Computer Sciences Commons^™

Softswitch: A Centralized Honeypot-Based Security Approach Using Software-Defined Switching For Secure Management Of Vlan Networks, Muhammet Baykara, Resul Daş

Turkish Journal of Electrical Engineering and Computer Sciences

Honeypot systems are traps for intruders which simulate real systems such as web, application, and database servers used in information systems. Using these systems, unauthorized and malicious access can be efficiently detected. Honeypot is an entity which acts as a source of valued information and its behavior can be monitored. The inability or difficulty of intrusion detection is a serious security problem in networks including virtual local area network (VLAN). According to the literature, the use of honeypots for intrusion detection and prevention in networks including VLAN is strongly recommended. In this paper, in order to provide security and to …

Introducing The Global Data Privacy Prize, Fred H. Cate, Christopher Kuner, Orla Lynskey, Christopher Millard, Nora Ni Loideain, Dan Jerker B. Svantesson

Articles by Maurer Faculty

No abstract provided.

The Influence Of Identifiable Personality Traits On Nurses’ Intention To Use Wireless Implantable Medical Devices, Vincent Molosky

CCAC Theses and Dissertations

Technically-driven medical devices such as wireless implantable medical devices (WIMD) have become ubiquitous within healthcare. The use of these devices has changed the way nurses administer patient care. Consequently, the nursing workforce is large and diverse, and with it comes an expected disparity in personalities. Research involving human factors and technology acceptance in healthcare is not new. Yet due to the changing variables in the manner of which patient care is being administered, both in person and in the mechanism of treatment, recent research suggests that individual human factors such as personality traits may hold unknown implications involving more successful …

User Information Security Behavior In Professional Virtual Communities: A Technology Threat Avoidance Approach, Vivienne Forrester

CCAC Theses and Dissertations

The popularization of professional virtual communities (PVCs) as a platform for people to share experiences and knowledge has produced a paradox of convenience versus security. The desire to communicate results in disclosure where users experience ongoing professional and social interaction. Excessive disclosure and unsecured user security behavior in PVCs increase users’ vulnerability to technology threats. Nefarious entities frequently use PVCs such as LinkedIn to launch digital attacks. Hence, users are faced with a gamut of technology threats that may cause harm to professional and personal lives. Few studies, however, have examined users’ information security behavior and their motivation to engage …

Usability Challenges With Insulin Pump Devices In Diabetes Care: What Trainers Observe With First-Time Pump Users, Helen Birkmann Hernandez

CCAC Theses and Dissertations

Insulin pumps are designed for the self-management of diabetes mellitus in patients and are known for their complexity of use. Pump manufacturers engage trainers to teach patients how to use the devices correctly to control the symptoms of their disease. Usability research related to insulin pumps and other infusion pumps with first-time users as participants has centered on the relationship between user interface design and the effectiveness of task completion. According to prior research, the characteristics of system behavior in a real life environment remain elusive. A suitable approach to acquire information about potential usability problems encountered by first-time users …

The Impact Of Cross-References On The Readability Of The U.S. Internal Revenue Code, Jeffrey A. Lasky

CCAC Theses and Dissertations

Scholars and practitioners have long argued that U.S. income tax law (“the Tax Code”) is excessively complex and difficult to understand, and hence imposes non-trivial adjudication, administration, planning, and compliance costs across the spectrum of income tax stakeholders: the courts, the Internal Revenue Service, tax practitioners, business managers, and individual taxpayers. Hence, there is considerable interest in reducing the effort needed to accurately understand and apply the provisions of income tax law. Prior scholarly work has strongly argued that exceptions to Tax Code provisions as expressed by cross-references embedded in the Tax Code text constitute a major source of reading …

Optimization Methods For Learning Graph-Structured Sparse Models, Baojian Zhou

Legacy Theses & Dissertations (2009 - 2024)

Learning graph-structured sparse models has recently received significant attention thanks to their broad applicability to many important real-world problems. However, such models, of more effective and stronger interpretability compared with their counterparts, are difficult to learn due to optimization challenges. This thesis presents optimization algorithms for learning graph-structured sparse models under three different problem settings. Firstly, under the batch learning setting, we develop methods that can be applied to different objective functions that enjoy linear convergence guarantees up to constant errors. They can effectively optimize the statistical score functions in the task of subgraph detection; Secondly, under stochastic learning setting, …

Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee

Legacy Theses & Dissertations (2009 - 2024)

Deep Learning is the new state-of-the-art technology in Image Processing. We applied Deep Learning techniques for identification of diseases from Radiographs made publicly available by NIH. We applied some Feature Engineering approach to augment the data from Anterior-Posterior position to Posterior-Anterior position and vice-versa for all the diseases, at the same point we suppressed ‘No Finding’ radiographs which contributed to more than 50% (approximately 60,000) of the dataset to top 1000 images. We also prepared a model by adding a huge amount of noise to the augmented data, which if need be can be deployed at rural locations which lack …

Efficient Algorithms For Mining Healthcare Data :, Yan Hu

Legacy Theses & Dissertations (2009 - 2024)

Data-Driven Healthcare (DDH) is defined as the usage of available medical big data to provide the best and most personalized care, which is believed to be one of the most promising directions for transforming healthcare. The healthcare data includes claims and cost data, clinical data, pharmaceutical R&D data, patient behavior and sentiment data, and health data on the web. There has been a remarkable upsurge in the adoption of healthcare data over the past several years. In particular, it has been used for medical concept extraction, patient trajectory modeling, disease inference, etc.

Towards The Development Of A Concurrent Programming Language, Marrium Ayesha

Legacy Theses & Dissertations (2009 - 2024)

In order to fully utilize the potential of current architectures, programmers must program withconcurrency in mind. Concurrent processes can be extremely challenging to reason about due tounexpected program behavior that may emerge from interaction between processes. One approachto deal with this difficulty is to study new programming languages that offer an abstraction forconcurrency. This thesis focuses on developing a logical interpretation for concurrent processesand incorporating it in an existing functional programming language called SML. We developthis feature upon the fact that a proof of a theorem in logic can be expressed as a program in aprogramming language. This relation allows …

Autonomous Spectrum Enforcement : A Blockchain Approach, Maqsood Ahamed Abdul Careem

Legacy Theses & Dissertations (2009 - 2024)

A core limitation in existing wireless technologies is the scarcity of spectrum, to support the exponential increase in Internet-connected and multimedia-capable mobile devices and the increasing demand for bandwidth-intensive services. As a solution, Dynamic Spectrum Access policies are being ratified to promote spectrum sharing for various spectrum bands and to improve the spectrum utilization. This poses an equally challenging problem of enforcing these spectrum policies. The distributed and dynamic nature of policy violations necessitates the use of autonomous agents to implement efficient and agile enforcement systems. The design of such a fully autonomous enforcement system is complicated due to the …

Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar

Legacy Theses & Dissertations (2009 - 2024)

Emotion forecasting is the task of predicting the future emotion of a speaker, i.e., the emotion label of the future speaking turn–based on the speaker’s past and current audio-visual cues. Emotion forecasting systems require new problem formulations that differ from traditional emotion recognition systems. In this thesis, we first explore two types of forecasting windows(i.e., analysis windows for which the speaker’s emotion is being forecasted): utterance forecasting and time forecasting. Utterance forecasting is based on speaking turns and forecasts what the speaker’s emotion will be after one, two, or three speaking turns. Time forecasting forecasts what the speaker’s emotion will …

Data Mining The Effects Of Testing Conditions And Specimen Properties On Brain Biomechanics, Folly Patterson, Osama Abuomar, Mike Jones, Keith Tansey, R. K. Prabhu

Computing Sciences

Traumatic brain injury is highly prevalent in the United States. However, despite its frequency and significance, there is little understanding of how the brain responds during injurious loading. A confounding problem is that because testing conditions vary between assessment methods, brain biomechanics cannot be fully understood. Data mining techniques, which are commonly used to determine patterns in large datasets, were applied to discover how changes in testing conditions affect the mechanical response of the brain. Data at various strain rates were collected from published literature and sorted into datasets based on strain rate and tension vs. compression. Self-organizing maps were …

Activity - Binary Code, Robert J. Domanski

Open Educational Resources

A Binary Code activity and worksheet for CS0 students. Part of the CUNY CS04All project.

On Cluster Robust Models, José Bayoán Santiago Calderón

CGU Theses & Dissertations

Cluster robust models are a kind of statistical models that attempt to estimate parameters considering potential heterogeneity in treatment effects. Absent heterogeneity in treatment effects, the partial and average treatment effect are the same. When heterogeneity in treatment effects occurs, the average treatment effect is a function of the various partial treatment effects and the composition of the population of interest. The first chapter explores the performance of common estimators as a function of the presence of heterogeneity in treatment effects and other characteristics that may influence their performance for estimating average treatment effects. The second chapter examines various approaches …

Qoe Driven Multimedia Service Schemes In Wireless Networks Resource Allocation: Evolution From Optimization, Game Theory, To Economics, Shuan He

CGU Theses & Dissertations

In order to deal with the Quality of Experience (QoE) improvement issue in the wireless networks services. In this dissertation we first investigated the Device to Device (D2D) relaying approach in the conventional Base Station (BS) to User Equipment (UE) two entities multimedia service system. In this part, the Multiple Input Multiple Output (MIMO) technology will be implemented in the D2D communication. Furthermore, factors such as the multimedia content distribution (i.e., Quad-tree fractal image compression method), the power allocation strategy, and modulation size are jointly considered to improve the QoE performance and energy efficiency. In addition, the emerging Non-Orthogonal Multiple …

Towards Misleading Connection Mining, Md Main Uddin Rony

Electronic Theses and Dissertations

This study introduces a new Natural Language Generation (NLG) task – Unit Claim Identification. The task aims to extract every piece of verifiable information from a headline. The Unit Claim identification has applications in other domains; such as fact-checking where the identification of each verifiable information from a check-worthy statement can lead to an effective fact-check. Moreover, the extracting of the unit claims from headlines can identify a misleading news article, by mapping evidence from contents. For addressing the unit claim identification problem, we outlined a set of guidelines for data annotation, arranged in-house training for the annotators and obtained …

Molecular Exchange Monte Carlo. A Generalized Method For Identity Exchanges In Grand Canonical Monte Carlo Simulations, Mohammad Soroush Barhaghi

Wayne State University Theses

A generalized identity exchange algorithm is presented for Monte Carlo simulations in the grand canonical ensemble. The algorithm, referred to as Molecular Exchange Monte Carlo (MEMC), may be applied to multicomponent systems of arbitrary molecular topology, and provides significant enhancements in the sampling of phase space over a wide range of compositions and temperatures. Three different approaches are presented for the insertion of large molecules, and the pros and cons of each method are discussed. The performance of the algorithms is highlighted through grand canonical Monte Carlo histogram-reweighting simulations performed on several systems, including 2,2,4-trimethylpentane+neopentane, butane+perfluorobutane, methane+n-alkanes, and water+impurity. Relative …

Collaboration Pattern Model For Student Participation In Problem-Solving Typed Chat, Duy Quang Bui

Theses

This project measures different collaborative dialogue acts between students who are working together to solve problems in a computer programming class. In COMPS (Computer-Mediated Problem Solving) exercises students work together via online typed-chat. Transcripts of these conversations were annotated with four categories of collaborative utterance: sharing ideas, negotiating ideas, regulating problem-solving, and maintaining communication. The annotated transcripts were then applied to answer four different research questions. A) Among the several students in a conversation, there are measurable quantitative differences in dialogue behavior that correlate with the relative preparedness for solving the problem. The most prepared student not only talks more …

Effective Evaluation Of The Non-Technical Skills In The Computing Discipline, Maurice Danaher, Kevin Schoepp, Ashley Ater Kranov

All Works

© 2019, Journal of Information Technology Eucation Research. Aim/Purpose Assessing non-technical skills is very difficult and current approaches typically assess the skills separately. There is a need for better quality assessment of these skills at undergraduate and postgraduate levels. Background A method has been developed for the computing discipline that assesses all six non-technical skills prescribed by ABET (Accreditation Board for Engineering and Technology), the accreditation board for engineering and technology. It has been shown to be a valid and reliable method for undergraduate students Methodology The method is based upon performance-based assessment where a team of students discuss and …

Attacker Capability Based Dynamic Deception Model For Large-Scale Networks, Md Ali Reza Al Amin, Sachhin Shetty, Laurent Njilla, Deepak K. Tosh, Charles Kamhoua

Computational Modeling & Simulation Engineering Faculty Publications

In modern days, cyber networks need continuous monitoring to keep the network secure and available to legitimate users. Cyber attackers use reconnaissance mission to collect critical network information and using that information, they make an advanced level cyber-attack plan. To thwart the reconnaissance mission and counterattack plan, the cyber defender needs to come up with a state-of-the-art cyber defense strategy. In this paper, we model a dynamic deception system (DDS) which will not only thwart reconnaissance mission but also steer the attacker towards fake network to achieve a fake goal state. In our model, we also capture the attacker’s capability …

Exploring Strategies For Implementing Information Security Training And Employee Compliance Practices, Alan Robert Dawson

Walden Dissertations and Doctoral Studies

Humans are the weakest link in any information security (IS) environment. Research has shown that humans account for more than half of all security incidents in organizations. The purpose of this qualitative case study was to explore the strategies IS managers use to provide training and awareness programs that improve compliance with organizational security policies and reduce the number of security incidents. The population for this study was IS security managers from 2 organizations in Western New York. Information theory and institutional isomorphism were the conceptual frameworks for this study. Data collection was performed using face-to-face interviews with IS managers …

Python Loops, Natalia Novak

Open Educational Resources

The following topics are covered:

• While Loops
• For Loops
• Nested loops
• Break and continue
• Loops else
• enumerate()

Applications: Turtle library with loops and decision procedures.

Prior knowledge of variables, assignments, expressions, input-output, lists, and conditionals is recommended.

For CS0 students. Part of the CUNY CS04All project.

Scalable Clustering For Immune Repertoire Sequence Analysis, Prem Bhusal

Browse all Theses and Dissertations

The development of the next-generation sequencing technology has enabled systems immunology researchers to conduct detailed immune repertoire analysis at the molecule level. Large sequence datasets (e.g., millions of sequences) are being collected to comprehensively understand how the immune system of a patient evolves over different stages of disease development. A recent study has shown that the hierarchical clustering (HC) algorithm gives the best results for B-cell clones analysis - an important type of immune repertoire sequencing (IR-Seq) analysis. However, due to the inherent complexity, the classical hierarchical clustering algorithm does not scale well to large sequence datasets. Surprisingly, no algorithms …

Adaptive Knowledge Networks: A Time Capsule, Swati Padhee, Anurag Illendula, Amit Sheth, Krishnaprasad Thirunarayan, Valerie L. Shalin

Kno.e.sis Publications

❖ Real world events are dynamic in nature Periodic events e.g. US Presidential Election Non-periodic events e.g. Cyclone Idai

❖ Need for real-time predictive analysis, trend analysis, spatio-temporal decision making, public opinion analysis for events.

❖ Current state-of-the-art curates dynamic knowledge graph from structured text.

❖ We propose creating an Adaptive Knowledge Network from incoming real-time multimodal spatio-temporally evolving data.

The New Legal Landscape For Text Mining And Machine Learning, Matthew Sag

Faculty Articles

Now that the dust has settled on the Authors Guild cases, this Article takes stock of the legal context for TDM research in the United States. This reappraisal begins in Part I with an assessment of exactly what the Authors Guild cases did and did not establish with respect to the fair use status of text mining. Those cases held unambiguously that reproducing copyrighted works as one step in the process of knowledge discovery through text data mining was transformative, and thus ultimately a fair use of those works. Part I explains why those rulings followed inexorably from copyright's most …

Reinforcement Learning For Optimal Control Of Network Epidemic Processes, Alec H. Kerrigan

Honors Undergraduate Theses

Our society is increasingly interconnected, making it easy for cascades/epidemic (diseases, disinformation etc). Current epidemic control efforts are based on approximate network epidemic models, which often ignore the unique complexity and rich information embedded in the complex interconnections of real-world networks/populations.Deep reinforcement learning (RL) is a powerful tool at learning policies for these nonlinear, complex processes in high-dimension. To control an epidemic outbreak on a Susceptible-Infected-Susceptible network epidemic model, we design a RL framework with a custom reward structure using the node2vec embedding technique. Results indicate deep RL is able to determine and converge on an optimal intervention policy in …

Visual-Textual Video Synopsis Generation, Aidean Sharghi Karganroodi

Electronic Theses and Dissertations

In this dissertation we tackle the problem of automatic video summarization. Automatic summarization techniques enable faster browsing and indexing of large video databases. However, due to the inherent subjectivity of the task, no single video summarizer fits all users unless it adapts to individual user's needs. To address this issue, we introduce a fresh view on the task called "Query-focused'' extractive video summarization. We develop a supervised model that takes as input a video and user's preference in form of a query, and creates a summary video by selecting key shots from the original video. We model the problem as …

Towards More Reliable Neural Network Learning Models, Navid Kardan

Electronic Theses and Dissertations

Ideally, when a neural network makes a wrong decision or encounters an out-of-distribution example, its predictive confidence should be as low as possible. Three primary contributions in this dissertation address this challenge. The first two contributions are new approaches to mitigate overconfident predictions in modern neural networks. In the first (1), called competitive overcomplete output layer neural networks, several classifiers, as part of the same output layer, are trained simultaneously and later their consensus produces more reliable predictions. The second approach (2) reformulates the original classification problem into several new versions by combining classes together and training a classifier on …

The Application Of Cloud Resources To Terrain Data Visualization, Gregory J. Larrick

EWU Masters Thesis Collection

In this thesis, the recent trends in cloud computing, via virtual machine hosted servers, are applied to the field of big data visualization. In particular, we investigate the visualization of terrain data acquired from several major open data sets with a graphics library for browser based rendering. Similar terrain data visualization solutions have not fully taken advantage of remote computational resources. In this thesis, we show that, by using a collection of Amazon EC-2 machines for fetching and decoding of terrain data, in conjunction with modern graphics libraries, three dimensional terrain information may be viewed and interacted with by many …