Computing and Information Systems - Research Publications

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    Algorithmic Decisions, Desire for Control, and the Preference for Human Review over Algorithmic Review
    Lyons, H ; Miller, T ; Velloso, E (ASSOC COMPUTING MACHINERY, 2023)
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    Lossy Compression Options for Dense Index Retention
    Mackenzie, J ; Moffat, A (ASSOC COMPUTING MACHINERY, 2023)
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    The Future Can’t Help Fix The Past: Assessing Program Repair In The Wild
    Kabadi, V ; Kong, D ; Xie, S ; Bao, L ; Azriadi Prana, GA ; Le, T-DB ; Le, X-BD ; Lo, D (IEEE, 2023-10-01)
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    Repeated Builds During Code Review: An Empirical Study of the OpenStack Community
    Maipradit, R ; Wang, D ; Thongtanunam, P ; Kula, RG ; Kamei, Y ; McIntosh, S (IEEE, 2023-01-01)
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    A Group Formation Game for Local Anomaly Detection
    Ye, Z ; Alpcan, T ; Leckie, C (IEEE, 2023-01-01)
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    Towards a Haptic Taxonomy of Emotions: Exploring Vibrotactile Stimulation in the Dorsal Region
    Villa, S ; Nguyen, TD ; Tag, B ; Machulla, TK ; Schmidt, A ; Niess, J (ACM, 2023-10-08)
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    Workshop on Understanding and Mitigating Cognitive Biases in Human-AI Collaboration
    Boonprakong, N ; He, G ; Gadiraju, U ; Van Berkel, N ; Wang, D ; Chen, S ; Liu, J ; Tag, B ; Goncalves, J ; Dingler, T (ACM, 2023-10-14)
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    Uncertainty in Selective Bagging: A Dynamic Bi-objective Optimization Model
    Maadi, M ; Akbarzadeh Khorshidi, H ; Aickelin, U (SIAM, 2023)
    Bagging is a common approach in ensemble learning that generates a group of classifiers through bootstrapping for classification tasks. Despite its wide applications, generating redundant classifiers remains a central challenge in bagging. In recent years, many selective bagging models have been presented to deal with this challenge. These models mostly focused on the accuracy of classifiers and the diversity among them. Despite the importance of uncertainty in the performance of ensemble classifiers, this criterion has been neglected in selective bagging models. In this paper, we propose a two-stage selective bagging model. In the first stage, we formalize the selective bagging problem as a bi-objective optimization model considering both the uncertainty and accuracy of classifiers. We propose an adaptive evolutionary Two-Arch2 algorithm, named Diverse-Two-Arch2, to solve the bi-objective model. The output of this stage is a subset of classifiers that are diverse, certain about correct predictions, and uncertain about incorrect predictions. While most selective bagging models focus on the selection of a fixed subset of classifiers for all test samples (static approach), our proposed model has a dynamic approach to the selection process. So, in the second stage of the model, we select only certain classifiers to make an ensemble prediction for each test sample. Experimental results on twenty data sets and comparing with two ensemble models, and five state-of-the-art dynamic selective bagging models show the outperformance of the proposed model. We also compare the performance of the proposed Diverse-Two-Arch2 to alternative evolutionary computation methods.
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    Semantic Shifts in Mental Health-Related Concepts
    Baes, N ; Haslam, N ; Vylomova, E ; Tahmasebi, N ; Montariol, S ; Dubossarsky, H ; Kutuzov, A ; Hengchen, S ; Alfter, D ; Periti, F ; Cassotti, P (Association for Computational Linguistics, 2023)
    The present study evaluates semantic shifts in mental health-related concepts in two diachronic corpora spanning 1970-2016, one academic and one general. It evaluates whether their meanings have broadened to encompass less severe phenomena and whether they have become more pathology related. It applies a recently proposed methodology (Baes et al., 2023) to examine whether words collocating with a sample of mental health concepts have become less emotionally intense and develops a new way to examine whether the concepts increasingly co-occur with pathology-related terms. In support of the first hypothesis, mental health-related concepts became associated with less emotionally intense language in the psychology corpus (addiction, anger, stress, worry) and in the general corpus (addiction, grief, stress, worry). In support of the second hypothesis, mental health-related concepts came to be more associated with pathology-related language in psychology (addiction, grief, stress, worry) and in the general corpus (grief, stress). Findings demonstrate that some mental health concepts have become normalized and/or pathologized, a conclusion with important social and cultural implications.