Reviewerly/exHarmony huggingface dataset:
Joint Scalable Video Coding and Transcoding Solutions for Fog-Computing-Assisted DASH Video Applications

Video streaming solutions have increased their importance in the last decade, enabling video on demand (VoD) services. Among several innovative services, 5G and Beyond 5G (B5G) systems consider the possibility of providing VoD-based solutions for surveillance applications, citizen information and e-tourism applications, to name a few. Although the majority of the implemented solutions resort to a centralized cloud-based approach, the interest in edge/fog-based approaches is increasing. Fog-based VoD services result in fulfilling the stringent low-latency requirement of 5G and B5G networks. In the following, by resorting to the Dynamic Adaptive Streaming over HTTP (DASH) technique, we design a video-segment deployment algorithm for streaming services in a fog computing environment. In particular, by exploiting the inherent adaptation of the DASH approach, we embed in the system a joint transcoding and scalable video coding (SVC) approach able to deploy at run-time the video segments upon the user’s request. With this in mind, two algorithms have been developed aiming at maximizing the marginal gain with respect to a pre-defined delay threshold and enabling video quality downgrade for faster video deployment. Numerical results demonstrate that by effectively mapping the video segments, it is possible to minimize the streaming latency while maximising the users’ target video quality.
Pengaruh Kualitas Pelayanan dan Fasilitas Perpustakaan terhadap Kepuasan Pengguna di Institut Agama Islam Nasional Laa Roiba Bogor

This study discusses the quality of service and library facilities on user satisfaction at the National Islamic Institute of Religion Library Laa Roiba Bogor based on the opinions of library users. The method used is quantitative, the population in this study is library visitors, namely students of the National Islamic Institute of Islam Laa Roiba as many as 355 people. This population is obtained from the number of library visitors. The type of sample selection in this study uses simple random sampling, where the sampling of members of the population is carried out randomly without regard to the existing strata in the population. In determining the number of samples using the Slovin formula. With the Slovin formula, it is known that the sample is 78 people. Based on the results of the study, according to library users, the quality of service and library facilities at the Laa Roiba National Islamic Institute Library got a good score. This is evidenced by the results of regression analysis that the correlation coefficient of service quality competence and library facilities is positive. &#x0D; Keywords: Service Quality, Library Facilities, User Satisfaction
3D reconstruction of non-structural surface of a stiffened hull plate based on RGB-D images

Deformation measurement of hull structure is of great significance for enhancing the quality of shipbuilding. In this paper, a three-dimensional (3D) reconstruction method based on RGB-D images is presented for non-structural surface of a stiffened hull plate. The algorithm for object surface region nodes (OSRN) is proposed to extract the features of point clouds and generate higher-order fitting surface model. Firstly, random sample consensus (RANSAC) algorithm and least square method are applied to eliminate the noise of point clouds. Spatial position of targets in RGB image and singular value decomposition (SVD) are utilized to accomplish the registration of point clouds from multiple camera views. Then, the point clouds data are clustered and fused to fit the quadric surface. On this basis, the 3D surface model is reconstructed through higher-order panel element. Two comparative experiments, including the measurements via the RGB-D camera and 3D Laser Scanner, are carried out to verify the applicability and accuracy of the proposed method.
Mixture Modeling of Time-to-Event Data in the Proportional Odds Model

Subgroup analysis with survival data are most essential for detailed assessment of the risks of medical products in heterogeneous population subgroups. In this paper, we developed a semiparametric mixture modeling strategy in the proportional odds model for simultaneous subgroup identification and regression analysis of survival data that flexibly allows the covariate effects to differ among several subgroups. Neither the membership or the subgroup-specific covariate effects are known a priori. The nonparametric maximum likelihood method together with a pair of MM algorithms with monotone ascent property are proposed to carry out the estimation procedures. Then, we conducted two series of simulation studies to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of German breast cancer data is further provided for illustrating the proposed methodology.
Performance Evaluation for the use of ELMo Word Embedding in Cyberbullying Detection

Communication using modern internet technologies has revolutionized the ways humans exchange information.. Despite the numerous advantages offered by such technology, its applicability is still limited due to problems stemming from personal attacks and pseudoattacks. On social media platforms, these toxic contents may take the form of texts (e.g., online chats, emails), speech, and even images and movie clips. Because the cyberbullying of an individual via the use of such toxic digital content may have severe consequences, it is essential to design and implement, among others, various techniques to automatically detect, using machine learning approaches, cyberbullying on social media. It is important to use word embedding techniques to represent words for text analysis, typically in the form of a real-valued vector that encodes the meaning of words. The extracted embeddings are used to decide if a digital input contains cyberbullying contents. Supplying strong word representations to classification methods is a key facet of such detection approaches. In this paper, we evaluate the ELMo word embedding against three other word embeddings, namely, TF-IDF, Word2Vec, and BERT, using three basic machine learning models and four deep learning models. The results show that the ELMo word embeddings have the best results when combined with neural network-based machine learning models.
Using Graphs to Represent Japanese Words for Serious Games

The aim of this paper is to explore the representation problem of how Japanese words are constructed from Kanjis (i.e. symbols for concepts). We discuss the various implications of such representations and demonstrate a fully functional game built upon a Neo4J implementation.
Semiotic Conceptual Analysis of Part-Whole Relationships in Diagrams

Proverbially, a picture is worth a 1000 words because it conveys a multitude of concepts, their parts and relationships simultaneously. But it is difficult to precisely describe why and how a picture achieves this. This paper employs Semiotic Conceptual Analysis as a means for providing formal methods for analysing diagrams and other graphical representations, in particular, by modelling part-whole relationships amongst representations and defining quantitative measures and certain qualitative features for comparing different types of representations. The general background for this research is analysing diagrams for teaching purposes.
Cyberbullying Detection using Ensemble Method

Cyberbullying is a form of bullying that occurs across social media platforms using electronic messages. This paper proposes three approaches and five models to identify cyberbullying on a generated social media dataset derived from multiple online platforms. Our initial approach consists in enhancing Support Vector Machines. Our second approach is based on DistilBERT, a lighter and faster Transformer model than BERT. Staking the first three models we obtain two more ensemble models. Contrasting the ensemble models with the three others, we observe that the ensemble models outperform the base model concerning all evaluation metrics except precision. While the highest accuracy, of 89.6%, was obtained using an ensemble model, we achieved the lowest accuracy, at 85.53% on the SVM model. The DistilBERT model exhibited the highest precision, at 91.17%. The model developed using the different granularity of features outperformed the simple TF-IDF.
Continuous Models

This chapter examines phenomena that change continuously, primarily in time. Examples are drawn from life sciences and engineering, including yeast populations, predator-prey interactions, laser beams, oscillations of a quartz crystal, sensors of magnetic fields, and pharmokinetic systems.
Explainable and Explorable Decision Support

An effective decision support system requires a user’s trust in its results, which are based on expected utilities of different action plans. As such, a result needs to be explainable and explorable, providing alternatives and additional information in a proactive way, instead of retroactively answering follow-up questions to a single action plan as output. Therefore, this paper presents LEEDS, an algorithm that computes alternative action plans, identifies groups of interest, and answers marginal queries for those groups to provide a comprehensive overview supporting a user. LEEDS leverages the strengths of gate models, lifting, and the switched lifted junction tree algorithm for efficient explainable and explorable decision support.
Study on path planning method for unknown 3D environment exploration driven by compound boundary points

Aiming at the low exploration level, slow speed and repeated exploration of UAVs in unknown environments, this paper improves the selection of boundary points based on the boundary driving method, and proposes a method suitable for UAVs to quickly explore in unknown environments. Planning method: while using the depth image (RGBD) sensor to receive environmental information, the octree map of the known environment is constructed in real time, and the common indoor environment is divided into six local environmental types. The drone observes according to the RGBD camera on board. The local environment type generates instantaneous speed commands, and designs composite boundary points, from which the point with the largest information gain and the smallest yaw angle is weighed as the boundary guide point. Finally, a simulation experiment was carried out and comparing with the existing methods. The time required to explore the apartment environment in the next best viewpoint (NBV) in Document with the present method is 68.7% less than the exploration time in the paper. In addition, this paper designs There are four types of environments. In these environments, the present method reduces the average exploration time by 97.1% comparing with the method in Document. The experimental results show that the present method can effectively improve the exploration efficiency and has strong feasibility.
Ramsey's Lost Counterfactual

In contemporary works on conditionals, the Ramsey test is a procedure for the evaluation of conditional sentences. There are several versions of the test, all inspired by a footnote by the British philosopher and mathematician Frank Ramsey, in his General Propositions and Causality. However, no study on Ramsey's own account of conditionals has been put forth so far. Furthermore, the footnote seems to cover indicative conditionals only, and this has led to the belief that no account of counterfactuals can be found in Ramsey's work. In this paper, I recover Ramsey's account of counterfactuals and show that it is sketched in the footnote too. The result is a well-developed account of counterfactuals that resembles many contemporary ones. But Ramsey uses the same approach also for other types of conditionals, and this casts doubts on the current criteria for the classification of this type of sentences.
Vulnerabilities and Ethical Issues in Machine Learning for Smart City Applications

In modern era, technology is transforming throughout the world fast-paced with numerous technological advancements and developments. Any use of technology, however, brings with it new concerns and obstacles. Many aspects of smart cities rely on data transfer, storage and learning technology and cyber-security issues have an impact on how they operate. First part of this chapter is focused on different aspects of vulnerabilities of machine learning in privacy, data security, privacy and public administration, scarcity of skilled professionals, huge capital, unemployment, economic disparity and artificial stupidity. Secondly, different ethical issues of ML in societal, implementation and data collection levels are presented. The use cases of smart city applications are presented to emphasize the vulnerabilities and ethical issues and to address and regulate such use of information.
Helping Hand: A GMM-Based Real-Time Assistive Device for Disabled Using Hand Gestures

In recent years, the emergence of computer vision (CV) and machine learning techniques for human to communicate with computer and vice versa has paved the way to fulfill the needs of people with physical disabilities and the elderly, by breaking the communication barrier they face. The vision-based hand gesture recognition system eliminates the need for additional devices and the physical contact with the device to identify the hand gesture shown by the subject to express their willingness. The assistive device uses webcam or the video camera connected with the laptop or desktop placed in front of the disabled person's bed. The hand gesture shown by the disabled person is segmented using the machine learning-based probabilistic Gaussian mixture model (GMM) algorithm by eliminating the background. The hand features are extracted using Hough transform, as it can tolerate changes in orientation and hand shape deformation as the disabled person may not always give a clear view of any shown gesture. The recognition of hand gesture is carried out by comparing the extracted features with the most likely used pre-stored features used by the disabled and elderly like the need for food, water, medicine, wash room, poser change and wheel me out. The prediction of hand gesture is carried out using the K-NN algorithm. Once predicted, the audio file corresponding to the recognized gesture stored in the database is played since the family members or the caretaker may not be with the disabled person every second or minute for immediate attention. The overall gesture prediction accuracy of the proposed system is found to be 95%, and the frequency distribution of gesture usage per day is found to be uniformly distributed with the chi-square test.
Classification and Tracking of Vehicles Using Videos Captured by Unmanned Aerial Vehicles

Road congestion levels currently seen in the main cities of the world, have generated problems associated with environmental air quality, road safety and excessive trip times. Intelligent Transport Systems (ITS) emerge as an important alternative to assist urban planning by monitoring the different urban road corridors. Traditionally, ITS use fixed camera systems which are sometimes insufficient to monitor and extend the traceability of the vehicles with extended coverage. Video capture mechanisms supported by Unmanned Aerial Vehicles (UAVs) e.g. drones, have greater continuous visibility of road segments, better visibility and allow more precise analysis on the density and behaviour of vehicle flows. This chapter presents detection, classification and tracking analysis for three categories of vehicles: Light (automobiles), Heavy (trucks and buses) and Motorcycles corresponding to 95% of urban traffic in emerging countries. For this purpose, 3 datasets with more than 2.5 million annotated objects are introduced using video sequences captured from UAVs from different views. An analysis of detection ability is carried out by considering traditional methods (prior to deep learning detectors) and using state-of-the-art detectors such as YOLO v3 and Faster R-CNN, obtaining results up to 98.5% AP (Average Precision) in all the analysed categories. Finally, multiple object tracking techniques such as Deep Sort are investigated, achieving results of up to 82% MOTP (Multiple Object Tracking Precision) in light vehicles.
Review on<i>Modelling and Verification of Secure Exams</i>

The Model Curricula Subcommittee of the IEEE Computer Society Education Committee was formed some 16 months ago to develop an IEEE Computer Society-sanctioned four-year curriculum in computer science and engineering to be used by colleges and ...
The Digital Transformation of Higher Education

The improvements in digital technologies have changed societies in many ways. This process is commonly called “digital transformation.” Many sectors have been affected by digital transformation, including higher education institutions. Also, the COVID-19 pandemic, which started in 2019 in China, triggered the use of digital technologies in everyday activities and at every level of education. This chapter primarily presents the process of digital transformation in higher education by giving examples from various countries based on recent research, providing challenges of digital transformation of higher education, and then examining the challenges and reflections of the COVID-19 pandemic on the digital transformation of higher education. Lastly, the current situation is evaluated, and suggestions are proposed.
USE OF TOOLS FOR THE EDUCATIONAL PROCESS DIGITALIZATION IN THE FRAMEWORK OF THE IMPLEMENTATION OF THE BLENDED LEARNING MODEL

Abstract. The circumstances of recent years have led to the rapid development of the digitalization of the educational environment. Thus, in modern medical education, the successful application of the blended learning model is noted, which consists in the rational combination of digital and traditional forms of education, using their advantages and minimizing disadvantages. The purpose of this work was to assess the level of digitalization of education at the Department of Microbiology, Virology, Immunology of the Kursk State Medical University, as well as to identify its strengths and weaknesses in order to further adjust the educational process and achieve maximum teaching efficiency. The main source of knowledge at the department for students is the course of lectures, the implementation of which is currently taking place in a remote format based on the cloud-based conference platform Zoom. The advantages of this format of lectures were the absence of restrictions associated with geographical remoteness, comfortable conditions for each conference participant, the opportunity for students to save lecture material, the development of self-education skills among students and an increase in the level of technical literacy among the teaching staff. A significant drawback of distance lectures is the lack of personal contact between the lecturer and the audience and the lack of control of students. In general, the level of preparation of students for practical classes revealed the effectiveness of using the distance lecture format. Preparation for practical classes at the department takes place in a mixed format, which is due to the impossibility of mastering the necessary practical skills by students remotely. Of the electronic elements of the educational process, digital automatic testing based on the Moodle e-learning system is widely used, the main advantage of which is saving the time of the teaching staff, since the mechanism independently processes students' answers and calculates the final result. During the discussion of the topic in practical classes, the teachers of the department demonstrate tables and reference materials on an interactive whiteboard or TV, which eliminates the need for a large amount of poster materials. A powerful tool in the learning process are multimedia aids, which are more visual, accessible and mobile. Thus, we have chosen a vector aimed at using the strengths and weaknesses of traditional and electronic formats, creating a kind of hybrid of them to increase the efficiency of our activities. The study of this aspect is necessary for further improvement in order to achieve greater efficiency.
PECULIARITIES OF USING THE AXIOMATIC METHOD OF INTRODUCING ELEMENTARY FUNCTIONS IN THE TEACHING OF FUTURE MATHEMATICS TEACHERS AT THE UNIVERSITY

Abstract. The concept of the development of mathematical education in the Russian Federation focuses on the importance of teaching mathematics as a system-forming factor in human education, the development of his cognitive abilities, logical thinking. At the same time, the problems that exist in the field of mathematical training of schoolchildren are also indicated. In this regard, the problem of improving the quality of professional training of future teachers of mathematics becomes relevant and significant. In the modern specialized school, the axiomatic approach in teaching mathematics is traditionally used to present the geometry course. It should be noted that in order to achieve subject learning outcomes that meet the requirements of educational standards, it is advisable to use formalized methods for studying other mathematical sections. The purpose of the work is to present the features of studying mathematics at a university using the axiomatic method of introducing elementary functions in the professional training of teachers. Observation, surveys, questionnaires, conversations with trainees were used as the main research methods; Elements of statistical analysis were used to process the experimental data. The author has developed diagnostic tools for assessing the level of formation of the main components of the methodological competence of future teachers of mathematics. The results of studies conducted with students of pedagogical areas of study of Pitirim Sorokin Syktyvkar State University (profile "Mathematics"), and their statistical analysis showed the effectiveness of using the axiomatic method in the process of developing the methodological competence of future teachers. The methodology for teaching future teachers of mathematics the axiomatic approach for introducing elementary functions, developed in an experimental study, has been successfully introduced into the educational process of the university.
The Necessary Digital Transformation for a Post-COVID-19 Scenario of Mérida's Roman City

This chapter addresses the importance of the Roman Archaeological Ensemble of Merida as a tourist destination and the necessary revitalisation of the tourism sector through digital innovation. Despite the different policies to reactivate the sector implemented in the post-COVID-19 scenario, the authors consider that there is still much work. They propose how digital technologies play a crucial role in the conservation and dissemination of cultural heritage, and they are committed to mobile and augmented narratives as an ideal strategy for urban archaeological tourism. Throughout the chapter, they review key notions that articulate the essential elements of the tourist experience and comment on previous projects that could serve as a starting point.
The Integration of Mobile-Assisted Language Learning

The process of modern communication has become effective due to the use of modern devices and tools. In education, the value of teacher and student communication is pivotal as it can help to improve learning. One of the latest methods to elevate learning processes is mobile-assisted language learning (MALL). MALL has emerged as a modern learning and teaching way all around the world. Looking at the Pakistani society, the use of latest devices and techniques is absent. There is a dire need to employ some latest ways and methods that can overhaul the already established system of learning. Importantly, before applying any particular method, there is a strong need to focus upon the perception of the users. Therefore, this study looks for the perceptions of ESL pre-service Pakistani teachers regarding MALL. Data were collected through interviews, and it was explored that pre-service teachers had positive perceptions regarding MALL.