Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF ISO 9001 QUALITY MANAGEMENT SYSTEM IN UNS CENTER LIBRARY

The purpose of this study was studied to determine the implementation of the ISO 9001 Quality Management System in the UNS Central Library. The research method used is qualitative with a phenomenal approach. The phenomena that have occurred are the transformation of ISO 9001, program innovation carried out by the UNS Central Library, and Predicate A for the National University Libraries. The results of this study were reviewed by 10 ISO clauses with the PDCA pattern (Plann, Do, Check, and Act). So that getting a program that has been running includes Processing Library Materials, Cooperation Administration, Digital Library Services, Academic Internships, Maintenance of Infrastructure, Website Management, Book Returns, HR, Information Literacy, Scientific Library Clinics, Library Tours, News Coverage, Environmental Maintenance, to the processing of journals, magazines, and newspapers. All of these programs are the result of implementing ISO 9001 itself by compiling Quality Procedures and Work Instructions for all elements of the UNS Central Library.
MBS model for control and optimization of a hydrofoil boat

Abstract The most efficient hull concept for racing boats is a hull completely lifted out of the water by a configuration of hydrodynamic foils. Due to its similarity to an inverted pendulum, the system is intrinsically unstable and requires control mechanisms. This paper describes a typical configuration of a hull and the hydrofoils and the corresponding MBS model. The MBS‐model allows studying parameters for control and hydrofoil positions and is coupled with a global optimizer to choose the most beneficial set of parameters.
Constructing a Relevance-oriented Dataset for Training Transformer Rankers for Medical Search

As there is usually sparse relevance assessment data for training, state-of-the-art transformer-based rankers may under-perform when directly employed in the medical domain. In particular, the lack of human-annotated labels has become a bottleneck of learning the data-hungry deep IR models. In this work, we introduce an effective approach to generating data from existing medical corpora for training transformer-based re-ranking models. Inspired by the fact that the title is usually closely relevant to the abstract in a scientific article, we create a dataset by regarding titles as queries and the corresponding abstracts as relevant documents, and use an unsupervised IR model (e.g., BM25) to sample negative examples. Thereafter, we train the re-ranking model on this relevance-oriented dataset before training the model on the target collection. Experimental results on the standard TREC Precision Medicine track benchmark show that our approach, despite its simplicity, is able to outperform strong recent baselines. The dataset is available for download <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Research on the Application of "Internet + MOOC" Mixed Teaching Model in English Subject

At this stage, the information-based teaching model based on the Internet and information technology as the main foundation and support has gradually been widely used in the teaching of related courses. The application of the Internet-based hybrid teaching model in English subjects has also been continuously promoted to promote Curriculum teaching reform and innovation provide new ideas and new methods. With the development and improvement of the MOOC teaching model, the current hybrid teaching model of Internet + MOOC has also been verified by teaching practice in English subjects. This article introduces the basic characteristics and advantages of the hybrid teaching model of Internet + MOOC, analyzes the current situation of hybrid teaching in English subjects, and explores the application strategies of the hybrid teaching model of Internet + MOOC in English subjects.
DETEKSI KECURANGAN LAPORAN KEUANGAN MENGGUNAKAN BENEISH M – SCORE DAN PREDIKSI KEBANGKRUTAN MENGGUNAKAN ALTMAN Z – SCORE (Studi Empiris pada Perusahaan yang Termasuk dalam Indeks LQ – 45 Tahun 2016 – 2018)

Financial statements can be the one of the source of financial information in a company. Fraud can be seen through financial reports, which can be interpreted as acts intended to get personal benefit or in a group that can harm other parties directly or indirectly, which is usually done by parties from within or outside the organization and is an illegal activity. Through the company's financial statements can be seen the financial condition of a company. If the company experiences economic difficulties, the company is unable to pay off its obligations or debt. If the company is exposed to financial distress, the company is considered to have failed in their business. This study was conducted with the aim of finding out companies listed on the Indonesia Stock Exchange (IDX) and those included in the LQ-45 index who were manipulating on their financial statements and who had financial difficulties reflected in the financial statements presented. The sample used in this study were 24 companies listed on the Indonesia Stock Exchange (IDX) and included in the LQ-45 index in 2016 - 2018. The type of data used in this study is secondary data, which is in the form of company annual reports. This study uses quantitative descriptive methods to analyze data. Beneish M - Score is used to detect financial statement fraud and Altman Z - Score is used to predict bankruptcy in companies. The results of this study indicate that in 2016 there were 42% of companies classified as manipulators, in 2017 there were 33% of companies classified as manipulators, and in 2018 there were 29% of companies classified as manipulators. In addition, in 2016 there were no companies experiencing financial difficulties or going bankrupt and in 2017 and 2018 there were 1 (one) or 4% of companies experiencing financial difficulties or who were going bankrupt.
Dynamic Connected Neural Decision Classifier and Regressor with Dynamic Softing Pruning

To deal with datasets of different complexity, this paper presents an efTo deal with various datasets over different complexity, this paper presents an self-adaptive learning model that combines the proposed Dynamic Connected Neural Decision Networks (DNDN) and a new pruning method–Dynamic Soft Pruning (DSP). DNDN is a combination of random forests and deep neural networks that enjoys both the advantages of strong classification capability of tree-like structure and representation learning capability of network structure. Based on Deep Neural Decision Forests (DNDF), this paper adopts an end-to-end training approach by representing the classification distribution with multiple randomly initialized softmax layers, which further allows an ensemble of multiple random forests attached to layers of neural network with different depth. We also propose a soft pruning method DSP to reduce the redundant connections of the network adaptively to avoid over-fitting simple dataset. The model demonstrates no performance loss compared with unpruned models and even higher robustness over different data and feature distribution. Extensive experiments on different datasets demonstrate the superiority of the proposed model over other popular algorithms in solving classification tasks.ficient learning model that combines the proposed Dynamic Connected Neural Decision Networks (DNDN) and a new pruning method–Dynamic Soft Pruning (DSP). DNDN is a combination of random forests and deep neural networks thereby it enjoys both the properties of powerful classification capability and representation learning functionality. Different from Deep Neural Decision Forests (DNDF), this paper adopts an end-to-end training approach by representing the classification distribution with multiple randomly initialized softmax layers, which enables the placement of the forest trees after each layer in the neural network and greatly improves the training speed and stability. Furthermore, DSP is proposed to reduce the redundant connections of the network in a soft fashion which has high flexibility but demonstrates no performance loss compared with previous approaches. Extensive experiments on different datasets demonstrate the superiority of the proposed model over other popular algorithms in solving classification tasks.
A Design of Direct Granular Model based on Takagi-Sugeno Fuzzy Model

In this paper, a direct granular TS fuzzy model (DGFM) trained from numerical data is proposed. The DGFM extracts a set of weighted data from the rules of numerical TS model and generates information granules from weighted data as the output of the granular model. The DGFM can evidently reduce number of parameters in model training process and can reduce computational complexity during model construction and derivation. In addition, an improved performance index for the granular model is designed, which can reflect the deviation between the output information granule and the target value by improving the coverage criterion and making the evaluation of the granular model more comprehensive. Experimental results are reported for both synthetic data and real-world datasets to show the performance of the DGFM.
Prediksi Kunjungan Wisatawan Mancanegara Ke Indonesia Menggunakan Jaringan Saraf Tiruan Dengan Algoritma Backpropagation

This study aims to see the development of the number of foreign tourist arrivals in the following year. With these predictions, it is expected to be able to assist the government in making policies related to foreign tourists in accordance with their home regions. Sources of data obtained from the Central Statistics Agency. In this study, researchers used the Backpropagation Algorithm. Backpropagation Algorithm is an algorithm that serves to reduce the error rate by adjusting the weight based on the desired output and target. From the test results of foreign tourist visit data obtained in 1-4-1 architecture which shows the target is reduced by the output that SSE 1.03218 which shows that there is an increase in the number of visits as a target. From the data obtained, that the performance calculation of artificial neural networks with Backpropagation Algorithm is 83.3This research contributes to the government and the community to further increase the provision of facilities, infrastructure, infrastructure, transportation and accommodation in order to get a satisfying predicate to improve the quality of the world market.
THE EFFECT OF TEACHING TECHNIQUE AND GRAMMAR MASTERY TOWARD READING COMPREHENSION AT THE EIGHT GRADE STUDENTS OF JUNIOR HIGH SCHOOL IN PANDEGLANG

The purpose of this study was (1) to show the effect of teaching techniques on students' reading comprehension at the eighth grade students of junior high school in Pandeglang (2) to show the effect of grammar mastery on students' reading comprehension at the eighth grade students of junior high school in Pandeglang (3) to show the effect teaching techniques and grammar mastery simultaneously toward students' reading comprehension at the eighth grade students of junior high school in Pandeglang. The research method used was an experimental method with two-way ANOVA analysis. The sample was eighty students of the eighth grade of senior high school Pandeglang and the sampling was done through a qualitative approach, data collection was carried out by means of a test filled with respondents. Data analysis consisted of: descriptive statistical analysis, data analysis requirements test (normality and homogeneity test), and hypothesis testing with two-way ANOVA. The statistical test used probability test (Sig.). This research was conducted from April to July 2015. The results showed that there is an effect of teaching techniques on students' reading comprehension. This is proved by the Sig. = 0.000 <0.05. There is an effect of grammar mastery on students' reading comprehension, this is evidenced by the Sig. = 0.000 <0.05. There is an effect of teaching techniques and grammar mastery simultaneously on students' reading comprehension, this is proved by the Sig. = 0.000 <0.05.
On Imitation and Innovative Teaching Method of Image Processing

With the rapid development of China's science and technology, the country's demand for technical talents has gradually increased. Under such circumstances, the development of school teaching has been significantly affected. In the course of teaching reform, the school focuses on cultivating students' creative thinking ability and attaches great importance to students' computer skills. Among them, the cultivation of students' creative thinking ability is the teaching goal of computer image processing. This article focuses on analyzing and exploring the imitation and innovation teaching method of image processing, breaking the traditional teaching model, and mobilizing students' learning motivation through the imitation and innovation method.
Memory State Tracker: A Memory Network based Dialogue State Tracker

Dialogue State Tracking (DST) is a core component towards task oriented dialogue system.It fills manuallyset slots at each turn of an utterance, which indicate the current topics or user requirement.In this work we propose a memory based state tracker that includes a memory encoder which encodes the dialogue history into a memory vector, and then connects to a pointer network which makes predictions.Our model reached a joint goal accuracy of 49.16% on MultiWOZ 2.0 data set (Budzianowski et al., 2018) and 47.27% on MultiWOZ 2.1 data set (Eric et al., 2019), outperforming the benchmark result.
Penerapan Komputer Dalam Mengidentifikasi Virus dan Bakteri Penyakit Ichtyosis Menerapkan Algoritma Rete

Most people use it not only for commercial purposes, but also to obtain information on the detection of diseases quickly and efficiently with computer-based applications that can help the general public to find out the causes and symptoms of the disease. For this reason, we need a system designed to be able to imitate the expertise of an expert in answering questions and solving a problem in accordance with the knowledge of an expert that is entered into a computer system. The development of artificial intelligence technology that occurred has enabled expert systems to be applied in detecting diseases using programming languages. One of them is in providing information about various problems, especially hepatitis. The expert system method used is the Rete Algorithm used to deal with uncertainty in the diagnosis of ichthyosis. With the facilities provided for the user, allowing users to use this system according to their individual needs. Users are given the ease of knowing information about the symptoms of ichthyosis, and its prevention.
Review of Sign Language Recognition Based on Deep Learning

Sign language recognition involves computer vision, pattern recognition, human-computer interaction, etc. It has important research significance and application value. The flourishing of deep learning technology brings new opportunities for more accurate and real-time sign language recognition. This paper reviews the sign language recognition technology based on deep learning in recent years, formulates and analyzes the algorithms from two branches - isolated words and continuous sentences. The isolated-word recognition technology is divided into three structures: Convolutional Neural Network (CNN), Three-Dimensional Convolutional Neural Network (3D-CNN) and Recurrent Neural Network (RNN) based method. The model used for continuous sentence recognition has higher complexity and is usually assisted with certain kind of long-term temporal sequence modeling algorithm. According to the major structure, there are three categories: the bidirectional LSTM, the 3D convolutional network model and the hybrid model. Common sign language datasets at home and abroad are summarized. Finally, the research challenges and development trends of sign language recognition technology are discussed, concluding that the robustness and practicality on the premise of high-precision still requires to be promoted.
Predicting Prescriptions via DSCA-Dual Sequences with Cross Attention Network

Mining Electronic Health Records (EHRs) is of great significance to improve the efficiency and quality of medical services. In recent years, researchers have used deep recurrent neural networks to predict patients' next-period prescriptions. The main challenges of predicting next-period prescriptions are as follows: i) The latent interdependence information which can be utilized to enhance the representation of the data between heterogeneous features is dynamic. However, most existing approaches do not consider capturing and utilizing this information. ii) Conventional recurrent neural networks cannot store historical interdependent information between heterogeneous features and take advantage of it. To address these challenges, We propose a novel attention mechanism named Cross Attention (CA) that can capture the interdependence between two sequences. We further propose three types of recurrent neural networks that can capture and utilize current and historical latent interdependence between the two sequences. Extensive experiments on real-world data demonstrate that DSCA networks can capture the interdependence information between sequences and outperform state-of-the-art methods.
Pathogenic Factors Mining Based on Medical Big Data Using Association Analysis Algorithm: A Case of Anorectal Diseases

Guangdong Province is a district with a high incidence of anorectal diseases. In addition, the pathogenic factors of anorectal diseases are complex and diverse. Existing clinical diagnostic techniques are difficult to accurately analyze the pathogenic factors of anorectal diseases. Therefore, in this paper, an association analysis algorithm is proposed for solving the problem of pathogenic factors mining. In order to dig out the valuable information of medical big data, an improved Apriori algorithm is designed. Compared with the classical Apriori algorithm, by deleting the unrelated itemsets which do not contain the specific items, the proposed Apriori algorithm can decrease the run time. Finally, simulation results show the superiority of the proposed algorithm.
Ensemble of Pre-Trained Pedestrian Detectors via Non-Maximum Suppression for Better Generalization and Consistent Performance

Pedestrian Detection (PD) is one of the most challenging problems for advanced driving assistance systems. Although deep models have achieved significant performance, their limited generalization ability prevents real-life implementation. The reason behind this limitation is shown to be the data-dependency of the deep learning, especially when model architecture and parameters are tweaked to boost the performance. As a result, pre-trained deep models tend to perform much worse when they are tested on a different dataset. By focusing on this issue, this paper investigates the use of ensembles consisting of vanilla-style deep models as weak classifiers. The main aim is to investigate if multiple models are used by their default parameters (without any fine-tuning considering the test set performance); would their ensemble reach better generalization capacity and have consistent performance on different datasets? Designing an ensemble for PD significantly differs from conventional classifier ensembles due to the importance of bounding box properties (location, size etc.) found by the detector. Thus, an extensive experimentation is carried out on how to combine model score with bounding box properties. Both simple ensemble strategies and learning based methods have been tested to present a simple and effective pipeline. Finally, non-maximum suppression, which is currently being used for bounding box regression, is modified to develop an ensemble method and the results show that it provides better improvements compared to existing ensemble strategies.
PENINGKATAN AKTIFITAS PEMBELAJARAN BAGI PESERTA DIDIK SEKOLAH DASAR DI MASA PANDEMI COVID-19 MELALUI WHATSAPP DAN GOOGLE CLASSROOM

Abstrak: Pembelajaran online melalui aplikasi WhatsApp dan Google Classroom ini adalah pengalaman baik ( best practice ) penulis dalam memberikan layanan terhadap peserta didik di SD Negeri Kademangan 4 Kota Probolinggo di masa pandemi Covid-19. Tujuan pembelajaran online ini adalah untuk memberikan layanan yang mudah diakses oleh peserta didik dan orang tua murid dan menjaga agar peserta didik tetap menaruh minat untuk belajar. Hasil pembelajaran secara umum adalah baik. Keterlibatan peserta didik kelas I berjumlah 30 orang, peserta didik yang aktif sebanyak 29 orang atau 96,67% sedangkan yang tidak aktif 1 orang atau 3,45%. Kelas II berjumlah 28 orang, peserta didik yang aktif sebanyak 23 orang atau 82,14% sedangkan yang tidak aktif 5 orang atau 21,74%. Kelas III berjumlah 28 orang, peserta didik yang aktif sebanyak 27 orang atau 96,43% sedangkan yang tidak aktif 1 orang atau 1 orang atau 3,45%. Kelas IV berjumlah 32 orang, peserta didik yang aktif sebanyak 32 orang atau 100%. Kelas V berjumlah 32 orang, peserta didik yang aktif sebanyak 27 orang atau 84,38% sedangkan yang tidak aktif 5 orang atau 18,52%. Kelas VI berjumlah 28 orang, peserta didik yang aktif sebanyak 22 orang atau 78,57% sedangkan yang tidak aktif 6 orang atau 27,27%. Kendala yang dihadapi diantaranya adanya peserta didik yang tidak mempunyai handphone sendiri, jaringan internet yang kurang lancar, dan tidak punya kuota. Kata Kunci: Aktifitas pembelajarn; aplikasi WhatsApp ; dan Google Classroom . IMPROVEMENT OF LEARNING ACTIVITIES FOR PRIMARY SCHOOL STUDENTS IN THE PANDEMIC OF COVID-19 THROUGH WHATSAPP AND GOOGLE CLASSROOM Abstract: Online learning through the WhatsApp and Google Classroom applications is the author's best practice in providing services to students at SD Negeri Kademangan 4, Probolinggo City during the Covid-19 pandemic. The purpose of this online learning is to provide services that are easily accessible to students and parents and to keep students interested in learning. Overall learning outcomes are good. The involvement of class I students was 30 people, active students were 29 people or 96.67% while those who were not active were 1 person or 3.45%. Class II totaled 28 people, active students were 23 people or 82.14% while those who were not active were 5 people or 21.74%. Class III totaled 28 people, active students were 27 people or 96.43%, while those who were not active were 1 person or 1 person or 3.45%. Class IV totaled 32 people, active students were 32 people or 100%. Class V totaled 32 people, active students were 27 people or 84.38%, while those who were not active were 5 people or 18.52%. Class VI numbered 28 people, active students were 22 people or 78.57%, while those who were not active were 6 students or 27.27%. The obstacles faced include students who do not have their own cellphones, internet networks that are not available, and do not have quotas. Keywords: learning activities; WhatsApp application; and Google Classroom.
Principal Component Analysis with Quantum Whale Optimization Algorithm

Principal component analysis (PCA) is a classical supervised linear dimension reduction algorithm in machine learning. PCA solves the optimal dimension reduction direction by maximizing the variance of projection points. The classical methods to realize optimization are gradient ascend method or stochastic gradient ascend method depending on gradient information. However, the above methods are easy to fall into the trap of local extremum, which is not conducive to global optimization. In this paper, the quantum whale algorithm is introduced into the optimization part of PCA, which enhances the parallelism and globality of optimization, and gets rid of the dependence on gradient.