machine learning techniques are used to reconstruct particle collision pathways. CERN (Conseil européen pour la recherche nucléaire) uses a massive underground particle collider, called the Large Hadron Collider or LHC, to produce particle collisions at extremely high speeds. There are several layers of detectors in the collider that track the pathways of particles as they collide. The data produced from collisions contains an extraneous amount of background noise, i.e., decays from known particle collisions produce fake signal. Particularly, in the first layer of the detector, the pixel tracker, there is an overwhelming amount of background noise that …

Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel

SMU Data Science Review

Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model …

Predicting Twitch.Tv Donations Using Sentiment Analysis, Alexander J. Gilbert, Jason Herbaugh, Feby Cheruvathoor, Ben Williams, Alex Tozzo

SMU Data Science Review

Twitch.tv streamers have a rare opportunity to receive immediate feedback from their audience through a real-time chat log that is rife with sentiment information. Tools that can help a streamer understand how they need to influence their audience can be useful in increasing the donations and subscriptions they earn. Although millions around the world stream on Twitch, only a minuscule fraction of these streamers earn a living streaming alone. This paper aimed to provide muchneeded guidance to enable more streamers to succeed. We used stream logs, known as VODs (video on demand), which can be easily accessed through Twitch’s API …

Reinforcement Learning For Predicting The Us Gdp Output Gap, Paul Swenson, Anish Patel, David Stroud, Jules Stacy

SMU Data Science Review

If a bank can successfully predict the economic fundamentals of the country they operate in, they have a significant advantage when determining interest rate risk against their competitors. This in turn is advantageous when determining investment risk against their competitors. This paper explores using Reinforcement Learning (RL) as a method for predicting The United States' Gross Domestic Product (GDP) on a quarterly basis. Various RL algorithms are compared based on how accurately they predict the GDP output gap for the following quarter. This research was unable to accurately predict the GDP output gap on a quarterly basis, but further research …

Hierarchical Neural Networks (Hnn): Using Tensorflow To Build Hnn, Rick Fontenot, Joseph Lazarus, Puri Rudick, Anthony Sgambellone

SMU Data Science Review

This research demonstrates the use of TensorFlow to build a Hierarchical Neural Network (HNN). Constructing and engineering neural networks to maximize accuracy and efficiency is an active field of research in machine learning. HNN, along with several other applications of split networks have been developed as recently as 2017. However, implementations thus far have required custom-built and coded HNNs. The research conducted here uses TensorFlow to validate this structure by building entirely separate neural nets with logical relations between the output of one net and the inputs of the nets that are downstream. Research has shown that Hierarchical Neural Networks …

Examining Bias In Jury Selection For Criminal Trials In Dallas County, Megan Ball, Brandon Birmingham, Matt Farrow, Katherine Mitchell, Bivin Sadler, Lynne Stokes

SMU Data Science Review

One of the hallmarks of the American judicial system is the concept of trial by jury, and for said trial to consist of an impartial jury of your peers. Several landmark legal cases in the history of the United States have challenged this notion of equal representation by jury—most notably Batson v. Kentucky, 476 U.S. 79 (1986). Most of the previous research, focus, and legal precedence has centered around peremptory challenges and attempting to prove if bias was suspected in excluding certain jurors from serving. Few studies, however, focus on examining challenges for cause based on self-reported biases from the …

Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, Cameron Stewart, Michael Mazel, Bivin Sadler

SMU Data Science Review

Women’s beach volleyball is one of the fastest growing collegiate sports today. The increase in popularity has come with an increase in valuable scholarship opportunities across the country. With thousands of athletes to sort through, college scouts depend on websites that aggregate tournament results and rank players nationally. This project partnered with the company Volleyball Life, who is the current market leader in the ranking space of junior beach volleyball players. Utilizing the tournament information provided by Volleyball Life, this study explored replacements to the current ranking systems, which are designed to aggregate player points from recent tournament placements. Three …

Assessing The Effectiveness Of The Mover Program For Treating Attention Deficit In Children (Mover: Movement Opportunities Through Vestibular Engagement Rhythm), Lindsay B. Williams, Camille Skubik-Peplaski

Student Journal of Occupational Therapy

Background: The purpose of this study was to investigate the effects of participation in a movement- to music program on the attention span of elementary school-aged children who have attention deficits. The hypothesis was that participants would demonstrate improved attention on the Test of Sustained Selective Attention (TOSSA) (Kovacs, 2015).

Methods: This quantitative study involved nine children who participated in four movement-to-music sessions with a staggered stop over a period of six weeks. The study incorporated a single group pre-test/post-test design, and a non-parametric Sign Test was utilized to analyze data from the TOSSA subcategories of concentration, detection, …

Resilience Development In Children With Adverse Childhood Experiences: The Role Of The Occupational Therapist And The Interdisciplinary Team, Kayla Frederick

Student Journal of Occupational Therapy

Research has revealed adverse childhood experiences (ACEs) can have a negative effect on a child’s development and put a child at an increased risk of physical and mental health problems in adulthood including obesity, diabetes, depressed mood, and attempted suicide (Anda et al., 2006; Felitti et al., 1998; Gilbert et al., 2015; Putamen, 2006). Some children have been able to counteract the negative effects of ACEs and achieve positive life outcomes using a principle called resilience (Zolkoski & Bullock, 2012). While researchers have begun to identify the key skills and character traits associated with resilience in children, few have explored …

Market Segmentation And Recency Frequency Monetary Value Analysis For A Freemium Mobile Game, Satvik Ajmera, Taylor Bonar, Dylan Scott, Carol Miu, Alana Manuel

SMU Data Science Review

Bricks ‘N Balls is a freemium game that relies on in-app purchases and ad monetization from users to be profitable at no upfront cost to the players. This study explores how in-game data analytics and purchase data can be used to segment players. Features taken into consideration for segmentation include past purchasing habits along with the players interactions within the missions. This study uses the Recency Frequency Monetary Value (RFM) framework to extract insights on player purchasing behavior to segment players into clusters and predict how much users will spend in the future.

The Intersection Between Race, Ethnicity, And Identity Formation: Implications For Counseling Multiracial Clients, Diana Garcia, Chantel Johnson, Quietya D. Walker

National Cross-Cultural Counseling & Education Conference for Research, Action & Change (2016-2024)

Learning Outcome 1: Participants will understand the lived experiences of multiracial individuals whose parents are from two or more minority statuses.

Learning Outcome 2: Participants will be able to explore the determinants of racial and ethnic self‐identities and the potential consequences of adopting one of these labels on an individual's self‐esteem and perceptions of discrimination.

Learning Outcome 3: Participants will understand the counseling and educational implications of working with multiracial clients.

Volume 1972

Promethia

No abstract provided.

Out Of The Octagon And Into The Courtroom: The Ufc’S Antitrust Lawsuit, John Milas

DePaul Journal of Sports Law

No abstract provided.

The First Step: Student-Athletes Finally Get The Right To Be Compensated For Their Names, Images, And Likenesses, Joseph Ranieri

DePaul Journal of Sports Law

No abstract provided.

From Bet Slips To Bank Accounts: The Federal Legalization Of Sports Betting, Brandon Grant

DePaul Journal of Sports Law

No abstract provided.

The Employment Status Of The Twenty-First Century Ncaa Collegiate Athlete: An Evaluation Of The Fair Labor Standards Act And The National Labor Relations Act, Danielle L. Kennebrew

DePaul Journal of Sports Law

Many individuals believe that the twenty-first century NCAA collegiate athlete should not be classified as an employee of their respective universities due to the longstanding tradition of amateurism governing collegiate athletics. However, such a proposition does not analysis the statutory test articulated by the Fair Labor Standards Act (FLSA) and the National Labor Relations Act (NLRA) when determining a worker’s employment statues. Upon review of the economic realities test utilized by the FLSA and the common-law agency test utilized by the NLRB, there are strong arguments for collegiate athletes holding employee status resulting from the compensation they receive in the …