Fine-Grained Visual Classification (FGVC) is a computer vision task that involves classifying subtle differences in images. While the Vision Transformer (ViT) is excellent at capturing long-range dependencies in seque...
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Backdoor attacks pose significant threats to Natural Language Processing (NLP) models. Various backdoor defense methods for NLP models primarily function by identifying and subsequently manipulating backdoor triggers ...
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Most previous studies on discourse parsing have utilized discriminative models to construct tree structures. However, these models tend to overlook the global perspective of the tree structure as a whole during the st...
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This paper introduces various aspects of social perception skills and scene awareness for interactive robots. The low-level audio-visual perceptual cues e.g., interruption events, eye contact, speech energy, etc. have...
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With the continuous improvement of various high-performance computing systems, various data centers had also been fully expanded. Energy consumption and actual performance measurement were very important indicators, w...
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This study investigates the effects on different racial/ethnic groups of middle school students when learning with a digital learning game, Decimal Point, and a comparable computer tutor. Using data from three classro...
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ISBN:
(纸本)9783031723148;9783031723155
This study investigates the effects on different racial/ethnic groups of middle school students when learning with a digital learning game, Decimal Point, and a comparable computer tutor. Using data from three classroom studies with 835 students, we compared learning outcomes and engagement among students from racial/ethnic groups that are well-represented in STEM (white and Asian) to those that are underrepresented in STEM (Black, Hispanic/Latine, Indigenous, and multiracial). Relative to students from underrepresented groups, students from well-represented groups in STEM scored higher on all tests (pre, post, and delayed, despite similar learning gains from pre-to-post and pre-to-delayed) and showed more engagement and less anxiety. The game also enhanced the experience of mastery only among students from well-represented groups. At the same time, students from underrepresented groups learned from the intervention and matched students from well-represented groups in learning efficiency. In short, we found similar learning gains from the game and tutor interventions among students from well-represented and underrepresented racial/ethnic groups, despite the lower performance and lower engagement among students from underrepresented groups. These insights highlight how students from diverse backgrounds may engage differently with educational technology, guiding future efforts in making Decimal Point - as well as digital learning tools in general - more inclusive.
Business process anomaly detection enables the prevention of misuse and failures. Existing approaches focus on detecting anomalies in control, temporal, and resource behavior of individual instances, neglecting the co...
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We consider two models of computation for Tarski’s order preserving function f related to fixed points in a complete lattice: the oracle function model and the polynomial function model. In both models, we find the f...
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This paper briefly analyzes the summary of network embedding and node similarity, emphasizes the discovery, and takes numerical simulation as the entry point to study the evaluation index, real network and artificial ...
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Recent advances in AI demonstrate its capacities to not only automate more and more everyday tasks, but also make direct Human-AI-Interaction possible. When using AI models in production, we might face situations wher...
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ISBN:
(纸本)9783031592348;9783031592355
Recent advances in AI demonstrate its capacities to not only automate more and more everyday tasks, but also make direct Human-AI-Interaction possible. When using AI models in production, we might face situations where the AI is confronted with input data that is very different from the data it was trained on. Such situations are called Out-of-Distribution situations and can result in misleading AI inferences. We argue that identification, handling, and prevention of Out-of-Distribution situations is key for creating production-ready interactive AI components. In this paper, we test the robustness of state-of-the-art AI/ML approaches in Out-of-Distribution situations and propose a research agenda to gather a deeper understanding of how to identify, handle, and prevent such situations in interactive applications.
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