Reviewerly/exHarmony huggingface dataset:
Perbandingan Algoritma N-gram dan Algoritma Knuth Morris Pratt untuk Mengukur Tingkat Akurasi Plagiarisme pada Dokumen Abstrak Skripsi Berbasis Website

&#x0D; &#x0D; &#x0D; &#x0D; Plagiarism is a crime that often occurs in the academic world, plagiarism occurs because of theft of other people's work that is illegally recognized as if the work is his own. N-gram is an algorithm by cutting as many characters as N-characters in a sentence or word. While the Knuth Morris Pratt (KMP) algorithm is a string search algorithm, this algorithm is used to maintain information that is used to carry out the number of shifts whenever there is no matched patency in the text. The purpose of this study is to create a system to measure the comparison of the accuracy of the N-gram algorithm with a website-based KMP on a thesis abstract document. This research uses the waterfall system development method which has stages, namely analysis, design, coding, and testing. The KMP test results are better than N-gram where kmp has an average percentage of 3.8% while the N-gram 3.5% results are obtained from an average of 10 trials and 5 documents tested.&#x0D; &#x0D; &#x0D; &#x0D;
Parallel graph algorithms by blocks

In today's data-driven world and heterogeneous computing environments, processing large-scale graphs in an architecture agnostic manner has become more crucial than ever before. In terms of graph analytics frameworks, on the one side, there has been a significant interest in developing hand-optimized high-performance computing solutions. On the systems side, following the big data movement and to bring parallel computing to the masses, researchers have proposed several graph processing and management systems to handle large-scale graphs. Hand optimized HPC approaches require high expertise and are expensive to maintain and develop, and graph processing frameworks suffer from limited expressibility and performance. We propose Parallel Graph Algorithms by Blocks (PGAbB), a block-based graph algorithms framework for shared-memory, multi-core, multi-GPU machines. PGAbB offers a sweet spot between efficient parallelism and architecture agnostic algorithm design for a wide class of graph problems while performing close to hand-optimized HPC implementations.
A New Method Using Naive Bayes And RGBD Facial Identification Based on Extracted Features from Image Pixels

Nowadays, life seems to have been resilient, particularly for those with physical disabilities. Recognition of AV letters is one of the critical and famously the difficult structures. This research has been developed based on the potential of the features in some applications than the statistical properties. While, these features have been resolved the lip movement for AV letters recognition, Naive Bayesian and Red green blue and depth RGBD have been adopted for visual letter identification. Naive Bayesian has 73.33% for usual recognition with three letters, each with ten frames, while RGBD classifier is 100%. Within that for this case, two scenarios were made with different forms of noise placed on the face of normal, normal + 10%, normal + 25% and normal + 75% noise. The first one trains and understands all classes, one after another. While the other is training 95 percent of RGBD and 83.3 percent for Naive Bayesian with recognition of one of the inflicted forms. RGBD identification is 100 percent for the second one, while 49.99 for the Naive Bayesian.
Deep Learning for Natural Language Processing

In this paper we discuss Natural Language Processing, normally abbreviated as NLP, is a part of computerized reasoning that manages the collaboration among PCs and people utilizing the characteristic language. A definitive target of NLP is to peruse, interpret, comprehend, and sort out the human dialects in a way that is significant. Most NLP strategies depend on AI to get importance from human dialects. deep learning strategies for language preparing and demonstrating. Improvement of factual language models assists with anticipating an arrangement of perceived words and phonemes, and can be utilized for improving discourse preparing and discourse acknowledgment. Be that as it may, right now the field of language demonstrating is moving from factual language displaying techniques to neural organizations and deep learning strategies. Thusly, one of the strategies for successful language demonstrating with the utilization of deep learning procedures is introduced in this paper. Introduced results concerns the demonstrating of the Polish language yet the accomplished exploration results and ends can likewise be applied to language displaying application for different dialects.
Improved Algorithm of Similarity Measure Based on Matrix Factorization Filling and Filling Confidence

Although collaborative filtering recommendation has been very mature in practical application, the problem of data sparsity has been difficult to solve.Sparse data seriously weakens the accuracy of similarity measurement, and also affects the recommendation accuracy of recommendation system.Therefore, this paper makes a deep study on this problem and proposes an improved similarity measure algorithm based on matrix factorization filling and filling confidence.The core idea of the algorithm is to fill the original sparse data set first, and then calculate the similarity based on the filling.In the process of matrix filling, considering that the matrix decomposition model can effectively alleviate the sparsity of data by associating users and items with hidden features, this paper chooses the BiasSVD matrix decomposition model as the basis for filling improvement.Considering that the value of user's interest or behavior will decline with the change of time, the user's recent interaction data can better reflect their current interest than the long-term interaction data, so the improved algorithm integrates the time decline function into the BiasSVD algorithm when filling, and uses the time decline function to give different weights to the user's historical data at different stages, In order to achieve better filling effect.At the same time, after filling, considering that the traditional matrix filling algorithm ignores the credibility difference between the real data and the filled data, and there is no distinction in the next step of similarity calculation, which affects the performance of the recommendation.Therefore, the concept of filling confidence is introduced in the calculation of similarity, which fully distinguishes the reference of the real score and the predicted score.
Insider Threat Prevention, Detection, and Mitigation

This chapter looks at the history of insider threat from its roots in espionage to individuals who have access to people, information, material, and facilities and who could compromise the critical assets of an organization in the government or private sector. It examines behaviors associated with an insider threat from the decades of the past when things were driven by pen and paper, to the current world in which activity is deeply rooted in technology and where business is conducted virtually and globally. In addition to understanding the threat and the dimensions of a malicious complacent or ignorant insider, focus will be directed toward thinking about mitigating that threat, through the development of a holistic and risk-based insider threat program. The use of a framework that is focused on prevention, detection, and response is presented. Key issues addressed include policy and its relationship to setting behavioral expectations, communication and training, vetting employees and third parties, and defining potential risk indicators that reflect critical behaviors indicating a potential risk. The chapter defines and outlines how behavior can be captured in data and correlated using technology (user behavioral analytics) to proactively identify changes in behavioral patterns over time. Such technology identifies escalation and triages alerts to anomalous activity in the service of interrupting forward motion of a potential threat. Finally, the chapter highlights several statistics that define the change of insider threats today, and leading practices to help develop a strategy to mitigate the insider threat and focus on a holistic and risk-based approach to this threat management issue.
Review on Generally used Data Security Techniques in Cloud Computing

Cloud computing gaining a lot of popularity these days because of growing the demand of data to secure in the cloud computing. Cloud computing is coming with lots of protecting against trouble which are gaining lot of attention and praises these days. These protections include record safety, community security, virtualization, protection, identification control and software integrity. Lot of data and information is shared on cyber world which include documents. Now a day’s information protection is one of the fact business received transfer of its facts to far of machines if these may be no guarantee for the record protection from cloud services computing. Many strategies and plans are counseled for information safety in cloud computing. Most popular protection techniques encomplise service soket layer(SSL) encryption on intrusion on detection device. The main purpose of this paper is analyses and examine the maximum crucial safety for the problems.
Cryptography from Sublinear-Time Average-Case Hardness of Time-Bounded Kolmogorov Complexity.

Let MKtP[s] be the set of strings x such that Kt(x) ≤ s(|x|), where Kt(x) denotes the t-bounded Kolmogorov complexity of the truthtable described by x. Our main theorem shows that for an appropriate notion of mild average-case hardness, for every e>0, polynomial t(n) ≥ (1+e)n, and every “nice” class F of super-polynomial functions, the following are equivalent: (i) the existence of some function T ∈ F such that T-hard one-way functions (OWF) exists (with non-uniform security); (ii) the existence of some function T ∈ F such that MKtP[T−1] is mildly average-case hard with respect to sublinear-time non-uniform algorithms (with running-time nδ for some 0tP[poly logn] (resp. MKtP[2O(√logn))]) w.r.t. sublinear-time non-uniform algorithms. We additionally note that if we want to deduce T-hard OWFs where security holds w.r.t. uniform T-time probabilistic attackers (i.e., uniformly-secure OWFs), it suffices to assume sublinear time hardness of MKtP w.r.t. uniform probabilistic sublinear-time attackers. We complement this result by proving lower bounds that come surprisingly close to what is required to unconditionally deduce the existence of (uniformly-secure) OWFs: MKtP[polylogn] is worst-case hard w.r.t. uniform probabilistic sublinear-time algorithms, and MKtP[n−logn] is mildly average-case hard for all O(t(n)/n3)-time deterministic algorithms.
Interactive data science at scale

A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, we discuss our development of suffix array and graph algorithms in the context of Arkouda, a NumPy-like replacement for interactive data science on tens of terabytes of data. Many real-world applications in bioinformatics, web information search and analysis, and lossless compression can be abstracted as string analysis. Suffix arrays are a very efficient data structure to support quick search of any string patterns. We have integrated the suffix array data structure (including its enhanced Longest Common Prefix (LCP) array) and the corresponding construction algorithm into Arkouda, thus providing Python users with a powerful method to support different types of string analysis. Our novel approach integrates a suffix array algorithm library into Arkouda so that the Arkouda runtime can select the large suffix array construction algorithm dynamically based on the dataset properties. Two of the implemented methods on the back-end of Arkouda include our novel O(n) time complexity skew algorithm in Chapel, and the DivSufSoft suffix array construction algorithm in C, which has higher time complexity but often is faster in practice. Experimental results show that, supported by Arkouda, Python users can build a large scale string's suffix array and LCP array in a Jupyter notebook easily without losing any performance compared with the directly back-end operation. Our future work is extending our self-developed algorithm to support multi-locale parallel execution, so that our algorithm can handle large strings on distributed systems. Graphs are widely used to abstract problems in domains such as social sciences, biological systems, and information systems. To support real-world large graph analysis in Arkouda, we first developed the array-based graph data structure which can be used like an adjacency matrix or incidence matrix but with much less memory. At the same time, it naturally works well with Arkouda's array operators. Based on this succinct graph data structure, we have developed two typical graph algorithms, breadth-first search (BFS) and triangle counting. Both algorithms have been successfully integrated into Arkouda. Both are multi-locale algorithms so they can handle a very large graph on distributed systems. Experimental results of BFS on a 32-node cluster system show that our method can build large graphs into distributed memory and execute the parallel BFS algorithm on typical sparse graph benchmarks and R-MAT generator-based graphs successfully. The performance results show that the distributed graph building time and BFS time will increase linearly with the total number of edges. For future work, we will further optimize these graph algorithms and investigate the streaming versions in Arkouda. We acknowledge Mike Merrill and Bill Reus, the founding developers of the open-source Arkouda framework. This is joint work with research scientist Dr. Zhihui Du, and doctoral student Oliver Alvarado Rodriguez.
Augmented reality blended learning instruction: The impact on growing motivation, attitudes, and knowledge in 3D geometry

The COVID 19 pandemic promotes several new technological products to facilitate learning process. However, there are still few studies on the integration of Augmented Reality (AR) technology into Blended Learning (BL), particularly on the education of prospective mathematics teachers. This research examines the effect of using augmented reality blended learning during the COVID 19 epidemic on 3D geometry, as well as the learning motivation and attitudes. 15 prospective teachers, including 4 males and 11 females, at one of Indonesia's private universities were engaged in to determine how 3D geometry is understood. Data on learning motivation and attitudes were collected through questionnaires and interviews and analyzed using descriptive statistics and paired-samples t-test. The results show that during the COVID-19 pandemic, augmented reality blended learning facilitates the comprehension of 3D geometry knowledge by prospective teacher applicants and promotes learning motivation and attitudes. Therefore, the use of new technology is significant to prospective teachers' needs, especially during the COVID 19 pandemic, to facilitate their teaching practice in real classrooms. © 2021 Karadeniz Technical University. All rights reserved.
The Bayesian Polynomial Regression Model Through INLA Using Gaussian Method with Empirical Bayesian, CCD and GRID Strategy

Relationship rice between with height can be described with scatter diagram.Scatter diagram can product the quadratic polynomial regression mod els and the qubic polynomial regression mode which possible.The quadratic polynomial regression model the best variable height and height 2 significant.Then parameter in the quadratic polynomial regression model can be estimated with Bayesian INLA with Gaussian and Empirical Bayes Strategy compare CCD, GRID Strategy.The posterior marginal intercept β0, coefficient β1 and β2 with Gaussian method and different strategy product similar.The posterior marginal of random effects the grid strategy different with CCD and empirical Bayes strategy.
Learning Strategies Used By Twelfth Grade Students Of English Tutorial Program To Develop Vocabulary Repertoire (A Case Study On Ririn Bimbel, Plosorejo)

This study deals with the memory strategies used by twelfth grade students of English Tutorial Program to develop vocabulary repertoire (A case study on RirinBimbel, Plosorejo). This research objective is to describe the memory strategies which is used by twelfth grade students of English Tutorial Program in Ririn Bimbel in developing form, meaning, and use of vocabulary. The research data were students’s memory strategies used by the twelfth grade students of English Tutorial program in Ririn Bimbel. The data sources of this research were informants consisting of twelfth grade students of English Tutorial program in RirinBimbel. The data collection was done by using memory strategies questionnaire and interview. The data were analyzed through some steps, those were data reduction, data display, and conclusion. The results of this research showed that: (1) the memory strategies used by the students in developing meaning were creating mental linkages, applying images and sounds, and reviewing well; (2) the memory strategies used by the students in developing form were creating mental linkages, applying images and sounds, reviewing well, and employing action; (3) the memory strategies used by the students in developing use covered creating mental linkages, applying images and sounds, and employing action.It can be conclude that All those strategies are used by the students in different frequency, thus creating mental linkages and applying images and sounds strategies become more popular strategies used by the students rather than reviewing well and employing action.
Library Guides: Trades: Standards

This is a curated list of resources relevant to Vocational Education Training in the Trades area. To view all databases, please go to the Library's A-Z Databases page.
『석명』 해석에 보이는 유가 철학적 인식 특징 ― 전통적 윤리관을 중심으로

The Shi-Ming (釋名) is a classification vocabulary book compiled by Liu Xi (劉 熙. 160~?), a scholar of Chinese classics of the late Han dynasty, and is a vocabulary dictionary of Chinese Exegetics that interprets all of the listed Chinese characters using 聲訓法, which interprets title letters similar in their reading sounds. Since LiuXi (劉熙) studied Chinese classics, Shi-Ming (釋名) has its Confucian interpretation, through which I am to examine the changes compared to the perspective of 春秋繁露(Chunqiufanlu), a representative book of Neo-Confucianism. By content of Shi-Ming (釋名), this study is classified into the theory of human nature and Yin-Yang and the Five Elements theory. From the perspective of human nature theory, Liu Xi (劉熙)’s interpretation of Shi-Ming (釋名) on the ideas of 仁, 誼, 禮 and 德 reflects both the view of traditional Confucianism of the Pre-Qin period (先秦) and Neo-Confucianism, indicating that the Confucian viewpoint of Dong Zhongshu (董仲舒) was widespread and influencing until the end of Han dynasty. This view has been passed on and developed since the Pre-Qin period (先秦), but it seems to have become more solid and commonplace by the time of Han dynasty. Shi-Ming (釋名) is a classification vocabulary book compiled by Liu Xi (劉熙), a scholar of Chinese classics of the late Han dynasty, and is a vocabulary dictionary of Chinese Exegetics that interprets all of the listed Chinese characters using 聲訓法, which interprets title letters similar in their reading sounds.
A Collaborative Filtering Model for Link Prediction of Fusion Knowledge Graph

In order to solve the problem that collaborative filtering recommendation algorithm completely depends on the interactive behavior information of users while ignoring the correlation information between items, this paper introduces a link prediction algorithm based on knowledge graph to integrate ItemCF algorithm. Through the linear weighted fusion of the item similarity matrix obtained by the ItemCF algorithm and the item similarity matrix obtained by the link prediction algorithm, the new fusion matrix is then introduced into ItemCF algorithm. The MovieLens-1M data set is used to verify the KGLP-ItemCF model proposed in this paper, and the experimental results show that the KGLP-ItemCF model effectively improves the precision, recall rate and F1 value. KGLP-ItemCF model effectively solves the problems of sparse data and over-reliance on user interaction information by introducing knowledge graph into ItemCF algorithm.
Correction to: On rapid multidisciplinary response aspects for Samos 2020 M7.0 earthquake

A correction to this paper has been published: https://doi.org/10.1007/s11600-021-00592-8
PENERAPAN MODEL PEMBELAJARAN QUANTUM TEACHING UNTUK MENINGKATKAN PRESTASI MATA PELAJARAN GEOGRAFI SMA

Quantum Teaching is one of learning models creating fun and more meaningful learning atmosphere. This model has not been widely used in geography learning. Quantum Teaching application research model implemented in SMA Negeri 1 Kretek aims to improve the learning achievement of students in geography subjects of IPS class of XI semester. This was a classroom action research with research subjects involving the IPS class 2 students of XI semester. The research used the Kemmis and Taggart’s model with several cycles. Each cycle consisted of four stages, namely: planning, acting, observation, and reflection. Data were collected through observations, interviews and document reviews, and descriptive quantitative analysis. The results show improvement which student achieved through the application of quantum teaching model consists of three things namely: (1) Increase student achievement learning. (2) Increasing the percentage of student activities. (3) Improvement in student attitudes. At the end of the second cycle more than 50% of students have a very good attitude.
Analisis Perbandingan Unjuk Kerja Motor Bensin Dengan Penambahan Groundstrap dengan Material Tembaga Dan Perak Pada Kabel Koil Busi

This research aimed to (1) Determining the differences in power and torque to use of a coil standard, a copper, and silver wire ground strap variation coils. (2) Determining the color characteristics of the sparks' size generated from the use of standard coils, variation coils of copper, and silver ground strap. This research used an experimental method. The data analysis technique used descriptive analysis, namely, observing and recording the test results and then concluding in tables and graphs. The test results show that an increase in power, and torque is generated in this test. The highest results were obtained using a silver ground strap variation coil with a power of 11.3 hp and torque of 12.05 Nm. In this case, the percentages are 7.6% and 7.8% from the lowest results of the standard coil. There are different colors, sizes, and characters obtained in the test of sparks. The dark blue-purplish sparks color is obtained from the silver wire ground strap variation coil, which can increase motor performance in a motorcycle. These results suggest that the ground strap on the spark plug coil wire can provide an increment in engine performance.
ARMemNet

Recently, several studies show the powerful capability of neural networks to capture non-linear features from time series which have multiple seasonal patterns. However, existing methods rely on convolution kernels implicitly, hence neglect to capture strong long-term patterns and lack interpretability. In this paper, we propose a memory-augmented neural network named AutoRegressive Memory Network (ARMemNet) for multivariate time series forecasting. ARMemNet utilizes memory components to explicitly encode intense long-term patterns. Furthermore, each encoder is designed to leverage inherently essential autoregressive property to represent short-term patterns. In experiments on real-world dataset, ARMemNet outperforms existing baselines and validates effectiveness of memory components for complex seasonality which is prevalent in time series datasets.