Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of the glutathione administration via dietary on intestinal microbiota of white shrimp, Penaeus vannamei, under cyclic hypoxia conditions

Environmental stress can impair the survival, growth performance, and intestinal environment of shrimp. The objective of this study was to investigate the effect of glutathione (GSH) on the survival, growth performance, intestinal oxidation, microbiota, and histology of shrimp under hypoxia. Four treatments were used: (1) normoxia, (2) cyclic serious/medium hypoxia (CSMH), (3) CSMH and 75 mg kg−1 GSH, and (4) CSMH and 150 mg kg−1 GSH. White shrimp (Penaeus vannamei) in groups 3 and 4 were fed a commercial diet supplemented with 75 and 150 mg kg−1 GSH for a 28 day period, respectively, and they were cultured under CSMH (0.8 – 3.5 mg L−1 dissolved oxygen) for the last 14 days of the experiment. P. vannamei supplemented with 75 mg kg−1 GSH showed significantly improved survival and growth performance under CSMH compared with the CSMH condition alone. The dose of 75 mg kg−1 GSH completely eliminated overproduction of reactive oxygen species and malondialdehyde to suppress serious histopathological lesions and improve bacterial diversity and the relative abundance of beneficial bacteria, such as Rhodobacteraceae, thereby preventing pathogen (e.g., Vibrio) invasion in the intestine of shrimp under CSMH. However, the dose of 150 mg kg−1 GSH was excessive, as it led to serious impairment of survival and growth. These results indicate that 75 mg kg−1 GSH has the potential to control shrimp mortality and growth inhibition under CSMH in the shrimp farm setting.
Dreicer mechanism of runaway electron generation in presence of high-Z impurities

It is expected that massive runaway electrons (REs) may be generated during plasma disruptions in ITER. An uncontrolled loss of such REs can cause localized damage of the Plasma Facing Components (PFCs). Then a Disruption Mitigation System (DMS) is being developed in ITER to control or suppress RE generation. One of the most promising techniques employed in the future DMS is the injection of High-Z impurities [1]. Although the avalanche mechanism of RE production is anticipated to be the dominant mechanism in ITER [2], the avalanche multiplication of the runaways after the thermal quench (TQ) still requires a seed RE current. One of the mechanisms of RE generation recognized in the past as an important RE seed provider is the diffusive leak of electrons from the maxwellian core into the high-energy “runaway” region under the influence of the driving electric field [3] (“Dreicer generation”). In ITER the cold post thermal quench plasma will be characterized by the large amount of partially ionized impurities. This makes the problem statement on RE generation different from that in [3]. In the present study we solve numerically the Fokker-Planck equation for electron distribution function to calculate the Dreicer RE generation in ITER-like plasmas taking into account electron interaction with partially ionized high-Z impurities according to model [4]. The comparison with the existing results for fully-ionized plasmas and the implications for ITER are discussed.
Design and Testing of Safer, More Effective Preservatives for Consumer Products

Preservatives deter microbial growth, providing crucial functions of safety and durability in composite materials, formulated products, and food packaging. Concern for human health and the environmental impact of some preservatives has led to regulatory restrictions and public pressure to remove individual classes of compounds, such as parabens and chromated copper arsenate, from consumer products. Bans do not address the need for safe, effective alternative preservatives, which are critical for both product performance (including lifespan and therefore life cycle metrics) and consumer safety. In this work, we studied both the safety and efficacy of a series of phenolic preservatives and compared them to common preservatives found in personal care products and building materials. We quantified antimicrobial activity against Aspergillus brasiliensis (mold) and Pseudomonas aeruginosa (Gram negative bacteria), and we conducted a hazard assessment, complemented by computational modeling, to evaluate the human.
Sense Your Power

Energy-constrained sensor nodes can adaptively optimize their energy consumption if a continuous measurement is provided. This is of particular importance in scenarios of high dynamics such as with energy harvesting. Still, self-measuring of power consumption at reasonable cost and complexity is unavailable as a generic system service. In this article, we present ECO, a hardware-software co-design that adds autonomous energy management capabilities to a large class of low-end IoT devices. ECO consists of a highly portable hardware shield built from inexpensive commodity components and software integrated into the RIOT operating system. RIOT supports more than 200 popular microcontrollers. Leveraging this flexibility, we assembled a variety of sensor nodes to evaluate key performance properties for different device classes. An overview and comparison with related work shows how ECO fills the gap of in situ power attribution transparently for consumers and how it improves over existing solutions. We also report about two different real-world field trials, which validate our solution for long-term production use.
Analysis of Uvama Urubugal in Tamil Sangam Literatures

Uvama urubugal in Tamil are used to explain a particular context by citing another equivalent context. This is referred to as “Uvamaiyani” in Tamil Grammar rules as stated in Tholkappiam. The is called as simile in English. Similes bring out many beautiful poetic contexts. Automatic extraction of such similes can help to build better Natural Language Generation applications such as, story generation systems and lyric suggestion systems. This paper attempts to automatically extract the uvama urubugal from Tamil Sangam Literatures. Natrinai and Mullai Pattu have been used for the analysis. There are 12 uvama urupugal in Tamil as per Nanool and this paper has attempted to analyze the usage of these 12 uvama urubugal in Sangam Literatures and compares their usage distribution in the Tamil Film songs data set comprising of 4215 songs. It was found that only two uvama urubugal were used in the current-day Tamil Film songs. This comparison was done to reveal the diminishing usage of these beautiful uvama urubugal by the current generation and the urge to use them again.
Adaptive Assembly Workstations and cobots: a qualitative assessment involving senior and adult workers

Collaborative robots (cobots) are increasingly installed at manufactory plants to keep up with the market demands, combining the benefits of advanced automation and flexible production machines. Yet, the workers’ acceptance of this technology is crucial to fully exploit their potential and to make cobots a valuable support for operators themselves. The present paper reports and discusses the preliminary results of a semi-structured interview study, which investigates how adult workers of different age groups receive a cobot installed in an Adaptive Assembly Workstation (AAW), which is a cutting-edge tool customizable to match the physical features of the operator. More specifically, participants had the opportunity to experience a realistic working activity with the cobot before being interviewed. Data from the interviews indicate that participants considered the robot useful and safe. Additionally, some design proposals emerged.
The Role of Virtual Spaces and Interactivity in Emergency Training

Virtual Reality (VR) allows to re-create risky situations in a safe and realistic environment and, in recent years, it has been proposed as a versatile training tool for fire emergencies. However, there is still the need to compare VR with other training formats, to better understand when and how it is advantageous to invest in virtual systems for emergency training. In this preliminary study, we compare different formats of a training program for teaching how to extinguish office-related fires. Formats vary in the presence and realism of the spatial representation, namely an immersive and interactive VR environment, a non-interactive desktop Video and traditional paper-based material. Participants’ reactions and knowledge acquisition have been measured using both quantitative (accuracy, reaction times, questionnaires) and qualitative (open questions) methods. Results show that, at performance level, all three training formats significantly improved accuracy, but only participants in VR and Video conditions became faster in choosing the correct extinguisher. In terms of participants’ evaluation, the VR format was judged more positively compared to the other trainings.
European Language Grid: A Joint Platform for the European Language Technology Community

Europe is a multilingual society, in which dozens of languages are spoken. The only option to enable and to benefit from multilingualism is through Language Technologies (LT), i.e., Natural Language Processing and Speech Technologies. We describe the European Language Grid (ELG), which is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella platform for the European LT landscape, including research and industry, enabling all stakeholders to upload, share and distribute their services, products and resources. At the end of our EU project, which will establish a legal entity in 2022, the ELG will provide access to approx. 1300 services for all European languages as well as thousands of data sets.
Empowering resilient crisis response training through purposefully designed boundary objects in a simulation-gaming exercise approach: Supporting ad-hoc team interaction

This paper reports on experiences of creating resilience-empowering simulation-gaming exercises concerning disruptions in the payment system. The aim of the exercises was to utilize gaming-simulation as a training environment where teams of practitioners can learn to develop in-depth understanding of systemic interaction (i.e., cascading effects of disruptions) and learn how to develop collaborative resilience across many different critical infrastructures. Interaction between different stakeholders in the payment system, such as business owners, banks, municipality crisis managers are considered as the foundation for building collaborative resilience. Designing the exercises in such a way that they encourage and support such interactions are therefore seen as a primary design goal. This paper describes lessons learned from the process of conducting the 17 exercises in terms of creating a well-balance simulation and an immersive experience.
Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi

In this paper, we develop Sindhi subjective lexicon using a merger of existing English resources: NRC lexicon, list of opinion words, SentiWordNet, Sindhi-English bilingual dictionary, and collection of Sindhi modifiers. The positive or negative sentiment score is assigned to each Sindhi opinion word. Afterwards, we determine the coverage of the proposed lexicon with subjectivity analysis. Moreover, we crawl multi-domain tweet corpus of news, sports, and finance. The crawled corpus is annotated by experienced annotators using the Doccano text annotation tool. The sentiment annotated corpus is evaluated by employing support vector machine (SVM), recurrent neural network (RNN) variants, and convolutional neural network (CNN).
Domain Expert Platform for Goal-Oriented Dialog Collection

Today, most dialogue systems are fully or partly built using neural network architectures. A crucial prerequisite for the creation of a goal-oriented neural network dialogue system is a dataset that represents typical dialogue scenarios and includes various semantic annotations, e.g. intents, slots and dialogue actions, that are necessary for training a particular neural network architecture. In this demonstration paper, we present an easy to use interface and its back-end which is oriented to domain experts for the collection of goal-oriented dialogue samples. The platform not only allows to collect or write sample dialogues in a structured way, but also provides a means for simple annotation and interpretation of the dialogues. The platform itself is language-independent; it depends only on the availability of particular language processing components for a specific language. It is currently being used to collect dialogue samples in Latvian (a highly inflected language) which represent typical communication between students and the student service.
Micro-electromechanical system based optimized steering angle estimation mechanism for customized self-driving vehicles

In an automated steering system of the self-driving vehicles, the steering wheel angle is measured by the absolute angular displacement sensors or relative angle sensors. However, these sensors either encompass global navigation satellite systems (GNSS)/gyroscope – Micro Electromechanical-Sensor (MEMS) based solutions or comprise of the complex gear-based mechanical structure which results in latency and additive bias in the accumulative steering angle assessment. To address these issues, we propose a novel steering angle assessment system based on enhanced gear mechanism along with the adapted rotation paradigm for the customized self-driving vehicles. Additionally, a digital signal processing system has been introduced to resolve the issues in the identification of absolute central and max-bounding steering wheels position in self-driving vehicles. In assistance with the proposed mechanism, an algorithm has also been proposed to optimize the computed steering angle to minimalize the effect of additive bias in the accuracy. The proposed mechanism has been installed in the customized self-driving testbed vehicle and rigor validation has been performed in the straight and curvy road scenarios. Finally, the comparison study has been carried out between the conventional relative sensor and the proposed mechanism to show the accuracy and effectiveness of the proposed mechanism in terms of error rate, stability, and deviation.
ONE: Toward ONE model, ONE algorithm, ONE corpus dedicated to sentiment analysis of Arabic/Arabizi and its dialects

Arabic is the official language of 22 countries, spoken by more than 400 million speakers. Each one of this country use at least on dialect for daily life conversation. Then, Arabic has at least 22 dialects. Each dialect can be written in Arabic or Arabizi Scripts. The most recent researches focus on constructing a language model and a training corpus for each dialect, in each script. Following this technique means constructing 46 different resources (by including the Modern Standard Arabic, MSA) for handling only one language. In this paper, we extract ONE corpus, and we propose ONE algorithm to automatically construct ONE training corpus using ONE classification model architecture for sentiment analysis MSA and different dialects. After manually reviewing the training corpus, the obtained results outperform all the research literature results for the targeted test corpora.
Code-size-aware Scheduling of Synchronous Dataflow Graphs on Multicore Systems

Synchronous dataflow graphs are widely used to model digital signal processing and multimedia applications. Self-timed execution is an efficient methodology for the analysis and scheduling of synchronous dataflow graphs. In this article, we propose a communication-aware self-timed execution approach to solve the problem of scheduling synchronous dataflow graphs on multicore systems with communication delays. Based on this communication-aware self-timed execution approach, four communication-aware scheduling algorithms are proposed using different allocation rules. Furthermore, a code-size-aware mapping heuristic is proposed and jointly used with a proposed scheduling algorithm to reduce the code size of SDFGs on multicore systems. The proposed scheduling algorithms are experimentally evaluated and found to perform better than existing algorithms in terms of throughput and runtime for several applications. The experiments also show that the proposed code-size-aware mapping approach can achieve significant code size reduction with limited throughput degradation in most cases.
Balancing guidance and flexibility in the design of an inclusive handweaving loom: Balancing guidance and flexibility

The paper describes a design case of an accessible handweaving loom that allows people with different kinds of disabilities to weave autonomously promoting self-expression and stimulating creativity. The project developed through several iterative cycles resulting in the redesign of a commercial loom enhanced and transformed using digital fabrication technology. People with various types of disabilities, their caregivers and weaving experts were engaged in a series of participatory design workshops focused on eliciting requirements and desires and inspiring the design process. The discussion of the design case offers a contribution on design for accessibility and a concrete case to reflect on the importance of supporting and stimulating self-expression and creativity in people with special needs.
N-gram and Neural Models for Uralic Language Identification: NRC at VarDial 2021

We describe the systems developed by the National Research Council Canada for the Uralic language identification shared task at the 2021 VarDial evaluation campaign. We evaluated two different approaches to this task: a probabilistic classifier exploiting only character 5-grams as features, and a character-based neural network pre-trained through self-supervision, then fine-tuned on the language identification task. The former method turned out to perform better, which casts doubt on the usefulness of deep learning methods for language identification, where they have yet to convincingly and consistently outperform simpler and less costly classification algorithms exploiting n-gram features.
Whit’s the Richt Pairt o Speech: PoS tagging for Scots

In this paper we explore PoS tagging for the Scots language. Scots is spoken in Scotland and Northern Ireland, and is closely related to English. As no linguistically annotated Scots data were available, we manually PoS tagged a small set that is used for evaluation and training. We use English as a transfer language to examine zero-shot transfer and transfer learning methods. We find that training on a very small amount of Scots data was superior to zero-shot transfer from English. Combining the Scots and English data led to further improvements, with a concatenation method giving the best results. We also compared the use of two different English treebanks and found that a treebank containing web data was superior in the zero-shot setting, while it was outperformed by a treebank containing a mix of genres when combined with Scots data.
Kawarith: an Arabic Twitter Corpus for Crisis Events

Social media (SM) platforms such as Twitter provide large quantities of real-time data that can be leveraged during mass emergencies. Developing tools to support crisis-affected communities requires available datasets, which often do not exist for low resource languages. This paper introduces Kawarith a multi-dialect Arabic Twitter corpus for crisis events, comprising more than a million Arabic tweets collected during 22 crises that occurred between 2018 and 2020 and involved several types of hazard. Exploration of this content revealed the most discussed topics and information types, and the paper presents a labelled dataset from seven emergency events that serves as a gold standard for several tasks in crisis informatics research. Using annotated data from the same event, a BERT model is fine-tuned to classify tweets into different categories in the multi- label setting. Results show that BERT-based models yield good performance on this task even with small amounts of task-specific training data.
Adapting the Portuguese Braille System to Formal Semantics

Since the seminal work of Richard Montague in the 1970s, mathematical and logic tools have successfully been used to model several aspects of the meaning of natural language. However, visually impaired people continue to face serious difficulties in getting full access to this important instrument. Our paper aims to present a work in progress whose main goal is to provide blind students and researchers with an adequate method to deal with the different resources that are used in formal semantics. In particular, we intend to adapt the Portuguese Braille system in order to accommodate the most common symbols and formulas used in this kind of approach and to develop pedagogical procedures to facilitate its learnability. By making this formalization compatible with the Braille coding (either traditional and electronic), we hope to help blind people to learn and use this notation, essential to acquire a better understanding of a great number of semantic properties displayed by natural language.
OCTIS: Comparing and Optimizing Topic models is Simple!

In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach. The proposed solution integrates several state-of-the-art topic models and evaluation metrics. These metrics can be targeted as objective by the underlying optimization procedure to determine the best hyper-parameter configuration. OCTIS allows researchers and practitioners to have a fair comparison between topic models of interest, using several benchmark datasets and well-known evaluation metrics, to integrate novel algorithms, and to have an interactive visualization of the results for understanding the behavior of each model. The code is available at the following link: https://github.com/MIND-Lab/OCTIS.