Reviewerly/exHarmony huggingface dataset:
Strategist of the libraries of the future. To the 70th anniversary of Yakov L. Shrayberg

Ensuring the sustainable development of Russia in a dynamic, controversial and diverse world requires a creative approach on the part of library strategists who are aware of their responsibility for the preservation of cultural heritage and the future development of Russian civilization. Against the background of the creative activity of one of the brightest representatives of the cohort of modern library strategists, Yakov Leonidovich Shrayberg, the dynamics of library strategies in Russia is shown, including their origin, current state and problems of the strategy of the future. Strategy is defined as a prudently designed long-term program of organized action that ensures the effective use of available resources to achieve desired goals. The article considers the formation of the personality of a library strategist in the conditions of the State Public Library for Science and Technology of Russia, which includes two strategic goals: firstly, on the basis of information technologies, to actively promote the growth of the social significance of libraries, increase their contribution to the development of education, science and culture, to the formation of social harmony and political stability; secondly, the preservation of the traditional book and traditional library in the future based on their coexistence with electronic libraries. Depending on the semantic orientation, three types of strategies are distinguished: a) global, solving the problems of all mankind; b) national, covering regionally limited state and socio-ethnic issues; c) sectoral, focused on individual sectors of national production and culture. Relationships between library strategies and other global, national and sectoral strategies are shown. It is concluded that it is necessary to strengthen the humanistic orientation of library strategies.
Availability of Voice Deepfake Technology and its Impact for Good and Evil

Artificial Intelligence and especially Machine Learning and Deep Learning techniques are increasingly populating today's technological and social landscape. These advancements have overwhelmingly contributed to the development of Speech Synthesis, also known as Text-To-Speech, where speech is artificially produced from text by means of computer technology [1]. But currently, there is a fundamental common drawback: unnatural, robotic and impersonal synthesized voices [2].
The Effectiveness of Lapbook in Teaching Reading Comprehension

This pre-experimental study aimed to determine the effectiveness of the lapbook and its effect on students’ reading comprehension mastery. A cluster random sampling was used to select the participants, the eighth-grade students of MTs Negeri 02 Pontianak. The data was collected by using tests. Try out was conducted to validate the test. The data collected were analyzed using descriptive and inferential statistics to know the process and result caused by the implementation of the wapbook which was elaborated into three main procedures; pretest, treatments, and post-test. The data analysis shows that the lapbook implementation affected the students’ reading comprehension achievement. The mean score from pre-test was 53.51 and the means score of the post-test was 71.89. The calculated data of the t-test confirms that tobtain was higher than ttable, which means that the alternative hypothesis was accepted. Furthermore, the effect size calculation was 1.07 ( 1.00) which indicates the standard effectiveness of the test is categorized as strong effect according to Cohens’ effect size qualification. In summary, the lapbook is significantly effective and encourages the other English teachers to use it to teach reading comprehension.
Randomization of Explainable and Interactive Simulated Annealing Strategy for Better Learning to Rank Models

Abstract This short paper is about the first Explainable and Interactive Learning to Rank (LTR) Package in Information Retrieval (IR). This application is based on Combining Simulated Annealing Strategy with (1+1)-Evolutionary Strategy (SAS-Rank) which was introduced before as learning algorithm for ranking in the previous studies. In this application, the ranking models for offspring and parent chromosomes were showed in the runing for each iteration. Furthermore, there are three options for changing the SAS-Rank parameters and seeing the evaluation results obtained for that. This application is the first application introduces interactive learning in the ranking problem domain for IR.
Modelling Ontology of Craft Convection as Small and Medium Enterprises

Knowledge elicitation is a process to obtain and transfer knowledge (that is, for example, accumulated inside the minds of experts in a certain domain) from humans to abstract and effective representations, to organize, model, and ultimately, communicate it in such form that is easier to understand and utilized through formal representation. Knowledge elicitation uses ontology approach so that knowledge representation can be presented in knowledge management system. This knowledge will be utilized mainly in Craft Convection Small Medium Enterprises (SMEs). Ontology as an instrument to represent knowledge has been studied and applied in various fields such as information science, knowledge engineering, artificial intelligence, or digital library. In the field of ontology and design, ontology can be applied in knowledge sharing. Using methontology approach, ontology modeling and implementation are developed to identify patterns and concepts of knowledge related to Craft Convection SMEs.
Evaluating Quality Improvement Techniques Within the Linked Data Generation Process

Linked Data datasets when they are published typically have varying levels of quality. These datasets are created using mapping artefacts, which define the transformation rules from non-graph based data into graph based RDF data. Currently, quality issues are detected after the mapping artefact has been executed and the Linked Data has already been published. It is argued in this paper that addressing quality issues within the mapping artefacts will positively improve the quality of the resulting dataset that is generated. Furthermore, we suggest that an explicit quality process for mappings will improve quality, maintenance, and reuse. This paper describes the evaluation of the Mapping Quality Vocabulary (MQV) Framework, which aims to guide linked data producers in producing high quality datasets, by enabling the quality assessment and subsequent improvement of the mapping artefacts. The evaluation of the MQV framework consisted of 58 participants with varying level of background knowledge.
On the translation from quantified modal logic to Counterpart Theory

Abstract Lewis (1968) claims that his language of Counterpart Theory (CT) interprets modal discourse and he adverts to a translation scheme from the language of Quantified Modal Logic (QML) to CT. However, everybody now agrees that his original translation scheme does not always work, since it does not always preserve the ‘intuitive’ meaning of the translated QML-formulas. Lewis discusses this problem with regard to the Necessitist Thesis, and I will extend his discourse to the analysis of the Converse Barcan Formula. Everyone also agrees that there are CT-formulas that can express the QML-content that gets lost through the translation. The problem is how we arrive to them. In this paper, I propose new translation rules from QML to CT, based on a suggestion by Kaplan. However, I will claim that we cannot have ‘the’ translation scheme from QML to CT. The reason being that de re modal language is ambiguous. Accordingly, there are different sorts of QML, depending on how we resolve such ambiguity. Therefore, depending on what sort of QML we intend to translate into CT, we need to use the corresponding translation scheme. This suggests that all the translation problems might just disappear if we do what Lewis did not: begin with a fully worked out QML that tells us how to understand de re modal discourse.
How to Dissect a Muppet: The Structure of Transformer Embedding Spaces

Abstract Pretrained embeddings based on the Transformer architecture have taken the NLP community by storm. We show that they can mathematically be reframed as a sum of vector factors and showcase how to use this reframing to study the impact of each component. We provide evidence that multi-head attentions and feed-forwards are not equally useful in all downstream applications, as well as a quantitative overview of the effects of finetuning on the overall embedding space. This approach allows us to draw connections to a wide range of previous studies, from vector space anisotropy to attention weights.
Symmetry or Asymmetry? Complex Problems and Solutions by Computational Intelligence and Soft Computing

What is the role of symmetry in the seemingly far away topics of solving complex applied problems by approaches offered by Soft Computing and Computational Intelligence [...]
Financial Early Warning Model for Listed Companies Based on the Smart Sensor Data Network

This paper uses intelligent sensors to build a data network, collects information about listed enterprises in all aspects, performs frequency statistics and semantic analysis based on the financial domain lexicon on information related to listed enterprises, and introduces big data variables into a nonlinear support vector machine early warning model of enterprises combined with financial indicators. This paper introduces online reviews as big data indicators based on financial early warning theories and methods. Suitable financial indicators and big data indicators are selected for the financial early warning model to filter the available indicators. The prediction results of only financial indicators are compared with the prediction results of incorporating big data indicators. Quantify the comment information related to enterprises on the platform through sentiment classification and statistics on the number of comment information posted. The cost-sensitive support vector machine is used as the base classifier of the improved AdaBoost algorithm to build a dynamic imbalance warning model. To address the problems of large complexity and computation of unbalanced big data classification, high reliance on a priori knowledge, and classification performance to be improved, the classification and detection method of the intelligent sensor data network is proposed. By introducing migration learning, the problem of knowledge acquisition and training efficiency of high-dimensional complex data feature extraction in a big data environment is effectively solved, and the network performance is optimized by a conjugate gradient descent algorithm. Through simulation experiments, the prediction accuracy of the model for positive class samples is significantly improved after the nonequilibrium processing, the recall rate of the network model reaches 84.15%, and the prediction accuracy of the network model under different time steps reaches more than 90%. The experiment proves that the model can send out financial alert information more quickly and efficiently and accurately when a financial crisis may occur, relative to the traditional financial forecasting methods.
Research and Application of Business Law Information Management System Based on Big Data

China’s manufacturing industry has set off a wave of digital transformation innovation, and the commercial law information management system, as one of the important tools to enhance the intelligent management ability of enterprises, will also face a new round of reform and upgrade. Building an “intelligent commercial law information management system platform” is the primary transformation path for traditional manufacturing enterprises to realize intelligent upgrading of commercial law information management system. The “intelligent commercial law information management system platform” is a management information sharing service platform that enables enterprises to achieve the goal of collaborative management by integrating their own business models and product technology needs with the help of emerging digital technologies, which can help enterprises realize the organic integration of business processes, risk management, value creation, and other management needs and change the original. The platform can help enterprises realize the organic integration of business processes, risk management, value creation, and other management needs and change the focus of the traditional financial shared service center, so that the enterprise business law information management system can be shifted to the enterprise strategy-oriented, focusing on risk control, decision support, and other high-level management. Therefore, it is particularly important to evaluate and optimize the “intelligent business law information management system platform” of manufacturing enterprises in the process of digital transformation. In this context, this paper conducts research and application of commercial law information management system based on big data, conducts an in-depth study on the current status of commercial law information management system in China, and provides an outlook on commercial law information management in China by establishing a relevant model system.
Teaching Tech to Talk: K-12 Conversational Artificial Intelligence Literacy Curriculum and Development Tools

With children talking to smart-speakers, smart-phones and even smart-microwaves daily, it is increasingly important to educate students on how these agents work-from underlying mechanisms to societal implications. Researchers are developing tools and curriculum to teach K-12 students broadly about artificial intelligence (AI); however, few studies have evaluated these tools with respect to AI-specific learning outcomes, and even fewer have addressed student learning about AI-based conversational agents. We evaluate our Conversational Agent Interface for MIT App Inventor and workshop curriculum with respect to eight AI competencies from the literature. Furthermore, we analyze teacher (n=9) and student (n=47) feedback from workshops with the interface and recommend that future work leverages design considerations from the literature to optimize engagement, collaborates with teachers, and addresses a range of student abilities through pacing and opportunities for extension. We found students struggled most with the concepts of AI ethics and learning, and recommend emphasizing these topics when teaching. The appendix, including a demo video, can be found here: https://gist.github.com/jessvb/1cd959e32415a6ad4389761c49b54bbf
Social Robots: The case of Robot Sophia

In recent years there has been an increasing use of robots in various areas of public and private life. This leads to a set of ethical, social and interpersonal dilemmas. The subject of this paper concerns attitudes towards social robots, starting with the issue of citizenship and moving on to the roles that can be attributed to a robot with Artificial Intelligence. A central example was the social robot Sophia, which has artificial intelligence, and it has been given to it a citizenship. In order to investigate the issue, a literature review of previous research articles related to social robots was initially conducted. An attitudes’ questionnaire was then constructed, to which 137 participants answered. The results showed that, the majority of the sample did not want robots to acquire citizenship and equal rights as humans, nor did they want robots to be used for roles involved in interpersonal relationships, such as raising children, work, friendship, or love. In general, it has been observed that the research sample was not particularly prepared for the existence of social robots in society, while they have been associated with negative or malicious purposes. Gender and age also play an important role regarding attitudes towards social robots. However, this is an open issue that leaves much more unanswered questions and concerns.
Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization

While there has been recent progress in abstractive summarization as applied to different domains including news articles, scientific articles, and blog posts, the application of these techniques to clinical text summarization has been limited. This is primarily due to the lack of large-scale training data and the messy/unstructured nature of clinical notes as opposed to other domains where massive training data come in structured or semi -structured form. Further, one of the least explored and critical components of clinical text summarization is factual accuracy of clinical summaries. This is specifically crucial in the healthcare domain, cardiology in particular, where an accurate summary generation that preserves the facts in the source notes is critical to the well-being of a patient. In this study, we propose a framework for improving the factual accuracy of abstractive summarization of clinical text using knowledge-guided multi-objective optimization. We propose to jointly optimize three cost functions in our proposed architecture during training: generative loss, entity loss and knowledge loss and evaluate the proposed architecture on 1) clinical notes of patients with heart failure (HF), which we collect for this study; and 2) two benchmark datasets, Indiana University Chest X-ray collection (IU X-Ray), and MIMIC-CXR, that are publicly available. We experiment with three transformer encoder-decoder architectures and demonstrate that optimizing different loss functions leads to improved performance in terms of entity-level factual accuracy.
PENINGKATAN PRESTASI BELAJAR MELALUI APLIKASI CANVA SELAMA PEMBELAJARAN DARING MASA PANDEMI COVID-19

This study aims to determine the implementation of online learning at SDN Krembangan Selatan III during the COVID-19 pandemic, to measure the ability of teachers to design and implement creative and innovative online learning according to student conditions during School from Home (SFH). In addition, this research was conducted to see the learning achievement of class VI students when participating in online learning. Teachers carry out digital-based learning using the Canva for education application in delivering learning materials to students. The features used in the delivery of the material is presentation. The presentation features display in the form of power points which can be recorded by showing the faces of the presenters (educators) in delivering material to students. Using descriptive qualitative research with the main focus of improving student learning outcomes. The results showed that the creativity of online learning has achieved the expected goals, especially after students used the Canva application, it becomes more innovative, and interested in participating in online learning. It also has impact on student academic achievement. The average value showed in the pre-test of 65.4 and post-test of 82.2. This showed that the student's academic results while using Canva are in a very good category. The use of the Canva application also affects the percentage who passed the grade, from 20% to 90%. Based on the results, it can be concluded that Canva for education can be an innovative digital learning media and can improve the learning achievement of sixth grade students at SDN Krembangan Selatan III Surabaya.
ENGAGING STUDENTS SPEAKING SKILL USING GOOGLE MEET PLATFORM: STUDENTS PERCEPTION

This study was conducted to find out students perception of engaging speaking skill using Google Meet platform and to find out the students perceptions of speaking skill using Google Meet platform. This study used a qualitative case study, and the data were taken through online classroom observation and close-ended questionnaire. Twenty four speaking students in the first semester of the English education department in one private university in Ciamis were involved in this study. In analyzing the data of observation, the researcher classified, interpreted and commented the results of observation from google meet application. The data from the students’ questionnaire item were analyzed by using percentage compilation. Based on the finding, there are three key points of the present research. First, Google Meet online was conducted in one private university in Ciamis to improve students’ engagement speaking skill. Second, the students were generally perceives google meet as an application that enables them to learn English optimally, with enjoyed, relax, and enthusiasm. Third, google meet application gave students more opportunity to practice English rather than regular classroom. Keywords: Speaking, Google Meet, Online Class.
A NARRATIVE ANALYSIS OF ENGLISH TEACHER’S EXPERIENCES IN TEACHING PUBLIC SPEAKING THROUGH ONLINE LEARNING-BASED EMERGENCY REMOTE TEACHING

This article is focused to investigate the way the English teacher prepares, implements and evaluates, what problems are faced, and how to solve problems that occur during the learning activity takes place. The study employed a narrative analysis method to find out the English teachers’ experiences, the problems and its solutions in the implementation of teaching public speaking through online learning based-emergency remote teaching in the Covid-19 pandemic situation. The first result of this study reveals that there are several things that must be considered to carry out learning activities, namely the preparation stage, the implementation stage and the stage for evaluating student learning outcomes. The second result indicates that several problems emerged on its implementation, namely poor internet connections, the lack of student internet quotas, and the problem of direct interaction between teacher and students during virtual meetings. The third result come with the solutions that were found to overcome network problems and internet quotas is to make learning video recordings posted on Google Classroom or Telegram; so students can watch them at a later time after having a good internet connection and sufficient quota, and the provision of Telegram as a forum for indirect interaction between teachers and students outside of direct interaction in virtual meetings.Keywords: Narrative analysis, Public speaking, Online Learning, Emergency Remote Teaching
Implementasi Association Rule Dalam Menganalisis Data Penjualan Sheshop dengan Menggunakan Algoritma Apriori

Sheshop is a business activity engaged in the field of hamper making services. Since the last 2 (two) years, sales transactions in Sheshop have been increasing, the transaction data is only used as a report and is not used to regulate business strategies. The transaction data should be used to see the attachment of each type of product purchased by the customer simultaneously. The amount of sales transaction data on Sheshop can be used as an analysis of customer behavior in making purchases of hampers at Sheshop. This study performs data analysis by implementing the Apriori algorithm method because this algorithm handles data mining processes quickly on large amounts of data, from the results of this study Sheshop can make decisions on what items need more inventory compared to other items by looking at the value confidence and support by using the RapidMiner application. The results of this study indicate that the association rule formed from 568 Sheshop sales data uses a minimum support value of 10% and a minimum confidence of 50% produces 6 (six) association rules with a confidence value of 58% to 75% with all rules having a positive correlation level. Based on the 6 (six) association rules obtained, 2 products are often purchased at the same time, namely the Koran and tasbih with a confidence value of 75%.