Reviewerly/exHarmony huggingface dataset:
Τεχνικές μηχανικής μάθησης για κατηγοριοποίηση βιοϊατρικών πολυδιάστατων σημάτων

In recent years, due to the technological progress of computer science, it has become possible to have and observe huge data sets. The processing of which often leads to inexhaustible knowledge. Thus, in the field of health, the data also exist and their processing can lead to a great improvement of the health systems, to the initial diagnoses of patients, to the analysis of the clinical picture of the patients and consequently to an increase in the life expectancy.The aim of this thesis is to observe various biomedical data processing techniques. These techniques were divided into two major categories. Machine learning techniques for data classification were first analyzed. Then, data volume management techniques as well as their dimensional reduction were presented.In addition to the theoretical background that was developed and analyzed, an experimental model for predicting the clinical picture of the elderly was created through the categorization of biomedical signals as well as their tumor management. This data was collected as part of a European program and contained data on the clinical profile of the elderly using a vest with sensors measuring heart and lung rate.The model was asked to classify the samples in terms of their Frailty into three categories: frail, pre-frail, no-frail. To achieve this goal a method was developed which was based on Multi Instance Learning (MIL). The signals were analyzed by extracting statistical features from them using sliding windows techniques and not their raw form, in order to reduce the volume of data. Then they were stored in a tensor. Finally, to achieve dimensional reduction to the data, tensor decomposition techniques were used, thus reaching the final data used in both machine learning and multiple instance learning algorithms.
Improved protocols of secure quantum communication using w states

Two effective stable quantum communication protocols using 3-qubit and 4-qubit symmetric W states have recently been suggested. These two protocols based on dense coding are extended and their efficiencies are greatly increased. Simplebounds are obtained on the qubit efficiency of determinist safe quantum communication (DSQC) and quantum secure direct communication (QSDC) protocols, and it is shown that dense coding is not sufficient for the design of DSQC and QSDC protocols that are maximally effective. This fact is used to develop DSQC and QSDC protocols that are maximally effective using 3-qubit and 4-qubit W states.
IMPLEMENTASI MODEL PROBLEM BASED LEARNING (PBL) BERBASIS ROLE PLAYING GAME (RPG) PADA MULTIMEDIA PEMBELAJARAN UNTUK MENINGKATKAN PEMAHAMAN SISWA PADA MATA PELAJARAN PEMROGRAMAN DASAR

Penelitian ini bertujuan untuk meningkatan pemahaman ekstrapolasi pada siswa setelah diberikan perlakuan berupa model Problem Based Learning (PBL) berbasis Role Playing Game (RPG) pada multimedia pembelajaran interaktif pada mata pelajaran pemrograman dasar. Metode penelitian yang digunakan adalah Research and Development (RD---Aim of this research is to improve student extrapolation understanding after practice problem based learning model (PBL) based on Role Playing Game (RPG) in interactive learning multimedia in basic programing subject. The research practice research and development method (R&D) by comprehensive life cycle (SHM) model. The multimedia development tested by media and material expert. The assessment of media expert is 86.33% with very good category and assessment of material expert is 83.75% with very good category. The test practices twice. The initial testing evaluation result is 81.42% with very good category and there are some points that require revisions. Therefore, a testing practice after revision process. Testing assessment after improvement is 84.43% with very good category. The experiment class divided into three category, upper, middle, and lower. The gain average value is 0.23 with low category for the upper class, 0.31 with average category for the middle class, and 0.36 with average category for lower class. According to gain data from the experiment class groups explain an improvement of student extrapolation understanding after problem based learning model practice based on role playing game in interactive learning multimedia on the basic programming subject.
Support Vector Machine Based Supervised Machine Learning Algorithm for Finding ROC and LDA Region

Breast Cancer is one of the most dangerous forms of conditions in the world, according to the Breast Cancer Institute (BCI). Breast cancer is a leading cause of death for women. Several empirical studies have explored the use of machine and predictive systems for breast cancer. Breast cancer can be the major cause of women’s death. Many claim their algorithms are faster, easier or more precise, as according to cancer. Net, for the most cancer groups and associated hereditary syndromes individualized literature exhibits over 100 and 20 varieties. The purpose of this study was to optimize the algorithm used to carry out the study, including hybrid models mixed from various algorithms for Machine Learning (ML), including Vector Machine (SVM), to K-Nearest Neighbor (KNN) for efficient breast cancer detection. Two kinds of tumors exist. One is a brain tumor, and the other is malignant, benign tumors which are not primarily types of cancer and malignant tumors. Keywords: Accuracy, Gaussian blend show, SVM, KNN, ROC, Wisconsin breast cancer Cite this Article: Nalli Vinaya Kumari, G. S. Pradeep Ghantasala. Support Vector Machine Based Supervised Machine Learning Algorithm for Finding ROC and LDA Region. Journal of Operating Systems Development & Trends. 2020; 7(1): 26–33p.
Hand Gesture Controlled Omnidirectional Vehicle

The purpose of this project was to study how hand gesture control can be implemented on a vehicle that utilizes mecanum wheels in order to move in all directions. Furthermore, it was investigated h ...
Educar a distancia, un nuevo reto

espanolLas actuales circunstancias que vivimos han transformado los domicilios de profesorado y alumnado en aulas improvisadas. La atencion educativa se ha redisenado para seguir desarrollando la tarea docente a distancia. En este cometido, han tomado una relevancia fundamental las plataformas educativas, aplicaciones y recursos online que permiten mantener el proceso de ensenanza-aprendizaje y el acompanamiento tutorial. Algunas aplicaciones como Wordwall, Liveworksheets, Worksheet Works o el portal ARASAAC son algunos ejemplos de aplicaciones que pueden favorecer el exito en este proceso. EnglishPresent circumstances have transformed our teachers and students’ homes into improvised classrooms. Educational attention has been redesigned in order to continue carrying out the teaching task from distance. In this mission, educational platforms, online applications and resources have gained a fundamental relevance, allowing the sustainability of the teaching-learning process and the tutor’s follow-up. Some apps such as Wordwall, Liveworksheets, Worksheet Works or ARASAAC portal are some examples of applications that can favour success in this process.
Differentially-private software frequency profiling under linear constraints

Differential privacy has emerged as a leading theoretical framework for privacy-preserving data gathering and analysis. It allows meaningful statistics to be collected for a population without revealing ``too much'' information about any individual member of the population. For software profiling, this machinery allows profiling data from many users of a deployed software system to be collected and analyzed in a privacy-preserving manner. Such a solution is appealing to many stakeholders, including software users, software developers, infrastructure providers, and government agencies. We propose an approach for differentially-private collection of frequency vectors from software executions. Frequency information is reported with the addition of random noise drawn from the Laplace distribution. A key observation behind the design of our scheme is that event frequencies are closely correlated due to the static code structure. Differential privacy protections must account for such relationships; otherwise, a seemingly-strong privacy guarantee is actually weaker than it appears. Motivated by this observation, we propose a novel and general differentially-private profiling scheme when correlations between frequencies can be expressed through linear inequalities. Using a linear programming formulation, we show how to determine the magnitude of random noise that should be added to achieve meaningful privacy protections under such linear constraints. Next, we develop an efficient instance of this general machinery for an important subclass of constraints. Instead of LP, our solution uses a reachability analysis of a constraint graph. As an exemplar, we employ this approach to implement differentially-private method frequency profiling for Android apps. Any differentially-private scheme has to balance two competing aspects: privacy and accuracy. Through an experimental study to characterize these trade-offs, we (1) show that our proposed randomization achieves much higher accuracy compared to related prior work, (2) demonstrate that high accuracy and high privacy protection can be achieved simultaneously, and (3) highlight the importance of linear constraints in the design of the randomization. These promising results provide evidence that our approach is a good candidate for privacy-preserving frequency profiling of deployed software.
Research and Practice of Autonomous Learning and Evaluation Mode Based on Mobile Client

This article focuses on the exploration and construction of a mobile online autonomous learning and evalua-tion model, and on the cultivation and guidance of students’ autonomous learning methods and abilities, to complete the construction and optimization of formative evaluation. The basic teaching of computer application in the freshman year is the experimental object and content, and the experimental and control groups with strict variables are built to carry out a comparative analysis, discussion, and in-depth exploration through experimental research on how to build a mo-bile and autonomous learning and evaluation model with perfect content and structure, to promote better online teaching of various majors in the school and improve autonomous learning and evaluation models.
Research Guides: Electronic Resources at The Evergreen Library: Electronic Books

This guide aims to bring together all the electronic resources (e-books, e-journals, databases, streaming media) and digital content available through The Evergreen State College Library.
Ordinary Differential Equations (ODEs) Initial-Value Problems

Differential equations are relationships between a function and its derivatives. They are used to model systems in numerous engineering and science fields. For example, a simple differential equation relates the acceleration of a car with its position. Unlike differentiation where analytic solutions can usually be computed, in general, finding exact solutions to differential equations is difficult. Therefore, numerical solutions are critical to making these equations useful for designing and understanding engineering and science systems. Because differential equations are so common in engineering, physics, and mathematics, the study of them is a vast and rich field that cannot be covered in this introductory text. This chapter covers ordinary differential equations with specified initial values, which is a subclass of differential equations problems called initial value problems. To reflect the importance of this class of problem, Python has a whole suite of functions to solve such equations. By the end of this chapter, you should understand what ordinary differential equation initial value problems are, how to pose these problems to Python, and how these Python solvers work.
INTERACTIVE AS A PEDAGOGICAL INNOVATION IN AN INFORMATION SOCIETY

The article analyzes the scientific sources on the classification of interactive technologies in primary and higher schools, presents the basic classifications of methods of interactive learning on the basis of various features: communicative functions, degree of involvement of participants in the educational process, forms of organization of educational activities, etc. The main problems in creating a unified classification of interactive methods have been identified. A new, practically oriented classification is developed, based on the sign of motivation of educational activity. Based on the research, we can state the need to create a new classification that would be universal for two levels of education - primary and higher education. The basis for its formation is a set of motives that each participant in the educational process pursues. The first group of methods are those that meet the social needs of the subjects of the educational process. These include the motive for self-expression and self-assertion in the group and the motive for achieving a collective goal. The second group of interactive methods is distinguished on the basis of motivating motives. Interviews aimed at meeting the vocational-value (training-value) needs make up the third group of classification. The fourth is interactive methods aimed at realizing cognitive motives. The fifth group of interactive methods, aimed at satisfying the mercantile motives in the future and synthesizing the previous varieties, is a set of all the above motives. The choice of methods in this category is aimed at a comprehensive realization of all educational needs that will find their way into future activities, including professional ones. In the course of the study, we came to the conclusion that the active use of interactive technologies at a new angle will facilitate the training of motivated and positively oriented students and highly qualified specialists, who are able to integrate theoretical knowledge and practical skills in the future professional activity.
FEATURES OF THE VALUE-MOTIVATIONAL SPHERE OF IT SPECIALISTS

The article presents the results of an empirical study of the features of the value-motivational sphere in the professional activity of IT specialists, where the professional success of an IT specialist is considered from the point of view of the level of sophistication and the specifics of its motivation and value orientations. The features of motivation and value orientations of IT specialists were identified, and a comparison was also made of the motivation and value orientations of IT specialists of different ages, work experience, gender, different professions, with the presence of higher education and without it, different frequencies of changing jobs. The data was obtained that the satisfaction of basic needs and personality development, the integration of lifestyles, autonomy and service are paramount value orientations for IT specialists in their professional activities. The least value is social recognition. It has been established that the key factors in the motivational sphere of IT specialists are professional success, professional recognition and approval, the possibility of professional and creative development. The results of the study can be used to unleash the potential, capabilities of an IT specialist, for a comparative assessment of the personal characteristics of an IT specialist as a subject of activity, and the research results can be used by specialists involved in the professional selection, adaptation and development of personnel in IT companies.
Application of the Steepest Ascent Hill Climbing (SAHC) Algorithm for Mobile-based Shortest Route Search

This study aims at early to determine the application of algorithms Steepest Ascent Hill Climbing (SAHC) for finding the shortest route-based Mobile in Humbang Hasundutan. Based on the results of the application of algorithms Steepest Ascent Hill Climbing (Sahc) To search based Shortest These Mobile in Humbang Hasundutan. So it can be concluded that the search for the shortest route based on Mobile can be solved using the Steepest Ascent Hill Climbing algorithm. In the manual calculation process using the Steepest Ascent Hill Climbing algorithm at the node from Humbang, there is a heuristic value of 0.0896184808, at the node from which the three intersections are originated there is a heuristic value of 0.1693780561, at the node from which there is a heuristic value of 0.367474152, at the node from which the waterfall falls sibabo has a heuristic value of 0.3823982675. Then the result of the shortest route from Sipinsur Geosite (F) to Simolap Waterfall (B) is F eD eB (Sipinsur GeoSite - intersection 4 - Simolap Waterfall) the total distance is 51 km and the time is 1 hour 34 minutes. So that the test results of the Steepest Ascent Hill Climbing algorithm process with the system in accordance with the manual calculation process of the Steepest Ascent Hill Climbing algorithm.
Pelatihan Tour Guide bagi Pemuda Karang Taruna

The partner of this service program was the youth cadets of Batu Milla Village, Maiwa, Enrekang Regency, South Sulawesi, Indonesia. The problems of partner were the lack of understanding of the importance of mastering English speaking skills, the community still lacks knowledge about the Tour Guide and the community still lacks training to become professional Tour Guides. The expected outcome was the community (youth cadets) have an understanding of Tour Guide skills. The approach used in conducting this program consisted of two methods, namely the presentation and practice. The results achieved were the participants (1) have knowledge and understanding about Tour Guide profession, (2) are able to express greetings, introduction, explaining object and closing presentation in English, and (3) around 80% are able to practice the simulation to become a Tour Guide.
A conversation on using tech as an artist

A conversation with Andrea Pejrolo, Chair of the Contemporary Writing and Production Department at Berklee College of Music in Boston ...
Interpelando una (otra) educación en la (pos) pandemia: notas diaspóricas

In this article the authors assess the educational challenges for the period of the pandemic and post COVID-19. The methodology used is qualitative, based on the methods: documentary analysis, literature review and participant observation. The text places in perspective the impacts for the education of vulnerable groups and asks questions such as: what educational policies and micro-policies of racial affirmation, anti-racism, interculturality and equity in this context? How will the design and implementation of these public and institutional educational agendas address the asymmetries generated by the intersection of race, class, gender, ethnic, territorial, and national structured, structural and structuring inequality and inequity with a focus on children, adolescents and young black / black, indigenous, migrant? Based on these questions, possible educational strategies are discussed at home and in the work of teachers. The article identifies that post-pandemic education must reorganize its ways of learning, and that requires a definitive break with banking education, as well as empowering creativity and mobilization of knowledge and experiences that lie in the peripheries, in the neighborhoods, in the territories quilombolas, in the palenques, in the villas, in the countryside.
Інтелектуальна система аналізу і прогнозування результатів соціологічних досліджень

The work is devoted to the analysis and forecasting of sociological researches results by means of the intellectual system, analysis of the basic problems and methods of their decision arising in the course of information processing of various formats of sociological data. The definition of the concept of «sociological research» is performed and the importance of correct adaptation of the structure of filling the research result us analyzed. The priority methods of raw sociological data processing for their further analysis and forecasting are demonstrated. The intelligent system in the Python programming language with an interface for analyzing and predicting the results of sociological research using analytical extrapolation, namely a power polynomial and neural networks, has been developed.
Police officer with other individuals in Maia village in Schrader Range

Title imposed. Caption on the rear of the photograph: Police Boy with 2 smaller pygmies Maia village in Schrader Range 5 Aug 1937 9.30 am bright. Captions reflect the attitudes of the author at the time they were written, and do not reflect the values of the archives. Stinear occasionally notes weather conditions and brightness when taking photographs. These photographs were taken during Stinear's time in Papua New Guinea from 1936-1937, in the Sepik, Ramu, and Gulf regions.
Mechanical Properties of a Shear-supported Polymer Elastic Element for Use of Robots

In recent years, polymer materials as an elastic component of robots are receiving attention. Among the polymer elastic materials, it has been well known that polyurethane has superior mechanical properties. This is because the polyurethane rubber spring has a larger working range of the deformation and viscosity compared to conventional steel springs. This study evaluates the elastic and viscous properties of polyurethane rubber spring in the shear direction, which could be useful for robotic revolute joints.
Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning

This paper tackles the challenging problem of estimating the intensity of Facial Action Units with few labeled images. Contrary to previous works, our method does not require to manually select key frames, and produces state-of-the-art results with as little as \(2\%\) of annotated frames, which are randomly chosen. To this end, we propose a semi-supervised learning approach where a spatio-temporal model combining a feature extractor and a temporal module are learned in two stages. The first stage uses datasets of unlabeled videos to learn a strong spatio-temporal representation of facial behavior dynamics based on contrastive learning. To our knowledge we are the first to build upon this framework for modeling facial behavior in an unsupervised manner. The second stage uses another dataset of randomly chosen labeled frames to train a regressor on top of our spatio-temporal model for estimating the AU intensity. We show that although backpropagation through time is applied only with respect to the output of the network for extremely sparse and randomly chosen labeled frames, our model can be effectively trained to estimate AU intensity accurately, thanks to the unsupervised pre-training of the first stage. We experimentally validate that our method outperforms existing methods when working with as little as \(2\%\) of randomly chosen data for both DISFA and BP4D datasets, without a careful choice of labeled frames, a time-consuming task still required in previous approaches.