Reviewerly/exHarmony huggingface dataset:
Hybrid Quantum–Classical Generative Adversarial Network for High-Resolution Image Generation

Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems such as classification and identification tasks. A subclass of QML methods is quantum generative adversarial networks (QGANs) which have been studied as a quantum counterpart of classical GANs widely used in image manipulation and generation tasks. The existing work on QGANs is still limited to small-scale proof-of-concept examples based on images with significant down-scaling. Here we integrate classical and quantum techniques to propose a new hybrid quantum-classical GAN framework. We demonstrate its superior learning capabilities over existing quantum techniques by generating 28×28 pixels grey-scale images without dimensionality reduction or classical pre/post-processing on multiple classes of the standard MNIST and Fashion MNIST datasets, which achieves comparable results to classical frameworks with three orders of magnitude less trainable generator parameters. To gain further insight into the working of our hybrid approach, we systematically explore the impact of its parameter space by varying the number of qubits, the size of image patches, the number of layers in the generator, the shape of the patches and the choice of prior distribution. Our results show that increasing the quantum generator size generally improves the learning capability of the network. The developed framework provides a foundation for future design of QGANs with optimal parameter set tailored for complex image generation tasks
Microwave-driven <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>i</mml:mi></mml:math> swap-like gate for fixed-frequency superconducting transmon qutrits

High-fidelity two-qubit gates are crucial for the scalability of superconducting quantum processors. While quantum information processing is typically based on qubits, qutrits (or qudits) provide a larger state space for quantum information storage and processing. In this study, we analyze a high-fidelity two-qutrit gate using microwave pulses on fixed-frequency superconducting transmon qutrits and show that a high-fidelity imaginary swap-like ($i$swap-like) gate can be achieved. Perturbation theory is employed to derive an effective interaction Hamiltonian, allowing us to estimate the gate time for the two-qutrit system. Moreover, by employing a microwave-activation scheme, we can realize a high-fidelity $i$swap-like gate on different excited states of the two qutrits. Our numerical results indicate that the proposed scheme can be readily extended to multiqutrit scenarios. Additionally, we investigate the impact of microwave pulse ramp time and qutrit relaxation on gate fidelity. The results demonstrate that, with a longer ramp time and the currently accessible qutrit relaxation time, the gate error remains sufficiently small. This proposed gate scheme enables the implementation of qutrit-qutrit entangling gates, providing a promising approach to realizing superconducting quantum computation.
A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning

Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.
The Future of Cyber Security Starts Today, Not Tomorrow

This study dives into the field of cybersecurity, analyzing current trends and laying out a conceptual framework for spotting new issues with societal bearing that call for more study in the area. The research eventually seeks to better society's general cybersecurity posture by effectively spotting trends and new risks by using text mining to examine cybersecurity material distributed between 2008 and 2018 in both scholarly and media sources. There is a significant time-based connection between the resources, as shown by the study's discovery of both convergences and divergences between the two cybersecurity corpora. Overall, the study's methodology shows how well automatic methods can be used to offer insightful information on socially significant and new cybersecurity subjects. The framework directs future academic study in this area with the intention of strengthening society's overall cybersecurity stance.
Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey

Adversarial attacks and defenses in machine learning and deep neural network (DNN) have been gaining significant attention due to the rapidly growing applications of deep learning in communication networks. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on DNN-based classification models for communication applications. Specifically, we conduct a comprehensive classification of recent adversarial attack methods and state-of-the-art adversarial defense techniques based on attack principles, and present them in visually appealing tables and tree diagrams. This is based on a rigorous evaluation of the existing works, including an analysis of their strengths and limitations. We also categorize the methods into counter-attack detection and robustness enhancement, with a specific focus on regularizationbased methods for enhancing robustness. New avenues of attack are also explored, including search-based, decision-based, dropbased, and physical-world attacks, and a hierarchical classification of the latest defense methods is provided, highlighting the challenges of balancing training costs with performance, maintaining clean accuracy, overcoming the effect of gradient masking, and ensuring method transferability. At last, the lessons learned and open challenges are summarized with future research opportunities recommended.
RealityReplay

Humans easily miss events in their surroundings due to limited short-term memory and field of view. This happens, for example, while watching an instructor's machine repair demonstration or conversing during a sports game. We present RealityReplay, a novel Mixed Reality (MR) approach that tracks and visualizes these significant events using in-situ MR visualizations without modifying the physical space. It requires only a head-mounted MR display and a 360-degree camera. We contribute a method for egocentric tracking of important motion events in users' surroundings based on a combination of semantic segmentation and saliency prediction, and generating in-situ MR visual summaries of temporal changes. These summary visualizations are overlaid onto the physical world to reveal which objects moved, in what order, and their trajectory, enabling users to observe previously hidden events. The visualizations are informed by a formative study comparing different styles on their effects on users' perception of temporal changes. Our evaluation shows that RealityReplay significantly enhances sensemaking of temporal motion events compared to memory-based recall. We demonstrate application scenarios in guidance, education, and observation, and discuss implications for extending human spatiotemporal capabilities through technological augmentation.
Learning an Effective Transformer for Remote Sensing Satellite Image Dehazing

The existing remote sensing (RS) image dehazing methods based on deep learning have sought help from the convolutional frameworks. Nevertheless, the inherent limitations of convolution, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., local receptive fields and independent input elements, curtail the network from learning the long-range dependencies and non-uniform distributions. To this end, we design an effective RS image dehazing Transformer architecture, denoted as RSDformer. Firstly, given the irregular shapes and non-uniform distributions of haze in RS images, capturing both local and non-local features is crucial for RS image dehazing models. Hence, we propose a detail-compensated transposed attention to extract the global and local dependencies across channels. Secondly, to enhance the ability to learn degraded features and better guide the restoration process, we develop a dual-frequency adaptive block with dynamic filters. Finally, a dynamic gated fusion block is designed to achieve fuse and exchange features across different scales effectively. In this way, the model exhibits robust capabilities to capture dependencies from both global and local areas, resulting in improving image content recovery. Extensive experiments prove that the proposed method obtains more appealing performances against other competitive methods.
Efficient Speed Control for DC Motors Using Novel Gazelle Simplex Optimizer

This paper addresses the design of an optimally executed proportional-integral-derivative (PID) controller, tailored for the speed regulation of a direct current (DC) motor. To achieve this objective, we present a novel hybrid algorithm, combining the gazelle optimization algorithm (GOA) with the effective simplex search method known as the Nelder-Mead (NM) technique. The fusion of these algorithms yields an innovative hybridized version, striking the balance between exploration and exploitation. The proposed approach, named the gazelle simplex optimizer (GSO), showcases great promise when applied to the task of controlling the speed regulation of a DC motor using the PID controller. To identify the optimal values for PID gains, we harness the power of a novel objective function as well, which guides the GSO in determining the most favorable controller settings. Rigorous comparative simulations are then undertaken, where we pit the GSO against several other algorithms, namely the reptile search algorithm, prairie dog optimization algorithm, weighted mean of vectors optimization, and the original GOA algorithm. These simulations allow us to assess the system’s behavior through various lenses, such as statistical tests, time and frequency domain responses, robustness analysis, and changes in the objective function. The evaluations from these comprehensive tests demonstrate the superiority of the GSO-based PID controlled DC motor speed regulation system. The GSO exhibits better performance than the alternative algorithms, providing solid evidence of its effectiveness. Furthermore, the proposed GSO approach outperforms other reported PID tuning methods, affirming its prowess in achieving superior speed regulation for DC motors.
Fine-Tuning Transformer Models Using Transfer Learning for Multilingual Threatening Text Identification

Threatening content detection on social media has recently gained attention. There is very limited work regarding threatening content detection in low-resource languages, especially in Urdu. Furthermore, previous work explored only mono-lingual approaches, and multi-lingual threatening content detection was not studied. This research addressed the task of Multi-lingual Threatening Content Detection (MTCD) in Urdu and English languages by exploiting transfer learning methodology with fine-tuning techniques. To address the multi-lingual task, we investigated two methodologies: 1) Joint multi-lingual, and 2) Joint-translated method. The former approach employs the concept of building a universal classifier for different languages whereas the latter approach applies the translation process to transform the text into one language and then perform classification. We explore the Multilingual Representations for Indian Languages (MuRIL) and Robustly Optimized BERT Pre-Training Approach (RoBERTa) with fine-tuning that already demonstrated state-of-the-art in capturing the contextual and semantic characteristics within the text. For hyper-parameters, manual search and grid search strategies are utilized to find the optimum values. Various experiments are performed on bi-lingual English and Urdu datasets and findings revealed that the proposed methodology outperformed the baselines and showed benchmark performance. The RoBERTa model achieved the highest performance by demonstrating 92% accuracy and 90% macro f1-score with the joint multi-lingual approach.
State-of-the-Art Human-Computer-Interaction in Metaverse

With the increasing popularity of the Metaverse concept, human beings have stepped to a new height in the intelligent technology progress. This work presents a literature review of the current research on Human-Computer Interaction in the Metaverse. Keywords like "Metaverse," "Human-Computer Interaction (HCI)," "virtual space," "virtual technology," "three-dimensional (3D) reconstruction," "parallel universe," "independent identity," "access to interests," and "blockchain" are used to search for relevant articles in the literature in databases like Scopus, Web of Science, and Google Academic. Nearly 100 advanced studies on the Metaverse are selected from over 20,000 documents covering 2018-2023. Finally, the current application state of the Metaverse's underlying technologies is explored and described using the PRISMA principles; these technologies include the fifth-generation (5 G) communication, HCI, virtual technology, blockchain, and 3D reconstruction. Moreover, the predictions for HCI's future growth in the Metaverse are made. The review concludes that the rapid advancement of the 5 G connection has made the concept of the Metaverse possible. The blockchain ensures the safety of currency transactions in the Metaverse virtual space. There will be a shift toward "invisibility" in the way that humans and computers interact in virtual worlds. In other words, HCI will be transparent to the user in the digital realm, and people will get along with the computer in a natural and equitable way. In the interaction, wearable devices allow for an immersive experience in the interaction, but they restrict the participants' freedom of movement and perception. The more humanized somatosensory connection will gain traction in the future, bringing people one step closer to the Metaverse.
Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations

The success of touristic businesses, attractions, and destinations heavily relies on travel agents’ recommendations, which significantly impact client satisfaction. However, the underlying recommendation process employed by travel agents remains poorly understood. This study presents a conceptual model of the recommendation process and empirically investigates the influence of tourism categories on agents’ destination recommendations. By employing collaborative filtering-based recommendation systems and comparing various algorithms, including matrix factorization and deep learning models, such as the bilateral variational autoencoder (BiVAE) and light graph convolutional neural network, this research provides insights into the performance of different techniques in the context of tourism. The models were evaluated using a tourism dataset and assessed through a range of metrics. The results indicate that the BiVAE algorithm outperformed others in terms of ranking and prediction metrics, underscoring the significance of considering multiple measurements and exploring diverse techniques. The findings have practical implications for tourism marketers seeking to influence travel agents and offer valuable insights for researchers investigating this domain. Additionally, the proposed model holds potential for applications in travel recommendation systems, including attraction recommendations.
A quaternion Sylvester equation solver through noise-resilient zeroing neural networks with application to control the SFM chaotic system

&lt;abstract&gt;&lt;p&gt;Dynamic Sylvester equation (DSE) problems have drawn a lot of interest from academics due to its importance in science and engineering. Due to this, the quest for the quaternion DSE (QDSE) solution is the subject of this work. This is accomplished using the zeroing neural network (ZNN) technique, which has achieved considerable success in tackling time-varying issues. Keeping in mind that the original ZNN can handle QDSE successfully in a noise-free environment, but it might not work in a noisy one, and the noise-resilient ZNN (NZNN) technique is also utilized. In light of that, one new ZNN model is introduced to solve the QDSE problem and one new NZNN model is introduced to solve the QDSE problem under different types of noises. Two simulation experiments and one application to control of the sine function memristor (SFM) chaotic system show that the models function superbly.&lt;/p&gt;&lt;/abstract&gt;
Beyond generalization: a theory of robustness in machine learning

Abstract The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning varies depending on context and community. Researchers either focus on narrow technical definitions, such as adversarial robustness, natural distribution shifts, and performativity, or they simply leave open what exactly they mean by robustness. In this paper, we provide a conceptual analysis of the term robustness , with the aim to develop a common language, that allows us to weave together different strands of robustness research. We define robustness as the relative stability of a robustness target with respect to specific interventions on a modifier. Our account captures the various sub-types of robustness that are discussed in the research literature, including robustness to distribution shifts, prediction robustness, or the robustness of algorithmic explanations. Finally, we delineate robustness from adjacent key concepts in ML, such as extrapolation, generalization, and uncertainty, and establish it as an independent epistemic concept.
FedSULP: A communication-efficient federated learning framework with selective updating and loss penalization

Federated Learning allows a myriad of clients to train a global model in a collaborative process over a period of rounds without essentially sharing their privately-stored data. The high frequency of communicating model updates to and from the central server, coupled with indiscriminate updating of the global model during training, makes communication a bottleneck for federated learning. Several methods, including model compression techniques, have been introduced to improve the communication efficiency of federated learning. However, some of these methods are computationally expensive and time-consuming whiles others result in information loss and low convergence rates. In this paper, we propose a new federated learning framework, FedSULP, that significantly reduces communication costs by accelerating learning convergence through gradient-based selective updating and clients' loss penalization. Specifically, we first investigate the gradient direction of the global model and then estimate the significance of the local updates to the convergence of the global model. Subsequently, we perform selective updating of the global model based on the estimation outcome. Additionally, we optimally penalize the loss objectives of the clients if their collective gradients and learned parameters largely deviate from the global model. Extensive experiments performed on real-world image classification data sets demonstrated that our proposed framework ensures faster learning convergence and reduces communication up to 2x, compared to other existing baseline and state-of-the-art frameworks, while developing good-performing models.
Contrast Enhancement-Based Preprocessing Process to Improve Deep Learning Object Task Performance and Results

Excessive lighting or sunlight can make it difficult to judge visually. The same goes for cameras that function like the human eye. In the field of computer vision, object tasks have a significant impact on performance depending on how much object information is provided. Light presents difficulties in recognizing objects, and recognition is not easy in shadows or dark areas. In this paper, we propose a contrast enhancement-based preprocessing process to obtain improved results in object recognition tasks by solving problems that occur due to light or lighting conditions. The proposed preprocessing process involves the steps of extracting optimal values, generating optimal images, and evaluating quality and similarity, and it can be applied to the generation of training and input data. As a result of an experiment in which the preprocessing process was applied to an object task, the object task results for areas with shadows or low contrast were improved while the existing performance was maintained for datasets that require contrast enhancement technology.
New Technology Deployment and Corporate Responsibilities in the Metaverse

The term “metaverse” came to the fore in 2021 when Facebook rebranded its corporate identity to Meta and signalled its intention to invest at least USD 10 billion in developing the concepts and related products that year. However, there is still little consensus in defining what constitutes the metaverse, although there is a widespread, though not universal, agreement that it will bring a wide range of benefits across society. More specifically, the advent and continuing evolution of the metaverse has strategic and operational implications for, and impacts on, industry and business at large. Adopting an inductive, interpretivist approach, this exploratory research article presents case examples of the guidance on the responsible development of the metaverse provided by two IT business services companies. This article identifies the major risks and responsibilities associated with the metaverse and assesses how companies might address these responsibilities. Very little research has been published in this area, and this article attempts to make a small contribution to filling this gap in the literature. This article finds that these responsibilities are largely in line with those currently associated with corporate digital responsibility, and concludes that the strategic impact and extent of regulatory change will depend on the nature of the metaverse that materialises in the forthcoming decade.