This work-in-progress research paper reports our initial attempt at integrating generative artificial intelligence (Gen AI) into our two credit hour ethics course required for computer science majors. Course content includes ethical frameworks, presentations, current event and stakeholder analysis, formal debates, job seeking, and codes of conduct. Given the wide applicability of computer ethics and the crowded schedule in the course, our inquiry seeks to find ways of utilizing Gen AI to streamline content delivery, to provide opportunities for independent student exploration, and to aid students during preparation for class activities. In this paper, we first describe a novel assignment integrating Gen AI given to students enrolled in the Spring 2024 offering of our computer science ethics course. The findings from a survey addressing student use of Gen AI before and during the assignment and the analysis of assignment artifacts submitted by students are then reported. Finally, we present additional student data and results from our separate experimentation, both of which focus on the use of Gen AI for debate preparation. Our efforts reveal that Gen AI can be a useful instructional tool for a computer science ethics course but should be integrated carefully.

In the domain of epidemiology, logistic regression modeling is widely used to explain the relationships among explanatory variables and dichotomous outcome variables. However, logistic regression modeling faces challenges such as overfitting, confounding, and multicollinearity when there is a large number of explanatory variables. For example, in the birth defect study presented in this paper, variable selection for building high quality models to identify risk factors from hundreds of pollutant variables is difficult. To address this problem, we propose a novel visual analytics approach to logistic regression modeling for high-dimensional datasets. It leverages the traditional modeling pipeline by providing (1) intuitive visualizations for inspecting statistical indicators and the relationships among the variables and (2) a seamless, effective dimension reduction pipeline for selecting variables for inclusion in high quality logistic regression models. A fully working prototype of this approach has been developed and successfully applied to the birth defect study, which illustrates its effectiveness and efficiency. Its application in an insurance policy study and feedback from domain experts further demonstrate its usefulness.

It is widely acknowledged that many freshmen go to university without any prior grounding in computer science. Recent studies conducted in the US have shown that not only do high school students lack any exposure, but also they possess ill-conceived notions of what computer science is, a problem also affecting their parents, teachers and regional school superintendents. For incoming students, the extent to which computing technology impacts their daily lives is very likely at odds with the extent to which they have considered how software works and its ethical implications. The ability to critically analyze and consider related ethical consequences of computing is an important life skill for every twenty-first century adult and not just computer scientists. With this this in mind, this paper presents a unique CS0 introduction to programming and computer ethics for pre CS and non majors. It has been defined as a service course to promote digital literacy and to ensure that students appreciate what computer science is and its socio-ethical implications. The paper outlines the course and its content in detail, as well as providing quantitative and qualitative evidence of its benefit and appeal to female students.

Scientists, business analysts, and others in a growing number of fields are trying to cope with the vast amount of data being generated. The lack of software that can efficiently process large data sets hinders insight into complex relationships. One of the most important concepts in learning how to construct efficient code is time complexity analysis with Big-O notation. Students often find time complexity difficult to learn and too abstract to apply in any meaningful way. Common instructional methods consist of a combination of mathematics and intuitive analysis which are often too cumbersome for practical application or cannot be extended to complex algorithms. Unfortunately, there are few tools available for teaching time complexity that students find concrete, straightforward, and applicable to real problems. In this paper, we present O-Charts, a first step in the development of a practical toolkit for teaching and applying time complexity analysis. O-Charts allow the systematic analysis of deeply nested loops where the use of control variables makes the number of executions difficult to define, calculate, and explain. We report initial results and our plans for future work.

Previous studies on E-transaction time-series have mainly focused on finding temporal trends of transaction behavior. Interesting transactions that are time-stamped and situation-relevant may easily be obscured in a large amount of information. This paper proposes a visual analytics system, Visual Analysis of E-transaction Time-Series (VAET), that allows the analysts to interactively explore large transaction datasets for insights about time-varying transactions. With a set of analyst-determined training samples, VAET automatically estimates the saliency of each transaction in a large time-series using a probabilistic decision tree learner. It provides an effective time-of-saliency (TOS) map where the analysts can explore a large number of transactions at different time granularities. Interesting transactions are further encoded with KnotLines, a compact visual representation that captures both the temporal variations and the contextual connection of transactions. The analysts can thus explore, select, and investigate knotlines of interest. A case study and user study with a real E-transactions dataset (26 million records) demonstrate the effectiveness of VAET.

Understanding what causes proteins to change shape and how the resulting shape influences function will expedite the design of more narrowly focused drugs and therapies. Shape alterations are often the result of flexibility changes in a set of localized neighborhoods that may or may not act in concert. Computational models have been developed to predict flexibility changes under varying empirical parameters. In this paper, we tackle a significant challenge facing scientists when analyzing outputs of a computational model, namely how to identify, examine, compare, and group interesting neighborhoods of proteins under different parameter sets. This is a difficult task since comparisons over protein subunits that comprise diverse neighborhoods are often too complex to characterize with a simple metric and too numerous to analyze manually. Here, we present a series of novel visual analytics approaches toward addressing this task. User scenarios illustrate the utility of these approaches and feedback from domain experts confirms their effectiveness.

Community structure is an important characteristic of many real networks, which shows high concentrations of edges within special groups of vertices and low concentrations between these groups. Community related graph analysis, such as discovering relationships among communities, identifying attribute-structure relationships, and selecting a large number of vertices with desired structural features and attributes, are common tasks in knowledge discovery in such networks. The clutter and the lack of interactivity often hinder efforts to apply traditional graph visualization techniques in these tasks. In this paper, we propose PIWI, a novel graph visual analytics approach to these tasks. Instead of using Node-Link Diagrams (NLDs), PIWI provides coordinated, uncluttered visualizations, and novel interactions based on graph community structure. The novel features, applicability, and limitations of this new technique have been discussed in detail. A set of case studies and preliminary user studies have been conducted with real graphs containing thousands of vertices, which provide supportive evidence about the usefulness of PIWI in community related tasks.

Abstract The knowledge gained from biology datasets can streamline and speed‐up pharmaceutical development. However, computational models generate so much information regarding protein behavior that large‐scale analysis by traditional methods is almost impossible. The volume of data produced makes the transition from data to knowledge difficult and hinders biomedical advances. In this work, we present a novel visual analytics approach named WaveMap for exploring data generated by a protein flexibility model. WaveMap integrates wavelet analysis, visualizations, and interactions to facilitate the browsing, feature identification, and comparison of protein attributes represented by two‐dimensional plots. We have implemented a fully working prototype of WaveMap and illustrate its usefulness through expert evaluation and an example scenario.