Reviewerly/exHarmony huggingface dataset:
When technology meets educational sciences: Combining virtual reality and microteaching to train pre-service teachers’ kindergarten classroom management strategies.

In our study, we asked 19 pre-service teachers (PSTs) about their experiences with and perceptions of a microteaching-supported virtual reality (VR) kindergarten classroom where they practiced and experimented with different classroom management strategies (CMS). We analyzed quantitative and qualitative responses to gain insight into the perceived usefulness of this learning environment for CMS development and transfer to internship practices. The quantitative results show that practicing in the VR classroom supported mastery of CMS and helped PSTs to manage behaviors at their internships. The qualitative data suggest that the microteaching approach can support transfer of CMS practiced in the VR-kindergarten classroom to in-situ classroom management situations, possibly because of the opportunity to collaboratively engage in reflexive practices. Moreover, PSTs raised valuable suggestions to further improve the microteaching structure to strengthen the effect of peer feedback. The results of this study underline the importance of purposefully grounding VR applications in proven instructional theories.
A new approach of image denoising based on adaptive multi-resolution technique

Medical imaging and diagnostic techniques have become popular over the last two decades with the advancement of data science, data analysis, data storage, and the internet. The impact of this evolution can be seen in the fields of telemedicine and medical sciences, which allow more effective detection and treatment of various diseases. Like any other form of imaging technique, medical images are sensitive to noise and artifacts. The images become unclear with the presence of noise, and the diseases cannot be identified properly. Therefore, image denoising plays a vital role in the field of biomedical image processing. As a result, work must be done to minimize noise without sacrificing image quality. Various methods for reducing noise have already been proposed in the literature. Each method has its own set of benefits and drawbacks. In this paper, we introduce a bi-dimensional empirical mode decomposition (BEMD)-based image de-noising approach. The principal purpose of this research is to decompose noisy images depending on frequency and create a hybrid algorithm that incorporates existing de-noising approaches. The proposed algorithm is an image-dependent technique that decomposes the noisy image into several IMFs with residue, then considering the individual attributes of the IMFs, they are separately filtered. Furthermore equalization is applied to the residue for preserving the edge information. A comprehensive study is conducted over the experimental results of the benchmark test images using different performance measure matrices to quantify the effectiveness of the presented approach. In terms of subjective and objective evaluation, the reconstructed image is found to be more accurate and visually pleasing. It also outperforms the state-of-the-art image-denoising methods, especially in terms of PSNR, RMSE, correlation, and structural similarity.
Effective Cyber Threat Hunting: Where and how does it fit?

Traditionally threat detection in organisations is reactive through pre-defined and preconfigured rules that are embedded in automated tools such as firewalls, anti-virus software, security information and event management (SIEMs) and intrusion detection systems/intrusion prevention systems (IDS/IPS). As the fourth industrial revolution (4IR) brings with it an exponential increase in technological advances and global interconnectivity, the cyberspace presents security risks and threats the scale of which is unprecedented. These security risks and threats have the potential of exposing confidential information, damaging the reputation of credible organisations and/or inflicting harm. The regular occurrence and complexity of cyber intrusions makes the guarding enterprise and government networks a daunting task. Nation states and businesses need to be ingenious and consider innovative and proactive means of safeguarding their valuable assets. The growth of technological, physical and biological worlds necessitates the adoption of a proactive approach towards safeguarding cyber space.&#x0D; This paper centers on cyber threat hunting (CTH) as one such proactive and important measure that can be adopted. The paper has a central contention that effective CTH cannot be an autonomous ‘plug in’ or a standalone intervention. To be effective CTH has to be synergistically integrated with relevant existing fields and practices. Academic work on such conceptual integration of where CTH fits is scarce. Within the confines of the paper we do not attempt to integrate CTH with many of the various relevant fields and practices. Instead, we limit the scope to postulations on CTH’s interface with two fields of central importance in cyber security, namely Cyber Counterintelligence (CCI) and Cyber Threat Monitoring and Analysis (CTMA). The paper’s corresponding two primary objectives are to position CTH within the broader field of CCI and further contextualise CTH within the CTMA domain. The postulations we advanced are qualified as tentative, exploratory work to be expanded on. The paper concludes with observations on further research.
System Of Formation Of Psychophysical Potential Of Transport Industry Specialists’ Development

The high dynamics of information, technical and technological, logistics and organizational indicators of the development of transport production complicates the requirements for professional training, dictating the need to form a competitive specialist, whose effectiveness and success are determined by the criteria of adaptability and mobility in the changing conditions of the professional world.A distinctive feature of the professional activity of specialists in the transport industry is the cumulative impact of occupational risks (physical, psychological, social, technospheric), the consequences of which may be reflected in danger to the life and health of employees and passengers; the occurrence of states of anxiety and stress, professional burnout; loss of control, violations of labour and technological discipline; emergencies; financial losses for the industry.The analysis of scientific research made it possible to determine the essence of the concept of "psychophysical potential of professional development of transport industry specialists" as an integrative personality quality, the structure of which is represented by motivational and value, activity and technological and reflexive components.The indicators of these components are expressed by the abilities and qualities of the individual and allow to resist the complex influence of professional risks.
Pedagogical Support of Students’ Vocational Self-Determination Under Conditions of an Advanced Vocational Training Center

The article substantiates the relevance of the idea that career guidance is a significant factor of students’ pedagogical support in a changing labor market and demanding requirements of society and employers for professional's background.&#x0D; The paper displays the author's approaches to changing priorities, identifying new meanings in the career guidance, focused on a vision of the future, the "professions (occupations) of the future" and the formation of such meta-professional personal qualities that will allow learners and students to become competitive and sought-after specialists on the labor market throughout their lives.&#x0D; The article describes some particular forms of career guidance performed for learners, students and adults under conditions of an advanced (proactive) professional training center and educational and technological cluster, based on close interaction and cooperation with enterprises and social partners.
Weaponization of Mandarin Chinese

This article investigates the weaponization of Mandarin Chinese by analyzing four components of weaponized language: propaganda, disinformation, censorship, and mundane discourse deployed by the Chinese Communist Party (CCP). The weaponization of Mandarin Chinese particularly targets religious people, including Christians, Falun Gong practitioners, and believers of sects unsanctioned by CCP, minorities, and prodemocracy activists. The weaponization of the Chinese language demonstrates that the CCP authorities possess the power to manipulate language so that it shapes public discourse and affects public perception, opinion, and behavior.
Implications of Internet of Things (IoT) on the Education for students with disabilities: A Systematic Literature Review

The world today has greatly been influenced by the advancements in technology, which has resulted to changes across all sectors of life, which includes the education sector.Moreover, the COVID-19 pandemic as of March 2020 resulted to more ICT integration in most sectors including education, leveraging on emerging technologies like the Internet of Things (IoT), Mircea et al., 2021.IoT refers to the network of interconnected people and devices to the world wide web (www) powered by the internet and is already in use in Education where learning and academic institutions are now incorporating IoT in educational activities, considering the ubiquitous nature of the IoT devices, Kassab et al., 2020a.However, despite the many technological advances, the adoption, and the impact of IoT in education is still in its infancy, Dai et al., 2021.According to the United Nations sustainable goals of 2015, quality education is both Inclusive and equitable, and promotes lifelong learning.Therefore, regardless of the adopted technologies, quality education should be facilitated to all people within the population, Kayhan et al., 2015a.Considering the world population of 15% that is of people living with disabilities according to World Health Organization, this paper therefore is a systematic review of literature for the period 2010 to 2021, on the implications of Internet of Things (IoTs) on the Education for students with disabilities, to highlight benefits, challenges, adopted scenarios, technologies, as well as gaps and notable areas for further research.
Penerapan Metode Hybrid Case Base Pada Sistem Pakar Diagnosa Penyakit Obesitas

Obesity is a disease that is caused by the condition of a person's body that has a lot of fat in his body and is overweight or very fat. This condition is usually caused by the intake of calories into the human body that exceeds the capacity required by the body, where this condition causes a buildup of calories that turn into saturated fat. These conditions cause various chronic diseases such as diabetes, hypertension and coronary heart disease. Therefore, in order to avoid disease conditions caused by obesity, treatment and knowledge are needed so that it is not too late to take action so that the disease does not become severe. One of them uses an expert system, so that people can check for diseases without having to see a doctor. The method used Hybrid Case Base is used to make the best decision from a number of symptoms or alternatives from the diagnosed disease. The aim is to make it easier for the public to find out the diseases they suffer from obesity and be able to check them early. Where this expert system applies the Hybrid Case Base method which has the final result of this research producing an accurate diagnosis with a sample of symptom data used and getting a similarity measurement percentage of 77% with a diagnosis of the possibility of having Type 2 Diabetes.
Teamwork Skills As Marker Of Communicative Module Successful Integration Into Extra-Curricular Activities

The study aims to present a theoretical argument for the marker 'teamwork skills' as one of the criteria of communicative universal learning activities (CULA) developed by junior high school students and prove empirically the necessity to develop and integrate a communicative module into extracurricular activities that promote the development of the studied skill.The following methods are used: theoretical (analysis, generalization, comparison); empirical (pedagogical observation, testing); mathematical-statistical (quantitative and qualitative analysis, mode and median analysis).The correlation analysis revealed statistically significant differences between the group cohesiveness index before and after the experiment (r= 0, 778) (the correlation is significant at the 0.01 level (2-sides), which proves the positive impact of integrating the communicative module in the extracurricular activity program on the development of teamwork skills for younger students.The conducted research confirmed the necessity of developing a communicative module based on systemic-activity and axiological approaches and integrating it into all extracurricular programmes for primary classes by introducing students to different types of questions, rules of communication and cooperation, ways of working with information during joint projects with classmates and parents in interaction with a teacher.The communicative module is variable and shortterm (from 2 to 8 hours).We recommend integrating it at the beginning of the extracurricular programme, while some of its content can be dispersed depending on the focus of the programme, the activities and interpersonal situations that may be among students.
Resource- and Power-Efficient High-Performance Object Detection Inference Acceleration Using FPGA

The success of deep convolutional neural networks in solving age-old computer vision challenges, particularly object detection, came with high requirements in terms of computation capability, energy consumption, and a lack of real-time processing capability. However, FPGA-based inference accelerations have recently been receiving more attention from academia and industry due to their high energy efficiency and flexible programmability. This paper presents resource-efficient yet high-performance object detection inference acceleration with detailed implementation and design choices. We tested our object detection acceleration by implementing YOLOv2 on two FPGA boards and achieved up to 184 GOPS with limited resource utilization.
Using Machine Learning on Imbalanced Guideline Compliance Data to Optimize Multidisciplinary Tumour Board Decision Making for the Management of Breast Cancer Patients

Complex breast cancer cases that need further multidisciplinary tumor board (MTB) discussions should have priority in the organization of MTBs. In order to optimize MTB workflow, we attempted to predict complex cases defined as non-compliant cases despite the use of the decision support system OncoDoc, through the implementation of machine learning procedures and algorithms (Decision Trees, Random Forests, and XGBoost). F1-score after cross-validation, sampling implementation, with or without feature selection, did not exceed 40%.
When happy accidents spark creativity: Bringing collaborative speculation to life with generative AI

Generative AI techniques like those that synthesize images from text (text-to-image models) offer new possibilities for creatively imagining new ideas. We investigate the capabilities of these models to help communities engage in conversations about their collective future. In particular, we design and deploy a facilitated experience where participants collaboratively speculate on utopias they want to see, and then produce AI-generated imagery from those speculations. In a series of in-depth user interviews, we invite participants to reflect on the generated images and refine their visions for the future. We synthesize findings with a bespoke community zine on the experience. We observe that participants often generated ideas for implementing their vision and drew new lateral considerations as a result of viewing the generated images. Critically, we find that the unexpected difference between the participant's imagined output and the generated image is what facilitated new insight for the participant. We hope our experimental model for co-creation, computational creativity, and community reflection inspires the use of generative models to help communities and organizations envision better futures.
Evaluation Of Road Channel As A Drainage Function For Agricultural Land Remediation

The Ring Road section on the West Ring Road - Madiun City is an alternative road which connecting the outside of Madiun city to the Madiun city. That road is a road with both side of it is a rice field. As a road, it must be clean from puddles when it rains. So that it does not cause traffic problems in the form of traffic disorder. Bad road channels have an impact on flooding due to rain and make the level of road service in the area decrease. On the other hand, the road channel in the area can also be used as a channel for removing pollutants from agricultural land in the rice fields. So that the process of remediation of agricultural land can run smoothly. This highway channel is highly influenced by the local climate, especially the existing of rainfall. Based on that, there is a need for Adequacy Evaluation of the Roadside of the Rice Fields as a drainage function in Agricultural Land Remediation, with a case study on the Ring Road channel in the West Ring Road - Madiun City. From the results of the analysis in this study, it was found that there are two size of Highway Channel Size on the Ring Road in the West Ring Road - Madiun City and both of them are in good conditions. Channel A1 with size b = 0.71 m, H = 0.36 m and h = 0.11 m. Channel A2 with size b = 0.75 m, H = 0.38 m and h = 0.11 m. The results of the design flood rainfall calculation show that there are variations in values that do not much differ in the return period of 2 years to 25 years and can be used as a reference in the management of small irrigation, drainage systems, and small-scale weir buildings, including calculation of road channels on the Ring Road in the Ring Road Section West-Madiun City. From the analysis, it was found that the road channels on the West Ring Road of Madiun City are still good and adequate. From the analysis of the square and trapezoidal dimensions, it is found that the channel discharge is &gt; the planned discharge, so the dimensions of the channel can accommodate the flood discharge that will occur in a period of 25 years, both as a function of road drainage and as drainage in Agricultural Land Remediation in the rice fields along the road. It is suggested that the need for channel maintenance on the road sections on the Ring Road in the West Ring Road - Madiun City and for further studies regarding other road infrastructure and facilities to support road service levels and as a function for agricultural land drainage
Automatic Segmentation of Calcification Areas in Digital Breast Images

In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction with intelligent segmentation techniques, mammography can be made more effective in diagnosing breast abnormalities and aiding in the early diagnosis of breast cancer, hence increasing its overall effectiveness. The methodology, which includes some concepts of digital imaging and machine learning techniques, will be described in the following section after a review of the literature on breast cancer (categories, prevention involving the environment and lifestyle, diagnosis, and tracking of the disease) has been completed (neural networks and random forests). It was possible to achieve these results by working with an image collection that previously had questionable regions (per the given technique). Fiji software extracted problematic candidate regions from mammography images, which were subsequently subjected to further examination. To categorize the results of the picture segmentation, they were sorted into three groups, which were as follows: random forest and neural networks both generated promising results in the segmentation of suspicious parts that were emphasized in the highlight of the image, and this was true for both algorithms. Detection of contours of the regions was carried out, indicating that cuts of these segmented sections may be created. Later on, automatic categorization of the targets can be carried out using a learning algorithm, as illustrated in the experiment.
Points2NeRF: Generating Neural Radiance Fields from 3D point cloud

Contemporary registration devices for 3D visual information, such as LIDARs and various depth cameras, capture data as 3D point clouds. In turn, such clouds are challenging to be processed due to their size and complexity. Existing methods address this problem by fitting a mesh to the point cloud and rendering it instead. This approach, however, leads to the reduced fidelity of the resulting visualization and misses color information of the objects crucial in computer graphics applications. In this work, we propose to mitigate this challenge by representing 3D objects as Neural Radiance Fields (NeRFs). We leverage a hypernetwork paradigm and train the model to take a 3D point cloud with the associated color values and return a NeRF network's weights that reconstruct 3D objects from input 2D images. Our method provides efficient 3D object representation and offers several advantages over the existing approaches, including the ability to condition NeRFs and improved generalization beyond objects seen in training. The latter we also confirmed in the results of our empirical evaluation.
Segmentation and classification of benign and malignant breast tumors via texture characterization from ultrasound images

The present paper deals with breast tumors classification from ultrasound images.The proposed procedure consists of four steps, namely preprocessing, segmentation, feature extraction and classification.To improve the quality of ultrasound images, the preprocessing step consists of anisotropic filtering and histogram equalization that are performed on the original images.The segmentation is performed on the preprocessed images using the Level Set method that allows to extract the region of interest (ROI) and to reduce its size at the same time.Two feature extraction methods are used in this work namely, the local binary pattern (LBP) method and the histogram of oriented gradients (HOG) method.The two methods (LBP and HOG) are techniques of textures analysis and allow to characterize the ROI.The extracted feature sets constitute the inputs for three classifiers namely, support vector machines (SVM), k-nearest neighbors (KNN) and decision trees (DT).In this work, the best results are obtained by the concatenation of the two feature vectors namely LBP and HOG associated to the SVM classifier.This allows to achieve an accuracy of 96%, a sensitivity of 97% and a specificity of 94%.
Development of Literacy Programs for Students in School Libraries in Indonesia

The research aims to explain the development of literacy programs in school libraries in Indonesia. This research is field research with a phenomenological approach with research locations in 10 junior high school libraries in Indonesia. Data collection is carried out using interviews, observations and documentation. Data analysis with reduction, classification, and verification. The results of this study found that the development of literacy programs in school libraries was carried out through habituation and learning activities. Habituation activities are carried out by inviting students to carry out literacy activities based on writing, religion, digital, reading angles, literacy ambassadors, reading challenges, and awarding. While learning activities are carried out by holding a class literacy club, mandatory visits to the library, and procuring class making.