Reviewerly/exHarmony huggingface dataset:
Institution of Higher Education and Science as a Factor of Economic Development

The modern economy is undergoing a structural transformation that enhances the role of knowledge and technology for economic development. Knowledge and technologies are created by researchers who received professional training in universities. The higher education system in Russia is changing similar to those in Western countries earlier. Institutional design should analyse knowledge about management routines, values, working rules, and the functioning of the institutional environment. The institutions operating in society can be identified by studying the narratives of the actors and the explanations for their actions. In the article, the in-depth interview is used for studying institutional changes in education and science. Data collected in projects «Identification of institutions and organizational mechanisms for the merger of universities in the context of the socio-economic development of the region» (10 interviews) and «Institutional traps of optimization of the sphere of education and science» (20 interviews, 3 expert focus group interviews). The narrative analysis identified interest groups in education and science that are involved in the functioning of institutional traps. The presence of special interest groups is an integral attribute of institutional traps that arise in conditions of quasi-optimal equilibrium. Combining research data shows the importance of the institution of higher education and science for economic development.
Recommendation Algorithm Based on Dynamic Profile of Post Users on Electric Power Major

The era of knowledge intelligence is increasingly affecting the transformation of traditional business training models. However, current job training in Electric Power Major has problems such as a wide range of learning resources, a large number of learning resources, management difficulty, and low efficiency. Due to the continuous update and change of business knowledge required by various professional positions, and the lack of timely and effective supplements, it is hard to meet the personalized and precise learning needs of employees in different positions. How to recommend suitable learning resources to users has become a research hotspot in recent years. From the perspective of the learning group, the users in the post have the same job content, job requirements, and job responsibilities, which are common. From the perspective of the individual user, each person has different knowledge reserves and learning abilities which would bring diversity in schedule they are learning and makes difference between themselves. This paper takes job user learning as the point of departure in research, designs an intelligent recommendation algorithm based on job profile, recommending the individual test for the users, and providing targeted test exercises to help users consolidate the knowledge they have learned. This algorithm fully considers the personalities and commonalities of post employees, which is of great significance for improving the learning efficiency of post employees.
Reading Comprehension: Influencing Factors and Deterrents in the Case of Undergraduate Students

This study was conducted to determine the factors which are crucial in deciding the degree of reading comprehensiveness in higher classes. This research also aimed to find the detriments to comprehension, among secondary level students with respect to the English language. These were assessed considering various aspects. Teaching approaches applied in primary classes which contribute to slow and partial learning were also found. 23 students from varying educational backgrounds were interviewed. They were given comprehension passages followed by questions to determine their level of understanding. Based on the responses, their comprehension skills were assessed. To understand the problems they encountered during the process, their perceptions were considered.
Testing DNN image classifiers for confusion &amp; bias errors

We found that many of the reported erroneous cases in popular DNN image classifiers occur because the trained models confuse one class with another or show biases towards some classes over others. Most existing DNN testing techniques focus on per-image violations, so fail to detect class-level confusions or biases. We developed a testing technique to automatically detect class-based confusion and bias errors in DNN-driven image classification software. We evaluated our implementation, DeepInspect, on several popular image classifiers with precision up to 100% (avg. 72.6%) for confusion errors, and up to 84.3% (avg. 66.8%) for bias errors.
A Data Hiding Technology by Applying Interpolation in Extended Local Binary Pattern

This paper proposes a data hiding technology by applying interpolation in Extended Local Binary Pattern (ELBP). The proposed method uses interpolation to predict pixels in blocks so as to reduce image distortion when compressing images using Extended Local Binary Pattern (ELBP). After that, LSB substitution is used to embed the secret data into the compression code, and then the compression code with secret data is optimized by Optimal Pixel Adjustment Process (OPAP). To make OPAP work well, the proposed method rearranges and encodes the difference values between original pixels and predictive values so as to get better results. According to the experimental results, the proposed method improves PSNR values about 1.25 dB. The proposed method can produce high quality stego images.
Sources of Knowledge and Their Individuation

Why is reading never thought of as a source of knowledge? This chapter analyzes, first, what it is for something to be a source of knowledge and, second, by what kind of principles acknowledged sources of knowledge have been individuated. It is shown that epistemologists have used five kinds of principles, and it is argued that reading can be individuated by means of some of those principles.
Performance Evaluation of Left Hand of Violin-playing Robot

This report presents the design and software of the left hand for our violin-playing robot, and its experimental results. The hand has four fingers with two active joints actuated by RC servo motors. We redesigned the hand so that it can be placed under the neck of the violin. The distance between the adjacent fingers can be adjusted so that each finger can push the appropriate positions on the strings to produce sounds with the correct pitch. We conducted frequency analysis on the produced sound and confirmed that the hand can produce sounds with the correct pitch. We then investigated the delay time of the finger motion and found that the average delay time is approximately 0.0587 s.
A Study on the Effective Application of Evolutionary Required Operational Capability (ROC) in the Build-up of Korean Military Power

In the era of the 4th Industrial Revolution, this study aims to present major trends and prospects related to the artificial intelligence(AI)-based defense industry in preparation for future defense. To this end, major domestic and foreign cases, including the U.S., and the current status of the Ministry of National Defense’s implementation policy, and short-term, medium- and long-term products and services promoted by artificial intelligence(AI) in the defense sector were discovered to distinguish the target projects. Through research related to artificial intelligence(AI) in the defense field, it was possible to seek directions for the development of artificial intelligence(AI)-related policies of the Ministry of National Defense, and to present general restrictions and considerations.
White-Box Attacks on the CNN-Based Myoelectric Control System

Convolutional neural networks (CNN) have been widely used in myoelectric control field, such as prosthesis control, physical rehabilitation and human-computer interaction. Nevertheless, it was found that CNN models are very easily tricked by adversarial instances, which are normal instances with tiny intentional perturbations. In this study, an attack framework based on universal adversarial perturbations (UAP) was proposed to attack the CNN-based myoelectric control system. The performance of the proposed framework was evaluated with data recorded by a High-density surface EMG electrode array of 8 subjects during performing 6 finger and wrist extension movements. The experiment results demonstrated the effectiveness of two adversarial attack algorithms including DeepFool-based UAPs and Total Loss Minimization-based UAPs on the CNN-based EMG gesture recognition network for both target and non-target attacks. To our knowledge, this is the first work on the vulnerability of the CNN classifier in EMG-based gesture recognition system, which hopefully can draw more attention to the security of muscle-computer interface.
Impact on the Volatility of the Bitcoin Price with the Implementation of Macro-Regulation

After year two thousand, with the booming of internet networks, virtual currency has appeared into the public's view.In the midst of all (including 8861 numbers of virtual currency, calculate by Coinmarketcap), bitcoin has become the most represented currency for us to discuss.This paper returns the closed adjusted price calculates rates.ARMA models are used to test whether the time series is stationary or not.Dummy variables are set according to the time by specific policies are implemented.According to the GARCH model we used, the implementation of the policy does lead to a sharp decline in the price of bitcoin.Accordingly, in the development process of bitcoin, supervision does give a positive effect in maintaining the price to be balanced and a positive control on malicious speculation to monopolize the bitcoin market and blind purchasing.
Identification of Extreme Guilt and Grave Fault in Bengali Language using Machine Learning

Though huge amount of study has been done on the Bengali Language for information retrieval, but none of them deals with extreme guilt ( ) and grave fault ( ) in the Bengali Language. In this study, we have described extreme guilt ( ) and grave fault ( ). We have used three machine learning methods, such as Logistic Regression (LR), Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB) as the baseline classifiers among the baseline classifier, MNB shows the accuracy of 89%. Ensemble learning has been used to improve the baseline classifiers. We have implemented an Ada Boost algorithm and Maximum voting classification decision method depending on the results of baseline classifiers. Maximum voting and Ada-Boost algorithms have shown an accuracy of 91% and 92% respectively. We have modified the Ada-boost algorithm using Principal Component Analysis (PCA) and named it JR-Ada-Boost. It outperforms all algorithms and gives an accuracy of 94%.
A Comparative Review of Incremental Clustering Methods for Large Dataset

Several algorithms have developed for analyzing large incremental datasets. Incremental algorithms are relatively efficient in dynamic evolving environment to seek out small clusters in large datasets. Many algorithms have devised for limiting the search space, building, and updating arbitrary shaped clusters in large incremented datasets. Within the real time visualization of real time data, when data in motion and growing dynamically, new data points arrive that generates instant cluster labels. In this paper, the comparative review of Incremental clustering methods for large dataset has done.
Modified Spider Monkey Optimization Algorithm Based on Self-Adaptive Inertia Weight

Spider monkey optimization (SMO) algorithm is a new swarm intelligence optimization algorithm proposed in recent years. It simulates the foraging behavior of spider monkeys which have fission-fusion social structure (FFSS). In this paper, a modified spider monkey optimization algorithm is proposed. The self-adaptive inertia weight is introduced in the local leader phase to enhance the self-learning ability of the spider monkey. According to the function value of an individual, the distance from the optimal value is determined, so the inertia weight related the individual function value is added to strength the global search ability or local search ability. The proposed algorithm is tested on 20 benchmark problems and compared with the original SMO and the hybrid algorithm SMOGA and GASMO. The numerical results show that the proposed algorithm has a certain degree of improvement in convergence accuracy and convergence speed. The performance of the proposed algorithm is also inspected by two classical engineering design problems.
Analysis of Audio Visual Feature Extraction Techniques for AVSR System

Recently, Speech technology has started to change the way the human communicate with devices.Speech as a medium of man-machine interaction has been gaining its importance in the modern computer/mobile era.As a result, many computer programs incorporate cutting-edge voice technologies to do diverse jobs.Feature extraction is the foremost step in speech recognition.Understanding the importance of feature extraction methods, this work demystifies the concept behind feature extraction techniques used in audio visual speech recognition.
A Coarse-to-fine Approach for Fast Super-Resolution with Flexible Magnification

We perform fast single image super-resolution with flexible magnification for natural images. A novel coarse-to-fine super-resolution framework is developed for the magnification that is factorized into a maximum integer component and the quotient. Specifically, our framework is embedded with a light-weight upscale network for super-resolution with the integer scale factor, followed by the fine-grained network to guide interpolation on feature maps as well as to generate the super-resolved image. Compared with the previous flexible magnification super-resolution approaches, the proposed framework achieves a tradeoff between computational complexity and performance. We conduct experiments using the coarse-to-fine framework on the standard benchmarks and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.
Is carousel interaction really usable?

In this paper a novel and significant study into the usability of carousel interaction in the context of desktop interaction is presented. Two equivalent prototypes in an e-commerce context were developed. One version had a carousel and the other version did not have a carousel. These were then compared with each other in an empirical experiment with 40 participants. The data collected were statistically analysed and overall results showed that in terms of performance the Web site version without carousel outperformed the version with carousel. Furthermore, the subjective preferences of the participants were strongly in favour of the without carousel version of the site. The results of this study make an important contribution to knowledge suggesting that in many cases implementing a carousel is not the best design decision. The results of this paper are particularly significant in relation to desktop versioned Web sites and goal-driven tasks. Serendipitous-type tasks and mobile versioned web sites used on mobile devices with touch screens were not part of the scope of this work.
KORELASI KECANDUAN MOBILE LEGENDS TERHADAP PRESTASI BELAJAR MAHASISWA

The purpose of the study was to determine the correlation between the level of online game addiction and student achievement in the Mathematics Education Study Program, UIN Raden Intan Lampung. The method in this research was quantitative with the type of correlation research. The data collection technique used a questionnaire and tested it to see the validity and reliability. The data analysis technique in this study used the normality test using the Kolmogorov Smirnov method and the linearity test using SPSS software version 24.0, and hypothesis testing using a correlation test with a significant level of 0.05 or 5%. The study results were that of the 30 questionnaires tested, 25 questions were feasible to use. The reliability test has a value of rcount &gt; rtable. The normality test got a significant value of 0.065 and 0.066 on online game addiction and learning achievement. So it can be said that there is a significant correlation between student achievement and online game addiction, and the value is 0.307, which means correlation
Conclusion

Chapter 11, the Conclusion, begins with analysis of a viral video of a young boy performing ballet in the mud in Lagos, viewed by tens of millions of people in the summer of 2020. This video highlights potential dreams and hopes involving future paths, with potential that may not be realized, given the paucity of available opportunities. Potential future paths in the digital technology era resulting from past and present paths are then discussed.
On two conjectures about the intersection distribution

Recently, Li and Pott proposed a new concept of intersection distribution concerning the interaction between the graph $$\{(x,f(x))~|~x\in {\mathbb {F}}_{q}\}$$ of f and the lines in the classical affine plane AG(2, q). Later, Kyureghyan et al. proceeded to consider the next simplest case, and derived the intersection distribution for all degree three polynomials over $${\mathbb {F}}_{q}$$ with q both odd and even. They also proposed several conjectures therein. In this paper, we completely solve two conjectures of Kyureghyan et al. Namely, we prove two classes of power functions having intersection distribution: $$v_{0}(f)=\frac{q(q-1)}{3},~v_{1}(f)=\frac{q(q+1)}{2},~v_{2}(f)=0,~v_{3}(f)=\frac{q(q-1)}{6}$$ . We mainly make use of the multivariate method and a certain type of equivalence on 2-to-1 mappings. The key point of our proof is to consider the number of the solutions of some low-degree equations.
Algorithmic and Advanced Programming in Python - Syllabus in Computer Science, Decision Making &amp;amp; Data - Masterclass 5

This paper is the fifth of a series of 9 lecture notes in Algorithmic and Advanced Programming in Python that are part of the Syllabus of Dauphine PSL's Master in Computer Science, Decision Making & Data. It taught advanced data structure in python with a focus on core and practical details to learn the ins and outs of advanced data structures. In this lecture, you will learn about binary trees and binary trees order traversals (preOrder, InOder, and PostOrder)
Experimental Verification of Estimated Block Size of BKZ Algorithm Against LWE

Cryptographic constructions based on the Learning with Errors (LWE) problem have received much attention in recent years for the process of standardizing post-quantum cryptography. The most effective solution against LWE is the BKZ algorithm and the complexity is dominated by block size. Alkim et al . (USENIX 2016) proposed an estimate of the block size which is required to solve LWE via the BKZ algorithm (2016 estimate). In this paper, we verify the fundamental assumption of the 2016 estimate which is applied to some proposed cryptosystems by executing some experiments using the BKZ algorithm. We also show our conjecture of why it is possible to solve LWE with smaller block sizes. Furthermore, we discuss the reason why the LWE solution is recovered earlier than expected by the 2016 estimate.
Model of Methodology for Organizing Project-based Biology Training in Non-core Institutions of Secondary Vocational Education

Biological knowledge is one of the fundamental components of a common human culture, because without knowledge of biology it is unthinkable to develop an ecological way of thinking, to ensure the perception of the scientific principles of interaction of the Man-Nature system. The formation of a healthy lifestyle, the preservation of ecosystems, and the development of mankind are based on knowledge of biology. The purpose of the article is to develop and use a model of a methodology for organizing project-based education in biology in non-core institutions of secondary vocational education. The interrelation of theoretical knowledge and practical skills is a prerequisite for increasing the level of training of future specialists in any industry. In connection with the introduction of new FSES SPE into the work of educational institutions, secondary schools are required to improve the organization of the educational process and teaching materials. The involvement of students in the implementation of various projects leads to the development and development of project competence, and its level is determined by the number and quality of developed projects. The author concludes that the formation of competencies among students at secondary schools through project activities takes place.
Power and Coherence Spectra

A concise introduction to geophysical data processing - many of the techniques associated with the general field of time series analysis - for advanced students, researchers, and professionals. The textbook begins with calculus before transitioning to discrete time series via the sampling theorem, aliasing, use of complex sinusoids, development of the discrete Fourier transform from the Fourier series, and an overview of linear digital filter types and descriptions. Aimed at senior undergraduate and graduate students in geophysics, environmental science, and engineering with no previous background in linear algebra, probability, or statistics, this textbook draws scenarios and datasets from across the world of geophysics, and shows how data processing techniques can be applied to real-world problems using detailed examples, illustrations, and exercises (using MATLAB or similar computing environment). Online supplementary resources include datasets for students, and a solutions manual and all the figures from the book as PowerPoints for course instructors.
Verification of the effectiveness of pre-learning in neural machine translation between Early-Modern Japanese literary language and modern colloquial Japanese

In this paper, The authors show the effectiveness of pre-learning in old style, new writing style pair and Wikipedia Japanese in neural machine translation from Early-Modern Japanese literary to colloquial Japanese. Early-Modern Japanese literary style is difficult to read easily. In addition, there are quite a few sentences in which Early-Modern Japanese literary styles are translated into modern colloquial Japanese, and it is difficult to create large-scale language pairs.
Fail-Safe Neural Network Inference Accelerator

Deep learning techniques like Convolution Neural Networks (CNN) are nowadays popular for performing many artificial intelligence tasks (e.g. object detection, semantic segmentation) across market segments. Automotive and Industrial market segments especially, are finding a lot of traction in using these techniques to solve several problems. These markets have higher functional safety goals due to potential impact of failure resulting in severe consequences including loss of life. The traditional approach to achieve higher safety goal for hardware is full redundancy, which has inherent disadvantage of doubling cost and power of given solution. This paper presents novel and area efficient hardware architecture using matrix checksum properties and duplication of nonlinear operations to enable higher safety goal for NN computation. The paper also proposes multiple architecture variations which allow trade-offs ranging from full performance throughput, low latency error detection to lowest area solution. The proposed solution is designed and simulated in Python HDL. The proposed solution can be realized with 5% additional silicon area as matrix computation is nearly majority part of NN computation. The simulation shows benefits of solution i.e. full performance throughput, while single errors in input matrix and matrix multiplications getting detected, meeting functional safety goals.
Graph Neural Networks

Many important real-world data sets are available in the form of graphs or networks: social networks, world-wide web (WWW), protein-interaction networks, brain networks, molecule networks, etc. See some examples in Fig. 8.1. In fact, the complex interaction in real systems can be described by different forms of graphs, so that graphs can be a ubiquitous tool for representing complex systems.