Reviewerly/exHarmony huggingface dataset:
Online Hospitality and Tourism Education

The study attempts to understand students' apprehensions, satisfaction, and experience concerning online hospitality and tourism education (HTE) during the COVID-19 pandemic. Focused group discussions were conducted to gather student experiences implementing ICT in hospitality and tourism education. The group discussions were recorded, transcribed, and analyzed in search of themes to identify and validate the constructs to develop the questionnaire for the study. The questionnaire was then presented before the subject experts to cross-check the validity of constructs. Importance-Performance Analysis (IPA) was used to determine students' satisfaction by comparing students' perceptions to students' expectations. The study's findings show the importance of practical classes and labs and on-site instructor comments on students' overall satisfaction. Online teaching can complement traditional classroom teaching but cannot fully replace lab sessions with instructor feedback. Transitioning to an online platform requires effective tools and curriculum modifications to fill the gap in industry expectations regarding student employability. Online teaching has immense capability, but it cannot be generalized and requires subject-specific attention and feedback. Online education needs to keep evolving alongside contemporary classroom teaching to meet student expectations. Future research would concentrate on communication, interpersonal, and technology skills and their effect on the study's results.
A ½ spin fiber model for the electron

The Standard Model and Quantum Mechanics studies the quantum world as a collection of probabilistically interacting particles and does not tell us what a single isolated particle like the electron is, thus its intrinsic detailed field mechanics and how these are generating its intrinsic property values like charge, spin, magnetic moment and handedness. We herein are translating these intrinsic physical properties of the electron to a novel 4-dimensional (i.e. three spatial plus one temporal) fiber spinor and showing their possible deeper correlation and interconnection combined under a single energy manifold. From physical quantum emulation observations, mathematical analysis and Wolfram Alpha parametric polar simulations and mathematical 4D animations we calculated a fiber model for the dressed bare mass electromagnetic field of the electron that results to all of its known measured intrinsic properties. Therefore our model is an intrinsic mechanics model for the electron at rest and shows the possibility that the elementary electron although it has no inner sub-particles, it can posses a specific energy flux manifold. Why an electron is actually a Â½ spin geometry, twisted photon. The fine structure constant is explained as a topological feature, proportionality constant, embedded inside our proposed fiber model for the electron. Our novel fiber model opens up a new door on theoretical intrinsic mechanics physics of elementary particles beyond the Standard Model.Â
On modelling of complex networks

A network is considered, which is modelled by a system of ordinary differential equations.The dynamics of a network depends on the attracting sets in a phase space.The problem of control and management of this network is in a focus of our study.The specific case is considered, where attractors are periodic trajectories.We provide the way of controlling the network by changing of a certain group of parameters.The numerical approach combined with the analytical solutions is used.As a by-product, the existence of multiple periodic solutions is proved.They are constructed explicitly for a specific three-dimensional system.Problems of control and management of systems of this kind are challenging issues in the theory of genetic networks.
Cybersecurity in the Local Government Sector in Poland: More Work Needs to be Done

Although cybersecurity is an important and complex issue that should be addressed by all government levels, so far little research has been devoted to cybersecurity at the local level. Existing literature lacks information on whether municipalities have implemented cybersecurity policies, if such policies are applied in practice and what they encompass. A CAWI method was used to collect the required data. The results indicate that while most municipalities have a document defining their security policy, they do not always apply it in practice. There is still little awareness regarding countering cyber-attacks. Therefore, more emphasis should be placed on such issues as: integrating cybersecurity policies into local government management, the rising threat of cyber-attacks, consultations with security auditors, and cybersecurity management training. Based on all Polish municipalities, the research described in this paper partly fills the identified gap.
Preserving Location Privacy in the IoT against Advanced Attacks using Deep Learning

Location-based services (LBSs) have received a significant amount of recent attention from the research community due to their valuable benefits in various aspects of society.In addition, the dependency on LBS in the performance of daily tasks has increased dramatically, especially after the spread of the COVID-19 pandemic.LBS users use their real location to build LBS queries to take benefits.This makes location privacy vulnerable to attacks.The privacy issue is accentuated if the attacker is an LBS provider since all information about users is accessible.Moreover, the attacker can apply advanced attacks, such as map matching and semantic location attacks.In response to these issues, this work employs artificial intelligence to build a robust defense against advanced location privacy attacks.The key idea behind protecting the location privacy of LBS users is to generate smart dummy locations.Smart dummy locations are false locations with the same query probability as the real location, but they are far from both the real location and each other.Relying on the previous two conditions, the deep-learning-based intelligent finder ensures a high level of location privacy protection against advanced attacks.The attacker cannot recognize the dummies from the real location and cannot isolate the real location by a filtering process.In terms of entropy (the privacy protection metric), accuracy (the deep learning metric), and total execution time (the performance metric) and compared to the well-known DDA and BDA systems, the proposed system shows better results, where entropy = 15.9, accuracy = 9.9, and total execution time = 17 sec.
Yoga Trainer for Beginners Via Machine Learning

Yoga is the practice for both mind and body as it has been proven scientifically on various occasions. Due to the modern advancements of technology, remote Yoga practice sessions have been increasing in popularity following the increase of demand for professional Yoga instructors. In order to tackle this problem, we proposed a system that uses machine learning techniques utilizing an ANN (Artificial Neural Network) model and a human pose tracking model to classify Yoga Hatha movements and detect Incorrect Yoga poses while providing real-time constructive feedback for practitioners to get them to maintain the correct posture for a specific Yoga Hatha pose. This system aims to enhance the learning experience and reduce the practice time for beginners while still retaining a versatile environment. In this paper, we managed to achieve a testing accuracy of 82.2% for our proposed model and were successfully able to reduce the average practice time by an average of 6.4 seconds when tested on 20 participants of different body features.
Learning to Classify Open Intent via Soft Labeling and Manifold Mixup

Open intent classification is a practical yet challenging task in dialogue systems. Its objective is to accurately classify samples of known intents while at the same time detecting those of open (unknown) intents. Existing methods usually use outlier detection algorithms combined with K-class classifier to detect open intents, where K represents the class number of known intents. Different from them, in this paper, we consider another way without using outlier detection algorithms. Specifically, we directly train a (K+1)-class classifier for open intent classification, where the (K+1)-th class represents open intents. To address the challenge that training a (K+1)-class classifier with training samples of only K classes, we propose a deep model based on Soft Labeling and Manifold Mixup (SLMM). In our method, soft labeling is used to reshape the label distribution of the known intent samples, aiming at reducing model’s overconfident on known intents. Manifold mixup is used to generate pseudo samples for open intents, aiming at well optimizing the decision boundary of open intents. Experiments on four benchmark datasets demonstrate that our method outperforms previous methods and achieves state-of-the-art performance. All the code and data of this work can be obtained at. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Object Detection and Tracking using YOLO v3 Framework for Increased Resolution Video

The proposed system is used for vehicle detection and tracking from the high-resolution video. It detects the object (vehicles) and recognizes the object comparing its features with the features of the objects stored in the database. If the features match, then object is tracked. There are two steps of implementation, online and offline process. In offline process the data in the form of images are given to feature extractor and then after to the trained YOLO v3 model and weight files is generated form the pre-trained YOLO v3 model. In online phase, real-time video is applied to feature extractor to extract the features and then applied to the pre-trained YOLO v3 model. The other reference to YOLO v3 model pre-trained is the output of weight file. The YOLO v3 model process on the video frame and weight file extracted features, the model output is classified image. In YOLO v3 Darknet-53 is used along with Keras, some libraries with OpenCV, Tensor Flow, and Numpy. The proposed system is implemented on PC Intel Pentium G500, 8GB and operating system Windows 7 is used for processing our system. The system is tested on PASCAL VOC dataset and the results obtained are accuracy 80%, precision 80%, recall 100%, F1-Score 88%, mAP 76.7%, and 0.018%. The system is implemented using python 3.6.0 software and also tested using real-time video having 1280x720 and 1920x1080 resolutions. The execution time for one frame of video having resolution of 1280x720 (HD) and 1920x1080 (FHD) and 1280x720 (HD) are 1.840 second and 4.414808 seconds respectively with accuracy is 80%.
User Authentication Protocol Based on the Location Factor for a Mobile Environment

The way the internet is used by billions of users around the world has been revolutionized by mobile devices.The capabilities of smartphones are constantly growing, and the number of services available for mobile devices is also increasing.This undeniable trend makes smartphones terminals for accessing services that process confidential data, which make smartphones priceless targets of cyberattacks.Along with an increasing number of mobile services, the methods of securing the confidentiality, integrity and availability of systems used have also evolved and adapted to the capabilities of a mobile environment.One of the important security services is the user authentication process.This process often implements the postulates of strong authentication, multistage authentication based on factors from the knowledge, position and inherence categories.Unfortunately, the implementation of the factors belonging to these categories is not always possible due to the limitations of smartphones, such as the lack of interfaces for the implementation of biometrics or environmental factors -problems with network or internet access in various countries and regions.Therefore, there is a need to analyse the possibility of implementing a strong authentication process based on additional information about users, e.g., based on location data.The article analyses the requirements for the authentication process and authentication factors.Based on the performed analysis, the criteria that each authentication factor must meet were defined.This article presents a proposal for a user authentication protocol based on the location factor for a mobile environment.The method can be used in the case of problems with the implementation of strong authentication or as an additional authentication factor that increases the security of the user identity confirmation process.The presented protocol has been analysed in terms of performance, security and compliance with the requirements related to the authentication factors.
Learning from the initial deployment of digital contact tracing apps

Smartphone applications for digital contact tracing were a truly innovative intervention in the COVID-19 pandemic. These apps aim to automatically identify and rapidly notify individuals who have been in close contact with people who have tested positive for SARS-CoV-2. These apps were deployed in many countries in record time to support or complement traditional contact tracing systems. Epidemiological models showed the potential impact of this intervention under specific epidemic conditions,1–3 but months into the apps' deployment, real-world evaluations are now required to identify shortcomings and look for improvements.
Natural language processing and machine learning based cyberbullying detection for Bangla and Romanized Bangla texts

The popularity of social media has been increasing tremendously in recent times and thus cyberbullying towards people has also increased at an alarming rate. Many cyberbullying texts can be found in the comment sections of many well-known Bangladeshi social media personalities YouTube videos. It has the potential to cause severe emotional and psychological distress. Therefore, texts containing cyberbullying should be detected at the earliest stage and prevented from being displayed. In this study, we use natural language processing (NLP) techniques and various machine learning classifiers and presented model for cyberbullying detection in Bangla and Romanized Bangla texts obtained from YouTube video comments. We developed our own datasets using YouTube application programming interface (API) version 3.0. We collected 5000 Bangla comments, as well as 7000 Romanized Bangla comments from videos of different well-known social media personals. These two datasets, as well as a third dataset of 12000 texts which was the combination of the first two datasets were used to train the classifiers. These datasets were used to train machine learning classifiers after being preprocessed using NLP techniques. With an accuracy score of 76%, support vector machine (SVM) outperformed the other classifiers for the first dataset. The highest accuracy scores for the second and third datasets were 84% and 80%, respectively, which were both achieved by multinomial naive Bayes.
Decentralization Of Information Using Blockchain Technology On Mobile Apps E-Journal

Decentralization is the division of centralized power processes as a whole into separate ones. Blockchain technology is a basic technology that enables decentralization and gives all users the opportunity to become part of the validator or node for future transactions. The purpose of this research is to develop and develop a tamper-resistant E-Journal Mobile Application system using Blockchain Technology, which supports reliable and auditable computing using a decentralized network, not only that, this research can also develop and develop an E-Journal Application System. Cellular that is resistant to interference from hackers or system crashes. By using Blockchain technology, it can solve problems that are reliable and can be done and controlled using a decentralized network. with great hope that choosing this type of decentralized blockchain technology will be useful in preventing errors and errors in the system. This is one of the main strengths of Blockchain Technology. With this decentralized transaction log, it will not be easy and it is impossible for a database consisting of hundreds to thousands of computers to be hacked or tampered with easily. This research is a model that can be implemented as a Blockchain technology development on Mobile Apps E-journals. In this study will be made using 3 methods, namely the data analysis method, the analysis method using SWOT and the development method using a waterfall with a combination of the three methods will make this research more effective and efficient.
An Augmented Reality Children’s Book Edutainment through Participatory Content Creation and Promotion Based on the Pastoral Life of Psiloritis

Augmented reality (AR) provides excellent learning potential, especially in a school environment. Multiple students can share the virtual scene and interact with it using the mobile interface as a hand-held display in AR children’s books. Students’ participation is an essential element of learning, and one of AR’s greatest strengths is its ability to promote collaborative experiences. An augmented reality children’s book edutainment through participatory content creation and promotion based on the pastoral life of Psiloritis has been recommended through this study, highlighting the features of AR to reveal educational values unique to AR and studying approaches for incorporating these characteristics into the typical education curriculum.
LITERASI DIGITAL DALAM PEMBELAJARAN DARING

The success of online learning is determined by several aspects, namely aspects of facilities and infrastructure as well as aspects of the ability of lecturers and students in organizing online learning. This article aims to examine the role of digital literacy in online learning. Based on the results of the study, it can be concluded that digital literacy skills have an important role in online learning. Students with good digital literacy skills will strive to find and select important information and understand, communicate, and convey ideas in the digital space. In addition, digital literacy skills will open opportunities for students to think, communicate, and work which ultimately leads to student learning success.
Management of educational potential through extracurricular activities

This article aims at studying the impact and role of extracurricular activities on the young and healthy formation and development of young people.The survey outlined the organizational arrangements, the steps teachers should take to ensure that extracurricular activities are successful and can achieve their original goal / goal, with the emphasis on learning.Reducing school dropout, reducing youth delinquency, reducing professional indecision, learning a job are some of the roles played by extracurricular activities; thus, education systems should encourage and support the realization of as many of these activities as possible.Also, various extracurricular activities from several countries / countries were presented in the study.
On Quadratic Interpolation of Image Cross-Correlation for Subpixel Motion Extraction

Digital image correlation techniques are well known for motion extraction from video images. Following a two-stage approach, the pixel-level displacement is first estimated by maximizing the cross-correlation between two images, then the estimation is refined in the vicinity of the cross-correlation peak. Among existing subpixel refinement methods, quadratic surface fitting (QSF) provides good performances in terms of accuracy and computational burden. It estimates subpixel displacement by interpolating cross-correlation values with a quadratic surface. The purpose of this paper is to analytically investigate the QSF method. By means of counterexamples, it is first shown in this paper that, contrary to a widespread intuition, the quadratic surface fitted to the pixel-level cross-correlation values in the neighborhood of the cross-correlation peak does not always have a maximum. The main contribution of this paper then consists in establishing the mathematical conditions ensuring the existence of a maximum of this fitted quadratic surface, based on a rigorous analysis. Algorithm modifications for handling the failure cases of the QSF method are also proposed in this paper, in order to consolidate it for subpixel motion extraction. Experimental results based on two typical types of images are also reported.
How do Scrum Methodologies Influence the Team's Cultural Values? A Multiple Case Study on Agile Teams in Nonsoftware Industries

The cultural component of the project team is recognized as one of the most critical factors in the implementation of agile project management (APM), especially in nonsoftware industries, where the diffusion of APM still involves several challenges. Particularly, the successful implementation of scrum—the most diffused APM methodology—seems related to the project teams’ subculture, which may differ from the overall organizational culture of the company. This article contributes to the APM literature in nonsoftware contexts by studying the cultural values that develop inside agile teams and the scrum principles and practices that are particularly relevant for fostering these values. Using interview data collected from seven manufacturing and service organizations, we use the competing value framework as the theoretical model to understand the cultural profiles of their organizations, how they deploy into the project teams’ subculture, and what, if any, connections exist with the adoption of scrum principles and practices. We find that clan and market values are the dominant subcultures in agile teams. These cultural values are fostered at a strategic level by a subset of scrum values (i.e., courage, openness, and respect) and pillars (i.e., transparency and adaptation). At an operational level, retrospective meetings and the definition of particular artifacts also contribute to develop these dominant cultural values.
Development of an App and Teaching Concept for Implementation of Hyperspectral Remote Sensing Data into School Lessons Using Augmented Reality

For the purpose of expanding STEM (science, technology, engineering, mathematics) education with remote sensing (RS) data and methods, an augmented reality (AR) app was developed in combination with a worksheet and lesson plan. Data from the Hyperspectral Imager for the Coastal Ocean (HICO) was searched for topics applicable to STEM curricula, which was found in the example of a harmful algal bloom in Lake Erie, USA, in 2011. Spectral shape algorithms were applied to differentiate between less harmful green and more harmful blue algae in the lake. The data was pre-processed to reduce its size significantly without losing too much information and then integrated into an app that was developed in Unity with the Vuforia extension. It was designed to let students browse and understand the raw data in RGB and a tangible hyperspectral cube, as well as to analyze algae maps derived from it. The app runs on Android smartphones with minimized data usage to make it less dependent on school funding and the socioeconomic background of students. Using educational concepts, such as active and collaborative learning, moderate constructivism, and scientific inquiry, the data was integrated into a lesson about environmental problems that was enhanced by the AR app. The app and worksheet were evaluated in two advanced geography courses (n = 36) and found to be complex, but doable and understandable, for the target group of German high school students in their final two school years. Thus, hyperspectral data can be used for STEM lessons using AR technology on students’ smartphones with several limitations both in the technology used and gainable knowledge.