Reviewerly/exHarmony huggingface dataset:
Examining Gender Mandates as Representation of Women in Political Speech

The Discourse Analysis of Teun A. Van Dijk tries to examine the gender mandate of the heads of political parties led by women in showing the representation of women in fighting for gender. Speech text is basically not just a string of words that is only linguistic, but social. Van Dijk's model is divided into three levels: a) macro structure, b) superstructure, and c) micro structure, which is used to analyze discourse critically. The data source uses You Tube through the text of political party speeches. Qualitative research methodology used to examine messages as gender mandates in speechwomen-led politics. Based on the research on political speeches, it can be concluded: There is a significant difference in the thematic structure between MSP and GN, the dominant theme of MSP's political speech is about the political, economic, and historical role of the party, and cadres are considered as party officials. The thematics of GN's speech criticized social realities, the struggle for gender, emancipation, women's wages, and polygamy. The micro structure in the schematic is compiled and assembled on economic studies, state politics, and political messages, commemorating the Asia-Africa Conference, while GN's speech emphasized women such as: Emancipation of gender equality, feelings of party leaders or the mission of the Party which are both attached to one. Stylistics in the choice of diction are used as expressions that have the power to inspire those who hear, using a stylistic style for PDI-P cadres, of course, it can be lively. Meanwhile, the diction used to highlight the stylistics used by GN is more about gender messages and mandates that attract millennials. The use of rhetorical language style in the micro structure was used by the two political party leaders as the party's mandate and mission.
Telessaúde com Idosos em Tempos de Pandemia: Experiência de uma Residência Multiprofissional

Objective: To describe the experience of multiprofessional telehealth with elderly from operating groups in the field of action of the Multiprofessional Residency in Family and Community Health at a Public University of Piaui. Methods: This is a descriptive study, experience report type, referring to telehealth actions carried out by residents, in the categories of Physiotherapy, Nutrition and Psychology, with emphasis on the elderly population. The actions were carried out from April 2020 to March 2021 with elderly people from two Basic Health Units and a Social Living Center in a city in Piaui. Results and Discussion: The challenges of telehealth with the elderly public are diverse and are related to the difficulties of their interactions through technology, but even if they exist, it was observed through this study that the adhesion to teleservices both in telemonitoring and in Online health care services were considered positive as the elderly were available and interested in sharing the proposed practices satisfactorily, serving both prevention and promotion of mental and bodily health. Conclusion: Despite the challenges, the benefits found with the use of telemonitoring and teleconsultation outweigh the initial difficulties. This experience expands the residents' view of the use of these tools with the elderly, in order to expand and improve health outcomes and sensitize residents to act differently from the traditional way.
Solar radiation estimates at Meerut workplace

The research is concerned with the assessment and analysis of solar radiation in a workplace with longitude 77.706 (77°43' east) and latitude 28.984 (28°59' north) located in western Uttar Pradesh and the National Capital Region of India. Solar radiation energy which varies according to geographical location and weather conditions and also depends on the variation in components of solar radiation such as global and diffused components. Every year from June to September except the rainy season, the spreading components of solar radiation are more and there is a significant amount of solar energy available in this area. This work estimates the variation in solar components of complete solar radiation and the possible availability of solar radiation energy, may play an important role in reducing the electrical energy crisis in the Meerut region.
The Path Modelling of the Robot Motion Between Obstacles in Matlab® Software

The paper deals with the path modelling and simulation of the drive robot motion. The paper presents the method of finding locally optimal path with respect to some physical conditions. The polyline and circular segments represent the path. Our main tool of solving this task is Matlab® software. Thanks to this Matlab scripts we found a trajectory more suited to drive robots and faster. Used with an adapted trajectory planning method, this algorithm can be useful for finding a path between two points. Depending on the stability constraints of the robot, the speed can be adapted in turns, looking at the centrifugal force that undergoes the robot.
Persepsi Guru Mengenai Kemampuan Membaca KritisSiswa Kelas IV dan V di SD Muhammadiyah ProgramKhusus Kottabarat Surakarta

Critical reading means reading by analyzing the writer's writing, then criticizingassessing the merits of a reading. This research aims to: 1) Describe the teachers'perceptions of the critical reading skills of class IV and V students at theMuhammadiyah Elementary School Special Program Kottabarat Surakarta; 2)Analyzing teacher perceptions about critical reading learning for students ingrades IV and V at SD Muhammadiyah Kottabarat Special Program Surakarta.This type of research uses a qualitative approach with descriptive design. Datacollection techniques used were interviews, observation, documentation andquestionnaires. Data were analyzed using an interactive model through the stepsof data reduction, data presentation, data verification, and drawing conclusions.The technique of checking the validity of the data uses method triangulation andsource triangulation. The results showed that: 1) The teacher has the perceptionthat some students already understand and have a general picture of criticalreading skills. The teacher sees students' abilities from the way they read andunderstand texts that have been adapted and provided by the teacher; 2) Theteacher considers that implementing critical reading learning is very important byproviding two or more texts that can train students' critical abilities for reading. Inaddition, by looking for new innovations in teaching such as presenting files,videos, reading print media texts in turn to improve the development of students'critical thinking.
Sketchy Scene Captioning: Learning Multi-level Semantic Information from Sparse Visual Scene Cues

To enrich the research about sketch modality, a new task termed Sketchy Scene Captioning is proposed in this paper. This task aims to generate sentence-level and paragraph-level descriptions for a sketchy scene. The sentence-level description provides the salient semantics of a sketchy scene while the paragraph-level description gives more details about the sketchy scene. Sketchy Scene Captioning can be viewed as an extension of sketch classification which can only provide one class label for a sketch. To generate multi-level descriptions for a sketchy scene is challenging because of the visual sparsity and ambiguity of the sketch modality. To achieve our goal, we first contribute a sketchy scene captioning dataset to lay the foundation of this new task. The popular sequence learning scheme, e.g., Long Short-Term Memory neural network with visual attention mechanism, is then adopted to recognize the objects in a sketchy scene and infer the relations among the objects. In the experiments, promising results have been achieved on the proposed dataset. We believe that this work will motivate further researches on the understanding of sketch modality and the numerous sketch-based applications in our daily life. The collected dataset is released at https://github.com/SketchysceneCaption/Dataset .
Relación entre motivación por las matemáticas y aprendizaje activo a través de herramientas digitales

espanolExiste una relacion clara entre la motivacion que el alumnado muestra por las matematicas y el rendimiento academico. Un aspecto al que debe prestar atencion el profesorado es el fomento de la motivacion de su alumnado a traves de metodologias activas. En este trabajo mostramos los resultados de un proyecto de trabajo con alumnado de sexto curso de primaria. A traves del cual, se pudo realizar un aprendizaje autonomo utilizando herramientas digitales para la busqueda de informacion y elaboracion de resultados. Los datos obtenidos muestran que el empleo de metodologias activas y herramientas digitales resultan motivadoras y fomentan el aprendizaje EnglishThere is a clear relationship between the motivation that students show for mathematics and academic performance. One aspect that teachers should pay attention to is promoting the motivation of their students through active methodologies. In this work we show the results of a work project with sixth grade primary school students. Through which the students were able to carry out autonomous learning using digital tools to search for information and produce results. The data obtained show that the use of active methodologies and digital tools are motivating and promote learning
Intensional and Extensional Views in DL-Lite Ontologies

The use of virtual collections of data is often essential in several data and knowledge management tasks. In the literature, the standard way to define virtual data collections is via views, i.e., virtual relations defined using queries. In data and knowledge bases, the notion of views is a staple of data access, data integration and exchange, query optimization, and data privacy. In this work, we study views in Ontology-Based Data Access (OBDA) systems. OBDA is a powerful paradigm for accessing data through an ontology, i.e., a conceptual specification of the domain of interest written using logical axioms. Intuitively, users of an OBDA system interact with the data only through the ontology's conceptual lens. We present a novel framework to express natural and sophisticated forms of views in OBDA systems and introduce fundamental reasoning tasks for these views. We study the computational complexity of these tasks and present classes of views for which these tasks are tractable or at least decidable.
Parenting style in building learning motivation in children: A case study in migrant workers’ families in Indonesia

This study aims to describe the role of parenting to students whose parents work as immigrants with substance in the form of student opinions, as well as to describe the learning motivation of students whose parents work as migrant workers with several approaches related to learning motivation. The study used a qualitative approach that took data based on the results of interviews and direct observations as a data collection instrument and then carried out descriptive analysis. The sample of this study was conducted on students at MI Al-Khoiriyah and SDN Langenharjo 2 schools whose parents worked as immigrants. The results showed that students realized that with the parenting pattern of parents who worked as immigrants this situation was only temporary, their parents worked for money to be able to meet their needs at home and students' educational needs. However, in general, students feel sad when their parents are away from them for a long time so that students experience a lack of direct parental love. Therefore, in general, students whose parents work as immigrants do not show good or high learning motivation. This can be seen from the incompleteness of eight indicators of student learning motivation at school. Suggestions for further research in the future are also discussed.
Time-multiplexed optical systems for reservoir computing and coherent Ising machines

Multiple photonic systems show great promise for providing practical yet powerful hardware substrates for neuromorphic computing. Among those, delay-based systems offer -through a time-multiplexing technique - a simple technological implementation route. We discuss our advances in the development of passive coherent fibre-ring cavities and semiconductor lasers with integrated delay for reservoir computing. Time-multiplexed systems are also highly suitable for coherent Ising machines as they allow to implement a fully interconnected large scale system with few components. We have recently proposed a system based on opto-electronic oscillators subjected to self-feedback with improved calculation time and solution quality.
Clustering Structure of Microstructure Measures

This paper builds the clustering model of measures of market microstructure features which are popular in predicting stock returns. In a 10-second time-frequency, we study the clustering structure of different measures to find out the best ones for predicting. In this way, we can predict more accurately with a limited number of predictors, which removes the noise and makes the model more interpretable.
Fuzzy-Engineered Multi-Cloud Resource Brokering for Data-intensive Applications

Multi-cloud resource brokering is becoming a critical requirement for applications that require high scale, diversity, and resilience. Applications demand timely selection of distributed data storage and computation platforms that span local private cloud resources as well as resources from multiple cloud service providers (CSPs). The distinct capabilities and policies, as well as performance/cost of the cloud services, are amongst the prime factors for CSP selection. However, application owners who need suitable cyber resources in community/public clouds, often have preliminary knowledge and preferences of certain CSPs. They also lack expert guidance to handle the problem of overwhelming resource choice from CSPs, and optimization to compensate for service dynamics. In this paper, we address this challenge of optimal resource selection while also leveraging limited user's expertise and preferences towards CSPs through multi-level fuzzy logic modeling based on convoluted factors of performance, agility, cost, and security. We evaluate the efficiency of our fuzzy-engineered resource brokering in improving allocation of resources as well as user satisfiability by using case studies and independent validations of CSPs evaluation.
Introduction to Azure Functions

Function as a service (FaaS) is getting more popular every day on all the major cloud platforms. With FaaS, you can build small chunks of code that run for a short time and host them on the FaaS cloud offering. You get billed for the time your function runs, and you do not need to bother about the hosting infrastructure and the scaling aspects.
LeMe-PT: A Medical Package Leaflet Corpus for Portuguese

The current trend on natural language processing is the use of machine learning. This is being done on every field, from summarization to machine translation. For these techniques to be applied, resources are needed, namely quality corpora. While there are large quantities of corpora for the Portuguese language, there is the lack of technical and focused corpora. Therefore, in this article we present a new corpus, built from drug package leaflets. We describe its structure and contents, and discuss possible exploration directions.
Teacher-Students’ Interaction in EFL Teaching: Analyzing Patterns of Classroom Interaction

Abstract. Teacher-students’ interaction patterns in EFL teaching can be considered crucial to be investigated. It is due to the reason that interaction in English classroom may facilitate pupils in practicing the language skills they have learnt in a real context. In doing this, Classroom Discourse Analysis (CDA) can be an alternative way in order to seek the level of accuracy in portraying classroom activities. Dealing with this, the present study aimed at analyzing teacher-students’ interaction patterns that occurred in EFL classroom interaction. This was a descriptive qualitative study. The participants of this study were an English teacher at one vocational school students and 36 students of XII grade. In collecting the data, classroom observation was administered. The data then were transcribed and analyzed by using SMSLEFA (Synergetic Multilayered Students’ Learning Engagement Framework of Analysis) framework as a part of Classroom Discourse Analysis proposed by Suherdi (2010) and Berry’s Exchange System Network (ESN). The data revealed that both non-anomalous exchanges and anomalous exchanges occurred during the teaching and learning process, specifically there were 54 K-oriented exchanges, 36 S-oriented exchanges, and 6 A-oriented exchanges. In addition, Dk1-initiated patterns and Ds1-initiated patterns appeared as the dominant patterns in the corpus. The findings implied that the activities conducted by the teacher focused on checking students’ understanding about the text and developing students’ skill, particularly students’ reading skill and speaking skill. the result of this study can be utilize to investigate the effort that learners devote to the learning process that are linked to their achievement. Keywords: teacher-students’ interaction, Classroom Discourse Analysis, SMSLEFA
Anti-Vice Recommendation Systems Based on Face Images Using Machine Learning

Face attribute analysis is a valuable aid in biometric-based human identification system. It is a challenging task to perform due to variations in illumination, occlusion, pose and other variables with respect to a face presentation. This paper proposes a model for determining whether a driver has consumed alcohol or drugs, or is under the control of some other vices based on the images of the face taken at a specific period of time. In order to fulfill this task images of the face and parts of the face are captured by a camera device. These various parts of the face mainly the cheek, neck, eyes, ear, etc., are used to detect alcohol consumption based on deviations of the face with respect to color, change in size of eyes and other parts of the face. A three layer network of neurons is used for experimentation. The neural network is trained using a back propagation algorithm and accuracy is checked for the model by applying the test data to the trained model. The expected results from the model involve a prediction stating the detection of a vice.
Social Media and Inclusive Human Development in Africa

This study investigates the relationship between social media and inclusive human development in 49 African countries for the year 2012. Social media is measured with Facebook penetration whereas inclusive human development is proxied by the inequality- adjusted human development index. The empirical evidence is based on Ordinary Least Squares, Tobit and Quantile regressions. Ordinary Least Squares provided baseline results, Tobit regressions account for the limited range in the outcome variable while Quantile regressions are engaged to control for initial levels of inequality-adjusted human development. From Ordinary Least Squares and Tobit results, Facebook penetration is positively associated with inclusive human development. Quantile regressions confirm this positive nexus and further establish that the positive association is slightly higher in magnitude in the above-median sub-sample. From a comparative assessment, it is apparent that with the exception of the resource-wealth sub-samples, higher levels of Facebook penetration are associated with comparatively higher levels of inclusive human development. Accordingly, the positive association between Facebook penetration and inclusive human development is: (i) a positive function of income levels and (ii) more apparent in Middle East and North African countries (compared to Sub-Saharan African countries), English common law countries (compared to their French civil law counterparts), and coastal countries (in relation to landlocked countries).
Creativity in Machines: Music Composition Using Artificial Intelligence that Passes Lovelace 2.0 Test

In this paper, we are using different AI techniques to train the model on musical data sequences with different compositions, so the model will be able to generate musical notes of various moods. The proposed system is based on PALASH 1.0 framework (Kumar et al., in Asian J Conv Technol, [1]), which generates different moods of music which is like human-created compositions. These AI-generated music is produced using the creativity of machines in such a way that it passes the Lovelace 2.0 test (Turing in Mind, New Series 59(236):433–460, [2]).
Regression Loss in Transformer-based Supervised Neural Machine Translation

Transformer-based model has achieved human-level performance in supervised neural machine translation (SNMT), much better than the models based on recurrent neural networks (RNNs) or convolutional neural network (CNN). The original Transformer-based model is trained through maximum likelihood estimation (MLE), which regards the machine translation task as a multilabel classification problem and takes the sum of the cross entropy loss of all the target tokens as the loss function. However, this model assumes that token generation is partially independent, without realizing that tokens are the components of a sequence. To solve the problem, this paper proposes a semantic regression loss for Transformer training, treating the generated sequence as a global. Upon finding that the semantic difference is proportional to candidate-reference distance, the authors considered the machine translation problem as a multi-task problem, and took the linear combination of cross entropy loss and semantic regression loss as the overall loss function. The semantic regression loss was proved to significantly enhance SNMT performance, with a slight reduction in convergence speed.
Power system distributed state estimation method based on transfer learning under sparse data

With the development of smart grids and the continuous penetration of new energy power generation, the scale of the power grid has increased, and the structure and operation methods have become increasingly complex. In order to more accurately grasp the operating status of the power grid, it is necessary to estimate the operating status of the power system. In recent years, with the development of artificial intelligence and data mining technology, neural networks have also been widely used in the field of state estimation. However, there are few researches on artificial intelligence algorithms in active distribution network state estimation, and the small amount of data in the bad data detection link that needs to be carried out before state estimation also limits the application of many artificial intelligence algorithms that require a large number of training samples. A small sample learning method applied to the detection of bad data for power system state estimation is proposed, which successfully solves the problem of the lack of available training data in building a neural network model for bad data detection, and solves the traditional method calculation speed brought by the massive increase of grid data. The problem of serious decline, and the accuracy of the traditional state estimation method is improved by more than 47%.
Research on the Reform of College Physical Education Teaching Mode in the New Era

Deepening the reform of physical education in colleges and universities, in the new era of college physical education reform, we face a series of practical difficulties, single teaching mode, imperfect teaching system, single evaluation system, etc., affect the development of physical education, and it is difficult to complete the set goals and tasks of physical education. For the problems existing in physical education teaching in colleges and universities, this paper analyzes the specific countermeasures for the reform of physical education teaching mode in colleges and universities, and provides some suggestions for the deepening reform of physical education teaching in the new era.