Digital Commons Network^™

Enrichment Of Turkish Question Answering Systems Using Knowledge Graphs, Okan Çi̇ftçi̇, Fati̇h Soygazi̇, Selma Teki̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Recent capabilities of large language models (LLMs) have transformed many tasks in Natural Language Processing (NLP), including question answering. The state-of-the-art systems do an excellent job of responding in a relevant, persuasive way but cannot guarantee factuality. Knowledge graphs, representing facts as triplets, can be valuable for avoiding errors and inconsistencies with real-world facts. This work introduces a knowledge graph-based approach to Turkish question answering. The proposed approach aims to develop a methodology capable of drawing inferences from a knowledge graph to answer complex multihop questions. We construct the Beyazperde Movie Knowledge Graph (BPMovieKG) and the Turkish Movie Question Answering …

Detection And Classification Of Unauthorized Use Of Irrigation Motors In Agricultural Irrigation, Önder Ci̇velek, Sedat Görmüş, Hali̇l İbrahi̇m Okumuş, Orhan Gazi̇ Kederoglu

Turkish Journal of Electrical Engineering and Computer Sciences

The decarbonisation of electricity generation requires the real-time monitoring and control of grid components in order to efficiently and timely dispatch demand. This highly automated system, known as the Smart Grid, relies on smart or sensor-equipped distribution network components to optimise energy flow and minimise losses. However, energy theft, a major obstacle to efficient resource utilisation, poses a significant challenge to achieving this goal. This study proposes and evaluates a real-time telemetry and control system designed to mitigate energy theft in agricultural irrigation applications. The system increases energy efficiency by tracking the energy use in agricultural irrigation. The key challenge …

A New Dynamic Classifier Selection Method For Text Classification, İsmai̇l Terzi̇, Alper Kürşat Uysal

Turkish Journal of Electrical Engineering and Computer Sciences

The primary objective of employing multiple classifier systems (MCS) in pattern recognition is to enhance classification accuracy. Dynamic classifier selection (DCS) and dynamic ensemble selection (DES) are two purposeful forms of multiple classifier systems. While DES involves the selection of a classifier set followed by decision combination, DCS opts for the choice of a single competent classifier, eliminating the necessity for classifier combination. As a consequence, DCS methods exhibit superior efficiency in terms of processing time and memory usage compared to DES methods. Moreover, a substantial performance gap exists between the performance of Oracle and both DES and DCS methods. …

A Real-Time Embedded System Designed For Nilm Studies With A Novel Competitive Decision Process Algorithm, Sai̇d Mahmut Çinar, Rasi̇m Doğan, Emre Akarslan

Turkish Journal of Electrical Engineering and Computer Sciences

This paper explores the determination of any load or load combination in a power system at any moment. This process requires measurements at the main electric utility service entry of a house, known as nonintrusive measurement. To accurately identify loads, total harmonic distortion, RMS, third harmonic currents, and power consumption are considered their fingerprints. Based on these fingerprints, an algorithm called the competitive decision process is developed and integrated into an embedded system. This algorithm has a two-level decision mechanism. In the first stage, the winner loads with the highest similarity scores from each feature are determined, and the loads …

Multi-Label Voice Disorder Classification Using Raw Waveforms, Gökay Di̇şken

Turkish Journal of Electrical Engineering and Computer Sciences

Automated voice disorder systems that distinguish pathological voices from healthy ones have been developed with the aid of machine learning methods. Both clinicians and patients can benefit from these systems as they provide many advantages, compared to the invasive techniques. These systems can produce binary (healthy/pathological) or multi-class (healthy/selected pathologies) decisions. However, multiple disorders might exist in an individual’s voice. Multi-label classification should be considered in such cases. By this time, only a single report is available on this topic, where hand-crafted features were used, and a data augmentation technique was utilized to overcome class imbalances. In this study, a …

Network Intrusion Detection Based On Machine Learning Strategies: Performance Comparisons On Imbalanced Wired, Wireless, And Software-Defined Networking (Sdn) Network Traffics, Hi̇lal Hacilar, Zafer Aydin, Vehbi̇ Çağri Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, …

Tuning Pid Controller For Quadrotor Using Particle Swarm Optimization, Eric X. Rodriguez, Qi Lu

Computer Science Faculty Publications

Energy expenditure for quadrotor control has a likelihood of being costly given parameter-dependent controllers that are less than optimal. The cost can grow proportionally when applied to multiple quadrotors for tracking and collaborative navigation tasks. This research aims to establish a basic approach to tuning PID (Proportional-Integral-Derivative) parameters for a simulated quadrotor drone. A PID controller for autonomy provides a straightforward method for correcting robotic movement based on its current state. However, applying a PID system to a flight controller poses challenges with an inherently under-actuated system, which includes the likelihood of large overshoots and lengthy adjustment times. To address …

A Scalable Parallel Processing Design For The Data Washing Machine: An Unsupervised Entity Resolution System, Nicholas Kofi Akortia Hagan

Theses and Dissertations

Entity Resolution (ER) has been one of the bedrocks in the creation of information systems by ensuring ambiguous entities are identified and resolved by linking. One common design approach of traditional ER systems is to run in single-threaded mode, which makes the system prone to out-of-memory error when processing larger datasets. The Data Washing Machine (DWM) as a proof-of-concept of an unsupervised cluster ER system is indifferent from this common design bottleneck. The original prototype design of the DWM requires shared memory tables and dictionaries of tokens, and its single-threaded nature makes it not scalable, hence not viable for real-world …

Predicting Choroidal Nevus Transformation To Melanoma Using Machine Learning, Prashant D. Tailor, Piotr K. Kopinski, Haley S. D'Souza, David A. Leske, Timothy W. Olsen, Carol L. Shields, Jerry A. Shields, Lauren A. Dalvin

Wills Eye Hospital Papers

PURPOSE: To develop and validate machine learning (ML) models to predict choroidal nevus transformation to melanoma based on multimodal imaging at initial presentation.

DESIGN: Retrospective multicenter study.

PARTICIPANTS: Patients diagnosed with choroidal nevus on the Ocular Oncology Service at Wills Eye Hospital (2007-2017) or Mayo Clinic Rochester (2015-2023).

METHODS: Multimodal imaging was obtained, including fundus photography, fundus autofluorescence, spectral domain OCT, and B-scan ultrasonography. Machine learning models were created (XGBoost, LGBM, Random Forest, Extra Tree) and optimized for area under receiver operating characteristic curve (AUROC). The Wills Eye Hospital cohort was used for training and testing (80% training-20% testing) with …

Memetic Memory As Vital Conduits Of Troublemakers In Digital Culture, Alexander O. Smith, Jordan Loewen-Colón

School of Information Studies - Post-doc and Student Scholarship

Recent fears of data capitalism and colonialism often argue using implicit assumptions about cybernetic technology’s ability to automate data about culture. As such, the level of data granularity made possible by cybernetic engineering can be used to dominate society and culture. Here we unpack these implicit assumptions about the datafication of culture through memes, which both act as cultural data and cultural memory. Using Alexander Galloway’s critical method of protocological analysis and descriptions of media tactics, we respond to fears of cybernetic domination. Protocols – the source by which cybernetic technologies enable automated datafication – enables us to respond to …

Using Cache Files To Improve The Efficiency Of The Ucgretina Simulation Code, Blake Mcnulty

Physics and Astronomy Summer Fellows

UCGretina is a program that is used to simulate the Gretina gamma-ray tracking array used in experiments at the Facility for Rare Isotope Beams (FRIB) to collect gamma rays produced in reactions such as proton scattering. This is done by generating a simulation of the beam particles used and sending them into a simulated recreation that mimics the experimental environment. My job this summer was to tackle a major issue that the program had which is the speed or efficiency at which it ran. The main issue was that it would take days for certain simulation sets to run which …

Task Analysis Methods Based On Deep Reinforcement Learning, Xue Gong, Pengfei Peng, Li Rong, Yalian Zheng, Jun Jiang

Journal of System Simulation

Abstract: In response to the high coupling of task interaction and many influencing factors in task analysis, a task analysis method based on sequence decoupling and deep reinforcement learning (DRL) is proposed, which can achieve task decomposition and task sequence reconstruction under complex constraints. The method designs an environment for deep reinforcement learning based on task information interaction, while improving the SumTree algorithm based on the difference between the loss functions of the target network and the evaluation network, achieving the priority evaluation among tasks. The activation function operation mechanism is introduced into the deep reinforcement learning network, followed by …

Using Gamification To Enhance Mastery Of Network Security Concepts, Kevin Hilliard, Xiaohong Yuan, Kelvin Bryant, Jinsheng Xu, Jinghua Zhang

Journal of Cybersecurity Education, Research and Practice

Gamification has proven to be effective in engaging and encouraging people to work towards and achieve goals. Many students struggle to focus on schoolwork, due to a lack of interest, lack of understanding, or other factors unique to the student. Applying gamification elements to education can help engage these students in learning their course material and help them excel academically. This study examines the effectiveness of using gamification techniques to enhance the learning experience in college Computer Science courses. A video game application is utilized to review and reinforce cybersecurity concepts that students have already been taught in class. Previous …

Parallel Multigrid In Time For Chaotic Dynamical Systems, David Alan Vargas

Mathematics & Statistics ETDs

Despite the fact that Parallel-in-Time (PinT) methods are predicted to become necessary to fully utilize next-generation exa- and zettascale machines, there are currently no known practical methods which scale well with the length of the time-domain for chaotic problems, due to exponential dependence of the condition number on the fastest chaotic timescale. I present modifications to the coarse-grid equations along with a novel rediscretization approach which together greatly improve convergence of the multigrid reduction in time (MGRIT) algorithm and allow the first known PinT speedup for a chaotic PDE. The novel Local Shadowing Relaxation (LSR) is presented as an alternative …

Inclusion4eu: Towards A Framework Of Inclusive Software Design Processes And Practices, Damian Gordon, Andrea Curley, Emma Murphy, John Gilligan, Anna Becevel, Dympna O'Sullivan

Articles

This research is based on a trans-European research project aimed at enhancing software design and engineering practices to promote inclusivity, involving stakeholders from academia, industry, and disability advocacy services. To explore the nature and meaning of inclusion, a number of different approaches to inclusion are outlined, including models of equitable design (Accessible Design, Inclusive Design, Universal Design, and Design for All), User Design processes (Co-Production, Co-Creation, User-Centred Design, Co-Design, and Participatory Design), and Software Engineering Methodologies (Linear, Spiral, and Agile). These three categories of models are combined to form a three-dimensional environment in which software development projects can be mapped …

Trec Ikat 2023: A Test Collection For Evaluating Conversational And Interactive Knowledge Assistants, Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee, Jeffrey Dalton, Leif Azzopardi

Computer Science Faculty Research & Creative Works

Conversational information seeking has evolved rapidly in the last few years with the development of Large Language Models (LLMs), providing the basis for interpreting and responding in a naturalistic manner to user requests. The extended TREC Interactive Knowledge Assistance Track (iKAT) collection aims to enable researchers to test and evaluate their Conversational Search Agent (CSA). The collection contains a set of 36 personalized dialogues over 20 different topics each coupled with a Personal Text Knowledge Base (PTKB) that defines the bespoke user personas. A total of 344 turns with approximately 26,000 passages are provided as assessments on relevance, as well …

Doing Personal Laps: Llm-Augmented Dialogue Construction For Personalized Multi-Session Conversational Search, Hideaki Joko, Shubham Chatterjee, Andrew Ramsay, Arjen P. De Vries, Jeff Dalton, Faegheh Hasibi

Computer Science Faculty Research & Creative Works

The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that span multiple sessions and reflect real-world user preferences. Previous approaches rely on experts in a wizard-of-oz setup that is difficult to scale, particularly for personalized tasks. Our method, LAPS, addresses this by using large language models (LLMs) to guide a single human worker in generating personalized dialogues. This method has proven to speed up the creation process and improve quality. LAPS can collect large-scale, human-written, multi-session, and multi-domain conversations, including extracting user preferences. …

Design Of Long-Distance Entanglement Distribution Protocols For Quantum Networks, Stav Haldar

LSU Doctoral Dissertations

Future quantum technologies such as quantum communication, quantum sensing, and distributed quantum computation, will rely on networks of shared entanglement between spatially separated nodes. Distributing entanglement between these nodes, especially over long distances, currently remains a challenge, due to limitations resulting from the fragility of quantum systems, such as photon losses, non-ideal measurements, and quantum memories with short coherence times. In the absence of full-scale fault-tolerant quantum error correction, which can in principle overcome these limitations, we should understand the extent to which we can circumvent these limitations. In this work, we provide improved protocols and policies for entanglement distribution …

Concolic Testing For Scripting Languages, Zhe Li

Dissertations and Theses

Scripting languages, such as JavaScript and Lua, are becoming more and more popular. They are typically easy to learn and use, making them accessible to a wide range of developers, even those with limited programming experience. Lua, for instance, is a lightweight, efficient, and versatile scripting language. It is designed to be easy to integrate into other systems and is often used as an embedded scripting language in larger applications such as NMap, which is a network scanning tool.

As another example, web front-end development with JavaScript (JS) is a popular choice for developers due to its ability to add …

The Shell Tutor: An Intelligent Tutoring System For The Unix Command Shell And Git, Jaxton Winder, Erik Falor, Seth Poulsen, John Edwards

Computer Science Faculty and Staff Publications

The command shell and Git are important tools for computer scientists to learn and is taught in many computer science curricula. Many tools used by computer scientists are primarily interfaced through the command shell, such as Git. However, there have been few studies and interventions designed to assist in understanding student behaviors in the command shell and better teaching of it. This paper aims to provide an overview and reflection of a novel intelligent tutoring system we developed, the Shell Tutor, which assists in teaching students the command shell and Git. This paper will also analyze features of this tool …

Human Centered Approaches And Taxonomies For Explainable Artificial Intelligence, Helen Sheridan, Emma Murphy, Dympna O'Sullivan

Conference papers

Recent interest within the research community related to explainable artificial intelligence (XAI) has led to a profuse amount of literature on the subject. Those who wish to tackle the domain from an HCI focus may be presented with overwhelming material, most of which does not pertain to human aspects of XAI. Taxonomies can serve to categorize a subject into topic areas and distill content into an overview of the field. This late breaking work intends to help those within the HCI community with a focus on XAI to understand relevant aspects of human centered XAI. We also present a taxonomy …

Design And Implementation Of Truly Random Number Generation Using Memristors For In-Memory Computing, Nick Felker

Theses and Dissertations

This paper proposes a new security module based on non-volatile memory. The module uses a memristor-based true random number generator to generate random numbers which can be used for cryptography. The module is implemented in software using a modified RISC-V instruction set architecture. The paper evaluates the performance of the module using the RISC-V simulator Gem5. The results show that the module can generate random numbers at a rate of 63 microseconds per number, which is faster than the standard C library’s random number generator. The module can also be used to scramble strings of characters and generate hashes of …

If Subsequent Results Are Too Easy To Obtain, The Proof Most Probably Has Errors: Explanation Of The Empirical Observation, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Many modern mathematical proofs are very complex, checking them is difficult; as a result, errors sneak into published proofs, even into proofs published in highly reputable journals. Sometimes, the errors are repairable, but sometimes, it turns out that the supposedly proven result is actually wrong. When the error is not noticed for some time, the published result is used to prove many other results -- and when the error is eventually found, all these new results are invalidated. This happened several times. Since it is not realistic to more thoroughly check all the proofs, and we want to minimize the …

Is Alaska Negative-Tax Arrangement Fair? Almost: Mathematical Analysis, Chon Van Le, Vladik Kreinovich

Departmental Technical Reports (CS)

In the State of Alaska there is no state income tax. Instead, there is a negative tex: every year every resident gets some money from the state. At present, every resident -- from the poorest to the richest -- gets the exact same amount of money: in 2024, it is expected to be around $1500. A natural question is: Is this fair? Maybe poor people should get more since their needs are greater? Maybe the rich people should get proportionally more, since fairness means equal added happiness for all, and for rich people, extra $1500 is barely noticeable? There have …

For 2 X N Cases, Proportional Fitting Problem Reduces To A Single Equation, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, for each of two classifications, we know the probabilities that a randomly selected object belong to different categories. For example, we know what proportion of people are below 20 years old, what proportion is between 20 and 30, etc., and we also know what proportion of people earns less than 10K, between 10K and 20K, etc. In such situations, we are often interested in proportion of people who are classified by two classifications into two given categories. For example, we are interested in the proportion of people whose age is between 20 and 30 and whose …

Development Of The Roadway Pothole Management Program, Dingxin Cheng

Mineta Transportation Institute

Addressing the issue of potholes is a primary concern for maintaining urban infrastructure. The research team has developed a prototype pothole management program. The program includes a mobile application and two machine learning models. The mobile app enables users to upload images of potholes, report relevant information, and provide driving directions to the pothole location. With the help of this application, the user can seamlessly capture images of the potholes, record pertinent information, and submit the data for necessary action. The mobile application is an essential tool in the Pothole Management Program (PMP), as it enhances the program's efficiency, effectiveness, …

Introduction To Programming And Applied Analytics Using Python, Matt Brown

ATU Faculty OER Books and Materials

This open electronic textbook is a collection of lecture notes, assignments, and additional background material for a junior level analytics course targeted for business students, it is free to use and copy. The text assumes readers have not had prior programming or computing courses, but have had at least one analytics course. The textbook differs from other textbooks because it serves a dual purpose, to first introduce to students the Python programming language and secondly to introduce analytics programming in Python. It is not meant to be a comprehensive book on the Python language or data analytics, rather a semester’s …

Empirical Insights Into Ai-Assisted Game Development: A Case Study On The Integration Of Generative Ai Tools In Creative Pipelines, Andrew Begemann, James Hutson

Student Scholarship

This study conducts an empirical exploration of generative Artificial Intelligence (AI) tools across the game development pipeline, from concept art creation to 3D model integration in a game engine. Employing AI generators like Leonardo AI, Scenario AI, Alpha 3D, and Luma AI, the research investigates their application in generating game assets. The process, documented in a diary-like format, ranges from producing concept art using fantasy game prompts to optimizing 3D models in Blender and applying them in Unreal Engine 5. The findings highlight the potential of AI to enhance the conceptualization phase and identify challenges in producing optimized, high-quality 3D …

Enhancing Adult Learner Success In Higher Education Through Decision Tree Models: A Machine Learning Approach, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key …

Reinforcement Learning For Strategic Airport Slot Scheduling: Analysis Of State Observations And Reward Designs, Anh Nguyen-Duy, Duc-Thinh Pham, Jian-Yi Lye, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Due to the NP-hard nature, the strategic airport slot scheduling problem is calling for exploring sub-optimal approaches, such as heuristics and learning-based approaches. Moreover, the continuous increase in air traffic demand requires approaches that can work well in new scenarios. While heuristics rely on a fixed set of rules, which limits the ability to explore new solutions, Reinforcement Learning offers a versatile framework to automate the search and generalize to unseen scenarios. Finding a suitable state observation and reward structure design is essential in using Reinforcement Learning. In this paper, we investigate the impact of providing the Reinforcement Learning agent …