Reviewerly/exHarmony huggingface dataset:
Are teachers techno-optimists or techno-pessimists? A pilot comparative among teachers in Bolivia, Brazil, the Dominican Republic, Ecuador, Finland, Poland, Turkey, and Uruguay

The aim of the article is to highlight the key elements related to the implementation of new technologies in education from the perspective of the opinions and experiences of educators in the field in Bolivia, Brazil, the Dominican Republic, Ecuador, Finland, Poland, Turkey, and Uruguay. The text compares issues related to attitudes towards the use of new media in education, experiences with different forms of e-learning, and the level of restrictions on the use of smartphones in school. These variables are juxtaposed with the self-assessment of digital competence and how cyberspace is used. The survey was conducted using a standardised survey questionnaire translated into the relevant national languages in the first half of 2019, and involved a sample of 873 teachers representing eight countries. On the basis of the pilot studies it was noted that: 1) Teachers from LAC and EU like to use digital media - this is a constant trend independent of geographical location; 2) Teachers note that new technologies are not always better than analogue didactic aids; 3) Teachers from selected countries (the Dominican Republic, Brazil, Turkey, and Uruguay) have much greater techno-optimism in themselves than teachers from Bolivia, Poland, Finland and Turkey in terms of the impact of ICT on student motivation and engagement; 4) In all countries teachers prefer free online courses (the different forms of e-learning are used most often by those in the Dominican Republic, and the least often in Bolivia and Poland); 5) In each country teachers who highly value their own digital competences and have a positive attitude towards new media use ICT much more actively; 6) There is also a global trend in that the extensive use of cyberspace (typical e-services) appears in combination with the extensive use of various forms of e-learning; 7) Teachers from Ecuador are most likely to want to ban the use of smartphones in schools. The most liberal approach in this respect is taken by the Uruguayans; 8) The knowledge of the conditions related to restricting the use of smartphones goes beyond the analyses related to the style of use and attitude towards new media. This article is the result of pilot studies conducted within the framework of the SMART ECOSYSTEM FOR LEARNING AND INCLUSION project carried out in selected Latin American, Caribbean (LAC) and European (EU) countries.
Augmented Reality for Head and Neck Carcinoma Imaging: Description and Feasibility of an Instant Calibration, Markerless Approach

Augmented reality (AR) can help to overcome current limitations in computer assisted head and neck surgery by granting "X-ray vision" to physicians. Still, the acceptance of AR in clinical applications is limited by technical and clinical challenges. We aim to demonstrate the benefit of a marker-free, instant calibration AR system for head and neck cancer imaging, which we hypothesize to be acceptable and practical for clinical use.We implemented a novel AR system for visualization of medical image data registered with the head or face of the patient prior to intervention. Our system allows the localization of head and neck carcinoma in relation to the outer anatomy. Our system does not require markers or stationary infrastructure, provides instant calibration and allows 2D and 3D multi-modal visualization for head and neck surgery planning via an AR head-mounted display. We evaluated our system in a pre-clinical user study with eleven medical experts.Medical experts rated our application with a system usability scale score of 74.8 ± 15.9, which signifies above average, good usability and clinical acceptance. An average of 12.7 ± 6.6 minutes of training time was needed by physicians, before they were able to navigate the application without assistance.Our AR system is characterized by a slim and easy setup, short training time and high usability and acceptance. Therefore, it presents a promising, novel tool for visualizing head and neck cancer imaging and pre-surgical localization of target structures.
Lightweight authentication for IoT/Cloud-based forensics in intelligent data computing

Numerous application domains in practice related to target- tracking, monitoring, and transportation have utilized the wide usage of Wireless Sensor Networks (WSN’s) technology. These domains use physical networking objects connecting over the Internet to collect and exchange the data. Using the advancement of cloud computing technologies and aggravation of big-data growth caused by the incorporation of the Internet of Things (IoT), secure user authentication for remote access is playing a crucial role. Since it has limited authorization and authentication privileges for mobility users, an approach of mobile-sink has been instigated for the improvisation of remote user authentication i.e. cloud-based IoT applications. As a result, this article proposes a lightweight smartcard based secure authentication (LS-BSA) approach using the mathematical assumption of bilinear-pairing/mapping, elliptic-curve cryptosystems, and fuzzy verifier. An extensive security investigation demonstrates that the proposed LS-BSA not only guarantees the AKA security properties but also prevents significant vulnerabilities. Furthermore, the proposed LS-BSA uses lightweight operations to establish a seamless data connectivity over a secure network. It maintains the compatibility standards including low-cost and low-power to mitigate the computation and communication cost of cloud-based intelligent data computing. Formal security verification of BAN-logic is introduced to show that LS-BSA offers proper mutual user authentication and secret secure-session key agreement between the real-time entities. In addition, a network scenario has been set up using the NS-3 simulator to prove that the proposed LS-BSA is more efficient than other existing schemes in terms of packet delivery ratio, end-to-end delay, and throughput rate.
Self-supervised Outdoor Scene Relighting

Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo. Current techniques are completely supervised, requiring high quality synthetic renderings to train a solution. Such renderings are synthesized using priors learned from limited data. In contrast, we propose a self-supervised approach for relighting. Our approach is trained only on corpora of images collected from the internet without any user-supervision. This virtually endless source of training data allows training a general relighting solution. Our approach first decomposes an image into its albedo, geometry and illumination. A novel relighting is then produced by modifying the illumination parameters. Our solution capture shadow using a dedicated shadow prediction map, and does not rely on accurate geometry estimation. We evaluate our technique subjectively and objectively using a new dataset with ground-truth relighting. Results show the ability of our technique to produce photo-realistic and physically plausible results, that generalizes to unseen scenes.
Identifying Illicit Addresses in Bitcoin Network

Bitcoin has attracted a lot of attentions from both researchers and investors since it was first proposed in 2008. One of the key characteristics of Bitcoin is anonymity, which makes the Bitcoin market unregulated and a large number of criminal and illicit activities are associated with bitcoin transactions. Therefore, it's necessary to identify the illicit addresses in the Bitcoin network for safeguarding financial systems and protecting user's assets. To identify the illicit addresses in the Bitcoin network, first, we collect a large dataset of illicit addresses. The illicit addresses come mainly from some specific websites, public forums, and research papers. Second, we make a careful design of the features of illicit addresses. The features include basic features that refer to the related papers and the novel proposed features (topological features and temporal features). Third, we apply various machine learning algorithms (RF, SVM, XGB, ANN) to evaluate our features, which indicates that the proposed features are discriminating and robust. Besides, the paper discusses the class imbalance problem and achieves a better enhancement when using the cost-sensitive approach. Moreover, the paper proposes a model that incorporates LSTM into auto-encoder to generate temporal features. Results show that the generated features are helpful for the illicit addresses identification. Finally, the dataset and code are released in Github .
HAR-Net: Joint Learning of Hybrid Attention for Single-Stage Object Detection

Object detection has been a challenging task in computer vision. Although significant progress has been made in object detection with deep neural networks, the attention mechanism has yet to be fully developed. In this paper, we propose a hybrid attention mechanism for single-stage object detection. First, we present the modules of spatial attention, channel attention and aligned attention for single-stage object detection. In particular, dilated convolution layers with symmetrically fixed rates are stacked to learn spatial attention. A channel attention mechanism with the cross-level group normalization and squeeze-and-excitation operation is proposed. Aligned attention is constructed with organized deformable filters. Second, the three types of attention are unified to construct the hybrid attention mechanism. We then plug the hybrid attention into Retina-Net and propose the efficient single-stage HAR-Net for object detection. The attention modules and the proposed HAR-Net are evaluated on the COCO detection dataset. The experiments demonstrate that hybrid attention can significantly improve the detection accuracy and that the HAR-Net can achieve a state-of-the-art 45.8% mAP, thus outperforming existing single-stage object detectors.
On Converse Bounds for Classical Communication Over Quantum Channels

We explore several new converse bounds for classical communication over quantum channels in both the one-shot and asymptotic regimes. First, we show that the Matthews-Wehner meta-converse bound for entanglement-assisted classical communication can be achieved by activated, no-signalling assisted codes, suitably generalizing a result for classical channels. Second, we derive a new efficiently computable meta-converse on the amount of classical information unassisted codes can transmit over a single use of a quantum channel. As applications, we provide a finite resource analysis of classical communication over quantum erasure channels, including the second-order and moderate deviation asymptotics. Third, we explore the asymptotic analogue of our new meta-converse, the $\Upsilon$-information of the channel. We show that its regularization is an upper bound on the classical capacity, which is generally tighter than the entanglement-assisted capacity and other known efficiently computable strong converse bounds. For covariant channels we show that the $\Upsilon$-information is a strong converse bound.
What is Interpretability?

We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: (1) Are networks explainable, and if so, what does it mean to explain the output of a network? And (2) what does it mean for a network to be interpretable? We argue that accounts of "explanation" tailored specifically to neural networks have ineffectively reinvented the wheel. In response to (1), we show how four familiar accounts of explanation apply to neural networks as they would to any scientific phenomenon. We diagnose the confusion about explaining neural networks within the machine learning literature as an equivocation on "explainability," "understandability" and "interpretability." To remedy this, we distinguish between these notions, and answer (2) by offering a theory and typology of interpretation in machine learning. Interpretation is something one does to an explanation with the aim of producing another, more understandable, explanation. As with explanation, there are various concepts and methods involved in interpretation: Total or Partial, Global or Local, and Approximative or Isomorphic. Our account of "interpretability" is consistent with uses in the machine learning literature, in keeping with the philosophy of explanation and understanding, and pays special attention to medical artificial intelligence systems.
Adaptive Partial Multi-View Hashing for Efficient Social Image Retrieval

Social networks allow users to actively upload images and descriptive tags, which has led to an explosive growth in the number of social images. Multi-view hashing is an efficient technique for supporting large-scale social image retrieval because of its desirable capabilities of encoding multi-view features into compact binary hash codes with extremely low storage costs and fast retrieval speeds. However, existing methods require multi-view features to be fully paired at both the offline model training and online query stages. This requirement cannot be easily satisfied for social image retrieval, where social images that lack descriptive tags are common in social networks. In this paper, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unsupervised Adaptive Partial Multi-view Hashing</i> (UAPMH) method to handle the partial-view hashing problem for efficient social image retrieval. Specifically, the shared and view-specific latent representations of fully paired and partial-view images, respectively, are learned separately by an adaptive partial multi-view matrix factorization module within the identical semantic space. In particular, instead of adopting simple fixed view combination weights, we develop a parameter-free weight learning scheme to adaptively learn the weights to capture the view variations and the discriminative capabilities of different views. With such a design, our model can sufficiently exploit the available partial-view samples with separate hash code learning and effectively preserve the latent relations of images and tags in hash codes with semantic space sharing. Moreover, to avoid relaxing errors and improve the learning efficiency, binary hash codes are directly learned in a fast mode with simple and efficient operations. Finally, we extend UAPMH to the supervised learning paradigm as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Supervised Adaptive Partial Multi-view Hashing</i> (SAPMH) with the supervision of pair-wise semantic labels to further enhance the discriminative capability of hash codes. The experiments demonstrate the state-of-the-art performance of the proposed approaches on public social image retrieval datasets. Our source codes and testing datasets can be obtained at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ChaoqunZheng/APMH</uri> .
CLAIRE: A Distributed-Memory Solver for Constrained Large Deformation Diffeomorphic Image Registration

With this work we release CLAIRE, a distributed-memory implementation of an effective solver for constrained large deformation diifeomorphic image registration problems in three dimensions. We consider an optimal control formulation. We invert for a stationary velocity field that parameterizes the deformation map. Our solver is based on a globalized, preconditioned, inexact reduced space Gauss‒Newton‒Krylov scheme. We exploit state-of-the-art techniques in scientific computing to develop an eifective solver that scales to thousands of distributed memory nodes on high-end clusters. We present the formulation, discuss algorithmic features, describe the software package, and introduce an improved preconditioner for the reduced space Hessian to speed up the convergence of our solver. We test registration performance on synthetic and real data. We Demonstrate registration accuracy on several neuroimaging datasets. We compare the performance of our scheme against diiferent flavors of the Demons algorithm for diifeomorphic image registration. We study convergence of our preconditioner and our overall algorithm. We report scalability results on state-of-the-art supercomputing platforms. We Demonstrate that we can solve registration problems for clinically relevant data sizes in two to four minutes on a standard compute node with 20 cores, attaining excellent data fidelity. With the present work we achieve a speedup of (on average) 5× with a peak performance of up to 17× compared to our former work.
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge

Scaling up the convolutional neural network (CNN) size (e.g., width, depth, etc.) is known to effectively improve model accuracy. However, the large model size impedes training on resource-constrained edge devices. For instance, federated learning (FL) may place undue burden on the compute capability of edge nodes, even though there is a strong practical need for FL due to its privacy and confidentiality properties. To address the resource-constrained reality of edge devices, we reformulate FL as a group knowledge transfer training algorithm, called FedGKT. FedGKT designs a variant of the alternating minimization approach to train small CNNs on edge nodes and periodically transfer their knowledge by knowledge distillation to a large server-side CNN. FedGKT consolidates several advantages into a single framework: reduced demand for edge computation, lower communication bandwidth for large CNNs, and asynchronous training, all while maintaining model accuracy comparable to FedAvg. We train CNNs designed based on ResNet-56 and ResNet-110 using three distinct datasets (CIFAR-10, CIFAR-100, and CINIC-10) and their non-I.I.D. variants. Our results show that FedGKT can obtain comparable or even slightly higher accuracy than FedAvg. More importantly, FedGKT makes edge training affordable. Compared to the edge training using FedAvg, FedGKT demands 9 to 17 times less computational power (FLOPs) on edge devices and requires 54 to 105 times fewer parameters in the edge CNN. Our source code is released at FedML (this https URL).
Adaptive Transform Domain Image Super-Resolution via Orthogonally Regularized Deep Networks

Deep learning methods, in particular, trained Convolutional Neural Networks (CNN) have recently been shown to produce compelling results for single image Super-Resolution (SR). Invariably, a CNN is learned to map the Low Resolution (LR) image to its corresponding High Resolution (HR) version in the spatial domain. We propose a novel network structure for learning the SR mapping function in an image transform domain, specifically the Discrete Cosine Transform (DCT). As the first contribution, we show that DCT can be integrated into the network structure as a Convolutional DCT (CDCT) layer. With the CDCT layer, we construct the DCT Deep SR (DCT-DSR) network. We further extend the DCT-DSR to allow the CDCT layer to become trainable (i.e., optimizable). Because this layer represents an image transform, we enforce pairwise orthogonality constraints and newly formulated complexity order constraints on the individual basis functions/filters. This Orthogonally Regularized Deep SR network (ORDSR) simplifies the SR task by taking advantage of image transform domain while adapting the design of transform basis to the training image set. Experimental results show ORDSR achieves state-of-the-art SR image quality with fewer parameters than most of the deep CNN methods. A particular success of ORDSR is in overcoming the artifacts introduced by bicubic interpolation. A key burden of deep SR has been identified as the requirement of generous training LR and HR image pairs; ORSDR exhibits a much more graceful degradation as training size is reduced with significant benefits in the regime of limited training. Analysis of memory and computation requirements confirms that ORDSR can allow for a more efficient network with faster inference.
Healthchain: Secure EMRs Management and Trading in Distributed Healthcare Service System

Electronic medical records (EMRs) are the most critical data in human health management. As in traditional centralized healthcare service systems (HSSs), user privacy security, EMRs data leakage, tampering, and island are some significant problems. However, blockchain is a promising technology to protect the privacy and realize cross-institutional data sharing for solving these problems. In this article, a novel peer-to-peer EMRs data management and trading system called healthchain has been proposed based on consortium blockchain technology. Through this distributed system, the patient can access their EMRs in different institutions freely, and the EMRs can be traded among different users conveniently. Then, to balance EMRs data supply and demand, we establish a Stackelberg pricing model to evaluate EMRs data providers and consumers' interactions. The optimal unit price and data amounts can be found by applying the backward induction method, and the maximizing benefits of the participants can be obtained by achieving the Nash equilibrium in the proposed game. Moreover, security analysis shows the healthchain can provide secure EMRs management and trading, and the simulation results show that the proposed pricing model can help the healthchain achieve social welfare maximization.
An evolutionary-based predictive soft computing model for the prediction of electricity consumption using multi expression programming

Proper estimation of electricity consumption is one of the influential factors for sustainability and cleaner production in both developed and developing countries. Many studies have been conducted to present accurate prediction models for forecasting electricity demand. However, researchers are still working to develop models with higher accuracy. This study applies a newer branch of Genetic Programming (GP) as a soft computing technique, known as Multi Expression Programming (MEP) to predict the electricity consumption of China for the first time based on the data collected from 1991 to 2019. Specifically, a robust mathematical model was developed using MEP for this purpose. Different predictive techniques known as Gene Expression Programming (GEP) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used to compare the accuracy of the model. Based on the results, the proposed MEP model is more powerful and accurate than both GEP and ANFIS. In addition, a sensitivity analysis was conducted to present the impact of each factor on the electricity consumption of China. It was shown that among the four independent factors (Population, Gross Domestic Product (GDP), Import, and Export), Population has the highest impact, followed by Export, Import and GDP, respectively.