Reviewerly/exHarmony huggingface dataset:
ANALISIS PERMASALAHAN DALAM PENGGUNAAN PLATFORM SEBAGAI MEDIA PEMBELAJARAN BIOLOGI: STUDI KASUS PADA SMA NEGERI 1 TARAKAN

Abstract: This study aims to analyze the problems in using the platform as a medium for learning biology for class X students at SMA Negeri 1 Tarakan. The research method uses a qualitative approach with an explanatory case study type of research. The research subjects were 59 students of SMA Negeri 1 Tarakan class X MIPA. The data was obtained from the results of online interviews using the zoom meeting platform. The results obtained in the communication aspect 39 students said they had problems, in the media aspect in the form of design 18 students said they had problems, in the media aspect in the form of effectiveness 19 students said they had problems, in the content aspect 18 students had problems and on the technical aspect. internet 59 students said they had problems. Based on the results of research and data analysis, it can be seen that in every aspect there are problems in using the platform as a medium for learning biology in class X MIPA SMA Negeri 1 Tarakan. Keywords: Analysis, Platform, Aspect
THE LITERARY TEXTS IN THE TEXTBOOKS FOR LEARNING ITALIAN AS FOREIGN LANGUAGE: EXPERIENCE IN THE MACEDONIAN UNIVERSITY CONTEXT

Луси Караниколова-
Minimization of Handover Decisions with Quality of Service Using Fuzzy Logic Prediction Model

Artificial Intelligence (AI) based network technologies considered best method to enhance the Quality of Service (QoS) of handoff algorithms due to its ability to handle huge data in fast processing. It helps to take effective handoff decision based on Received Signal Strength (RSS), traffic intensity, speed and diversity. In this paper the fuzzy logic prediction model has been developed for handoff decisions. On retrieving the network, the RSS was developed to form a time series data over a period of time. The data is then proceeded with the newly proposed fuzzy logic prediction model for estimation and prediction coefficients, while the predicted values of RSS are organized as fuzzy sets and in conjunction with other measured parameters of network. Moreover, the Received Signal Strength Indicator (RSSI), traffic load in the network, channel capacity, network load (NL), Bit Error Rate (BER), received signal power level has been estimated throughput the Signal to Noise Ratio (SNR), In addition, to user preferences such as the security and cost of the network. The overall performance of proposed fuzzy logic prediction model is capable to predict the handover decision ahead then the available RSS method and other handover necessity estimation techniques. This model also reduces the ping-pong effect associated with other techniques of handover.
A Writer in the Making - A Comparative Analysis of Ellen G. White’s Writings on the Resurrection of Christ -

재림교회 창시자 중 한 사람인 엘렌 지 화잇은 많은 저술을 남겼다. 그의 글은 신학자들에 의해 광범위하게 연구되었지만 문학적 또는 언어학적 관점에서 조사한 연구는 상대적으로 적다. 화잇은 작문에 대한 정규 수업을 거의 받지 못했지만 그의 글은 지금까지 수많은 사람에게 읽히고 있다. 그러므로 그녀가 작가로서어떻게 성숙했는지 파악하는 연구는 가치가 있다. 특별히 이 연구는 번역본을읽었기 때문에 그의 텍스트가 보여주는 다양한 글쓰기 스타일에 익숙하지 않은해외 독자들에게 흥미있는 연구가 될 것이다. 소수의 기존 연구는 대쟁투 총서특히 화잇이 60대였을 때 출판한 ≷시대의 소망≸과 같은 대표적인 작품을 중심으로 그의 글쓰기 스타일을 비교 조사했다. 이러한 연구는 그의 글의 독창성에 의문을 제기한 회의론자들을 반박하는 것에 주로 초점을 맞췄다. 현재까지 화잇의 초기저작물을 ≷시대의 소망≸이 출판되기 수십 년 전에 작성된 저작물과 문체면에서어떻게 다른지 비교 조사한 연구는 찾아보기 힘들다. 이 간극을 메우기 위해 이논문은 20년 간격으로 저술된 두 권의 책인 ≷영적선물 I≸과 ≷예언의 신 III≸에서예수의 부활을 다루는 두 발췌문을 비교 분석한다. 이 연구는 작가로서의 화잇의성장을 조명하기 위해 문단의 구성, 언어, 어조, 문체 및 수사적 기교에 초점을맞춘다. 이 연구는 화잇이 두 텍스트에서 유사한 문장을 재사용하지만 이후 텍스트에서 내용, 구성, 문법 및 문구를 다양하게 변경하여 보다 향상된 문어체 스타일과 어조를 썼으며, 이러한 성숙한 스타일은 ≷시대의 소망≸에 대한 이전 연구에서관찰한바와 일치함을 보여준다.Ellen G. White, one of the founders of the Seventh-day Adventist (SDA) Church, was a prolific writer. While her writings have been extensively studied by theologians, relatively few studies have examined her writings from a literary or linguistic perspective. Despite her lack of formal education and training in writing, her writings continue to draw and inspire readers. Therefore, it would be worthwhile to examine White’s texts to understand how she developed as a writer. This can be a particularly illuminating task for international readers who have mainly read translations of her books, and therefore, are not as familiar with the different writing styles that her various texts exhibit. A small number of existing studies have comparatively examined White’s writing styles at different time periods by focusing on some of her most representative works, such as the Conflict of the Ages series, and The Desire of the Ages, in particular, which was published when White was in her 60s. These studies largely focused on addressing skeptics who questioned the originality of her writing. To date, there is a dearth of studies that examined how White’s earlier writings compare to those written in the decades prior to the publication of The Desire of Ages. To help fill this gap, this paper comparatively analyzes two excerpts that deal with Jesus’ resurrection from White’s two different books—Spiritual Gifts I, and The Spirit of Prophecy III―which were written two decades apart. The current study focuses on the organization, language, tone, style, and rhetorical moves in order to shed further light on her development as a writer. The study shows that White reuses similar sentences in the two texts but makes various changes to the content, organization, grammar, and wording in her later text, which in turn create a more formal style and tone, and that the improved style aligns with the observations made by previous studies of The Desire of Ages.
Fuzzy and Model Based Clustering Methods: Can We Fruitfully Compare Them?

During the last years, fuzzy and model-based approaches to clustering have received a great deal of attention and have been increasingly used in several empirical contexts. Even if they are very different from a theoretical point of view, they are similar in practice. In fact, model-based clustering gives posterior probabilities of component, treated as cluster, membership. Fuzzy clustering assigns observations to clusters through fuzzy membership degrees, while no probabilistic assumption is made to represent the clusters. The aim of this work is to compare the performance of some clustering methods belonging to the two approaches, in terms of recovering the true clusters, in a large scale simulation study.
Pattern recognition of grooves in human lips for improved authentication in cyber-physical systems using U-Net architecture

Latent Lip groove application is been a notable topic in forensic applications like crime and other investigations. The detection of lip movement is been a challenging task since it is a smaller integral part of the human face. The conventional models operate on the available public or private dataset but it is constrained to the large population and unconstrained environment. The study aims at developing a deep learning model in a multimodal system using the deep U-Net Convolutional Neural Network architecture. It also aims at improving biometric authentication through a deep pattern recognition that involves the feature extraction of grooves present in the human lips. An examination of grooves present in the input lip image is conducted by the present system to check the authenticity of the person entering the cyber-physical systems. The lip images are collected from the public security cameras via high-definition cameras in crowded areas that help the proposed method in forensic investigation and further, it considers various unconstrained scenarios to improve the efficacy of the system. The study involves initially pre-processing of lip image, and feature extraction of lip grooves to improve the efficacy of the lip trait. The simulation is conducted on the MATLAB tool to examine the efficacy of the model against various existing methods. Further, the study does not take into account the datasets available on the websites and lip images are only collected from a large set population in a real-time environment. The results of the simulation show that the proposed method achieves a higher degree of accuracy in extracting the grooves from the input lip images.
Efficient Direct Convolution Using Long SIMD Instructions

This paper demonstrates that state-of-the-art proposals to compute convolutions on architectures with CPUs supporting SIMD instructions deliver poor performance for long SIMD lengths due to frequent cache conflict misses. We first discuss how to adapt the state-of-the-art SIMD direct convolution to architectures using long SIMD instructions and analyze the implications of increasing the SIMD length on the algorithm formulation. Next, we propose two new algorithmic approaches: the Bounded Direct Convolution (BDC), which adapts the amount of computation exposed to mitigate cache misses, and the Multi-Block Direct Convolution (MBDC), which redefines the activation memory layout to improve the memory access pattern. We evaluate BDC, MBDC, the state-of-the-art technique, and a proprietary library on an architecture featuring CPUs with 16,384-bit SIMD registers using ResNet convolutions. Our results show that BDC and MBDC achieve respective speed-ups of 1.44× and 1.28× compared to the state-of-the-art technique for ResNet-101, and 1.83× and 1.63× compared to the proprietary library.
Instructional Program for Comprehensive Machine Manufacturing Practical Training

To begin, the role and positioning of comprehensive practical training in mechanical engineering teaching system are planned, as is the teaching purpose of comprehensive practical training in mechanical engineering.Second, on this basis, the teaching contents of comprehensive practical training in mechanical manufacturing are detailed planned in four aspects, and methods for implementing the teaching contents are provided.Finally, for many years of actual implementation, the verification of the implementation effect on the teaching contents is provided.
PEMODELAN TOPIK ULASAN APLIKASI NETFLIX PADA GOOGLE PLAY STORE MENGGUNAKAN LATENT DIRICHLET ALLOCATION

Reviews given by application users have an essential role in the decisions of potential users before deciding to use a service. Users can provide reviews of application services on the Google Play Store. A large number of reviews makes the data collection and analysis difficult if done manually. Therefore, a particular method is needed to collect and analyze these reviews automatically to obtain the hidden essential information. Topic modeling is an extension of text analysis that can find main themes or trends hidden in large sets of unstructured documents. This study applies topic modeling with the Latent Dirichlet Allocation (LDA) method for Netflix application review data sourced from the Google Play Store web. The Latent Dirichlet Allocation (LDA) method is a probability model from textual data that can explain the hidden semantic themes in the review document. This research aims to analyze hidden topics that application users discuss. The results of topic modeling show that of the twelve topics generated, the most discussed topic by users is payment methods.
PREDIKSI TINGKAT TEMPERATUR KOTA SEMARANG MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM)

Temperature is one of the most important attributes of climate, temperature affects life in many different ways such as in agriculture, aviation, energy, and life in general. Temperatur prediction is needed to make the right step to prevent the negative impact of climate change. Long Short-Term Memory (LSTM) is the method that can predict time series data, using unique design of neural network, LSTM can help to prevent vanishing gradient from happening which allows us to use more data from the past to predict the future. Hyperparameters like LSTM unit, epochs, and batch size are used to make the best model, the best model is the one with the lowest loss function. This research used climate data from 1 January 2019 until 31 December 2021 consist of 1096 data in total. The best prediction in this research is made by the model with 1 hidden layer, 64 LSTM units, 8 batch sizes, and 400 epochs with the lowest loss function 0.012769, this model give MAPE value of 1.97325 % and RMSE value of 0.683.
Development of a learning disabilities test a case study at elementary school

This study aims to determine the procedure and to assess the quality of the development of test instruments in measuring the learning disabilities of elementary school students. Learning disabilities are formulated as disorders that occur in learning activities such as dyslexia, dysgraphia, dyscalculia. The type of the research is research and development by using Martini Jamaris’s Model. The subjects were 90 students of elementary school in Jakarta. The number of items was 75 items consisting of 19 items (dyslexia), 29 items (dysgraphia), and 27 items (dyscalculia). The results obtained in the validity test were declared valid were only 54 of the 75 items. Reliability of the test was stated to be reliable with very high interpretation in all dimensions. The results of the analysis of learning disabilities using a learning disabilities test that have been developed were obtained 90% of students experience learning disabilities.
Making nature compute for us

Artificial intelligence is all the hype lately. Behind many of the mind-blowing breakthroughs of the past decade is a single workhorse: More compute. As engineers work hard to supply the necessary electronics, researchers are turning to less conventional ideas in hopes of finding the next big thing. We showed how to employ the complex computations nature does, free-of-charge, for neural networks.
Comparative Analysis of Kannada Formant Synthesized Utterances and their Quality

Objectives:The goal of this work is to synthesize Kannada utterances using a modified Klatt type formant synthesizer to evaluate its performance by comparing against eSpeak synthesizer in terms of intelligibility and quality of the utterances generated.Methods: Kannada utterances viz., vowels, diphthongs, Consonant-Vowel (CV) coarticulations and simple words are generated using a modified Klatt type formant synthesizer and eSpeak.The vowels and diphthongs generated by both the synthesizers are compared with natural recorded utterances using F1-F2 formants and the CV co-articulations are compared using spectrograms.The synthesized word utterances are compared with natural recorded utterances using Log Spectral Distance to find out which synthesizer outputs the frequency spectrum that is closest to the frequency spectrum of the natural utterances.Also, the synthesized word utterances are evaluated for their intelligibility and quality using Mean Opinion Score (MOS) obtained from 10 native Kannada language speakers.Findings: The word utterances synthesized by the modified Klatt type formant synthesizer scored a MOS of 86% and 4.46 out of 5 for the parameters of intelligibility and quality whereas for the same two parameters eSpeak scored 70% and 4.14 out of 5 respectively.Novelty: Klatt type formant synthesizer that uses pitch synchronous parameter update method synthesizes good quality Kannada sound utterances and storing the control parameters of the synthesizer using polynomials reduces the database footprint.
Kernel controlled real-time Complex Langevin simulation

This study explores the utility of a kernel in complex Langevin simulations of quantum realtime dynamics on the Schwinger-Keldysh contour.We give several examples where we use a systematic scheme to find kernels that restore correct convergence of complex Langevin.The schemes combine prior information we know about the system and the correctness of convergence of complex Langevin to construct a kernel.This allows us to simulate up to 1.5 on the real-time Schwinger-Keldysh contour with the 0+1 dimensional anharmonic oscillator using = 1, = 24, which was previously unattainable using the complex Langevin equation.
Police Efforts in Overcoming Anarchic Actions Performed by Motorcycle Gangs

The peculiarity of bike groups is overflowing in a few major urban communities in Indonesia and is typically generally finished by youngsters.All moves initiated by bike posses that cause disorder are vexing to the encompassing local area.In this review, the records used to help the exploration are as works and news connected with the endeavors of the police in handling the wrongdoing of bike posses.The record being referred to is Regulation no. 8 of 1981 concerning the Criminal Method Code, Regulation no. 2 of 2002 concerning the Public Police of the Republic of Indonesia, Perkap Polri No. 23 of 2010 concerning the Authoritative Construction of the Retreat Police and Area Police, Public Police Perkap No. 14 of 2012 concerning Rules for Criminal Examination, the job of the police in endeavors to beat demonstrations of viciousness carried out by cruiser groups which are sought after in three ways, i.e prudently, preventively, and oppressively.Preplanned is done by encouraging bike gangsters who then, at that point, say something to disband from the cruiser posse bunch, preventive endeavors are completed by giving lawful guiding to the local area and junior and secondary school understudies in Garut Rule and as well as passing on requests or messages.
Multiple Person Detection and Tracking using Convolutional Neural Network

Tracking multiple persons is a challenging task when persons move in groups and occlude each other. Existing group based methods have extensively investigated how to make group division more accurate in a tracking-by-detection framework. However, few of them quantify the group dynamics or consider the group in a dynamic view. Inspired by the sociological properties of pedestrians, this work proposes a network that tracks the moving persons.This work uses CNN algorithm by extracting the ROI based HOG features to track more accurately without any interference and to obtain a robust free result. It can be done by considering the thresholding values of the person and based on the thresholding bboxes are assigned to the persons to keep the track s of the persons being detected. Implementation of algorithm, creation of user Interface, leads to observe the performance criteria of the persons being tracked which will gives us accurate and robust free results and widely used in video surveillance and generates direct response. As in the case of any accidental decisions taken by a manual observation can be replaced by using this kind of network. CNN based tracking can overcome the problem of manual observation very accurately based on region of Interest.
A Novel Approach of Watermarking for Multiple Images with DWT-DCT

A novel approach is discussed, to embed the multiple images (more than one image) with single cover image. More than one image coefficients are can be effectively embedded into cover image coefficients. The 3 images (cover image, 2 watermark images) are transformed into wavelet coefficients. One of the coefficients of watermark images LLw1 and LLw2 and cover image LLc and HHc are follows DCT and embedding process respectively. The resultant cover image hiding multiple watermark images respectively. The quantitative and qualitative analysis is discussed with Normalized Correlation (NC), Mean square error (MSE) and Peak Signal to noise ratio (PSNR).
Weakening and Iterating Laws using String Diagrams

Distributive laws are a standard way of combining two monads, providing a compositional approach for reasoning about computational effects in semantics. Situations where no such law exists can sometimes be handled by weakening the notion of distributive law, still recovering a composite monad. A celebrated result from Eugenia Cheng shows that combining $n$ monads is possible by iterating more distributive laws, provided they satisfy a coherence condition called the Yang-Baxter equation. Moreover, the order of composition does not matter, leading to a form of associativity. The main contribution of this paper is to generalise the associativity of iterated composition to weak distributive laws in the case of $n = 3$ monads. To this end, we use string-diagrammatic notation, which significantly helps make increasingly complex proofs more readable. We also provide examples of new weak distributive laws arising from iteration.
CFM: a Convolutional network for First Motion polarity classification of earthquake waveforms.

The knowledge of the crustal stress field is essential in the evaluation of the seismic hazard of an area.To this aim, it is necessary to derive reliable focal mechanisms mainly when small earthquakes have to be included in the computation. The first motion focal mechanism solution techniques are still widely used in modern softwares. The determination of P-wave polarities with manual procedures can lead to human errors and it is time-consuming. Automatic procedures can avoid these drawbacks. Polarity identification is not a classification task easily expressed in terms of mathematical procedures, in fact classical automatic procedures can lead to worse results than those obtained by human operators. For this reason, the use of machine learning approaches results necessary to accomplish this task.With low computational costs, real-time analysis capabilities, no need for complicated pre-processing procedures, and truly competitive results, properly designed convolutional networks can be the answer to various problems, including those related to seismology.&amp;#160;In our work, we present the Convolutional First Motion (CFM) network, a Deep Convolutional Neural Network (DCNN) used to classify seismic traces based on first motion polarities of P-waves. We used waveforms contained in two datasets. We prepared the first dataset selecting approximatively 150&amp;#729;000 waveforms contained in the Italian seismic catalogue INSTANCE, specifically designed for the application of machine learning techniques. To this end we devised an analysis procedure using Principal Component Analysis and Self-Organising Maps, through which a clustering process individuated groups of suitable traces. A second dataset, not specifically designed for machine learning techniques, is prepared manually picking approximatively 4&amp;#729;000 waveforms of earthquakes occurred between 2010 and 2014 at Mt. Pollino area in Italy, avoiding possible overlapping of waveforms between the two datasets. The network, trained on ~130&amp;#729;000 time windows centred on P-wave arrival times of waveforms in the INSTANCE catalogue, achieved accuracies of 95.7% and 98.9% on two test sets: the Mt. Pollino dataset and part of the INSTANCE catalogue. Further testing showed that if we give the network waveforms with uncertain arrival times, it acquires robustness to this type of noise, still showing high-level of performance.We infer that the CFM network would be suitable in succession to automatic techniques that derive P-wave arrival times, for example techniques in which deep learning is used, in order to cover the entire data processing phase with machine learning. Given the incredible ability of DCNNs to model and process large volumes of data and their remarkable performance, it is reasonable to assume that deep learning will soon become the norm even in the context of first-motion polarity determination.&amp;#160;This work was partially supported by the PRIN-2017 MATISSE project (no. 20177EPPN2), funded by the Ministry of Education and Research.