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Articles 61 - 90 of 269
Full-Text Articles in Computer Sciences
Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula
Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula
Browse all Theses and Dissertations
Malware detection is a critical task in ensuring the security of computer systems. Due to a surge in malware and the malware program sophistication, machine learning methods have been developed to perform such a task with great success. To further learn structural semantics, Graph Neural Networks abbreviated as GNNs have emerged as a recent practice for malware detection by modeling the relationships between various components of a program as a graph, which deliver promising detection performance improvement. However, this line of research attends to individual programs while overlooking program interactions; also, these GNNs tend to perform feature aggregation from neighbors …
Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman
Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman
Browse all Theses and Dissertations
Insider threats to information security have become a burden for organizations. Understanding insider activities leads to an effective improvement in identifying insider attacks and limits their threats. This dissertation presents three systems to detect insider threats effectively. The aim is to reduce the false negative rate (FNR), provide better dataset use, and reduce dimensionality and zero padding effects. The systems developed utilize deep learning techniques and are evaluated using the CERT 4.2 dataset. The dataset is analyzed and reformed so that each row represents a variable length sample of user activities. Two data representations are implemented to model extracted features …
Fuzzing Php Interpreters By Automatically Generating Samples, Jacob S. Baumgarte
Fuzzing Php Interpreters By Automatically Generating Samples, Jacob S. Baumgarte
Browse all Theses and Dissertations
Modern web development has grown increasingly reliant on scripting languages such as PHP. The complexities of an interpreted language means it is very difficult to account for every use case as unusual interactions can cause unintended side effects. Automatically generating test input to detect bugs or fuzzing, has proven to be an effective technique for JavaScript engines. By extending this concept to PHP, existing vulnerabilities that have since gone undetected can be brought to light. While PHP fuzzers exist, they are limited to testing a small quantity of test seeds per second. In this thesis, we propose a solution for …
Unsupervised-Based Distributed Machine Learning For Efficient Data Clustering And Prediction, Vishnu Vardhan Baligodugula
Unsupervised-Based Distributed Machine Learning For Efficient Data Clustering And Prediction, Vishnu Vardhan Baligodugula
Browse all Theses and Dissertations
Machine learning techniques utilize training data samples to help understand, predict, classify, and make valuable decisions for different applications such as medicine, email filtering, speech recognition, agriculture, and computer vision, where it is challenging or unfeasible to produce traditional algorithms to accomplish the needed tasks. Unsupervised ML-based approaches have emerged for building groups of data samples known as data clusters for driving necessary decisions about these data samples and helping solve challenges in critical applications. Data clustering is used in multiple fields, including health, finance, social networks, education, and science. Sequential processing of clustering algorithms, like the K-Means, Minibatch K-Means, …
Anomaly Detection In Multi-Seasonal Time Series Data, Ashton Taylor Williams
Anomaly Detection In Multi-Seasonal Time Series Data, Ashton Taylor Williams
Browse all Theses and Dissertations
Most of today’s time series data contain anomalies and multiple seasonalities, and accurate anomaly detection in these data is critical to almost any type of business. However, most mainstream forecasting models used for anomaly detection can only incorporate one or no seasonal component into their forecasts and cannot capture every known seasonal pattern in time series data. In this thesis, we propose a new multi-seasonal forecasting model for anomaly detection in time series data that extends the popular Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Our model, named multi-SARIMA, utilizes a time series dataset’s multiple pre-determined seasonal trends to increase …
Path-Safe: Enabling Dynamic Mandatory Access Controls Using Security Tokens, James P. Maclennan
Path-Safe: Enabling Dynamic Mandatory Access Controls Using Security Tokens, James P. Maclennan
Browse all Theses and Dissertations
Deploying Mandatory Access Controls (MAC) is a popular way to provide host protection against malware. Unfortunately, current implementations lack the flexibility to adapt to emergent malware threats and are known for being difficult to configure. A core tenet of MAC security systems is that the policies they are deployed with are immutable from the host while they are active. This work looks at deploying a MAC system that leverages using encrypted security tokens to allow for redeploying policy configurations in real-time without the need to stop a running process. This is instrumental in developing an adaptive framework for security systems …
Data-Driven Strategies For Disease Management In Patients Admitted For Heart Failure, Ankita Agarwal
Data-Driven Strategies For Disease Management In Patients Admitted For Heart Failure, Ankita Agarwal
Browse all Theses and Dissertations
Heart failure is a syndrome which effects a patient’s quality of life adversely. It can be caused by different underlying conditions or abnormalities and involves both cardiovascular and non-cardiovascular comorbidities. Heart failure cannot be cured but a patient’s quality of life can be improved by effective treatment through medicines and surgery, and lifestyle management. As effective treatment of heart failure incurs cost for the patients and resource allocation for the hospitals, predicting length of stay of these patients during each hospitalization becomes important. Heart failure can be classified into two types: left sided heart failure and right sided heart failure. …
Understanding And Enhancing The Efficiency And Efficacy Of Machine Learning-Assisted Software Vulnerability Detection, Daniel J. Grahn
Understanding And Enhancing The Efficiency And Efficacy Of Machine Learning-Assisted Software Vulnerability Detection, Daniel J. Grahn
Browse all Theses and Dissertations
As our world has become dependent upon software for nearly every aspect of modern society, software security has followed as an essential feature. The first line of defense against vulnerabilities is secure coding. While today’s programmers are carefully taught secure coding best practices, they can make mistakes or intentionally introduce vulnerable code. The traditional backstop to human errors and insider threats is the adoption of automated security analysis tools. These analysis tools have limitations. Static analysis suffers from high false positive rates that may cause annoyance and complacency among developers. Dynamic analysis can be difficult to set up and very …
Virtual Laboratories For Stem Education: An Evaluation Model And Comparison, Jumana Mahmoud Kharsa
Virtual Laboratories For Stem Education: An Evaluation Model And Comparison, Jumana Mahmoud Kharsa
Theses
Laboratory work is key to science education, and virtual environments play a vital role in remote learning. This thesis is concerned with the evaluation of virtual laboratories used in educational fields, mainly in STEM courses. This research investigates the basic criteria for evaluating virtual environments used in science education in order to create an evaluation scale. We reviewed the literature to highlight the main guidelines of evaluating virtual laboratories and found that the most common evaluation features for virtual tools are Ease of Use, Usefulness, Motivation, Interface Design, and Realism. Upon generating the assessment scale, we selected two web-based interactive …
Computer Enabled Interventions To Communication And Behavioral Problems In Collaborative Work Environments, Ashutosh Shivakumar
Computer Enabled Interventions To Communication And Behavioral Problems In Collaborative Work Environments, Ashutosh Shivakumar
Browse all Theses and Dissertations
Task success in co-located and distributed collaborative work settings is characterized by clear and efficient communication between participating members. Communication issues like 1) Unwanted interruptions and 2) Delayed feedback in collaborative work based distributed scenarios have the potential to impede task coordination and significantly decrease the probability of accomplishing task objective. Research shows that 1) Interrupting tasks at random moments can cause users to take up to 30% longer to resume tasks, commit up to twice the errors, and experience up to twice the negative effect than when interrupted at boundaries 2) Skill retention in collaborative learning tasks improves with …
Virtual Reality-Based Serious Role-Playing Games As Digital Experiential Learning Tools To Deliver Healthcare Skills Through Mobile Devices, Dixit Bharatkumar Patel
Virtual Reality-Based Serious Role-Playing Games As Digital Experiential Learning Tools To Deliver Healthcare Skills Through Mobile Devices, Dixit Bharatkumar Patel
Browse all Theses and Dissertations
Inadequate professional training and practices related to health care may result in severe complications to care experiences and outcomes. Moreover, healthcare professionals are as susceptible to the possibility of implicit biases as any other group. Importantly, the health care training is critical and challenging as minor prejudicial beliefs have an adverse influence or serious consequences on patients' health outcomes. Thus, facilitating serious role-playing virtual care practices along with raising awareness of healthcare professionals about the enduring impact of implicit/explicit biases and Social Determinants of Health (SDH) on health outcomes assist to advance the patient-provider relation, care experiences (e.g., healthcare experience …
Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng
Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng
Engineering Management & Systems Engineering Faculty Publications
A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords …
A Probabilistic Perspective Of Human-Machine Interaction, Mustafa Canan, Mustafa Demir, Samuel Kovacic
A Probabilistic Perspective Of Human-Machine Interaction, Mustafa Canan, Mustafa Demir, Samuel Kovacic
Engineering Management & Systems Engineering Faculty Publications
Human-machine interaction (HMI) has become an essential part of the daily routine in organizations. Although the machines are designed with state-of-the-art Artificial Intelligence applications, they are limited in their ability to mimic human behavior. The human-human interaction occurs between two or more humans; when a machine replaces a human, the interaction dynamics are not the same. The results indicate that a machine that interacts with a human can increase the mental uncertainty that a human experiences. Developments in decision sciences indicate that using quantum probability theory (QPT) improves the understanding of human decision-making than merely using classical probability theory (CPT). …
Characterizing Students’ Engineering Design Strategies Using Energy3d, Jasmine Singh, Viranga Perera, Alejandra Magana, Brittany Newell
Characterizing Students’ Engineering Design Strategies Using Energy3d, Jasmine Singh, Viranga Perera, Alejandra Magana, Brittany Newell
Discovery Undergraduate Interdisciplinary Research Internship
The goals of this study are to characterize design actions that students performed when solving a design challenge, and to create a machine learning model to help future students make better engineering design choices. We analyze data from an introductory engineering course where students used Energy3D, an open source computer-aided design software, to design a zero-energy home (i.e. a home that consumes no net energy over a period of a year). Student design actions within the software were recorded into text files. Using a sample of over 300 students, we first identify patterns in the data to assess how students …
Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe
Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe
Engineering Management & Systems Engineering Faculty Publications
Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …
A Tutorial And Future Research For Building A Blockchain-Based Secure Communication Scheme For Internet Of Intelligent Things, Mohammad Wazid, Ashok Kumar Das, Sachin Shetty, Minho Jo
A Tutorial And Future Research For Building A Blockchain-Based Secure Communication Scheme For Internet Of Intelligent Things, Mohammad Wazid, Ashok Kumar Das, Sachin Shetty, Minho Jo
Computational Modeling & Simulation Engineering Faculty Publications
The Internet of Intelligent Things (IoIT) communication environment can be utilized in various types of applications (for example, intelligent battlefields, smart healthcare systems, the industrial internet, home automation, and many more). Communications that happen in such environments can have different types of security and privacy issues, which can be resolved through the utilization of blockchain. In this paper, we propose a tutorial that aims in desiging a generalized blockchain-based secure authentication key management scheme for the IoIT environment. Moreover, some issues with using blockchain for a communication environment are discussed as future research directions. The details of different types of …
Scalable, Pluggable, And Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems, Michael Partin
Scalable, Pluggable, And Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems, Michael Partin
Browse all Theses and Dissertations
Features and attributes that describe an event (disasters, social movements, etc.) are heterogeneous in nature. For virtually all events that impact humans, technology enables us to capture a large amount and variety of data from many sources, including humans (i.e., social media) and sensors/internet of things (IoTs). The corresponding modalities of data include text, imagery, voice and video, along with structured data such as gazetteers (i.e., location-based data) and government and statistical data. However, even though there is often an abundance of information produced, this information is fragmented across the various modalities and sources. The DisasterRecord system aims to provide …
Towards Interpretable And Reliable Deep Neural Networks For Visual Intelligence, Ning Xie
Towards Interpretable And Reliable Deep Neural Networks For Visual Intelligence, Ning Xie
Browse all Theses and Dissertations
Deep Neural Networks (DNNs) are powerful tools blossomed in a variety of successful real-life applications. While the performance of DNNs is outstanding, their opaque nature raises a growing concern in the community, causing suspicions on the reliability and trustworthiness of decisions made by DNNs. In order to release such concerns and towards building reliable deep learning systems, research efforts are actively made in diverse aspects such as model interpretation, model fairness and bias, adversarial attacks and defenses, and so on. In this dissertation, we focus on the research topic of DNN interpretations for visual intelligence, aiming to unfold the black-box …
Aateam: Achieving The Ad Hoc Teamwork By Employing The Attention Mechanism, Shuo Chen, Ewa Andrejczuk, Zhiguang Cao, Jie Zhang
Aateam: Achieving The Ad Hoc Teamwork By Employing The Attention Mechanism, Shuo Chen, Ewa Andrejczuk, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
In the ad hoc teamwork setting, a team of agents needs to perform a task without prior coordination. The most advanced approach learns policies based on previous experiences and reuses one of the policies to interact with new teammates. However, the selected policy in many cases is sub-optimal. Switching between policies to adapt to new teammates' behaviour takes time, which threatens the successful performance of a task. In this paper, we propose AATEAM – a method that uses the attention-based neural networks to cope with new teammates' behaviour in real-time. We train one attention network per teammate type. The attention …
Deepcon-Pre: Improved Protein Contact Map Prediction Using Inverse Covariance And Deep Residual Networks, Nachammai Palaniappan
Deepcon-Pre: Improved Protein Contact Map Prediction Using Inverse Covariance And Deep Residual Networks, Nachammai Palaniappan
Theses
As with most domains where machine learning methods are applied, correct feature engineering is critical when developing deep learning algorithms for solving the protein folding problem. Unlike the domains such as computer vision and natural language processing, feature engineering is not rigorously studied towards solving the protein folding problem. A recent research has highlighted that input features known as precision matrix are most informative for predicting inter-residue contact map, the key for building three-dimensional models. In this work, we study the significance of the precision matrix feature when very deep residual networks are trained. Using a standard dataset of 3456 …
Law Library Blog (August 2019): Legal Beagle's Blog Archive, Roger Williams University School Of Law
Law Library Blog (August 2019): Legal Beagle's Blog Archive, Roger Williams University School Of Law
Law Library Newsletters/Blog
No abstract provided.
On Hybrid Temporal Basis Functions For Stable Numerical Solution Of Time Domain Boundary Integral Equations, Fang Q. Hu
On Hybrid Temporal Basis Functions For Stable Numerical Solution Of Time Domain Boundary Integral Equations, Fang Q. Hu
Mathematics & Statistics Faculty Publications
Problems in unsteady aerodynamics and aeroacoustics can sometimes be formulated as integral equations, such as the boundary integral equations. Numerical discretization of integral equations in the time domain often leads to so-called March-On-in-Time (MOT) schemes. In the literature, the temporal basis functions used in MOT schemes have been largely limited to low-order shifted Lagrange basis functions. In order to evaluate the accuracy and effectiveness of the temporal basis functions, a Fourier analysis of the temporal interpolation schemes is carried out. Based on the Fourier analysis, the spectral resolutions of various temporal basis functions are quantified. It is argued that hybrid …
A Novel Methodology For Timely Brain Formations Of 3d Spatial Information With Application To Visually Impaired Navigation, Spyridon Manganas
A Novel Methodology For Timely Brain Formations Of 3d Spatial Information With Application To Visually Impaired Navigation, Spyridon Manganas
Browse all Theses and Dissertations
Human brain analysis and understanding pose several challenges due to the great complexity of the structural organization and the functional connectivity that characterizes the human brain. The ability of the brain to adapt in dynamic changes over time such as normal aging, neurodegenerative diseases or congenital brain malformations renders the brain’s exploration a particularly demanding and difficult task. In recent years, advances in brain imaging modalities and lately the multimodal fusion, combined with improvements in related technologies have greatly assisted the development of brain maps by providing insights regarding the overall brain structure and functionality. Even though the existence of …
Improve Operating Room Utilization Through Distributed Scheduling Workflow And Automation, Miteshkumar Mahendrabhai Vasoya
Improve Operating Room Utilization Through Distributed Scheduling Workflow And Automation, Miteshkumar Mahendrabhai Vasoya
Browse all Theses and Dissertations
Operating room (OR) plays a crucial role in health care, contributing more than 50% of the hospital’s revenue and incurring over 35% of the hospital’s expense, ultimately determining the hospital’s profitability. Moreover, because the OR is a primary source of admissions, it is virtually impossible to streamline hospital‐wide workflow without first streamlining patient flow through the OR. Unfortunately, current OR scheduling practices often limit the utilization of OR, one of the most expensive resources in the health care industry, to around 60%. On the other hand, many patients have to wait an excessively long time before their surgeries can be …
Ieee Access Special Section Editorial: Wirelessly Powered Networks, And Technologies, Theofanis P. Raptis, Nuno B. Carvalho, Diego Masotti, Lei Shu, Cong Wang, Yuanyuan Yang
Ieee Access Special Section Editorial: Wirelessly Powered Networks, And Technologies, Theofanis P. Raptis, Nuno B. Carvalho, Diego Masotti, Lei Shu, Cong Wang, Yuanyuan Yang
Computer Science Faculty Publications
Wireless Power Transfer (WPT) is, by definition, a process that occurs in any system where electrical energy is transmitted from a power source to a load without the connection of electrical conductors. WPT is the driving technology that will enable the next stage in the current consumer electronics revolution, including battery-less sensors, passive RF identification (RFID), passive wireless sensors, the Internet of Things and 5G, and machine-to-machine solutions. WPT-enabled devices can be powered by harvesting energy from the surroundings, including electromagnetic (EM) energy, leading to a new communication networks paradigm, the Wirelessly Powered Networks.
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework, Nir Nissim, Aviad Cohen, Jian Wu, Andrea Lanzi, Lior Rokach, Yuval Elovici, Lee Giles
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework, Nir Nissim, Aviad Cohen, Jian Wu, Andrea Lanzi, Lior Rokach, Yuval Elovici, Lee Giles
Computer Science Faculty Publications
Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library …
Using Github In Large Software Engineering Classes: An Exploratory Case Study, Miroslav Tushev
Using Github In Large Software Engineering Classes: An Exploratory Case Study, Miroslav Tushev
LSU Master's Theses
GitHub has been recently used in Software Engineering (SE) classes to facilitate col- laboration in student team projects. The underlying tenet is that the technical and social feature of GitHub can help students to communicate and collaborate more effectively as a team as well as help teachers to evaluate individual student contribution more objectively. To shed more light on this, in this case study, we explore the benefits and drawbacks of using GitHub in SE classes. Our study is conducted in a software engineering class of 91 students divided into 18 teams. Our research method includes an entry and an …
The Rock 2018, School Of Engineering And Computer Science
The Rock 2018, School Of Engineering And Computer Science
The Rock
No abstract provided.
Evaluating Student Perceptions And Learning Outcomes: Differences Between Sla-Able And Non-Sla-Able Introductory Programming Courses, Christina M. Frederick, Matthew B. Pierce, Andrew Griggs, Lulu Sun
Evaluating Student Perceptions And Learning Outcomes: Differences Between Sla-Able And Non-Sla-Able Introductory Programming Courses, Christina M. Frederick, Matthew B. Pierce, Andrew Griggs, Lulu Sun
Publications
Engineering, computer science and subsequently knowledge of programming language is an increasingly vital skill in today’s workforce. First year engineering students are introduced to programming in addition to rigorous course loads in their first year. Second Language Acquisition (SLA) has been applied to programming course content delivery and has shown promise as an effective means of better educating new students. Results will be presented from a NSF funded study conducted over the past two years. SLA was applied to an introductory engineering course that teaches basic programming skills in a Blended learning environment (SLA-aBLe). This study examined four semesters worth …
The Rock 2017, School Of Engineering And Computer Science
The Rock 2017, School Of Engineering And Computer Science
The Rock
No abstract provided.