Reviewerly/exHarmony huggingface dataset:
Human resource training for digital economy: current problems and targets

This article reviews the problem of training a new generation of specialists in the sphere of information and communication technologies. Relevance of the problem is substantiated by the tasks for overcoming technological gap and human resourcing for the digital transformation of national economy. The theoretical and methodological framework is comprised of the provisions that reveal complex and contradictory nature of the digital transformation process due to being affected by various social, cultural, economic and political factors. The key role is assigned to the educational institutions intended for formation of the new set of knowledge, competences and values. The goal consist in determination of factors that define staff requirements of the information and communication technologies sector (ICT. It is demonstrated that targets in human resource training for the information and communication technologies sector rely on the overall evaluation of economic situation and do not take into account a number of specific factors. It is underlined that the key characteristics of the Russian ICT sector consist in its heterogeneity and dependence on the general trends in the economy and job market, and staff shortage is substantiated by the outcome of previous stage in the development of ICT sector and specificity of the country's economy. In conclusion, the author emphasizes the importance of differentiated approach towards determination of the need and requirements of training of digital personnel in the context of isolated branches, regions and development trends of with regard of information and communication technologies.
Increasing Trust in Development Processes Using Robust, Data-Driven Markov Games: An Application to PRESTIGE

In this article, we consider how to increase trust in development processes in which there is a risk for adversarial manipulation, and the adversary's objectives and resources are either ill-specified, imprecisely specified, or unknown. In such problems, we must hedge against the risk of misapprehension of attacker objectives and resources, which is further complicated in the absence of adversarial training data. We show how to model dynamic agent interaction, on the basis of partially observed or noise-corrupted data, using a partially observable Markov game (POMG) framework. We then propose a threefold heuristic solution procedure that: 1) uses the POMG to generate potential adversarial policies; 2) explicitly incorporates these adversarial policies in the construction of a robust defender policy by solving a robust dynamic program (DP); and 3) employs a probability matching heuristic in partially observable environments.
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems

Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in the model training pipeline results in predictions of the unobserved parameters that lead to higher decision quality. Unfortunately, this process comes at a large computational cost because the optimization problem must be solved and differentiated through in each training iteration; furthermore, it may also sometimes fail to improve solution quality due to non-smoothness issues that arise when training through a complex optimization layer. To address these shortcomings, we learn a low-dimensional surrogate model of a large optimization problem by representing the feasible space in terms of meta-variables, each of which is a linear combination of the original variables. By training a low-dimensional surrogate model end-to-end, and jointly with the predictive model, we achieve: i) a large reduction in training and inference time; and ii) improved performance by focusing attention on the more important variables in the optimization and learning in a smoother space. Empirically, we demonstrate these improvements on a non-convex adversary modeling task, a submodular recommendation task and a convex portfolio optimization task.
Incremental approach for multi-modal face expression recognition system using deep neural networks

Facial expression recognition (FER) is still one of the most challenging tasks. Convolutional neural network (CNN) and deep convolutional neural network (DCNN) has evolved as an efficient tool for FER models, but they differ significantly in terms of their network configuration and architecture. There exists a variety of bottlenecks in existing FER systems, such as they lack in generalising their algorithms. In this paper, we propose a model based on DCNN to overcome these challenges. Firstly, the proposed model focuses on the selection of an appropriate activation function depending on its accuracy and training loss over a database. Secondly, an incremental strategy is used in which deeper models are developed simultaneously from shallower networks to increase the accuracy with less training loss. Lastly, by an ensemble of CNN and DCNNs, the model achieves an accuracy of 74.15% for FER2013, 96.20% for CK+, and 98.25% for JAFFE databases, outperforming previous work.
Big Data Analysis on Out-of-School Youth Arts and Culture Education

The purpose of this study was to closely analyze the perceptions of arts and cultural education for out-of-school youth by using the big data analysis method. Specifically, it aimed to suggest the implications for the policy direction of arts and cultural education for out-of-school youth by grasping the connection by keyword based on words derived from the web. This study selected social media data left on Naver, Daum, Google, YouTube and Twitter by selecting arts and cultural education for out-of-school youth as a keyword. The analysis period of the data was conducted from January 14, 2019 to January 14, 2020 for the last one year, and the text mining, semantic connection network, N-gram network, and Word Cloud were conducted in stages. The main analysis results of the study are as follows. First, the top 10 words derived based on the keyword of arts and cultural education for out-of-school youth (11,319), out-of-school youth (9102), cultural support (8909), arts and cultural (5393), art (2834), cooperation (1985), youth shelter (1985), arts and cultural education program (1834), local government (1797), and experience (1612). Second, as a result of conducting the N-gram network craft, the words in the top 10 are youth cultural support (4,882), arts and cultural youth (1,794), local government cooperation (1,783), cultural support out of school (835), dreams Darak Saturday school of culture(632), Saturday culture school support (612), child-youth (590), positive effect (553), learning positive(492), out-of-school youth shelter (482), cultural support (478).
Cross-validation Confidence Intervals for Test Error

This work develops central limit theorems for cross-validation and consistent estimators of its asymptotic variance under weak stability conditions on the learning algorithm. Together, these results provide practical, asymptotically-exact confidence intervals for k-fold test error and valid, powerful hypothesis tests of whether one learning algorithm has smaller k-fold test error than another. These results are also the first of their kind for the popular choice of leave-one-out cross-validation. In our real-data experiments with diverse learning algorithms, the resulting intervals and tests outperform the most popular alternative methods from the literature.
From Language to Language-ish: How Brain-Like is an LSTM’s Representation of Nonsensical Language Stimuli?

The representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. However, these decoding results are usually based on the brain’s reaction to syntactically and semantically sound language stimuli. In this study, we asked: how does an LSTM (long short term memory) language model, trained (by and large) on semantically and syntactically intact language, represent a language sample with degraded semantic or syntactic information? Does the LSTM representation still resemble the brain’s reaction? We found that, even for some kinds of nonsensical language, there is a statistically significant relationship between the brain’s activity and the representations of an LSTM. This indicates that, at least in some instances, LSTMs and the human brain handle nonsensical data similarly.
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables

Recently generating natural language explanations has shown very promising results in not only offering interpretable explanations but also providing additional information and supervision for prediction. However, existing approaches usually require a large set of human annotated explanations for training while collecting a large set of explanations is not only time consuming but also expensive. In this paper, we develop a general framework for interpretable natural language understanding that requires only a small set of human annotated explanations for training. Our framework treats natural language explanations as latent variables that model the underlying reasoning process of a neural model. We develop a variational EM framework for optimization where an explanation generation module and an explanation-augmented prediction module are alternatively optimized and mutually enhance each other. Moreover, we further propose an explanation-based self-training method under this framework for semi-supervised learning. It alternates between assigning pseudo-labels to unlabeled data and generating new explanations to iteratively improve each other. Experiments on two natural language understanding tasks demonstrate that our framework can not only make effective predictions in both supervised and semi-supervised settings, but also generate good natural language explanation.
Blockchain-Based Protected Digital Copyright Management with Digital Watermarking

In the digital era, more and more information is shared through the internet. This may lead to editing, transmitting, misusing detection and usage of information by unauthorized persons. To avoid such a problem we use the digital right management (DRM) with blockchain. In our daily life, blockchain is a powerful utensil to share the data in decentralized and distributed technology. The DRM process provides security and authentication for the content and owner of the data. In our proposed system, the following steps have been implemented Watermark Embedding with Discrete Wavelet Transform (DWT), Arnold Transform (AT), Perceptual Hash, Hash Code generation (md5), Watermark Extraction and Blockchain Creation. Discrete Wavelet Transform provides better image quality and robustness. Arnold Transform provides the best watermark encryption. The hash code generation gives a numeric value which helps in the recognizable proof of a ediget during equality testing. Blockchain is used to store the information in a secured manner and also provide timestamp authentication. Watermark extraction is achieved with the inverse of Arnold transform, inverse Discrete Wavelet Transform with the matching of hash code. This system provides secure transactions with copyright-protected data and safe transparent management.
15 Years of Reuse Experience in Evolutionary Prototyping for the Defense Industry

At Thales Defense Mission Systems, software products first go through an industrial prototyping phase. We elaborate evolutionary prototypes which implement complete business behavior and fulfill functional requirements. We elaborate and evolve our solutions directly with end-users who act as stake-holders in the products’ design. Prototypes also serve as models for the final products development. Because software products in the defense industry are developed over many years, this prototyping phase is crucial. Therefore, reusing software is a high-stakes issue in our activities. Component-oriented development helps us to foster reuse throughout the life cycle of our products. The work presented in this paper stems from 15 years of experience in developing prototypes for the defense industry. We directly reuse component implementations to build new prototypes from existing ones. We reuse component interfaces transparently in multiple prototypes, whatever the underlying implementation solutions. This kind of reuse spans prototypes and final products which are deployed on different execution platforms. We reuse non-component legacy software that we integrate in our component architectures. In this case, we seamlessly augment standard classes with component behavior, while preserving legacy code. In this paper, we present our component programming framework with a focus on component reuse in the context of evolutionary prototyping. We report three scenarios of reuse that we encounter regularly in our prototyping activity.
Product Recommendation System with Explicit Feedback Using Deep Learning Methods

Today, efforts are made to develop and improve recommendation systems that will direct users to the right product according to their individual preferences during internet shopping. In this study, the recommendation system was designed with Autoencoders, which are one of the methods of deep learning and MovieLens dataset. While designing the system, various optimization algorithms, namely Gradient Descent, Gradient Descent with Momentum, RmsProp and Adam (Adaptive Momentum Optimization), were tried by using TensorFlow in the Python programming language. Moreover, the effect of increasing the amount of the data on the optimization algorithm was analyzed. Consequently, it was effectively demonstrated that the most successful one was the Adam algorithm with a test error of 1.363. It was also observed that decreasing the sparsity on the training data leads to a lower test error.
A Comprehensive Study of Pros and Cons on Implementation of Blockchain for IoT Device Security

Internet of Things (IoT) one of the biggest buzz words is going around. The question arises what is it? Is it a group of computable devices and appliances connected using sensors, which use the Internet for data exchange? You might be right. We have to remember that if we are going for wireless transmission as we normally do in IoT solutions then the risk in these networks is very high. In this world of IoT 4.0 every device is connected with every device and these devices are used in all kinds of applications starting from the Military, Healthcare, Education, Home and list go on. One has to keep in mind that an IoT solution relies on wired, cellular, or adhoc communication networks. IoT is considered to play a very important role in the next decade or two. There is also an exponential rise of IoT-based devices so that there is a seamless exchange of data between the connected devices and hence decided to be taken based on the data that can be taken faster for the betterment of mankind. As the number of devices has increased it has given rise to various security concerns. We have to remember that confidentiality, authentication, access control, and integrity should never be compromised in the IoT environment. Currently, researchers are trying to develop efficient security and privacy protocols in IoT networks. IoT creates new security challenges for enterprises in scope and scale-like device identity, secure network scale, data security and physical security. This chapter deals with the issues and challenges of IoT devices and the prevention of security loopholes via Blockchain implementation.
ANALISIS SISTEM INFORMASI AKUNTANSI PERSEDIAAN OBAT PUSKESMAS PEMBANTU MOJOSARIREJO

Drug supply information system in the health center is needed by all government agencies in the health sector, the existing system must be clear from how the drug supply planning system until the drug is ready for distribution and of course the drug supply system has the aim of perfecting inventory, one of which is to avoid lack of drug stock, avoiding overlapping authority and application of systems for inventory management. This study uses descriptive qualitative methods and uses primary and secondary data collection. This study also uses data collection techniques by interview and observation. This research has a weakness in the LPPO letter whose request sometimes does not match the reception, the recording system uses manuals because sometimes manual recording has many obstacles in its writing.
RESULTS OF ASSESSING THE FORMATION OF METASUBJECT COMPETENCES OF TEACHERS IN URBAN AND RURAL SCHOOLS

The article presents the results of assessing the formation of metasubject competencies of teachers; the features of the activity of a teacher in rural school are described; substantiation of the complex of modern professional competencies of a teacher is given; an emphasis is placed on a cluster of metasubject competencies, a complex of assessment tools for their assessment is presented – competency passports, a system of test tasks, a computer program for testing; shows the results of a study of metasubject competencies of 770 teachers from 68 schools in the Yaroslavl region within the framework of the project «Improving the quality of education in schools with low learning outcomes (SHNOR) and in schools operating in unfavorable social conditions (SHSU)»; the general test results are described, a comparative analysis of the test results of metasubject competences of teachers in urban and rural schools is provided; recommendations were given to management teams of schools to eliminate professional deficiencies of teachers in the field of meta-subject competencies.
INVESTIGATING THE EFFECT OF COACHING LEADERSHIP ON JOB AUTONOMY AND JOB COMMITMENT

Coaching skills not only meet the needs of members of the organization, is placed on growth and achievement in building capacity of members. This paper empirically investigated the effect of coaching leadership in the education section. This study is to construct the sub-dimensions(direction,development, accountability) of coaching leadership, job autonomy and job commitment. To accomplish the purpose of this study, we conducted a questionnaire survey of 230working members of the school in Gyeongnam province and Pusan in Korea, and collected 223cases and verified the hypotheses through SPSS and AMOS analysis with final data of 205. As a result, coaching leadership that constructs sub-dimensions of direction, development, and accountabilityhas a positive effect on job autonomy and job commitment, respectively. Besides, job autonomy has a significantly positive effect on job commitment. Consequently, we proved that coaching leadership is an important factor to affect job autonomy of members and organization effectiveness. This study showed that direction element, development and accountability of a principal influence the teachers job attitude in schools. These results suggested that coaching is a new and effective leadership style that influences members of school's attitude or organization effectiveness. Therefore, it is meant to provide an implication that coaching leadership should be exercised to raise the job autonomy and commitment of employees.
Analyzing the Impact of Refactoring Variants on Feature Location

Due to the increasing importance of feature location process, several studies evaluate the performance of different techniques based on IR strategies and a set of software variants as input artifacts. The proposed techniques attempt to improve the results obtained but it is often a difficult task. None of the existing feature location techniques considers the changing nature of the input artifacts, which may undergo series of refactoring changes. In this paper, we investigate the impact of refactoring variants on the feature location techniques. We first evaluate the performance of two techniques through the ArgoUML SPL benchmark when the variants are refactored. We then discuss the degraded results and the possibility of restoring them. Finally, we outline a process of variant alignment that aims to preserve the performance of the feature location.
Environmental education in light of the digital culture

The importance of environmental education cannot be stressed enough as humanity populated the majority of the Earth and its activity effects the whole planet. Environmental education is lifestyle education that cannot be started early enough. The point is not only to properly provide scientific knowledge besides personality development. It is essential that children discover and understand cause and effect relationships so they will understand the processes on the whole. The processes leading to the solutions of environmental questions and problems can link different subjects, can build a global point of view suitable to be supported by digital culture for more reasons. The present article in addition to the definition of concepts of environmental education and its practical implementation, describes the accentuated role and possibilities of the digital culture subject in the process of education for sustainability and it provides concrete tasks by age groups along with detailed methodological guidance
Multivariate Real Time Series Data Using Six Unsupervised Machine Learning Algorithms

The development of artificial intelligence (AI) algorithms for classification purpose of undesirable events has gained notoriety in the industrial world. Nevertheless, for AI algorithm training is necessary to have labeled data to identify the normal and anomalous operating conditions of the system. However, labeled data is scarce or nonexistent, as it requires a herculean effort to the specialists of labeling them. Thus, this chapter provides a comparison performance of six unsupervised Machine Learning (ML) algorithms to pattern recognition in multivariate time series data. The algorithms can identify patterns to assist in semiautomatic way the data annotating process for, subsequentially, leverage the training of AI supervised models. To verify the performance of the unsupervised ML algorithms to detect interest/anomaly pattern in real time series data, six algorithms were applied in following two identical cases (i) meteorological data from a hurricane season and (ii) monitoring data from dynamic machinery for predictive maintenance purposes. The performance evaluation was investigated with seven threshold indicators: accuracy, precision, recall, specificity, F1-Score, AUC-ROC and AUC-PRC. The results suggest that algorithms with multivariate approach can be successfully applied in the detection of anomalies in multivariate time series data.
Active Object Estimation for Human-Robot Collaborative Tasks

In the current exploring of interpreting human activities of daily living (ADLs), rarely we can see a specific model for training robot helpers, which in some domains has shown promising prototypes. In our proposed scenario, we aim to build a model for training a robot helper to assist human being to conduct certain activities, and for this, we are interested in (1) which objects will the subject interact with; (2) how will the subject interacts the object, or in this paper, how will the objects moving; So that in limited conditions the robotic helper can help the human conduct such interactions. The setting also includes a fixed IR based stereo camera and based on its RGB-D stream feed we utilise a generative adversarial network (GAN) for the objective movement prediction. Then object detection is applied to the produced future frame, which is compared with the last input frame, to resolve the movement of the object. IR frame is also handled, to produce the 3D distance of the object to the camera, leading to the actual 3D location of the object in the certain feature time frame. Experiment results show promising in our model.