Computing and Information Systems - Research Publications

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    On the impact of initialisation strategies on Maximum Flow algorithm performance
    Alipour, H ; Munoz, MA ; Smith-Miles, K (PERGAMON-ELSEVIER SCIENCE LTD, 2024-03)
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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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    Directive Explanations for Actionable Explainability in Machine Learning Applications
    Singh, R ; Miller, T ; Lyons, H ; Sonenberg, L ; Velloso, E ; Vetere, F ; Howe, P ; Dourish, P (ASSOC COMPUTING MACHINERY, 2023-12)
    In this article, we show that explanations of decisions made by machine learning systems can be improved by not only explaining why a decision was made but also explaining how an individual could obtain their desired outcome. We formally define the concept of directive explanations (those that offer specific actions an individual could take to achieve their desired outcome), introduce two forms of directive explanations (directive-specific and directive-generic), and describe how these can be generated computationally. We investigate people’s preference for and perception toward directive explanations through two online studies, one quantitative and the other qualitative, each covering two domains (the credit scoring domain and the employee satisfaction domain). We find a significant preference for both forms of directive explanations compared to non-directive counterfactual explanations. However, we also find that preferences are affected by many aspects, including individual preferences and social factors. We conclude that deciding what type of explanation to provide requires information about the recipients and other contextual information. This reinforces the need for a human-centered and context-specific approach to explainable AI.
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    Scalable Approximate Butterfly and Bi-triangle Counting for Large Bipartite Networks
    Zhang, F ; Chen, D ; Wang, S ; Yang, Y ; Gan, J (Association for Computing Machinery (ACM), 2023-12-08)
    A bipartite graph is a graph that consists of two disjoint sets of vertices and only edges between vertices from different vertex sets. In this paper, we study the counting problems of two common types of em motifs in bipartite graphs: (i) butterflies (2x2 bicliques) and (ii) bi-triangles (length-6 cycles). Unlike most of the existing algorithms that aim to obtain exact counts, our goal is to obtain precise enough estimations of these counts in bipartite graphs, as such estimations are already sufficient and of great usefulness in various applications. While there exist approximate algorithms for butterfly counting, these algorithms are mainly based on the techniques designed for general graphs, and hence, they are less effective on bipartite graphs. Not to mention that there is still a lack of study on approximate bi-triangle counting. Motivated by this, we first propose a novel butterfly counting algorithm, called one-sided weighted sampling, which is tailored for bipartite graphs. The basic idea of this algorithm is to estimate the total butterfly count with the number of butterflies containing two randomly sampled vertices from the same side of the two vertex sets. We prove that our estimation is unbiased, and our technique can be further extended (non-trivially) for bi-triangle count estimation. Theoretical analyses under a power-law random bipartite graph model and extensive experiments on multiple large real datasets demonstrate that our proposed approximate counting algorithms can reach high accuracy, yet achieve up to three orders (resp. four orders) of magnitude speed-up over the state-of-the-art exact butterfly (resp. bi-triangle) counting algorithms. Additionally, we present an approximate clustering coefficient estimation framework for bipartite graphs, which shows a similar speed-up over the exact solutions with less than 1% relative error.
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    The Impact of Judgment Variability on the Consistency of Offline Effectiveness Measures
    Rashidi, L ; Zobel, J ; Moffat, A (ASSOC COMPUTING MACHINERY, 2024-01)
    Measurement of the effectiveness of search engines is often based on use of relevance judgments. It is well known that judgments can be inconsistent between judges, leading to discrepancies that potentially affect not only scores but also system relativities and confidence in the experimental outcomes. We take the perspective that the relevance judgments are an amalgam of perfect relevance assessments plus errors; making use of a model of systematic errors in binary relevance judgments that can be tuned to reflect the kind of judge that is being used, we explore the behavior of measures of effectiveness as error is introduced. Using a novel methodology in which we examine the distribution of “true” effectiveness measurements that could be underlying measurements based on sets of judgments that include error, we find that even moderate amounts of error can lead to conclusions such as orderings of systems that statistical tests report as significant but are nonetheless incorrect. Further, in these results the widely used recall-based measures AP and NDCG are notably more fragile in the presence of judgment error than is the utility-based measure RBP, but all the measures failed under even moderate error rates. We conclude that knowledge of likely error rates in judgments is critical to interpretation of experimental outcomes.
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    TransCP: A Transformer Pointer Network for Generic Entity Description Generation With Explicit Content-Planning
    Trisedya, BD ; Qi, J ; Zheng, H ; Salim, FD ; Zhang, R (IEEE COMPUTER SOC, 2023-12-01)
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    Focused Contrastive Loss for Classification With Pre-Trained Language Models
    He, J ; Li, Y ; Zhai, Z ; Fang, B ; Thorne, C ; Druckenbrodt, C ; Akhondi, S ; Verspoor, K (Institute of Electrical and Electronics Engineers (IEEE), 2023-01-01)
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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)