Miracle-dz/UniFAR-FTUs huggingface dataset:
CRAB: Class Representation Attentive BERT for Hate Speech Identification in Social Media

In recent years, social media platforms have hosted an explosion of hate speech and objectionable content. The urgent need for effective automatic hate speech detection models have drawn remarkable investment from companies and researchers. Social media posts are generally short and their semantics could drastically be altered by even a single token. Thus, it is crucial for this task to learn context-aware input representations, and consider relevancy scores between input embeddings and class representations as an additional signal. To accommodate these needs, this paper introduces CRAB (Class Representation Attentive BERT), a neural model for detecting hate speech in social media. The model benefits from two semantic representations: (i) trainable token-wise and sentence-wise class representations, and (ii) contextualized input embeddings from state-of-the-art BERT encoder. To investigate effectiveness of CRAB, we train our model on Twitter data and compare it against strong baselines. Our results show that CRAB achieves 1.89% relative improved Macro-averaged F1 over state-of-the-art baseline. The results of this research open an opportunity for the future research on automated abusive behavior detection in social media
What can your body tell about your flow state? Dimensionality and heart rate variability analyses of flow

Flow has been widely studied in different disciplines and contexts; but there is a lack of consensus about its dimensionality, measurement and structure. This research presents two studies to better understand the state of flow. On the one hand, the first study tests the dimensionality of flow, through an online survey with a sample of 771 Spanish social commerce users. This study empirically analyzes the state of flow using the statistical software EQS 6, considering that in social commerce the flow state is reached if enjoyment during the purchasing process, concentration on the action being performed and temporal distortion are presented together. On the other hand, the second study, conducted in Japan, presents a pilot experiment focused on studying the experience of flow among individuals within different tasks. The specific objectives of this second study in progress are, firstly, to test how different levels of interactivity affect the state of flow in order to study whether there are some common patterns on heart rate variability when experiencing flow according to the LF/HF (the index of sympathetic activity); and, secondly, to study how the flow state can be experienced or not in various everyday life experiences. The findings will allow companies to know what factors are needed to generate a state that leads users to repeat the experience.
Decoding Aging: Understanding the Complex Relationship among Aging, Free Radicals, and GSH

N-aryl maleimides can undergo a 1,4-Michael-type addition reaction with reduced glutathione (GSH), leading to a decreased concentration of GSH and an increased concentration of free radicals (FRs) in cells. GSH is a critical scavenging molecule responsible for protecting cells from oxidation and for maintaining redox homeostasis. N-aryl maleimides disturb redox homeostasis in cells because they scavenge thiol-containing molecules, especially GSH. This study aimed at measuring the concentrations of GSH and FRs by electronic paramagnetic resonance (EPR), in the brain and liver tissue of male Wistar rats (ex vivo) at different ages and after treatment with 3,5-dimaleimylbenzoic acid (3,5-DMB). Our results showed a relationship between age and the concentrations of GSH and FRs in cells. In young rats, the concentration of GSH was higher than in old rats, while the concentration of FRs was higher in adult rats than in young rats, suggesting an inverse relationship between GSH and FRs. On the other hand, the reaction of 3,5-DMB (an electrophilic maleimide) with cellular GSH increased the FR content. The results of this study contribute to the awareness that the process of aging implies not only a loss of tissue function but also essential changes in the molecular contents of cells, especially the concentrations of FRs and GSH.
Gestop: Customizable Gesture Control of Computer Systems

The established way of interfacing with most computer systems is a mouse and keyboard. Hand gestures are an intuitive and effective touchless way to interact with computer systems. However, hand gesture based systems have seen low adoption among end-users primarily due to numerous technical hurdles in detecting in-air gestures accurately. This paper presents Gestop, a framework developed to bridge this gap. The framework learns to detect gestures from demonstrations, is customizable by end-users and enables users to interact in real-time with computers having only RGB cameras, using gestures.
The motion of respiratory droplets produced by coughing

Coronavirus disease 2019 has become a global pandemic infectious respiratory disease with high mortality and infectiousness. This paper investigates respiratory droplet transmission, which is critical to understanding, modeling, and controlling epidemics. In the present work, we implemented flow visualization, particle image velocimetry, and particle shadow tracking velocimetry to measure the velocity of the airflow and droplets involved in coughing and then constructed a physical model considering the evaporation effect to predict the motion of droplets under different weather conditions. The experimental results indicate that the convection velocity of cough airflow presents the relationship t−0.7 with time; hence, the distance from the cougher increases by t0.3 in the range of our measurement domain. Substituting these experimental results into the physical model reveals that small droplets (initial diameter D ≤ 100 μm) evaporate to droplet nuclei and that large droplets with D ≥ 500 μm and an initial velocity u0 ≥ 5 m/s travel more than 2 m. Winter conditions of low temperature and high relative humidity can cause more droplets to settle to the ground, which may be a possible driver of a second pandemic wave in the autumn and winter seasons.
Dispersive Divergence-Free Vector Meshless Method for Time-Domain Analysis of Frequency-Dependent Media

The dispersive meshless method with scalar basis function has been successfully applied for analysis of frequencydependent media. However, as scalar-based meshless methods are not always divergence-free in the absence of source, inaccurate or even wrong solutions may be found in the results. To overcome this problem, in this paper, a new dispersive formulation of meshless method with vector basis function is proposed for analysis of dispersive materials. The divergencefree property of this method improves the accuracy of simulation by eliminating the artificial charges generated due to the numerical analysis. Moreover, the frequency behavior of the dispersive medium is modeled using auxiliary-differentialequations (ADE) method. The efficiency and divergence-free property of the proposed method are verified and compared with dispersive meshless method with scalar basis function by a numerical example.
A First Pilot Study to Compare Virtual Group Meetings using Video Conferences and (Immersive) Virtual Reality

Face-to-face communication has evolved as most natural means for communication. However, virtual group meetings have received considerable attention as an alternative for allowing multiple persons to communicate over distance, e. g. , via video conferences or immersive virtual reality (VR) systems, but they incur numerous limitations and challenges. In particular, they often hinder spatial perception of full-body language, deictic relations, or eye-to-eye contact. The differences between video conferences and immersive VR meetings still remain poorly understood. We report about a pilot study in which we compared virtual group meetings using video conferences and VR meetings with and without head-mounted displays (HMDs). The results suggest that participants feel higher sense of presence when using an immersive VR meeting, but only if an HMD is used. Usability of video conferences as well as immersive VR is acceptable, whereas non-immersive VR without HMD was not acceptable.
Modeling and Simulation of a Point to Point Spherical Articulated Manipulator Using Optimal Control

This paper aims to design an optimal stability controller for a point to point trajectory tracking 3 degree of freedom (DoF) articulated manipulator. The Denavit Hartenberg (DH) convention is used to obtain the forward and inverse kinematics of the manipulator. The manipulator dynamics are formulated using the Lagrange Euler (LE) method to obtain a nonlinear system. The complicated nonlinear equations obtained are then linearized in order to implement the optimal linear quadratic regulator (LQR). The simulations are performed in MATLAB and Simulink and the optimal controller's performance is tested for various conditions and the results are presented. The results obtained prove the superiority of LQR over conventional PID control.
Medicinal Effect, In Silico Bioactivity Prediction, and Pharmaceutical Formulation of Ageratum conyzoides L.: A Review

Goat weed (Ageratum conyzoides L.), or bandotan in Indonesia, is an herbaceous plant that broadly grows up in both subtropical as well as tropical areas. This herb contains many phytoconstituents which have many benefits in different aspects. The essential oil contains phytochemicals such as phenol, phenolic ester, and coumarin, whereas many compounds can been identified in the whole part such as terpenoid, steroid, chromene, pyrrolizidine alkaloid, and flavonoid. Empirically, this herb has been used as an antihemorrhagic, antiseptic, antileprosy, and wound-healing agent. This article reviews the potency of the herb in medication according to the chemical substances being deposited, which are collected from numerous studies, followed by its in silico bioactivity prediction as well as its pharmaceutical dosage form formulation.
Subdivision Surfaces for use in Cut Finite Element Methods

The finite element method has for some time been a popular choice for approximating PDEs over a geometry domain. We usually use a FEM mesh that matches the geometry description. By FEM mesh we mean the mesh where we find the solution to the PDE. In order to use finite element method where the geometry description does not match the FEM mesh, we need a technique to modify the FEM around the geometry description. An essential part of such techniques is to find the intersection points between the geometry description and the FEM mesh. We say that FEM mesh is cut when the geometry description does not match the FEM mesh, this is also referred to as CutFEM. In this thesis, we have contributed to a suitable algorithm for finding intersection points between the geometry description and the FEM mesh. We base our algorithm on Newton’s method to detect these intersection points. The results show that the intersection points are very accurate for regular triangles. The intersection points are found directly on the limit surface of Loop’s subdivision surfaces.
Constraint Translation Candidates: A Bridge between Neural Query Translation and Cross-lingual Information Retrieval

Query translation (QT) is a key component in cross-lingual information retrieval system (CLIR). With the help of deep learning, neural machine translation (NMT) has shown promising results on various tasks. However, NMT is generally trained with large-scale out-of-domain data rather than in-domain query translation pairs. Besides, the translation model lacks a mechanism at the inference time to guarantee the generated words to match the search index. The two shortages of QT result in readable texts for human but inadequate candidates for the downstream retrieval task. In this paper, we propose a novel approach to alleviate these problems by limiting the open target vocabulary search space of QT to a set of important words mined from search index database. The constraint translation candidates are employed at both of training and inference time, thus guiding the translation model to learn and generate well performing target queries. The proposed methods are exploited and examined in a real-word CLIR system--Aliexpress e-Commerce search engine. Experimental results demonstrate that our approach yields better performance on both translation quality and retrieval accuracy than the strong NMT baseline.
Clustering Contextualized Representations of Text for Unsupervised Syntax Induction

We explore clustering of contextualized text representations for two unsupervised syntax induction tasks: part of speech induction (POSI) and constituency labelling (CoLab). We propose a deep embedded clustering approach which jointly transforms these representations into a lower dimension cluster friendly space and clusters them. We further enhance these representations by augmenting them with task-specific representations. We also explore the effectiveness of multilingual representations for different tasks and languages. With this work, we establish the first strong baselines for unsupervised syntax induction using contextualized text representations. We report competitive performance on 45-tag POSI, state-of-the-art performance on 12-tag POSI across 10 languages, and competitive results on CoLab.
Position and Rotation Invariant Sign Language Recognition from 3D Point Cloud Data with Recurrent Neural Networks

Sign language is a gesture based symbolic communication medium among speech and hearing impaired people. It also serves as a communication bridge between non-impaired population and impaired population. Unfortunately, in most situations a non-impaired person is not well conversant in such symbolic languages which restricts natural information flow between these two categories of population. Therefore, an automated translation mechanism can be greatly useful that can seamlessly translate sign language into natural language. In this paper, we attempt to perform recognition on 30 basic Indian sign gestures. Gestures are represented as temporal sequences of 3D depth maps each consisting of 3D coordinates of 20 body joints. A recurrent neural network (RNN) is employed as classifier. To improve performance of the classifier, we use geometric transformation for alignment correction of depth frames. In our experiments the model achieves 84.81% accuracy.
Learning to Recognize Dialect Features

Building NLP systems that serve everyone requires accounting for dialect differences. But dialects are not monolithic entities: rather, distinctions between and within dialects are captured by the presence, absence, and frequency of dozens of dialect features in speech and text, such as the deletion of the copula in “He ∅ running”. In this paper, we introduce the task of dialect feature detection, and present two multitask learning approaches, both based on pretrained transformers. For most dialects, large-scale annotated corpora for these features are unavailable, making it difficult to train recognizers. We train our models on a small number of minimal pairs, building on how linguists typically define dialect features. Evaluation on a test set of 22 dialect features of Indian English demonstrates that these models learn to recognize many features with high accuracy, and that a few minimal pairs can be as effective for training as thousands of labeled examples. We also demonstrate the downstream applicability of dialect feature detection both as a measure of dialect density and as a dialect classifier.
Feline procedural sedation and analgesia: When, why and how

Practical relevance: Procedural sedation and analgesia (PSA) describes the process of depressing a patient’s conscious state to perform unpleasant, minimally invasive procedures, and is part of the daily routine in feline medicine. Maintaining cardiopulmonary stability is critical while peforming PSA. Clinical challenges: Decision-making with respect to drug choice and dosage regimen, taking into consideration the cat’s health status, behavior, any concomitant diseases and the need for analgesia, represents an everyday challenge in feline practice. While PSA is commonly perceived to be an uneventful procedure, complications may arise, especially when cats that were meant to be sedated are actually anesthetized. Aims: This clinical article reviews key aspects of PSA in cats while exploring the literature and discussing complications and risk factors. Recommendations are given for patient assessment and preparation, clinical monitoring and fasting protocols, and there is discussion of how PSA protocols may change blood results and diagnostic tests. An overview of, and rationale for, building a PSA protocol, and the advantages and disadvantages of different classes of sedatives and anesthetics, is presented in a clinical context. Finally, injectable drug protocols are reported, supported by an evidence-based approach and clinical experience.
Interpreting Uncertainty in Model Predictions for Covid-19 Diagnosis

COVID-19, due to its accelerated spread has brought in the need to use assistive tools for faster diagnosis in addition to typical lab swab testing. Chest X-Rays for COVID cases tend to show changes in the lungs such as ground glass opacities and peripheral consolidations which can be detected by deep neural networks. However, traditional convolutional networks use point estimate for predictions, lacking in capture of uncertainty, which makes them less reliable for adoption. There have been several works so far in predicting COVID positive cases with chest X-Rays. However, not much has been explored on quantifying the uncertainty of these predictions, interpreting uncertainty, and decomposing this to model or data uncertainty. To address these needs, we develop a visualization framework to address interpretability of uncertainty and its components, with uncertainty in predictions computed with a Bayesian Convolutional Neural Network. This framework aims to understand the contribution of individual features in the Chest-X-Ray images to predictive uncertainty. Providing this as an assistive tool can help the radiologist understand why the model came up with a prediction and whether the regions of interest captured by the model for the specific prediction are of significance in diagnosis. We demonstrate the usefulness of the tool in chest x-ray interpretation through several test cases from a benchmark dataset.
Tractable contextual bandits beyond realizability

Tractable contextual bandit algorithms often rely on the realizability assumption -- i.e., that the true expected reward model belongs to a known class, such as linear functions. We investigate issues that arise in the absence of realizability and note that the dynamics of adaptive data collection can lead commonly used bandit algorithms to learn a suboptimal policy. In this work, we present a tractable bandit algorithm that is not sensitive to the realizability assumption and computationally reduces to solving a constrained regression problem in every epoch. When realizability does not hold, our algorithm ensures the same guarantees on regret achieved by realizability-based algorithms under realizability, up to an additive term that accounts for the misspecification error. This extra term is proportional to T times the (2/5)-root of the mean squared error between the best model in the class and the true model, where T is the total number of time-steps. Our work sheds light on the bias-variance trade-off for tractable contextual bandits. This trade-off is not captured by algorithms that assume realizability, since under this assumption there exists an estimator in the class that attains zero bias.
Fields of definition of abelian subvarieties

In this paper we study the field of definition of abelian subvarieties $B\subset A_{\overline{K}}$ for an abelian variety $A$ over a field $K$ of characteristic $0$. We show that, provided that no isotypic component of $A_{\overline{K}}$ is simple, there are infinitely many abelian subvarieties of $A_{\overline{K}}$ with field of definition $K_A$, the field of definition of the endomorphisms of $A_{\overline{K}}$. This result combined with earlier work of R\'emond gives an explicit maximum for the minimal degree of a field extension over which an abelian subvariety of $A_{\overline{K}}$ is defined with varying $A$ of fixed dimension and $K$ of characteristic $0$.
Ping pong for health: the meaning of space in a sport based health intervention at the workplace

ABSTRACT Purpose: This is a study on a sport-based intervention, with a focus on physical activity, social relations, and learning, to promote health and well-being in the workplace lived space. Lived space is situated and associated with social and cultural conventions which affect the quality of the perceived space at work. The aim of the paper is to elucidate the participant’s experiences of the intervention and how health and well-being were affected. Methods: The intervention was conducted with employees from the warehouse of a company within the retail sector. The design consisted of one initial workshop as a baseline, a sport-based intervention, three group interviews, and a final workshop. A hermeneutic phenomenological analysis focused on experiences of the intervention and the meaning of the workplace as the lived space. Results: Three themes emerged in the analysis; Expressing positive individual effects, Expressing improved work environment and The meaning of the workplace as lived space. The themes are discussed in relation to three basic health foci: physical activity, social relations and learning. Conclusion: The workplace as a lived space offers a valuable opportunity for sport-based interventions that improve health and well-being through physical activity, social relations, and learning.
Towards model-based development of decentralised peer-to-peer data vaults

Using centralised data storage systems has been the standard practice followed by online service providers when managing the personal data of their users. This method requires users to trust these providers and, to some extent, users are not in full control over their data. The development of applications around decentralised data vaults, i.e., encrypted storage systems located in user-managed devices, can give this control back to the users as sole owners of the data. However, the development of such applications is not effort-free, and it requires developers to have specialised knowledge, such as how to deploy secure and peer-to-peer communication systems. We present Vaultage, a model-based framework that can simplify the development of data vault applications. We demonstrate its core features through a social network application case study and include some initial evaluation results, showing Vaultage's code generation capabilities and some profiling analysis of the generated network components.
Lyapunov-Based Reinforcement Learning State Estimator

In this paper, we consider the state estimation problem for nonlinear stochastic discrete-time systems. We combine Lyapunov's method in control theory and deep reinforcement learning to design the state estimator. We theoretically prove the convergence of the bounded estimate error solely using the data simulated from the model. An actor-critic reinforcement learning algorithm is proposed to learn the state estimator approximated by a deep neural network. The convergence of the algorithm is analysed. The proposed Lyapunov-based reinforcement learning state estimator is compared with a number of existing nonlinear filtering methods through Monte Carlo simulations, showing its advantage in terms of estimate convergence even under some system uncertainties such as covariance shift in system noise and randomly missing measurements. To the best of our knowledge, this is the first reinforcement learning based nonlinear state estimator with bounded estimate error performance guarantee.