Reviewerly/exHarmony huggingface dataset:
Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges

Nowadays, deep learning (DL) is a popular tool used in various applications in different fields, including the medical domain. DL techniques can cope with several challenges, which are difficult to resolve via traditional artificial intelligence (AI) techniques. Cervical cancer (CC) is one of the leading reasons for death in females and ranks second after breast cancer, with more than 700 mortalities daily. This number is estimated to be 400,000 annually by 2030. However, if the cancer is detected in the early and precancerous stages, it is completely curable. Pap smear and colposcopy are the most widely used screening methods for the detection of cervical cancer. But manual screening approach suffers from a high false rate due to human errors. To overcome this challenge, machine learning (ML) and DL-based computer-aided diagnostic (CAD) techniques are being extensively expanded to automatically segment and categorize cervical cytology and colposcopy images. These methods increase the accuracy of detecting different stages of cervical cancer. Hence, there is an increased interest in creating computer-aided solutions for CC screening, especially in less-developed countries where the majority of cervical cancer-related fatalities occur. This review overviews state-of-the-art approaches that use DL techniques to analyze cervical cytology and screening images. It reviews and discusses relevant DL techniques, their architectures, classification methods, and the segmentation of cervical cytology and colposcopy images. Finally, it reviews the DL algorithms that are currently used in CC screening and offers useful insights, research opportunities and future directions in this field.
Review of Security Methods Based on Classical Cryptography and Quantum Cryptography

Classical cryptography is the process of hiding information and it manages the secret knowledge by encrypting the plain text message through the translation of it to an unintelligible message. Quantum cryptography is also hiding the plain text through encryption and it works based on the law of quantum physics for providing absolute security of data communication. It uses the idea of quantum mechanics to gadget a cryptographic system and the key problems are solved by intrusion detection including the Eavesdropping detection using quantum expertise. On the contrary, principles of secured communication protocol systems are demonstrated with classical quantum cryptography where the keys are distributed securely and are applied in quantum key distribution as well. In this paper, we provide a survey of works on classical cryptography and quantum cryptography and compare them with respect to time, security level and the classification of the data. Moreover, we perform a concise analysis of perspective classical cryptography and the conception of Quantum cryptography with various protocols and highlight the benefits of classical cryptography and Quantum cryptography in different applications. Finally, we provide a set of recommendations for selecting the suitable encryption model for securing the communication.
Next-Gen Mulsemedia: Virtual Reality Haptic Simulator’s Impact on Medical Practitioner for Higher Education Institutions

Immersive technology is one of the emerging trends in education in the twenty-first century, whether that be university training programs, or real-world technical training. However, there has been very little research into the effects and consequences of virtual reality. Various types of eLearning have been used to transmit information in recent years, and especially for medical education, virtual reality plays a vital role in terms of providing effective training; the virtual reality app bridged the gap between traditional learning and practical exposure. This unified reality environment enables users to simulate real-life scenarios and obtain useful information that would otherwise be unavailable. In the real world, it is difficult to grasp. In India’s education sector, virtual reality technology is also being researched at an early stage. The goal of this research paper is to assess and explain the impact of virtual reality simulators on medical students’ desire to learn. In the classroom, the core motivation hypothesis is used to boost motivation. The attention, relevance, confidence, and satisfaction (ARCS) model influenced the interpretation of virtual reality’s impact on student motivation and content update implementation. The study examined the numerous variables of virtual reality simulators and their impact on medical education, using the ARCS model as a factor analysis. According to the study, students wsould learn more and be more motivated if virtual reality simulators were used. Attention, relevance, satisfaction, and confidence indicators were used to develop motivational variables, and the results were significant. We have taken the sample of 607 students’ data for this analysis, through which we have identified the potential of VR made available to students, as well as the faculty, which has the potential to transform medical education. Instructors may be wary of incorporating new technology like VR into their curriculums, but with the support of their students’ learning habits, this may not be a problem. It may help instructors feel more confident, while also enhancing the relationship between faculty, librarians, and students.
Federated Learning as a Privacy Solution - An Overview

The Fourth Industrial Revolution suggests smart and automated industrial solutions by incorporating Artificial Intelligence into it. Today, the world of technology is highly dependent on Machine Learning (ML) and Deep Learning (DL) and their applications. All these ML/DL models, which bring huge benefits and provide Industry 4.0 solutions, require a bulk of data, extensive computational power, and storage for enhanced performance and accuracy. With the current jurisdictions on privacy all over the world, it is hard to access the required amount of data without giving the data ownership to the centralized silos. Taking model to the data source is the idea that makes Federated Learning (FL) a unique and better-suited solution in this situation. In this paper, we present a review of FL, its learning models, aggregation algorithms, frameworks, and the challenges faced by this new paradigm of decentralized and distributed Machine Learning. We discuss the potential applications of FL in various domains that can help improve the efficiency and flexibility of industrial processes. We also talk about their impact on changing the model training trends altogether in terms of data privacy, decentralization, security, and resource management. The main contribution of this work is to provide a comprehensive and concise review and comparative analysis of various frameworks and aggregation algorithms, followed by a discussion of challenges currently faced by FL.
Blockchain in Online Learning: A Systematic Review and Bibliographic Visualization

In the contemporary era of global and sustainable information management, blockchain has made a cutting-edge transformation in online learning. To apprehend this new trajectory, this current systematic review with bibliographic visualization aims to identify the thematic clusters of underlying aspects concerning the sustainable nexus of blockchain and online learning. Using the updated guidelines of the PRISMA flowchart, a total of 434 scholarly research papers from the mainstream research databases, i.e., Web of Science, Scopus, IEEE Xplore, and ScienceDirect, were inspected to be categorized into 15 relevant publications. Bibliographic data were assembled and analyzed accordingly to construct network visualization maps, such as co-authorship, citation, co-citation, bibliographic coupling, and term co-occurrence using VOSviewer 1.6.18. Significant terms were reported and later cross-mapped with those identified by critically reviewing the applicable 15 papers. Standardized scholarship, behavior pattern, and digital badging have been found and derived as themes from the connected clusters. Therefore, current research findings reveal these three broad clutches of themes concerning the sustainable nexus of blockchain and online learning.
Earthquake Phase Association with Graph Neural Networks

Seismic phase association connects earthquake arrival time measurements to their causative sources. Effective association must determine the number of discrete events, their location and origin times, and it must differentiate real arrivals from measurement artifacts. The advent of deep learning pickers, which provide high rates of picks from closely overlapping small magnitude earthquakes, motivates revisiting the phase association problem and approaching it using the methods of deep learning. We have developed a Graph Neural Network associator that simultaneously predicts both source space-time localization, and discrete source-arrival association likelihoods. The method is applicable to arbitrary geometry, time-varying seismic networks of hundreds of stations, and is robust to high rates of sources and input picks with variable noise and quality. Our Graph Earthquake Neural Interpretation Engine (GENIE) uses one graph to represent the station set and another to represent the spatial source region. GENIE learns relationships from data in this combined representation that enable it to determine robust source and source-arrival associations. We train on synthetic data, and test our method on real data from the Northern California (NC) seismic network using input generated by the PhaseNet deep learning phase picker. We successfully re-detect ~96% of all events M>1 reported by the USGS during 500 random days between 2000$\unicode{x2013}$2022. Over a 100-day continuous interval of processing in 2017$\unicode{x2013}$2018, we detect ~4.2x the number of events reported by the USGS. Our new events have small magnitude estimates below the magnitude of completeness of the USGS catalog, and are located close to the active faults and quarries in the region. Our results demonstrate that GENIE can effectively solve the association problem under complex seismic monitoring conditions.
Intelligent multimodal pedestrian detection using hybrid metaheuristic optimization with deep learning model

For video surveillance, pedestrian detection assists in providing baseline data for crowd monitoring, people counting, and event detection; for smart transport system, pedestrian detection acts as a vital part in the semantic understanding of the environment. Pedestrian detection is frequently confronted with substantial intra-class variability because human tends to have great variation in human appearance and pose. Currently, the emergence of deep learning (DL) model has received considerable attention in computer vision techniques like object detection and object classification, and this application is based on supervised learning which required labels. Convolution neural networks (CNN) have assisted substantial improvement in pedestrian recognition due to the stronger representative capability of the CNN feature. But it is usually hard to decrease false positives on hard negative samples namely poles, tree leaves, traffic lights, and so on. Therefore, this study develops an intelligent multimodal pedestrian detection and classification using hybrid metaheuristic optimization with deep learning (IPDC-HMODL) algorithm. The major aim of the presented IPDC-HMODL model is the recognition and classification of multiple pedestrians exist in the input frames. It follows a three stage process namely multimodal object detection, pedestrian classification, and parameter tuning. At the initial stage, the IPDC-HMODL model uses multimodal object detector using YOLO-v5 and RetinaNet model. In addition, the IPDC-HMODL model applies kernel extreme learning machine (KELM) algorithm for pedestrian classification. Finally, hybrid salp swarm optimization (HSSO) model is used for optimal parameter adjustment. To depict the improvised outcomes of the IPDC-HMODL technique, a wide spread simulation analysis was conducted. The comparison study highlighted the enhanced outcomes of the IPDC-HMODL model over other approaches on multimodal pedestrian detection.
Thoughts on Architecture

The term architecture has evolved considerably from its original Greek roots and its application to buildings and computers to its more recent manifestation for minds. This article considers lessons from this history, in terms of a set of relevant distinctions introduced at each of these stages and a definition of architecture that spans all three, and a reconsideration of three key issues from cognitive architectures for architectures in general and cognitive architectures more particularly.
Metaverse and Tourism: From a New Niche to a Transformation

Abstract Metaverse is named among the technologies that are predicted to transform everyday life. The proliferation of such technologies as the Internet and smartphones has triggered major transformations in the tourism industry. This paper conceptualises the phenomenon of Metaverse towards the phenomenon of tourism. It applies a semi-systematic literature review methodology to identify existing alignment between the phenomena. The paper concludes that there is a conceptual alignment between the critical dimensions of the Metaverse and tourism. Tourism should be ready for the reciprocal effects of metaverse development on tourism and vice versa, from new opportunities to enhance tourist experience to a possible dissolution of the contemporary understanding of tourism.
University Students’ Engagement in Mobile Learning

The implementation of mobile learning seems to be an emerging topic in many educational institutions. As recently noticed, mobile technology has employed wireless technologies to communicate, think, learn, and share in order to spread and exchange information. Therefore, using mobile technologies in learning and teaching can create a positive environment in higher education. Hence, the purpose of this study is to evaluate mobile learning engagement among educational technology students. Data from three focus group discussions and 15 semi-structured interviews with students who experienced mobile learning were gathered using a qualitative approach design. A total of seventeen basic themes and four organizing themes were extracted, where the researchers categories of engagement, i.e., social engagement, cognitive engagement, emotional engagement, and behavioral engagement. In the present research, the findings indicate that social engagement themes included social-mobile interaction, building community, developing relationships, and competition. The cognitive engagement themes included attention, cognitive and meta-cognitive strategies, immersion, and cognitive curiosity. Emotional engagement themes included excitement and enjoyment, instructor comforting students, motivation, and emotional safety. Behavioral engagement themes included effort and time on task, attendance, participation, and positive conduct.
DBO-Net: Differentiable bi-level optimization network for multi-view clustering

Multi-view clustering on traditional optimization methods is derived from different theoretical frameworks, yet it may be inefficient in dealing with complex multi-view data compared to deep models. In contrast, deep multi-view clustering methods for implicit optimization have excellent feature abstraction ability but are inscrutable due to their black-box problem. However, very limited research was devoted to integrating the advantages of the above two types of methods to design an efficient method for multi-view clustering. Focusing on these problems, this paper proposes a differentiable bi-level optimization network (DBO-Net) for multi-view clustering, which is implemented by incorporating the traditional optimization method with deep learning to design an interpretable deep network. To enhance the representation capability, the proposed DBO-Net is constructed by stacking multiple explicit differentiable block networks to learn an interpretable consistent representation. Then all the learned parameters can be implicitly optimized through back-propagation, making the learned representation more suitable for the clustering task. Extensive experimental results validate that the strategy of bi-level optimization can effectively improve clustering performance and the proposed method is superior to the state-of-the-art clustering methods.
Deepfake Histologic Images for Enhancing Digital Pathology

A pathologist's optical microscopic examination of thinly cut, stained tissue on glass slides prepared from a formalin-fixed paraffin-embedded tissue blocks is the gold standard for tissue diagnostics. In addition, the diagnostic abilities and expertise of pathologists is dependent on their direct experience with common and rarer variant morphologies. Recently, deep learning approaches have been used to successfully show a high level of accuracy for such tasks. However, obtaining expert-level annotated images is an expensive and time-consuming task, and artificially synthesized histologic images can prove greatly beneficial. In this study, we present an approach to not only generate histologic images that reproduce the diagnostic morphologic features of common disease but also provide a user ability to generate new and rare morphologies. Our approach involves developing a generative adversarial network model that synthesizes pathology images constrained by class labels. We investigated the ability of this framework in synthesizing realistic prostate and colon tissue images and assessed the utility of these images in augmenting the diagnostic ability of machine learning methods and their usability by a panel of experienced anatomic pathologists. Synthetic data generated by our framework performed similar to real data when training a deep learning model for diagnosis. Pathologists were not able to distinguish between real and synthetic images, and their analyses showed a similar level of interobserver agreement for prostate cancer grading. We extended the approach to significantly more complex images from colon biopsies and showed that the morphology of the complex microenvironment in such tissues can be reproduced. Finally, we present the ability for a user to generate deepfake histologic images using a simple markup of sematic labels.
The effect of feature extraction and data sampling on credit card fraud detection

Abstract Training a machine learning algorithm on a class-imbalanced dataset can be a difficult task, a process that could prove even more challenging under conditions of high dimensionality. Feature extraction and data sampling are among the most popular preprocessing techniques. Feature extraction is used to derive a richer set of reduced dataset features, while data sampling is used to mitigate class imbalance. In this paper, we investigate these two preprocessing techniques, using a credit card fraud dataset and four ensemble classifiers (Random Forest, CatBoost, LightGBM, and XGBoost). Within the context of feature extraction, the Principal Component Analysis (PCA) and Convolutional Autoencoder (CAE) methods are evaluated. With regard to data sampling, the Random Undersampling (RUS), Synthetic Minority Oversampling Technique (SMOTE), and SMOTE Tomek methods are evaluated. The F1 score and Area Under the Receiver Operating Characteristic Curve (AUC) metrics serve as measures of classification performance. Our results show that the implementation of the RUS method followed by the CAE method leads to the best performance for credit card fraud detection.
The determinants of issuing central bank digital currencies

Central Bank Digital Currencies (CBDC) are considered ‘digital fiat currencies’ that do not have a physical form, which is a key distinction from conventional fiat money. This study aims to identify factors that influence central banks’ decisions in taking advanced actions to issue CBDC, namely, the economic, market, demographic and technical factors. Data is collected from the CBDC Tracker and the WB database for the period 2013–2021. We applied the Pooled OLS estimations to examine the impact of the key factors on being in an advanced stage for issuing CBDC; moreover, probit and logistic regression are employed to robust our results and overcome the limitations of Pooled OLS. The findings demonstrate that underdeveloped economies are more engaged in issuing CBDC. Besides, better regulations, FDI inflow, young populations, and more urban societies would increase the probability of CBDC issuance. Nevertheless, results show the impact of technical factors is heterogeneous across countries.
On the value of expert knowledge in estimation and forecasting of solar photovoltaic power generation

Reliable estimates and forecasts of Photovoltaic (PV) power output form a fundamental basis to support its large-scale integration. This is recognized in literature, where a growing amount of studies deal with the development of PV power estimation and forecasting models. In particular, machine learning techniques received significant attention in the past decade. Yet, the importance of predictor variables are consistently ignored in such developments and as a result those models fail to acknowledge the value of including physics-based models. In this study we quantify the value of predictor variables for PV power estimation and forecasting, assess deficiencies in estimation and forecasting models, and introduce a number of pre-processing steps to improve the overall estimation or forecasting performance. To this end, we use common physical models to create so-called expert variables and test their impact on the performance of single-point and probabilistic models. In addition, we investigate the optimal selection of predictor variables for PV power estimation and forecasting. By means of a sensitivity analysis, the paper shows how the value of expert variables is affected by the tilt angle of the PV system. To allow for a deeper insight into the importance of predictor variables, two case studies in different climate regions are considered in the numerical evaluation.
CNOT-Efficient Circuits for Arbitrary Rank Many-Body Fermionic and Qubit Excitations

Efficient quantum circuits are necessary for realizing quantum algorithms on noisy intermediate-scale quantum devices. Fermionic excitations entering unitary coupled-cluster (UCC) ansätze give rise to quantum circuits containing CNOT "staircases" whose number scales exponentially with the excitation rank. Recently, Yordanov et al. [Phys. Rev. A102, 062612 (2020); Commun. Phys.4, 228 (2021)] constructed CNOT-efficient quantum circuits for both Fermionic- (FEB) and qubit-excitation-based (QEB) singles and doubles and illustrated their usefulness in adaptive derivative-assembled pseudo-Trotterized variational quantum eigensolver (ADAPT-VQE) simulations. In this work, we extend these CNOT-efficient quantum circuits to arbitrary excitation ranks. To illustrate the benefits of these compact FEB and QEB quantum circuits, we perform numerical simulations using the recently developed selected projective quantum eigensolver (SPQE) approach, which relies on an adaptive UCC ansatz built from arbitrary-order particle-hole excitation operators. We show that both FEB- and QEB-SPQE decrease the number of CNOT gates compared to traditional SPQE by factors as large as 15. At the same time, QEB-SPQE requires, in general, more ansatz parameters than FEB-SPQE, in particular those corresponding to higher-than-double excitations, resulting in quantum circuits with larger CNOT counts. Although ADAPT-VQE generates quantum circuits with fewer CNOTs than SPQE, SPQE requires orders of magnitude less residual element evaluations than gradient element evaluations in ADAPT-VQE.