Reviewerly/exHarmony huggingface dataset:
Design of Energy-Efficient Gaussian Filters by Combining Refactoring and Approximate Adders

The Gaussian image filter is a compute-intensive approach to reduce undesirable artifacts and generally serves as a pre-processing technique for emerging applications related to visual computing systems. This work evaluates alternatives for the design of power-efficient Gaussian Filters. The proposed optimization strategy combines: 1) a refactored function to minimize the arithmetic operations, and 2) a design space exploration investigating different approximation scenarios applied to the full adders. The exact version of our refactored Gaussian Filter architecture reduces the total power consumption and the circuit area by 18% and 12%, respectively compared with the baseline Gaussian Filter architecture. Moreover, the combination of different approximation levels with the refactored architecture provides design options with power reductions from 21% to 59% compared with the baseline Gaussian Filter architecture.
Foreign language virtual teaching optimization: sensory-somatic enhancers as an alternative to rational and emotional ones

Based on the use of potentials of human analogue thinking and cognitive effects as an indispensable component of the cognitive activity, the article considers atypically located in the frame structure and suggestively loaded rational and emotional motivators for learning a foreign language. The article focuses on sensory-somatic enhancers of the new information reception. It is shown that stimulation of the analogue language learning is mostly successfully carried out with suggestive or emotional motivation, as well as through visual, audio, kinesthetic channels alongside with the verbal one during games. It is proved that such games were used in the history of human ontogenesis. Game resources of virtual reality are considered as new stimulating suggestions, since a game is a way to detach from the profane in favor of the sacred information space. The ways of optimization of virtual foreign language learning by means of sensory-somatic enhancers are described.
The Privacy Cost of Currency

Banknotes, or cash, can be used continuously by any person for nearly every transaction and provide anonymity for the parties. However, as digitization increases, the role and form of money is changing. In response to pressure produced by the increase in new forms of money and the potential for a cashless society, states are exploring potential substitutes to cash. Governments have begun to investigate the intersection of digitization and fiat currency: Central Bank Digital Currencies (“CBDC”). States have begun researching and developing CBDCs to serve in lieu of cash. Central banks are analyzing the potential for a CBDC that could be made available to the public and serve as a substitute for cash by providing an alternate, safe, and robust payment instrument. However, the greatest attribute of cash is that it protects purchaser anonymity. Fully eliminating cash, without a substitute that safeguards anonymity, would undermine privacy of individuals. The creation of a CBDC in response to the potential cashless society raises the question whether the anonymity previously provided by cash must be safeguarded by the state. This note posits that a central bank in a cashless society must opt for the token-based form of CBDC, which provides the most privacy to individuals. States that choose an account-based CBDC will be in violation of fundamental international privacy principles. This note begins by drawing the crucial distinction between account-based and token-based currencies. Then, this note argues that the broad right to privacy in the digital age is inclusive of personal financial data; this data is subject to the lawful and arbitrary standards of article 17 of the International Covenant on Civil and Political Rights (“ICCPR”). Applying the ICCPR framework, it becomes abundantly clear that the privacy of individuals must be protected, even in the rapidly changing landscape of payments in the digital age.
Performance of Fuzzy Inference System for Calorific Value Predicting by Using The Mamdani Method

It is well known that the energy content of fuel is characterized by its calorific value which is usually measured by using a bomb calorimeter. In many analysis, the calorific value is usually related with the proximate analysis results. In this study, the calorific value predicting program based on the proximate analysis data using the fuzzy inference system with Mamdani method has been developed. In our analysis, the proximate analysis data of moisture and volatile matter contents are used as input and the calorific value as output. The results showed that the influence of the moisture content is more dominant in determining the calorific value of fuel than volatile matter content. The performance of the calorific value predicting program also shown that the prediction error is about 0.75% to 5.2%. The obtained calorific value is well reproduced the experimental data.
Speeding up Natural Language Text Search using Compression

Text search is a well-known problem in computer science where the valid shifts of a pattern P in a text string T are found.This paper shows how to speed up text search by searching for P in a compressed version of T. A fast compression algorithm was designed for this aim.This algorithm is based on the assumption that T is restricted to the letters of a single natural language.Relying on this assumption, a letter, in T or P, is encoded into a single byte instead of the two-byte unicode which shortens the string on which a text search algorithm works.The main disadvantage of this approach is the restriction of the alphabet of T to be from a single natural language.However, wide range of text documents complies to this assumption.Another issue is the overhead that is required to compress P and T, but it was found that the proposed compression algorithm is so fast such that its run-time can be paid for and still save text search time.Different approaches to store compressed T are also explored.The conducted experimental study showed that this approach does actually reduce the text search time.
Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation.

Labeling data for modern machine learning is expensive and time-consuming. Latent variable models can be used to infer labels from weaker, easier-to-acquire sources operating on unlabeled data. Such models can also be trained using labeled data, presenting a key question: should a user invest in few labeled or many unlabeled points? We answer this via a framework centered on model misspecification in method-of-moments latent variable estimation. Our core result is a bias-variance decomposition of the generalization error, which shows that the unlabeled-only approach incurs additional bias under misspecification. We then introduce a correction that provably removes this bias in certain cases. We apply our decomposition framework to three scenarios -- well-specified, misspecified, and corrected models -- to 1) choose between labeled and unlabeled data and 2) learn from their combination. We observe theoretically and with synthetic experiments that for well-specified models, labeled points are worth a constant factor more than unlabeled points. With misspecification, however, their relative value is higher due to the additional bias but can be reduced with correction. We also apply our approach to study real-world weak supervision techniques for dataset construction.
Analisis Decision Support System Perbandingan Metode Smarter dan Saw Dalam Menentukan Pemilihan Staff Pegawai Terbaik Laboratorium Komputer

The best employees will make an agency to increase in its operations and can develop rapidly. However, relations on the part of an agency are still not optimal in selecting the best staff. Therefore, human resource management is needed in an agency with the selection of the best employee staff to spur staff morale in improving operations, dedication and performance in an agency, so that it becomes better, namely by creating a Decision Support System in selecting the best staff employees by using the Smarter and Saw method comparison method. The Smarter and Saw method is a decision-making method using multiplication to determine the value of a criterion, where the value for each criterion must be determined in advance with the weight of each criterion concerned. By utilizing the advantages and disadvantages of each method, the comparison of this method is able to give a value of 79.5 for the Smarter method, 93 for using SAW. Decision support systems using this comparison method can assist in the selection of the best employees and provide an alternative choice of ranking results
Low mathematical literacy of adolescents of Ukraine and ways to increase the level of teaching mathematics in educational institutions

Олійник О. П., Олійник С
THE EFFECT OF ECONOMIC GROWTH AND DEMOGRAPHIC CONDITION ON CRIMINALITY IN EAST JAVA PROVINCE

Introduction: This study aims to determine how much the effect of economic growth and demographic conditions on the crime rate in East Java Province. This study is using secondary data for 6 years from 2013-2018.Methods: The data analysis method used in this study is panel data regression analysis method with the Fixed Effect Model (FEM) approach. The dependent variable used in this study is the crime rate, while the independent variables are economic and demographic growth, which includes population density and the number of poor people.Results: Based on the results of this study, it can be concluded that economic growth has a positive but insignificant relationship to the crime rate in East Java Province. Population density has a positive and significant relationship to the crime rate in East Java Province. Meanwhile, the number of poor people has a negative and significant relationship with the crime rate in East Java Province. The results of the study also show that economic growth, population density and the number of poor people have an effect and significant relationship on the crime rate in East Java Province.Conclusion and suggestion: This needs an equitable distribution of income and empowerment of human resources through improving educational facilities and infrastructure as well as skills so that productivity and community income increase. In addition, it is necessary to have equitable development and the provision of employment opportunities in each region so that population density is not concentrated in one or several areas.
Summarization in English as a Foreign Language: A Study Comparing Summary Performances to Summarizers’ Vocabulary Size

The study examined the impact of a first language&amp;rsquo;s summarizing skill and second language vocabulary size on summary performances in a second language. A total of 40 English as a Foreign Language (EFL) learners from a Japanese university with a mixed level of English language proficiency were asked to write a summary in English (i.e., their non-native language, L2) and in Japanese (their native language, L1) from a text written English and Japanese respectively. The effect of L1 summarizing skill on L2 summary performances was examined using multiple regression analysis. L1 summary performances (i.e., summarizing skill) slightly influenced English summary performances for summary writers with lower-level English language proficiency but not L2 summary performances for those with higher-level English language proficiency. The participants&amp;rsquo; vocabulary size measured by Nation&amp;rsquo;s (2007) test was positively correlated with their English summary performances. Moreover, the results showed that the vocabulary size in the highest and smallest-vocabulary size groups was correlated with scores on two rating scales (i.e., Language use and Source use) in their English summary. In contrast, the vocabulary size in the middle-level vocabulary size groups was correlated with their scores on two different rating scales (i.e., Main idea coverage and Integration) in their English summary. This study concluded that L1 summary performance had not impact on L2 summary performances because several characteristics influence of summary writers&amp;rsquo; English vocabulary size. The study made several recommendations to EFL teachers who teach summary writing and for further study.
Enhancing Reading Comprehension of Junior High School Students Through Scaffolding Instruction

Scaffolding, as one of the structured-teaching learning strategies on language skills, has been developed worldwide including in Indonesian context.This paper investigates the effect of using scaffolding to enhance reading comprehension of ninth-grade students in SMP Negeri 04 Batu, East Java, Indonesia.The present study employs a quantitative research design which involves 30 participants (i.e. 12 males and 18 female), and their average ages are 15 years old.The instruments used in the study are pre-test and post-test besides questionnaire.The tests are done before and after using the scaffolding strategy and the questionnaire are used to identify the students' perception and feeling after the implementation of the strategy.Data analysis are carried out using paired-samples t-test and the questionnaire (5-Likert scales) are analyzed descriptively.The results of the study show a significant difference between before and after using the scaffolding strategy.It indicates that the scaffolding strategy practiced by the students could build their reading comprehension skills by means of steps by steps activity.Besides, the students feel more comfortable and eager to read more authentic reading material on account of this strategy.Therefore, it is recommended for teachers and syllabus designers to include scaffolding instruction to enhance the students reading comprehension.
Sistem Pendukung Keputusan Pengadopsian Anak Menggunakan Metode Analytical Hierarchy Process Dan Metode Perbandingan Eksponensial

YPAB is an institution that keeps and takes care babies and children who don’t have parents. The babies and the children can be adopted, but the institution has their own regulations for potential adopters who want to adopt children. All this time, adopter who adopted the babies or the children from YPAB institution is done manually which need long time. Therefore, DSS is needed for decide a potential adopter. The purpose of this research is developing a DSS in the process of selecting potential adopters. Meanwhile, the research method uses the Analytical Hierarchy Process (AHP) and the Exponential Comparison Method (MPE) and the System testing uses black-box and validity testing. System testing is used black-box and validity testing by comparing the real YPAB data and the calculation system results. The test results show that the adoption system using the Analytical Hierarchy Process (AHP) method and the Exponential Comparison Method (MPE) has a good performance so that the system is feasible to be implemented in YPAB.Keywords: DSS, MPE, Children adoption, YPAB.
Constrained dynamics of maximally entangled bipartite system

Abstract The classical and quantum dynamics of two particles constrained on $$S^1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>S</mml:mi><mml:mn>1</mml:mn></mml:msup></mml:math> is discussed via Dirac’s approach. We show that when state is maximally entangled between two subsystems, the product of dispersion in the measurement reduces. We also quantify the upper bound on the external field $$\vec {B}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mi>B</mml:mi><mml:mo>→</mml:mo></mml:mover></mml:math> such that $$\vec {B}\ge \vec {B}_{upper }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mover><mml:mi>B</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mo>≥</mml:mo><mml:msub><mml:mover><mml:mi>B</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mrow><mml:mi>upper</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> implies no reduction in the product of dispersion pertaining to one subsystem. Further, we report on the cut-off value of the external field $$\vec {B}_{cutoff }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mover><mml:mi>B</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mrow><mml:mi>cutoff</mml:mi></mml:mrow></mml:msub></mml:math> , above which the bipartite entanglement is lost and there exists a direct relationship between uncertainty of the composite system and the external field. We note that, in this framework it is possible to tune the external field for entanglement/unentanglement of a bipartite system. Finally, we show that the additional terms arising in the quantum Hamiltonian, due to the requirement of Hermiticity of operators, produce a shift in the energy of the system.
Evaluasi Sistem Informasi Human Resources Management Systems PT MMC Metal Fabrication Bekasi

The HRMS (Human Resources Management System) Information System functions to process employee data related to the Human Resources Department. After running this HRMS Information System for several years, the leadership of PT MMC Metal Fabrication wanted to find out the extent of maturity and the shortcomings found in the HRMS Information System. To find out the extent of maturity of the HRMS Information System, an HRMS Information System Evaluation is needed. To adjust the standards in Indonesia, Information System Evaluation is made using the Index KAMI (Information Security). Index KAMI is an Information System Evaluation framework adopted from ISO 27001 that has been adjusted to the standards in Indonesia. The results of this study can provide the maturity level of the HRMS Information System along with the findings.
AN INVESTIGATION OF ENGLISH SPEAKING SKILLS PERFORMANCE OF INTROVERT STUDENTS IN SPEAKING CLASS

&lt;span lang="EN-GB"&gt;This research aims to know the personality (introvert) affects the students on their speaking English performance and influences the students in their communication,&lt;/span&gt;&lt;span lang="EN-GB"&gt;and to know the effort of the introvert students to handle the personality problem in&lt;/span&gt;&lt;span lang="EN-GB"&gt;speaking English performance. In order to collect the data, observation,&lt;/span&gt;&lt;span lang="EN-GB"&gt;questionnaire and interview was three research instrument used.&lt;/span&gt;&lt;span lang="EN-GB"&gt;The participant of this research was a 26 students in the second semester of English&lt;/span&gt;&lt;span lang="EN-GB"&gt;Education Department, Ibn Khaldun University Bogor. Finally for the result of this&lt;/span&gt;&lt;span lang="EN-GB"&gt;research revealed that for the score was 2.23 in average of students speaking&lt;/span&gt;&lt;span lang="EN-GB"&gt;performance, it means fair slightly good. This statement was strengthened with the&lt;/span&gt;&lt;span lang="EN-GB"&gt;data analysis that the most affecting factors from psychological aspects towards&lt;/span&gt;&lt;span lang="EN-GB"&gt;introvert students in speaking skill were depressed (92%). From the linguistics&lt;/span&gt;&lt;span lang="EN-GB"&gt;factors, eight of thirteen students said vocabulary and grammar was the common&lt;/span&gt;&lt;span lang="EN-GB"&gt;problem. And six of thirteen students experienced difficulties in pronunciation.&lt;/span&gt;&lt;span lang="EN-GB"&gt;Introvert students used a various strategies to overcome their public speaking&lt;/span&gt;&lt;span lang="EN-GB"&gt;problems, several students try to improving their speak ability by read a book and&lt;/span&gt;&lt;span lang="EN-GB"&gt;record it, sing an English song, have a conversation every day, by join the&lt;/span&gt;&lt;span lang="EN-GB"&gt;community outside of the university, help their brother/sister do their homework,&lt;/span&gt;&lt;span lang="EN-GB"&gt;talk with the foreigner, watch YouTube.&lt;/span&gt;
Fractional moment-preserving initialization schemes for training deep neural networks

A traditional approach to initialization in deep neural networks (DNNs) is to sample the network weights randomly for preserving the variance of pre-activations. On the other hand, several studies show that during the training process, the distribution of stochastic gradients can be heavy-tailed especially for small batch sizes. In this case, weights and therefore pre-activations can be modeled with a heavy-tailed distribution that has an infinite variance but has a finite (non-integer) fractional moment of order $s$ with $s<2$. Motivated by this fact, we develop initialization schemes for fully connected feed-forward networks that can provably preserve any given moment of order $s \in (0, 2]$ over the layers for a class of activations including ReLU, Leaky ReLU, Randomized Leaky ReLU, and linear activations. These generalized schemes recover traditional initialization schemes in the limit $s \to 2$ and serve as part of a principled theory for initialization. For all these schemes, we show that the network output admits a finite almost sure limit as the number of layers grows, and the limit is heavy-tailed in some settings. This sheds further light into the origins of heavy tail during signal propagation in DNNs. We prove that the logarithm of the norm of the network outputs, if properly scaled, will converge to a Gaussian distribution with an explicit mean and variance we can compute depending on the activation used, the value of s chosen and the network width. We also prove that our initialization scheme avoids small network output values more frequently compared to traditional approaches. Furthermore, the proposed initialization strategy does not have an extra cost during the training procedure. We show through numerical experiments that our initialization can improve the training and test performance.
Neural Weighted A*: Learning Graph Costs and Heuristics with Differentiable Anytime A*

Recently, the trend of incorporating differentiable algorithms into deep learning architectures arose in machine learning research, as the fusion of neural layers and algorithmic layers has been beneficial for handling combinatorial data, such as shortest paths on graphs. Recent works related to data-driven planning aim at learning either cost functions or heuristic functions, but not both. We propose Neural Weighted A*, a differentiable anytime planner able to produce improved representations of planar maps as graph costs and heuristics. Training occurs end-to-end on raw images with direct supervision on planning examples, thanks to a differentiable A* solver integrated into the architecture. More importantly, the user can trade off planning accuracy for efficiency at run-time, using a single, real-valued parameter. The solution suboptimality is constrained within a linear bound equal to the optimal path cost multiplied by the tradeoff parameter. We experimentally show the validity of our claims by testing Neural Weighted A* against several baselines, introducing a novel, tile-based navigation dataset. We outperform similar architectures in planning accuracy and efficiency.