Reviewerly/exHarmony huggingface dataset:
Discovering the Old Dubrovnik Cathedrals: Stone Fragments Analysis, Cataloguing and Structured Terminology Development in Digital Environment

The research and educational project Discovering the Old Dubrovnik Cathedrals was launched in 2015. Its activities were focused on the study of archaeological finds originating from the site of two consecutive medieval cathedral buildings under the actual Baroque Cathedral in Dubrovnik. The archaeological excavations at the site were undertaken after the earthquake of 1979, and the resulting finds have not been studied since. The primary activity of the educational project that so far involved over 40 students was the study of stone and glass fragments, as well as fragments of wall paintings and pottery. Out of approx. 3000 stone fragments, 250 were selected for detailed cataloguing, analysis and indexing. A metadata record was created for each object and each record was accompanied by multiple photographic representations of the fragment. Whole-part relationships between records allow the structuring and simulation of certain architectural units (e.g. the Romanesque ambo). Another important result of the project is the development of structured terminology via multilingual thesaurus (equivalence, associative and hierarchical relationships have been established among concepts). The aim of the article is to present an application of data processing in the field of digital art history and in the broader context of digital humanities.
Contact tracing applications and informational privacy amidst the pandemic in India

Various technological interventions introduced during the COVID-19 pandemic outbreak have resulted in numerous challenges, including the protection of informational privacy. Contact tracing applications are one such method adopted by governments worldwide to contain the pandemic. Their key features involve the collection and use of sensitive personal data, such as an individual’s personal information, health record, and location, raising concerns among regulators. Regulators must ensure that these technologies align with informational privacy protection. The current legal regime in India is not sophisticated enough to delve into issues that concern contact tracing technology. In India, the introduction of the Aarogya Setu Application (contact tracing application) gained attention when the Ministry of Home Affairs (MHA) made downloading it mandatory for individuals. Eventually, doing so was made voluntary. Considering these current trends, this paper aims to examine developments in data privacy issues specifically regarding surveillance technologies and tracing applications in India. It highlights and analyzes them considering private players which announced their partnership in creating a robust contact tracing application. The study also adopts a comparative examination of existing contact tracing applications and their privacy policy along with the Indian version and explores how one deploys jurisprudence for informational privacy as perceived in India.
Path Aggregation Model for Attributed Network Embedding with a Smart Random Walk

Abstract Network embedding has attracted a surge of attention recently. In this field, how to preserve high-order proximity has long been a difficult task. Graph convolutional network (GCN) and random walk-based approaches can preserve high-order proximity to a certain extent. However, they partially concentrate on the aggregation process and sampling process respectively. Path aggregation methods combine the merits of GCN and random walk, and thus can preserve more high-order information and achieve better performance. However, path aggregation framework has not been applied in attributed network embedding yet. In this paper, we propose a path aggregation model for attributed network embedding, with two main contributions. First, we claim that there always exists implicit edge weight in networks, and design a tweaked random walk algorithm to sample paths accordingly. Second, we propose a path aggregation framework dealing with both nodes and attributes. Extensive experimental results show that our proposal outperforms the cutting-edge baselines on downstream tasks, such as node clustering, node classification, and link prediction.
Civil engineering professional certification management information system

With the improvement of information technology and the promotion of professional certification, the informationization of professional certification is bound to become the mainstream trend of professional certification in various disciplines. Based on the three basic requirements of engineering education professional accreditation, this paper designs and develops a management information system for civil engineering professional accreditation based on the existing traditional academic affairs system with four new functional modules such as civil engineering professional accreditation assessment system. The system realizes the whole cycle and process control of civil engineering students' study and career development, further improves the theoretical level and practical ability of engineering students in universities, cultivates a large number of high-quality engineering talents for the society, and also standardizes the quality standards of civil engineering and related majors.
Chapter 12. Exploring variation in translation with probabilistic language models

While some authors have suggested that translationese fingerprints are universal, others have shown that there is a fair amount of variation among translations due to source language shining through, translation type or translation mode. In our work, we attempt to gain empirical insights into variation in translation, focusing here on translation mode (translation vs. interpreting). Our goal is to discover features of translationese and interpretese that distinguish translated and interpreted output from comparable original text/speech as well as from each other at different linguistic levels. We use relative entropy (Kullback-Leibler Divergence) and visualization with word clouds. Our analysis shows differences in typical words between originals vs. non-originals as well as between translation modes both at lexical and grammatical levels.
Research on the Path Planning of the Construction Robot Based on BIM

Aiming at the problem of the optimal global path planning of the construction robot entering the target point of the construction site and the efficient construction of the navigation map, a BIM(Building information modeling)-based construction robot job planning method is proposed. The traditional method of building a map environment for construction robots is obtained by its own sensors, which has a large amount of calculation. Firstly, the real construction environment is established through the BIM model, which effectively improves the efficiency of generating navigation map . Secondly, adding goal-directed functions to the RRT algorithm, combined with the target bias expansion algorithm based on the idea of artificial potential fields to further accelerate the search efficiency. Finally, the pruning algorithm is used to reduce the redundant points, which shortens the running time of the algorithm and the simulation time of the robot. The improved RRT algorithm before and after was compared and simulated in the construction environment of BIM in V-rep. The experimental results demonstrate a reduction of more than 50% in the final path simulation time for the improved RRT algorithm.
Multilingual Translational Optical Character Recognition System for Printed Telugu Text

OCR, an acronym for &#x201C;Optical Character Recognition&#x201D; is a system that automatically grabs information one needs from scanned images of typewritten or printed text by translating them into machine-encoded text. OCR today is embedded in many applications, websites, etc., but most of these systems operate for Latin-based scripts such as Roman and English. India is a multilingual country with more than 19,500 languages or dialects spoken as mother tongues. Due to this diversity, many works are not reported in Indian languages. Most of the Indian language has large character sets that are complex in structure compared to Latin-based scripts. Transfer learning of Latin-based OCR systems to Telugu is hence a difficult undertaking. Neural networks are best equipped to meet the difficulty of Telugu OCR. This work aims to develop a multilingual translation OCR system that can recognize the basic printed texts of Telugu scripts.
On the Issue of Modernization and Integration of Long-term Electronic Archives

Over a period of about twenty years, the IIS SB RAS has created information systems of historical orientation: the Electronic Archive of Academician A. P. Ershov, the Photo Archive of the Siberian Branch of the RAS, the Archive of the newspaper "Science in Siberia", the Open Archive of the SB RAS, etc. Each of the resources has its own specifics but in general their content is based on the general social and territorial basis of scientific and public activities of the SB AS USSR/RAS and the Novosibirsk Akademgorodok. In this report we will look at some of the problems of integrating/disintegrating disparate electronic resources into a common platform using existing and emerging tools.
Analysis of Breast Spectral Data Based on Machine Learning

Breast cancer has seriously threatened the health of women in China, however, the conventional detection method has its limitations, so it is of great significance to explore a rapid, noninvasive and sensitive detection technology. In recent years, spectroscopy has been explored as a tool for not using molybdenum targets to determine breast density, and then predict the occurrence of the breast cancer. Based on this idea, this study attempts to do preliminary processing and analysis on the actual spectral data collected by experiments, building models and comparing three classification methods which based on machine learning, which are logical regression, support vector machine(SVM) and random forest algorithm. The accuracy achieved 90.48%(logical regression), 85.71% (SVM) and 95.24%(random forest) in the recognition of breast hyperplasia. In addition, the prediction method of combining spectral data with breast cancer risk factor data was explored, and the accuracy of SVM method was 77.8%. In this study, the random forest algorithm is characterized as the best in the analysis with the participation of only mammary gland spectrum, and in the analysis with the participation of spectral data and risk data SVM shows the better performance than random forest although there is still no ideal effect and needs more data support. This study provides a new idea for the spectral detection of breast cancer in China and provides a possibility for the development of a new breast cancer detection method.
Classification Trees for Imbalanced Data: Surface-to-Volume Regularization

Classification algorithms face difficulties when one or more classes have limited training data. We are particularly interested in classification trees, due to their interpretability and flexibility. When data are limited in one or more of the classes, the estimated decision boundaries are often irregularly shaped due to the limited sample size, leading to poor generalization error. We propose a novel approach that penalizes the Surface-to-Volume Ratio (SVR) of the decision set, obtaining a new class of SVR-Tree algorithms. We develop a simple and computationally efficient implementation while proving estimation consistency for SVR-Tree and rate of convergence for an idealized empirical risk minimizer of SVR-Tree. SVR-Tree is compared with multiple algorithms that are designed to deal with imbalance through real data applications. Supplementary materials for this article are available online.
Application of Machine Learning Algorithm Based on Neural Network Technology

Abstract Neural networks have strong characteristics for processing data and information. At the same time, the current computer technology is also very advanced, and many kinds of very powerful information technologies have been developed under the promotion and promotion of modern science and technology. Therefore, relevant personnel will carry out advanced technology and neural network structure methods. Fusion, and then an artificial neural network was established on this basis. In a broad sense, machine learning refers to how to enable a machine to acquire relevant knowledge through autonomous learning, and the purpose is to enable the machine to have relevant skills similar to what people need to acquire knowledge. The research in this article aims to explore the machine learning algorithms based on neural network technology, and through literature research methods, case analysis methods, etc., to have an in-depth understanding of machine learning algorithms in neural network technology, and then through the analysis of machine learning algorithms in neural network technology The learning advantage and its influencing factors are designed based on the machine learning algorithm in the neural network and experimented. Experimental results show that LSTM performs well in replication tasks, and the performance of LSTM even far exceeds that of NTM, but the performance of LSTM in addition and multiplication tasks is much lower than that of NTM. Although the accuracy of NTM on the test set is higher than that of LSTM and RNN, the performance of the model is still relatively poor.
Low illumination image enhancement technology based on iterative dense residual network

The images collected by imaging equipment in low-light environments often have low brightness, low contrast, loss of image details, etc., which will cause huge interference in certain application scenarios such as video surveillance, automatic driving, face recognition, etc., according to this article A low-illuminance image enhancement algorithm based on an iterative dense residual network is used to solve the above problems. The network uses the image brightness component V as input, uses a new type of dense residual block to extract image features, adds an attention mechanism to enhance feature learning, and finally obtains an enhanced image through image reconstruction. Experimental results show that compared with mainstream algorithms, the algorithm in this paper performs well in subjective and objective evaluation, and meets the requirements of natural color, contrast, and robustness for image enhancement.
IMPLEMENTASI MODEL PEMBELAJARAN DARING PADA MASA PANDEMI COVID – 19

Penelitian ini merupakan penelitian kualitatif deskriptif. Penelitian ini dilaksanakan di Sekolah Dasar Negeri Cengkong II Kecamatan Purwasari kabupaten Karawang. Subjek penelitian ini adalah 3 orang yaitu guru kelas IV, 1 orang siswa dan 1 orangtua siswa. Objek penelitian ini adalah proses pembelajaran daring pada masa covid-19. Teknik pengumpulan data yang digunakan dalam penelitian ini adalah pengamatan dan wawancara. Instrumen pengumpulan data terdiri atas pengamatan dan pedoman wawancara. Analisis data dilakukan secara deskriptif dengan melakukan Teknik analisis data model Miles and Humberman.&#x0D; Hasil dari pengamatan dan wawancara diperoleh bahwa Sekolah Dasar Negeri Cengkong II Kecamatan Purwasari Kabupaten Karawang pada masa pandemi covid-19 melakukan pembelajaran jarak jauh (PJJ) dengan menggunakan model pembelajaran daring / online. Aplikasi whatsapp, google meet, google form, zoom dan kine master adalah aplikasi yang digunakan dalam proses pembelajaran daring / online. Handphone, kuota dan jaringan internet yang kuat dan stabil merupakan faktor utama dalam proses pembelajaran daring / online, agar tercapainya materi ajar dan tujuan pembelajaran selama guru menjelaskan dan menyampaikan materi yang telah disiapkan. Selain itu terdapat kendala dalam proses pembelajaran daring yaitu: jaringan internet yang tidak stabil dan tidak semua siswa memiliki handphone / gadget.&#x0D; Kata kunci : Implementasi pembelajaran, model pembelajaran daring / online.&#x0D; This research is a descriptive qualitative research. This research was conducted at Cengkong II State Elementary School, Purwasari District, Karawang Regency. The subjects of this study were 3 people, namely the fourth grade teacher, 1 student and 1 student's parent. The object of this research is the online learning process during the covid-19 period. Data collection techniques used in this study were observation and interviews. The data collection instrument consisted of observations and interview guidelines. Data analysis was carried out descriptively by using the Miles and Humberman model data analysis technique.&#x0D; The results from observations and interviews showed that the Cengkong II State Elementary School, Purwasari District, Karawang Regency during the covid-19 pandemic carried out distance learning (PJJ) using an online / online learning model. WhatsApp, Google Meet, Google Form, Zoom and Kine Master applications are applications that are used in the online learning process. Mobile phones, quotas and a strong and stable internet network are the main factors in the online learning process, in order to achieve teaching materials and learning objectives as long as the teacher explains and conveys the material that has been prepared. In addition, there are obstacles in the online learning process, namely: unstable internet network and not all students have cellphones / gadgets.&#x0D; Keywords: Implementation of learning, online learning model / online.
Semi-supervised Semantic Segmentation Based on Confrontation Network

We propose a semi-supervised semantic segmentation method using adversarial networks. Different from most existing discriminators, we use spatial resolution to distinguish the predicted probability map and the real ground segmentation distribution. The identifier can improve the accuracy of semantic segmentation by coupling the antagonism loss of the model and the standard cross entropy loss. In addition, full convolution discriminator is used to achieve semi-supervised learning, which can provide additional monitoring signals. Compared with the existing methods using weak labeled images, our method enhances the segmentation model. The experimental results on VOC2012 data set prove the effectiveness of the algorithm.
Barrier Forming: Separating Polygonal Sets with Minimum Number of Lines

In this work, we carry out structural and al-gorithmic studies of a problem of barrier forming: selecting the minimum number of straight line segments (barriers) that separate several sets of mutually disjoint objects in the plane. The problem models the optimal placement of line sensors (e.g., infrared laser beams) for isolating many types of regions in a pair- wise manner for practical purposes (e.g., guarding against intrusions). The problem is NP-hard even if we want to find the minimum number of lines to separate two sets of points in the plane. Under the umbrella problem of barrier forming with minimum number of line segments, three settings are examined: barrier forming for point sets, point sets with polygonal obstacles, polygonal sets with polygonal obstacles. We describe methods for computing the optimal solution for the first two settings with the assistance of mathematical programming, and provide a 2-OPT solution for the third. We demonstrate the effectiveness of our methods through extensive simulations.
Higher Education Student Emotions

This article presents the perception of 611 students from public universities in Mexico about online classes in pandemic times. Exploratory factor analysis was conducted. Vygotsky's contributions are taken, especially those related to emotional manifestations. It is concluded that the most important factor in this change is the emotions that are identified as an impulse to act during online classes, given the didactic and pedagogical components, resources, and supports for students that promote learning. This proposes a framework for considering the emotional aspects of positive engagement in students learning online. There is evidence of some balance between the advantages and disadvantages of online education. It also suggests that women perceive more negative emotions such as disappointment and frustration in the online education experience. The teacher, being a vicarious learning model, can innovate in education by promoting peripheral participation with other playful activities that involve abstract, applied, and situated learning.
Computer-Aided Language Learning (CALL) used in the example of Sakarya University

Considering the ubiquity of computers and the Internet in this ever changing technological era, it is definitely normal that they would affect the language learning process. As an institution, Sakarya University is supposed to keep pace with the technology in every field. Thereupon, CALL started being used at English Language Preparatory Class of Sakarya University. Computer-Aided Language Learning (CALL) is the fulfilment of software and web-based programs to ease the learning of English. CALL can be a good supplementary tool it cannot only be used like traditional face-to-face instruction. An increased use of computers and the advent of the Internet in every field of modern lifestyle make computer-aided language learning irreplaceable in all aspects. Being new to those who were fond of the high technology and who were fed up with using books, CALL grabbed the students’ attention, which made the study easier. In the system, Computers were tools that students mainly utilized. Every student had a computer reserved for himself/herself. It was an amazing experience for those using books. In this study it is aimed to evaluate CALL implemented at English Language Preparatory Class of Sakarya University and assess the students’ performances. The data analysis method was used. The data were able to be managed and analyzed properly and effectively after the conclusions of the research. The success results of the Sakarya University preparatory class students were evaluated. The data utilized in the study are restricted to the period of three academic years. According to results CALL has been successfully applied in years when the software used was up-to-date. In line with the results, CALL is a useful tool that helps teachers to ease the language learning process.
Chinese character relationship extraction based on BERT and CNN-BiGRU-ATT

Among Chinese-oriented character relationship extraction methods, most traditional deep learning methods use word2vec as word vector embedding, and use a single convolutional neural network (CNN) or recurrent neural network (RNN). Word2vec cannot solve the problem of polysemy, CNN is not suitable for processing sequence tasks, and RNN cannot fully describe local features. Aiming at the above problems, we propose a method of character relationship extraction based on bidirectional encoder representations from transformers (BERT), CNN and bidirectional gated recurrent unit (BiGRU). In this paper, first, we use BERT to dynamically characterize the characteristics of word vectors. Second, combine the prominent ability of CNN to extract local features and BiGRU's ability to handle serialization tasks. At the same time, we introduce an attention mechanism (ATT) into the model. The experimental results of Soochow University & Setaria Company-Personal relationship extraction data set show that this method has achieved 81 % accuracy. Compared with the single CNN and RNN with word2vec, it has improved the Recall and F1-score.