Reviewerly/exHarmony huggingface dataset:
Enhanced Intra Prediction Scheme in Point Cloud Attribute Compression

3D point cloud compression (PCC) has been an attractive field with increasing applications in recent years. Moving Picture Experts Group (MPEG) is building an open standard for point cloud compression, consisting of two solutions, video-based point cloud compression (V-PCC) and geometry-based point cloud compression (G-PCC). As an essential process in G-PCC, K-nearest neighbors (KNN) algorithm is adopted to perform intra prediction, which only considering distance-based local similarity but neglecting the overall geometric distribution of the neighbor set. In this paper, we propose an enhanced intra prediction scheme based on point-cloud geometric distribution. The centroid-based criterion is introduced to measure the uniformity of spatial distribution of predictive reference points. Our scheme is implemented in G-PCC reference software. Experimental results demonstrate that our proposed method can optimize the selection of predictors, which leads to better rate-distortion (R-D) performance than the G-PCC anchor on point cloud datasets under the common test conditions (CTC).
Implementing a lightweight Schmidt-Samoa cryptosystem (SSC) for sensory communications

Abstract One of the remarkable issues that face wireless sensor networks (WSNs) nowadays is security. WSNs should provide a way to transfer data securely particularly when employed for mission-critical purposes. In this paper, we propose an enhanced architecture and implementation for 128-bit Schmidt-Samoa cryptosystem (SSC) to secure the data communication for wireless sensor networks (WSN) against external attacks. The proposed SSC cryptosystem has been efficiently implemented and verified using FPGA modules by exploiting the maximum allowable parallelism of the SSC internal operations. To verify the proposed SSC implementation, we have synthesized our VHDL coding using Quartus II CAD tool targeting the Altera Cyclone IV FPGA EP4CGX22CF19C7 device. Hence, the synthesizer results reveal that the proposed cryptographic FPGA processor recorded an attractive result in terms of critical path delay, hardware utilization, maximum operational frequency FPGA thermal power dissipation for low-power applications such as the wireless sensor networks.
The Effects of The Presence of Tutor in The Learning Video on Cognitive Load and Academic Achievement

This study was intended to determine the effect of the presence of tutors in the learning video on cognitive load and academic achievement. The design used in this study was the posttest control group design. The subjects of this study were 30 students of eleventh-grade science of senior high school number 1 Ungaran. The measurements of the data were carried out using cognitive load questionnaires, eye-tracking measurements, and biological matter of reproductive materials (prior knowledge and posttest teaching). The result of analysis using the Multivariate analysis of variance test showed that the presence of tutors in the learning video did not have a significant effect on extraneous load (F(2.27)=2.6, p&gt;0.05), the presence of tutors in the learning video did not have a significant effect on germane load in the subjective measurements (F(2.27)=0.06, p&gt;0.05), however, the presence of tutors in the learning video has a significant effect on the objective measurements (F(2.27)=24.47, p&lt;0.05) and (F(2.27)=28.14, p&lt;0.05). Furthermore, the presence of tutors in the learning video has a significant effect on academic achievement with (F(2.27)=3.38, p&lt;0.05). The findings of this study proved that there were effects of the presence of tutor in the learning video on germane load and academic achievement.
COACHING AS THE PEDAGOGICAL TECHNOLOGY IN PROFESSIONAL TRAINING OF FUTURE SPECIALISTS

The article deals with theoretical analysis of coaching – as innovative pedagogical technology. Relying on a comparative analysis of scientific and pedagogical sources, the author notes common problems in the scientific research of domestic and foreign scientists. It is pointed out that the technology of coaching went through the theoretical analysis, wide practical application in the theory and practical research by foreign scientists.It is shown that common features in domestic and foreign studies of coaching are: the construction of educational processes based on motivational interaction; creation of opportunities to disclose individual’s potential; the equitable creative activity of participants in the educational process; partnership to support people in achieving their goals.It has been proved that coaching has all signs of pedagogical technology, common pedagogical activity model of design, organization, and introduction in the educational process; the organizational component of the structure of different forms in native classes, other types realization of pedagogical consideration of subjects in educational process; Possibility for the development of the student’s personal potential and realization of teacher’s potential; The stages of coaching realization on the basis of knowledge are characterized (twelve stages with realization of practical tasks on each of them).The author identifies the benefits (enhancement of the activation of the educational process and the motivation of students to study the course, identify students’ abilities that reveal them as creative people capable of logical thinking, the development of students leadership qualities and emotional intelligence, high productivity of the educational process and the improvement of communication skills) and the disadvantages of using coaching technology in the training of future specialists in a higher education institution. It is shown that coaching develops leadership skills for students, which stimulates the formation of successful teams that provide training for competent professionals in a higher education institution.
Relationship of Digital Literacy and Teacher’s Perception of 21st Century Learning: A Study of SMK Bako Kuching, Sarawak

This paper aimed to determine teachers' knowledge level in digital literacy and teacher’s perception of the 21st Century Learning in SMK Bako Kuching Sarawak. Many teachers’ nowadays have difficulty to know their level knowledge in digital literacy, especially in the 21st century learning in school. Besides, some teachers have huge interpretation of job burden in spite of teaching using technology. Since there is no study done on this subject in Sarawak, this is the first research done in study teacher’s level perception in digital literacy in school. The objectives of this study were to determine the level of teacher’s digital literacy in SMK Bako. Second, it was to identify significant relationship between antecedents (information literacy, visual literacy, software literacy, technology literacy and computer literacy) and teacher’s perception for 21st century learning in SMK Bako. Third was to determine the moderating effect of gender relationship between digital literacy and teacher’s perception for 21st century learning in SMK Bako. The data was analysed using Pearson Correlation and Multiple Regression to test the hypotheses. This quantitative study shows that there was a significant relationship with digital literacy and teacher’s perception for 21st century learning in SMK Bako Kuching, Sarawak. It was also found that gender moderated the relationship between digital literacy and teachers’ perception in 21st century learning. This study can be a guideline for other researchers, especially in the education sector to evaluate the teacher’s level in digital literacy perception in the future.
The Use of Innovative Pedagogical Technologies for Automation of the Specialists’ Professional Training

The purpose of this study was to find out how students and teachers perceive the automation of the specialists’ professional training process and the impact factors of perceiving the learning activity of such kind by students and faculty. The experimental model of automated learning was based on an express course in the academic subjects "Roman Private Law" and "Latin (Latin Law Phraseology)". The following methods were used to analyze the quantitative data: Chi-Square statistical method and triangulation. STATA Software was used to process the data. An online Text Analyzer utility was used to process the answers of the focus group respondents to determine the research categories. Automation of the professional training process has a positive impact on education and greatly enhances the opportunities for both teachers and students making it possible to effectively solve the key task of higher education – to teach the student an autonomous learning, as it forms the skills of managing their own time, self-organization, self-motivation, and reflection. Automation of the professional training process through the use of innovative pedagogical technologies brings about a number of new opportunities and advantages, such as: prominence (detailed elaboration of professional processes with different levels), interactivity (ability to control and influence the process), focusing (allows to remove distracting factors, to concentrate on the material). In the proposed automated model, Chatbot can be programmed so that the course participant will not feel the difference between the language of the real person and the machine. Queries that cannot be processed by Chatbot are answered by the course administrator/moderator via email. This model can be adapted and upgraded to teach other professionally oriented theoretical and applied courses. In addition, Chatbot can be used by higher education institutions in managing a university admissions process to provide applicants with information about admission requirements, programmes, specialties, etc.
Breast microcalcifications detection based on fusing features with DTCWT

BACKGROUND:Breast cancer is a common disease in women. Early detection and early treatment can reduce breast cancer mortality. Studies have shown that breast cancer microcalcifications is one of the important clinical manifestations of early breast cancer, and sometimes even the only manifestation. When the mammography image shows typical malignant microcalcification, it can be diagnosed as breast cancer without any other signs of malignancy. In the aided diagnosis of microcalcifications, it is a crucial step to automatically find and locate regions of interest containing microcalcifications. However, the existing feature extraction method for microcalcifications only extracts features in the time domain or wavelet domain, and does not completely represent all the information of the region of interest. An extraction method based on the combination of Dual-Tree Complex Wavelet Transform (DTCWT) and texture features is proposed in the paper. METHODS:First, the processing operations including denoising, enhancement, and edge detection were performed on mammograms. Sub-image segmentation is then performed. DTCWT features and texture features are extracted for each sub-image.DTCWT features are combined with texture features, and then genetic algorithm is used for feature optimization. The features are classified by the Extreme Learning Machine (ELM) to achieve rapid detection and automatic extraction of ROI with microcalcifications. The experimental results verify that the feature model proposed in this paper has the highest detection rate for ROI regions. The ROI region extracted by the proposed feature model was used as subsequent experimental data. Three different methods were used to detect the microcalcifications, including Top-hat, wavelet transform, and methods combining Top-Hat and wavelet transform. RESULTS:The method was applied to 100 mammograms from the mammograms database of women in Northeast China. In the automatic extraction of ROI, the accuracy, sensitivity, specificity, positive accuracy and negative accuracy of the proposed model combined with DTCWT were 95.92%, 96.71%, 92.20%, 93.65%, 96.33%, respectively. When the Top-hat algorithm was used for microcalcifications detection, the sensitivity reached 89.6%, and the false positive detection rate was 2.6. When the wavelet transform algorithm was used for microcalcifications detection, the sensitivity was 91.1%, and the false positive detection rate was 3.28. When the combined algorithm was used for microcalcifications detection, the sensitivity was 86.7%, and the false positive detection rate decreased to 1.35. CONCLUSIONS:The proposed model combined with DTCWT features achieves better result in the automatic extraction of ROI. Moreover, in the subsequent detection of microcalcifications based on three methods, the three methods achieved better results in sensitivity and false positive detection rate, respectively.
University brand management in the conditions of education digitalization

Humanitarian workers operate in complex environments with various challenges and demanding working conditions.These challenges put aid workers in a range of risks and under the pressure.However, human resources are crucial for success of humanitarian operations in general.At the same time, each humanitarian operation is reliant on logistics and logistics activities are always connected with logistic staff.Understanding what motivates logisticians to join the humanitarian sector is essential information for humanitarian organizations and for recruiters within.Also, knowing which factors influence motivation and job satisfaction of humanitarian logisticians could help the organizations to struggle with the extremely turnover they have to face.Up to this moment, needed skills and the performance of humanitarian logisticians were examined.Also, the motivators of humanitarian workers are covered in previous research.Therefore, the additional aim of this research is to extend the knowledge about the human resources in humanitarian sector as well.
A Trading Model Based on Legal Contracts Using Smart Contract Templates

Smart contracts are often used to automate the execution of transactions so that the fairness, credibility and traceability can be ensured. Generally, for different trading events, different smart contracts are needed to be developed so as to meet the needs. This may lead to a reduction in efficiency, especially when large-scale transactions occur. In addition, trading contracts have complex life cycle and terms. Developing a smart contract based on the context of a contract is extremely difficult. These characteristics may do harm to the application of smart contracts in e-commerce. To this end, this paper proposes a trading model based on legal contracts using smart contract templates. The model can dynamically construct, store, and invoke smart contracts based on a smart contract template and the context of a trading contract, thereby improving the reusability of smart contracts and reducing the difficulty for use. Third-party information, like logistics information, is used as the triggering condition of smart contracts to enhance the reasonableness of execution. In this paper, the garment acquisition contract is token as an example to describe the construction method of a smart contract template. Finally, combined with the experiment and the life cycle of a legal contract, the validity and rationality of the model is discussed. The comparison with legal trading contracts in performance is also conducted.
Patterns of Means and Standard Deviations with Binary Variables: A Key to Detecting Fraudulent Research

Walter R. Schumm*1, Duane W Crawford1 and Lorenza Lockett2 Author Affiliations 1School of Family Studies and Human Services, Kansas State University, USA 2Department of Sociology, Anthropology, and Social Work, Kansas State University, USA Received: November 13, 2019 | Published: November 20, 2019 Corresponding author: Walter R Schumm, School of Family Studies and Human Services, Kansas State University, 1700 Anderson Avenue, Manhattan, KS, USA DOI: 10.26717/BJSTR.2019.23.003851
Learning How to Avoiding Obstacles for End-to-End Driving with Conditional Imitation Learning

Obstacle avoiding is one of the most complex tasks for autonomous driving systems, which was also ignored by many cutting-edge end-to-end learning-based methods. The difficulties stem from the integrated process of detection and interpretation of environment and obstacles and generation of proper behaviors. We make the use of CARLA, a simulator for autonomous driving research, and collect massive human drivers' reactions to obstacles on road subjecting to given driving commands, i.e. follow, go straight, turn left and turn right for about 6 hours. A behavior-Cloning neural network architecture is proposed with the modified loss that enlarge the effects of errors for steer, which indicates the benefit to high an accuracy. We found the data augmentation of the image is crucial to the training of the proposed network. And a reasonable limit allows avoiding unexpected stop. The experiments demonstrate 3 obstacle avoidance cases: for the same type as the training dataset, other automobile and two-wheeled vehicles. Finally, the CARLA benchmark is also tested.
Perancangan Aplikasi Sistem Pakar Penyakit Roseola Dengan Menggunakan Metode Certainty Factor

Measles Roseola usually attacks infants with transmission from a sprinkling of sufferers' saliva. Roseola must be treated quickly because it can cause liver and brain inflammation. Roseola's disease for people whose economy is low is not given much attention because it is often diagnosed in severe or acute illness. This is because the red rash in infants is a common measles. Early diagnosis of Roseola's disease is a good way to avoid adverse consequences for the baby's health. The Roseola disease expert system is able to help low-income people to self-diagnose the disease. Roseola's disease expert system is applied knowledge gained from experts, namely specialist dermatologists in children. Processing of symptoms based on facts with the value and calculation of the Certainty Factor method. Certainty Factor determines good results by combining expert values and user values.
Development and Application of the e-Portfolio System in High School

The article is devoted to the development of practical approaches to the implementation of the system of electronic portfolio of students in the university and the analysis of the experience of its application. The functionality of the software implementation of the example of the electronic portfolio, as well as the software architecture, is considered, the main sections of the electronic portfolio system are formulated. Automation of business processes of working with the portfolio allows you to quickly create, store and process information about the status of the basic parameters of activity of students in various fields of activity, which can be useful for both teachers and employees of various interested departments in the university.
An excursion in finite geometry

A particular Pappus configuration gives us the road map for an excursion into the world of small finite geometries.
Potential of ASTER’s VNIR, SWIR, and TIR bands for phosphate exploration in central west Tunisia

Owing to the inaccessibility of most of the mineral deposits as well as cost and time requirements for conventional ground-based methods, remote sensing techniques become very useful for the rapid detection, mapping, and multitemporal tracking of different mineralization surface states. In this context, a methodological approach based on hyperspectral spectroscopy and Advanced Spaceborne Thermal Emission and Reflection (ASTER) data is proposed for the phosphate exploration and mapping in a specific geological context in Tunisia. ASTER’s thermal infrared (TIR) portion has been particularly expedited in phosphate detection and mapping. Visible near-infrared (VNIR), shortwave infrared (SWIR), and TIR atmospherically and geometrically corrected data are thus processed using the matched filtering (MF) method and VNIR-SWIR reflectance spectra of the collected samples. Results revealed that different absorption features of carbonate minerals (calcite and dolomite) in the SWIR region have to be considered for an efficient identification of rock phosphate. Proposed band combinations using MF fraction maps have successfully discriminated phosphate mineralization and outcrops. Moreover, the surface temperature map derived from TIR bands using the emissivity normalization method reveals the usefulness of the proposed method for rock phosphate delineation. Both geological map and published prospects map as well as x-ray diffraction (XRD) analysis results are used for the validation purposes. A high kappa coefficient value of 0.92, 0.97, and 0.88, is thus calculated, respectively, for VNIR, SWIR, and TIR data-based maps. The high kappa coefficient values implicate the contribution of proposed methodological approach in exploration and mapping phosphates in the Chaketma mine site in central west Tunisia.
A Coding-Free Software Framework for Developing Lightweight Web Data Management Systems

Increasingly more enterprises are intending to deploy data management systems in the cloud. However, the complexity of software development significantly increases both time and learning costs of data management system development. In this paper, we investigate the coding-free construction of a data management system based on Software-as-a-Service (SaaS) architecture, in which a practical application platform and a set of construction methods are proposed. Specifically, by extracting the common features of data management systems, we design a universal web platform to quickly generate and publish customized system instances. Then, we propose a method to develop a lightweight data management system using a specific requirements table in a spreadsheet. The corresponding platform maps the requirements table into a system instance by parsing the table model and implementing the objective system in the running stage. Finally, we implement the proposed framework and deploy it on the web. The empirical results demonstrate the feasibility and availability of the coding-free method for developing lightweight web data management systems.
A Comparison Study of VAE and GAN for Software Fault Prediction

Software fault is an unavoidable problem in software project. How to predict software fault to enhance safety and reliability of system is worth studying. In recent years, deep learning has been widely used in the fields of image, text and voice. However it is seldom applied in the field of software fault prediction. Considering the ability of deep learning, we select the deep learning techniques of VAE and GAN for software fault prediction and compare the performance of them. There is one salient feature of software fault data. The proportion of non-fault data is well above the proportion of fault data. Because of the imbalanced data, it is difficult to get high accuracy to predict software fault. As we known, VAE and GAN are able to generate synthetic samples that obey the distribution of real data. We try to take advantage of their power to generate new fault samples in order to improve the accuracy of software fault prediction. The architectures of VAE and GAN are designed to fit for the high dimensional software fault data. New software fault samples are generated to balance the software fault datasets in order to get better performance for software fault prediction. The models of VAE and GAN are trained on GPU TITAN X. SMOTE is also adopted in order to compare the performance with VAE and GAN. The results in the experiment show that VAE and GAN are useful techniques for software fault prediction and VAE has better performance than GAN on this issue.