Databases and Information Systems Commons^™

Qlens: Visual Analytics Of Multi-Step Problem-Solving Behaviors For Improving Question Design, Meng Xia, Reshika P. Velumani, Yong Wang, Huamin Qu, Xiaojuan Ma

Research Collection School Of Computing and Information Systems

With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students’ problem-solving processes unfold step by step to infer whether students’ problem-solving logic matches their design intent. They also need to compare the behaviors of different groups (e.g., students from different grades) to distribute questions to students with the right level of knowledge. The availability of fine-grained interaction data, such as mouse movement trajectories ...

Visual Analysis Of Discrimination In Machine Learning, Qianwen Wang, Zhenghua Xu, Zhutian Chen, Yong Wang, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set ...

A Bert-Based Two-Stage Model For Chinese Chengyu Recommendation, Minghuan Tan, Jing Jiang, Bingtian Dai

Research Collection School Of Computing and Information Systems

In Chinese, Chengyu are fixed phrases consisting of four characters. As a type of idioms, their meanings usually cannot be derived from their component characters. In this paper, we study the task of recommending a Chengyu given a textual context. Observing some of the limitations with existing work, we propose a two-stage model, where during the first stage we re-train a Chinese BERT model by masking out Chengyu from a large Chinese corpus with a wide coverage of Chengyu. During the second stage, we fine-tune the retrained, Chengyu-oriented BERT on a specific Chengyu recommendation dataset. We evaluate this method on ...

Analysis Of Theoretical And Applied Machine Learning Models For Network Intrusion Detection, Jonah Baron

Masters Theses & Doctoral Dissertations

Network Intrusion Detection System (IDS) devices play a crucial role in the realm of network security. These systems generate alerts for security analysts by performing signature-based and anomaly-based detection on malicious network traffic. However, there are several challenges when configuring and fine-tuning these IDS devices for high accuracy and precision. Machine learning utilizes a variety of algorithms and unique dataset input to generate models for effective classification. These machine learning techniques can be applied to IDS devices to classify and filter anomalous network traffic. This combination of machine learning and network security provides improved automated network defense by developing highly-optimized ...

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

All Graduate Theses and Dissertations

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 ...

Tripdecoder: Study Travel Time Attributes And Route Preferences Of Metro Systems From Smart Card Data, Xiancai Tian, Baihua Zheng, Yazhe Wang, Hsao-Ting Huang, Chih-Cheng Hung

Research Collection School Of Computing and Information Systems

In this paper, we target at recovering the exact routes taken by commuters inside a metro system that are not captured by an Automated Fare Collection (AFC) system and hence remain unknown. We strategically propose two inference tasks to handle the recovering, one to infer the travel time of each travel link that contributes to the total duration of any trip inside a metro network and the other to infer the route preferences based on historical trip records and the travel time of each travel link inferred in the previous inference task. As these two inference tasks have interrelationship, most ...

Scope: Building And Testing An Integrated Manual-Automated Event Extraction Tool For Online Text-Based Media Sources, Matthew Crittenden

Undergraduate Honors Theses

Building on insights from two years of manually extracting events information from online news media, an interactive information extraction environment (IIEE) was developed. SCOPE, the Scientific Collection of Open-source Policy Evidence, is a Python Django-based tool divided across specialized modules for extracting structured events data from unstructured text. These modules are grouped into a flexible framework which enables the user to tailor the tool to meet their needs. Following principles of user-oriented learning for information extraction (IE), SCOPE offers an alternative approach to developing AI-assisted IE systems. In this piece, we detail the ongoing development of the SCOPE tool, present ...

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 ...

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 ...

Neural Methods For Answer Passage Retrieval Over Sparse Collections, Daniel Cohen

Doctoral Dissertations

Recent advances in machine learning have allowed information retrieval (IR) techniques to advance beyond the stage of handcrafting domain specific features. Specifically, deep neural models incorporate varying levels of features to learn whether a document answers the information need of a query. However, these neural models rely on a large number of parameters to successfully learn a relation between a query and a relevant document.

This reliance on a large number of parameters, combined with the current methods of optimization relying on small updates necessitates numerous samples to allow the neural model to converge on an effective relevance function. This ...

Collections As Data At Florida International University, Jamie Rogers

Works of the FIU Libraries

This presentation provides an overview of the concept of collections as data; shares information about our "dLOC as Data" grant initiative, a collaboration between the Digital Library of the Caribbean (dLOC), the Florida International University (FIU) Libraries Digital Collections Center, and the University of Florida Libraries, funded by the Mellon sub-award program, "Collections as Data: Part to Whole" ; as well as provides an opportunity to talk about how we can share more collections as data resources and undertake new and exciting projects at FIU.

Although the concept of collections as data isn't new, it is becoming more mainstream. As ...

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 ...

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 ...

Building And Using Digital Libraries For Etds, Edward A. Fox

The Journal of Electronic Theses and Dissertations

Despite the high value of electronic theses and dissertations (ETDs), the global collection has seen limited use. To extend such use, a new approach to building digital libraries (DLs) is needed. Fortunately, recent decades have seen that a vast amount of “gray literature” has become available through a diverse set of institutional repositories as well as regional and national libraries and archives. Most of the works in those collections include ETDs and are often freely available in keeping with the open-access movement, but such access is limited by the services of supporting information systems. As explained through a set of ...

Analysis Of System Performance Metrics Towards The Detection Of Cryptojacking In Iot Devices, Richard Matthews

Masters Theses & Doctoral Dissertations

This single-case mechanism study examined the effects of cryptojacking on Internet of Things (IoT) device performance metrics. Cryptojacking is a cyber-threat that involves stealing the computational resources of devices belonging to others to generate cryptocurrencies. The resources primarily include the processing cycles of devices and the additional electricity needed to power this additional load. The literature surveyed showed that cryptojacking has been gaining in popularity and is now one of the top cyberthreats. Cryptocurrencies offer anyone more freedom and anonymity than dealing with traditional financial institutions which make them especially attractive to cybercriminals. Other reasons for the increasing popularity of ...

Mass Incarceration In Nebraska: Data And Historical Analysis Of Inmates From 1980-2020, Anna Krause

Honors Theses, University of Nebraska-Lincoln

This study examines Nebraska Department of Corrections inmate data from 1980-2020, looking specifically at inmate demographics and offense trends. State-of-the-art data analysis is conducted to collect, modify, and visualize the data sources. Inmates are organized by each decade they were incarcerated within. The current active prison population is also examined in their own research group. The demographic and offense trends are compared with previous local and national research. Historical context is given for evolving trends in offenses. Solutions for Nebraska prison overcrowding are presented from various interest groups. This study aims to enlighten all interested Nebraskans on who inhabits their ...

The Dna Cloud: Is It Alive?, Theodoros Bargiotas

LSU Doctoral Dissertations

In this analysis, I will firstly be presenting the current knowledge concerning the materiality of the internet based Cloud, which I will henceforth be referring to as simply the Cloud. For organisation purposes I have created two umbrella categories under which I place the ongoing research in the field. Scholars have been addressing the issue of Cloud materiality through broadly two prisms: sociological materiality and geopolitical materiality. The literature of course deals with the intricacies of the Cloud based on its present ferromagnetic storage functionality. However, developments in synthetic biology have caused private tech companies and University spin-offs to flirt ...

The Mycobacterium Tuberculosis Transposon Sequencing Database (Mtbtndb): A Large-Scale Guide To Genetic Conditional Essentiality [Preprint], Adrian Jinich, Anisha Zaveri, Michael A. Dejesus, Emanuel Flores-Bautista, Clare M. Smith, Christopher M. Sassetti, Jeremy M. Rock, Sabine Ehrt, Dirk Schnappinger, Thomas R. Ioerger, Kyu Rhee

University of Massachusetts Medical School Faculty Publications

Characterization of gene essentiality across different conditions is a useful approach for predicting gene function. Transposon sequencing (TnSeq) is a powerful means of generating genome-wide profiles of essentiality and has been used extensively in Mycobacterium tuberculosis (Mtb) genetic research. Over the past two decades, dozens of TnSeq screens have been published, yielding valuable insights into the biology of Mtb in vitro, inside macrophages, and in model host organisms. However, these Mtb TnSeq profiles are distributed across dozens of research papers within supplementary materials, which makes querying them cumbersome and assembling a complete and consistent synthesis of existing data challenging. Here ...

Towards Identity Relationship Management For Internet Of Things, Mohammad Muntasir Nur

Masters Theses & Doctoral Dissertations

Identity and Access Management (IAM) is in the core of any information systems. Traditional IAM systems manage users, applications, and devices within organizational boundaries, and utilize static intelligence for authentication and access control. Identity federation has helped a lot to deal with boundary limitation, but still limited to static intelligence – users, applications and devices must be under known boundaries. However, today’s IAM requirements are much more complex. Boundaries between enterprise and consumer space, on premises and cloud, personal devices and organization owned devices, and home, work and public places are fading away. These challenges get more complicated for Internet ...

A Consent Framework For The Internet Of Things In The Gdpr Era, Gerald Chikukwa

Masters Theses & Doctoral Dissertations

The Internet of Things (IoT) is an environment of connected physical devices and objects that communicate amongst themselves over the internet. The IoT is based on the notion of always-connected customers, which allows businesses to collect large volumes of customer data to give them a competitive edge. Most of the data collected by these IoT devices include personal information, preferences, and behaviors. However, constant connectivity and sharing of data create security and privacy concerns. Laws and regulations like the General Data Protection Regulation (GDPR) of 2016 ensure that customers are protected by providing privacy and security guidelines to businesses. Data ...