Reviewerly/exHarmony huggingface dataset:
Detail Reconstruction of 3D Face Model from Single Image

Abstract With the development of computer vision technology, 3D face model reconstruction technology has a great breakthrough. In view of the current popular deep learning method, we use an algorithm based on neural network to restore the details of 3D face model. We design a high frequency detail generation network to generate displacement map based on the texture extracted from the original image. The displacement map contains a large number of high frequency detail features of the target face. By embedding the displacement map into the face model, the details of the model can be restored.
20세기 초 일본 신어의 현대 일본어와 한국어에서의 정착과 쇠퇴

In order to investigate settlement of Japanese new words accepted in early 20th century, we analyzed the Japanese new words in lists of dictionary and Corpus. And it was compared with a number of the words which is searched in Google(NWSG). The new words which has higher NWSG shows relatively higher registration ratio in dictionary both in Korean and Japanese.Base on this result, we divide the new words into settled and unsettled words. Interesting point is that there is a lot of common settled and unsettled words in Korean and Japanese. The ratios of settled and unsettled words were ca. 40% and 20%, respectively. This results demonstrate that the Japanese new words accepted in early 20th century influenced greatly on modern Korean and Japanese.
The Use of Youtube Video Series by Non English Major Students Of the Faculty of Economics for Speaking Skill

This study was conducted to investigate the use of social media especially YouTube by students of Non English Major of the Faculty of Economics at the University of Palangka Raya. It was also to see the impact of using social media especially YouTube to improve their speaking ability. The study design of this study was descriptive qualitative. Fifteen students of the Faculty of Economics were selected as participants to speak in front of hand phone camera with the topics already prepared. Five online meetings were done to know their progress. Semi structured interview was also done to collect the supporting data of deeper information needed. The result showed that use of social media especially YouTube gave high impact for the speaking ability of Non English Major students as seen from their video and deep  information from the interview. However, there were various resistance dealing with the efforts to increase their speaking ability, they are psychological factors (less motivated, not confident), environment factor (the relationship with their environment), linguistic factors (finding references, vocabulary selection, speaking style and gesture). The use of social media especially YouTube might make the students feel easier in overcoming the various resistance to improve speaking ability.
Virtual Pinboard - A Method for Displaying İnformation using Web Based Augmented Reality on Smartphones

A pin board is also known as a bulletin board or  notice board. It is a surface that facilitates the addition and removal of messages and is used for posting important dates, projects, schedules, important announcements and public messages, for example, to advertise items wanted or for sale, announce events, or to provide information. Generally made of materials like cork, rubber or foam, pin boards are widely used in universities, corporate workspaces, local shops, cafes and at many sports and cultural events. The beauty of the pin board is its simplicity and versatility. Augmented reality (AR) and virtual reality (VR) are technologies that have seen a lot of progress in the past decade and are continuing to rise in popularity. Their applications are now found in almost all the fields and more devices are built with AR/VR support. Considering the global pandemic situation and the processing capability of current smartphones, we propose a way of contactless display of information virtually using augmented reality on handheld devices (Smartphone and tablets). Virtual pin boards can be used in numerous places, for example in schools, universities, workplaces, homes, fairs, tourist locations and crime scenes, etc., as a means to display information.
СТРУКТУРА И СОДЕРЖАНИЕ ПРОФЕССИОНАЛЬНО-ИДЕНТИФИЦИРУЮЩЕЙ КОМПЕТЕНЦИИ УЧИТЕЛЯ ИНОСТРАННОГО ЯЗЫКА В НАЧАЛЬНОЙ ШКОЛЕ

The article is focused on the formation of professional competences as part of training a primary school foreign language teacher. The subejective and procedural content of the article is built on the competence-based model of professional education related to the changing needs of society and the labor market in modern conditions. The aim of research is to identify the most characteristic features of the modern competence-based approach. The goals and objects of the formation of competencies are considered, the organizational requirements for their formation of students are described, the content of key competencies and its functions and structural features are revealed. The foreign language teachers’ professional competences (key, subject and special) are considered in the article, which are necessary for good teaching, on the basis of the competency model of S.S. Kunanbayeva «Professional readiness of graduates».
Literature review on software metrics and a New Proposal

The use of metrics is important in software development activities as they make it possible to check quality, identify failures and other benefits. The objective of this paper is to propose a new software metric based on a bibliometric study and a literature review on software metrics. The bibliometric research was carried out in the Scopus and Web of Science databases to identify the distribution of articles by year of publication, the main authors, affiliation, country, the most common languages, the types of documents, journals with more publications, areas of knowledge, and the keyword clusters. Twenty-three articles were subsequently selected for reading to compose the literature review. The results of the bibliometric research show that (i) there is no defined core of research; (ii) there is a fluctuation of the number of published articles; (iii) the predominant language is English, and the country with the highest index of publications is the United States; (iv) the main area of knowledge is computer science; (v) in relation to affiliation, Florida Atlantic University stands out; (vi) the journal with the largest number of publications is the Journal of Systems and Software. The literature review showed that many software metrics can be used for different purposes, but most of them are related to code, and none are related to acceptance. As such, a support metric for the software acceptance process is proposed to facilitate the delivery phase of the software product, providing security for the customer and cost savings for the developing company.
GRADIENT CALCULATION FOR QUANTUM CIRCUIT USING TENSOR FLOW QUANTUM

Although AI (ML) does not exactly duplicate frameworks in nature, it can discover a model of a framework and predict its performance. In recent years, traditional ML models have proven to be reliable in handling logical concerns, advancing picture preparation for immoral discovery, assessing seismic tremor post-quake tremors, predicting fantastic climatic examples, and establishes rules exoplanets. With the continued growth in quantum processing, the emergence of the most recent quantum ML models could have a significant impact on the world's most pressing concerns, resulting in breakthroughs in the fields of medicine, materials, detection, and correspondences. A quantum model can communicate with and summarize the data from a quantum mechanical source. However, to achieve quantum models, four categories will constantly be provided: cognitive knowledge and cross-breeding quantum old style models. Quantum information displays superposition and tangle, resulting in potential exchanges that would require an exponential number of local computational as-sets to communicate with or store. In this paper, we provide Tensor Flow Quantum in session 1, Concepts of Quantum Machine Learning with session 2, as well as a hybrid quantum-classical model and gradient computations for a proposed quantum circuit in session 3, which compares and contrasts two different models.
Special issue: Selected papers of the 13th International Conference on Language and Automata Theory and Applications, LATA 2019

We give a computational interpretation to an abstract formulation of Krull's theorem, by analysing its classical proof based on Zorn's lemma. Our approach is inspired by proof theory, and uses a form of update recursion to replace the existence of maximal ideals. Our main result allows us to derive, in a uniform way, algorithms which compute witnesses for existential theorems in countable abstract algebra. We give a number of concrete examples of this phenomenon, including the prime ideal theorem and Krull's theorem on valuation rings.
Queued Commuter Counting System by using Caffe Deep Learning Technique

The problem of using public transport is that the volume of transport vehicles is insufficient for the number of commuters. Commuters who have to wait in line lose time and opportunity. Solving this problem by increasing vehicle estimates might not be easy due to budget constraints. Transportation management is essential in order to meet the needs of commuters as much as possible. In this article, we proposed a system for displaying the number of commuters queuing at tram stations using a visual identification technique. The system consists of Raspberry Pi 3, Web camera, Firebase application, Caffe model, personal computer and HTML. A key component of the system is the person identification by the Caffe deep learning technique. Commuters pictures at each station are captured and processed. Finally, the quantity of the commuters are displayed on a website. We tested the system in a case study of the electric cars transportation on campus. The experimental results showed that the system worked well with the nearest distance of the passenger to the camera. The lower number of passengers, the better accuracy of person detection (different testing period). The result of detection people without mask on showed 100% accuracy for all distances.
A STUDY ON GOING CASHLESS WITH CRYPTOCURRENCY IN INDIA AND ITS IMPACT IN BANKING INDUSTRY

India is a developing country and one such development is transforming to be a Digital India. With the announcement of demonetisation in 2016, India took a huge step of transforming to Cashless Economy. The idea of transforming into digitalization was to eliminate black money in the country. Banking system has faced a major change in this transformation by introducing Net Banking, Mobile Banking. Being the industry with the most leading financial sector of the country and one of the most important reasons for economic development, with the innovation of certain digital currencies the banks was not satisfied. One of the digital transformations is the evolution of Crypto currency. Cryptocurrency is a digital or virtual currency which is moulded to use as a medium of exchange used by a strong cryptography (used for solving codes) to secure financial transactions and verify the transfer of assets. They are considered to be one such future development of Block chain technology. But the awareness regarding to cryptocurrencies is not appreciated. The Banking sector is opposing to investment or transactions with cryptocurrencies as the banks do not earn much profits with the innovation of cryptocurrency. The objective of the study is (i)To assess the level of awareness among the investors on investment in crypto currency (ii) To suggest measures on bringing new form of crypto currencies for progressing towards a cashless society
New Performance Metrics for Offline Content-Based TV Recommender System

The past decade has seen a fast rise in popularity of recommendation systems provided by many entertainment and social media services. However, despite the recognised advances in different recommendation approaches and technologies, there remain many challenges, particularly in TV content recommendation systems. More precisely, machine learning based TV content recommendation systems suffer from a class imbalance problem; hence, it is difficult to evaluate the system using traditional metrics. Moreover, specific challenges arise during the development phase, when the system operates in ‘offline’ mode. This means the recommendations are not actually presented to users - making it even more difficult to measure the quality of those recommendations. This paper presents a proof-of-concept demonstrator of a television recommendation system, based on Content-based Filtering, as a contribution towards building a full-scale intelligent recommendation system. New evaluation metrics are proposed for ‘offline’ testing mode, while also tackling the class imbalance problem. The experimental results, based on real usage data, are promising and help in defining the future path as presented along with the conclusion.
Visually grounded fine-grained speech representations learning

Visually grounded speech representation learning has shown to be useful in the field of speech representation learning. Studies of learning visually grounded speech embedding adopted speech-image cross-modal retrieval task to evaluate the models, since the cross-modal retrieval task allows to jointly learn both modalities and find their relationships. Specifically, the two modalities, i.e., audio and visual, were jointly embedded into a common space where the speech embeddings and the image embeddings were learned in this process. The obtained embeddings were evaluated by cross-modal retrieval task to see the model performance. Currently, the studies worked on visually grounded speech representation learning trained on scene-based datasets, such as Flickr8k, etc., which learn different objects to infer a new scene. The works that investigating the visually grounded speech representation model's ability to combine different attribute information to infer new objects are lacking. Therefore, this thesis presented a visually grounded speech representation model trained on the fine-grained datasets that contain high level details of objects to learn attribute information associated with objects to infer new objects. The proposed model adopted dual-encoder structure and used different DNN models to extract visual and audio features. An adapted batch loss was used to calculate the similarities between two modalities. Experiments were conducted to test the model performance: 1) The parameter adjusting to obtain a better-performed model. 2) Comparing with state-of-the-art models in speech-image cross-modal retrieval field and fine-grained text-image cross-modal retrieval field. 3) Ablation studies to evaluate components in the model. 4) Research on attention module to see its effectiveness. The results indicated that the proposed model was able to learn the relationships between attributes and objects to retrieve new visual objects and outperformed other visually grounded speech learning models.
PENGEMBANGAN PERMAINAN STRAT ADVENTURE BERBASIS KOMPUTER SEBAGAI MEDIA PEMBELAJARAN STRUKTUR ATOM UNTUK SISWA SMA

AbstrakPenelitian yang dilakukan bertujuan untuk mengembangkan permainan StrAt Adventure berbasis komputer sebagai media pembelajaran struktur atom untuk siswa SMA. Kelayakan dari permainan ditinjau dari aspek validitas, kepraktisan, dan keefektifan. Penelitian ini menggunakan desain pengembangan model jenis penelitian yang digunakan adalah R&D (Research and Development) yang telah dimodifikasi sampai pada uji coba permainan. Aspek validitas diperoleh dari hasil penilaian validasi oleh validator yang terdiri dari dua dosen kimia dan satu guru mata pelajaran kimia. Hasil validasi isi mendapatkan persentase dalam rentang 81%-100% dan validasi konstruk mendapatkan persentase dalam rentang 61%-80% dan 81%-100% termasuk pada kriteria valid dan sangat valid. Aspek kepraktisan diperoleh dari hasil observasi aktivitas siswa sebesar 100% termasuk pada kriteria sangat praktis dan hasil angket respon siswa dan memperoleh persentase sebesar 87,5%, 85%, dan 82,22% termasuk pada kriteria sangat praktis. Aspek keefektifan diperoleh dari hasil belajar siswa memperoleh sebesar 100%. Berdasarkan hasil dari penelitian maka permainan StrAt Adventure layak digunakan sebagai media pembelajaran struktur atom untuk siswa SMA.Kata Kunci : Permainan StrAt Adventure, Media Pembelajaran, Struktur Atom.AbstractThis study aims to develop a computer-based StrAt Adventure game as an atomic structure learning media for high school students. The feasibility of the game in terms of validity, practicality, and effectiveness. This study uses the design development model type of research used is R&D (Research and Development) that has been modified at the trial game. The validity aspect is obtained from the results of the validation assessment by the validator consisting of two chemistry lecturers and one chemistry subject teacher. The content validation results get a percentage in the range of 81% -100% and construct validation get the percentage in the range 61% -80% and 81% -100% included in the criteria valid and very valid. The practical aspects obtained from the observations of student activities at 100% included in the criteria are very practical and results of student questionnaire responses and obtained a percentage of 87.5%, 85%, and 82,22% included in the very practical criteria. The effectiveness aspects obtained from student learning outcomes obtained at 100%. Based on the results of the resesarch conducted, the game StrAt Adventure is worthy of being used as an atomic structure learning media for high school students. Keywords: StrAt Adventure Game, Learning Media, Atomic Structure.
Internet of Medical Things (IoMT) based healthcare monitoring system for patients in smart hospitals

In this modern world, the remote healthcare system using Internet of Medical Things (IoMT) is emerged as the boon for the patients. The elderly patients, rural patients and disabled people who can’t reach to the Hospital in emergency situations can take benefit from the IoMT technology for their treatment. The wireless sensors attached to the human body area networks senses the body temperature, heartbeats, brain neurological signals and send to the remote doctor using the internet with the help of smart mobile phone. In this paper, we propose a remote healthcare system using IoMT technology which can be helpful for regular health monitoring and to save the patient’s life in emergency conditions.
Nutri-bullets: Summarizing Health Studies by Composing Segments

We introduce Nutri-bullets, a multi-document summarization task for health and nutrition. First, we present two datasets of food and health summaries from multiple scientific studies. Furthermore, we propose a novel extract-compose model to solve the problem in the regime of limited parallel data. We explicitly select key spans from several abstracts using a policy network, followed by composing the selected spans to present a summary via a task specific language model. Compared to state-of-the-art methods, our approach leads to more faithful, relevant and diverse summarization -- properties imperative to this application. For instance, on the BreastCancer dataset our approach gets a more than 50% improvement on relevance and faithfulness.
Integrated learning as a means of organizing the modern educational process

Integration is a new approach to teaching subjects. Such lessons save time, since they make it possible not to duplicate material on different subjects. Integration of the subject Informatics and Information with other subjects is carried out in the form of combining courses in subjects. This approach requires one teacher to know two subjects. Using information technology tools and the level of preparedness of students, you can build an integrated lesson, create integrated assignments, and conduct an integrated module for students of any age.
Developing a set of key performance indicators for monitoring sustainability of forest functions in the Hyrcanian forests

This study identifies key performance indicators for monitoring the sustainability of forest functions in the northern forests of Iran. This process was conducted in two phases through the Delphi method, and Fuzzy Cognitive Mapping (FCM). The Delphi method classified indicators according to two properties, importance and measurability. These were used to identify four classes: (a) important and measurable, (b) important and less measurable, (c) less important and measurable, (d) less important and less measurable. Indicators placed in groups (a) and (b) were then used to develop a set of key performance indicators using FCM. The FCM enabled the evaluation of the overall cumulative impacts of all indicators within the network, individually and collectively, through their interactions with other indicators. The selected methods and possible application of the key performance indicators could be used to determine how to achieve the sustainable management of a forest area.
GEOMORPHOLOGICAL MAPPING OF INTERTIDAL AREAS

Abstract. Spatiotemporal geomorphological mapping of intertidal areas is essential for understanding system dynamics and provides information for ecological conservation and management. Mapping the geomorphology of intertidal areas is very challenging mainly because spectral differences are oftentimes relatively small while transitions between geomorphological units are oftentimes gradual. Also, the intertidal areas are highly dynamic. Considerable challenges are to distinguish between different types of tidal flats, specifically, low and high dynamic shoal flats, sandy and silty low dynamic flats, and mega-ripple areas. In this study, we harness machine learning methods and compare between machine learning methods using features calculated in classical Object-Based Image Analysis (OBIA) vs. end-to-end deep convolutional neural networks that derive features directly from imagery, in automated geomorphological mapping. This study expects to gain us an in-depth understanding of features that contribute to tidal area classification and greatly improve the automation and prediction accuracy. We emphasise model interpretability and knowledge mining. By comparing and combing object-based and deep learning-based models, this study contributes to the development and integration of both methodology domains for semantic segmentation.
ASSESSING THE PERFORMANCE OF SOME RE-SAMPLING METHODS USING LOGISTIC REGRESSION

This research reports on the performance of two re-sampling methods (Bootstrap and Jackknife) relationship and significance of social-economic factors (age, gender, marital status and settlement) and modes of HIV/AIDS transmission to the HIV/AIDS spread. Logistic regression model, a form of probabilistic function for binary response was used to relate social-economic factors (age, sex, marital status and settlement) to HIV/AIDS spread. The statistical predictive model was used to project the likelihood response of HIV/AIDS spread with a larger population using 10,000 Bootstrap re-sampled observations and Jackknife re-sampled. From the analysis obtained from the two re-sampling methods, we can conclude that HIV transmission in Kebbi state is higher among the married couples than single individuals and concentrate more in the rural areas.