Reviewerly/exHarmony huggingface dataset:
The nexus between democratic governance and sustainable development in Nigeria

Democracy has been critiqued severally in literature for its impact in sustaining development of countries that have adopted that system of government. This paper therefore analyses Nigeria's democratic experience as it relates to these vital development indices with a view to ascertaining the extent these indices have translated development since the country adopted democracy in 1999. Inverse relationship between Knowledge Economy Index (KEI), Human Development Index (HDI) and democratic governance were also found as the country has experienced weak KEI and low HDI over the years. Findings revealed a negative relationship between democratic governance and gender equality. The study recommends that programs to reduce poverty should target the poorest of the poor and government should strengthen anti-graft and law enforcement agencies as well as legal institutions to reduce corruption. The educational sector should be strengthened and sufficiently funded to improve KEI and HDI and conform to global best practice.
DIGITALIZATION OF UNIVERSI

The universities are facing many challenges nowadays. On one hand stays the question, how to attract the most talented students, on the other how to recruit and keep academic staff. One of the ways to deal with these arising challenges is through branding the corporate image of the universities as Yordanova, 2022 discusses. The corporate identity of the university becomes crucial as a way to differentiate between each other especially in the dynamically changing business environment. The unexpected pandemic situation not only surprised all educational institutions, but has become an outstanding factor for their survival and competitiveness. The present chapter draws attention on digitalization as a way to respond and to adapt to the pandemic situation. What is more, it presents author’s view on factors, affecting on the level of digitalization of universities. Best practices of universities implementing digitalizations are also discussed.
On the Efficacy of Boosting-Based Ensemble Learning Techniques for Predicting Employee Absenteeism

Employee absenteeism is a substantial problem faced by many organizations. It severely affects the productive operations in organizations. In the recent years, predictive modeling using ensemble learning techniques has increased the attention of researchers to develop competent models for various predictive tasks. Ensemble techniques combine the predictions of multiple models to yield a single consolidated decision. Thus, predictive modeling with the help of ensemble learning techniques can predict employee absenteeism so that the human resource department can devise intervention policies and lessen monetary losses due to absenteeism. Therefore, in this direction, this study develops models with the help of boosting-based ensemble learning aggregate with data balancing to predict employee absenteeism. The predictive accuracy of the absenteeism prediction models is evaluated using strong performance measures; area under receiver operator characteristics curve, balance and geometric mean. The results are also examined statistically with statistical analysis. The boosting-based ensemble learning techniques are effective for predicting employee absenteeism according to the results of the study.
Korean university students’ perceptions of rote memorization as a form of meaningful learning

The purpose of this study is to identify the perceptions of 20 Korean university students about the practice of rote memorization as a learning strategy in the Republic of Korea. Some educators and students have criticized rote memorization as a learning method that stifles critical and creative thinking and only promotes a type of superficial learning. However, several studies maintain that rote memorization can lead to deeper or meaningful learning. The participants in this study took part in individual interviews to identify their general perceptions about the use of rote memorization as a learning strategy in Korea and the type of learning it promotes. The results reveal that the high majority of participants view memorization as an extremely important skill in Korea but also suggest a variety of other learning strategies to promote meaningful learning. It is believed that the results of this study have significant implications for learners and educators.
PictoCal: a digital calendar of daily activity based on pictograms

This work presents a web service that allows the entity’s professionals to create digital calendars for each of the people affected by some type of autism disorder and work with them through pictograms. With this tool, both therapists and families will be able to create calendars and complete each day of any month of the year with pictograms that describe the scheduled activities. These calendars will be completely adapted to the needs and preferences of the users to whom it is addressed. They will be able to create several calendars for different or even the same person, with all the data hosted in the cloud and accessible from anywhere via the Internet. In addition, this web service provides an integrated online search tool for pictograms, so you can search for all the pictograms that are needed to complete the calendar without leaving the platform.
A 2D Synthesized Image Improves the 3D Search for Foveated Visual Systems

Current medical imaging increasingly relies on 3D volumetric data making it difficult for radiologists to thoroughly search all regions of the volume. In some applications (e.g., Digital Breast Tomosynthesis), the volumetric data is typically paired with a synthesized 2D image (2D-S) generated from the corresponding 3D volume. We investigate how this image pairing affects the search for spatially large and small signals. Observers searched for these signals in 3D volumes, 2D-S images, and while viewing both. We hypothesize that lower spatial acuity in the observers’ visual periphery hinders the search for the small signals in the 3D images. However, the inclusion of the 2D-S guides eye movements to suspicious locations, improving the observer’s ability to find the signals in 3D. Behavioral results show that the 2D-S, used as an adjunct to the volumetric data, improves the localization and detection of the small (but not large) signal compared to 3D alone. There is a concomitant reduction in search errors as well. To understand this process at a computational level, we implement a Foveated Search Model (FSM) that executes human eye movements and then processes points in the image with varying spatial detail based on their eccentricity from fixations. The FSM predicts human performance for both signals and captures the reduction in search errors when the 2D-S supplements the 3D search. Our experimental and modeling results delineate the utility of 2D-S in 3D search—reduce the detrimental impact of low-resolution peripheral processing by guiding attention to regions of interest, effectively reducing errors.
Effect of Lifelong Education Support Learning Programs using University Resources on the Successful Retirement, Retirement Life Plans and LSIA of the Middle-aged and Elderly in the Community

목적: 본 연구는 경상남도의 K시를 중심으로 대학 자원을 활용한 노인활동지도 평생교육지원학습프로그램을 통해 지역사회 중장년층의 성공적 노후, 노후생활계획, 생활만족도를 측정하여 지역사회 평생교육의 발전을 견인할 목적을 가진다. 연구방법: 2021년 8월 9일부터 11월 15일까지 15주간 김해시에 거주하는 40세 이상 중장년층 26명을 대상으로 성공적 노후, 노후생활계획과 생활만족도를 조사하였으며, SPSS 25.0 Version을 사용하여 통계분석 하였다. 연구결과: 평생교육지원학습프로그램의 결과, 성공적 노후, 노후생활계획과 생활만족도 모두 유의한 차이를 보였다(p<.05). 직업의 유무에 따라 비교한 결과, 성공적인 노후와 생활만족도에서 프로그램 전에는유의한 차이를 보였으나(p<.05), 후에는 유의한 차이를 보이지 않았다. 프로그램 후의 성공적 노후, 노후생활계획과 생활만족도의 상관에서 유의한 차이를 보였다(p<.01). 결론: 평생교육지원학습프로그램은 중장년층에게 성공적 노후, 노후생활계획과 생활만족도에서 삶의 긍정적인 영향을 미칠 수 있음을 의미하고 있다. 향후 중장년층을 대상으로 한 평생교육의 기회의 확대를 기대한다.Objective : This study aims to lead the development of lifelong education in the community by measuring the successful aging, retirement life plan, and life satisfaction of the middle-aged in the community through the lifelong education support learning program for elderly citizens using university resources, centering on K city in Gyeongsangnamdo. Methods : For 15 weeks from August 9 to November 15, 2021, 26 middle-aged people over 40 older residing in Gimhae City were surveyed for successful aging, retirement plan, and life satisfaction, and statistical analysis was performed using SPSS 22.0 Version. Results : As a result of the lifelong education support learning program, there were significant differences in both successful aging, retirement life plan, and life satisfaction(p<.05). As a result of comparing according to the presence or absence of a job, there was a significant difference before the program in successful aging and life satisfaction(p<.05), but there was no significant difference after the program. There was a significant difference in the correlation between successful aging, retirement life plan, and life satisfaction after the program(p<.01). Conclusion : Lifelong education support learning program means that it can have a positive effect on life in successful aging, retirement life plan, and life satisfaction for middle-aged people. We look forward to expanding opportunities for lifelong education for the middle-aged in the future.
Nursing Student's Experience of Class in COVID-19 New Infectious Disease Crisis Situation: A Qualitative Content Analysis

Purpose: This study was conducted to analyze the class experiences of nursing students in the COVID-19 new infectious disease crisis situation to increase understanding of non-face-to-face(NFTF) classes. Methods: This study was conducted on 108 nursing students who participated in NFTF classes and submitted reflection journals at the department of nursing located in Busan from July 1 to July 15, 2022. As for the data analysis method, qualitative content analysis was conducted. Result: The three categories were positive aspects of NFTF classes, negative aspects of NFTF classes, and experiences of mixed classes. The 10 subcategories are the comfort of NFTF classes, repetitive viewing of lectures, flexible use of time, no inconvenience in commuting, difficulty in concentrating, stuffy and uncomfortable, communication problems, deterioration in class quality, confusion and discomfort during mixed classes. Conclusion: In the future, it seems necessary to develop a teaching method that can be approached from a holistic point of view without distinguishing between face-to-face(FTF) and NFTF classes.
A study on the support, exchange and cooperation of education during and after unification of Germany

This study is to analyse the support, exchange and cooperation of education during and after unification of Germany and to extract implications for unification education in South Korea from it. The support and cooperation in education during the process of unification of Germany are summarized in four aspects: joint committee of education between East- and West Germany, educational cooperation between East-and West Berlin, cooperation of non-state organizations between East- and West Germany and recommendations and supports from the education of West Germany. After unification of Germany are analysed the support, exchange and cooperation of education. As the support of education were fulfilled financial, personal and material support from old federal states, educational support by international organization, and support by private foundations. There were also educational cooperations between East- and West Germany in various levels. Furthermore, there were various exchange of students, teachers, teacher organizations, schools between old and new federal states, and exchange of educational field as a part of city exchanges as well. We can extract following implications for unification education in South Korea from this study: the need for broad discussions on which level of unification teleological is in education, preparation of alternatives for overcoming the educatinal problems of South-and North Korea, recovery of the educational exchanges and cooperations between South-and North Korea in the level of province office of education, discussion on construction of joint committee of education between South-and North Korea, active investigation of educational support methods by international organization.
Community Awareness Education in the Era of Artificial Intelligence: The Third Neighbor

We are living in the age of artificial intelligence(AI). In other words, our daily life is based on AI. At the same time, we are facing serious global warming issues, as known as the climate crisis. Against the backdrop, this study identified the necessity to explore educational tasks based on the global warming problems in the AI era. To this end, this study first explored the value of the times with regard to community awareness education under the theme of “Community Awareness Education in the Era of Artificial Intelligence: The Third Neighbor,” and explained that the emergence of AI is a product of coevolution of living things and minerals from a fundamental level. In other words, the history of evolution is the process of coevolution between living and non-living beings. Therefore, we need to go beyond various dichotomous worldviews; in this context, the existence of AI can be positioned as another neighbor, the third one in our lives. In addition, this study emphasized that, since we have entered the age of AI, humans should get out of the limited imagination of human intelligence. In this context, this study argued that it is necessary to discuss the direction for the development of AI as co-existing intelligence and suggested AI humanities based on this.
Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Masking

Automatic speech recognition (ASR) models are used widely in applications for voice navigation and voice control of domestic appliances. ASRs have been misused by attackers to generate malicious outputs by attacking the deep learning component within ASRs. To assess the security and robustnesss of ASRs, we propose techniques within our framework SPAT that generate blackbox (agnostic to the DNN) adversarial attacks that are portable across ASRs. This is in contrast to existing work that focuses on whitebox attacks that are time consuming and lack portability. Our techniques generate adversarial attacks that have no human audible difference by manipulating the input speech signal using a psychoacoustic model that maintains the audio perturbations below the thresholds of human perception. We propose a framework SPAT with three attack generation techniques based on the psychoacoustic concept and frame selection techniques to selectively target the attack. We evaluate portability and effectiveness of our techniques using three popular ASRs and two input audio datasets using the metrics- Word Error Rate (WER) of output transcription, Similarity to original audio, attack Success Rate on different ASRs and Detection score by a defense system. We found our adversarial attacks were portable across ASRs, not easily detected by a state-of the-art defense system, and had significant difference in output transcriptions while sounding similar to original audio.
Phishing threatens state and corporate cybersecurity

Significance One group is based in Russia, the other in Iran. The advisory highlights how little some of the primary tactics and techniques used by cybercriminals and state intelligence agencies have evolved over the last decade. It also underlines the difficulty of curbing such attacks. Impacts Tactics used by cybercriminals and state hackers will continue to converge as both run up against tighter corporate cybersecurity. Similarities between criminal and espionage tactics will complicate attribution and cyberinsurance covering state-linked attacks. Tighter cybersecurity controls risk increasing employee fatigue with more complex protocols, and causing operational disruption. Workarounds to access links and attachments, such as forwarding messages to personal accounts, create new vulnerabilities. Corporate cybersecurity will need to assume the certainty of human error, but remedial measures such as training have an imperfect record.
Malicious encrypted network traffic flow detection using enhanced optimal deep feature selection with DLSTM

This paper plans to implement a novel detection model of maliciously encrypted internet protocol network flow using the deep structured concept. The major processing levels are (i) data collection, (ii) feature extraction, (iii) optimal feature selection, and (iv) detection. In the beginning, the standard dataset is taken from online databases. The deep convolutional neural network (DCNN) is introduced for the deep feature extraction process. The accurate features are chosen by the crossover decision-based krill herd algorithm (CD-KHA) which helps to minimize the training complexity of the deep structured architecture. These selected features are given to the hybridized deep learning with long short-term memory (LSTM) and deep neural network (DNN). Here, the structural design of the model is improved by the same CD-KHA. Through the comparison and analysis, the accuracy rate of the offered method shows higher performance than the other baseline approaches.
Social Networks and Their Contribution in the University Educational Field: Systematic Review

With the massification of social networks and the incursion of data science in the university environment, it requires us to investigate and reflect on whether these tools are contributing to the improvement of the teaching-learning process, academic research, administrative services and social welfare. This paper has formulated three questions, with the purpose of conducting and achieving the goals of literature review, RQ1: What social networks are applied in the university educational field? RQ2: In what areas of the university educational system are they applied social networks? and RQ3: How do social networks contribute in their application to the university educational environment? The authors have carried out a systematic review based on the PRISMA statement (Preferred Reporting of Items for Systematic Reviews and Meta-Analysis), on findings published in Spanish and English, in Scopus, Eric and Google Scholar databases, within the period between 2010 and 2022. The descriptors used as part of the search strategy were “Social network”, “Twitter”, “Instagram”, “WhatsApp”, “University service”, “academic services”, “administrative services” and “Social welfare services”. The results focused on the review of 17 articles, finding that the most used social network in the university environment is Twitter, focusing its application on academic services such as the teaching learning and research process. However, it is evident in the state of the art that there is a lack of institutional policies that formalise the use of social networks as well as norms that establish their good practices of use.
Knowledge-based Data Processing for Multilingual Natural Language Analysis

Natural Language Processing (NLP) aids the empowerment of intelligent machines by enhancing human language understanding for linguistic-based human-computer communication. Recent developments in processing power, as well as the availability of large volumes of linguistic data, have enhanced the demand for data-driven methods for automatic semantic analysis. This paper proposes multilingual data processing using feature extraction with classification using deep learning architectures. Here, the input text data has been collected based on various languages and processed to remove missing values and null values. The processed data has been extracted using Histogram Equalization based Global Local Entropy (HEGLE) and classified using Kernel-based Radial basis Function (Ker_Rad_BF). These architectures could be utilized to process natural language. We present solutions to the multilingual sentiment analysis issue in this research article by implementing algorithms, and we compare precision factors to discover the optimum option for multilingual sentiment analysis. For the HASOC dataset, the proposed HEGLE_ Ker_Rad_BF achieved an accuracy of 98%, a precision of 97%, a recall of 90.5%, an f-1 score of 85%, RMSE of 55.6% and a loss curve analysis attained 44%. For the TRAC dataset, the accuracy of 98%, the precision attained is 97%, the Recall is 91%, the F-1 score is 87%, and the RMSE of the proposed neural network is 55%.
Comparative Analysis of Globalisation Techniques for Medical Document Classification

Medical document classification is one of the important topics of text mining. Globalisation techniques play a major role in text classification. It is also known that globalisation techniques play an important role in text classification. Our aim in the study is to conduct a detailed analysis on two data sets with English and Turkish content by using medical text summaries of Turkish articles. These datasets consist of Turkish and English text summaries of the same articles. To observe how successful local feature selection methods in the field of text classification affect the classification performance on these two equivalent data sets by applying different globalisation techniques. The feature selection methods used are CHI2, MI, OR, WLLR. Globalisation techniques are SUM, AVG, MAX. Classifiers are MNB, DT, and SVM.
Multi-Criteria Decision Making Using Additive Ratio Assessment in Digital Voice Recorder Selection System

Digital Voice Recorder has many uses, usually used for interviews, recording voices and songs, recording meeting results, and can be used for learning. Currently, various Digital Voice Recorder products have been circulating and have different functions and specifications that are created according to the needs of users. For this reason, users must be observant in choosing a Digital Voice Recorder to support their work. So, we need a system that can provide recommendations and help in making decisions to choose the right Digital Voice Recorder. This study aims to develop a decision support system with Multiple Criteria Decision Making (MCDM) using Additive Ratio Assessment (ARAS) to assist in selecting a Digital Voice Recorder, so that it can assist in selecting the best solution appropriately and according to user needs. The ARAS method is used as a model that can select the best alternative based on the utility level of each alternative to determine the best alternative. Based on the case studies conducted, the utility values of each alternative were obtained, namely: Zoom Handy Recorder with a value of 0.5672, Sony PX470 with a value of 0.6147, Ruizu X52 with a value of 0.4664 and Tascam DR-22ML with a value of 0.9096. So, the best alternative is the Tascam DR-22ML. Based on testing through the black-box testing method, it shows that the system built has been running well
Feature Extraction in Content based Image Retrieval

A technique for Content Based Image Retrieval (CBIR) for the generation of image content descriptor which exploiting the advantage of low complexity Order Dither Block Truncation Coding (ODBTC). The quantizer and bitmap image are the compressed form of image obtained from the ODBTC technique in encoding step. Decoding is not performed in this method. It has two image feature such as Color Co-occurrence Feature (CCF) and Bit Pattern Feature (BPF) for indexing the image. These features are directly obtained from ODBTC encoded data stream. By comparing with the BTC image retrieval system and other earlier method the experimental result show the proposed method is superior. ODBTC is suited for image compression and it is a simple and effective descriptor to index the image in CBIR system. Content-based image retrieval is a technique which is used to extract the images on the basis of their content such as texture, color, shape and spatial layout. In order to minimize this gap many concepts was introduced. Moreover, Images can be stored and extracted based on various features and one of the prominent feature is Texture.