6 research outputs found
Interpretable Visual Understanding with Cognitive Attention Network
While image understanding on recognition-level has achieved remarkable
advancements, reliable visual scene understanding requires comprehensive image
understanding on recognition-level but also cognition-level, which calls for
exploiting the multi-source information as well as learning different levels of
understanding and extensive commonsense knowledge. In this paper, we propose a
novel Cognitive Attention Network (CAN) for visual commonsense reasoning to
achieve interpretable visual understanding. Specifically, we first introduce an
image-text fusion module to fuse information from images and text collectively.
Second, a novel inference module is designed to encode commonsense among image,
query and response. Extensive experiments on large-scale Visual Commonsense
Reasoning (VCR) benchmark dataset demonstrate the effectiveness of our
approach. The implementation is publicly available at
https://github.com/tanjatang/CANComment: ICANN2
A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification
A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high dimensional input space of the training samples into a low dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications. Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum. Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map. In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach
The Effectiveness of Hybrid Learning in Improving of Teacher-Student Relationship in Terms of Learning Motivation
The Advanced Mathematical Thinking (AMT) ability is one of the prioritized mathematical abilities needed to be developed in learning mathematics during tertiary education. The present study sought to test the effectiveness of hybrid learning in improving students' advanced mathematical thinking. The research used a quasi-experimental design with a non-equivalent control group design. The subject of this study was students of a mathematics education study program at a university in Bandung who attended lecture for the multi-variable in a calculus course. The sampling technique used was purposive sampling. Of the many variable calculus classes consisting of 2 classes, one class was chosen as the experiment group and the other class as the control group. The sample consists of 40 people for each group. Data analysis used the MANOVA test with normality and homogeneity tests as a prerequisite test. The results showed a difference in AMT's significance between the hybrid learning and conventional groups, where hybrid learning had a higher AMT. Other than that, there is a difference in the significance of AMT between the high motivation group and the low motivation group, where high motivation has a higher AMT, and there is an interaction of learning models and motivational factors to increase AMT. Doi: 10.28991/esj-2021-01288 Full Text: PD
Preventing Discriminatory Decision-making in Evolving Data Streams
Bias in machine learning has rightly received significant attention over the
last decade. However, most fair machine learning (fair-ML) work to address bias
in decision-making systems has focused solely on the offline setting. Despite
the wide prevalence of online systems in the real world, work on identifying
and correcting bias in the online setting is severely lacking. The unique
challenges of the online environment make addressing bias more difficult than
in the offline setting. First, Streaming Machine Learning (SML) algorithms must
deal with the constantly evolving real-time data stream. Second, they need to
adapt to changing data distributions (concept drift) to make accurate
predictions on new incoming data. Adding fairness constraints to this already
complicated task is not straightforward. In this work, we focus on the
challenges of achieving fairness in biased data streams while accounting for
the presence of concept drift, accessing one sample at a time. We present Fair
Sampling over Stream (), a novel fair rebalancing approach capable of
being integrated with SML classification algorithms. Furthermore, we devise the
first unified performance-fairness metric, Fairness Bonded Utility (FBU), to
evaluate and compare the trade-off between performance and fairness of
different bias mitigation methods efficiently. FBU simplifies the comparison of
fairness-performance trade-offs of multiple techniques through one unified and
intuitive evaluation, allowing model designers to easily choose a technique.
Overall, extensive evaluations show our measures surpass those of other fair
online techniques previously reported in the literature