Computer Sciences Commons^™

Submodularity And Local Search Approaches For Maximum Capture Problems Under Generalized Extreme Value Models, Tien Thanh Dam, Thuy Anh Ta, Tien Mai

Research Collection School Of Computing and Information Systems

We study the maximum capture problem in facility location under random utility models, i.e., the problem of seeking to locate new facilities in a competitive market such that the captured user demand is maximized, assuming that each customer chooses among all available facilities according to a random utility maximization model. We employ the generalized extreme value (GEV) family of discrete choice models and show that the objective function in this context is monotonic and submodular. This finding implies that a simple greedy heuristic can always guarantee a (1−1/e) approximation solution. We further develop a new algorithm combining a greedy heuristic, …

Hyperspectral Image Analysis Of Food For Nutritional Intake, Shirin Nasr Esfahani

UNLV Theses, Dissertations, Professional Papers, and Capstones

The primary object of this dissertation is to investigate the application of hyperspectral technology to accommodate for the growing demand in the automatic dietary assessment applications. Food intake is one of the main factors that contribute to human health. In other words, it is necessary to get information about the amount of nutrition and vitamins that a human body requires through a daily diet. Manual dietary assessments are time-consuming and are also not precise enough, especially when the information is used for the care and treatment of hospitalized patients. Moreover, the data must be analyzed by nutritional experts. Therefore, researchers …

Reachability And Turn Constraint Paths, Sabrina Wallace

UNLV Theses, Dissertations, Professional Papers, and Capstones

Problems dealing with the development of efficient algorithms for constructing collision-free paths have been explored extensively. We review existing algorithms for constructing collision-free paths under turn-angle constraints. We examine the problem of computing collision-free paths in the presence of polygonal obstacles. We present an algorithm for identifying the placement of a source vertex so that the maximum number of obstacle vertices can be reached via the shortest path tree under turn-angle requirements. We also present some experimental results on the construction of collision-free paths in the presence of polygonal obstacles.

Structure Aware Smart Encoding And Decoding Of Information In Dna, Shoshanna Llewellyn

Boise State University Theses and Dissertations

Our increasingly information driven world is growing the demand for new storage technologies. Current estimates place the total storage demands exceeding the supply of usable silicon by 2040 [1]. DNA is an attractive technology due to its incredible density, almost negligible energy requirements, and data retention measured in centuries [1]. DNA does, however, come with new challenges. It is an organic compound with complex internal interactions which complicate the design and synthesis of DNA sequences for the purpose of data storage. In this work we demonstrate a new encoding-decoding process that accounts for some of the challenges in encoding and …

The Ukicer 2022 Conference Poster: Techmate: A Best Practice Toolkit For Driving Sustainable Acceleration Towards Gender Equality In Technology Disciplines In Heis., Alina Berry

Conference papers

TechMate is a research project that is being developed to enhance gender balance in technology disciplines, in particular computing higher education in Ireland and beyond. Gender imbalance in computing education is a well-known issue: in Ireland, less than 15% of the student population in computer science, ICT and related disciplines are women. Despite a significant amount of research and practical work conducted in the recent decades, the problem still persists and this research initiative aims to improve the situation.

Among the main aims of this project, there is a development of a toolkit to drive sustainable acceleration towards gender equality …

Towards Making Transformer-Based Language Models Learn How Children Learn, Yousra Mahdy

Boise State University Theses and Dissertations

Transformer-based Language Models (LMs), learn contextual meanings for words using a huge amount of unlabeled text data. These models show outstanding performance on various Natural Language Processing (NLP) tasks. However, what the LMs learn is far from what the meaning is for humans, partly due to the fact that humans can differentiate between concrete and abstract words, but language models make no distinction. Concrete words are words that have a physical representation in the world such as “chair”, while abstract words are ideas such as “democracy”. The process of learning word meanings starts from early childhood when children acquire their …

Ontology-Based Formal Approach For Safety And Security Verification Of Industrial Control Systems, Ramesh Neupane

Boise State University Theses and Dissertations

Control logics, as part of the Industrial Control Systems (ICS), are used to control the physical processes of the critical infrastructures such as power plants, water, and gas distribution, etc. Most commonly, the Programmable Logic Controller (PLC) manages these processes through actuators based on information received from sensor readings. Any safety issues or cyberattacks on these systems may have catastrophic consequences on human lives and the environment. In an effort to improve the resilience and security of control logics, this thesis provides algorithms and tools to formally define the safety and security requirements w.r.t. the physical processes, and the industrial …

Improving Children's Authentication Practices With Respect To Graphical Authentication Mechanism, Dhanush Kumar Ratakonda

Boise State University Theses and Dissertations

A variety of authentication mechanisms are used for online applications to protect user’s data. Prior literature identifies that adults and children often utilize weak authentication practices and our own initial research corroborates that children often create weak usernames and passwords. One reason children adopt weak authentication practices is due to difficulties in remembering their usernames and passwords. Existing literature suggests that people are better at remembering graphical information than text and words. In this dissertation, my research goal is to improve the usability and security of children’s authentication mechanisms. My research includes designing, developing, and evaluating a new graphical …

Automated Detection Of Sockpuppet Accounts In Wikipedia, Mostofa Najmus Sakib

Boise State University Theses and Dissertations

Wikipedia is a free Internet-based encyclopedia that is built and maintained via the open-source collaboration of a community of volunteers. Wikipedia’s purpose is to benefit readers by acting as a widely accessible and free encyclopedia, a comprehensive written synopsis that contains information on all discovered branches of knowledge. The website has millions of pages that are maintained by thousands of volunteer editors. Unfortunately, given its open-editing format, Wikipedia is highly vulnerable to malicious activity, including vandalism, spam, undisclosed paid editing, etc.

Malicious users often use sockpuppet accounts to circumvent a block or a ban imposed by Wikipedia administrators on the …

Syntax Exercises And Their Effect On Computational Thinking, Marina Johnson

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Abstract—Job opportunities and the need for programmers are increasing. Companies are looking for new hires who have the ability to learn how to learn, who have computational thinking skills. Student dropout rate in computer science is the highest among college majors. Educators are striving to find a way to teach efficiently and effectively the technical and the problem solving skills students need. In this paper we will be studying the effects of syntax exercises on a subject’s ability to think computationally and precisely. We tested our process on professionals and students. Half of the professionals were in the computer science …

Programming Process, Patterns And Behaviors: Insights From Keystroke Analysis Of Cs1 Students, Raj Shrestha

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

With all the experiences and knowledge, I take programming as granted. But learning to program is still difficult for a lot of introductory programming students. This is also one of the major reasons for a high attrition rate in CS1 courses. If instructors were able to identify struggling students then effective interventions can be taken to help them. This thesis is a research done on programming process data that can be collected non-intrusively from CS1 students when they are programming. The data and their findings can be leveraged in understanding students’ thought process, detecting patterns and identifying behaviors that could …

Academic Hats And Ice Cream: Two Optimization Problems, Valery F. Ochkov, Yulia V. Chudova

Journal of Humanistic Mathematics

This article describes the use of computer software to optimize the design of an academic hat and an ice cream cone!

Facial-Sketch Synthesis: A New Challenge, Deng Ping Fan, Ziling Huang, Peng Zheng, Hong Liu, Xuebin Qin, Luc Van Gool

Machine Learning Faculty Publications

This paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high cost of obtaining hand-drawn sketch datasets, there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last decade. We first introduce a high-quality dataset for FSS, named FS2K, which consists of 2 104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difficulty, diversity, and scalability and should thus facilitate the progress of FSS research. Second, we present the largest-scale FSS …

The Dope Distance Is Sic: A Stable, Informative, And Computable Metric On Ordered Merge Trees, Jose Arbelo, Antonio Delgado, Charley Kirk, Zach Schlamowitz

Mathematics Summer Fellows

When analyzing time series data, it is often of interest to categorize them based on how different they are. We define a new dissimilarity measure between time series: Dynamic Ordered Persistence Editing (DOPE). DOPE satisfies metric properties, is stable to noise, is as informative as alternative approaches, and efficiently computable. Satisfying these properties simultaneously makes DOPE of interest to both theoreticians and data scientists alike.

Meta-Detr: Image-Level Few-Shot Detection With Inter-Class Correlation Exploitation, Gongjie Zhang, Zhipeng Luo, Kaiwen Cui, Shijian Lu, Eric P. Xing

Machine Learning Faculty Publications

Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is still constrained by several factors, such as (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes. Such limitations hinder the generalization of base-class knowledge for the detection of novel-class objects. In this work, we design Meta-DETR, which (i) is the first image-level few-shot detector, and (ii) introduces a novel inter-class correlational meta-learning strategy to capture and leverage the correlation among different classes for robust and accurate few-shot object detection. Meta-DETR works …

Accessdesign: An Inclusive Co-Design Toolkit For The Creation Of Accessible Digital Tools., Claudia Fernandez-Rivera, Sarah Boland, Eamon Aswad, John Gilligan, Dympna O'Sullivan, Emma Murphy

Articles

Existing toolkits and resources to support co-design are not always accessible to designers and co-designers with disabilities. In this paper we present a study based on an innovative co-design programme, in collaboration with St John of God Community Services, where 3rd year computer science students work with service users with intellectual disabilities to create digital applications together. We conducted a series of co-design focus group sessions involving the service users who were previously involved in the co-design collaboration with SJOG Services and TU Dublin. The data collected during these design sessions has been integrated to form an accessible design toolkit …

Deep Reinforcement Learning For End-To-End Network Slicing: Challenges And Solutions, Qiang Liu, Nakjung Choi, Tao Han

School of Computing: Faculty Publications

5G and beyond is expected to enable various emerging use cases with diverse performance requirements from vertical industries. To serve these use cases cost-effectively, network slicing plays a key role in dynamically creating virtual end-to-end networks according to specific resource demands. A network slice may have hundreds of configurable parameters over multiple technical domains that define the performance of the network slice, which makes it impossible to use traditional model-based solutions to orchestrate resources for network slices. In this article, we discuss how to design and deploy deep reinforcement learning (DRL), a model-free approach, to address the network slicing problem. …

Semantic-Aligned Matching For Enhanced Detr Convergence And Multi-Scale Feature Fusion, Gongjie Zhang, Zhipeng Luo, Yingchen Yu, Jiaxing Huang, Kaiwen Cui, Shijian Lu, Eric Xing

Machine Learning Faculty Publications

The recently proposed DEtection TRansformer (DETR) has established a fully end-to-end paradigm for object detection. However, DETR suffers from slow training convergence, which hinders its applicability to various detection tasks. We observe that DETR's slow convergence is largely attributed to the difficulty in matching object queries to relevant regions due to the unaligned semantics between object queries and encoded image features. With this observation, we design Semantic-Aligned-Matching DETR++ (SAM-DETR++) to accelerate DETR's convergence and improve detection performance. The core of SAM-DETR++ is a plug-andplay module that projects object queries and encoded image features into the same feature embedding space, where …

Application In Medicine: Has Artificial Intelligence Stood The Test Of Time, Mir Ibrahim Sajid, Shaheer Ahmed, Usama Waqar, Javeria Tariq, Mohsin Chundrigar, Samira Shabbir Balouch, Sajid Abaidullah

Medical College Documents

Artificial intelligence (AI) has proven time and time again to be a game-changer innovation in every walk of life, including medicine. Introduced by Dr. Gunn in 1976 to accurately diagnose acute abdominal pain and list potential differentials, AI has since come a long way. In particular, AI has been aiding in radiological diagnoses with good sensitivity and specificity by using machine learning algorithms. With the coronavirus disease 2019 pandemic, AI has proven to be more than just a tool to facilitate healthcare workers in decision making and limiting physician-patient contact during the pandemic. It has guided governments and key policymakers …

Pervasive Healthcare Internet Of Things: A Survey, Kim Anh Phung, Cemil Kirbas, Leyla Dereci, Tam Van Nguyen

Computer Science Faculty Publications

Thanks to the proliferation of the Internet of Things (IoT), pervasive healthcare is gaining popularity day by day as it offers health support to patients irrespective of their location. In emergency medical situations, medical aid can be sent quickly. Though not yet standardized, this research direction, healthcare Internet of Things (H-IoT), attracts the attention of the research community, both academia and industry. In this article, we conduct a comprehensive survey of pervasive computing H-IoT. We would like to visit the wide range of applications. We provide a broad vision of key components, their roles, and connections in the big picture. …

Simulating Sub-Threshold Communication Channels Through Neurons, Richard Maina

School of Computing: Dissertations, Theses, and Student Research

Molecular Communication is an emerging paradigm with the potential to revolutionize the technology behind wearable and implantable devices and the broad range of functions they support, from tracking physical activity to medical diagnostics. This can be achieved through intra-body communication networks that take advantage of natural biological processes as a means of transmitting, propagating and receiving information. In this thesis we focus particularly on using the neuron as a means to facilitate information transfer for interconnected wearable or implantable devices through a technique known as sub-threshold electrical stimulation. We develop upon a prior work by introducing a linear model of …

An Algorithm For Task Allocation And Planning For A Heterogeneous Multi-Robot System To Minimize The Last Task Completion Time, Abhishek Patil, Jungyun Bae, Myoungkuk Park

Michigan Tech Publications, Part 1

This paper proposes an algorithm that provides operational strategies for multiple heterogeneous mobile robot systems utilized in many real-world applications, such as deliveries, surveillance, search and rescue, monitoring, and transportation. Specifically, the authors focus on developing an algorithm that solves a min-max multiple depot heterogeneous asymmetric traveling salesperson problem (MDHATSP). The algorithm is designed based on a primal-dual technique to operate given multiple heterogeneous robots located at distinctive depots by finding a tour for each robot such that all the given targets are visited by at least one robot while minimizing the last task completion time. Building on existing work, …

Code Cyber: A Curated Collection Of Cybersecurity Career Learning And Preparation Resources, Kazi Tasin, Ethan Pruzhansky, Jason Lin, Tanvir Rahman, Patrick J. Slattery

Publications and Research

Since we are living in a digital age, the need to protect ourselves and those who are vulnerable to cyber-attacks is paramount to prevent cyber attacks that steal information such as banking accounts and important sensitive information.

Our research team extensively investigated the five aspects of cybersecurity such as identity, protection, detection, and response. By conducting various interviews with cybersecurity professionals, we gathered information about these five aspects for example security intelligence or security operations and response, (thread hunting, response orchestration) identity access management, (identity management, and data protection), and risks (risk perspective). Our main goal is to look into …

Evaluation Of Automation Techniques For Data Quality Assessment For Party And Product Master Data, Mahmood Mohammed

Theses and Dissertations

In an era, where data is being used by organizations in achieving their business goals and driving their business decisions, it is key to ensure the quality of data. For an organization, the most important data assets include master data assets such as product master data and customer master data, supplier master data, employee master data which can be generalized as party master data. There has been significant growth and variety in data in recent years because of which the traditional rules-based approach and dependency on data experts for assessing data quality is no longer working. This dissertation evaluates and …

Towards Smart City Security: Violence And Weaponized Violence Detection Using Dcnn, Toluwani Aremu, Li Zhiyuan, Reem Alameeri, Moayad Aloqaily, Mohsen Guizani

Machine Learning Faculty Publications

In this ever connected society, CCTVs have had a pivotal role in enforcing safety and security of the citizens by recording unlawful activities for the authorities to take actions. In a smart city context, using Deep Convolutional Neural Networks (DCNN) to detection violence and weaponized violence from CCTV videos will provide an additional layer of security by ensuring real-time detection around the clock. In this work, we introduced a new specialised dataset by gathering real CCTV footage of both weaponized and non-weaponized violence as well as non-violence videos from YouTube. We also proposed a novel approach in merging consecutive video …

Data Vu: Why Breaches Involve The Same Stories Again And Again, Woodrow Hartzog, Daniel Solove

Shorter Faculty Works

In the classic comedy Groundhog Day, protagonist Phil, played by Bill Murray, asks “What would you do if you were stuck in one place and every day was exactly the same, and nothing that you did mattered?” In this movie, Phil is stuck reliving the same day over and over, where the events repeat in a continual loop, and nothing he does can stop them. Phil’s predicament sounds a lot like our cruel cycle with data breaches.

Every year, organizations suffer more data spills and attacks, with personal information being exposed and abused at alarming rates. While Phil …

2-Dimensional String Problems: Data Structures And Quantum Algorithms, Dhrumilkumar Patel

LSU Master's Theses

The field of stringology studies algorithms and data structures used for processing strings efficiently. The goal of this thesis is to investigate 2-dimensional (2D) variants of some fundamental string problems, including \textit{Exact Pattern Matching} and \textit{Longest Common Substring}.

In the 2D pattern matching problem, we are given a matrix $\M[1\dd n,1\dd n]$ that consists of $N = n \times n$ symbols drawn from an alphabet $\Sigma$ of size $\sigma$. The query consists of a $ m \times m$ square matrix $\PP[1\dd m, 1\dd m]$ drawn from the same alphabet, and the task is to find all the locations of $\PP$ …

Addressing Ethical Issues In The Design Of Smart Home Technology For Older Adults And People With Disabilities., Jonathan Turner, Dympna O'Sullivan, Damian Gordon, Yannis Stavrakakis, Brian Keegan, Emma Murphy

Articles

Unique ethical, privacy and safety implications arise for people who are reliant on home-based smart technology due to health conditions or disabilities. In this paper we highlight a need for a reflective, inclusive ethical framework that encompasses the life cycle of smart home technology. We present key ethical considerations for smart home technology for older adults and people with disabilities and argue for ethical frameworks which combine these key considerations with existing models of design and development.

Computational Models To Detect Radiation In Urban Environments: An Application Of Signal Processing Techniques And Neural Networks To Radiation Data Analysis, Jose Nicolas Gachancipa

Beyond: Undergraduate Research Journal

Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This article presents a computational model …

Self-Distilled Vision Transformer For Domain Generalization, Maryam Sultana, Muzammal Naseer, Muhammad Haris Khan, Salman Khan, Fahad Shahbaz Khan

Computer Vision Faculty Publications

In recent past, several domain generalization (DG) methods have been proposed, showing encouraging performance, however, almost all of them build on convolutional neural networks (CNNs). There is little to no progress on studying the DG performance of vision transformers (ViTs), which are challenging the supremacy of CNNs on standard benchmarks, often built on i.i.d assumption. This renders the real-world deployment of ViTs doubtful. In this paper, we attempt to explore ViTs towards addressing the DG problem. Similar to CNNs, ViTs also struggle in out-of-distribution scenarios and the main culprit is overfitting to source domains. Inspired by the modular architecture of …