Reviewerly/exHarmony huggingface dataset:
Syntactic-Semantic Classes of Context-Sensitive Synonyms Based on a Bilingual Corpus

This paper summarizes findings of a three-year study on verb synonymy in translation based on both syntactic and semantic criteria and reports on recent results extending this work. Primary language resources used are existing Czech and English lexical and corpus resources, namely the Prague Dependency Treebank-style valency lexicons, FrameNet, VerbNet, PropBank, WordNet and the parallel Prague Czech-English Dependency Treebank, which contains deep syntactic and partially semantic annotation of running texts. The resulting lexicon (called formerly CzEngClass, now SynSemClass) and all associated resources linked to the existing lexicons and corpora following from this project are publicly and freely available. While the project proper assumes manual annotation work, we expect to use the resulting resource (together with the existing ones) as a necessary resource for developing automatic methods for extending such a lexicon, or creating similar lexicons for other languages.
Analysis of Energy Efficient Load Balancing of Virtual Machine in Cloud Environment

Cloud computing is the hottest trend in distributed computing. It provides the services based on demand. The services are distributed such as applications, software’s and data bases. The data center handles 53% of load during the peak time. It consumes around 0.67 Zeta bytes of data and the energy usage is 0.96 billion kilo watts of power during the peak time. The main challenge in the cloud data center is to manage load during the peak time. To reduce the energy consumption, the virtual machine needs to be reconfigured based on the memory, CPU usage and workload conditions. In this paper, the Virtual Machine allocation is done using proposed method viz Multiplier algorithm and performance during different workload conditions are analyzed and it is found that the energy efficiency increases from 5% to 6% in the cloud data center.
Varying strategies to maximize the effectiveness of ELT: Lessons from training sessions

Several factors determine the effectiveness of language learning, one of which is the variety of strategies carried out by the teacher in the teaching and learning process. Defined as any efforts made to maximize the learning achievements, strategy encompasses material development, classroom management, and teaching techniques. The question is in what ways the teacher implements such a variety, and what the effect is. This descriptive study was conducted in an in-service teacher training which aimed to improve the trainees’ mastery of ELT strategies and the teaching language. The data were collected using questionnaires, observation, documentation, interviews, and reflection. The subjects were the trainers and the teacher trainees who have mixed language abilities and teaching experiences. The findings show a range of varieties was employed during the training process, covering the use of diverse materials, fun classroom activities, learner-centered approach, affect-lowering classroom management, and varied interaction patterns. As effective learning is dependent on the fresh minds, teachers should always strive to prevent their students from exhausting monotony.
Recommendation Based on Attention Degree and Entropy

With the development of the Internet, the problem of information overload becomes more and more serious. The personalized recommendation technology can establish user profiles through the user’s behavior and other information, and automatically recommend the items that best match the user’s preferences, thus effectively reducing the information overload problem. Although scholars from all over the world have proposed many solutions to the personalized news recommendation system, there are still problems such as computing redundancy, incomplete data, and the inability to make a personalized recommendation to users. Based on the problem, this paper will focus on selected IHUMCF algorithm in computing the user similarity neighbor reason is inadequate to some extent, and the attention degree impact factor is put forward, and then puts forward the L-HUMCF algorithm, and then this paper based on L-HUMCF recommended to improve, as a result, joined the user of information entropy, EL-HUMCF algorithm is proposed, and experimental verification algorithm is effective. Experimental results show that the proposed algorithm is better than other personalized recommendation algorithms.
DIFFERENTIAL ITEM FUNCTIONING METHOD AS AN ITEM BIAS INDICATOR FOR BIG DATA ASSESSMENT IN THE 21ST CENTURY

Differential item functioning is an approach that is widely used to find out test items that have bias, especially as it regards to Big Data Assessment. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. This study sought to find out items that are biased using differential item functioning approach in relation to school type (private and public schools), school location (urban and rural schools) using National Business and Technical Examination Board (NABTEB) Agricultural Science, Biology and Physics Multiple choice Test items used in 2015 NABTEB Examination. The research design employed in this study was Expo-Facto research design. Based on 10 percentage of the population, a sample of 1, 644 students (Agricultural Science 543, Biology 583 and Physics 518 students) in SSS 3 who enrolled for the Senior School Certificate Examination in 2018 were used. The test contained 50 items each which were administered to the students. Logistic regression was used to analyze the data. The research findings showed that some items in NABTEB Agricultural Science, Biology and Physics were biased in relation to school type and some items in relation to school location. The implication of these findings is that NABTEB Agricultural Science, Biology and Physics examinations questions have item biases which could be deleted. From the result of the findings, it was then recommended that test experts and developers should explore the use of DIF approach to detect biased items in Big Data Assessment.
Digital Library: A Resource for Information Dissemination in Digital Environment

The concept of libraries has been revolutionized by the Information and Communication Technology. In digital era each and every library is slowly getting digitalization. The digital collections, services and infrastructure to support lifelong learning, research, scholarly communication as well as preservation and conservation of our recorded knowledge are comprised in the digital library. The process of democratization of information is also involved in it. In this paper the authors are discussed the factors that will necessitate the traditional libraries to get digitalization, as well as the definition, need, resources of the digital library, requirements for digital library and characteristics, advantages and disadvantages of digital libraries. To describe the role of librarian in the new environment the emphasis is also put up in this paper.
Patent Examination Practices regarding AI-Related Inventions: Comparison in the EPO, USPTO and JPO

With the advent of the so-called Fourth Industrial Revolution, AI-related technologies are beginning to be applied in a variety of fields. In addition, the number of patent applications for AI-related inventions is increasing rapidly as AI-related technologies are implemented in society. Under these circumstances, it would be of great value to thoroughly investigate the current state of AI-related inventions in Europe, the United States, and Japan. Therefore, in this paper, I focus on the current status of examination practices of AI-related inventions in Japan, the U.S. and Europe, and discuss future issues.First, the latest guidelines/cases regarding Computer Implemented Software (CII) are scrutinized since AI related inventions are subcategories of CII and these guidelines are generally applied to them as well. Although each patent office has established examination guidelines for CII, AI-related inventions may have different characteristics from the conventional CII, and so there are cases where it is difficult to apply them to the conventional guidelines for CII as they are. Therefore, second, I have clarified how the various patent offices have responded to this situation by analyzing the latest precedents, guidelines, and movements in legal reform. In addition, in order to deepen the understanding of the examination practices of each patent office regarding AI-related inventions, some case studies were conducted and future issues were extracted.
ATTITUDE OFSTUDENTS TO THE PROBLEM OF EMPLOYMENT

The article analyzes the problem of student employment during the period of their education. The peculiarities influencing the choice of criteria for employment among students are revealed. On the basis of a sociological survey, the attitude of students to the problem under consideration is revealed.
PENGARUH SISTEM INFORMASI AKUNTANSI TERHADAP EFEKTIVITAS PENGENDALIAN INTERNAL PADA PT BOSOWA BERLIAN MOTOR MAKASSAR

SRI LESTARI PEBRIANTI.2019. The Effect of Accounting Information Systems on The Effectiveness of Internal Control at PT Bosowa Berlian Motor Makassar under the guidance of Faridah SE., M.Si., AK, CA and H.I Nyoman Mariantha BA, SE, M.Si.The purpose of this study was to determine The Effect of Accounting Information Systems on The Effectiveness of Internal Control at PT Bosowa Berlian Motor Makassar. The data used are primary data in the form of questionnaires distributed to employees who use company information systems at PT Bosowa Berlian Motor Makassar. The questionnaire distributed to respondents was 45 and the questionnaires returned were 45. The analytical method used was Simple Linear Regression Analysis.The results of the study show that the independent variable (Accounting Information System) has a positive and significant effect on the dependent variable (Effectiveness of Internal Control).
Authenticated Data Structures as Functors in Isabelle/HOL.

Merkle trees are ubiquitous in blockchains and other distributed ledger technologies (DLTs). They guarantee that the involved systems are referring to the same binary tree, even if each of them knows only the cryptographic hash of the root. Inclusion proofs allow knowledgeable systems to share subtrees with other systems and the latter can verify the subtrees' authenticity. Often, blockchains and DLTs use data structures more complicated than binary trees; authenticated data structures generalize Merkle trees to such structures.We show how to formally define and reason about authenticated data structures, their inclusion proofs, and operations thereon as datatypes in Isabelle/HOL. The construction lives in the symbolic model, i.e., we assume that no hash collisions occur. Our approach is modular and allows us to construct complicated trees from reusable building blocks, which we call Merkle functors. Merkle functors include sums, products, and function spaces and are closed under composition and least fixpoints. As a practical application, we model the hierarchical transactions of Canton, a practical interoperability protocol for distributed ledgers, as authenticated data structures. This is a first step towards formalizing the Canton protocol and verifying its integrity and security guarantees.
A Comparison of Two Neural Network Based Methods for Human Activity Recognition

In this paper, two diﬀerent methods of human activity recognition based on video signals are introduced. The first method explores the eﬀectiveness of combining feature descriptors obtained by local descriptors and artiﬁcial neural network classiﬁer. It is used in the traditional approach and the local descriptors extract interest points or local patches from the videos, and the feature vectors are later constructed based on the intrests, and eventually feature vectors are used as the input of a two-layer feed-forward artiﬁcial neural network (ANN). Experimental results show that using the HOG3D descriptor with ANN gives the best performance. On the other hand, deep learning architectures have attracted much consideration for automatic feature extraction in the last years, so an improved 3D convolutional neural network architecture is also designed as the second method. They are implemented and compared with state-of-the-art approaches on two data sets. The results exhibit that method 1 is superior when the shortage of sample data is the main restriction. It respectively achieves recognition accuracies of 97.8% and 99.8% for the Weizmann and KTH action data sets. In addition, method 2 is considerable for its automatic features extraction, and achieves an acceptable result with lots of original training data. As a result, it gets recognition accuracy of 92% for the KTH data set while this value is drastically reduced for the Weizmann data set.
AN ERROR ANALYSIS IN MOOD MADE BY THE SEVENTH SEMESTER ENGLISH STUDENTS OF STKIP BUDIDAYA BINJAI IN THE YEAR 2017/2018

This research was purposed to find out the types of error and the sources of error made by the seventh semester English students of STKIP Budidaya Binjai in theyear 2017/2018. The researcher formulated the problems of this research were: “What types of error are made by students in Mood?” and “What sources of errors are made by students in Mood?”. The design of the research was a qualitative research. Based on the results of the data analysis, the findings of this research can be stated as follows: (1) The seventh semester English students of STKIP Budidaya Binjai made error in writing Mood of the sentences. (2) The types of error which students made were: Omission, Addition (double marking), Misformation and Misordering. The type of error which the most dominantly made by the students was error of Omission. (3) The sources of error which students made were: Interlingual Transfer, Intralingual Transfer, Context of Learning andCommunication Strategy. The source of error which the most dominantly made by the students was Communication strategy.
LibGuides: Evaluating Sources of Information: Popular vs Scholarly

Sources should be considered for currency, relevance, authority, accuracy, objectivity and purpose.
EFEKTIVITAS PENERAPAN SISTEM INFORMASI AKUNTANSI,PEMANFAATANSISTEM INFORMASI AKUNTANSI DAN KEPERCAYAAN SISTEM INFORMASI AKUNTANSI TERHADAP KINERJA KARYAWAN

Abstract&#x0D; The aim to be achieved from this study is to provide empirical evidence that the effect of the effectiveness of accounting information systems, the use of accounting information systems, accounting information system trust significantly influence employees at PT. Delta DuniaTekstil II. The population in this study were all employees of the administrative staff produced by PT. Delta DuniaTekstil II. The sample of this study was 30 employees of the administration of the production of CD units. The sampling technique used was purposive sampling. The analysis technique consists of classic assumption tests, multiple linear regression analysis, t test, F test, and R. Questionnaires in the Test using Validity Test and Variable Test obtain valid and reliable results. The results of hypothesis testing indicate that the effectiveness of the SIA variable has a positive and significant effect on employee performance variables. The variable utilization of SIA has a positive and significant effect on employee performance variables. The SIA trust variable has a positive and significant effect on employee performance variables. Normality test results show that the assumption of normality, multicollinearity test does not have multicollinearity, heteroscedasticity test there are no variables that occur heteroscedasticity and autocorrelation test does not occur symptoms of autocorrelation. The adjusted R2 value of 82.5% means that the variability of employee performance can be explained by the variable effectiveness of the application of SIA, the use of SIA and the trustworthiness of the SIA while the remaining 17.5% is influenced by other factors outside the variables studied.
Generating Mutually Inductive Theorems from Concise Descriptions.

We describe defret-mutual-generate, a utility for proving ACL2 theorems about large mutually recursive cliques of functions. This builds on previous tools such as defret-mutual and make-flag, which automate parts of the process but still require a theorem body to be written out for each function in the clique. For large cliques, this tends to mean that certain common hypotheses and conclusions are repeated many times, making proofs difficult to read, write, and maintain. This utility automates several of the most common patterns that occur in these forms, such as including hypotheses based on formal names or types. Its input language is rich enough to support forms that have some common parts and some unique parts per function. One application of defret-mutual-generate has been to support proofs about the FGL rewriter, which consists of a mutually recursive clique of 49 functions. The use of this utility reduced the size of the forms that express theorems about this clique by an order of magnitude. It also greatly has reduced the need to edit theorem forms when changing definitions in the clique, even when adding or removing functions.
Construction and Operation Mechanism of Practice Training Base for Industry-Education Integration

The continuous development of the times has made the society's demand for talents higher and higher. In order to ensure that the students they nurture, they not only have solid professional knowledge and skills, but also have strong practical capabilities, so they have been in line with local enterprises. Contact to establish a training base to ensure that students can get better development in a broader practice platform. Therefore, this article mainly starts with the positive significance of constructing a training base for the integration of industry and education, and explores the construction of its operating mechanism in conjunction with the actual situation, in order to cultivate more high-quality talents needed by more countries.
Annotations on the Relationship Among Discriminant Functions

Different forms of discriminant functions and the essence of their appearances were considered in this study.Various forms of classification problems were also considered, and in each of the cases mentioned, classification from simple functions of the observational vector rather than complicated regions in the higher-dimensional space of the original vector were made.Ever since the emergence of the Linear Discriminant Function (LDF) by Fisher, several other classification statistics have emerged and violation of condition of equal variance covariance matrix for Linear Discriminant Function (LDF) results to Quadratic Discriminant Function (QDF).While the Best Linear Discriminant Function (BLDF) is referred to Best Sample Discriminant Function (BSDF) when the parameters are estimated from a sample and also optimal in the same sense as Quadratic Discriminant Function (QDF), Rao statistic is best for discriminating between options that are close each other.The relationships among the classification statistics examined were established: Among the methods of classification statistics considered, Anderson's (W) and Rao's (R) statistics are equivalent when the two sample sizes n 1 and n 2 are equal, and when a constant is equal to 1, W, R and John-Kudo's (Z) classification statistics are asymptotically comparable.A linear relationship is also established between W and Z classification.
Design Of Sales Application With Data Mining Using Analysis Basket Market Methodand Apriori Algorithm In The Jetlag Coffee: Design Of Sales Application With Data Mining Using Analysis Basket Market Methodand Apriori Algorithm In The Jetlag Coffee

The tight competition in sales, made the owner of Jetlag Coffe must be smart in analyzing the market. In addition, the availability of goods sold is one of the factors that must be analyzed so that customer needs can be met. To support this, previous sales data can be used to analyze the market and customer needs with market basket analysis. Addition of a priori algorithms including the types of association rules in data mining, a priori algorithms that aim to find items sets run on a set of data. Based on this relationship, it is possible to promote goods with a pattern of attachment to the item. Consumers who purchase products will be interested in purchasing different products, than those usually purchased by other consumers before. By offering products contained in the pattern of sales of these products. By using this market basket analysis and a priori algorithm method, it is hoped that it can also optimize the database, increase sales, and minimize product promotions that have low sales levels. By minimizing the promotion of goods that are not bought, consumers will not be disturbed by the promotion of products that do not have a pattern of attachments, so that promotions will be more effective.
McDonalds Inner Child makes a McWarming Christmas Advert

We take a look into the Emotional world of Christmas Adverts, and came up with McDonalds pulling ahead with their new #ReindeerReady Campaign.
Dimension-Preserving Reductions Between SVP and CVP in Different $p$-Norms

$ \newcommand{\SVP}{\textsf{SVP}} \newcommand{\CVP}{\textsf{CVP}} \newcommand{\eps}{\varepsilon} $We show a number of reductions between the Shortest Vector Problem and the Closest Vector Problem over lattices in different $\ell_p$ norms ($\SVP_p$ and $\CVP_p$ respectively). Specifically, we present the following $2^{\eps m}$-time reductions for $1 \leq p \leq q \leq \infty$, which all increase the rank $n$ and dimension $m$ of the input lattice by at most one: $\bullet$ a reduction from $\widetilde{O}(1/\eps^{1/p})\gamma$-approximate $\SVP_q$ to $\gamma$-approximate $\SVP_p$; $\bullet$ a reduction from $\widetilde{O}(1/\eps^{1/p}) \gamma$-approximate $\CVP_p$ to $\gamma$-approximate $\CVP_q$; and $\bullet$ a reduction from $\widetilde{O}(1/\eps^{1+1/p})$-$\CVP_q$ to $(1+\eps)$-unique $\SVP_p$ (which in turn trivially reduces to $(1+\eps)$-approximate $\SVP_p$). The last reduction is interesting even in the case $p = q$. In particular, this special case subsumes much prior work adapting $2^{O(m)}$-time $\SVP_p$ algorithms to solve $O(1)$-approximate $\CVP_p$. In the (important) special case when $p = q$, $1 \leq p \leq 2$, and the $\SVP_p$ oracle is exact, we show a stronger reduction, from $O(1/\eps^{1/p})\text{-}\CVP_p$ to (exact) $\SVP_p$ in $2^{\eps m}$ time. For example, taking $\eps = \log m/m$ and $p = 2$ gives a slight improvement over Kannan's celebrated polynomial-time reduction from $\sqrt{m}\text{-}\CVP_2$ to $\SVP_2$. We also note that the last two reductions can be combined to give a reduction from approximate-$\CVP_p$ to $\SVP_q$ for any $p$ and $q$, regardless of whether $p \leq q$ or $p > q$. Our techniques combine those from the recent breakthrough work of Eisenbrand and Venzin (which showed how to adapt the current fastest known algorithm for these problems in the $\ell_2$ norm to all $\ell_p$ norms) together with sparsification-based techniques.
Hyper-parameter Optimization for Monte Carlo Tree Search using Self-play

The Monte Carlo tree search (MCTS) is a popular method for implementing an intelligent game program. It has several hyper-parameters that require an optimization for showing the best performance. Due to the stochastic nature of the MCTS, the hyper-parameter optimization is difficult to solve. This paper uses the self-playing capability of the MCTS-based game program for optimizing the hyper-parameters. It seeks a winner path over the hyper-parameter space while performing the self-play. The top-q longest winners in the winner path compete for the final winner. The experiment using the 15-15-5 game (Omok in Korean name) showed a promising result.