Reviewerly/exHarmony huggingface dataset:
¿Habla Español? Do Financial Aid Offices Use Chatbots…and Can They Speak Spanish?

This study examines 341 financial aid office websites hosted by Title IV participating institutions of higher education and their usage of chatbot technologies. Moreover, we explored whether these chatbots can communicate with Internet users in Spanish. Results suggest only 22% of financial aid websites integrate chatbots and fewer than 5% can converse in Spanish.
Social Distancing Detection By Using Deep Neural Network

This article helps in presenting a clear view of detecting social distance inorder to evaluate the distance covered by the people from eachother. It helps in providing alarming notification to the people in making safety during this pandemic period by the use of video feed. The frame of the video clipping is used as an input and is implemented based on the hybrid computer vision. The Deep neural network dependent algorithm named YOLOv3 has been used in alarming the detection of distance between the people. It is used along with the mapping technique known as Inverse Perspective Mapping (IPM) which is one of the tracking mechanisms that helps in monitoring the social distance. It is tested against MS COCO and image datasets that has been obtained from Google. The precision was found to be 97% that helps to design the outlook of the place where the public involvement is too high. It could help in controlling the violations of trespassers who does not obey the rules of social distancing and also servers to be precaution to control the disease prone zones.
PROFESSIONAL FORMATION OF TEACHERS OF ART DISCIPLINES

The article deals with one of the most important aspects of future teachers of art disciplines preparation for future professional activity. We consider professional development as a certain stage of development of consciousness of future teachers between being a student and how he thinks of himself already in the status of a teacher. The authors claim that a person becomes a professional when he not only imagines, but already identifies himself as a specialist. A student can imagine himself as a teacher on the basis of mental action, which creates an imaginary image of his I and which never really happened. In contrast, thinking of oneself as a specialist means knowing oneself (distinguishing oneself, identifying oneself) as a professional who actually has (and proved this reality in his own experience) a sufficient level of development of professional qualities.Attention is drawn to the fact that the theory of professional development of future teachers of art disciplines should mark the unity of professional training of teachers and special training of artists - musicians, artists, choreographers at the level of technological patterns, principles and methods of professional development.It is emphasized that the focus of artistic and pedagogical thinking on the mechanisms of aesthetic development of professional consciousness is crucial. That is, the area that brings the subject teacher (translator of knowledge, skills and abilities) beyond the narrow framework of knowledge of his subject to the level of the teacher, ie the person who educates, and therefore forms other personalities and opens opportunities for further development and self-improvement.
STUDY OF INDIVIDUAL CAPABILITIES OF FUTURE DESIGNERS FOR SUCCESSFUL PROFESSIONAL ACTIVITY

Obtaining valid and reliable information on predicting the success of the formation of professionally important personal qualities is facilitated by the usage of wide range diagnostic methods for assessing the professional aptitude of future specialists, capabilities for their professional development and achieving success in professional activity. The development of individual creative abilities of future designers is the key to their successful professional implementation and building a professional career. Professional aptitude is determined by a set of mental and psychophysiological characteristics of the individual, which are necessary and sufficient to achieve socially acceptable efficiency in professional activity. The determination of professional aptitude consists in evaluating the capabilities of future designers to master the content of design activities and draws up a specialized diagnostic procedure. In the process of diagnostics, the interrelationships of motivational, cognitive, operational and affective components of future designers, their readiness of individual typological properties for professional activity are studied. Based on the results of diagnostics, characterological features are analyzed and professional orientation is investigated. Determining the characteristics of the future specialist is an important condition for improving professional knowledge and skills, the key to their professional skills, achieving productivity, the formation of individuality through the results of work. Diagnosing the professional suitability of future designers is a multidimensional, multifaceted, multilevel system that reflects the content of the development of personality, ensures the effectiveness of identifying the most suitable individual qualities of future professionals to design.
Implementasi Metode Certainty Factor dalam Mengetahui Kerusakan Sepeda Motor Type Injeksi

Motorcycles are the main means of transportation that many people use to support their activities. Before the development of technology, motorcycles still use carburetors. However, motorcycles that still use the carburetor system are still considered wasteful. With the development of technology, currently there is a Yamaha brand motorcycle with an injection system that is able to supply gasoline according to the engine's needs. Motorcycles with injection technology are much more efficient than motorcycles with carburetor technology, besides being economical, this type of motorcycle has reliable engine performance and power. However, there are often obstacles that cause damage to Yamaha injection motorcycles so that they interfere with user activities. An expert system is a system that adopts the expertise of an expert expert in a particular field into a computer system or program that is presented with a display that can be used by users who are not experts so that with this system users can make decisions or determine policies like an expert in solving problems. problem. The Certainty factor method is used to obtain the certainty value of the type of damage from an injection motorcycle expert, so that the user also gets a certainty value of damage from the selected symptoms from expert experience. The results of the diagnosis on the expert system that have been made that if the user checks for symptoms such as the engine stuttering when the gas is turned on, the electric starter is difficult/not lifting, the engine is difficult to start when using the kick starter, the exhaust often emits an explosive sound, it can be concluded from the results of the analysis that The damage is on the spark plug with a value of MB = 0.99, MD = 0.22 and a Certainty Factor value of 76.66%. The results of diagnostics that have been carried out by visitors or mechanics can be accessed again by entering the email in the database
Detecting Speaker Personas from Conversational Texts

Personas are useful for dialogue response prediction. However, the personas used in current studies are pre-defined and hard to obtain before a conversation. To tackle this issue, we study a new task, named Speaker Persona Detection (SPD), which aims to detect speaker personas based on the plain conversational text. In this task, a best-matched persona is searched out from candidates given the conversational text. This is a many-to-many semantic matching task because both contexts and personas in SPD are composed of multiple sentences. The long-term dependency and the dynamic redundancy among these sentences increase the difficulty of this task. We build a dataset for SPD, dubbed as Persona Match on Persona-Chat (PMPC). Furthermore, we evaluate several baseline models and propose utterance-to-profile (U2P) matching networks for this task. The U2P models operate at a fine granularity which treat both contexts and personas as sets of multiple sequences. Then, each sequence pair is scored and an interpretable overall score is obtained for a context-persona pair through aggregation. Evaluation results show that the U2P models outperform their baseline counterparts significantly.
Encrypted And Hiding Audio Data Using Lorentz Chaotic Equation

Because of the widespread use of the Internet, it has become a great need to find ways to secure military lines of communication, for both Army and Police. The use of Chaos equations is one of the most important methods to use in the carry the data whether it was encrypted or not. As much as the data was encrypted as much as the security increased. In this paper, we introduce encrypted data, transported using chaos equation as carrying media after hiding this data in the chaos equation on one of the Lorenz Chaos equations. The security of the data gained from using the DES use of encryption plus the security of high encryption of the chaos equations plus the use of hiding of the encrypted data at one channel of Lorenz equations.
Learning from Noisy Labels for Entity-Centric Information Extraction.

Recent information extraction approaches have relied on training deep neural models. However, such models can easily overfit noisy labels and suffer from performance degradation. While it is very costly to filter noisy labels in large learning resources, recent studies show that such labels take more training steps to be memorized and are more frequently forgotten than clean labels, therefore are identifiable in training. Motivated by such properties, we propose a simple co-regularization framework for entity-centric information extraction, which consists of several neural models with identical structures but different parameter initialization. These models are jointly optimized with the task-specific losses and are regularized to generate similar predictions based on an agreement loss, which prevents overfitting on noisy labels. Extensive experiments on two widely used but noisy benchmarks for information extraction, TACRED and CoNLL03, demonstrate the effectiveness of our framework. We release our code to the community for future research.
TOURISM IN BAKA STUPAS AND SURROUNDING WITH HAHSLM PERSPECTIVE

Tourism activity is a sector that plays an important role in regional development and development efforts by contributing to the income of an area. One of the areas that has developed due to the contribution of the tourism sector is Magelang Regency, Central Java. The existence of the tourist area of Borobudur Temple which has an international scale contributes greatly to the income of the people around it. 72 Borobudur stupas which are a reflexivity of the interference quantum physics pattern based on the Hahslm constant 472319 approach in the Covid-19 economic era. Modern physics is phenomenal in quantum reality with its wave interfer-ence. Similarity is found in the meaning of 7 waves and 2 gaps in the interference physics process, then reflected in the code of the 72 stupas that were built. The conclusion of the research is that 72 Borobudur stupas are a reflection of the interference part of the Hahslm 472319 constant in the Covid-19 economic era. The method used is the Hahslm 472319 equation which refers to QS. Al-Hijr [15]: 87. The result obtained is that the largest historical temple building in the world, namely Borobudur has 72 stupas, refer-ring to phenomenal events with absolute and differentiation.
Contrastive Out-of-Distribution Detection for Pretrained Transformers.

Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution. However, in real-world scenarios, the model often faces out-of-distribution (OOD) instances that can cause severe semantic shift problems at inference time. Therefore, in practice, a reliable model should identify such instances, and then either reject them during inference or pass them over to models that handle another distribution. In this paper, we develop an unsupervised OOD detection method, in which only the in-distribution (ID) data are used in training. We propose to fine-tune the Transformers with a contrastive loss, which improves the compactness of representations, such that OOD instances can be better differentiated from ID ones. These OOD instances can then be accurately detected using the Mahalanobis distance in the model’s penultimate layer. We experiment with comprehensive settings and achieve near-perfect OOD detection performance, outperforming baselines drastically. We further investigate the rationales behind the improvement, finding that more compact representations through margin-based contrastive learning bring the improvement. We release our code to the community for future research.
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams

Adversarial examples are a widely studied phenomenon in machine learning models. While most of the attention has been focused on neural networks, other practical models also suffer from this issue. In this work, we propose an algorithm for evaluating the adversarial robustness of $k$-nearest neighbor classification, i.e., finding a minimum-norm adversarial example. Diverging from previous proposals, we take a geometric approach by performing a search that expands outwards from a given input point. On a high level, the search radius expands to the nearby Voronoi cells until we find a cell that classifies differently from the input point. To scale the algorithm to a large $k$, we introduce approximation steps that find perturbations with smaller norm, compared to the baselines, in a variety of datasets. Furthermore, we analyze the structural properties of a dataset where our approach outperforms the competition.
Application of Mix-Up Method in Document Classification Task Using BERT

The mix-up method (Zhang et al., 2017), one of the methods for data augmentation, is known to be easy to implement and highly effective.Although the mix-up method is intended for image identification, it can also be applied to natural language processing.In this paper, we attempt to apply the mix-up method to a document classification task using bidirectional encoder representations from transformers (BERT) (Devlin et al., 2018).Since BERT allows for two-sentence input, we concatenated word sequences from two documents with different labels and used the multi-class output as the supervised data with a one-hot vector.In an experiment using the livedoor news corpus, which is Japanese, we compared the accuracy of document classification using two methods for selecting documents to be concatenated with that of ordinary document classification.As a result, we found that the proposed method is better than the normal classification when the documents with labels shortages are mixed preferentially.This indicates that how to choose documents for mix-up has a significant impact on the results.
The EMC Society Meets with IBIS Forum!

With great pleasure, I would like to share the following report on the IBIS Summit held at the 2021 IEEE Virtual Symposium on EMC+SIPI from Randy Wolff (IBIS chair) and Mike LaBonte (IBIS secretary). This is the second year for the IBIS Summit at our IEEE EMC Society's flagship symposium.
[Teaching contact tracing during the SARS-CoV-2 pandemic : experiences from the Swiss canton of Vaud]

The rapid evolution of the SARS-CoV-2 epidemic required the implementation of contact tracing at an unprecedented scale in the Swiss cantons. Hundreds of contact tracers with different professions, most without medical background, had to be recruited and educated for tasks that usually are carried out by small teams of experts in communicable diseases. Teaching materials and courses about contact tracing, especially in French, were scarce. Thus, the learning team at the contact tracing centre of the canton of Vaud supported by clinicians and epidemiologists developed a method to dynamically create and apply a series of teaching modules. We describe this process and its results. The teaching materials are freely available upon contact with the authors.
Learning State Representations from Random Deep Action-conditional Predictions

In this work, we study auxiliary prediction tasks defined by temporal-difference networks (TD networks); these networks are a language for expressing a rich space of general value function (GVF) prediction targets that may be learned efficiently with TD. Through analysis in an illustrative domain we show the benefits to learning state representations of exploiting the full richness of TD networks, including both action-conditional predictions and temporally deep predictions. Our main (and perhaps surprising) result is that deep action-conditional TD networks with random structures that create random prediction-questions about random features yield state representations that are competitive with state-of-the-art hand-crafted value prediction and pixel control auxiliary tasks in both Atari games and DeepMind Lab tasks. We also show through stop-gradient experiments that learning the state representations solely via these unsupervised random TD network prediction tasks yield agents that outperform the end-to-end-trained actor-critic baseline.
Deontic Logic Based on Inquisitive Semantics

This paper introduces deontic logic based on inquisitive semantics. A semantics for action formulas is introduced where each action formula is associated with a set of alternatives. Deontic operato ...
Study on the Lower Extremity Rehabilitation Device RHleg

&lt;p class="0abstract"&gt;This paper introduces a device to support lower extremity rehabilitation for stroke patients. First, the device's mechanical structure has been presented; the device has a simple structure and fully meets the exercise function for the patient. The equipment is controlled by control and monitoring software, which is designed from Visual Basic software. The control circuit is the Arduino 2560 circuit, responsible for receiving the angle sensor signal and measuring the current during operation. In addition, the control circuit provides control signals to the actuator and communicates with the software using the rs232 cable. Next, the mathematical model of the device has been established, and we use MatLab software to simulate the response of the device to the PID controller. Simulation results for fast response time and slight overshoot. Finally, an experiment on volunteers was conducted, the results showed that the device was stable and safe to operate. Thus, the RHleg device has been successfully designed and manufactured, tested on volunteers with good results, and this is the basis for us to continue moving towards patient testing.&lt;/p&gt;
La nueva normalidad en las aulas de Formación del profesorado de Enseñanza Secundaria

espanolLa situacion excepcional vivida en el planeta durante los dos ultimos cursos ha obligado al profesorado a adaptar su docencia, transformando sus metodologias didacticas presenciales a telematicas. Este esfuerzo ha tenido un desarrollo desigual en funcion de los medios con los que han podido contar los profesores y alumnos y, sobre todo, en funcion de la Competencia Digital Didactica (CDD) que tenia el profesor. Presentamos una experiencia, en un master de formacion del profesorado de secundaria, realizada por docentes que disponen de esta competencia altamente desarrollada. Se ha establecido una metodologia didactica que integra, de una manera fluida, a estudiantes que asisten al aula presencial con otros que siguen las clases a distancia en un modelo hibrido. El resultado indica que es posible esta integracion, los estudiantes siguen las clases sin dificultad desde la distancia, realizando las mismas tareas que quienes estan en el aula, sintiendose parte del grupo, participando incluso en mayor medida que los del aula y sin dificultar el ritmo general de la clase presencial. Ademas, la percepcion de los estudiantes sobre esta metodologia es que puede tener continuidad una vez superada la excepcionalidad de la pandemia y que sus equipamientos y redes permiten estas experiencias. EnglishThe exceptional situation experienced on the planet during the last two academic years has forced teachers to adapt their teaching. They had to pass their face-to-face teaching methodologies to online teaching. This effort has had an uneven development depending on the means that teachers and students have been able to count on and, above all, depending on the Teacher's Digital Didactic Competence (DDC). We present an experience, in a master's degree in secondary teacher training, carried out by professors who have this competence highly developed. This has led them to establish a hybrid didactic methodology that perfectly integrates students who attend the classroom with others who follow the classes online. The results indicate that this integration is possible as students at home could follow the classes without difficulty, performing the same tasks as those in the classroom, feeling part of the group, participating even to a greater extent than those in class and without hindering the general rhythm of the face-to-face class. Furthermore, students believe that this methodology can continue once the exceptionality of the pandemic has been overcome and that their equipment allows these experiences.
The Effectiveness of Using Jigsaw in Teaching Reading Comprehension across different Personality

The purposes of the study were to test assumption underlying the effect of the jigsaw in teaching reading, the sign in the achievement of an extrovert student and extrovert student, and the interaction between jigsaw and personality. Before the researcher give the treatment in experiment class the researcher give questionnaire about personality into class, the questionnaire from EPI. The place of the research at MA Sabilul Muttaqin with the students 47 of eleventh grade that consist of 25 student in experimental they are 17 extrovert students and 8 introvert student and 22 in control they are 14 extrovert students and 8 introvert student. The researcher used experimental factorial design is a procedure of testing a hypothesis by setting up a situation in which strength of the relationship between variable can be tasted. And the research used Two Way ANOVA to calculate the data. From the result is jigsaw technique more effective than conventional technique, and there are differences on significant between extrovert and extrovert. An interaction between jigsaw and personality. Keyword: Effectiveness, Jigsaw, Descriptive text, Personality