Reviewerly/exHarmony huggingface dataset:
A Tool for Automating IT Management in Small Schools

The outbreak of COVID-2019 has resulted in the adaptation of the teaching and learning style in schools to become more online, conducting teaching and learning from any places without classroom meeting. Systems such as School Management, Online Meeting, and Online library, have been deployed to support all school members including students, teachers, parents, and administrators. These systems need to be properly managed. For business enterprises, this job falls on the shoulders of the IT department, which is usually well-staffed and well-equipped as companies realize their competitive edge depends on it. For educational institutions, especially in small schools, only 1 or 2 "computer specialists" assume the responsibility of the whole IT department. This can be overwhelming for them and, when IT tasks are poorly managed, dissatisfaction and productivity loss among school members ensue. This paper describes a system that we have designed and developed called Admin Task Management Center (ATMC). It aims to significantly reduce the manual workload of IT staff in small schools in document management, system monitoring, and other IT-related tasks. Our ATMC is currently being deployed at Satit Kaset IP (Kasetsart University Laboratory School, Center for Educational Research and Development, International Program). Our evaluation shows that the ATMC considerably raises the productivity level of IT staff, as well as other members of the school. We have released version 1 of our ATMC tool as open-source software. It is available on Github.
Factors Affecting the Adoption of Smart Waste Management

In recent years, there has been minimal research on the adoption of smart waste management in organizations. The study objective, therefore, is to explore factors affecting the adoption of smart waste management in organizations. The study adopted a systematic review based on quantitative content analysis to explore factors affecting the adoption of smart waste management in organizations using the TOE(Technological, Organizational, and Environmental) Framework as a theoretical lens. The quantitative content analysis helped examine literature using qualitative data quantitatively using a statistical package. The study results indicate the technological factors affect smart waste management more in organizations than organizational and environmental factors. The study contributes to the body of knowledge of factors affecting the adoption of smart waste in an organization.
VULNERABILITY ANALYSIS PIPELINE USING COMPILER BASED SOURCE TO SOURCE TRANSLATIONS AND DEEP LEARNING

A major problem in modern systems are vulnerable application. With modern operating systems becoming more user friendly, a huge part of its users are inexperienced and are not trained to prevent the exploitation of their system from vulnerabilities. Many users do not update their system regularly, which makes them vulnerable to public vulnerabilities that have been exploited. Forecasts show that security related expenditure are becoming extremely expensive. This only reiterates the fact that prevention is a key business plan for multiple corporations and exploitable vulnerabilities tend to cost a lot more. One such example is a report from IBM where an analysis between July 2018 and April 2019 based on 507 companies concluded the fact that the average data breach cost is 8.19 million dollars. In this context, the need for automatic vulnerability detection has risen, especially given the large array of available libraries and open source programs. When an application is written, a trust is placed in any of the libraries it links to and also in any other application that is communicating with it. This paper aims to improve the results obtained for code vulnerability detection with an implementation of a C/C++ vulnerability detection method based on project source code lexicon normalization with the help of tokens and classification with the help of deep learning approaches. The proposed pipeline is a three staged process which includes: scraping source files from GitHub using a static analyzer to produce labels, training the deep learning model on the previous labeled and normalized samples and analyzing the results based on different deep learning methods. We supplement available labeled vulnerability datasets from an array of labeled open-source projects gathered with a web-crawler from GitHub. We improve the vulnerability detection tool implementation with binary crossentropys, and adam. We evaluated our implementation with real software packages, NIST SATE IV and Draper VDISC benchmark datasets and obtained 96% accuracy with our analysis.
SMARTBOARD IN E-TEACHING: PRELIMINARY RESULTS IN ELECTRONICS LABORATORIES

Students that are currently enrolled in bachelor studies belong to Generation Z, making them highly comfortable with using various devices with Internet access. Their permanent connection to the virtual world leads to a shift in the way they accumulate knowledge. Classical means of study, such as reading books or lecture notes, have lost ground to online oriented methods of instruction. The challenge for today's academia is to adapt their teaching methods to these new trends, so that the students' attention is gained and maintained throughout the entire teaching activity. In teaching electronics, the laboratory classes are the most challenging part of the subject, for students, as they get to actively work with components, devices, lab instrumentation. However, a minimum amount of theoretical explanations is necessary, before actually building and analyzing any given circuit. Using a regular whiteboard for this part would make the students focus on writing down the explanations and drawing the circuit diagrams, rather than paying attention to what is explained. Moreover, the amount of information that fits on a whiteboard is limited, making the access to previously discussed topics virtually impossible. This case study presents the effects of using a smartboard in teaching electronics laboratories to 1st and 2nd year students, an average of 200 students a year. The study was conducted over the last two academic years (2018-2019, 2017-2018) and the results are validated against two years of using a regular whiteboard (2016-2017, 2015-2016). Some of the qualitative effects are: active participation during the theoretical explanations; undivided attention to explanations; ability to switch through the slides; emphasizing important content and comparing practical measurements to theoretical explanations; unlimited additional information can be added, to any of the slides, at any given time; easy access to laboratory explanations, emailed as pdf to the students; student access to correct laboratory explanations written by the teacher; possibility to use laboratory explanations as additional material in preparing for the final written exam. Measureable effects include: a shortening of the theoretical explanations part at the beginning of the lab session (from 40 minutes to 25 minutes, on average), increased time for the practical experiments, increased grades at the final practical test, from 8.90 to 9.30, on average.
Application of Deep Learning Model of Small Image Data Based on Active Transfer Learning

Deep learning models are known for their ability to learn complex patterns and make accurate predictions from data, but their performance is heavily dependent on the quantity and quality of the data used to train them. In many cases, a large amount of labeled data is required to achieve high performance, but collecting and labeling data can be time-consuming and expensive. To solve these problems, this paper proposes a method to utilize active transfer learning-based image small-scale data. This allows us to learn only 74% less data on Fashion-MNIST data and derive 84.9% accuracy.
FACIAL ANALYSIS METHOD FOR EMOTION RECOGNITION USING LBP OPERATOR

In terms of interpersonal relationships, emotional expressions are vital to the development and regulation, and to sustain this, there are three examples that facial expressions should be involved in the formation of attachments, regulation, and acceleration or deceleration of aggression. Talking about human beings, the face is the body part that reveals the most information about the person itself. The face can give details about intention and attentiveness, about mood, and, on the other hand, the face is the main path in recognition of a person. In a word, the face is the most distinctive and the most used method in finding a person's identity. It will be implemented a method for basic emotion recognition (happiness, sadness, anger, surprise, disgust, fear, neutral) in pictures with facial expressions will be implemented. The method will start with face detection and face fiducial points localization using DLIB public library. The Local Binary Patterns (LBP) features will be computed in areas of interest on the face and the classification will be done using a support vector machine (SVM). Pictures in which the person has a neutral expression and, respectively, pictures in which the expression is at the apex of the emotion will be used. The method will be tested on a public database containing sequences of images starting from the neutral expression and going to the apex expression for different persons. The classifier will be tested in different scenarios: leave-one-person-out, leave-one-sequence-out, etc. The implementation will be done in Python and it will be used OpenCV, DLIB and LibSVM public libraries.
DESIGNING AN ONTOLOGY FOR KNOWLEDGE-BASED PROCESSING IN ROMANIAN ACADEMIC LIBRARIES

The analysis of ontologies' role in knowledge processing has both theoretical and pragmatic foundations, taking into account that ontologies are a core component of knowledge representation within the semantic web. The necessity for the development of the Lib2Life ontology emerged alongside the Smart Search project that has as a central objective the design of a semantic indexing and search system for digitized documents shared by the Romanian central university libraries. This project has a high degree of innovation taking into account the fact that, at national scale, there is no dedicated platform that aggregates the joint collection of digitized documents. Thus, the following research question arises, centered on identifying a suitable data model to corresponding map topics and improve content discovery. The proposed methodology for ontology design includes: (a) ontology purpose set up; (b) conceptualization of the subject domains; (c) formalization; (d) implementation; (e) evaluation and maintenance. The conceptualization stage involves defining the glossary of terms, as well as building taxonomies for 17 specific categories. Afterwards, the ontology was semantically enhanced with properties and axioms (i.e., description of binary relations, class attributes, instance attributes, and formal axioms). Our model was designed using the Prot?g? editor, we used OWL as ontology standard language, and OntoGraph for visualization and navigation. Overall and considering knowledge organization systems, ontologies enclose specific features: coherent in terms of structure, organizing information and meaning of notions; accurate by problem solving and reasoning; extensible, allowing conceptual developments according to science evolution and users' needs, and reusable for follow-up experiments. Moreover, ontology design applied onto knowledge domains improves the efficiency and effectiveness of semantic content search and retrieval process of relevant meta-tagged resources.
DESIGNING A DOCUMENT IMAGE ANALYSIS SYSTEM ON 3 AXIS: EDUCATION, RESEARCH AND PERFORMANCE

Technology advances to make life easier for people. We tend to surround us with devices as small as possible and with the highest computing power. The need for data access from everywhere is an important detail. As a consequence, digital documents have been gaining ground on printed ones and for some sectors, the latter were even replaced. The need and the obligation to preserve the written cultural heritage, represented by books and valuable documents, some of them rare and even unique, forced us to imagine a system that protects the patrimony but makes it also accessible. In order to make books easily available to the public and at the lowest possible risk for the protection of the originals, we came to the idea of designing and creating an efficient digitization system of these records. The current article presents the proposed architecture of a Document Image Analysis System that will process the information with individual modules for each type of operation. The main scope for such tool is to recognize information from the documents and extract them for electronic use. The flow of operations are indicated by user, some steps can be eliminated depending on the user's desire and needs. In order to design an efficient Document Image Analysis System, we need a 3 axis approach: Education - involving students that can receive tasks for replacing modules and validating their homework, Research - performing various tests and Performance - testing the module interconnection and enabling the system to be extremely configurable. No matter what axis is considered, the main scope is the flexibility of the system - performed by individual modules as physical binaries or collection of binaries that are linked via scripts. Each module is designed to accomplish a certain major task by executing several sub-tasks whose results, in most cases, are subject to an intelligent voting process that produces the module's output data.
NTC (NIKOLA TESLA CENTER) CAMP AND INFORMATION AND COMMUNICATION TECHNOLOGIES – KIDS EVALUATION OF INNOVATIVE LEARNING TECHNIQUES

The paper presents the NTC (Nikola Tesla Centre) learning methodologies. The researches that shows the importance of movement for children, especially before primary school, are presented. One of the basic principles of the NTC learning system is to help children, parents and educators for a whole development, in order to achieve the full potential of the child. To gain this result the NTC learning system provides exercises that include rotation, balance, dynamic accommodation of the eye, fine motoric, stimulation of the foot, coordination eye - hand, speech, etc.. The paper presents also the importance of usage the Information and communication technologies (ICT) for educational purposes and for a proper use of ICT in education. The second part of the paper presents the results of a pilot research project on the NTC camps held in Croatia and Serbia. The population analysed is a sample of 68 kids from Serbia, Croatia and Bosnia and Hercegovina from 7 to 12 years old. The aim was to understand how children acquire new learning methodologies and to define which activities they like most. The activities were: morning gym, NTC learning methodologies, playing in nature and on the beach or mountain. The paper analyses also the perception of the possibility to use the NTC learning techniques for learning at the school. The last part of the questionnaire analyses if the children own a smartphone. If yes, how much they use it during the workweek and during the weekend and how to they spend their free time
THE KOLMOGOROV COMPLEXITY AS A TOOL OF PHD STUDENTS’ PREPARATION TO BE A RESEARCHER

This paper is about one new approache to teaching in Bauman Moscow State Technical University, Russia, carring on now in the framework of the ERASMUS+ Capacity Building for Higher Education just starting a project (# 586060-EPP-1-2017-1-RO-EPPKA2-CBHE-JP). The name of the project is "Excellence in Engineering Education through Teacher Training and New Pedagogic Approaches in Russia and Tajikistan". It concerns to Kolmogorov Complexity usage in PhD students curricula towards. Introduction is about the growth of massive amount of data generated by different sources at real time or near real time. Generally data is heterogeneous by its content and exists at every human sphere such as education, government, finance, medicine and so on. They cause fundamental issues in data storage, data security and high speed access to data. Such issues directly related to new research aims. The fierst part of the paper discribes an example of one PhD student's approach to her research using Kolmogorov complexity. The second part of the paper is about the managing immense volumes of data which was possible and was held only with help of supercomputers that lead to great expenses. Applying of distributed computing systems allow to assign and handle operations on homogeneous or heterogeneous nodes with less expenses compared to supercomputers. However data and operation partition and distribution among nodes could be a challenge by itself. Furthermore, nodes interaction can cause significant difficulties as generally distributed systems consist of great number of nodes which could be constant in quantity or constantly growing. In practice, most systems should be scalable due to incessant data growth and tasks for processing that means that any system needs to meet with such circumstances and not to be limited by constant number of nodes. The third part of the paper is about the possibility's analyses of Kolmogorov complexity usage for optimal way of data dividing and processing in distributed computing systems without quantity nodes limitations. The Conclusion show that the investigation is fundamental as distributed systems could be beneficial at any spheres and organizations including universities and business companies and could be used by PhD students as an investigation tools. Besides, this paper's aims is to describe the advantages, disadvantages of Kolmogorov complexity with criticism and arguments.
Rapid Estimation of Disaster Losses by Physics-based Simulation for the M6.8 Luding Earthquake on September 5, 2022

An M6.8 earthquake occurred in Luding, Sichuan province, China on September 5, 2022. Since towns and villages in the earthquake-stricken area are densely populated, the earthquake caused severe fatalities and economic losses. Rapid estimation of earthquake intensity and disaster losses is significantly important for post-earthquake emergency rescue, scientific anti-seismic deployment, and the reduction of casualties and financial losses. Therefore, we make a preliminary rapid estimation of the earthquake intensity and disaster losses in the aftermath of the Luding earthquake. The seismic intensity represents the distribution of earthquake disasters and the degree of ground damages and can be directly converted from the peak acceleration velocity (PGV) map. To obtain a reliable PGV distribution map of this earthquake, we combined the finite-fault model constrained by seismic observations, with the complex three-dimensional (3D) geological environment and topographical features to perform strong ground motion simulation. Then, we compared the consistency between the simulated ground motion waveforms and observations, indicating the plausibility and reliability of simulations. In addition, we transformed the PGV simulation results into intensity and obtained a physics-based map of the intensity distribution of the Luding earthquake. The maximum simulated intensity of this earthquake is IX, which is consistent with the maximum intensity determined from the post-earthquake field survey. Based on the simulated seismic intensity map of the Luding earthquake and the earthquake disaster loss estimation model, we rapidly estimated the death and economic losses caused by this earthquake. The estimation results show that the death toll caused by this earthquake is most probably in the range of 50-300, with a mathematic expectation of 89 The government should launch a Level II earthquake emergency response plan. The economic losses are likely to be in the range of 10-100 billion RMB, with a mathematical expectation of 23.205 billion RMB. Such seismic intensity simulations and rapid estimation of disaster losses are expected to provide a preliminary scientific reference for governments to carry out the targeted deployment of emergency rescue and post-disaster reconstruction.
Multi-temporal UAS surveys for reconstructing soil water content of ploughland plots through multispectral and thermal infrared imagery

Soil water content (SWC) estimation is a crucial issue of agricultural production, and its mapping is an important task. We aimed to study the efficacy of UAS-based thermal (TH) and multispectral (MS) cameras in SWC mapping.The study area for the analysis is situated at NE-Hungary near the town of T&amp;#233;pe. the plot is a part of a larger area of intensive agriculture, where the arable crop at the year of surveys was maize. The experimental AOI was set to a maximum size of 200 x 200 meters due to the time-demand and limitations of the multi-sensor surveys. On the plot 3 major soil types are found meanwhile relative relief differences are also notable. Soil samples were collected at the time of surveys to measure the reference SWC rates in laboratory conditions using the gravimetric method.The aerial mapping tasks were carried out using a DJI Matrice M210 payloads: 1) Micasense RedEdge-MX Dual, 2) Zenmuse XT2. High resolution DEM of the initial surface were mapped by a DJI Matrice M210 RTK v2 + a Zenmuse X7 lens. All imagery were processed in Pix4D Mapper. Machine Learning algorithms were then utilized to model the relationship between reflectance values, land surface temperature and the reference SWC values.Our surveys were dedicated to a sensitivity analysis on the different settings of Pix4D regarding the downscaling to different pixel resolution of the multispectral data and spectral reflectance calibration too. We have analyzed the differences on the SWC modeling accuracies on the different soil types and relief conditions to develop a more robust estimation for precision drainage designs.-----------The research is supported by the NKFI K138079 project.