Miracle-dz/UniFAR-FTUs huggingface dataset:
Homophobia, Transphobia Detection in Tamil, Malayalam, English Languages using Logistic Regression and Code-Mixed Data using AWD-LSTM

This paper presents the submission of the shared task “Homophobia, Transphobia Detection of YouTube Comments” organized by DravidianLangTech. Our team has participated in Task - B, which tries to identify the comments on youtube are Non-anti LGBTQ+ content or Homophobic or Transphobic in code-mixed(Tamil-English), Tamil, Malayalam, and English. We proposed the AWD-LSTM model for the code-mixed(Tamil-English) language data set and Logistic Regression for Tamil, Malayalam, and English languages. Our AWD-LSTM model achieved a 0.33 macro average F1 score for code-mixed(Tamil-English) language and Logistic Regression achieved a 0.55 macro average F1 score in the Tamil language, 0.98 macro average F1-score in the Malayalam language, 0.91 macro average F1-score in the English language.
Intelligent Control of Industrial Compensating Devices Based on the Application of Fuzzy Logic

The process of reactive power compensation has been formalized using the example of daily electricity consumption by the power substation of the enterprise. The choice of the fuzzy control principle for the operation of compensating devices is justified. Results of computer modeling of the reactive power compensation process based on actual electricity consumption data from powerful substations of the mining and beneficiation plant are presented. The effectiveness of using the fuzzy control principle for compensating devices has been analyzed. The experimental data from January and June 2019 for one of the main substations of the enterprise were used by the authors for testing. The proposed algorithm for controlling the compensation process is implemented based on fuzzy control.
COVID-19 vaccine stance classification from tweets

Since the discovery and betterment of vaccines for human diseases, Anti-Vaccine rhetoric and resistance have been prevalent in social circles. These sentiments adversely affect the effectiveness of preventing the contraction of deadly contagious diseases, such as COVID-19. With the advent of social media platforms, the expression of anti-vaccine stances has a far greater reach in society. In this paper, we tackle the task of COVID-19 vaccine stance detection to gauge people's receptiveness towards vaccines and subsequently understand the effectiveness of the vaccination drives. © 2021 Copyright for this paper by its authors.
A Survey on COSMIC Students Estimation Challenge

Software project management is a significant software engineering practice that is highly related to achieving software-specific project goals. This study aims to share students’ perceptions of incorporating an international software estimation challenge called “COSMIC Students’ Estimation Challenge” into a software project management course. For this aim, students were taught the COSMIC Functional Size Measurement method and entered the competition. After the competition, a questionnaire asking for the students’ opinions was collected. The objective of the research is to get an insight into to what extent incorporating this type of competition activity -a challenge-can contribute to students’ learning perceptions. In the long run, the findings can contribute to creating a foresight about making the necessary curriculum arrangements to form a more up-to-date and dynamic education plan by including the methods applied in the software industry in Software Engineering education. The results suggest that this kind of competition experience and preparation is helpful for students to learn the COSMIC method.
A Classification Approach to Detect Public Sentiments towards COVID-19 Vaccines

In this paper, team University of Botswana Computer Science (UBCS) investigate the opinions of Twitter users towards vaccine uptake. In particular, we build three different text classifiers to detect people's opinions and classify them as provax-for opinions that are for vaccination, antivax for opinions against vaccination and neutral-for opinions that are neither for or against vaccination. Two different datasets obtained from Twitter, 1 by Cotfas and the other by Fire2022 Organizing team were merged to and used for this study. The dataset contained 4392 tweets. Our first classifier was based on the basic BERT model and the other 2 were machine learning models, Random Forest and Multinomial Naive Bayes models. Naive Bayes classifier outperformed other classifiers with a macro-F1 score of 0.319. © 2022 Copyright for this paper by its authors.
An Empirical Study on Quality Issues of Deep Learning Platform

In recent years, deep learning (DL) has been increasingly adopted in many application areas. To help deep learning developers better train and test their models, enterprises have built dedicated, multi-tenant platforms equipped with a mass of computing devices like GPUs. The service quality of these platforms plays a critical role in system efficiency and user experience. Nevertheless, there indeed exist diverse types of quality issues that not only waste computing resources significantly but also slow down development productivity severely. In this paper, we present a comprehensive empirical study on quality issues of Platform-X in Microsoft. Platform-X is an internal production deep learning platform that serves hundreds of developers and researchers. We have manually examined 360 real issues and investigated their common symptoms, root causes, and mitigation actions. Our major findings include: (1) 28.33% of the quality issues are caused by hardware (the GPU, network, and compute node) faults; (2) 28.33% of them result from system-side faults (e.g., system defects and service outages); (3) User-side faults (e.g., user bugs and policy violation) account for more than two-fifths (43.34%) of all the common causes; (4) More than three-fifths of all the quality issues can be mitigated by simply resubmitting jobs (34.72%) and improving user code (24.72%). Our study results provide valuable guidance on promoting the service quality of deep learning platforms from both the development and maintenance aspects. The results further motivate possible research directions and tooling support.
Improving Speaker Verification Model for Low-Resources Languages

Speaker verification is an essential task in speech processing. Implementation this task based on convolutional neural networks. Several key metrics were evaluated, including equal error rate and precision top-K, and were compared the performance of different architectures and loss functions. The experiments are conducted using a Ukrainian dataset and include comparisons of models trained on multilingual data, as well as models trained on clean and augmented data. The results are presented in tables and figures, showing that even for low-resource languages, the models can achieve good performance metrics. The authors also discuss the implications of their findings and the potential for transferring skills to other languages. The paper provides valuable insights for researchers working in the field of speaker verification.
Unsupervised Photo-to-Caricature Generation with Adaptive Select Layer-Instance Normalization and Semi-cycle Consistency

Unpaired photo to caricature generation is a challenging but meaningful task. Gener-ating high quality caricatures with rich texture/color and plausible exaggeration is important. Previous methods often respectively deal with the shape transformation and texture/color style. We argue that shape transformation can be treated as same as texture/color. Thereby, shape transformation and texture/color can be transferred at the same time. In this paper, we proposed a new method namely AdsSe-GAN for photo-to-caricature generation, which consists of a new normalization function called AdaSLIN and a new semi-cycle consistency loss. The AdaSLIN adaptively selects Layer Normalization or Instance Normalization to simultaneously transfer texture/color and shape transformation. Besides we present semi-cycle consistency loss which only imposes L1 norm on caricature-to-photo process, which is diﬀerent from existing methods that apply cycle consistency loss to preserve the original domain information. In fact, while generating caricature, taking no account of the cycle restriction makes our model generate caricature with more distinct exaggeration and higher quality. Experimental results on a public caricature dataset, WebCaricature, show the eﬀectiveness of our proposed method compared with the state-of-the-art models.
LE SYST ` EME DE NAVIER-STOKES INCOMPRESSIBLE

Resume. — Ce texte commence par une analyse de l'article fondamental de Jean Leray sur lesde Navier-Stokes et un bref survol du probleme de la re- gularite globale. Puis, nousles proprietes lagrangiennes des solutions des ´equations de Navier-Stokes. Dans une derniere section, nousune estima- tion qui decrit en particulier l'effet regularisant de l'´equation de Navier-Stokes en termes d'analyticite. Abstract (The noncompressible Navier-Stokes system seventy years after Jean Leray) This text first analyzes the seminal paper of Jean Leray about Navier-Stokes equations. Then we present a brief overview of the problem of global regularity. Then we study lagrangian properties of the solutions of Navier-Stokes equations. In the last section, we establish an estimate which describes the regularization effect of the Navier-Stokes equations in terms of analyticity.
Leveraging BERT, MWE, and ML Models to Detect Emotions and Threats in Urdu

Our participation in the EmoThreat 2022 challenge relied on 3 distinct methods. We used algorithms based on BERT, a traditional statistic method and unsupervised Multi Word Expression (MWE) extraction to resolve the two proposed tasks to classify Urdu tweets. Task A consisted in classifying tweets into 6 classes of emotions. Task B was subdivided into 2 sub-tasks: B1) detecting threat and B2) specifying if the threat targets a single person or a group. For task A, the unsupervised MWE extraction method obtained the best results. For task B, the BERT based approach was more efficient to identify threats and the ML traditional method turned out to be the best among challenge participants to detect individual or collective threat. After discussing the types of errors, we suggest some guidelines for further improvement of those models.
Evaluation of the Satisfaction Levels of Inpatients in the University of Gaziantep Medical Faculty Hospital

Patient satisfaction is an important criterion for evaluating the quality of health services. The aim of this study was to determine the satisfaction of the patients receiving inpatient treatment. Patient satisfaction was evaluated on 230 patients in Hospital of Gaziantep University. Analyzing data frequency, correlations and chi-square tests were used. 73.5% of patients were reported to „good‟ general hospital care.
Analysis of Tax Crimes Committed by Tax Personnel in Lampung Province

Criminal acts in the field of taxation refer to violations or crimes related to the tax provisions of a country. Tax crime often involve manipulation or violation of the law by individuals in order to avoid or reduce the tax obligations they should pay. This study aimed to analyze tax crimes committed by unscrupulous tax personnel in Lampung province. This research is a qualitative observational study and uses a literature study. Secondary data such as laws and regulations, legal theory, and doctrine are used to explain legal phenomena. This study highlights the phenomenon of tax crime in the Bengkulu and Lampung DGT areas. There are five suspects in tax crimes during 2021. In addition, there is also an investigation into the alleged criminal act of garbage retribution corruption at the Bandarlampung Environment Agency (DLH) for the 2019, 2020, and 2021 fiscal years. The legal basis used in settlement of criminal acts in the field of taxation is Law Number 6 of 1983 concerning general provisions and tax procedures and their amendments. In addition, the norms of criminal acts in the field of taxation also include provisions contained in the Criminal Code and other laws and regulations. In conclusion, corruption is an act of enriching oneself or others, which can harm the economy and state finances. Legal subjects of corruption can be corporations and civil servants.
An Investigation of Concentration and Health Impacts of Aldehydes Associated with Cooking in 29 Residential Buildings

Indoor air quality can be influenced by various indoor activities. The indoor air pollutants generated by cooking activities can cause severe risks to occupants’ health in residential buildings. The present study conducted field experiments to measure indoor pollutants associated with cooking in 29 residential buildings. Due to an open plan in Korean residential buildings, the emission of indoor pollutants was measured in the kitchen and living room. Focusing on aldehydes, the indoor emission levels for various cooking methods such as grilling, frying, and boiling were analyzed. As a result, the emission of formaldehyde, acetaldehyde, and propionaldehyde was highly increased for all cooking methods. The increase rate of the emission was higher in the kitchen than that in the living room for grilling and frying. In the case of boiling, the highest concentration of aldehydes was observed. Moreover, the indoor level of aldehydes was higher in the living room than that in the kitchen. Moreover, the health risk such as cancer for occupants was assessed based on the measured data for different cooking methods. The assessment results showed that all the emissions of aldehydes for different cooking methods required instant actions to avoid cancer risk for occupants.
What does finance democracy look like?: thinking beyond fintech and regtech

ABSTRACT The paper addresses the purported ‘democratisation’ of finance as it has been heralded among many proponents of decentralized finance (‘DeFi’). It places the emergence of decentralised platform financing in the context of a continuing neo-liberalization of public services to interrogate the democratisation claim. Following a brief presentation of the guiding principles for both Canadian and U.S. securities regulation law, the paper explains the fast evolution of DeFi and argues that its driving impetus is the inclusion of individuals into a financial system that features little centralisation and even less regulatory oversight. While access to online investment forms is broadened, it remains questionable to which degree such a system can be an instrument in the pursuit of long-term oriented public goods goals.
Robust control by extended static output feedback.

This paper presents a novel approach for the controller synthesis in linear time invariant systems (LTI) with uncertainty polytopical type, by means of the extended static feedback of the measured output. The method consists of designing feedback gains for the injection of the output and its derivative, which corresponds to the control signal. Conditions for the existence of such controllers are established. The uncertainty parameters are supposed to belong to a polytope. The stabilization problem is formulated in the context of linear matrix inequalities (LMI). Multiobjective performance indices in H2/H& are also considered, which are described as LMIs. The extended H2/H& conditions are obtained by means of the well known projection lemma. Performance of the proposed synthesis technique is illustrated by numerical example.
Social Inclusion of Transgender People in Intercollegiate Sports—A Scoping Review

Transgender individuals face discrimination and exclusion in various areas of society, including sports. Notably, intercollegiate athletics suffer criticism for their lack of inclusivity towards transgender athletes. Despite the increasing visibility of transgender individuals and ongoing efforts towards greater inclusivity, there is a significant lack of research on their social integration within college sports. This scoping review aimed to explore the then-current state of research on the social integration of transgender individuals in intercollegiate athletics, identify gaps in the literature, and suggest areas for further investigation. The study examined articles published between 2013 and 2023, using databases such as PubMed, ERIC, and EBSCO Essentials, as well as relevant citations from selected articles. The inclusion criteria for articles were their focus on the social integration of transgender individuals in sports events, publication in English, and relevance to the research question. A standardized technique based on the PRISMA flow diagram 2020 was used to locate, assess, and extract information from eligible research. The results of the study are expected to inform policy and strategy in transgender participation in college sports and promote greater inclusivity for transgender individuals in sports institutions and groups.
Field validation as a tool for mitigating uncertainty in species distribution modeling for conservation planning

Despite the success of species distribution models (SDMs) in rare species conservation planning, inherent uncertainty in predictions can facilitate mis-use or misapplication. Field validation is important as model predictions can vary greatly due to low species prevalence or spatial bias; traits frequently encountered in rare species data. This study outlines an effective SDM approach to map the potential distribution of a Florida endemic plant species, Macbridea alba , which is transferable to other rare species that: (1) utilize presence data to create species distribution maps, (2) conduct field validation to gather independent data, (3) and use independent data for model evaluation and improvement. Independent data was used to assess each model's ability to predict the occurrence of M. alba and identify habitat for conservation and reintroduction efforts. Our study documents that for low prevalence species, models can produce significant variation in optimized threshold and evaluation metrics, which impact outcomes when converting continuous predictions to binary predictions, a common practice in model application. Our species distribution models were useful in finding new populations of M. alba . Field validation is vital to improving model performance, threshold selection, and evaluating model design and should be carefully considered when suggesting model use and application.
Utilizing The iNaturalist Application for Biology Research and Learning

This literature study aims to describe the utilization of the iNaturalist application (https://www.inaturalist.org/) in biology research and learning activities. iNaturalist is an Online Citizen Science (OCS) application available on the web and Android (Apps) platforms. This application has various features for collecting, identifying, and mapping varied biodiversity worldwide. It is suitable for biodiversity research and can also be used in learning biology on relevant concepts such as biodiversity, ecology, environment, plantae, animalia, and others. This article will discuss the advantages of the iNaturalist application, how to use it, and the utilization of the application in learning biology. The working principle of the iNaturalist application is to utilize artificial intelligence technology in the form of image processing so that it can identify various uploaded biodiversity data. This application offers community learning, the process of identifying research data involving the iNaturalist community worldwide. The way the iNaturalist application works is that users record data, share data with fellow naturalists and discuss research results. iNaturalist in research can be used for collecting, identifying, and publishing ecological, environmental, and biodiversity research data. The use of iNaturalist application in learning can be used in biology learning to train science process skills, especially observation skills, biodiversity learning, taxonomy learning, and research skills. In addition, this application supports Citizen Science activities that are suitable for use in education biology because it can create contextual and collaborative learning.
Low-Complexity Model-Free Combined Control of Grid-Connected Converters Under Normal and Abnormal Grid Conditions

This article proposes a low-complexity model-free control method for grid-connected converters. It controls the grid current vector under normal and abnormal grid conditions through hysteresis controllers with a novel dynamic switching logic without any current/voltage reference frame transformation and sequence extraction. In addition, the proposed method has overcurrent protection, including under fault conditions due to a direct current magnitude regulation. Compared to the existing combined control method, it is model-free, enjoys current limiting and stable operation under fault conditions and power factor control mode, and lacks any estimation procedure. Compared to the predictive control method, it encounters a lower computation burden and is robust to the system model parameters. The performance effectiveness and superiority of the system under the proposed control and existing methods in terms of stability and robustness are shown by extensive analytical, simulation, and experimental studies.
Late Quaternary dynamics of Arctic biota from ancient environmental genomics

Wang, Yucheng; Pedersen, Mikkel Winther; Alsos, Inger Greve; De Sanctis, Bianca; Racimo, Fernando; Prohaska, Ana; Colssac, Eric; Owens, Hannah Lois; Merkel, Marie Kristine Føreid; Fernandez-Guerra, Antonio; Rouillard, Alexandra; Lammers, Youri; Alberti, Adriana; Denoeud, France; Money, Daniel; Ruter, Anthony H. ; McColl, Hugh; Larsen, Nicolaj Krog; Cherezova, Anna A. ; Edwards, Mary E. ; Fedorov, Grigory B. ; Halle, James; Orlando, Ludovic; Vinner, Lasse; Korneliussen, Thorfinn Sand; Bellman, David W. ; Bjørk, Anders A. ; Cao, Jialu; Dockter, Christoph; Esdale, Julie; Gusarova, Galina; Kjeldsen, Kristian K. ; Mangerud, Jan; Rasic, Jeffrey T. ; Skadhauge, Birgitte; Svendsen, John Inge; Tikhonov, Alexei; Wincker, Patrick; Xing, Yingchun; Zhang, Yubin; Froese, Duane G. ; Rahbek, Carsten; Nogués-Bravo, David; Holden, Philip B. ; Edwards, Neil R. ; Durbin, Richard; Meltzer, David J.; Kjaer, Kurt H. ; Möller, Per and Willerslev, Eske (2021). Late Quaternary dynamics of Arctic biota from ancient environmental genomics. Nature, 600 pp. 86–92.
A Computational Linguistics Study of Compound Nouns in Thai 1

This paper presents the boundary of Thai compound nouns, the internal relation, and the properties of the predicates and arguments by the approach of computational linguistics. Nouns referring to scientific instruments are selected as the representation of Thai compound nouns. The logical structure is used for finding the boundary of the compounds, the relation and the properties of any elements, as well. The result is that the predicate functions, as the head of the compound nouns, mostly expresses [INSTRUMENT], [SHAPE], or [CONTAINER]. For the boundary of the compounds, it can be told by calculating the number of the arguments which occur with their predicates. The compounds have at most 2 arguments. The first one expresses [PURPOSE], [CHARACTERISTIC], [STATE], [METHOD], or [PROCESS]. The other expresses [PURPOSE] or [CHARACTERISTIC].