Reviewerly/exHarmony huggingface dataset:
Identification of Variables that Predict Teachers’ Attitudes toward ICT in Higher Education for Teaching and Research: A Study with Regression

The aim of this research is to analyse the attitudes towards information and communication technologies (ICT) of higher education teachers from an affective, cognitive and behavioural model for teaching and research. It also aimed to explore different factors that can predict such attitudes. A non-experimental study was proposed using a survey technique and descriptive and inferential analyses were carried out using a multiple linear regression model (MLR). In total, the sample was formed by 867 university professors from Spain belonging to different areas of knowledge. The results show that these teachers have a medium total attitudinal level, so the lowest attitudes have been represented by the behavioural ones, followed by the affective ones. Regarding the predictor variables, variables that can predict such attitudes were found to be age, participation in projects, gender and teaching in face-to-face and/or online universities (ordered from highest to lowest priority).
Virtual reality is more efficient in learning human heart anatomy especially for subjects with low baseline knowledge

New technologies make their way into education and one of the most prominent of them is the immersive Virtual Reality (IVR). But the scientific data about its efficiency in the educational process is controversial. In the present study three randomized groups of students, who did not have biological and medical classes amongst their courses, studied human heart anatomy using three different learning methods – a paper (text and images); a 3D interactive human heart model presented on a computer display; an IVR human heart model. Prior and after learning session students performed the test of human heart anatomy with 28 open questions. The IVR group showed the increase of correct answers within the group and compared with other groups. Also, the subjects with lesser baseline knowledge in IVR group showed the greater increase of correct answers following IVR session. The structure and the way how the learning material should be presented in IVR and how it affects the learning outcomes are discussed.
Motion Sickness in Virtual Reality: An Empirical Evaluation

Due to rapid growth in Virtual Reality (VR) technology, the industry of VR is expected to grow around $26.89 billion by 2022. However, with its extensive growth and immersive inclusion in human life, health-related issues are reported including, but not limited to nauseated feeling, vomiting, dizziness and cold sweats. These issues introduce a well-known side effect termed as motion sickness in VR users. Consequently, motion sickness limits the VR community in the full adaptation of this immersive technology. Since there is no lack of literature investigating motion sickness caused by VR, yet researches on the effect of VR on human's physiology is still in its infancy. This study presents novel findings, by comparing different factors such as gender, motion sickness experience, 3D games experience and VR experience. Furthermore, it reports the impact of concerning factors in a within-subjects design (46 participants participated in an experiment) under different virtual environment genres. The key findings of this article report that there is a significant difference in the amount of motion sickness when shifting from pleasant to the horror genre of the environment and having a strong dependence on gender. Moreover, the type of virtual environment is an essential factor that has a notable effect on the user's blood pressure, blood sugar and heart rate. However, past experiences with motion sickness and 3D games show no significant impact on the user's level of motion sickness.
Implications of Virtual Reality in Arts Education: Research Analysis in the Context of Higher Education

Technological advances have posed a challenge in university learning ecosystems in terms of the application of immersive technologies that offer an educational and innovative framework to the student. The evolution of global research on this topic during the period 1980 to 2019 was studied. For this purpose, a bibliometric analysis of 1296 articles was applied, obtaining results of the scientific productivity of the journals, authors, institutions, and countries that contribute to this research. The evidence shows a growing interest, especially in the last three years, in the study of the application of virtual reality in higher education. The main subject area is Social Science. The most productive research institution is the Complutense University of Madrid. The United States is the country with the most publications and citations. In addition, The United States, Spain and the United Kingdom are the countries with the most international collaborations in their publications. The study detected five new directions for future research. The growing worldwide trend of scientific production demonstrates the interest in developing aspects of the use of virtual reality in arts education in the context of higher education. This study contributes to the academic, scientific and institutional discussion around the improvement of decision making based on the available information.
Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs.GNN-based EA methods present promising performance by modeling the KG structure defined by relation triples.However, attribute triples can also provide crucial alignment signal but have not been well explored yet.In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently.Besides, the performances of current EA methods are overestimated because of the name-bias of existing EA datasets.To make an objective evaluation, we propose a hard experimental setting where we select equivalent entity pairs with very different names as the test set.Under both the regular and hard settings, our method achieves significant improvements (5.10% on average Hits@1 in DBP15k) over 12 baselines in crosslingual and monolingual datasets.Ablation studies on different subgraphs and a case study about attribute types further demonstrate the effectiveness of our method.Source code and data can be found at https://github.com/thunlp/explore-and-evaluate.
Matching-Theory-Based Low-Latency Scheme for Multitask Federated Learning in MEC Networks

Nowadays, there is an ever-increasing interests in federated learning, which allows end devices to collaboratively train a global machine learning model in a decentralized paradigm without sharing individual data. Despite the advantages of low communication cost and preserving data privacy, federated learning is also facing with new challenges to address. Practically, end devices will consider the resources cost and willingness caused by machine learning model training when they are invited to participate a federated learning task. So, how to assign the preferable tasks to the devices with high willingness has to be considered. Besides, the end devices have the property of high mobility, which means the time of devices localizing within the network is limited. Therefore, to reduce the task execution time is necessary. To address these problems, we first analyze and formulate the latency minimization problem for multitask federated learning in a multiaccess edge computing (MEC) network scenario. Then, we model the corresponding problem as a matching game to find the optimal task assignment solutions. Moreover, considering the large-scale Internet-of-Things (IoT) scenario, it is almost impossible for two sides to know the details of every individual of the other side so that the complete preference list (CPL) cannot be built in reality. Therefore, we propose an algorithm for large-scale matching with the incomplete preference list to address the problem. Finally, we conduct the numerical simulation in various cases to demonstrate the effectiveness of our proposed method. The results show that our approach can achieve similar performance with the CPL case.
A robust blind medical image watermarking approach for telemedicine applications

In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the original image. The proposed approach is applied in the frequency domain. For this purpose, four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform and discreet cosine transform. All these transforms was combined with Schur decomposition and the watermark bits were integrated in the upper triangular matrix. To obtain a satisfactory compromise between robustness and imperceptibility, the integration was performed in the medium frequencies of the image. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality of watermarked images and are remarkably robust against several conventional attacks.
Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model

Insulator fault detection is one of the essential tasks for high-voltage transmission lines’ intelligent inspection. In this study, a modified model based on You Only Look Once (YOLO) is proposed for detecting insulator faults in aerial images with a complex background. Firstly, aerial images with one fault or multiple faults are collected in diverse scenes, and then a novel dataset is established. Secondly, to increase feature reuse and propagation in the low-resolution feature layers, a Cross Stage Partial Dense YOLO (CSPD-YOLO) model is proposed based on YOLO-v3 and the Cross Stage Partial Network. The feature pyramid network and improved loss function are adopted to the CSPD-YOLO model, improving the accuracy of insulator fault detection. Finally, the proposed CSPD-YOLO model and compared models are trained and tested on the established dataset. The average precision of CSPD-YOLO model is 4.9% and 1.8% higher than that of YOLO-v3 and YOLO-v4, and the running time of CSPD-YOLO (0.011 s) model is slightly longer than that of YOLO-v3 (0.01 s) and YOLO-v4 (0.01 s). Compared with the excellent object detection models YOLO-v3 and YOLO-v4, the experimental results and analysis demonstrate that the proposed CSPD-YOLO model performs better in insulator fault detection from high-voltage transmission lines with a complex background.
Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection

The popularity and promotion of depth maps have brought new vigor and vitality into salient object detection (SOD), and a mass of RGB-D SOD algorithms have been proposed, mainly concentrating on how to better integrate cross-modality features from RGB image and depth map. For the cross-modality interaction in feature encoder, existing methods either indiscriminately treat RGB and depth modalities, or only habitually utilize depth cues as auxiliary information of the RGB branch. Different from them, we reconsider the status of two modalities and propose a novel Cross-modality Discrepant Interaction Network (CDINet) for RGB-D SOD, which differentially models the dependence of two modalities according to the feature representations of different layers. To this end, two components are designed to implement the effective cross-modality interaction: 1) the RGB-induced Detail Enhancement (RDE) module leverages RGB modality to enhance the details of the depth features in low-level encoder stage. 2) the Depth-induced Semantic Enhancement (DSE) module transfers the object positioning and internal consistency of depth features to the RGB branch in high-level encoder stage. Furthermore, we also design a Dense Decoding Reconstruction (DDR) structure, which constructs a semantic block by combining multi-level encoder features to upgrade the skip connection in the feature decoding. Extensive experiments on five benchmark datasets demonstrate that our network outperforms $15$ state-of-the-art methods both quantitatively and qualitatively. Our code is publicly available at:https://rmcong.github.io/proj_CDINet.html.
Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data

Capturing ground truth data to benchmark super-resolution (SR) is challenging. Therefore, current quantitative studies are mainly evaluated on simulated data artificially sampled from ground truth images. We argue that such evaluations overestimate the actual performance of SR methods compared to their behavior on real images. Toward bridging this simulated-to-real gap, we introduce the Super-Resolution Erlangen (SupER) database, the first comprehensive laboratory SR database of all-real acquisitions with pixel-wise ground truth. It consists of more than 80k images of 14 scenes combining different facets: CMOS sensor noise, real sampling at four resolution levels, nine scene motion types, two photometric conditions, and lossy video coding at five levels. As such, the database exceeds existing benchmarks by an order of magnitude in quality and quantity. This paper also benchmarks 19 popular single-image and multi-frame algorithms on our data. The benchmark comprises a quantitative study by exploiting ground truth data and qualitative evaluations in a large-scale observer study. We also rigorously investigate agreements between both evaluations from a statistical perspective. One interesting result is that top-performing methods on simulated data may be surpassed by others on real data. Our insights can spur further algorithm development, and the publicy available dataset can foster future evaluations.
Haze and Thin Cloud Removal via Sphere Model Improved Dark Channel Prior

Haze and cloud seriously degrade the quality of optical remote sensing images, which largely decrease their interpretability and intelligibility. In this letter, we propose a two-stage haze and thin cloud removal method based on homomorphic filtering (HF) and sphere model improved dark channel prior (DCP). Compared with current dehazing methods, the most advantage of the proposed method is that our method can deal with uneven haze, thick haze, and thin cloud. We observe that haze and cloud are highly related to the illumination component and mainly located in the low frequency of an image. Thus, we adapt HF to enhance the haze image, which makes the distribution of haze more even. In the second stage, we analyze the drawback of DCP, i.e., the transmission estimated by DCP is very sensitive to noise. To draw this issue, we propose a novel sphere model to estimate a more accurate transmission map. The sphere model improved DCP is more suitable for thick haze images than the traditional DCP. Extensive experimental results show that the proposed method significantly outperforms the compared state-of-the-art methods. The source code and data sets used in the letter are made public.
The Value of Diagnostic Information in Personalised Healthcare: A Comprehensive Concept to Facilitate Bringing This Technology into Healthcare Systems

Health systems around the world seek to address patients' unmet health needs for a range of acute and chronic diseases. Simultaneously, governments strive to keep healthcare spending sustainable, while providing equal access to high-quality care. This has fuelled debate around what constitutes a valuable healthcare intervention in a health system and the corollary consideration of what governments are willing to pay for a certain health intervention. Until recently, the value of information in general, and the value of diagnostic information (VODI) specifically, was not part of the discussion.However, investment in diagnostic information can be a key development as information may guide more effective and efficient healthcare and help maintain an affordable health system. This paper therefore explores ways to best define, evaluate, and reward the value created from diagnostics in healthcare and how to include these value considerations in decision-making processes for diagnostics. The authors ultimately call for a holistic VODI framework that accounts for the full range of potential benefits of diagnostic testing, beyond the traditional clinical and health economic domains, and that is essential to recognise, measure, and fully leverage the benefits of diagnostics for patients, health systems, and society.
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification

Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-GCN model improves state-of-the-art baselines on all of the challenging node classification tasks we consider: Cora, Citeseer, Pubmed, and PPI. In addition, our proposed method has other desirable properties, including generalization to recently proposed semi-supervised learning methods such as GraphSAGE, allowing us to propose N-SAGE, and resilience to adversarial input perturbations.
The 100-Digit Challenge: Algorithm jDE100

Real parameter optimization problems are often very complex and computationally expensive. We can find such problems in engineering and scientific applications. In this paper, a new algorithm is proposed to tackle the 100-Digit Challenge. There are 10 functions representing 10 optimization problems, and the goal is to compute each function's minimum value to 10 digits of accuracy. There is no limit on either time or the maximum number of function evaluations. The proposed algorithm is based on the self-adaptive differential evolution algorithm jDE. Our algorithm uses two populations and some other mechanisms when tackling the challenge. We provide the score for each function as required by the organizers of this challenge competition.
Variational AutoEncoder for Regression: Application to Brain Aging Analysis

While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified probabilistic model for learning the latent space of imaging data and performing supervised regression. Based on recent advances in learning disentangled representations, the novel generative process explicitly models the conditional distribution of latent representations with respect to the regression target variable. Performing a variational inference procedure on this model leads to joint regularization between the VAE and a neural-network regressor. In predicting the age of 245 subjects from their structural Magnetic Resonance (MR) images, our model is more accurate than state-of-the-art methods when applied to either region-of-interest (ROI) measurements or raw 3D volume images. More importantly, unlike simple feed-forward neural-networks, disentanglement of age in latent representations allows for intuitive interpretation of the structural developmental patterns of the human brain.
Don't Use Large Mini-batches, Use Local SGD

Mini-batch stochastic gradient methods (SGD) are state of the art for distributed training of deep neural networks. Drastic increases in the mini-batch sizes have lead to key efficiency and scalability gains in recent years. However, progress faces a major roadblock, as models trained with large batches often do not generalize well, i.e. they do not show good accuracy on new data. As a remedy, we propose a \emph{post-local} SGD and show that it significantly improves the generalization performance compared to large-batch training on standard benchmarks while enjoying the same efficiency (time-to-accuracy) and scalability. We further provide an extensive study of the communication efficiency vs. performance trade-offs associated with a host of \emph{local SGD} variants.
Differentiable Reasoning on Large Knowledge Bases and Natural Language

Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering. General neural architectures that jointly learn representations and transformations of text are very data-inefficient, and it is hard to analyse their reasoning process. These issues are addressed by end-to-end differentiable reasoning systems such as Neural Theorem Provers (NTPs), although they can only be used with small-scale symbolic KBs. In this paper we first propose Greedy NTPs (GNTPs), an extension to NTPs addressing their complexity and scalability limitations, thus making them applicable to real-world datasets. This result is achieved by dynamically constructing the computation graph of NTPs and including only the most promising proof paths during inference, thus obtaining orders of magnitude more efficient models 1. Then, we propose a novel approach for jointly reasoning over KBs and textual mentions, by embedding logic facts and natural language sentences in a shared embedding space. We show that GNTPs perform on par with NTPs at a fraction of their cost while achieving competitive link prediction results on large datasets, providing explanations for predictions, and inducing interpretable models.
Neural Machine Translation with Byte-Level Subwords

Almost all existing machine translation models are built on top of character-based vocabularies: characters, subwords or words. Rare characters from noisy text or character-rich languages such as Japanese and Chinese however can unnecessarily take up vocabulary slots and limit its compactness. Representing text at the level of bytes and using the 256 byte set as vocabulary is a potential solution to this issue. High computational cost has however prevented it from being widely deployed or used in practice. In this paper, we investigate byte-level subwords, specifically byte-level BPE (BBPE), which is compacter than character vocabulary and has no out-of-vocabulary tokens, but is more efficient than using pure bytes only is. We claim that contextualizing BBPE embeddings is necessary, which can be implemented by a convolutional or recurrent layer. Our experiments show that BBPE has comparable performance to BPE while its size is only 1/8 of that for BPE. In the multilingual setting, BBPE maximizes vocabulary sharing across many languages and achieves better translation quality. Moreover, we show that BBPE enables transferring models between languages with non-overlapping character sets.