Reviewerly/exHarmony huggingface dataset:
Reorganizing Educational Institutional Domain using Faceted Ontological Principles

The purpose of this work is to find out how different library classification systems and linguistic ontologies arrange a particular domain of interest and what are the limitations for information retrieval. We use knowledge representation techniques and languages for construction of a domain specific ontology. This ontology would help not only in problem solving, but it would demonstrate the ease with which complex queries can be handled using principles of domain ontology, thereby facilitating better information retrieval. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as, Identification of the terminology, Analysis, Synthesis, Standardization and Ordering. Firstly, for purposes of conceptualization OntoUML has been used which is a well-founded and established language for Ontology driven Conceptual Modelling. Phase transformation of “the same mode” has been subsequently obtained by OWL-DL using Protégé software. The final OWL ontology contains a total of around 232 axioms. These axioms comprise 148 logical axioms, 76 declaration axioms and 43 classes. These axioms glue together classes, properties and data types as well as a constraint. Such data clustering cannot be achieved through general use of simple classification schemes. Hence it has been observed and established that domain ontology using faceted principles provide better information retrieval with enhanced precision. This ontology should be seen not only as an alternative of the existing classification system but as a Knowledge Base (KB) system which can handle complex queries well, which is the ultimate purpose of any classification system or indexing system. In this paper, we try to understand how ontology-based information retrieval systems can prove its utility as a useful tool in the field of library science with a particular focus on the education domain.
The Reading Ability Can Be Predicted by a Universal Verbal Working Memory Factor Across Different Writing Systems: Evidence from Reading Span Task

Most Chinese college students have an experience of learning English as second language for more than ten years.To explore whether the ability of reading Chinese script and English script was related to a universal working memory capacity, and whether the working memory capacity can predict a change in reading ability, we measured the general working memory span (via N-back task), verbal working memory span of Chinese and English (via reading span task), and reading comprehension ability of Chinese and English of our participants.We also collected the score of Chinese and English of their College Entrance Exam, which is tested at least half a year before now.Our results showed that both Chinese and English reading ability can be predicted by English reading span, which suggested a universal reading ability across different writing systems.The results also showed that reading span failed to predict the past reading score, and Chinese reading span failed to predict the reading comprehension of either Chinese and English, the possible reasons were discussed.
Solar radiation nowcasting through advanced CNN model integrated with ResNet structure

Although a range of solar radiation forecasting methods have been developed for predicting photovoltaic generation, only a few of them focus on solar radiation forecasting for building energy demand. From the perspective of building performance modelling, solar radiation forecasting needs to meet several critical requirements including high spatial resolution (1m-2km) and high temporal resolution (5-60mins), the accurate value of Direct Normal Irradiance (DNI) and Diffuse Horizontal Irradiance (DHI), which differs the requirement for predicting photovoltaic generation. As the geometric sum of DNI and DHI, accurate prediction of Global Horizontal Irradiance (GHI) with high spatialtemporal resolution tends to be the prerequisite for precise prediction of DNI and DHI. This research aims to construct a hybrid nowcasting model to predict GHI in high spatial-temporal resolution. In this article, the authors adopt an advanced Convolutional Neural Network (CNN) model with Residual Neural Network (ResNet) structure to identify the cloud image information and predict the GHI at 10 minutes intervals merely using cloud images captured by a ground-based sky camera. On this basis, several ResNet structures are compared to achieve the optimal nowcasting model for GHI. The results present that the ResNet structure can efficiently capture the cloud information and the ResNet152 achieves better performance than other alternative structures on the nowcasting of GHI. Finally, the authors discussed the calculation of synchronous DNI and DHI using the predictive GHI and Dirint model, and the application of DNI and DHI as the input for the simulation of building energy management. Key Contributions • Classifying solar radiation forecasting methods and reviewing recent representative articles. • Proposing a hybrid method using CNN-ResNet models to nowcast solar irradiance. • Only using ground-based cloud images as input to predict solar irradiation. • Comparing several ResNet structures to achieve the optimal nowcasting model for GHI. Practical Implications This study makes it possible for relevant researchers to utilise predictive solar radiation data in a convenient manner, taking only cloud images as input instead of other difficult-to-obtain weather data.
High-Performance Hardware Implementation of the KATAN Lightweight Cryptographic Cipher

Lightweight cryptography has been proposed recently as an attractive solution to provide security for the ever-growing number of IoT resource-constrained devices. Many of the proposed lightweight cryptographic ciphers have been implemented in software. However, for practical embedded IoT applications, hardware implementations are preferred because they have small silicon area and low-power consumption. In this paper, we present a transistor-level hardware implementation of the well-known KATAN lightweight cipher. This cipher has been chosen due to its operational simplicity and high levels of security. Moreover, the structure of the KATAN cipher lends itself naturally for transistor-level hardware implementation. The design has been implemented at the transistor level using the advanced new 28-nm CMOS technology which facilitates optimized designs for the resource-constrained IoT devices. The proposed VLSI KATAN encryption and decryption circuits have been designed and simulated using the Synopsys Custom Designer Tool using 28-nm technology, 0.9 v supply voltage and a 1 GHz clock signal. The KATAN encryption circuit has 312 GE (Gate Equivalent) without key and irregular update registers, and 1081 GE for the overall design, and the decryption circuit has 390 GE without memory registers and 6867 GE for the overall design.
SPEAKING INTERACTION PROBLEMS AMONG INDONESIAN EFL STUDENTS

EFL students have become more prodigious in Indonesia as the use of English has also increased globally at present. EFL students possibly tend to get numerous problems in practising their speaking communicatively. Due to the fact that EFL students are more likely to feel shame to practice their English constantly because of several factors among them. Besides, the use of English does not become habitual on a daily basis and social interaction. The quality of their knowledge of English in the previous study also takes part as the reason why they still have problems in speaking performance. That explanation leads to the speaking problems that arise among the EFL students. This research applied descriptive qualitative research. The participants of this research were the students in majoring the English education program in the 2020/2021 academic year. In this research, the researcher used classroom observation and interviews as the instrument. The interview used a semi-structured interview that was given to the students to see what problems they face during speaking performances and what efforts they have done to solve the problems. From the data analysis, the researcher found that the students have speaking problems in speaking performances in the case of pronunciation, lack of vocabulary and less self-confidence. All of the problems are caused by a lack of practice or interest in studying and memorizing vocabulary. Moreover, the attempts that students take as the solution in solving their speaking problem such as; practising more, increasing vocabulary through memorizing, learning to speak through applications for instance; YouTube, song apps, and Duolingo (English learning application).
The Impact of Media Literacy Education on Academic Competency: Focusing on the Case of Undergraduate International Students Class

The purpose of this study is to examine the effect of media literacy education on the improvement of academic interest and thinking ability of undergraduate international students, and to verify the change in thinking ability through learners’ writing activity. Media analysis can enhance the thinking ability of international students and strengthen their domain of expression. Learners can organize their thoughts and present their opinions through discussion in the process of understanding the contents of the media as well as analyzing and evaluating information. This study defines academic competency reinforcement as the positive change in the ability to organize and express one’s opinions through discussion and writing activities in the process of analyzing media contents related to class topics. In the process of analyzing the media, learners shared their opinions and recognized changes in their speaking and writing skills. While analyzing various images from a critical point of view, they experienced improvement in their thinking ability, and they showed high satisfaction with media literacy education. Since the critical perspective in the process of distinguishing media contents has a positive effect on the improvement of thinking, writing and speaking skills of the learners, it is necessary to review media literacy education plans for undergraduate international students in domestic universities.&#x0D;
Online Speech Therapy System

Abstract: This paper provides information about online speech therapy system. The power of internet is endless. In today’s world with the advancement of knowledge and technology we can achieve any challenge in life. This paper is about providing assistance to people with speech disorders. Speech disorder is a common problem world wide and some people with this disorder face many challenges and embarrassment in their life. This paper aims at providing assistance to these people with artificial intelligence technology used. Keywords: Artificial intelligence, neural network, speech and language disorder, speech and language assistance
Human Activity Recognition for Surveillance

Abstract: Human activity recognition is important in human-to-human contact and interpersonal relationships. One of the key objects of research in the scientific fields of computer vision and machine learning is the human ability to identify another person's activity. With the introduction of tiny sensor technologies that can be worn on the body, it is now possible to gather and retain data on various aspects of human mobility under free living settings. This technique has the potential to be employed in automated activity profiling systems that generate a continuous record of activity patterns over time. These activity profiling systems rely on classification algorithms to properly interpret body-worn sensor data and identify various activities. This article examines the many strategies used to classify normal activities and/or identify falls using body-worn sensor data. The study is organized according to the many analytical methodologies and highlights the wide range of approaches that have previously been used in this sector. Although tremendous progress has been achieved in this critical field, there is still much room for improvement, particularly in the application of sophisticated classification approaches to situations requiring a wide range of activities. Keywords: Deep Learning, Classification, Detecting Human activity, Image Processing, Feature Extraction, CNN, LR ect.
Multi-target Filter and Detector for Unknown-number Speaker Diarization

A strong representation of a target speaker can aid in extracting important information regarding the speaker and detecting the corresponding temporal regions in a multi-speaker conversation. In this study, we propose a neural architecture that simultaneously extracts speaker representations that are consistent with the speaker diarization objective and detects the presence of each speaker frame by frame, regardless of the number of speakers in the conversation. A speaker representation (known as a z-vector) extractor and frame-speaker contextualizer, which is realized by a residual network and processing data in both the temporal and speaker dimensions, are integrated into a unified framework. Testing on the CALLHOME corpus reveals that our model outperforms most methods presented to date. An evaluation in a more challenging case of concurrent speakers ranging from two to seven demonstrates that our model also achieves relative diarization error rate reductions of 26.35% and 6.4% over two typical baselines, namely the traditional x-vector clustering model and attention-based model, respectively.
Impact of tics in the post-pandemic university teaching process: opportunities and challenges

This research deals with the impact of ICTs in the teachingprocess in relation to the pandemic Covid-19 as a driver of ICTs in highereducation, which will show everything related to the use of ICTs and arecurrently being the protagonists of the teaching - learning process themeasures taken to safeguard the safety of the entire university communityby its authorities one of them is the confinement or quarantine to preventtheir community is exposed to contagion of Covid-19 and so that academicactivities can continue, We will have concepts and data on Covid-19, ICTs,Higher Education, as well as on the use of ICTs. Surveys were conducted toboth students and teachers about ICTs in post-pandemic higher education,its opportunities and challenges, we will indicate our appreciation of theacademic processes and the benefits that have the use of technologies.
Weighted Least Square Filter for Improving the Quality of Depth Map on FPGA

This paper proposes a post-filtering system for improving the quality of depth maps for 3D projection on FPGA. We propose to implement the Weighted Least Square (WLS) filter on Field-programmable Gate Array (FPGA), which can predict the disparities, which cannot be measured, by using the values of the neighboring pixels. In our design, we optimized the architecture of WLS filter at the algorithm level. For hardware acceleration, we use the High-Level Synthesis (HLS) description to accelerate the algorithm. To break through the bottleneck brought by the limited memory resources on FPGA, we used UltraScale Architecture Memory Resources (URAM) on board and reduced memory consumption of Block RAMs (BRAM) from 140% to 80%. Through our approach, we can improve the quality of the depth map on the FPGA named M-KUBOS. And, the WLS filter can smooth the depth map with 130 MHz operational frequency on M-KUBOS at the speed of 10 frames per second (FPS), which achieves a 76.43% performance acceleration compared to the software execution on the ARM Cortex A9 at 1.2 GHz clock.
Conhecimentos e ações estratégicas de enfermeiros para profilaxia de tromboembolismo venoso

The aim was to verify the knowledge of nurses from inpatient units about risk factors and venous thromboembolism prophylaxis. The study is descriptive and prospective with a quantitative approach and data were collected from the analysis of medical records and questionnaires applied to nurses. The results showed the existence of an important deficit in the knowledge of nurses regarding the prophylaxis of venous thromboembolism and it was identified that these professionals are not inserted in the process of prophylaxis of the disease, while they do not record information about risks, prophylaxis or conducts aimed at preventing the onset of the disease. It was concluded that the non-inclusion of nurses in the process of screening for venous thromboembolism risks negatively impacts their prophylaxis, generating high costs to the Unified Health System due to a preventable disease. And the non-recognition of its role in disease prevention limits its insertion in the prophylaxis process and reduction of this serious public health problem.
Security Recommendations for Your Cloud Environment

The cloud will not solve all of your computing problems, enable all of your future business initiatives, nor will it always save your business money. In many use cases, the cloud may be more expensive and be a trade-off in staffing skills that would support a legacy solution if cloud washed. Regulations, compliance, and security best practices will dictate how to proceed in the cloud, but implementing an end-to-end solution will require a secure architecture along with many of the mitigating controls discussed in this book.
A Study of Traditional Motifs in the Context of Digitalization

The lion motif was the earliest animal heraldic motif to emerge, and its stylized postures are the richest of all heraldic motifs.After the lion was introduced to China, lion motifs developed, and lion art was localized in China, with early lions in caves, lions in murals, lion motifs in gold and silver, lion brocades, porcelain lion motifs and other forms of folk lion art gradually coming to the fore.This paper will analyze the different uses of lion prints in gold and silverware, weaving, sculpture and furniture, explore the history of the localization of lion prints and interpret lion prints from multiple perspectives.The current rapid development of the internet and big data provides new technologies and forms for the inheritance and development of traditional prints, which will be displayed to the public through modern digital technology in their typical forms and most essential meanings.The traditional patterns will be presented to the public in their typical form and most essential meaning through modern digital technology.
HUBUNGAN ANTARA SARAPAN PAGI DENGAN TINGKAT KONSENTRASI BELAJAR SISWA

The results of the 2018 Program for International Student Assessment (PISA) survey show that Indonesian student achievement is ranked 74th out of 79 countries. Indonesia's score is still low. Learning achievement is closely related to learning concentration. One of the effects of learning concentration is the calories of breakfast in the morning, because a breakfast with sufficient calories can meet the body's needs so that the body can continue to optimize metabolism as fuel for the brain to work. The purpose of this study was to determine the relationship between breakfast and the level of concentration in student learning.This study uses a correlational analytical design with a cross sectional time approach. A sample of 40 respondents was taken by simple random sampling. Data was taken by using observation. While the statistical test used is the Spearman's Test.The results showed that as many as 20 students (50%) had breakfast in accordance with the standard needs and had a good concentration of learning as many as 19 students (47.5%). The results of the Spearman's test show that there is a relationship between breakfast and the concentration level of students' learning (p value = 0.000). The conclusion that breakfast affects learning concentration. Breakfast must meet the daily nutritional needs of 30% of the total calorie needs.
P51-12 The development of nomogram for predicting thyroid cancer in subjects with single thyroid nodule in Bali, Indonesia

There were many cases of thyroid cancer proven from histopathology test after lobectomy from single thyroid nodules, which were not being predicted in advanced. The challenge for the clinician is thus to predict the malignancy preoperative to prevent unnecessary second completion thyroidectomy. This study aimed to determine the cancer probability risk of a single thyroid nodule by using preoperative parameters.
Asset Management

Asset management is one of the most fundamental cybersecurity best practices. Regardless of whether the technology is on-premise, in the cloud, or operating in a hybrid environment, understanding and documenting all your assets is critical. After all, you cannot adequately develop a security strategy against threats if you are unaware that an asset exists and needs protection. And in the cloud, even though it may not be your computer or resource, tracking and classifying the asset is crucial. This will help ensure that it does not become a risk from identity management attacks, data governance issues, and the persistence of exploitable vulnerabilities.
Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning

Abstract Machine learning and artificial intelligence have become crucial factors for the competitiveness of individual companies and entire economies. Yet their successful deployment requires access to a large volume of training data often not even available to the largest corporations. The rise of trustworthy federated digital ecosystems will significantly improve data availability for all participants and thus will allow a quantum leap for the widespread adoption of artificial intelligence at all scales of companies and in all sectors of the economy. In this chapter, we will explain how AI systems are built with data science and machine learning principles and describe how this leads to AI platforms. We will detail the principles of distributed learning which represents a perfect match with the principles of distributed data ecosystems and discuss how trust, as a central value proposition of modern ecosystems, carries over to creating trustworthy AI systems.
Regulatory Compliance

A threat actor does not care about the law, compliance, regulations, or security best practices. In fact, they are hopeful that your cloud environment is deficient on many of these concepts and recommendations to help facilitate the success of their malicious activity. While regulatory compliance is designed to provide legally binding guidelines for industries and governments, they do not provide the necessary means to stay secure, and do not think for one moment that a cloud service provider will do it for you out of the box. That just does not happen – compliance does not equal security. Regulatory compliance measures are enforced guidance toward good cybersecurity hygiene, but implementing them without good processes, people, training, automation, and diligence will leave you susceptible to a breach. Therefore, when reviewing regulatory compliance initiatives, consider the following for your cloud deployments:
The authorship of George a Greene, the Pinner of Wakefield

Abstract Establishing the authorship of George a Greene, the Pinner of Wakefield can be seen within the wider context of detecting a collaborative or single-authored canon of early Shakespeare plays which, perhaps, he did not want to see as part of his dramatic work if we are to trust Heminges’ and Condell’s references to ‘surrepetitious copies, maimed, and deformed by the frauds and stealthes of injurious imposters, that expos’d them’ (First Folio preface). The fact that the remaining plays of the First Folio [are] ‘absolute in their numbers as he [Shakespeare] conceived them,’ has in some way a ring of self-amputation. The conclusion of the present study, based on the acknowledged methodological advances in non-traditional stylometric tools, contained in the R Stylo suite, is that George a Greene, the Pinner of Wakefield was originally written by William Shakespeare, even though the play may have been shortened for performances outside London with a reduced company of players during the plague years of 1592 and 1593.