Computer Sciences Commons^™

Deep Data Analysis On The Web, Xuanyu Liu

Master's Projects

Search engines are well known to people all over the world. People prefer to use keywords searching to open websites or retrieve information rather than type typical URLs. Therefore, collecting finite sequences of keywords that represent important concepts within a set of authors is important, in other words, we need knowledge mining. We use a simplicial concept method to speed up concept mining. Previous CS 298 project has studied this approach under Dr. Lin. This method is very fast, for example, to mine the concept, FP-growth takes 876 seconds from a database with 1257 columns 65k rows, simplicial complex only …

A Machine Learning Approach To Determine Oyster Vessel Behavior, Devin Frey

LSU New Orleans Theses and Dissertations

A support vector machine (SVM) classifier was designed to replace a previous classifier which predicted oyster vessel behavior in the public oyster grounds of Louisiana. The SVM classifier predicts vessel behavior (docked, poling, fishing, or traveling) based on each vessel’s speed and either net speed or movement angle. The data from these vessels was recorded by a Vessel Monitoring System (VMS), and stored in a PostgreSQL database. The SVM classifier was written in Python, using the scikit-learn library, and was trained by using predictions from the previous classifier. Several validation and parameter optimization techniques were used to improve the SVM …

Spatial Data Mining Analytical Environment For Large Scale Geospatial Data, Zhao Yang

LSU New Orleans Theses and Dissertations

Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend …

What Is Answer Set Programming To Propositional Satisfiability, Yuliya Lierler

Computer Science Faculty Publications

Propositional satisfiability (or satisfiability) and answer set programming are two closely related subareas of Artificial Intelligence that are used to model and solve difficult combinatorial search problems. Satisfiability solvers and answer set solvers are the software systems that find satisfying interpretations and answer sets for given propositional formulas and logic programs, respectively. These systems are closely related in their common design patterns. In satisfiability, a propositional formula is used to encode problem specifications in a way that its satisfying interpretations correspond to the solutions of the problem. To find solutions to a problem it is then sufficient to use a …

Web-Based Integrated Development Environment, Hien T. Vu

Master's Projects

As tablets become more powerful and more economical, students are attracted to them and are moving away from desktops and laptops. Their compact size and easy to use Graphical User Interface (GUI) reduce the learning and adoption barriers for new users. This also changes the environment in which undergraduate Computer Science students learn how to program. Popular Integrated Development Environments (IDE) such as Eclipse and NetBeans require disk space for local installations as well as an external compiler. These requirements cannot be met by current tablets and thus drive the need for a web-based IDE. There are also many other …

The Paradox Of Social Media Security: A Study Of It Students’ Perceptions Versus Behavior On Using Facebook, Zahra Y. Alqubaiti

Master of Science in Information Technology Theses

Social media plays an essential role in the modern society, enabling people to be better connected to each other and creating new opportunities for businesses. At the same time, social networking sites have become major targets for cyber-security attacks due to their massive user base. Many studies investigated the security vulnerabilities and privacy issues of social networking sites and made recommendations on how to mitigate security risks. Users are an integral part of any security mix. In this thesis, we explore the relationship between users’ security perceptions and their actual behavior on social networking sites. Protection motivation theory (PMT), initially …

Distributed All-Ip Mobility Management Architecture Supported By The Ndn Overlay, Zhiwei Yan, Guanggang Geng, Sherali Zeadally, Yong-Jin Park

Information Science Faculty Publications

Two of the most promising candidate solutions for realizing the next-generation all-IP mobile networks are Mobile IPv6 (MIPv6), which is the host-based and global mobility supporting protocol, and Proxy MIPv6 (PMIPv6), which is the network-based and localized mobility supporting protocol. However, the unprecedented growth of mobile Internet traffic has resulted in the development of distributed mobility management (DMM) architecture by the Internet engineering task force DMM working group. The extension of the basic MIPv6 and PMIPv6 to support their distributed and scalable deployment in the future is one of the major goals of the DMM working group. We propose an …

Bgsu Minecraft Initiative Website, Jacob Gusching

Honors Projects

A website for the BGSU Minecraft Initiative, a program that uses Minecraft as an educational tool to engage younger students to learn. This website is a communication tool to showcase the work of BGSU students and to spread the knowledge and lesson to plans to interested parties.

Algorithm For Premature Ventricular Contraction Detection From A Subcutaneous Electrocardiogram Signal, Iris Lynn Shelly

Dissertations and Theses

Cardiac arrhythmias occur when the normal pattern of electrical signals in the heart breaks down. A premature ventricular contraction (PVC) is a common type of arrhythmia that occurs when a heartbeat originates from an ectopic focus within the ventricles rather than from the sinus node in the right atrium. This and other arrhythmias are often diagnosed with the help of an electrocardiogram, or ECG, which records the electrical activity of the heart using electrodes placed on the skin. In an ECG signal, a PVC is characterized by both timing and morphological differences from a normal sinus beat.

An implantable cardiac …

Argumentation For Knowledge Representation, Conflict Resolution, Defeasible Inference And Its Integration With Machine Learning, Luca Longo

Conference papers

Modern machine Learning is devoted to the construction of algorithms and computational procedures that can automatically improve with experience and learn from data. Defeasible argumentation has emerged as sub-topic of artificial intelligence aimed at formalising common-sense qualitative reasoning. The former is an inductive approach for inference while the latter is deductive, each one having advantages and limitations. A great challenge for theoretical and applied research in AI is their integration. The first aim of this chapter is to provide readers informally with the basic notions of defeasible and non-monotonic reasoning. It then describes argumentation theory, a paradigm for implementing defeasible …

Joint And Selective Periodic Component Carrier Assignment In Lte-A, Husnu S. Narman, Mohammed Atiquzzaman, Mehdi Rahmani-Andebili, Haiying Shen

Computer Sciences and Electrical Engineering Faculty Research

The bandwidth demand for mobile Internet access is significantly increased with the number of mobile users. Carrier aggregation has been proposed to answer this demand in mobile networks. In carrier aggregation, the best available one or more component carriers of each band are assigned to each user to provide efficient services. Several works have been reported in the literature on mandatory and periodic component carrier assignment methods. Although the former works, especially periodic component carrier assignment methods, have significantly improved the performance of LTE-A systems, many limitations still exist. One limitation of previous works is that data transfer is interrupted …

Multimodal Spontaneous Emotion Corpus For Human Behavior Analysis, Zheng Zhang, Jeffrey M. Girard, Yue Wu, Xing Zhang, Peng Liu, Umur Ciftci, Shaun Canavan, Michael Reale, Andrew Horowitz, Huiyuan Yang, Jeffrey F. Cohn, Qiang Ji, Lijun Yin

Computer Science Faculty Research & Creative Works

Emotion is expressed in multiple modalities, yet most research has considered at most one or two. This stems in part from the lack of large, diverse, well-annotated, multimodal databases with which to develop and test algorithms. We present a well-annotated, multimodal, multidimensional spontaneous emotion corpus of 140 participants. Emotion inductions were highly varied. Data were acquired from a variety of sensors of the face that included high-resolution 3D dynamic imaging, high-resolution 2D video, and thermal (infrared) sensing, and contact physiological sensors that included electrical conductivity of the skin, respiration, blood pressure, and heart rate. Facial expression was annotated for both …

Can They Use It? Studying The Usability Of The Canvas Learning Management System At Bowling Green State University, James Faisant

Honors Projects

Students’ use of the Canvas learning management system (LMS) as implemented by Bowling Green State University (BGSU) is a substantial part of their learning experience. A well designed and easy to use LMS not only allows students to be more efficient, it allows students to engage effectively with their coursework. Students’ ability to effectively use the LMS is examined to understand whether the system is usable, and if not, what changes should be made. Research included two distinct elements. First, students were asked to complete nine tasks identified as common tasks within Canvas, while being timed. Additionally, students responded to …

Improving The Prediction Accuracy Of Text Data And Attribute Data Mining With Data Preprocessing, Priyanga Chandrasekar

Master of Science in Computer Science Theses

Data Mining is the extraction of valuable information from the patterns of data and turning it into useful knowledge. Data preprocessing is an important step in the data mining process. The quality of the data affects the result and accuracy of the data mining results. Hence, Data preprocessing becomes one of the critical steps in a data mining process.

In the research of text mining, document classification is a growing field. Even though we have many existing classifying approaches, Naïve Bayes Classifier is good at classification because of its simplicity and effectiveness. The aim of this paper is to identify …

College Of Engineering Senior Design Competition Fall 2016, University Of Nevada, Las Vegas

Fred and Harriet Cox Senior Design Competition Projects

Part of every UNLV engineering student’s academic experience, the senior design project stimulates engineering innovation and entrepreneurship. Each student in their senior year chooses, plans, designs, and prototypes a product in this required element of the curriculum. A capstone to the student’s educational career, the senior design project encourages the student to use everything learned in the engineering program to create a practical, real world solution to an engineering challenge. The senior design competition helps focus the senior students in increasing the quality and potential for commercial application for their design projects. Judges from local industry evaluate the projects on …

Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh

Conference papers

Accurate classification of astronomical objects currently relies on spectroscopic data. Acquiring this data is time-consuming and expensive compared to photometric data. Hence, improving the accuracy of photometric classification could lead to far better coverage and faster classification pipelines. This paper investigates the benefit of using unsupervised feature-extraction from multi-wavelength image data for photometric classification of stars, galaxies and QSOs. An unsupervised Deep Belief Network is used, giving the model a higher level of interpretability thanks to its generative nature and layer-wise training. A Random Forest classifier is used to measure the contribution of the novel features compared to a set …

A Survey Of Visual Analytics Tools For Effective Decision-Making, R. Jordan Crouser, Erina Fukuda, Subashini Sridhar

Computer Science: Faculty Publications

Over the past decade, the visualization for cybersecurity (VizSec) research community has adapted many information visualization techniques to support the critical work of cyber analysts. While these efforts have yielded many specialized tools and platforms, the community lacks a unified approach to the design and implementation of these systems. In this work, we provide a retrospective analysis of the past decade of VizSec publications, with an eye toward developing a more cohesive understanding of the emerging patterns of design:

• We identify common thematic groupings among existing work, as well as interesting patterns of design around the utilization of various …

Looking Into The Crystal Ball: Requirements Evolution Over Time, Alicia M. Grubb, Marsha Chechik

Computer Science: Faculty Publications

Goal modeling has long been used in the literature to model and reason about system requirements, constraints within the domain and environment, and stakeholders' goals. Goal model analysis helps stakeholders answer 'what if' questions enabling them to make tradeoff decisions about their project requirements. However, questions concerning the evolution over time of stakeholder requirements or changes in actor intentionality are not explicitly addressed by current approaches. In this paper, we tackle this problem by presenting a method for specifying changes in intentions over time, and a technique that uses simulation for asking a variety of 'what if' questions about such …

Ransomware In High-Risk Environments, Shallaw M. Aziz

Information Technology Capstone Research Project Reports

In today’s modern world, cybercrime is skyrocketing globally, which impacts a variety of organizations and endpoint users. Hackers are using a multitude of approaches and tools, including ransomware threats, to take over targeted systems. These acts of cybercrime lead to huge damages in areas of business, healthcare systems, industry sectors, and other fields. Ransomware is considered as a high risk threat, which is designed to hijack the data. This paper is demonstrating the ransomware types, and how they are evolved from the malware and trojan codes, which is used to attack previous incidents, and explains the most common encryption algorithms …

Towards Decision Making Under Interval Uncertainty, Andrzej Pownuk, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we know the exact form of the objective function, and we know the optimal decision corresponding to each values of the corresponding parameters x_i. What should we do if we do not know the exact values of x_i, and instead, we only know each x_i with uncertainty -- e.g., with interval uncertainty? In this case, one of the most widely used approaches is to select, for each i, one value from the corresponding interval -- usually, a midpoint -- and to use the exact-case optimal decision corresponding to the selected values. …

Why Rsa? A Pedagogical Comment, Pedro Barragan Olague, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

One of the most widely used cryptographic algorithms is the RSA algorithm in which a message m encoded as the remainder c of m^e modulo n, where n and e are given numbers -- forming a public code. A similar transformation c^d mod n$, for an appropriate secret code d, enables us to reconstruct the original message. In this paper, we provide a pedagogical explanation for this algorithm.

Specifying A Global Optimization Solver In Z, Angel F. Garcia Contreras, Yoonsik Cheon

Departmental Technical Reports (CS)

NumConSol is an interval-based numerical constraint and optimization solver to find a global optimum of a function. It is written in Python. In this document, we specify the NumConSol solver in Z, a formal specification language based on sets and predicates. The aim is to provide a solid foundation for restructuring and refactoring the current implementation of the NumConSol solver as well as facilitating its future improvements. The formal specification also allows us to design more effective testing for the solver, e.g., generating test cases from the specification.

Efficient Processing Of Similarity Queries With Applications, Mingjie Tang

Open Access Dissertations

Today, a myriad of data sources, from the Internet to business operations to scientific instruments, produce large and different types of data. Many application scenarios, e.g., marketing analysis, sensor networks, and medical and biological applications, call for identifying and processing similarities in "big" data. As a result, it is imperative to develop new similarity query processing approaches and systems that scale from low dimensional data to high dimensional data, from single machine to clusters of hundreds of machines, and from disk-based to memory-based processing. This dissertation introduces and studies several similarity-aware query operators, analyzes and optimizes their performance.

The first …

How To Make Machine Learning Robust Against Adversarial Inputs, Gerardo Muela, Christian Servin, Vladik Kreinovich

Departmental Technical Reports (CS)

It has been recently shown that it is possible to "cheat" many machine learning algorithms -- i.e., to perform minor modifications of the inputs that would lead to a wrong classification. This feature can be used by adversaries to avoid spam detection, to create a wrong identification allowing access to classified information, etc. In this paper, we propose a solution to this problem: namely, instead of applying the original machine learning algorithm to the original inputs, we should first perform a random modification of these inputs. Since machine learning algorithms perform well on random data, such a random modification ensures …

Aiddata Gis International Fellowship: Ghana West-Africa, Jason N. Ready

Sustainability and Social Justice

My internship, or fellowship as it was commonly referred to, was funded by a non-profit organization out of Williamsburg Virginia called AidData. This fellowship took place in in the country of Ghana, West-Africa beginning in May of 2016 and continued for 14 weeks with 40 hours each week. The objective of this internship was to provide in-depth training on the use of geographic Information Systems to Private and Public sectors within the country to allow for increased efficiency, and transparency through data visualization. In accordance with the requirement of Clark Universities GISDE master’s program this paper will delve into the …

A System For Detecting Malicious Insider Data Theft In Iaas Cloud Environments, Jason Nikolai, Yong Wang

Research & Publications

The Cloud Security Alliance lists data theft and insider attacks as critical threats to cloud security. Our work puts forth an approach using a train, monitor, detect pattern which leverages a stateful rule based k-nearest neighbors anomaly detection technique and system state data to detect inside attacker data theft on Infrastructure as a Service (IaaS) nodes. We posit, instantiate, and demonstrate our approach using the Eucalyptus cloud computing infrastructure where we observe a 100 percent detection rate for abnormal login events and data copies to outside systems.

Students' Explanations In Complex Learning Of Disciplinary Programming, Camilo Vieira

Open Access Dissertations

Computational Science and Engineering (CSE) has been denominated as the third pillar of science and as a set of important skills to solve the problems of a global society. Along with the theoretical and the experimental approaches, computation offers a third alternative to solve complex problems that require processing large amounts of data, or representing complex phenomena that are not easy to experiment with. Despite the relevance of CSE, current professionals and scientists are not well prepared to take advantage of this set of tools and methods. Computation is usually taught in an isolated way from engineering disciplines, and therefore, …

Optimal Group Decision Making Criterion And How It Can Help To Decrease Poverty, Inequality, And Discrimination, Vladik Kreinovich, Thongchai Dumrongpokaphan

Departmental Technical Reports (CS)

Traditional approach to group decision making in economics is to maximize the GDP, i.e., the overall gain. The hope behind this approach is that the increased wealth will trickle down to everyone. Sometimes, this happens, but often, in spite of an increase in overall GDP, inequality remains: some people remain poor, some groups continue to face economic discrimination, etc. This shows that maximizing the overall gain is probably not always the best criterion in group decision making. In this chapter, we find a group decision making criterion which is optimal (in some reasonable sense), and we show that using this …

A Modification Of Backpropagation Enables Neural Networks To Learn Preferences, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

To help a person make proper decisions, we must first understand the person's preferences. A natural way to determine these preferences is to learn them from the person's choices. In principle, we can use the traditional machine learning techniques: we start with all the pairs (x,y) of options for which we know the person's choices, and we train, e.g., the neural network to recognize these choices. However, this process does not take into account that a rational person's choices are consistent: e.g., if a person prefers a to b and b to c, this person should also prefer a and …

For Fuzzy Logic, Occam's Principle Explains The Ubiquity Of The Golden Ratio And Of The 80-20 Rule, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In this paper, we show that for fuzzy logic, the Occam's principle -- that we should always select the simplest possible explanation -- explains the ubiquity of the golden ratio and of the 80-20 rule.