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1. Hierarchical Grouping of Simple Visual Scenes

   Rice, P. J. ; Mao, L. ; Zhu, A. ; Wu, E.' & ( January 2023 , Proceedings of the Forty-Fifth Annual Meeting of the Cognitive Science Society)

   Human visual grouping processes consolidate independent visual objects into grouped visual features on the basis of shared characteristics; these visual features can themselves be grouped, resulting in a hierarchical representation of visual grouping information. This “grouping hierarchy“ promotes ef- ficient attention in the support of goal-directed behavior, but improper grouping of elements of a visual scene can also re- sult in critical behavioral errors. Understanding of how visual object/features characteristics such as size and form influences perception of hierarchical visual groups can further theory of human visual grouping behavior and contribute to effective in- terface design. In the present study, participants provided free- response groupings of a set of stimuli that contained consistent structural relationships between a limited set of visual features. These grouping patterns were evaluated for relationships be- tween specific characteristics of the constituent visual features and the distribution of features across levels of the indicated grouping hierarchy. We observed that while the relative size of the visual features differentiated groupings across levels of the grouping hierarchy, the form of visual objects and features was more likely to distinguish separate groups within a partic- ular level of hierarchy. These consistent relationships between visual feature characteristics and placement within a grouping hierarchy can be leveraged to advance computational theories of human visual grouping behavior, which can in turn be ap- plied to effective design for interfaces such as voter ballots. 

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2. Hierarchical Grouping of Simple Visual Scenes

   Rice, P. J. ; Mao, L. ; Zhu, A. ; Wu, E.' & ( January 2023 , Proceedings of the Forty-Fifth Annual Meeting of the Cognitive Science Society)

   Human visual grouping processes consolidate independent visual objects into grouped visual features on the basis of shared characteristics; these visual features can themselves be grouped, resulting in a hierarchical representation of visual grouping information. This “grouping hierarchy“ promotes ef- ficient attention in the support of goal-directed behavior, but improper grouping of elements of a visual scene can also re- sult in critical behavioral errors. Understanding of how visual object/features characteristics such as size and form influences perception of hierarchical visual groups can further theory of human visual grouping behavior and contribute to effective in- terface design. In the present study, participants provided free- response groupings of a set of stimuli that contained consistent structural relationships between a limited set of visual features. These grouping patterns were evaluated for relationships be- tween specific characteristics of the constituent visual features and the distribution of features across levels of the indicated grouping hierarchy. We observed that while the relative size of the visual features differentiated groupings across levels of the grouping hierarchy, the form of visual objects and features was more likely to distinguish separate groups within a partic- ular level of hierarchy. These consistent relationships between visual feature characteristics and placement within a grouping hierarchy can be leveraged to advance computational theories of human visual grouping behavior, which can in turn be ap- plied to effective design for interfaces such as voter ballots. 

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3. Experimental precipitation of apatite pseudofossils resembling fossil embryos

   https://doi.org/10.1111/gbi.12264  

   Crosby, C. H. ; Bailey, J. V. ( October 2017 , Geobiology)

   Certain phosphatic grains preserved in the rock record are interpreted as microfossils representing a diversity of microorganisms from bacteria to fossil embryos. In addition tobona fideprimary biological features, phosphatic microfossils and fossil embryos commonly exhibit features that result from abiotic precipitation or diagenetic alteration. Distinguishing between abiotic and primary biological features can be difficult, and some features thought to represent biological tissue could instead be artifacts that are unrelated to the original morphology of a preserved organism. Here, we present experimentally generated, abiotically produced mineral precipitates that morphologically resemble biologically produced features, some of which may be observed in the rock record or noted in extant organisms, including embryos. These findings extend the diversity of biomorphic features known to result from abiotic precipitation.

    

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4. Automated Assessment of Quality of Jupyter Notebooks Using Artificial Intelligence and Big Code

   https://doi.org/10.32473/flairs.v34i1.128560  

   Oli, Priti ; Banjade, Rabin ; Tamang, Lasang Jimba ; Rus, Vasile ( May 2021 , The International FLAIRS Conference Proceedings)

   We present in this paper an automated method to assess the quality of Jupyter notebooks. The quality of notebooks is assessed in terms of reproducibility and executability. Specifically, we automatically extract a number of expert-defined features for each notebook, perform a feature selection step, and then trained supervised binary classifiers to predict whether a notebook is reproducible and executable, respectively. We also experimented with semantic code embeddings to capture the notebooks' semantics. We have evaluated these methods on a dataset of 306,539 notebooks and achieved an F1 score of 0.87 for reproducibility and 0.96 for executability (using expert-defined features) and an F1 score of 0.81 for reproducibility and 0.78 for executability (using code embeddings). Our results suggest that semantic code embeddings can be used to determine with good performance the reproducibility and executability of Jupyter notebooks, and since they can be automatically derived, they have the advantage of no need for expert involvement to define features. 

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5. Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

   https://doi.org/10.1145/3461702.3462597  

   Kommiya Mothilal, Ramaravind ; Mahajan, Divyat ; Tan, Chenhao ; Sharma, Amit ( May 2021 , Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society)

   null (Ed.)

   Feature attributions and counterfactual explanations are popular approaches to explain a ML model. The former assigns an importance score to each input feature, while the latter provides input examples with minimal changes to alter the model's predictions. To unify these approaches, we provide an interpretation based on the actual causality framework and present two key results in terms of their use. First, we present a method to generate feature attribution explanations from a set of counterfactual examples. These feature attributions convey how important a feature is to changing the classification outcome of a model, especially on whether a subset of features is necessary and/or sufficient for that change, which attribution-based methods are unable to provide. Second, we show how counterfactual examples can be used to evaluate the goodness of an attribution-based explanation in terms of its necessity and sufficiency. As a result, we highlight the complimentary of these two approaches. Our evaluation on three benchmark datasets --- Adult-Income, LendingClub, and German-Credit --- confirms the complimentary. Feature attribution methods like LIME and SHAP and counterfactual explanation methods like Wachter et al. and DiCE often do not agree on feature importance rankings. In addition, by restricting the features that can be modified for generating counterfactual examples, we find that the top-k features from LIME or SHAP are often neither necessary nor sufficient explanations of a model's prediction. Finally, we present a case study of different explanation methods on a real-world hospital triage problem. 

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