Reviewerly/exHarmony huggingface dataset:
MENINGKATKAN HASIL BELAJAR DAN KETERAMPILAN BERKOMUNIKASI SISWA DALAM MATERI SHOWING CARE MELALUI METODE PERMAINAN KUIS DI KELAS X SEKOLAH MENENGAH ATAS (SMA) NEGERI 1 CISARUA KABUPATEN BANDUNG BARAT

This study aims to (1). Improve students' communication skills in Showing care material through playing quizzes. (2). Knowing the learning process in the Showing care material through playing quizzes. (3). Measure how much the students' ability to improve in showing care is achieved through quizzes. The results showed that by applying the quiz game method. Showing care learning, student learning outcomes in this material can be improved. Variations in the application of this learning method can also avoid the boredom of students in learning English so that student learning outcomes have increased from 70% in the first cycle to 100% in the second cycle. Based on research that the learning outcomes of English obtained by students of class X MIPA-1 of SMA Negeri 1 Cisarua, West Bandung Regency, the average value increased in cycle I, namely 72.05 to 79.55 in cycle II. From the description above, the researcher concludes that the quiz play learning method applied by the teacher can improve student learning outcomes, therefore the researcher suggests that the quiz play learning method be socialized and used as an alternative in learning English.Keywords: quiz game, communication skills, learning outcomes, showing care material
Ownership Concentration and Corporate Social Responsibility in Emerging Economies

We investigate the impact of ownership concentration on corporate social responsibility (CSR) practices in emerging economies. CSR practices are viewed as private provision of public goods, leading...
Panel Discussion: Covid-19, Technology and Ethics

The Covid-19 pandemic has led to considerable disruption and raised a number of issues for both individuals and society as a whole. Many of these issues have both technology and ethics dimensions. Several of them are discussed in this paper. In the session the authors will be making brief presentations from their perspectives and the experiences of Covid-19 in their different countries. This will be followed by a comment/question and answer session and final summing up by the authors.
Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution.

It is often posited that more predictable parts of a speaker’s meaning tend to be made less explicit, for instance using shorter, less informative words. Studying these dynamics in the domain of referring expressions has proven difficult, with existing studies, both psycholinguistic and corpus-based, providing contradictory results. We test the hypothesis that speakers produce less informative referring expressions (e.g., pronouns vs. full noun phrases) when the context is more informative about the referent, using novel computational estimates of referent predictability. We obtain these estimates training an existing coreference resolution system for English on a new task, masked coreference resolution, giving us a probability distribution over referents that is conditioned on the context but not the referring expression. The resulting system retains standard coreference resolution performance while yielding a better estimate of human-derived referent predictability than previous attempts. A statistical analysis of the relationship between model output and mention form supports the hypothesis that predictability affects the form of a mention, both its morphosyntactic type and its length.
A Cnn Model Based Approach For Offline HandwrittenTamil Text Recognition System

Computers may dominate our lives but using pen for writing is still mightier than keyboard. Computer vision applications like handwriting recognition model is playing a vital role now-a-days. Handwritten text recognition (HTR) is the most efficient way to digitize handwritten documents. For every handwritten text recognition model, feature extraction is the foremost task. In CNN architecture, feature extraction is done automatically. Lot of attention has received in the past years but research has focused only on Latin, Urdu and English. For Tamil language very fewer studies were done. Instead of predicting by word, first the individual characters from the text should be segregated then the segregated character is given to the trained CNN model to predict handwritten Tamil characters.The dataset used here is an isolated handwritten Tamil character dataset which was developed by HP labs India. Hyper-parameter tuning is performed to tune the model and achieved good accuracy results.
Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health.

Many statistical models have high accuracy on test benchmarks, but are not explainable, struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily integrate domain expertise. These factors limit their use, particularly in settings such as mental health, where it is difficult to annotate datasets and model outputs have significant impact. We introduce a micromodel architecture to address these challenges. Our approach allows researchers to build interpretable representations that embed domain knowledge and provide explanations throughout the model’s decision process. We demonstrate the idea on multiple mental health tasks: depression classification, PTSD classification, and suicidal risk assessment. Our systems consistently produce strong results, even in low-resource scenarios, and are more interpretable than alternative methods.
A Coarse-to-Fine Labeling Framework for Joint Word Segmentation, POS Tagging, and Constituent Parsing

The most straightforward approach to joint word segmentation (WS), part-of-speech (POS) tagging, and constituent parsing (PAR) is converting a word-level tree into a char-level tree, which, however, leads to two severe challenges.First, a larger label set (e.g., ≥ 600) and longer inputs both increase computational cost.Second, it is difficult to rule out illegal trees containing conflicting production rules, which is important for reliable model evaluation.If a POS tag (like VV) is above a phrase tag (like VP) in the output tree, it becomes quite complex to decide word boundaries.To deal with both challenges, this work proposes a two-stage coarse-to-fine labeling framework for joint WS-POS-PAR.In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i.e., phrase, subphrase, word, subword).The tree is guaranteed to be legal via constrained CKY decoding.In the fine labeling stage, the model expands each coarse label into a final label (such as VP, VP * , VV, VV * ).Experiments on Chinese Penn Treebank 5.1 and 7.0 show that our joint model consistently outperforms the pipeline approach on both settings of without and with BERT, and achieves new state-of-the-art performance.
When Does Contrastive Learning Preserve Adversarial Robustness from  Pretraining to Finetuning?

Contrastive learning (CL) can learn generalizable feature representations and achieve the state-of-the-art performance of downstream tasks by finetuning a linear classifier on top of it. However, as adversarial robustness becomes vital in image classification, it remains unclear whether or not CL is able to preserve robustness to downstream tasks. The main challenge is that in the self-supervised pretraining + supervised finetuning paradigm, adversarial robustness is easily forgotten due to a learning task mismatch from pretraining to finetuning. We call such a challenge 'cross-task robustness transferability'. To address the above problem, in this paper we revisit and advance CL principles through the lens of robustness enhancement. We show that (1) the design of contrastive views matters: High-frequency components of images are beneficial to improving model robustness; (2) Augmenting CL with pseudo-supervision stimulus (e.g., resorting to feature clustering) helps preserve robustness without forgetting. Equipped with our new designs, we propose AdvCL, a novel adversarial contrastive pretraining framework. We show that AdvCL is able to enhance cross-task robustness transferability without loss of model accuracy and finetuning efficiency. With a thorough experimental study, we demonstrate that AdvCL outperforms the state-of-the-art self-supervised robust learning methods across multiple datasets (CIFAR-10, CIFAR-100, and STL-10) and finetuning schemes (linear evaluation and full model finetuning).
A novel secure approach of selecting the key or nonce by Guessing in Cryptography

The world of Cryptography and Encryption is evolving rapidly with new technologies being upgraded or invented. The area of Blockchain, Quantum computing, normal sharing of data over the network all uses the techniques of encrypting the data using some sort of keys. As the Machines and technology are becoming more and more powerful, legacy way of handling the communication needs a secure approach. This paper propose an idea of guessing the nonce or key, in such a way that it would evolve with the machines and technology. The Idea of Proposed adaptive algorithm works on challenges and using different challenges, the key is guess. There are three variations of this approach Direct, Separation and Pooling. The Messages are encrypted from the pooled of solved challenge keys and using the proposed techniques, nonce or key is selected mutually. Using Poission distribution, it is found that the identification of key becomes more robust as the level of communication increases. This Methodology allows the flexibility to change or make the challenge more complex depending upon their needs, which indeed increase the probability of guessing. This can also solve the problem of Denial of service(DOS) and spamming. In future of advance computations and development, this approach will be helpful in making the system secure.
The influence of university science on the Russian regions’ development

Introduction. One of the drivers of the Russian regions’ development is traditionally considered to be local universities and the scientific activity development, in particular. However, such a belief is usually based on speculative conclusions and is not subjected to detailed empirical testing. The purpose of the study is to assess the relationship between the development of science in universities in Russian regions and indicators of regional development according to 2017—18 data. Methods. The authors use methods of generalization, grouping, assessment of indicators of central tendency and variations, as well as methods of correlation analysis. The study is based on the data from regional statistics presented on the official website of Russian Statistics Agency, as well as data from monitoring the universities effectiveness, commissioned by the Ministry of Science and Higher Education of the Russian Federation. Scientific novelty of the research. For the first time, the authors present an empirical analysis of the relationship between indicators of socioeconomic development of Russian regions and indicators of scientific activity in universities in the region. The study is characterized by using a sample of homogeneous regions. The sample excluded regions that did not have significant scientific results, as well as regions that are leaders in economic development. As a result, a “core” of 57 Russian regions with similar characteristics of both economic development and the results of scientific activity was investigated. This increased the practical significance of the conclusions and their reliability. Results. As a result of the study, it was found out that there is a statistically significant relationship between all indicators of regional development and science in universities. However, a deeper analysis made it possible to establish that this relationship is indirect. In some cases, we can talk about the opposite influence - regional development on scientific results in universities. Conclusions. It was found that the indicators used in the public administration system for assessing the effectiveness and efficiency of scientific activity are not key in terms of their impact on regional development. It was revealed that publications in leading world databases and in the Russian Science Citation Index (RSCI) are equally related to regional development. It has been determined that in a number of cases, within the considered sample, it is possible to distinguish two groups of regions with a different nature of the relationship between regional development and science in universities.
Task-Oriented Clustering for Dialogues

A reliable clustering algorithm for task-oriented dialogues can help developer analysis and define dialogue tasks efficiently. It is challenging to directly apply prior normal text clustering algorithms for task-oriented dialogues, due to the inherent differences between them, such as coreference, omission and diversity expression. In this paper, we propose a Dialogue Task Clustering Network model for task-oriented clustering. The proposed model combines context-aware utterance representations and cross-dialogue utterance cluster representations for task-oriented dialogues clustering. An iterative end-to-end training strategy is utilized for dialogue clustering and representation learning jointly. Experiments on three public datasets show that our model significantly outperform strong baselines in all metrics.
On the Contribution of Per-ICD Attention Mechanisms to Classify Health Records in LanguagesWith Fewer Resources than English

We introduce a multi-label text classifier with per-label attention for the classification of Electronic Health Records according to the International Classification of Diseases.We apply the model on two Electronic Health Records datasets with Discharge Summaries in two languages with fewer resources than English, Spanish and Swedish.Our model leverages the BERT Multilingual model (specifically the Wikipedia, as the model have been trained with 104 languages, including Spanish and Swedish, with the largest Wikipedia dumps 1 ) to share the language modelling capabilities across the languages.With the perlabel attention, the model can compute the relevance of each word from the EHR towards the prediction of each label.For the experimental framework, we apply 157 labels from Chapter XI -Diseases of the Digestive System of the ICD, which makes the attention especially important as the model has to discriminate between similar diseases.
A Study on the Relationship between U-Learning Yoga Class Flow and Satisfaction According to ego-resilience University Students

The purpose of this study is to investigate the effect of U-Learning yoga on class flow and satisfaction according to the ego-resilience of University students. For the study subjects, 115 people were selected from a University located in the Northern Gyeonggi region using the convenience sampling method among the non-probability sampling methods. A total of 115 questionnaires, a total of 92 were used for the analysis, excluding those with poor responses. For data analysis, IBM SPSS Statistics 25 statistical program was used, and descriptive statistics were performed to investigate the ego-resilience, class flow, and class satisfaction of University students, and non-parametric difference tests and correlation analysis were performed. To verify the relative influence, multiple regression analysis was performed, and after the verification of the mediating effect according to the regression analysis procedure, the Sobel Test was conducted to verify the significance of the mediating effect of cless flow. As a result of the study, first, it was found that the ego-resilience of University students had a significant effect on U-Learning yoga cless flow. Second, the effect of University students
Efficient Explanations from Empirical Explainers

Amid a discussion about Green AI in which we see explainability neglected, we explore the possibility to efficiently approximate computationally expensive explainers. To this end, we propose feature attribution modelling with Empirical Explainers. Empirical Explainers learn from data to predict the attribution maps of expensive explainers. We train and test Empirical Explainers in the language domain and find that they model their expensive counterparts surprisingly well, at a fraction of the cost. They could thus mitigate the computational burden of neural explanations significantly, in applications that tolerate an approximation error.
Existing and Newer Technologies in US Higher Education

This paper looks at the current technological tendencies in American higher education through content and textual analysis of numerous articles and reports. The research results in the classification into three thematic technological categories of courses, curricula, and campuses. US courses are likely to embrace an expansion of existing technological usage along with the growth of newer technologies.
An improved moth-flame optimization algorithm based on fusion mechanism

Moth-flame optimization algorithms are widely employed to solve optimization problems and achieve good performance. However, the algorithms suffer the shortcoming of prematurity because of the early gathering of flames. To solve this problem, the flame fusion mechanism is integrated to improve the exploratory behavior of the moth-flame optimization algorithm. The flame fusion mechanism provides a new way to evaluate the state of flame aggregation based on the distribution of flames and moths. When the concentration of flames is higher than the fusion threshold, the better flame will fuse other flames. And the fused flames will be regenerated to enhance the exploration behavior of the algorithm. At the same time, the fusion rate that determines the probability of flame fusion is introduced. The fusion rate changes during iteration to balance the exploration and exploitation behaviors of the algorithm. The improved moth-flame optimization is validated by ten benchmark functions. The results show that the optimization ability of the improved moth-flame optimization algorithm is improved, and the stability is higher than compared algorithms as well.
Models and specifics of interaction between universities and stakeholders

One of the world's leading social trends in development of higher education of the twenty-first century is progression of relationships between universities and business. The main purpose of this paper is to compare the models of integration between universities and business, considering the triple model of social partnership, the model with the dominant role of the state, and the model of mixed representation, typical for the Central European countries. The methodological framework of this research includes methods of statistical and comparative analysis and synthesis. From the results of this research, European higher education has developed and effectively operates the developed models of interaction between institutions of higher education and business. The obtained results allow us to evaluate the possibilities of applying positive foreign experience in Russian reality.&#x0D; Keywords: university, education, government, business, research, integration of education and business, models of interaction between universities and business.
COMMUNICATION STRATEGIES USED BY ENGLISH TEACHER IN TEACHING ENGLISH AS FOREIGN LANGUAGE

The aim of this study is to find out the communication strategies used by English Senior High School Teachers in Bengkulu Tengah. This study belongs to mix method which involved questionnaire and interview to collect the required data. Questionnaire was distributed to 21 teachers who were teaching at the senior high school 2020/2021 academic year. Then interview, was employed to enrich the data. The data were analyzed by using Dornyei & Scott’s (1997) taxonomy of communication strategies. The results show that the English teachers in Bengkulu Tengah more prefer to use communication strategy in the form of prefabricated patterns, nonlinguistic mean and stalling or time gaining. They also try to use those communication strategies in good order from change the word that the student unfamiliar with to familiar word, then they use the body gesture to stress the meaning and last, they use code mix or partial translation to make students get the meaning. The use of those communication strategies is underlying two main reason which are solving the speaking problems and making interactive speaking situation. Teachers employed the various communication strategies to stimulate students to be more active during teaching and learning process. Keywords :  Communication Strategies, English Teacher, Teaching English as Foreign Language
Threat Diagnostic Checklists of Security Service in 5G Communication Network Virtualization Environment

The purpose of this paper is to review the direction of the slicing security policy, which is a major consideration in the context of standardization in 5G communication network security, to derive security vulnerability diagnosis items, and to present about analyzing and presenting the issues of discussion for 5G communication network virtualization. As for the research method, the direction of virtualization security policy of 5G communication network of ENISA (European Union Agency for Cybersecurity), a European core security research institute, and research contents such as virtualization security policy and vulnerability analysis of 5G communication network from related journals were used for analysis. In the research result of this paper, the security structure in virtualization security of 5G communication network is arranged, and security threats and risk management factors are derived. In addition, vulnerability diagnosis items were derived for each security service in the risk management area. The contribution of this study is to summarize the security threat items in 5G communication network virtualization security that is still being discussed, to be able to gain insights of the direction of European 5G communication network cybersecurity, and to derive vulnerabilities diagnosis items to be considered for virtualization security of 5G communication network. In addition, the results of this study can be used as basic data to develop vulnerability diagnosis items for virtualization security of domestic 5G communication networks. In the future, it is necessary to study the detailed diagnosis process for the vulnerability diagnosis items of 5G communication network virtualization security.
METHODS OF TEACHING ADVANCED MATH CONNECTED WITH PRACTICAL TRAINING FOR STUDENTS OF INDUSTRIAL UNIVERSITY

Purpose of this study is to develop methods of teaching public education in association with vocational training for students of universities of technology. Its mission is researching the theoretical basis of public education teaching at university level in association with the practice of vocational training. Author performs research on the practical basis of the actual situation of teaching and learning advanced education in universities of technology. Then study present solutions expressed through pedagogical measures in teaching public education for students of universities of technology. And author conducted pedagogical experiment to test the correctness of scientific hypothesis and feasibility of the proposed solution.