Reviewerly/exHarmony huggingface dataset:
A Study on the Utilization of Catholic Cultural Resources in Naepo Area

‘Religion has been associated with human history and has created a unique culture of a nation as a factor that has a great impact on human life. Therefore, the ratio of religious cultural heritage is absolutely high among existing world cultural heritage. Although there are many religious cultural heritage in Korea, these cultural resoures are not utilized in various ways. Religious cultural resources can be used for missionary work and they can also be used for cultural contents aspects in various ways.&#x0D; In particular, the religious cultural contents should be something that not only religious people but everyone in the nation can understand and sympathize in terms of cultural contents aspect. In terms of psycho-cultural aspects, religious and cultural contents can also be utilized for relaxation, healing, and humanistic education for modern people. The values pursued by religions such as respect for life, love and peace can be used for humanistic education, which is an important part of the realization of the public good.&#x0D; Naepo area was the center of the Catholic faith in the late Joseon Dynasty and many martyrs were dispatched during the era of persecution. Therefore, there are many sacred places and materials related to martyrdom. It was an area which was considered to be a good place to live in Joseon Dynasty, both geographically and economically. Such conditions became factors for the Catholic faith to spread easily and settle in the nation.&#x0D; For this reason, I examined the Catholic history and the cultural resources of Naepo area and tried to find out how to utilize them. To do this, I suggested humanistic education that relates to the natural resources in the bay area, and developing web services that relates to the cultural resources in Naepo. Through this study, I look forward to the revitalization of the local and religious culture in Naepo.
Facial Recognition based Attendance

This paper shows an approach for face detection which can process images and attaining high detection rates. that is accomplished via a three-key concept - integral image, AdaBoost and Cascade. An integral image is a new image, introduced to permit the features to be computed. The AdaBoost (Adaptive Boosting), a statistical classification meta-algorithm, picks a certain number of distinguishable features from a set and returns classifiers that efficiently detect those features. The Cascade is a technique where an image is traversed pixel by pixel. Complex classifiers are combined during this traversal ignoring the image background and spending more computation on the region that appears to be a face. It can be viewed as a focusing mechanism which targets specific objects and ensures that the discarded regions aren't probable to comprise of the object of interest. This approach can be used to record attendance. This saves time and is a cheat free method to record attendance.
A Fine Three-dimensional Adaptive Color Segmentation Algorithm for Real-time Vision System of Soccer Robot

The aim of the vision system for small soccer robots is to recognize and track robots and soccer ball in a semi-open environment. Traditional color segmentation methods are usually sensitive to external conditions, for example light changes or uneven distribution. Machine learning-based methods usually require advanced hardware as well as huge amount of data. To overcome these limitations, we propose a fine three-dimensional adaptive color classification method in this paper. The presented method determines the thresholds in color space dynamically. For distinguishing the overlapping area in color space, Naive Bayes classifier is used to classify the color categories which obey the Multivariate Normal Distribution. Dynamic adaptive lighting is introduced to enhance the performance, and the robustness of classification is increased by combining multiple brightness separable color space. This method is embedded in a vision system for Soccer Robot to enhance the processing performance. The experiments show that the system is robust and achieves high precision. It can process the soccer video in real-time with detection rate of 99.68 % and precision of 99.92 %.
Frame Duplication Detection Using CNN-Based Features with PCA and Agglomerative Clustering

In a court of law, surveillance videos and recordings are the major sources of evidence for any incident or crime. With a simple video editor, the reality could be easily manipulated. This introduces the challenge of verifying the authenticity of contents before they may be used in any critical application domains. In this paper, a video forensic technique is proposed to detect frame duplication forgery in surveillance videos. The proposed technique utilizes convolutional neural network for integrity embedding generation and matching, for video frames duplication detection and localization. Through this work, we compare the efficacy of various deep learning models in generating true embeddings of surveillance footages. To perform experiments a dataset of over 100 authentic and 300 forged, high-resolution HEVC-coded video clips is prepared from surveillance clips. Experimental results indicate that the proposed technique is a good replacement of traditional hand-crafted and compression domain feature-based approaches.
Low-cost visible reflectance spectrophotometer for classification of small intestine cancer lesion degree

Some studies demonstrated the application of visible light reflectance spectrophotometry in cancer diagnoses, such as skin cancer and an oral lesion. This method is quantified, able to be digitalized, affordable, and easy to use. However, the use of visible light spectrophotometry has not been used for small intestine cancer lesions. This pilot study aims to evaluate the potency of a simple or low-cost visible light reflectance spectrophotometer to classify mice’s small intestine cancer lesion degree based on intensity measurement. This analytical cross-sectional study used paraffin block to preserve Mus musculus mice’s small intestine tissue (normal, pre-cancer, and cancer). The samples were grouped according to the lesion degree that a pathology expert had evaluated. The best machine learning to classify lesion degree based on light intensity was performed by Support Vector Machine (SVM) with accuracy 85.2%, AUC 93,4%, and precision of 85.3%.
A Novel Passive L1/L2 Edge Loop Detection Observing MAC addresses of L3 Core Switches

A L1 and/or L2 loop in a network may cause congestion and incur communication failures. It is, therefore, important to quickly and accurately detect a loop and locate its origin in order to eliminate the loop. To address this issue, this paper proposes a novel passive loop detection on edge ports of edge switches where end users' routers or terminals are accom-modated. The basic idea of the proposed detection is inspired by the nature that a MAC address of a L3 core switch should never be observed on an edge port. The MAC address is then observed by always-accepting MAC address authentication that can be easily deployed. The proposed detection can, therefore, accurately locate an edge port where a loop is formed, and avoid a failure to notify a network operator of a loop. In addition, the proposed detection can reduce a load on an edge switch more than the existing active detecting methods. Our evaluations in the real campus network have shown that the proposed method can detect the loop even where the existing methods cannot.
External algebraic representation of strategies for some types of routine activities

The paper considers the concepts of "routine modeling", "routine project activity", "routine research activity", defined axiomatically. Activity management is usually carried out through standard activity plans. As an axiom, the statement is accepted that the subject of activity perceives any item of the activity plan either as a link to an accessible algorithm of activity, or as a link to the goal of the activity, for which the method of achieving it is not indicated, and this perception is subjective. As a mechanism for creating action plans, we consider the strategy. The work is devoted to the presentation of the strategy of routine modeling, the strategy of routine project activity and the strategy of routine research activity in the form of combinations of basic strategies. The work is based on the author's interpretation of the algebraic approach, which we understand as a system of three components: 1) a system of basic objects; 2) systems of typical transformations and typical combinations of objects; 3) an approximation mechanism intended for an approximate representation of an object as a result of applying type transformations and type combinations of objects to basic objects. The results can be used in the organization of training and in the creation of information and control systems.
Design and implementation of college entrance examination volunteer intelligent filling system based on big data

Voluntary application for college entrance examination is the key link for parents and candidates after college entrance examination. With the development of information technology and the diversification of information access, "Internet+Volunteer Application" has become one of the main ways for students and parents to fill out their volunteer applications. But how to use the huge amount of information on the internet to find suitable colleges and majors for students is an important issue. Therefore, this paper designs an intelligent volunteer application system based on big data and collaborative recommendation technologies to provide intelligent decision-making solutions for candidates to better select universities and majors.
Research and application of improved k-means algorithm based on educational big data

This paper aims to conduct in-depth mining of student performance data. It is helpful to provide teachers with teaching strategies and improve teaching quality. Compared with the previous research on the k-means algorithm, the main innovation of this paper lies in the selection of the initial cluster center and providing teaching strategies for universities. First, we collect the students' transcripts of three subjects and process the original data including cleaning data, extracting key fields, and integrated processing of data. Then, we use the k-means algorithm, to get the cluster center and the category. Finally, we visualized the clustering results.
A Novel Approach for e-Voting System Using Blockchain

With the inventions of new systems such as blockchain, the technological world gets new opportunities to develop new projects. The topic is relatively new hence the issues such as legal issues are hindering the growth of these technologies. In this paper, we are going to implement a thought experiment on Blockchain technology which is widely regarded as open-source to conduct a trial and error process for adopting a new voting system that could be used in elections and will have a blockchain base. The advantages of a Blockchain-based system are that it will provide security, reliability, and anonymity of a voter which are core fundamentals of hosting a free and fair election while and it can assist the government to widespread their narrative as there is no chance of cracking the hash stored. We have seen election commissions fail at various levels of security and privacy so having an alternative method will help in designing the blueprint of elections in many unreliable areas. This paper will first analyze and showcase the newly proposed method that is blockchain-based voting. How the blockchain is engineered and its benefits will be discussed in this paper, and current research being conducted to widespread this technology will also be discussed.
Factors of Students' Reading Lateness

Background: Reading is still considered trivial by Indonesian, especially parents of students. Purpose: This research aims to find out what are the factors that cause students to be late in reading. Design and methods: This research use a type of literature review research method. researchers seek information about research from several relevant journals and articles, read and record information obtained from data sources and compile research articles from data and information that has been collected. The author will discuss the factors of delay in learning to read in students. Results: Some of the factors that students to be late in reading are: 1) external factors that can cause reading difficulties are family and school conditions and 2) internal factors that are found in the individual himself.
Data Encryption and Hiding for Secured Data Transmission

The secured data hiding for future transfer is an application proposed for a secured data transmission in which the application alters the originality of the file to an encrypted form and embeds that file into a video file which will be transferred. The motive of the application is to provide the ease for the user to pass the information with the encryption standards and algorithms then store the information in an unreadable format. In the next phase, the application has a reverse process where the embedded video can be de-embedded, and the encrypted file can be decrypted to its actual data upon proper user request. In the process of encryption and decryption, the application should confirm the authenticity of the user. The application is generated using the tiny encryption algorithm to encrypt the data. This algorithm is a 64-bit block cipher with a variable length key. The tiny encryption algorithm is used because requires less memory, and as a compliment, it uses only simple operations; thus, it is easy to implement.
Understanding and Applying Deep Learning

The past 10 years have witnessed an explosion in deep learning neural network model development. The most common perceptual models with vision, speech, and text inputs are not general-purpose AI systems but tools. They automatically extract clues from inputs and compute probabilities of class labels. Successful applications require representative training data, an understanding of the limitations and capabilities of deep learning, and careful attention to a complex development process. The goal of this view is to foster an intuitive understanding of convolutional network deep learning models and how to use them with the goal of engaging a wider creative community. A focus is to make it possible for experts in areas such as health, education, poverty, and agriculture to understand the process of deep learning model development so they can help transition effective solutions to practice.
A Lightweight Deep Learning RF Fingerprint Recognition Method

Current deep learning-based RF fingerprint recognition methods have the problems of large models and high operational complexity, for which this paper proposes an RF fingerprint recognition method (CNN-GRU network) using a combination of convolutional neural networks and gated recurrent unit networks. The algorithm uses a convolutional layer to extract IQ orthogonal correlation features and learning temporal data, then extracts temporal features through the GRU network, and finally has an output layer to output recognition results. The number of parameters of the algorithm is reduced by using small convolutional kernels and replacing multiple fully connected layers with gated cyclic units. Experimental results show that the algorithm achieves a recognition rate of 98.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> on a software radio dataset with multiple USRP X310s, and the algorithm has fewer parameters and is more lightweight than network models with the same recognition rate, which is of better engineering application.
SOAP messaging to provide quality of protection through Kerberos Authentication

Service-oriented architecture (SOA) is a widely adopted architecture that uses web services, which have become increasingly important in the development and integration of applications. Its purpose is to allow information system technologies to interact by exchanging messages between sender and recipient using the simple object access protocol (SOAP), an XML document, or the HTTP protocol. We will attempt to provide an overview and analysis of standards in the field of web service security, specifically SOAP messages, using Kerberos authentication, which is a computer network security protocol that provides users with high security for requests between two or more hosts located in an unreliable location such as the internet.Everything that has to do with Kerberos has to deal with systems that rely on data authentication.
Fast Multiple-Precision Integer Division Using Intel AVX-512

This paper reports on the implementation of a large integer division method that uses Intel Advanced Vector Extensions 512 (AVX-512), which is a 512-bit Single Instruction Multiple Data (SIMD) instruction set, and proposes a modification to a conventional division algorithm that makes it more SIMD instruction-friendly. More specifically, we use the Integer Fused Multiply-Add AVX-512 (AVX-512IFMA) subset, which is an instruction set that works well with large integer multiplication, to compute large integer divisions via a multiplication-based approach with a reciprocal. For the division process, we apply the most basic algorithm and divide-and-conquer methods and then use several techniques to compute efficiently with SIMD instructions in our implementation. We then combine these techniques and methods to implement our division function so that it can flexibly handle various sizes. To evaluate the performance of our proposed implementation, we executed our division program and the GNU Multiple Precision Arithmetic Library (GMP) on a Cannon Lake microarchitecture processor. A comparison of the execution times for our division program and GMP with various sizes showed that our method resulted in performance improvements of 25% to 35% on average, thus indicating that SIMD instructions are effective for fast arbitrary precision integer divisions.
Academic English in higher education in Montenegro during the Covid-19 pandemic

The Covid-19 pandemic in 2020 led to the closure of all educational institutions in Montenegro, necessitating rapid and extensive changes for both teachers and students. This chapter examines how the teaching of Academic English in two Montenegrin universities, the University of Montenegro and the University “Mediterranean” Podgorica, initially responded, the challenges faced, and the developmental support that was (and was not) provided. Crucial to the Montenegrin experience was a widespread lack of digital skills concerning pedagogical uses of technology. However, different faculties handled the move from the traditional classroom to the online environment in different ways, and this chapter compares the online teaching and learning in the faculty of art versus science alongside the digital competences of ESP teachers at these faculties. Particular attention is given to the participation of ESP teachers in the ReFLAME (Erasmus+) project, which has the principal aim of introducing and integrating information technology in ESP. This chapter highlights novel approaches that the universities suggested for fulfilling the motivation, assessment, and testing needs of their students while considering the benefits and disadvantages of online learning in this setting for insight on better practices that can be developed in south-east Europe and globally.
CCSS: An Effective Object Detection System for Classroom Crowd Statistics

The crowd statistics technology has been widely applied to smart classroom, manual roll call and campus security in recent years. However, due to challenges like low resolution, shooting angels and partial overlapping of students in the classroom, it's extremely hard to estimate the number of students accurately. Inspired by the improvements of object detection models in image target classification and location, we implements a classroom crowd statistics system (CCSS) to provide statistical information on the number of students for the construction of the wisdom classroom. In addition, we introduce a new large-scale classroom dataset, which contains 3,070 images in the classroom environment and 106,304 student annotations. To the best of our knowledge, this is the first student counting dataset collected under the classroom settings, which will greatly promote the development of classroom crowd statistics based on deep learning. In order to further improve the accuracy and speed of students detecting, we also modify the YOLOv4 algorithm to make it more adaptive for this task. The experimental results show that our model gains a significant improvement over the selected baselines on the proposed dataset.
Neighborhood Focused Critic Policy Gradients for Multi-agent Reinforcement Learning

Centralized Training and Decentralized Exucetion (CTDE) is typical training pattern in multi-agent reinforcement learning. In actor-critic method, the actor updates based on the centralized critic which takes global state as criteria for individual contribution. While in cooperative multi-agent tasks, estimating individual contribution in the range of all agents leads to the overlook of local coordination, and thus intensify the mislead in credit assignment. This paper propose Neighbourhood Focused Critic (NFC) Policy Gradients for multi-agent reinforcement learning, parameterizing critic by neighbourhood focused graph neural network in Actor-critic method. NFC allows the centralized critic focus on local coordination in each agent's neighbourhood, and decentralized actors optimizes policies according to the critic estimation, thus alleviating the credit assignment problem. We test our NFC in StarCraft Multi-Agent Challenge (SMAC) environment, the results show that it improves performance and convergence speed significantly comparing to methods with globally focused critic.
A Curated Personas and Design Guidelines Tool for Better Supporting Diverse End-users

Many work and living activities in modern society are increasingly dependent on web and app software. However, much existing software lacks consideration of many diverse end-user characteristics, such as age, mental and physical challenges, language proficiency, culture, socio-economic status, educational attainment and so on. Many developers do not have lived experience of most of these challenges, fail to empathise with those who have them, and lack knowledge of how to address them in their software design and evaluation activities. To address this issue, we designed a curated persona and design guidelines tool to help developers consider and address diverse end-user needs during the software development process. Our tool helps software development teams to take a more holistic view of human nature and diverse end-uses during the software development cycle and to design software with multiple diverse end-user needs in mind. Our evaluation with 23 real world software developers shows that the use of such human-centric persona and guideline tools in the early stages of software development can help to reduce software end-user bias and increase software accessibility.
An Answer Recommendation Algorithm Based on Semantic Fusion Heterogeneous Information Network

Question answering system is an important direction of natural language processing and has been widely used in various fields. However, existing answer recommendation research mainly confronts the problems of text semantic extraction and multi-feature fusion. In this paper, we propose an Answer Recommendation algorithm based on Semantic Fusion Heterogeneous Information Network, called ARSHIN. Firstly, we employ the PCFG method to mine phrases in texts, and perform phrase embedding to obtain phrase similarity relationships. Secondly, we utilize a heterogeneous information network to fuse text semantics and entity-relationship features. Finally, we give the recommended answers by calculating entity correlations. Comparative experiments on three real datasets demonstrate that ARSHIN has advantages in answer recommendation research.