Reviewerly/exHarmony huggingface dataset:
Speech-Based Traffic Reporting: An Automated Data Collecting Approach for Intelligent Transportation Systems

In this paper, we explore the possibility of an automatic data collecting feature for Intelligent Traffic Systems (ITS). Our proposed feature collects users’ speech reports and uses deep learning approaches to analyze the unstructured data, then provides traffic information to the community, helping to increase users’ awareness and better routing system. The basic procedure is to transform speech report to text form, we used the popular Conformer model for this automatic speech recognition (ASR) task, and then extract useful information from the output, this is the task for intent classification and slot filling, our choice is the state-of-the-art JointIDSF model for Vietnamese language. We trained the Conformer on the VIVOS dataset and gain promising result (25 and 25.5% WER on the train and test set, respectively). For the JointIDSF, we got 0.63 F1 score on the test set. We experimented this feature on the existing urban traffic estimation system deployed for real-world applications in Ho Chi Minh City, namely UTraffic, to confirm the effectiveness of the proposed solution.
Rectifying Homographies for Stereo Vision: Analytical Solution for Minimal Distortion

Stereo rectification is the determination of two image transformations (or homographies) that map corresponding points on the two images, projections of the same point in the 3D space, onto the same horizontal line in the transformed images. Rectification is used to simplify the subsequent stereo correspondence problem and speeding up the matching process. Rectifying transformations, in general, introduce perspective distortion on the obtained images, which shall be minimised to improve the accuracy of the following algorithm dealing with the stereo correspondence problem. The search for the optimal transformations is usually carried out relying on numerical optimisation. This work proposes a closed-form solution for the rectifying homographies that minimise perspective distortion. The experimental comparison confirms its capability to solve the convergence issues of the previous formulation. Its Python implementation is provided.
Information Fusion Based on Deep Learning

Recently deep learning has achieved outstanding performances in many fields. Compared with conventional shallow models, deep learning methods use deeper architectures to more powerfully model the complex distributions of the real-world datasets. This chapter proposes two deep learning based fusion methods that can fuse two branches of networks into a unique feature. After reading this chapter people can have preliminary knowledge on deep learning based fusion methods.
Factors Affecting Employees’ Susceptibility to Cyber Attacks

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On the Suitability of Augmented Reality for Safe Experiments on Radioactive Materials in Physics Educational Applications

Laboratory experiences have proved to be a key moment of the educational path in most of the so-called Sciences, Technology, Engineering and Mathematics (STEM) subjects. Having the opportunity of practicing on actual experiments about the theoretical knowledge achieved during the classroom lectures is a fundamental step from a didactic point of view. However, lab activities could be forbidden in the presence of tests characterized by safety issues, thus limiting students’ cultural growth; this is particularly true for physics experiments involving radioactive materials, sources of dangerous radiations. To face the considered problems, the authors propose hereinafter a mixed-reality solution involving augmented reality (AR) at students-side and actual instrumentation at laboratory-side. It is worth noting that the proposed solution can be applied for any type of experiment involving the remote control of measurement instruments and generic risk conditions (physical, chemical or biological). As for the considered case study on gamma radiation measurements, an ad-hoc AR application along with a microcontroller-based prototype allows students, located in a safe classroom, to (i) control distance and orientation of a remote actual detector with respect to different radioactive sources and (ii) retrieve and display on their smartphones the corresponding energy spectrum. The communication between classroom equipment and remote laboratory is carried out by means of enabling technologies typical of Internet of Things paradigm, thus making it possible a straightforward integration of the measurement results in cloud environment as dashboard, storage or processing.
GeoCloud4EduNet: Geospatial Cloud Computing Model for Visualization and Analysis of Educational Information Network

Information regarding a particular college is available online but a centralized system that maps all the colleges and their faculties to their research works is needed for the aspiring students who are willing to join on their preferred department. In the present research paper, Geospatial cloud computing model, i.e., GeoCloud4EduNet proposed for educational information infrastructure to tackle big data problem, which works on the distributed computing environment. As a case study, this paper currently focusing on the National Institutes of Technology (NITs) and their various departments as well as the specialization or the research interest of faculties. NITs are autonomous public institutes of higher education, located in India. The key challenge is for the students to connect with the right faculty of their domain as this kind of issue has not been addressed in the past.
Artificial Speech is Culture:

As conversational voice assistants (CVAs) like Alexa, Siri, and Google Home have become ubiquitous in some parts of the world, lab-based reception studies have captured perceived failures in their usability or accounted for ways that these devices reproduce problematic (often racialized and gendered) cultural conditions. But examining these devices solely as end products fails to account for the ways that UX design, like other expert labor, is a social practice born of daily workplace negotiations and inevitable compromises. This U.S. and Japan-based study argues that anthropological methods are critical to our understanding of conversation design and device training processes and explores why we must go beyond quantifying device usability to examine the work of the usability professionals themselves. For the conversational user experience (CUX) designers and researchers I interviewed and with whom I am conducting ongoing fieldwork, current technology frustrates their capacity to design for systems that go beyond being tools to also be enjoyable conversation partners. In some cases, the process of creating this category of AI turns the canonical literature on computers as social actors on its head, by identifying conscious intentions on the part of designers to replicate human personality and use it to drive the interactions we have with CVAs. It also suggests that the end users who most successfully adopt these tools are frequently those who best reproduce the culture of UX designers themselves.
Digital Skills, Digital Teaching Competencies, and Online Learning: Legislative and Regulatory Environment

Purpose: The introduction of distance learning technologies that was urgently adopted on a large scale in the spring of 2020 again resumed discussions about the digital competence of teachers. The necessity of improvement of content components of educational process is shown. Design/Methodology/Approach: The article substantiates the methodological basis for changing approaches to the staffing of pedagogical workers in the field of digitalization of education. The thesis is considered on the need to institutionalize educational activities in the digital environment, first of all, in solving issues of a regulatory and economic nature. Findings: The requirements for the teacher are reflected in scientifically based concepts and legal regulation. The used competence-based approach led to allocation of such key significant properties of the teacher as value orientation, general culture, educational and informative, info-communicative, social and labor. Originality/Value: The authors found that the process of digitalization of education is quite regulated, but the issues of quality control of knowledge are not fully regulated by law. In this regard, it is proposed to form ideas about the developing regulation of the model of digital competencies of a teacher, the development of distance education in general education, about systemic organizational measures, and support infrastructure that can regulate this area, supporting the direction of personification of education. The need to create the concept of additional professional education of pedagogical personnel as a set of provisions and ideas, taking into account the formation of new professional competencies, is methodologically justified; identify trends, patterns, principles, methods, and forms in the formation of the teacher's retraining process.
MemMode method of memorizing terms and definitions for distance learning of transport organizations employees

In the context of the development of the digital economy and increased competition in the field of education in transport, digitalization of the educational process and its gamification in connection with the peculiarities of the perception of educational materials by the younger generation is becoming a prerequisite. In response to this challenge the modern time, the author’s method of memorizing terms and definitions “MemMode” was developed, the purpose of which was to form in students some of the basic competencies obtained in the framework of bachelor’s and secondary vocational education: knowledge of basic terminology and mastery of professional vocabulary in transport, for example. Among the numerous tasks that can be easily solved in the process of applying the method, the formation of not only “hard skills” – mastering the theory of the discipline and working with it, but also “soft skills” – teamwork and an objective assessment of competitors’ products, stand out. The target audience for the application of the author’s method are students of secondary vocational education and bachelor’s degree in transport, students of advanced training and professional retraining courses for transport organizations employees. The MemMode method of memorizing terms and definitions was formed, edited, and tested for 10 years. As a result, its effectiveness, favorable perception by students, the possibility of commercialization and full adaptability in the framework of the process of gamification and digitalization of education have been proved.
Application Design in Preschool Education Resource System Platform Based on Deep Learning Technology

The goal of deep learning is to achieve high-quality education, which is an educational pursuit in a new society. This research mainly discusses the application design of preschool education resource system platform based on deep learning technology. In the educational resource platform for preschool children, reasonable learning scenarios or scenes can be created, and the teaching content can be presented in the form of games, music, videos, etc. according to the children’s cognitive preferences. Situational learning in deep learning can provide children with good hints or enlightenment. In the pre-school education science activities, teachers can use a variety of questioning methods in combination with the pre-school education resource system to guide children to think positively, explore creatively, and promote deep learning. Approximately 90% of children’s attention can be focused on learning when using the pre-school education resource platform based on deep learning technology, and only a few children have the phenomenon of slipping. This research will promote the promotion of deep learning technology in the field of education.
Privacy-Preserving Contrastive Explanations with Local Foil Trees

We present the first algorithm that combines privacy-preserving technologies and state-of-the-art explainable AI to enable privacy-friendly explanations of black-box AI models. We provide a secure algorithm for contrastive explanations of black-box machine learning models that securely trains and uses local foil trees. Our work shows that the quality of these explanations can be upheld whilst ensuring the privacy of both the training data, and the model itself. An extended version of this paper is found at Cryptology ePrint Archive [16].
Deployment and Validation of the CS-AWARE Solution at Two Pilot Sites: A Combined Agile Software Development and Design-Based Research Approach

The main objective of this chapter is to discuss the deployment of the CS-AWARE solution at two local public administration (LPA) user sites as pilot case studies, and to evaluate the results and success of the solution through end user validation. This objective is achieved through local pilot-specific development and learning. This means that we evaluate the specific dynamics of each pilot to see to what extent this case realises the goals that have been set by the LPA and relevant stakeholders. We involve users in all phases of deployment, and especially in a number of usability sessions. Furthermore, through assessment and comparative benchmarking, the results from the different pilots will be compared to evaluate similarities and differences. This type of evaluation is needed to draw more generalisable conclusions about cybersecurity solutions. Finally, the results of the pilots allow to derive lessons learned and to draw conclusions from each pilot to help form the final shape of the CS-AWARE solution.
A Multidimensional Perspective on Digital Competence, Curriculum and Teacher Training in Italy. A Scoping Review on Prospective and Novice Teachers

The study, here described, approaches the analysis of selected sources in a scoping review for mapping the key aspects related to digital competence, at national level, in the chosen research area and target audience, that is, pre-service teachers, including novice faculty members. The review process is supported by providing a brief overview of the Italian initiatives in the lens of European reference frameworks and a focus on training paths and profiles of prospective teachers in Italy. The specificities of the above-mentioned aspects can, in fact, facilitate the discussion of data and the collection of inputs for future research. The review highlights some of the existing gaps in the literature due to the difficulty in selecting a broad range of studies focussed specifically on assessment and shows that a common trend, in the selected studies, is the clear connection both theoretically and empirically to a cross analysis with European initiatives and tools, including for self-assessment inclination.
Rare Pattern Mining from Data Stream Using Hash-Based Search and Vertical Mining

Rare itemset mining is the emerging research domain in data mining. Patterns with low support and high confidence are referred to as Rare Patterns, which are very interesting compared to frequent Patterns in certain application domains like analysis of network logs, online customer purchase behavior, online banking transaction analysis, sensor data analysis, stock market data analysis. Many applications generate large volumes of the continuous data streams. To analyze such data streams and to identify rare patterns, we need efficient algorithms that can process data streams. Many research articles on rare pattern mining are available for static databases. However, it is not possible to apply the algorithms designed for static databases to data streams. Hence, we need algorithms that are specifically designed for data stream processing, to mine important rare patterns. Rare pattern mining from the data stream is in the budding stage and only a few algorithms are available. To address this, we have proposed algorithm HEclat-RPStream, an Eclat based method to mine rare patterns from a data stream using a vertical mining with bitsets. The discovered patterns are maintained in a prefix-based rare pattern tree, it uses double hashing to maintain a rare pattern in the data stream. The algorithm also uses the Breadth First Search (BFS) and Depth First Search (DFS) to discover interesting large itemsets. To handle data streams, we have used a time sensitive sliding window approach which captures most recent patterns. The pruning technique based on two items is used to optimize the performance. The experimental results of the proposed method demonstrated good performance concerning execution time and the total number of rare patterns generated.
Noise Aware L₂-LP Decomposition-Based Enhancement in Extremely Low Light Conditions With Web Application

In this article, an algorithm is introduced which not only focuses on enhancing low light images but also denoises images to produce more visually pleasing outputs. It is observed that various methods ignore noise during enhancement processes. This causes, enhancement of noise which leads to significant information lost. The paper projected a novel framework for low light images that performs enhancement and denoising jointly. The proposed paper uses <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{P}$ </tex-math></inline-formula> image decomposition to obtain reflectance and illumination concurrently. The illumination layer is corrected using the proposed weighting distribution and generates enhanced output. Further to this, noise suppressed bilateral filter is employed here for denoising process and gamma function is applied in subsequent step for additional contrast improvement. Experimental trials illustrate that the proposed method yields result with good contrast and brightness when it is compared to various enhancement techniques.
Secure Multi-party Computation and Its Applications

There is an urgent need for secure data sharing solutions, such that different organisations can jointly compute with their data, without revealing sensitive data to each other. Secure multi-party computation is an innovative cryptographic technology that has recently become more mature, such that it can be used to obtain very secure data sharing solutions for real life applications. Its broad usability is illustrated by different use cases from various domains.
Deep Contextual Learning for Event-Based Potential User Recommendation in Online Social Networks

Event recommendation allows people to identify various recent upcoming social events.Based on the Profile or User recommendation people will identify the group of users to subscribe the event and to participate, despite it faces cold-start issues intrinsically.The existing models exploit multiple contextual factors to mitigate the cold-start issues in essential applications on profile recommendations to the event.However, those existing solution does not incorporate the correlation and covariance measures among various contextual factors.Moreover, recommending similar profiles to various groups of the events also has not been well analyzed in the existing literature.The proposed prototype model Correlation Aware Deep Contextual Learning (CADCL) solves the mentioned issues.CADCL explores correlation on the different perspectives of user and event features to alleviate the sparsity problem.Latent Dirichlet Allocation (LDA) has been employed to extract the latent contextual information to increase the high relevancy rate in a hidden layer of the deep learning architecture.Finally, decision of the profile recommendation to the events is integrated on basis of influence weight to the correlation.Experimental analysis of the proposed architecture on Meetup dataset is cross-validated and performance metrics such as Precision 0.99%, Recall 0.88% and F Measure 0.93% are proved to be better on comparing with current state of art approaches.
Wisdom Science of Image Conceptualization

This paper proposes a model of human creative activities using the framework of wisdom science. The two main components of the model are the concept processing and image processing modules, which function in parallel with information exchange links between them. The model is also discussed related to the model of tacit and explicit knowledge. The concept of system science serves as the basic framework for the modeling and analysis.
Research on the Identification of Hand-Painted and Machine-Printed Thangka Using CBIR

Thangka is a unique painting art form in Tibetan culture.As Thangka was awarded as the first batch of national intangible cultural heritage, it has been brought into focus.Unfortunately, illegal merchants sell fake Thangkas at high prices for profit.Therefore, identifying hand-painted Thangkas from machineprinted fake Thangkas is important for protecting national intangible cultural heritage.The paper uses Content-Based Image Retrieval (CBIR) techniques to analyze the color, shape, texture, and other characteristics of hand-painted and machine-printed Thangka images, in order to identify Thangkas.Based on the database collected and established by this project team, we use Local Binary Pattern (LBP) texture analysis combined with the color histogram of Hue,aturation, Value (HSV) space, scale invariance, K-Means clustering, perceptual Difference Hash (DHASH) and other algorithms to extract the color lines and texture features of Thangka images, in order to identify hand-painted and machineprinted Thangkas.Three algorithms, LBP algorithm and HSV algorithm and DHASH algorithm, are compared, and the experimental results show that the color histogram algorithm based on HSV space is efficient.This algorithm can be applied broadly to retrieve and identify hand-painted Thangkas and help protect this precious intangible cultural heritage.
Applicability of Tunicate Swarm Optimization as Mppt for Pv Systems

This paper proposes a new maximum power point tracking (MPPT) for photovoltaic (PV) systems using Tunicate Swarm Optimization (TSO), this method showed high efficiency, fast convergence speed, and robustness. It is compared to the conventional P&O . The comprehensive assessments are carried out, first during standard conditions of irradiance and temperature, then under changes in irradiance levels. These tests are carried out for a standalone photovoltaic system in the MATLAB platform. The results show that TSO is capable of tracking the MPP with high efficiency and low power loss under various applied conditions compared to the selected P&O technique. So, it can be concluded that the proposed Tunicate Swarm Optimization is useful as MPPT to deal with all phenomena that interrupt the operating of the photovoltaic system.
Design and Implementation of Online Education System Based on Big Data Technology

The comprehensive effect of tourism can promote the development of regional economy. Urbanization is an important support for the development of tourism. There are mutual constraints, mutual promotion and mutual coupling between tourism and urbanization. In the information age, big data plays a key role in promoting the integration of new urbanization and tourism. Big data has become the core of urban science and the key of smart tourism, which drives the scientific and intelligent development of tourism towns.