Miracle-dz/UniFAR-FTUs huggingface dataset:
URJC-Team at EmoEvalEs 2021: BERT for Emotion Classification in Spanish Tweets

This paper describes the participation of the URJC-Team in the EmoEvalEs 2021 task of the IberLEF evaluation campaign. The task consists of classifying the emotion expressed in a tweet among seven diﬀerent classes of emotion. Our proposal is based on transfer learning using BERT language modeling. We train three ﬁne-tuned BERT models ﬁnally selecting for the submitted runs two of them, along with a sys-tem that combines all the models by means of an ensemble method. We obtained competitive results in the challenge, ranking ﬁfth. Additional work needs to be done to improve the results.
Latest enhancements in the Spanish DBpedia

The Spanish DBpedia is a data source used initially to support the Spanish community. However, our logs show that the Spanish language goes beyond Spanish speakers and many non-Spanish speakers use the Spanish DBpedia on a daily basis. In the last months we have made two important enhancements to the Spanish DBpedia: (1) we publish a nonstandard dataset containing the type of resources that in the standard distribution have no type, and (2) we update automatically our data every week by using the DBpedia databus. In this way, we satisfy a frequent request made by companies and we foster the usage of the Spanish language, the second mother language by the number of speakers (after Chinese), and the second in scientiﬁc papers (after English).
CISUC at IDPT2021: Traditional and Deep Learning for Irony Detection in Portuguese

These notes describe the participation of the CISUC team in the IDPT 2021 shared task. Irony detection was tackled as a text clas-siﬁcation task, where both traditional and transformer-based (BERT) approaches were explored. The former performed ok, but not everything went well, and the results achieved by BERT were not evaluated, due to an issue with our oﬃcial submissions. Nevertheless, we still discuss some of the options taken, identify important features, and present validation results in the training data.
SortCache

Sparse data applications have irregular access patterns that stymie modern memory architectures. Although hyper-sparse workloads have received considerable attention in the past, moderately-sparse workloads prevalent in machine learning applications, graph processing and HPC have not. Where the former can bypass the cache hierarchy, the latter fit in the cache. This article makes the observation that intelligent, near-processor cache management can improve bandwidth utilization for data-irregular accesses, thereby accelerating moderately-sparse workloads. We propose SortCache, a processor-centric approach to accelerating sparse workloads by introducing accelerators that leverage the on-chip cache subsystem, with minimal programmer intervention.
Using the RFC 7575 and Models at Runtime for Enabling Autonomic Networking in SDN

The programmable network architectures that emerged in the last decade have allowed new ways to enable Autonomic Networks. However, there are several open issues to address before making such a possibility into a feasible reality. For instance, defining network goals, translating them into network rules, and granting the correct functioning of the network control loop in a self-adaptive manner are examples of complex tasks required to enable an autonomic networking environment. Fortunately, architectures based on the concept of Models at Runtime (MART) provide ways to overcome such complexity. This paper proposes a MART-based framework – using the RFC 7575 as reference (i.e., definitions and design goals for autonomic networking) – to implement autonomic management into a programmable network. The evaluation shows the proposed framework is suitable for satisfying the functional and performance requirements of a simulated network.
Preparation and characterization of N-benzyl-N , O-succinyl chitosan polymeric micelles for solubilization of poorly soluble non-steroidal anti-inflammatory drugs

Purpose: To investigate the solubilization of poorly water-soluble non-steroidal anti-inflammatory drugs (NSAIDs) in N-benzyl-N,O-succinyl chitosan (BSCS) polymeric micelles Methods: BSCS was synthesized by reductive amination and succinylation, respectively. NSAIDs; meloxicam (MX), piroxicam (PRX), ketoprofen (KP) and indomethacin (IND) were entrapped in the hydrophobic inner cores by evaporation method. The effects of drug structure on loading efficiency, particle size and surface charge of micelles were investigated. Results: The critical micelle concentration of BSCS micelles was 0.0385 mg/mL and cytotoxicity on Caco-2 cells depends on the polymer concentration (IC50 = 3.23 ± 0.08 mg/mL). BSCS micelles were able to entrap MX, PRX, KP and IND and also improve the solubility of drugs. Drug loading efficiency was highly dependent on the drug molecules. The drug loading capacity of these BSCS micelles was in the following rank order: KP (282.9 μg/mg) > PRX (200.8 μg/mg) > MX (73.7 μg/mg) > IND (41.2 μg/mg). The highest loading efficiency was observed in KP-loaded BSCS micelles due to the attractive force between phenyl groups of KP and benzyl groups of the polymer. Particle size and surface charge were in the range of 312 433 nm and -38 to -41 mV, respectively. Conclusion: BSCS copolymer presents desirable attributes for enhancing the solubility of hydrophobic drugs. Moreover, BSCS polymeric micelles might be beneficial carrier in a drug delivery system.
Optical transitions of native defects in single-walled carbon nanotubes: Time-dependent density functional theory study

ABSTRACT Optical transitions of a pristine (8,0) single-walled carbon nanotube (SWCNT) and the (8,0) SWCNT containing three types of defects were calculated based on electronic structures obtained using the density functional theory (DFT) corrected by the van der Waals (vdW) interactions. The photon absorption energies (PAE) of the pristine (8,0) SWCNT and defected (8,0) SWCNTs were calculated using time-dependent density functional theory (TDDFT). We found that all three defects studied, namely, single vacancy, diatom vacancy, and Stone-Wales, have characteristic PAEs lower than the PAE of the pristine SWCNT. The calculated PAE could be used to identify native defects in semiconducting (8,0) SWCNT.
A Study to Determinants of Risk Management and Prevention in Credit Card Business in Commercial Banks

The purpose of this study is to explore and determinants the risk management and prevention in credit card business of Malaysian commercial banks. There were five factors derived through the literature and experimental investigation that are affecting the banks due to credit card which are External Fraud Risk, Internal Operational Risk, Credit Risk of Cardholders, Science and Technology and Intermediary Agency Trades Risk. Hence, 250 respondents participated in this study from state of Selangor and Federal Capital Kuala Lumpur. The data was collected and analyzed by SPPS software and it was concluded that that External fraud risk, Internal operational risk, Credit risk of Cardholder, Science and technology are positively correlated with the Credit card business risks of commercial Banks in Malaysia. These papers have highlighted suggestion for future research to develop this study further by considering the data derived from this study by using Delphi Technique
Ilias El Aita_Manufacturing oral solid dosage forms using pressure-assisted microsyringe 3D-printing

34 Chapter III – On-demand manufacturing of immediate release levetiracetam tablets using pressure-assisted microsyringe printing . 35 Pretext . 35 Evaluation of authorship . 35 Abstract . 36. 36 Chapter IV – 3D-Printing with precise layer-wise dose adjustments for paediatric use via pressure-assisted microsyringe printing . 37 Pretext . 37 Evaluation of authorship . 37 Abstract . 38. 38 Chapter V – Investigation of semi-solid formulations for 3D printing of drugs after prolonged storage to mimic real-life applications . 39 Pretext . 39 Evaluation of authorship . 39 Abstract . 40. 40 Chapter VI – Conclusion and future perspective . 41 Summary . 45 Original publications . 47
Vietnamese Sentence Paraphrase Identification using Pre-trained Model and Linguistic Knowledge

The paraphrase identification task identifies whether two text segments share the same meaning, thereby playing a crucial role in various applications, such as computerassisted translation, question answering, machine translation, etc. Although the literature on paraphrase identification in English and other popular languages is vast and growing, the research on this topic in Vietnamese remains relatively untapped. In this paper, we propose a novel method to classify Vietnamese sentence paraphrases, which deploys both the pre-trained model to exploit the semantic context and linguistic knowledge to provide further information in the identification process. Two branches of neural networks built in the Siamese architecture are also responsible for learning the differences among the sentence representations. To evaluate the proposed method, we present experiments on two existing Vietnamese sentence paraphrase corpora. The results show that for the same corpora, our method using the PhoBERT as a feature vector yields 94.97% F1-score on the VnPara corpus and 93.49% F1-score on the VNPC corpus. They are better than the results of the Siamese LSTM method and the pre-trained models. Keywords—Paraphrase identification; Vietnamese; pre-trained model; linguistics; neural networks
Deaths of Despair: Prescriptive Authority of Psychologists and Suicides

The Center for Growth and Opportunity at Utah State University is a university-based academic research center that explores the scientific foundations of the interaction between individuals, business, and government. This working paper represents scientific research that is intended for submission to an academic journal. The views expressed in this paper are those of the author(s) and do not necessarily reflect the views of the Center for Growth and Opportunity at Utah State University or the views of Utah State University.
Charge Density and Band Offsets at Heterovalent Semiconductor Interfaces

The well‐known insensitivity of the band offset (BO) of isovalent heterojunctions with the zincblende structure to the orientation, abruptness, and atomic structure of the interface was recently shown to be attributable to a localness in the dependence of charge density on the atomic structure. In contrast, a sharp dependence of the BO on interface specifics has been observed at heterovalent heterojunctions. Here, detailed analyses of the relationship between the BO, interface structure, and charge distribution have been carried out for many lattice‐matched heterovalent interfaces between zincblende and diamond structure semiconductors. From thermodynamic considerations, three types of neutral interfaces were investigated, each with equal densities of donor‐ and acceptor‐like heterovalent bonds, constructible in all orientations. Distinctively different, yet approximately orientation‐independent, valence BOs were found. The equilibrium charge density of the heterovalent interface could be recreated with the charge densities of bulk semiconductors and oligo‐cells. Because charge transfer between heterovalent bonds is identifiable with that for dopants in semiconductor and its effect accountable by linear response, a combination of neutral polyhedra theory, previously developed for isovalent heterojunctions, and dielectric screening theory was found to explain BO trends throughout, allowing a strategy that facilitates adjustment in the BO of all isovalent heterojunctions.
Gait Recognition through Static and Activity Specific Parameters

: A gait recognition system that recoups static body and gait parameters of subjects as they walk is introduced. This approach is a case of a activity specific biometric: a technique for separating distinguishing properties of a person or then again of a person's conduct that is material just when an individual is performing that particular activity. To assess our parameters, authors determine a normal confusion metric - related to mutual information - rather than announcing a percent right with a constrained database. This measurement predicts how well a given feature vector will filter character in a huge population. Authors test the utility of an assortment of body what's more, gait parameters recouped in various review conditions on a database comprising of 15 to 20 subjects walking at both a calculated and frontal-equal view as for the camera, both inside and out. Authors likewise examine motion capture information of the subjects to find whether confusion in the parameters is intrinsically a physical or a visual estimation error property.
Overall heritability in popcorn estimated by meta-analysis

The objective of this study was to conduct a meta-analysis and test its efficiency in summarizing the heterogeneous data of heritability estimates for the traits of grain yield (GY) and popping expansion (PE), and to provide reliable estimates of selection gains in popcorn. Therefore, 97 heritability estimates ( ) for popcorn GY and PE in the broad and narrow sense were used. The main procedures underlying the estimation of the combined heritability ( ) using the technique of meta-analysis consisted of i) an exploratory analysis of the set of heritability estimates to detect outliers using a box-plot chart, ii) the verification of the required statistical assumptions, iii) testing the involved heritability estimates for homogeneity, and iv) the calculation of the estimates of combined heritability. The meta-analysis facilitated the synthesis of the information pertaining to heritability in popcorn. The combined heritability estimates ( ) in the broad sense for GY and PE were 0.5208 ± 0.0229 and 0.6356 ± 0.0209, respectively, and in the narrow sense were 0.3290 ± 0.0292 and 0.3083 ± 0.0298, respectively.
Synchronization in complete networks of reaction-diffusion equations of FitzHugh-Nagumo type with nonlinear coupling

The synchronization in complete network consisting of nodes is studied in this paper. Each node is connected to all other ones by nonlinear coupling and is represented by a reaction-diffusion system of FitzHugh-Nagumo type which can be obtained by simplifying the famous Hodgkin-Huxley model. From this complete network, the sufficient condition on the coupling strength to achieve the synchronization is found. The result shows that the networks with bigger in-degrees of nodes synchronize more easily. The paper also presents the numerical simulations for theoretical result and shows a compromise between the theoretical and numerical results.
COVID-19 Diagnosis with Deep Learning Diagnóstico de COVID-19 con Deep Learning

The coronavirus disease 2019 (COVID-19) is fatal and spreading rapidly. Early detection and diagnosis of the COVID-19 infection will prevent rapid spread. This study aims to automatically detect COVID-19 through a chest computed tomography (CT) dataset. The standard models for automatic COVID-19 detection using raw chest CT images are presented. This study uses convolutional neural network (CNN), Zeiler and Fergus network (ZFNet), and dense convolutional network-121 (DenseNet121) architectures of deep convolutional neural network models. The proposed models are presented to provide accurate diagnosis for binary classification. The datasets were obtained from a public database. This retrospective study included 757 chest CT images (360 confirmed COVID-19 and 397 non-COVID-19 chest CT images). The algorithms were coded using the Python programming language. The performance metrics used were accuracy, precision, recall, F1-score, and ROC-AUC. Comparative analyses are presented between the three models by considering hyper-parameter factors to find the best model. We obtained the best performance, with an accuracy of 94,7%, a recall of 90%, a precision of 100%, and an F1-score of 94,7% from the CNN model. As a result, the CNN algorithm is more accurate and precise than the ZFNet and DenseNet121 models. This study can present a second point of view to medical staff.
Oral verrucous carcinoma: a 24-year epidemiological study and case report

Introduction: Verrucous carcinoma is a non-metastatic variant of squamous cell carcinoma. It was first reported by Ackerman in 1948. It is a verrucous exophytic tumor and, although it is a rare lesion, it mainly affects the oral cavity, with slow growth and can be locally invasive. Objective: To report a case and present an observational and retrospective analysis of medical records containing biopsy data from patients diagnosed with oral verrucous carcinoma (OVC) in an oral diagnostic referral service for a period of 24 years. Material and methods: The following data were collected: age, sex, ethnicity, anatomical location and management. Result: Eight cases of OVC were found, all in Caucasian patients (n = 8, 100%), aged between 57 and 102 years. 62% of the injuries affected women. The most affected region was the jugal mucosa (n = 4, 50%). In addition, Smoking patients were not found. In all cases, incisional biopsy was performed and referral to the head and neck surgeon. Conclusion: Caucasian women in the sixth and seventh decades of life were the most affected by OVC. The absence of smoking patients corroborates the literature, which reports that cigarettes appear to be unrelated to this injury. The dentist's role is to recognize the disease early, diagnose it and refer it for medical treatment.
An overview on Ethnopharmacologicals studies carried out on Lannea microcarpa Engl. & K. Krause (Anacardiaceae): a medicinal plant used in Burkina Faso for the treatment of hypertension

Salfo Ouédraogo1,2, Mathieu Nitiéma1, Lazare Belemnaba1, Tata Kadiatou Traoré*2, Bavouma C Sombié2, Hermine Zime Diawara2, Sylvin Ouédraogo1, Rasmané Semdé2 1Department of Traditional Medicine and Pharmacopoeia-Pharmacy (MEPHATRA-PH), Health Science Research Institute (IRSS/CNRST), 03 BP 7047 Ouaga 03, Burkina Faso 2Drug Development Laboratory (LADME), Doctoral School of Health, Joseph Ki-Zerbo University, 03 BP 7021 Ouaga 03, Burkina Faso
Candidate Background Verification Using Machine Learning and Fuzzy Matching

— Recruiters usually spend less than a minute looking at each resume when deciding whether it’s worth continuing the recruitment process with the candidate. They focus on the keywords, and it is impossible to guarantee a fair process of candidate selection [5]. They have to go through thousands of resumes before deciding which candidate to select within a short time. Thus, making it nearly difficult to perform a thorough background check of the candidates. Recruiters may often encounter cases where candidates provide false information on their resume regarding their education, experience or even skills. The main scope of this paper is to introduce a data-driven approach to verify any candidate’s resume by using social media platforms automatically and give recruiters time to only examine promising candidates. Once, the candidate profile is validated, the ranking score is calculated. The score describes how well the candidate’s profile matches with the information retrieved from his/her social media accounts. For example, we check a candidate’s educational background, work experience and skills from LinkedIn. Later, this paper illustrates a prototype application which can increase the productivity of recruiters. This application shows how to improve the hiring process by giving an unbiased hiring decision support.
Baseline data of the presence of meso and microplastics in digestive tract of a commercially important teleost fish from the Rio de la Plata Estuary System (Southwest Atlantic Ocean)

In this paper, we analyze the occurrence, abundance, and type of plastic debris (PD) present on the digestive tracts of 65 white croakers (Micropogonias furnieri) catches from Rio de la Plata Estuary System (RLPES). Eighty percent of individuals analyzed had ingested PD, of which 156 were thread-like plastics (FO = 83.9 %) and 30 were fragments (FO = 16.1%). The average number of pieces per digestive tract was 2.83 ± 3.14. Microplastic represented 85% of PD, the majority of these pieces were <2 mm, and the preeminent color was blue. The RLPES is the area of the Southwest Atlantic Ocean that concentrates most of the records of plastic debris on the environment, where the white croaker is the main commercial fish. In addition, M. furnieri constitutes a key prey of several predators in the area. Although preliminary, this is a relevant contribution to the knowledge of marine debris pollution and its impact on the Southwest Atlantic Ocean organisms, particularly at the RLPES.
Crop Prediction System, Crop Disease Identification and Its Solution Using Machine Learning

: - India is agricultural land and farming is that the major source of economy. 70% of the Indian population directly relies on agriculture. The common problem existing among the Indian farmers is to settle on the proper crop supported by the soil and atmospheric requirements. thanks to this, they face a significant setback in productivity. Our work proposes to assist farmers to determine the soil quality by analyzing its various parameters and suggesting crops supported by the results obtained using a machine learning approach. The system uses the Classification algorithm of K nearest neighbor to enhance the efficiency of the Crop Recommendation System. The system maps the soil and crop data to predict the list of suitable crops for the soil and it also provides knowledge about nutrients that are deficient in soil for the actual crop. Hence it leaves upon the user to make a decision on the crop to be sown. Thus, the system helps to supply knowledge to the dilettante farmers.
Synthesis, thin-film self-assembly, and pyrolysis of ruthenium-containing polyferrocenylsilane block copolymers

Polyferrocenylsilane (PFS)-containing block copolymers have previously been shown to self-assemble into metal-rich nanodomains in thin films or the bulk phase. This patterning of the metalloblock can enable the fabrication of arrays of Fe nanoparticles for applications in catalytic carbon nanotube growth. Herein, we report the preparation of block copolymers with a ruthenium-containing polyferrocenylsilane (RuPFMEtS) segment from a polystyrene-block-polyferrocenylmethyl(trimethylsilylethynyl)silane (PS-b-PFMEt(TMS)S) precursor. The latter was prepared via the photocontrolled ring-opening polymerisation of methyl(trimethylsilylethynyl)sila[1]ferrocenophane and a cyclopentadienyl-terminated polystyrene homopolymer. Deprotection of the Si(CH3)3 groups from the ethynyl substituents on the PFS block was carried out by using NaOMe. Incorporation of Ru3(CO)9H clusters into the block copolymer was achieved by reaction with Ru3(CO)12 to obtain PS-b-RuPFMEtS. This afforded two block copolymers, highly metallised ruthenium-based segments, PS265-b-RuPFMEtS10 and PS196-b-RuPFMEtS31, containing ca. 10 and 20% Ru by mass, respectively. Phase-separation of the resulting block copolymers was investigated in the bulk and thin films and was found to yield spherical or cylindrical domains of RuPFMEtS in a PS matrix, respectively. Pyrolysis of PS265-b-RuPFMEtS10 and PS196-b-RuPFMEtS31 block copolymers at 500 or 800 °C for 2 h led to the formation of either amorphous (ca. 2 nm in diameter at 500 °C) or polycrystalline (ca. 14 nm in diameter at 800 °C) Fe/Ru nanoparticles in a carbonaceous matrix. These NP composites are promising candidates for use as heterogeneous hydrogenation catalysts. The pyrolysed materials were characterised by high resolution transmission electron microscopy, energy-dispersive X-ray spectroscopy, selected area electron diffraction, scanning electron microscopy and powder X-ray diffraction.