Reviewerly/exHarmony huggingface dataset:
Graph attention network with dynamic representation of relations for knowledge graph completion

Knowledge graph completion (KGC) aims to predict the missing element in a triple based on known triples or facts. Recently, plenty of representation learning methods for KGC have achieved the promising performance, especially ones based on graph neural networks and their variants. Those methods exploit local neighborhood information to update the embedding of target entities. However, the existing works have the following two problems. First, those approaches focus on the representation learning of entities, while the relation representation usually adopts a simple linear transformation, which cannot capture the distinctive semantic intensions of the same relation in different triples. Second, different types of entity information are simply combined together, resulting in the loss of global properties including the type and the global importance of entities, which is prone to cause over-smoothing phenomenon. To address these two problems, we propose a Graph Attention Network with Dynamic Representation of Relations and global information (DRR-GAT) for knowledge graph completion. Specifically, the task of dynamic representation of relations is to learn the distinctive representation of the same relation in different triples. This goal is achieved via a path Transformer. To this end, path Transformer is designed to take the path information as its input, where only those paths from the target entity to the neighborhood relations with the same type as the target relation are considered. Sequentially, the mechanism of global embeddings is incorporated into graph attention network to capture the global information of entities and relations. Experimental performance outperforms the state-of-the-art methods, indicating the effectiveness of our proposed approach.
Examining the influence of virtual reality tourism on consumers' subjective wellbeing

Tourism consumption has been outlined as having a positive effect on individuals' subjective wellbeing, however, we had limited empirical or conceptual understanding on the propensity of VR tourism in fostering positive subjective wellbeing and the subsequent effects this has for tourism providers and policymakers. A vacation transports one's self to an alternative world, physically. In parallel, VR transports one's self to an alternative world, virtually. Accordingly, through a multiple quantitative study research-design and drawing on the theoretical lens of Presence-Theory and Effort-Recovery-Theory, this research uncovers the positive effect of VR tourism on individuals' subjective wellbeing, the nuance of the sense of presence in VR tourism in enabling psychological-detachment and enhancing consumer wellbeing along with the subsequent attitude and behaviour intentions stimulated by consumer wellbeing. Our results pertain that not only can VR tourism provide important societal and health benefits in recovery and enhancing individuals' wellbeing, but also benefits from an economic perspective.
Swin-JDE: Joint Detection and Embedding Multi-Object Tracking in Crowded Scenes Based on Swin-Transformer

Multi-object tracking (MOT) is a highly valued and challenging research topic in computer vision. To achieve more robust tracking performance, recently published MOT methods tend to use anchor-free object detectors, which have the advantage of dealing with the identity ambiguity problem encountered by anchor-based methods in learning appearance features. However, in practical applications, it is found that the detection accuracy of the anchor-free object detector based on classical convolutional neural networks in crowded scenes will be significantly reduced. In order to have better detection and tracking performance in crowded scenes, this paper proposes an anchor-free joint detection and embedding (JDE) MOT method based on Transformer architecture, called Swin-JDE. The proposed method includes a novel Patch-Expanding module, which can improve the spatial information of feature maps by up-sampling processing through neural network learning and Einops Notation-based rearrangement to enhance the detection and tracking performance of the MOT model. In terms of training method, we propose a two-step training method that trains the detection branch separately from the appearance branch to enhance the detection robustness of anchor-free predictors. Furthermore, during the training process, we also propose an examination method to remove occluded targets from the training dataset to improve the accuracy of the appearance embedding layer. In terms of data association, we propose a new post-processing method, which simultaneously considers the three factors of detection confidence, appearance embedding distance, intersection-over-union (IoU) distance to match each tracklet and the detection information to improve the tracking robustness of the MOT model. Experimental results show that the proposed method achieves 70.38% multiple object tracking accuracy (MOTA) and 69.53% identification F1-score (IDF1) results in the MOT20 benchmark dataset, and the identification switch (ID Switch) is reduced to 2026. Compared with FairMOT, the proposed method improves MOTA and IDF1 by 8.58% and 2.23%, respectively.
Does deidentification of data from wearable devices give us a false sense of security? A systematic review

Wearable devices have made it easier to generate and share data collected on individuals. This systematic review seeks to investigate whether deidentifying data from wearable devices is sufficient to protect the privacy of individuals in datasets. We searched Web of Science, IEEE Xplore Digital Library, PubMed, Scopus, and the ACM Digital Library on Dec 6, 2021 (PROSPERO registration number CRD42022312922). We also performed manual searches in journals of interest until April 12, 2022. Although our search strategy had no language restrictions, all retrieved studies were in English. We included studies showing reidentification, identification, or authentication with data from wearable devices. Our search retrieved 17 625 studies, and 72 studies met our inclusion criteria. We designed a custom assessment tool for study quality and risk of bias assessments. 64 studies were classified as high quality and eight as moderate quality, and we did not detect any bias in any of the included studies. Correct identification rates were typically 86-100%, indicating a high risk of reidentification. Additionally, as little as 1-300 s of recording were required to enable reidentification from sensors that are generally not thought to generate identifiable information, such as electrocardiograms. These findings call for concerted efforts to rethink methods for data sharing to promote advances in research innovation while preventing the loss of individual privacy.
HyGGE: Hyperbolic graph attention network for reasoning over knowledge graphs

Recently, hyperbolic embedding has successfully demonstrated its superiority over Euclidean analogues in representing hierarchical data. As the scale-free network that usually exhibits rich hierarchical structures, knowledge graphs naturally become a field where hyperbolic embedding shows its talents. Furthermore, hyperbolic embedding is also an expected solution to comprehensively reproduce the semantic features and underlying structures of KGs in the embedding space, which will significantly optimize the interpretability and performance of embedding models. However, most of the several existing hyperbolic studies only individually learn semantic information indicated by triples individually, making the embedding space relatively one-sided and simplified. In addition, many issues that limit reasoning performance are still ignored and unresolved in the context of hyperbolic geometry, like the response to complex relations and relation patterns. Motivated by these concerns, we propose the hyperbolic embedding model for KG reasoning, HyGGE. It is based on an innovative hyperbolic graph attention network. Furthermore, the response to complex relations, which is a well-known problem that constrains reasoning performance is also discussed in HyGGE. On the one hand, the focus on neighborhood structures and relation features makes up for the singularity that the embedding space is completely induced by triples individually, thereby optimizing the expressiveness of the embedding space. On the other hand, they cooperate with the effect of hyperbolic geometry to capture hierarchical features contained in local structures, and thus giving the hyperbolic embedding a fuller play to its advantages. Extensive experiments have validated the effectiveness and advantages of HyGGE.
Plots.jl – A User Extendable Plotting API for the Julia Programming Language

There are many excellent plotting libraries. Each excels at a specific use case: one is particularly suited for creating printable 2D figures for publication, another for generating interactive 3D graphics, while a third may have excellent L<small>A</small>T<small>E</small>X integration or be ideal for creating dashboards on the web. The aim of <tt>Plots.jl</tt> is to enable the user to use the same syntax to interact with a range of different plotting libraries, making it possible to change the library that does the actual plotting (the <em>backend</em>) without needing to touch the code that creates the content – and without having to learn multiple application programming interfaces (API). This is achieved by separating the specification of the plot from the implementation of the graphical backend. This plot specification is extendable by a <em>recipe</em> system that allows package authors and users to create new types of plots, as well as to specify how to plot any type of object (e.g. a statistical model, a map, a phylogenetic tree or the solution to a system of differential equations) without depending on the <tt>Plots.jl</tt> package. This design supports a modular ecosystem structure for plotting and yields a high code reuse potential across the entire Julia package ecosystem. <tt>Plots.jl</tt> is publicly available at <a href="https://github.com/JuliaPlots/Plots.jl" target="_blank">https://github.com/JuliaPlots/Plots.jl</a>.
Silicon-based decoder for polarization-encoding quantum key distribution

Silicon-based polarization-encoding quantum key distribution (QKD) has been widely studied, owing to its low cost and robustness. However, prior studies have utilized off-chip devices to demodulate the quantum states or perform polarization compensation, given the difficulty of fabricating polarized independent components on the chip. In this paper we propose a fully chip-based decoder for polarization-encoding QKD. The chip realizes a polarization state analyzer and compensates for the BB84 protocol without requiring additional hardware. It is based on a polarization-to-path conversion method that uses a polarization splitter-rotator. The chip was fabricated using a standard silicon photonics foundry; it has a compact design and is suitable for mass production. In the experimental stability test, an average quantum bit error rate of 0.59% was achieved through continuous operation for 10 h without any polarization feedback. Furthermore, using the developed feedback algorithm, the chip enabled the automatic compensation of the fiber polarization drift, which was emulated by a random fiber polarization scrambler. In the case of the QKD demonstration, we obtained a finite-key secret rate of 240 bps over a fiber spool of 100 km. This study represents an important step toward the integrated, practical, and large-scale deployment of QKD systems.
Efficient Image Super-Resolution Using Vast-Receptive-Field Attention

The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechanism. We start from a simple pixel attention module and gradually modify it to achieve better super-resolution performance with reduced parameters. The specific approaches include: (1) increasing the receptive field of the attention branch, (2) replacing large dense convolution kernels with depthwise separable convolutions, and (3) introducing pixel normalization. These approaches paint a clear evolutionary roadmap for the design of attention mechanisms. Based on these observations, we propose VapSR, the Vast-receptive-field Pixel attention network. Experiments demonstrate the superior performance of VapSR. VapSR outperforms the present lightweight networks with even fewer parameters. And the light version of VapSR can use only 21.68% and 28.18% parameters of IMDB and RFDN to achieve similar performances to those networks. The code and models are available at https://github.com/zhoumumu/VapSR .
You Can Handle, You Can Teach It: Systematic Review on the Use of Extended Reality and Artificial Intelligence Technologies for Online Higher Education

Over the past year, defined by the COVID-19 pandemic, we have witnessed a boom in applying key emerging technologies in education. In such challenging situations, technology and education expanded their work together to strengthen and interactively impact the learning process in the online higher education context. From a pedagogical perspective, extended reality (XR) and artificial intelligence (AI) were accessible toolboxes to amplify an active and learner-centered teaching method. Whether and how such activities will continue in a post-COVID-19 situation remains unclear. In this systematic literature review, we document the application of XR and AI in online higher education settings and build up an accurate depiction of their influence after the COVID-19 pandemic outbreak. A significant contribution of the thorough analysis conducted was the corroboration of the growing interest of these fast-emerging technologies and their impact on learner agency and outcomes, making online education more accessible, effective, engaging, collaborative, self-paced, and adapted to the diverse academic trajectories. The momentum brought about by the pandemic has served as an impulse for educators and universities to expand the use of these technologies progressively, meet new challenges, and shape the future of online higher education.
An Adaptive and Scalable Multi-Object Tracker Based on the Non-Homogeneous Poisson Process

This paper proposes a new adaptive framework for tracking multiple objects in the presence of data association uncertainty and heavy clutter, either with or without knowledge of the measurement rates and/or target shapes. Built upon an online Gibbs sequential Markov chain Monte Carlo sampling scheme, the adaptive tracker is Bayesian optimal and robust, requiring no additional approximations or measurement partition steps. With a non-homogeneous Poisson process measurement model, our tracker can tackle the data association task with linear computational complexity. Meanwhile, we study generalised inverse Gaussian and inverse Wishart distributions for modelling Poisson rates and object shapes, respectively; these prior models ensure closed-form full conditionals in our online Gibbs sampling steps, under which object states and shapes can be jointly estimated with associations and Poisson rates in a parallel fashion. Furthermore, a fast Rao-Blackwellisation scheme for linear Gaussian dynamics is designed and demonstrated to significantly improve both tracking efficiency and accuracy. We validate the efficacy of our method on real and simulated data.
Advances and Challenges in Multimodal Remote Sensing Image Registration

Over the past few decades, with the rapid development of global aerospace and aerial remote sensing technology, the types of sensors have evolved from the traditional monomodal sensors (e.g., optical sensors) to the new generation of multimodal sensors (e.g., multispectral, hyperspectral, light detection and ranging (LiDAR), and synthetic aperture radar (SAR) sensors). These advanced devices can dynamically provide various and abundant multimodal remote sensing images (MRSIs) with different spatial, temporal, and spectral resolutions according to different application requirements. Since then, it is of great scientific significance to carry out the research of MRSI registration, which is a crucial step for integrating the complementary information among multimodal data and making comprehensive observations and analysis of the Earth’s surface. In this work, we will present our own contributions to the field of multimodal image registration, summarize the advantages and limitations of existing multimodal image registration methods, and then discuss the remaining challenges and make a forward-looking prospect for the future development of the field.
A Blockchain-Inspired Attribute-Based Zero-Trust Access Control Model for IoT

The connected or smart environment is the integration of smart devices (sensors, IoT devices, or actuator) into the Internet of Things (IoT) paradigm, in which a large number of devices are connected, monitoring the physical environment and processes and transmitting into the centralized database for advanced analytics and analysis. This integrated and connected setup allows greater levels of automation of smart systems than is possible with just the Internet. While delivering services to the different processes and application within connected smart systems, these IoT devices perform an impeccably large number of device-to-device communications that allow them to access the selected subsets of device information and data. The sensitive and private nature of these data renders the smart infrastructure vulnerable to copious attacks which threat agents exploit for cyberattacks which not only affect critical services but probably bring threat to people’s lives. Hence, advanced measures need to be taken for securing smart environments, such as dynamic access control, advanced network screening, and monitoring behavioural anomalies. In this paper, we have discussed the essential cyberthreats and vulnerabilities in smart environments and proposed ZAIB (Zero-Trust and ABAC for IoT using Blockchain), a novel secure framework that monitors and facilitates device-to-device communications with different levels of access-controlled mechanisms based on environmental parameters and device behaviour. It is protected by zero-trust architecture and provides dynamic behavioural analysis of IoT devices by calculating device trust levels for each request. ZAIB enforces variable policies specifically generated for each scenario by using attribute-based access control (ABAC). We have used blockchain to ensure anonymous device and user registrations and immutable activity logs. All the attributes, trust level histories, and data generated by IoT devices are protected using IPFS. Finally, a security evaluation shows that ZAIB satisfies the needs of active defence and end-to-end security enforcement of data, users, and services involved in a smart grid network.
Quantum machine learning: from physics to software engineering

Quantum machine learning is a rapidly growing field at the intersection of quantum technology and artificial intelligence. This review provides a two-fold overview of several key approaches that can offer advancements in both the development of quantum technologies and the power of artificial intelligence. Among these approaches are quantum-enhanced algorithms, which apply quantum software engineering to classical information processing to improve keystone machine learning solutions. In this context, we explore the capability of hybrid quantum-classical neural networks to improve model generalization and increase accuracy while reducing computational resources. We also illustrate how machine learning can be used both to mitigate the effects of errors on presently available noisy intermediate-scale quantum devices, and to understand quantum advantage via an automatic study of quantum walk processes on graphs. In addition, we review how quantum hardware can be enhanced by applying machine learning to fundamental and applied physics problems as well as quantum tomography and photonics. We aim to demonstrate how concepts in physics can be translated into practical engineering of machine learning solutions using quantum software.
Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.
UMGAN: Underwater Image Enhancement Network for Unpaired Image-to-Image Translation

Due to light absorption and scattering underwater images suffer from low contrast, color distortion, blurred details, and uneven illumination, which affect underwater vision tasks and research. Therefore, underwater image enhancement is of great significance in vision applications. In contrast to existing methods for specific underwater environments or reliance on paired datasets, this study proposes an underwater multiscene generative adversarial network (UMGAN) to enhance underwater images. The network implements unpaired image-to-image translation between the underwater turbid domain and the underwater clear domain. It has a great enhancement impact on several underwater image types. Feedback mechanisms and a noise reduction network are designed to optimize the generator and address the issue of noise and artifacts in GAN-produced images. Furthermore, a global–local discriminator is employed to improve the overall image while adaptively modifying the local region image effect. It resolves the issue of over- and underenhancement in local regions. The reliance on paired training data is eliminated through a cycle consistency network structure. UMGAN performs satisfactorily on various types of data when compared quantitatively and qualitatively to other state-of-the-art algorithms. It has strong robustness and can be applied to various enhancement tasks in different scenes.