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1. Biology Undergraduate Students’ Graphing Practice in Digital Versus Pen and Paper Graphing Environments

   https://doi.org/10.1007/s10956-020-09886-w  

   Gardner, Stephanie M.; Suazo-Flores, Elizabeth; Maruca, Susan; Abraham, Joel K.; Karippadath, Anupriya; Meir, Eli (June 2021, Journal of Science Education and Technology)

   null (Ed.)

   Abstract Graphing is an important practice for scientists and in K-16 science curricula. Graphs can be constructed using an array of software packages as well as by hand, with pen-and-paper. However, we have an incomplete understanding of how students’ graphing practice vary by graphing environment; differences could affect how best to teach and assess graphing. Here we explore the role of two graphing environments in students’ graphing practice. We studied 43 undergraduate biology students’ graphing practice using either pen-and-paper (PP) ( n  = 21 students) or a digital graphing tool GraphSmarts (GS) ( n  = 22 students). Participants’ graphs and verbal justifications were analyzed to identify features such as the variables plotted, number of graphs created, raw data versus summarized data plotted, and graph types (e.g., scatter plot, line graph, or bar graph) as well as participants’ reasoning for their graphing choices. Several aspects of participant graphs were similar regardless of graphing environment, including plotting raw vs. summarized data, graph type, and overall graph quality, while GS participants were more likely to plot the most relevant variables. In GS, participants could easily make more graphs than in PP and this may have helped some participants show latent features of their graphing practice. Those students using PP tended to focus more on ease of constructing the graph than GS. This study illuminates how the different characteristics of the graphing environment have implications for instruction and interpretation of assessments of student graphing practices. 

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2. Information-Theoretic Topology-Hiding Broadcast: Wheels, Stars, Friendship, and Beyond

   https://doi.org/10.4230/LIPIcs.ITC.2024.1  

   Banoun, D'or; Boyle, Elette; Cohen, Ran (January 2024, ITC)

   Aggarwal, Divesh (Ed.)

   Topology-hiding broadcast (THB) enables parties communicating over an incomplete network to broadcast messages while hiding the network topology from within a given class of graphs. Although broadcast is a privacy-free task, it is known that THB for certain graph classes necessitates computational assumptions, even against "honest but curious" adversaries, and even given a single corrupted party. Recent works have tried to understand when THB can be obtained with information-theoretic (IT) security (without cryptography or setup assumptions) as a function of properties of the corresponding graph class. We revisit this question through a case study of the class of wheel graphs and their subgraphs. The nth wheel graph is established by connecting n nodes who form a cycle with another "center" node, thus providing a natural extension that captures and enriches previously studied graph classes in the setting of IT-THB. We present a series of new findings in this line. We fully characterize feasibility of IT-THB for any class of subgraphs of the wheel, each possessing an embedded star (i.e., a well-defined center connected to all other nodes). Our characterization provides evidence that IT-THB feasibility may correlate with a more fine-grained degree structure - as opposed to pure connectivity - of the corresponding graphs. We provide positive results achieving perfect IT-THB for new graph classes, including ones where the number of nodes is unknown. Further, we provide the first feasibility of IT-THB on non-degenerate graph-classes with t > 1 corruptions, for the class of friendship graphs (Erdös, Rényi, Sós '66). 

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3. Experiential Explanations for Reinforcement Learning

   https://doi.org/10.1007/s00521-024-10951-3  

   Alabdulkarim, Amal; Singh, Madhuri; Mansi, Gennie; Hall, Kaely; Ehsan, Upol; Riedl, Mark O (April 2025, Neural Computing and Applications)

   Abstract Reinforcement learning (RL) systems can be complex and non-interpretable, making it challenging for non-AI experts to understand or intervene in their decisions. This is due in part to the sequential nature of RL in which actions are chosen because of their likelihood of obtaining future rewards. However, RL agents discard the qualitative features of their training, making it difficult to recover user-understandable information for “why” an action is chosen. We propose a techniqueExperiential Explanationsto generate counterfactual explanations by traininginfluence predictorsalong with the RL policy. Influence predictors are models that learn how different sources of reward affect the agent in different states, thus restoring information about how the policy reflects the environment. Two human evaluation studies revealed that participants presented with Experiential Explanations were better able to correctly guess what an agent would do than those presented with other standard types of explanation. Participants also found that Experiential Explanations are more understandable, satisfying, complete, useful, and accurate. Qualitative analysis provides information on the factors of Experiential Explanations that are most useful and the desired characteristics that participants seek from the explanations. 

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4. A Robot Ethics Dilemma: To Side with Folk Morality or the Experts?

   Borenstein, J.; Arkin, R.C.; Wagner, A. (July 2022, 2022 Meeting of the International Association of Computing and Philosophy)

   Our research team has been investigating methods for enabling robots to behave ethically while interacting with human beings. Our approach relies on two main sources of data for determining what counts as “ethical” behavior. The first are the views of average adults, which we refer to “folk morality”, and the second are the views of ethics experts. Yet the enterprise of identifying what should ground a robot’s decisions about ethical matters raises many fundamental metaethical questions. Here, we focus on one main metaethical question: would reason dedicate that it is more justifiable to base a robot’s decisions on folk morality or the guidance of ethics experts? The goal of this presentation is to highlight some of the arguments for and against each respective point of view, and the implications such arguments might have for the endeavor to encode ethical decision-making processes into robots. 

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5. The Turing Test for Graph Drawing Algorithms

   Purchase, H; Archambault, D; Kobourov, S; Noellenburg, M; Pupyrev, S; Wu, H-Y (October 2020, 28th International Symposium on Graph Drawing and Network Visualization (GD))

   Do algorithms for drawing graphs pass theTuringTest?That is, are their outputs indistinguishable from graphs drawn by humans? We address this question through a human-centred experiment, focusing on ‘small’ graphs, of a size for which it would be reasonable for someone to choose to draw the graph manually. Overall, we find that hand-drawn layouts can be distinguished from those generated by graph drawing algorithms, although this is not always the case for graphs drawn by force- directed or multi-dimensional scaling algorithms, making these good candidates for Turing Test success. We show that, in general, hand-drawn graphs are judged to be of higher quality than automatically generated ones, although this result varies with graph size and algorithm. 

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