Reviewerly/exHarmony huggingface dataset:
METODE K-MEANS UNTUK PENGELOMPOKAN MASYARAKAT MISKIN DENGAN MENGGUNAKAN JARAK KEDEKATAN MANHATTAN CITY DAN EUCLIDEAN (STUDI KASUS KOTA BINJAI)

The occurrence of poverty in the community is caused by the inability of the family head economically to meet the primary needs of family members, namely clothing, food, shelter, health, and education needs. The poor are almost present in every country, city, and region, this becomes a common problem. The current poverty data obtained from the Binjai City Central Bureau of Statistics is from 2012, and the number of poor people has increased in 2016 by 17,800 people with a poverty line (Rp. /Cap/month) of Rp. 343,078 and the latest data obtained in 2017 were 18,230 people with Poverty Lines (Rp. / Kap / Bulan) Rp. 371,387. In the database of the Binjai City Central Bureau of Statistics there are very diverse data on the poor, with this data, researchers try to explore data from the poor city of Binjai to obtain new information by grouping poverty data using the k-means clustering data mining method using distance the closeness of Manhattan City and Euclidean, so that groups of variables that are very influential in the community of poverty can be identified. The observed variables such as the level of education of the household head, education level of housewives, employment, number of family members, and other observed variables affect poverty. And the results of the k-means method for grouping poor people using proximity to Manhattan city and euclidean that can provide additional information in optimizing poverty alleviation in the city of Binjai.
Structure Learning with Side Information: Sample Complexity

Graphical models encode the stochastic dependencies among random variables (RVs). The vertices represent the RVs, and the edges signify the conditional dependencies among the RVs. Structure learning is the process of inferring the edges by observing realizations of the RVs, and it has applications in a wide range of technological, social, and biological networks. Learning the structure of graphs when the vertices are treated in isolation from inferential information known about them is well-investigated. In a wide range of domains, however, often there exist additional inferred knowledge about the structure, which can serve as valuable side information. For instance, the gene networks that represent different subtypes of the same cancer share similar edges across all subtypes and also have exclusive edges corresponding to each subtype, rendering partially similar graphical models for gene expression in different cancer subtypes. Hence, an inferential decision regarding a gene network can serve as side information for inferring other related gene networks. When such side information is leveraged judiciously, it can translate to significant improvement in structure learning. Leveraging such side information can be abstracted as inferring structures of distinct graphical models that are {\sl partially} similar. This paper focuses on Ising graphical models, and considers the problem of simultaneously learning the structures of two {\sl partially} similar graphs, where any inference about the structure of one graph offers side information for the other graph. The bounded edge subclass of Ising models is considered, and necessary conditions (information-theoretic ), as well as sufficient conditions (algorithmic) for the sample complexity for achieving a bounded probability of error, are established. Furthermore, specific regimes are identified in which the necessary and sufficient conditions coincide, rendering the optimal sample complexity.
Estimating Entropy of Distributions in Constant Space

We consider the task of estimating the entropy of $k$-ary distributions from samples in the streaming model, where space is limited. Our main contribution is an algorithm that requires $O\left(\frac{k \log (1/\varepsilon)^2}{\varepsilon^3}\right)$ samples and a constant $O(1)$ memory words of space and outputs a $\pm\varepsilon$ estimate of $H(p)$. Without space limitations, the sample complexity has been established as $S(k,\varepsilon)=\Theta\left(\frac k{\varepsilon\log k}+\frac{\log^2 k}{\varepsilon^2}\right)$, which is sub-linear in the domain size $k$, and the current algorithms that achieve optimal sample complexity also require nearly-linear space in $k$. Our algorithm partitions $[0,1]$ into intervals and estimates the entropy contribution of probability values in each interval. The intervals are designed to trade bias and variance. Distribution property estimation and testing with limited memory is a largely unexplored research area. We hope our work will motivate research in this field.
Students’ Understanding on Simple Past Tense through VOA Learning English YouTube Channel

The objective of the study is to find out the effect of VOA Learning English YouTube Channel on students’ simple past tense. This study was conducted at 8th grade students of SMP Pustek Serpong in academic year 2017/2018. The researcher used quantitative analysis with a quasi-experimental design. As the sample, the researcher uses one class with total number of 37 students. By using t-test, the finding showed that tcalculation (7.89) was higher than ttable (2.026) at a significant level of 5%. It indicates that VOA Learning English YouTube Channel gives the positive significant result in improving students’ simple past tense.
Quantum Distributed Algorithm for Triangle Finding in the CONGEST Model

This paper considers the triangle finding problem in the CONGEST model of distributed computing. Recent works by Izumi and Le Gall (PODC'17), Chang, Pettie and Zhang (SODA'19) and Chang and Saranurak (PODC'19) have successively reduced the classical round complexity of triangle finding (as well as triangle listing) from the trivial upper bound $O(n)$ to $\tilde O(n^{1/3})$, where~$n$ denotes the number of vertices in the graph. In this paper we present a quantum distributed algorithm that solves the triangle finding problem in $\tilde O(n^{1/4})$ rounds in the CONGEST model. This gives another example of quantum algorithm beating the best known classical algorithms in distributed computing. Our result also exhibits an interesting phenomenon: while in the classical setting the best known upper bounds for the triangle finding and listing problems are identical, in the quantum setting the round complexities of these two problems are now $\tilde O(n^{1/4})$ and $\tilde \Theta(n^{1/3})$, respectively. Our result thus shows that triangle finding is easier than triangle listing in the quantum CONGEST model.
An Improved Struck Tracking Algorithm Based on Scale Adaptation and Selective Updating

In order to solve the problem of tracking failures caused by scale variation, occlusion and fast motion, a selective updating ASMS(scale-adaptive meanshift)-Struck algorithm which combines scale-adaptive meanshift and Perceptual hash method is proposed. By introduced scale-adaptive, the improved method can effectively track targets with scale variation and fast motion; then, the perceptual hash algorithm and Hamming distance are introduced to select a good update module in real time, which effectively avoids the error information introduced by the classifier due to scale variation, occlusion or illumination variation, thus improving the tracking robustness and accuracy. OTB-50 standard datasets is used to verify and evaluate our method. The experimental results demonstrate that the improved Struck algorithm outperforms the classical Struck in accuracy and robustness, and it is competitive with other state-of-the-art trackers on several challenging sequences.
Perbandingan Distance Metric pada Nearest Neighbour untuk Klasifikasi Sel Darah Putih

White blood cells, have a function to protect human body from viruses, bacteria and any other harmful substance. In this research, Local Binary Pattern was proposed for feature extraction using Euclidean distance, Chebyshev distance and Minkowski distance as classifier.
All Parameters as Dimensions

The purpose of explaining all parameters as dimensions is to simplify our understanding of this universe. In order to achieve this universe as a perfect system, the soul is also assumed to have parameters called soul level dimensions. Considering soul level dimensions assumption will help us to achieve the closure. The present day science does it without considering the soul level dimensions and hence the closure is not achieved.
Learning Bayesian Networks with Low Rank Conditional Probability Tables

In this paper, we provide a method to learn the directed structure of a Bayesian network using data. The data is accessed by making conditional probability queries to a black-box model. We introduce a notion of simplicity of representation of conditional probability tables for the nodes in the Bayesian network, that we call ``low rankness''. We connect this notion to the Fourier transformation of real valued set functions and propose a method which learns the exact directed structure of a `low rank` Bayesian network using very few queries. We formally prove that our method correctly recovers the true directed structure, runs in polynomial time and only needs polynomial samples with respect to the number of nodes. We also provide further improvements in efficiency if we have access to some observational data.
Despeckling of Ultra sound Images using spatial filters - A Fusion Approach

Ultra sound images are normally affected by speckle noise which is typically multiplicative in nature. This study proposes different fusion based despeckling methods for ultra sound images. The output of existing spatial domain despeckling methods viz. Lee filter, Bayesian Non Local Means (BNLM) filter and Frost filter are fused pairwise. Fusion is implemented in two steps, first an inter-scale stationary wavelet coefficient fusion followed by an intra-scale wavelet coefficient fusion. Analysis of these projected despeckling strategies are conducted using metrics like Peak Signal to Noise Ratio (PSNR), Equivalent Number of Looks (ENL), Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI). The results show that the performance of fusion based methods is better than the respective individual filters for despeckling ultra sound images.
Banknotes classification system through image processing and pattern recognition for people with visual impairment

In Colombia, the recognition of banknotes by blind people is increasingly difficult, because it requires extensive training and is increasingly difficult due to the emergence of new bills, aging, and circulation of counterfeit bills. To contribute to this recognition process, a classification system was carried out for eleven denominations of Colombian banknotes applying image processing and pattern recognition techniques. A prototype was developed, consisting of a frontal lighting system to eliminate shadows and detail textures. For the recognition of the banknotes, two stages were developed: The detection was carried out by means of image processing, the background was eliminated by means of binarization. Then, uniform interest points were taken over the entire bill, and a set of descriptors was obtained in a different color space. For identification, a database of 1100 samples was created, 100 of each bill, which was used to train different neural networks MLP, structuring a pipeline to vary the configuration parameters to obtain the model with greater accuracy. 70% of the data was used for training and 30% for verification. An initial accuracy of 85% was obtained by cross-validation. A significant improvement was achieved by adding new features and increasing the number of samples to 7700 by manipulating them, modifying the brightness and rotating the bills. With the new data, it was possible to increase the accuracy to 95% by cross-validation. The system was mounted on a Raspberry PI 3 for its practical application. A final test was done with 240 images captured in real time, 20 images from each banknote, making each prediction in 0.9 seconds and getting a general accuracy of 97%.
Tracking and managing deemed abilities

Information about the powers and abilities of acting entities is used to coordinate their actions in societies, either physical or digital. Yet, the commonsensical meaning of an acting entity being deemed able to do something is still missing from the existing specification languages for the web or for multi-agent systems. We advance a general purpose abstract logical account of evidence-based ability. A basic model can be thought of as the ongoing trace of a multi-agent system. Every state records systemic confirmations and disconfirmations of whether an acting entity is able to bring about something. Qualitative inductive reasoning is then used in order to infer what acting entities are deemed able to bring about in the multi-agent system. A temporalised modal language is used to talk about deemed ability, actual agency, and confirmation and disconfirmation of deemed ability. What constitutes a confirmation and a disconfirmation is left to the modeller as in general it depends on the application at hand. So to illustrate the methodology we propose two extended examples, one in practical philosophy, the other in system engineering. We first use a logic of agency and ability to obtain a version of Mele's general practical abilities. Then, we look at the management of abilities in a supervised system.
Deep kernel learning for integral measurements

Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions. We present a method that makes this approach feasible for problems where the data consists of line integral measurements of the target function. The performance is illustrated on computed tomography reconstruction examples.
REPRESENTASI PENERAPAN MEDIA AUDIO VISUAL PADA MATA PELAJARAN SANITASI, HIGIENE DAN KESEHATAN KESELAMATAN KERJA DI SMK

Research aims to representation the application of audio-visual media including planning, implementation and evaluation, inhibiting and supporting factors. This type of research is qualitative. Data collection: interviews, observation, documentation. The validity of the data uses triangulation techniques. Data analysis techniques: data collection, data reduction, data presentation, data verification. The results of the study (1) the application of audio-visual media include: planning carried out well, audio-visual media contained in the RPP, the implementation has been carried out quite well, the infrastructure facilities in the classroom are inadequate, the evaluation has been carried out quite well, the end of the questioning but evaluation of the use of audio visual media has not been implemented. (2) Factors supporting the easy access of the internet to obtain material and the inhibiting factors for the use of audio-visual media are inadequate infrastructure.
Time Series Prediction Using Recurrent Neural Network

Typical algorithms for deep learning include DNN (deep neural network), CNN (convolution neural network), and RNN (recurrent neural network) algorithms. Among them, RNN is excellent at dealing with sequential data. Sequential data such as time series data can be handled without losing gradient by LS...
Games for Security Under Adaptive Adversaries

This work explores methods for proving and disproving security of systems under adaptive adversaries. Adaptive adversaries are ones which make their next move based on the previous observations. Our first contribution is a new game based characterization of security. We show that the game accurately captures security of deterministic and probabilistic systems against adaptive (probabilistic) adversaries. In addition, we build on top of the game characterization and present techniques that expedite proving the existence of attacker and defender strategies, and consequently proving security or vulnerability of systems. The first is what we call attack (and defense) slopes which give simple sufficient criteria for existence of winning strategies (for attacker and defender). The second is reductions of one game to another achieved by mapping a strategy of one to that of the other. We show that such reductions can prove or disprove security by reducing from a game of a secure system or reducing to that of a non-secure system.
Optimal Selection of Cryptographic Algorithms in Blockchain Based on Fuzzy Analytic Hierarchy Process

As a collection of innovative technologies, blockchain has solved the problem of reliable transmission and exchange of information on untrusted networks. The underlying implementation is the basis for the reliability of blockchain, which consists of various cryptographic algorithms for the use of identity authentication and privacy protection of distributed ledgers. The cryptographic algorithm plays a vital role in the blockchain, which guarantees the confidentiality, integrity, verifiability and non-repudiation of the blockchain. In order to get the most suitable cryptographic algorithm for the blockchain system, this paper proposed a method using Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and score the comprehensive performance of the three types of cryptographic algorithms applied in the blockchain, including symmetric cryptographic algorithms, asymmetric cryptographic algorithms and hash algorithms. This paper weighs the performance differences of cryptographic algorithms considering the aspects of security, operational efficiency, language and hardware support and resource consumption. Finally, three cryptographic algorithms are selected that are considered to be the most suitable ones for block-chain systems, namely ECDSA, sha256 and AES. This result is also consistent with the most commonly used cryptographic algorithms in the current blockchain development direction. Therefore, the reliability and practicability of the algorithm evaluation pro-posed in this paper has been proved.
Installing the MySQL Shell

While the MySQL server still includes the old MySQL client (mysql), the new MySQL Shell should be considered the default client to interact with your MySQL servers. It has many advantages over the previous client that was bundled with the server; the most powerful being the ability to use Python or JavaScript directly from the shell. So, how do we get the new MySQL Shell?
Algorithms for Belief State Compression

A knowledge base is an integral part of a logic-based artificial intelligence system. In this paper, we present a variety of algorithms for knowledge base size reduction. Our approach differs from previous approaches in at least three aspects. First, it takes its objects to be support-structured sets of unconstrained, rather than flat sets of syntactically-constrained, logical formulas, which we refer to as belief states. Second, classical notions of minimality and redundancy are replaced by weaker, resource-bounded alternatives based on the support structure. Third, in “lossy” variants of compression, the compressed knowledge base logically implies only a practically-relevant subset of the original knowledge base.
Package Placement Application Based on Location Tracking on Android Platform

Abstract JNE has a pickup service program to pick up a package of loyal online shop customers. This system still has limitations in terms of interaction with customers and JNE customer service. This causes less than the maximum number of customers or partners to get better service. Based on these problems starting with the manufacture of input-output design with the tracking location method will facilitate customers in the package pick-up process. This research will produce a package pickup application on the Android platform that is created using the Java programming language and uses the MySQL database so that JNE can do its business activities in a shorter and timely manner. With the use of this application can cause process time savings reaching package pick-up more efficiently, this application can be a better substitute for the previous system.
Kernel Node Embeddings

Learning representations of nodes in a low dimensional space is a crucial task with many interesting applications in network analysis, including link prediction and node classification. Two popular approaches for this problem include matrix factorization and random walk-based models. In this paper, we aim to bring together the best of both worlds, towards learning latent node representations. In particular, we propose a weighted matrix factorization model which encodes random walk-based information about the nodes of the graph. The main benefit of this formulation is that it allows to utilize kernel functions on the computation of the embeddings. We perform an empirical evaluation on real-world networks, showing that the proposed model outperforms baseline node embedding algorithms in two downstream machine learning tasks.
Sistem Informasi Akuntansi Siklus Pendapatan Pada Usaha Bengkel Budi Barokah Sokaraja

Abstract: Computer-based accounting information systems can be used by companies to improve the efficiency, effectiveness and accuracy of the processes of business transactions that occur in the company. The income accounting cycle which is one of the cycles in an accounting information system needs attention because this cycle produces revenue transactions which are a financial source for the company. The use of computer-based systems in this cycle can also provide added value in the form of improving services to customers. To implement a computer-based accounting information system, a software is needed to process revenue transactions. In this study, the authors developed an application of accounting information systems to process transactions in the income cycle found in vehicle workshop businesses. This application processes transaction data starting from ordering repair services, recording spare parts usage, repair service fees, bill payments and and making income reports on workshop business activities.&#x0D; &#x0D; Keywords: revenue cycle, repairation service, accounting information system
The Effect of Transformational Leadership and Involvement on Employees’ Job Performance in Abu Dhabi National Oil Company

Employee performance is a measure of the effectiveness of one's job.It is also the way an individual does a job evaluated by its effectiveness.Hence, optimal job performance is often the primary outcome that organizations seek to attain.Therefore, this study examined the effect of leadership behaviour on the job performance of employees at Abu Dhabi National Oil Company, through the mediating role of involvement.In this quantitative research study, a structured questionnaire using a 5-point Likert scale was used to collect data from a total of 450 respondents from Abu Dhabi National Oil Company.The validity and reliability of the measurement and the structural models were ascertained.The Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test the hypotheses.Findings revealed that there was a substantial mediation effect of involvement in the relationship between leadership behaviour and job performance.The findings add to the extant literature by integrating the factors that could improve job performance.Based on the findings of the study, it was recommended that oil and gas organizations in UAE improve their organizational involvement and appoint managers who possess good leadership qualities.By doing so, employee job performance can be enhanced, thus leading to a positive and significant effect on the overall organizational performance.The implications for practice and future research were also discussed.