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The 10th Annual Computer Science Workshop, Submissions, Abstract Template, Computer Science Department

Computer Science Faculty Proceedings & Presentations

This is the abstract template for the 10th Annual Computer Science Graduate Research Workshop (2022). To learn more about this workshop, please visit: https://digitalcommons.unomaha.edu/csworkshop/2022/.

The 10th Annual Computer Science Workshop, Submissions, Abstract Template, Computer Science Department

Computer Science Faculty Proceedings & Presentations

This is the abstract template for the 10th Annual Computer Science Graduate Research Workshop (2020). To learn more about this workshop, please visit: https://digitalcommons.unomaha.edu/csworkshop/2020/.

Using The Solo Taxonomy To Understand Subgoal Labels Effect In Cs1, Adrienne Decker, Lauren E. Margulieux, Briana B. Morrison

Computer Science Faculty Proceedings & Presentations

This work extends previous research on subgoal labeled instructions by examining their effect across a semester-long, Java-based CS1 course. Across four quizzes, students were asked to explain in plain English the process that they would use to solve a programming problem. In this mixed methods study, we used the SOLO taxonomy to categorize student responses about problem-solving processes and compare students who learned with subgoal labels to those who did not. The use of the SOLO taxonomy classification allows us to look deeper than the mere correctness of answers to focus on the quality of the answers produced in terms …

Dual Modality Code Explanations For Novices: Unexpected Results, Briana B. Morrison

Computer Science Faculty Proceedings & Presentations

The research in both cognitive load theory and multimedia principles for learning indicates presenting information using both diagrams and accompanying audio explanations yields better learning performance than using diagrams with text explanations. While this is a common practice in introductory programming courses, often called "live coding," it has yet to be empirically tested. This paper reports on an experiment to determine if auditory explanations of code result in improved learning performance over written explanations. Students were shown videos explaining short code segments one of three ways: text only explanations, auditory only explanations, or both text and auditory explanations, thus replicating …

Smt-Based Constraint Answer Set Solver Ezsmt (System Description), Benjamin Susman, Yuliya Lierler

Computer Science Faculty Proceedings & Presentations

Constraint answer set programming is a promising research direction that integrates answer set programming with constraint processing. Recently, the formal link between this research area and satisfiability modulo theories (or SMT) was established. This link allows the cross-fertilization between traditionally different solving technologies. The paper presents the system EZSMT, one of the first SMT-based solvers for constraint answer set programming. It also presents the comparative analysis of the performance of EZSMT in relation to its peers including solvers EZCSP and CLINGCON that rely on the hybrid solving approach based on the combination of answer set solvers and constraint solvers. Experimental …

Learning Loops: A Replication Study Illuminates Impact Of Hs Courses, Briana B. Morrison, Adrienne Decker, Lauren E. Margulieux

Computer Science Faculty Proceedings & Presentations

A recent study about the effectiveness of subgoal labeling in an introductory computer science programming course both supported previous research and produced some puzzling results. In this study, we replicate the experiment with a different student population to determine if the results are repeatable. We also gave the experimental task to students in a follow-on course to explore if they had indeed mastered the programming concept. We found that the previous puzzling results were repeated. In addition, for the novice programmers, we found a statistically significant difference in performance based on whether the student had previous programming courses in high …

Constraint Answer Set Programming Versus Satisfiability Modulo Theories, Yuliya Lierler, Benjamin Susman

Computer Science Faculty Proceedings & Presentations

Constraint answer set programming is a promising research direction that integrates answer set programming with constraint processing. It is often informally related to the field of Satisfiability Modulo Theories. Yet, the exact formal link is obscured as the terminology and concepts used in these two research areas differ. In this paper, we make the link between these two areas precise.

A Dynamic Run-Profile Energy-Aware Approach For Scheduling Computationally Intensive Bioinformatics Applications, Sachin Pawaskar, Hesham Ali

Computer Science Faculty Proceedings & Presentations

High Performance Computing (HPC) resources are housed in large datacenters, which consume exorbitant amounts of energy and are quickly demanding attention from businesses as they result in high operating costs. On the other hand HPC environments have been very useful to researchers in many emerging areas in life sciences such as Bioinformatics and Medical Informatics. In an earlier work, we introduced a dynamic model for energy aware scheduling (EAS) in a HPC environment; the model is domain agnostic and incorporates both the deadline parameter as well as energy parameters for computationally intensive applications. Our proposed EAS model incorporates 2-phases. In …

Information Seeking Practices Of Parents: Exploring Skills, Face Threats And Social Networks, Betsy Disalvo, Parisa Khanipour Roshan, Briana B. Morrison

Computer Science Faculty Proceedings & Presentations

Parents are often responsible for finding, selecting, and facilitating their children's out-of-school learning experiences. One might expect that the recent surge in online educational tools and the vast online network of information about informal learning would make this easier for all parents. Instead, the increase in these free, accessible resources is contributing to an inequality of use between children from lower and higher socio-economic status (SES). Through over 60 interviews with a diverse group of parents, we explored parents' ability to find learning opportunities and their role in facilitating educational experiences for their children. We identified differences in the use …

Performance Tuning In Answer Set Programming, Matt Buddenhagen, Yuliya Lierler

Computer Science Faculty Proceedings & Presentations

Performance analysis and tuning are well established software engineering processes in the realm of imperative programming. This work is a step towards establishing the standards of performance analysis in the realm of answer set programming -- a prominent constraint programming paradigm. We present and study the roles of human tuning and automatic configuration tools in this process. The case study takes place in the realm of a real-world answer set programming application that required several hundred lines of code. Experimental results suggest that human-tuning of the logic programming encoding and automatic tuning of the answer set solver are orthogonal (complementary) …

Subgoals, Context, And Worked Examples In Learning Computing Problem Solving, Briana B. Morrison, Lauren E. Margulieux, Mark Guzdial

Computer Science Faculty Proceedings & Presentations

Recent empirical results suggest that the instructional material used to teach computing may actually overload students' cognitive abilities. Better designed materials may enhance learning by reducing unnecessary load. Subgoal labels have been shown to be effective at reducing the cognitive load during problem solving in both mathematics and science. Until now, subgoal labels have been given to students to learn passively. We report on a study to determine if giving learners subgoal labels is more or less effective than asking learners to generate subgoal labels within an introductory CS programming task. The answers are mixed and depend on other features …

Prepositional Phrase Attachment Problem Revisited: How Verbnet Can Help, Dan Bailey, Yuliya Lierler, Benjamin Susman

Computer Science Faculty Proceedings & Presentations

Resolving attachment ambiguities is a pervasive problem in syntactic analysis. We propose and investigate an approach to resolving prepositional phrase attachment that centers around the ways of incorporating semantic knowledge derived from the lexico-semantic ontologies such as VERBNET and WORDNET.

An Abstract View On Modularity In Knowledge Representation, Yuliya Lierler, Miroslaw Truszczyński

Computer Science Faculty Proceedings & Presentations

Modularity is an essential aspect of knowledge representation and reasoning theory and practice. It has received substantial attention. We introduce model-based modular systems, an abstract framework for modular knowledge representation formalisms, similar in scope to multi-context systems but employing a simpler information-flow mechanism. We establish the precise relationship between the two frameworks, showing that they can simulate each other. We demonstrate that recently introduced modular knowledge representation formalisms integrating logic programming with satisfiability and, more generally, with constraint satisfaction can be cast as modular systems in our sense. These results show that our formalism offers a simple unifying framework for …

The Winograd Schema Challenge And Reasoning About Correlation, Dan Bailey, Amelia Harrison, Yuliya Lierler, Vladimir Lifschitz, Julian Michael

Computer Science Faculty Proceedings & Presentations

The Winograd Schema Challenge is an alternative to the Turing Test that may provide a more meaningful measure of machine intelligence. It poses a set of coreference resolution problems that cannot be solved without human-like reasoning. In this paper, we take the view that the solution to such problems lies in establishing discourse coherence. Specifically, we examine two types of rhetorical relations that can be used to establish discourse coherence: positive and negative correlation. We introduce a framework for reasoning about correlation between sentences, and show how this framework can be used to justify solutions to some Winograd Schema problems.

Subgoals Help Students Solve Parsons Problems, Briana B. Morrison, Lauren E. Margulieux, Barbara Ericson, Mark Guzdial

Computer Science Faculty Proceedings & Presentations

We report on a study that used subgoal labels to teach students how to write while loops with a Parsons problem learning assessment. Subgoal labels were used to aid learning of programming while not overloading students' cognitive abilities. We wanted to compare giving learners subgoal labels versus asking learners to generate subgoal labels. As an assessment for learning we asked students to solve a Parsons problem – to place code segments in the correct order. We found that students who were given subgoal labels performed statistically better than the groups that did not receive subgoal labels or were asked to …

Usability And Usage Of Interactive Features In An Online Ebook For Cs Teachers, Barbara Ericson, Steven Moore, Briana B. Morrison, Mark Guzdial

Computer Science Faculty Proceedings & Presentations

There are too few secondary school computing teachers to meet international needs for growing secondary school computing education. Our group has created an ebook to help prepare secondary teachers to teach the programming and big data concepts in the new AP Computer Science Principles course. The ebook was designed using principles from educational psychology, specifically worked examples and cognitive load. The ebook interleaves worked examples and interactive practice activities, which we believe will lead to more efficient and effective learning than more typical approaches to learning programming. This paper reports the results from initial studies of our ebook. First, we …

Abstract Disjunctive Answer Set Solvers, Remi Brochenin, Yuliya Lierler, Marco Maratea

Computer Science Faculty Proceedings & Presentations

A fundamental task in answer set programming is to compute answer sets of logic programs. Answer set solvers are the programs that perform this task. The problem of deciding whether a disjunctive program has an answer set isΣ^P₂ -complete. The high complexity of reasoning within disjunctive logic programming is responsible for few solvers capable of dealing with such programs, namely DLV, GNT, CMODELS and CLASP. We show that transition systems introduced by Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers can be adapted for disjunctive answer set solvers. In particular, we present transition systems for CMODELS …

Abstract Modular Inference Systems And Solvers, Yuliya Lierler, Miroslaw Truszczyński

Computer Science Faculty Proceedings & Presentations

Integrating diverse formalisms into modular knowledge representation systems offers increased expressivity, modeling convenience and computational benefits. We introduce the concepts of abstract inference modules and abstract modular inference systems to study general principles behind the design and analysis of model-generating programs, or solvers, for integrated multilogic systems.We show how modules and modular systems give rise to transition graphs, which are a natural and convenient representation of solvers, an idea pioneered by the SAT community. We illustrate our approach by showing how it applies to answer-set programming and propositional logic, and to multi-logic systems based on these two formalisms.

A Critique Of “Gamification” In Khan Academy, Betsy Disalvo, Briana B. Morrison

Computer Science Faculty Proceedings & Presentations

Khan Academy (Kahn Acadamy, 2013) is an informal online learning platform that is adding game elements that participants encounter as they move through curricula. This “gamification”, includes the addition of badges, accomplishment statistics and skill tree visualizations that reflect the completion of different learning task and participation in the online community. In this paper we outline the types of gamification used in Khan academy and reflect on their effectiveness in relation to learning theory and motivation theory.

Research Challenges And Opportunities In Knowledge Representation, Section 2.4.2 Advances In Satisfiability And Answer Set Programming, Natasha Noy, Deborah Mcguinness, Yuliya Lierler

Computer Science Faculty Proceedings & Presentations

Final report edited by Natasha Noy and Deborah McGuinness.

Report Section 2.4.2, Advances in satisfiability and answer set programming, authored by Yuliya Lierer, UNO faculty member.

Research Challenges And Opportunities In Knowledge Representation, Section 4.1.1 Hybrid Kr, Natasha Noy, Deborah Mcguinness, Yuliya Lierler

Computer Science Faculty Proceedings & Presentations

Final report edited by Natasha Noy and Deborah McGuinness.

Report Section 4.1.1 Hybrid KR, co-authored by Yuliya Lierer, UNO faculty member.

Research Challenges And Opportunities In Knowledge Representation, Section 2.3.2: Applications Based On Formal Models, Natasha Noy, Deborah Mcguinness, Yuliya Lierler

Computer Science Faculty Proceedings & Presentations

Final report edited by Natasha Noy and Deborah McGuinness.

Report Section 2.3.2, Applications based on formal models, authored by Yuliya Lierer, UNO faculty member.

Modular Answer Set Solving, Yuliya Lierler, Miroslaw Truszczyński

Computer Science Faculty Proceedings & Presentations

Modularity is essential for modeling large-scale practical applications.We propose modular logic programs as a modular version of answer set programming and study the relationship of our formalism to an earlier concept of lp-modules.

Logic Programs Vs. First-Order Formulas In Textual Inference, Yuliya Lierler, Vladimir Lifschitz

Computer Science Faculty Proceedings & Presentations

In the problem of recognizing textual entailment, the goal is to decide, given a text and a hypothesis expressed in a natural language, whether a human reasoner would call the hypothesis a consequence of the text. One approach to this problem is to use a first-order reasoning tool to check whether the hypothesis can be derived from the text conjoined with relevant background knowledge, after expressing all of them by first-order formulas. Another possibility is to express the hypothesis, the text, and the background knowledge in a logic programming language, and use a logic programming system. We discuss the relation …

Towards A Tight Integration Of Syntactic Parsing With Semantic Disambiguation By Means Of Declarative Programming, Yuliya Lierler, Peter Schüller

Computer Science Faculty Proceedings & Presentations

We propose and advocate the use of an advanced declarative programming paradigm – answer set programming – as a uniform platform for integrated approach towards syntax-semantic processing in natural language. We illustrate that (a) the parsing technology based on answer set programming implementation reaches performance sufficient for being a useful NLP tool, and (b) the proposed method for incorporating semantic information from FRAMENET into syntactic parsing may prove to be useful in allowing semantic-based disambiguation of syntactic structures.

Prolog And Asp Inference Under One Roof, Marcello Balduccini, Yuliya Lierler, Peter Schüller

Computer Science Faculty Proceedings & Presentations

Answer set programming (ASP) is a declarative programming paradigm stemming from logic programming that has been successfully applied in various domains. Despite amazing advancements in ASP solving, many applications still pose a challenge that is commonly referred to as grounding bottleneck. Devising, implementing, and evaluating a method that alleviates this problem for certain application domains is the focus of this paper. The proposed method is based on combining backtracking-based search algorithms employed in answer set solvers with SLDNF resolution from PROLOG. Using PROLOG inference on non-ground portions of a given program, both grounding time and the size of the ground …

Abstract Modular Systems And Solvers, Yuliya Lierler, Miroslaw Truszczyński

Computer Science Faculty Proceedings & Presentations

Integrating diverse formalisms into modular knowledge representation systems offers increased expressivity, modeling convenience and computational benefits. We introduce concepts of abstract modules and abstract modular systems to study general principles behind the design and analysis of modelfinding programs, or solvers, for integrated heterogeneous multi-logic systems. We show how abstract modules and abstract modular systems give rise to transition systems, which are a natural and convenient representation of solvers pioneered by the SAT community. We illustrate our approach by showing how it applies to answer set programming and propositional logic, and to multi-logic systems based on these two formalisms.

Hybrid Automated Reasoning Tools: From Black-Box To Clear-Box Integration, Marcello Balduccini, Yuliya Lierler

Computer Science Faculty Proceedings & Presentations

Recently, researchers in answer set programming and constraint programming spent significant efforts in the development of hybrid languages and solving algorithms combining the strengths of these traditionally separate fields. These efforts resulted in a new research area: constraint answer set programming (CASP). CASP languages and systems proved to be largely successful at providing efficient solutions to problems involving hybrid reasoning tasks, such as scheduling problems with elements of planning. Yet, the development of CASP systems is difficult, requiring non-trivial expertise in multiple areas. This suggests a need for a study identifying general development principles of hybrid systems. Once these principles …

An Energy-Aware Bioinformatics Application For Assembling Short Reads In High Performance Computing Systems, Julia Warnke, Sachin Pawaskar, Hesham Ali

Computer Science Faculty Proceedings & Presentations

Current biomedical technologies are producing massive amounts of data on an unprecedented scale. The increasing complexity and growth rate of biological data has made bioinformatics data processing and analysis a key and computationally intensive task. High performance computing (HPC) has been successfully applied to major bioinformatics applications to reduce computational burden. However, a naïve approach for developing parallel bioinformatics applications may achieve a high degree of parallelism while unnecessarily expending computational resources and consuming high levels of energy. As the wealth of biological data and associated computational burden continues to increase, there has become a need for the development of …

A Tarskian Informal Semantics For Answer Set Programming, Marc Denecker, Yuliya Lierler, Miroslaw Truszczyński, Joost Vennekens

Computer Science Faculty Proceedings & Presentations

In their seminal papers on stable model semantics, Gelfond and Lifschitz introduced ASP by casting programs as epistemic theories, in which rules represent statements about the knowledge of a rational agent. To the best of our knowledge, theirs is still the only published systematic account of the intuitive meaning of rules and programs under the stable semantics. In current ASP practice, however, we find numerous applications in which rational agents no longer seem to play any role. Therefore, we propose here an alternative explanation of the intuitive meaning of ASP programs, in which they are not viewed as statements about …