Reviewerly/exHarmony huggingface dataset:
Decentralized Patient-Centric Report and Medical Image Management System Based on Blockchain Technology and the Inter-Planetary File System

Several academicians have been actively contributing to establishing a practical solution to storing and distributing medical images and test reports in the research domain of health care in recent years. Current procedures mainly rely on cloud-assisted centralized data centers, which raise maintenance expenditure, necessitate a large amount of storage space, and raise privacy concerns when exchanging data across a network. As a result, it is critically essential to provide a framework that allows for the efficient exchange and storage of large amounts of medical data in a secure setting. In this research, we describe a unique proof-of-concept architecture for a distributed patient-centric test report and image management (PCRIM) system that aims to facilitate patient privacy and control without the need for a centralized infrastructure. We used an Ethereum blockchain and a distributed file system technology called the Inter-Planetary File System in this system (IPFS). Then, to secure a distributed and trustworthy access control policy, we designed an Ethereum smart contract termed the patient-centric access control protocol. The IPFS allows for the decentralized storage of medical metadata, such as images, with worldwide accessibility. We demonstrate how the PCRIM system design enables hospitals, patients, and image requestors to obtain patient-centric data in a distributed and secure manner. Finally, we tested the proposed framework in the Windows environment by deploying a smart contract prototype on an Ethereum TESTNET blockchain. The findings of the study indicate that the proposed strategy is both efficient and practicable.
Hooked on the metaverse? Exploring the prevalence of addiction to virtual reality applications

Similar to debates about other new media technologies in the past, with the popularization of virtual reality (VR) technologies, concerns are raised about their potential to breed media addiction. In response to these concerns, the aim of this research was to provide a first examination of the prevalence of addiction to VR application use. An online survey was conducted among frequent VR users ( N = 754), and measures of the different components of addiction were obtained, as well as demographics, hours of weekly use, type of apps used, and feelings of spatial presence and embodiment during VR app use. The results indicate that between 2% and 20% of users reveal compulsive VR use, depending on the classification criteria used. These prevalence estimates are similar to those of other activities such as the use of (non-VR) video games or the use of social networking sites. Therefore, the results suggest that VR applications do not have a higher addictive potential than other more traditional technologies. However, feelings of embodiment when using VR positively predict addiction. This may suggest that future developments of VR technology could, perhaps, also increase its addictive potential compared to other technologies.
New CNN-based tool to discriminate anthropogenic from natural low magnitude seismic events

SUMMARY With the deployment of high quality and dense permanent seismic networks over the last 15 yr comes a dramatic increase of data to process. In order to lower the threshold value of magnitudes in a catalogue as much as possible, the issue of discrimination between natural and anthropogenic events is becoming increasingly important. To achieve this discrimination, we propose the use of a convolutional neural network (CNN) trained from spectrograms. We built a database of labelled events detected in metropolitan France between 2020 and 2021 and trained a CNN with three-component 60 s spectrograms ranging frequencies from 1 to 50 Hz. By applying our trained model on independent French data, we reach an accuracy of 98.2 per cent. In order to show the versatility of the approach, this trained model is also applied on different geographical areas, a post-seismic campaign from NW France and data from Utah, and reaches an accuracy of 100.0 and 96.7 per cent, respectively. These tests tend to hypothesize that some features due to explosions compared to earthquakes are widely shared in different geographical places. In a first approach, we propose that it can be due to a contrast in the energy balance between natural and anthopogenic events. Earthquake seismic energies seem to be more continuous as a function of frequency (vertical bands features in a spectrogram) and conversely for explosions (horizontal strips).
Explainable software systems: from requirements analysis to system evaluation

Abstract The growing complexity of software systems and the influence of software-supported decisions in our society sparked the need for software that is transparent, accountable, and trustworthy. Explainability has been identified as a means to achieve these qualities. It is recognized as an emerging non-functional requirement (NFR) that has a significant impact on system quality. Accordingly, software engineers need means to assist them in incorporating this NFR into systems. This requires an early analysis of the benefits and possible design issues that arise from interrelationships between different quality aspects. However, explainability is currently under-researched in the domain of requirements engineering, and there is a lack of artifacts that support the requirements engineering process and system design. In this work, we remedy this deficit by proposing four artifacts: a definition of explainability, a conceptual model, a knowledge catalogue, and a reference model for explainable systems. These artifacts should support software and requirements engineers in understanding the definition of explainability and how it interacts with other quality aspects. Besides that, they may be considered a starting point to provide practical value in the refinement of explainability from high-level requirements to concrete design choices, as well as on the identification of methods and metrics for the evaluation of the implemented requirements.
Virtual reality for geospatial education: immersive technologies enhance sense of embodiment

This article focuses on the role of embodiment in learning spatial phenomena in virtual reality in the context of geospatial education. We conducted an empirical study on the geovisualization of global earthquake locations in immersive VR and desktop VR. We asked students to visualize cross-sections of the geometry of earthquake locations in subduction zones and answer pre- and post-experience questions. The degree of the sense of embodiment in immersive VR versus desktop VR was researched. We defined the sense of embodiment based on two embodied affordances: (1) embodied relative reference frame and (2) bodily engagement. We found that immersive VR has a significantly higher sense of embodiment than desktop VR. Additionally, students showed a significantly higher level of reflective thinking in immersive VR. In terms of learning performance, students significantly gained knowledge in both conditions when comparing their pre- and post-knowledge. However, we could not find any significant difference between immersive and desktop VR. The lower level of embodiment in desktop VR seemed sufficient to improve students' grades. These results encourage more research studies on the effect of the sense of embodiment on learning in the geospatial and earth sciences.
Digital Transformation in Higher Education: Intelligence in Systems and Business Models

Purpose—The purpose of this chapter is to analyze how higher education institutions can successfully manage digital transformation. Originality/Value—The future of higher education depends on the ability of its managers to envision and manage the digital transformation process in the new competitive landscape, which is further challenged with frequent and varied “crisis” situations such as COVID-19. The originality of this chapter is in its providing a review and reflection of the emerging digital transformation trends and an illustration of good practices in higher education. To this effect, it contributes to the on-going debate about why and how the higher education sector should adopt a business mentality and embrace business model innovation as a key driver to sustain the future competitiveness of higher education institutions.
Dental X-ray Identification System Based on Association Rules Extracted by k-Symbol Fractional Haar Functions

Several identification approaches have recently been employed in human identification systems for forensic purposes to decrease human efforts and to boost the accuracy of identification. Dental identification systems provide automated matching by searching photographic dental features to retrieve similar models. In this study, the problem of dental image identification was investigated by developing a novel dental identification scheme (DIS) utilizing a fractional wavelet feature extraction technique and rule mining with an Apriori procedure. The proposed approach extracts the most discriminating image features during the mining process to obtain strong association rules (ARs). The proposed approach is divided into two steps. The first stage is feature extraction using a wavelet transform based on a k-symbol fractional Haar filter (k-symbol FHF), while the second stage is the Apriori algorithm of AR mining, which is applied to find the frequent patterns in dental images. Each dental image’s created ARs are saved alongside the image in the rules database for use in the dental identification system’s recognition. The DIS method suggested in this study primarily enhances the Apriori-based dental identification system, which aims to address the drawbacks of dental rule mining.
UniSKGRep: A unified representation learning framework of social network and knowledge graph

The human-oriented applications aim to exploit behaviors of people, which impose challenges on user modeling of integrating social network (SN) with knowledge graph (KG), and jointly analyzing two types of graph data. However, existing graph representation learning methods merely represent one of two graphs alone, and hence are unable to comprehensively consider features of both SN and KG with profiling the correlation between them, resulting in unsatisfied performance in downstream tasks. Considering the diverse gap of features and the difficulty of associating of the two graph data, we introduce a Unified Social Knowledge Graph Representation learning framework (UniSKGRep), with the goal to leverage the multi-view information inherent in the SN and KG for improving the downstream tasks of user modeling. To the best of our knowledge, we are the first to present a unified representation learning framework for SN and KG. Concretely, the SN and KG are organized as the Social Knowledge Graph (SKG), a unified representation of SN and KG. For the representation learning of SKG, first, two separate encoders in the Intra-graph model capture both the social-view and knowledge-view in two embedding spaces, respectively. Then the Inter-graph model is learned to associate the two separate spaces via bridging the semantics of overlapping node pairs. In addition, the overlapping node enhancement module is designed to effectively align two spaces with the consideration of a relatively small number of overlapping nodes. The two spaces are gradually unified by continuously iterating the joint training procedure. Extensive experiments on two real-world SKG datasets have proved the effectiveness of UniSKGRep in yielding general and substantial performance improvement compared with the strong baselines in various downstream tasks.
Efficient transformer with code token learner for code clone detection

Deep learning techniques have achieved promising results in code clone detection in the past decade. Unfortunately, current deep learning-based methods rarely explicitly consider the modeling of long codes. Worse, the code length is increasing due to the increasing requirement of complex functions. Thus, modeling the relationship between code tokens to catch their long-range dependencies is crucial to comprehensively capture the information of the code fragment. In this work, we resort to the Transformer to capture long-range dependencies within a code, which however requires huge computational cost for long code fragments. To make it possible to apply Transformer efficiently, we propose a code token learner to largely reduce the number of feature tokens in an automatic way. Besides, considering the tree structure of the abstract syntax tree, we present a tree-based position embedding to encode the position of each token in the input. Apart from the Transformer that captures the dependency within a code, we further leverage a cross-code attention module to capture the similarities between two code fragments. Our method significantly reduces the computational cost of using Transformer by 97% while achieves superior performance with state-of-the-art methods. Our code is available at https://github.com/ArcticHare105/Code-Token-Learner.
LG-IQA: Integration of local and global features for no-reference image quality assessment

The visual physiology research shows that the human visual system (HVS) first rapidly and unconsciously produces a global perception, and then gradually focuses on specific local areas for the perception of image quality. Therefore, in order to better simulate the perception of image quality by the HVS, we need to consider both local and global information when evaluating image quality. Based on above analysis, this paper proposes a new method by integrating local and global features for no-reference image quality assessment (IQA), which is named local and global IQA (LG-IQA). Specifically, a test image is transformed by the dual-tree complex wavelet transform (DTCWT) to get the wavelet coefficients, and then the local binary pattern (LBP) operator is applied on the reconstructed image of high-frequency wavelet sub-bands. The obtained results are used as local features. After the DTCWT, we estimate parameters using an asymmetric generalized Gaussian distribution (AGGD) model by fitting the wavelet coefficients, and take the results as global features. Finally, the extracted local and global features are combined together, and the relationship between image features and image quality scores will be learned by an AdaBoosting back-propagation (BP) neural network. It is worth mentioning that this is the first attempt to integrate the LBP and DTCWT together in the IQA research based on neural network. Experimental results demonstrate that the predicted scores of our proposed LG-IQA correlate well with the subjective quality scores.
Deep manifold regularized semi-nonnegative matrix factorization for Multi-view Clustering

With the development and application of modern technology, multi-view data exists widely. The deep matrix factorization model is widely used because it mines the hierarchical information of the samples, but it lacks a geometric structure protection mechanism. Moreover, in multi-view representation learning, strong representations have consensus from multi-view data and are more likely to exist in the structural relationships of data objects. To address these issues, we propose Deep Manifold Regularized Semi-Nonnegative Matrix Factorization for Multi-view Clustering (DMRMF_MVC), which preserves the geometric structure of data objects through multi-layer embedding graph regularization. The graph regularizer combines the nearest neighbor relationship with the exclusive relationship of data objects to express the internal manifold structure of the data fully. Furthermore, to distinguish the value of each view data, the adaptive weight is associated with representation learning to maximize the complementarity between the views. Extensive experimental results show that DMRMF_MVC outperforms other state-of-the-art algorithms based on multiple indicators on various datasets.
Quantum key secure communication protocol via enhanced superdense coding

In this study, an improvement, and a generalization to more than two bits of the superdense coding protocol are presented. Based on both innovations, a novel quantum key secure communication protocol is developed, which uses an N-bit key, optical multiplexers and demultiplexers, and quantum repeaters, which work thanks to entanglement swapping. In this way, the new protocol allows the simultaneous transmission of N-bits encrypted through optical channels. This study incorporates implementations on two platforms: the Quirk simulator, and the 16-qubits Melbourne processor of the IBM Q Experience program. Errors in terms of the number of quantum repeaters are studied. Finally, this study is completed by analyzing the advantages of the optical link, with which the protocol works, versus the commonly used electromagnetic link for quantum communication between submerged submarines in the presence of a third party that acts as an eavesdropper.
Use of theory to guide integration of virtual reality technology in nursing education: A scoping study

There is growing interest among academic nurse educators in using virtual reality (VR) environments as a pedagogical tool to enhance student learning.The aim of this scoping study was to identify the theories or conceptual models that informed the application of virtual reality technology in nursing education programs.A scoping study using the Arksey and O'Malley (2005) framework was conducted. A search strategy recommended and executed by a research librarian yielded 18 final articles for review.There is limited use of theory or a conceptual model, particularly of nursing origin, to guide the integration of VR technology for student learning.More research is needed to define best practices for application of VR technology for specific curricular needs, and for theory development based on specific concepts and disciplinary perspectives of learning.
Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records

Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challenging and currently underserved field of feature engineering in machine learning workflows. Machine learning workflows are being increasingly used to analyze medical records with heterogeneous phenotypic, genotypic, and related medical terms to improve patient care. We performed a retrospective study using neuropathology reports from the German Neuropathology Reference Center for Epilepsy Surgery at Erlangen, Germany. This cohort included 312 patients who underwent epilepsy surgery and were labeled with one or more diagnoses, including dual pathology, hippocampal sclerosis, malformation of cortical dysplasia, tumor, encephalitis, and gliosis. We modeled the diagnosis terms together with their microscopy, immunohistochemistry, anatomy, etiologies, and imaging findings using the description logic-based Web Ontology Language (OWL) in the Epilepsy and Seizure Ontology (EpSO). Three tree-based machine learning models were used to classify the neuropathology reports into one or more diagnosis classes with and without ontology-based feature engineering. We used five-fold cross validation to avoid overfitting with a fixed number of repetitions while leaving out one subset of data for testing, and we used recall, balanced accuracy, and hamming loss as performance metrics for the multi-label classification task. The epilepsy ontology-based feature engineering approach improved the performance of all the three learning models with an improvement of 35.7%, 54.5%, and 33.3% in logistics regression, random forest, and gradient tree boosting models respectively. The run time performance of all three models improved significantly with ontology-based feature engineering with gradient tree boosting model showing a 93.8% reduction in the time required for training and testing of the model. Although, all three models showed an overall improved performance across the three-performance metrics using ontology-based feature engineering, the rate of improvement was not consistent across all input features. To analyze this variation in performance, we computed feature importance scores and found that microscopy had the highest importance score across the three models, followed by imaging, immunohistochemistry, and anatomy in a decreasing order of importance scores. This study showed that ontologies have an important role in feature engineering to make heterogeneous clinical data accessible to machine learning models and also improve the performance of machine learning models in multilabel multiclass classification tasks.
Intelligent data deduplication with Deep Transfer Learning Enabled Classification Model for Cloud-based Healthcare System

The extensive adoption of cloud computing (CC) enabled healthcare systems in attaining medical data from different data sources sustained by heterogeneous cloud providers. Data deduplication (DD) is a proficient method to effectually share and store data in the cloud server. At the same time, deep learning (DL) models can be applied for effective decision-making in the cloud-based healthcare system. With this motivation, this study develops an Intelligent DD with Deep Transfer Learning Enabled Classification Model for Cloud-based Healthcare System (IDDTLC-CHS) model. The presented IDDTLC-CHS model aims to accomplish effective DD and classification in the cloud-enabled healthcare environment. For DD, the neighbourhood correlation sequence (NCS) algorithm is employed which generates optimum code words and is then compressed by Deflate model. Besides, the data classification module involves a series of processes namely fuzzy c-means (FCM) segmentation, Xception feature extraction; bidirectional gated recurrent unit (BiGRU), and seagull optimization algorithm (SGO). The SGO algorithm is applied for optimal adjustment of the parameters involved in the BiGRU model. To assess enhanced outcomes of the IDDTLC-CHS model, a wide-ranging simulation analysis is carried out using the benchmark dataset. The comparative analysis reported the betterment of the IDDTLC-CHS model compared to other recent approaches.
Nonlocal quantum correlations in a bipartite quantum system coupled to a bosonic non-Markovian reservoir

In this paper, we explore the dynamics of nonlocal correlations in a two-qubit system, that is, first prepared in a Gisin state and then interacts with a bosonic non-Markovian environment. We employ uncertainty-induced nonlocality (UIN) and the Horodecki measure (Bell function) to characterize the degree of nonclassical correlations and quantum nonlocality in the system, taking into account the influence of the non-Markovian reservoir. The dynamics of the UIN and the Bell nonlocality are next examined using the various parameters that define the non-Markovian reservoir and the initial Gisin state. Our results show that the amount of nonlocal correlations and the degree of violation of Bell’s inequality can be modulated by varying the physical parameters characterizing the non-Markovian reservoir and the initial Gisin state. We also show that in some specific cases, the system exhibits nonclassical correlations while the evolved Gisin state does not violate Bell’s inequality. Our results also confirm that UIN is robust than Bell’s nonlocality in the presence of decoherence induced by the interaction with the non-Markovian reservoir.