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Teacher Retention In One Urban School District Why Do Teachers Stay: A Case Study
No full textTeacher attrition can have a profound effect on the educational experiences of students. Five schools in one New England school district that experienced the least amount of teacher turnover over a three year time period were purposefully selected for this research study which served to provide a clear understanding of why some teachers stay in urban schools, while so many others leave. Maslow's Hierarchy of Needs (1943) was the theory that framed this study. The participants provided valuable insight into understanding their motivation to stay in the schools. The themes of altruism and generativity emerged in the survey and interview data, allowing the researcher to suggest hiring practices to the Board of Education be structured to include questions that focus on altruistic behaviors and generativity
Groundswell Spring 2017
No full textThis is the 2017 Spring Edition of Groundswell the University of Bridgeport's Student Literary Magazine. This edition may also include poetry, art, and photography as well as literary contributions. The editor is Katherine Collado. The Advisory board includes Edward Geist, Eric D. Lehman, Amy Nawrocki, Diane Krumrey and Emily Lamed
Fully Autonomous Reproduction Robotic System
No full textSelf-reproduction robotics systems are capable of producing other robotic systems; such that the resulting systems are fully functional and autonomous. In this poster, a novel method for producing modular robotic systems is presented, where the resulting robots consist of Cubelets - modular robots kit - and the producing robot is built using Lego Mindstorms EV3
Social Media Big Data Approach to Emotional Intelligence
No full textThis paper aims to research the possibility of detecting regional sentiment by analyzing Social Media Big Data (SMBD). It can be said that the vast majority of people with some form of internet access participate in one Social Media platform or the other. The importance of the insight derivable from SMBD cannot be overemphasized. Many theories have been postulated that show that human emotion is extremely powerful and drives our thought process. Noting that our communication is driven by our thoughts makes social media data a huge wealth of information from which an average regional sentiment analysis can be deduced. This sentimental knowledge becomes a valuable tool in policing (security) and marketing (commerce)
Rapid Detection of Active Tuberculosis Using Coal-Derived Quantum Dots
No full textChidi Igweh, Prabir Patra, and Alicia Petryk's poster on utilizing quantum dots as a detection system within patients with an active tuberculosis infection
Organizational Structure’s Relationship to Job Satisfaction: Moderating Effects of Personality
No full textThe present study examined the relationship between organizational structure and job satisfaction, as well as investigated the moderating effects of conscientiousness and agreeableness on both variables. Results showed that while there is a strong relationship between organizational structure and satisfaction, personality traits also make a significant contribution to satisfaction and engagement at work. In the present study, personality factors showed moderating effects on the structure-satisfaction relationship in a manner that agreeableness drove higher satisfaction in organic organizational structures that it did in mechanistic structures. Post hoc investigations revealed extremes of high/low agreeableness driving or depressing job satisfaction depending on individual level on conscientiousness
High Altitude Thermal Control
No full textOn September 1, 2016, a high altitude student platform (HASP) was launched from NASA’s Columbia Scientific Balloon Facility in Fort Sumner, New Mexico with 12 student payloads. The University of Bridgeport sent a robotic arm with three servo-motors and servo-motor testbed to the fringes of space. The objective was to test the motors at very low temperatures and in vacuum conditions. The payload has heaters and temperature sensors fitted on all its walls that can automatically respond to the thermal conditions to assure that the motors operate
The Influence of Emotional States on Short-Term Memory Retention by Using Electroencephalography (EEG) Measurements: A Case Study
No full textThe electroencephalogram (EEG) is a non-invasive test that records the electrical activity of the brain as wave patterns generated by various brain structures. The electrical activity is recorded from the scalp surface after being picked up by metal electrodes (small metal discs) and conductive media. This study explored how emotions can impact short-term memory retention, and thus the process of learning, by analyzing five mental tasks: relaxation, memorization of ten words, memorization of ten two-digit numbers, visual exposure to emotional stimuli, and recall of the 10 words and 10 numbers. The word list contained ten words as five event-related (directly related to the type of emotion triggered) and five not event-related words. The visual task was separated into three categories corresponding to the type of image extracted from the public domain International Affective Picture System (IAPS) library, which in turn correspond to three emotional states assumed to be evoked by them: neutral, negative (e.g. sadness), and positive (e.g. happiness). Event-related potentials (ERP) were measured by EEG with the ANT Neuro system. The ASA software and EEGLab were utilized for the analysis of ERPs in five EEG bands: Delta (0-3.9Hz), Theta (4-7.9Hz), Alpha (8-12.9Hz), Beta (13-30 Hz), and Gamma (31-50 Hz). Eleven participants (ten males and one female between 20 and 25 years old) were included in this case study. To date, no other studies have been reported to use EEG measurements in the evaluation of the influence of emotional states on short-term memory retention
From Noob to Smurf: Advanced Analytics for League of Legends
No full textStandard metrics for multiplayer online battle arena (MOBA) games like League of Legends (LoL) are very simple: kills, deaths, and the like. At Vantage Sports, we use a proprietary method to generate unique metrics that are more useful for professional players. These metrics are then calculated for hundreds of thousands of amateur player games, and the results used to determine which ones most contribute to winning. Some of the most important ones are worthless deaths and smart kills, which refine the standard metrics based on whether the team overall benefited from the activity. A new player rating model described here correlates strongly with winning even though it is essentially based on just one individual's contribution to a five-on-five game
Face Recognition - Algorithmic Approach For Large Datasets And 3D Based Point Clouds
No full textThis work proposes solutions for two different scenarios in face recognition and verification. The first scenario involves large scale unconstrained unsupervised face recognition. The proposed system for this scenario is a complete face recognition framework. The proposed system first studies the performance of unsupervised face recognition for frontalized captured faces in the wild under the effect of a single image super-resolution algorithm. The system also introduces new high dimensional features based on LBP and SURF that perform better than the state-of-the-art features for unconstrained unsupervised face recognition. To solve the large scale recognition process, a new algorithm has been designed to manipulate face images in the dataset. This new algorithm represents all training face images as a fully connected graph. The algorithm then divides the fully connected graph into simpler sub-graphs to enhance the overall recognition rate. The sub-graphs are generated dynamically, and a comparison between different sub-graph selection techniques including minimizing edge weight sums, random selection, and maximizing sum of edge weights inside the sub-graph is provided. Results show that the optimized hierarchical dynamic technique developed with sub-graphs selection increases the recognition rate in large benchmark image dataset by more than 40% for rank 1 recognition rate compared to the original single large graph method. The approach developed in this research is tested on different datasets, especially if the number of images per person in the training data is low. Furthermore, in order to improve rank 1 recognition rates and to reduce the computation time of the recognition process, a new technique that combines the hierarchical face recognition algorithm and a deep learning neural network using Siamese structure for face verification is proposed. The second part of this work addresses the usage of neural generative models for 3D faces with an application in face recognition when 3D datasets are utilized separately without the existence of texture information scenarios. An improved technique is developed to construct new representations for point clouds containing 3D information. The technique employs a regression neural network model trained using Levenberg-Marquardt (LM) algorithm. One of the advantages of this new representation is the significant reduction in storage space required for point clouds due to the utilization of a regression model for depth map regeneration. Moreover, the trained neural models can be used to generate a super-resolution version of the original 3D point clouds. The proposed regression representation is also used with a deep Siamese neural system to implement a complete depth-based neural face recognition and verification framework. The results indicate that the proposed system provides highly accurate and efficient face recognition results with 3D information only without texture information