Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Method to Improve Running Economy and Maximal Aerobic Power in Athletes: Endurance Training With Periodic Carbon Monoxide Inhalation

Background Altitude training stimulates erythropoietin hormone (EPO) release and increases blood hemoglobin (Hb) mass, which may result in improved oxygen (O2) transport capacity. It was hypothesized in the present study that periodic inhalation of carbon monoxide (CO) might elicit similar physiological adaptations compared to altitude training. Methods Twelve male college student athletes, who were well-trained soccer players, participated. They performed a 4-week treadmill-training program, five times a week. Participants were randomly assigned into an experimental group with inhaling CO (INCO) (1 mL/kg body weight for 2 min) in O2 (4 L) before all training sessions and a control group without inhaling CO (NOCO). CO and EPO concentrations in venous blood were first measured acutely at the 1st, 2nd, 4th, 6th, and 8th hour after INCO, and total hemoglobin mass (tHb), running economy and VO2max were measured before and after the 4 weeks training intervention. Results HbCO% increased from 0.7 to 4.4% (P < 0.05) after 1 h of CO inhalation and EPO increased from 1.9 to 2.7 mIU/mL after 4 h post CO inhalation (P < 0.05) acutely before the intervention. After the training, the tHb and VO2max in the INCO group increased significantly by 3.7 and 2.7%, respectively, while no significant differences were observed in the NOCO condition. O2 uptake at given submaximal speeds declined by approximately 4% in the INCO group. Conclusion Acutely, EPO increased sharply post CO inhalation, peaking at 4 h post inhalation. 4-weeks of training with CO inhalation before exercise sessions improved tHb and VO2max as well as running economy, suggesting that moderate CO inhalation could be a new method to improve the endurance performance in athletes.
Coach Turnover in Top Professional Brazilian Football Championship: A Multilevel Survival Analysis

In this study, we examined the probability of coaches’ survival in the top Brazilian professional football championship considering variation across the competitive seasons between 2012 and 2017, considering a multilevel framework. We also considered whether previous coaching experience in the top Brazilian professional football championship would change the probability of coaches’ survival across the season. The data considered 4,560 games from the top professional Brazilian football league (Campeonato Brasileiro Série A) between the 2012 and 2017 seasons. At the start of each season, the coach from each team was followed, being recorded at the time the event occurred, i.e., the coach being sacked. A total survival of 120 coaches was considered between the seasons of 2012 and 2017, i.e., 20 coaches at the beginning of each season. Coaches were assigned as novice (no previous experience as head coach in the top Brazilian championship) or experienced (with at least some previous experience as head coach in the top Brazilian championship). Data were available and extracted from the official website of the Brazilian Football Confederation1. On average and considering un-pooled observations, the median life of a coach was about 16.5 rounds. Considering variation between 2012 and 2017 seasons, only about 26.3% (95% CI: 18.2–36.1) of the coaches ended a season without being sacked. By mid-season, at round 19, the probability of coaches’ survival was 0.42 (95% CI: 0.32–0.53). Variation between season on survival estimates per round was substantial (between-season standard deviation = 0.48, 95% credible intervals: 0.25–0.95; corresponding to an inverse logit = 0.62, 95% CI: 0.56–0.72). There was no substantial variation between novice and experienced coaches’ survival probability. The present results expose the vulnerability of the coaching context in Brazilian football, potentially highlighting an excessive emphasis on short-term results to mediate club management decisions.
Validation of Application SuperDuplicates (AS) Enumeration Tool for Free-Roaming Dogs (FRD) in Urban Settings of Panchkula Municipal Corporation in North India

A cost-effective estimation of the number of free-roaming dogs is an essential prerequisite for the control of rabies in countries where the disease is endemic, as vaccination of at least 70% of the population is recommended to effectively control the disease. Although estimating the population size through sight-resight based maximum likelihood methodology generates an estimate closest to the actual size, it requires at least five survey efforts to achieve this. In a rural setting in India, a reliable estimate of at least 70% of the likely true population of free-roaming dogs was obtained with the Application SuperDuplicates shinyapp online tool using a photographic sight-resight technique through just two surveys. We tested the wider applicability of this method by validating its use in urban settings in India. Sight-resight surveys of free-roaming dogs were conducted in 15 sectors of the Panchkula Municipal Corporation in north India during September- October 2016. A total of 1,408 unique dogs were identified through 3,465 sightings on 14 survey tracks. The estimates obtained by the Application SuperDuplicates shinyapp online tool after two surveys were compared with the maximum likelihood estimates and it was found that the former, after two surveys, provided an estimate that was at least 70% of that obtained by the latter after 5–6 surveys. Thus, the Application SuperDuplicates shinyapp online tool provides an efficient means for estimating the minimum number of free-roaming dogs to vaccinate with a considerably lower effort than the traditional mark-resight based methods. We recommend use of this tool for estimating the vaccination target of free-roaming dogs prior to undertaking mass vaccination efforts against rabies.
The Association Between Disgust Sensitivity and Negative Attitudes Toward Homosexuality: The Mediating Role of Moral Foundations

Previous studies have found that “disgust-sensitive” individuals have negative attitudes toward gay and lesbian people, but the underlying mechanisms for such attitudes remain unclear. Based on moral foundations theory, the current paper assumes that the relationship between disgust sensitivity and attitudes toward homosexuality are mediated by moral foundations. In order to test this assumption, the current authors examined the questionnaire answers from a total of 452 Chinese undergraduates who participated in this study. The results showed that disgust sensitivity was positively correlated with negative attitudes toward homosexuality, and positively correlated with moral concerns in five domains (care, fairness, loyalty, authority, and sanctity). Authority and sanctity were both associated with attitudes toward homosexuality, while only sanctity mediated the relationship between disgust sensitivity and attitudes toward homosexuality. Overall, the results suggest that considering moral foundations (especially sanctity) may lend more insight to the associations between disgust sensitivity and negative attitudes toward gay and lesbian people.
The Cost-Based Lean Approach to the Information Logistics Business System Modelling

The paper highlights the problems of modelling the complex logistics manufacturing system which is to meet the needs of the contemporary lean approach to manufacturing. It is assumed that the system consists of identically arranged and equipped subsystems, however, each of them is characterised by different operating and fixed costs. The software engineering method used to create the simulator of the discussed system is emphasised to show how the necessary assumptions turn the real manufacturing system into the mathematical model of the logistic-business system, and subsequently into the simulator. Manufacturing strategies as well as heuristic algorithms are introduced in order to control making the order. The case study presents the method of searching for the most adequate manufacturing subsystem for making the order on the basis of cost calculation. The key points responsible for excessive costs are detected. A thorough analysis based on modified operating as well as fixed costs is carried out.
NH-MADM Strategy in Neutrosophic Hesitant Fuzzy Set Environment Based on Extended GRA

Pranab BISWAS, Surapati PRAMANIK, Bibhas C. GIRI 1Department of Mathematics, Jadavpur University, Kolkata–700032, West Bengal, India 2Department of Mathematics, Nandalal Ghosh B.T College, Panpur, Narayanpur 743126, West Bengal, India 3Department of Mathematics, Jadavpur University, Kolkata–700032, West Bengal, India e-mail: prabiswas.jdvu@gmail.com, paldam2010@gmail.com, surapati.math@gmail.com, bcgiri.jumath@gmail.com
Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs

Text classification models have been heavily utilized for a slew of interesting natural language processing problems. Like any other machine learning model, these classifiers are very dependent on the size and quality of the training dataset. Insufficient and imbalanced datasets will lead to poor performance. An interesting solution to poor datasets is to take advantage of the world knowledge in the form of knowledge graphs to improve our training data. In this paper, we use ConceptNet and Wikidata to improve sexist tweet classification by two methods (1) text augmentation and (2) text generation. In our text generation approach, we generate new tweets by replacing words using data acquired from ConceptNet relations in order to increase the size of our training set, this method is very helpful with frustratingly small datasets, preserves the label and increases diversity. In our text augmentation approach, the number of tweets remains the same but their words are augmented (concatenation) with words extracted from their ConceptNet relations and their description extracted from Wikidata. In our text augmentation approach, the number of tweets in each class remains the same but the range of each tweet increases. Our experiments show that our approach improves sexist tweet classification significantly in our entire machine learning models. Our approach can be readily applied to any other small dataset size like hate speech or abusive language and text classification problem using any machine learning model.
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change

State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.
A Non-Uniform Tuning Method for SQL-on-Hadoop Systems

A SQL-on-Hadoop query consists of a workflow of MapReduce jobs with a single point of configuration. This means that the developer tunes hundreds of tuning parameters directly in the query source code (or via terminal interface), but the system assumes the same configuration to every running job. The lurking problem is that the system allocates computing resources uniformly to every running job, even if they present different consumption needs (after all the tuning setup is the same). In this paper, we demonstrate that such uniform allocation of resources drives the query to inefficient performance. We claim that applying a non-uniform allocation method to define a customized tuning setup for each job can outperform the uniform allocation. Ultimately, we demonstrate that searching for specific tuning setup is an optimization problem with polynomial cost.
Some New Operations Over Fermatean Fuzzy Numbers and Application of Fermatean Fuzzy WPM in Multiple Criteria Decision Making

Fermatean fuzzy sets (FFSs), proposed by Senapati and Yager (2019a), can handle uncertain information more easily in the process of decision making. They deﬁned basic operations over the Fermatean fuzzy sets. Here we shall introduce three new operations: subtraction, division, and Fermatean arithmetic mean operations over Fermatean fuzzy sets. We discuss their properties in details. Later, we develop a Fermatean fuzzy weighted product model to solve the multi-criteria decision-making problem. Finally, an illustrative example of selecting a suitable bridge construction method is given to verify the approach developed by us and to demonstrate its practicability and eﬀectiveness.
Detecting Syntactic Change Using a Neural Part-of-Speech Tagger

We train a diachronic long short-term memory (LSTM) part-of-speech tagger on a large corpus of American English from the 19th, 20th, and 21st centuries. We analyze the tagger’s ability to implicitly learn temporal structure between years, and the extent to which this knowledge can be transferred to date new sentences. The learned year embeddings show a strong linear correlation between their first principal component and time. We show that temporal information encoded in the model can be used to predict novel sentences’ years of composition relatively well. Comparisons to a feedforward baseline suggest that the temporal change learned by the LSTM is syntactic rather than purely lexical. Thus, our results suggest that our tagger is implicitly learning to model syntactic change in American English over the course of the 19th, 20th, and early 21st centuries.
Infant Contact-less Non-Nutritive Sucking Pattern Quantification via Facial Gesture Analysis

Non-nutritive sucking (NNS) is defined as the sucking action that occurs when a finger, pacifier, or other object is placed in the baby's mouth, but there is no nutrient delivered. In addition to providing a sense of safety, NNS even can be regarded as an indicator of infant's central nervous system development. The rich data, such as sucking frequency, the number of cycles, and their amplitude during baby's non-nutritive sucking is important clue for judging the brain development of infants or preterm infants. Nowadays most researchers are collecting NNS data by using some contact devices such as pressure transducers. However, such invasive contact will have a direct impact on the baby's natural sucking behavior, resulting in significant distortion in the collected data. Therefore, we propose a novel contact-less NNS data acquisition and quantification scheme, which leverages the facial landmarks tracking technology to extract the movement signals of baby's jaw from recorded baby's sucking video. Since completion of the sucking action requires a large amount of synchronous coordination and neural integration of the facial muscles and the cranial nerves, the facial muscle movement signals accompanying baby's sucking pacifier can indirectly replace the NNS signal. We have evaluated our method on videos collected from several infants during their NNS behaviors and we have achieved the quantified NNS patterns closely comparable to results from visual inspection as well as contact-based sensor readings.
Investigating the Lombard Effect Influence on End-to-End Audio-Visual Speech Recognition

Several audio-visual speech recognition models have been recently proposed which aim to improve the robustness over audio-only models in the presence of noise. However, almost all of them ignore the impact of the Lombard effect, i.e., the change in speaking style in noisy environments which aims to make speech more intelligible and affects both the acoustic characteristics of speech and the lip movements. In this paper, we investigate the impact of the Lombard effect in audio-visual speech recognition. To the best of our knowledge, this is the first work which does so using end-to-end deep architectures and presents results on unseen speakers. Our results show that properly modelling Lombard speech is always beneficial. Even if a relatively small amount of Lombard speech is added to the training set then the performance in a real scenario, where noisy Lombard speech is present, can be significantly improved. We also show that the standard approach followed in the literature, where a model is trained and tested on noisy plain speech, provides a correct estimate of the video-only performance and slightly underestimates the audio-visual performance. In case of audio-only approaches, performance is overestimated for SNRs higher than -3dB and underestimated for lower SNRs.
Combining Physics-Based Domain Knowledge and Machine Learning using Variational Gaussian Processes with Explicit Linear Prior

Centuries of development in natural sciences and mathematical modeling provide valuable domain expert knowledge that has yet to be explored for the development of machine learning models. When modeling complex physical systems, both domain knowledge and data contribute important information about the system. In this paper, we present a data-driven model that takes advantage of partial domain knowledge in order to improve generalization and interpretability. The presented model, which we call EVGP (Explicit Variational Gaussian Process), uses an explicit linear prior to incorporate partial domain knowledge while using data to fill in the gaps in knowledge. Variational inference was used to obtain a sparse approximation that scales well to large datasets. The advantages include: 1) using partial domain knowledge to improve inductive bias (assumptions of the model), 2) scalability to large datasets, 3) improved interpretability. We show how the EVGP model can be used to learn system dynamics using basic Newtonian mechanics as prior knowledge. We demonstrate that using simple priors from partially defined physics models considerably improves performance when compared to fully data-driven models.
Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference

Dialogue contexts are proven helpful in the spoken language understanding (SLU) system and they are typically encoded with explicit memory representations. However, most of the previous models learn the context memory with only one objective to maximizing the SLU performance, leaving the context memory under-exploited. In this paper, we propose a new dialogue logistic inference (DLI) task to consolidate the context memory jointly with SLU in the multi-task framework. DLI is defined as sorting a shuffled dialogue session into its original logical order and shares the same memory encoder and retrieval mechanism as the SLU model. Our experimental results show that various popular contextual SLU models can benefit from our approach, and improvements are quite impressive, especially in slot filling.
Monopulse-based THz Beam Tracking for Indoor Virtual Reality Applications

Terahertz spectrum is being researched upon to provide ultra-high throughput radio links for indoor applications, e.g., virtual reality (VR), etc. as well as outdoor applications, e.g., backhaul links, etc. This paper investigates a monopulse-based beam tracking approach for limited mobility users relying on sparse massive multiple input multiple output (MIMO) wireless channels. Owing to the sparsity, beamforming is realized using digitally-controlled radio frequency (RF) / intermediate-frequency (IF) phase shifters with constant amplitude constraint for transmit power compliance. A monopulse-based beam tracking technique, using received signal strength indi-cation (RSSI) is adopted to avoid feedback overheads for obvious reasons of efficacy and resource savings. The Matlab implementation of the beam tracking algorithm is also reported. This Matlab implementation has been kept as general purpose as possible using functions wherein the channel, beamforming codebooks, monopulse comparator, etc. can easily be updated for specific requirements and with minimum code amendments.
An Optimization Framework for Resilient Batch Estimation in Cyber-Physical Systems

This article proposes a class of resilient state estimators for linear time-varying discrete-time systems. The dynamic equation of the system is assumed to be affected by a bounded process noise. As to the available measurements, they are potentially corrupted by a noise of both dense and impulsive natures. The latter, in addition to being arbitrary in its form, need not be strictly bounded. In this setting, we construct the estimator as the set-valued map, which associates with the measurements the minimizing set of some appropriate performance functions. We consider a family of such performance functions, each of which yielding a specific instance of the proposed general estimation framework. It is then shown that the proposed class of estimators enjoys the property of resilience, i.e., it induces an estimation error, which, under certain conditions, is independent of the extreme values of the (impulsive) measurement noise. Hence, the estimation error may be bounded, while the measurement noise is virtually unbounded. Moreover, we provide several error bounds (in different configurations), whose expressions depend explicitly on the degree of observability of the system being observed and on the considered performance function. Finally, a few simulation results are provided to illustrate the resilience property.
A Sustainable Multi-Modal Multi-Layer Emotion-Aware Service at the Edge

Limited by the computational capabilities and battery energy of terminal devices and network bandwidth, emotion recognition tasks fail to achieve good interactive experience for users. The intolerable latency for users also seriously restricts the popularization of emotion recognition applications in the edge environments such as fatigue detection in auto-driving. The development of edge computing provides a more sustainable solution for this problem. Based on edge computing, this article proposes a multi-modal multi-layer emotion-aware service (MULTI-EASE) architecture that considers user’s facial expression and voice as a multi-modal data source of emotion recognition, and employs the intelligent terminal, edge server and cloud as multi-layer execution environment. By analyzing the average delay of each task and the average energy consumption at the mobile device, we formulate a delay-constrained energy minimization problem and perform a task scheduling policy between multiple layers to reduce the end-to-end delay and energy consumption by using an edge-based approach, further to improve the users’ emotion interactive experience and achieve energy saving in edge computing. Finally, a prototype system is also implemented to validate the architecture of MULTI-EASE, the experimental results show that MULTI-EASE is a sustainable and efficient platform for emotion analysis applications, and also provide a valuable reference for dynamic task scheduling under MULTI-EASE architecture.