Reviewerly/exHarmony huggingface dataset:
Improving graph prototypical network using active learning

Abstract Due to the growth of using various devices and applications in modern life, the amount of data available is skyrocketing, but labeling all of this data is beyond the reach of data scientists. Thus, it is necessary to categorize data with a small amount of labeled data. In fact, it should be possible to prioritize data for labeling. To achieve this goal in this study, we have used few-shot learning with active learning and also used the power of graph convolutional networks in classifying data with a graphical structure. To implement the proposed model, we use two graph convolutional networks in parallel to calculate the embedding and the importance of each node. Using the output of both networks, we create prototypes of classes, and then, we classify them according to the distance of each node of these prototypes. We have also used active learning to select data more intelligently, which improves the overall model performance. As well as this, we have tested our proposed model in the field of electronic commerce for tagging goods in big online stores, which encounter a large number of diverse products, where high accuracy categorization in a short time without the interference of human factor and with the help of artificial intelligence is needed to reduce costs. The results of implementing the model on the Amazon dataset and its comparison with the state-of-the-art models in this field show the superiority of our method.
Novel Watermarking efficiency using New Discrete Laguerre Wavelets Transform (NDLWT)

Watermarking in protecting brands from possible counterfeiting is considered one of the methods of processing digital and color images. This technique requires the discovery of advanced methods to obtain the best results by discovering a new filter instead of the standard filters that were used without reaching the peak of the required and optimal values ​​through which the images are analyzed. Colored with the proposed watermark in the process of immersion and separation the new filter is derived from Laguerre polynomials and using mathematical methods to derive the filter. And using it in analyzing the image to reach the best results, and through the proposed New Discrete Laguerre Wavelets Transform NDLWT for watermark algorithms it becomes clear the efficiency of the proposed theory in reading the most important criteria for the quality of the extracted image.
An optimizing method for performance and resource utilization in quantum machine learning circuits

Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. It makes certain kinds of problems be solved easier compared to classical computers. This advantage of quantum computing can be used to implement many existing problems in different fields incredibly effectively. One important field that quantum computing has shown great results in machine learning. Until now, many different quantum algorithms have been presented to perform different machine learning approaches. In some special cases, the execution time of these quantum algorithms will be reduced exponentially compared to the classical ones. But at the same time, with increasing data volume and computation time, taking care of systems to prevent unwanted interactions with the environment can be a daunting task and since these algorithms work on machine learning problems, which usually includes big data, their implementation is very costly in terms of quantum resources. Here, in this paper, we have proposed an approach to reduce the cost of quantum circuits and to optimize quantum machine learning circuits in particular. To reduce the number of resources used, in this paper an approach including different optimization algorithms is considered. Our approach is used to optimize quantum machine learning algorithms for big data. In this case, the optimized circuits run quantum machine learning algorithms in less time than the original ones and by preserving the original functionality. Our approach improves the number of quantum gates by 10.7% and 14.9% in different circuits respectively. This is the amount of reduction for one iteration of a given sub-circuit U in the main circuit. For cases where this sub-circuit is repeated more times in the main circuit, the optimization rate is increased. Therefore, by applying the proposed method to circuits with big data, both cost and performance are improved.
THEORETICAL ISSUES OF FUTURE TEACHERS’ SOFT SKILLS DEVELOPMENT

"In the given article the importance of future teachers’ soft skills development is considered. The importance of soft skills is considered through the prism of future teachers preparation. The system of future teachers’ soft skills is structured in four groups: basic communication, foresight management, effective thinking skills, self-management. Also the practical implementation of mentioned soft skills in future teacher’s work is analyzed. The role of «flexible» skills is reviewed. The main aim of the article is to analyze the role of soft skills development in terms of future teachers education, also to strengthen the focus on soft skills which will be the most prominent in future teachers’ works. The modern labor market requires the development of soft skills among the core competencies of students. In the process of developing «flexible» technologies, many scientists single out self-actualization as the leading factor in achieving the heights of professionalism, including the categories of inspiration, motivation «from within», participation, which in turn are also necessary in the process of developing soft skills. "
The situation of implementing the homeroom teacher competencies through practicum for pre-service teacher training in Vietnam

The research determines the situation of implementing the homeroom teacher competencies for pre service teachers along with practicum in Vietnam, in regard to training teacher adapting national curriculum innovation in Vietnam. There were 342 participants involving data collection. They are pre-service teachers, lecturers from pedagogy universitie. The findings show that homeroom teacher competencies play an importance part in teacher training. Pre service teachers often performed competencies during practicum. However, the effectiveness of implementation was uneven, especially the competencies of psychological counseling for students. Student teachers had many difficulties in implementing activities in the real classroom. Common areas of activity should be diversified between universities of pedagogy and schools tighterly to increase the time student teacher practicum at school in the role of homeroom teacher. The factors should forcus on improving the homeroom teacher competencies for student teachers along with practicum to develop the professionalism of pre-service students to prepare for future careers.&#x0D; &#x0D; Keywords: Homeroom teacher, practicum, pre-service teacher, teacher training, competency
Attitude and social context in MALL classes: A view from midwifery learners

Previous studies on learning technology have focused on attitudinal dimensions and social aspects restricted to the general purpose of language classes. This obviously provides prospects for further investigation, particularly for adult learners with specific goals. Based on this rationale, this study seeks to examine the impact of Mobile Assisted Language Learning (MALL) on the attitudes and social level of the learner perspective in a Midwifery classroom. This study also explores the level of significance impact and hypothetical correlation that occurs between attitudes and social contexts. This study employs attitude questionnaires (personal, learning environment, and behavioral) and social context questionnaires (student-student relationships, formal student-instructor relationships, informal student-instructor relationships, and students as instructors). By obtaining 310 respondents who have participated in the MALL program in the Midwifery class, the data have been analyzed using the Paired Sample T-test, and Pearson Correlation Coefficient (SPSS version 25 is utilized). The results show that the learners’ attitude and their level of social context achieved significant changes after the MALL class. However, the correlation between attitude and social level revealed a low correlation. However, several sub-factors in both aspects are incredibly and strongly correlated, especially in the aspects of anxiety, self-efficacy, self-regulation, and usefulness affecting aspects of both formal and informal student-instructor relationships. Suggestions are welcomed for further correlation tests at the sub-factor level on diverse subjects (different gender and field studies). Keywords: Attitude, social context, MALL, Midwifery Class;
Transition from computer-based testing of national benchmark tests to adaptive testing: Robust application of fourth industrial revolution tools

In 2020, the COVID-19 pandemic strongly affected all sectors, including education. Across the globe, governments enacted policies restricting people's movement, affecting the educational assessment industry. During the lockdown, South Africa's National Benchmark Tests (NBTs) were cancelled. In contrast to paper-pencil and linear tests deployed for NBTs, computer-adaptive testing (CAT) is a modern alternative. CAT technology is widely used for licensing and certification exams in most developed nations. CAT is expected to be used by many educational assessment companies in sub-Saharan Africa for their high-stakes exams due to its reduced testing time and algorithms that produce stable and reliable results. This paper aims to provide an overview of the 4IR tools that will enable creation of a comprehensive CAT, such as feasibility studies, item bank development, test-and-calibrate item banks, and specifications for the final CAT. To successfully implement CAT, these steps are essential. The importance of thorough research and documentation in each stage of the CAT cannot be overemphasized. Based on Fourth Industrial Revolution (4IR) artificial intelligence tools, we conclude that CAT is an excellent modality to adopt for ensuring accurate evaluations of examinees' abilities within high-stakes exams. Keywords: CAT framework, Computerised adaptive testing, Fourth industrial revolution, Linear testing, National benchmark tests
Effectiveness of instruction-based information literacy training on the readiness of lifelong learning and the students’ information literacy ability

This research aims to study the effectiveness instruction-based literacy information training to improve the readiness of lifelong learning of secondary school students. In this study, 250 high school students from 10 schools in the Bandung area, Indonesia. Samples were randomly selected and put into the experimental and control groups. The results showed that instructional-based information literacy training could improve students' lifelong learning readiness and improve students' information literacy skills. Increased readiness for independent learning can be seen from the readiness to learn independently, the ability to overcome learning barriers, and the ability to respond to learning triggers. The improvement of information literacy skills is seen from the ability of students to search for information, assess the credibility of information, and use information to support their studies and learning assignments. The implication of this research is that the findings can be used by stakeholders from the curriculum, training implementers, and libraries in designing systems for smooth study and preparing individuals for community life. Information literacy is the main foundation to support other abilities. Keywords: information literacy ability, lifelong learning readiness, literacy of information, training, library
Enhancing critical thinking skills for low-grade elementary school students using mobile apps

This research was motivated by elementary school students’ low critical thinking ability and their familiarity with gadgets. They use gadgets with various Android-based mobile applications that can be easily downloaded. Thus, this research was designed using an experimental method by explaining to the respondents the instructions for using SBL Apps before operating the application. The population was all lower grade elementary school students in Sumedang Regency, West Java, Indonesia. A random sample of 16 third graders of SDN 1 Citimun was selected. The students were given an explanation first, about how to use SBL Apps, and then they operated the application independently. Data were collected through observation, interviews, and questionnaires, and then processed by qualitative and quantitative analysis. The research results showed that 1) students' responses were positive and significant toward SBL Apps, and 2) the use of SBL Apps could significantly improve students' critical thinking skills. Keywords: Enhancing, Critical thinking; Low-grade students; Mobile Apps, SBL Apps
Depth camera based dataset of hand gestures

The dataset contains RGB and depth version video frames of various hand movements captured with the Intel RealSense Depth Camera D435. The camera has two channels for collecting both RGB and depth frames at the same time. A large dataset is created for accurate classification of hand gestures under complex backgrounds. The dataset is made up of 29718 frames from RGB and depth versions corresponding to various hand gestures from different people collected at different time instances with complex backgrounds. Hand movements corresponding to scroll-right, scroll-left, scroll-up, scroll-down, zoom-in, and zoom-out are included in the data. Each sequence has data of 40 frames, and there is a total of 662 sequences corresponding to each gesture in the dataset. To capture all the variations in the dataset, the hand is oriented in various ways while capturing.
Principals of basic education schools practice in the Sultanate of Oman of ethical leadership dimensions

The study aimed to identify the degree to which principals of basic education schools in the Sultanate of Oman practice of the dimensions of ethical leadership, the study used the descriptive analytical method, and the questionnaire was a tool, which was applied to a sample of (270) male and female teachers, the study reached the following results: The degree of practicing the dimensions of ethical leadership among the principals of basic education schools in the second stage was at a high level in the topic of availability of ethical behavior, and the degree of practice is medium in the topics of ethical behavior practice and topic of promoting ethical behavior, the study also confirmed that the arrangement of the dimensions of ethical leadership topic came as follows: The topic of availability of ethical behavior ranked first with arithmetic mean of (3.63), the axis of promoting ethical behavior in the second place, with an arithmetic mean of 3.00, and finally came the axis of ethical behavior practice with an arithmetic mean (2.72), the study also confirmed that there are no statistically significant differences at the significance level 0.05 in the degree to which managers practice the dimensions of ethical leadership according to the variable (Gender - Educational Qualification). The study recommends the need to spread the ethical culture through programs and practical training courses in ethical leadership . &#x0D; Keywords: Dimensions of ethical leadership , Ethical leadership , Basic Education.
Disclosing #MeToo in 2022

&lt;p&gt;In 2017, the #MeToo movement went viral on social media (Bogen et al., 2021). Survivors of sexual assault and harassment shared their experiences on Twitter using the hashtag #MeToo (Nutbeam &amp; Mereish, 2021) to raise awareness of the frequency of sexual assault and harassment and inspired calls for social change (Bogen et al., 2021). Since the movement's height, there has been considerable scholarly engagement with #MeToo (Quan-Haase et al., 2021), including research on the use of the hashtag for sexual assault disclosures and the ‘social reactions’ to those disclosures (Bogen et al., 2019; Bogen et al., 2021; Lindgren, 2019; Schneider &amp; Carpenter, 2020). Social reactions refer to how people respond to a survivor’s disclosure of sexual victimization, including positive responses such as emotional support or offering resources and negative responses like victim-blaming (Ullman, 2000). Informed by Bogen et al. (2021) and Schneider and Carpenter’s (2020) studies on social reactions to #MeToo, this qualitative pilot study aimed to capture current themes in the social reactions to #MeToo sexual assault disclosures, as well as how those reactions may have changed over time. The pilot study applied small-scale, primary data qualitative content analysis to replies (&lt;em&gt;N &lt;/em&gt;= 268) to #MeToo disclosure tweets (&lt;em&gt;N &lt;/em&gt;= 19) that were published between late 2021 and mid-2022. The replies were coded for social reaction themes and subthemes based on the two aforementioned social reaction studies, which analyzed tweets using the hashtag #MeToo related to issues of sexual assault and harassment, the movement more broadly, and reactions to specific disclosures. While earlier studies found primarily positive social reactions to the #MeToo hashtag (Bogen et al., 2019; Bogen et al., 2021; Lindgren, 2019; Schneider &amp; Carpenter, 2020), the potential for negative and largely anonymous reactions such as trolling remained a concern (Schneider &amp; Carpenter, 2020). The tangible impacts of social reactions on survivors of sexual assault are known, with negative responses influencing worse outcomes following an assault (Ullman, 2000). Far less is known about the impacts of online disclosure and its unique risk factors for harming survivors who disclose and survivors who witness the responses (Bogen et al., 2019; Bogen et al., 2021; Schneider &amp; Carpenter, 2020). The current study showed the importance of ongoing analysis of social reactions to disclosures of sexual victimization, specifically surrounding online social media platforms such as Twitter. This pilot study strove to centre the voices and experiences of survivors of sexual victimization (Gill, 2018) to identify current social reactions to #MeToo disclosure tweets. The current research could inform future efforts that will be needed to improve online responses and promote more positive outcomes to empower survivors of sexual assault (Sherman et al., 2019). &lt;/p&gt;
Time-Efficient Constant-Space-Overhead Fault-Tolerant Quantum Computation

Abstract Scalable realization of quantum computing to attain substantial speedups over classical computing requires fault tolerance. Conventionally, protocols for fault-tolerant quantum computation (FTQC) demand excessive space overhead of physical qubits per logical qubit. A more recent protocol to achieve constant-space-overhead FTQC using quantum low-density parity-check (LDPC) codes thus attracts considerable attention but suffers from another drawback: it incurs polynomially long time overhead. To address these problems, we here introduce an alternative approach using a concatenation of multiple small-size quantum codes for the constant-space-overhead FTQC rather than a single large-size quantum LDPC code. We develop techniques for concatenating different quantum Hamming codes with growing sizes. As a result, we construct a low-overhead protocol to achieve constant space overhead and only quasi-polylogarithmic time overhead simultaneously. Our protocol accomplishes FTQC even if a decoder has non-constant runtime, unlike the existing constant-space-overhead protocol. These results establish a foundation for FTQC realizing a large class of quantum speedups within feasibly bounded space overhead yet negligibly short time overhead. This achievement opens a promising avenue for the low-overhead FTQC based on code concatenation.
Open Challenges of Interactive Video Search and Evaluation

During the last 10 years of Video Browser Showdown (VBS), there were many different approaches tested for known-item search and ad-hoc search tasks. Undoubtedly, teams incorporating state-of-the-art models from the machine learning domain had an advantage over teams focusing just on interactive interfaces. On the other hand, VBS results indicate that effective means of interaction with a search system is still necessary to accomplish challenging search tasks. In this tutorial, we summarize successful deep models tested at the Video Browser Showdown as well as interfaces designed on top of corresponding distance/similarity spaces. Our broad experience with competition organization and evaluation will be presented as well, focusing on promising findings and also challenging problems from the most recent iterations of the Video Browser Showdown.
Explaining Dementia Using Graph Theory and Machine Learning

Dementia is a central nervous system neurodegenerative disease. Dementia has been discussed around different types. Among all, Alzheimer’s disease (AD) and vascular dementias (VaD) are the commonly caused disease. There is a huge research gap that graph theory has rarely been followed to explain the cognitive systems functioning particularly diseases like dementia. Thus, theoretical models of graphs can be used to interrogate the cognitive systems and the likely presence of dementia to understand the reasonings behind and thus the treatment. In this paper, three studies have been discussed in which dementia is investigated through graph theory and machine learning by using theoretical foundations to support the evidence. The first study discusses the significance of graph theory techniques and its coined ideas. There are fundamental designed parameters; connectivity, diameter vertex centrality, betweenness centrality, clustering coefficient, degree distribution, cluster analysis and graph cores. In addition to this, these are featured to analyze magneto-encephalography data to find out functional network intensity in Alzheimer’s disease affected patients. The second study explores particularly the changed structure of Alzheimer’s disease. The third study coins the significance of machine leaning philosophy that paves the way for the black box and diagnosis.
Digging Into Normal Incorporated Stereo Matching

Despite the remarkable progress facilitated by learning-based stereo matching algorithms, disparity estimation in low-texture, occluded, and bordered regions still remain bottlenecks that limit the performance. To tackle these challenges, geometric guidance like plane information is necessary as it provides intuitive guidance about disparity consistency and affinity similarity. In this paper, we propose a normal incorporated joint learning that framework consisting of two specific modules named non-local disparity propagation(NDP) and affinity-aware residual learning(ARL). The estimated normal map is first utilized for calculating a non-local affinity matrix as well as a non-local offset to perform spatial propagation at the disparity level. To enhance geometric consistency, especially in low-texture regions, the estimated normal map is then leveraged to calculate a local affinity matrix which provides the residual learning with information about where the correction should refer and thus improve the residual learning efficiency. Extensive experiments on several public datasets including Scene Flow, KITTI 2015, and Middlebury 2014 validate the effectiveness of our proposed method. By the time we finished this work, our approach ranked 1st for stereo matching across foreground pixels on the KITTI 2015 dataset and 3rd on the Scene Flow dataset among all the published works.