Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Adaptive Protection based on IEC 61850

In electrical power systems, the overcurrent relays (ANSI/IEEE 50/51) are widely used for protection, and it is one of the main alternatives for identifying system faults. In radial systems supplied by a single source, protection and selectivity can be achieved using the traditional techniques based on time-current curves coordination. However, in a system with multiple sources and operational conditions, traditional techniques are not sufficient to obtain selectivity. This paper presents an adaptative protection system which, based on IEC 61850, adjusts the protection to changes in the power system. The proposal uses a communication network to change the overcurrent settings according to the sources in operation. The performance of the proposed system was evaluated in a case study. The system is composed of five independent power supplies, a utility and four cogeneration units with different rated power; thus 15 combinations of sources are possible during islanding conditions. The results demonstrate that the proposal can provide the selectivity of the system in any operational condition.
Synergistic enhancement of arc ablation resistance and mechanical properties of epoxy resin insulation

Arc ablation is one of the important inducements leading to the surface insulation failure of epoxy resin (EP) break insulation in a HVDC circuit breaker. As a promising method to enhance the arc ablation resistance of EP insulation, the use of heat-conducting nano-fillers has always been confronted with the contradiction between the enhancement of thermal properties and that of mechanical strength. Up to now, the ablation characteristics of EP nanocomposite under short-time partial arc ablation is still poorly understood, and the synergistical enhancement of arc ablation resistance and mechanical strength of EP insulation is not realized as yet. Focused on these uncharted areas, in this work, we prepared a novel EP nanocomposite by blending method using dopamine-modified h-BN flakes and rigid α-Al2O3 microsphere as binary modifiers. The results indicate that, though the arc ablation resistance of EP insulation can be improved to some extent after introducing functionalized h-BN, however, the tensile strength decreases significantly. In comparison, the h-BN&α-Al2O3 binary nanofillers may not only improve the apparent activation energy of EP insulation in thermal decomposition, but also regulate the interfacial interaction between particle swarms and EP matrix. This is beneficial to relieve the partial carbonization on the insulation surface thereby improving the toughness of the composite, which provides a feasible method to synergistically enhance the multiple properties of EP insulation.
Dimensioning and Layout Planning of 5G-Based Vehicular Edge Computing Networks Towards Intelligent Transportation

Fast-response communication is crucial for Vehicular Ad Hoc Network (VANET). In practice, the conventional VANETs, suffering from the high mobility of the vehicles and the ever-growing data to percept and process, cannot meet the demand of fast response currently. In this paper, we study the Dimensioning and Layout Planning (DLP) problem under 5G-based Vehicular Edge Computing Network (VECN) architecture which integrates the 5G Micro Base Station (gNB) and Edge Computing (EC) to reduce the response time. The DLP problem aims to minimize the total placement cost under the constraint of the full coverage. This paper formulates the DLP problem as an integer linear program (ILP) and then proposes a Greedy Algorithm (GA) and a Cost-Effective Heuristic Algorithm (CEHA) toimprove the computation efficiency. The case studies have verified the feasibility and scalability of the DLP formulation and showed that the proposed CEHA is fairly effective and efficient to solve the DLP problem.
Seizing Requirements Engineering Issues through Supervised Learning Techniques

In recent years, the popularity of machine learning techniques has grown due to the availability of larges volumes of data and the increased processing capacity of computers. Despite the inherent value of these techniques, few studies have attempted to summarize how machine learning algorithms, especially supervised learning have contributed to task automation and resolving challenges in Requirements Engineering. This paper proposes a systematic mapping of the literature to identify and analyze proposals which employ supervised learning in Requirements Engineering between 2002-2018. The goal of this research is to identify trends, datasets, and methods used. Thirty-three studies were selected based on defined inclusion and exclusion criteria. The results show that researches using these techniques focuses on eight broad categories: detection of linguistic problems in requirements documents and artifacts written in natural language, classification of document content, traceability, effort estimation, requirements analysis, failures prediction, quality and detection of business rules. The most used supervised learning algorithms were Support Vector Machine, Naive Bayes, Decision Tree, K-Nearest Neighbour, and Random Forest. Twenty-five public and twenty -eight private data sources were identified. Among the most used public data sources are Predictor Models in Software Engineering, iTrust Electronic Health Care System and Metric Data Program from NASA.
Characteristics of Poly-Si Junctionless FinFETs With HfZrO Using Forming Gas Annealing

In this study, an effective method was proposed to enhance the current drivability of junctionless field-effect transistors (JL-FETs) by utilizing ferroelectric effects. The ferroelectric layers were deposited on JL-FinFETs. The poly-Si junctionless FinFETs (JL-FinFETs) with HfZrO were successfully fabricated and demonstrated. The subthreshold slope (S.S.) of JL-FinFETs with HfZrO was very sensitive to post-metal annealing (PMA) conditions and fin width. With PMA at 700 °C, steeper S.S. and Ion/Ioff>107 could be obtained owing to the ferroelectric effect. JL-FinFETs with PMA at 700 °C possessed lower Ioff and offered the promise of higher integration flexibility for Si CMOS compatible process for future applications. Besides, the JL-FinFETs with forming gas annealing (FGA) had a small hysteresis and achieved the improved S.S.
Kalman Filtering Based Adaptive Transfer in Energy Harvesting IoT Networks

In this paper, we investigate an energy prediction algorithm based on Kalman filtering in energy harvesting IoT networks. The IoT nodes harvest renewable energy from nature and powered by green energy only. Owing to the space-time instability and non-uniformity of renewable energy, the IoT nodes may have insufficient energy supply. An unresolved challenge is accurately predicting the available renewable energy, and developing low complexity solutions that incorporate a lossless transfer guarantee. With this in mind, we propose the energy prediction algorithm based on Kalman filtering to bridge the gap between lossless transfer and unstable renewable energy. The energy prediction is performed at the access point in order to dynamically adjust the number of bits to be sent, and the data loss due to receiver energy depletion will be improved. In addition, real solar and wind energy profiles are exploited by simulations. The simulation results show that the proposed energy prediction algorithm can improve transmission efficiency in terms of the rate of drop bits and the number of time slots needed to transmit a given payload, and reduce the wastes of harvested renewable energy.
A Hybrid Classification Method via Character Embedding in Chinese Short Text With Few Words

Last decades have witnessed the significance development of research in short text classification. However, most existing methods only focus on the text which contained dozens of words like Twitter or MicroBlog, but not take the short text with few words like news headline or invoice name into consideration. Meanwhile, contemporary short text classification methods either to expand feature of short text with external corpus or to learn the feature representation from all the texts, which have not take the difference between words of short text into full consideration. Notably, the classification of short text with few words are usually determined by a few specific key words contrary to documents classification or traditional short text classification. To address these problems, this paper propose a hybrid classification method of Attention mechanism and Feature selection via Character embedding in Chinese short text with few words, called AFC. More specifically, firstly, the character embedding is computed to represent Chinese short texts with few words, which takes full advantage of short text information without external corpus. Secondly, attention-based LSTM is introduced in our method to project the data into feature representation space with weighting, which make the keywords in classification have more subtle value. Furthermore, the semantic similarity between content and class label information is calculated for feature selection, which reduces the possible negative influence of some redundant information on classification. Experiments on real-world datasets demonstrate the effectiveness of our method compared to other competing methods.
A High Frequency Dual Inverted Mesa QCM Sensor Array With Concentric Electrodes

Quartz Crystal Microbalance (QCM) is a candidate technology for high sensitivity application. This mass sensor’s property depends on the piezoelectric property of the AT-cut quartz crystal. QCM has been widely employed for gas detection due to its comparative advantages including high mass sensitivity, potential for array configuration, low cost, ease of fabrication as well as wide range of available sensitive material compatibility. However, its application has been limited due to two main disadvantages of the non-uniform mass sensitivity across the electrodes and lower frequency of operation. In this work, in order to overcome these disadvantages, a novel concentric electrode structure combined with the dual inverted mesa structure has been proposed and implemented. It is demonstrated that the developed and optimized concentric electrode has provided a uniform displacement across the QCM’s sensing electrode. In addition, the dual inverted mesa design has been implemented in the QCM array in which a high fundamental resonant frequency of 33 MHz has been achieved without interference between the adjacent channels in the sensor array. The interference between the adjacent high frequency QCM channels has been eliminated and therefore, each QCM can function as an individual gas sensor when coated with the designed sensing layers. For 33 MHz, the interference has been eliminated at the optimal center to center between electrode distance (c2c). In the case of 10 MHz array, c2c value of 6 mm is achieved which is lower than the value of c2c achieved on a traditionally un-etched QCM array which was found equal to 6.5 mm. Therefore, the proposed QCM array design is shown to reduce the overall size while enhancing the device sensitivity, uniformity and performance
New Synthesis of Benzo-δ-carbolines, Cryptolepines, and Their Salts: In Vitro Cytotoxic, Antiplasmodial, and Antitrypanosomal Activities of δ-Carbolines, Benzo-δ-carbolines, and Cryptolepines

The paper describes, in its first part, a new synthesis of benzo-δ-carbolines, cryptolepines, and their salts. The strategy is based on the association between halogen-dance and hetero-ring cross-coupling. It is fully convergent and regioselective with interesting overall yields from 27% to 70%. A halogen-dance mechanism in quinoline series is also proposed. The formal synthesis of potential antimalarial compounds and the first total synthesis of 11-isopropylcryptolepine are also described. In the second part, cytotoxic activity against mammalian cells and activities against Plasmodium falciparum and Trypanosoma cruzi of benzo-δ-carbolines and δ-carbolines were evaluated in vitro to study the structure−activity relationships. For benzo-δ-carbolines, methylation at N-5 increases the cytotoxic and antiparasitic activities. A further alkylation on C-11 generally increases the cytotoxic activity but not the antiparasitic activity, cryptolepine and 11-methylcryptolepine being the most active on both parasites.
Mapping of Quantitative Trait Loci for the Occurrence of White-Back Kernels Associated with High Temperatures During the Ripening Period of Rice (Oryza sativa L.)

To identify the chromosomal regions controlling the occurrence of undesirable white-back kernels (WBK) in rice under growth conditions at high temperatures, we performed a quantitative trait locus (QTL) analysis in F6 recombinant inbred lines (RILs). We constructed a linkage map using 106 simple sequence repeat (SSR) markers from 107 RILs derived from a cross between the japonica cultivar ‘Chiyonishiki’, which shows a high incidence of WBK at high temperatures, and the japonica cultivar ‘Koshijiwase’, which is tolerant to high temperatures and displays a low incidence of WBK. Each RIL was exposed to high temperatures in a greenhouse during the ripening period (from heading to maturity). The number of heat-damaged kernels was counted to obtain the rates of WBK in each RIL. Four putative QTLs for the occurrence of WBK–qWK1-1, qWK1-2, qWK2 and qWK8–were detected near the SSR markers RM8068 and RM5501 on chromosome 1, RM5916 on chromosome 2, and RM2680 on chromosome 8, respectively. The Koshijiwase alleles at the qWK1-2, qWK2 and qWK8 loci decreased the incidence of WBK and showed additive effects. qWK1-2 explained 15.0% of the total phenotypic variance, while qWK1-1, 8.8%, qWK2, 9.3%, and qWK8, 9.2%.
HRTF Measurement by Means of Unsupervised Head Movements With Respect to a Single Fixed Speaker

In a standard state-of-the-art measurement the head-related transfer function (HRTF) is obtained in an anechoic room with an elaborate setup involving multiple calibrated loudspeakers. In search for a simplified method that would open up the possibility for an HRTF measurement in a home environment, it has been suggested that this setup could be replaced with one with a single, fixed loudspeaker. In such a setup, the subject samples different directions by moving the head with respect to this loudspeaker, while the head movements are tracked in some way. In this paper, the feasibility of such an approach is studied. To this end, the HRTF is measured in an unmodified (non-anechoic) room by means of a single external speaker and a high resolution head tracking system. The differences between the dynamically obtained HRTF and the standard static HRTF are investigated, and are shown to be mostly due to variable torso reflections.
Rental Prediction in Bicycle-Sharing System Using Recurrent Neural Network

As the rapid development of smart city and Internet of Things (IoT), related research issues have attracted much attention from industry and academia around the world, and Bicycle-Sharing System (BSS) is one of the thriving applications of smart transportation system. BSS is a system that allows users to rent the bicycle from any automatic rental station. If there’re some stations that don’t have enough bicycles or free places, then it is usually handled by dedicated vehicles to rebalance the bicycles. Thus, predicting the rental (i.e. the number of renting or returning bicycles) from users in the future is important to improve the service quality. This research uses Recurrent Neural Network (RNN) to predict the rental from users. The RNN consists of three parts: period, closeness, and general. Each of them represents the historical records in different time intervals in the past time respectively. After inputting the historical rental data into RNN and the training process, we can predict the bicycle rental in the coming day by inputting the rental records of the past time into RNN. Finally, we compare the effectiveness among this and the method of Poisson by real YouBike data and prove that our model outperforms it.
The importance of live coral for small-sized herbivorous reef fishes in physically challenging environments

Tropical coral reefs are facing increasing levels of disturbance, with predictions of ongoing reductions in live coral cover. The response of herbivorous fishes to loss of coral cover was investigated by analysing the relationship between the abundance of different sized herbivorous fishes and live coral cover, on a high flow reef slope and a sheltered back reef on Ningaloo Reef, Western Australia. We found positive relationships between coral cover and the abundance of small (10-20 cm) excavating and scraping parrotfishes on the slope, but a negative relationship between corals and small (10-15 cm) grazers. These relationships were not evident on back reefs. Generally, the abundance of large individuals were not correlated with live coral in either habitat. We suggest that in physically demanding environments small parrotfishes may utilise the structural complexity of corals to resist strong water flow and/or to reduce predation. Small acanthurid grazers may school to reduce energy costs and minimise predation risk. The loss of coral cover appears to be particularly important for small fishes in high energy habitats, especially non-schooling species.
Exploring the Enrichment of Basque WordNet with a Sentiment Lexicon

Wordnets are lexical databases where the semantic relations of words and concepts are established. These resources are useful for manyNLP tasks, such as automatic text classification, word-sense disambiguation or machine translation. In comparison with other wordnets,the Basque version is smaller and some PoS are underrepresented or missing e.g. adjectives and adverbs. In this work, we explore anovel approach to enrich the Basque WordNet, focusing on the adjectives. We want to prove the use and and effectiveness of sentimentlexicons to enrich the resource without the need of starting from scratch. Using as complementary resources, one dictionary and thesentiment valences of the words, we check if the word of the lexicon matches with the meaning of the synset, and if it matches we addthe word as variant to the Basque WordNet. Following this methodology, we describe the most frequent adjectives with positive andnegative valence, the matches and the possible solutions for the non-matches.
Evaluation Dataset for Zero Pronoun in Japanese to English Translation

In natural language, we often omit some words that are easily understandable from the context. In particular, pronouns of subject, object, and possessive cases are often omitted in Japanese; these are known as zero pronouns. In translation from Japanese to other languages, we need to find a correct antecedent for each zero pronoun to generate a correct and coherent translation. However, it is difficult for conventional automatic evaluation metrics (e.g., BLEU) to focus on the success of zero pronoun resolution. Therefore, we present a hand-crafted dataset to evaluate whether translation models can resolve the zero pronoun problems in Japanese to English translations. We manually and statistically validate that our dataset can effectively evaluate the correctness of the antecedents selected in translations. Through the translation experiments using our dataset, we reveal shortcomings of an existing context-aware neural machine translation model.
Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods

In this paper we present the first full morphological analysis and disambiguation system for Gulf Arabic. We use an existing state-of-the-art morphological disambiguation system to investigate the effects of different data sizes and different combinations of morphological analyzers for Modern Standard Arabic, Egyptian Arabic, and Gulf Arabic. We find that in very low settings, morphological analyzers help boost the performance of the full morphological disambiguation task. However, as the size of resources increase, the value of the morphological analyzers decreases.
Adaptation of Deep Bidirectional Transformers for Afrikaans Language

The recent success of pretrained language models in Natural Language Processing has sparked interest in training such models for languages other than English. Currently, training of these models can either be monolingual or multilingual based. In the case of multilingual models, such models are trained on concatenated data of multiple languages. We introduce AfriBERT, a language model for the Afrikaans language based on Bidirectional Encoder Representation from Transformers (BERT). We compare the performance of AfriBERT against multilingual BERT in multiple downstream tasks, namely part-of-speech tagging, named-entity recognition, and dependency parsing. Our results show that AfriBERT improves the current state-of-the-art in most of the tasks we considered, and that transfer learning from multilingual to monolingual model can have a significant performance improvement on downstream tasks. We release the pretrained model for AfriBERT.
Fintan - Flexible, Integrated Transformation and Annotation eNgineering

We introduce the Flexible and Integrated Transformation and Annotation eNgeneering (Fintan) platform for converting heterogeneous linguistic resources to RDF. With its modular architecture, workflow management and visualization features, Fintan facilitates the development of complex transformation pipelines by integrating generic RDF converters and augmenting them with extended graph processing capabilities: Existing converters can be easily deployed to the system by means of an ontological data structure which renders their properties and the dependencies between transformation steps. Development of subsequent graph transformation steps for resource transformation, annotation engineering or entity linking is further facilitated by a novel visual rendering of SPARQL queries. A graphical workflow manager allows to easily manage the converter modules and combine them to new transformation pipelines. Employing the stream-based graph processing approach first implemented with CoNLL-RDF, we address common challenges and scalability issues when transforming resources and showcase the performance of Fintan by means of a purely graph-based transformation of the Universal Morphology data to RDF.
The Brain-IHM Dataset: a New Resource for Studying the Brain Basis of Human-Human and Human-Machine Conversations

This paper presents an original dataset of controlled interactions, focusing on the study of feedback items. It consists on recordings of different conversations between a doctor and a patient, played by actors. In this corpus, the patient is mainly a listener and produces different feedbacks, some of them being (voluntary) incongruent. Moreover, these conversations have been re-synthesized in a virtual reality context, in which the patient is played by an artificial agent. The final corpus is made of different movies of human-human conversations plus the same conversations replayed in a human-machine context, resulting in the first human-human/human-machine parallel corpus. The corpus is then enriched with different multimodal annotations at the verbal and non-verbal levels. Moreover, and this is the first dataset of this type, we have designed an experiment during which different participants had to watch the movies and give an evaluation of the interaction. During this task, we recorded participant’s brain signal. The Brain-IHM dataset is then conceived with a triple purpose: 1/ studying feedbacks by comparing congruent vs. incongruent feedbacks 2/ comparing human-human and human-machine production of feedbacks 3/ studying the brain basis of feedback perception.