Reviewerly/exHarmony huggingface dataset:
EVALUASI PROGRAM OUTBOUND DI TK MENGGUNAKAN MODEL ALKIN

The problem in this study is that there has been no evaluation of outbond programs. This research aims to evaluate the Outound Program in Pilang Kindergarten 01, Masaran, Sragen School Year 2019/2020. This study uses mixed methods that are a combination of qualitative and quantitative research using a descriptive approach. The study was conducted from January to April 2020. The subjects in this study were students and informants were teachers and principals. Data collection methods use observations, interviews and documentation and questionnaires. Data analysis techniques use interactive analysis, while questionnaires is analyzed using percentage analysis (percentile). The conclusion of the outbound program input evaluation gets a score of 80%. The evaluation process obtained a score of 75%. The output evaluation scored 85%. Based on the evaluation results, the recommendation is to continue the program, because the implementation of the program has been running in accordance with expectations and providing useful results. Keywords: Outbound Program Evaluation, Alkin Model
On Sampling Based Algorithms for k-Means.

We generalise the results of Bhattacharya et al. [Bhattacharya et al., 2018] for the list-k-means problem defined as - for a (unknown) partition X₁, ..., X_k of the dataset X ⊆ ℝ^d, find a list of k-center-sets (each element in the list is a set of k centers) such that at least one of k-center-sets {c₁, ..., c_k} in the list gives an (1+e)-approximation with respect to the cost function min_{permutation π} [∑_{i = 1}^{k} ∑_{x ∈ X_i} ||x - c_{π(i)}||²]. The list-k-means problem is important for the constrained k-means problem since algorithms for the former can be converted to {PTAS} for various versions of the latter. The algorithm for the list-k-means problem by Bhattacharya et al. is a D²-sampling based algorithm that runs in k iterations. Making use of a constant factor solution for the (classical or unconstrained) k-means problem, we generalise the algorithm of Bhattacharya et al. in two ways - (i) for any fixed set X_{j₁}, ..., X_{j_t} of t ≤ k clusters, the algorithm produces a list of (k/(e))^{O(t/(e))} t-center sets such that (w.h.p.) at least one of them is good for X_{j₁}, ..., X_{j_t}, and (ii) the algorithm runs in a single iteration. Following are the consequences of our generalisations: 1) Faster PTAS under stability and a parameterised reduction: Property (i) of our generalisation is useful in scenarios where finding good centers becomes easier once good centers for a few clusters have been chosen. One such case is clustering under stability of Awasthi et al. [Awasthi et al., 2010] where the number of such bad clusters is a constant. Using property (i), we significantly improve the running time of their algorithm from O(dn³) (k log{n})^{poly(1/(β), 1/(e))} to O (dn³ (k/(e)) ^{O(1/βe²)}). Another application is a parameterised reduction from the outlier version of k-means to the classical one where the bad clusters are the outliers.2) Streaming algorithms: The sampling algorithm running in a single iteration (i.e., property (ii)) allows us to design a constant-pass, logspace streaming algorithm for the list-k-means problem. This can be converted to a constant-pass, logspace streaming PTAS for various constrained versions of the k-means problem. In particular, this gives a 3-pass, polylog-space streaming PTAS for the constrained binary k-means problem which in turn gives a 4-pass, polylog-space streaming PTAS for the generalised binary 𝓁₀-rank-r approximation problem. This is the first constant pass, polylog-space streaming algorithm for either of the two problems. Coreset based techniques, which is another approach for designing streaming algorithms in general, is not known to work for the constrained binary k-means problem to the best of our knowledge.
Secure Quantum Extraction Protocols.

Knowledge extraction, typically studied in the classical setting, is at the heart of several cryptographic protocols. The prospect of quantum computers forces us to revisit the concept of knowledge extraction in the presence of quantum adversaries.
Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training.

Online reviews are a vital source of information when purchasing a service or a product. Opinion spammers manipulate these reviews, deliberately altering the overall perception of the service. Though there exists a corpus of online reviews, only a few have been labeled as spam or non-spam, making it difficult to train spam detection models. We propose an adversarial training mechanism leveraging the capabilities of Generative Pre-Training 2 (GPT-2) for classifying opinion spam with limited labeled data and a large set of unlabeled data. Experiments on TripAdvisor and YelpZip datasets show that the proposed model outperforms state-of-the-art techniques by at least 7% in terms of accuracy when labeled data is limited. The proposed model can also generate synthetic spam/non-spam reviews with reasonable perplexity, thereby, providing additional labeled data during training.
On the (Ir)Replaceability of Global Setups, or How (Not) to Use a Global Ledger.

In universally composable (UC) security, a global setup is intended to capture the ideal behavior of a primitive which is accessible by multiple protocols, allowing them to share state. A representative example is the Bitcoin ledger. Indeed, since Bitcoin—and more generally blockchain ledgers—are known to be useful in various scenarios, it has become increasingly popular to capture such ledgers as global setup. Intuitively, one would expect UC to allow us to make security statements about protocols that use such a global setup, e.g., a global ledger, which can then be automatically translated into the setting where the setup is replaced by a protocol implementing it, such as Bitcoin.
Multi-Client Oblivious RAM with Poly-Logarithmic Communication.

Oblivious RAM enables oblivious access to memory in the single-client setting, which may not be the best fit in the network setting. Multi-client oblivious RAM (MCORAM) considers a collaborative but untrusted environment, where a database owner selectively grants read access and write access to different entries of a confidential database to multiple clients. Their access pattern must remain oblivious not only to the server but also to fellow clients. This upgrade rules out many techniques for constructing ORAM, forcing us to pursue new techniques. MCORAM not only provides an alternative solution to private anonymous data access (Eurocrypt 2019) but also serves as a promising building block for equipping oblivious file systems with access control and extending other advanced cryptosystems to the multi-client setting.
INFLUENCE OF EXTRA-LEARNING DEVELOPMENT OF ELECTRONIC RESOURCES ON THE DEVELOPMENT OF PERSONAL QUALITIES OF SCHOOLCHILDREN

The article describes and substantiates approaches to the development of extracurricularactivities of schoolchildren, providing for the development of new electronic resources bythem. The problem of finding ways to technological support extracurricular activities withstudents, identifying new ways to implement their creative activities through the use ofinformation technology is relevant in connection with updated social orders for the educationsystem. The aim of the study was practical confirmation of the effectiveness of the proposedapproaches to informatization of extracurricular activities at school and their positive impacton the development of personal qualities of students.A pedagogical study was conducted on the basis of the formation of the control (52 people)and experimental (54 people) groups of fifth graders from two Kazakhstan schools. A seriesof extracurricular activities were implemented in the experimental group, in the framework ofwhich schoolchildren developed electronic resources using selected means of informatization.Comparative diagnostics of the level of nine different personality traits of schoolchildren wascarried out using methods of mathematical statistics and specially designed questionnaires.The study found that the development and implementation of the proposed methodologicalapproaches to extracurricular activities at school, based on the project creation by students ofelectronic resources that can be used in the main educational process, as well as thepreliminary selection of the necessary informatization tools for this, contribute to thedevelopment of significant personal qualities.The positive effect of informatization of extracurricular work and the creation of electronicresources by students in the framework of this work on the development of cognitivemotivation, independence, the ability to find and organize information, set goals and planwork to achieve them, communicate and collaborate with other people, as well as otherpersonal qualities, has been experimentally proved. schoolchildren.
WITHDRAWN: Lightweight and authenticated key exchange based on self linear pairings

Currently, various lightweight online applications communicate on storing data and forwarded to wireless communication which is insecure from hackers. Resolve this type of problem, the two or more than existing users must exchange authenticated session key. Firstly, Menezes et al. introduced multiple key exchange protocol which does not apply the hash function during own protocol. Later, Lai and Lin suggested a scheme for exchange the key in ECC cryptosystem and pointed out that some weakness of Menezes et al. protocol. In this paper, we introduced the lightweight cryptography based on a self-pairing algorithm which is more efficient compare with all existing schemes. Finally, we compared the protocol which is based on lightweight cryptosystems and self-linear based protocol in the sense of computational cost.
NORAA [machinic doodles]

How does machine learning contribute to our understanding of how ideas are communicated through drawing? Specifically, how can networks capable of exhibiting dynamic temporal behaviour for time sequences be used for the generation of line (vector) drawings? Can machine-learning algorithms reveal something about the way we draw? Can we better understand the way we encode ideas into drawings from these algorithms?
Analysis of Tectonic Ore-Controlling Factors Using of Geophysical Data on the Example of the Elkon Ore District (Aldan Shield, North-Asian Craton)

Abstract The author investigated the gravitational and the magnetically fields of the Elkon ore unit. The gold-uranium deposits of the Elkon ore district this is unique of his scale and matching with biggest ore units of world. The study of the regularities of forms, location and direction of extension of tectonic ore-controlling structures in magnetic and gravitational fields, the study of their transformation using Surfer 8, Koscad 3D and ArcGIS. As a result, the author obtained get two-dimensional autocorrelation functions, local and regional components of geophysical fields, and their gradients. The most informative transformations of fields are regional component of the magnetically field and local component of the gravitational field by results statistical analysis to study the tectonic structure like Elkon ore unit.
LibGuides: Art & Art History: Additional Resources

This public library LibGuide provides resources around art, artists, and art history for teens and adults.
LibGuides: Primary Sources: Subject Resources

This is a research guide to finding primary sources that are accessible through the MSU Duane G. Meyer Library or through the open web. Explore a variety of subjects.
Studi Evaluasi Fungsi Surat Keterangan Pendamping Ijazah (SKPI) dalam Dunia Pendidikan bagi Pengguna

This mini-research discusses the evaluation study of the function of a certificate accompanying a diploma or abbreviated as SKPI in the world of education, in this case, the school as a user. The purpose of the birth of the SKPI as an additional document describing the competence of graduates who are not listed on the diploma and academic transcripts has not been achieved. This is because the socialization to schools has not been carried out evenly (not yet arrived). The research method used is a descriptive method with a questionnaire sheet instrument distributed to 211 respondents of the alumni of the PBS Indonesia IKIP Siliwangi Study Program from four graduates (batch 2016, 2017, 2018, and 2019). The results obtained from the questionnaire data processing illustrate that the function of the certificate companion certificate (SKPI) has not been used optimally and most users do not know this SKPI document, respondents stated that only 8.06% of users already know about SKPI.Keywords: Evaluation Study, Certificate Companion Diploma, SKPI Function
DATA CLUSTER MAPPING OF GLOBAL COVID-19 PANDEMIC BASED ON GEO-LOCATION: DATA CLUSTER MAPPING OF GLOBAL COVID-19 PANDEMIC BASED ON GEO-LOCATION

The spread of the covid-19 virus pandemic is very fast, where was start of the virus spreading from Wuhan City, Hubei Province, China and suddenly spread out widely to almost around the word. According that kind of pandemic phenomena, this research was conducted to make clustering based on data of latitude and longitude due global spreading of Covid-19 use DBSCAN (Density-Based Spatial Clustering Of Applications With Noise) and K-Means to find a level accurate and suitable in calculating for this pandemic case as the alternative choices for condition analyse in decision making purpose. The algorithm had developed calculate based on characterization from geolocation of the country which is to determine the number quality of cluster use Silhoutte Coefficient and Elbow Methods. Therefore, from calculated results can be analyse similarity of covid-19 spreading pattern refer to clustering in each province or country. From data testing show that DBSCAN method separate the data of noise points with eps=22 and minimum pts=4, and for K-Means method with k = 3. After calculation by use the two methods, finally, can visualize the mapping cluster continent of Asia, Europe and Africa with showing the pattern of increasing covid19 cases that can began controlled. The other result show cluster for continent of north and South America have increased significant and the Australian Continent cluster gets the lowest case and can controlled.
Algebraic features of algorithm composition for calculating fractal structure

The construction methods analysis of known geometric fractals allows us to reveal algebraic features of fractal algorithms composition. The main concept of the analysis results is fractal operators which are basic operations for constructing fractals of a certain type. The result of using operators for geometric fractals is a structure with fractures of triangular, square, trapezoidal and similar forms. Multiplication and addition operations are introduced for operators, as well as the concept of the unit, null and inverse operator, which allows us to define periodic and quasiperiodic fractal structures. An algorithm for the formation of periodic and stochastic fractal structures is proposed, a distinctive feature of which is the implementation of the probabilistic choice of the basic geometric primitive on the current iterative cycle. The software implementation of the proposed algorithm confirmed the validity of the algebraic approach in studying and modeling fractal with a complex structure.
Neural fuzzy regression modelling for forecasting

Recently statistical model especially forecasting model has developed to soft model. The model is more computerize in line to computer development and it is not based on strict rules, such as it has to fulfil classical or soft assumption. The model is called as soft statistical model. By using soft model and soft assumption, there are many models can be constructed, such as Artificial Neural Network (ANN) or Multi Layers Perceptions (MLP). There are three layers in ANN, it is called input, hidden and output layer. The optimum weight of each layer is processed using back propagation approach. In this research, ANN model – especially Neural Fuzzy Regression (NFR) model – is applied to find best forecasting model, specifically forecasting model of the stock price. The data is the stock price of a mining sector emitted and the exchange rate US$ to IDR from January 2015 until February 2019. The Data is collected from publication of Indonesia Stock Exchange and Indonesia Central Bank. The stock price shows positive trends recently and there is a correlation between the stock price and the exchange rate. Based on autocorrelation function, there are four previous data that have significant relationship with the current data. NFR model has five nodes in input layer (four lag time and exchange rate), some nodes in hidden layer and a node in output layer. The best model is model with five nodes as input, seven nodes in hidden layer and an output. The model has accuracy of MSE 34.0850, MAPE 2.9026, and MAD 28.7377.
Perancangan Komik E-Book “Mari Membuat Komik Sendiri” Pembelajaran Pembuatan Komik Untuk Remaja

The creation of this work aims to provide an educational media about the process of making comics that entertain and attract teenagers in the world of comics, especially for local comics. The background for the selection of this comic-making process as material for the creation of works, is because comics are one of the visual media that has become popular among the public. It is proven by the existence of printed comics with locals and digital comic platforms such as Webtoon, Ciayo, and so on. Basically, comics are liked by children because of the many illustrations. Comics with educational elements that are given material inserts on how to make comics themselves are expected to become additional alternative media. In addition, comics about the process of making comics themselves are expected to help those who read to understand the ways that might be incomprehensible when there are only theories or materials in the form of writing without examples of illustrations.
Partially Observable Markov Decision Processes

This chapter covers Partially Observable Markov Decision Processes (POMDPs), that extend MDPs for when the state is not completely observable. After a general introduction to POMDPs, their formal representation and properties are described. The representation of the value function as a set of linear equations ( $$\alpha -vectors$$ ) is presented via a series of examples, which is the bases of several solution algorithms. The value iteration and point-based value iteration algorithms for finding the optimal policy are described. Finally, two application examples are presented: automatic adaption for virtual rehabilitation and task planning for service robots.
Obiezione di coscienza nell’interruzione di gravidanza ancora a giudizio? Punti fermi e prospettive future

The essay examines the problems connected to the exercise of the right of conscientious objection in the matter of voluntary termination of pregnancy, starting with the two recent decisions of the European Court of Human Rights against Sweden. Taking into account these two decisions and those of the European Committee of Social Rights against Italy, as well as the Italian constitutional principles, the essay focuses on the position of doctors and health professionals who decide to refuse services strictly connected to a freely chosen profession.
Computing Opinions for Twitter Review Data

Customers and users post their opinions or reviews on social networking sites and it has increased the amount of data WWW. With this users from all over world try to share their opinions and sentiments on the blogging sites every day. Internet is being used in form of web pages, social media, and sometimes blogs which increases online portals sentiments, reviews, opinions, references, scores, and feedbacks are also generated by people. Twitter is the most famous micro-blogging site where users express their opinions in the form of tweets. The user can express their sentiments about various aspects e.g., books, celebrities, restaurants, various products, research, events, etc. All these opinions plays vital roles and they are quite important for various businesses, for government schemes, and for individual human being as well. Still, there are many curbs in mining reviews or opinions and process to calculate them. These limitations have turned into highland in investigating the actual gist of opinions and measuring its polarity. Hence, we recommend an inventive way to compute the sentiments for given reviews or opinions. This recommendation is centered on the social networking sites’ information of various Tweets, a word-emotion-association-network is put up in association to represent opinions and semantics that decides the base for the emotions (sentiment) analysis of opinion or reviews.
Credit Card Fraud Detection Using Fuzzy Rule Based Classifier

Fraud detection is an emerging topic of notable importance. Data mining strategies have been applied most considerably to the detection of insurance fraud, monetary fraud and financial fraud. This project will mainly focus on detecting fraudulent credit card transactions. Fraud detection in telecommunication systems, particularly the case of extraordinary imposed fraud, providing an anomaly detection technique supported by way of a signature schema, fraud deals with cases regarding criminal purposes that typically are different to identify, have additionally attracted a a tremendous deal of attention in latest years. The use of credit cards has dramatically increased because of a fast advancement inside the electronic commerce technology. Credit card will become the most popular mode of payment for each on line as properly as ordinary purchase, in instances of fraud related to it are also growing day through day. In this research sequence of operations in credit card transaction processing using a Fuzzy rule based classifier and accuracy is improved in the detection of frauds compared to other algorithms. A Naïve Bayes is initially trained with the everyday behaviour of a card holder. If an incoming credit card transaction is not accepted by the trained version with sufficiently excessive probability, it’s considered to be fraudulent. At the same time, it ensures that true transactions aren’t rejected. Supervised learning requires prior type to anomalies. In this research fuzzy rule primarily based category set of rules used for modelling real world credit card information statistics and detecting the anomaly usage of credit card information’s. Whenever anomaly credit card usage detected the system will capture the anomaly user face and freeze the anomaly user system. Django framework is used for web app creation.
A Generalized A* Algorithm for Finding Globally Optimal Paths in Weighted Colored Graphs

Both geometric and semantic information of the search space is imperative for a good plan. We encode those properties in a weighted colored graph (geometric information in terms of edge weight and semantic information in terms of edge and vertex color), and propose a generalized A* to find the shortest path among the set of paths with minimal inclusion of low-ranked color edges. We prove the completeness and optimality of this Class-Ordered A* (COA*) algorithm with respect to the hereto defined notion of optimality. The utility of COA* is numerically validated in a ternary graph with feasible, infeasible, and unknown vertices and edges for the cases of a 2D mobile robot, a 3D robotic arm, and a 5D robotic arm with limited sensing capabilities. We compare the results of COA* to that of the regular A* algorithm, the latter of which finds the shortest path regardless of uncertainty, and we show that the COA* dominates the A* solution in terms of finding less uncertain paths.