Reviewerly/exHarmony huggingface dataset:
Digital Generation Influence on the Post-COVID-19 Use of Digital Technologies in Engineering Education: A Statistical Study

The COVID-19 pandemic caused a global health crisis that led to a sudden migration of many educational activities to digital environments. This migration affected the digitization process of higher education. This paper conducts a quantitative statistical analysis of the impact that the COVID-19 pandemic had on the habits of use of information and communication technologies (ICT) among 426 Latin American university professors in engineering areas. In particular, this impact has been analyzed in terms of the digital generation of the participating professors. To achieve this, the responses given by them on a validated questionnaire were examined for the purposes of this research. As a result, the COVID-19 pandemic has led to an increase of up to 47% in the use of ICT among engineering professors, but unevenly according to the different uses of these tools within the teaching activity. In addition, in engineering areas, it seems that digital natives have increased their use of ICT more than digital immigrants after the pandemic (between 8% and 20%, depending on the type of digital tool in question). It is suggested that universities take measures for the digital integration of older professors.
Experimental Study of Morphological Analyzers for Topic Categorization in News Articles

Natural language processing refers to the ability of computers to understand text and spoken words similar to humans. Recently, various machine learning techniques have been used to encode a large amount of text and decode feature vectors of text successfully. However, understanding low-resource languages is in the early stages of research. In particular, Korean, which is an agglutinative language, needs sophisticated preprocessing steps, such as morphological analysis. Since morphological analysis in preprocessing significantly influences classification results, ideal and optimized morphological analyzers must be used. This study explored five state-of-the-art morphological analyzers for Korean news articles and categorized their topics into seven classes using term frequency–inverse document frequency and light gradient boosting machine frameworks. It was found that a morphological analyzer based on unsupervised learning achieved a computation time of 6 s in 500,899 tokens, which is 72 times faster than the slowest analyzer (432 s). In addition, a morphological analyzer using dynamic programming achieved a topic categorization accuracy of 82.5%, which is 9.4% higher than achieve when using the hidden Markov model (73.1%) and 13.4% higher compared to the baseline (69.1%) without any morphological analyzer in news articles. This study can provide insight into how each morphological analyzer extracts morphemes in sentences and affects categorizing topics in news articles.
Multiple templates transformer for visual object tracking

Matching the similarity between a template and search region is crucial in Siamese trackers. However, due to the limited information provided by a fixed template, existing trackers are not robust enough in complex scenarios, such as severe deformation, background clutters, out-of-view, illumination variation, low resolution, scale variation, fast motion, and full occlusion. Therefore, it is essential to use an informative template. Additionally, since the Transformer has superior model capability compared to traditional cross-correlation in tracking, some Siamese trackers have integrated Transformers and achieved exceptional performance. In this paper, we present a novel tracking architecture with Multiple Templates Transformer (MTT) to address the above issues. By utilizing multiple templates, the proposed method can grasp more contextual information and historical changes about the target, which can be leveraged to enhance the response in the search region using an encoder-decoder framework. We also explore different mechanisms to fuse templates effectively to achieve higher accuracy. We evaluate MTT in several famous benchmarks such as GOT-10k, TrackingNet, UAV123, OTB2015, VOT2018, and LaSOT. Extensive experimental results indicate that our tracker is capable of achieving better robustness in the face of different challenges while maintaining a considerable real-time speed.
Comparative Analysis of YOLOv8 and Mack-RCNN for People Counting on Fish-Eye Images

Monitoring the number of people in a building environment is key to achieving power efficiency, comfort, and safety in building use. The usage of fish-eye cameras in combination with neural network (NN) can provide a well-balanced compromise between price and measurement accuracy (ACC). Unfortunately, due to fish-eye camera distortion, solutions built for projective cameras may not work well. In addition, people can be counted with NN that solve different types of tasks, such as object detection or instance segmentation. As such, NN have different loss functions, and it is hard to forecast which approach could provide better performance for people counting on fish-eye images.To answer this question, the authors of this study compared YOLOv8 architecture vs Mask-RCNN under different training and pre-processing conditions. For this purpose, a custom data set was generated from different view angle cameras, and additional metrics were used apart from standard ones usually used for mask and bounding box evaluation. Experiments indicate that YOLOv8-N is a better solution as it requires fewer resources and at the same time provides top people count ACC of 95.
ANALYSIS OF THE MAIN METHODS OF DRAWINGS RECOGNITION AND THE POSSIBILITIES OF TRANSFORMATION 2D IN 3D

The article presents an analysis of the main methods for recognizing drawings and the possibilities of transforming two-dimensional models (2D) into three-dimensional models (3D). Despite the rapid development of IT, the question of accuracy and speed of transformation of two-dimensional models into three-dimensional ones remains open. As machine design technologies and corresponding automated decision-making systems (CAD) develop, the number of methods and models that can potentially be used in the task of drawing recognition and 2D to 3D transformation is rapidly increasing. Today, there are quite a large number of methods for recognizing drawings and converting them into a three-dimensional model, but each of them has a certain number of shortcomings. Therefore, there is a need to carry out a comprehensive analysis of these methods, which can potentially be applied in the context of solving problems of drawing recognition and 2D to 3D transformation. It should be noted that there is a contradiction between the traditional procedure for preparing drawing documentation on paper media until the 80s and 90s of the 20th century and the new methods of 3D modelling that have been developed since the mid-90s. This gives designers truly unlimited opportunities to prepare design and technical documentation, without focusing on the problem of preparing design and drawing documentation and the features of entering input data. Application software significantly facilitates this process. Note that most 3D systems (for example, software products Autodesk TinkerCAD, DesignSpark Mechanical, FreeCAD, Autodesk AutoCAD, ZBrush, Blender, etc.) use approaches that allow synthesizing a frame or boundary representation of an object modelled in space. Professional systems (for example Autodesk 3ds Max, Autodesk Maya) use generalized models of spatial objects. This idea assumes that the designers have appropriate information arrays, which a priori should correspond to all projections of the object in the three main planes.
A Survey on Cross-Domain Few-Shot Image Classification

Due to the limited availability of labelled data in many real-world scenarios, we have to resort to data from other domains to improve models’ performance, which prompts the advancement of research regarding the cross-domain few-shot image classification task. In this paper, we systematically review existing cross-domain few-shot image classification algorithms published in recent years. We categorize these algorithms into data-augmentation and feature-alignment paradigms and present their recent progress. We summarize three commonly-used cross-domain datasets for benchmarking few-shot image classification tasks and relevant scenarios. Finally, we outline existing limitations and future perspectives.
HMGR: A Hybrid Model for Geolocation Recommendation

The Geosocial networks integrate geographical location information into traditional social networks, bridging the gap between people's real-life experiences and the virtual world. As a significant application of geosocial networks, location recommendation suggests places that individuals may find interesting, offering valuable references for their travels and greatly enhancing their lives. Consequently, the challenge of recommending relevant locations to users from a vast pool of geographic options has become a prominent topic in academic research. Collaborative filtering algorithms stand as one of the classic solutions in the field of recommendations. While they partially address the problem of information overload, they often encounter a common obstacle known as the cold start problem. To overcome this issue, this study makes two primary contributions: Firstly, it proposes the utilization of Markov chains to mitigate the cold start problem. Secondly, it introduces a hybrid recommendation model called HMGR for location-based recommendations, which effectively enhances the accuracy of suggestions. We evaluate the efficacy of the Markov chain and HMGR model through extensive experimentation. The results demonstrate that the implementation of Markov chains successfully alleviates the cold start problem, and our HMGR model significantly improves the precision of recommendations.
Blockchain Protocols: Transforming the Web We Know

The concept of Decentralized Internet or Web3.0 has gained the audience’s attention and has shown exponential growth. The researcher’s community believes that Web3.0 is the next big step in the progression of the web we know now. Bitcoin whitepaper made Blockchain famous among researchers and developers. Blockchain is the technology that provides Decentralization, Auditabilty, Consistency, and Anonymity. The introduction of blockchain protocols compatible with smart contracts such as Ethereum, Near, Polygon, Solana, etc., makes it easier for the developer to quickly build Decentralized Applications, i.e., Dapps. These Decentralized Applications create Decentralized Internet and an alternative to web2.0. In this research paper, We have extensively discussed the core Blockchain technology inspired by the Bitcoin whitepaper and four Blockchain protocols: Ethereum, Near, Solana, and Polygon. We have distinguished these protocols based on the Consensus mechanism, Level of Blockchain, Speed of Transactions, Smart Contracts, etc.
Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering

Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computing. To protect data privacy, federated learning learns shared models by locally executing distributed training on participating devices and aggregating local models into global models. There is a problem in federated learning, that is, the negative impact caused by the non-independent and identical distribution of data across different user terminals. In order to alleviate this problem, this paper proposes a strengthened federation aggregation method based on adaptive OPTICS clustering. Specifically, this method perceives the clustering environment as a Markov decision process, and models the adjustment process of parameter search direction, so as to find the best clustering parameters to achieve the best federated aggregation method. The core contribution of this paper is to propose an adaptive OPTICS clustering algorithm for federated learning. The algorithm combines OPTICS clustering and adaptive learning technology, and can effectively deal with the problem of non-independent and identically distributed data across different user terminals. By perceiving the clustering environment as a Markov decision process, the goal is to find the best parameters of the OPTICS cluster without artificial assistance, so as to obtain the best federated aggregation method and achieve better performance. The reliability and practicability of this method have been verified on the experimental data, and its effectiveness and superiority have been proved.
Systematic knowledge modeling and extraction methods for manufacturing process planning based on knowledge graph

Process knowledge is an important part of intelligent manufacturing, which has become the trend of global manufacturing industry. However, process planning in manufacturing heavily relies on experience and historical knowledge that cannot be effectively reused. Also, process knowledge is complicatedly structured, widely distributed, and weakly associated, which makes it more difficult to be shared and reused. Moreover, existing methods of organizing process knowledge are labor-intensive, inefficient and inevitable to extensive manual annotation. To overcome the above problems, a systematic method of knowledge graph construction for process knowledge is presented. First, through key concepts and relationships analysis, a domain ontology for process knowledge is proposed. Second, a pattern-based bootstrapping framework with a 2-level masking technique is established to perform process knowledge extraction, which does not require manual annotation and avoids overfitting issues. Third, a meta path-based question answering over knowledge graph approach is presented to support process planning, which can effectively capture keywords of the input question, present the intention and match the designed path. Finally, taking the production process of radiator as a research object, a corresponding manufacturing process knowledge graph is constructed and applied to process planning scenario, and experiments validate the feasibility of the proposed methods.
Understanding Shadow IT usage intention: a view of the dual-factor model

Purpose Despite the growing concern about security breaches and risks emerging from Shadow IT usage, a type of information security violation committed by organizational insiders, this phenomenon has received little scholarly attention. By integrating the dual-factor theory, unified theory of acceptance and use of technology (UTAUT) and social control theory, this research aims to examine facilitating and deterring factors of Shadow IT usage intention. Design/methodology/approach An online survey was performed to obtain data. As this study aims at investigating the behavior of organizational insiders, LinkedIn, an employment-oriented network site, was chosen as the main site to reach the potential respondents. Findings The results show that while performance expectancy, effort expectancy and subjective norms considerably impact intention to use Shadow IT, personal norms and sanctions-related factors exert no influence. Besides, an organizational factor of ethical work climate is found to significantly increase individual perceptions of informal controls and formal controls. Originality/value This work is the first attempt to extend the generalizability of the dual-factor theory and UTAUT model, which primarily has been utilized in the context of system usage, to the new context of information security. This study is also one of few studies that simultaneously take both organizational and individual factors into consideration and identify its impacts on user's behaviors in the information security context.
Fraud Detection Using Decision Tree Algorithm to Curb Identity Theft

Identity theft is a growing concern that can cause significant financial and emotional harm to individuals. One way to detect and prevent identity theft is by using machine learning algorithms, such as decision tree. In this study, we investigate the effectiveness of using a decision tree algorithm in detecting and preventing identity theft. A dataset consisting of personal information, as well as information on suspicious activity, was collected from a financial institution. The dataset included a total of 284,807 rows of data and 30 columns. The decision tree algorithm was implemented using the Python programming language and the scikit-learn library. The algorithm was trained on the training set and used to classify new cases as either fraudulent or non-fraudulent. The performance of the decision tree algorithm was evaluated using several performance metrics such as accuracy, precision, recall and F1-score. Results showed that the decision tree algorithm was effective in detecting and preventing identity theft, with an overall accuracy of 99%. These findings demonstrate the potential of using decision tree algorithms in detecting and preventing identity theft, which can help to curb the increasing problem of identity theft and protect individuals from financial and emotional harm.
A Qualitative Study of the Effects of Socio—organizational Factors on the Information Security Culture of Employees in a Financial Institution

Information communication technology (ICT) is constantly evolving, it is currently used in virtually every aspect of human life, such as financial transactions. Due to its ongoing development, the internet serves as the basis for all service platforms. Financial institutions are responding to the development of ICT by following suit in an effort to improve services and maintain existing technology. Similarly, worldwide cybercrime has increased, resulting in financial losses. As a result, information security has become a pressing concern for any institution tasked with providing adequate services. Developing complex security methods that address people, processes, and technology is required to strike a balance between the technical and behavioural components of information security. This qualitative study will examine how socio-organizational factors can enhance employees' attitudes toward information security culture at any financial institution. To fulfil the above aim, a qualitative methodology is utilised. Interviews (prior and main) and document analysis (Review & Analysis of Internal Materials in the banking sector of Nigeria) will be used to collect data. The Quota Sampling method is used in recruiting participants for interviews. With the assistance of academic experts, fellow researchers, and information security professional experts, the interview questions will be pretested. The Thematic analysis will be utilised to deduce occurrences, patterns, and use of concepts. Based on the analysis of data gathered from 38 top management of a financial institution, the study concludes that improving information security culture in any institution requires strong leadership, accountability, education/awareness/training/experience, commitment & trust, monitoring & internal control (audits), process integration, technology, permissible/institutional pressure, societal pressure, personal beliefs, values, and attitudes. The study expands on'behavioural information security', a crucial area of study. It bridges the gap in research most especially in Africa as it expands knowledge of information security by critically exploring human factors in the form of employees in the financial sector of a cross-cultural country Nigeria. From a theoretical perspective, the study presents socio-organizational factors that can enhance employees' attitudes toward information security culture. From a practical perspective, it is a study which provides guideline for better articulation, formation, and implementation of an organisation's Information security laws, policies, and procedures as well as to create a framework which can aid top management in recruiting. The usefulness of this thesis is not limited to the financial institution of Nigeria/Africa.
Indian Sign Language Recognition System for Interrogative Words Using Deep Learning

With around 63 million population of deaf/mute society in India, an effective way of communicating with them is needed in order to understand them as deaf/mute people use hand shape, facial expressions, and body movements for communication. Developing an effective, accessible, and reliable way of communication is vital research in progress in machine translation. In this paper, proposed methodology considers isolated dynamic Indian Sign Language (ISL) gestures to recognize human actions on interrogative words. A self-recorded dataset of such gestures is made to contour the unavailability and variation of such dataset. Video frames are extracted from these dynamic gestures which are fed into proposed CNN architecture for training followed by prediction using Single-Frame CNN method on input videos. The proposed method has been tested with different optimizers and has achieved highest accuracy of 99.6% with Adam optimizer.
Henry Giroux

This entry describes the life and works of educational theorist Henry Giroux.
A Novel &amp; Innovative Blockchain-Empowered Federated Learning Approach for Secure Data Sharing in Smart City Applications

The very existence of smart cities forms the stepping stone in the evolution of many technological advancements in the future era. While smart cities have already grown in their way, the tremendous amount of data generated from them paves the way for new perspectives of development. This is because security and privacy remain to be the major constraint across smart city applications. Further, smart city applications such as smart homes, smart transportation, and smart healthcare are generating a huge amount of data every day and it is often complex to collect and manage all the data together at a single location. To address these constraints, this paper presents a novel and innovative blockchain-assisted federated learning approach for secure data sharing in IoT Smart Cities. Here, we implement a federated learning approach, where the process of learning is made in a distributed fashion. The use of blockchain in turn adds more security and resilience to smart city applications. The security analysis proves that the proposed approach offers comparatively better performance and remains more resistant to various security threats and vulnerabilities.
A Novel Approach of Machine Learning Application in Astrophysics: Morphological Feature Wrapping Based Ensemble Method for Galaxy Shape Classification Using GAMA Dataset

The numerous strategies for the automated morphological categorization of galaxies, which uses a variety of supervised machine learning techniques, have not been well examined or compared. As the majority of star galaxy classifiers in use today use condensed summary data from catalogues, rigorous feature extraction and selection are required. With the aid of Deep Convolutional Neural Networks (CNN), a development in machine learning, it may automate the process of feature detection from data by a computer, therefore lowering the demand for qualified human input. Low-level artificial classification has made great progress. While this is the case, Deep Learning consistently outperforms traditional computers. analyzing large datasets while learning. We examine three machine learning techniques for categorizing morphological galaxies: Support Vector Machines (SVM), Random Forests (RF), and Naive Bayes (NB). We examine the efficacy of several machine learning algorithms on each feature representation of a galaxy using a collection of morphological features produced by image analysis as well as the raw image pixel data compressed using PCA (Principal Component Analysis) into PCA features. According to our experiments, RF outperformed SVM and NB. The remainder of the time, morphological features outperformed our PCA features in performance. Thus, the current mechanism is not extremely scalable. A probabilistic classifier that can scale, is based on source data, and requires the least amount of human interaction is essential to resolving these problems.