Reviewerly/exHarmony huggingface dataset:
AUGMENTED REALITY IN PORTUGAL: ITS USE, ITS DEVELOPMENT, AND CONTEXTS

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 1823-1830ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0463Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
LESSONS LEARNED FROM TEACHING IN THE PANDEMIC: IMPACTS, RESPONSES AND WHAT WORKED

Appears in: ICERI2022 Proceedings Publication year: 2022Page: 2795 (abstract only)ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0687Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
INFORMATION SECURITY AND DIGITAL RIGHTS MANAGEMENT: A BRIEF LITERATURE REVIEW

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 76-80ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0046Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
THE DESIGN AND DEVELOPMENT OF AN INSTITUTIONAL DIGITAL TOOLKIT TO ENHANCE RESEARCH TEACHING INTEGRATION

THE DESIGN AND DEVELOPMENT OF AN INSTITUTIONAL DIGITAL TOOLKIT TO ENHANCE RESEARCH TEACHING INTEGRATION
A CONTEMPORARY ADOLESCENT’S PRACTICAL MODEL OF UPBRINGING: PEDAGOGICAL POSSIBILITIES OF NETWORKING

1 St Petersburg University (RUSSIAN FEDERATION)2 State Budgetary Educational Institution Gymnasium № 343 of the Nevsky District of St. Petersburg (RUSSIAN FEDERATION)
THE CHALLENGES AND BENEFITS OF MOVING MUSICAL IMPROVISATION ONLINE: LESSONS FOR EDUCATIONAL DESIGN AND PRACTICE

THE CHALLENGES AND BENEFITS OF MOVING MUSICAL IMPROVISATION ONLINE: LESSONS FOR EDUCATIONAL DESIGN AND PRACTICE
DIGITAL SKILLS OF STUDENTS

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 7561-7564ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1925Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
EMPIRICAL RESEARCH ON HOW EFFICIENTLY DIGITALIZATION IS BEING INTEGRATED IN SECONDARY, BACCALAUREATE AND VOCATIONAL EDUCATION IN SPAIN

EMPIRICAL RESEARCH ON HOW EFFICIENTLY DIGITALIZATION IS BEING INTEGRATED IN SECONDARY, BACCALAUREATE AND VOCATIONAL EDUCATION IN SPAIN
IDENTIFICATION OF THE INFLUENCE OF UNFORESEEN EXTERNAL INFLUENCES (COVID-19 PANDEMIC) ON EDUCATIONAL OUTCOMES IN THE GENERAL EDUCATION SYSTEM

IDENTIFICATION OF THE INFLUENCE OF UNFORESEEN EXTERNAL INFLUENCES (COVID-19 PANDEMIC) ON EDUCATIONAL OUTCOMES IN THE GENERAL EDUCATION SYSTEM
AGILE, HYBRID TEACHING METHODS OF DISTRIBUTED PROJECT MANAGEMENT AND INTERCULTURAL SKILLS FOR GLOBAL SOFTWARE ENGINEERING DURING THE PANDEMIC

AGILE, HYBRID TEACHING METHODS OF DISTRIBUTED PROJECT MANAGEMENT AND INTERCULTURAL SKILLS FOR GLOBAL SOFTWARE ENGINEERING DURING THE PANDEMIC
ENTREPRENEURSHIP AS A FUNDAMENTAL TRANSVERSAL COMPETENCE IN FIRST-YEAR STUDENTS OF THE DEGREE IN CHEMISTRY

ENTREPRENEURSHIP AS A FUNDAMENTAL TRANSVERSAL COMPETENCE IN FIRST-YEAR STUDENTS OF THE DEGREE IN CHEMISTRY
DIGITAL TRANSFORMATION IN LANGUAGE EDUCATION DURING THE COVID-19

1 Aoyama Gakuin University (JAPAN)2 National Institute of Advanced Industrial Science and Technology (JAPAN)
TECHNICAL TRANSLATION IN TRANSLATOR TRAINING

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 930-937ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0273Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
DIGITAL PEDAGOGIES IN TEACHER EDUCATION POST COVID-19

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 408-414ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0141Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
Artificial Intelligence and Causal Inference

This book presents a theoretical overview of recent developments at the interface between deep neural networks and causal inference (although the title mentions Artificial Intelligence, the methods discussed are exclusively neural networks). Chapters 1–4 introduce deep learning models, including variational autoencoders, generative adversarial networks and neural networks as Gaussian processes. Chapter 5 then discusses causal models, including Pearl’s do-calculus, mediation analysis, confounding, instrumental variables and how these can be integrated with neural networks. The remaining chapters discuss various topics that combine both deep neural networks and causal inference, such as reinforcement learning and counterfactual reasoning. Both deep learning and causal inference are fast-moving fields, and the author covers the latest topics and methods well. The book has a high ratio of equations to text, and even more technical material is contained in appendices at the end of each chapter. No worked examples with data are provided to illustrate how the methods should be implemented; however, at the end of each chapter links to software packages and code (primarily GitHub repositories) developed by others are provided. Although readers will gain a mathematical understanding of the latest methods, they will have to look elsewhere for examples of how to analyse and interpret the results.
Chronological Study of Sediments from Alambari River, São José dos Campos, São Paulo State, Brazil

Several studies in Nuclear Geophysics have adopted the 210Pb chronological method to determine sedimentation rates in hydrographic basins. In this paper, such technique was utilized to investigate sediments collected at Alambari River, located in the vicinity of the Henrique Lage Refinery (Revap - Petrobras), in the municipality of São José dos Campos, São Paulo State, Brazil. The study was carried out in order to observe the potential anthropic influences of the refinery on the Alambari River sediments. For the collected sediments, analysis including loss on ignition (LOI), grain size, chemical composition, and sedimentation rates/sediments age were carried out, in this case, adopting the 210Pb Constant Rate of Supply (CRS) model. The average sedimentation rate varied between 0.363 g/cm².year and 1.68 g/cm².year. Silica was identified as the main constituent in all three sediment cores analyzed in this study, with average concentrations of 41.63-82.58%. Silica correlated inversely with aluminum, iron and titanium, among other oxides. The X-ray fluorescence spectrometry also allowed identify the presence of elements that could be associated with petroleum and its derivatives, such as palladium, nickel and vanadium, possibly indicating the contamination of sediments of the Alambari River by effluents or leaks due to the refinery's activities.
Minimalism for the Win: User-Centered Design for Guidance in Industrial Maintenance

We conducted an exploratory study to test the delivery of technical instructions built on the principles of minimalism. The aim was to investigate how we could support target users’ skill levels in a context-sensitive manner. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Literature review:</b> Related work examines minimalism, user needs and profiling, and industrial maintenance and technician experience. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research questions:</b> 1. How can the semantic structure of DITA XML be utilized in delivering technical information to users based on their skill levels? 2. How would a layered system of information support the principles of minimalism? <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</b> We created material and tested the concept in user studies with maintenance personnel in three countries. We collected feedback through participant observation, interviews, and questionnaires. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results and discussion:</b> The minimalist approach of delivering information to maintenance technicians was well received and supported users with varying skill levels. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</b> The context-sensitive level of expertise concept empowers users to decide on the depth of technical information that they require to complete the task at hand. The semantic structure of DITA XML works well in the delivery of technical information to the users based on their skill levels. Many of the key principles of minimalism are applicable to hardware maintenance instructions.
Motion Embedded Image: A combination of spatial and temporal features for action recognition

Demand for human activity recognition from videos has rapidly increased in many real-life applications, e.g., video surveillance, entertainment, healthcare, child and old homes, etc. Moreover, the explosion of short-form videos on social networking platforms such as Tiktok, Facebook, Youtube, etc., makes this problem gain much more attention. In this paper, we focus on the problem of human activity recognition in general short videos. Compared with still images, clips provide both spatial and temporal information, and the challenge is to capture the complementary information on appearance from still frames and motion between frames. Our contribution is two-fold. First, we study an approach of using motion embedded Image in a variation of two-stream ConvNet architecture: one stream is a motion stream capable of capturing and recognizing motion based on embedded batches of frames; another one is a normal image classification ConvNet being fed still frames to classify static appearance and recognize the missing spatial information from the first stream. Second, we build a brand new dataset of Southeast Asian Sports short videos, consisting of both standard videos with no effect and non-standard videos with effects - a modern factor that all currently available datasets being used for benchmarking models lack. Our model is trained and evaluated using different backbone architectures and on two benchmarks: UCF-101 and SEAGS-V1. The result shows that this is a model with competitive performance compared to previous attempts to use deep nets for human activity recognition in short-form videos.
Camera Calibration Based on Neural Network Method

Camera calibration is an important step in soil deformation measurement with digital image cross-correlation method. Its purpose is to transform the measured soil deformation in image plane into real space. This chapter presented a three-layer, feed-forward neural network for camera calibration which achieves very good performance.
A Modified Teaching-Learning-Based Optimization Algorithm for Traveling Salesman Problem

In this investigation, we propose a modified teaching-learning-based optimization algorithm (mTLBO) for solving the traveling salesman problems. We design an mTLBO with Boltzmann selection, novel upgradation strategy in the teaching phase, and interactive group-based crossover for learners in the learning phase. In the teaching phase, we focus on different learning abilities of different subjects of individual learners and in the learning phase, learners are randomly divided to form different groups; it helps to maintain the diversity of the population and to avoid premature convergence. The proposed algorithm is tested against benchmark functions from TSPLIB. The results are compared with the proposed mTLBO, TLBO and standard Genetic Algorithm with Roulette wheel selection, cyclic crossover and random mutation. The effectiveness of the proposed algorithm is shown through statistical test ANOVA.
Goal-oriented conditional variational autoencoders for proactive and knowledge-aware conversational recommender system

Conversational recommender system is designed to proactively elicit the user preferences in a dialogue manner, which could effectively improve the user experience as well as the accuracy of recommendation compared with the traditional static recommender systems. As a powerful technique to flexibly produce context-dependent responses, generative dialogue systems have been widely studied. To further improve the meaningfulness and diversity of responses, a number of remarkable researches have been proposed to enrich the inputs of generative model and utilize them effectively. In this work, we focus on capturing the discourse-level features of responses to improve the quality of generation, and propose a novel goal-oriented conditional variational autoencoders model. Our model uses the latent variable guided by dialogue goal to learn the distribution over potential responses and generates informative and diverse results. Moreover, a response-aware knowledge discernment mechanism is proposed which employs the discourse-level response features to accurately discern related knowledge facts and further facilitate the response generation. Extensive experimental studies are conducted to prove the effectiveness of the proposed approaches, the results of the automatic evaluation, the human evaluation as well as the ablation studies demonstrate the potential of this work.1
Entrepreneurial Corporate Social Responsibility in the Post-COVID-19 Pandemic Era: Perspectives in Sub-Saharan Africa

The chapter highlights the dynamics of entrepreneurship and corporate social responsibility (CSR) in the post-COVID-19 era from an African perspective. CSR is nowadays a business cost that enterprises bear; it predicts successes in the core business and fosters relationships with other stakeholders like employees, host communities and consumers. Most of the big firms operating in African states are foreign-owned. They operate in communities that have historical social ties by virtue of having cultural and environmental ownership. However, one notes in the African states the absence of functional social responsibility through jobs, and environmental protection, among other things. What is also obvious is the prevalence of rampant tax avoidance through CSR schemes, which again, robs local states of funds for social services. Therefore, chapter discusses the resource curse theory, which presents a dilemma for resource-rich African states. The question that begs an answer is: Will African communities become true beneficiaries of CSR schemes in the post COVID-19? The findings of this chapter suggest that African states have a long way to go in ensuring that social responsibility benefits their societies.
SiamDA: distribution-aware Siamese network for visual tracking

Siamese-based trackers have achieved remarkable performance and widespread applications in visual tracking. However, most of the existing Siamese trackers are usually restricted by the difficulty of handling the misalignment between target localization and state estimation. To address the misalignment issue, we propose an efficient Siamese distribution-aware (DA) anchor-free (SiamDA) network for object tracking. First, the bounding box of regression targets is modeled by a flexible general distribution, avoiding the ambiguity of traditional rectangle representation in some complex scenes, such as occlusion and background clutter. On this basis, to eliminate the misalignment issue, a DA module is designed to connect the target localization and state estimation. In addition, the Pseudo-Intersection over Union sample assignment rule provides our anchor-free tracker with high-quality samples effectively during the training process, making it possible to further improve the tracking performance. Extensive experiments on official visual tracking benchmarks demonstrate the effectiveness and efficiency of SiamDA.
Graph Database System for COVID-19 Vaccine Supply

The COVID-19 virus has been spreading at an alarming rate causing life-threatening conditions in many human beings. Since vaccines to prevent this disease have been allowed for public usage, it has become extremely important to quickly immunize people to prevent fatalities, which subsequently implies the necessity of an efficient vaccine supply system. In any supply system, technology can enable the transfer and processing of large amounts of data in a quick and secure manner, for all entities involved in the process. It is useful for planning, execution, and analysis. It is helpful for tracking and real-time updates so that the journey of a commodity to be supplied is known to all entities at any given time, and this can be useful to catch any faults or for improving the process. The vaccines often need to be supplied over long distances and thus, there is an evident need to have a database system to model the supply of these vaccines effectively. For this, relational databases have been used for a long time to create a structured and well-defined model. However, when it comes to efficiency and flexibility, modern technology like graph databases can be a better fit while still keeping the structure of data in mind. In this paper, we propose a graph database system for the supply of COVID-19 vaccines and describe its advantages when compared to a traditional relational database system.
Authenticate Medical Data Using SWT and PCA-Based Watermarking Technique

Telemedicine has aided healthcare specialists in obtaining a high quality diagnosis by allowing them to connect with one another via extensive and faster access to patient electronic medical records, such as medical photographs. Because of security threats such as picture alteration to compromise misleading data, incorrect prognosis, and treatment may result. We use a hybrid strategy using stationary wavelet transform (SWT) and principal component analysis (PCA) to embed and extract invisible clinical image watermarks. The host image is treated as a medical image, whereas the Electronic Patient’s History (EPH) is treated as a watermark. The RGB host picture was transformed to YCbCr and split into ROI and RONI portions. As a fragile watermark, the EPH and ROI that are formed from the Y segment applied host image will be generated. The overall performance is primarily assessed using the metrics such as PSNR and NC and SSIM, and the quality is evaluated through a series of manipulated assaults. Our proposed system's primary objective is to provide confidentiality and authentication to the medical data. Patients suffering from major illnesses may get depressed as a result of others’ knowledge of their illnesses. With this method, data is more secure, and only authorized persons, patients, and their family members have access to his/her condition and diagnosis information. Results show that the proposed technique withstands image processing and false-positive attacks with high quality in terms of robustness and imperceptibility.
Recent Trends for Practicing Steganography Using Audio as Carrier: A Study

In today’s digital world, confidential information exchange over public communication channel suffers from several issues. Steganography is considered to be one of the concepts to solve these security issues. There are different types of steganography depending on the carrier medium chosen. Audio steganography is one of the types of steganography which uses audio files as carrier for concealing messages. The paper presents a study based on recent audio steganography approaches. The paper discusses the recent approaches available in literature recommended by researcher for performing audio steganography. Paper also presents the comparative evaluation of discussed methods based on three major parameters. It was well established by the presented study that although various approaches were proposed in literature for performing audio steganography, but there is still a scope of development of new methods which can perform well on these three parameters.
Far3Det: Towards Far-Field 3D Detection

We focus on the task of far-field 3D detection (Far3Det) of objects beyond a certain distance from an observer, e.g., $>$50m. Far3Det is particularly important for autonomous vehicles (AVs) operating at highway speeds, which require detections of far-field obstacles to ensure sufficient braking distances. However, contemporary AV benchmarks such as nuScenes underemphasize this problem because they evaluate performance only up to a certain distance (50m). One reason is that obtaining far-field 3D annotations is difficult, particularly for lidar sensors that produce very few point returns for far-away objects. Indeed, we find that almost 50% of far-field objects (beyond 50m) contain zero lidar points. Secondly, current metrics for 3D detection employ a "one-size-fits-all" philosophy, using the same tolerance thresholds for near and far objects, inconsistent with tolerances for both human vision and stereo disparities. Both factors lead to an incomplete analysis of the Far3Det task. For example, while conventional wisdom tells us that high-resolution RGB sensors should be vital for 3D detection of far-away objects, lidar-based methods still rank higher compared to RGB counterparts on the current benchmark leaderboards. As a first step towards a Far3Det benchmark, we develop a method to find well-annotated scenes from the nuScenes dataset and derive a well-annotated far-field validation set. We also propose a Far3Det evaluation protocol and explore various 3D detection methods for Far3Det. Our result convincingly justifies the long-held conventional wisdom that high-resolution RGB improves 3D detection in the far-field. We further propose a simple yet effective method that fuses detections from RGB and lidar detectors based on non-maximum suppression, which remarkably outperforms state-of-the-art 3D detectors in the far-field.
Developing a Graph Analytics Code : An Analysis of the Chapel Programming Environment.

algorithms for breadth-first search and triangle counting graph kernels in both environments. We discuss the code in each of the environments, and compile performance data on a Cray Aries and an Infiniband platform. Our results show that the library-based approach of UPC++ currently provides strong performance while Chapel provides a high-level abstraction that, harder to optimize, still provides comparable performance. We argue that various language primitives and runtime library features should be considered in conjunction with each other to design future graph analytics libraries so as to incorporate the best of both worlds.
Lightweight and Efficient Source Authentication Scheme Based on Blockchain Approach in Open Data Environment

The growth of information and the era of Internet popularity. Make it easier for people to get information. The government’s laws and policies have also kept up with the evolution of technology and created many convenient electronic systems to make the people more convenient. However, because of advances in technology, information warfare has also started. Much false news began to be born. The most frightening thing is not the enemies who spread false news, but the masses who do not intend to spread one. This paper is based on the open data environment and uses blockchain related mechanisms to reduce the occurrence of this case. Therefore, a lightweight source authentication scheme is designed to be used in the non-repudiation feature of digital signatures. When malicious articles are disseminated, it is possible to find out who agrees and disseminate malicious articles effectively.
Design and Application of Handwritten Numeral Recognizer Based on Convolutional Neural Network

In the field of artificial intelligence, target recognition is always a very concerned and challenging task. Scholars use various methods to indicate the accuracy rate of recognition. Convolutional neural network is a kind of artificial neural network, which is used in the field of image recognition widely. In this paper, we design and implement a handwritten numeral recognizer through data preprocessing, image normalization and noise reduction, and test relevance between recognition results and the parameters of convolutional neural network by experiment and comparison of different models, so as to obtain convolutional neural network model with the best effect.