Reviewerly/exHarmony huggingface dataset:
Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise

Abstract Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function of complex, high-dimensional, non-Gaussian, and anisotropic noise. This is a major limitation for precision measurements in many domains of science, including astrophysics, for example, for the analysis of the cosmic microwave background, gravitational waves, gravitational lensing, and exoplanets. This work presents Score-based LIkelihood Characterization, a framework that resolves this issue by building a data-driven noise model using a set of noise realizations from observations. We show that the approach produces unbiased and precise likelihoods even in the presence of highly non-Gaussian correlated and spatially varying noise. We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. In combination with the Jacobian of the physical model of the signal, we use Langevin sampling to produce independent samples from the unbiased likelihood. We demonstrate the effectiveness of the method using real data from the Hubble Space Telescope and James Webb Space Telescope.
Interpretable Quantum Advantage in Neural Sequence Learning

Quantum neural networks have been widely studied in recent years, given their potential practical utility and recent results regarding their ability to efficiently express certain classical data. However, analytic results to date rely on assumptions and arguments from complexity theory. Due to this, there is little intuition as to the source of the expressive power of quantum neural networks or for which classes of classical data any advantage can be reasonably expected to hold. Here, we study the relative expressive power between a broad class of neural network sequence models and a class of recurrent models based on Gaussian operations with non-Gaussian measurements. We explicitly show that quantum contextuality is the source of an unconditional memory separation in the expressivity of the two model classes. Additionally, as we are able to pinpoint quantum contextuality as the source of this separation, we use this intuition to study the relative performance of our introduced model on a standard translation data set exhibiting linguistic contextuality. In doing so, we demonstrate that our introduced quantum models are able to outperform state of the art classical models even in practice.
Witnessing Quantum Correlations in a Hybrid Qubit‐Qutrit System Under Intrinsic Decoherence

Abstract Studying qubit‐qutrit systems is crucial to harnessing their quantum advantages, especially in quantum secure technologies. In this regard, the present work consists of considering a qubit‐qutrit system under intrinsic decoherence. For this hybrid‐spin system, the relationship between negativity, local quantum uncertainty, and local quantum Fisher information is discussed. The results show that it is possible to generate and preserve the maximally correlated states by tuning the system parameters to certain fixed values. It is also demonstrated the occurrence of the sudden death/birth phenomenon by examining the behavior of quantum correlation functions.
Introducing Prolog in Language-Informed Ways

We argue that using students’ conscious and unconscious language and grammar proficiency, plus applying what we know about language acquisition, can make Prolog learning much more accessible. This strategy could increase our potential to extend its influence widely, simultaneously helping develop computational, logical and subject-specific concepts and skills. Our position elicits a nontraditional -and in our view, more pedagogical and efficient- ordering of themes and tools.
Mobile educational tool based on augmented reality technology for tooth carving: results of a prospective cohort study

Augmented reality (AR) technology has been shown to be effective in displaying information and presenting three-dimensional objects. Although AR applications are commonly used by learners via mobile devices, plastic models or two-dimensional images are still commonly used in tooth carving practice. Learners practicing tooth carving face a challenge due to the three-dimensional features of teeth as there is a lack of tools available that provide sequential guidance. In this study, we developed an AR-based tooth carving practice tool (AR-TCPT) and compared it to a plastic model to evaluate its potential as a practice tool as well as its user experience.To model tooth carving, we created a three-dimensional object from sequential steps that included the maxillary canines and maxillary first premolars (16 steps), mandibular first premolars (13 steps), and mandibular first molars (14 steps). Image markers, created using Photoshop software, were assigned to each tooth. An AR-based mobile application was developed using the Unity engine. For tooth carving, 52 participants were randomly assigned to a control group (n = 26; using a plastic tooth model) or an experimental group (n = 26; using the AR-TCPT). User experience was evaluated using a 22-item questionnaire. Data were comparatively analyzed using the nonparametric Mann-Whitney U test via the SPSS program.The AR-TCPT detects image markers with the mobile device camera and displays three-dimensional objects for tooth fragmentation. Users can manipulate the device to view each step or examine the shape of a tooth. The results of the user experience survey revealed that the AR-TCPT experimental group scored significantly higher in tooth carving experience compared with the control group that used the plastic model.Compared with the conventional plastic model, the AR-TCPT provided a better user experience for tooth carving. The tool is highly accessible as it is designed to be used on mobile devices by users. Further studies are required to determine the educational impact of the AR-TCTP on quantitative scoring of carved teeth as well as individual user's carving abilities.
A Novel Ensemble Learning System for Cyberattack Classification

Nowadays, IT systems rely mainly on artificial intelligence (AI) algorithms to process data. AI is generally used to extract knowledge from stored information and, depending on the nature of data, it may be necessary to apply different AI algorithms. In this article, a novel perspective on the use of AI to ensure the cybersecurity through the study of network traffic is presented. This is done through the construction of a two-stage cyberattack classification ensemble model addressing class imbalance following a one-vs-rest (OvR) approach. With the growing trend of cyberattacks, it is essential to implement techniques that ensure legitimate access to information. To address this issue, this work proposes a network traffic classification system for different categories based on several AI techniques. In the first task, binary models are generated to clearly differentiate each type of traffic from the rest. With binary models generated, an ensemble model is developed in two phases, which allows the separation of legitimate and illegitimate traffic (phase 1) while also identifying the type of illegitimate traffic (phase 2). In this way, the proposed system allows a complete multiclass classification of network traffic. The estimation of global performance is done using a modern dataset (UNSW-NB15), evaluated using two approaches and compared with other state-of-art works. Our proposal, based on the construction of a two-step model, reaches an F1 of 0.912 for the first level of binary classification and 0.7754 for the multiclass classification. These results show that the proposed system outperforms other state-of-the-art approaches (+0.75% and +3.54% for binary and multiclass classification, respectively) in terms of F1, as demonstrated through comparison together with other relevant classification metrics.
Keyless Lightweight Encipher using Homomorphic and Binomial Coefficients for Smart Computing Applications

Globally, advanced technologies play a vital role to offer services to the user. But the major issue raised here is that the data utilization rate increasing day by day which causes attacks on data. With the increasing use of the internet and digital media, data theft, and piracy making became easy due to the lack of proper security services offered techniques. Existing techniques such as DES, AES, RSA are techniques that failed to achieve immutable, and decentralized features with Lightweight enciphers. In spite of this, attackers continue to attack sensitive data due to the technology's fast progress and the inherent fragility. As a result, a homomorphic encryption method was suggested based on the transformed ASCII values assigned to each character in plain text. In this proposed system, the secret key generated is a tetrahedral-based key, a Pentatope-based key. There is no need for the transmission of keys between users. This work proposes novel encryption using Homomorphic operations and binomial coefficients to secure sensitive information related to the academic industry.
Deep learning models for cancer stem cell detection: a brief review

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor cells that persist within tumors as a distinct population. They drive tumor initiation, relapse, and metastasis through self-renewal and differentiation into multiple cell types, similar to typical stem cell processes. Despite their importance, the morphological features of CSCs have been poorly understood. Recent advances in artificial intelligence (AI) technology have provided automated recognition of biological images of various stem cells, including CSCs, leading to a surge in deep learning research in this field. This mini-review explores the emerging trend of deep learning research in the field of CSCs. It introduces diverse convolutional neural network (CNN)-based deep learning models for stem cell research and discusses the application of deep learning for CSC research. Finally, it provides perspectives and limitations in the field of deep learning-based stem cell research.
Cross-Modality Earth Mover’s Distance for Visible Thermal Person Re-identification

Visible thermal person re-identification (VT-ReID) suffers from inter-modality discrepancy and intra-identity variations. Distribution alignment is a popular solution for VT-ReID, however, it is usually restricted to the influence of the intra-identity variations. In this paper, we propose the Cross-Modality Earth Mover's Distance (CM-EMD) that can alleviate the impact of the intra-identity variations during modality alignment. CM-EMD selects an optimal transport strategy and assigns high weights to pairs that have a smaller intra-identity variation. In this manner, the model will focus on reducing the inter-modality discrepancy while paying less attention to intra-identity variations, leading to a more effective modality alignment. Moreover, we introduce two techniques to improve the advantage of CM-EMD. First, Cross-Modality Discrimination Learning (CM-DL) is designed to overcome the discrimination degradation problem caused by modality alignment. By reducing the ratio between intra-identity and inter-identity variances, CM-DL leads the model to learn more discriminative representations. Second, we construct the Multi-Granularity Structure (MGS), enabling us to align modalities from both coarse- and fine-grained levels with the proposed CM-EMD. Extensive experiments show the benefits of the proposed CM-EMD and its auxiliary techniques (CM-DL and MGS). Our method achieves state-of-the-art performance on two VT-ReID benchmarks.
Deep Convolutional Neural Networks Transfer Learning Comparison on Arabic Handwriting Recognition System

Around 27 languages and more than 420 million people worldwide use Arabic letters. That makes the Arabic language one of the most used languages. However, the Arabic language has a challenge, namely the difference in letters based on their position. Arabic handwriting recognition is important for various applications, such as education and communication. One example is during a pandemic when most education has turned digital, making recognizing students' Arabic handwriting difficult. This paper aims to create a model that can recognize Arabic handwriting by comparing several CNN architectures using transfer learning to classify Arabic, Hijja, and AHCD handwriting datasets. Transfer learning is a model that has been trained by previous datasets to other datasets and is suitable for use in models with small datasets because it can improve model accuracy even with small datasets. The datasets were split into 60%, 20%, and 20% for training, validation, and testing. Each model uses data augmentation and 50% dropout on a fully connected layer to reduce overfitting. Some of the CNN architectures used in this study to create Arabic writing recognition models are ResNet, DenseNet, VGG16, VGG19, InceptionV3, and MobileNet. The models were compiled and trained with various parameters. The best model achieved to classify AHCD and Hijja dataset is VGG16 with Adam optimizer and 0.0001 learning rate. Based on this research, it is expected to know the performance of the best model for classifying Arabic handwriting.
Classification of Malaria Cell Image using Inception-V3 Architecture

Malaria is a severe global public health problem caused by the bite of infected mosquitoes. It can be cured, but only with early detection and effective, quick treatment. It can cause severe conditions if not properly diagnosed and treated at an early stage. In the worst scenario, it can cause death. This study aims at focusing on classifying malaria cell images. Malaria is classified as a dangerous disease caused by the bite of the female Anophles mosquito. As such, it leads to mortality when immediate action and treatment fails to be administered. In particular, this study aims to classify malaria cell images by utilizing the Inception-V3 architecture. In this study, training was conducted on 27,558 malaria cell image data through Inception-V3 architecture by proposing 3 scenarios. The proposed scenario 1 model applies the SGD optimizer to generate a loss value of 0.13 and an accuracy value of 0.95; scenario 2 model applies the Adam optimizer to generate a loss value of 0.09 and an accuracy value of 0.96; and lastly scenario 3 implements the RMSprop optimizer to generate a loss value of 0.08 and an accuracy value of 0.97. Applying the three scenarios, the results of the study apparently indicate that the Inception-V3 model using the RMSprop optimizer is capable of providing the best accuracy results with an accuracy of 97% with the lowest loss value, compared to scenario 1 and scenario 2. Further, the test results confirms that the proposed model in this study is capable of classifying malaria cells effectively.
HistoStarGAN: A unified approach to stain normalisation, stain transfer and stain invariant segmentation in renal histopathology

Virtual stain transfer is a promising area of research in Computational Pathology, which has a great potential to alleviate important limitations when applying deep-learning-based solutions such as lack of annotations and sensitivity to a domain shift. However, in the literature, the majority of virtual staining approaches are trained for a specific staining or stain combination, and their extension to unseen stainings requires the acquisition of additional data and training. In this paper, we propose HistoStarGAN, a unified framework that performs stain transfer between multiple stainings, stain normalisation and stain invariant segmentation, all in one inference of the model. We demonstrate the generalisation abilities of the proposed solution to perform diverse stain transfer and accurate stain invariant segmentation over numerous unseen stainings, which is the first such demonstration in the field. Moreover, the pre-trained HistoStarGAN model can serve as a synthetic data generator, which paves the way for the use of fully annotated synthetic image data to improve the training of deep learning-based algorithms. To illustrate the capabilities of our approach, as well as the potential risks in the microscopy domain, inspired by applications in natural images, we generated KidneyArtPathology, a fully annotated artificial image dataset for renal pathology.
Neural embedding: learning the embedding of the manifold of physics data

In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be a powerful step in the data analysis pipeline for many applications. Using progressively more realistic simulated collisions at the Large Hadron Collider, we show that this embedding approach learns the underlying latent structure. With the notion of volume in Euclidean spaces, we provide for the first time a viable solution to quantifying the true search capability of model agnostic search algorithms in collider physics (i.e. anomaly detection). Finally, we discuss how the ideas presented in this paper can be employed to solve many practical challenges that require the extraction of physically meaningful representations from information in complex high dimensional datasets.
Do you want to promote recall, perceptions, or behavior? The best data visualization depends on the communication goal

Data visualizations can be effective and inclusive means for helping people understand health-related data. Yet numerous high-quality studies comparing data visualizations have yielded relatively little practical design guidance because of a lack of clarity about what communicators want their audience to accomplish. When conducting rigorous evaluations of communication (eg, applying the ISO 9186 method), describing the process simply as evaluating "comprehension" or "interpretation" of visualizations fails to do justice to the true range of outcomes being studied. We present newly developed taxonomies of outcome measures and tasks that are guiding a large-scale systematic review of the health numbers communication literature. Using these taxonomies allows a designer to determine whether a specific data presentation format or feature supports or inhibits the desired audience cognitions, feelings, or behaviors. We argue that taking a granular, outcomes-based approach to designing and evaluating information visualization research is essential to deriving practical, actionable knowledge from it.
Quantum reinforcement learning via policy iteration

Quantum computing has shown the potential to substantially speed up machine learning applications, in particular for supervised and unsupervised learning. Reinforcement learning, on the other hand, has become essential for solving many decision-making problems and policy iteration methods remain the foundation of such approaches. In this paper, we provide a general framework for performing quantum reinforcement learning via policy iteration. We validate our framework by designing and analyzing quantum policy evaluation methods for infinite-horizon discounted problems by building quantum states that approximately encode the value function of a policy, and quantum policy improvement methods by post-processing measurement outcomes on these quantum states. Last, we study the theoretical and experimental performance of our quantum algorithms on two environments from OpenAI’s Gym.
Does the adoption of blockchain technology add intangible benefits to the industrial sector? Evidence from Jordan

Purpose This study aims to examine the feasibility of adopting blockchain technology in Jordan’s industrial sector and its intangible benefits. It also analyzes the influence of factors like technological, process, cultural and leadership readiness on the willingness of enterprises to adopt blockchain. Design/methodology/approach To gain insights into the potential adoption of blockchain technology and its intangible benefits for enterprises in the Jordanian industrial sector, this study gathered perspectives from a broad range of professionals, including financial managers, internal control staff, accounting departments, IT department managers and IS-related personnel. This was achieved through the administration of a comprehensive questionnaire designed to capture their opinions. Findings This study highlights the importance of technological and leadership readiness in adopting blockchain. It also shows that blockchain adoption can yield significant intangible benefits for enterprises. However, the study did not find a significant relationship between process readiness, cultural readiness and the intention to adopt blockchain. Practical implications The study’s outcomes underscore the importance of prioritizing technological and leadership readiness for enterprises and policymakers intending to adopt blockchain technology. By doing so, they can increase their willingness to adopt this technology and leverage its benefits. Originality/value This pioneering study investigates the adoption of blockchain technology and its intangible benefits for Jordanian businesses. It also examines the influence of factors like technological, process, cultural and leadership readiness on the decision to adopt blockchain in the industrial sector.
Understanding and preventing the advertisement and sale of illicit drugs to young people through social media: A multidisciplinary scoping review

Abstract Issues The sale of illicit drugs online has expanded to mainstream social media apps. These platforms provide access to a wide audience, especially children and adolescents. Research is in its infancy and scattered due to the multidisciplinary aspects of the phenomena. Approach We present a multidisciplinary systematic scoping review on the advertisement and sale of illicit drugs to young people. Peer‐reviewed studies written in English, Spanish and French were searched for the period 2015 to 2022. We extracted data on users, drugs studied, rate of posts, terminology used and study methodology. Key Findings A total of 56 peer‐reviewed papers were included. The analysis of these highlights the variety of drugs advertised and platforms used to do so. Various methodological designs were considered. Approaches to detecting illicit content were the focus of many studies as algorithms move from detecting drug‐related keywords to drug selling behaviour. We found that on average, for the studies reviewed, 13 in 100 social media posts advertise illicit drugs. However, popular platforms used by adolescents are rarely studied. Implications Promotional content is increasing in sophistication to appeal to young people, shifting towards healthy, glamourous and seemingly legal depictions of drugs. Greater inter‐disciplinary collaboration between computational and qualitative approaches are needed to comprehensively study the sale and advertisement of illegal drugs on social media across different platforms. This requires coordinated action from researchers, policy makers and service providers.