Computer Sciences Commons^™

Siguria Dhe Privatësia Në Shfyrtëzimin E Internetit, Rina Nebihu

Theses and Dissertations

Teknologjia e informacionit përfshinë të gjitha llojet e teknologjisë së përdorur për shkëmbimin, ruajtjen, përdorimin ose krijimin e informacionit. Teknologjia e informacionit përfiton nga bota e biznesit duke lejuar organizatat të punojnë në mënyrë më efikase dhe maksimizojnë produktivitetin. Komunikimi më i shpejtë, ruajtja elektronike dhe mbrojtja e të dhënave janë përparësi që teknologjia informative mund të ketë në ndërmarrjen tuaj. Siguria dhe privatësia e atyre të dhënave ka rëndësi jashtzakonisht të madhe, sidomos në ditët e sotme, ku siguria dhe privatësia mund të cenohen mjaft lehtë.

Tema e marrë në studim është një çështje shumë e rëndësishme për të …

Fraud Detection Using Data-Driven Approach, Arianit Mehana

Theses and Dissertations

The extensive use of internet is continuously drifting businesses to incorporate their services in the online environment. One of the first spectrums to embrace this evolution was the banking sector. In fact, the first known online banking service came in 1980. It was deployed from a community bank located in Knoxville, called the United American Bank. Since then, internet banking has been offering ease and efficiency to costumers in completing their daily banking tasks.

The ever increasing use of internet banking and the large number of online transactions, increased fraudulent behaviour also. As if fraud increase wasn’t enough, the massive …

Përdorimi I Teknikave Të Mësuarit Të Makinave Për Personalizimin E Rrugëve Mësimore Të Studentëve, Burbuqe Beqiraj

Theses and Dissertations

Krijimi i rrugëve mësimore duke u përshtatur në mënyrë dinamike me nevojat individuale të studentëve ka potencialin të luajnë rol të rëndësishëm në procesin mësimor. Prandaj, qëllimi kryesor i punimit është trajtimi i teknikave dhe algoritmeve të ndryshme si pjesë e mësimit të makinave që kanë ndikim në personalizimin e procesit mësimor në aspektin e mësimnxënies varësisht variablave të ndryshme të profilit të studentëve.

Gjithashtu, punimi është fokusuar në krijimin e modelit për personalizimin e rrugëve mësimore. Bazuar në modelin e propozuar është zhvilluar një prototip i cili mundëson personalizimin e procesit mësimor sipas nevojave të secilit student. Ky prototip …

Semi-Supervised Deep Learning With Applications In Surgical Video Analysis And Bioinformatics, Sheng Wang

Computer Science and Engineering Dissertations - Archive

In the current era of big data, deep learning has been the state-of-the-art model for various applications. Image-based applications such as image classification, object detection, image segmentation, benefit most from deep learning networks. One reason for the successful applications of deep learning is that there are a large number of labeled training samples for the model to learn from. People are interested in reducing the cost of getting labeled training samples, and there are various research going on with unsupervised, semi-supervised, and self-supervised deep learning. The cost of health-related data is even higher. Labeling the surgical videos with tools being …

Sl2mf: Predicting Synthetic Lethality In Human Cancers Via Logistic Matrix Factorization, Yong Liu, Min Wu, Chenghao Liu, Xiao-Li Li, Jie Zheng

Student Publications

Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms, or cell lines. Therefore, computational prediction methods are needed to address these issues. This paper proposes a novel SL prediction method, named $\mathsf{SL}^2 \mathsf{MF}$, which employs logistic matrix factorization to learn latent representations of genes from the observed SL data. The probability that two genes are likely to form SL is modeled by the linear combination of gene latent vectors. As known SL pairs are more trustworthy …

Efficient Network Design For High Dimensional Data, Xin Miao

Computer Science and Engineering Dissertations - Archive

Due to the powerful feature representation capabilities, deep learning has became a powerful tool in the field of computer vision. Especially in the aspect of high-dimensional images, deep learning can achieve fast inference compared with most traditional methods. This paper focuses on how to design an efficient neural network and apply it to two high-dimensional images application, video facial landmarks detections and compressive imaging system. In this first part of this paper, we focus on landmarks detection for video facial images. Existing methods for facial landmarks detection mainly rely on cascaded regression. It is an indirect method and progressively estimates …

Heuristics For Sparsest Cut Approximations In Network Flow Applications, Fernando Vilas

Computer Science and Engineering Theses and Dissertations

The Maximum Concurrent Flow Problem (MCFP) is a polynomially bounded problem that has been used over the years in a variety of applications. Sometimes it is used to attempt to find the Sparsest Cut, an NP-hard problem, and other times to find communities in Social Network Analysis (SNA) in its hierarchical formulation, the HMCFP. Though it is polynomially bounded, the MCFP quickly grows in space utilization, rendering it useful on only small problems. When it was defined, only a few hundred nodes could be solved, where a few decades later, graphs of one to two thousand nodes can still be …

Dfl-Opt : A Daily Fantasy Lineup Optimizer, Francis Aurori

Theses, Dissertations and Culminating Projects

[Background] Daily fantasy sports (DFS) are a variety of fantasy sports where contests take place in a matter of days or hours rather than over a whole season. A disparity exists between skilled professionals and casual participants in the creation of line-ups (i.e. teams) w.r.t their chances of winning in these contests. The purpose of the current project was to create a user-friendly, open source platform (named DFL-Opt) for participants of all skill levels to utilize in the creation of DFS line-ups. In addition, efficacy of the DFL-Opt platform was determined by playing the lineups generated by the DFL-Opt tool …

Enhancing Cellular Communications For Uavs Via Intelligent Reflective Surface, Dong Ma, Ming Ding, Mahbub Hassan

Research Collection School Of Computing and Information Systems

Intelligent reflective surfaces (IRSs) capable of reconfiguring their electromagnetic absorption and reflection properties in real-time are offering unprecedented opportunities to enhance wireless communication experience in challenging environments. In this paper, we analyze the potential of IRS in enhancing cellular communications for UAVs, which currently suffers from poor signal strength due to the down-tilt of base station antennas optimized to serve ground users. We consider deployment of IRS on building walls, which can be remotely configured by cellular base stations to coherently direct the reflected radio waves towards specific UAVs in order to increase their received signal strengths. Using the recently …

Cornac: A Comparative Framework For Multimodal Recommender Systems, Aghiles Salah, Quoc Tuan Truong, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Cornac is an open-source Python framework for multimodal recommender systems. In addition to core utilities for accessing, building, evaluating, and comparing recommender models, Cornac is distinctive in putting emphasis on recommendation models that leverage auxiliary information in the form of a social network, item textual descriptions, product images, etc. Such multimodal auxiliary data supplement user-item interactions (e.g., ratings, clicks), which tend to be sparse in practice. To facilitate broad adoption and community contribution, Cornac is publicly available at https://github.com/PreferredAI/cornac, and it can be installed via Anaconda or the Python Package Index (pip). Not only is it well-covered by unit tests …

Shakespeare In The Eighteenth Century: Algorithm For Quotation Identification, Marion Pauline Chiariglione

Graduate Theses and Dissertations

Quoting a borrowed excerpt of text within another literary work was infrequently done prior to the beginning of the eighteenth century. However, quoting other texts, particularly Shakespeare, became quite common after that. Our work develops automatic approaches to identify that trend. Initial work focuses on identifying exact and modified sections of texts taken from works of Shakespeare in novels spanning the eighteenth century. We then introduce a novel approach to identifying modified quotes by adapting the Edit Distance metric, which is character based, to a word based approach. This paper offers an introduction to previous uses of this metric within …

Achieving Causal Fairness In Machine Learning, Yongkai Wu

Graduate Theses and Dissertations

Fairness is a social norm and a legal requirement in today's society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974) have been established to prohibit discrimination and enforce fairness on several grounds, such as gender, age, sexual orientation, race, and religion, referred to as sensitive attributes. Nowadays machine learning algorithms are extensively applied to make important decisions in many real-world applications, e.g., employment, admission, and loans. Traditional machine learning algorithms aim to maximize predictive performance, e.g., accuracy. Consequently, certain groups may get unfairly treated when those algorithms are applied for decision-making. Therefore, it is an imperative …

Data Breach Consequences And Responses: A Multi-Method Investigation Of Stakeholders, Hamid Reza Nikkhah

Graduate Theses and Dissertations

The role of information in today’s economy is essential as organizations that can effectively store and leverage information about their stakeholders can gain an advantage in their markets. The extensive digitization of business information can make organizations vulnerable to data breaches. A data breach is the unauthorized access to sensitive, protected, or confidential data resulting in the compromise of information security. Data breaches affect not only the breached organization but also various related stakeholders. After a data breach, stakeholders of the breached organizations show negative behaviors, which causes the breached organizations to face financial and non-financial costs. As such, the …

Cybersecurity Methods For Grid-Connected Power Electronics, Stephen Joe Moquin

Graduate Theses and Dissertations

The present work shows a secure-by-design process, defense-in-depth method, and security techniques for a secure distributed energy resource. The distributed energy resource is a cybersecure, solar inverter and battery energy storage system prototype, collectively called the Cybersecure Power Router. Consideration is given to the use of the Smart Green Power Node for a foundation of the present work. Metrics for controller security are investigated to evaluate firmware security techniques. The prototype's ability to mitigate, respond to, and recover from firmware integrity degradation is examined. The prototype shows many working security techniques within the context of a grid-connected, distributed energy resource. …

Stay-At-Home Motor Rehabilitation: Optimizing Spatiotemporal Learning On Low-Cost Capacitive Sensor Arrays, Reid Sutherland

Graduate Theses and Dissertations

Repeated, consistent, and precise gesture performance is a key part of recovery for stroke and other motor-impaired patients. Close professional supervision to these exercises is also essential to ensure proper neuromotor repair, which consumes a large amount of medical resources. Gesture recognition systems are emerging as stay-at-home solutions to this problem, but the best solutions are expensive, and the inexpensive solutions are not universal enough to tackle patient-to-patient variability. While many methods have been studied and implemented, the gesture recognition system designer does not have a strategy to effectively predict the right method to fit the needs of a patient. …

Learning Abstractions For Planning, Brian Charles Cook

Computer Science and Engineering Dissertations - Archive

Planners for hard problems must exploit domain-specific structure to find solutions efficiently. Yet, hand-engineered solutions and optimizations are often expensive and difficult or impossible to adapt to other problems. This work applies automatic machine learning techniques to increase planner performance for specific problem domains and to learn useful abstract representations for planning. In particular, this dissertation develops methods to address important aspects of learning in planning in four different areas: State-of-the-art domain-independent classical planners utilize multiple search heuristics and decide how to allocate computational effort between heuristics prior to planning. This work presents a heuristic planning algorithm that uses a …

Fire Progression, Ashwini Badgujar, L. Zheng, S. Hu, Aj Purdy

Creative Activity and Research Day - CARD

Fires have grown up to 70% in recent years. Fire Progression is a Machine Learning research project wherein we are trying to predict the direction in which the fire might grow in future. We are using Machine Learning technique and features like surface temperature, air temperature, moisture, precipitation and other additional parameters to predict the progression.

Active/Transfer Learning With Medical Imaging, Nicholas Kebbas, Maggie Lu

Creative Activity and Research Day - CARD

We've developed a robust web application to assist researchers in improving the accuracy of their Machine Learning Models. The system provides an image labeling web interface so that researchers can label and classify the models, and multiple data visualizations that help researchers identify and resolve inconsistencies between the classifications determined by their models and user feedback.

Evaluation Of Visualization Techniques For Communicating Off-Screen Data, Tony Jimenez

Creative Activity and Research Day - CARD

Worldwide, the use of mobile devices like tablets has begun to integrate themselves in people’s daily lives. People have thus begun to bring over desktop applications, more specifically visualization applications, into the mobile atmosphere. However, this brings forth some challenges, like how to manage screen space and how the visualizations should present the relevant information. Our take on this was to use various aggregations that would allow users to see data elements that would otherwise be off-screen. We thought it was best to create a variety of different aggregations, allowing us to figure out the best method of the group. …

Hierarchical Reinforcement Learning With Integrated Discovery Of Salient Subgoals, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Hierarchical Reinforcement Learning (HRL) is a promising approach to solve more complex tasks which may be challenging for the traditional reinforcement learning. HRL achieves this by decomposing a task into shorter-horizon subgoals which are simpler to achieve. Autonomous discovery of such subgoals is an important part of HRL. Recently, end-to-end HRL methods have been used to reduce the overhead from offline subgoal discovery by seeking the useful subgoals while simultaneously learning optimal policies in a hierarchy. However, these methods may still suffer from slow learning when the search space used by a high level policy to find the subgoals is …

Learning Expensive Coordination: An Event-Based Deep Rl Approach, Runsheng Yu, Xinrun Wang, Rundong Wang, Youzhi Zhang, Bo An, Zhen Yu Shi, Hanjiang Lai

Research Collection School Of Computing and Information Systems

Existing works in deep Multi-Agent Reinforcement Learning (MARL) mainly focus on coordinating cooperative agents to complete certain tasks jointly. However, in many cases of the real world, agents are self-interested such as employees in a company and clubs in a league. Therefore, the leader, i.e., the manager of the company or the league, needs to provide bonuses to followers for efficient coordination, which we call expensive coordination. The main difficulties of expensive coordination are that i) the leader has to consider the long-term effect and predict the followers’ behaviors when assigning bonuses, and ii) the complex interactions between followers make …

Improving Energy-Efficiency Through Smart Data Placement In Hadoop Clusters, Ahmed Mostafa

Theses and Dissertations

Hadoop, a pioneering open source framework, has revolutionized the big data world because of its ability to process vast amounts of unstructured and semi-structured data. This ability makes Hadoop the ‘go-to’ technology for many industries that generate big data, thus it also aids in being cost effective, unlike other legacy systems. Hadoop MapReduce is used in large scale data parallel applications to process massive amounts of data across a cluster and is used for scheduling, processing, and executing jobs. Basically, MapReduce is the right hand of Hadoop, as its library is needed to process these large data sets. In this …

Cyber Security’S Influence On Modern Society, Nicholas Vallarelli

Honors College Theses

The world of cyber security is evolving every day, and cyber-criminals are trying to take advantage of it to gain as much money and power as possible. As the Internet continues to grow, more people around the world join the Internet. The purpose of this is to see how much of an importance cyber security has and how cyber-criminals are able to utilize the cyberworld for their own personal gain. Research has been done on how the cyberworld got where it is today. Additionally, individual research has been done in an effort to learn how to hack. A hack lab …

A Framework For Vector-Weighted Deep Neural Networks, Carter Chiu

UNLV Theses, Dissertations, Professional Papers, and Capstones

The vast majority of advances in deep neural network research operate on the basis of a real-valued weight space. Recent work in alternative spaces have challenged and complemented this idea; for instance, the use of complex- or binary-valued weights have yielded promising and fascinating results. We propose a framework for a novel weight space consisting of vector values which we christen VectorNet. We first develop the theoretical foundations of our proposed approach, including formalizing the requisite theory for forward and backpropagating values in a vector-weighted layer. We also introduce the concept of expansion and aggregation functions for conversion between real …

Toward Productivity Improvements In Programming Languages Through Behavioral Analytics, Patrick Daleiden

UNLV Theses, Dissertations, Professional Papers, and Capstones

Computer science knowledge and skills have become foundational for success in virtually every professional field. As such, productivity in programming and computer science education is of paramount economic and strategic importance for innovation, employment and economic growth. Much of the research around productivity and computer science education has centered around improving notoriously difficult compiler error messages, with a noted surge in new studies in the last decade. In developing an original research plan for this area, this dissertation begins with an examination of the Case for New Instrumentation, draw- ing inspiration from automated data mining innovations and corporate marketing techniques …

A Machine Learning Approach To Predict Retention Of Computer Science Students At University Of Nevada, Las Vegas, Sudhir Deshmukh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Student retention is an important measure when in determining student success. Retention refers to the first-time full-time student from previous fall term who returned to the same university for the following fall term. Decline in retention rate have adverse effect on stakeholders, parents, and students view about the institution, revenue generated from tuition cost and obtaining outside funds. In an effort to increase retention rates, universities have started analyzing the factors that correlate with students dropping out. Many universities have identified some of these factors and are working on developing intervention programs to help students to elevate their academic performance …

Meta-Analysis Of Biological Research Literature, Evan Suggs

Honors Theses

Comparative studies have been powerful tools in generating a broad understanding about the evolution of animal social systems but they currently rely on the slow, manual process of reading thousands of abstracts and papers from research databases. A web application was created for researchers conducting a comparative survey, in order to speed up their research. This web application automates the retrieval of research papers and their selection process. Using previously obtained data sets on the orders Artiodacytla and Lagomorph, a machine learning application was created to classify the papers. These techniques and tools should greatly increase the speed at which …

Vzwam Web-Based Lookup, Ruben Claudio

Masters Theses & Doctoral Dissertations

This web-based lookup will allow V employees to find territory sales rep much faster. It will simplify the process and eliminate manual processes.

At the moment, a combination of multiple manual processes is needed to find territory sales reps. The company’s CRM does not allow to find rep sales quickly. When an in-house sales representative is talking to a prospect, this sales rep has to go through a few series of steps to find an outside or territory sales rep --which is usually needed to schedule in-person meetings, that results in delays while doing transactions with the prospects. Besides, because …

Robotic Swarms: Assembly And Complexity, Angel Adrian Cantu Suarez

Theses and Dissertations

This thesis focuses on the assembly of robotic swarms that move according to some global signal in a model called the “tilt” model. The model consists of a 2D board that contain open and blocked spaces, along with tiles or polyominoes that move toward a signaled cardinal direction. We look at two variations of this model called the single-step and full-tilt model, where the elements move single distances or maximally, respectively, when a signal is send. We show different methods of shape construction, defining board configurations that are universal for a set of shapes. Afterwards, we analyze different computational problems …

Iclab: A Global, Longitudinal Internet Censorship Measurement Platform, Arian Akhavan Niaki, Shinyoung Cho, Zachary Weinberg, Nguyen Phong Hoang, Abbas Razaghpanah, Nicolas Christin, Phillipa Gill

Computer Science: Faculty Publications

Researchers have studied Internet censorship for nearly as long as attempts to censor contents have taken place. Most studies have however been limited to a short period of time and / or a few countries; the few exceptions have traded off detail for breadth of coverage. Collecting enough data for a comprehensive, global, longitudinal perspective remains challenging.In this work, we present ICLab, an Internet measurement platform specialized for censorship research. It achieves a new balance between breadth of coverage and detail of measurements, by using commercial VPNs as vantage points distributed around the world. ICLab has been operated continuously since …