Reviewerly/exHarmony huggingface dataset:
Comparison of Sequence Models for Text Narration from Tabular Data

This paper demonstrates our work on the survey of pre-trained transformer models for text narration from tabular data. Understanding the meaning of data from tables or any other data source requires human effort and time to interpret the content. In this era of internet where data is exponentially growing and massive improvement in technology, we propose an NLP (Natural Language Processing) based approach where we can generate the meaningful text from the table without the human intervention. In this paper we propose transformer-based models with the goal to generate natural human interpretable language text generated from the input tables. We propose transformer based pre-trained model that is trained with structured and context rich tables and their respective summaries. We present comprehensive comparison between different transformer-based models and conclude with mentioning key points and future research roadmap.
Reduction between the Syllogism OAO-3 and the Remaining 23 Valid Syllogisms

With the help of the definitions of three negative quantifiers of Aristotelian quantifiers (i.e.all, no, some and not all), the symmetry of no and some, and some basic inference rules in propositional logic, one can deduce the remaining 23 valid syllogisms only from the syllogism OAO-3.In other words, there is reducible relations between/among different forms and different figures of valid traditional syllogisms.And these reducible relations actually reflect the transformation relations between the monotonicity of the four Aristotelian quantifiers.This paper provides a computational level of reasoning for syllogistic logic and an important theoretical basis for knowledge representation and knowledge reasoning in computers.
Practical Mechanisms for Training of Education Managers for Innovation and Evaluation of their Effectiveness

The article is devoted to the preparation and assessment of the effectiveness of training of education managers for innovations in the system of higher pedagogical education by conducting an empirical study; structural and logical evaluation scheme is described; analysis of the dynamics of managers' readiness for innovative management in comparison groups is carried out.
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation

Current machine learning models have shown high efficiency in solving a wide variety of real-world problems. However, their black box character poses a major challenge for the understanding and traceability of the underlying decision-making strategies. As a remedy, many post-hoc explanation and self-explanatory methods have been developed to interpret the models' behavior. These methods, in addition, enable the identification of artifacts that can be learned by the model as class-relevant features. In this work, we provide a detailed case study of the self-explaining network, ProtoPNet, in the presence of a spectrum of artifacts. Accordingly, we identify the main drawbacks of ProtoPNet, especially, its coarse and spatially imprecise explanations. We address these limitations by introducing Prototypical Relevance Propagation (PRP), a novel method for generating more precise model-aware explanations. Furthermore, in order to obtain a clean dataset, we propose to use multi-view clustering strategies for segregating the artifact images using the PRP explanations, thereby suppressing the potential artifact learning in the models.
Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer's Disease

In this work, we propose three explainable deep learning architectures to automatically detect patients with Alzheimer`s disease based on their language abilities. The architectures use: (1) only the part-of-speech features; (2) only language embedding features and (3) both of these feature classes via a unified architecture. We use self-attention mechanisms and interpretable 1-dimensional ConvolutionalNeural Network (CNN) to generate two types of explanations of the model`s action: intra-class explanation and inter-class explanation. The inter-class explanation captures the relative importance of each of the different features in that class, while the inter-class explanation captures the relative importance between the classes. Note that although we have considered two classes of features in this paper, the architecture is easily expandable to more classes because of its modularity. Extensive experimentation and comparison with several recent models show that our method outperforms these methods with an accuracy of 92.2% and F1 score of 0.952on the DementiaBank dataset while being able to generate explanations. We show by examples, how to generate these explanations using attention values.
Exploring English Grammar Learning Strategies in Online Learning Used by Thai University Students

The objectives of this study were: 1 to explore the extent to which the university students employed English grammar learning strategies (GLS) in online learning; 2. to examine the differences between the use of GLS among the students with different English proficiency levels; and 3. to investigate factors which have an impact on the application of GLS. Data was collected from a class of thirty First-Year English major students enrolling in the course called Grammar 2, using the GLS and focus-group interviews. They were classified into three groups with different grammar proficiency levels, measured by their grade obtained in a grammar course last semester, namely higher-level learners (A, B+), intermediate-level learners (B, C+), and basic-level learners (C, D+, and D). The quantitative data was statistically analyzed by frequency, percentage, means, standard deviation, and ANOVA. Thematic analysis was used to analyze data from focus-group interviews. The findings showed that the students used GLS in general at a high level. The social strategies were used the most while metacognitive strategies were found to be used the least. Moreover, the comparison of GLS use among the three groups revealed no significant differences in their strategy use. Therefore, learners’ grammar proficiency did not have a direct effect on GLS use among the students in this study. Instead, focus-group interviews further revealed a range of factors mediating GLS use namely motivation, learning styles, learning situations, and teachers.
PaNOSC – Making FAIR open data a reality for Photon and Neutron Science

PaNOSC – Making FAIR open data a reality for Photon and Neutron Science The Photon and Neutron Open Science Cloud (PaNOSC) is a European project enabling open science through the adoption of FAIR principles across photon and neutron (PaN) facilities in Europe. To this aim, PaNOSC has developed and provided common policies and software and services connected to and made accessible via the European Open Science Cloud (EOSC).
Use of Website as Information Media and Learning Media for Madrasah Students in Ngawi Regency

This study aims to describe the quality of information submitted on the website at one of the Madrasahs in Ngawi Regency about the use of technology as a medium for disseminating information. The method used in this study is to use qualitative descriptive research. The data was obtained from content analysis and interviews as well as in-depth observations conducted in one of the Madarasahs in Ngawi Regency. The results showed that the use of information technology that is actively used is Instagram and YouTube, information dissemination and promotion that is actively managed and used by Madrasah.
A Grammatically and Structurally Based Part of Speech (POS) Tagger for Arabic Language

In this paper we report on an experimental syntactically and morphologically driven rule-based Arabic tagger. The tagger is developed using Arabic language grammatical rules and regulations. The tagger requires no pre-tagged text and is developed using a primitive set of lexicon items along with extensive grammatical and structural rules. It is tested and compared to Stanford tagger both in terms of accuracy and performance (speed). Obtained results are quite comparable to Stanford tagger performance with marginal difference favoring the developed tagger in accuracy with huge difference in terms of speed of execution. The newly developed tagger named MTE Tagger has been tested and evaluated. For the evaluation of its accuracy of tagging, a set of Arabic text was manually prepared and annotated. Compared to Stanford tagger, the MTE tagger performance was quite comparable. The developed tagger makes use of no pre-annotated datasets, except of some simple lexicon consisting of list of words representing closed word types like demonstrative nouns or pronouns or some particles. For the purpose of evaluation of the new tagger, it was run on multiple datasets and results were compared to those of Stanford tagger. In particular, both taggers (the MTE and the Stanford) were run on a set of 1226 sentences with close to 20,000 tokens that was human annotated and verified to serve as testbed. The results were very encouraging where in both test runs, the MTE tagger outperformed the Stanford tagger in terms of accuracy of 87.88% versus 86.67% for the Stanford tagger. In terms of speed of tagging and in comparison Stanford tagger, MTE Taggers’ performance was on average 1:50. More improved accuracy is possible in future work as the set of rules are further optimized, integrated and more of Arabic language properties such as end of word discretization are used.
Robust Watermarking Method for Secure Transmission of Medical Images in EHR Systems

Confidentiality of Electronic Health Record (EHR) and privacy are two important security requirements for healthcare systems. Many devices on the EHR network utilize little or no encryption, which makes data in transit vulnerable to exploitative attacks, such as Man-in-the-Middle and other filtration methods. Recently, watermarking algorithms as an efficient response to these requirements is in the underline. In this paper, we present a robust watermarking method conceived as part of an Electronic Health Record platform. In this method a chaotic encryption and blind medical image watermarking technique was incorporated into the system as an authorization mechanism to ensure confidentiality and integrity of electronic health information. We present a hybrid watermarking method based on a combination of discrete wavelet transform (DWT), hessenberg Decomposition (HD), Singular value decomposition (SVD) and an original chaos crypto system based on the Arnold Transform (AT) of Singular Value Decomposition. In order to spread the robustness of our algorithm and provide additional security, an improved SVD-AT embedding and extraction procedure has been used to scramble the EHR data in the preprocessing step of the proposed method. In the process of watermark embedding, an R-level discrete wavelet transform was applied to the host image. The low frequency wavelet coefficients are selected to carry this scrambled-watermark. In extraction process, the stored used plain image is obtained from the trusted authority server to complete the verification process. The receiver should compare the unsigned watermark with the extracted watermark again. The verification can be done before clinical procedures and diagnosis. The proposed watermarking method endures entirety attacks and rightly extracts the hidden watermark without significant degradation in the image quality, thus, when the Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) performance of the proposed algorithm is performed.
DEVELOPING MATERIALS FOR YOUNG LEARNERS

The article views the principles of materials development for young learners. It suggests a three-steps model in teaching materials development. The first step is reading and reviewing picture books and thinking of possible activities, the second one is choosing suitable video materials and creating activities for the corresponding age group – primary students or pre-school children, and the third step is creating their own stories, visualizing and digitalizing them and creating activities. Finally, it presents materials for young and very young learners developed by the student teachers at College – Dobrich, Shumen University.
“A study to assess the knowledge regarding warning signs of cancer among home-maker women in the selected areas of Pune city”

Introduction: When aberrant cells proliferate uncontrolled, cross their normal borders to infiltrate other body parts and/or move to other organs, the result is cancer, a broad category of disorders that may begin in almost any function or muscle. The latter phase, known as metastasizing, is a significant contributor to cancer-related mortality. Other synonyms for cancer include neoplasm and malignant tumour. Approximately 9.6 million fatalities, or one in every six deaths, were attributed to cancer in 2018, making it the second largest cause of death worldwide. Methodology: In the present study Descriptive Survey research design was used. Data collected on 500 samples. Tool was constructed to identify the demographic variables, and a set of self-structured questionnaires on knowledge regarding warning signs of cancer in home- maker women. Result: The main results about the degree of awareness of cancer warning signals among Home-Maker women are that 497 (99.4%) of the women have average knowledge, 3 (0.6%) have poor knowledge, and 0 (0%), have high knowledge. The mean awareness of cancer warning signals amongst Home-Maker women is 9.37, with a standard deviation of 0.74. Conclusion: The research was carried out to see how well Home-Maker women in certain parts of Pune city were informed about cancer warning indicators. The analysis was conducted to evaluate Home-Maker women’s awareness of cancer warning indicators. It demonstrates the average knowledge score of most married women. The level of understanding and their demographics did not significantly correlate with one another. Recommendation: A similar study can be done on urban population.
A hybrid modified Bat Algorithm with Dynamic Spiral Method for solving mixed integer non-linear programming optimization

Most optimization problems in engineering are nonlinear, with many constraints. Consequently, finding optimal solutions to such nonlinear problems requires efficient optimization methods. Most of the methods used so far are gradient-based methods. In gradient-based methods, the objective function must be differentiable, and the search for the optimum solution usually starts with a guess-point. For multimodal objectives, the search will likely get stuck at a local optimum. Nowadays, new metaheuristic methods are being developed for solving nonlinear optimization problems. Metaheuristic methods neither require a guess-point nor a derivative of the objective function. Metaheuristic methods, called Bat Algorithm and Spiral Dynamic Method, have been developed for solving optimization problems. Each of the methods has the strength to solve the problem. We propose combining Bat Algorithm (BA) and Dynamic Spiral Method (DSM) for solving mixed integer optimization. The bat Algorithm is used for the exploration stage to find some candidate solutions. Meanwhile, the dynamic spiral method is used in local search to find the best optimum solution. The result obtained by Bat Algorithm-Dynamic Spiral Method (BA-DSM) were more effective than the standard Bat Algorithm in solving the problem.
IBD: An Interpretable Backdoor-Detection Method via Multivariate Interactions

Recent work has shown that deep neural networks are vulnerable to backdoor attacks. In comparison with the success of backdoor-attack methods, existing backdoor-defense methods face a lack of theoretical foundations and interpretable solutions. Most defense methods are based on experience with the characteristics of previous attacks, but fail to defend against new attacks. In this paper, we propose IBD, an interpretable backdoor-detection method via multivariate interactions. Using information theory techniques, IBD reveals how the backdoor works from the perspective of multivariate interactions of features. Based on the interpretable theorem, IBD enables defenders to detect backdoor models and poisoned examples without introducing additional information about the specific attack method. Experiments on widely used datasets and models show that IBD achieves a 78% increase in average in detection accuracy and an order-of-magnitude reduction in time cost compared with existing backdoor-detection methods.
Ensuring Security Using Blockchain Technology

The world is heading toward an era of digitization and automation, especially in the development of Blockchain technology. Blockchain in the Internet of Things network is an innovative approach that can make communication between such network devices distributed, autonomous and secure. Blockchain, in this context, is a set of cryptographically linked blocks. Transactions in the network act as the primary carrier of information about the state of the nodes, as well as output information from the nodes themselves for autonomous network functions. A node is a "smart" device, sensor, or microcontroller that connects a group of sensors. Blockchain will be used to provide secure data transmission and device processing on the Internet of Things network. This study discusses the main challenges of applying technology in distributed networks.
THE DEVELOPMENT OF DIGITAL ILLUSTRATION TO HELP STUDENT’S COMPREHENSION IN READING NARRATIVE TEXT: A RESEARCH AND DEVELOPMENT

This study aims to know the lecturer’s and student’s perspectives on the use of digital illustrations or digital storybooks, this study also aims to develop a digital picture book for learning to read narrative text. This study is about research and development or also known as R&amp;D, which produces products that help students’ comprehension in reading narrative text. The data was taken from the questionnaire and open-ended interviews that have been shared by the respondents. According to Borg and Gall (1983), R&amp;D is “The process used to develop and validate educational products”. The results of this research not only develop existing products but also on finding knowledge, middle ground, or answers to existing problems. This study found that the respondents like story books with pictures and colors, they also assume that illustration can help their understanding of reading narrative text, and they think that the use of the visual learning method is a good method and not monotonous to teach and learn especially in English learning, they also said that this product can be developed further, and for the last, they think that learning media via easy-to-access link is a good medium for learning.&#x0D; Keywords: Digital illustration, Narrative text, Research and development, Visual, Learning, Media
LITERATURE ON SCREEN - PROBLEMS AND STRATEGIES OF ONLINE LEARNING

The article talks about the transition from reading fiction on screen to teaching literature through the screen of online educational platforms. The peculiarities of "vertical" screen reading do not lead to durability and depth of reading, but it saves time and allows for multiple returns to the text. Constructive changes as a result of online learning are mainly related to the acquisition of new digital competencies for teachers and students, which support in different ways the reading and understanding of fiction. This opens the prospect of hybrid education with the best of face-to-face learning and the effectiveness of distance learning.
TECHNOLOGY USE IN LEARNING TO READ: A LITERATURE REVIEW

Learning to read in the 4.0 revolution era should be more focused on learning in the use of technology. Hence, the purpose of the research in this article was to analyze the learning process based on methods, analytical techniques, and findings in the terms of applying reading learning technology findings and analyzing the findings of publications on Google Scholar and Scopus. The type of research was literature research using a systematic review, which provided an overview of the publication of articles with the number of found data 56 articles. After conducting the review process, it was obtained that 25 articles matched the criteria. The research results showed that the most publications on the use of technology were in 2014, 2019, and 2021. Related to the place of publication, most publications used Scopus and Sinta 3 as many as 6 articles. The use of research was based on the research methods, which were mostly used on development methods at 48%. Thus, it could be seen from the results of the research that the use of technology in learning to read is able to improve reading skills.
Differentially Private Block Coordinate Descent for Linear Regression on Vertically Partitioned Data

We present a differentially private extension of the block coordinate descent algorithm by means of objective perturbation. The algorithm iteratively performs linear regression in a federated setting on vertically partitioned data. In addition to a privacy guarantee, we derive a utility guarantee; a tolerance parameter indicates how much the differentially private regression may deviate from the analysis without differential privacy. The algorithm’s performance is compared with that of the standard block coordinate descent algorithm on both artificial test data and real-world data. We find that the algorithm is fast and able to generate practical predictions with single-digit privacy budgets, albeit with some accuracy loss.
Influence of Peer Factor on Pupils’ Academic Performance in Public Primary Schools in Tindiret Sub-County, Nandi County, Kenya

The study determined the influence of peer factors on pupils’ academic performance in public primary schools in Tindiret Sub-County. The research was a descriptive survey design. The study targeted 100 public primary schools with target respondents 500 teachers from these 100 public primary schools. The study utilised a simple random sampling technique for teachers while head teachers and civil society organisations stratified sampling was used. A structured questionnaire was used as a data collection tool. Data was analysed using SPSS version 23 and results were expressed by descriptive statistical data, which included mean and standard deviation. In addition, a correlation analysis was computed to check on the association between peer factors and academic performance. The study found that there was a significant correlation between peer pressure and parental financial status on the academic performance of public in public primary schools in Tindiret Sub-County, Nandi County with p = 0.000. When a low-performing student interacts with a high-performing peer, their performance improves significantly. The study recommends that the ministry of education should ensure that trained counsellors are stationed in all schools and institutions to provide counselling services to schools