Reviewerly/exHarmony huggingface dataset:
Space-time Templates based Features for Patient Activity Recognition

Human activity recognition has been the popular area of research among the computer vision researchers. The proposed work is focused on patient activity recognition in hospital room environment. We have investigated the optimum supportive features for the patient activity recognition problem. Exploiting the strength of space-time template approaches for activity analysis, Motion-Density Image (MDI) is proposed for patient's activities when used supportively with Motion-History Image (MHI). The final feature vector is created by combining the description of MHI and MDI using Motion Orientation Histograms (MOH) and then applying Linear Discriminant Analysis (LDA) for dimensionality reduction. The LDA technique not only reduced the complexity cost required for classification but also played vital role to get best recognition results by increasing between-class separation and decreasing the with-in class variance.To validate the proposed approach, we recorded a video dataset containing 8 activities of patients performed in hospital room environment under indoor conditions.We have successfully validated the results of the proposed approach on our dataset by training the SVM classifier and achieved 97.9% average recognition accuracy.
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables

Probabilistic graphical models (PGMs) provide a compact representation of knowledge that can be queried in a flexible way: after learning the parameters of a graphical model once, new probabilistic queries can be answered at test time without retraining. However, when using undirected PGMS with hidden variables, two sources of error typically compound in all but the simplest models (a) learning error (both computing the partition function and integrating out the hidden variables is intractable); and (b) prediction error (exact inference is also intractable). Here we introduce query training (QT), a mechanism to learn a PGM that is optimized for the approximate inference algorithm that will be paired with it. The resulting PGM is a worse model of the data (as measured by the likelihood), but it is tuned to produce better marginals for a given inference algorithm. Unlike prior works, our approach preserves the querying flexibility of the original PGM: at test time, we can estimate the marginal of any variable given any partial evidence. We demonstrate experimentally that QT can be used to learn a challenging 8-connected grid Markov random field with hidden variables and that it consistently outperforms the state-of-the-art AdVIL when tested on three undirected models across multiple datasets.
Higher inclusive education for persons with special educational needs as a socio-pedagogical problem

Higher education is one of the most effective ways of organizing life. It gives freedom of life choice, spiritual and material independence, forms a worldview and life goals, develops a person's ability to adapt to a changing social situation, gives life resilience and harmonizes existence, which is especially important for young people with disabilities.
Multilingual Transfer Learning for QA using Translation as Data Augmentation

Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a QA model on English and test on other languages. In this work, we explore strategies that improve cross-lingual transfer by bringing the multilingual embeddings closer in the semantic space. Our first strategy augments the original English training data with machine translation-generated data. This results in a corpus of multilingual silver-labeled QA pairs that is 14 times larger than the original training set. In addition, we propose two novel strategies, language adversarial training and language arbitration framework, which significantly improve the (zero-resource) cross-lingual transfer performance and result in LM embeddings that are less language-variant. Empirically, we show that the proposed models outperform the previous zero-shot baseline on the recently introduced multilingual MLQA and TyDiQA datasets.
RESEARCH CHALLENGES IN TEXT MINING AND EMPIRICAL RESEARCH DIRECTIONS

Document categorization is one among the prime successive and fundamental issues in viewpoints of information examination, with applications from data recovery as well as spam sifting to content personalization and etymological communication content measure.Automated text order classification is an especially difficult assignment in present day information investigation, both from an observational and from a hypothetical viewpoint.This issue is of focal interest in numerous web applications, and therefore it has received consideration from specialists in such assorted zones.Quickly streaming surges of text are created by online news, web-based media and perpetual various applications, along with subsequently the need to precisely and adaptively sort them into the sub-streams could be a significant one.The emphasis on exclusively making utilization of delimited resources could be a result of size of particular streams: each time and memory should be held under the influence.The economical analysis of the huge datasets is the one among the most challenges in trendy machine intelligence and data processing applications.In this paper, we extensively surveyed significant developments occurred in this domain over past years.We have listed some significant existing methods, tools, standard datasets for performing text mining and analysis.We also given an argument on the various open challenges involved in this domain along with the problem identification and our possible research directions / objectives to overcome these challenges.
Assessing Institution’s Footprint in Web 2.0: A Comparative Analysis of University Library Websites in Bangladesh

The objective of this study was to compare the availability of web 2.0 tools on selected public and private university library websites in Bangladesh. Existing studies of web 2.0 seem to have ignored the possibility of comparing public university library websites with private university library websites. Conducting a study like this will provide an overview about the existing status of Web 2.0 tools. Content analysis was adopted as the method for the study. Twenty university library websites, comprised of ten top-ranked public and ten top-ranked private universities in Bangladesh, were studied for data collection. The findings demonstrate that the use of web 2.0 tools on the selected university library websites is still in its early stage, but it has been growing rapidly. More than 90% of the university libraries apply one or more web 2.0 tools to their websites. The private university library websites are ahead of public university libraries in terms of the integration of web 2.0 tools. Internal applications that are not publicly accessible contain information that may not be collected through content analysis since these links usually cannot be found on library web sites.This study is unique in terms of comparing public and private university library’s web contents. The findings from this comparative analysis will be useful for a university to track their own position and also those are planning to develop a website.
MACHINE LEARNING ALGORITHMS BASED BREAST CANCER PREDICTION MODEL

Bosom threatening development accepts a basic work within the extended passing rate annually. Course of actioncalculations and information mining approaches could be a capable sort of data characterization. Particularly, withinthe range of therapeutic services, they are connected to analyze and examine for fundamental authority. Course ofaction is one of the foremost noteworthy and essential undertaking in AI and data mining. Around an awesomebargain of inspect has been organized to relate data mining also AI on different restorative datasets to organizebosom Cancer. In this examination, we investigate the utilize of different AI calculations in specificNaive Bayes(NB), J48, artificial neural framework (ANN) and k Closest Neighbors (k-NN) proceeding the Wisconsin BreastCancer (one of a kind) datasets. The objective is to look at the judgment of the shown calculations within the datacharacterization prepare subordinate on the ampleness of each procedure subordinate on precision, precision,affectability and expressness.
Geological and Geophysical Criteria for The Localization of Gold Ore Nodes and Fields of The Central Kolyma Region

Summary Was carried out A high-precision aeromagnetic and aerogamma-spectrometric survey on a scale of 1: 50,000 (Central Kolyma region). The goal is to identify the predictive and prospecting criteria and signs of gold-ore clusters and fields of the Yano-Kolyma province manifested in geophysical fields. The results showed the transformation of geophysical fields; they have stratigraphic, lithological, tectonic, magmatic, metasomatic features and criteria for gold ore reference areas. An important role belongs to the AGS data and the improvement of the methodology for their interpretation. The identified geological and geophysical features are elements of predictive and prospecting geophysical models of ore clusters and fields with gold-quartz mineralization in a carbonaceous-terrigenous strata and in granite stocks. The developed models can be used to improve the reliability of the forecast assessment of gold mineralization in the Yano-Kolyma province.
An Integrated Cloud Web Hosting and Data Centric Approach Using Amazon Web Services (AWS)

Our old-style Hosting services comes with a predefined set amount of RAM, CPU, Disk Space, Bandwidth etc. and can provide better performance and processing facilities, but it lacks scalability, flexibility, availability and less time-to-set up. On the other side Cloud Computing is providing more flexible likelihoods. Cloud Computing is used to store and access the data over the internet with pay as or operate servers; we just have to pay for running our website or web services on them and leave the maintenance and operations to someone else to deal with them. In our paper we are going to demonstrate how AWS plays a key role in cloud hosting and data centric approach abstemiously than traditional hosting approach. In this competitive market, choosing of a cloud service provider is one the crucial business decision that has to make. There are so many factors to consider before choosing a leading cloud provider like Amazon Azure, Salesforce.com and Google as their offerings are very closely competing with each other. As there are many players in the market, AWS is leading the market with better pricing model according to security, scalability, availability and accessibility with the largest market share.Through our paper we can conclude that AWS(Cloud computing) is all about computing as an utility based services. As an end user perspective it is accessible at whichever location or hour it is and also from all computers, without the requirement of inserting, or enhancing and fixing the software implementations bodily on a domestic desktop or server.This paper strives to explore how AWS enables a wide range of organizations to concentrate on their key business tools with minimal cost.
Web-based Information System Services in a Textile Industry

Abstract. This purpose of this study is to rebuild a website in a major office of textile industry and be able to build a system which can be used there. The research method used in this paper was qualitative descriptive method, which is an actual or accurate systematic description or writing about facts, as well as the relation between phenomena in detail. This research method was used since the process of scientific research conducted was interpreting and explaining the data concerned with the situation that is happening in the Central Textile. The goal is to create a new system using that major office of textile industry website. And customers will find it easier and faster to perform business processes.
Election Security: Technology, Risks, and Mitigations

Most government and state officials serve at the hands of the people – that is they are elected to their positions. This is typically accomplished by voting, a formal expression of one’s opinion or choice. Voting is a fundamental process in a democratic system; however, the right to vote is sometimes taken for granted. The privilege to determine a country’s leadership is not universal. The ability and requirement to vote varies from country to country. The spectrum ranges from compulsory voting where citizens in 22 countries face fines if they do not vote, to optional voting where it is a civic right not duty, to 20 autocratic countries where there is no ability to vote (ITV, 2015). Many countries that allow citizens to vote require citizens to be a minimum of 18 years old; however, the age requirements vary from 16 to 21 years old (Chughtai, 2019). The ability to vote in the United States (US), is a right granted to citizens by the Constitution and has been modified over time to be more inclusive. So, what happens when the validity of a citizen’s vote or the integrity of the voting system is compromised? From a US Presidential election perspective, this paper will analyze the history of elections focusing on potential compromises, the technologies used to support elections, and outline the risks and associated mitigations to prevent election compromise. The paper also will compare election systems between the US and Australia. Finally, this paper will provide recommendations for securing the election process based on research and the stated comparisons.
Variational Leakage: The Role of Information Complexity in Privacy Leakage

We study the role of information complexity in privacy leakage about an attribute of an adversary's interest, which is not known a priori to the system designer. Considering the supervised representation learning setup and using neural networks to parameterize the variational bounds of information quantities, we study the impact of the following factors on the amount of information leakage: information complexity regularizer weight, latent space dimension, the cardinalities of the known utility and unknown sensitive attribute sets, the correlation between utility and sensitive attributes, and a potential bias in a sensitive attribute of adversary's interest. We conduct extensive experiments on Colored-MNIST and CelebA datasets to evaluate the effect of information complexity on the amount of intrinsic leakage.
МЕТОДИКА ОБУЧЕНИЯ СТУДЕНТОВ УМЕНИЮ ВЫЯВЛЯТЬ ДИДАКТИЧЕСКИЙ ПОТЕНЦИАЛ СРЕДСТВ ИНФОРМАЦИОННО-КОММУНИКАЦИОННЫХ ТЕХНОЛОГИЙ ДЛЯ ФОРМИРОВАНИЯ И РАЗВИТИЯ УНИВЕРСАЛЬНЫХ УЧЕБНЫХ ДЕЙСТВИЙ (ПРОФЕССИОНАЛЬНЫХ КОМПЕТЕНЦИЙ) ОБУЧАЮЩИХСЯ

Within the framework of solving the pedagogical problem of the formation (development) of methodological knowledge of students of pedagogical specialties, the need to develop a methodology for teaching students the skills to identify and substantiate the didactic potential of information and communication technologies (ICT) and their means is formulated. Based on the essence of the activity-based and competence-based approaches, the authors consider the detailing (decomposition) of learning outcomes, their differentiation, and the allocation of the activity components of the ICT tools used as a necessary condition for constructing a teaching methodology. Based on the establishment of the correspondence between the operational composition of actions, the components of universal educational actions (or professional competencies) and the activities implemented in specific ICT tools, a three-level gradation of the didactic potential of ICT tools is proposed. The content-activity components of the teaching methodology are presented in the format of a summary table. The proposed methodology for identifying and substantiating the didactic potential is universal and can be used to identify it with a different modern target orientation.
Blockchain-Based Smart Supply Chain Management System

Supply chain management (SCM) is a process of delivering the goods from the manufacturer to the customer with various stakeholders. Maintenance of transparency is important from the process of manufacturing to delivery in SCM. The current SCM is not shared and does not have transparent data of the raw material, quality of the product, transportation, and delivery of goods. The current SCM is not well-equipped with the tracking the details of the product. A record of the particular product data should be maintained with a date of service, next service date, etc. The supply chain is not only moving the product from the manufacturer to the end user, but it also goes backward if the customer wants to return the product. Trust is questionable in SCM if there is no tracking mechanism. Trusting all of the participants is required when people use the supply chain even though it is not a trusted environment.
PRACTICALITY OF USING BROWN’S RUBRIC AS A TOOL IN ASSESSING STUDENT’S WRITING SKILL

This research was due to some problems found in the field. It has been done at SMA KEC X KOTO because most of the teachers of Senior High School still used traditional technique in evaluating students’ writing. The teacher assessed only the accuracy in the use of tense and word spelling, teacher crossed out of words that are considered wrong without giving an explanation and teacher gave assessments to students’ writing without clear criteria, so that the assessment was done subjective. This research used descriptive quantitative research. The researcher used a questionnaires and documentation collect the data. The population of this research was SMA KEC X KOTO. The samples that English teacher at SMA N 1 X KOTO, SMA AL HIRA, SMA NURUL IKHLAS consist 10 teachers by using total sampling. The data were got through questionnaires consists 29 list question items that teachers must answer based on their opinion. To analyze the data, researcher used Likert Scale and then, to find out percentage of each item of questionnaire, the researcher used the formula by Sudijono p:f /n x 100% , after that researcher interpreted of the practicality percentage table.The finding reveals that Brown’s rubric using students writing at SMA X KOTO is very practical. Mostly teacher respond to every statements such as the organization which consists of 8 indicators only answer strongly agree and agree. The interpretation table shows that the percentage of teacher’s mean score was 89%. Thus, the use Brown’s rubric as a tool in assessing students writing was very practical ( 86% - 100%). It is recomended for teachers to use Brown’s Rubric.Key word: Writing, Analytic rubric, Very Practical
PRACTICAL APPLICATION OF THE PROJECT APPROACH ANYPLACE AND ANYTIME ENCOURAGING THE COGNITIVE ACTIVITY OF STUDENTS

Applying the method of project approach in modern schools, helps students get permanent motivation of studying, develop their cognitive activity and work online anytime, anyplace. This method motivates students, increases their self-confidence and sureness in their own abilities.Interactive methods of teaching are a great challenge for the teacher.The meaning of the cognitive motive as a basic one in the student’s activity is a significant part of the project work during the educational process.Project work is an interactive method, which is a process of previously planned actions with a certain aim, ways of achieving it, terms, resources, and students often work in groups.One of the basic approaches in the educational process of IT is the project based approach.In this approach students’ work is organized on a certain project. The given task should result an obvious product for one or more classes. Supporting the thorough development of students is leading. The school should provide the participation of students in various activities, which will enrich their knowledges and form their individuality.The choice of the theme to research “Practical applying of the project approach anytime and anyplace”, was dictated by the following:1. Orientation of education in provoking thinking, independence, forming skills of teamwork, instead of memorizing and reproducing.2. Practical thinking improves the development of students’ cognitive abilities.3. Advantages of project work in evolving practical thinking. In project work, students learn how to combine thinking and action, to differentiate important from unimportant, to detect problems and find a way to solve them, to plan.During the process of work with the students it became clear that using innovative methods of education in teaching IT in 10 grade, increases the students’ interest for the subject
Il ruolo della rete sociale nell’acquisizione dell’articolo italiano in parlanti ucrainofone

The quality and quantity of the input to which a learner of an L2 is exposed are essential factors for the development, in the interlanguage, of complex traits such as the article. The sociolinguistics of immigration, which correlates acquisition variables with social aspects, postulates that the type of social network in which the learner is inserted determines his/her exposure to the input and, therefore, the results of the acquisition. Assuming this theoretical perspective, in this article we analyse the production of the article in the speech of 12 Ukrainian-speaking spontaneous learners of L2 Italian.
Research on Mathematics Classroom Teaching Based on the Model of Hierarchical Teaching Management

With the implementation of nine-year compulsory education, and our school is located at the border of urban and rural areas, there are many children in rural areas and the quality of students varies. The problems of mathematics learning in our school have come to the fore. Some do not learn at all, and some cannot learn. Coupled with the traditional thinking that mathematics is a “big problem” in students’ minds, students’ learning initiative and self-consciousness are generally higher. Poor, it was difficult to improve at one time. On the other hand, limited by students’ large classes, number of class hours and 45 minutes of class time, classroom teaching can provide students of different levels with limited participation opportunities. Therefore, as a mathematics teacher in actual work, they will constantly try to use better methods. To change the problem of student differences in class teaching mode. By analyzing the status quo of mathematics teaching in the middle school, studying the reform practice of mathematics teaching at home and abroad, it is decided to conduct experimental research of layered teaching in mathematics classroom teaching of our school. Hierarchical teaching conforms to the dialectical theory of materialism, the theory of pedagogy, the learning psychology of students, and the principles of teaching students in accordance with their aptitude in the teaching theory. It has a broad theoretical foundation.
A Dataflow Architecture for Real-Time Full-Search Block Motion Estimation

Motion estimation is the cornerstone of main video compression standards, which are based on the reduction of the temporal redundancy between consecutive frames. Although the mechanism is simple, the best method, Full Search, uses a brute-force approach which is not suited for real-time applications. This work introduces a high performance architecture for performing on-the-fly full-search block matching estimation in FPGA devices, which has been modeled using C++ programming language and synthesized with Vivado HLS for a Xilinx ZC706 prototyping board. The architecture is based on a dataflow datapath and it is configurable, enabling a fast and easy exploration of the solution space. On-board results achieve a maximum performance of 743 fps, 247 fps and 110 fps for VGA, HD and FHD video resolutions, respectively, for a typical macroblock size of $$16 \times 16$$ pixels and a search area of $$\pm 16$$ pixels.