Reviewerly/exHarmony huggingface dataset:
Application of a 3D Talking Head as Part of Telecommunication AR, VR, MR System: Systematic Review

In today’s digital era, the realms of virtual reality (VR), augmented reality (AR), and mixed reality (MR) collectively referred to as extended reality (XR) are reshaping human–computer interactions. XR technologies are poised to overcome geographical barriers, offering innovative solutions for enhancing emotional and social engagement in telecommunications and remote collaboration. This paper delves into the integration of (AI)-powered 3D talking heads within XR-based telecommunication systems. These avatars replicate human expressions, gestures, and speech, effectively minimizing physical constraints in remote communication. The contributions of this research encompass an extensive examination of audio-driven 3D head generation methods and the establishment of comprehensive evaluation criteria for 3D talking head algorithms within Shared Virtual Environments (SVEs). As XR technology evolves, AI-driven 3D talking heads promise to revolutionize remote collaboration and communication.
Performance Comparison of Various YOLO Models for Vehicle Detection: An Experimental Study

Development of automatic vehicle detection (AVD) systems using either images or videos from traffic scenarios would be quite beneficial for making an automated traffic management system. There is an abundance of AVD-based research articles that have been published in the literature. This paper focuses on three major object detection algorithms under the You Only Look Once (YOLO) family, namely YOLOv5, YOLOv7, and YOLOv8 for AVD. This paper also discusses the architectural differences found in these variants of YOLO models. For experimental evaluation, we have used two recently introduced AVD datasets developed for the Indian subcontinent, namely JUVDsi v1 and IRUVD. We have achieved a satisfactory outcome on the IRUVD dataset with a mAP score of 0.96 using the YOLOv7 model and mAP score of 0.817 on the JUVDsi v1 dataset using the YOLOv8 model. The code and detailed results can be found at: https://github.com/JUVDsi/yolo-comparison.git .
RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification. This paper analyses VGG-16, InceptionResNetV2, DenseNet121, Inception V3, and MobileNetV2 models to classify real-life waste when trained on pristine and unadulterated materials, versus samples collected at a landfill site. When training on DiversionNet, the unadulterated material dataset with labels required for landfill modelling, classification accuracy was limited to 49.69% in the real environment. Using real-world samples in the newly formed RealWaste dataset showed that practical applications for deep learning in waste classification are possible, with Inception V3 reaching 89.19% classification accuracy on the full spectrum of labels required for accurate modelling.
Blockchain-Based Renewable Energy Certificate Trade for Low-Carbon Community of Active Energy Agents

The future distribution grid is a peer-to-peer (P2P) community formed by a large number of active energy agents (AEAs), and renewable energy certificate (REC) trading is an efficient way to realize a low-carbon AEA community. AEAs can trade not only electricity but also RECs among themselves to economically and efficiently meet the renewable portfolio standard (RPS) requirements. Aiming to lower the market barrier and increase the trading benefits for market participants, this paper proposes a blockchain-based renewable energy certificate (BCREC) that supports divisible and multiple transactions. The trade process includes four stages: setup, pre-transaction, transaction, and post-transaction. A scheme based on blockchain oracles and smart contracts is implemented to achieve decentralized BCREC issuance and transaction and to support a more flexible trading market. By exploring two typical market scenarios, we verify the advantages of BCREC trading and evaluate its impacts on AEA profits and market efficiency.
Tweet acts and quote-tweetable acts

Abstract Online communication can often seem different to offline talk. Structural features of social media sites can shape the things we do with words. In this paper, I argue that the practice of ‘quote-tweeting’ can cause a single utterance that originally performed just one speech act to later perform several different speech acts. This describes a new type of illocutionary pluralism—the view that a single utterance can perform multiple illocutionary acts. Not only is this type more plural than others (if one utterance can acquire many kinds of illocutionary force), but it also shows how illocutionary forces can be accumulated over time. This is not limited to online utterances—some offline contexts are similarly structured, and so offline utterances can also come to perform many different speech acts.
Fermionic simulators for enhanced scalability of variational quantum simulation

Near-term quantum simulators are mostly based on qubit-based architectures. However, their imperfect nature significantly limits their practical application. The situation is even worse for simulating fermionic systems, which underlie most of material science and chemistry, as one has to adopt fermion-to-qubit encodings which create significant additional resource overhead and trainability issues. Thanks to recent advances in trapping and manipulation of neutral atoms in optical tweezers, digital fermionic quantum simulators are becoming viable. A key question is whether these emerging fermionic simulators can outperform qubit-based simulators for characterizing strongly correlated electronic systems. Here we perform a comprehensive comparison of resource efficiency between qubit and fermionic simulators for variational ground-state emulation of fermionic systems in both condensed matter systems and quantum chemistry problems. We show that the fermionic simulators indeed outperform their qubit counterparts with respect to resources for quantum evolution (circuit depth) as well as classical optimization (number of required parameters and iterations). In addition, they show less sensitivity to the random initialization of the circuit. The relative advantage of fermionic simulators becomes even more pronounced as interaction becomes stronger, or tunneling is allowed in more than one dimension, as well as for spinful fermions. Importantly, this improvement is scalable, i.e., the performance gap between fermionic and qubit simulators only grows for bigger system sizes.
Spatial prediction of the turbulent unsteady von Kármán vortex street using echo state networks

The spatial prediction of the turbulent flow of the unsteady von Kármán vortex street behind a cylinder at Re = 1000 is studied. For this, an echo state network (ESN) with 6000 neurons was trained on the raw, low-spatial resolution data from particle image velocimetry. During prediction, the ESN is provided one half of the spatial domain of the fluid flow. The task is to infer the missing other half. Four different decompositions termed forward, backward, forward–backward, and vertical were examined to show whether there exists a favorable region of the flow for which the ESN performs best. Also, it was checked whether the flow direction has an influence on the network's performance. In order to measure the quality of the predictions, we choose the vertical velocity prediction of direction (VVPD). Furthermore, the ESN's two main hyperparameters, leaking rate (LR) and spectral radius (SR), were optimized according to the VVPD values of the corresponding network output. Moreover, each hyperparameter combination was run for 24 random reservoir realizations. Our results show that VVPD values are highest for LR ≈ 0.6, and quite independent of SR values for all four prediction approaches. Furthermore, maximum VVPD values of ≈0.83 were achieved for backward, forward–backward, and vertical predictions while for the forward case VVPDmax=0.74 was achieved. We found that the predicted vertical velocity fields predominantly align with their respective ground truth. The best overall accordance was found for backward and forward–backward scenarios. In summary, we conclude that the stable quality of the reconstructed fields over a long period of time, along with the simplicity of the machine learning algorithm (ESN), which relied on coarse experimental data only, demonstrates the viability of spatial prediction as a suitable method for machine learning application in turbulence.
Relationships Between Teachers' Technological Competency Levels and Self-Regulated Learning Behavior

In order to provide readers with an overview and summarize the content, the purpose of the study in this chapter is stated as investigating the relationships between how teachers use their technological competency levels in a blended learning environment and improving the self-regulated learning (SRL) skills and behaviors of secondary school learners. Against the background of the architecture and technological advancements of Education 4.0, issues regarding e.g., massive open online courses (MOOCs), are discussed.
Research on Accounting Data Encryption Processing System based on Artificial Intelligence

To address the issues of low confidentiality and poor real-time performance in the encryption process of accounting system data, a proposed solution based on artificial intelligence is presented. Firstly, by studying the knowledge and reasoning abilities utilized by artificial intelligence technology to simulate human experts in solving complex problems, the fundamental structure of the accounting system is obtained. Then, the data prior to encryption is processed using the wavelet denoising method to eliminate noise points from the data. Additionally, a chaotic parameter modulation encryption method is introduced, utilizing a logistic chaotic mapping model to modulate the data parameters. Simultaneously, during encryption and decryption analysis, iterative criteria for data parameters are established to reduce the number of iterations and achieve real-time data encryption. Finally, experimental results demonstrate that this method possesses higher efficiency in terms of real-time performance, integrity, and confidentiality, providing superior real-time data encryption capabilities.
Applications and Software

This chapter discusses the software development aspects of DIRECT-type algorithms. It explores the implementation of both sequential and parallel versions of these algorithms. Various software tools, including the DIRECTGO toolbox and the GENDIRECT algorithmic framework, are discussed in detail. Furthermore, this chapter provides an overview of the DIRECTGOLib library, which includes a collection of global optimization tests and practical problems in the real world. We conduct a thorough examination of test benchmark cases and practical real-world optimization problems and offer a detailed implementation analysis. A significant portion of this chapter is devoted to a numerical benchmark study. As part of this study, various DIRECT-type algorithms are compared with state-of-the-art approaches, including stochastic ones, using different classes of problems, such as box-constrained test problems, problems with various constraints, and real-world practical problems.
IDENTITY CONSTRUCTION BASED ON A COMMUNAL LAYER OF CTI: A CASE ON BOOKSTAGRAM

&lt;p&gt;In this digital information age, Instagram, as one of the online social media platforms, has gained more popularity among young generations as a virtual space to express themselves, including their hobby of reading. Since the number of Instagram users who enjoy reading has been growing rapidly, so has the community of readers and book lovers in this virtual space; hence, the terms bookstagram and bookstagrammer have become more well-known. This study investigates how a Bookstagrammer constructs her language identity and online identity on Instagram by analysing the bookstagrammer’s feeds on Instagram and implementing one layer of the Communication Theory of Identity (CTI), the communal identity. Using a method of virtual ethnography, it has been found that the bookstagrammer used English as the predominant language in her posts to connect and expand the interaction with other bookstagrammers worldwide, even though it was not her first language. Alongside, Instagram’s features, such as hashtags and tags in her posts, were her visible efforts to build her identity by actively engaging in the Bookstagram community that supported her to actively participate in the bookstagram community, which eventually enriched the communal identity of bookstagram. A recommendation is offered since the communal layer, and the other three layers of CTI are interconnected and have the potential to bring a more thorough understanding of one’s identity construction on social media.&lt;/p&gt;
Pitfalls of the Sublinear QAOA-based Factorization Algorithm

Quantum computing devices are believed to be powerful in solving the prime factorization problem, which is at the heart of widely deployed public-key cryptographic tools. However, the implementation of Shor’s quantum factorization algorithm requires significant resources scaling linearly with the number size; taking into account an overhead that is required for quantum error correction the estimation is that 20 millions of (noisy) physical qubits are required for factoring 2048-bit RSA key in 8 hours. Recent proposal by Yan et. al. claims a possibility of solving the factorization problem with sublinear quantum resources. As we demonstrate in our work, this proposal lacks systematic analysis of the computational complexity of the classical part of the algorithm, which exploits the Schnorr’s lattice-based approach. We provide several examples illustrating the need in additional resource analysis for the proposed quantum factorization algorithm.
ML-Based Translation Methods for Protocols and Data Formats

In order to exchange information between systems, the information must get encoded into a predefined data format, and it must be transferred in a protocol that the communicating parties have agreed upon. This works well if all parties follow the same protocol standard and use the same data description schemes. If systems use different data formats or protocols, then some sort of translation is required. Protocol and data format translation has been attempted previously through rule-based approaches, ontologies, and also by using machine learning (ML) techniques. Due to the current advances related to AI/ML methods, tools, and infrastructure, the accuracy and feasibility of “translation” with ML-approaches improved significantly. This paper introduces a generic approach and methodology for translating data formats and protocols with ML-based methods and presents our initial results through JSON-XML and JSON-SenML translation.
Combining Color and Spatial Image Features for Unsupervised Image Segmentation with Mixture Modelling and Spectral Clustering

The demand for accurate and reliable unsupervised image segmentation methods is high. Regardless of whether we are faced with a problem for which we do not have a usable training dataset, or whether it is not possible to obtain one, we still need to be able to extract the desired information from images. In such cases, we are usually gently pushed towards the best possible clustering method, as it is often more robust than simple traditional image processing methods. We investigate the usefulness of combining two clustering methods for unsupervised image segmentation. We use the mixture models to extract the color and spatial image features based on the obtained output segments. Then we construct a similarity matrix (adjacency matrix) based on these features to perform spectral clustering. In between, we propose a label noise correction using Markov random fields. We investigate the usefulness of our method on many hand-crafted images of different objects with different shapes, colorization, and noise. Compared to other clustering methods, our proposal performs better, with 10% higher accuracy. Compared to state-of-the-art supervised image segmentation methods based on deep convolutional neural networks, our proposal proves to be competitive.
Cryptographically Secured Pseudo-Random Number Generators: Analysis and Testing with NIST Statistical Test Suite

There are several areas of knowledge in which (pseudo-)random numbers are necessary, for example, in statistical–mathematical simulation or in cryptography and system security, among others. Depending on the area of application, it will be necessary that the sequences used meet certain requirements. In general, randomness and uniformity conditions are required in the generated sequences, which are checked with statistical tests, and conditions on sequence unpredictability if the application is in security. In the present work, a literature review on cryptographically secure pseudo-random number generators (CSPRNGs) is carried out, they are implemented, and a critical analysis of their statistical quality and computational efficiency is performed. For this purpose, different programming languages will be used, and the sequences obtained will be checked by means of the NIST Statistical Test Suite (NIST STS). In addition, a user’s guide will be provided to allow the selection of one generator over another according to its statistical properties and computational implementation characteristics.
A Privacy-Preserving Blockchain Scheme for the Reliable Exchange of IoT Data

The Internet of Things (IoT) system has been claimed to deliver comfort and a more satisfactory lifestyle. The data flows that connect IoT sub-systems are critical to the success of the whole system. However, there are concerns about how privacy-preserving these flows are. Most existing single-server architecture solutions to the privacy problem have limitations regarding user privacy and anonymity. They may lead to revealing users’ regular activities and affects their integrity and confidentiality. Despite Blockchain technology is suitable for improving IoT systems’ security and privacy, there is a lack of privacy protection in blockchain when accessing personal user data due to privacy threats from internal parties. Thus, we aim to build a protected environment for users to exchange data and maintain privacy with an efficient authentication solution. We consider Blockchain technology using Elliptic Curve Integrated Encryption Scheme (ECIES) and message authentication regulation to enhance security and privacy for the transmitted data. This provides reliable auditing of the users’ access history and efficient authentication within the system. We evaluate the outcomes to show the usefulness of this approach, targeting IoT data, and compare it with current work in the field.
Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle

Motorcycle detection and collision warning are essential features in advanced driver assistance systems (ADAS) to ensure road safety, especially in emergency situations. However, detecting motorcycles from videos captured from a car is challenging due to the varying shapes and appearances of motorcycles. In this paper, we propose an integrated and innovative remote sensing and artificial intelligence (AI) methodology for motorcycle detection and distance estimation based on visual data from a single camera installed in the back of a vehicle. Firstly, MD-TinyYOLOv4 is used for detecting motorcycles, refining the neural network through SPP (spatial pyramid pooling) feature extraction, Mish activation function, data augmentation techniques, and optimized anchor boxes for training. The proposed algorithm outperforms eight existing YOLO versions, achieving a precision of 81% at a speed of 240 fps. Secondly, a refined disparity map of each motorcycle’s bounding box is estimated by training a Monodepth2 with a bilateral filter for distance estimation. The proposed fusion model (motorcycle’s detection and distance from vehicle) is evaluated with depth stereo camera measurements, and the results show that 89% of warning scenes are correctly detected, with an alarm notification time of 0.022 s for each image. Outcomes indicate that the proposed integrated methodology provides an effective solution for ADAS, with promising results for real-world applications, and can be suitable for running on mobility services or embedded computing boards instead of the super expensive and powerful systems used in some high-tech unmanned vehicles.
Filtering in Triplet Markov Chain Model in the Presence of Non-Gaussian Noise with Application to Target Tracking

In hidden Markov chain (HMC) models, widely used for target tracking, the process noise and measurement noise are in general assumed to be independent and Gaussian for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For instance, in a typical radar tracking application, the measurement noise is correlated over time as the sampling frequency of a radar is generally much higher than the bandwidth of the measurement noise. In addition, target maneuvers and measurement outliers imply that the process noise and measurement noise are non-Gaussian. To solve this problem, we resort to triplet Markov chain (TMC) models to describe stochastic systems with correlated noise and derive a new filter under the maximum correntropy criterion to deal with non-Gaussian noise. By stacking the state vector, measurement vector, and auxiliary vector into a triplet state vector, the TMC model can capture the complete dynamics of stochastic systems, which may be subjected to potential parameter uncertainty, non-stationarity, or error sources. Correntropy is used to measure the similarity of two random variables; unlike the commonly used minimum mean square error criterion, which uses only second-order statistics, correntropy uses second-order and higher-order information, and is more suitable for systems in the presence of non-Gaussian noise, particularly some heavy-tailed noise disturbances. Furthermore, to reduce the influence of round-off errors, a square-root implementation of the new filter is provided using QR decomposition. Instead of the full covariance matrices, corresponding Cholesky factors are recursively calculated in the square-root filtering algorithm. This is more numerically stable for ill-conditioned problems compared to the conventional filter. Finally, the effectiveness of the proposed algorithms is illustrated via three numerical examples.