Reviewerly/exHarmony huggingface dataset:
SIGNBASE: A DATA-DRIVEN APPROACH TO ABSTRACT SIGNS IN THE PALEOLITHIC

In the Paleolithic around 100,000 to 10,000 years ago, abstract motives also referred to as signs, patterns, or marks are abundant in parietal art as well as on mobile objects. In the case of parietal art, several studies have been dealing with such abstract signs. However, studies scrutinizing signs on mobile objects, such as figurines, tools, or personal ornaments, are rare and mostly limited to either single objects, or to particular assemblages. Our project SignBase aims to enable large-scale comparisons by collecting abstract motives on mobile objects from all over the European Paleolithic, the African Middle Stone Age, as well as further finds of the Near East and South East Asia. In contrast to the chronological difficulties of dating parietal art, abstract motives on mobile objects are usually well dated, at least with reference to the given techno-complexes. Our project ultimately aims to enable quantitative comparative studies on the development of abstract graphical expressions before the emergence of writing systems. This includes the application of classification algorithms allowing us to study the signs in geographical and chronological dimensions. Furthermore, while any inference about their meaning is inevitably speculative, information-theoretic analyses can shed light on the evolution of their information encoding potential and compare it to later graphical behavior such as early written language.
Automated Feature Extraction techniques for Histopathology Images with Machine Learning based Classification Techniques

Cancer is a leading cause of death worldwide. It is the primary reason why research in this area is difficult. Pathologists most commonly use histopathological images to detect cancer. The shape, morphology, intensity, and texture of histopathology images can be used to classify the cancer status. Because of the large amount of data, using full high-resolution histopathology images will require more time to extract all information. The classification results are compared in this study using different feature extraction algorithms capable of extracting various features from histopathological image texture. The successful feature extraction algorithms GLCM, LBP, PCA, and DCP have been chosen for this study. These features are classified using NB, ANN, DT, and K-NN Classifiers. The most successful feature extraction algorithm and classification algorithm for histopathological images are determined.
Memory-Efficient Approximation Algorithms for Max-k-Cut and Correlation Clustering

Max-k-Cut and correlation clustering are fundamental graph G=(V,E) partitioning problems. The methods with the best approximation guarantees for Max-k-Cut and the Max-Agree variant of correlation clustering involve solving SDPs with $n^2$ variables and $n^2$ constraints (where $n$ is the number of vertices). Large-scale instances of SDPs, thus, present a memory bottleneck. In this paper, we develop a simple polynomial-time Gaussian sampling-based algorithms for these two problems that use $O(n+|E|)$ memory and nearly achieve the best existing approximation guarantees. For dense graphs arriving in a stream, we eliminate the dependence on $|E|$ in the storage complexity at the cost of a slightly worse approximation ratio by combining our approach with sparsification.
Pengamanan Data Pelanggan dan Penjualan Menggunakan Implementasi Algoritma Kriptografi

Data security is an important priority, as it relates to privacy, integrity, authentication, confidentiality, and customer and sales data. The confidentiality and security of customer and sales data is the most important thing in conducting business in the industrial 4.0 era, which is now all-digital. Therefore, the research objective in this paper is to secure customer data using the RSA (Rivest Shamir Adleman) cryptographic algorithm implementation. The strength and security of the RSA algorithm lie in the level of difficulty in factoring numbers into prime factors. The research in this paper uses customer data and car sales at PT. Sutan Indo Various Cars Pematangsiantar. Based on these data, a decryption encryption application will be built using the RSA algorithm. This RSA cryptographic algorithm uses a key length of 1024 bits and plaintext in the form of files with the extension Microsoft Office. The first step is to convert the plaintext and the key into hexadecimal form. The results showed that the length of the file used would affect the encryption and decryption process time. The longer the file size, the longer it will take for the encryption and decryption process. So it can be concluded that the RSA algorithm is effectively used to secure data that is not too large in file size.
Requirements Engineering in The Integration of Social Media Based Interactive Learning

Due to the pandemic Covid-19, Department of Industrial Engineering, University of Diponegoro started requiring online learning for each subject using the Microsoft platform Teams. These changes caused several obstacles faced by students. The Microsoft Teams app requires stable connectivity and high quota usage because online learning takes place in real-time. For this reason, it is necessary to increase better facilities by meeting the needs of students in online learning. This study aims to map user needs in online learning into telegram-based social media features and WordPress-based websites. This study uses the Requirement Engineering method in designing its information system. The Computer Simulation course at the Department of Industrial Engineering focuses on designing this online learning application. The analysis of user requirements obtained will be implemented as the basis for making information systems. Lecturers can provide course material as learning content into Telegram groups or websites. The telegram bot feature is used to present learning material shortly, concisely, and clearly.
Efek Perubahan Nilai SWR Pada Siaran Televisi Mobil

There is some signal which is take effect  to the antena among others SWR the Standing Wave Ratio which is a comparison or ratio of the existing resistance in a circuit, which is a comparison between the external resistance and the internal resistance. Calculation or the magnitude of this comparison or ratio will have an effect on the existing circuit.The magnitude of the SWR value has been  classified in the name of ideal, less ideal or not ideal. The influence of the SWR value that has been summarized in a classification will certainly have a felt effect for the user of the tool or instrument that has the SWR value. With the classification that has been summarized previously coupled with existing calculations, the influence of the magnitude of the SWR value will be explained.Before getting a bad influence or effect from the SWR value, the user will be able to anticipate or prevent the bad thing from happening. In addition, there is a possibility that users can also eliminate or minimize the occurrence of such disturbances or unfavorable influences.By knowing all the disturbances, the user will be able to prevent it so that everything that happens is as expected. From what has happened, the data will conclude about the effect of the SWR value on car television broadcasts which are directly related to the antenna.Keywords: SWR, Standing Wave Ratio, Ideal, Classification, Car Television
THE IMPLEMENTATION OF M.U.R.D.E.R TECHNIQUE TOENHANCE STUDENTS' READING COMPREHENSION INRECOUNT TEXT AT THE SECOND GRADE OF MTSMIFTAHUL HUDA KARANGSONO

ABSTRACTMaskur, Hilda Zunaida. NIM. 17203153136. 2019. Implementation ofM.U.R.D.E.R Technique to Enhance Students' Reading Comprehensionin Recount Text at the Second Grade of MTs Miftahul HudaKarangsono.Thesis. English Education Department. Faculty of Tarbiyahand Teacher Training. State Islamic Institute (IAIN) of Tulungagung.Advisor: Nanik Sri Rahayu, M.Pd.,Keywords: M.U.R.D.E.R Technique, Enhance students' readingcomprehension, Recount text.Reading is the most important skill in learning english. In Indonesia,English becomes compulsary subject in the junior high school up to universitylevel. As mentioned by Brown (2007:185) that reading is the most essential skillin educational context as it can be the assessments for students' general ability. So,the successful of academic process depend on the reading process.This study is intended to answer the single question “How does MURDERtechnique enhance students' reading comprehension at the second grade of MTsMiftahul Huda Karangsono?. The aim of this study is to know the enhancementof students' reading comprehension at the second grade of MTs Miftahul HudaKarangsono through MURDER technique.This research design was Classroom Action Research (CAR) addapted byKemmis and Taggart. This research was conducted in the VIII A that consisted of29 students in the academic year of 2018/2019. The instrument of this researchwas interview guide, reading comprehension test and documentation. The datawas taken from the result of interview with the English teacher and observationsheet that analyzed during the process of teaching and learning in the class.This research was carried out in two cycles which each cycle consisted ofthree meetings. The result of pre-eliminary test, test for cycle 1 and cycle 2showed possitive progress. In the test of cycle 1, the mean score was only 59,1and the implementation of MURDER did not successful yet. Students weredifficult in determining main idea and felt confused in their role. In the test ofcycle 2, the mean score was 76,3. It showed that the mean score enhanced thanbefore. The conclusion of this research showed that students' readingcomprehension could be enhanced by using MURDER technique.
Distilling Word Meaning in Context from Pre-trained Language Models

In this study, we propose a self-supervised learning method that distils representations of word meaning in context from a pre-trained masked language model.Word representations are the basis for context-aware lexical semantics and unsupervised semantic textual similarity (STS) estimation.A previous study transforms contextualised representations employing static word embeddings to weaken excessive effects of contextual information.In contrast, the proposed method derives representations of word meaning in context while preserving useful context information intact.Specifically, our method learns to combine outputs of different hidden layers using selfattention through self-supervised learning with an automatically generated training corpus.To evaluate the performance of the proposed approach, we performed comparative experiments using a range of benchmark tasks.The results confirm that our representations exhibited a competitive performance compared to that of the state-of-the-art method transforming contextualised representations for the context-aware lexical semantic tasks and outperformed it for STS estimation.
Pedestrian Motion Recognition Algorithms Using Smartphone Accelerometer

Pedestrian motion recognition algorithms using the accelerometer on a smartphone are implemented, and performance evaluation results are presented. The features for classifying the decision tree and design parameters of a CNN (Convolutional Neural Network) for recognizing pedestrian motion are listed. The accelerometer outputs of a smartphone that was used to monitor six adults who performed six motion types were collected. The decision tree thresholds were determined, and the CNN weights were learned from the collected data. With the derived thresholds and weights, the real-time pedestrian motion recognition algorithms were implemented on a smartphone, and their performance was evaluated. The performance evaluation results show that the CNN-based motion recognition algorithm has better accuracy than the decision tree-based motion recognition algorithm.
Rancang Bangun Website Sekolah Dengan Metode User Centered Design (studi Kasus: Sekolah Insan Teladan)

Insan Teladan School does not yet have a container containing school information as a promotional medium. So, we need a website school that functions as a forum for providing information so that people can access information more quickly. The purpose of this research is to build a school website with functionality that suits user needs and has an attractive appearance according to the user's wishes. The application of the method User Centered Design (UCD) that focuses on the needs and desires of users is expected to realize the research objectives. The UCD method applied is the use of user personas as the basis for designing mockups and prototypes. All system functionality has been fulfilled by conducting the User Acceptance Test which got 51 test items running well. Based on the 5 second test, it was found that 90% of prospective users had a good experience when using Insan Teladan School website the test Usability Testing resulted in the first highest score on the aspect satisfaction of 90.48% and the second highest score on the aspect learnability of 90, 38% so that it can be concluded that Insan Teladan School website has an attractive appearance and is user friendly.
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)

It is generally recognized that finite learning rate (LR), in contrast to infinitesimal LR, is important for good generalization in real-life deep nets. Most attempted explanations propose approximating finite-LR SGD with Ito Stochastic Differential Equations (SDEs), but formal justification for this approximation (e.g., (Li et al., 2019)) only applies to SGD with tiny LR. Experimental verification of the approximation appears computationally infeasible. The current paper clarifies the picture with the following contributions: (a) An efficient simulation algorithm SVAG that provably converges to the conventionally used Ito SDE approximation. (b) A theoretically motivated testable necessary condition for the SDE approximation and its most famous implication, the linear scaling rule (Goyal et al., 2017), to hold. (c) Experiments using this simulation to demonstrate that the previously proposed SDE approximation can meaningfully capture the training and generalization properties of common deep nets.
THE ENGLISH EDUCATION DEPARTMENT STUDENTS PERCEPTIONS OF IDEAL ENGLISH TEACHER IN HIGH SCHOOL LEVEL

Ideal English teacher is very important in teaching and learning English especially in high schools. According to Indonesia government regulation, an ideal English teacher is an English teacher who has 4 competencies. They are pedagogical, personal, social, and professional competence. This article is a report of the research aims to know the students of English Education Department perception of an ideal English teacher in high school level. It is designed through descriptive qualitative research. To collect the data, the writer use questionnaire. The result of this research shows that according to the students, in pedagogical competence, an ideal English teacher should understand the students’ characteristic. While in personal competence, an ideal English teacher should have high responsibilities. Furthermore, in social competence, the students think that an ideal English teacher should not discriminate the students based on the background of the family. In professional competence, an ideal English teacher should use many sources in delivering the material. Finally, the writer hopes this research can be used by the English teacher as a suggestion to be the truly ideal English teacher.
HUBUNGAN DUKUNGAN SOSIAL ORANG TUA TERHADAP MOTIVASI BELAJAR BAHASA INGGRIS PADA SISWA KELAS VIII DI SMPN 1 CIKUPA

In the learning process, motivation is so important to achieve the goal. It cannot be separated from the support of parents who are influential in the development and growth of children. Objectives: Knowing the relationship of parental social support to motivation to learn English. This research is quantitative with a descriptive correlational technique with a cross sectional approach. A population of 297 calculated using the Slovin formula obtained 170 samples with random samping technique. Based on Chi-Square analysis, the P Value is 0,047 where if the significance value or P Value <0.05 means Ha is accepted and Ho is rejected, this means that there is a relationship between parental social support and motivation to learn English in class VIII students at SMPN 1 CIKUPA. There is significant relationship between parental social support and motivation to learn English. It is hoped that the result of the study can be taken into consideration in observing the relationship between parental social support and motivation to learn English.
An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier

An efficient phishing website detection plugin service was developed using machine learning technique based on the prevalent phishing threat while using existing web browsers in critical online transactions.The study gathered useful information from 27 published articles and dataset consisting of 11,000 data points with 30 features downloaded from phishtank.A unique architectural framework for detecting phishing websites was designed using random forest machine learning classifier based the aim and objectives of the study.The model was trained with 90% (9,900) of the dataset and tested with 10% (1,100) using Python programming language for better efficiency.Microsoft Visual Studio Code, Jupiter Notebook, Anaconda Integrated Development Environment, HTML/CSS and JavaScript was used in developing the frontend of the model for easy integration into existing web browsers.The proposed model was also modeled using use-case and sequence diagrams to test its internal functionalities.The result revealed that the proposed model had an accuracy of 0.96, error rate of 0.04, precision of 0.97, recall value of 0.99 and f1-score of 0.98 which far outperform other models developed based on literatures.Future recommendations should focus on improved security features, more phishing adaptive learning properties, and so on, so that it can be reasonably applied to other web browsers in accurately detecting real-world phishing situations using advanced algorithms such as hybridized machine learning and deep learning techniques.
Search Mechanism with Specialised Genetic Operators

This chapter introduces how to improve the quality of generated offspring via genetic programming for dynamic scheduling by measuring the importance of subtrees and selecting crossover points based on the subtree importance. This chapter proposes two strategies to measure the subtree importance. One is based on the frequency of features. The other one is based on the correlation coefficient between the behaviour of the subtrees and the whole tree. The results show that the proposed algorithm with correlation coefficient can detect important and unimportant subtrees in an individual effectively. The results also show that the way to replace an unimportant subtree of a parent with an important subtree from the other parent can improve the quality of the generated offspring effectively. The depth distributions of selected unimportant and important subtrees are further analysed in this chapter.
RESEARCH THE EFFICIENCY OF PROFESSIONAL SELF-REALIZATION OF EMPLOYEES OF BANKING INSTITUTIONS

The author of the article reveals the psychological features of management and provides the results of a study of the relationship between the effectiveness of professional self-realization of various employeesin banking institutions (economists, financial advisers, operators) with their individual psychological characteristics. One of the main psychological factors of activation of personal andprofessional potential of bank employees are readiness and ability for selfdevelopment, realization of the process of progressive self-change andself-realization in professional activity. The author defines professional self-realization as a conscious self-development of the bank’s staff in the process of which potential opportunities are revealed, the result of which is the constant achievement of personal and socially significant results. The correlation analysis of the ratio of professional efficiency of three groups of employees of banking institutions with their individual psychological characteristics are revealed the presence of several stable relationships, significant at &lt;0.05.
End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs

We propose a novel problem within end-to-end learning of task oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting). Such dialogs are grounded in domain-specific flowcharts, which the agent is supposed to follow during the conversation. Our task exposes novel technical challenges for neural TOD, such as grounding an utterance to the flowchart without explicit annotation, referring to additional manual pages when user asks a clarification question, and ability to follow unseen flowcharts at test time. We release a dataset (FLODIAL) consisting of 2,738 dialogs grounded on 12 different troubleshooting flowcharts. We also design a neural model, FLONET, which uses a retrieval-augmented generation architecture to train the dialog agent. Our experiments find that FLONET can do zero-shot transfer to unseen flowcharts, and sets a strong baseline for future research.
BINARY LOGISTIC REGRESSION IN DETERMINING AFFECTING FACTORS STUDENT GRADUATION IN A SUBJECT

Good communication and coordination between lecturers are needed in delivering material by different lecturers to ensure the relatively uniform quality of education. Knowing the success information from several classes to predict other classes, should be completed by significant parameters used in the algorithm. This research is using a quantitative analysis method with binary logistic regression methodology in determining critical factors of train data on “Introduction to Information Technology” subject in the university of XYZ. Several statistical testing are conducted to give the expected results using software excel with Real Statistics add-ins and Orange Data Mining in testing the pass-prediction from the given data training. The successive model can also be used to classify graduation for the different subjects.
Learning and Evaluating Chinese Idiom Embeddings

We study the task of learning and evaluating Chinese idiom embeddings.We first construct a new evaluation dataset that contains idiom synonyms and antonyms.Observing that existing Chinese word embedding methods may not be suitable for learning idiom embeddings, we further present a BERT-based method that directly learns embedding vectors for individual idioms.We empirically compare representative existing methods and our method.We find that our method substantially outperforms existing methods on the evaluation dataset we have constructed.
On the significance of the ideological struggle against Freudianism in modern medicine and philosophy

Currently, the old theory of Freud, with its primacy of instincts over consciousness, is manifested in the so-called sociological direction of psychoanalysis, called neo-Freudianism. Representatives of this new trend moved away from a purely Freudian position, explaining neuroses in terms of only one sexual desire.