Reviewerly/exHarmony huggingface dataset:
INTEGRATED FLASHCARD AND LUDO VOCABULARY LEARNING GAME TO INCREASE VOCABULARY ACHIEVEMENT OF THE FIRST GRADE OF JUNIOR HIGH SCHOOL: RESEARCH AND DEVELOPMENT

This research was aimed to establish, to find out the effectiveness, and to know the teacher’s and student’s responses of Flash Ludo Game as a vocabulary learning media for first grade students of Junior High School. The researcher was applied Research and Development (R&amp;D) design whis is adapted from Sugiyono. It consisted of five phases, they were: data collection and finding potentials and problems, design and development, expert validation and revision, product trial, and final product. The final product of the model consisted of three kinds of flashcards, a set of ludo game, and three kinds of guidebooks. The result of design and development showed that the model was very valid, the average of material and media experts was 92.5%. The product trial was done with 20 seventh grade students as participants. The one-group pre-post test showed that before using the model, the average student’s score was (45.80) and after using the model, it reached (84.05). From the t-test analysis, the researcher found that the sig (2.tailed) value was .000, it showed that the Sig. value (2-tailed) &lt;0.05, in means that was a significant difference between pre-test and post-test. The result of teacher’s and student’s responses showed that the model was very proper, with the average result was 94%. The vocabulary learning media was effective and improve student’s vocabulary achievement for the first grade students of Junior High School.
Interactive Lesson Video Model To Develop Pronunciation Skill The Tenth Grade Students’ English Pronunciation Skill : Research And Development.

This research was aimed to develop interactive video model for teaching pronunciation to the tenth grade students. This strategy was choosen because it could interested the students to learn pronunciation.&#x0D; The researcher applied Research and Development (R&amp;D) design. The subject of this research was students of senior high school. The data of the research were quantitative and qualitative data. The quantitative data were the outcome of the pre-test and post-test results with included the results of experts content material and experts content of media toward the developed interactive video model.&#x0D; Based on the recapitulation results of interactive video model, the presentage of component mterial was 86,06% and the results of media was 84,74%. the results recapitulation of students mean score in students learning with interactive video model in pre-test was 62,58 and post-test was 75,70. It means the interactive video model included in “very feasibility” category. So, the interactive video model can use to teach reading in the classroom.
RDRS: Represent Document-level Relation with Sentence-level Relation by Distant Supervision

This paper aims to study how to use remote monitoring for relation extraction, so as to identify the relationship between entities and provide logical facts. In recent years, document-level relation extraction (DocRE) has attracted much attention. Distant supervision (DS) has been playing an important role in sentence-level relation extraction (RE). Compared with traditional relation extraction methods, DS requires less data. However, the performance of document-level DS is limited by the noise in the long distant text. Previous DS works represent intra-sentential and inter-sentential representations in the same way, and neglect the difference between noise in them. This paper proposes an architecture called RDRS, which represents inter-sentence and intra-sentence relations in different ways, and uses a sentence-level relation model to represent document-level relations. The RDRS contains various modules, in which pairs of entities are separated according to the distribution of their mentions. In addition, this paper also improves the performance of inter-sentence representation by associating intra-sentence representation with inter-sentence representation. The experiments are carried out on Document-level Relation Extraction Dataset (DocRED). Experiments on DocRED show that the method has a significant improvement over the baseline. This model is better than previous models that represent inter-sentence and intra-sentence relations in the same way.
Completeness Thresholds for Memory Safety of Array Traversing Programs

We report on intermediate results of -- to the best of our knowledge -- the first study of completeness thresholds for (partially) bounded memory safety proofs. Specifically, we consider heap-manipulating programs that iterate over arrays without allocating or freeing memory. In this setting, we present the first notion of completeness thresholds for program verification which reduce unbounded memory safety proofs to (partially) bounded ones. Moreover, we demonstrate that we can characterise completeness thresholds for simple classes of array traversing programs. Finally, we suggest avenues of research to scale this technique theoretically, i.e., to larger classes of programs (heap manipulation, tree-like data structures), and practically by highlighting automation opportunities.
Upward Pass Semantics in Arithmetic Circuit Inference

An Arithmetic Circuit (AC) is a deep learning probabilistic model that is compiled by eliminating every variable in a given Bayesian Network (BN).We introduce a special case of AC, called a p-AC, in which every node is a 1, marginal, or conditional of the joint distribution defined by the given BN.This is accomplished by using wait-sets to restrict the elimination ordering used.We show both theoretical and practical advantages of p-ACs over ACs, including that there is no increase in network size.Lastly, we observe and analyze an interesting graphical relationship between semantics in deep learning inference and causal analysis.
Dependency enhanced session-based recommendation based on GNN

Session-based recommendations can predict the next item through the user's current session sequence. Session-based recommendation with graph neural network has become the mainstream method in this domain. However, the session sub-graph constructed by the session sequence will bring fake dependencies issue. Adjacent items may be noise, non-adjacent items may have true dependencies; Moreover, the information outside the target session is ignored. To solve these problems, a dependency enhanced session-based recommendation model based on graph neural network (GID-GNN) is proposed. Firstly, it uses GNN to learn session features, meanwhile uses self-attention to capture session items’ dependencies to obtain session level representation; Then a global graph is constructed to obtain effective global level information and global level representation; Finally, the prediction is made by combining two part representations. Experiments were tested on two real datasets, the results proved effectiveness of GID-GNN model.
Development of soft skills of high school students as an opportunity to implement the requirements of modern education

The article considers the main features of the formation process of “soft skills” of high school students in the training based on modern educational technologies. The first part of the article describes the features of the world and Kazakhstan society development over the past three decades, and concludes that there is a need for further reforms in the field of professional guidance in the secondary education system. Matching the needs of modern society, the latter should develop student’s not only professional orientation, but also supra-professional, “flexible”, or “soft” skills. The importance of the vocational education cycle in developing “soft skills” among secondary school pupils is also discussed. In the final part of the article an overview of advanced pedagogical technologies was given, the effectiveness of which in terms of the development of soft skills has been confirmed by both pedagogical science and practice. Significant work in this direction is implemented in further plans to address the problem of soft skills development in terms of effective career guidance work in schools in Kazakhstan. The study shows the main forms and results of the development of soft skills in modern high school in the professional education school system
Software Hope Design for Children with ASD.

This article describes the analysis, design, implementation, and evaluation of a software called Hope to help children with autism spectrum disorder (ASD) to express themselves through dance. The proposed software is based on augmented reality and allows strengthening teaching learning processes that include aspects related to imitation, perception, gross and fine motor skills, and visual coordination. The design process conducted in an interactive way, centered on the human being, with the participation and guidance of a multidisciplinary team. The characteristics considered to design and implement the software are explained, which was tested in a Ludic Place Therapeutic Center with children with ASD, in addition, a pedagogical intervention proposal was built where parameters for evaluation are defined; the start-up of Hope required constant interaction, participatory design methods, likewise the software was improved with the recommendations of the participants, some students from the therapeutic center tried the application, they did so initially accompanied by their caregivers and progressively they achieved individual use of the system, Hope was assessed by 5 children with ASD, in addition to 5 parents, 5 IT specialists, followed by 5 experts (teachers, psychologists, therapists, doctors). In the end, encouraging results achieved that included recognition of the body, nonverbal dialogue, less directive, structured expressions, and the ability to create and make the participants’ thinking more flexible.
Some Fundamentals of Mathematics of Blockchain

This section is introductory. We set the very first mathematics needed for any blockchain modeling, by considering a blockchain itself. The other parts of this chapter are depending on the contents of this section. The reader is supposed to be familiar with the basic mathematics, including probability and the used algebra, as well as the notions of bit, bytes, and hexadecimal in computer science. We re-introduce these notions first of all, for the convenience of the reader.
Analysis of Machine Learning Algorithms for Detection of Cyberbullying on Social Networks

The use of electronic communications to bully a person, usually by sending a threatening message, is called cyberbullying. Cyberbullying is abusive online behaviour that can cause very serious social problems. Twitter is the microblogging site where cyberbullying is caused very easily. People can hide their identities and bully others. They can send bad text messages and other media anonymously. In this project, machine learning methods used are random forest, support vector machine, bagging classifier, naive Bayesian, k-nearest neighbour, logistic regression, decision tree, AdaBoost and stochastic gradient descent . Moreover, these methods are analysed for detection of cyberbullying. The parameters considered for analysis are accuracy, precision, recall and F1-Score. All these algorithms are compared and the algorithm with the highest accuracy will be projected as the best solution for the above problem statement.
Extractive and Abstractive Text Summarization Model Fine-Tuned Based on BERTSUM and Bio-BERT on COVID-19 Open Research Articles

Bio-BERT (BERT for Bio-medical Text Mining) is a Natural Language Processing (NLP) model, pre-trained on massive bio-medical data. Bio-BERT is effective in an extensive variety of NLP tasks that can be applied to bio-medical data. BERTSUM, BERTSUMABS, and BERTSUMEXTABS are NLP models built for the task of Extractive Text Summarization (ETS) and Abstractive Text Summarization (ATS). These models are evaluated on CNN/DailyMail and Extreme Summarization datasets. In this chapter, the objective is to achieve ETS and ATS for CORD-19 dataset. A hybrid NLP model based on Bio-BERT, BERTSUM, BERTSUMABS, and BERTSUMEXTABS has been proposed. As the objective is to find ETS and ATS on bio-medical datasets, Bio-BERT has been chosen as it is pre-trained on bio-medical PubMed full-text articles. BERTSUM, BERTSUMABS, and BERTSUMEXTABS models are chosen as they were fine-tuned for the task of text summarization. As there is a rapid acceleration in the novel COVID-19 publications, there is a need to obtain a summary of these publications in order to save time. The model generated summary has to be on par with the human written summary. Experiments were conducted on the CORD-19 dataset, and the proposed hybrid model has been evaluated based on ROUGE metric. The proposed model is compared with BERT-based BERTSUM, BERTSUMABS, and BERTSUMEXTABS on CORD-19 dataset and is found to achieve the highest ROUGE values for the task of ETS and ATS.
Hintikka’s conception of syntheticity as the introduction of new individuals

Abstract In a series of papers published in the sixties and seventies, Jaakko Hintikka, drawing upon Kant’s conception, defines an argument to be analytic whenever it does not introduce new individuals into the discussion and argues that there exists a class of arguments in polyadic first-order logic that are to be synthetic according to this sense. His work has been utterly overlooked in the literature. In this paper, I claim that the value of Hintikka’s contribution has been obscured by his formalisation of the original definition. Therefore, I provide (i) a brief reconstruction of the historical framework of the problem and the revolutionary import of Hintikka’s contribution, (ii) a clarification of the most complicated steps of Hintikka’s elaboration of his insight, (iii) a criticism of several features that play a fundamental role in Hintikka’s formalisation and (iv) a selection from Hintikka’s own material of some valuable suggestions towards a clear and workable formalisation. As for the pars construens , I isolate in the approach of depth-bounded first-order logics (D'Agostino et al. 2021) an alternative formalisation of the notion of syntheticity as the introduction of new individuals in the reasoning, and I show that it is not affected by the same difficulties as Hintikka’s proposal. In so doing, I hope to have contributed to the realisation of the project of rehabilitating Kant’s analytic–synthetic distinction in the context of modern first-order logic with the purpose of showing, against the logical empiricist movement, that logic is not analytic.
Edge Detection of Satellite Image for Water Body Identification using Novel Marr-Hildreth Algorithm and comparing with Hough Transform Algorithm to Enhance Accuracy and Contrast

The Marr-Hildreth algorithm and the Hough Transform Algorithm are used to analyze a software project using image processing algorithms, such as the Marr-Hildreth and Hough Transform Algorithm, in this study. Here the pretest power analysis was carried out with 80% and there are totally 2 groups and the sample size is 20. The power analysis was held up with 80% and the sample amount for the two factions is 40. The Results of Software effort analysis was done using Marr-Hildreth algorithm and Hough Transform Algorithm with the accuracy of 10 and 10 respectively. There is a statistically significant variation in precision for two algorithms is 0.045 (p<0.05) by conducting dominant samples t-tests. The Conclusion is that the Marr-Hildreth algorithm performs significantly better than the Hough Transform Algorithm.
Siamese Network Object Tracking Algorithm Combined with Attention Mechanism

Object tracking is one of the important research topics in the direction of computer vision. And occlusion, deformation and scale change are the most challenging issues in the realm of tracking. In an effort to address the issue that the Siamese network-based object tracking algorithm’s resilience and success rate are both low and weak in complicated circumstances such as similar object interference and object occlusion, a Siamese network Object tracking algorithm combined with attention mechanism based on the existing Siamese network algorithm is proposed. Firstly, in order to extract the features of the template picture and the sample picture, the modified AlexNet convolutional neural network <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> is utilized as the feature extraction network. Secondly, in order to increase the model’s precision and increase tracking’s rate of success, the module for the attention mechanism, which consists of the channel attention mechanism and the spatial attention mechanism, is introduced. The channel attention mechanism can focus on the information useful for tracking the Object, suppress unimportant background information, and the spatial attention mechanism is capable of picking up on the object’s abundant spatial information. Experiments show that the proposed Siamese network target tracking algorithm combined with attention mechanism achieves contestable results on the OTB100 test dataset, and the enhanced method outperforms the original approach in terms of performance.
The Potential Energy of Context in Brand Communication Education Reform from the Perspective of Mobile Media

Mobile media is profoundly changing the format of brand communication, and it also puts forward new requirements for the cultivation of brand communication talents. The reform of brand communication education is imperative. As a new entrance to mobile media, context provides a new opportunity for innovating education models and optimizing teaching paths. Based on the logic of mobile communication and the support of application technology, this study attempts to explore the potential energy of context in brand communication education reform from three aspects of context construction, context experience, and context mindset.
Social Recommendation Algorithm Based on Learning Interests and Styles for Online Blended Learning Platform

Information overload is a common problem in the construction of online platforms for blended learning, and personalized recommendation is a solution to this challenge. However, the traditional collaborative filtering recommendation algorithm is not fully applicable to online blended learning platforms characterized by sparse rating data and rich text and social information. In this paper, a new model of user learning interest similarity measurement was constructed using the author-topic model based on user-generated text and behavior information. Comprehensive user trust relationships were constructed and comprehensive trust was calculated based on user interaction behavior information by taking trust and distrust factors into full account. User learning styles were automatically identified by analyzing users’ multidimensional learning behavior patterns. Finally, a social recommendation algorithm integrating interest similarity, comprehensive trust, and learning styles was proposed. In addition, an experimental analysis was conducted using the actual data set of the online blended learning platform China University MOOC. The results indicated that the recommended method in this paper can better recommend learning resources of interest to users and effectively improve the recommendation accuracy, thus improving users’ learning effect.
Efektivitas Metode Jigsaw Terhadap Kegiatan Membaca dan Berhitung dalam Proses Pembelajaran Peserta Didik

The background of this research was based on the findings regarding the low activity and mastery of students' concepts in reading and numeracy learning in class II SD IT Aroja Kisaran, Asahan Regency. SD IT Aroja Kisaran. The subjects in this study were 28 students of class II SD IT Aroja Kisaran, 2020-2021 academic year. The research method used was classroom action research which was carried out in two cycles. Each cycle consists of four stages, namely Planning, Implementation, Observation and Reflection. Data collection was carried out through tests, observation of student and teacher activities, interviews and documentation. Results: The results showed that: (1) The reasoning ability of students before learning to read had not yet reached completeness where the average score obtained only reached 72.67. (2) Students' reading reasoning ability after learning in cycle II has reached completeness where the average value obtained reaches 81.25. Meanwhile (3) the students' reasoning abilities before learning to count had not yet reached completeness where the average value obtained only reached 74.46. (2) and the students 'reading reasoning ability after learning in cycle II has reached completeness where the average value obtained reaches 80. The jigsaw type cooperative learning model is very effective in the reading and numeracy learning process to support the students' reasoning abilities
Research on Internal Control Risk and Prevention under Financial Sharing Mode of Baosteel

Because of the sustainable development of society and economy and the expansion of the scale of business, many large group companies with many children and sun companies have resulted in the financial organization structure is complicated, the management is chaotic, the capital cost remains high.In this context, many large, small and medium-sized companies have adopted the way of "accounting", that is, through the establishment of "financial resources sharing", "unified" way to achieve "centralized management" and "centralized accounting".Baosteel Co., Ltd. is a representative enterprise.The company has established a "financial sharing" organization, which can greatly reduce the cost of the enterprise, and effectively improve the business condition of the enterprise, and enhance the financial management level of the enterprise.Although financial sharing has many advantages, its advantages are not obvious.The disadvantages are also obvious.For example, the establishment of a financial allocation caused by the personnel flow, the replacement of posts, the adoption of new financial methods and financial system caused by administrative gaps, etc.The above problems have a great impact on the traditional internal management mode.This paper uses the theory of financial sharing and internal control, combined with the theory of internal management of Baosteel Group, discusses the internal control risk faced by the company in the course of operation, and puts forward the corresponding countermeasures.
THE EFFECTIVENESS OF USING COOPERATION READING AND COMPOSITION (CIRC) TOWARDS WRITING ACHIEVEMENT ACROSS STUDENTS’ SELF-EFFICACY AT SMAN 16 PEKANBARU

This study proves the Effectiveness of Using Cooperation Reading and Composition (CIRC) on Writing Achievement in All Students' Self-Efficacy. Researchers conducted this research at SMAN 16 Pekanbaru. The purpose of this research, the researcher wants to prove, is there any influence from CIRC in arousing students' self-efficacy in writing. Which is the population for this study namely eleventh grade students, and for the sample the students in class XI MIPA-1 and XI IPS-2. And for the sampling technique the researcher used a purposive method. The type of research that researchers use is quasi-experimental design. For this study, researchers will measure students' self-efficacy using the CIRC method in learning to write. And the results show that there is no effect of this method in increasing students' self-efficacy in learning to write.