Reviewerly/exHarmony huggingface dataset:
Australia in Oak, Copper, and Quartz

This article describes a map I made based on a trip to Western Australia I took with my wife in 2014. The map is sculptural, constructed using a combination of wood, metal, and semi-precious gemstones. For the base of the map, I used a 44.5 × 40.5 in (113 × 103 cm) piece of quarter-sawn white oak veneer plywood. Guided by a 1-inch (2.54 cm) grid system, I drilled 773 holes of four different diameters to show the land area and general shape of the continent. I chose two different gauges of copper wire to represent driving and train routes. Amethyst stones represent alkaline saline lakes that Holly—an extremophile microbiologist—sampled for resident microbiota. For the one acidic saline lake she sampled (pH 3.5), I used rose quartz instead of amethyst. I highlighted the stromatolites we observed at Shark Bay (Western Australia) with a green diopside mineral. Finally, anywhere we stayed of note is represented with one (or more) red map pins. The final product is 44.5 × 40.5 in (113 × 103 cm) at a scale of 1:4,118,400 (1 in = 65 mi; 1 cm = 41.2 km).
EDUCATIONAL VALUES FROM THE CHARACTERS OF EL SHIRAZY'S NOVEL 'AYAT-AYAT CINTA': A SOCIOLOGY OF LITERATURE PERSPECTIVE

This article aimed at finding out the educational values reflected by characters in the novel Ayat-ayat Cinta written by Habiburrahman El Shirazy from sociological approach to literature. This was a descriptive-qualitative research with textual data in the form of words, phrases, clauses, sentences, and discourse uttered by the characters and narrated by the author in the novel. The result shows that the novel contains some moral messages as well as educational values. Each character in this novel carries educational messages through their characterizations and plot of the story.
Analisis Non-Hierarchical Clustering dan Lagrangean Multiplier dalam Penentuan Bobot Portofolio Optimal Saham Perbankan Indonesia

Investment banking is a financial entity. Investment banking is an important financial intermediary that is indispensable in the capital market. The increasing interest of investors in investing in the stock market has increased because the stock market offers flexible and transparent money options opportunities for risk sharing with the potential for positive returns. Every investor wants a high rate of return with a low risk value. Risk cannot be eliminated, but can be minimized. Clustering means finding and defining a number of clusters (or groups) in a data set. Grouping is done based on numerical equations or distances (dissimilarities). The k-means algorithm is a grouping algorithm based on distance. In the k-means algorithm, the smaller the distance between the samples, the higher the similarity. The k-medoids algorithm is used to find medoids in a cluster which is the center point of the cluster. The return value or commonly known as the return on the funds invested in the investment. Portfolio is a collection of several investment instruments formed with the aim of meeting investment goals. obtained 5 clusters using k-means algorithm with total of wss 23,606. The optimal portfolio weight percentage for each stock formed is BMAS (9.11%%), BANK (32.78%), ARTO (28.84%), BBYB (16.72%), and BBHI (12.54%). Furthermore, from the results of the optimization calculations that have been obtained,the expected return portfolio value is and the portfolio variance is . Investing Rp.1.000.000.000,-, then the expected return value that will be obtained when choosing this portfolio is Rp.11.094.300,-.
Statistical Downscaling Menggunakan Pengelompokan Expectation-Maximization pada Data CFSRv2

General Circulation Model (GCM) is a numerical model that produce a number of data from various climate parameters that are used to estimate climate, one of which is precipitation. GCM is a global scale data and has high resolution. So, the GCM cannot consider the local-scale areas with a higher resolution than the GCM. Therefore, to be able to use the GCM for estimating the local rainfall, namely Statistical Downscaling (SD). SD is a technique used to get the relationship between global-scale (GCM) and local-scale data. SD use the GCM which consist of dependent variables that have multicollinearity. So in this research, the principal component regression (PCR) and partial least square regression (PLSR) will be used to reduce the multicollinearity. In addition, to reduce the RMSEP and increase the correlation value, a clustering technique will be applied before modeling, namely Expectation-Maximization (EM) clustering. This research use CFSRv2 data as GCM and local rainfall data at four rainfall stations in West Java (January 2011 to December 2017). Based on this research, PCR is a good modeling than PLSR and EM clustering get the lower RMSEP and higher correlation value than without clustering before modeling. The conclusion is PCR with EM clustering is a good method for estimating local rainfall using the SD technique especially rainfall in West Java and CFSRv2 data.
Analisis Sentimen pada Program Transportasi Publik JakLingko dengan Metode Support Vector Machine

As a metropolitan city with high mobility, public transportation plays an important role in facilitating economic, business and government activities in DKI Jakarta. DKI Jakarta provincial government launched the JakLingko program to create an integrated, convenient, efficient, and affordable public transportation system. Knowledge of public opinion can help improve the service quality of the JakLingko program. The use of social media is becoming very popular nowadays. Through social media, anyone can easily express their opinion about an issue. It is used to obtain objective and latest public opinion. Sentiment analysis is a method that can be used to analyze public opinion. Through sentiment analysis whose data was collected from Twitter, it can be seen how the public opinion toward JakLingko program. In this study, public sentiment will be classified into positive sentiment or negative sentiment. As for the classification, the Support Vector Machine (SVM) algorithm is used.
IMPLEMENTASI IMRPOVED CHI-SQUARE AUTOMATIC INTERACTION DETECTION PADA KLASIFIKASI FAKTOR-FAKTOR YANG MEMPENGARUHI LITERASI INFORMASI GENERASI MUDA

Information Literacy skills are needed to find quality sources and manage and sort information so that it can be used to improve the quality of life and community empowerment. The number of factors that affect information literacy causes the need for classification. The method used is Improved Chi-Square Automatic Interaction Detection (Improved CHAID), which aims to classify influencing factors with Information Literacy abilities. This study uses primary data, namely 237 Mananggu Young Generation (15-24 years), with Information Literacy as the dependent variable. The independent variables consist of reading interest, reading habits, gender, digital literacy, information needs, critical thinking, and information-seeking behavior. Based on the Improved CHAID analysis, the factors that significantly affect information literacy are Reading Habits (83%), Information Needs (89%), and Critical Thinking (94%). The classification performance of Testing Data is 40%, with a classification accuracy of 77% or from 95 samples, there are 73 samples that are properly classified. The sensitivity of 78% shows that the classification results are able to predict samples that have information literacy, 74% specificity indicates that the classification results are able to predict samples that do not have information literacy, and Press's Q 27.38 indicates a stable classification or statistically significant.
Klasifikasi Pemilih dalam Pemilu 2019 di Indonesia Menggunakan Regresi Logistik Multinomial dan Chi-Square Automatic Decision Tree (CHAID)

General Elections (Elections) in Indonesia are conducted once in every 5 years. This paper focused on the election of the president and his representatives. Voters in elections are regulated by law. There are many aspects of voter’s background that influence voters in making decisions during elections. In this paper, the focus is on voter background factors in geographical, demographic, and socio-economic aspects. The geographical aspect is the region where voters live; demographic aspects are gender, age, recent education, marital status, and religion; socioeconomic aspects are the household expenses, household income, personal expenses, type of work, type of residence, and status of residence. There are 3 categories of voter decisions in the election, namely candidate pair A, candidate pair B, and refuse to vote. The objectives of this paper are to find out what variables significantly influence voters in making decisions during elections, and to classify voters based on the significance of variables in geographical, demographic, and socio-economic aspects related to voter choice in elections. Multinomial logistic regression analysis is used to answer the first objective, while the CHAID (Chi-Square Automatic Decision Tree) decision tree is used to answer the second objective. Through multinomial logistic regression analysis, it can be seen that the variable type of region, age, recent education, religion, household expenditure, household income, house type and home status influence voter decisions in elections. Through the CHAID decision tree, the results obtained are 5 types of voters based on a decision tree of several independent variables that are significant to the dependent variable.
Evaluation of Frame Synchronization Efficiency for Non-Separable Factorial Codes Depending on Synchronization Parameters

This paper aims at implementing the principles of establishing frame synchronism for non-separable factorial codes, as well as applying the operation of interleaving fragments received from the communication channel to increase the efficiency of finding permutation boundaries. We apply the algorithm for establishing frame synchronism with parameters defined for the upper limit of the communication channel bit error probability P0_max = 0,495 for environments with a bit error probability P0 ≤ 0,495. The parameters of the algorithm for establishing frame synchronism with the upper limit of the communication channel bit error probability P0_max = 0,4 are determined. The efficiency of using the interleaving operation of fragments received from the communication channel is evaluated. The efficiency of implementing algorithms for establishing frame synchronism with parameters determined for the upper limit of the communication channel bit error probability P0_max ≤ 0,495 and P0_max = 0,4 is compared, for environments with a bit error probability P0 ≤ 0,4. We give recommendations for selecting the synchronization algorithm parameters for communication channels where the bit error probability upper limit P0 ≤ 0,4 is known, and for channels where this value is variable. These recommendations can be used to improve the efficiency of algorithms for establishing frame synchronism when designing telecommunication systems with non-separable data factorial coding in conditions of natural or artificially created high-level communication channel noise.
Profile of Interactive Learning Media Based Digital Literacy Needs on Elemental Chemistry Concepts

Interactive learning media integrated with technology are needed to facilitate the explanation of abstract material, including elemental chemistry. The study aims to analyze the profile of interactive learning media-based digital literacy needs on elemental chemistry concepts in high school students. The research uses a descriptive qualitative research method. The subjects of this study consisted of chemistry teachers and high school students from four sub-districts in Trenggalek Regency. Based on perspective of teachers and students, they need learning media that can help explain abstract material to be more communicative and interactive to increase learning effectiveness. The results of questionnaire answers show the application of interactive digital learning media based digital literacy fromthe teachers and students are 39% and 37%, respectively. The percentage shows that digital literacy-based learning media is still lacking. Furthermore, interactive learning media based on digital literacy could be developed to fulfill a need using specific criteria.
Dynamic demand response strategy based on source-grid-load integration and optimal energy efficiency

With the deployment of various types of distributed flexible resources within the station area and the application of advanced sensing interconnection technology, a technical basis is provided for the dynamic interactive regulation of the flexible resources in the high-permeability new energy station area. This paper firstly analyzes the dynamic interaction timing characteristics of source-load-storage in the station area, and establishes a global interaction regulation strategy model of source-grid-load in the station area with the objective of optimal energy efficiency and the constraints of new energy active output, electric vehicle charging response, and user comfort by means of non-dominated ranking genetic algorithm. Finally, the economic and environmental benefits of the interactive regulation strategy are verified based on a realistic demonstration scenario, and the optimal comprehensive benefits of the new energy are realized with the mutual coordination of local consumption and external electricity.
Remote Sensing Image Registration Technology based on AKAZE

This paper proposes a remote sensing image registration method based on AKAZE. Firstly, feature points are extracted from the source image by AKAZE algorithm. Secondly, the nearest neighbor search matching and cross validation were performed on the obtained feature points to remove the poor feature points and shorten the subsequent registration time. Finally, the application of this algorithm in remote sensing image registration is verified.
Newly Discovered Route Takeover and DNS Hijacking Attacks in Openshift

OpenShift uses Route objects to expose web applications to the outside world through HAproxy. One of the challenges of managing web application routing in containerized environments such as OpenShift is securely transferring information and allowing access to the applications running in those environments. This paper will examine two possible attacks discovered during security research on OpenShift networking: Route takeover and DNS hijacking. While writing this paper, we didn’t find related works discussing the attacks in containerized environments like Openshift. The novelty of the discovered attacks is the way those attacks are implemented and leveraged in the Openshift environment. The techniques used to gain route takeover and DNS hijacking can work only on Openshift clusters. Next, in the paper, we will briefly present and explain how users can prevent those possible attacks by following specific security practices.
Accelerating Experience Replay for Deep Q-Networks with Reduced Target Computation

Mnih’s seminal deep reinforcement learning paper that applied a Deep Q-network to Atari video games demonstrated the importance of a replay buffer and a target network. Though the pair were required for convergence, the use of the replay buffer came at a significant computational cost. With each new sample generated by the system, the targets in the mini batch buffer were continually recomputed. We propose an alternative that eliminates the target recomputation called TAO-DQN (Target Accelerated Optimization-DQN). Our approach focuses on a new replay buffer algorithm that lowers the computational burden. We implemented this new approach on three experiments involving environments from the OpenAI gym. This resulted in convergence to better policies in fewer episodes and less time. Furthermore, we offer a mathematical justification for our improved convergence rate.
An Empirical Evaluation of Writing Style Features in Cross-Topic and Cross-Genre Documents in Authorship Identification

In this paper, an investigation was done to identify writing style features that can be used for cross-topic and cross-genre documents in the Authorship Identification task from 2003 to 2015. Different writing style features were empirically evaluated that were previously used in single topic and single genre documents for Authorship Identification to determine whether they can be used effectively for cross-topic and crossgenre Authorship Identification using an ablation process. The dataset used was taken from the 2015 PAN CLEF Forum English collection consisting of 100 sets. Furthermore, it was investigated whether combining some of these feature sets can help improve the authorship identification task. Three different classifiers were used: Naïve Bayes, Support Vector Machine, and Random Forest. The results suggest that a combination of a lexical, syntactical, structural, and content feature set can be used effectively for cross topic and cross genre authorship identification, as it achieved an AUC result of 0.837.
Essential characteristics of moral education of convicts in correctional facilities of Ukraine 2nd half of the 20th – beginning of the 21st century

Methodological aspects of teaching the discipline "Social work in the field of employment"In modern Ukrainian society, social work as a professional activity is aimed at helping clients of different social groups who find themselves in difficult life circumstances and in a group of social risk.As a rule, social risks include those circumstances that negatively affect a person's quality of life, such as the death of a breadwinner, disability, temporary incapacity for work, low level of material security and loss of a job.Citizens who have lost their jobs and want to get a job are registered at the State Employment Center.The discipline "Social work in the field of employment" deals with the training of future social workers who supplement their knowledge of current issues in the field of employment and the employment system.In particular, in Ukraine over the last decade the problem of employment and low material security has become more important, which is why there is a need to improve the employment system and provide social guarantees.Therefore, there is an urgent need to help students get acquainted with the problems in the field of employment, which is the main task of the discipline.It has been established that the discipline has main tasks aimed at successful assimilation of educational material by students during lectures and practical work.The educational process is conducted in various forms, namely: lectures, seminar assignments and independent work.Based on the results of the analysis of the theoretical material, it is proven that, in addition to teaching the academic discipline, students have the opportunity to participate in scientific conferences and write a paper, which will help to improve the score, understand the topic and learn research activities.The student's work is evaluated according to various criteria: work on seminar and independent works is taken into account, and the results of modular and control works are also taken into account.During the academic semester, the teacher evaluates the student's work, and subsequently forms a score, which also depends on scientific activity.
Polynomial color compensation model with neural network solver for color-coded fringe patterns in phase-measuring deflectometry

The total image display/projection and image acquisition time in a projector-based phase-measuring deflectometry (PMD) measurement is reduced when color-coded fringe patterns are used since only a single image of the fringe pattern is sufficient to extract phase information using the phase-stepping method. This benefit is, however, offset by the difficulty in resolving various sources of color intensity errors of the fringe patterns in the camera view, such as uneven illumination, color imbalance, color crosstalk, and deviation of color intensity response of the camera and the projector from the calibrated color targets. Thus, a polynomial color compensation model is proposed in this study to compensate for color intensity errors. The model coefficients were estimated using the relationship between the captured and targeted color intensities, which are sequences of monochromatic 8-bit red, green, and blue (RGB) images for projection. The model was applied to modify the color intensities of the generated fringe patterns before projection. The modified color fringe patterns were then used for PMD measurement on spherical concave and convex mirrors. Based on the experimental results, the root-mean-squared (RMS) color intensity errors in R, G, and B channels were reduced by 33.3%, 58.3%, and 36.4%, respectively, after conducting the polynomial color compensation. The RMS phase errors in the fringe patterns were reduced by 71.9%. The RMS height errors and the radius percentage errors of the reconstructed concave and convex heightmaps were 1.87 μm (0.12%) and 1.55 μm (0.17%), respectively. The performance of the proposed polynomial color compensation model in reducing color intensity errors of the captured fringe patterns for accurate PMD measurement was demonstrated successfully.
Bringing data minimization to digital wallets at scale with general-purpose zero-knowledge proofs

Today, digital identity management for individuals is either inconvenient and error-prone or creates undesirable lock-in effects and violates privacy and security expectations. These shortcomings inhibit the digital transformation in general and seem particularly concerning in the context of novel applications such as access control for decentralized autonomous organizations and identification in the Metaverse. Decentralized or self-sovereign identity (SSI) aims to offer a solution to this dilemma by empowering individuals to manage their digital identity through machine-verifiable attestations stored in a "digital wallet" application on their edge devices. However, when presented to a relying party, these attestations typically reveal more attributes than required and allow tracking end users' activities. Several academic works and practical solutions exist to reduce or avoid such excessive information disclosure, from simple selective disclosure to data-minimizing anonymous credentials based on zero-knowledge proofs (ZKPs). We first demonstrate that the SSI solutions that are currently built with anonymous credentials still lack essential features such as scalable revocation, certificate chaining, and integration with secure elements. We then argue that general-purpose ZKPs in the form of zk-SNARKs can appropriately address these pressing challenges. We describe our implementation and conduct performance tests on different edge devices to illustrate that the performance of zk-SNARK-based anonymous credentials is already practical. We also discuss further advantages that general-purpose ZKPs can easily provide for digital wallets, for instance, to create "designated verifier presentations" that facilitate new design options for digital identity infrastructures that previously were not accessible because of the threat of man-in-the-middle attacks.