Reviewerly/exHarmony huggingface dataset:
Apparel Goods Recommender System Using image feature

Recommender system is an information-filtering tool used in solving the problem that the user's preference in information overload. In recent years, with the development of deep learning, more and more research has begun to try the combination of deep learning and recommender system, and achieved good results. In this paper, we propose a novel probabilistic model. This model integrates a deep neural network model for extracting image shape features into the apparel goods recommender system to achieve good performance.
Measuring the digital competence of future primary school teachers

Reforming the education system in Ukraine dictates changes in the training of future primary school teachers. This is particularly relevant when the formation of the digital competence of future educators is considered. The research highlights the results of theoretical and empirical study concerning the diagnostics and measurement of students’ digital competence: the definition of criteria, indicators and description of levels of its formation. The author identifies digital competence as a crucial component of the professional competence that combines the ability to use digital technologies to create effective teaching and learning process, critically evaluate digital resources in terms of expediency of their application in professional activities, and acquire technological innovations. In view of this, four groups of criteria and indicators have been established regarding the acquisition of digital technology ability: the development of students’ cognitive skills; the development of students’ creative skills; the development of student’s communicative skills; the development of students’ collaborative skills. Based on these developed criteria and indicators, the author characterizes the three levels of the formation of the digital competence of the future primary school teacher as: high (student strives for self-development, creatively solves the tasks, organizes a mutual work, helps other participants), adequate (student uses the offered digital technologies and improves the ability to work with already known tools, solves the tasks using the given recommendations, follows ethical principals in the course of network communication, responsibly treats a group work), low (student does not feel an inner need for self-development via the usage of digital technologies, has no own ideas, has few or none participation in mutual work, strives to do the work separately from the group).
Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization

Semi-supervised ordinal regression (S$^2$OR) problems are ubiquitous in real-world applications, where only a few ordered instances are labeled and massive instances remain unlabeled. Recent researches have shown that directly optimizing concordance index or AUC can impose a better ranking on the data than optimizing the traditional error rate in ordinal regression (OR) problems. In this paper, we propose an unbiased objective function for S$^2$OR AUC optimization based on ordinal binary decomposition approach. Besides, to handle the large-scale kernelized learning problems, we propose a scalable algorithm called QS$^3$ORAO using the doubly stochastic gradients (DSG) framework for functional optimization. Theoretically, we prove that our method can converge to the optimal solution at the rate of $O(1/t)$, where $t$ is the number of iterations for stochastic data sampling. Extensive experimental results on various benchmark and real-world datasets also demonstrate that our method is efficient and effective while retaining similar generalization performance.
RAINFALL FORECASTING MODEL USING ADALINE AND REGRESSION ALGORITHM

High rainfall intensity could cause flooding or inundation which needs to be forecasted to estimate the amount of rainfall that will come. Forecasting is the process of making future predictions based on past and present data and most commonly with trend analysis. Computational models can be used using artificial statistics and statistics. The advantage of forecasting using artificial intelligence is able to calculate with non-linear data and limited parameters while statistical forecasting is the opposite. Adaptive Linear Neuron (ADALINE) is one of the learning algorithms of the artificial neural network algorithm. The learning algorithm is able to recognize historical data patterns so that it can provide future pattern predictions. This study have compared the performance of computational models using ADALINE artificial intelligence with statistical computing models, namely regression. The rainfall data used was the rainfall data in South Kalimantan Province 2008-2013. Comparison of the performance fro the two algorithms was using root mean square error (RMSE). The smaller the RMSE value was more better. The results of the two algorithms has been obtained RMSE ADALINE algorithm was 0.0729 while RMSE REGRESSION algorithm was 0.0900. This shown that the performance of the ADALINE algorithm was better than the REGRESSION algorithm for forecasting rainfall data in the Province of South Kalimantan in 2008-2013.
An unsupervised learning method for bearing fault diagnosis based on sparse feature extraction

Data-driven based methods have shown their strong feature extraction ability in intelligent fault diagnosis. But most methods always need sample labels in network training process, which is undoubtedly increasing the cost of network training. So developing an unsupervised feature learning method without training labels is desirable. In our work, sparse filtering, an effective unsupervised learning method, is presented for bearing fault diagnosis. The fault features are first extracted from frequency spectra of vibration signals by sparse filtering automatically, and eLU is adopted as activation function for further sparse processing of the features. Then the learned features are input to softmax regression to identify the corresponding fault types. The classification result shows that our method can extract much sparser features and is better than the comparison method.
The Role of The Students Association in The Promotion of Students' Speech Skills at The Khalid Bin Al Walid Institute at University of Muhammadiyah Mataram (ar)

The purpose of this research is to explain the importance of the student organizations and describe their efforts to improve the speaking skills of students at the Mahad Khalid Bin Al Waleed at the University of Muhammadiyah Mataram. This research uses the qualitative approach with the descriptive type. The result shows the student organizations play an important role based on their objectives and functions. The objectives are to help the foundation and all parties in the Ma'had develop the students’ potential and qualification, and to be the place for the students to share their problems and complaints, while the functions are as one of the media to develop students’ quality, both the members of the non-member, and as the good examples and pioneers of any good deeds. The efforts done by student organizations in improving speaking skills are such as by making activities that lead to improving students' speaking skills like sticking vocabularies in each class and Friday activities such as language game, Arabic debate and short lecture.
Multi-Agent Parallel Implementation To Solve Nonlinear Equality Constrained Multiobjective Optimization Problem – Case of Unmanned Aerial Vehicle (UAV)

This paper investigates a decomposition-coordination method using Multi-agent systems implementation for solving the nonlinear equality constrained multiobjective optimization problem (NECMOP), where several nonlinear objective functions must be optimized in a conflicting situation. A practical case study of an unmanned aerial vehicle was found to fit the characteristics of the model and was then taken to test the effectiveness of the method. The NECMOP is converted to an equivalent scalar optimization problem (SOP) which is then decomposed into several separable subproblems. These latter are independent from each other, which makes them processable in parallel and allows nonlinearity to be treated at a local level by an agent which lives in a container. The agents communicate the results to the master agent who coordinates the intermediate solutions using Lagrange multipliers and requests a new loop until it finds an optimal solution that satisfies all the constraints.
The impact of using digital technologies in German lessons

In today's world, digital technologies have become a factor that unites people and culture, makes culture accessible and diffuses it. Today, the upbringing process is acquiring special significance in the context of aspects of digital technologies related to socialization, since they are part of the upbringing reality of modern man. The result of the civilization progress, and consequently, of information progress, is the qualitative and quantitative changes in educational technologies that create opportunities for the use of new didactic tools in the educational process with the aim of creating a personality and its effective functioning in the public environment. Digital technologies in education have three main functions: educational and cognitive, characterized by a variety of information that is transmitted using drawings, words, sounds, actions. In this case, the dynamics of information transfer contributes to the development of abstract perception and activity of the student; emotional and educational, which relates to the involvement in the process of cognition of the world of a person’s personality and its mental processes. It plays an important role in the process of education and socialization by forming a system of values, beliefs and attitudes; activity-interactive, which is available in motor and communicative activity of a person as a result of the formation of ability and manual skills, as well as in communication and information exchange. As a result of the study, it was found out that digital technologies have become an important component and the key to an effective educational process in educational institutions, and now they are entrusted with the task of attracting students to study as such, and not only in classroom time. Negative aspects here reveal a decrease in direct contact and lively communication, which in the case of less self-organizing and socially adapted persons can lead to a decrease in the level of knowledge.
Quantum Computer Control using Novel, Hybrid Semiconductor-Superconductor Electronics

Inspired by recent interest in quantum computing and recent studies of cryo CMOS for control electronics, this paper presents a hybrid semiconductor-superconductor approach for engineering scalable computing systems that operate across the gradient between room temperature and the temperature of a cryogenic payload. Such a hybrid computer architecture would have unique suitability to quantum computers, scalable sensors, and the quantum internet. The approach is enabled by Cryogenic Adiabatic Transistor Circuits (CATCs), a novel way of using adiabatic circuits to substantially reduce cooling requirements. In a hybrid chip of CATCs and a second technology, such as Josephson junctions (JJs) or cryo CMOS, the CATCs complement the speed, power, and density of the second technology as well as becoming a longsought cryogenic memory. This paper describes higher-level design principles for CATC hybrids with a quantum computer control system that includes CATC memory, an FPGA-like logic module that uses CATC for dense configuration logic and JJs for fast configured logic, and I/O subsystems including microwave modulators and low frequency control signals.
Image Spam Detection Using FENOMAA Technique

Today’s most important mean of communication is the e-mail that allows people all over the world to communicate, share data, and perform business. Yet there is nothing worse than an inbox full of spam; i.e., information crafted to be delivered to a large number of recipients against their wishes. Numerous anti-spam methods and solutions have been proposed and deployed, but they are not effective as most mail servers rely on blacklists and rules engine leaving a big part on the user to identify the spam, while others rely on filters that might carry high false positive rate. The proposed solution is “Image Spam Detection using FENOMAA Technique”. FENOMAA which stands for Feature Extraction Neural network with OCR enhanced by Mail Authentication and Analyzer of context is an aggregation of methods that detects spam and specifically image spam and is composed of four essential parts that include authentication, OCR enhanced by Neural Network, Feature Extraction enhanced by Neural Network, and Context Analyzer Spell Checker. The authentication method in which the SPF record is encapsulated within the Domain Keys Identified Mail (DKIM) header along with the use of trusted third party in DKIM as an alternative to DNS, thus allowing additional verification and strength to the proposed method. Also, The OCR and the Feature Extraction methods are enhanced by the addition of Neural Network where the neural net is composed and trained according to the criteria mentioned in this paper in order to generate the desired results. The Context Analyzer Spell Checker is present in order to correct, verify, and optimize the results taken from both neural nets.
NSF International Conference on Social and Cultural Nexus of Science and Technology Development (2019)

Sri Lanka Journal of Social Sciences (SLJSS) publishes social science articles on topics relevant to Sri Lanka in particular and South Asia in general. The journal also has a website at http://www.nsf.gov.lk/.
A política pública de arquivo: a gestão de documento arquivístico no Arquivo Nacional de Timor-Leste

The article has the general objective of describing public policies of the East Timor National Archives in the action plan between 2018-2022, to seek to concretize in practical and tangible terms its potential and relevance in the preservation and conservation of the country's patrimony. The action plan was the result of the Graduation in Archivology at the Federal University of Santa Catarina in partnership with East Timor of ANTL, guaranteeing the integrity, reliability and documentary authenticity. Specific objectives: to develop the conceptual basis for understanding archive policies; analyze the collection processes developed by the public agencies managed by ANTL; present the public policies of archives in document management in ANTL. The methodology was qualitative descriptive and exploratory and bibliographical, a technical visit was carried out in the ANTL in the period between ten. from 2017 to March. of 2018 to observe and describe the condition and behavior in the archive and of the technicians, with respect to the archival standardization. It is concluded that the ANTL is in an inadequate situation in its archival technical activities, lack of political awareness to support projects and works in the archive; professional archivists; archival methodology; creation of guidelines and standards to assist in the implementation of the Public Policy of the National Archives of East Timor in access to information.
«Счеты». Происхождение «Счет»«Счеты». Происхождение «Счет»From the Experience of Teaching the History of Informatics Using Case Technology

In the article describes the basics of case technology. Examples of cases on the history of Informatics are given. Methodical approaches to the development of cases and the organization of the educational process with their use are explained.
A Schmitt Trigger Based Oscillatory Neural Network for Reservoir Computing

With the increase in communication bandwidth and frequency, the development level of communication technology is also constantly developing.The scale of the Internet of Things (IoT) has shifted from single point-to-point communication to mesh communication between sensors.However, the large sensors serving the infrastructure place a burden on real-time monitoring, data transmission, and even data analysis.The information processing method is experimentally demonstrated with a non-linear Schmitt trigger oscillator.A neuronally inspired concept called reservoir computing has been implemented.The synchronization frequency prediction tasks are utilized as benchmarks to reduce the computational load.The oscillator's oscillation frequency is affected by the sensor input, further affecting the storage pattern of the oscillatory neural network.This paper proposes a method of information processing by training and modulating the weights of the intrinsic electronic neural network to achieve the next step prediction.The effects on the frequency of a single oscillator in a coupled oscillatory neural network are studied under asynchronous and synchronization modes.Principle Component Analysis (PCA) is used to reduce the data dimension, and Support Vector Machine (SVM) is used to classify the synchronous and asynchronous data.We define that oscillator with stronger coupling weight (lower coupling resistance) as a leader oscillator.From the spice simulation, when OSC 1 and OSC 2 work as leader oscillator, the ONN almost always achieve synchronization; and the synchronization frequency is close to the average value of the leader oscillators.By training the emerging synchronous and asynchronous data, we can predict the synchronization status of an unknown dataset.Weight retrieval can be achieved by adjusting the slope and bias of the separation boundary.
APPLICATION OF MOBILE TECHNOLOGIES IN THE PROCESS OF TEACHING FUTURE PHYSICIANS AT THE UNIVERSITIES OF THE USA

The development as well as the global and local spread of mobile devices, mainly smart phones and tablets, have led to the emergence of a new direction of electronic learning in the field of higher medical education, namely mobile learning. This phenomenon causes continuous research, study and application of mobile technologies for learning purposes by medical students and academics. In order to make mobile learning effective, higher medical institutions have to take proper measures. The academic staff should create required learning tools and methodological support, provide proper material and technical conditions for the efficient and safe usage of mobile technologies. The researchers should study the experience of developed countries in this direction. A profound analysis of foreign scientific evidence has shown that the USA ranks a leading position in this branch of innovation. This country has a long history of implementing mobile technologies into the undergraduate university training aimed at improving clinical thinking and communication skills of future physicians. Thus, our paper highlights: the conditions of implementation of electronic mobile technologies in the US higher medical education; current state of their application at medical schools and colleges of the USA; the requirements and risks of new technologies application in university training of future physicians; potentials of social media and gaming technologies as the professional training tools at US higher medical institutions. Priceless experience of the USA should be taken into account while designing new curricula and modernizing an academic process at undergraduate level in Ukraine. The preliminary conditions for this process primarily include the use of mobile applications, social media tools and “smart games” for learning. The electronic mobile technologies involving the tactics of a serious learning game are promising and can aid evolution of m-learning in the Ukrainian higher medical education institutions. However, electronic mobile technologies, including the above listed tools applied in the academic process could be beneficial only in case of their gradual, methodologically correct, grounded and controlled implementation and usage involving proper on-line behavior – digital professionalism.
A New Variable Ordering Method for the K2 Algorithm

K2 is an algorithm used for learning the structure of a Bayesian networks (BN). The performance of the K2 algorithm depends on the order of the variables. If the given ordering is not sufficient, the score of the network structure is found to be low. We proposed a new variable ordering method in order to find the hierarchy of the variables. The proposed method was compared with other methods by using synthetic and real-world data sets. Experimental results show that the proposed method is efficient in terms of both time and score.
The challenges faced by early childhood English teachers: Categorical analysis of the challenges and the differential based on teaching experience

This study focused on challenges faced by early childhood English teachers in Korea against the backdrop of the local rise of English education for young learners. A mixed-method was used to collect data, including open-ended survey questionnaires and interviews. Six teachers from 250 early childhood English teachers were selected for this study based on the length of teaching experience. A number of challenges emerged from the analysis of the survey, including classroom management, mixed-level class, motivation, teaching methodology, parents involvement, restrained English education in the context of Korea, the teacher’s level of English, lack of assessment and materials which fit early childhood language learners, and balancing between interest and learning. Nine distinct themes were found based on calculated frequency. They were cross-tabulated by two groups of teachers depending on whether teachers had less or more than three years of teaching experience. Results showed challenges and difficulties faced by teachers with lengthy teaching experience. Findings of this study suggest a way to build teacher education programs to support the challenges identified by teachers.
Development of a Module with Comprehensive Intelligence about Fungi Material for Class X SMA/MA

Quality of education can be realized with the procurement of instructional materials in the form of module in accordance with demands of the curiculum 2013. Module in accordance with the curiculum 2013 containing values of spiritual intelligence, social and emational intelligence, intelctual intelligence and skilfulness intelligence. These four intelligence are known as comprehensive intelligence, but the module wich is contains a comprehensive intelligence valid and practical. This research is design development study that uses a PLOMP method, wich is consisting of three phases: preliminary research, development or prototyping phase, and assesment phase. The research was conducted in SMAN 4 padang with the subject of the research of the students of grade X MIA 1 and grade X MIA 5 SMAN 4 Padang and the object in this research is a module prodct containing comprehensive intelligence about the function material for class X SMA/MA. The data is primary data namely the data obtain directly through questionnaire validity and practicality on the subject of research. From the result of this research, found that the module has a comprehensive intelligence on valid and practical materials, the resulting module is very practical by teachers and learners in terms of language criticism, learning efficiencies and benefits.