5153 research outputs found
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Artemis Next Generation Aviation Emergency Recorder Locator: An Improved Underwater Pinger Locator for Downed Aircraft
No full textThomas Arciuolo, Joseph Bango, and Humaira Islam's poster proposing a new form of underwater Pinger locator
The Impact of Social Isolation on Coping Style Utilization
No full textThis study explores the different coping styles used depending on the individuals perceived level of social isolation. 151 undergraduate students from the University of Bridgeport were recruited to complete scales used to measure coping styles (Carver, 2013) and Social Isolation (UCLA Loneliness scale; Russell, 1996). Results demonstrated that styles of coping were impacted when comparing high vs. low perceived social isolation. This has ramifications for future studies, which should explore the socio-cognitive mechanisms underlying these changes
The Influence of a Transition Framework on College-Level Preparation Strategies Used in University Business Schools: A Case Study
No full textThe 21st century ushered in changes regarding how people conduct business across the globe and what businesses need from their employees. The extent to which business schools are responding to these changes remains unknown. This qualitative multiple-case study explored the perceptions of business school deans and instructors regarding college-level strategies in their business schools that prepare undergraduate students for the demands of the workplace. The researcher also explored the extent to which elements of Schlossberg's 4S analytical framework (situation, self, support, strategy) influenced college students' preparation strategies at two Connecticut private business schools. Primary data sources for the study included semi-structured interviews. A convenience sample of 16 participants (4 business school deans and 12 business instructors) participated in the study. Interview transcription and analysis revealed clusters of concomitant themes salient to the research study using Schlossberg's 4S analytical framework to organize the data. Findings from the study revealed that university leaders and educators examined factors that establish best practices aligned to 21st century business practices and needs. The four elements of Schlossberg's analytical framework influenced business schools' preparation strategies
Transformative Learning: Preparing for Contemporary Chinese Student Pedagogy Demands -- A Paper Presented at Columbia University
No full textTimothy Dorr's poster on his research into meeting Chinese student pedagogy needs in Western universities
Privacy Preserving HIPAA-Compliant Access Control Model for Web Services
No full textSoftware applications are developed to help companies and organizations process and manage data that support their daily operations. However, this data might contain sensitive clients’ information that should be protected to ensure the clients’ privacy. Besides losing the clients’ trust, neglecting to ensure the clients’ data privacy may also be unlawful and inflict serious legal and financial consequences. Lately, different laws and regulations related to data privacy have been enacted specially in vital sectors such as health care, finance, and accounting. Those regulations dictate how clients’ data should be disclosed and transmitted within the organization as well as with external partners. The privacy rules in these laws and regulations presented a challenge for software engineers who design and implement the software applications used in processing the clients’ private data. The difficulty is linked to the complexity and length of the letter of the law and how to guarantee that the software application is maintaining the clients’ data privacy in compliance with the law. Some healthcare organization are trying to perform their own interpretation of the law privacy rules by creating custom systems. However, the problems with such approach is that the margin of error while interpreting the letter of the law is high specially with separate efforts carried out by individual companies. According to a survey carried out to check the Healthcare Insurance Portability and Accountability Act (HIPAA) requirements interpretation created for medical and healthcare related applications, none of the frameworks were well developed to capture the relationships specified in the law. To solve this problem, a standard framework is required that will analyze the regulatory text and provide a method to extract the relevant component that can be used during software roles engineering and development. The extracted components will include all the possible arrangements of roles, purposes, permissions, temporal factors, and any carried out obligations. In this work we propose a framework to analyze, extract, model, and enforce the privacy requirements from HIPAA regulatory text. The framework goal is to translate the law privacy rules text into more manageable components in the form of entities, roles, purposes, and obligations. Those components together can be used as building blocks to create formal privacy policies. The process concentrates on two main components; entities and their roles, and data access context. To accomplish the first part, the framework will parse the privacy sections of the regulatory text to mine all the subjects, and then categorize those subjects into roles based on their characterization in the law. To acquire the access context, the process will extract all the purposes, temporal clauses and any carried out obligations and classify them based on their permissibility
Providing Security, Immutability and Transparency to Voting System Using Blockchain Technology
No full textToday’s election system is one of the most suffered problem in many countries. Rigging of votes and unsecured electronic voting machine (EVM), changing of votes, and polling booth capturing are the concerned issues that are to be addressed in present voting system. Blockchain technology is the solution to overcome these problems and to provide a safe voting system. As this technology is booming with a solution to provide security, integrity and authentication to many other fields, Voting system can be implemented using this blockchain technology for a safe and secure future. Voters can gain security and belief in the voting system
What Expectations Do Young Adults With Autism Spectrum Disorders And Their Families Have For The Transition into Adulthood?
No full textResearch Question - Are the expectations for adulthood different for young adults with autism? Do they want the same options for adulthood that other young adults desire? What can professionals and programs do to ensure that they have a successful transition into adulthood? Methodology - Interviews were conducted by the researcher with young adults and their parents. A series of questions related to work, independent living, recreation and relationships were presented to the interviewees. A review of records was used to triangulate the data
Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection
No full textThe security of networked systems has become a critical universal issue that influences individuals, enterprises and governments. The rate of attacks against networked systems has increased dramatically, and the tactics used by the attackers are continuing to evolve. Intrusion detection is one of the solutions against these attacks. A common and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. The performance of an IDS is significantly improved when the features are more discriminative and representative. This study uses two feature dimensionality reduction approaches: (i) Auto-Encoder (AE): an instance of deep learning, for dimensionality reduction, and (ii) Principle Component Analysis (PCA). The resulting low-dimensional features from both techniques are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. This research effort is able to reduce the CICIDS2017 dataset’s feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6% in multi-class and binary classification. Furthermore, in this paper, we propose a Multi-Class Combined performance metric CombinedMc with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset.http://dx.doi.org/10.3390/electronics803032
Analytics to understand coups d'état occurrence in U.S.A.
No full textMy research question is why hasn’t there been a coups d’état in the United States of America, USA? By analyzing a large dataset of 122 countries and over 2000 coups attempts, I try to understand analytically what can cause a coups d’état to occur in the USA. I combine the semiparametric and extreme bounds data analysis techniques to effectively make a conclusion that democracy and GDP/capita play a big role in success or fail of a coups d'état and how this plays out for my research
Autonomous Task-Based Evolutionary Design of Modular Robots
No full textIn an attempt to solve the problem of finding a set of multiple unique modular robotic designs that can be constructed using a given repertoire of modules to perform a specific task, a novel synthesis framework is introduced based on design optimization concepts and evolutionary algorithms to search for the optimal design. Designing modular robotic systems faces two main challenges: the lack of basic rules of thumb and design bias introduced by human designers. The space of possible designs cannot be easily grasped by human designers especially for new tasks or tasks that are not fully understood by designers. Therefore, evolutionary computation is employed to design modular robots autonomously. Evolutionary algorithms can efficiently handle problems with discrete search spaces and solutions of variable sizes as these algorithms offer feasible robustness to local minima in the search space; and they can be parallelized easily to reducing system runtime. Moreover, they do not have to make assumptions about the solution form. This dissertation proposes a novel autonomous system for task-based modular robotic design based on evolutionary algorithms to search for the optimal design. The introduced system offers a flexible synthesis algorithm that can accommodate to different task-based design needs and can be applied to different modular shapes to produce homogenous modular robots. The proposed system uses a new representation for modular robotic assembly configuration based on graph theory and Assembly Incidence Matrix (AIM), in order to enable efficient and extendible task-based design of modular robots that can take input modules of different geometries and Degrees Of Freedom (DOFs). Robotic simulation is a powerful tool for saving time and money when designing robots as it provides an accurate method of assessing robotic adequacy to accomplish a specific task. Furthermore, it is difficult to predict robotic performance without simulation. Thus, simulation is used in this research to evaluate the robotic designs by measuring the fitness of the evolved robots, while incorporating the environmental features and robotic hardware constraints. Results are illustrated for a number of benchmark problems. The results presented a significant advance in robotic design automation state of the art