Reviewerly/exHarmony huggingface dataset:
Formation of digital communication in the organizational and pedagogical activity of the university

Problem statement. The active development of digital technologies has had a serious impact on the field of education and led to the formation of a new information and communication environment at universities. Within the framework of the study, the features of the formation of digital communication of organizational and pedagogical activity of a modern university were considered. Methodology. The main methods chosen are a SWOT analysis of opportunities and threats associated with the introduction of the digital environment at the university, and a sociological survey to assess the attitude of all participants in the educational process to the digitalization of the university. Results. The study of the process of digital communication of the university's activities made it possible to design a pilot communication model of organizational and pedagogical activity of the university in a new digital environment. The trends in the development of the knowledge society focused on the activities of higher educational institutions and the sphere of educational services are also identified. The main provisions in the field of the processes of formation of elite consciousness and professional domestic elite capable of continuing the development of progress have been formed. Conclusion. The proposed pilot version of modeling the digital environment of the university helped to formulate the basic requirements for the organization of higher school activities and highlight the current directions of development: activation of innovative and creative potential of students, scientific and project activity of the teaching staff, improvement of the competence-based professional level of training of personnel and employees of the university, contributing, among other things, to personal development and increased labor efficiency. This strategy will help the university become a brand and take a stable position in a competitive environment, create stable economic development, and, as a result, expand partnerships, scientific and educational projects that give the right to enter the modern socio-cultural space of the country and the world.
Optical anisotropy of transparent polymer materials fabricated via 3D printing and their application in photoelasticity

Additive manufacturing has been used to fabricate photoelastic models for several years. In the 3D-printing techniques that use photosensitive resins, models are built layer by layer, and the influence of these layers on light propagation and photoelastic fringes is still ambiguous. In this study, a plane polarization arrangement is used to examine the influence of thin layers on light propagation using three disks printed along different directions. The influence of the layers on the light intensity is negligible for the disk where the layer planes are perpendicular to the direction of light propagation. Identical light polarization is observed for the disks where the layer planes are parallel to the direction of light propagation. This is consistent with a polarizer. The evaluation of the thin layers using scanning electron microscopy indicated that the influence of the microstructures on light propagation is the potential cause of these phenomena. The influence of light polarization on photoelastic fringes was presented by comparing the fringes of different disks under circularly polarized light. The results can improve the application of the 3D-printed models in photoelasticity and the understanding of the optical anisotropy of 3D-printed polymer.
Understanding the Role of Human-Related Factors in Security Requirements Elicitation

Context and motivation: Many requirements engineering (RE) activities depend not only on the nature of the system itself, but also on human-centric characteristics of the RE teams. Question/problem: What role do human-related factors of RE teams play in eliciting high-quality security requirements? Principal ideas/results: This research preview presents our preliminary work in discovering the cognitive factors that represent the intentions and motivations of RE teams to develop secure systems from early stages of the system development, and how these factors impact the quality of the elicited requirements. We outline a framework, with an illustrative example, for describing the variables that affect the decisions of RE teams when they elicit security requirements to address security concerns. Contribution: The proposed framework helps to characterize the different aspects of human-related factors, and the correlation between the impact of these factors on the quality of the requirements elicitation phase. This is a novel research direction which positions our long-term research agenda, and we urge community contributions in this direction to achieve an enhanced understanding of the role of human-related factors in requirements engineering for security domains.
Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer’s KYC and anonymise the collected visual data to ensure the customer’s privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> and A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> lattice vector quantization for the secure transmission and storage of in-person banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations.
Remote Sensing Image Encryption Using Optimal Key Generation-Based Chaotic Encryption

The Internet of Things (IoT) offers a new era of connectivity, which goes beyond laptops and smart connected devices for connected vehicles, smart homes, smart cities, and connected healthcare. The massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for users. With the increasing use of multimedia in communications, the content security of remote-sensing images attracted much attention in academia and industry. Image encryption is important for securing remote sensing images in the IoT environment. Recently, researchers have introduced plenty of algorithms for encrypting images. This study introduces an Improved Sine Cosine Algorithm with Chaotic Encryption based Remote Sensing Image Encryption (ISCACE-RSI) technique in IoT Environment. The proposed model follows a three-stage process, namely pre-processing, encryption, and optimal key generation. The remote sensing images were preprocessed at the initial stage to enhance the image quality. Next, the ISCACE-RSI technique exploits the double-layer remote sensing image encryption (DLRSIE) algorithm for encrypting the images. The DLRSIE methodology incorporates the design of Chaotic Maps and deoxyribonucleic acid (DNA) Strand Displacement (DNASD) approach. The chaotic map is employed for generating pseudorandom sequences and implementing routine scrambling and diffusion processes on the plaintext images. Then, the study presents three DNASD-related encryption rules based on the variety of DNASD, and those rules are applied for encrypting the images at the DNA sequence level. For an optimal key generation of the DLRSIE technique, the ISCA is applied with an objective function of the maximization of peak signal to noise ratio (PSNR). To examine the performance of the ISCACE-RSI model, a detailed set of simulations were conducted. The comparative study reported the better performance of the ISCACE-RSI model over other existing approaches.
Supporting Shared Understanding in Asynchronous Communication Contexts

[Context and motivation] The success of software projects depends on developing a system that satisfies the stakeholders' wishes and needs according to their mental models of the intended system. However, stakeholders may have different or misaligned mental models of the same system, resulting in conflicting requirements. For this reason, aligned mental models and thus a shared understanding of the project vision is essential for the success of software projects. [Question/problem] While it is already challenging to achieve shared understanding in synchronous contexts, such as meetings, it is even more challenging when only asynchronous contexts, like messaging services, are possible. When multiple stakeholders are involved from different locations and time zones, primarily asynchronous communication occurs. Despite the frequent use of software tools, like Confluence, to support asynchronous contexts, their use for the development of a shared understanding has hardly been analyzed. [Principal ideas/results] In this paper, we propose five concepts to help stakeholders develop a shared understanding in asynchronous communication contexts. We assess the adaptability of three existing software tools to our concepts, adapt these software tools accordingly, and develop our own prototype that implements all five concepts. In an experiment with 30 participants, we evaluate these four software tools and compare them to a control group that had no support in developing a shared understanding. [Contribution] Our results show the suitability of our concepts, as the participants using our concepts were able to achieve a higher level of shared understanding compared to the control group.
Scope Determined (D) and Scope Determining (G) Requirements: A New Categorization of Functional Requirements

Context: Some believe that Requirements Engineering (RE) for a computer-based system (CBS) should be done up front, producing a complete requirements specification before any of the CBS's software (SW) is written. Problem: A common complaint is that (1) new requirements never stop coming; so upfront RE goes on forever with an ever growing scope. However, data show that (2) the cost to modify written SW to include a new requirement is at least 10 times the cost of writing the SW with the requirement included from the start; so upfront RE saves development costs, particularly if the new requirement is one that was needed to prevent a failure of the implementation of a requirement already included in the scope. The scope of a CBS is the set of requirements that drive its implementation. Hypothesis: We believe that both (1) and (2) are correct, but each is about a different category of requirements, (1) scope determininG (G) or (2) scope determineD (D), respectively. Past Work: Re-examination of the reported data of some past case studies through the lens of these categories indicates that when a project failed, a large number of its defects were due to missing D requirements, and when a project succeeded, the project focused its RE on finding all of its D requirements. Conclusions: The overall aim of the future research is to empirically show that focusing RE for a chosen scope, including for a sprint in an agile development, on finding all and only the D requirements for the scope, while deferring any G requirements to later releases or sprints, allows upfront RE (1) that does not go on forever, and (2) that discovers all requirements whose addition after implementation would be wastefully expensive, wasteful because these requirements are discoverable during RE if enough time is devoted to looking for them.
Requirements Engineering Issues Experienced by Software Practitioners: A Study on Stack Exchange

[Context and Motivation] Requirements engineering (RE) is central to software development. Despite its importance, there are many issues related to its enactment. Question and answer platforms, such as the Stack Exchange, are paramount in contemporary software development. They discuss and bring to light practitioners' viewpoints on software engineering issues. Approaching those platforms focusing on RE deserves investigation because it can reveal current issues experienced by software practitioners and possible solutions for them. [Question/Problem] This work investigates RE issues, their causes, effects, and possible solutions as discussed by software practitioners in the Software Engineering Stack Exchange (SWESE). For that, we mine, curate, and analyze a set of 61 discussions related to RE, composed of 414 posts and 770 comments extracted from SWESE. [Principal Ideas/Results] We identify 50 issues and their relations with requirements phases. Customers' unable to describe system requirements and the need for detailed specifications are among the most commonly discussed issues. We also list 20 causes, 23 effects, and 59 solutions for the mined issues. Examples of causes for RE issues are lack of technical knowledge and communication issues. Examples of the effects of RE issues are rework and unstable requirements. Solutions encompass practices such as clearly defining requirements and using prototypes. [Contribution] This work organizes the mined RE issues in a Sankey diagram, relating them to RE phases and solutions, which may assist practitioners experiencing them and serve as guidance for future research.
A Requirements Engineering Perspective to AI-Based Systems Development: A Vision Paper

Context and motivation: AI-based systems (i.e., systems integrating some AI model or component) are becoming pervasive in society. A number of characteristics of AI-based systems challenge classical requirements engineering (RE) and raise questions yet to be answered. Question: This vision paper inquires the role that RE should play in the development of AI-based systems with a focus on three areas: roles involved, requirements' scope and non-functional requirements. Principal Ideas: The paper builds upon the vision that RE shall become the cornerstone in AI-based system development and proposes some initial ideas and roadmap for these three areas. Contribution: Our vision is a step towards clarifying the role of RE in the context of AI-based systems development. The different research lines outlined in the paper call for further research in this area.
A Novel Internet of Medical Thing Cryptosystem Based on Jigsaw Transformation and Ikeda Chaotic Map

Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image data. Medical picture encryption is a crucial step in many cloud-based and healthcare applications. In this study, a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things (IoMT) and cloud storage. A disorganized three-dimensional map is the foundation of the proposed cipher. The dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding process. The suggested cryptosystem enhances the security of the delivered medical images by performing many operations. To validate the efficiency of the recommended cryptosystem, various medical image kinds are used, each with its unique characteristics. Several measures are used to evaluate the proposed cryptosystem, which all support its robust security. The simulation results confirm the supplied cryptosystem’s secrecy. Furthermore, it provides strong robustness and suggested protection standards for cloud service applications, healthcare, and IoMT. It is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998, which is near its most excellent value of 8, and a typical NPCR value of 99.62%, which is also near its extreme value of 99.60%. Moreover, the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.
Transformers in Automatic Speech Recognition

The Transformer, a model relying entirely on the attention mechanism, brought significant improvements in performance on several natural language processing tasks. This chapter presents its impact on the speech processing domain and, more specifically, on the automatic speech recognition task. A short history of the evolution of automatic speech recognition systems is also given. A selection of important works making use of transformers are presented, as well as pretraining self-supervised architectures.
Computational Argumentation &amp; Cognitive AI

This tutorial examines the role of Computational Argumentation at the theoretical and practical level of Human-centric AI. It rests on the central role that argumentation has in human cognition rendering argumentation as a possible foundation for the two basic elements of intelligence, namely learning and reasoning, in a way that is suitable for human-centric AI. The tutorial examines argumentation as a basis for cognitive technologies of Learning and Explainable Inference or Decision Making and their application in today’s AI.
Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

The analysis of overcrowded areas is essential for flow monitoring, assembly control, and security. Crowd counting’s primary goal is to calculate the population in a given region, which requires real-time analysis of congested scenes for prompt reactionary actions. The crowd is always unexpected, and the benchmarked available datasets have a lot of variation, which limits the trained models’ performance on unseen test data. In this paper, we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene. The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization (EvoNorm). This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples. The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters, which enables real-time processing and solves the density drift problem due to its large receptive field. Five benchmark datasets are used in this study to assess the proposed model, resulting in the conclusion that it outperforms conventional models.
Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification (TLBOML-ERC) model for Sentiment Analysis on tweets made in the Arabic language. The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets. To attain this, the proposed TLBOML-ERC model initially carries out data pre-processing and a Continuous Bag Of Words (CBOW)-based word embedding process. In addition, Denoising Autoencoder (DAE) model is also exploited to categorise different emotions expressed in Arabic tweets. To improve the efficacy of the DAE model, the Teaching and Learning-based Optimization (TLBO) algorithm is utilized to optimize the parameters. The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset. The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.
A Novel Unbiased Deep Learning Approach (DL-Net) in Feature Space for Converting Gray to Color Image

Gray to Color conversion causes difficulties because of the nature of its intrinsic multimodality.Despite recent significant advancements in this domain by numerous learning-based approaches, there still have two drawbacks: i) implausible color assignment and ii) contextual ambiguity.Recently deep learning models are being used for colorization as they outperform others.In a training image, desaturated color components are greater than saturated color components due to the larger background areas (clouds, pavement, dirt, walls, etc.) compared to the focused objects.This imbalanced feature representation biases the learning model in favor of major features.However, small regions with specific colors are the region of interest.To solve this problem, we proposed the Deep Localization Network (DL-Net) by modifying the mean squared error backpropagation algorithm.We compute chromatic component-based Local Losses (LLs) which are the primary component of the proposed DL-Net.The LL employs priority on rare semantic components of the original image features.It works to improve diverse-range dependency modeling in an effort to reduce contextual ambiguity and color leakage that promotes the production of more plausible coloring.With a number of current methodologies, we contrast our proposed approach.The experimental findings demonstrate that our proposed method produces good colorization of images and outperforms other methods in terms of SSIM, MSE, and PSNR quality criteria.