Reviewerly/exHarmony huggingface dataset:
Supporting Linked Engineering Data Management of Smart Product Systems Through Semantic Platform Services

The increasing industrial relevance of connected smart product systems, enhanced individualized customer requirements, regulatory boundary conditions as well as advanced value networks and volatile international markets determine a new level of engineering complexity. Current engineering processes are enabled by a large number of IT applications, which are partially orchestrated by product lifecycle management solutions. Considering the dramatically increasing complexity, the IT applications are often not adequately connected as a whole, leading to interruptions in the process chains and finally resulting in quality problems, time expenditure, and additional costs. To support seamless engineering processes, a sufficiently linked information system structure is necessary. Due to the often-existing best-of-breed solutions and historically grown IT infrastructures, the direct point-to-point coupling of IT systems among each other requires disproportionately high efforts. As an approach to improve the existing situation, integration platforms utilizing loose coupling of individual heterogeneous engineering IT applications by semantic web technologies could be used to create a comprehensive, linked engineering data structure. This paper presents a conceptual IT-Service approach that utilizes this linked data structure to address problems in the area of information retrieval, data exchange, and data quality. A fundamental service function enables easy access to the information scattered throughout the whole organization. This approach could contribute to a high level of transparency within the company’s internal processes and relationships between data across individual engineering IT applications. The proposed concepts for IT platform services facilitate the exchange of data between different software applications and the standardized checking and documentation of data quality.
Are Students Satisfied with the Current School-Based Curriculum of Chinese Traditional Culture? A Survey of 120 Elementary and Middle Schools in China

The school-based curriculum is one of the crucial ways of Chinese traditional culture education. Therefore, it is essential to discuss the current elementary and middle school students’ satisfaction with the traditional cultural school-based curriculum. A survey of the curriculum satisfaction of 120 elementary and middle schools in China with a traditional cultural school-based curriculum found that students’ satisfaction with these curriculums is generally average. However, students believed that the quality of the curriculum is still low. Its main manifestations were passive satisfaction, compromise satisfaction, excellent satisfaction, fall satisfaction, and autonomous satisfaction. This highlighted the problems of some traditional cultural school-based curriculums like the positioning is based on subjective guesswork, the content has not been effectively screened, and the implementation method is a single indoctrination. Based on this, we suggest that: (i) create an all-round atmosphere for students to learn traditional culture actively; (ii) reshape the traditional culture in the curriculum according to the value of the times; (iii) guarantee the cultural resources and professional teachers of curriculum implementation with discipline construction, and (iv) focus on the experience of the implementation process.
PERSEPSI MAHASISWA TENTANG PENERAPAN E-LEARNING PADA MASA DARURAT COVID-19

Teaching and learning activities in long distance. Moving the learning process from classroom to online classroom requires careful preparation and methods. Both in terms of facilities and human resources. One of the human resources in this case is a student. This research describes about the perceptions of students in application of e-learning at STIA Muhammadiyah Selong. From data analysis, the perception of STIA Muhammadiyah Selong’s student is quite good for the application of e-learning. This can be seen from the independence of students in completing assignments and understanding lecture material,  student independence belong to medium categori(62%). The ease of getting teaching material belong to high categori. But understanding of material is low categori, 73,6% students get trouble to understanding material and enrichment.
Static Type Analysis by Abstract Interpretation of Python Programs (Artifact)

Python is an increasingly popular dynamic programming language, particularly used in the scientific community and well-known for its powerful and permissive high-level syntax. Our work aims at detecting statically and automatically type errors. As these type errors are exceptions that can be caught later on, we precisely track all exceptions (raised or caught). We designed a static analysis by abstract interpretation able to infer the possible types of variables, taking into account the full control-flow. It handles both typing paradigms used in Python, nominal and structural, supports Python’s object model, introspection operators allowing dynamic type testing, dynamic attribute addition, as well as exception handling. We present a flow- and context-sensitive analysis with special domains to support containers (such as lists) and infer type equalities (allowing it to express parametric polymorphism). The analysis is soundly derived by abstract interpretation from a concrete semantics of Python developed by Fromherz et al. Our analysis is designed in a modular way as a set of domains abstracting a concrete collecting semantics. It has been implemented into the MOPSA analysis framework, and leverages external type annotations from the Typeshed project to support the vast standard library. We show that it scales to benchmarks a few thousand lines long, and preliminary results show it is able to analyze a small real-life command-line utility called PathPicker. Compared to previous work, it is sound, while it keeps similar efficiency and precision.
A Multi-Cluster Random Forests-Based Approach to Super-Resolution of Abdominal CT Images Using Deep Neural Networks

To enhance the resolution of abdominal computed tomography slices, a super-resolution method based on random forests is proposed. We classify input LR patches using an Auto-Encoder network, and we employ several random forest machines for the estimation of HR data. The random forest machines are trained, and they are used to reconstruct HR patches in the test phase. Compared to the conventional K-means clustering and interpolation techniques, our results improved the performance of the SR algorithm by at least 0.02 and 5.7% using the SSIM and PSNR measures. While the conventional RF-based method generated unwanted spots in the HR image, the quality of our results outperforms the standard RF-based approach. Moreover, the proposed intensity profile follows that of the original image and preserves the edges better; however, the conventional technique generates false high-frequency oscillations.
A Regional University in the Conditions of the Transition to Digital Technologies: Trends, Opportuni-ties and Prospects for Managing Research Activities

Speeding up in the digitalization of the country's economy has a positive effect on the economic development of the regions, but this requires not only legislative initiatives, but also their scientific justification. The conceptual apparatus for managing the research activities of a regional university in the context of the transition to digital technologies was developed in the paper. It has been proven that in the new economy, universities make a significant contribution to the development of innovation and economic growth.
Optical character recognition <b>and the smart ancient script database</b>

The initial success of optical character recognition (OCR) for ancient scripts has opened the floodgates for ‘smart’ ancient script research. ‘Smart’ ancient script research requires the support of a smart ancient script database. In order to compile the big data necessary for this smart research, smart ancient script database software must be able to recognize all aspects and all levels of all ancient script materials. Therefore, in addition to the integration of OCR functionality into this software, the other primary imperative moving forward is to innovate a new digitized ancient script data system, one that includes full-scale supplementation to include all available materials, as well as newly inputted image data. This data must include variant graphic forms, variant written forms, handwriting, graphic components, calligraphic styles, and other of the inexhaustible different variations in script construction. This database must contain a multi-level framework with an annotated arrangement of the fullest range of meanings for words within linguistic context. It must also contain a digitally integrated multiple-path indexed arrangement of the important paleographical interpretations in the field. Our strategy for the construction of this smart ancient script database is to push forward with both algorithm writing and data input work simultaneously and in mutual support, following an open-sourced community supported model, making this project an exercise in interdisciplinary collaboration within the paleography community.
On the Chinese Development of Computer-Assisted Translation under the Background of Artificial Intelligence

With the rapid development of science and technology, artificial intelligence (AI) technology has a high-rapid growth at the same time. Under the background of artificial intelligence, computer-assisted translation (CAT) was born at the right moment, which not only helps people to learn the methods of translation, but also contributes the better environment for development on translation in China. Therefore, the study is focused on the development of artificial intelligence, which points out the direction of computer-assisted translation at present.
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles

Ever since the COVID-19 pandemic broke out, the academic and scientific research community, as well as industry and governments around the world have joined forces in an unprecedented manner to fight the threat.Clinicians, biologists, chemists, bioinformaticians, nurses, data scientists, and all of the affiliated relevant disciplines have been mobilized to help discover efficient treatments for the infected population, as well as a vaccine solution to prevent further the virus' spread.In this combat against the virus responsible for the pandemic, key for any advancements is the timely, accurate, peer-reviewed, and efficient communication of any novel research findings.In this paper we present a novel framework to address the information need of filtering efficiently the scientific bibliography for relevant literature around COVID-19.The contributions of the paper are summarized in the following: we define and describe the information need that encompasses the major requirements for COVID-19 articles' relevancy, we present and release an expert-curated benchmark set for the task, and we analyze the performance of several state-of-the-art machine learning classifiers that may distinguish the relevant from the non-relevant COVID-19 literature.
Review on the principle, Progress and Application of Block chain Technology

Abstract In recent years, electronic currencies such as bitcoin have been favored by people. As the core technology of Bitcoin, blockchain has attracted the attention of more and more experts and scholars at home and abroad. The changes of its classification mode, service mode and application requirements lead to the diversified development of core technology forms. Blockchain is a distributed ledger technology, which evolves into a complete storage system based on logic control functions such as intelligent contracts. Block chain technology is based on decentralized point-to-point transmission network, using cryptography principles and consensus mechanism to ensure the coherence of each node participating in the distributed system, and to ensure the accuracy, consistency, non-tampering or deletion of the recorded information. All these ensure the establishment of a sharing system with high privacy, high security and high efficiency. In this paper, the basic principles and characteristics of blockchain technology are analyzed, and the research status and application of this technology in the financial system are summarized in detail.
Justifications for Goal-Directed Constraint Answer Set Programming.

Ethical and legal concerns make it necessary for programs that may directly influence the life of people (via, e.g., legal or health counseling) to justify in human-understandable terms the advice given. Answer Set Programming has a rich semantics that makes it possible to very concisely express complex knowledge. However, justifying why an answer is a consequence from an ASP program may be non-trivial -- even more so when the user is an expert in a given domain, but not necessarily knowledgeable in ASP. Most ASP systems generate answers using SAT-solving procedures on ground rules that do not match how humans perceive reasoning. We propose using s(CASP), a query-driven, top-down execution model for predicate ASP with constraints to generate justification trees of (constrained) answer sets. The operational semantics of s(CASP) relies on backward chaining, which is intuitive to follow and lends itself to generating explanations that are easier to translate into natural language. We show how s(CASP) provides minimal justifications for, among others, relevant examples proposed in the literature, both as search trees but, more importantly, as explanations in natural language. We validate our design with real ASP applications and evaluate the cost of generating s(CASP) justification trees.
The Bibliographic Work: History, Theory, and Practice

The bibliographic work has assumed a great deal of importance in modern cataloging. But the concept of the work has existed for over a century, and even some of the earliest catalog codes differentiate between the intellectual work and its instances. This article will delve into the history and theory of the work, providing a basic overview of the concept as well as a summary of the myriad uses of the work throughout the history of cataloging. In addition to monographs, this paper will look at the work as applied to music, moving images, serials, and aggregates.
The Acquisition of English Vocabulary for Children at Grade III in SD Islam Nurul Hidayah

AbstractThis study is an effort to understand the acquisition of the English language by students at SD Islam Nurul Hidayah in Bojong Sari. The aim of this study is to identify and evaluate the acquisition of the English language by students at SD Islam Nurul Hidayah in Bojong Sari. The use of simple statistical analysis response research query, a qualitative approach was used. This study used the theory on telegraphic and formulaic speech from Tabors and Snow. In this study, there were 17 students as samples and the collection of data techniques was taken through interviews.The results of the interviews indicate that the development of English language vocabulary by children is in two stages (telegraphic/formulaic expression and constructive language): body part is 39% telegraphic and 61% productive, transportation is 62% telegraphic and 38% productive, color is 44% telegraphic and 56% productive, classroom is 40% telegraphic and 60% productive, job is 45% telegraphic dan 55% productive, nation is 43% telegraphic and 57% productive, universe is 81% telegraphic and 19% productive, self-introduction is 57% telegraphic and 43% productive, prayer before learning is 100% with 0% productive language, and prayer after learning is 100% and 0% productive language. This study then recommends that the teacher of SD Islam Nurul Hidayah in Bojong Sari increase the student's motivation to learn English and to practice English more frequently. It is recommended to do further research focusing on students in preschool or kindergarten to examine their acquisition of English. Keywords: Second Language Acquisition; Children; Elementary School
The Effect of Class Noise on Continuous Test Case Selection: A Controlled Experiment on Industrial Data

Continuous integration and testing produce a large amount of data about defects in code revisions, which can be utilized for training a predictive learner to effectively select a subset of test suites. One challenge in using predictive learners lies in the noise that comes in the training data, which often leads to a decrease in classification performances. This study examines the impact of one type of noise, called class noise, on a learner’s ability for selecting test cases. Understanding the impact of class noise on the performance of a learner for test case selection would assist testers decide on the appropriateness of different noise handling strategies. For this purpose, we design and implement a controlled experiment using an industrial data-set to measure the impact of class noise at six different levels on the predictive performance of a learner. We measure the learning performance using the Precision, Recall, F-score, and Mathew Correlation Coefficient (MCC) metrics. The results show a statistically significant relationship between class noise and the learner’s performance for test case selection. Particularly, a significant difference between the three performance measures (Precision, F-score, and MCC) under all the six noise levels and at 0% level was found, whereas a similar relationship between recall and class noise was found at a level above 30%. We conclude that higher class noise ratios lead to missing out more tests in the predicted subset of test suite and increases the rate of false alarms when the class noise ratio exceeds 30%.
The Application of Computer-supported Mind Map in College English Reading Teaching

This paper starts with the analysis of the essence of computer-supported mind maps and traditional mind maps, and then makes an in-depth analysis of the application of computer-supported mind maps in English teaching, including detailed discussion and planning on the development of mind maps, scaffolding teaching mode and brainstorming teaching strategies. Finally, the essential differences between English reading teaching and reading are elaborated in detail. Starting from the reality of college English reading teaching, while improving the development of college English teaching, the rapid development of computer-supported mind map in college English reading teaching is promoted.
Signatures' stability evaluation in a multi-device scenario

On-line signatures can be acquired adopting specifically devoted stylus-pad system as well as with general purpose tablet/smartphone. The resulting acquired signal is strongly influenced by the device itself as well as by the user interaction modality. It is evident that it is desirable to identify the most stable features for verification aims. In this work, a set of 39 function feature domains is investigated in terms of stability adopting the weighted Direct Matching Points approach. Experiments have been performed on the e-biosign dataset providing many evidences related to the different scenarios.
Opinion Mining Analysis of Twitter Users of Particular Topic

Opinion mining consists of many different fields like natural language processing, text mining, decision making and linguistics. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.
A Comparative Study of Keywords and Sentiments of Abstracts by Python Programs

Four corpora are created to investigate the self-mentions, keywords and sentiment of abstracts. First, self-mentions are categorized to examine the authorial interactions with the reader. Then, the study of high-frequency words and keywords is conducted with different Python programs and the software AntConc. The keywords generated with WordCloud and TF-IDF-LDA methods show a definite relation with high-frequency words generated by Jieba_Counter and NLTK FreqDist. Further, the sentiment analysis is performed with SnowNLP and TextBlob yielding different results, which verifies the authorial interactions with the reader and increased factual information respectively. Finally, the verification by reference corpora validates the consistency of the sentiment analysis by these two methods. The research suggests that the methods for high-frequency words generation, keywords generation and sentiment analysis be selected discriminatively since different methods generate different results; meanwhile, the study verifies that the objectivity remains in the writing of abstracts. The investigation is conducive to the choices of keywords generation and self-mentions in writing.
Complementary Libraries to TensorFlow 2.x

Now that we covered the basics of machine learning and deep learning, we can slowly move on to the applied side of deep learning. As you know, every machine learning application, including deep learning applications, has a pipeline consisting of several steps. TensorFlow offers us several modules for all these steps. Even though TensorFlow is very powerful for model building, training, evaluation, and making predictions, we still need other complementary libraries for certain tasks, especially for data preparation. Although the potential libraries you may use in a deep learning pipeline may vary to a great extent, the most popular complementary libraries are as follows:
Efficient Metaballs-Based Collision Detection for VR Neurosurgery Simulation on GPU

This paper presents a novel hybrid model comprising both surface mesh and the metaballs which occupy organs’ interior for the soft tissue modeling. Through the utility of metaballs, we are capable of simplifying the organ interior using a set of overlapping spheres with different radii. We first develop an adaptive approach based on Voronoi Diagram for the initialization of inner metaballs. Then, we resort to global optimization and devise an electrostatic attraction technique to drive the metaballs to best fill the space inside the organ’s boundary. We simplify the surgical instrument as a collection of cylinders with different radii and orientation, and develop an adaptive collision detection method to facilitate the collision between the surgical instrument and metaballs. Our framework is built on the parallel computation architecture of CUDA, and thus can afford interactive performance on a commodity desktop. To illustrate the effectiveness, the above techniques have all been integrated into a VR-based ventriculoscopic surgery simulator.