Reviewerly/exHarmony huggingface dataset:
Continuous learning and advancing technologies: a framework for professional development and training in artificial intelligence

Purpose The purpose of this article is to assist organizations with professional development and training by using Fink’s (2017) Taxonomy of Significant Learning (TSL) to engage employees with continuous learning, in the context of artificial intelligence (AI). By using TSL, employees engage in six interactive stages that stimulate cognitive and affective learning. This stimulation is highly imperative because employees must acquire and apply new knowledge, develop new attitudes, and change learning behavior to help organizations undergo endless changes shaped by technological advancements. Design/methodology/approach This article provides a structure on how to professionally develop and train an organization’s workforce in AI through the six tenets of the TSL model. It also provides guidance on analyzing situational factors and assessing employee learning. Findings This conceptual study reveals how organizations can advance employee thinking and shape employee behavior to prepare future leaders during technological shifts. Practical implications By integrating the TSL in professional development and training strategies, organizations can achieve employee outcomes that will grow and sustain organizations as technology continues to impact markets. Originality/value This paper contributes to existing literature by providing a systematic and quality approach to transform employees into active learners within advanced technological environments. In addition, this paper discusses assessment methods to evaluate the effectiveness of learning.
High Synthetic Image Coding System

Compressing an image and reconstructing it without degrading its original quality is one of the challenges that still exist now a day. A coding system that considers both quality and compression rate is implemented in this work. The implemented system applies a high synthetic entropy coding schema to store the compressed image at the smallest size as possible without affecting its original quality. This coding schema is applied with two transform-based techniques, one with Discrete Cosine Transform and the other with Discrete Wavelet Transform. The implemented system was tested with different standard color images and the obtained results with different evaluation metrics have been shown. A comparison was made with some previous related works to test the effectiveness of the implemented coding schema.
Framework for unsupervised incremental evolution of stylized images

Abstract This paper examines and showcases a framework to generate artworks using evolutionary algorithms. Based on the idea of an incremental abstract artistic process stylized images are generated from different input images without human supervision. After explaining the underlying concept, the solution space of different styles is explored and its properties for style consistency and style variety are discussed. A first step towards better control of the outcome is implemented through masking, followed by a discussion about potential improvements and further research.
Application of Deep Learning System Technology in Identification of Women’s Breast Cancer

Background and Objectives: The classification of breast cancer is performed based on its histological subtypes using the degree of differentiation. However, there have been low levels of intra- and inter-observer agreement in the process. The use of convolutional neural networks (CNNs) in the field of radiology has shown potential in categorizing medical images, including the histological classification of malignant neoplasms. Materials and Methods: This study aimed to use CNNs to develop an automated approach to aid in the histological classification of breast cancer, with a focus on improving accuracy, reproducibility, and reducing subjectivity and bias. The study identified regions of interest (ROIs), filtered images with low representation of tumor cells, and trained the CNN to classify the images. Results: The major contribution of this research was the application of CNNs as a machine learning technique for histologically classifying breast cancer using medical images. The study resulted in the development of a low-cost, portable, and easy-to-use AI model that can be used by healthcare professionals in remote areas. Conclusions: This study aimed to use artificial neural networks to improve the accuracy and reproducibility of the process of histologically classifying breast cancer and reduce the subjectivity and bias that can be introduced by human observers. The results showed the potential for using CNNs in the development of an automated approach for the histological classification of breast cancer.
MANRS Statistical analysis and adoption in india as a collaborative security tool

ABSTRACTABSTRACTThe Internet is a vast network of networks and autonomous numbering systems, including enterprise networks, content delivery networks, Internet service providers, and cloud computing companies such as Microsoft Cloud and Google. The Border Gateway Protocol (BGP) ensures that data can be routed between networks in the most efficient way, much like the navigation system GPS, which maintains a database of addresses that can take distance and high traffic volumes into account when deciding the best route to a destination. Due to the highly decentralized and dispersed sharing of routing announcements and routing policy data among network operators, there are multiple opportunities for error or tampering. Major revisions are needed to address the most common routing threats. The global Mutually Agreed Norms for Routing Security (MANRS) initiative aims to work with operators, enterprises, and legislators to create rules for routing security. This paper explores potential solutions to the problems associated with routing, as well as potential technical and policy issues.KEYWORDS: Autonomous systemBGPMANRSroutingnetworkscybersecurity Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.
Survey of Distances between the Most Popular Distributions

We present a number of upper and lower bounds for the total variation distances between the most popular probability distributions. In particular, some estimates of the total variation distances in the cases of multivariate Gaussian distributions, Poisson distributions, binomial distributions, between a binomial and a Poisson distribution, and also in the case of negative binomial distributions are given. Next, the estimations of Lévy–Prohorov distance in terms of Wasserstein metrics are discussed, and Fréchet, Wasserstein and Hellinger distances for multivariate Gaussian distributions are evaluated. Some novel context-sensitive distances are introduced and a number of bounds mimicking the classical results from the information theory are proved.
Aнгли хэлний өгүүлэгдэхүүн, өгүүлэхүүний хоорондын зохицлын хамаарал бүхий алдаанд хийсэн дүн шинжилгээ (ШУС, Багш гадаад хэлний боловсрол хөтөлбөрийн оюутны жишээгээр)

The purpose of the present research study was to carry out an error analysis on the use of the subject and verb agreement. We investigated the errors in a corpus of 13 reports written by 13 intermediate students of the School of Art and Sciences at NUM. The instrument used for this study was students’ written reports in the English language. All the errors related to the subject and verb agreement in this article were identified, described, explained and evaluated according to Stephen Pit Corder’s taxonomy. Specifically, we aimed to identify the sources of the errors. Furthermore, we wrote a recommendation for the students to enhance their language proficiency.
Analysis Algorithm of Biomedical Signals in Remote Monitoring Systems of Human Health

The article considers the problems of adaptation of existing and development of new diagnostic algorithms and methods of remote monitoring of the physiological state of a person in relation to the Internet of Things technology. In order to reduce the energy consumption of the wearable unit and biomedical signal sensors, reduce the redundancy of the recorded and transmitted diagnostic information, the critical situation recognition process is divided into two stages. At the first stage, the main indicators (heart rate and human fall signal) are monitored. If they do not comply with the norm, additional signals are analyzed (the second stage) to confirm the critical situa tion and determine the degree of alarm.
ROLE OF ARTS AND LITERATURE IN PERSONALITY DEVELOPMENT

The present research paper is a discussion on an essential area of human life. The personality and its development with art and literature is the focal point of discussion here. Art which includes five dimensions of skill is an integral part of human life. From ancient times man could never escape his life form literature and art. Art works for the inclusion of harmony, rhythm, balance, unity, proportion, love, compassion, sympathy, fluency, compatibility in human life. These qualities lead a man towards a better version of his personality. The area in which art, particularly literature, is most easily demonstrated as an investigatory technique per se is of course that of personality study. Characterization in the master pieces of literary works portrays virtues and follies before reader. The psychological study of human personality is determined by the action of man, but the betterment of these actions can be learnt through art and literature. A writer, painter or sculptor with their artistic impression create classics and leaves the reader, spectator, or art lover to identify and absorb the best rather suitable for one. The formation of personality begins with the birth of a man. It includes songs sung by mother, folk literature in form of mythologies like ‘Ramayana’ and ‘Mahabharata’ or heroic tales of country heroes told by grandparents, folk art forms like ‘mandana’ which is a reflection of cultural perspective of traditions and the whole process seems invisible but before the beginning of any education process lays the foundation of a personality. Man is a social animal and grows in the society. Therefore, the values, believes, experiences and expressions art and literature display belong to that society and reflects the culture of it but for sure makes a man able to transform into a useful human.
Validation and Evaluation of a Tool for Developing an Integrated View of ICT-Based Educational Innovation

The increasing digitalization of education requires an integrated view of innovation and related expertise development. This study validated and evaluated the content and use of the ”Integrated ICT Motion Sensor”, a tool for stimulating awareness about ICT-based educational innovation among multiple stakeholders within higher education institutions. The Motion Sensor offers administrators, managers, ICT specialists, educational designers, and lecturers a guiding tool for entering into a dialogue about substantive themes such as vision and policy, leadership, professional development, and ICT infrastructure within the institution. The validation of the content by questionnaires and a focus group with 10 experts showed that the experts’ input was almost in line with the Motion Sensor prototype, which was based on the literature. The use of the Motion Sensor was evaluated in a pilot study within four higher educational institutions. Results from the questionnaires showed that participants generally appreciated the nature and course of the conversation. Moreover, the Motion Sensor led to more awareness about ICT-based educational innovation among participants.
Preface to “Swarm and Evolutionary Computation—Bridging Theory and Practice”

Swarm and evolutionary computation (SEC) [...]
The system of situations of understanding as a trajectory for gaining a wholesome experience of meaning-activity

The chapter deals with the issues of meaning-oriented education in higher school. It proves the relevance and significance of such education for the preparation of future specialists to solve professional problems. Within the framework of the concept developed by the author, the essence of the concepts &amp;quot;understanding&amp;quot;, &amp;quot;case of understanding&amp;quot;, meaning, &amp;quot;meaning-activity&amp;quot;, &amp;quot;text-material &amp;quot;, &amp;quot;task for meaning&amp;quot; are revealed. The didactic resource of case of understanding and procedures of meaning-activity is considered. The chapter reveals the author's point of view on the content of the case of understanding as a specific developing environment.
Image Captioning in Tamil Language using Encoder-Decoder Architecture

Image captioning is the process of using clear, meaningful words to describe the characteristics of an image. This feature has wide applications in social networking applications such as Facebook and Instagram, and video streaming platforms such as YouTube and Netflix, where the need to verbalize an image or video is evident. Image captioning is also one of the most requested features in next-generation AI systems. It has huge applications in the Deep Learning domain. Much research is actively being done on image captioning, which can solve a good deal of real time problems such as the need for a system that can aid visually disabled people, creating effective captions that can be incorporated in self-driving vehicles, etc. This elaborate yet useful feature can be incorporated with the help of various technical concepts such as Natural Language Processing, Computer vision, Image Processing, etc. The image captioning feature has already been attempted on English language and with the help of extensive research and technical advancements these attempts have been fruitful and successful. Nowadays, there are many applications and models available based on image captioning of English language. This has paved a path for further advancements in this domain. A lot of research are now being undertaken to incorporate this highly useful feature with non-English languages. English being the native language for a relatively smaller proportion of people, it would be helpful for people whose native language is not English, to get their images captioned in the language of their choice. This research focuses on image captioning in Tamil language and its underlying methodology and architecture. Moreover, the paper also includes experiments related to this with the help of an image captioning model which uses a combination of Convolution Neural Network and Long Short -Term Memory models.
Restoring the Subjects in Sexual Violence Victim’s Statements by Sentence-wise Rule-based Zero Anaphora Resolution

자연어 처리 기술을 이용해 진술의 신빙성을 판별하는 소프트웨어에 대한 연구가 대두되고 있다. 이때 피해자의 진술에서 빈번히 생략되는 주어의 정확한 복원이 이루어진다면 컴퓨터의 정확한 문장 이해를 통해 진술신빙성 판단 성능을 높일 수 있다. 기존 한국어 무형대용어 해결 연구에서 주로 사용되었던 머신러닝 및 딥러닝 모델은 명사구에 초점을 맞춘 것으로, 일정 범위 내에 선행어가 존재하여야 생략어 복원이 가능하다는 등의 한계점이 있다. 성폭력 피해자 면담 조사 진술서의 특성상 서술어에 초점을 맞춘다면 효율적인 복원이 가능하다. 이에 기반해 본 연구에서는 구문 분석기와 규칙기반 모델을 이용해 생략된 가해자 또는 피해자 주어를 문장 단위로 복원하는 방법을 채택했다. 구축한 모델로 실제 성폭력 피해자 조사 면담 기록물에서 피해자 또는 가해자의 주어가 생략된 2,396문장에 대해 주어 복원 실험을 진행하였다. 본 모델의 복원 성능은 F1 스코어는 90.79%이며 낮지 않은 수준으로 판단한다. Research on software that determines the credibility of statements using natural language processing technology is emerging. If accurate restoration of subjects that are frequently omitted from victim"s statements is performed, the performance of judging the credibility of statements can be improved through accurate sentence understanding of the computer. Machine learning and deep learning models, which were mainly used in existing research on Korean Zero Anaphora Resolution (ZAR), have limitations such that anaphora can be restored only when preceding words exist. Due to the nature of the investigative interview, efficient sentence-wise restoration is possible based on the predicate. Therefore, this study adopts a method of restoring omitted perpetrators or victims as the subject on a sentence-by-sentence basis using parsers and a rule-based model. We conducted an experiment using 2,396 sentences. Our model outperforms and its F1 score is 90.79%, which is considered decent for the credibility check system.
Does ICT diffusion reduce poverty? Evidence from SAARC countries

Abstract The present study aims to explore the relationship between ICT diffusion and poverty reduction in SAARC countries using panel data from 2005 to 2020. This study uses econometric techniques robust to cross‐sectional dependence (CSD) including Pesaran's CSD tests; second‐generation unit root test; Pedroni, Kao, Westerlund cointegration tests; CS‐ARDL, Driscoll‐Kraay (DK) standard error approach; and D&amp;H causality test. The investigation is based on the ICT diffusion index constructed using principal component analysis (PCA). The study's major finding shows that ICT diffusion reduces poverty both in the long and short run, indicating the favorable impact of ICT on the development process in SAARC countries. Further, economic growth, financial development, and remittances all serve to minimize the poverty level. The causality test reveals bidirectional causation between ICT diffusion and poverty reduction. The study highlights the crucial role of ICT diffusion and selected economic variables in reducing poverty. The findings of the present research shall benefit policymakers to formulate appropriate policies and programs to improve the well‐being of people and enhance macroeconomic performance, which impacts both the societal and environmental development of a country.
Random Forest based Method for Predicting the Timing of Intervention During Virtual Reality-based Job Training

최근 들어 가상현실 기술로 개발된 실감 콘텐츠가 발달장애 학생들의 직업훈련을 위한 수단으로 활용되고 있다. 그러나 발달장애 학생들은 예상하지 못한 상황을 대처하는 능력이 부족할 수 있으므로 교사의 도움 없이 콘텐츠만으로 훈련을 완수하기엔 한계가 있을 수 있다. 본 논문에서는 가상현실 기술로 개발된 실감 콘텐츠로 발달장애 학생들이 바리스타 훈련을 수행하는 도중에 도움, 즉 중재가 필요한 시점을 파악하기 위한 연구의 일환으로 머신러닝 기법의 하나인 랜덤 포레스트(Random forest) 예측 모형을 구축하여 중재가 필요한 학생과 그렇지 않은 학생을 구별하는 중요 요인을 탐색하였다. 이를 위해, 바리스타 훈련을 수행할 때 사용하는 14개의 주요 도구들로부터 왼쪽 컨트롤러와의 거리, 그리고 오른쪽 컨트롤러와의 거리 정보로 구성된 28가지 변수를 투입하였고 각 변수의 중요도를 도출하였다. Recently, virtual reality-based training system has been actively used in job training for students with developmental disabilities. These students may lack the ability to cope with unexpected situations and may experience limitations in completing training unless they receive help from others. This is a part of a study identifying the moment they require and receive intervention so that they may correctly complete the training. This paper implements a Random Forest prediction model and explores important features that distinguish students who need intervention from those who do not. To this end, 28 feature variables consisting of the distance from the left controller to each of 14 major barista tools and the distance from right controller to each of 14 major barista tools were selected, and the importance of each feature variable was established.
The digital literacy, awareness, and educational needs of virtual reality among nursing students

Purpose: This study’s aim was to investigate digital literacy, awareness of virtual reality, and educational needs of virtual reality-based nursing education among nursing students. Methods: Structured self-reported questionnaires were used to measure the nursing students’ digital literacy, awareness, and educational needs of virtual reality. This study was conducted on 162 nursing students in Seoul between December 13 and December 24, 2021. The data were analyzed using descriptive statistics, t-test, and one-way ANOVA with SPSS/WIN 24.0. Results: The mean scores on digital literacy, awareness, and educational needs of virtual reality were 4.38±0.42, 4.71±0.64, and 4.08±0.61, respectively. Concerning the educational needs of the subjects, the need for safety education was the highest, followed by the need for oxygenation education, elimination education, and activity and exercise education. Significant relationships were found between digital literacy and the awareness of virtual reality (r=.34, &lt;i&gt;p&lt;/i&gt;&lt;.001), between digital literacy and the educational needs of virtual reality (r=.17, &lt;i&gt;p&lt;/i&gt;=.028), and between awareness and the educational needs of virtual reality (r=.51, &lt;i&gt;p&lt;/i&gt;&lt;.001). Conclusion: This study suggests that it is necessary to develop a virtual reality-based nursing educational program that reflects the educational needs of nursing students.
Formirovanie novykh standartov vysshego obrazovaniia kak professional'nogo obrazovaniia -- vazhneishii faktor obespecheniia ekonomicheskoi i tekhnologicheskoi nezavisimosti Rossii

For a long time, higher education in Russia was organized within the framework of the Bologna system «bachelor's-master's». However, the implementation of the FSES HE according to foreign standards did not give the desired effect, since domestic organizations were not interested in graduates of domestic universities, as in professionals. As a result, higher education ceased to exist as a professional education, and society lost the specialists it needed in the person of university graduates who were trained as bachelors. At the same time, the Federal State Educational Standards of Higher Education were constantly «improved», since they did not provide the necessary quality of training for university graduates. However, such a reform, «squeezed» by the framework of the Bologna system, did not take into account the needs of the domestic economy, Russian organizations in the specialists they needed. At the same time, a large number of commercial universities appeared that did not train specialists, but earned money from it. And today, even in many state universities there is a set of students for paid education, as a result of which not all talented young people can enter a university, since there are not enough budget places for them, and those who can pay for education go to paid education. The author emphasizes the need to ensure the technological and economic independence of Russia, which cannot be provided without the appropriate organization of students' education in domestic universities. And this must be done urgently and without «looking back» to foreign «wishes».
SLRProp: A Back-Propagation Variant of Sparse Low Rank Method for DNNs Reduction

Application of deep neural networks (DNN) in edge computing has emerged as a consequence of the need of real time and distributed response of different devices in a large number of scenarios. To this end, shredding these original structures is urgent due to the high number of parameters needed to represent them. As a consequence, the most representative components of different layers are kept in order to maintain the network's accuracy as close as possible to the entire network's ones. To do so, two different approaches have been developed in this work. First, the Sparse Low Rank Method (SLR) has been applied to two different Fully Connected (FC) layers to watch their effect on the final response, and the method has been applied to the latest of these layers as a duplicate. On the contrary, SLRProp has been proposed as a variant case, where the relevances of the previous FC layer's components were weighed as the sum of the products of each of these neurons' absolute values and the relevances of the neurons from the last FC layer that are connected with the neurons from the previous FC layer. Thus, the relationship of relevances across layer was considered. Experiments have been carried out in well-known architectures to conclude whether the relevances throughout layers have less effect on the final response of the network than the independent relevances intra-layer.