Reviewerly/exHarmony huggingface dataset:
IDENTIFICATION AND PREVENTION OF EXPECTED CYBERSECURITY THREATS DURING 2022 FIFA WORLD CUP IN QATAR

Purpose: This research aimed to identify cybersecurity threats expected at the upcoming FIFA World Cup in Qatar in 2022 and assess how they can be prevented.&#x0D; Methodology: This was done by adopting a quantitative research design and survey strategy with 167 respondents from Qatar. The respondents were purposively sampled from the event industry, and a Likert scale was used to quantify the responses for further statistical analysis. The quantitative data collected was analysed using the SPSS version 25 for data analysis. A hypothesis was tested as to whether the perceived expected cybersecurity threats are significantly associated with the perceived quality of measures to tackle these threats. The testing was done using multiple methods, including Principal Component Analysis (PCA) and cross-sectional linear regression analysis. Further analysis was done using One-way ANOVA and correlation analysis, as well as, independent samples t-test. Descriptive statistics, such as percentages and frequencies were used, with tables and charts used in presenting the findings.&#x0D; Findings: The results revealed high loadings of potential cyberattacks on sponsors, fans, online ticket sales, government and the FIFA website based on the PCA. The regression analysis revealed a statistically significant association between the perception of the cybersecurity risks and perceived quality of measures undertaken to address the cyber threats. The research was limited, however, by not covering technical issues of cybersecurity, including the development of improvements to current security systems, which presents an area for future research with the implementation of machine learning technologies, big data and AI training.&#x0D; Contribution: The study provided recommendations for policymakers to invest in technologies for the protection of sensitive data, including online databases and hiring competent specialists in the field of cybersecurity. To address the risks for fans, policymakers are recommended to start a campaign aimed at increasing the awareness of cyberattacks on personal and financial information at large events.
Human posture prediction by Deep Learning

İnterpreting the human posture in human videos and pictures constitutes the most basic structure of human posture prediction. A system is created that decides what the movement is and what purpose it is made by evaluating pictures and videos. In this way, a structure has been created that determines and classifies human movements as an automatic system. A mechanism of motional meaning contained in the created system has been recognized in such away that the pattern is expressed. It is intended to take advantage of these components by taking instant information. A result was obtained by primarily inferring instant still images and eliminating time intervals that do not contain information range. A classification was made according to their accuracy. Based on the location coordinates of the images and videos, it was tried to determine how people might react in the neck stage. Thanks to the analysis performed through the joints with optical flow calculation, motion information was obtained and classifications and analyses expressing the power of motion were created. Motion information on the region determined in the image is determined by the detection of joints, revealing the power generated by movement. The created histograms provide ease of classification of motion. Based on the reliability of the descriptions, which include the concept of the time in a sequential way with the detection of joints, it was desired to create a sliding classification mechanism within the framework of these joints. As a result of this study, it was aimed to obtain a functional structure that can recognize and understand the autonomous movement of stationary or moving beings. An efficient structure has been created in terms of providing a useful and facilitating mechanism by solving the problems in estimation.
An analytical approach to real-time cloud services on IoT-based applications for smart city planning

This paper illustrates the cloud-based services on next generation smart living technology, first by providing the concept of smart technology of the next generation, followed by the wide area of application in this sector. Next, the current generation technological enhancement, IoT (Internet of Things) is brought into the smart living. The entire IoT-based Smart Application sector has then been divided into five categories: Power and Energy Sector, Transport Sector, Healthcare Sector, Retail Sector and Education Sector. Each sector has been analysed in detail with respect to possible real-time cloud services, which can be incorporated into the respective area. Consequently, existing cloud services, current trends, limitations and future scopes have been discussed followed by recommendations in each section. For example, for IoT based application in power and energy sector, contribution includes finding the limitations of cloud service have been found such as unoptimised communication scheme, data complexity, interoperability, cyber security risk and data integrity issue. To address these limitations respectively, a policy to ensure backward and horizontal compatibility, proposal to increase local processing, data compression and prediction, implementation of big data techniques, authentication and encryption, planning and redundancy have been included as recommendation. For other four categories, an intense analysis has been carried out in similar fashion. Finally, the recommendation has been added for each category for the next barrier scope of research.
Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics

Machine learning in photonics has potential in many industries. However, research on patent portfolios is still lacking. The purpose of this study was to assess the status of machine learning in photonics technology and patent portfolios and investigate major assignees to generate a better understanding of the developmental trends of machine learning in photonics. This can provide governments and industry with a resource for planning strategic development. I used data-mining methods (correspondence analysis and K-means clustering) to explore competing technological and strategic-group relationships within the field of machine learning in photonics. The data were granted patents in the USPTO database from 2019 to 2020. The results reveal that patents were primarily in image data processing, electronic digital data processing, wireless communication networks, and healthcare informatics and diagnosis. I assessed the relative technological advantages of various assignees and propose policy recommendations for technology development.
Sign Language Generation - A survey of techniques

Sign language is a visual language that uses body postures and facial expressions. It is generally used by hearing-impaired people as a source of communication. According to the World Health Organization (WHO), around 466 million people (5% of the world population) are with hearing and speech impairment. Normal people generally do not understand this sign language and hence there is a communication gap between hearing-impaired and other people. Different phonemic scripts were developed such as HamNoSys notation that describes sign language using symbols. With the development in the field of artificial intelligence, we are now able to overcome the limitations of communication with people using different languages. Sign language translating system is the one that converts sign to text or speech whereas sign language generating system is the one that converts speech or text to sign language. Sign language generating systems were developed so that normal people can use this system to display signs to hearing-impaired people. This survey consists of a comparative study of approaches and techniques that are used to generate sign language. We have discussed general architecture and applications of the sign language generating system.
Novel Approach to Choosing Principal Components Number in Logistic Regression

The confirmed approach to choosing the number of principal components for prediction models includes exploring the contribution of each principal component to the total variance of the target variable. A combination of possible important principal components can be chosen to explain a big part of the variance in the target. Sometimes several combinations of principal components should be explored to achieve the highest accuracy in classification. This research proposes a novel automatic way of deciding how many principal components should be retained to improve classification accuracy. We do that by combining principal components with the ANOVA selection. To improve the accuracy resulting from our automatic approach, we use the bootstrap procedure for model selection. We call this procedure the Bootstrapped-ANOVA PCA selection. Our results suggest that this procedure can automate the principal components selection and improve the accuracy of classification models, in our example, the logistic regression. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Towards adequate cybersecurity risk management in SMEs

Small and medium-sized enterprises (SMEs) are core to the growth of the African economy. Their continued dependency on technology is driving them deeper into risks. SMEs that are developing technology-based solutions need an effective way to manage cyber risks. This study sought to determine the key cybersecurity risks faced by SMEs in Kenya that develop technology-based solutions. Using a case study approach and based on the National Institute of Standards and Technology (NIST) cybersecurity framework, an in-depth evaluation of the cybersecurity risk management practices within a selected SME was undertaken. The paper reports on the components identified as critical in the management of cyber risks within SMEs. An implementation strategy has been developed to provide a roadmap to assist in the management of cyber risks as part of their business risks. Insights provided will assist the government and regulatory bodies assess the adequacy of current cybersecurity legislations and guidelines. This study has practical implications for SME managers in fostering a cybersecurity culture in this growing sector.
Model Selection for Cross-lingual Transfer

Transformers that are pre-trained on multilingual corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive crosslingual transfer capabilities.In the zero-shot transfer setting, only English training data is used, and the fine-tuned model is evaluated on another target language.While this works surprisingly well, substantial variance has been observed in target language performance between different fine-tuning runs, and in the zero-shot setup, no target-language development data is available to select among multiple fine-tuned models.Prior work has relied on English dev data to select among models that are fine-tuned with different learning rates, number of steps and other hyperparameters, often resulting in suboptimal choices.In this paper, we show that it is possible to select consistently better models when small amounts of annotated data are available in auxiliary pivot languages.We propose a machine learning approach to model selection that uses the finetuned model's own internal representations to predict its cross-lingual capabilities.In extensive experiments we find that this method consistently selects better models than English validation data across twenty five languages (including eight low-resource languages), and often achieves results that are comparable to model selection using target language development data.
Reinforcement Learning

Reinforcement learning is an area of machine learning that searches for the behavior of an agent when performing actions in a particular environment. The objective of the behavior is to maximize the notion of cumulative reward by identifying the best action to take for a given environment.
CYBERBULLYING IN ONLINE CLASSES: THE CASE OF COVID-19

The digital economy has evolved due to the Covid-19 pandemic. Like businesses, the educational sector is also affected by this change. Students are using online educational platforms to continue their studies during the pandemic. The digital world provides resilience but it has also caused trust and privacy issues for students. Cyberbullying has significantly increased in online classrooms during the pandemic and it is essential to understand the increased risks of cyberbullying in online classrooms. It is vital to understand its effect on students. This study explores the impacts of cyber violence and cyber oppression that students face in online classrooms. An online survey is conducted to gain knowledge of (i) cyberbullying in online classrooms, (ii) its negative impacts on students, and (iii) the lack of awareness, policies and management regarding cyber laws in educational institutions under the pandemic. Based on this study&apos;s finding, students&apos; online safety is at risk. Proper anti-cyberbullying policies are almost non-existent, and there is a lack of awareness about existing anti-cyberbullying laws. This survey also highlights the lack of research regarding successful practices, strategies, anti-cyberbullying policies, and legal reactions for cyberbullying victims. Therefore, a cyberbullying prevention framework is proposed in which reactive and preventive measures and educational institutions&apos; responsibilities are suggested. The proposed framework will help to formulate cyberbullying anticipation programs for online educational platforms and it will also help in minimising students&apos; trust and privacy concerns due to digitalised educational systems.
Volume Measurement For Silos

Silos are large containers that can hold materials like sand, grain, or fermented feed. Since containers are large, remotely managing the storage is a challenging task. The grain poured into the silos form the uneven surface, collecting the data and calculating the volume of an uneven surface is the main aim of the experiment. These large structures can be of different sizes ranging from 20 to 40 meters in height. The whole structure of the silo looks like a cylinder with a cone-shaped top and there can be two types according to the shape of the base, cone based or flat based. LIDAR (Light Detection and ranging) sensor, a distance measuring device is used to generate 3D point cloud data. It works mainly on the principle of time of flight. Pan and tilt mechanism allows the sensor to rotate 360 degrees on one axis and 180 degrees on another axis. With the help of this mechanism, distance to most of the points inside the silos is measured. The 3D coordinates are mapped with the distance information from the sensor and also a visualization is done using python programming. Along with this, the volume of grain is also calculated.
Analysis of Information Technology (IT) Goods Sales Patterns Using the FP-Growth Algorithm

Determination of sales patterns is very important in marketing. Sales pattern serves to conduct an effective analysis in improving marketing. Sales analysis aims to explore new knowledge that can help design effective strategies by utilizing sales transaction data. This study processes sales data for 12 transaction days containing 47 items using the Fp-Growth algorithm. The results of this study are items with a minimum value of support &gt; 0.10 and confidence 0.60 and will be compared with testing data using RapidMiner to test whether the results are valid so that the test results can help in designing sales strategies.
Research on Video Tracking Algorithm which based on improved CamShift and Particle Kalman Filter

The original CamShift moving target tracking algorithm will appear local maximization when the target is occluded or the background color is close to the target color, resulting in target loss. In order to solve this problem, the CamShift algorithm is improved in this paper, and the particle filter and Kalman filter are fused together. A joint video tracking algorithm (CPKF) based on the fusion of improved CamShift algorithm, Particle Filter and Kalman Filter is proposed. The color feature and texture feature are integrated in CamShift to improve its anti-jamming ability in complex scenes. By improved Camshift algorithm to extract target feature and predict the location of the object, Camshift candidate area was calculated by the Bhattacharyya distance and the similarity of the target area is to determine whether the target obscured, if obscured, using particle filter to track local area, and passing the optimal state estimation to kalman filter to track the global area. If not occluded, Continue tracking with improved Kalman filtering. Experimental results show that the proposed algorithm can effectively track moving targets in complex environments in real time, which is significantly better than the traditional target tracking algorithm.
Classification of diseases for rice plant based on Naive Bayes classifier with a combination of PROMETHEE

Rice is a staple food crop for most of the population in Indonesia. The need for food, such as rice, is increasing every year. However, in recent years, the low productivity is due to the prolonged dry season. Also, quality rice production can be influenced by several factors, such as the presence of pests and diseases that attack rice plants so that farmers have difficulty dealing with them. Most of the researchers have applied a measure with an intelligence-based measurement; however, the obtained accuracy cannot achieve maximum. Therefore, in this research, a new approach was carried out using the Naive Bayes and PROMETHEE method to determine diseases and pests in rice that have been proposed to reduce the risk of errors and shorten the time in decision making. The contribution of this research is to identifying these problems, including the search for prior probability, conditional probability, posterior probability, and ranking that can use the Naive Bayes and PROMETHEE method to result the highest accuration value. So, this research can be use rapid decision making based on learning data. From the sample's data of the Agriculture Office of Lamongan Regency, East Java pointed out that 38 symptoms can cause 13 types of diseases and rice plants' pests based on the learning process has 73.91% accuracy with k=3. Testing the system used data on pest and disease disturbances as many as 180 data and the data division using k is 4. It proves that the naive bayes and PROMETHEE method is able to give better results.
Experience from the Project Approach in Teaching Technology Subjects at the University of Chemical Technology Prague – Teaching for Practice

A proposal was formulated at the University of Chemical Technology (UCT) Prague in 2016 to implement the project approach in teaching technology subjects. A grant was awarded for an Operational Program Research Development and Education project called "Education Improvement – UCT Prague Priority" from the Ministry of Education, Youth and Sports of the Czech Republic. The project's main goals were that UCT graduates would understand the application of chemistry in practice simultaneously in the context of the given field of study, as well as the law, economy, safety and environmental impact, realize the importance of viability and profitability of technical projects, learn about usable methods applied in the implementation of technical projects, and get acquainted with the principles of creation of teams and team work. A team of teachers and outside consultants from practice have been created. Using the Internet and personal contacts, experience with the use of project approach in teaching at the Czech universities has been gathered. Experience from the project approach in teaching at UCT Prague has been summed up. Basic 12 modules of real-life technical projects have been defined and described in a Methodical Handbook which also includes a number of tools for students. A new subject has been created on the use of scheduling software. A total of 8 technology subjects have been selected for piloting of the project in 2018–2021, employing the created tools. The experience with teaching the pilot subjects was used in the lifelong education of the university teachers. The results will also be used for the teaching other technology subjects at UCT Prague. It can be expected that the project will contribute to a better employability of fresh graduates, their faster adaptation to the requirements of practice, a faster career growth, and that it will improve the feedback from graduates to the teaching staff and their activities at UCT Prague.
Terminal Control with Guaranteed Accuracy Based on the Interval System Model

The problem of optimal terminal control of a linear stationary system the parameters of which are known with accuracy up to intervals is formulated, and the necessary and sufficient conditions for its stability and controllability are determined. A set of control actions that guarantee the specified accuracy of solving the optimal control problem given an interval model of the initial data is obtained. The necessary and sufficient conditions for the existence of this set are determined, and algorithms for its construction and obtaining its rectangular subset of the maximum volume are developed. A priori requirements for the accuracy of identifying the system parameters are formulated taking into account the requirements for the control problem solution accuracy.
Playout Optimization for Monte-Carlo Search Algorithms. Application to Morpion Solitaire

In Monte-Carlo based algorithms, we generate a lot of playouts by successively playing moves. Building the current list of possible moves for each turn of a game becomes a time-consuming task and this last task can be even more costly if a too large area of the gameboard is analyzed. We introduce the Range Query technique which speeds up playouts' generation by a factor of about 10. This rather general technique can be reused with any game where the possible moves are modified only locally around the played move. This technique uses very little memory and all the game data can remain in the L1 CPU cache which helps to improve performance. We also propose SSE2 optimization to improve the performance of list processing. The performance gain can impact any algorithm based on playouts' generation. For the experiments, we test our technique with the NMCS and NRPA algorithms on the 5D and 5T variants of the Morpion Solitaire.