Reviewerly/exHarmony huggingface dataset:
Appendix: Entangled beauty and CP violation

Subject Astronomy and Astrophysics Mathematical and Statistical Physics Computational Physics Collection: Oxford Scholarship Online
An Emotion-Adaptive VR Experience for Recreational Use in Eldercare

Virtual Reality (VR) technology provides the elderly, and people with dementia, the opportunity to reminisce by exploring places outside their (care) home, free from age-related (physical) restrictions. However, the elderly are particularly vulnerable to overstimulation. Irresponsible VR design can cause stress and anxiety, potentially even exacerbating cognitive decline, and diminishing well-being. We present an electromyography (EMG) driven stress- and emotion-adaptive VR environment for the elderly that provides an immersive but controlled experience targeted at preventing negative emotions. We report our results and insights from a pilot study with elderly participants (N=3). Our system detects and mitigates signs of stress and negative emotions while promoting pleasant recollections.
Deciphering the footprint of Emil Grosse from Germany to Brazil: a brief review

After the German geologist Emil Jakob Grosse concluded his work as head of the General Geological Commission of Colombia in April 1931, he lived and worked between Germany and Brazil, where his record has been largely unknown to the present date. In this contribution, we document some historical aspects of his life and briefly review Emil Grosse’s trajectory from Germany to Brazil. The oldest record of Emil Grosse in Brazil dates from August 20, 1936, when he worked at the “Fábrica mine” until 1939. The Fábrica mine contains an iron deposit located southeast of the São Francisco Craton in what is now known as Quadrilátero Ferrífero, within the municipality of Ouro Preto. Grosse worked in a hematite ore site located in a banded iron formation (BIF), but there is no record of him having worked in the coal deposits. Years after, in 1946, the Fábrica mine changed owners and name to Ferteco, a subsidiary of the German corporation Industriegewerkschaft Bergbau, with headquarters in Bochum, Germany, where Grosse possibly worked to validate the deposit. In the 1940s, Grosse lived between the steelworks town of Peine, Germany, and Belo Horizonte, Brazil. There is a record of his trip in the middle of the Second World War on October 26, 1942. In 1946, his work on the Fábrica mine was finally published in Volume XI (No. 62) of the Brazilian journal Mineração e Metalurgia. Later, on August 18, 1949, Grosse obtained his permanent visa through the mediation of the Brazilian Military Mission and the German control council. Finally, he settled in Würzburg, Germany, where in 1971, he died at the age of 91.
Local Connectivity-Based Density Estimation for Face Clustering

Recent graph-based face clustering methods predict the connectivity of enormous edges, including false positive edges that link nodes with different classes. However, those false positive edges, which connect negative node pairs, have the risk of integration of different clusters when their connectivity is incorrectly estimated. This paper proposes a novel face clustering method to address this problem. The proposed clustering method employs density-based clustering, which maintains edges that have higher density. For this purpose, we propose a reliable density estimation algorithm based on local connectivity between K nearest neighbors (KNN). We effectively exclude negative pairs from the KNN graph based on the reliable density while maintaining sufficient positive pairs. Furthermore, we develop a pairwise connectivity estimation network to predict the connectivity of the selected edges. Experimental results demonstrate that the proposed clustering method significantly outperforms the state-of-the-art clustering methods on large-scale face clustering datasets and fashion image clustering datasets. Our code is available at https://github.com/illian01/LCE-PCENet
FEUWNet: A Fast and Effective Baseline for Underwater Image Enhancement

Underwater optical images are one of the important media for humans to explore the oceans, but it is difficult to directly obtain high-quality underwater images because of the unique physical and chemical properties of underwater, most underwater images exhibit disadvantages such as color decay, low contrast, and blurred details. In order to solve the above problems, this paper designed a fast and effective baseline for underwater image enhancement called FEUWNet. FEUWNet uses a plug-and-play module consists with downsampling encoding block and up-sampling decoding block to balance enhancement performance and computational speed. After experimental comparison, FEUWNet has achieved good results in underwater image enhancement.
Tweet recommendation using Clustered Bert and Word2vec Models

This work presents a tweet recommendation algorithm based on Clustered Bert and Clustered word2vec models. The paper proposes a hybrid recommender system using content and collaborative filtering techniques. In the first phase, the content of the tweets is captured and processed via clustered Bert and word2vec models. These models work on clustering the tweet embeddings generated from Bert and Word2vec models giving out tweet clusters as an output. In the second phase, Collaborative filtering algorithms is then used to recommend tweets to a user based on the historic tweet cluster of interest of the user. The work uses Agglomerative and Kmeans clustering algorithms to cluster similar tweets together. Results show that the recommendations generated from Clustered Bert model have a better performance than Clustered Word2vec model.
Towards symbolic representation-based modeling of Temporal Knowledge Graphs

Symbolic representation helps us to represent information in a well-defined rule-driven fashion. Currently, there are several ways to represent Knowledge Graphs in general. However, in this work, we extended the implementation of symbolic representation to model domain-oriented temporal Knowledge Graphs. For symbolic representation, we incorporated Horn rules and SWRL (Semantic Web Rule Language). The presented approach is semi-autonomous: (i) we extracted hand-crafted rules and (ii) we utilized the PSyKE (Platform for Symbolic Knowledge Extraction) package to extract some rules automatically from raw data logs. For domain modeling, we targeted a smart industry environment. To validate the proposed model, we conducted a counterfactual study using Knowledge Graph and network analysis for fact-finding and filtering.
Development of a Humanoid Reading System with Voice Lip Synchronization

With the increasing use of service robots in people’s daily lives, there is a growing expectation that robots not only perform service tasks independently but also play a role in education. To improve the interactive capabilities and functionality of humanoid robots, we have developed a humanoid robot reading system that can read books. The system comprises two main modules: A document character recognition module and a lip synchronization reading module. The document character recognition module detects and recognizes the captured document images from the cameras and extracts the text from the image, and the lip synchronized reading module outputs the reading text by synchronizing voice and lip movements. Our team has independently developed a humanoid robot that has successfully implemented this system.
SentMask: A Sentence-Aware Mask Attention-Guided Two-Stage Text Summarization Component

The text summarization task aims to generate succinct sentences that summarise what an article tries to express. Based on pretrained language models, combining extractive and abstractive summarization approaches has been widely adopted in text summarization tasks. It has been proven to be effective in many existing pieces of research using extract-then-abstract algorithms. However, this method suffers from semantic information loss throughout the extraction process, resulting in incomprehensive sentences being generated during the abstract phase. Besides, current research on text summarization emphasizes only word-level comprehension while paying little attention to understanding the level of the sentence. To tackle this problem, in this paper, we propose the SentMask component. Taking into account that the semantics of sentences that are filtered out during the extraction process is also worth considering, the paper designs a sentence-aware mask attention mechanism in the process of generating a text summary. By applying the extractive approach, the paper first selects the most essential sentences to construct the initial summary phrases. This information leads the model to modify the weights of the attention mechanism, which provides supervision for the generative model to ensure that it focuses on the sentences that convey important semantics while not ignoring others. The final summary is constructed based on the key information provided. The experimental results demonstrate that our model achieves higher ROUGE and BLEU scores compared to other baseline models on two benchmark datasets.
Enhancing heart disease detection and classification using fuzzy logic and metaheuristic algorithms in a blockchain assisted healthcare environment

Heart disease (HD) is a leading cause of mortality worldwide, emphasizing the need for accurate and efficient detection and classification methods. Recently, Blockchain (BC) provides seamless and secure sharing of heart disease data amongst healthcare providers, specialists, and researchers. This allows collaborative efforts, data exchange, and integration of diverse datasets, leading to a more comprehensive analysis and accurate detection of heart diseases. BC provides a decentralized and tamper-proof platform for storing sensitive patient data related to heart disease. This ensures the integrity and security of the data, reducing the risk of unauthorized access or data manipulation. Therefore, this study presents a new blockchain-assisted heart disease detection and classification model with feature selection with optimal fuzzy logic (BHDDC-FSOFL) technique. The presented BHDDC-FSOFL technique uses BC technology to store healthcare data securely. In addition, the disease detection module encompasses the design of biogeography teaching and learning-based optimization (BTLBO) algorithm for feature selection (FS) procedure. Moreover, an adaptive neuro-fuzzy inference system (ANFIS) classifier can be exploited for HD detection and classification. Furthermore, the ebola search optimization (ESO) algorithm is used for the parameter tuning of the ANFIS classifier. The integration of ANFIS classifier enables the modeling of uncertainty and imprecision in HD data, while metaheuristic algorithms aid in optimizing the classification process. Additionally, the utilization of BC technology ensures secure and transparent storage and sharing of healthcare data. To demonstrate the enhanced HD classification results of the BHDDC-FSOFL technique, a detailed experimental analysis was made on the HD dataset. The extensive result analysis pointed out the improved performance of the BHDDC-FSOFL technique compared to recent approaches in terms of different measures. Therefore, the proposed model offers a reliable and privacy-enhancing solution for healthcare providers and patients in a BC-assisted healthcare environment.
Potholes and Power: A Multimodal Critical Discourse Analysis of “Look At This F*ckin’ Street” on Instagram

The condition of road infrastructure in New Orleans is recognized among citizens as unacceptably poor. This problem is one in a long list of problems with the municipal government and city services that have combined to create an atmosphere of distrust and deep frustration. On Instagram, “Look At This F*ckin Street” (LATFS) exists to document failing local infrastructure and has over 94,000 followers and an active culture of crowdsourced user submissions and regular engagement in comments and reposts. In this article, I utilize Multimodal Critical Discourse Analysis to explore all of the relevant discursive modalities seen in the account in order to uncover how they work to challenge and ultimately undermine power. Via an open coding process, I identify three main strategies employed by the anonymous account manager and the participating followers: Shaming, Mocking, and Exposing. Within each of these strategies, I explore the specific techniques observed within these discourses that contribute to the effectiveness of these strategies. I argue that LATFS is an effective and powerful participatory platform for exposing a broad range of systemic problems and their causes, allowing residents to take back the narrative of their city’s infrastructure challenges, diminish and demean the powerful interests responsible, and ultimately attempt to reclaim the power lost to negligent or even bad-faith municipal authorities in New Orleans.
StepFormer: Self-Supervised Step Discovery and Localization in Instructional Videos

Instructional videos are an important resource to learn procedural tasks from human demonstrations. However, the instruction steps in such videos are typically short and sparse, with most of the video being irrelevant to the procedure. This motivates the need to temporally localize the instruction steps in such videos, i.e. the task called key-step localization. Traditional methods for key-step localization require video-level human annotations and thus do not scale to large datasets. In this work, we tackle the problem with no human supervision and introduce StepFormer, a self-supervised model that discovers and localizes instruction steps in a video. StepFormer is a transformer decoder that attends to the video with learnable queries, and produces a sequence of slots capturing the key-steps in the video. We train our system on a large dataset of instructional videos, using their automatically-generated subtitles as the only source of supervision. In particular, we supervise our system with a sequence of text narrations using an order-aware loss function that filters out irrelevant phrases. We show that our model outperforms all previous unsupervised and weakly-supervised approaches on step detection and localization by a large margin on three challenging benchmarks. Moreover, our model demonstrates an emergent property to solve zero-shot multi-step localization and outperforms all relevant baselines at this task.
Occlusion-Free Scene Recovery via Neural Radiance Fields

Our everyday lives are filled with occlusions that we strive to see through. By aggregating desired background information from different viewpoints, we can easily eliminate such occlusions without any external occlusion-free supervision. Though several occlusion removal methods have been proposed to empower machine vision systems with such ability, their performances are still unsatisfactory due to reliance on external supervision. We propose a novel method for occlusion removal by directly building a mapping between position and viewing angles and the corresponding occlusion-free scene details leveraging Neural Radiance Fields (NeRF). We also develop an effective scheme to jointly optimize camera parameters and scene reconstruction when occlusions are present. An additional depth constraint is applied to supervise the entire optimizaion without labeled external data for training. The experimental results on existing and newly collected datasets validate the effectiveness of our method. Our project page: https://freebutuselesssoul.github.io/occnerf.
PersonNeRF : Personalized Reconstruction from Photo Collections

We present PersonNeRF, a method that takes a collection of photos of a subject (e.g. Roger Federer) captured across multiple years with arbitrary body poses and ap-pearances, and enables rendering the subject with arbitrary novel combinations of viewpoint, body pose, and appearance. PersonNeRF builds a customized neural volumetric 3D model of the subject that is able to render an entire space spanned by camera viewpoint, body pose, and appearance. A central challenge in this task is dealing with sparse observations; a given body pose is likely only observed by a single viewpoint with a single appearance, and a given appearance is only observed under a handful of different body poses. We address this issue by recovering a canonical T-pose neural volumetric representation of the subject that allows for changing appearance across different observations, but uses a shared pose-dependent motion field across all observations. We demonstrate that this approach, along with regularization of the recovered volumetric geometry to encourage smoothness, is able to recover a model that renders compelling images from novel combinations of viewpoint, pose, and appearance from these challenging unstructured photo collections, outperforming prior work for free-viewpoint human rendering.
Learning Decorrelated Representations Efficiently Using Fast Fourier Transform

Barlow Twins and VICReg are self-supervised representation learning models that use regularizers to decorrelate features. Although these models are as effective as conventional representation learning models, their training can be computationally demanding if the dimension <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> of the projected embeddings is high. As the regularizers are defined in terms of individual elements of a cross-correlation or covariance matrix, computing the loss for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> samples takes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(nd^{2})$</tex> time. In this paper, we propose a relaxed decorre-lating regularizer that can be computed in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(nd\log d)$</tex> time by Fast Fourier Transform. We also propose an inexpensive technique to mitigate undesirable local minima that develop with the relaxation. The proposed regularizer exhibits accuracy comparable to that of existing regularizers in down-stream tasks, whereas their training requires less memory and is faster for large <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> . The source code is available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/yutaro-s/scalable-decorrelation-ssl.git
NeRFLight: Fast and Light Neural Radiance Fields using a Shared Feature Grid

While original Neural Radiance Fields (NeRF) have shown impressive results in modeling the appearance of a scene with compact MLP architectures, they are not able to achieve real-time rendering. This has been recently addressed by either baking the outputs of NeRF into a data structure or arranging trainable parameters in an explicit feature grid. These strategies, however, significantly increase the memory footprint of the model which prevents their deployment on bandwidth-constrained applications. In this paper, we extend the grid-based approach to achieve real-time view synthesis at more than 150 FPS using a lightweight model. Our main contribution is a novel architecture in which the density field of NeRF-based representations is split into N regions and the density is modeled using N different decoders which reuse the same feature grid. This results in a smaller grid where each feature is located in more than one spatial position, forcing them to learn a compact representation that is valid for different parts of the scene. We further reduce the size of the final model by disposing of the features symmetrically on each region, which favors feature pruning after training while also allowing smooth gradient transitions between neighboring voxels. An exhaustive evaluation demonstrates that our method achieves real-time performance and quality metrics on a pair with state-of-the-art with an improvement of more than 2× in the FPS/MB ratio.
Quantitative Manipulation of Custom Attributes on 3D-Aware Image Synthesis

While 3D-based GAN techniques have been successfully applied to render photo-realistic 3D images with a variety of attributes while preserving view consistency, there has been little research on how to fine-control 3D images without limiting to a specific category of objects of their properties. To fill such research gap, we propose a novel image manipulation model of 3D-based GAN representations for a fine-grained control of specific custom attributes. By extending the latest 3D-based GAN models (e.g., EG3D), our user-friendly quantitative manipulation model enables a fine yet normalized control of 3D manipulation of multi-attribute quantities while achieving view consistency. We validate the effectiveness of our proposed technique both qualitatively and quantitatively through various experiments.
Deep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model

Semantic editing of images is the fundamental goal of computer vision. Although deep learning methods, such as generative adversarial networks (GANs), are capable of producing high-quality images, they often do not have an inherent way of editing generated images semantically. Recent studies have investigated a way of manipulating the latent variable to determine the images to be generated. However, methods that assume linear semantic arithmetic have certain limitations in terms of the quality of image editing, whereas methods that discover nonlinear semantic pathways provide non-commutative editing, which is inconsistent when applied in different orders. This study proposes a novel method called deep curvilinear editing (DeCurvEd) to determine semantic commuting vector fields on the latent space. We theoretically demonstrate that owing to commutativity, the editing of multiple attributes depends only on the quantities and not on the order. Furthermore, we experimentally demonstrate that compared to previous methods, the nonlinear and commutative nature of DeCurvEdfacilitates the disentanglement of image attributes and provides higher-quality editing.
Grit Influencing Factors in taekwondo College Athlete: Focusing on Goal Perception, Perceptions of Error, Failure Mindset

The purpose of this study is to explore the causal relationship of Goal Perception, Perceptions of Error, Failure Mindset and Grit. To this end, 388 taekwondo players from University in Seoul, Gyeonggi, Incheon, Chungbuk, and Jeonbuk universities were selected as a sample and using Spss 23 and Amos 23, frequency analysis, skewness, kurtosis, correlation analysis, reliability analysis, CFA factor analysis, multiple regression analysis results were verified. As a result of this study, First, Goal Perception had a significant effect on Grit. Second, Perceptions of Error had a significant effect on Grit. Third, Failure Mindset had a significant effect on Grit. Based on the above results, it will serve as an academic basis for recognizing the importance of positive perception of failure and improving grit by increasing Goal Perception of university taekwondo players.