Reviewerly/exHarmony huggingface dataset:
Current Situation Research on Integrated Development and Application of China's "Publishing + VR/AR"

As the development of information technology and change in people's reading habits, the publishing industry is integrating with the modern information technology and transforming to the digitization. The virtual reality (VR) and augmented reality (AR) technologies have been developed rapidly since 2016, which have begun to be applied in some areas and made remarkable achievements in just a few years. Multiple publishing units in China respond to the call of the times, and conduct the innovative development by combining with their own resource advantages. In this paper, the application status of VR/AR technology in book publishing, newspaper media, periodicals and magazines is summarized, and some representative cases are illustrated the application of VR/AR technologies in the publishing industry does give readers an extraordinary reading experience, and promotes the interaction among readers, authors and publishing units, which finds a new innovation point for the innovation of the whole publishing industry.
Deep Visual Anomaly detection with Negative Learning

With the increase in the learning capability of deep convolution-based architectures, various applications of such models have been proposed over time. In the field of anomaly detection, improvements in deep learning opened new prospects of exploration for the researchers whom tried to automate the labor-intensive features of data collection. First, in terms of data collection, it is impossible to anticipate all the anomalies that might exist in a given environment. Second, assuming we limit the possibilities of anomalies, it will still be hard to record all these scenarios for the sake of training a model. Third, even if we manage to record a significant amount of abnormal data, it's laborious to annotate this data on pixel or even frame level. Various approaches address the problem by proposing one-class classification using generative models trained on only normal data. In such methods, only the normal data is used, which is abundantly available and doesn't require significant human input. However, these are trained with only normal data and at the test time, given abnormal data as input, may often generate normal-looking output. This happens due to the hallucination characteristic of generative models. Next, these systems are designed to not use abnormal examples during the training. In this paper, we propose anomaly detection with negative learning (ADNL), which employs the negative learning concept for the enhancement of anomaly detection by utilizing a very small number of labeled anomaly data as compared with the normal data during training. The idea is to limit the reconstruction capability of a generative model using the given a small amount of anomaly examples. This way, the network not only learns to reconstruct normal data but also encloses the normal distribution far from the possible distribution of anomalies.
Design and Development of Activated Carbon-Based Purification System for Automobile Emissions

Activated carbon, also known as activated charcoal, is a type of carbon processed to have tiny, low-volume pores that enhance the surface area available for adsorption or chemical reactions. Because of its high degree of micro porosity, one gram of activated carbon has a surface area in surplus of 3,000 m2 (32,000 sq. ft) as determined by gas adsorption. An activation level enough for useful application may be obtained exclusively from high surface area. Further chemical treatment often enhances adsorption properties. Carbon adsorption has a number of applications in eliminating pollutants from air or water streams both in the field and in industrial processes like: ground water remediation, spill clean-up, air purification, drinking water filtration, volatile organic compounds capture from painting, dry cleaning, gasoline dispensing operations, and several other processes. Carbon filtering is a process of filtering that uses a bed of activated carbon to eliminate contaminants and impurities, using chemical adsorption. Activated carbon works through a process called adsorption, whereby pollutant molecules in the fluid to be treated are trapped inside the pore structure of the carbon substrate. Carbon filtering is generally used for water purification, air filtering and industrial gas processing. Distinctive particle sizes that can be removed by carbon filters range from 0.5 to 50 micrometres. The particle size will be used as part of the filter depiction. Lot of efforts are made to reduce the air pollution from petrol and diesel engines and regulations for emission restrictions are also imposed in USA and in a small number of cities of India. A wide-ranging analysis of energy usage and pollution shows that substitute power systems are still a long way behind the conventional ones. Additionally, developments in petrol and diesel engines, combined with improvements in the vehicles, will make fuel consumption reduction of 40% or more in the future cars. This, in turn, will decrease the CO 2 emissions, a gas which is answerable for greenhouse effect.
경기도 일부 어린이집 조리종사자의 내분비계 장애물질 인식도 및 노출 저감화 행동 조사

Endocrine-disrupting chemicals (EDCs) are chemicals present in the environment that interfere with the normal hormone functions of various organisms and cause genetopathy, deformities, or cancer. This study surveyed the awareness of EDCs with 242 cooking staff at 242 meal service facilities for children located in a part of Gyeonggi Province. To minimize infants’ exposure to EDCs, the subjects were provided with information on EDCs for two months. The behavior of reducing exposure to EDCs was analyzed according to the awareness of EDCs and work ethics. In addition, the effects before and after being provided with the information were evaluated. According to the results, the levels of awareness of EDCs and work ethics’ scores of the cooking staff were high with 3.95 and 4.39 points, respectively, out of five points. In addition, a higher awareness of EDCs and a higher work ethics’ score were associated with an improved behavior of reducing exposure to EDCs (P＜0.05). The overall cooking and cleaning behavior for reducing exposure to EDCs showed improvement after providing information (P＜0.01). Therefore, these results suggest that it is important to provide continuous education to enhance the awareness of EDCs, work ethics, and behavior to reduce exposure to EDCs.
Seeking String Theory in Free Algebras

After decades of study, string theory still lacks a formulation which is non-perturbative, background independent and duality invariant. The free Lie algebra can be resolved into cyclic structures which can be mapped onto a closed string-like state in a given background. From this observation the idea that string theories may be reformulated in background free algebraic terms is explored.
Revisiting Knowledge Distillation for Object Detection

The existing solutions for object detection distillation rely on the availability of both a teacher model and ground-truth labels. We propose a new perspective to relax this constraint. In our framework, a student is first trained with pseudo labels generated by the teacher, and then fine-tuned using labeled data, if any available. Extensive experiments demonstrate improvements over existing object detection distillation algorithms. In addition, decoupling the teacher and ground-truth distillation in this framework provides interesting properties such: as 1) using unlabeled data to further improve the student's performance, 2) combining multiple teacher models of different architectures, even with different object categories, and 3) reducing the need for labeled data (with only 20% of COCO labels, this method achieves the same performance as the model trained on the entire set of labels). Furthermore, a by-product of this approach is the potential usage for domain adaptation. We verify these properties through extensive experiments.
Challenges Kindergarten Administrators Face in Gaza City in Using E-learning in Light of Corona Pandemic: الصعوبات التي تواجه مديرات رياض الأطفال بمحافظة غزة في استخدام التعليم الإلكتروني في ظل جائحة كورونا

The study aims to identify the challenges that kindergarten administrators face in Gaza city with using e-learning in light of the Coron pandemic. The study uses the descriptive and analytical approach, and the study covers all the kindergartens in Gaza city, which they are (350) managers. The researcher chose the kindergarten directors in the West Gaza District, which they are (132) managers as a sample for the study. The results showed that kindergarten administrators in Gaza city face difficulties in using e-learning in light of the Corona pandemic to a large extent, with an arithmetic average (1.92, and a relative weight (83.85%), and at the level of fields, the technical difficulties ranked first with an arithmetic average (4.23), With a relative weight (84.67%) and a very large degree, then administrative and financial challenges followed, and it's ranked second, with an arithmetic average (4.18), and a relative weight (83.65%) and to a large degree the difficulties related to the concept of e-learning ranked third, with an arithmetic average (4.161), with relative weight (83.23%), and to a large extent, The study illustrates the solutions to how to reduce these challenges, which they have educated the community about the importance of electronic education, the need to unify the educational platforms for children as a base, and train kindergarten administrators to use websites. The results did not reveal any statistically significant differences in the difficulties that kindergarten managers face in using E-learning refers to a variable of academic qualification, years of experience, or kindergarten type; The study recommended and illustrate the necessity to implement educational programs for kindergarten directors to train them on how to use an e-learning by using activating seminars and training programs for directorates to develop their electronic skills, and the necessity of conducting research studies in the field of e-learning in kindergartens in light of the current conditions.
Model Clusterization of WebGIS-based Mobile Internet Provider Connections

The spread of the corona virus or COVID-19 pandemic has presented its own challenges for educational institutions in Indonesia. As a result of this policy, the education sector such as universities stopped the face-to-face learning process. Ibn Khaldun Bogor University conducts an online learning process which can be carried out from each student's home. The easy teaching and learning process, cost efficiency, easy access anywhere and anytime have made many educational institutions adopt the e-learning method. Geographical Information System (GIS) is information based on a work system that enters, manages, manipulates, and analyzes data and explains descriptions. Process Clusterization Model of Cellular Internet Provider Connection Based on Webgis, the information arrangement needs to be packaged in a geographic information system using the Kernel Density method displayed in a WebGIS.
DYNAMIC MODELING OF TIME SERIES DATA USING BEKK-GARCH MODEL

The Vector Autoregressive Moving Average (VARMA) model is one of the models that is often used in modeling multivariate time series data. In time-series data of economics, especially data return, they usually have high fluctuations in some periods, so the return volatility is unstable. In modeling data return of share prices ADRO and ITMG, the behavior of high volatility will be considered. This study aims to find the best model that fits the data return of share price of the energy companies of PT Adaro Energy Tbk (ADRO) and PT Indo Tambangraya Megah Tbk (ITMG), to analyze the behavior of impulse response of the variables data return ADRO and ITMG, to analyze the granger causality test, and to forecast the next 12 periods. Based on the selection of the best model using the criteria of AICC, HQC, AIC, and SBC, it was found that the VARMA (2.2) -GARCH (1.1) model is the best one for the data in this study. The model VARMA(2,2)-GARCH (1,1) is then written as a univariate model. For the univariate ADRO model, the test statistics F = 4,73 and P-value = 0,0084, which indicates the model is very significant; and for the univariate ITMG model, the test statistics is F = 5,82 and P-value 0,0001, which indicates the model is significant. Based on the best model selected, the impulse response, Granger causality test, and forecasting for the next 12 periods are discussed.
Integrated Reasoning Engine for Pointer-related Code Clone Detection

Detecting similar code fragments, usually referred to as code clones, is an important task. In particular, code clone detection can have significant uses in the context of vulnerability discovery, refactoring and plagiarism detection. However, false positives are inevitable and always require manual reviews. In this paper, we propose Twin-Finder+, a novel closed-loop approach for pointer-related code clone detection that integrates machine learning and symbolic execution techniques to achieve precision. Twin-Finder+ introduces a formal verification mechanism to automate such manual reviews process. Our experimental results show Twin-Finder+ that can remove 91.69% false positives in average. We further conduct security analysis for memory safety using real-world applications, Links version 2.14 and libreOffice-6.0.0.1. Twin-Finder+ is able to find 6 unreported bugs in Links version 2.14 and one public patched bug in libreOffice-6.0.0.1.
Attention-constraint facial expression recognition.

To make full use of existing inherent correlation between facial regions and expression, we propose an attention-constraint facial expression recognition method, where the prior correlation between facial regions and expression is integrated into attention weights for extracting better representation. The proposed method mainly consists of four components: feature extractor, local self attention-constraint learner (LSACL), global and local attention-constraint learner (GLACL) and facial expression classifier. Specifically, feature extractor is mainly used to extract features from overall facial image and its corresponding cropped facial regions. Then, the extracted local features from facial regions are fed into local self attention-constraint learner, where some prior rank constraints summarized from facial domain knowledge are embedded into self attention weights. Similarly, the rank correlation constraints between respective facial region and a specified expression are further embedded into global-to-local attention weights when the global feature and local features from local self attention-constraint learner are fed into global and local attention-constraint learner. Finally, the feature from global and local attention-constraint learner and original global feature are fused and passed to facial expression classifier for conducting facial expression recognition. Experiments on two benchmark datasets validate the effectiveness of the proposed method.
Lightweight Cross-Lingual Sentence Representation Learning

Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such large-scale models are usually impractical due to memory limitations. In this work, we introduce a lightweight dual-transformer architecture with just 2 layers for generating memory-efficient cross-lingual sentence representations. We explore different training tasks and observe that current cross-lingual training tasks leave a lot to be desired for this shallow architecture. To ameliorate this, we propose a novel cross-lingual language model, which combines the existing single-word masked language model with the newly proposed cross-lingual token-level reconstruction task. We further augment the training task by the introduction of two computationally-lite sentence-level contrastive learning tasks to enhance the alignment of cross-lingual sentence representation space, which compensates for the learning bottleneck of the lightweight transformer for generative tasks. Our comparisons with competing models on cross-lingual sentence retrieval and multilingual document classification confirm the effectiveness of the newly proposed training tasks for a shallow model.
Differentially Private Densest Subgraph Detection

Densest subgraph detection is a fundamental graph mining problem, with a large number of applications. There has been a lot of work on efficient algorithms for finding the densest subgraph in massive networks. However, in many domains, the network is private, and returning a densest subgraph can reveal information about the network. Differential privacy is a powerful framework to handle such settings. We study the densest subgraph problem in the edge privacy model, in which the edges of the graph are private. We present the first sequential and parallel differentially private algorithms for this problem. We show that our algorithms have an additive approximation guarantee. We evaluate our algorithms on a large number of real-world networks, and observe a good privacy-accuracy tradeoff when the network has high density.
Senake Bandaranayake library classification system for archaeology (SBC)

Senake Bandaranayake library classification system is based on the Library collection of the Postgraduate Institute of Archaeology (PGIAR), University of Kelaniya. It was introduced by Emeritus Professor Senaka Bandaranaike in 1987. It has formulated its own classification system to suit the needs of a small to medium scale library. Archaeology is a multi-disciplinary subject. Therefore the use of a general classification system was problematic when organizing a specialized collection, such as archaeology. The Dewey Decimal Classification (DDC) and the Universal Decimal Classification (UDC) are the most widely used classification systems in the world. It is observed that neither the DDC nor UDC is suitable to classify an archaeological collection of this nature. There is no other generally accepted scheme for a special collection such as archaeology. Therefore, Prof. Senake Bandaranayake has decided to formulate its own classification scheme to serve the needs. This classification system is designed mainly for the purpose of organizing a collection of archeology and related subjects, giving priority to Sri Lanka. This makes it easier for the reader to access the information they need. For over 30 years, this classification system has been used by the Postgraduate Institute of Archaeology to organize their library collection. However, this classification has not been discussed in the field of Library Science in Sri Lanka. The main objective of this article is to introduce the Senaka Bandaranayake classification system for archaeology and to get suggestions and ideas from the library community.
A Comparative Study Between Students Personality in Speaking Skill at English Education Departmentof UIN Antasari Banjarmasin

Personality plays an important role to improve the speaking ability. People with different personality have a different way to acquire language because people are different and have their own way to learn. In this research the discussion is narrowed to the extrovert and the introvert only. Therefore the researcher interested in conducting a comparative research between extrovert students and introvert students at English Departmen of UIN Antasari.This research used comparative study which is aimed to answer 1) how is the extroverts and introverts students achievement in public speaking class? 2) is there any significant difference between extrovert and introvert students in public speaking class?. The participants of this research are 86 fifth semester students from C, D, E class of 2016 accademic year 2018/2019. The data of this research were the classification groups of extroverts and introverts based on the questionnaire scores and the students final scores from the lecturer. In conducting this research the researcher use two instruments that were questionnaire and documentary. The analysis of this research was quantitatively comparative study with independent sample t-test by using SPSS 15.The result of this research , speaking ability of extrovert and introvert students’ in public speaking class was in good category which the mean score of extroverts students ware 88.9 and introverts’ mean score are 88.7. Refered to the finding and the disscussion there is no different between extrovert and introvert students’ in public speaking class, based on the tcount is -116 < t table 1.99. T-table is higher than t-count Ho is accepted and Ha is rejected.
Electronic Resources of Libraries of First-Generation Indian Institute of Management (IIM): An Evaluative Analysis

The study analyses the availability and distribution of electronic resources among the selected libraries of the Indian Institute of Management (IIMs) in India. Researchers have chosen six IIMs identified as first-generation libraries and compare various dimensions of electronic resources such as availability of different types of resources, its source of availability, mode of access, and the customized services through specialized tools and collaborative networks. It is found from the study that all the first-generation IIM libraries are part of IIM library consortia and are the major source of electronic resources rather than individual subscriptions and the majority of the libraries are providing customized services through specialized access tools.
Virtual epitexts in the dissemination of children’s books: Toward an analytical model for author websites

Author websites form a corpus of epitexts (Lluch et al., 2015) that in thecontext of the knowledge society need to be studied to identify how authors presentand disseminate their books and promote reading on the social web. The aim of theresearch is to identify the key aspects for an analytical model for the websites of authorsof children’s books. To this end, from within the portal Picturebook Makers, we selectedthe websites of three authors: Oliver Jeffers, Isol and Manuel Marsol. We analysedthese websites using a selection of three of parameters. The results show that this vir-tual public epitext is in some cases an adaptation of analogue models of dissemination,while in others, such as the site of Jeffers, it leans toward a transmedia discourse inwhich the book-trailer is an essential tool for promoting the work.
초등 예비교사들의 모바일 기반 과학 문제해결 과정에서 형성된 규범의 특징 - 디지털 시민성의 관점으로 -

This study analyzed the features of norms formed in mobile-based science problem-solving and interpreted them from the perspective of digital citizenship. For this, we implemented two mobile-based science problemsolving activities for nine elementary school preparatory teachers composed of two groups, and analyzed the norms observed in their activities. As a result, four norms were found as follows. First, the information presented as a basis should be scientifically reliable. Second, the information need to be searched widely, but the information should be selected and reconstructed in relation to the problem. Third, in a mobile environment, the ideas should be clearly expressed and understood. Fourth, courtesies in mobile interaction should be represented more politely than in face-to-face interaction. Based on the four norms found in this study, we discussed the characteristics and factors of digital citizenship for judging scientifically reliable and relevant information and expressing ideas clearer in mobile environment. Finally, we suggested the educational implications for fostering digital citizens who can judge and practice ‘science issues’ in a ‘mobile environment’.