Reviewerly/exHarmony huggingface dataset:
Education game development technology training

The modern educational system places high demands on the participants in the educational process. Teachers are faced with the task of finding new technologies for interacting with the target audience. One of the key technologies is game technology, which has the phenomenological features of a game as an existential, metaphysical, socio-cultural and pedagogical phenomenon. Training under the additional advanced training program "School of Game Engineering" will provide opportunities for generating ideas on the organization and content of the system for the formation and dissemination of the culture of the game. The program reveals the methodological foundations of gamification technologies, the definition of their place and role in educational games in the content, organizational and personal-semantic aspects. The formation of mechanisms of game-technical skills in the field of gamification of project activities and interactive forms of education will expand the scale and increase the efficiency of modern education.
A Network Flow Interpretation of Robust Goal Legibility in Path Finding

In this paper, we define goal legibility in a multi-agent path-finding setting. We consider a set of identical agents moving in an environment and tasked with reaching a set of locations that need to be serviced. An observer monitors their movements from a distance to identify their destinations as soon as possible. Our algorithm constructs a set of paths for the agents, one to each destination, that overlap as little as possible while satisfying a budget constraint. In this way, the observer, knowing the possible agents' destinations as well as the set of paths they might follow, is guaranteed to determine with certainty an agent's destination by looking at the shortest possible fragment of the agent's trajectory, regardless of when it starts observing. Our technique is robust because the observer's inference mechanism requires no coordination with the agents' motions. By reformulating legible path planning into a classical minimum cost flow problem, we can leverage powerful tools from combinatorial optimization, obtaining fast and scalable algorithms. We present experiments that show the benefits offered by our approach.
A novel machine learning approach in Image Pattern Recognition under invariance constraints

Two of the main challenges of image recognition in radar, acoustic or T-ray imaging regard the view-point variation of the pattern and the feature extraction techniques that must retrieve the most discriminative information about different classes. In this paper, we focus on feature extraction and image classification techniques by using a Rotation Invariant Wavelet Packet Decomposition and a novel entropy-based feature extraction technique to characterize an image. The entropy-based characterization described in the paper offers an extended analysis compared to usual approaches such as the energy of the wavelet sub bands. The computed features will be further used to train a Graph Neural Network adapte d to a quad-tree decomposition which has the powerful advantage of considering the structural information of the rotation-invariant decomposition. We successfully classified the images with an accuracy of 99.3%. The results are compared to other classic feature extraction techniques such as k-NN, SVM, DCT and WPD, proving the increased capability of our method.
USING EMOTIONAL DESIGN TO RAISE AWARENESS OF INFORMATION SECURITY

Awareness raising is a crucial step in achieving a higher level of information security (InfoSec), which is needed, in turn, for successful digitization.To encourage employees to feel positive toward InfoSec and as a means to support their learning process, awareness-raising measures can make use of the principles of emotional design.In this paper, we present the development and evaluation of digital serious games that use emotional design principles, such as personalization, the possibility of identification, appealing multimedia elements, and storytelling.The results of the user tests indicate that the digital serious games that have been developed are positively accepted by employees and moderately enhance and intensify InfoSec-related knowledge.
E-learning Service Framework Implementation for COVID-19 pandemic (Case study Informatic dept. Universitas Muhammadiyah Magelang)

Work from home is a way to reduce the development of the coronavirus termed COVID-19. In the world of education, we have been familiar with e-learning systems where learning is done with any time, any ware, anybody, and anyplace. Nevertheless, the readiness for implementation is not relaxed because there are technical and non-technical obstacles. There are four roles in e-learning wherein the role of instructor and student's role is the core, but the readiness of infrastructure and integration between systems is a problem in itself. The consequence of Work from home is to provide resources independently at home, with cost and geographical challenges. The development of E-learning 3.0, with its U-learning concept, gives a different dimension, especially in the use of cloud service provider applications. In this paper, the E-learning Service Framework is proposed by considering the results of a review of the LearningCube modeling, Interactive Mode Architecture, and Critical Success Factors.
News recommendation system with category awareness based on pre-trained Electra

A news recommendation system is crucial to help users locate interesting news and relieve information overload. How to accurately model news text is the key to a news recommendation system. Traditional news recommendation systems use news encoders that directly model the text of all historical reading news. However, pre-trained language model (PLM) trained on large-scale corpora often have more robust reading comprehension capabilities and can extract more profound implicit expressions of news texts. Users have their preferences when reading different categories of news. Using a large number of training samples to learn such preferences will consume many computing resources. Adding category awareness to the news recommendation system can efficiently and accurately model users and build a personalized News recommendation system. Therefore, this paper uses Electra as a news encoder to capture a more robust contextual implicit representation of news texts. At the same time, additional weights are used to model different news categories. On this basis, users' behavior logs are added to build a news recommendation model. The cosine similarity is used as a loss function to obtain the recommendation probability of candidate news. Experiments show that the AUC of this method is 2.37% higher than the current state-of-the-art methods on the Mind-small datasets.
Preface: Proceedings of the 8th Annual International Seminar on Trends in Science and Science Education (AISTSSE) 2021

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CatchPhish: Model for detecting homographic attacks on phishing pages

The growth in the numbers of phishing attacks, along with the volume of successful frauds, demonstrates vul-nerabilities of the protection tools and exposes the advance in the refinement of the attacks. In more than 70% of cases, the improvements rely on the presence of homographic terms as a mechanism to embed reliability in malicious pages. In this scenario, the present study proposes an intelligent approach denominated CatchPhish, which, through the attack target brand identification, can infer the veracity of the page evaluated. CatchPhish uses a Siamese neural network capable of identifying the presence of typosquatting mentions in phishing pages. In the experiments, the proposed approach achieved 99.30% of assertiveness. In addition, the proposed approach stands out for its ability to produce terms for training, so, instead of providing the tool with a high amount of distorted terms, it provides the mark preceded by the correct spelling, which circumvents a strong obstacle in the construction of protection mechanisms.
Retention Strategies in Print Media Industry – A Study Across Select Print Media Houses in Bengaluru City

Achieving goals requires empowered individuals all around the world since competitive advantage is characterised by innovation, speed to market, and productivity. Employee morale is declining due to a lack of top management support, inadequate human resource practices, lack of recognition and success, and lack of support for limited resources. Employers are boosting expenditures in employee engagement to obtain a competitive advantage. However, research shows that more investment does not improve employee engagement, putting employee-generated competitive advantage at risk. As a result, the current study examines factors influencing employee engagement, and makes recommendations for boosting employee engagement to improve organizational performance. The study is exploratory in nature and seeks to discover the elements impacting print media retention techniques. The study employs a well-structured questionnaire developed after a literature review and validated by a panel of five experts. The questionnaire had demographic data and Likert scale questions (1=Strong Disagreement, 5=Strong Agreement). There is no accurate data on the number of print media enterprises in Bangalore. According to KOMPASS, there are 215 print media enterprises in Bangalore, 56 of which have over 250 employees. Using the Cochran formula, a Convenience sample of 100 people is picked with a 10% margin of error and a 95% confidence. After removing the outliers, 121 replies were chosen for the research out of 117 responses (Glenn D isreal). Data was analysed using SPSS for descriptive statistics and AMOS for SEM. Only permanent personnel completed and returned the questionnaires. The study concluded that recognition and appreciation, the organisational climate, and the interaction between subordinates and superiors influence employee happiness. Well-being of employees is affected by several things, such as how they are treated by their employers and how much money they get. Employee engagement techniques for the Business Environment and superior subordinate collaboration are vital in the print media business, where work must be approved and supported by immediate superiors for magazines and other processes.
Development of a modified differential evolution for the problem of finding the pupil region of interest in real time

Video oculography (VOG) occupies a key role in many theoretical and practical studies: in ophthalmology, biometric identification systems, in the task of augmentative communication for people with neuroparalytic syndromes. The pupil registration step of VOG process takes considerable time and computational resources in existing algorithms (Hough transform, Starburst, morphological erosion) or has limitations in accuracy. It is necessary to develop a technique that improves the quality of the VOG process in conditions of limited computing resources in order to reduce the cost of the technology for individual use by people with disabilities and medical institutions. The author proposes to present the process of pupil registration as a global multidimensional optimization problem and its solution by the stochastic method of differential evolution (DE). The optimization problem is formalized and efficiency of DE application in video oculography is shown. The modification of the numerical method of DE is proposed, including new models of mutation, crossover, selection. The modification is based on the process of formation genetic isolations in the neighborhood of all local and global extremums, and the subsequent growth of the most adapted isolation and degeneration of others, according to the differential equation of Verhulst-Pearl. The proposed method uses low values of initial population size (∼24 pcs. for VOG) and find fast (∼0.009 s. for ARM Cortex-A52) and accurate extremum in complex multimodal functions with small solution space. Dependencies of the results on the input parameters, for optimal tuning of the method to VOG, are given.
Foreground Detection in Dynamic Scenes using Singular Value Decomposition Algorithm in Comparison with Gaussian Mixture Model to measure F-score

Aim:The purpose of this work is to represent a foreground detection method using a novel Singular Value Decomposition (SVD) algorithm that gives improved accuracy and F-score.Materials and Methods: ClinCalc is a tool used tocompute sample sizesand displays the results of sample analyses.Cdnet 2014 dataset demonstrates our foreground detection using novel Singular value decomposition algorithm.Here Singular value decomposition is compared with the Gaussian mixture model(GMM).Results: Poor detection of moving objects is improved by using Singular value decomposition with mean F-score rate of 83% and accuracy of 87%.Conclusion: Singular value decomposition subtracts the background and detects the moving objects with better accuracy and F-score compared with GMM.
Research on Cloud Printing Technology Based on LBS and Knowledge Sharing

In cloud print, this paper USES block chain as the underlying technology, the implementation of large database to ensure the safety of mass data storage, and based on this foundation to carry out knowledge sharing service, easy to play article includes more than five thousand specialization vocabulary and its derivative data table, auxiliary ai machine training, in order to provide the knowledge in knowledge sharing service type map search, Expand the search range and precision by plane search, and build intelligent large screen.Cross-domain services result in a 40% conversion rate and a 4 times gross profit margin.
Be Cyber Secure

This important edition focuses on the human factor in training, cautionary tales of breaches that occurred through human error, while also identifying storytelling as an effective tool in cyber education. Topics include: Addressing management issues Approaches to cyber defence Innovative tools for threat response Recruiting management GDPR
Some Tasks for the Use of Blockchain Technology to Improve Transparency in Maritime Transportation

Traditional maritime transportation has several problems, such as complicated transaction processes, high dependence on paper documents, difficulty in obtaining timely information, and difficulty in tracking cargo. Due to these problems, it is difficult to secure the visibility and transparency for transporting cargo. Therefore, we considered a plan to improve the visibility and transparency of maritime transportation through real-time tracking of the movement route and status of transporting cargo in the maritime transportation process through the application of blockchain technology in maritime transportation. For these purposes, we analyzed the problems faced by traditional maritime transportation, and examined the necessity and expected effects of blockchain technology in maritime transportation. And we reviewed the challenges to the widespread adoption of blockchain technology in maritime transport from the technical, legal, transactional, and environmental perspectives.
Futuristic flask with convolutional neural network for removing Gaussian noise from digital images

Noise removal is one of the chronic problems while dealing with the images. In this paper, we propose Futuristic Flask with Convolution Neural Network (FFCNN), a residual learning model in deep convolutional neural network which is trained with large dataset, showing up excellent results for removing Gaussian noise from digital Images. FFCNN is not only intended for training neural model with performance metrics but also intended to have presentation metrics for a user, using a micro web framework “Flask” to enable interactions. The architecture has feed- forward denoising neural network structure that conducts discriminative learning for image denoising especially for Additive White Gaussian Noise (AWGN) with specific noise level and also for blind Gaussian noise. The proposed algorithm is optimized and speeded by layers of batch normalization with GPU computing. The resulting PSNR and SSIM obtained are excellent proving efficiency and effectiveness of the model for several general image denoising task.
Reform and Research on Teaching Methods of Computer Operating System Course in Colleges and Universities

With the continuous development of information technology, computer operating system has become an important discipline in colleges and universities.In the face of the rapid development of science and technology, the teaching method of computer operating system should also be reformed accordingly, so that students can better master and use it.This paper discusses the necessity of teaching reform of computer operating system course, discusses its existing problems, and puts forward some suggestions for improvement.
Can We Effectively Use Smart Contracts to Stipulate Time Constraints?

Smart contracts provide the means to stipulate rules of interaction between mutually distrustful organizations. They encode contractual agreements on the basis of source code, which else need to be contractualized in natural language. While the mediation of contractual agreements via smart contracts is seamless in theory, it requires that the conditions of an interaction are accurately made available in the blockchain. Time is a prominent such condition. In the paper at hand, we empirically measure the consistency of a smart contract to yield equal results on the basis of the time of an interaction and its potentially inaccurate representation in the blockchain. We propose a novel metric called execution accuracy to measure this consistency. We specifically measure the execution accuracy of a time intervalconstrained smart contract that executes distinct logic within and without some constraint interval. We run experiments for the local Ganache and Quorum and the public Görli and Rinkeby Ethereum blockchains. Our experiments confirm our intuition that execution accuracy decreases near interval bounds. The novelty of our proposed metric resides in its capacity to quantify this decrease. We demonstrate how time constraints can be effectively stipulated on the basis of execution accuracy measurements.
Determination of Heavy Metals in Dump Site at Katsina States

Heavy Metals are any metallic element that has a relatively high density and is toxic or poisonous at low concentration.Heavy Metals are general collective term, which applies to the group of metals and metalloids with atomic density greater than 4 g/cm 3 .Most of these metal ions can be released from industries, agriculture and individual household and are in simple cationic forms.This study aimed at determining some heavy metals contents in dump soil along Federal College Education (F.C.E.) Katsina.Three samples each were collected differently at the vicinity of the tannery and the surrounding tanneries, 10m away a nearby farms and nearby growing plants.Control samples was also collected far away from these areas for comparison.A distance between 5 to 10 m was maintained between each sampling point to study the metal distribution of the study area.Atomic Absorption Spectrophotometer (AAS) with air acetylene flame was used for the metal analysis.The results of the three samples studied obviously indicates that all the soil samples have Manganese concentration in the in the highest degree (A 3.83 mg/kg, B 3.652 mg/kg, C 6.602 mg/kg) while Co: 0.048 mg/kg, Cd: 0.028 mg/kg, Cd: 0.026 mg/kg) respectively indicates lowest concentration in the three samples.All of the soil samples contain high manganese concentrations and other heavy metals are present in negligible amounts.This means that manganesecontaining waste is circulating in our environments at random.Contamination or soil pollution caused by high manganese concentrations in our environment is extremely dangerous since it puts domestic animals' and humans' health at risk.
The Role of Interactive and Immersive Technologies in Higher Education: A Survey

Abstract : The developments in the Engineering domain have risen at an unprecedented rate in the past three decades. New technologies emerge every year and find their use case in multiple industries, also impacting the higher education as a whole. The present ecosystem demands industry-ready graduates from higher education institutes. To meet this, inculcation of newly developed technologies in the engineering education domain is a must. The retention span of Millennials in the traditional classroom settings has lowered down, posing the biggest challenge in student learning outcome at the higher education level. To solve this problem, interactive and immersive technologies has played a significant role through the use of Augmented Reality (AR), Virtual Reality (VR), and Gamification in a modern classroom setting. These technologies are capable of being used online as well as offline, and promise to deliver an enhanced user experience. The use of such tools also improves the student engagement by virtue of interactive content during the classroom session. This results in improved student learning and motivation in various domains of higher education. This paper focuses on the engineering education domain and discusses the role of these technologies in improving student understanding of intricate concepts of various engineering disciplines. A study of research papers presenting different digital learning platforms developed using these technologies from 2017 to 2021 was done, and it was concluded that the trends and technologies discussed in this paper have been tested and proven to be beneficial in engineering education. Further, developing a low-cost learning system using immersive and interactive technologies, and upgrading the present classroom set-up and skill set of instructors to make them significantly capable of utilizing the benefits offered by these technologies is yet to be achieved. Keywords : Higher Education, Education Technology, Augmented and Virtual Reality, Immersive Technology, Interactive Teaching- Learning, Game-Based Learning, Online Teaching- Learning
Knowledge Map and Information Fusion Based on Personalized Recommendation Algorithm

With the improvement of people’s living standards, in the face of massive Internet information, information explosion reduces the utilization of information. Information recommendation technology is an important technical means to solve this problem. Traditional search engines often can not meet the personalized needs of users in X information recommendation, and users usually need to spend a lot of time screening information. Because the knowledge map effectively solves the problems of project cold start and sparse data, and its rich graph network structure, many relevant scholars have studied and applied it to the recommendation system.
Scope of Error Analysis in English written texts of ESL Students- A Case Study

Of all the language skills, writing is the most difficult skill for the students who learn English as a second language because they have less experience with written expression. In this research paper, efforts have been made to prove the relevance of error analysis in the English writing skills of the Intermediate Level students. To conduct the research, a random written sample of a Class XII student of Jawahar Navodaya Vidyalaya, Navsari, Gujarat, has been taken up. Errors in the written text have been categorized using the theory provided by Dulay, Burt, and Krashen (1982, pp.146). The need of this research arose after looking into the writing errors made by the JNV students of Intermediate Level, where English is taught and learned as a Second Language. The intent is to prove how error analysis can help to possibly identify sources of errors in learners’ writing; categorize the errors, and try to present a method for correction and improve the writing skills of the students. This study can be of great help to the English teachers of JNVs that were established in 1986 to bring out the best of Indian rural talent; to improve upon the writing skills of their students.
Preface: Proceedings of the 4th International Conference of Science and Education Science (IConSSE 2021)

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