Reviewerly/exHarmony huggingface dataset:
Multiple Teacher Distillation for Robust and Greener Models

The language models nowadays are in the center of natural language processing progress.These models are mostly of significant size.There are successful attempts to reduce them, but at least some of these attempts rely on randomness.We propose a novel distillation procedure leveraging on multiple teachers usage which alleviates random seed dependency and makes the models more robust.We show that this procedure applied to TinyBERT and Dis-tilBERT models improves their worst case results up to 2% while keeping almost the same best-case ones.The latter fact keeps true with a constraint on computational time, which is important to lessen the carbon footprint.In addition, we present the results of an application of the proposed procedure to a computer vision model ResNet, which shows that the statement keeps true in this totally different domain.
Reading Comprehension Test and Its Challenges In Students’ Perspective

There are two objectives of this research were first, to find the most difficult question category that students faced in reading test, and last, to investigate the causes of students' difficulties. This research used a qualitative method and it was conducted at the first semester students of c class English Department of UNTIRTA year 2019. The descriptive method was used to expose the result of this research. The data were collected through documents of reading comprehension final test first semester and an interview with the students. The document was used to find out the most difficult question category, and the result after analyzing the document is the students got difficulties in answering the question about vocabulary mastery. It is also supported by the result of interviewing the students. There were 17 students from 20 students or 85% felt that the vocabulary question category in part B was difficult. Then, the interview was also used to investigate the causes of the student's difficulties in reading test. The result got after analysis, the causes of the students got difficulties and confused to answer vocabulary question because they were lack of vocabulary mastery, and their reading habit was poor. So, it indicates that students’ capability of guessing meaning some unfamiliar vocabulary or words in the current context needs to be improved.
The Implementation and Testing of Online Self-Diagnose Covid19 Application Using CBR and UAT

Nowadays, at Bali Province (Indonesia), one of the public services that are still lacking related to the Covid19 pandemic is online self-diagnosis, so there are still many people who do not know whether they are exposed or not, based on general symptoms, unusual symptoms, or serious symptoms so that they can be treated immediately. In this research, Covid19CBR as a prototype of an online Covid19 self-diagnose application using the CBR (Case-Based Reasoning) method was developed, which can be run online via a web browser. Users simply input their personal data, choose the symptoms experienced or felt, then the system will provide diagnostic results online. This research use Design Science Research Methodology (DSRM) with the case study qualitative research methods, literature study data collection methods, with prototype design using Use Case Diagrams and Sequence Diagrams. The application was tested using User Acceptance Testing (UAT) with the results of 95% that mean user can use this application easily.
Learning Data Augmentation Schedules for Natural Language Processing

Despite its proven efficiency in other fields, data augmentation is less popular in the context of natural language processing (NLP) due to its complexity and limited results.A recent study (Longpre et al., 2020) showed for example that task-agnostic data augmentations fail to consistently boost the performance of pretrained transformers even in low data regimes.In this paper, we investigate whether datadriven augmentation scheduling and the integration of a wider set of transformations can lead to improved performance where fixed and limited policies were unsuccessful.Our results suggest that, while this approach can help train better models in some settings, the improvements are unsubstantial.This negative result is meant to help researchers better understand the limitations of data augmentation for NLP.
Improving Neural Language Processing with Named Entities

Pretraining-based neural network models have demonstrated state-of-the-art (SOTA) performances on natural language processing (NLP) tasks.The most frequently used sentence representation for neural-based NLP methods is a sequence of subwords that is different from the sentence representation of non-neural methods that are created using basic NLP technologies, such as part-of-speech (POS) tagging, named entity (NE) recognition, and parsing.Most neural-based NLP models receive only vectors encoded from a sequence of subwords obtained from an input text.However, basic NLP information, such as POS tags, NEs, parsing results, etc, cannot be obtained explicitly from only the large unlabeled text used in pretraining-based models.This paper explores use of NEs on two Japanese tasks; document classification and headline generation using Transformer-based models, to reveal the effectiveness of basic NLP information.The experimental results with eight basic NEs and approximately 200 extended NEs show that NEs improve accuracy although a large pretrainingbased model trained using 70 GB text data was used.
Retraction Note: Distribution of earthquake activity in mountain area based on embedded system and physical fitness detection of basketball

With the development of embedded system technology in recent years, it has become increasingly mature. The training model is to train the voice in the embedded system, and match the voice to be tested with the pattern, so as to complete the speech recognition of non-specific people to generate the training model. Therefore, in this paper, the embedded system is used to record the 2018–2020 mountain seismic data of the reservoir at a specific location downstream. For these earthquakes, the displacement, activity, and source parameters are studied, so as to analyze the general characteristics of the distribution of seismic activity in the mountain area, and summarize the double location. At the present stage of Chinese basketball, college basketball is one of the important foundations of its development. Basketball is not only one of the most popular and popular sports in the world, but also one of the most popular sports in China. Since it was introduced into China, it has been highly concerned by the masses and the whole country. Improving the level of college basketball plays an important role in promoting the level of Chinese basketball. Taking a college basketball team as an example, this paper expounds the importance of special basketball training and the role of basketball detection system, and provides effective tips for special physical training. Server data transmission technology and database technology design and implement the team sports physical fitness detection and evaluation system, greatly improve the security of basketball and training. In this paper, the embedded system technology is used to study the distribution of earthquake activity, basketball training, and physical fitness detection. In this paper, the distribution of seismic activity in mountainous areas and basketball physical fitness detection are studied and applied in practice, in order to promote its development and application.
Sistem Deteksi Orang Jatuh Dengan Menggunakan Sensor Kamera Kinect Dengan Metode AdaBoost

Fall cases of elderly people aged 65 or above put their health at risk because it could lead to hip bone fracture, concussion, even death. Immediate help is needed if fall happened which is why an automatic and unobtrusive fall detection system is needed. There are three approaches in fall detection system; wearable, ambience, and vision-based. Wearable approach has the drawback of its obtrusive nature while ambience approach is prone to high false positive value. Vision-based approach is chosen because its unobtrusive nature and low false positive value. This study uses Kinect camera because of its ability on extracting skeletal data. The methods that are used in the fall detection system are AdaBoost method and joint velocity thresholding method. Thresholding method is used as a comparison to AdaBoost method. Both methods use skeletal data from the subject recorded by the Kinect camera. AdaBoost method compares the skeletal data with model that was made before while thresholding method compares the joint velocity value with the threshold value. System test is done using training data, test data, and real-time data. The average accuracy obtained from the system test with AdaBoost method is 91.75% and with thresholding method is 68.22%.
Grammatical categories determination for Turkish and Kazakh languages based on machine learning algorithms and fulfilling dictionaries of link grammar parser

This research is aimed at identifying the parts of speech for the Kazakh and Turkish languages in an information retrieval system. The proposed algorithms are based on machine learning techniques. In this paper, we consider the binary classification of words according to parts of speech. We decided to take the most popular machine learning algorithms. In this paper, the following approaches and well-known machine learning algorithms are studied and considered. We defined 7 dictionaries and tagged 135 million words in Kazakh and 9 dictionaries and 50 million words in the Turkish language. The main problem considered in the paper is to create algorithms for the execution of dictionaries of the so-called Link Grammar Parser (LGP) system, in particular for the Kazakh and Turkish languages, using machine learning techniques. The focus of the research is on the review and comparison of machine learning algorithms and methods that have accomplished results on various natural language processing tasks such as grammatical categories determination. For the operation of the LGP system, a dictionary is created in which a connector for each word is indicated – the type of connection that can be created using this word. The authors considered methods of filling in LGP dictionaries using machine learning. The complexities of natural language processing, however, do not exclude the possibility of identifying narrower tasks that can already be solved algorithmically: for example, determining parts of speech or splitting texts into logical groups. However, some features of natural languages significantly reduce the effectiveness of these solutions. Thus, taking into account all word forms for each word in the Kazakh and Turkish languages increases the complexity of text processing by an order of magnitude
Compartilhamento do conhecimento entre docentes em uma escola técnica do estado de São Paulo

Knowledge Management (KM) aims to be a procedure for creating, organizing and accessing the knowledge existing in the organization. Any and all companies, regardless of their productive nature, must invest in the control of organizational knowledge, not as a main purpose, but as a practice capable of improving production and its results. This article aims to identify, in the perception of teachers of a state technical school, some of them members of the management team, the incidence of knowledge sharing (CC) actions, present in the SECI conversion process (socialization, externalization, combination and internalization). This is qualitative quantitative, descriptive, applied research, carried out from 2018 to 2020, which uses field study as a strategy. The field investigation was carried out by consulting institutional documents and, in November and December 2019, applying a questionnaire to high school / technical teachers and the institution's management and teaching staff. The state technical school, object of the research, is located in the northern region of the state of São Paulo and is administered by the Centro Paula Souza (CPS), an autarchy of the Government of the State of SP. The results indicated that the stages of externalization and combination are the ones that stand out the most, leaving the occurrence of constant records of pedagogical activities and aggregation of existing information. On the other hand, internal training actions and appreciation of the mission and organizational values need improvement. The investigation is relevant, not only because of the possibility of applying its results, but also because there are few studies dealing with KM in the academic area.
Towards explaining user satisfaction with contact tracing mobile applications in a time of pandemic: a text analytics approach

This research project investigates the critical phenomenon of the post-adoption use of ContactTracing Mobile Applications (CTMAs) in a time of pandemic.A panel data set of customer reviews was collected from March 2020 to June 2021.Using sentiment analysis, topic modeling and dictionarybased analytics, 10,337 reviews were analyzed.The results show that after controlling for review sentiment and length, user satisfaction is associated with users' perception of utilitarian benefits of CTMA, their CTMAspecific privacy concerns, and installation and use issues.Our methodological approach (using various text analysis techniques for analyzing public feedback) and findings (influential factors on consumers' satisfaction with CTMA) can inform the design and deployment of the next generation of CTMAs for managing future pandemics.
Tukey HSD Post Hoc Test untuk Perbandingan Karakteristik Lingkungan dan Sumber Daya Provinsi-Provinsi di Indonesia

Environmental Characteristics has several components, including the quality of the Environment and Environmental Resources which consist of mineral resources, energy resources, land, land resources, biological resources, and water resources. The provinces in Java, Sumatra, Kalimantan, dan Sulawesi still rely on natural resources for their economic growth. Environmental Quality can be measured by various indices, some of which are the Air Quality Index and the Water Quality Index. One of the problems in the National Economic Recovery during the Covid-19 Pandemic Period is the unavailability of studies and research related to similarities and differences in environmental characteristics to identify provinces that have the potential to prioritize the distribution of Funds/Budgets from the Central Government. Knowing the Similarities and Differences in Characteristics is also needed in determining the Destination Areas for comparative studies from one province to another. For this reason, it is necessary to recommend environmental sector policies as material for the preparation of regulations and the use of appropriate technology in overcoming environmental problems. Analysis of Variance Comparison of Environmental Characteristics and Resources of the Provinces on the four Big Islands in Indonesia shows a significant F - Statistics value, so a Tukey HSD Post Hoc Test is needed to recommend Priority Provinces based on similarities and differences in characteristics in the context of Strengthening the Environmental Sector, as well as recommendations for similarities between regions as a material for consideration for comparative studies between Regional / Provincial Governments.
CLOUDY TECHNOLOGIES AS A WAY TO IMPROVE THE EFFICIENCY OF TEACHING MATHEMATICS IN HIGHER EDUCATION INSTITUTIONS

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GAME PENGENALAN ORGAN TUBUH MANUSIA BERBASIS MULTIMEDIA MENGGUNAKAN STRUKTUR ALUR MULTIMEDIA HIERARKI

Natural science is the study of nature and all its contents in it, one of which studies the human body organs which are divided into two parts, namely internal organs and human external organs. The delivery of learning materials in schools is still conventional, namely using the media of printed books. The delivery media that is still conventional and many terms on the material of human organs cause students to have difficulty in understanding the material. There are many media that can be applied in learning human organs, one of which is by using learning game media. Game is a medium of delivery that is widely enjoyed by all people. So it is necessary to create an application system or multimedia-based learning game, as a solution to solving the problem of students' difficulties in understanding the material. Multimedia has aspects that are of interest to students because of its appearance in the form of images, animations, audio, and video. In designing this game, the human body uses a hierarchical multimedia flow structure, while the software used to build this game is Adobe Photoshop, Adobe Flash, and CorelDraw. The results of the game design will be implemented into a multimedia-based human organ recognition game using a multimedia hierarchy structure which is expected to be an effective way of delivering material in overcoming various problems that occur in learning human organs
The article is dedicated to the anniversary of Boris Vasilyevich Markov, the famous philosopher of Saint Petersburg, Russia. The author of the article, basing on many years of personal experience and professional communication with the hero of the day, presents an expressive and holistic image of Markov as a person and as a philosopher according to his biography and creativity. An attempt is made to consider the complex philosophical evolution of Markov, which took place in key periods for …

The article examines the epistemological parameters of the phenomenon of expert examination as well as the social and cognitive features of using scientific knowledge to substantiate the objectivity of expert evaluations. Today, the scope of expert activities has significantly expanded. Accordingly, the number of studies, including philosophical ones, considering this phenomenon, in particular, has increased primarily in connection with the growth of its role in assessing the social-humanitarian risks associated with the introduction of scientific-technical advances. At the same time, attention is directed to the fact that it is precisely due to the significant expansion of the scope of expert activity that the nature of the expert examination itself is distorted — its dependence on social contexts is increasing, but its objectivity is lost. The article aims to clarify the reasons for the growth of this dependence in connection with the specificity of the epistemological parameters of knowledge, which is used as a scientific basis for expert evaluations. This aspect of expert examination, as a rule, falls out of sight of both its researchers and the experts themselves. Modern philosophers and methodologists of science state the direct dependence of expert examination on applied (i. e., limited to practical requests) developments, while, in the author’s opinion, the condition for the objectivity of expert opinions is the obligatory appeal of experts to fundamental science, motivated by the commitment to expand the sphere of holistic knowledge concerning the world. This condition is highlighted due to the epistemological perspective of comprehending expert evaluations, which makes it possible to include additional criteria for their objectivity in the sphere of the expert’s self-awareness. The actualization of such criteria, according to the author, is now becoming a prerequisite for an effective expert examination that maintains a high social status.
Referenceless Parsing-Based Evaluation of AMR-to-English Generation

Reference-based automatic evaluation metrics are notoriously limited for NLG due to their inability to fully capture the range of possible outputs.We examine a referenceless alternative: evaluating the adequacy of English sentences generated from Abstract Meaning Representation (AMR) graphs by parsing into AMR and comparing the parse directly to the input.We find that the errors introduced by automatic AMR parsing substantially limit the effectiveness of this approach, but a manual editing study indicates that as parsing improves, parsing-based evaluation has the potential to outperform most reference-based metrics.
Driver State Alert Control using Head Shoulder Inclination and Facial Landmarks

Driver sleepiness is one in all the most important causes of most of the accidents within the world. Detecting the driver’s eye weariness is the simplest way for detecting the somnolence of the driver. The prevailing systems in the literature cannot discover sleepiness in folks having lagophthalmos (condition that forestalls eyes from closing utterly) and monocular vision (person with one-eyed or sight loss in one eye). To solve this downside a Driver State Alert Control system is projected which makes the use of head-shoulder inclination, face detection, eye detection, emotion recognition, eye openness estimation and blink counts for detecting the sleepiness and collision liability associated with robust emotional factors. The projected framework endlessly analyses the head shoulder inclination and facial lineaments of the driver to alert the driver by activating the alarm once he/she is drowsy or showing emotion unstable to drive. The typical separation between upper eyelid and lower eyelid of adults suffering from lagophthalmos is about 1-5mm. The EAR (Eye Aspect Ratio) is calculated supporting this separately for each eye. Thus, the proposed technique can be used for folks with lagophthalmos and monocular vision. Also, the entities don’t solely rely on blink count to ascertain the sleepiness, collision risks related to robust emotional factors are considered too. The projected system which is enforced with one camera view on open CV and raspberry pi setting illustrates the systems good efficiency in particulars of authentic sleep identification results and thus reduces road mishaps. It is easy to place in any type of vehicle and price effective too.
ARTIFICIAL INTELLIGENCE AS AN INVENTOR: STATE, PROBLEMS, DEVELOPMENT PROSPECTS

The present article considers the filing of an application under the procedure under the Patent Cooperation Treaty (PCT), in which the inventor indicates artificial intelligence. This is the first example in the history of mankind when the inventor in the patent application is not a person. Patent systems of different countries are not ready for such an event and the passage of such an application in the national phases of these countries will differ from each other. However, there is no doubt that in the world attempts to declare artificial intelligence as an inventor will continue. Currently, intellectual property experts are discussing the possible consequences of this phenomenon. This discussion has already suggested the need to develop and sign an international agreement to develop a unified approach to such applications in national and regional patent offices and changes in national laws governing this issue. The presented article details the passage of application PCT / IB2019 / 057809 in the national phase in the patent offices of Australia (IP Australia) and the United States (USPTO). Also, appeals and subsequent lawsuits on the mentioned applications were considered. The arguments of the parties presented in this article will be of interest to a wide range of experts in the field of intellectual property and, in particular, forensic experts in the field of intellectual property. In particular, a practical example of linguistic analysis used by a judge in a case before the Federal Court of Australia will be useful for forensic experts. Also, for professionals in the field of intellectual property will be useful references to sources that describe in detail the mentioned lawsuits. This article also provides a brief overview of the progress of this application in the national phases of other countries, with the relevant numbers of national applications, which will allow intellectual property professionals to monitor the status of the records of these applications in the future.