Reviewerly/exHarmony huggingface dataset:
Understanding Peanuts and Schulzian Symmetry: Panel Detection, Caption Detection, and Gag Panels in 17,897 Comic Strips Through Distant Viewing.

In this article, we applied distant viewing to a corpus of 17,897 comic strips from Charles Schulz’s Peanuts as a primary case study. Distant viewing uses computational techniques to study large-scale visual media, and draws upon interdisciplinary areas including visual media studies, cultural studies, data science, and semiotics. We focus on comic strips, particularly Peanuts , due to their widespread readership, historical and cultural cache, and complexity as a medium built on the interplay between text, image, and meaning. First, we discuss previous work done at the intersections of comics studies and computer vision. Next, we establish the processes for applying computer vision to comic strips. After that, we provide several examples, including: panel detection (variations in panel length over a cartoonist’s career); caption detection (identification and location of captions in panels); and comics paratext (computer vision analyses/exclusions of copyright text, signatures, dates, etc.). Combined studies of panel detection, caption detection, and comics paratext reveals new insights into the success, longevity, and influence of one of the world’s most famous newspaper comic strips. Ultimately, computer vision reveals a subtle stability and symmetry to Schulz’s artistry that played an understudied but significant role in the comic strip’s popularity.
Privacy and Security Through Blockchain in Industry 4.0

The name Industry 4.0 describes a synthesis of modern manufacturing techniques that help producers to hit targets more quickly. The manufacturing sector's trend is changing significantly because of digitalization and technological improvements. Technology that aids in comprehensive integration, such as IoT, cybersecurity, blockchain, big data, etc. Due to the internet's open nature as a means of data transmission, security and privacy must be given top priority. The integration of blockchain is currently taking place as part of Industry 4.0. Blockchain enables Industry 4.0 through bringing a variety of processing activities in a protected environment. The inherent characteristics of decentralization, blockchain technology is a promising platform for cybersecurity. This chapter's purpose is to highlight the issues on traditional production sectors, evaluation blockchain application in Industry 4.0, limitations of blockchain, privacy and security requirements, and adoption of blockchain inside this context of Industry 4.0.
Innovative Applications of Data Science

The travel and tourism sector is one among the important sectors that contribute or generate income to a country, which in turn raise the economy. More than one billion tourists travel to their desired international destinations as normal tourist, for their business, medical, study purpose, etc. every year contributes 9.8% of global GDP. This value represents 7% of the world total exports. Today with the natural language processing (NLP), most of the major challenges in these sectors have been addressed, which results in the increased revenue generation to the country. Moreover, NLP tries to provide a hassle-free experience to the tourist and thus improves the revenue generation. It helps to streamline the business opportunities and benefits all the interconnected industries. This chapter tries to provide the role of NLP in travel and tourism along with the challenges in this field.
Introduction to Augmented Reality

Recently, augmented reality (AR) has gained a lot of popularity. The fundamental tenet of augmented reality is to effectively facilitate interactive communication between people and computers. With the aid of specific tools, a person in the real world can enter the electronically produced reality. These days, this technology is included in every mobile filter we use, including those seen in Pokémon Go and the Instagram and Snapchat apps. Wearable versions of numerous miniature augmented reality models have been created. Numerous industries have been investigated using AR technology, including those in the realms of medicine, education, business, architecture, commerce, tourism, navigation, translation, the visual arts, fitness, flying training, the military, industrial manufacturing, etc. The roots of AR in Industry 4.0 are deepening day by day. The immense potential of AR can be utilized in an infinite number of ways to fulfill the objectives of Industry 4.0. This chapter shows the impact of AR in our society.
Empirical Study on Cyber-Crime and Cyber Terrorism

This chapter will give a clear idea of what cyber-crime is and cyber terrorism's causes and types and how these are done and what is the main difference between cyber-crimes and cyber terrorism. It will also explore what precautions can be taken that may help a user with these attacks.
Some Insights of Cyber Physical Systems in the Context of the Tourism and Travel Industry

Cyber-physical systems (CPS) facilitate the incorporation of humans, objects, and computing systems in their physical environment. These systems operate in real-time and are used to enable the systems and devices to self-organize and reconfigure to respond to their changing environments. They help to realize data-intensive interconnected platforms that allow data streams to run continuously on an autonomous basis. CPSs in the context of tourism and travel are considered important elements for developing efficient and effective smart tourism services. This research explores various facets of CPSs and proposes a blockchain-based framework to offer a smart tourism ecosystem, catering to the dynamic preferences and needs of the tourists. The key idea is to use a novel blockchain-based data security mechanism, which uses a set of policies to enforce data security for smart tourism services.
A Study on Impact of HRM Practices on Organizational Performancewith special reference to IT Companies in Hyderabad

The study explores the critical relationship between HRM practices and the performance oforganizations, with a specific focus on IT companies in Hyderabad. This research delves into thedynamic and competitive landscape of the IT sector and aims to shed light on how HRM strategiesimpact organizational success. Through an in-depth analysis of HRM practices such as recruitment,training, performance appraisal, and employee engagement, the study highlights their pivotal role inattracting and retaining top talent within IT firms. The research emphasizes the need for HRMpractices to align with the unique requirements of the IT industry, including technical skilldevelopment and adaptability. By investigating these aspects, the study provides valuable insightsfor IT companies in Hyderabad and similar tech hubs, offering actionable recommendations foroptimizing HRM strategies to ensure sustained organizational performance, growth, andcompetitiveness. A sample of 186 responses was gathered with the help of structured questionnairein few IT companies existing in Hyderabad. This study encapsulates the key objectives and findingsof the comprehensive study, making it a valuable resource for HR professionals and IT industrystakeholders seeking to enhance their understanding of the HRM-performance relationship.
Postdigital Learning Design

Learning design refers to the practice of creating opportunities for learning, crafted to support the achievement of defined learning objectives within a specific context. This work can be practical (e.g., educators developing and sharing their practice) or technical (e.g., researchers developing tools and strategies to support educators in their work). In both cases, the representation of learning designs is never neutral – design practice is always underpinned by one’s values and assumptions about how people learn. Understanding these values is central for the effective communication and implementation of design ideas across contexts. Over the years, learning design practice has been powerfully shaped by advances in learning theory and digital technologies, but also influenced by social, political, and economic developments more broadly. This entry describes the roots of learning design and its evolution across time, its connections to the postdigital, and implications for future developments.
Fitting an Equation to Data Impartially

We consider the problem of fitting a relationship (e.g. a potential scientific law) to data involving multiple variables. Ordinary (least squares) regression is not suitable for this because the estimated relationship will differ according to which variable is chosen as being dependent, and the dependent variable is unrealistically assumed to be the only variable which has any measurement error (noise).We present a very general method for estimating a linear functional relationship between multiple noisy variables, which are treated impartially i.e. no distinction between dependent and independent variables. The data are not assumed to follow any distribution, but all variables are treated as being equally reliable.Our approach extends the geometric mean functional relationship to multiple dimensions. This is especially useful with variables measured in different units as it is naturally scale invariant, whereas orthogonal regression is not. This is because our approach is not based on minimising distances, but on the symmetric concept of correlation.The estimated coefficients or parameters are easily obtained from the covariances or correlations, and correspond to geometric means of associated least squares coefficients. The ease of calculation will hopefully allow widespread application of impartial fitting to estimate relationships in a neutral way.
Entanglement capacity of fermionic Gaussian states

Abstract We study the capacity of entanglement as an alternative to entanglement entropies in estimating the degree of entanglement of quantum bipartite systems over fermionic Gaussian states. In particular, we derive the exact and asymptotic formulas of average capacity of two different cases—with and without particle number constraints. For the later case, the obtained formulas generalize some partial results of average capacity in the literature. The key ingredient in deriving the results is a set of new tools for simplifying finite summations developed very recently in the study of entanglement entropy of fermionic Gaussian states.
Development of valuable social ideas of future managers of scientific projects

The article examines the theoretical and methodological aspects of the problem of the development of valuable social ideas of future managers of scientific projects. A theoretical analysis of the scientific works of domestic and foreign scientists dedicated to the study of social perceptions is provided. Social representations are one of the forms of knowledge of social reality. The development of valuable social ideas of the individual takes place in the process of complex application of the most effective methods and means of education, informal education, self-education under the influence of the social environment. Highly developed value social representations of an individual determine its effective self-determination and social activity useful for society. Emphasis is placed on the importance of the development of valuable social ideas of future managers of scientific projects, which is due to the specifics of their future work associated with constant social interaction. Examples of methods and forms of work for the development of valuable social ideas of future managers of scientific projects are presented. The effectiveness of using a training form of work for the purpose of developing valuable social ideas of future managers of scientific projects is determined by their active participation, in the process of which they acquire new knowledge, skills, social skills and values, new experience of social interaction, and other personal qualities that fully correspond to the principles competence approach and partnership pedagogy. Prospects for further research are outlined, which consist in the study of the factors of development of valuable social perceptions of the individual, which will allow to determine the conditions of pedagogical influence on the individual within the framework of partnership pedagogy in order to improve its further social development and effective self-realization.
Building an individual educational trajectory of future teachers through digitization of education and distance learning based on pedagogical design

The article examines the influence of global digitalization and digital technologies on the formation of the individual educational trajectory of future teachers in the conditions of distance education, organized on the principles and rules of pedagogical design. The trends of development and rethinking of the educational process have been determined, including through the introduction of pedagogical design into the learning process. Issues of rethinking the educational process in the digital environment, its features and possible difficulties are considered. The purpose of using modern digital technologies in education is discussed. The prospects of using the impact of digitalization on the development and formation of professionally important competencies of future educators are considered. It was determined that digital technologies in pedagogical activities contribute to the development of innovations and the generation of new ideas that affect both the teacher and the students of education, as well as the educational process in general. Pedagogical design is defined as the collaborative process of developing a distance course using specific learning objectives and pedagogical theories to determine learning strategies, activities, and assessments to achieve desired educational outcomes. Pedagogical design has been found to use design principles to promote learner engagement and includes the development of structures, digital learning objects, and multimedia resources to improve the quality of teaching and learning. The basic principles of pedagogical design were identified, including the digital architecture of the e-course, which organizes educational information, the principle and method of interaction with the e-course, characterized by simplicity and ease of use, which contribute to effective learning.
Methodological principles of the problem of formation of cultural values of future primary school teachers

The article “Methodological foundations of the problem of formation of cultural values of future primary school teachers” focuses on research and analysis of the process of formation of cultural values of future primary school teachers. The author examines in detail the theoretical foundations of this problem, key methodological principles and paradigms. The definition and justification of the methodological foundations of the problem of the formation of cultural values of future primary school teachers are given. The article covers important aspects of pedagogical science, such as the role of cultural values in the modern educational process and the peculiarities of their formation in higher educational institutions. The article presents an analysis of the essence of the concept of «methodology» and «approach». Scientific approaches to the study of the problem of the formation of cultural values of future primary school teachers are disclosed: axiological, cultural, competence, activity, environmental, subjectsubject, reflective. Certain regularities of the formation of cultural values of future primary school teachers are singled out. The leading principles on which the methodology of this study is based are outlined, namely: taking into account the age, individual psychophysiological characteristics, abilities of future primary school teachers; systematicity and consistency; complexity; flexibility; self-actualization; subjectivity; the principle of axiologising the educational environment of higher education; the principle of cooperation of various subjects in the process of forming the cultural values of future primary school teachers through the interaction of higher education with various social institutions (educational institutions, cultural institutions, social institutions). The article is important for scientists, teachers, students in the field of pedagogy, as well as everyone who is interested in the problems of cultural values in modern education.
A Cross-Modal View to Utilize Label Semantics for Enhancing Student Network in Multi-label Classification

Knowledge transfer has become a promising approach for improving the performance and efficiency of relatively lightweight networks. Previous research has focused on identifying suitable knowledge and enhancing network structures to obtain more valuable knowledge. However, the introduction of extra information such as semantics remains an unexplored area. In this study, we introduce a multi-label classifier with label embeddings to replace the traditional GAP layer and incorporate semantics. Our approach adopts a cross-modal view for classification and employs the correlation matrix of visual and label modalities as knowledge to enhance the performance of the student. Furthermore, due to the same classification head, we initiate the student’s head with trained teacher’s and enable the label embeddings more representative. Experimental results show that our proposed method outperforms existing typical methods. Additionally, further analysis confirms the effectiveness of our approach.
DA-TSD: Double Attention Two-Stage 3D Object Detector from Point Clouds

In this paper, we focus on the problem of small and long-range object misses in 3D object detection on point clouds. We observed that in challenging situations, especially for hard objects such as small objects, the performance of the detector remains unsatisfactory. To address these issues, this paper proposes a voxel-based two-stage 3D object detector, named DA-TSD, which mainly includes a Double Attention (DA) module and a Pyramid Sampling (PS) module. The DA module comprehensively considers point-wise and channel-wise excitation attention, which can effectively enhance the crucial information of the object and suppress irrelevant noise. In addition, the stacked DA module utilizes not only the current level feature but also the multi-level feature attention. The PS module provides cross-layer feature mappings to obtain more comprehensive feature representations. The experimental results on the val set of the KITTI dataset demonstrate the superiority and effectiveness of DA-TSD. DA-TSD provides higher detection accuracy while maintaining real-time frame processing rate, running at a speed of 28.5 FPS on an NVIDIA GeForce RTX 3090 Ti GPU.
Context Enhancement Methodology for Action Recognition in Still Images

Action recognition in still images is a popular research topic in the field of computer vision, but it is to remain challenging due to the lack of motion information. Contextual information is a significant factor in the task of recognizing image action, which is inseparable from a predefined action class. And the existing research strategy does not ensure adequate use of contextual information. To address this issue, we propose a Contextual Enhancement Module (CEM) that combines the self-attention mechanism and the contextual attention mechanism. Specifically, the context enhancement module uses self-attention to learn pixel-level contextual information, after which separates the image into parts and uses contextual attention to learn region-level contextual information. In this way, the model can emphasize the significance of various pixels and regions in the image and significantly improve feature representation. We performed a lot of experiments on the PASCAL VOC 2012 Action dataset and the Stanford 40 Actions dataset. The results demonstrate that our method performs effectively, with the state-of-the-arts outcomes being obtained on both datasets.
Diversified Contrastive Learning For Few-Shot Classification

We argue that the current few-shot learning, which only uses contrastive learning as an auxiliary task, cannot fully realize the potential of contrastive learning. In this paper, we take a deeper exploration of how to combine contrastive learning and few-shot classification better. We use a two-stage training paradigm called pre-training and meta-training, respectively. During the pre-training phase, we differ from previous work that only extracted global features of images for contrastive learning. We extract the global features of the image and local features for contrastive learning, where the local feature contrastive loss is called the maximum matching local contrastive loss. To better integrate contrastive learning with few-shot learning, we propose a prototype contrastive module in the meta-training stage. During the meta-training phase, we record the feature vector representations of all base class prototypes and conduct class-level contrastive learning between K-way class prototypes obtained from the current task and all base class prototypes. Meanwhile, we dynamically update all stored base class prototypes as the training progresses. We validate our model on mimiImagenet and tired-Imagenet datasets. Our experimental results show meaningful improvements in few-shot classification and therefore demonstrate the usefulness of our model.
An Ensemble Scheme Based on the Optimization of TOPSIS and AdaBoost for In-Class Teaching Quality Evaluation

As artificial intelligence has grown, intelligent technology has steadily been used in the classroom. Intelligent in-class evaluation has gained popularity in recent years. In this study, we apply two models: AE-SIS (Analytic Hierarchy Process-Entropy Weight-TOPSIS) and AW-AB (Adjusted Weight in Adaptive Boosting) to evaluate in-class teaching quality. We provide an ensemble scheme for intelligent in-class evaluation that combines the benefits of the two models. We test the current in-class evaluation criteria using classroom datasets for comparison. The outcomes show how great and successful the suggested plan is.
Fusing Hand and Body Skeletons for Human Action Recognition in Assembly

As collaborative robots (cobots) continue to gain popularity in industrial manufacturing, effective human-robot collaboration becomes crucial. Cobots should be able to recognize human actions to assist with assembly tasks and act autonomously. To achieve this, skeleton-based approaches are often used due to their ability to generalize across various people and environments. Although body skeleton approaches are widely used for action recognition, they may not be accurate enough for assembly actions where the worker’s fingers and hands play a significant role. To address this limitation, we propose a method in which less detailed body skeletons are combined with highly detailed hand skeletons. We investigate CNNs and transformers, the latter of which are particularly adept at extracting and combining important information from both skeleton types using attention. This paper demonstrates the effectiveness of our proposed approach in enhancing action recognition in assembly scenarios.