Reviewerly/exHarmony huggingface dataset:
Analysis of Relation between Prediction Accuracy of Surrogate Model and Search Performance on Extreme Learning Machine Assisted MOEA/D

In recent years, evolutionary algorithms have been used for many real-world problems, but it takes enormous computation time to obtain the optimal solution due to its high calculation cost. Multi-objective evolutionary algorithms using surrogate models have been studied to reduce the computation time for the optimization. ELMOEA/D is one of the surrogate-assisted multi-objective evolutionary algorithms. ELMOEA/D combines MOEA/D with an extreme learning machine (ELM). This paper analyzes the relation between the estimation accuracy of the surrogate model and the search performance of ELMOEA/D. We experiment on several well-known multi-objective benchmark problems and compare the different number of generations. The experimental results reveal that the estimation accuracy and the search performance decrease as the number of generations increase.
Calibrated reservoir computers

We observe the presence of infinitely fine-scaled alternations within the performance landscape of reservoir computers aimed for chaotic data forecasting. We investigate the emergence of the observed structures by means of variations of the transversal stability of the synchronization manifold relating the observational and internal dynamical states. Finally, we deduce a simple calibration method in order to attenuate the thus evidenced performance uncertainty.
The Research of a Method to Identify a Workplace via a Monitor Snapshot

Digital technologies progress along with their ease of use, convenience and popularity inevitably leads to an increase in the number of cases of unauthorized copying of confidential information. Thus the popularity of photo and video recording digital devices has led to unregistered information leakage from the monitors of employees’ workplaces. Modern mobile devices send photos almost instantly over social networks, taking information outside the organization’s information space. The more numerous staff the harder is non-trivial task to prevent the leakage. The common solution is in applying special organizational and technical protection measures. Experience shows that these measures are quite effective, but not absolute. In this case, identifying the terminal from which the photo was taken would help not eliminate the leakage channel, but to establish the source at least. Since the terminal screen snapshot is taken by a device which is not wired to the workstation equipment, it is impossible to embed metadata on the place and time of registration in the recorded data. Consequently, one of the most effective ways to identify a terminal screen on a photo is to implant a digital watermark into the image displayed on a screen. Basically, watermarks protect the copyright of multimedia files. The paper considers implementing watermarks as a method to identify employees’ workplaces via monitor snapshots with mainly text information. Also we present the results of an experimental test of the method imperceptibility and robustness
Proteger os trabalhadores da saúde contra o SARS-CoV-2

COVID-19 is a serious threat to the responsiveness of health services. In this context, health workers (HW) are expected to work long hours, under great professional, family and emotional pressure, with resources often inadequate, while accepting the dangers inherent in the activity, the proximity to infectious people, inseparable dangers of work environments. The impact of the pandemic is reflected in HW as individuals, but it also affects their families, both in terms of less time available and in terms of mental health. Factors such as quantity, cost, availability as well as the lack of habit of use of personal protective equipment (PPE) affects its use by HW. The use of PPE is not sufficient protection if not accompanied by the adoption of other infection control measures, such as hand hygiene and physical distance. Priority should also be given to testing HW. The pandemic challenges health systems to a dynamic adaptation in the management of existing resources, with a special focus on information systems. Telemedicine services can potentially reduce direct contact with the patients and, therefore, the risk of SARS-CoV-2 infection. In the perspective of a SARS-CoV-2 vaccine soon, HW should be among the priority groups to receive it. HW are one of the most important resources in a country, so it is necessary to take care of these professionals in all aspects in health organizations, so that HW are able to respond to the demands of this pandemic and others that will eventually occur in the future. The pandemic does not recognize borders. It must be addressed in a way that strengthens international collaboration that enables solidarity and equitable resource sharing.
Digitalization Trends in Education and Blended Learning

The article under review actualizes the problem of using digital computer technologies in the process of blended learning. The notion of «blended learning» is determined and specified according to various sources. Various models and ways of organizing this kind of work as an alternative to the standard form of learning are presented. The article presents a positive experience of using blended learning technology. Much attention is paid to visualization as one of the teaching tools to create educational materials for blended learning. The synthesis of verbal materials (especially spoken) and visual elements (pictures, graphic notes, animations, films, plots, and diagrams, etc.) within the limits of one text is an instrument to optimize the process of semantic perception and understanding text information. All the benefits of this modern technology allow students to establish a holistic adoption of this model and a positive direction for the development of blended learning as an innovative teaching technology
Learning from Label Proportions: A Mutual Contamination Framework

Learning from label proportions (LLP) is a weakly supervised setting for classification in which unlabeled training instances are grouped into bags, and each bag is annotated with the proportion of each class occurring in that bag. Prior work on LLP has yet to establish a consistent learning procedure, nor does there exist a theoretically justified, general purpose training criterion. In this work we address these two issues by posing LLP in terms of mutual contamination models (MCMs), which have recently been applied successfully to study various other weak supervision settings. In the process, we establish several novel technical results for MCMs, including unbiased losses and generalization error bounds under non-iid sampling plans. We also point out the limitations of a common experimental setting for LLP, and propose a new one based on our MCM framework.
GPS Driven Camera Selection in Cyclocross Races for Automatic Rider Story Generation

Cyclocross races are a very popular winter sport in Belgium and the Netherlands. In this paper we present a methodology to calculate the proximity of riders to a number of cameras that are located on a cyclocross course in order to automatically select the correct camera for each rider. The methodology is based on two main input sources. The first input is the course with cameras positioned along it. As the course and camera information is usually available as pdf and isn’t directly processable by computer programs, we propose the conversion GeoJSON. The second requirement for our methodology is accurate location tracking of the athletes on the course with the help of wearable GPS trackers. We present an experimental camera proximity algorithm that uses both input sources and finds for every rider at any given moment in the race the closest camera or vice versa. The output of this methodology results in automatic identification of the filmed riders by a given camera at a given moment in the race and might benefit post-processing of the camera video streams for further computer vision-based analysis of the streams, for example, to pre-filter the camera streams or to generate rider and team stories.
Energy-Efficient Multiply-and-Accumulate using Silicon Photonics for Deep Neural Networks

We propose two optical hybrid matrix multipliers for deep neural networks. Our results indicate our all-optical design achieved the best performance in energy efficiency and latency, with an energy-delay product reduction of 33.1% and 76.4% for conservative and aggressive estimates, respectively.
A regularized logistic regression based model for supervised learning

In this work, we introduce a new regularized logistic model for the supervised classification problem. Current logistic models have become the preferred tools for supervised classification in many situations. They mostly use either L 1 or L 2 regularization of the weight vector of parameters. Here we take a different approach by applying regularization not to the weight vector but to the gradient vector of the function representing the separating hyper-surface. We present the mathematical analysis of the model in its continuous setting and provide experimental evidence to show that the new model is competitive with state of the art models.
Introduction to Web Scraping

In this chapter, you will learn about the common use cases for web scraping. The overall goal of this book is to take raw web crawls and transform them into structured data which can be used for providing actionable insights. We will demonstrate applications of such a structured data from a rest API endpoint by performing sentiment analysis on Reddit comments. Lastly, we will talk about the different steps of the web scraping pipeline and how we are going to explore them in this book.
Application for online conferences and meetings

Purpose This study aims to support an institution to hold an online meeting or conference in the middle of social distancing, which is currently in effect. Design/methodology/approach In developing this application, rapid application design methodology is used. The implementation used HTML5 and PHP for the Web and MySQL for the database and Agora Software Development Kit. To evaluate the application, the authors had a black box testing method. Findings This application will support the participant registration process, validation, payment, providing a link to the workshop to the participant, token and room name to be able to join an online meeting or conference up to the process of giving a digital attendance certificate to participants or members participating in it. Originality/value An integrated Web application provides full services, starting from the registration process, payment, the conference meeting itself and certificate of attendance.
PENINGKATAN KEMAMPUAN MENULIS PANTUN SISWA KELAS IV SD NEGERI 066044 KECAMATAN MEDAN HELVETIA KOTA MEDAN DENGAN MENGGUNAKAN MODEL COOPERATIVE LEARNING THINK PAIR SHARE

This research aims to improve the student’s ability for completing hiatus paragraphs and student activities. Research isclassroom action research with the number 37 students. The instruments used is the test of hiatus paragraph, observation ofstudent activity. Analysis of the data to see an increase the ability of narrative writing, and student activity have Ms. ExcelProgram. The results showed that peer tutoring learning model can improve the ability for completing the hiatus paragraphs.Cycle I obtained an average value of 71.38. The number of students who scored 65 (category enough) were 29 studentsfrom 37 students who take the test, or the level of mastery learning students is 78.38%. Cycle II obtained average value80.71. The number of students who earn a minimum category sufficient (minimum value of 65.0) were 31 students of the37 students who take the test, or the level of students' ability to complete the hiatus paragraph is 83.78%. Of the 16indicators of student activity in the first cycle, there are 4 (four) or indicators (25%) met the indicators of success measures.But on the second cycle, all indicators have met the indicators of success. The model of learning peer tutor may be analternative for teachers in presenting lessons on the material of hiatus paragraph completing.
The Formation of the Student's Objectivity in the University Educational Process

In this article, we highlight and consider one of the directions for improving the system of training specialists in higher education - the formation of student subjectivity in the educational process of the university. It should be emphasized that this task is interdisciplinary and requires the integration of the efforts of many specialists: teachers, psychologists, sociologists, etc. The author describes the main interactive methods used in teaching practice: the “Workshop of the Future” method, the case-study method, the “Decision tree” method, and the educational discussion. Also in the article, the author revealed the goal of interactive learning, which is to create comfortable learning conditions in which each student can feel his success and intellectual competence, which will allow him to simulate life and professional situations from pedagogical practice, to find a solution to the problem based on an analysis of the circumstances and the corresponding situation; to contribute to the formation of professional skills and abilities, to cultivate humanistic values in him, help to create an atmosphere of interaction and cooperation.
Short-Text Feature Expansion and Classification Based on Non-negative Matrix Factorization

In this paper, a Non-negative Matrix Factorization Feature Expansion (NMFFE) approach was proposed to overcome the feature-sparsity issue when expanding features of short-text. Firstly, we took the internal relationships of short texts and words into account when segmenting words from texts and constructing their relationship matrix. Secondly, we utilized Dual regularization Non-negative Matrix Tri-Factorization algorithm (DNMTF) to obtain the words clustering indicator matrix, which was used to get the feature space by dimensionality reduction methods. Thirdly, words with close relationship were selected out from the feature space and added into the short-text in order to solve the sparsity issue. The experimental results showed that the accuracy of short text classification of our NMFFE algorithm increased 25.77%, 10.89% and 1.79% on three datasets: Web snippets, Twitter sports and AGnews respectively compared with Word2Vec algorithm and Char-CNN algorithm. It indicated that the NMFFE algorithm was better than BOW algorithm and the Char-CNN algorithm in terms of classification accuracy and algorithm robustness.
Quine on Ontological Commitment in Light of Predicate-Functor Logic

Quine is renowned for offering a criterion of ontological commitment in terms of the values of bound variables. Simultaneously, however, Quine had a long-standing interest in formal systems that dispense with variables and quantifiers that permit variables to be explained away. The chapter seeks to resolve this often-neglected tension in a way that is sensitive to Quine’s allegiance to first-order logic, but is somewhat relaxed about the connection between semantics (/truth) and ontology.
Evolution and Revolution in Artificial Intelligence

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques wont to solve various real-world problems. For a primer on machine learning, you'll want to read this seven-part series that I wrote. While human-like deductive reasoning, presumption, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms
Tactile Scaling: An MR System for Experiencing Virtual Body Scaling

We present an MR system for experiencing human body scaling in the virtual world with enhancement of tactile stimuli. The device a user grabs has an outer surface based on a creased pattern that scales to all the directions; hence, is scaled by controlling air inside using an air pump with control circuits. We demonstrate an MR content in which a user experiences scaling in a virtual space with simultaneous tactile stimuli from the scaling device.
VARIASI KALIMAT BAHASA INDONESIA DALAM SKRIPSI MAHASISWA PROGRAM STUDI PENDIDIKAN GURU SEKOLAH DASAR UNIVERSITAS NAHDLATUL ULAMA SURABAYA

The purpose of this research is to describe variations of Indonesian sentences in the thesis of the students of the Nahdlatul Ulama University Primary School Teacher Education Study Program Surabaya. To achieve these objectives, qualitative research approaches that are descriptive are used. The data sources of this study were three PGSD student theses tested in 2019. The data were in the form of all sentences contained in the background of the problem. The results showed that there were 126 sentences with various variations or types of sentences namely (1) complete sentences, (2) elliptical sentences, (3) broad sentences, (4) active sentences, (5) passive sentences, (6) normal sentences, (7) inversion sentences, (8) news sentences, (9) positive sentences, (10) negative sentences, (11) single sentences, and (12) compound sentences. The most common types of sentences are broad sentences and news sentences, normal sentences, and positive sentences, compound sentences, complete sentences, and active sentences. Simple sentence types, question sentences, and command sentences were not found in this study.
Image Classification of Chicken Embryo Based on Matched Filter and Skeleton Curvature Feature

Abstract To solve the problem of nondestructive detection of egg formation activity in vaccine preparation, a new method based on matching filter for chicken embryo image blood vessels segmentation algorithm combined with the curvature feature of the blood vessels skeleton was proposed. Gaussian matching filter was used to enhance the contrast between the blood vessels and the background of the green channel image and the blood vessels were segmented from the background. Morphological methods was used to process the binary images to extract the vascular skeleton feature. The curvature of the vascular skeleton was calculated by using the least square method. Finally, the activity of chicken embryo was judged by combining the shape feature of blood vessel with the texture feature of chicken embryo extracted by gray level co-occurrence matrix algorithm. The experimental results show that this method has high accuracy in the classification experiment of chicken embryo images affected by spot noise, and meets the quality requirements of producing virus species in vaccine preparation.
A Design of Obstacle Avoiding Robot

Obstacle detection and avoidance can be considered as the central issue in designing mobile robots. This technology provides the robots with senses which it con use to troverse in unfamiliar environments without do morging itself. In this paper on Obstacle Avoiding Robot is designed which can detect obstacles in its path and maneuver around them without marking any collision. It is a robot vehicle that works on Arduino Microcontroller and employs ultrasonic distance censor to detect obstacles. The Arduino board was selected as the microcontroller platform and its Software counterpart, Arduino software, was used to carry out the programming. Being a fully autonomous robot, it successfully maneuvered in unknown environ- ments without any collision. The hardware used in this project is widely available and inexpensive which makes the robot easily replicable.
Federated Diabetes Mellitus Analysis via Homomorphic Encryption

Abstract Diabetes is one of the most extensive chronic diseases in the world. Most patients suffer from the disease and its complications for a long time due to the lack of accurate and standardized treatment at early stage. Therefore, by analyzing diabetes data and establishing relevant predictive models, it is very meaningful to give reasonable health advice to high-risk groups. The establishment of an accurate prediction model requires a large number of data sources as support, and multiple medical institutions participate in data contribution and collaborative learning. Federated learning provides a secure general architecture for distributed collaborative learning. However, in the process of federated learning, the data of participants is still subject to the risk of security attacks or indirect information leakage. For example, when a participant uploads the local model parameters to an honest but curious cloud server, the cloud server can obtain relevant information from the participant. In order to solve this problem, this paper proposes a federated forest algorithm based on homomorphic encryption to strengthen the protection of the data privacy of the participants while ensuring that the accuracy of data analysis does not decrease. Analysis proves that our algorithm has good performance in privacy protection and prediction accuracy.
Methodological Approaches to the Continuity of an Environmental Culture Forming as a Platform of Sustainable Development Culture

The article describes the changes taking place in culture and education of the 21st century in connection with the civilization search for ways of sustainable development. The role of environmental culture as a platform for the formation of a sustainable development culture is considered. The problem of developing methodological approaches to ensuring the continuity of the formation in general education of the foundations of a culture of sustainable development based on ecological culture is actualized. The conclusion is made about the new mission of environmental education as education for sustainable development, which forms the prerequisites for the development of the foundations of sustainable development culture. A comparative characteristic of environmental culture in its classical understanding as a result of natural-scientific ecological education of schoolchildren, and ecological culture for sustainable development, as the integration of cultural results of all general education, is given. The essence of culturological, cultural-creative, value-worldview, integrative, semiotic-hermeneutic and institutional approaches to ensuring the continuity of the formation of ecological culture and culture of sustainable development is revealed. The development degree of these approaches and the pedagogical means of their implementation in the general educational process are analyzed. It is concluded that the application of the identified methodological approaches requires a rethinking of the theory of constructing the environmental education content, its sources, criteria and indicators of its results, primarily personal
Multi-dimensional risk assessment of electricity market based on cloud model

Abstract Aiming at various potential risks in the electricity market, this paper establishes a risk assessment index system for the electricity market from four dimensions of structural risk, operation risk, fairness risk, and behavioral risk. The analytic hierarchy process is used to determine the subjective weight of risk evaluation indicators, and the entropy method is used to determine the objective Weight, according to the least squares optimization idea to get the comprehensive weight. Then, the basic theory and calculation method of cloud model are introduced, and the standard cloud of risk level evaluation is constructed. Finally, the analysis of a calculation example shows that the risk evaluation level of the selected regional electricity market is moderate, which verifies the validity and feasibility of the model proposed.