Reviewerly/exHarmony huggingface dataset:
<scp>EnLEFD‐DM</scp>: Ensemble Learning based Ethereum Fraud Detection using <scp>CRISP‐DM</scp> framework

Abstract Cryptocurrencies continue to captivate businesses and investors despite market fluctuations. The number of crypto users has risen rapidly in the last few years, and alarmingly, many appear to be unaware of the risks involved. These risks aren't confined to market hazards but include very sophisticated cybercrimes related to cryptocurrencies. As cryptocurrencies have become a breeding ground for a variety of cybercrimes, resulting in enormous financial losses, it hinders user adoption limiting the utility of blockchain technology. It has become crucial to spot such scams and devise intelligent techniques to make this technology safer for investors. This paper proposes a classification model based on the Cross Industry Standard Process for Data Mining (CRISP‐DM) framework to identify fraudulent transactions over the Ethereum blockchain. Its contribution is multi‐faceted; first, the available imbalanced Ethereum dataset has been balanced to enhance the accuracy of the classification model. Second, the correlation‐based feature selection technique has been applied to retain the best discriminating features. Thirdly, an effective machine learning‐based ensemble classification model has been adopted for the identification of fraudulent transactions over the Ethereum network. A comparative analysis of 10 machine learning techniques has been presented consisting of both individual and ensemble classifiers. Evaluated outcomes show that ensemble classifiers appear to yield better performance measures over individual classifiers, and among all, the LightGbm classifier outperformed with 99.2% accuracy. Further, extensive experiments indicate that the proposed method outperforms the state‐of‐the‐art method when applied to a similar dataset.
Heterogeneity in Extended Reality Influences Procedural Knowledge Gain and Operation Training

Extended reality (XR) is a general term for virtual reality (VR), augmented reality (AR), and mixed reality (MR). By converting abstract digital expressions into intelligent feedback through figures, one can effectively compensate for the poor performance of traditional learning in deep cognitive processing and operational skills training. However, the extant results are uncertain, and only a limited number of studies have investigated the influence mechanism of heterogeneity among VR, AR, and MR on procedural knowledge learning, higher-level presence and generative cognitive processing, higher-quality operational behavior, and knowledge transfer. This study explored and analyzed the influence of XR heterogeneity on procedural knowledge gain and operation training. Participants ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> = 127) were recruited from junior undergraduate students majoring in rail traffic signal and control. Based on the procedural disassembly and assembly of the ZD6 electric switch machine, an XR pretraining system and a 3-D-printed combined virtual-real procedural training system were developed to collect three types of data regarding knowledge gain, procedural training, and a set of questionnaires. Subsequently, a one-way analysis of variance was conducted with XR as the moderator variable. The results indicate that fully immersive VR is more conducive to procedural operation training; however, space and real factors must be considered. AR is consistent with the learner's operating habits, but the corresponding lack of immersion and interactivity restricts the improvement of procedural learning effects. MR multimodal perception leads to an increase in cognitive load, but moderate cognitive pressure is conducive to the cultivation of procedural cognition skills. Furthermore, this study proposes suggestions for using XR to intensify the perception and understanding of procedural cognitive actions.
Theories Supporting Central Bank Digital Currency Development and Its Usefulness

This chapter presents some theories that support central bank digital currency development and its usefulness. The theories provide useful explanations for the development and use of central bank digital currency in the economy. Some theories show that information about central bank digital currency, as well as the perceived usefulness and ease of use of central bank digital currency, is crucial for its success. Other theories show that central bank digital currency can facilitate the flow of funds to economic agebts, and enhance the functioning of the economic system, thereby contributing to economic growth. These theories are useful to economists, policymakers and researchers who are interested in how central bank digital currency affects economic activities.
Accountable Knowledge-aware Recommender Systems

Knowledge-aware algorithms represent one of the most innovative research directions in the area of recommender systems. The use of different types of content representation requires new methods to extract descriptive features to adopt in the recommendation process. The literature on knowledge-aware recommender systems is actually rich and constantly evolving in terms of both techniques and software libraries to implement them. This makes also difficult to define reproducible recommendation pipelines, making the accountability of recommender systems a challenge. This tutorial aims to discuss the most recent trends in the area of knowledge-aware recommender systems, including novel representation methods for textual content, and discuss how to implement reproducible pipelines for knowledge-aware recommender systems. We pursue our goals by using a comprehensive Python framework called ClayRS1 to deal with knowledge-aware recommender systems. We would like to provide: (i) common ground for researchers and practitioners interested in the latest knowledge-aware techniques for user modeling and recommender systems; (ii) a practical way for implementing the whole recommendation pipeline, ranging from the content processing for text to the generation of recommendations and the evaluation of their performance.
Service-based Presentation of Multimodal Information for the Justification of Recommender Systems Results

The current models for the explanation and justification of recommender systems results focus on qualitative and quantitative data about items, overlooking the power of images to describe the different aspects of experience that the consumer should expect from their selection to post-sales. In the present paper, we extend previous justification models by exploiting object recognition on images to support a service-oriented presentation of multimodal (textual, quantitative, and images) information about items. As a testbed for our model, we chose the home-booking domain. In a user study, we found that item comparison can be enhanced by empowering the user to filter multimodal data based on a set of evaluation dimensions describing the experience with items. These results encourage the introduction of service-based filters for multimodal information retrieval in product and service catalogs.
Modular Multi-Copter Structure Control for Cooperative Aerial Cargo Transportation

Abstract The control problem of a multi-copter swarm, mechanically coupled through a modular lattice structure of connecting rods, is considered in this article. The system’s structural elasticity is considered in deriving the system’s dynamics. The devised controller is robust against the induced flexibilities, while an inherent adaptation scheme allows for the control of asymmetrical configurations and the transportation of unknown payloads. Certain optimization metrics are introduced for solving the individual agent thrust allocation problem while achieving maximum system flight time, resulting in a platform-independent control implementation. Experimental studies are offered to illustrate the efficiency of the suggested controller under typical flight conditions, increased rod elasticities and payload transportation.
Amplifying Artists’ Voices: Item Provider Perspectives on Influence and Fairness of Music Streaming Platforms

The majority of music consumption nowadays takes place on music streaming platforms. Whichever artists, albums, or songs are exposed to consumers on these platforms therefore greatly influences what music is ultimately consumed. As a result, the impact of these platforms on artists—their main item providers—is considerable. The recommender systems at the core of streaming platforms, though, have traditionally been developed focusing on end consumer objectives. Only recently, researchers have started to include item provider objectives, though rarely through reaching out to item providers directly. By omitting this important stakeholder’s point of view, we risk not understanding what artists value most, and might miss first-hand ideas on how to improve streaming platforms and recommender systems. Therefore, we conducted semi-structured interviews to capture the artists’ view. Specifically, we explore artists’ considerations regarding fairness, transparency, and diversity in music recommender systems, and the role artists envision for streaming platforms regarding those topics. We identify some topics with a clear consensus among artists, such as desiring more control over which music is recommended to whom, and expecting streaming platforms to actively increase music diversity in recommendations. In contrast, artists’ opinions differ on whether platforms should actively intervene in recommender systems to, e.g., increase localization or gender balance. Further, we observe that artists often take user preferences into account and even suggest new platform functionality to benefit both users and item providers. We encourage utilizing these insights when designing and evaluating music streaming platforms and recommender systems.
Evaluating a Large Language Model on Searching for GUI Layouts

The field of generative artificial intelligence has seen significant advancements in recent years with the advent of large language models, which have shown impressive results in software engineering tasks but not yet in engineering user interfaces. Thus, we raise a specific research question: would an LLM-based system be able to search for relevant GUI layouts? To address this question, we conducted a controlled study evaluating how Instigator, an LLM-based system for searching GUI layouts of web pages by generative pre-trained training, would return GUI layouts that are relevant to a given instruction and what would be the user experience of (N =34) practitioners interacting with Instigator. Our results identify a very high similarity and a moderate correlation between the rankings of the GUI layouts generated by Instigator and the rankings of the practitioners with respect to their relevance to a given design instruction. We highlight the results obtained through thirteen UEQ+ scales that characterize the user experience of the practitioner with Instigator, which we use to discuss perspectives for improving such future tools.
Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks

In this article, we advocate for the design of ad hoc artificial intelligence (AI)/machine learning (ML) models to facilitate their usage in future smart infrastructures based on communication networks. To motivate this, we first review key operations identified by the 3GPP for transferring AI/ML models through 5G networks and the main existing techniques to reduce their communication overheads. We also present a novel communication-aware ML framework, which we refer to as Accordion, that enables an efficient AI/ML model transfer thanks to an overhauled model training and communication protocol. We demonstrate the communication-related benefits of Accordion, analyse key performance trade-offs, and discuss potential research directions within this realm.
Optimization of Blockchain Based E-voting

Modern technology has created the need for information flow to be fast and effective. we have been able to reach this through the internet; the medium, that is, the internet has expedited every folks, by creating the communication cheap and quick. One such innovative technology is blockchain. Having the properties such as immutability, integrity and decentralized architecture, one of the prospective application of blockchain is e-voting. The objective of e-voting system using blockchain is to facilitate transparent, fair and verifiable approach. But the existing e-voting systems using blockchain take more time to reflect the vote casted by the voters as the number of voters increases and make the system slow. In this paper we propose a system which uses a consensus algorithm called PoA to minimize the time for authentication, mining and verification of casted votes. The Proposed system which replaces the PoW consensus algorithm, in existing system, with PoA consensus algorithm, proved to be faster than existing system by more than 9 seconds. Thus this paper highlights how the existing e-voting system using blockchain is slow and how the proposed system optimizes the time required and makes the system faster.
Creation of Juices with Interactive Genetic Algorithm via Taste Communication between Users in Remote Locations

Creating media content suited to many users is crucial from the viewpoint of business and a situation with multiple users' participation. Interactive Evolutionary Computation (IEC) is known as a search method that finds good solutions; however, it is originally for each user. This study proposes an IEC that gathers evaluations of multiple users in remote locations and utilizes them in the search, and the search target is good blended juices. Each user's good solution is exchanged between the users as a taste-communication. We had the experiment to investigate the proposed IEC's effectiveness with ten participants in five pairs: two participants of the pair located in different locations. The experimental results showed the effectiveness with a significant increase in fitness values and a significant convergence in the search space. Furthermore, details of the exchange of the GA individuals were investigated by observing the pair's exchange and comparing the fitness values of the exchanged GA individuals.
Study of Machine Learning and Deep Learning Algorithms for the Detection of Email Spam based on Python Implementation

Spam is the act of sending unsolicited emails to a large number of users for phishing, spreading malware, etc. Internet Service Providers (ISPs) and email inbox providers (like Gmail, Yahoo Mail, AOL, etc.) rely on SPAM filters, firewalls, and blacklist directories to prevent "unsolicited" SPAM emails from entering your inbox. Spam mails are overrunning email inboxes, which significantly slows down internet performance. It is crucial to properly analyze the connections between these spammers and spam because the majority of us tend to provide them with crucial information, such as our contact information. Since the benefactor covers a large percentage of the costs related to spamming, it effectively serves as advertising for the cost of mailing. The study of existing work shows that machine learning and deep learning are frequently employed to effectively identify email spam. This research paper is secondary work in which we have studied, and implemented the various machine learning and deep learning approaches to identify email spam in Python. The four machine learning algorithms—KNN, Navies Bayes, BiLSTM, and Deep CNN—show that they can be utilized effectively to detect spam. Yet the Deep CNN outperforms the other three based on accuracy and the F1 score.
Using Convolutional Neural Network for Human Posture Estimation: A study of the effects of number of layers and number of neurons on accuracy

Human Posture estimation is a field which gathers huge researchers interest due to its variations in different machine learning (ML) & deep learning (DL) architectures to estimate human postures This work includes tweaking of layers with varying neurons in Convolutional Neural Network Architecture to test which pair of neurons and layers gives the best accuracy, also visualizing each of the pair with help of graphs. The results of this work provide great results with high accuracy.
URL Based Malicious Activity Detection Using Machine Learning

The constant use of the Internet has led to an increased vulnerability to malware attacks through malicious websites. The goal of this research is to create a machine-learning algorithm that will detect whether URLs contain susceptible activities such as viruses, phishing, malware, worms, etc. or are secure. Malicious URLs are compromised URLs that are employed in drive-by downloads and online attacks. Phishing and social engineering are common types of attacks that use malicious URLs. The fact that one-third of all websites have the potential to be harmful shows how widespread bad URLs are in online crime. This work deals with three machine learning models, such as random forest, light GBM, and XG Boost, to analyse our data and give the best one as per the results and analysis.
Medical Image Segmentation Approach via Transformer Knowledge Distillation

Numerous transformer-based medical image segmentation methods have been proposed and achieved good segmentation results. However, it is still a challenge to train and deploy transformer networks to mobile medical devices due to a large number of model parameters. To resolve the training and model parameter problems, in this paper, we propose a Transformer-based network for Medical Image Segmentation using Knowledge Distillation named MISTKD. The MISTKD consists of a teacher network and a student network. It achieves comparable performance to state-of-the-art transformer works using fewer parameters by employing the teacher network to train the student network. The training can be implemented by extracting the sequence in the teacher and student encoder networks during the training procedure. The losses between sequences are further calculated, thus the student network can learn from the teacher network. The experimental results on Synapse show that the proposed work achieves competitive performance using only one-eighth parameters.
High Fidelity Face Generation with Style Generative Adversarial Networks

This study proposes a novel Generative Adversarial Network (GAN)-based model for text-to-face generation, which generates high-fidelity, diverse and realistic facial images that are consistent with the provided textual descriptions. The model utilizes the StyleGAN architecture and is trained on a large dataset of real human faces. Various metrics are employed to assess the effectiveness of the proposed model. The potential applications of this technology are diverse and include criminal investigation, data augmentation for face recognition systems, computer graphics, and entertainment. The proposed model can generate realistic and visually appealing facial images that can be useful in a variety of fields. This study demonstrates the effectiveness of using GAN-based models for text-to-image generation, highlighting their potential for improving the accuracy and robustness of various applications.
Media Multitasking with M-Learning Technology in Real-Time Classroom Learning: Analysing the Dynamics in Formal Educational Settings for the Future of E-Learning in India

Teachers’ ability to integrate digital technology with formal pedagogical approaches is vital because it ensures that students retain the level of concentration required to understand complex arguments and conduct rigorous classroom-led research. The researcher here argues that classroom dynamics are increasingly at risk due to the addictive nature brought about by the pervasiveness of digital devices and social media, even though many studies have acknowledged the importance of technology in this media-rich world and the idea of “learn anytime, anywhere” associated with mobile learning. Due to the lack of comprehensive studies on the effects of technology on education, especially on the effects of “multitasking” on the teacher’s traditional roles as media orchestrators and learning facilitators, this article critically evaluates the existing literature on mobile learning. By discussing the pros and cons of incorporating technology into the traditional classroom, this article further attempts to give impetus to a discussion that has long been overdue
Blood Cell Classification using Neural Network Models

Blood cells classification is a crucial aspect in medical diagnosis. Several machine learning models have been proposed under various researches for classification of blood cells in recent years. However, the traditional machine learning algorithms are limited in the accurate detection of abnormal cells. In this study, we propose deep learning based approach for blood cell classification and evaluate the efficiency of multi-layer neural network model built for the classification of the various types of White Blood Cells using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in combination. The proposed method leverages the strengths of both CNN and RNN and gives better results.
Support vector regression model with variant tolerance

Most works on Support Vector Regression (SVR) focus on kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius [Formula: see text]-tube, affording good predictive performance on datasets. However, the fixed radius limitation prevents the adaptive selection of support vectors according to the data distribution characteristics, compromising the performance of the SVR-based methods. Therefore, this study proposes an “Alterable [Formula: see text]-Support Vector Regression” ([Formula: see text]-SVR) model by applying a novel [Formula: see text], named “Alterable [Formula: see text],” to the SVR model. Based on the data point sparsity at each location, the model solves the different [Formula: see text] at the corresponding position, and thus zoom-in or zoom-out the [Formula: see text]-tube by changing its radius. Such a variable [Formula: see text]-tube strategy diminishes noise and outliers in the dataset, enhancing the prediction performance of the [Formula: see text]-SVR model. Therefore, we suggest a novel non-deterministic algorithm to iteratively solve the complex problem of optimizing [Formula: see text] associated with every location. Extensive experimental results demonstrate that our approach can improve the accuracy and stability on simulated and real data compared with the baseline methods.
Strengthened Circle and Popov Criteria for the Stability Analysis of Feedback Systems With ReLU Neural Networks

This paper considers the stability analysis of a Lurie system with a static repeated ReLU (rectified linear unit) nonlinearity. Properties of the ReLU function are leveraged to derive new tailored quadratic constraints (QCs) which are satisfied by the repeated ReLU. These QCs are used to strengthen the Circle and Popov Criteria for this specialised Lurie system. It is shown that the criteria can be cast as a set of linear matrix inequalities (LMIs) with less restrictive conditions on the matrix variables. Many systems involving a neural network (NN) with ReLU activations are important instances of this specialised Lurie system; for example, a continuous time recurrent neural network (RNN) or the interconnection of a linear system with a feedforward NN. Numerical examples show the strengthened criteria strike an appealing balance between reduced conservatism and complexity, compared to existing criteria.