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10th Learning Analytics Summer Institute Spain, LASI Spain 2022

作者： Vázquez-Ingelmo, Andrea García-Peñalvo, Francisco José Therón, Roberto Byrd, Vetria Camba, Jorge D. GRIAL Research Group University of Salamanca Paseo de Canalejas 169 Salamanca Spain VisUSAL Computer Science Department Universidad de Salamanca Salamanca Spain Computer Graphics Technology Purdue University West LafayetteIN United States

Data visualizations and information dashboards are useful but complex tools. They must be fully understood to draw proper insights and to avoid misleading conclusions. However, several elements and factors are involved in this domain, which makes it difficult to learn. In previous works, we proposed a meta-model to capture the primitive elements that compose visualizations and dashboards. This meta-model has served as a framework for conducting data visualization research, but also to develop a graphical tool for generating data visualizations and dashboards. This tool (namely MetaViz) enables users to create data visualizations through fine-grained components based on the entities represented in the meta-model. The main goal of the system is to provide a learning experience in which users can freely add and configure elements to understand how they influence the final display. This work describes work in progress to validate the pedagogical value of MetaViz in terms of the understanding of data visualization concepts. © 2022 Copyright for this paper by its authors.

关键词： Data visualization

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Weighting Attributes Based on the Greedy Algorithm Properties

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Procedia computer Science 2024年 246卷 4883-4892页

作者： Beata Zielosko Urszula Stańczyk Kamil Jabloński University of Silesia in Katowice Institute of Computer Science Bȩdzińska 39 41-200 Sosnowiec Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Estimation of importance for considered features is an important issue for any knowledge exploration process and it can be executed by a variety of approaches. In the research reported in this study, the primary aim was the development of a methodology for creating attribute rankings. Based on the properties of the greedy algorithm for inducing decision rules, a new application of this algorithm has been proposed. Instead of constructing a single ordering of features, attributes were weighted multiple times. The input datasets were discretised with several algorithms representing supervised and unsupervised discretisation approaches. Each resulting discrete data variant was exploited to construct a ranking of attributes. The effectiveness of the obtained rankings was confirmed through a rule filtering process governed by weighted attributes. The methodology was applied to the stylometric task of authorship attribution. The experimental outcomes demonstrate the value of the proposed research method, as it generally led to improved predictions while taking into account a noticeably decreased sets of attributes and decision rules.

关键词： Greedy algorithm Weighting attributes Decision rules Rule filtering Authorship attribution

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Text-dependent Speaker Verification Challenge 2024: Exploring Shared and User-defined Passphrases

Text-dependent Speaker Verification Challenge 2024: Explorin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hossein Zeinali Kong Aik Lee Jahangir Alam Lukáš Burget Department of Computer Engineering Amirkabir University of Technology Tehran Iran Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Kowloon Hong Kong Computer Research Institute of Montreal Montreal Canada Department of Computer Graphics and Multimedia Brno University of Technology Brno Czech Republic

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In contrast to text-independent speaker verification, which has received significant attention from researchers and has many competitions dedicated to it, text-dependent speaker verification (TdSV) has been less explored recently. The TdSV Challenge 2024 was organized to analyze and explore novel methods for this type of speaker verification and aims to motivate participants to develop new approaches to TdSV, conduct comprehensive analyses, and investigate advanced techniques such as self-supervised learning. This challenge builds on the achievements of the short-duration speaker verification (SdSV) Challenges held in 2020 and 2021 and focuses specifically on TdSV in two distinct scenarios. The first scenario involves conventional TdSV, while the second focuses on speaker enrollment using user-defined passphrases. This paper provides a detailed description of both tasks, introduces the evaluation rules, and presents a comprehensive analysis of the results obtained from this challenge.

关键词： Self-supervised learning Organizations Signal processing Acoustics Speaker recognition Speech processing

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"Ultimately, it's a matter of safety, and resisting ostracization": Understanding Neurodivergent Masking with Online Communities 25

"Ultimately, it's a matter of safety, and resisting ostraciz...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Kritika Kritika Rua Mae Williams Kathryn E. Ringland Computational Media Department University of California Santa Cruz Santa Cruz California USA Computer Graphics and Technology Purdue University West Lafayette Indiana USA

ISBN: (纸本)9798400713941

Neurotypical modes of existence and interaction are enforced through traditional social norms, compelling individuals who diverge from these norms, such as those who are neurodivergent, to conform through "masking."technology research and design often also ascribe to these conventional norms, creating technology that reinforces neurodivergent people's need to mask. In this research, we turn to neurodivergent communities online to develop an understanding of masking behaviors. We adopt a two-tiered research approach consisting of a qualitative thematic analysis of TikTok videos and a survey questionnaire. Through this work, we initiate discussion on the complexities of neurodivergent masking as a pervasive social adaptation. We urge HCI researchers to critically reframe intervention design and research practices that may either perpetuate or seek to address masking. © 2025 Copyright held by the owner/author(s).

关键词： Neurodivergent Masking

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FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion

arXiv

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arXiv 2022年

作者： Hirner, Dominik Fraundorfer, Friedrich Graz University of Technology Austria Institute for Computer Graphics and Vision Austria

We propose an accurate and lightweight convolutional neural network for stereo estimation with depth completion. We name this method fully-convolutional deformable similarity network with depth completion (FCDSN-DC). This method extends FC-DCNN by improving the feature extractor, adding a network structure for training highly accurate similarity functions and a network structure for filling inconsistent disparity estimates. The whole method consists of three parts. The first part consists of fully-convolutional densely connected layers that computes expressive features of rectified image pairs. The second part of our network learns highly accurate similarity functions between this learned features. It consists of densely-connected convolution layers with a deformable convolution block at the end to further improve the accuracy of the results. After this step an initial disparity map is created and the left-right consistency check is performed in order to remove inconsistent points. The last part of the network then uses this input together with the corresponding left RGB image in order to train a network that fills in the missing measurements. Consistent depth estimations are gathered around invalid points and are parsed together with the RGB points into a shallow CNN network structure in order to recover the missing values. We evaluate our method on challenging real world indoor and outdoor scenes, in particular Middlebury, KITTI and ETH3D were it produces competitive results. We furthermore show that this method generalizes well and is well suited for many applications without the need of further training. The code of our full framework is available at: https://***/thedodo/FCDSN-DC. © 2022, CC BY.

关键词： Convolution

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Filtering Decision Rules Driven by Sequential Forward and Backward Selection of Attributes: An Illustrative Example in Stylometric Domain

Filtering Decision Rules Driven by Sequential Forward and Ba...

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Federated Conference on computer Science and Information Systems (FedCSIS)

作者： Beata Zielosko Urszula Stańczyk Kamil Jabloński University of Silesia in Katowice Institute of Computer Science Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Gliwice Poland

The paper presents investigations concerning the decision rule filtering process controlled by the estimated relevance of available attributes. In the conducted study, two search directions were used, sequential forward selection and sequential backward elimination. The steps of sequential search were governed by three rankings obtained for variables, all related to characteristics of data and rules that can be induced, as follows, (i) a ranking based on the weighting factor referring to the occurrence of attributes in generated decision reducts, (ii) the OneR ranking exploiting short rule properties, and (iii) the proposed ranking defined through the operation of greedy algorithm for rule induction. The three rankings were confronted and compared from the perspective of their usefulness for the selection of rules performed in the two directions and with two strategies for rule selection. The resulting sets of rules were analysed with respect to the properties of the constituent decision rules and from the point of performance for all constructed rule-based classifiers. Substantial experiments were carried out in the stylometric domain, treating the task of authorship attribution as classification. The results obtained indicate that for all three rankings and search paths it was possible to obtain a noticeable reduction of attributes while at least maintaining the power of inducers, at the same time improving characteristics of rule sets.

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SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning

arXiv

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arXiv 2023年

作者： Alonso-Fernandez, Fernando Hernandez-Diaz, Kevin Rubio, Jose Maria Buades Bigun, Josef School of Information Technology Halmstad University Sweden Computer Graphics and Vision and AI Group University of Balearic Islands Spain

The widespread use of mobile devices for various digital services has created a need for reliable and real-time person authentication. In this context, facial recognition technologies have emerged as a dependable method for verifying users due to the prevalence of cameras in mobile devices and their integration into everyday applications. The rapid advancement of deep Convolutional Neural Networks (CNNs) has led to numerous face verification architectures. However, these models are often large and impractical for mobile applications, reaching sizes of hundreds of megabytes with millions of parameters. We address this issue by developing SqueezerFaceNet, a light face recognition network which less than 1M parameters. This is achieved by applying a network pruning method based on Taylor scores, where filters with small importance scores are removed iteratively. Starting from an already small network (of 1.24M) based on SqueezeNet, we show that it can be further reduced (up to 40%) without an appreciable loss in performance. To the best of our knowledge, we are the first to evaluate network pruning methods for the task of face recognition. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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Semi-Supervised Language Models for Identification of Personal Health Experiential from Twitter Data: A Case for Medication Effects 20

Semi-Supervised Language Models for Identification of Person...

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20th Workshop on Biomedical Language Processing, BioNLP 2021

作者： Zhu, Minghao Jiang, Keyuan School of Computer Science and Technology Donghua University Shanghai 201620 China Department of Computer Information Technology and Graphics Purdue University Northwest HammondIN46323 United States

ISBN: (纸本)9781954085404

First-hand experience related to any changes of one’s health condition and understanding such experience can play an important role in advancing medical science and healthcare. Monitoring the safe use of medication drugs is an important task of pharmacovigilance, and first-hand experience of effects about consumers’ medication intake can be valuable to gain insight into how our human body reacts to medications. Social media have been considered as a possible alternative data source for gathering personal experience with medications posted by users. Identifying personal experience tweets is a challenging classification task, and efforts have been made to tackle the challenges using supervised approaches requiring annotated data. There exists an abundance of unlabeled Twitter data, and being able to use such data for training without suffering in classification performance is of great value, which can reduce the cost of laborious annotation process. We investigated two semi-supervised learning methods, with different mixes of labeled and unlabeled data in the training set, to understand the impact on classification performance. Our results from both pseudo-label and consistency regularization methods show that both methods generated a noticeable improvement in F1 score when the labeled set was small, and consistency regularization could still provide a small gain even a larger labeled set was used. © 2021 Association for Computational Linguistics

关键词： Social networking (online)

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Synthetic Augmentation for Anatomical Landmark Localization using DDPMs

arXiv

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arXiv 2024年

作者： Hadzic, Arnela Bogensperger, Lea Joham, Simon Johannes Urschler, Martin Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

Deep learning techniques for anatomical landmark localization (ALL) have shown great success, but their reliance on large annotated datasets remains a problem due to the tedious and costly nature of medical data acquisition and annotation. While traditional data augmentation, variational autoencoders (VAEs), and generative adversarial networks (GANs) have already been used to synthetically expand medical datasets, diffusion-based generative models have recently started to gain attention for their ability to generate high-quality synthetic images. In this study, we explore the use of denoising diffusion probabilistic models (DDPMs) for generating medical images and their corresponding heatmaps of landmarks to enhance the training of a supervised deep learning model for ALL. Our novel approach involves a DDPM with a 2-channel input, incorporating both the original medical image and its heatmap of annotated landmarks. We also propose a novel way to assess the quality of the generated images using a Markov Random Field (MRF) model for landmark matching and a Statistical Shape Model (SSM) to check landmark plausibility, before we evaluate the DDPM-augmented dataset in the context of an ALL task involving hand X-Rays. © 2024, CC BY-SA.

关键词： Generative adversarial networks

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Detecting DTI Using Graph Embedding and Multi-Head Attention Mechanism

Detecting DTI Using Graph Embedding and Multi-Head Attention...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoli Lin Yaoyu Chen Wenli Yang HaiPing Yu Zimo Wu Xiaolong Zhang Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China School of Information Wuhan Vocational College Professional Committee of Graphics and Big Data China Graphics Society Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-target interaction (DTI) prediction has an important role in drug discovery, significantly expediting the drug design and development process. This paper proposes a new model, ERW_BiAN, that seamlessly integrates graph embedding representations with deep learning network. Specifically, we utilize an improved graph embedding model, ERW, to generate comprehensive feature vectors for each node within the knowledge graph. Subsequently, these feature vectors are fed into the BiAN model, which leverages an attention mechanism to assign precise weights to sequences. Experimental results demonstrate the superior accuracy and predictive prowess of ERW_BiAN in DTI prediction tasks. Furthermore, the case about COVID-19 shows that ERW_BiAN has the better application potential for predicting drug-target interactions.

关键词： Deep learning Drugs COVID-19 Attention mechanisms Biological system modeling Knowledge graphs Predictive models Vectors Drug discovery Diffusion tensor imaging

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