Computer Sciences Commons^™

Unlinkable And Revocable Secret Handshake, Yangguang Tian, Yingliu Li, Guomin Yang, Guomin Yang

Research Collection School Of Computing and Information Systems

In this paper, we introduce a new construction for unlinkable secret handshake that allows a group of users to perform handshakes anonymously. We define formal security models for the proposed construction and prove that it can achieve session key security, anonymity and affiliation hiding. In particular, the proposed construction ensures that (i) anonymity against protocol participants (including group authority) is achieved since a hierarchical identity-based signature is used in generating group user's pseudonym-credential pairs and (ii) revocation is achieved using a secret sharing-based revocation mechanism.

A Survey On Ml4vis: Applying Machine Learning Advances To Data Visualization, Qianwen Wang, Zhutian Chen, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VIS is needed. In this article, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: “what visualization processes can be assisted by ML?” and “how ML techniques can be used to solve visualization problems? ” This survey reveals seven main processes where …

Vehicle Routing: Review Of Benchmark Datasets, Aldy Gunawan, Graham Kendall, Barry Mccollum, Hsin-Vonn Seow, Lai Soon Lee

Research Collection School Of Computing and Information Systems

The Vehicle Routing Problem (VRP) was formally presented to the scientific literature in 1959 by Dantzig and Ramser (DOI:10.1287/mnsc.6.1.80). Sixty years on, the problem is still heavily researched, with hundreds of papers having been published addressing this problem and the many variants that now exist. Many datasets have been proposed to enable researchers to compare their algorithms using the same problem instances where either the best known solution is known or, in some cases, the optimal solution is known. In this survey paper, we provide a list of Vehicle Routing Problem datasets, categorized to enable researchers to have easy access …

Dynamic Lane Traffic Signal Control With Group Attention And Multi-Timescale Reinforcement Learning, Qize Jiang, Jingze Li, Weiwei Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

Traffic signal control has achieved significant success with the development of reinforcement learning. However, existing works mainly focus on intersections with normal lanes with fixed outgoing directions. It is noticed that some intersections actually implement dynamic lanes, in addition to normal lanes, to adjust the outgoing directions dynamically. Existing methods fail to coordinate the control of traffic signal and that of dynamic lanes effectively. In addition, they lack proper structures and learning algorithms to make full use of traffic flow prediction, which is essential to set the proper directions for dynamic lanes. Motivated by the ineffectiveness of existing approaches when …

Integrating Empirical Analysis Into Analytical Framework: An Integrated Model Structure For On-Demand Transportation, Yuliu Su, Ying Xu, Costas Courcoubetis, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

On-demand transportation services have been developing in an irresistible trend since their first launch in public. These services not only transform the urban mobility landscape, but also profoundly change individuals’ travel behavior. In this paper, we propose an integrated model structure which integrates empirical analysis into a discrete choice based analytical framework to investigate a heterogeneous population’s choices on ownership, usage and transportation mode with the presence of ride-hailing. Distinguished from traditional discrete choice models where individuals’ choices are only affected by exogenous variables and are independent of other individuals’ choices, our model extends to capture the endogeneity of supply …

Automated Taxi Queue Management At High-Demand Venues, Mengyu Ji, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

In this paper, we seek to identify an effective management policy that could reduce supply-demand gaps at taxi queues serving high-density locations where demand surges frequently happen. Unlike current industry practice, which relies on broadcasting to attract taxis to come and serve the queue, we propose more proactive and adaptive approaches to handle demand surges. Our design objective is to reduce the cumulative supply-demand gaps as much as we could by sending notifications to individual taxis. To address this problem, we first propose a highly effective passenger demand prediction system that is based on the real-time flight arrival information. By …

A Lagrangian Column Generation Approach For The Probabilistic Crowdsourced Logistics Planning, Chung-Kyun Han, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

In recent years we have increasingly seen the movement for the retail industry to move their operations online. Along the process, it has created brand new patterns for the fulfillment service, and the logistics service providers serving these retailers have no choice but to adapt. The most challenging issues faced by all logistics service providers are the highly fluctuating demands and the shortening response times. All these challenges imply that maintaining a fixed fleet will either be too costly or insufficient. One potential solution is to tap into the crowdsourced workforce. However, existing industry practices of relying on human planners …

Graph-Based Seed Object Synthesis For Search-Based Unit Testing, Yun Lin, You Seng Ong, Jun Sun, Gordon Fraser, Jin Song Dong

Research Collection School Of Computing and Information Systems

Search-based software testing (SBST) generates tests using search algorithms guided by measurements gauging how far a test case is away from exercising a coverage goal. The effectiveness of SBST largely depends on the continuity and monotonicity of the fitness landscape decided by these measurements and the search operators. Unfortunately, the fitness landscape is challenging when the function under test takes object inputs, as classical measurements hardly provide guidance for constructing legitimate object inputs. To overcome this problem, we propose test seeds, i.e., test code skeletons of legitimate objects which enable the use of classical measurements. Given a target branch in …

Effective Digital Learning Practices For Is Design Courses During Covid-19, Eng Lieh Ouh, Benjamin Gan

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic has pushed educational institutions to adopt digital learning for an extended period. This research studies the effectiveness of digital learning practices based on student feedback data collected for two Information Systems design courses: human interaction design and solution architecture design. This paper leverages the data to analyze the effectiveness of a set of digital learning practices: ZOOM lectures, polling or Kahoot questions, self-reflection, virtual exercises and virtual mentorship. Our research questions are on the effectiveness of these learning practices to keep the student’s interest and learn the course materials. The research compares each learning practice and the …

Inter-Retailer Channel Competition: Empirical Analyses Of Store Entry Effects On Online Purchases, Qian Tang, Mei Lin, Youngsoo Kim

Research Collection School Of Computing and Information Systems

This study empirically examines the effect of offline store entry on a competing online retailer in the footwear industry and investigates how this effect depends on the relative product assortment and price between the offline store and the online retailer. Using transaction data from a large online footwear retailer and offline store entry data from 19 major shoe retail chains and 3 department store chains, we quantify the entry effect of offline stores. Categorizing offline stores by assortment and price, we find that the entry of regular-price narrow-assortment stores generates a complementary effect that increases online purchases, while the entry …

Fixed Pattern Noise Non-Uniformity Correction Through K-Means Clustering, Andres Imperial

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Imagery obtained with poorly calibrated sensors is often corrupted with fixed pattern noise. Fixed pattern noise presents itself through a non-uniform distribution and therefore is hard to target in noise removal. Traditional noise removal techniques assume that the noise is uniformly distributed and subsequently produces inadequate corrections. Noise correction methods that target fixed pattern noise rely on dynamically identifying present noise and adjust correction values appropriately using nearby information or general assumptions about the image’s composition. If noise identification is not accurate, the correction values will also suffer from low accuracy. Inaccurate correction values can affect the imagery’s quality, and …

Breast Ultrasound Image Segmentation Based On Uncertainty Reduction And Context Information, Kuan Huang

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Breast cancer frequently occurs in women over the world. It was one of the most serious diseases and the second common cancer among women in 2019. The survival rate of stages 0 and 1 of breast cancer is closed to 100%. It is urgent to develop an approach that can detect breast cancer in the early stages. Breast ultrasound (BUS) imaging is low-cost, portable, and effective; therefore, it becomes the most crucial approach for breast cancer diagnosis. However, BUS images are of poor quality, low contrast, and uncertain. The computer-aided diagnosis (CAD) system is developed for breast cancer to prevent …

Comparative Study Of Machine Learning Models On Solar Flare Prediction Problem, Nikhil Sai Kurivella

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Solar flare events are explosions of energy and radiation from the Sun’s surface. These events occur due to the tangling and twisting of magnetic fields associated with sunspots. When Coronal Mass ejections accompany solar flares, solar storms could travel towards earth at very high speeds, disrupting all earthly technologies and posing radiation hazards to astronauts. For this reason, the prediction of solar flares has become a crucial aspect of forecasting space weather. Our thesis utilized the time-series data consisting of active solar region magnetic field parameters acquired from SDO that span more than eight years. The classification models take AR …

Fitting Cell-Based Biomechanical Models From Spatiotemporal Data: A Gradient Descent Approach, Namita Raghuvanshi

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Biological systems that contain multiple living cells exhibit complex self-organization during development of an embryo as individual cells coordinate their behaviors to form intricate patterns. Understanding the mechanisms that underlie this emergent behavior is within reach because of advances in cell imaging that can now track hundreds of cells’ states and positions in real time. However, computational methods are needed that can fit physical scientific models to these observations in space and time. This work introduces a new method to solve this problem that applies automatic differentiation and gradient descent, techniques that underlie deep-learning advances. The method fits a biomechanical …

Intraday Stock Trading Using Reinforcement Learning: An Investigation Of Visual Representations Of Price, Kanak Tenguria

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In this study, we are developing a reinforcement learning-based strategy for the intraday trading of stocks. This study’s primary goals include developing an environment that can be used as a simulator for the day trading stock market and, train an agent to trade in this environment by performing actions and finding the optimal policy to maximize its reward.

This study also focuses on experimentation with different state representations to understand how data representation affects the learning system. We have experimented with three different state representations. All the representations focus on presenting the intraday stock data as images. We have chosen …

Computational Techniques For Elucidating Plant-Pathogen Interactions: A Case Study On Citrus-Hlb Interactome, Cristian D. Loaiza

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The Citrus fruit industry in the United States has been affected during the last two decades because of the outbreak of the citrus greening disease, also known as Huanglongbing (HLB). Many people and organizations are working in therapeutics to help mitigate the impact of this disease, unfortunately there is not a cure yet. There are many mechanisms that needs to be understood about this disease, especially at the molecular level. Not only HLB, but many other infectious diseases are controlled by the interaction of proteins from the host (Citrus in this case) and from the pathogen that causes the disease. …

Plug-And-Play Sql, Shubham Swami

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

We present an efficient model to retrieve data from a database by implementing plug-and-play queries using the query guards. The model is efficient in the sense that it saves time when writing a query and promotes query portability and reuse. A plug-and-play query is a freestanding query that can couple to any data socket and self determine whether it can be evaluated reliably on the data. We use hierarchies to improve SQL querying in a way that eliminates the need to write a view to construct virtual tables or a set of tables to run a query. The hierarchy is …

Metaxmorph: Hierarchical Transformation Of Data With Metadata, Shubham Airan

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

This research is about transforming data. Data comes in different shapes; it can be structured as a graph, a tree, a collection of tables, or some other shape. In this thesis, we focus on data structured as a tree, which is known as hierarchical data. The same data could be structured in many different tree shapes. Previously it was shown how to transform data from one tree shape, one hierarchy to another without losing any information. But sometimes the pieces of the hierarchy are annotated or associated with metadata, that is, with data about the data itself. The metadata can …

Deep Learning Data And Indexes In A Database, Vishal Sharma

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A database is used to store and retrieve data, which is a critical component for any software application. Databases requires configuration for efficiency, however, there are tens of configuration parameters. It is a challenging task to manually configure a database. Furthermore, a database must be reconfigured on a regular basis to keep up with newer data and workload. The goal of this thesis is to use the query workload history to autonomously configure the database and improve its performance. We achieve proposed work in four stages: (i) we develop an index recommender using deep reinforcement learning for a standalone database. …

Socio-Technical Perspective For Electronic Tax Information System In Tanzania, Lucas Ngowi, Ellen Kalinga

Tanzania Journal of Engineering and Technology (TJET)

Socio-technical systems theory has rarely been used by system architects in setting up computing systems. However, the role of socio-technical concepts in computing, which is becoming social in nature, has made the concepts more relevant and commercial. Tax information systems are examples of such systems because they are influenced by external variables such as the political environment, technological trends, and social environment, introducing complexity in their deployment and determining the type of e-services and their delivery to a diverse group of people. It was observed that in Tanzania there is resistance, reluctance and minimal use of electronic tax system because …

On The Vulnerability Of Openthread To Agile Denial Of Service Attacks, Casey Cronin, Sarah Diesburg Ph.D., Dheryta Jaisinghani Ph.D.

Summer Undergraduate Research Program (SURP) Symposium

The Internet of Things (IoT) includes physical devices such as sensors, connected home appliances, video monitoring systems, and smart classroom or smart warehouse applications. These devices can capture large amounts of data while using low amount of power to do it, as well as keep track of things going on around it and turn it into usable data for the user depending on what task it is performing.

IoT devices are not immune from security concerns, such as Denial of Service (DoS) attacks. These attacks are important to investigate because they can play a dangerous role in shutting down applications, …

Socioapp: Detecting Your Sociability Status With Your Smartphone, Aaron Walker, Dheryta Jaisinghani Ph.D., Sarah Diesburg Ph.D.

Summer Undergraduate Research Program (SURP) Symposium

We are still recovering from the pandemic’s psychological effects and figuring out how to break out of our quarantine. Students may not realize they have antisocial behaviors that have a negative affect on students’ grades. Our smartphones are packed with sensors that can help us to passively detect social status of a subject and propose prospective remedies. Our hypothesis is that you are social when co-doing certain activities like walking and talking. Thus, we begin with detecting activities such as, walking, sitting, and cooking using the movement sensors accelerometer and gyroscope; then, we detect proximity with another person using the …

Probability Distributions For Elliptic Curves In The Cgl Hash Function, Dhruv Bhatia, Kara Fagerstrom, Max Watson

Mathematical Sciences Technical Reports (MSTR)

Hash functions map data of arbitrary length to data of predetermined length. Good hash functions are hard to predict, making them useful in cryptography. We are interested in the elliptic curve CGL hash function, which maps a bitstring to an elliptic curve by traversing an inputdetermined path through an isogeny graph. The nodes of an isogeny graph are elliptic curves, and the edges are special maps betwixt elliptic curves called isogenies. Knowing which hash values are most likely informs us of potential security weaknesses in the hash function. We use stochastic matrices to compute the expected probability distributions of the …

Human-Centered Cybersecurity Research — Anthropological Findings From Two Longitudinal Studies, Anwesh Tuladhar

USF Tampa Graduate Theses and Dissertations

Cybersecurity is a pressing issue. Researchers have proposed numerous security solutions over the years in order to combat security issues but it is still common to find known, well understood security issues in production environments. In this thesis, I seek to find the underlying reasons to why existing security solutions and best practices are not consistently applied and how to improve the utilization of secure best practices. To this end, I adopt the anthropological research method of long term participant observation and embed myself in real-world settings in order to understand the existence of security issues and the perception of …

Graphical Models In Reconstructability Analysis And Bayesian Networks, Marcus Harris, Martin Zwick

Complex Systems Faculty Publications and Presentations

Reconstructability Analysis (RA) and Bayesian Networks (BN) are both probabilistic graphical modeling methodologies used in machine learning and artificial intelligence. There are RA models that are statistically equivalent to BN models and there are also models unique to RA and models unique to BN. The primary goal of this paper is to unify these two methodologies via a lattice of structures that offers an expanded set of models to represent complex systems more accurately or more simply. The conceptualization of this lattice also offers a framework for additional innovations beyond what is presented here. Specifically, this paper integrates RA and …

The Mystery Of The Dancing Men, Manmohan Kaur

Journal of Humanistic Mathematics

In this paper I describe an activity based on a 1903 Sherlock Holmes murder mystery, in which a substitution cipher is used to encrypt secret messages. The story provides a fun and interesting way to talk about frequency analysis, and can be used as a segue into mathematical constructs such as modular arithmetic and computation. The activity is accessible to ages twelve and above, and has been successfully used in mathematics outreach and popularization efforts as well as in general education and mathematics courses.

Boolean Logic Algebra Driven Similarity Measure For Text Based Applications, Hassan I. Abdalla, Ali A. Amer

All Works

In Information Retrieval (IR), Data Mining (DM), and Machine Learning (ML), similarity measures have been widely used for text clustering and classification. The similarity measure is the cornerstone upon which the performance of most DM and ML algorithms is completely dependent. Thus, till now, the endeavor in literature for an effective and efficient similarity measure is still immature. Some recently-proposed similarity measures were effective, but have a complex design and suffer from inefficiencies. This work, therefore, develops an effective and efficient similarity measure of a simplistic design for text-based applications. The measure developed in this work is driven by Boolean …

The Is Social Continuance Model: Using Conversational Agents To Support Co-Creation, Naif Alawi

USF Tampa Graduate Theses and Dissertations

With the rise of Agentic IS Artifact and the increasing integration of this technology within organizations, our understanding of the impact of this technology on individuals remains limited. Although IS use literature provides important guidance for organization to increase employees’ willingness to work with new technology implementations, the utilitarian view of prior IS use limits its application in light of the new evolving social interaction between humans and Agentic IS Artifacts. To that end, we contribute to the IS use literature by implementing a social view to understand the impact of Agentic IS Artifacts on an individual’s perception and behavior. …

A Wireguard Exploration, Alexander Master, Christina Garman

CERIAS Technical Reports

Internet users require secure means of communication. Virtual Private Networks (VPNs) often serve this purpose, for consumers and businesses. The research aims of this paper were an analysis and implementation of the new VPN protocol WireGuard. The authors explain the cryptographic primitives used, build server and client code implementations of WireGuard peers, and present the benefits and drawbacks of this new technology. The outcome was a functional WireGuard client and server implementation, capable of tunneling all Internet traffic through a cloud-based virtual private server (VPS), with minimal manual configuration necessary from the end user. The code is publicly available.

Raising Algorithm Bias Awareness Among Computer Science Students Through Library And Computer Science Instruction, Shalini Ramachandran, Steven Matthew Cutchin, Sheree Fu

Computer Science Faculty Publications and Presentations

We are a computer science professor and two librarians who work closely with computer science students. In this paper, we outline the development of an introductory algorithm bias instruction session. As part of our lesson development, we analyzed the results of a survey we conducted of computer science students at three universities on their perceptions about search-engine and big-data algorithms. We examined whether an information literacy component focused on algorithmic bias was beneficial to offer to students in the computational sciences and designed an instructional prototype. We studied qualitative data, including feedback from students and colleagues on our initial instruction …