Databases and Information Systems Commons^™

How Do Software Developers Use Github Actions To Automate Their Workflows?, Timothy Kinsman, Mairieli Wessel, Marco Gerosa, Christoph Treude

Research Collection School Of Computing and Information Systems

Automated tools are frequently used in social coding repositories to perform repetitive activities that are part of the distributed software development process. Recently, GitHub introduced GitHub Actions, a feature providing automated work-flows for repository maintainers. Although several Actions have been built and used by practitioners, relatively little has been done to evaluate them. Understanding and anticipating the effects of adopting such kind of technology is important for planning and management. Our research is the first to investigate how developers use Actions and how several activity indicators change after their adoption. Our results indicate that, although only a small subset of …

Data-Driven Recommendation Of Academic Options Based On Personality Traits, Aashish Ghimire

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The choice of academic major and, subsequently, an academic institution has a massive effect on a person’s career. It not only determines their career path but their earning potential, professional happiness, etc. [1] About 40% of people who are admitted to a college do not graduate within six years. Yet, very limited resources are available for students to help make those decisions, and each guidance counselor is responsible for roughly 400 to 900 students across the United States. A tool to help these decisions would benefit students, parents, and guidance counselors.

Various research studies have shown that personality traits affect …

Artificial Neural Network Based Approach For Malware Detection, Matthew Fletcher

Honors Capstone Projects and Theses

No abstract provided.

A Deep Analysis And Algorithmic Approach To Solving Complex Fitness Issues In Collegiate Student Athletes, Holly N. Puckett

Honors College Theses

Sports are not simply an entertainment source. For many, it creates a sense of community, support, and trust among both fans and athletes alike. In order to continue the sense of community sports provides, athletes must be properly cared for in order to perform at the highest level possible. Thus, their fitness and health must be monitored continuously. In a professional sense, one can expect individualized attention to athletes daily due to an abundance of funding and resources. However, when looking at college communities and student athletes within them, the number of athletes per athletic trainer increases due to both …

Mapping Renewal: How An Unexpected Interdisciplinary Collaboration Transformed A Digital Humanities Project, Elise Tanner, Geoffrey Joseph

Digital Initiatives Symposium

Funded by a National Endowment for Humanities (NEH) Humanities Collections and Reference Resources Foundations Grant, the UA Little Rock Center for Arkansas History and Culture’s “Mapping Renewal” pilot project focused on creating access to and providing spatial context to archival materials related to racial segregation and urban renewal in the city of Little Rock, Arkansas, from 1954-1989. An unplanned interdisciplinary collaboration with the UA Little Rock Arkansas Economic Development Institute (AEDI) has proven to be an invaluable partnership. One team member from each department will demonstrate the Mapping Renewal website and discuss how the collaborative process has changed and shaped …

Building A Data Washing Machine For Unsupervised Entity Resolution Of Unstandardized References Sources, Awaad K. Al Sarkhi

Theses and Dissertations

This dissertation describes a first attempt to build a data washing machine, a system able to take dirty data and through an unsupervised process, output clean data. The washing machine design described here focuses on two main aspects of the data curation process, token correction and data redundancy. It aims to simplify and automate the preparation of data used to create information products. In this approach, all these steps would be automated, thus saving the time and effort of the data analysts who ordinarily perform these actions. In other words, this is the opposite of the current approach to first …

Exploring Ai And Multiplayer In Java, Ronni Kurtzhals

Student Academic Conference

I conducted research into three topics: artificial intelligence, package deployment, and multiplayer servers in Java. This research came together to form my project presentation on the implementation of these topics, which I felt accurately demonstrated the various things I have learned from my courses at Moorhead State University. Several resources were consulted throughout the project, including the work of W3Schools and StackOverflow as well as relevant assignments and textbooks from previous classes. I found this project relevant to computer science and information systems for several reasons, such as the AI component and use of SQL data tables; but it was …

Non-Hazardous Industrial Solid Waste Tracking System, Justin Tank

Masters Theses & Doctoral Dissertations

The Olmsted Non-Hazardous Industrial Solid Waste Tracking System allows waste generators of certain materials to electronically have their waste assessments evaluated, approved, and tracked through a simple online process. The current process of manually requesting evaluations, prepopulating tracking forms, and filling them out on triplicate carbonless forms is out of sync with other processes in the department. Complying with audit requirements requires pulling physical copies and providing them physically to fulfill information requests.

Waste generators in Minnesota are required to track their waste disposals for certain types of industrial waste streams. This ensures waste is accounted for at the point …

Sql Injection & Web Application Security: A Python-Based Network Traffic Detection Model, Nyki Anderson

Cybersecurity Undergraduate Research Showcase

The Internet of Things (IoT) presents a great many challenges in cybersecurity as the world grows more and more digitally dependent. Personally identifiable information (PII) (i,e., names, addresses, emails, credit card numbers) is stored in databases across websites the world over. The greatest threat to privacy, according to the Open Worldwide Application Security Project (OWASP) is SQL injection attacks (SQLIA) [1]. In these sorts of attacks, hackers use malicious statements entered into forms, search bars, and other browser input mediums to trick the web application server into divulging database assets. A proposed technique against such exploitation is convolution neural network …

Implementing A Registry Federation For Materials Science Data Discovery, Raymond L. Plante, Chandler A. Becker, Andrea Medina-Smith, Kevin Brady, Alden Dima, Benjamin Long, Laura M. Bartolo, James A. Warren, Robert J. Hanisch

Copyright, Fair Use, Scholarly Communication, etc.

As a result of a number of national initiatives, we are seeing rapid growth in the data important to materials science that are available over the web. Consequently, it is becoming increasingly difficult for researchers to learn what data are available and how to access them. To address this problem, the Research Data Alliance (RDA) Working Group for International Materials Science Registries (IMRR) was established to bring together materials science and information technology experts to develop an international federation of registries that can be used for global discovery of data resources for materials science. A resource registry collects high-level metadata …

Predicting The Outcome Of Nba Games, Matthew Houde

Honors Projects in Data Science

The aim of the project is to create a machine learning model to predict NBA games. The purpose is to build upon and improve existing models. Research into other predictive sports models and machine learning techniques was conducted to understand what is currently being done to predict NBA games and how effective it is in doing so. After a thorough literary review, the model was created using Python and a variety of machine learning techniques. The dataset used had an array of team statistics for both the home and away team for each corresponding matchup and two supporting features were …

Buffer Overflow And Sql Injection In C++, Noah Warren Kapley

Masters Theses & Specialist Projects

Buffer overflows and SQL Injection have plagued programmers for many years. A successful buffer overflow, innocuous or not, damages a computer’s permanent memory. Safer buffer overflow programs are presented in this thesis for the C programs characterizing string concatenation, string copy, and format get string, a C program which takes input and output from a keyboard, in most cases. Safer string concatenation and string copy programs presented in this thesis require the programmer to specify the amount of storage space necessary for the program’s execution. This safety mechanism is designed to help programmers avoid over specifying the amount of storage …

The Role Of Privacy Within The Realm Of Healthcare Wearables' Acceptance And Use, Thomas Jernejcic

Masters Theses & Doctoral Dissertations

The flexibility and vitality of the Internet along with technological innovation have fueled an industry focused on the design of portable devices capable of supporting personal activities and wellbeing. These compute devices, known as wearables, are unique from other computers in that they are portable, specific in function, and worn or carried by the user. While there are definite benefits attributable to wearables, there are also notable risks, especially in the realm of security where personal information and/or activities are often accessible to third parties. In addition, protecting one’s private information is regularly an afterthought and thus lacking in maturity. …

Newslink: Empowering Intuitive News Search With Knowledge Graphs, Yueji Yang, Yuchen Li, Anthony Tung

Research Collection School Of Computing and Information Systems

News search tools help end users to identify relevant news stories. However, existing search approaches often carry out in a "black-box" process. There is little intuition that helps users understand how the results are related to the query. In this paper, we propose a novel news search framework, called NEWSLINK, to empower intuitive news search by using relationship paths discovered from open Knowledge Graphs (KGs). Specifically, NEWSLINK embeds both a query and news documents to subgraphs, called subgraph embeddings, in the KG. Their embeddings' overlap induces relationship paths between the involving entities. Two major advantages are obtained by incorporating subgraph …

Tour: Dynamic Topic And Sentiment Analysis Of User Reviews For Assisting App Release, Tianyi Yang, Cuiyun Gao, Jingya Zang, David Lo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

App reviews deliver user opinions and emerging issues (e.g., new bugs) about the app releases. Due to the dynamic nature of app reviews, topics and sentiment of the reviews would change along with app release versions. Although several studies have focused on summarizing user opinions by analyzing user sentiment towards app features, no practical tool is released. The large quantity of reviews and noise words also necessitates an automated tool for monitoring user reviews. In this paper, we introduce TOUR for dynamic TOpic and sentiment analysis of User Reviews. TOUR is able to (i) detect and summarize emerging app issues …

Homophily Outlier Detection In Non-Iid Categorical Data, Guansong Pang, Longbing Cao, Ling Chen

Research Collection School Of Computing and Information Systems

Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier …

Spectral Tensor Train Parameterization Of Deep Learning Layers, A. Obukhov, M. Rakhuba, A. Liniger, Zhiwu Huang, S. Georgoulis, D. Dai, Van Gool L.

Research Collection School Of Computing and Information Systems

We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context. The low-rank property leads to parameter efficiency and permits taking computational shortcuts when computing mappings. Spectral properties are often subject to constraints in optimization problems, leading to better models and stability of optimization. We start by looking at the compact SVD parameterization of weight matrices and identifying redundancy sources in the parameterization. We further apply the Tensor Train (TT) decomposition to the compact SVD components, and propose a non-redundant differentiable parameterization of fixed TT-rank tensor manifolds, termed the Spectral Tensor Train Parameterization (STTP). We …

Mixed Dish Recognition With Contextual Relation And Domain Alignment, Lixi Deng, Jingjing Chen, Chong-Wah Ngo, Qianru Sun, Sheng Tang, Yongdong Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Mixed dish is a food category that contains different dishes mixed in one plate, and is popular in Eastern and Southeast Asia. Recognizing the individual dishes in a mixed dish image is important for health related applications, e.g. to calculate the nutrition values of the dish. However, most existing methods that focus on single dish classification are not applicable to the recognition of mixed dish images. The main challenge of mixed dish recognition comes from three aspects: a wide range of dish types, the complex dish combination with severe overlap between different dishes and the large visual variances of same …

Boundary Precedence Image Inpainting Method Based On Self-Organizing Maps, Haibo Pen, Quan Wang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

In addition to text data analysis, image analysis is an area that has increasingly gained importance in recent years because more and more image data have spread throughout the internet and real life. As an important segment of image analysis techniques, image restoration has been attracting a lot of researchers’ attention. As one of AI methodologies, Self-organizing Maps (SOMs) have been applied to a great number of useful applications. However, it has rarely been applied to the domain of image restoration. In this paper, we propose a novel image restoration method by leveraging the capability of SOMs, and we name …

Do Users Care About Ad's Performance Costs? Exploring The Effects Of The Performance Costs Of In-App Ads On User Experience, Cuiyun Gao, Jichuan Zeng, Federica Sarro, David Lo, Irwin King, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Context: In-app advertising is the primary source of revenue for many mobile apps. The cost of advertising (ad cost) is non-negligible for app developers to ensure a good user experience and continuous profits. Previous studies mainly focus on addressing the hidden performance costs generated by ads, including consumption of memory, CPU, data traffic, and battery. However, there is no research on analyzing users’ perceptions of ads’ performance costs to our knowledge.Objective: To fill this gap and better understand the effects of performance costs of in-app ads on user experience, we conduct a study on analyzing user concerns about ads’ performance …

Efficient Retrieval Of Matrix Factorization-Based Top-K Recommendations: A Survey Of Recent Approaches, Dung D. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Top-k recommendation seeks to deliver a personalized list of k items to each individual user. An established methodology in the literature based on matrix factorization (MF), which usually represents users and items as vectors in low-dimensional space, is an effective approach to recommender systems, thanks to its superior performance in terms of recommendation quality and scalability. A typical matrix factorization recommender system has two main phases: preference elicitation and recommendation retrieval. The former analyzes user-generated data to learn user preferences and item characteristics in the form of latent feature vectors, whereas the latter ranks the candidate items based on the …

Dbl: Efficient Reachability Queries On Dynamic Graphs, Qiuyi Lyu, Yuchen Li, Bingsheng He, Bin Gong

Research Collection School Of Computing and Information Systems

Reachability query is a fundamental problem on graphs, which has been extensively studied in academia and industry. Since graphs are subject to frequent updates in many applications, it is essential to support efficient graph updates while offering good performance in reachability queries. Existing solutions compress the original graph with the Directed Acyclic Graph (DAG) and propose efficient query processing and index update techniques. However, they focus on optimizing the scenarios where the Strong Connected Components (SCCs) remain unchanged and have overlooked the prohibitively high cost of the DAG maintenance when SCCs are updated. In this paper, we propose DBL, an …

Towards Efficient Motif-Based Graph Partitioning: An Adaptive Sampling Approach, Shixun Huang, Yuchen Li, Zhifeng Bao, Zhao Li

Research Collection School Of Computing and Information Systems

In this paper, we study the problem of efficient motif-based graph partitioning (MGP). We observe that existing methods require to enumerate all motif instances to compute the exact edge weights for partitioning. However, the enumeration is prohibitively expensive against large graphs. We thus propose a sampling-based MGP (SMGP) framework that employs an unbiased sampling mechanism to efficiently estimate the edge weights while trying to preserve the partitioning quality. To further improve the effectiveness, we propose a novel adaptive sampling framework called SMGP+. SMGP+ iteratively partitions the input graph based on up-to-date estimated edge weights, and adaptively adjusts the sampling distribution …

Dismastd: An Efficient Distributed Multi-Aspect Streaming Tensor Decomposition, Keyu Yang, Yunjun Gao, Yifeng Shen, Baihua Zheng, Lu Chen

Research Collection School Of Computing and Information Systems

Tensor decomposition is a fundamental multidimensional data analysis tool for many data-driven applications, such as social computing, computer vision, and bioinformatics, to name but a few. However, the rapidly increasing streaming data nowadays introduces new challenges to traditional static tensor decomposition. It requires an efficient distributed dynamic tensor decomposition without re-computing the whole tensor from scratch. In this paper, we propose DisMASTD, an efficient distributed multi-aspect streaming tensor decomposition. First, we prove the optimal tensor partitioning problem is NP-hard. Second, we present two heuristic tensor partitioning approaches to ensure the load balancing. Third, we develop a distributed multi-aspect streaming tensor …

Enconter: Entity Constrained Progressive Sequence Generation Via Insertion-Based Transformer, Lee Hsun Hsieh, Yang Yin Lee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Pretrained using large amount of data, autoregressive language models are able to generate high quality sequences. However, these models do not perform well under hard lexical constraints as they lack fine control of content generation process. Progressive insertion-based transformers can overcome the above limitation and efficiently generate a sequence in parallel given some input tokens as constraint. These transformers however may fail to support hard lexical constraints as their generation process is more likely to terminate prematurely. The paper analyses such early termination problems and proposes the ENtity-CONstrained insertion TransformER (ENCONTER), a new insertion transformer that addresses the above pitfall …

Sentiment-Oriented Metric Learning For Text-To-Image Retrieval, Quoc Tuan Truong, Hady W. Lauw

Research Collection School Of Computing and Information Systems

In this era of multimedia Web, text-to-image retrieval is a critical function of search engines and visually-oriented online platforms. Traditionally, the task primarily deals with matching a text query with the most relevant images available in the corpus. To an increasing extent, the Web also features visual expressions of preferences, imbuing images with sentiments that express those preferences. Cases in point include photos in online reviews as well as social media. In this work, we study the effects of sentiment information on text-to-image retrieval. Particularly, we present two approaches for incorporating sentiment orientation into metric learning for cross-modal retrieval. Each …

Time Period-Based Top-K Semantic Trajectory Pattern Query, Munkh-Erdene Yadamjav, Farhana Murtaza Choudhury, Zhifeng Bao, Baihua Zheng

Research Collection School Of Computing and Information Systems

The sequences of user check-ins form semantic trajectories that represent the movement of users through time, along with the types of POIs visited. Extracting patterns in semantic trajectories can be widely used in applications such as route planning and trip recommendation. Existing studies focus on the entire time duration of the data, which may miss some temporally significant patterns. In addition, they require thresholds to define the interestingness of the patterns. Motivated by the above, we study a new problem of finding top-k semantic trajectory patterns w.r.t. a given time period and categories by considering the spatial closeness of POIs. …

Dycuckoo: Dynamic Hash Tables On Gpus, Yuchen Li, Qiwei Zhu, Zheng Lyu, Zhongdong Huang, Jianling Sun

Research Collection School Of Computing and Information Systems

The hash table is a fundamental structure that has been implemented on graphics processing units (GPUs) to accelerate a wide range of analytics workloads. Most existing works have focused on static scenarios and occupy large GPU memory to maximize the insertion efficiency. In many cases, data stored in hash tables get updated dynamically, and existing approaches use unnecessarily large memory resources. One naïve solution is to rebuild a hash table (known as rehashing) whenever it is either filled or mostly empty. However, this approach renders significant overheads for rehashing. In this paper, we propose a novel dynamic cuckoo hash table …

A Fully Dynamic Algorithm For K-Regret Minimizing Sets, Yanhao Wang, Yuchen Li, Raymond Chi-Wing Wong, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Selecting a small set of representatives from a large database is important in many applications such as multi-criteria decision making, web search, and recommendation. The k-regret minimizing set (k-RMS) problem was recently proposed for representative tuple discovery. Specifically, for a large database P of tuples with multiple numerical attributes, the k-RMS problem returns a size-r subset Q of P such that, for any possible ranking function, the score of the top-ranked tuple in Q is not much worse than the score of the kth-ranked tuple in P. Although the k-RMS problem has been extensively studied in the literature, existing methods …

Dram Failure Prediction In Aiops: Empirical Evaluation, Challenges And Opportunities, Zhiyue Wu, Hongzuo Xu, Guansong Pang, Fengyuan Yu, Yijie Wang, Songlei Jian, Yongjun Wang

Research Collection School Of Computing and Information Systems

DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been done on DRAM failure prediction mainly due to the lack of public available datasets. This paper presents a comprehensive empirical evaluation of diverse machine learning techniques for DRAM failure prediction using a large-scale multisource dataset, including more than three millions of records of kernel, address, and mcelog data, provided by Alibaba Cloud through PAKDD 2021 competition. Particularly, we first formulate the problem as a multiclass classification task and exhaustively evaluate seven popular/stateof-the-art …