Reviewerly/exHarmony huggingface dataset:
Scalable, Flexible Implementation of MBSE and DevOps in VSEs: Design Considerations and a Case Study

Abstract Model‐based systems engineering (MBSE) and development operations (DevOps) both provide organizations with increased system development capabilities by defining development process controls and defining sequential development, testing, and other processes. As the use and complexity of MBSE and DevOps processes for software systems development continues to rise, very small entities (&lt;25 employees) may struggle to implement full scale implementations of these engineering project management processes. This paper uses principles from MBSE and DevOps models to explore a reduced, simplified scope of both system development paradigms that VSEs can implement to obtain benefits of both processes.
Variability on System Architecture using Airbus MBPLE for MOFLT Framework

Abstract Model‐Based Product Line Engineering (MBPLE) and Variability Management are key enablers of Model‐Based Systems Engineering (MBSE). A MBSE Architecture Framework based on the SysML language has been developed at Airbus Group Level: R‐MOFLT ‐ Requirements, Mission, Operational, Functional, Logical, Technical Analysis. MBPLE4MOFLT is a feature‐based product line engineering framework based on the ISO 26580 (2021) standard, extending R‐MOFLT. This paper defines the methodological foundations of the Airbus MBPLE4MOFLT framework and provides insights into its application in practice.
Systems Engineering Isn't Scary for Agile Practitioners

Abstract Traditional Systems Engineering artifacts and terminology have a negative connotation in some companies and industries that use Agile development. This paper aims to show that the terms and concepts typically associated with traditional systems engineering are not scary for Agile software development. They can, and should, be tailored and utilized during the Agile development process. Many times, terminology and lack of understanding lead to Agile practitioners shying away from what are considered traditional Systems Engineering artifacts such as requirements, architectures designs, documented interfaces, verification, gate reviews, etc. Agile actually already employs many of these artifacts and concepts but simply uses a different terminology when referring to them. This paper helps advance the mindset that traditional Systems Engineering terms, concepts, and artifacts are not something to avoid in Agile by providing alternative ways to describe and think about them in an Agile setting.
Coping with Verification in Complex Engineered Product Development

Abstract Coping with system verification in complex systems is a challenging task. This paper reports on how the high complexity of closely integrated systems and long timelines in development affects the verification process in an engineering company in Norway. The literature on verification in development and a survey within the industry‐as‐laboratory method were used to research the practice of verification in the case company to identify where the company's practice deviates from the recommended practice according to the literature. The research found that the company is taking shortcuts in its documented processes resulting in integration testing late in the development. The authors propose the use of metrics to monitor the progress of the system of interest and the project. In conclusion, continuously tracking and improving the work process is recommended to cope with the complexity of developing the company products.
Extracting Temporal Association Rules Over Datacubes

Association rules are one of the most used data mining techniques. The first proposals have considered relations over time in different ways, resulting in the so-called temporal association rules (TAR). Although there are some proposals to extract association rules in OLAP systems, to the best of our knowledge, there is no method proposed to extract temporal association rules over multidimensional models in these kinds of systems. In this paper, we study the adaptation of TAR to multidimensional structures, identifying the dimension that establishes the number of transactions and how to find time relative correlations between the other dimensions. A new method called COGtARE is presented as an extension of a previous approach proposed to reduce the complexity of the resulting set of association rules. The method is tested in application to financial data of companies.
KALAKA-3 Database Language Classifier Through Convolutional Recurrent Neural Network

During the last few years, the field of automatic speech recognition (ASR) has been growing exponentially, due to the diverse applications and solutions it offers. For this reason, this paper presents a multiclass language classifier based on recurrent convolutional neural networks, whose objective is to classify the audios of the KALAKA-3 database, according to their language. To meet this objective, the mel frequency cepstral coefficients (MFCCs) were extracted from each of the audios in the database, with which the training process is carried out. A recurrent convolutional neural network (CRNN) was created for this process, resulting in an accuracy of 98% using the testing data, and 40% using the Eval data. This work sets a precedent for improving real-time translators, since in the future it would be possible to listen to a few seconds of a conversation, identify it, and automatically perform a translation process, which would be very useful in various applications.
Marketing research in the activity of a scientific organization

The main activity of a scientific organization is to conduct research and obtain new knowledge. The next area of work is to bring to the target audience up-to-date information about the topics of ongoing research and the achieved scientific results. The purpose of this work is to describe the operational marketing concepts and approaches that are in force in the scientific community, and the possible application of modern and promising working marketing tools in the activities of scientific organizations, as well as other objects to which their marketing efforts are directed. For researchers, unlike representatives of commercial organizations, marketing of relationships with representatives of the target audience is close. Therefore, informal interaction between scientists and potential consumers of the results of their research can become a starting point for the transfer and distribution of scientific products free of charge, on the basis of mutual interest, or on a reimbursable basis. Operational theory is used as a methodological tool in the study, the following concepts are introduced: the goals of implementing the marketing concept, the target audience, the object, the toolkit and the societal environment of the concept. It is shown that when implementing the marketing activities of a scientific organization, a combination of four types of considered marketing concepts can be used, in which the objects of application of marketing efforts can be: a scientific organization, a personal brand of a scientist, a branch of science, the results of fundamental or applied scientific research. Basic research may require long-term funding. It is formulated that in some cases it may be appropriate to form a special unit for the organization of applied marketing activities at universities, as well as at academic institutions.
WebLabel: OpenLABEL-compliant multi-sensor labelling

Abstract Annotated datasets have become crucial for training Machine Learning (ML) models for developing Autonomous Vehicles (AVs) and their functions. Generating these datasets usually involves a complex coordination of automation and manual effort. Moreover, most available labelling tools focus on specific media types (e.g., images or video). Consequently, they cannot perform complex labelling tasks for multi-sensor setups. Recently, ASAM published OpenLABEL, a standard designed to specify an annotation format flexible enough to support the development of automated driving features and to guarantee interoperability among different systems and providers. In this work, we present WebLabel, the first multipurpose web application tool for labelling complex multi-sensor data that is fully compliant with OpenLABEL 1.0. The proposed work analyses several labelling use cases demonstrating the standard's benefits and the application's flexibility to cover various heterogeneous requirements: image labelling, multi-view video object annotation, point-cloud view-based labelling for 3D geometries and action recognition.
A Language Lending Itself: Mapping Clusters of Contextually Close Cognates in Indo-European Languages

AbstractA key method for understanding the evolution of languages is to look for words which share etymological roots across languages. These words, called lexical cognates, allow historical linguists to group languages into families and study their structural history. Existing research on cognate clustering is chiefly based on analyses performed on lexical databases which limits the scope for exploring new cognates. Additionally, while the Indo-European family is a transcontinental one, its cognate analyses are largely limited to a number of European languages. In this research work we build a new dataset using word embeddings to search for cognates using phonetic matching between translations of words in different languages across the Indo-European family. The context-dependent positioning of words in word embeddings allows for comparisons with contextually similar words and hence clustering using unsupervised learning algorithms. We successfully find significant distinctions between these “contextually close clusters of cognates” among languages across the Indo-Iranian, Romance, and Germanic families. We release this novel method to demonstrate which words a language is more likely to lend to another.KEYWORDS: Word embeddingsCognatesUnsupervised clusteringLinguistic analysisNLP ACKNOWLEDGEMENTSSarthak Rastogi contributes to all parts of this project, including method design, data pre-processing, modelling, result analysis, and paper writing. Manpreet Kaur contributes to result analysis and paper writing.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSarthak RastogiSarthak Rastogi is an undergraduate student at Manav Rachna University as well as Indian Institute of Technology, Madras. He is currently engaged in research on the applications of LLMs in Human–Computer Interactions. Email: thesarthakrastogi@gmail.com.Manpreet KaurManpreet Kaur is serving as a professor in the department of Computer Science and Technology, Manav Rachna University. She has a rich academic experience of working at esteemed institutions for 15 years. Her research interests lie in the areas of natural language processing, information retrieval and machine learning. Corresponding author. Email: manpreet.kathuria@gmail.com.
Chatbot usability scale: Adaptation to turkish and validation/reliability analysis

The aim of this study is to adapt the 15-item Chatbot Usability Scale to the Turkish language and culture and evaluate the validity and reliability of the scale in the Turkish language and culture after the adaptation process. The necessary permissions were obtained, and the process was initiated. Proficient translators in both cultures were selected to ensure linguistic equivalence, and they carried out the translation process proficiently in both languages. The scale was translated back to its original language to enhance its suitability, and linguistic equivalence was ensured. The sample for the study included 406 students from eight different undergraduate and associate degree programs at a state university. Following the item analysis, the Cronbach's Alpha coefficient of the scale was found to be 0.935. Exploratory and confirmatory factor analyses were applied and it was found that the scale had a two-factor structure for Turkish university students. As a result of this study, the Chatbot Usability Scale has been adapted to the Turkish language and culture. As a suggestion, the generalizability of the scale can be increased by applying it to individuals from different age groups, genders, educational levels, and occupational groups and evaluating the test-retest reliability of the scale in future studies.
Statistical Analysis of the Influence of Teaching Experience on the Perception of Virtual Reality

The use of virtual reality (VR) technologies applied to higher education is experiencing a vertiginous increase, mainly in scientific-technical areas. In this work, quantitative descriptive research is carried out on the perceptions of the didactic use of VR technologies by Latin American professors of engineering and health sciences. Specifically, a statistical analysis is carried out to identify significant gaps in the assessments carried out by the participating professors according to their area of knowledge and to study the influence exerted on these assessments by the teaching experience of the professors. For this purpose, a questionnaire designed for this purpose has been used, which has been answered by 432 professors of engineering and health sciences from different Latin American universities. The results reveal that engineering professors give better evaluations than health sciences professors of the didactic aspects of VR and that teaching experience positively influences engineering professors’ evaluations but negatively those of health sciences professors. It is recommended to increase teacher training in the use of VR, considering the specificities identified in each area of knowledge.
Delivering Engaging Curricular Lessons – A Case of Mixed Reality Technology in Education

Student engagement is a fundamental construct that supports learning. As immersive technologies develop, their implementation in educational contexts grows, for instance, through the deployment of mixed reality (MR) environments. This forges the need to explore the strategies that teachers use to deliver their lessons in order to increase student engagement in immersive learning spaces. Therefore, this paper explores teachers’ perspectives on implementing a lesson within an MR context in which a remote student participates as a hologram. For this, a case study was carried out using an in-house-built teleimmersive platform prototype deployed at a Finnish primary school, where three 5th grade students (11–12 years old) and three teachers participated. Here we present the teachers’ perspectives in terms of the pedagogical, technological, and student engagement standpoints in relation to delivering an engaging lesson in an MR context. Furthermore, we discuss the affordances that MR technology should offer to support teachers in delivering engaging lessons to their students, as well as several teacher strategies for delivering engaging lessons within MR contexts. This work contributes to the pedagogically sound implementation of immersive technologies in education.
Introduction

The last decade has dramatically changed the way businesses operate. Among the principal driving forces of change, technology represented the main force. The business world transitioned from a semi-analogic paradigm to a fully digital one. Businesses were then obliged to switch the way they were operating, including data among their core resources, and starting to rely on machine-based decisions.
Morphological classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs

Out of the estimated few trillion galaxies, only around a million have been detected through radio frequencies, and only a tiny fraction, approximately a thousand, have been manually classified. We have addressed this disparity between labeled and unlabeled images of radio galaxies by employing a semi-supervised learning approach to classify them into the known Fanaroff-Riley Type I (FRI) and Type II (FRII) categories. A Group Equivariant Convolutional Neural Network (G-CNN) was used as an encoder of the state-of-the-art self-supervised methods SimCLR (A Simple Framework for Contrastive Learning of Visual Representations) and BYOL (Bootstrap Your Own Latent). The G-CNN preserves the equivariance for the Euclidean Group E(2), enabling it to effectively learn the representation of globally oriented feature maps. After representation learning, we trained a fully-connected classifier and fine-tuned the trained encoder with labeled data. Our findings demonstrate that our semi-supervised approach outperforms existing state-of-the-art methods across several metrics, including cluster quality, convergence rate, accuracy, precision, recall, and the F1-score. Moreover, statistical significance testing via a t-test revealed that our method surpasses the performance of a fully supervised G-CNN. This study emphasizes the importance of semi-supervised learning in radio galaxy classification, where labeled data are still scarce, but the prospects for discovery are immense.
Error-correcting output codes for multi-class classification based on Hadamard matrices and a CNN model

The error-correcting output codes(ECOC) is the ensemble method for the multi-class classification problem. In some applications of ECOC, Hadamard matrices are used because they have good properties to apply ECOC. However, due to the difficulties of the construction of Hadamard matrices of some specific orders, only Hadamard matrices of order a power of 2 constructed by Kronecker product were usually used. In this paper, we consider Hadamard matrices of various orders to the Hadamard ECOC to determine which orders of Hadamard matrices give a good performance compared to the number of classes. We apply the Hadamard ECOC to the image datasets including CIFAR-10, CIFAR-100, subsets of CIFAR-100, and EMNIST-Letters. We also compare the result of the Hybrid Hadamard ECOC with the Hadamard ECOC. For our experiment, a convolutional neural network(CNN) is chosen as a base learner, which is largely used in the image classification problems.
Detection of Hate Speech in Assamese Text

Hate speech events have been documented on almost every continent. The number of people using social media sites like Facebook, Twitter, YouTube, Instagram, and others has recently increased, and many people are increasingly holding conversations on these platforms. Experts claim that as more and more people have turned to the Internet, those who harbor racial, gendered, or homophobic prejudices have discovered online communities where hate speech can incite violence. The majority of earlier studies on the identification of hate speech were conducted using English and other languages. Our goal in this paper is to identify Assamese language hate speech on social media sites like Twitter. In order to experiment with multinomial naive Bayes (MNB), support vector machine (SVM) with various word embedding and n-gram models as classification algorithms, a dataset of 2000 words has been manually collected from various social sites such as YouTube, Facebook, Twitter, and Newspapers
A face recognition taxonomy and review framework towards dimensionality, modality and feature quality

This paper presents a comprehensive review of Automatic Facial Recognition Systems using integrative and systematic mapping approach. The review is grounded on criteria-attribute scheme formulated in proposed Face Recognition framework. The proposed framework provides a unified platform to identify, categorize and understand wholesome Face Recognition taxonomy based on different criteria (Modality, Dimensionality and Feature Quality) and their corresponding attributes (Unimodal-Multimodal, 2D-3D and Physiological-Behavioral). The framework facilitates a user to understand and select attributes across different criteria. The user selection of criteria-attribute is assisted through several selection parameters (Dataset Availability, Application, User Preference, System Complexity and Time Complexity). Depending on the user selection, a criteria-attribute scheme based model is formulated for Face Recognition. This paper also provides critical mathematical insights to understand each attribute extensively. Existing works are analyzed and compared comprehensively and quantitatively based on popular datasets and proposed criteria-attribute framework.
FSDA: Tackling Tail-Event Analysis in Imbalanced Time Series Data with Feature Selection and Data Augmentation

Efficient management of imbalanced time series data is of paramount importance when data located in the tails, particularly extreme values, have a substantial influence on predictive outcomes. This paper introduces FSDA (Feature Selection and Data Augmentation), a combined approach of feature selection and data augmentation, to address this issue. FSDA aims to identify the most predictive features for tail data, which may exhibit different sensitivities compared to the rest of the dataset. Data augmentation, a conventional technique for handling imbalanced data, is employed to enhance the accuracy of machine learning regression methods. Augmented information is strategically incorporated using time-warping and drift methods to maintain the temporal integrity of the data. Empirical evidence based on a use case in financial data reveals that FSDA consistently outperforms feature selection (FS) and data augmentation (DA) methods across all percentiles ranging from 85 to 99, demonstrating its efficacy in managing imbalanced time series data and improving predictive accuracy.
Blockchain‐based data deduplication using novel content‐defined chunking algorithm in cloud environment

Abstract The cloud environment is inherently dynamic as users are added immensely in a short duration. It is indeed difficult to manage such user profiles and associated data. Meanwhile, the cloud data expand at a twofold‐to‐threefold rate on average, making storage space management and data integrity maintenance a mandatory task but also risky. The main approaches for addressing these data maintenance challenges in a cloud context are deduplication and data protection. In order to manage storage space, finding and removing identical copies of the same data from the cloud are possible, resulting in a reduction in the amount of storage space needed. Furthermore, duplicate copies are considerably reduced in cloud storage owing to data deduplication. Here, a decentralized ledger public blockchain network is introduced to protect the Integrity of data stored in cloud storage. This research proposes data deduplication using speedy content‐defined Chunking (SpeedyCDC) algorithm in the public blockchain. Many people and businesses outsource sensitive data to remote cloud servers because it considerably eliminates the hassle of managing software and infrastructure. However, the ownership and control rights of users data are nonetheless divided because it is outsourced to cloud storage providers (CSPs) and kept on a distant cloud. As a result, users have a great deal of difficulty in verifying the Integrity of sensitive data. Analysis using datasets from Geospatial Information Systems (GIS) revealed that the throughput increased by 5%–6% over that of the fastCDC technique, which offered Integrity since a blockchain network secured it.