Reviewerly/exHarmony huggingface dataset:
Study and Analysis of Visual Saliency Applications Using Graph Neural Networks

GNNs (graph neural networks) are deep learning algorithms that operate on graphs. A graph's unique ability to capture structural relationships among data gives insight into more information rather than by analyzing data in isolation. GNNs have numerous applications in different areas, including computer vision. In this chapter, the authors want to investigate the application of graph neural networks (GNNs) to common computer vision problems, specifically on visual saliency, salient object detection, and co-saliency. A thorough overview of numerous visual saliency problems that have been resolved using graph neural networks are studied in this chapter. The different research approaches that used GNN to find saliency and co-saliency between objects are also analyzed.
Comprehensive Study of Face Recognition Using Feature Extraction and Fusion Face Technique

Face recognition is a process by which the identity of a person is determined from the face images stored in a face database. Face recognition is one of the most successful applications of image analysis. In the present scenario, face recognition plays a major role in commercial and law enforcement applications, such as surveillance system, passport, security, personal information accesses, human machine interaction, etc. At present, very reliable methods of biometric personal identification exist. In face recognition, a feature vector usually represents the salient characteristics that best describe a face image. However, these characteristics vary quite substantially while looking into a face image from different directions. This chapter addresses this issue by means of image fusion and presents a comprehensive study of different image fusion techniques for face recognition. Image fusion is done between the original captured image and its true/partial diagonal images.
Graph Neural Network and Its Applications

Graph neural network (GNN) is an emerging field in deep learning. Graphs have more expressive power than any other data structure. Graph neural network is one of the application areas of deep learning, and it has applications in different domains where traditional convolutional neural networks can't give the desired result. Graphs are basically connections of nodes through the edges. In the area of recommendation systems, image processing and fraud detection are some of the few application areas of graph neural networks. As graphs are moveable and mobile in nature, they are more flexible to apply in these domains. GNN deals with these types of problems more effectively than a convolution neural network. To apply GNN to a specific problem domain, data needs to be converted into a graphical format, and then neural network operations can be executed. The main feature of GNN is to inherit information from its neighborhood. This is called graph embedding. This chapter describes basic GNN architecture, GNN advantage over CNN, and its application in different domains.
Application and Some Fundamental Study of GNN In Forecasting

The chapter consists of the application of GNN with all applied fundamentals in different fields of application. Firstly, the discussion will be about the graph using graph theory connection to the mathematical aspect. Secondly, the basis of the data set will be for forecasting and predictive analysis, application, and fundamental concepts, which will help in decision making regarding the different unsolved problems. Third, knowledge about the models of the graph neural network with the examples will be a very important part of the chapter. This chapter is useful for fulfilling the research gap in the field of some forecasting models using graph neural networks with the application of machine learning on data analysis with a large number of examples.
Introduction to Graph Neural Network

Deep learning on graphs is an upcoming area of study. This chapter provides an introduction to graph neural networks (GNNs), a type of neural network that is designed to process data represented in the form of graphs. First, it summarizes the explanation of deep learning on graphs. The fundamental concepts of graph neural networks, as well as GNN theories, are then explained. In this chapter, different types of graph neural network (GNN) are also explained. At the end, the applications of graph neural network where GNN is used and for what purpose it is going to be used are explained. This also explores the various applications of GNNs in fields such as social network analysis, recommendation systems, drug discovery, computer vision, and natural language processing. With the increasing prevalence of graph data, GNNs are becoming increasingly important and will likely continue to play a significant role in many fields in the future.
Graph Classification of Graph Neural Networks

Graph neural networks have recently come to the fore as the top machine learning architecture for supervised learning using graph and relational data. An overview of GNNs for graph classification (i.e., GNNs that learn a graph level output) is provided in this chapter as pooling layers, or layers that learn graph-level representations from node-level representations, are essential elements for successful graph classification because GNNs compute node-level representations. Hence, the authors give a thorough overview of pooling layers. The constraints of GNNs for graph categorization are further discussed, along with developments made in overcoming them. Finally, they review some GNN applications for graph classification and give an overview of benchmark datasets for empirical analysis.
Guidelines for Developers and Recommendations for Users to Mitigate Phishing Attacks: An Interdisciplinary Research Approach

Phishing attacks are common these days. If successful, these attacks cause psychological, emotional, and financial damage to the victims. Such damages may have a long-term impact. The overall objective of this Ph.D. research is to contribute to mitigating phishing victimization risks by exploring phishing prevalence, user-related risk factors, and vulnerable target groups and by designing (1) guidelines for social website developers focused on internet user vulnerabilities and (2) recommendations for users to avoid such attacks. The Ph.D. research acknowledges that phishing attacks are technical in nature, while the impact is financial and psychological. Therefore, an interdisciplinary research approach focusing on empirical research methods from social sciences (i.e., focus groups and surveys) and computer science (i.e., data-driven techniques such as machine learning) is adopted for the research. In particular, we aim to use a machine learning model for data analytics and quantitative and qualitative research design for psychological analysis. The research outcome of this Ph.D. work is expected to provide recommendations for internet users and organizations developing social-media-based software systems through more phishing aware development practices.
Comparative Study of Unsupervised Keyword Extraction Methods for Job Recommendation in an Industrial Environment

Automatic keyword extraction has important applications in various fields such as information retrieval, text mining and automatic text summarization. Different models of keyword extraction exist in the literature. In most cases, these models are designed for English-language documents, including scientific journals, news articles, or web pages. In this work, we evaluate state-of-the-art unsupervised approaches for extracting keywords from French-language Curricula Vitae (CVs) and job offers. The goal is to use these keywords to match a candidate and a job offer as part of a job recommendation system. Our evaluation showed that statistical baselines obtain good results with an interesting processing time in an industrial context. It also allowed us to highlight, on the one hand, biases related to pre-trained word embedding models on corpora of a different nature than CVs and job offers, and on the other hand, the difficulties of annotation within the framework of job search platforms.
Business User-Oriented Recommender System of Data

Companies nowadays are increasingly dependent on data. In an environment that is more dynamic than ever, they are looking for tools to leverage those data and obtain valuable information in a rapid and flexible way. One way to achieve this is by using Data-Driven Decision Support Systems (Data-Driven DSS). In this project, I focus on one such type of DSS, namely the Self-Service Business Intelligence (SSBI) Systems. These systems are designed specifically to avoid the involvement of the IT department when creating business reports by empowering businesspeople in the production of their own reports, thereby reducing the time-to-release of a given report and improving the responsiveness of companies. Business decision-makers, when developing their own reports, however face barriers. These challenges are related to the current self-service features that are not sufficiently adapted to their business needs and their lack of technical knowledge. The objective of my project is to build a framework based on Artificial Intelligence (AI) techniques such as Natural Language Processing techniques, Semantic and Recommender Systems to solve one of the main challenges faced by businesspeople, namely: the data picking within technical and large current databases. These AI systems offer a number of benefits that are strongly linked to the problems encountered by business users in the data picking process. This paper introduces the three main research questions of my thesis and positions them in the current literature. It then elaborates on the different theoretical, methodological and empirical contributions I plan to advance as part of my project.
Using GPU and TPU Hardware Accelerators to Develop a Cloud-Based Genetic Algorithm System

Genetic algorithms (GAs) are widely used in the scientific and industrial sectors due to their speed and flexibility in solving real problems. This article discusses the creation of a cloud-based genetic algorithm system. Utilizing Google Colab to discover optimum solutions and test the system's overall performance across a variety of fundamental cloud run-time types that contain Tensor Processing Unit (TPU) and Graphics Processing Unit (GPU) resources. The suggested technique demonstrated that when implementing mathematical equations, TPU outperforms GPU by achieving a (460.35) average of generation with a (13.95) average of run time in TPU versus a (484) average of generation with a (14.32) average of run time in GPU.
A General Framework for Blockchain Data Analysis

Blockchain is a foundational technology that allows application paradigms to shift from trusting humans to trusting machines and from centralized to decentralized control. Along with its explosive growth, blockchain data analysis is getting increasingly important for both scientific research and commercial applications. The current blockchain analysis systems and frameworks have limitations and weaknesses; they have excessively focused on Bitcoin and a small set of features. This paper presents a framework for blockchain data analysis. The framework is general and can be applied to a wide range of data analyses. Our main contributions are as follows: (i) we formulate the requirements of the framework; (ii) we present the detailed design of the framework with multiple components to collect, extract, enrich, store, and do further processing with blockchain data; (iii) we implement the framework and evaluate its performance in a specific use case that analyzes token-transferring transactions. We also discuss the potential of the framework for a number of blockchain data analyses.
Indirect Adaptive Control of Nonlinear System Using Recurrent Neural Network

In this article, a locally recurrent neural network with input feed through (LRNNIFT) is presented for control of nonlinear dynamical systems. The rationale of using LRNNIFT is due to its modest structure and mathematical model, which gives it an edge over the existing Elman neural networks (ENN) and feed forward neural networks (FFNN). Results from simulation showed that LRNNIFT-based controller is able to achieve adaptive control in a nonlinear system. It is also tested and observed to counterbalance the effects of disturbances. A comparative analysis is presented with the help of simulation, and it is deduced that overall performance of LRNNIFT controller is better than that of FFNN and ENN controllers.
Interpretable Clustering via Soft Clustering Trees

Clustering is a popular unsupervised learning task that consists of finding a partition of the data points that groups similar points together. Despite its popularity, most state-of-the-art algorithms do not provide any explanation of the obtained partition, making it hard to interpret. In recent years, several works have considered using decision trees to construct clusters that are inherently interpretable. However, these approaches do not scale to large datasets, do not account for uncertainty in results, and do not support advanced clustering objectives such as spectral clustering. In this work, we present soft clustering trees, an interpretable clustering approach that is based on soft decision trees that provide probabilistic cluster membership. We model soft clustering trees as continuous optimization problem that is amenable to efficient optimization techniques. Our approach is designed to output highly sparse decision trees to increase interpretability and to support tree-based spectral clustering. Extensive experiments show that our approach can produce clustering trees of significantly higher quality compared to the state-of-the-art and scale to large datasets.
Scalable and Near-Optimal $$\varepsilon $$-Tube Clusterwise Regression

Clusterwise Regression (CLR) methods that jointly optimize clustering and regression tasks are useful for partitioning data into disjoint subsets with distinct regression trends. Due to the inherent difficulty in simultaneously optimizing clustering and regression objectives, it is not surprising that existing optimal CLR approaches do not scale beyond 100 s of data points. In an effort to provide more scalable and optimal CLR methods, we propose a novel formulation of the problem that takes inspiration from $$\varepsilon $$ -tubes in Support Vector Regression (SVR). The advantage of this novel formulation, which aims to assign data points to clusters in order to minimize the largest $$\varepsilon $$ -tube that encapsulates the regressed data, is that it admits an optimal MILP formulation. Furthermore, given that each constraint in our formulation corresponds to a single data point, we propose an efficient row generation solution that can optimally converge for the full dataset while only requiring optimization over a subset of the data. Our results on a variety of synthetic and benchmark real datasets show that our Clusterwise Regression MILP formulation provides near-optimal solutions up to 100,000 data points and the smallest data-encapsulating $$\varepsilon $$ -tubes among CLR alternatives.
Playing the Electrophonic Saxophone

'Playing the Electrophonic Saxophone' explores the expanded role of the instrument and relations made possible by such expansions. In and through artistic practice I research the many processes, tensions, and artistic possibilities that emerge through interplay. How can exploring possibilities and conditions for the acoustic saxophone in interplay with electric and electronic tools and sounds pave the way for new music, new insights, and expand the conception of what the instrument can be? How can playing with electrically and digitally manipulated sound facilitate the instrument’s ability to connect to musicians and artists from other fields of practice? The research’s main artistic outcome is the sum of a series of artistic projects presented in this exposition. The two texts Playing Air and Playing Parasitic constitute the ethical and methodological basis for the research and my practice and transfers to the projects’ two basic elements: roles and their relations. The band Møster! acts as the main project ensemble, and the PhD research is finalized with a public artistic presentation at the Nattjazz Festival, Røkeriet USF, Bergen on June 1st, 2021. 'Playing the Electrophonic Saxophone' utforsker instrumentets utvidete rolle, og relasjoner som en slik utvidelse åpner for. I og gjennom kunstnerisk praksis forsker jeg på de mange prosesser, spenninger, og kunstneriske potensial som vokser frem gjennom samspill. Hvordan kan utforskning av muligheter og premisser for akustisk saksofonspill i samspill med elektriske og elektroniske verktøy og lyder lede til ny musikk, ny innsikt, og utvide forståelsen av hva instrumentet kan være? Hvordan kan det å spille med elektrisk og digitalt manipulert lyd legge til rette for samhandling med musikere og artister fra andre kunstneriske felt, med ulike kunstneriske praksiser? Forskningens kunstneriske resultat er presentert som en samling prosjekter i denne eksposisjonen. De to tekstene 'Playing Air' og 'Playing Parasitic' utgjør det metodologiske og etiske grunnlaget for forskningen og min praksis, og kan leses inn i forskningens to hovedelementer: roller, og deres relasjoner. Bandet Møster! fungerer som hoved-ensemble, og PhD-prosjektet ble avsluttet med en offentlig konsertfremføring på Nattjazz, Røkeriet USF i Bergen, 1. juni 2021.
Combating counterfeits with the Blockchain-technology-supported platform under government enforcement

The intrinsic value of brand names has led to the widespread presence of counterfeits in multi-industries. Although government enforcement plays a role in combating counterfeits, it is difficult to fundamentally curb counterfeiting. This paper examines how the brand-name firm adopts the blockchain-technology-supported (BTS) platform in dealing with counterfeits under government enforcement. We consider a market with a manufacturer (i.e., a brand-name firm) and a counterfeiter. First, we develop a signaling game model to analyze the negative impact of deceptive sales. The results show that, under weak government enforcement and a high rampancy level of counterfeits, deceptive sales will decrease the expected demand for authentic products, the manufacturer's expected profit, the expected consumer surplus, and the expected social welfare. Although government enforcement forces the counterfeiter to implement non-deceptive sales, it will decrease the expected consumer surplus under symmetric quality information. Then, we develop a Stackelberg game model to analyze the manufacturer's adoption strategy for the BTS platform. We find that weaker government enforcement will incentivize the manufacturer to adopt the BTS platform. However, the manufacturer does not always adopt the BTS platform even if the counterfeiter implements deceptive sales. Finally, we analyze the effect of adopting the BTS platform under government enforcement. We reveal that the role of the BTS platform in improving consumer rights is stronger than that in combating counterfeits, and the effect of adopting the BTS platform will strengthen as government enforcement weakens.
Impact of Augmented Reality and Virtual Reality on Customer Purchase Behavior in the Virtual World

Metaverse is opening new avenues of opportunities for product manufacturers, as well as service providers, and with this focus in mind, the present research proposes the scope and challenges that the application of Neuromarketing in virtual worlds faces across different disciplines and business segments. The immense growth potential that is currently untapped in the metaverse domain can be taken to a different level altogether with the help of neuromarketing applications. This study will analyze the impact of augmented and virtual reality on customer purchase behavior in the virtual world. The stress has been provided to understand customer purchase behavior through a framework model of consumer behavior in augmented shopping reality. The study also discusses the various challenges for customer purchase behavior in the virtual world along with the future direction for research to fill the knowledge gaps in the area under study.
How Will Influencer Marketing Change in the Metaverse?

The metaverse is a technological phenomenon that is constantly expanding. Its users are multiplying every day, which encourages companies to use it to support the evolution of consumer needs and habits. The rise of the metaverse has forced the adoption of new marketing practices. This also concerns the practices considered within the framework of influencer campaigns. Influencer marketing continues to appeal to marketers who want to attract a large audience through non-intrusive marketing. This strategy has generated an estimated $13.8 billion in revenue by 2021 and $16.4 billion by 2022. Thus, marketing professionals are called upon to collaborate with influencers in order to design immersive experiences, monetize their collaborative projects and use virtual influencers in particular to manage influencer campaigns while having full control over the content. Thus, integrating the metaverse into influencer marketing strategies could be a beneficial tactic for brands looking to develop a competitive advantage in a rapidly evolving market.
Integrating Metaverse to Enhance the Learning Experience Benefits of Interactive Teaching Pedagogy for Students

Education is a pillar for success and every teacher tries to make sure to provide a better way for a student to learn. As time passed, the way of teaching also changed. Starting from no visual to chart paper to images to slides or real objects, the way of teaching changed to enhance the student's experience in learning. Now we are at the stage where slides/chart papers are getting old and 3D/4D views are coming into the picture. New technology like Metaverse is going to change interactive learning. An immersive experience will be new for teachers as well as students.
Metaverse-Based Higher Education

The advent of technology backed by digitalization is bringing innovative and new ways in imparting higher education. The high speed of the internet readily available in the higher education institutions is further supporting such activities. Especially post-COVID-19, parents who used to keep an eye on children towards usage of smartphones began to provide such devices themselves to children for educational purposes. The AI-supported technology facilitated the process by creating artificial environment to understand the real-world. It provides a shield to adversities of the real situations. With the further advancement in the emerging field of metaverse, learners can view themselves as one of the participants on screen in the form of an avatar. Since the students are not aware of the challenges and limitations of such emerging areas, the responsibility of educationists is on the rise. The researchers have tried to review the scope of higher education resting on metaverse as one of the major aspects in the near future.
Near MDS codes of non-elliptic-curve type from Reed-Solomon codes

An [n,k,d] linear code is said to be maximum distance separable (MDS) or almost maximum distance separable (AMDS) if d=n−k+1 or d=n−k, respectively. If a code and its dual code are both AMDS, then the linear code is called a near maximum distance separable (NMDS) code. NMDS codes correspond to some interesting objects in finite geometry and have nice applications in cryptography. The NMDS codes constructed from elliptic curves are referred as elliptic curve NMDS codes. In this paper, by adding two suitable columns to the generator matrices of famous Reed-Solomon codes, we can obtain two families of NMDS codes with parameters [q+3,4,q−1]q (q is a prime power with gcd⁡(q−1,3)=1) and [2m+3,5,2m−2]2m respectively. These NMDS codes are shown to be linearly inequivalent to elliptic curve NMDS codes. Besides, the weight enumerators and locality of these NMDS codes are completely determined. It turns out that the resultant NMDS codes and their dual codes are mostly distance-optimal and dimension-optimal locally recoverable codes.