Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Orthogonal Mechanics in Linguistic Annotation Games

Gamification has been recently growing in popularity among researchers investigating Information and Communication Technologies. Scholars have been trying to take advantage of this approach in the field of natural language processing (NLP), developing Games With A Purpose (GWAPs) for corpus annotation that have obtained encouraging results both in annotation quality and overall cost. However, GWAPs implement gamification in different ways and to different degrees. We propose a new framework to investigate the mechanics employed in the gamification process and their magnitude in terms of complexity. This framework is based on an analysis of some of the most important contributions in the field of NLP-related gamified applications and GWAP theory. Its primary purpose is to provide a first step towards classifying mechanics that mimic mainstream video games and may require skills that are not relevant to the annotation task, defined as orthogonal mechanics. In order to test our framework, we develop and evaluate Spacewords, a linguistic space game for synonymy annotation.
Biofeedback Methods in Entertainment Video Games

The area of biofeedback interaction has grown over recent years, thanks to the release of more affordable and reliable sensor technology, and the accessibility offered by modern game development tools. This article presents a systematic literature review focusing on how different biofeedback interaction methods have been used for entertainment purposes in video games, between 2008 and 2020. It divides previous contributions in terms of a proposed interaction classification criteria and five different biofeedback methods (with a sixth category combining them): electroencephalography, electrocardiography, eye tracking, electrodermal activity, electromyography, and multi-modal interaction. The review describes the properties, sensor technologies, and the type of data gathered for every included biofeedback method, and presents their respective interaction techniques. It summarizes a set of opportunities and challenges for each included method, based on the results from previous work, and discusses these findings. It also analyzes how these interaction techniques are distributed between different common game genres. The review is beneficial for people interested in biofeedback methods and their potential use for novel interaction techniques in future video games.
Game-based Climate Change Engagement

Video games have risen as a popular medium with the potential to become a powerful tool for public climate change engagement. However, little is known about how existing digital games can fulfill this role. This study systematically compiles 150 video games that represent climate change, including serious (n = 109) and for entertainment (n = 41). The games are analyzed by adapting an existing framework (15 dimensions: achievable, challenging, concrete, credible, efficacy-enhancing, experiential learning, feedback-oriented, fun, identity-driven, levelling-up, meaningful, narrative-driven, reward-driven, simulating, social) and statistically compared. The analysis reveals that most games comply with most recommended attributes, but credibility, achievability, meaningfulness, and social features are uncommon or rare. Statistical results comparing serious games and games for entertainment associate six attributes with serious games (achievable, challenging, credible, efficacy-enhancing, experiential learning, feedback-oriented), and one (narrative-driven) with games for entertainment. The findings suggest that researchers would benefit from widening their lens to detect previously overlooked opportunities for game-based climate change engagement, communication, and education. The study also provides a systematic mapping of extant games depicting climate change for interested developers, designers and educators.
UES-Br: Translation and Cross-Cultural Adaptation of the User Engagement Scale for Brazilian Portuguese

The User Engagement Scale (UES) is a self-reported attitude scale broadly used in user research to measure their engagement with interactive systems, such as digital games and applications. The present work presents a translation of UES to the Portuguese language and its cultural adaptation to the Brazilian context. We followed five steps for the cross-cultural adaptation process: Translation, Synthesis, Back translation, Expert committee review, and Pretesting. Seeking to assure that the translated version is equivalent to the original in terms of metrics and semantics, we run tests on two data sets collected from 432 users’ past experiences. As expected, T-tests for independent samples demonstrated that UES-Br provides significantly higher engagement experiences than scores for non-engaging experiences. An exploratory factor analysis indicated that most items were strongly correlated to the main factor (Engagement), showing a single underlying factor for all items. A Confirmatory Factor Analysis suggests that the original four-factor model provided an adequate fit to the data collected with UES-Br. The translation also demonstrated evidence of validity and reliability. This paper contributes to the global player-and human-computer interaction knowledge by enabling the Portuguese-speaking community to use a validated tool already established for native English speakers.
Identifying Commercial Games with Therapeutic Potential through a Content Analysis of Steam Reviews

While evidence supports that some commercial off-the-shelf video games may promote mental wellbeing, it is an extensive time investment to experimentally identify games that benefit players. The time delay between commercial games research and commercial game development can render such research out-of-date. In this work, we explore player-written game reviews as a way to expeditiously identifying games with potential benefits for mental wellbeing. Through a content analysis of review data, we found that players publicly disclose experiences consistent with self-care. Our analysis generated categories related to coping and recovery, emotional regulation, social connectedness, and obsessive play. Through this process, we identified several games as strong candidates for further research. Our work contributes to an emerging research agenda of commercial video games as therapy (VGTx), by providing a technique for rapidly identifying games with therapeutic potential. Further, we demonstrate that Steam user reviews are a valuable source of affective player experience data-a contribution with broad implications for player experience research.
Point of Contact

COVID-19 exposed the need to identify newer tools to understand perception of information, behavioral conformance to instructions and model the effects of individual motivation and decisions on the success of measures being put in place. We approach this challenge through the lens of serious games. Serious games are designed to instruct and inform within the confines of their magic circle. We built a multiplayer serious game, Point of Contact (PoC), to investigate effects of a serious game on perception and behavior. We conducted a study with 23 participants to gauge perceptions of COVID-19 preventive measures and quantify the change after playing PoC. The results show a significant positive change to participants' perceptions towards COVID-19 preventive measures, shifting perceptions towards following guidelines more strictly due to a greater awareness of how the virus spreads. We discuss these implications and the value of a serious game like PoC towards pandemic risk modelling at a microcosm level.
Under Pressure

Video games frequently invoke high-pressure circumstances in which player performance is crucial. These high-pressure circumstances are incubators for 'choking' and 'clutching'-phenomena that broadly address critical failures and successes in performance, respectively. The eruption of esports into the mainstream has vitalized the need to understand performance in video games, and particularly in competitive games spaces. In this work, we explore the potential mechanisms behind choking and clutching and how they are related to player traits and tendencies. We report the results of multiple regression analyses, finding that the propensity to choke is positively correlated with Reinvestment, Obsessive Passion, and Public Self-Consciousness, as well as Approach and Avoidance coping styles. Likewise, we find that the propensity to clutch is negatively correlated with Social Anxiety, and positively with Private Self-Consciousness and player experience with competitive gaming. We propose that these findings can be utilized to scaffold and support performance in high-pressure gaming spaces, such as esports. This work represents an initial step in the empirical exploration of choking and clutching in competitive video game contexts.
iLoci: robust evaluation of genome content and organization for provisional and mature genome assemblies

Background The rate at which new draft genome assemblies and corresponding annotation versions are being produced has long outpaced the scientific community’s capacity to refine these drafts into “finished,” reference-quality data resources to a standard typically expected from dedicated efforts of model organism research communities. Nonetheless, scientists must be able to evaluate newly sequenced genomes in the context of previously published data, requiring summaries of genome content and organization that can be quickly computed, updated, and meaningfully compared. As annotation quality will necessarily vary within and across data sets, the ability to select subsets of only those data that are well supported is critical for distinguishing technical artifacts from biological effects in genome-wide analyses. Results We introduce a new framework for genome analyses based on parsing an annotated genome assembly into distinct interval loci (iLoci), available as open source software as part of the AEGeAn Toolkit (https://github.com/BrendelGroup/AEGeAn). We demonstrate that iLoci provide an alternative coordinate system that is robust to changes in assembly and annotation versions and facilitates granular quality control of genome data. We discuss how statistics computed on iLoci reflect various characteristics of genome content and organization and illustrate how these statistics can be used to establish a baseline for assessment of the completeness and accuracy of the data. We also introduce a well-defined measure of relative genome compactness and compute other iLocus statistics that reveal genome-wide characteristics of gene arrangements in the whole genome context. Conclusions We present a coherent computational framework that calculates informative statistics from genome assembly/annotation data input. Given the fast pace of assembly/annotation updates, our AEGeAn Toolkit fills a niche in computational genomics based on deriving persistent and species-specific genome statistics. Gene structure model centric iLoci provide a precisely defined coordinate system that can be used to store assembly/annotation updates that reflect either stable or changed assessments. Large-scale application of the approach revealed species and clade specific genome organization in precisely defined computational terms, promising intriguing forays into the forces of shaping genome structure as more and more genome assemblies are being deposited.
Modified continuous gas stream method for measuring rates of respiration in fruits and vegetables.

A uniform suction system with modified flow meters to measure the respiration rate of 16 samples of fruits or vegetables and 1 control blank at the same time has been devised. It has a simple air inlet through towers containing concn. KOH and flakes of NaOH, and palm-type 17-way inlet and outlet manifold units at each end of the continuous gas stream assembly. The apparatus is efficient, simple and relatively inexpensive.
Robust T cell activation requires an eIF3-driven burst in T cell receptor translation

Activation of T cells requires a rapid surge in cellular protein synthesis. However, the role of translation initiation in the early induction of specific genes remains unclear. Here we show human translation initiation factor eIF3 interacts with select immune system related mRNAs including those encoding the T cell receptor (TCR) subunits TCRA and TCRB. Binding of eIF3 to the TCRA and TCRB mRNA 3’-untranslated regions (3’-UTRs) depends on CD28 coreceptor signaling and regulates a burst in TCR translation required for robust T cell activation. Use of the TCRA or TCRB 3’-UTRs to control expression of an anti-CD19 chimeric antigen receptor (CAR) improves the ability of CAR-T cells to kill tumor cells in vitro. These results identify a new mechanism of eIF3-mediated translation control that can aid T cell engineering for immunotherapy applications.
Pantothenate biosynthesis is critical for chronic infection by the neurotropic parasite Toxoplasma gondii

Coenzyme A (CoA) is an essential molecule acting in metabolism, post-translational modification, and regulation of gene expression. While all organisms synthesize CoA, many, including humans, are unable to produce its precursor, pantothenate. Intriguingly, like most plants, fungi and bacteria, parasites of the coccidian subgroup of Apicomplexa, including the human pathogen Toxoplasma gondii , possess all the enzymes required for de novo synthesis of pantothenate. Here, the importance of CoA and pantothenate biosynthesis for the acute and chronic stages of T. gondii infection is dissected through genetic, biochemical and metabolomic approaches, revealing that CoA synthesis is essential for T. gondii tachyzoites, due to the parasite’s inability to salvage CoA or intermediates of the pathway. In contrast, pantothenate synthesis is only partially active in T. gondii tachyzoites, making the parasite reliant on its uptake. However, pantothenate synthesis is crucial for the establishment of chronic infection, offering a promising target for intervention against the persistent stage of T. gondii . Coenzyme A (CoA) is an essential metabolite found in all organisms and its synthesis involves five conserved enzymatic steps and uses pantothenate (Pan) as a precursor. Here, Lunghi et al. examine the Pan synthesis pathway in Toxoplasma gondii and find that Pan is crucial for the establishment of chronic but not acute infection.
Predicting compound-protein interaction using hierarchical graph convolutional networks

Motivation Convolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a computational method based on convolutional neural networks to tackle a fundamental question in drug discovery and development, i.e. the prediction of compound-protein interactions based on compound structure and protein sequence. We propose a hierarchical graph convolutional network (HGCN) to encode small molecules. The HGCN aggregates a molecule embedding from substructure embeddings, which are synthesized from atom embeddings. As small molecules usually share substructures, computing a molecule embedding from those common substructures allows us to learn better generic models. We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction. Results Experiments using different datasets show the improvement of our model compared to other GCN-based methods and a sequence based method, DeepDTA, in predicting compound-protein interactions. Each prediction made by the model is also explainable and can be used to identify critical residues mediating the interaction. Availability and implementation https://github.com/banhdzui/cpi_hgcn. git
Monkey plays Pac-Man with compositional strategies and hierarchical decision-making

Humans can often handle daunting tasks with ease by developing a set of strategies to reduce decision making into simpler problems. The ability to use heuristic strategies demands an advanced level of intelligence and has not been demonstrated in animals. Here, we trained macaque monkeys to play the classic video game Pac-Man. The monkeys’ decision-making may be described with a strategy-based hierarchical decision-making model with over 90% accuracy. The model reveals that the monkeys adopted the take-the-best heuristic by using one dominating strategy for their decision-making at a time and formed compound strategies by assembling the basis strategies to handle particular game situations. With the model, the computationally complex but fully quantifiable Pac-Man behavior paradigm provides a new approach to understanding animals’ advanced cognition. One-Sentence Summary Macaque monkeys play Pac-Man with strategy-based hierarchical decision making, a cognitive capacity hitherto unknown in them.
Predicting the Activities of Drug Excipients on Biological Targets using One-Shot Learning

Excipients are a major component of drugs and are used to improve drugs attributes such as stability and appearance. Excipients approved by Food and Drug Administration (FDA) are regarded as safe for human in allowed concentration, but their potential interaction with drug targets have not been investigated systematically, which might influence drug’s efficacy. Deep learning models have been used for identification of ligands that could bind to the drug targets. However, due to the limited available data, it is challenging to reliably estimate the likelihood of a ligand-protein interaction. One-shot learning techniques provide a potential approach to address this low-data problem as these techniques require only one or a few examples to classify the new data. In this study, we apply one-shot learning models on datasets that include ligands binding to G-Protein Coupled Receptors (GPCRs) and Kinases. The predicted results suggest that one-shot learning models could be used for predicting ligand-protein interaction and the models attain better performance when protein targets contain conserved binding pockets. The trained models are also used to predict interactions between excipients and drug targets, which provides a potential efficient strategy to explore the activities of drug excipients. We find that a large number of drug excipients could interact with biological targets and influence their function. The results demonstrate how one-shot learning models can be used to make accurate prediction for excipient-protein interactions and these methods could be used for selecting excipients with limited drug-protein interactions.
Evolution of Weighted External Owned Accounts Trading Network on Ethereum

In this paper, we investigated the evolution of user behavior on Ethereum through building directed and weighted external owned accounts trading networks (DWETN) from August 10th, 2015 to June 9th, 2017. It is showed that the evolution of the structural properties of the DWETN and important events are closely related. The relationship between the number of users and the total number of transactions is linear, and each user will have 1.5 transactions on average. Out-degree, in-degree, out-strength, and in-strength distributions follow the power-law distributions, and the power exponent values of out-degree and in-degree distributions change much more than out-strength and in-strength distributions, which implies that, despite the changes in network structure, the transaction behavior pattern of users have inherent stability. The evolution of directed degree correlation coefficients and directed and weighted Pearson correlation coefficients of the network indicates that in-degree(in-strength) of the source node of an edge has a weak effect on the target node's in-degree(in-strength) and out-degree(out-strength), and as time evolves, the effect of out-degree(out-strength) of the source node of an edge on the target node's in-degree(in-strength) and out-degree(out-strength) from negative to weak.
pth input-to-state stability of stochastic bidirectional associative memory neural networks with infinite delays

In this paper, bidirectional associative memory neural networks (BAMNNs) with infinite delays under the influence of Brownian motion or Markovian switching are investigated respectively. By utilizing Itô formula and constructing new Halanay differential inequalities, two sufficient conditions ensuring the pth input-to-state stability (p-ISS) of the neural networks are obtained. A numerical example is given to illustrate the efficiency of the derived results.
Out-Of-Control Detection In Semiconductor Manufacturing using One-Class Support Vector Machines

Semiconductor manufacturing is a continuously challenging and competitive industry. It is important to detect any anomalies in the production facilities, or fabs, as they occur to avoid defect accumulations and loss of performance. In this paper we present a literature review of classification methods and detailed the chosen method which is One Class-Support Vector Machine (OC-SVM). This method is used for out-of-control detection in semiconductor manufacturing. The method is tested via an application using industrial data of the studied fab.
Further Stability Analysis for Load Frequency Control of Power Systems with Time-Varying Delays

This paper studies the stability problem of load frequency control (LFC) of a single area power system with communication delays both from remote measurements to the control centre and from control centre to generator units. The closed-loop LFC system with two transmission delays in communication networks can be expressed as the linear system with two additive time-varying delays. A stability criterion is derived via an affine second order Bessel-Legendre free-matrix-based inequality and Lyapunov stabiliity theory application. The stability criterion is in the form of linear matrix inequality (LMI), which can be easily solved via the MATLAB LMI-toolbox. Compared with some previously published results, the stability criterion in this paper is less conservative. Finally, an application of the LFC for a single area power system is presented to show the effectiveness of the proposed approach.
Automatic Generation of Kinematics and Dynamics Model Descriptions for Modular Reconfigurable Robot Manipulators

We propose a unified approach for automatically generating multiple robot-model descriptions for modular robot manipulators. Modular robots need models for kinematics and dynamics to deploy motion control schemes with high performance as it is possible for their fixed structure counterparts. Additionally, these models are also necessary for enabling the optimization of their assembly to optimally meet task and environmental constraints. Manual derivation of models for every different assembly is impractical as the number of possible arrangements can be large. We propose a systematic approach for characterizing single modules and for automatically generating the following widely adopted robot model descriptions for kinematics and dynamics: standard and modified Denavit-Hartenberg, product of exponentials formulation, and the unified robot description format. We numerically verify our approach also considering experimental data from a real modular robot manipulator.