Reviewerly/exHarmony huggingface dataset:
Optimalisasi Dikmas Lantas Mengurangi Pelanggaran Lalu Lintas

In the last 3 years, drugs crime is the most common case in the Bogor City area with 561 cases. Binmas Bintibmas unit has a role in efforts to tackle drug cases among high school students with guidance and counseling. This study is to analyze and describe the efforts and factors that influence the implementation of binluh by the bintibmas unit. Research location in Bogor City Polresta with the focus of the bintibmas unit. This study using qualitative methods with descriptive analysis techniques. Using triangulation (Moleong;2006:330)  of data which is sources, theories and techniques adn using primary, secondary and tertiary data sources. Data validity including data reduction, data present, conclusion making and verification. This research uses management function theory, management element theory and Force Field Analysis theory and effort concept, binluh concept, drug abuse concept and bintibmas concept. The sub-optimal implementation of binluh carried out by the bintibmas unit is analyzed from the planning function, preparing the material to determine binluh targets, organizing, preparing competent officers, implementation and control, assessment and monitoring in binluh activities. In the implementation factor that influence binluh are internal factors that influence that are the factors of people, budget, infrastructure and methods of binluh and external factors are community awareness and factors of other functions also the effort doing with coaching and relationship and work procedures. The conclusion is binluh inhibiting factors carried out by the bintibmas unit are member competencies, extension materials, budgets, cooperation with other functions and agencies, binluh target determination and binluh methods.
Open-Set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios.

An understanding and classification of driving scenarios are important for testing and development of autonomous driving functionalities. Machine learning models are useful for scenario classification but most of them assume that data received during the testing are from one of the classes used in the training. This assumption is not true always because of the open environment where vehicles operate. This is addressed by a new machine learning paradigm called open-set recognition. Open-set recognition is the problem of assigning test samples to one of the classes used in training or to an unknown class. This work proposes a combination of Convolutional Neural Networks (CNN) and Random Forest (RF) for open set recognition of traffic scenarios. CNNs are used for the feature generation and the RF algorithm along with extreme value theory for the detection of known and unknown classes. The proposed solution is featured by exploring the vote patterns of trees in RF instead of just majority voting. By inheriting the ensemble nature of RF, the vote pattern of all trees combined with extreme value theory is shown to be well suited for detecting unknown classes. The proposed method has been tested on the highD and OpenTraffic datasets and has demonstrated superior performance in various aspects compared to existing solutions.
Generation of densely-spaced time-bin entangled qubits on a chip

We present a compact scheme for the integrated generation of densely-spaced time-bin entangled two-photon qubits using an on-chip unbalanced interferometer and a spiral waveguide. We show quantum interference with visibilities as high as 99%.
QUESTIONS USED BY ENGLISH TEACHER IN TEACHING READING COMPREHENSION TO GRADE X AT SMAN 1 MUARA

This research focused on the types of questions used by teacher and the reason for using the questions. It was a qualitative research design. The subject of this study was an English teacher of the tenth grade students of SMA Negeri 1 Muara. The data were collected by observing, video recording, and interview. The result showed that the teacher only used 4 out of 5 types of questions, they were question of literal, question of reorganization, question of inference, and question of personal response. It was also found that there were other types of questions which categorized as students’ knowledge, vocabulary, and students’ experiences. Out of the 4 types of question, the type of questions frequently used was question of literal. Followed by question of reorganization came as the second, and the least used by the teacher was question of personal response. The reasons for using the types of questions were to measure students’ comprehension and to facilitate students towards comprehension.  Key words:  teaching reading comprehension, types of questions
CSTlemma version 8.1.2

CSTlemma is lemmatizer that treats pre- in- and suffixes alike.The CST's lemmatizer can be (and already is) trained for tens of languages, also ones that require lemmatization rules that change words by adding or removing prefixes and/or infixes to obtain the lemma for the word. In Dutch, for example, the word afgemaakt has the lemma afmaken, so the ge has to be removed, an a has to be inserted and the t-ending must be replaced by en.New in version 8 of CSTlemma is the possibility to output the rule by which given word is transformed to its lemma. It is also possible to just output unique identifier for that rule - in practice, this identifier is just some kind of pointer in the datastructure that comprises the rule set.Rules for CSTlemma must be created with the affixtrain program (https://github.com/kuhumcst/affixtrain), but ready-made rules can be obtained from the net. For example, the https://github.com/kuhumcst/texton-linguistic-resources repo contains rules for about 30 languages.If you want to build CSTlemma, you not only need the source code contained in https://github.com/kuhumcst/cstlemma, but also some source code files from https://github.com/kuhumcst/letterfunc and from https://github.com/kuhumcst/parsesgml, The easiest and best way to go forward is to copy https://github.com/kuhumcst/cstlemma/blob/master/doc/makecstlemma.bash to (linux, Mac?) folder and run that script. That will fetch all needed repositories and build cstlemma.
Spec work: Estudio de caso de la convocatoria “Talent Hunt” organizada por Jackie Smith (Agosto 2020, Argentina)

This paper analyzes the issue of spec work (speculative work) from a critical perspective.In order to go deeper in a situation where a company has carried out a design competition as a methodology for recruiting personnel, we propose a case of study: the Talent Hunt open call, organized by Jackie Smith during the month of August 2020 in Argentina.The empirical study uses the critical discourse analysis, based on previous research which tackles argumentation and rhetoric in the context of digital social media, from the original publication of the contest in Instagram and three others that reacted to this initiative.
SISTEM APLIKASI INVENTORY SEPEDA MOTOR PADA DEALER YAMAHA SURYA PRIMA GAMBUT BERBASIS VISUAL

Dealer is an agency or individual whose job is as the distribution hand from producers to consumers. In general trading it is also referred to as a sales agent, but in stock or foreign exchange trading, it is referred to as a broker. Yamaha Dealer Surya Prima Gambut is one of the most popular Yamaha dealers in Banjar. This dealer is located on Jl. A.Yani Km.13,7 Peat, Peat, Kayu Bawang, Peat, Banjar, South Kalimantan 70652. At this dealer the management system still uses Microsoft Office as a basic application that helps in managing motorcycle inventory data currently running.Based on these needs, the construction of a visual-based application system is expected to facilitate employees or application users who are appointed as administrators in every management and processing of motorcycle inventory data at Yamaha Surya Prima Gambut dealers.The final result of this study is the creation of an application system that can be applied to provide ease in managing motorcycle inventory data at Yamaha Surya Prima Peat dealers easily in data entry and report processing using Visual Studio 2010.
A research level of efficiency treatment of Covid-19 using data envelopment analysis

Coronavirus Disease 2019 (Covid-19) is a collection of viruses that can infect the respiratory system. This virus only causes mild respiratory infections, such as the flu. The process of handling Corona infection patients is indeed much different from other disease patients. Covid-19 can cause various symptoms in its sufferer. These symptoms depend on the type of corona virus that attacks, and how serious the infection is. The design of this application uses DFD and ERD modeling which is built using the web-based PHP programming language and uses a MySQL database. The assessment of the efficiency level of COVID-19 was carried out in one of the provinces in Indonesia, namely in the province of Aceh, using samples from 12 existing hospitals. All the hospitals that we have taken data every day will continue to increase and decrease. This application is built using the Data Envelopment Analysis (DEA) method, where the search process is determined by the lindo software. The input criteria in this application are suspected patients, probable patients, confirmed patients, and patients being treated. While the output of this application is the patient recovers, and the patient dies. All the data we get is real data that we get directly from the hospital and the results are confirmed via the Internet. All calculation processes use predetermined formulas. The computational result obtained from the DEA CCR Model on the 8 DMUs is 1, it is the gain from dividing the output variable by the input variable, then the results obtained from 8 DMUs are efficient and 4 DMUs are inefficient.
LibGuides: History 402A (Fall 2021): Metadata Literacy

Library instruction guide for the Fall 2020 semester of History 402A: Introduction to Public History.
Unsupervised Modified Clustering Technique Based on Fuzzy Set Theory to Categorize the Real-Life Data

The fuzzy C-means clustering algorithm is appropriate for segmenting datasets and is commonly utilized in real-life settings. The growth of huge data has posed numerous obstacles for clustering approaches. Due to their high density and execution time, traditional clustering approaches cannot be applied for such vast amount of real-world data. To address these challenges, we offer an unsupervised clustering method for automatically categorizing large-scale datasets without requiring labels, with a focus on the real-life dataset. Experiments on real-world datasets show that our suggested unsupervised technique performs well and has high precision. The outcomes reveal that the proposed approach effectively segregate unstructured real-world database into distinct clusters.
10 Cybersecurity & Blockchain

Blockchain is an emerging technology that can radically improve transactions security at banking, supply chain, and other transaction networks. It's estimated that Blockchain will generate $3.1 trillion in new business value by 2030. Essentially, it provides the basis for a dynamic distributed ledger that can be applied to save time when recording transactions between parties, remove costs associated with intermediaries, and reduce risks of fraud and tampering. This book explores the fundamentals and applications of Blockchain technology. Readers will learn about the decentralized peer-to-peer network, distributed ledger, and the trust model that defines Blockchain technology. They will also be introduced to the basic components of Blockchain (transaction, block, block header, and the chain), its operations (hashing, verification, validation, and consensus model), underlying algorithms, and essentials of trust (hard fork and soft fork). Private and public Blockchain networks similar to Bitcoin and Ethereum will be introduced, as will concepts of Smart Contracts, Proof of Work and Proof of Stack, and cryptocurrency including Facebook's Libra will be elucidated. Also, the book will address the relationship between Blockchain technology, Internet of Things (IoT), Artificial Intelligence (AI), Cybersecurity, Digital Transformation and Quantum Computing. Readers will understand the inner workings and applications of this disruptive technology and its potential impact on all aspects of the business world and society. A look at the future trends of Blockchain Technology will be presented in the book.
Wearable Sensor-based Hand Gesture Recognition of Construction Workers

Wearable Sensor-based Hand Gesture Recognition of Construction Workers Xin Wang and Zhenhua Zhu Pages 498-504 (2021 Proceedings of the 38th ISARC, Dubai, UAE, ISBN 978-952-69524-1-3, ISSN 2413-5844) Abstract: Maintaining good communication is important for keeping the construction site safe and the project running smoothly and on schedule. Hand gestures, as one of the common ways to communicate, are widely used on construction sites due to their simple but effective nature. However, the meaning of these hand gestures was not always captured precisely, which would lead to construction errors and even accidents. This paper presented a feasibility study on investigating whether the hand gestures could be captured and interpreted automatically with wearable sensors. A new dataset which is made of the accelerometer and gyroscope data is created. The created dataset contains 8 classes of hand gestures for instructing tower crane operations and is employed to compare two state-of-the-art deep learning networks, namely, Fully Convolutional Neural Network (FCN) and ResNet, and measure their hand gesture recognition performance. The comparison results indicate that a high classification accuracy (96.9%) could be achieved. Further, a pilot study was conducted in a laboratory environment to test whether the methods used in this study could serve as an interface to help workers control and/or interact with construction machines. Keywords: Hand Gesture Recognition; Wearable Sensor; Dataset Creation; Performance Comparison DOI: https://doi.org/10.22260/ISARC2021/0068 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
The essence and content of the professional competence of the future teacher of musical art

The article explores the issues of developing of the professional competence of the future teacher of musical art, as well as its importance for the successful realization of their educational work. The authors have carried out a theoretical analysis of psychological and pedagogical works, on the basis of which it can be argued that a competency-based approach is one of the priorities in the modern educational environment. The article defines the concepts: “competence of music teacher”, “professional competence”, “professional competence of music teacher”, “professional competence of the future teacher of musical art”. The authors highlight the structure of professional competence of the future teachers. According to the authors, the professional training of the future teacher of musical art should include a complex of competences, such as: artistic, communicative, professional, methodical and qualified. Based on the scientific achievements of modern scientists, the authors cover a range of studies of competencies in artistic education. Summarizing the views of Ye. Provorova, I. Poluboiaryn, N. Tsiuliupa and others on the essence of competencies, the authors highlight their main content dominants: integrated quality; set of knowledge and skills; motivation; readiness for effective professional activity.The work focuses on the individual’s motivation, because the consideration of motivation, according to scientists, is of key importance for the professional competence of the future teachers of musical art. Scientists have given their own definition of “professional competence of the future teacher of musical art”. In their opinion, this is the unity of theoretical and practical readiness for professional activity, which is a structured set of content components of teacher, which is based on a professional world outlook and is measured by certain criteria and standards. A component-based structure is proposed, which includes such components as: artistic and informative, facilitative, musical and professional.The ways of further scientific work in the direction of diagnostics of formation of professional competence of the future teacher of musical art and development of methodology with the use of multimedia technologies are determined.
Nursing care to systemic arterial hypertension patients assisted by the e-sus program from the patient's perspective

General objective: To know the nursing care provided to systemic arterial hypertension patients assisted by the e-sus program from the patient's perspective. Methodology: Exploratory research with a quantitative approach. A form was used as an instrument, applied through home visits to 50 elderly people in March 2020. CEP approval opinion no. 3,534,141, on August 27, 2019. Results and Discussion: It was found in the research that 60% of the elderly were between 60 and 75 years old, 70% were female and 60% were illiterate. Regarding quality of life, 64% of the elderly reported physical inactivity, 78% were obese or overweight, and 54% reported having another pathology. In addition, 60% reported not participating in educational activities, hindering health promotion and prevention. As for the use of antihypertensive medications, 94% of the users informed that they were taking them, and 45.3% cited the verification of BP as care taken by the team. Conclusion: Therefore, it becomes relevant the implementation of new strategies and the improvement in the approaches already used by professionals, aiming at a quality of life for the elderly.
Standard principles of managing digital rights to access electronic resources of library collections

The key principles of managing digital rights (DRM) of access to electronic documents applicable to book trade and library services are discussed. Digital document types and popular e-book formats where DRM-systems are applicable, are analyzed. The significance of DRM systems standardization, their friendliness to users and e-book rightsholders, is substantiated. The key provisions of international standards implemented this year to regulate typology of copyright security tools as well as DRM systems characteristics based on data coding, are examined. The most popular commercial solutions for DRM systems are reviewed; non-commercial international Readium project is described. The project is aimed at designing and developing software modules to integrate standard DRM technology into automated information systems, including library AIS.
Software Quality Prediction using Different Machine Learning Methods

Software quality estimation is an activity needed at various stages of software development. It may be used for planning the project`s quality assurance practices and for benchmarking. In earlier previous studies, two methods (Multiple Criteria Linear Programming and Multiple Criteria Quadratic Programming) for estimating the quality of software had been used. Also, C5.0, SVM and Neutral network were experimented with for quality estimation. These studies have relatively low accuracies. In this study, we aimed to improve estimation accuracy by using relevant features of a large dataset. We used a feature selection method and correlation matrix for reaching higher accuracies. In addition, we have experimented with recent methods shown to be successful for other prediction tasks. Machine learning algorithms such as XGBoost, Random Forest, Decision Tree, Logistic Regression and Naive Bayes are applied to the data to predict the software quality and reveal the relation between the quality and development attributes. The experimental results show that the quality level of software can be well estimated by machine learning algorithms.
Scientific and methodological heritage of the Russian National Public Library for Science and Technology: The background

Being the all-Union coordination center for bibliography in technologies and the methodological center for USSR’s scientific and special libraries, the State Public Scientific and Technical Library of the USSR issued reference bibliographic publications, special periodicals, serials and manuals based on the research accomplished by the Library. It developed the system of learning materials in every area of library services and functions (user services, collection development and organization, reference aids, bibliographic work, etc.). All materials were highly demanded by scientific and technical libraries of the country and were of great importance for the advance of scientific, technical and special libraries and national librarianship on the whole. The author reviews the key reference bibliographic and methodological materials produced by the State Public Scientific and Technical Library of the USSR in 1958–1980.
Stain Normalization of Histopathology Images using conditional Generative Adversarial Networks (cGAN)

The diagnosis of cancer is mainly performed by visual analysis of pathologists through examining the morphology of the tissue slices under a microscope. If the microscopic image of a specimen is not stained, it will look colorless and without texture. Therefore, chemical staining is required to create adequate contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, and types of illness, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to interpretive disparity among pathologists, is one of the main challenges in designing robust and flexible systems for automated analysis. Various strategies for stain normalization have been proposed as a pre-processing step in the pipeline of the automated systems. The pix2pix methodwhich is derived from the conditional Generative Adversarial Networks (cGAN) is one of the powerful methods for solving image-to-image translation problems. The main innovation of this paper is to present a new powerful method for the stain normalization of histopathology images using the Pix2Pix method, which is implemented and evaluated on the Mitos-Atypia-14 dataset.In the proposed method, grayscale images are given as input to the network, and then the system learns to restain the texture of the input image in a specific coloring style by preserving the structure and corresponding histopathological pattern. This method, compared to previous methods that relied on a reference image, instead uses the distribution of all images in the learning phase. The proposed method has achieved significant resultsboth in quantitative and qualitative evaluations comparing to some well-known methods in the literature.Moreover, as another innovation, the proposed method tested in a clinical use-case, namely breast cancer tumor classification,using the PatchCamelyon datasetand itshowsa 5% increase in the AUC parameter.
Successful Neural Network Architecture Design

The previous chapter, on meta-optimization, discussed the automation of neural network design, including automated design of neural network architectures. It may seem odd to follow such a chapter on the automation of neural network architectures with a chapter on successful (implicitly, manual) neural network architecture design, but the truth is that deep learning will not reach a point anytime soon (or at all) where the state of design could possibly dismiss the need to possess an understanding of key principles and concepts in neural network architecture design. You've seen, firstly, that Neural Architecture Search – despite making rapid advancements – is still limited in many ways by computational accessibility and reach in the problem domain. Moreover, Neural Architecture Search algorithms themselves need to make implicit architectural design decisions to make the search space more feasible to search, which human NAS designers need to understand and encode. The design of architectures simply cannot be wholly automated away.