Reviewerly/exHarmony huggingface dataset:
SAATHI: An Urdu Virtual Assistant for Elderly Aging in Place

Abstract With the rise of the digital age, life has become a lot easier for the vast majority of the population. However, the ever-increasing elderly population has suffered, especially in countries like Pakistan, where limited accessibility to technology, often due to language barriers, hinders elderly from reaping technological benefits. In this paper, an Urdu virtual assistant application is proposed which provides an intuitive and empathetic platform for the elderly in Pakistan that helps them perform essential tasks such as reminding them of their medications, organising their work, getting daily news highlights, and connecting them with their loved ones. It also provides entertainment in the form of user-specified video playlists or by positively engaging them in conversations on various topics.
Data Mining in the Big Data Era

While entering into the era of big data, seeking the secrets hidden in the data has become the main target in every single industry.This paper states the definition of data mining in the era of big data and interprets data mining methods in current popular industries such as sports, music, and health care.This paper also analyzes the future of data mining and some potential applications.The paper finds that in the era of big data, data mining, as a powerful method to seek the secret from the data, has had a significant impact on the fields of sports, health care, and the music industry.Data mining also has a brighter future when considering information security, business management, financial analysis, the education industry, etc.The rise of the big data era provides data mining with a suitable and stable environment, which makes jobs such as Data Analyst so popular.
Codenames as a Game of Co-occurrence Counting

Codenames is a popular board game, in which knowledge and cooperation between players play an important role.The task of a player playing as a spymaster is to find words (clues) that a teammate finds related to as many of some given words as possible, but not to other specified words.This is a hard challenge even with today's advanced language technology methods.In our study, we create spymaster agents using four types of relatedness measures that require only a raw text corpus to produce.These include newly introduced ones based on cooccurrences, which outperform FastText cosine similarity on gold standard relatedness data.To generate clues in Codenames, we combine relatedness measures with four different scoring functions, for two languages, English and Hungarian.For testing, we collect decisions of human guesser players in an online game, and our configurations outperform previous agents among methods using raw corpora only.
User Profile Mining through Linguistic Feature

Internet and social media usage have reached previously unheard-of levels in recent years.People express their opinions, share feelings, and reflect on news and current affairs on social media.The abundant conversation and expression that took place in those platforms contain valuable linguistic feature which helps researchers study people's social behavior and mining user's profile.This study summarizes previous studies and approaches to understand and analyze linguistic feature in social media.The study also highlights some valuable, meaningful, and accessible linguistic features that researchers should extract from messy information.The paper finds that social media language is more causal than the speaking and writing language, and contain more feature such as emoji, hashtags and URL which can contribute to user profile mining.
CYBERCRIME WITH RESPECT TO RIGHT TO PRIVACY

In today era the pace of technology development is expanding day by day. the people have turned completely dependent on the internet. It has become a necessity in people's life. The netizens have threatened their privacy in cyberspace. With the increasing use of internet, citizens privacy is being exploited. This concern is felt more in the case of youth and teenagers who constitute maturity of the internet addicts and are susceptible in understanding the threat of exposing themselves to the cyber world. crime is rapidly increasing against women is in all field of cybercrime The issue of 'internet privacy' has been increased by social networking sites, which are increasingly used for social interactions between individuals through uploading personal content. When a person's privacy is violated through internet, there are a number of questions arises, as what constitutes a violation. Who is responsible for this? And what is the remedy for this crime? One of the most important fundamental human rights is the right to privacy. In addition, courts in India have recognized it as a fundamental right, despite the fact that it is not stated in the Indian Constitution. There are laws that ensure the right to privacy is protected. As IT act 2000 has been existence for 21 years the cybercrimes are rising day by day and enforcement of law faces many challenges as a result of existing lacunae in this act. Many provisions regarding the cyberlaw are absent in this act. The present research paper includes various Indian laws which are for the protection of cybercrime, and how right to privacy can be protected and various remedies to counter the increasing cybercrime against women, children and suggestion can be given to improve present situation.
Chapter 3 Smittestopp Backend

Abstract An efficient backend solution is of great importance for any large-scale system, and Smittestopp is no exception. The Smittestopp backend comprises various components for user and device registration, mobile app data ingestion, database and cloud operations, and web interface support. This chapter describes our journey from a vague idea to a deployed system. We provide an overview of the system internals and design iterations and discuss the challenges that we faced during the development process, along with the lessons learned. The Smittestopp backend handled around 1.5 million registered devices and provided various insights and analyses before being discontinued a few months after its launch.
Fuzzy Periodic Patterns from Super-Market Datasets

Finding periodicity of the patterns from super-market data has been found to be an important data mining problem which many researchers encounter often. Such patterns reflect the buying nature of the customers in the super-market. There may be yearly, half-yearly, quarterly, monthly, daily, hourly or any other type of periodicity. In such patterns, it has been observed that the pattern is not exactly periodic that is the length of the time interval of frequency of a frequent itemset is not always equal. Furthermore, the time gap between successive time intervals of frequency of the frequent itemset is also not equal. However, there is a sufficient overlapping in the time intervals. In this situation, if the time intervals of frequency can be retained as a compact form it becomes fuzzy time interval describing the period of frequency of the frequent itemset. We designate the corresponding pattern as fuzzy periodic pattern. We propose here a method of discovering such patterns from the super-market dataset. The effectiveness of the method can be verified with the help of experiment conducted with a synthetic data collected from FIMI website.
CONDUCTING REMOTE BINARY CLASSES ON UKRAINIAN AS A FOREIGN LANGUAGE FOR STUDENTS THIRD COURSE (ECOLOGY)

The article is devoted to important stages of conducting binary classes on the Ukrainian language as a foreign language and studying the lexical and semantic structure of the language on the basis of popular science and technical texts.The definition of a binary lesson as one of the forms of realization of interdisciplinary connections and integration of knowledge from several subjects is clearly outlined.It is also a means of increasing the motivation to study the subject, as they create conditions for the practical application of knowledge; develop self-education skills, because most of the preparation for classes foreign students carry out independently and in extracurricular activities; in binary classes, skills are transferred to new areas that have not been studied before, which helps students to make decisions about creative production situations.A non-traditional type of lesson is considered, which is rather one of the forms of the project, which allows to integrate knowledge from different areas to solve one problem.In general, 5 types of binary classes are described: 1. Type of lesson: formation of new knowledge.2. Type of occupation: learning skills.3. Type of lesson: application of knowledge in practice.4. Type of occupation: repetition, systematization and generalization of knowledge, consolidation of skills.5. Type of occupation: control and testing of knowledge and skills.Examples are given for each type of binary session.The article presents important principles of understanding, mastering and accumulation of vocabulary on the basis of popular science and technical texts, which are the basis for the development of analytical skills, ingenuity and competence of foreign students.One of the examples of a binary lesson for learning the Ukrainian language in a foreign language environment is offered.An example of conducting a binary practical lesson in a group of third-year environmental students at the National University «Lviv Polytechnic» is given.Examples of pre-text and posttext exercises with examples that help foreign students to learn vocabulary and terminology of the Ukrainian language are described.The focus is on the problems of teaching Ukrainian as a foreign language in technical universities.The article presents observations on the use of modern pedagogical technologies as a form of learning organization.
Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings

While a great deal of work has been done on NLP approaches to lexical semantic change detection, other aspects of language change have received less attention from the NLP community.In this paper, we address the detection of sound change through historical spelling.We propose that a sound change can be captured by comparing the relative distance through time between the distributions of the characters involved before and after the change has taken place.We model these distributions using PPMI character embeddings.We verify this hypothesis in synthetic data and then test the method's ability to trace the well-known historical change of lenition of plosives in Danish historical sources.We show that the models are able to identify several of the changes under consideration and to uncover meaningful contexts in which they appeared.The methodology has the potential to contribute to the study of open questions such as the relative chronology of sound shifts and their geographical distribution.
Kognitiven pristop k motivaciji in učenju pri višješolskih študentih

In the article, we focused on the connections between learning or. academic motivation and learning strategies in higher education. In the theoretical part, we explained the basic concepts on the basis of the read literature listed in the bibliography, and in the empirical (research) part, we conducted a survey of students of Higher Vocational College. We used the freely available Questionnaire of Motivation and Learning Strategies, based on a cognitive approach to motivation and learning, which is characterized by emphasizing the intertwining of cognitive and emotional components in learning. There is a statistically significant medium-strong positive association between beliefs about self-efficacy and repetition and elaboration, and a weak positive association with the organization. There is a statistically significant weak positive association between metacognition and time and learning space, peer learning and seeking help - with more frequent use of metacognitive strategies, mastery of time and learning organization strategies, turning to peers or teachers for help increases statistically significantly.
Preface: International Conference on Recent Innovations in Science and Technology (RIST 2021)

First Page
Research Intention Towards Incremental Clustering

Incremental clustering is nothing but a process of grouping new incoming or incremental data into classes or clusters. It mainly clusters the randomly new data into a similar group of clusters. The existing K-means and DBSCAN clustering algorithms are inefficient to handle the large dynamic databases because, for every change in the incremental database, they simply run their algorithms repeatedly, taking lots of time to properly cluster those new ones coming data. It takes too much time and has also been realized that applying the existing algorithm frequently for updated databases may be too costly. So, the existing K-means clustering algorithm is not suitable for a dynamic environment. That’s why incremental versions of K-means and DBSCAN have been introduced in our work to overcome these challenges. To address the aforementioned issue, incremental clustering algorithms were developed to measure new cluster centers by simply computing the distance of new data from the means of current clusters rather than rerunning the entire clustering procedure. Both the K-means and the DBSCANDBSCAN algorithms use a similar approach. As a result, it specifies the delta change in the original database at which incremental K-means or DBSCANDBSCAN clustering outperforms prior techniques.
Learning Media Development Using the Lectora Inspire Application Refers to the 2013 Curriculum for Elementary School Students

The purpose of this study was to determine the feasibility of learning media using Lectora Inspire on the sub-theme of Animal Movement Organs.The subjects in this study were fifth grade of elementary school students.The results showed that the learning media using Lectora Inspire which was developed was classified as "Very Good" learning media criteria based on the results of the assessments of the three validators in the "Very Valid" category with details of the material expert validators obtained a score with an average of 95% very valid categories.In the aspect of media assessment by media experts, a score of 93.75% was obtained in the very valid category.In the aspect of language assessment by linguists, a score of 96.66% was obtained in the very valid category and the teacher response assessment aspect by the second-grade teacher was obtained with an average score of 97.61% in the very valid category.Based on the results of the three validations, the learning media using the Lectora Inspire Application on the sub-theme of Animal Movement Organs is appropriate for fifth grade elementary school students.
Kahoot as An Alternative Interactive Learning Media in Digital Era

Kahoot is an online game-based creative learning media used to teach in schools and other educational institutions.This learning media is attractive because it has many exciting features such as games, quizzes, discussions, and surveys.This study's objective was to investigate the use of Kahoot as a free online game-based learning media that provides classroom dynamics, students' interaction, and the learning process.This study's methodology was based on qualitative descriptive to obtain an overview of Kahoot as learning media in the classroom.This study's object includes 25 teachers of Wonotingal Elementary School Semarang, and the main conclusion is that Kahoot can have the alternative interactive learning media.However, they are also studied where Kahoot has no or little effect.The main challenges mentioned by teachers include technical problems and old age.We conducted observations, field notes, and interviews with the teachers to study the teacher's obstacles in implementing Kahoot as learning media in the classroom.Kahoot is used for particular learning and can also be combined with any learning method that suited the teacher's needs and student characteristics.
On the Robustness of Question Rewriting Systems to Questions of Varying Hardness

In conversational question answering (CQA), the task of question rewriting (QR) in context aims to rewrite a context-dependent question into an equivalent self-contained question that gives the same answer.In this paper, we are interested in the robustness of a QR system to questions varying in rewriting hardness or difficulty.Since there is a lack of questions classified based on their rewriting hardness, we first propose a heuristic method to automatically classify questions into subsets of varying hardness, by measuring the discrepancy between a question and its rewrite.To find out what makes questions hard or easy for rewriting, we then conduct a human evaluation to annotate the rewriting hardness of questions.Finally, to enhance the robustness of QR systems to questions of varying hardness, we propose a novel learning framework for QR that first trains a QR model independently on each subset of questions of a certain level of hardness, then combines these QR models as one joint model for inference.Experimental results on two datasets show that our framework improves the overall performance compared to the baselines 1 .
INTERNET SPACE AS A MEANS OF ORGANIZING EXTRA-COURSE ACTIVITIES OF STUDENTS IN THE DISCIPLINES OF THE NATURAL SCIENCE CYCLE

The modern information society defines the goals for young people not only to obtain information, but also to determine the special competencies necessary to perform certain professional tasks. The learning process has received a new character: there is a change in learning technology from the usual, habitual assimilation of knowledge that is transferred by the teacher to the formation of students' creative abilities, the ability to use solutions, and personal responsibility. The comprehensive penetration of informatization into all spheres of modern life requires the teacher to use innovative forms of education and upbringing, the use of which contributes to the development of students' cognitive activity, the development of their communicative competence and creative abilities. The article highlights the problem of the Internet space as a means of organizing extracurricular activities of students in the natural sciences on the example of the author's program "The World of the Living". The experiment included the following stages: 1. Ascertaining, the main task of which was to identify the attitude of students to the organization and various forms of extracurricular activities in the courses of the natural science cycle. 2. Formative, where it was envisaged to conduct various forms of extracurricular activities of students using elements of the Internet space within the framework of the developed program. 3. Control. The authors came to the conclusion that the use of the Internet space in the process of extracurricular activities contributes to the effective training of future specialists in the field of information competence of the disciplines of the natural science cycle.
Exploration on the teaching mode of public English based on learning link App

Under the condition of information technology, with the mobile teaching APPS help, it has become a new scenic spot in the classroom. The application of Chaoxing learning link in Higher Vocational English classroom provides a way to explore the online and offline English teaching mode. On the premise of investigating the mobile learning needs of higher vocational students, this study explores the online and offline public English teaching design, and provides a new perspective for the teaching strategies of English teaching.
Cloze Evaluation for Deeper Understanding of Commonsense Stories in Indonesian

Story comprehension that involves complex causal and temporal relations is a critical task in NLP, but previous studies have focused predominantly on English, leaving open the question of how the findings generalize to other languages, such as Indonesian.In this paper, we follow the Story Cloze Test framework of Mostafazadeh et al. (2016) in evaluating story understanding in Indonesian, by constructing a four-sentence story with one correct ending and one incorrect ending.To investigate commonsense knowledge acquisition in language models, we experimented with: (1) a classification task to predict the correct ending; and(2) a generation task to complete the story with a single sentence.We investigate these tasks in two settings: (i) monolingual training and (ii) zero-shot cross-lingual transfer between Indonesian and English.
Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation

How to learn highly compact yet effective sentence representation? Pre-trained language models have been effective in many NLP tasks. However, these models are often huge and produce large sentence embeddings. Moreover, there is a big performance gap between large and small models. In this paper, we propose Homomorphic Projective Distillation (HPD) to learn compressed sentence embeddings. Our method augments a small Transformer encoder model with learnable projection layers to produce compact representations while mimicking a large pre-trained language model to retain the sentence representation quality. We evaluate our method with different model sizes on both semantic textual similarity (STS) and semantic retrieval (SR) tasks. Experiments show that our method achieves 2.7-4.5 points performance gain on STS tasks compared with previous best representations of the same size. In SR tasks, our method improves retrieval speed (8.2×) and memory usage (8.0×) compared with state-of-the-art large models. Our implementation is available at https://github.com/XuandongZhao/HPD.
Graph Regularization Based Multi-view Dictionary Learning for Person Re-Identification

Person re-identification (re-ID) is an attractive task, while it has not yet reached the realistic applications. Most existing re-ID methods based on supervised scenario achieve satisfactory performance, which is mainly due to the requirement for a large number of paired-annotated training images with cross cameras. Nonetheless, this limits their generalization ability in real-world scene because of the huge annotation cost. We propose a graph regularization based dictionary learning for unsupervised re-ID. Specifically, in order to alleviate the difference between different cameras, we first project the samples under different cameras into a common space through asymmetric projections, i.e., specific projection for each camera. Then we construct a constraint item which is called Laplacian regularization via the visual similarity matrix to exploit the cross-view identity-discriminative information. In addition, we also extend our model to multi-view scene which aims at learning different dictionary for various visual features due to the confidence of the similarity matrix constructed from a single view is limited. In our experiments, the effectiveness of the proposed method has been manifested on several re-ID datasets.