Reviewerly/exHarmony huggingface dataset:
Edge maps assisted image super-resolution using spatial feature transform

Deep learning has revolutionized every field of computer vision, including single image super-resolution because of its remarkable performance pertaining to effectiveness and efficiency. Various recent methods try to predict the SR image by incorporating different prior knowledge of images. In this paper, we propose a new method that utilizes not only internal image features but also multi-level edge prior knowledge with richer information. Holding the intuition that edge information helps to deal with blurry edges and try to generate sharper results, we present a residual edge and channel attention super-resolution network to handle LR images, named RECAN. Our architecture consists of two basic modules: the first module is EdgeNet, which generates multi-level edge maps from the input image; and the second module takes advantage of significant information in input image along with edge maps, called SRNet. Specifically, the SRNet uses channel attention technique and spatial feature transform (SFT) layers to super-resolve an image. Qualitative and quantitative comparisons are presented with state-of-the-art methods, which show promising results of our method together with improved image quality.
Distributional statistical learning: How and how well can it be measured?

Individuals are readily able to extract and encode statistical information from their environment (or statistical learning). However, the bulk of the literature has primarily focused on conditional statistical learning (i.e. the ability to learn joint and conditional relationships between stimuli), and has largely neglected distributional statistical learning (i.e. the ability to learn the frequency and variability of distributions). In this paper, we investigate how and how well distributional learning can be measured by exploring the relationship between and psychometric properties of two measures: discrimination judgements and frequency estimates. Reliable performance was observed in both measures across two different distributional learning tasks (natural and artificial). Discrimination judgements and frequency estimates also significantly correlated with one another in both tasks, and performance on all tasks accounted for the majority of variance across tasks (55%). These results suggest that distributional learning can be measured reliably, and may tap into both the ability to discriminate between relative frequencies and to explicitly estimate them.
Reasons to Be Cheerful: The Benevolent Market Power of Decentralised Blockchains

Blockchain technology has in recent years captured attention and funding based on its promise to build trust among multiple parties without the need for a trusted intermediary. Born out of government-funded research on game theoretic Byzantine consensus, it offers the prospect of a more efficient way to build trust in transactions; trust in a product’s provenance, or trust in a contract’s execution. In competition policy, the focus of work on blockchains has, since the OECD’s thought leading work on the topic began in 2018, been on the possible anticompetitive risks posed by the technology, and the question of whether there was anything new about these risks. While we remain highly uncertain as to the prospects of a Blockchain revolution, in this paper, we therefore focus instead on the reasons for optimism, and in particular, why we think that decentralised permission-less blockchains offer the prospect of radical pro-competitive and inclusive efficiencies, and hence might contribute to a pro-competitive industrial policy. We therefore expect that blockchain will continue to capture the attention of policymakers that are keen to deliver shared prosperity in a post-COVID world, and we suggest that competition agencies might usefully engage in facilitating the emergence of a particular type of Blockchain, specifically decentralised permission-less blockchains with platform functionality.
A Study on the educational application of historical game

The importance of history education, regardless of country and age, is always emphasized. The importance of history education is already being emphasized in Korea, but there are many difficulties in getting students to understand history properly just by school classes, and there are also difficulties in attracting students to participate in classes. The effectiveness of education using games has been proven since 20 years ago, and the demand for game-based education is gradually increasing in the education community, which is becoming more open. Based on the effectiveness demonstrated by research on existing game-based education, this paper presents a method to evaluate and utilize commercially developed games in game-based education by improving and applying historical thinking, experiencing experiences, and problems of game-based training introduced in ESN reports. The selection conditions were based on a clear background of the times, clear historical evidence, and the existence of historical distortions, and the use of historical thinking, empirical historical experience, genre advantage, and time division factors as evaluation indicators of the selected game. In addition to the games introduced in this paper, we hope to meet the demand for game-based education through various historical games and functional games for educational purposes from the development stage.
Does LGBT inclusion promote national innovative capacity

This paper empirically investigates the extent to which the social inclusion of lesbian, gay, bisexual and transgender (LGBT) people matters for technological innovation. The central hypothesis, in particular, postulates that the acceptability of LGBT individuals helps foster human capital skills, thus strengthening innovation. The reverse holds true in societies where significant discrimination against homosexuality prevails. To test this proposition, I perform empirical analysis using data for a world sample of countries. The results indicate that social tolerance toward homosexuality is positively correlated with the economic complexity index, a novel measure of innovation. To achieve causal inference, I conduct several falsification exercises, none of which alters the baseline findings. Further, individual-level analysis, based on data from the World Values Survey, lends strong credence to the international evidence. I also find that LBGT inclusion exerts a positive influence on human capital skills, which is a potential mechanism explaining the baseline findings.
Image enhancement by deep neural networks using high-level information

A method is investigated for training neural networks for image enhancement, based on using information from the features of neural networks trained in image classification. Experiments are performed to identify the optimal loss function that achieves maximum precision in the classification of images superposed with noise or blurring. The dependence of the best configuration of training parameters on the type of detrimental influence and target is demonstrated. This is the first study, to our knowledge, to compare the influence of such a loss function on the precision of the restoration and recognition with the utilization of a single classifier trained under the influence of distorting factors. We show that it is reasonable to correct some simple distortions “outside” the classifier, while others are better corrected “inside.”
The development and analysis of extended architecture model for intelligent tutoring systems

Intelligent Tutoring Systems (ITS) are computer programs that use leamers knowledge level to providing individualized education. ITS research has successfully delivered systems efficiently supporting one-to-one tutoring. Most of these systems are actively used in real-worid settings and have even contributed to changing traditional education curricula. Instructional activities, learning examples, exploring interactive simulations and playing educational games can benefit from individualized computer-based assistance. To enhance ongoing research related to the improvement of tutoring, we present an extended knowledge mode! including besides the standard modules a common shared database and knowledge-based background, too. The external databases can improve the guality of the behavior models both in tutor and student models. The Python programming language and OWL are efficient tools to combine the ontology management and machine leaming functions to develop ITS systems. In this Paper, we survey ITS technologies andpresent a novel extended architecture model for Intelligent e-Tutoring Systems.
An Adaptive Swarm Optimization Technique for Load Balancing and Task Scheduling in Cloud Computing

In recent era of growing technology, cloud computing has attracted various industries and researchers due to itsignificant nature in parallel and distributed computing systems. This technology has grown drastically in variousreal-time applications such as medical field, health organizations, multi-media applications etc. cloud computingurge to provide the better quality of service for clients or users. However, increasing demand of applicationleads to the imbalance in cloud resources and causes huge power consumption in data centers. Due to multipletasks imbalance occurs in cloud platforms which degrade the quality of service for clients. Task scheduling isconsidered as an important aspect which can be used for load balancing in cloud systems. This helps to allocatebest available resources to complete the task by considering various other parameters such as computation time,scalability, makespan, throughput etc. However, various techniques have been proposed for task scheduling incloud computing but these techniques still suffer from various issues such as makespan, execution time etc. Toaddress this issue, an adaptive swarm optimization approach is presented for heterogeneous virtual machinesystems. Proposed approach addresses the multi-objective problem by developing a probabilistic model resultingin optimization and convergence rate improvement. During task execution in cloud computing, if any VM isoverloaded then the task is removed from that VM and proposed adaptive swarm optimization technique is usedto find any other available optimal resource for task completion. An extensive simulation is performed along withcomparative analysis. Experimental study shows that proposed approach outperforms when compared with stateof-art technique for load balancing in cloud computing systems.
An Analysis of Oil and Gas E-Learning CulturePre-Covid-19, During and Post-COVID Era

Since the 1920s, the Oil and Gas industry has adopted numerous approaches in learning. Increased collaborative knowledge management is a crucial effort in retooling the human capital. Remote learning via the digital platform is a win-win situation for the employer and employee. This paper discusses the challenges and recommendations of e-learning.A qualitative study was conducted with a Malaysian Oil and Gas establishment to study its organizational management trends. The research findings and literature review highlighted three main phenomena of exchanging knowledge in this industry. Due to global expansion, virtual collaboration is a norm even before the COVID-19 pandemic.The findings suggest the adoption of an operational matrix to dynamic resource management as a digital platform (or technology) that is the catalyst to becoming a sustainable post-Covid organization. In part 2, all the sampling and data analysis will be demonstrated to confirm the e-learning approach to the proposed conceptual framework as described in part 1.
Interferensi Kosakata Bahasa Jawa Dalam Ragam Lisan Siswa Pada Pembelajaran Bahasa Indonesia Kelas VII SMP Negeri 2 Kartasura

This study aims to describe (1) the from of Javanese language vocabulary interference in the use of Indonesia oral variety by students of Negeri 2 Kartasura Junior High School, (2) the meaning of Javanese language vocabulary interference in the use of Indonesia oral variety by Junior High School 2 Kartasura students, (3) factors causing Javanese vocabulary inteference in the use of Indonesia by students of Negeri 2 Kartasura Junior High School. The source of data in this study is the oral speech of students of State Junior High School 2 Karatsura. Data collection techniques used are listening, note and proficiency. Data were analyzed by quality research metods. The validity of the data is obtained through triangualtion with sources. The results of this study are as follows. The first from of Javanese language vocabulary interferences in Indonesia is the verbal rhythm used by students including basic forms, compound words, rewords and affixed word. Sechond, eating Javanese language vocabulary interference in Indonesia oral varety used by students, namely stating comparisons, stating affirmations, stating circumstances, stating time, stating a place, stating a cause, statinga a purpose, stating a question, stating a character, statingan action or action, stating number, declare a tool and function as a referrer. The three factors that cause Javanese language vocalbulary interference in the use of Indonesia students oral variety, namely there are i nternal and external factors. Internal factors include (1) biligual speakers with Javanese mother tongue, (2) speakers using thir native language, (3) limitations of speakers using spoken Indonesia. While external factors include (1) Javanese language usage habits in the family of speakers, (2) Javanese language usage habits in the speaker”s neighborhood, (3) language habits in the school environment.
Pengelompokan Data Kategorik Dengan Algoritma Robust Clustering Using Links

Cluster analysis is a technique of data mining that is used to group data based on the similarity of attributes of data objects. The problem that is often encountered in cluster analysis is the data on a categorical scale. Categorical scale data grouping can be done using the ROCK (RObust Clustering using linKs) algorithm. The ROCK algorithm is included in the of agglomerative hierarchical clustering algorithms in cluster analysis. This algorithm introduces a concept called neighbors and links in grouping data. Categorical data grouping with ROCK algorithm is done in three steps. The first step is counting similarities. The second step is determining the neighbors and the last is calculating the links between the observation objects. The value of the link is affected by θ. The optimum number of clusters in the ROCK algorithm is selected using a minimum ratio value of . The purpose of this study is to group 100 data of insurance customers of PT. Prudential Life Samarinda in 2018. Based on the analysis results, obtained that the optimum group is at θ = 0.1 with a ratio value of  is 0.1371. The optimum number of groups formed is 2 clusters. The first group consisted of 42 customers and the second group consisted of 58 customers.
Does Sorting Matter for Learning Inequality? Evidence From East Africa

Inequalities in children’s learning are widely recognized to arise from variations in both household and school-related factors. While few studies have considered the role of sorting between schools and households, even fewer have quantified how much sorting contributes to educational inequalities in low- and middle-income countries. We fill this gap using data on over 1 million children from three East African countries. Applying a novel variance decomposition procedure, our results indicate that sorting of pupils across schools accounts for at least 8 percent of the total test-score variance, eqyuivalent to half a year of schooling or more. This contribution tends to be largest for children from families at the ends of the socio-economic spectrum. Empirical simulations of steady-state educational inequalities reveal that policies to mitigate the consequences of sorting could substantially reduce inequalities in education.
THEORETICAL AND METHODOLOGICAL BASIS OF ADVANCED PROFESSIONAL TRAINING FOR SECONDARY VOCATIONAL EDUCATION SPECIALISTS

The relevance of the study lies in the importance of adopting innovative solutions for further evolution of the Russian System of SVE. The article is aimed at the development of theoretical and methodological grounds for advanced professional training of SVE specialists. The creation of educational and production clusters will provide professional resources for SVE organizations, resulting in the advanced approach in the vocational training in interaction with social partners. Using the comparison method, the study analyzes the experience of implementing public-private partnership programs in the sphere of SVE in Russia. The analysis revealed that the motivation of social partners to interact with vocational education institutions increases with the competition growth and is considered as a way to diversify production and business risks, as well as to reduce the costs for personnel training (retraining, advanced training). The analysis of the educational and production clusters functioning experience in experimental sites of leading educational organizations of SVE in the Siberian Federal District made it possible to identify and describe wide changes in the system of SVE, stages of implementing the mechanism for creating the system of advanced professional training for specialists with SVE, educational technologies required to organize the educational and production process, as well as conditions for the creation and implementation of the complete educational and production process in the SVE system.
Adaptive Finite Approximation of Continuous Noncooperative Games

A problem of solving continuous noncooperative games is considered. It is assumed that control in a system modeled by a continuous noncooperative game can be administered from just one side responsibility for making decisions in the system. Therefore, any solution regardless of the fact how the players interpret them, should be studied and in fact optimized only by the administrator. In accordance with this, a procedure of adaptive finite approximation of continuous noncooperative games is presented and aimed at obtaining approximate solutions suitable for practical implementation under conditions of one-sided administering or control of modeled systems. This procedure consists of two stages. At the first stage, the payoff functions of the players are sampled using a uniform dichotomized partition of the sets of their pure strategies. At the second stage, the corresponding finite noncooperative game is solved. If its solution satisfies the requirements of the administrator, then this solution is the result of an approximation. Otherwise, the procedure returns to the first stage, where the density of the partition is twice increased and a new "twice complicated" game is solved. Such returns and complications are iterated until a solution of the corresponding finite game satisfies the administrator. Only the administrator decides which type of utility, symmetry or equilibrium is preferable and whether the corresponding game solution is acceptable. The degree of closeness of the approximate solution to solution of the original continuous game is not established. The accuracy and quality of the approximation is considered in terms of how fast the finite game solution satisfies the administrator. The sequence of the already solved game is not removed but serves as a basis for selecting the most rational solution considering operation speed, utility, symmetry and equilibrium.
An Analysis of Writing Tasks in English Textbook for Office Administration Students entitled Bahasa Inggris SMA/MA/SMK/MAK Kelas XI

Vocational High School students majoring in Office Administration will be required to do writing tasks when they work in companies after graduating from school. This study aimed at finding out the appropriateness of writing tasks in English textbook entitled Bahasa Inggris SMA/MA/SMK/MAK Kelas XI for Office Administration students at Eleventh Grade. Content analysis method was employed in this study and the analysis was based on Nunan’s (2004) theory of task components (goal, input, procedure) and Hyland’s (2003) theory of writing procedures (graphology, scaffolding, composing). The results indicated that the textbook is inappropriate for Office Administration students. Among the three components of writing task, writing goals and texts (input) in the analyzed textbook do not correspond to the ones required by Office Administration students. The goals and input in the textbook are related to daily life situations, while the students are supposed to write business documents. In terms of procedure, the textbook leaves out graphology tasks and only provides the tasks for scaffolding and composing. Scaffolding has four categories: language familiarization (found in Chapter 1, 2, 5, 6), model analysis and manipulation (not found in any chapters), controlled composition based on model (found in Chapter 1, 2, 3, 4, 5, 6, 8), guided composition (found in Chapter 2, 3, 5, 6, 7, 8). Then, composing has two categories: composition heuristics (found in Chapter 4 and 8) and extended writing (found in Chapter 3 and 5). These findings imply that the needs arise to select more relevant writing tasks for Office Administration students or design new writing materials for the students.
A System for Ammeter Inspection Task

There are millions of ammeters working in China and daily manual inspection of their status consumes immense manpower and material resources. Besides, manual inspection is inevitably affected by factors such as the levels of evaluation skills and tiredness. To solve the problem, we propose an ammeter safety status assessment method and construct a software system for the ammeter inspection task, which encapsulates smart detection and evaluation algorithms for ammeter and its parts into an app and website. The use of this system has greatly reduced the workload of ammeter inspection tasks and unify the standards for defect judgment. Meanwhile, raw data and its analysis results are kept in the system and can be reused to improve the system further. The system has been deployed in the electric power system of Hangzhou and creates millions of economic values.
Implementasi Steganografi Menggunakan Metode Spread Spectrum Dalam Pengamanan Data Teks Pada Citra Digital

Various techniques are used to protect digital information, especially information that is kept secret from people who are not entitled to the right of access to information, one of which is the technique of steganography. Steganography technique is a technique used to secure data by inserting or hiding data into an object without changing the shape of the object; one of the objects is a digital image. The steganography technique also has several methods, namely the Spread Spectrum method. Spread Spectrum is a method of communication where information signals are spread across all available frequencies by selecting where to insert data at low frequencies and adding pseudo-noise (PN). This study secures text data with the Spread Spectrum steganography technique on digital image objects by the steps of changing the value of the RGB pixel image and text data into binary, spreading the text data, followed by key generation and the modulation process of spreading text data with the generated key results. To produce a stegano image with an RGB pixel value that changes the value of 0 to 1 value that does not affect the color reproduction of the RGB pixel image.