Miracle-dz/UniFAR-FTUs huggingface dataset:
Cultural evolution of precise and consensual semantic conventions in a multiplayer gaming app

The amount of information conveyed by linguistic conventions depends on their precision, yet the codes that humans and other animals use to communicate are quite ambiguous: they may map several vague meanings to the same symbol. How does semantic precision evolve, and what are the constraints that limit it? We studied the evolution of semantic conventions, in the form of variable mappings between symbols and their referents, in a multiplayer gaming app built around a referential communication game, where a sender had to indicate a colour to a receiver, using only black and white symbols. We expected that the players’ mappings between symbols and colours would grow more specific over time, precision being measured as sense entropy, through a selection process whereby precise mappings are preferentially copied. We found that players become increasingly more precise in their use of symbols over the course of their careers. This trend did not, however, result from selective copying of precise mappings. We explore the implications of this result for the study of lexical ambiguity, Zipf’s Law of Meaning, and disagreements over semantic conventions.
Disruption in Economics Learning through WhatsApp Group during Covid-19 Pandemic

The word ‘Disruption’ becomes more popular along with Industrial Revolution 4.0. This word appears in various aspects of life: socioeconomic, financial, transportation, and even educational aspects. This change called Disruption is challenging to some countries. But for some others, it can also help facilitate the society in facing various phenomena, for example, the current issues covid-19 pandemic that happens all over the world. The aim of this study is to describe how economics learning can be done at a distance during the pandemic, using WhatsApp Group media, especially in Indonesia. Many teachers, students, lecturers, and college students use this media to keep the learning going during home-learning phenomenon during this pandemic time. The methods used in this study is literature review, by collecting various theory, books, journals and data related to the use of WhatsApp in teaching and learning. The results show that, WhatsApp Group helps the communication between students and teachers, especially when learning takes place from different location, but it still has a lot of weakness and cannot be used in long term. Learning in class still becomes the first choice in Indonesia even though the education systems start to disrupt digitally, departing from this home-learning phenomenon.
On Discriminability and Diversity in Domain Adaptation

—In domain adaptation, the performance of classiﬁer trained in source domain degrades when bumping into the high data density near the decision boundary in target domain. A common solution is to enhance the discriminability by directly minimizing the Shannon Entropy to push the decision boundary far away from the high density area. However, entropy minimizatin also leads to severe reduction of the prediction diversity, and unfortunately this side effect was mostly ignored in previous study. In this paper, we reinvestigate two critical issues in domain adaptation, i.e. , the predicion discriminability and diversity. By studying the structure of the classiﬁcation output matrix of a randomly selected data batch, we ﬁnd by theoretical analysis that the prediction discriminability and diversity could be separately measured by the Frobenius-norm and rank of the batch output matrix. Meanwhile, the nuclear-norm is an upperbound of the Frobenius-norm, and a convex approximation of the matrix rank. Accordingly, to improve both discriminability and diversity, we propose Batch Nuclear-norm Maximization (BNM) on the target output matrix. We further investigate Batch Nuclear-norm Minimization, to increase the knowledge applicability learned by source data. To speed up the calculation of nuclear-norm, we reduce the computation complexity from O ( n 3 ) to O ( n 2 ) via approximation. Extensive experiments show that the proposed techniques could boost the adaptation procedure under three typical domain adaptation scenarios. The code is available at https://github.com/cuishuhao/BNM.
Exploiting epigenomic and sequence-based features for predicting enhancer-promoter interactions

How to discriminate distal regulatory elements to a gene target is challenging in understanding gene regulation and illustrating causes of complex diseases. Among known distal regulatory elements, enhancers interact with a target gene’s promoter to regulate its expression. Although the emergence of many machine learning approaches has been able to predict enhancer-promoter interactions (EPIs), global and precise prediction of EPIs at the genomic level still requires further exploration.In this paper, we develop an integrated EPIs prediction method, called EpPredictor with improved performance. By using various features of histone modifications, transcription factor binding sites, and DNA sequences among the human genome, a robust supervised machine learning algorithm, named LightGBM, is introduced to predict enhancer-promoter interactions (EPIs). Among six different cell lines, our method effectively predicts the enhancer-promoter interactions (EPIs) and achieves better performance in F1-score and AUC compared to other methods, such as TargetFinder and PEP.
Rating Crop Insurance Contracts with Nonparametric Bayesian Model Averaging

Crop insurance is plagued by relatively little historical information but significant spatial information. We investigate the efficacy of using nonparametric Bayesian model averaging (BMA) to incorporate extraneous information into the estimated premium rates. Nonparametric BMA is particularly suited to this application because it does not make any assumptions about parametric form or the extent to which yields are similar. We evaluate the proposed estimator under small-tomedium sample sizes and various geographical restrictions on the distance of spatial smoothing for policy relevance. The nonparametric BMA consistently decreases error and enables statistically significant and economically important rents to be captured.
Viscosity approximation method for solving the multiple-set split equality common fixed-point problems for quasi-pseudocontractive mappings in Hilbert spaces

We propose a parallel iterative scheme with viscosity approximation method which converges strongly to a solution of the multiple-set split equality common fixed point problem for quasi-pseudocontractive mappings in real Hilbert spaces. We also give an application of our result to approximation of minimization problem from intensity-modulated radiation therapy. Finally, we present numerical examples to demonstrate the behaviour of our algorithm. This result improves and generalizes many existing results in literature in this direction.
Studies on the Sulfur Dioxide Fumigation of Table Grapes

Table grapes were packed in standard lugs with a given number of botrytis infected berries and fumigated with different concentrations of SO2 for 20 minutes before precooling. This was followed by different concentrations of SO2 for 30 minutes every 7, 10 and 14 days in storage. The fruit was stored at 85, 90 and 95% relative humidity; all at O°C. Grapes at 95% RH required 15-20% more SO2 for a given exposure than those at 85%. Dosage had to be adjusted to storage RH and amount of fruit treated to maintain given exposure levels owing to container absorption. The amount of botrytis decay which developed in 3 months was inversely related both to the initial and storage SO2 treatments. It was directly related to the length of the interval between storage treatments. Fumigant injury to the fruit was directly related to the initial treatment and inversely to the interval between treatments. Best decay control with minimum injury was obtained with the 7-day interval. A formula and figure for calculating the dosage of fumigant are included which take into account the fruit-space ratio (absorption factor) and storage RH as well as the SO2 concentration.
Sℒ1-Simplex: Safe Velocity Regulation of Self-Driving Vehicles in Dynamic and Unforeseen Environments

This article proposes a novel extension of the Simplex architecture with model switching and model learning to achieve safe velocity regulation of self-driving vehicles in dynamic and unforeseen environments. To guarantee the reliability of autonomous vehicles, an ℒ1 adaptive controller that compensates for uncertainties and disturbances is employed by the Simplex architecture as a verified high-assurance controller (HAC) to tolerate concurrent software and physical failures. Meanwhile, the safe switching controller is incorporated into the HAC for safe velocity regulation in the dynamic (prepared) environments, through the integration of the traction control system and anti-lock braking system. Due to the high dependence of vehicle dynamics on the driving environments, the HAC leverages the finite-time model learning to timely learn and update the vehicle model for ℒ1 adaptive controller, when any deviation from the safety envelope or the uncertainty measurement threshold occurs in the unforeseen driving environments. With the integration of ℒ1 adaptive controller, safe switching controller and finite-time model learning, the vehicle’s angular and longitudinal velocities can asymptotically track the provided references in the dynamic and unforeseen driving environments, while the wheel slips are restricted to safety envelopes to prevent slipping and sliding. Finally, the effectiveness of the proposed Simplex architecture for safe velocity regulation is validated by the AutoRally platform.
Toward Fine-Grained Indoor Localization Based on Massive MIMO-OFDM System: Experiment and Analysis

Fine-grained indoor localization has attracted attention recently because of the rapidly growing demand for indoor location-based services (ILBS). Specifically, massive (large-scale) multiple-input and multiple-output (MIMO) systems have received increasing attention due to high angular resolution. This paper presents an indoor localization testbed based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system, which supports physical-layer channel measurements. Instead of exploiting channel state information (CSI) directly for localization, we focus on positioning from the perspective of multipath components (MPCs), which are extracted from the CSI through the space-alternating generalized expectation-maximization (SAGE) algorithm. On top of the available MPCs, we propose a generalized fingerprinting system based on different single-metric and hybrid-metric schemes. We evaluate the impact of the varying antenna topologies, the size of the training set, the number of antennas, and the effective signal-to-noise ratio (SNR). The experimental results show that the proposed fingerprinting method can achieve centimeter-level positioning accuracy with a relatively small training set. Specifically, the distributed uniform linear array obtains the highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from the high spatial resolution.
Comparative Investigation of Learning Algorithms for Image Classification with Small Dataset

ABSTRACT Increase in popularity of deep learning in various research areas leads to use it in resolving image classification problems. The objective of this research is to compare and to find learning algorithms which perform better for image classification task with small dataset. We have also tuned the hyperparameters associated with optimizers and models to improve performance. First, we performed several experiments using eight learning algorithms to come closer to optimal values of hyperparameters. Then, we executed twenty-four final experiments with near optimum values of hyperparameters to find the best learning algorithm. Experimental results showed that the AdaGrad learning algorithm achieves better accuracy, lesser training time, as well as fewer memory utilization compared to the rest of the learning algorithms.
Industrial Applications of Smoothed Particle Hydrodynamics

Smoothed Particle Hydrodynamics was created by Gingold and Monaghan (1977) and Lucy (1977) to address the challenges of computational modelling of astrophysical processes. These usually involve matter with a dynamic free surface boundary, undergoing rapid changes of scale with complex physics. The foundational idea of SPH was to replace a classical mesh with Lagrangian particles, interacting through compactly supported kernel functions. Monaghan (1994) pioneered the extension of SPH to certain engineering problems, with the first application to water waves. Since then, there has been sustained growth and innovation both in the mathematics and numerics of SPH and in the diversity and quality of validated engineering applications. Extensive analysis of the method has been given by Monaghan (2012), Price (2012), Violeau (2012), and Vacondio et al. (2021) among others. The SPH rEsearch and engineeRing International Community (SPHERIC, www.spheric-sph.org) was founded in 2005 to bring together developers and practitioners of SPH in all fields to foster innovation and collaboration. SPH is now a mature engineering tool for problems that are challenging for conventional mesh-based CFD. In this Special Issue of IJCFD we are honoured to present a broad selection of the state of the art of SPH modelling in industrially important applications. These cases typically involve flow with dynamic free surfaces, and often include multiple phases, heat transfer, fluid-structure interaction, or other physics. Amicarelli, Manenti, and Paggi (2021) use a 3D SPH model to predict a full-scale catastrophic 3D dambreak flood interacting with complex terrain including a reservoir, a residential area and a hydroelectric generation station. Cleary et al. (2021) systematically review industrial, geophysical and biophysical applications of SPH modelling, predicting very complex processes such as free surface hydrodynamics, fluidstructure interactions, challenging multiphase flows, and flows involving reactions and phase change. Cui, Habashi, and Casseau (2021) simulate freezing of Supercooled Large Droplets (SLD) after impingement at aircraft flight-representative conditions. Guan et al. (2021) combine an SPHmodel for fluid dynamics with a finite element computation of tyre deformation to investigate the effect of aircraft tyre configuration on water spray on runways. Groenenboom, Cartwright, and McGuckin (2021) develop an integrated model by coupling SPH with a Finite Element (FE) model to predict complex fluid-structure interaction. Huntley et al. (2021) present simulations of a spray containing short solid fibres in a compositematerials manufacturing process. Collectively, these articles demonstrate the experimentally validated capability of SPH as amature technique to provide valuable engineering insight into complex phenomena in industry and technology.
Performance Study of Minimax and Reinforcement Learning Agents Playing the Turn-based Game Iwoki

ABSTRACT Iwoki math is an abstract board game that consists on placing tiles and that combines the calculation of simple mathematical operations with the spatial perception of two-dimensional objects. Due to its inherent features, it is also a very challenging environment to test different artificial intelligence technologies and methods. In this paper, a series of intelligent agents with different reasoning and decision capacities have been developed based on different artificial intelligence techniques applied to game theory, such as Minimax or Reinforcement Learning. Their capabilities have been tested by playing games with each other, but also against human players, obtaining remarkable results. The experimental results ratify conclusions already known at a theoretical level but also provide a new contribution that could be the basis for future research.
Research productivity of library and information science faculty in India and the United States : A comparison based on publications, citations and h-index

The present study compared research productivity of Library and Information Science (LIS) faculties working in 61 government universities of India and 55 American Library Association-accredited LIS programs in the United States. A regression model is used to determine the effect of independent variables i.e. number of publications, number of citations per publication, and total number of citations for top-cited publication on h-index value of faculties. Further, k-means cluster analysis (three tier) is performed for each rank of faculties (full, associate & assistant professor) based on their publication and citation. The findings of this study indicate that for both countries h-index value of LIS faculties is related to number of publications and citations per publication, while mitigating the impact of a highly-cited publication. The top tier of Indian LIS faculties by publications, have more publications, citations for most-cited publication, and larger h-index than the lowest tier in the U.S. (though these researchers do have a higher number of citations per publication). The most productive LIS Faculties in the U.S. have about 3.5 times more publications, citations, and h-index than Indian LIS faculties. Finally, for the evaluation of LIS researchers for tenure and promotion decisions in regard to h-index, the study suggests that it may provide more equitable valuation of research productivity than looking at raw number of citation counts, while providing more information about quality of publications than just the raw number of publications alone.
Artificial intelligence in healthcare: transforming the practice of medicine

ABSTRACT Artificial intelligence (AI) is a powerful and disruptive area of computer science, with the potential to fundamentally transform the practice of medicine and the delivery of healthcare. In this review article, we outline recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective, reliable and safe AI systems, and discuss the possible future direction of AI augmented healthcare systems.
Adsorption of Toluidine Blue O onto Chitosan

Chitosan, a deacetylated polymer of chitin, was applied and characterized as an adsorbent for a dye Toluidine Blue O. The adsorption of Toluidine Blue O by chitosan was affected by the particle sire and mass of chitosan, initial dye concentration, reaction time, and solution pH. More dye was adsorbed the smaller the chitosan particles. When initial ratio of dye to chitosan was over 1 : 500, the adsorption of dye was rapidly declined. Adsorption rate of dye to chitosan showed 2 phases, rapidly occurred one at the beginning within 3 min and thereafter very slowly occurred one. The amount of dye adsorbed was increased with increase in pH.
Livelyzer: analyzing the first-mile ingest performance of live video streaming

Over-the-top (OTT) live video traffic has grown significantly, fueled by fundamental shifts in how users consume video content (e.g., increased cord-cutting) and by improvements in camera technologies, computing power, and wireless resources. A key determining factor for the end-to-end live streaming QoE is the design of the first-mile upstream ingest path that captures and transmits the live content in real-time, from the broadcaster to the remote video server. This path often involves either a Wi-Fi or cellular component, and is likely to be bandwidth-constrained with time-varying capacity, making the task of high-quality video delivery challenging. Today, there is little understanding of the state of the art in the design of this critical path, with existing research focused mainly on the downstream distribution path, from the video server to end viewers. To shed more light on the first-mile ingest aspect of live streaming, we propose Livelyzer, a generalized active measurement and black-box testing framework for analyzing the performance of this component in popular live streaming software and services under controlled settings. We use Livelyzer to characterize the ingest behavior and performance of several live streaming platforms, identify design deficiencies that lead to poor performance, and propose best practice design recommendations to improve the same.
Stress and burnout in Improving Access to Psychological Therapies (IAPT) trainees: a systematic review

Abstract For more than a decade, Improving Access to Psychological Therapies (IAPT) has been training a new workforce of psychological therapists. Despite evidence of stress and burnout both in trainee mental health professionals, and qualified IAPT clinicians, little is known about these topics in IAPT trainees. Consequently, this systematic review sought to establish the current state of the literature regarding stress and burnout in IAPT trainees. Electronic databases were searched to identify all published and available unpublished work relating to the topic. On the basis of pre-established eligibility criteria, eight studies (including six unpublished doctoral theses) were identified and assessed for quality. This review identifies that research into the experience of IAPT trainees is under-developed. Existing evidence tentatively suggests that IAPT trainees may experience levels of stress and burnout that are higher than their qualified peers and among the higher end of healthcare professionals more generally. The experience of fulfilling dual roles as mental health professionals and university students concurrently appears to be a significant source of stress for IAPT trainees. More research regarding the levels and sources of stress and burnout in IAPT trainees is urgently needed to confirm and extend these findings. Recommendations for future research in the area are given. Key learning aims (1) To establish the current state of the literature regarding stress and burnout in IAPT trainees. (2) To raise practitioner, service and education-provider awareness regarding the levels and perceived sources of stress and burnout in IAPT trainees. (3) To make recommendations regarding future research on the topic.
Acute mental health presentations before and during the COVID-19 pandemic

Background A number of community based surveys have identified an increase in psychological symptoms and distress but there has been no examination of symptoms at the more severe end of the mental health spectrum. Aims We aimed to analyse numbers and types of psychiatric presentations to inform planning for future demand on mental health services in light of the COVID-19 pandemic. Method We analysed electronic data between January and April 2020 for 2534 patients referred to acute psychiatric services, and tested for differences in patient demographics, symptom severity and use of the Mental Health Act 1983 (MHA), before and after lockdown. We used interrupted time-series analyses to compare trends in emergency department and psychiatric presentations until December 2020. Results There were 22% fewer psychiatric presentations the first week and 48% fewer emergency department presentations in the first month after lockdown initiated. A higher proportion of patients were detained under the MHA (22.2 v. 16.1%) and Mental Capacity Act 2005 (2.2 v. 1.1%) (χ2(2) = 16.3, P < 0.0001), and they experienced a longer duration of symptoms before seeking help from mental health services (χ2(3) = 18.6, P < 0.0001). A higher proportion of patients presented with psychotic symptoms (23.3 v. 17.0%) or delirium (7.0 v. 3.6%), and fewer had self-harm behaviour (43.8 v. 52.0%, χ2(7) = 28.7, P < 0.0001). A higher proportion were admitted to psychiatric in-patient units (22.2 v. 18.3%) (χ2(6) = 42.8, P < 0.0001) after lockdown. Conclusions UK lockdown resulted in fewer psychiatric presentations, but those who presented were more likely to have severe symptoms, be detained under the MHA and be admitted to hospital. Psychiatric services should ensure provision of care for these patients as well as planning for those affected by future COVID-19 waves.