Reviewerly/exHarmony huggingface dataset:
Decentralized Federated Learning for Electronic Health Records

Federated learning opens a number of research opportunities due to its high communication efficiency in distributed training problems within a star network. In this paper, we focus on improving the communication efficiency for fully decentralized federated learning (DFL) over a graph, where the algorithm performs local updates for several iterations and then enables communications among the nodes. In such a way, the communication rounds of exchanging the common interest of parameters can be saved significantly without loss of optimality of the solutions. Multiple numerical simulations based on large, real- world electronic health record databases showcase the superiority of the decentralized federated learning compared with classic methods.
No-Reference Image Quality Assessment: An Attention Driven Approach

In this paper, we tackle no-reference image quality assessment (NR-IQA), which aims to predict the perceptual quality of a distorted image without referencing its pristine-quality counterpart. Inspired by the free-energy principle, we assume that, while perceiving a distorted image, the human visual system (HVS) tends to predict the pristine image then estimates the perceptual quality based on the distorted-restored pair. Furthermore, the perceptual quality depends heavily on the way how human beings attend to distorted images, namely, the cooperation of foveal vision and the eye movement mechanism. Inspired by these properties of the HVS, given the distorted-restored pair, we implement an attention-driven NR-IQA method with reinforcement learning (RL). The model learns a policy to attend to several regions parallelly. The observations of the fixation regions are aggregated in a weighted average way, which is inspired by the robust averaging strategy. For policy learning, the rewards are derived from two tasks-classifying the distortion type and estimating the perceptual score. The goal of policy learning is to maximize the expectation of the accumulated rewards. Extensive experiments on LIVE, TID2008, TID2013 and CSIQ demonstrate the superiority of our methods.
Improving the Accuracy of Tesseract 4.0 OCR Engine Using Convolution-Based Preprocessing

Optical Character Recognition (OCR) is the process of identifying and converting texts rendered in images using pixels to a more computer-friendly representation. The presented work aims to prove that the accuracy of the Tesseract 4.0 OCR engine can be further enhanced by employing convolution-based preprocessing using specific kernels. As Tesseract 4.0 has proven great performance when evaluated against a favorable input, its capability of properly detecting and identifying characters in more realistic, unfriendly images is questioned. The article proposes an adaptive image preprocessing step guided by a reinforcement learning model, which attempts to minimize the edit distance between the recognized text and the ground truth. It is shown that this approach can boost the character-level accuracy of Tesseract 4.0 from 0.134 to 0.616 (+359% relative change) and the F1 score from 0.163 to 0.729 (+347% relative change) on a dataset that is considered challenging by its authors.
Inclusive development in environmental sustainability in sub‐Saharan Africa: Insights from governance mechanisms

Abstract This research examines the relevance of inclusive development in modulating the role of governance on environmental degradation. The study focuses on 44 countries in sub‐Saharan Africa for the period 2000–2012. The generalised method of moments is employed as the empirical strategy, and CO 2 emissions per capita is used to measure environmental pollution. Bundled and unbundled governance dynamics are employed, notably political governance (consisting of political stability/no violence and “voice and accountability”), economic governance (encompassing government effectiveness and regulation quality), institutional governance (entailing corruption‐control and the rule of law), and general governance (a composite measure of political governance, economic governance, and institutional governance). The following main findings are established. First, the underlying net effect in the moderating role of inclusive development in the governance‐CO 2 emissions nexus is not significant in regressions pertaining to political governance and economic governance. Second, there are positive net effects from the relevance of inclusive development in modulating the effects of regulation quality, economic governance, and general governance on CO 2 emissions. The significant and insignificant effects are elucidated. Policy implications are discussed.
Deep neural rejection against adversarial examples

Abstract Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i.e., input samples that are carefully perturbed to cause misclassification at test time. In this work, we propose a deep neural rejection mechanism to detect adversarial examples, based on the idea of rejecting samples that exhibit anomalous feature representations at different network layers. With respect to competing approaches, our method does not require generating adversarial examples at training time, and it is less computationally demanding. To properly evaluate our method, we define an adaptive white-box attack that is aware of the defense mechanism and aims to bypass it. Under this worst-case setting, we empirically show that our approach outperforms previously proposed methods that detect adversarial examples by only analyzing the feature representation provided by the output network layer.
Certificateless authenticated encryption with keyword search: Enhanced security model and a concrete construction for industrial IoT

The Industrial Internet of Things (IIoT) represents a variety of IoT applications in the industrial sector of economy and is heavily underpinned by computational capabilities of cloud computing to reduce the cost of on-demand services. However, cloud computing does not protect the privacy of the outsourced data unless some form of encryption is applied. Recently, some Certificateless Authenticated Encryption with Keyword Search (CLAEKS) schemes have been proposed. However, it is known in the literature that the security of these schemes is proved based on a weak security model. In this paper, we first provide a more powerful and realistic security model for CLAEKS schemes. Then, we propose a new CLAEKS scheme and prove its security in the enhanced security model. The comparison results show that the proposed CLAEKS scheme is the only existing searchable encryption scheme in the certificateless setting that is secure in the enhanced security model.
Novel Computer Architectures and Quantum Chemistry

Electronic structure theory (especially quantum chemistry) has thrived and has become increasingly relevant to a broad spectrum of scientific endeavors as the sophistication of both computer architectures and software engineering has advanced. This article provides a brief history of advances in both hardware and software, from the early days of IBM mainframes to the current emphasis on accelerators and modern programming practices.
Topology-learnable graph convolution for skeleton-based action recognition

Graph convolutional networks (GCNs) generalize convolutional neural networks into irregular graph-like structures. Generally, graph topologies are set by hand and fixed over all layers. Handcrafted connections may not be optimal and cannot fully use the self-learning ability of deep learning. In this work, we explore a topology-learnable graph convolution for skeleton-based action recognition. Specifically, a spatial graph convolution can be decomposed into a feature learning component that evolves the features of each graph vertex, and a graph vertex fusion component in which the latent graph topologies can be learned adaptively. Different initialization strategies for the learnable fusion matrix are evaluated. Experimental results that are based on the spatial-temporal GCNs for skeleton-based action recognition, demonstrate that convolution can work on graphs like on images, even if only a specific fusion matrix initialization that uses adjacency matrices is applied. Moreover, the self-learning process can learn the latent topology of a graph beyond the handcrafted topology, thereby making graph convolution flexible and universal.
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning: Examining Distributed and Centralized Stochastic Gradient Descent

We provide a discussion of several recent results which, in certain scenarios, are able to overcome a barrier in distributed stochastic optimization for machine learning (ML). Our focus is the so-called asymptotic network independence property, which is achieved whenever a distributed method executed over a network of n nodes asymptotically converges to the optimal solution at a comparable rate to a centralized method with the same computational power as the entire network. We explain this property through an example involving the training of ML models and sketch a short mathematical analysis for comparing the performance of distributed stochastic gradient descent (DSGD) with centralized SGD.
Aggregation and remuneration in Demand-Response with a blockchain-based framework

This article describes the possibility of using the blockchain technology for load and generation aggregation in a new distributed demand response (DR) service and customer remuneration system. The blockchain technology and the use of smart contracts for DR allow the creation of a distributed system, in which customers can communicate directly, in a transparent, secure, and traceable way, with the grid operator to provide their flexibility. In this article, the DR problem formulation takes into account several aspects, which are periodically executed. First, the blockchain records customers' energy consumption or production; then, the smart contract starts calculating the baseline and the potential support provided by each customer to fulfill the requested load adaptation. Customers' availability for generation and load profile modulation is also taken into account, as well as their privacy and an updated definition of the roles of grid and market operators in a new DR scenario supported by the blockchain technology. The blockchain used is Hyperledger Fabric, since it turned to be flexible for smart contract implementation while supporting multitenancy. Results show the possibility of successfully applying the blockchain technology to this particular topic, even considering privacy-preserving issues.
Self-attention driven adversarial similarity learning network

Similarity learning is a kind of machine learning algorithm that aims to measure the relevance between given objects. However, conventional similarity learning algorithms usually measure the distance between the entire given objects in the latent feature space. Consequently, the obtained similarity scores only represent how close are the entire given objects, but are incapable of demonstrating which part of them are similar to each other and how semantically similar are they. To address the above problems, in this paper, we propose a self-attention driven adversarial similarity learning network. Discriminative self-attention weights are firstly assigned to different regions of the given objects. The similarity learning step measures the relevance between these self-attention weighted feature maps of given objects under various topic vectors. The topic vectors are conditioned to capture and preserve hidden semantic information within data distribution by a generator-discriminator model with adversarial loss. This model aims to generate objects from topic vectors and propagates the difference between the generated and the real objects back to the similarity learning step, which forces the topic vectors to not only assign discriminative similarity scores to different object pairs but also further mine the hidden semantic information within data distribution. The final similarity scores represent how tight the given objects are connected to the topics. In addition, the regions with higher self-attention weights make more contribution to the discriminative similarity scores. The effectiveness of the proposed method is demonstrated through evaluations based on image retrieval task and document retrieval task and compared against various state-of-the-art algorithms in the field. The visualization results of topic vectors and self-attention weighted feature maps are demonstrated to make our proposed method explainable.
Communicating Robot Arm Motion Intent Through Mixed Reality Head-Mounted Displays

Efficient motion intent communication is necessary for safe and collaborative work environments with collocated humans and robots. Humans efficiently communicate their motion intent to other humans through gestures, gaze, and social cues. However, robots often have difficulty efficiently communicating their motion intent to humans via these methods. Many existing methods for robot motion intent communication rely on 2D displays, which require the human to continually pause their work and check a visualization. We propose a mixed reality head-mounted display visualization of the proposed robot motion over the wearer's real-world view of the robot and its environment. To evaluate the effectiveness of this system against a 2D display visualization and against no visualization, we asked 32 participants to labeled different robot arm motions as either colliding or non-colliding with blocks on a table. We found a 16% increase in accuracy with a 62% decrease in the time it took to complete the task compared to the next best system. This demonstrates that a mixed-reality HMD allows a human to more quickly and accurately tell where the robot is going to move than the compared baselines.
A neural blockchain for a tokenizable e-Participation model

Currently, Distributed Ledger Technologies (DLTs) and, especially, Blockchain technology represent a great opportunity for public institutions to improve citizen participation and foster democratic innovation. These technologies facilitate the simplification of processes and provide secure management of recorded data, guaranteeing the transmission and public transparency of information. Based on the combination of a Blockchain as a Service (BaaS) platform and G-Cloud solutions, our proposal consists of the design of an e-Participation model that uses a tokenizable system of the actions and processes undertaken by citizens in participatory processes providing incentives to promote greater participation in public affairs. In order to develop a sustainable, scalable and resilient e-Participation system, a new blockchain concept, which organizes the blocks as a neural system, is combined with the implementation of a virtual token to reward participants. Furthermore, this virtual token is deployed through a smart contract that the block itself produces, containing information about the transaction and all the documents involved in the process. Finally, our Neural Distributed Ledger (NDL) framework facilitates the interconnection of blockchain networks in a transparent, certified, secure, auditable, scalable and traceable way.
Entanglement-Based Quantum Radar: From Myth to Reality

Many quantum radars currently studied in the literature use a phenomenon called entanglement to address the problem of distinguishing signal from noise, which is one of the most important problems faced by any radar. Until recently, entanglement-based quantum radars at radio frequencies existed only in theory; their practicality was very much in doubt. The situation has changed with a recent experimental implementation of all the necessary components of a quantum two-mode squeezing (QTMS) radar, which operates at microwave frequencies and can be described as a quantum range finder. In this article, we lay out the exact problem solved by this prototype, namely quantum noise, and explain how entanglement can overcome the existence of this noise. By analyzing the QTMS radar prototype, we point out a technological route to an entanglement-based quantum radar that can, in principle, perform all tasks that radars can and must do, such as array processing, clutter suppression, and image processing (including synthetic aperture radar and inverse synthetic aperture radar).
Modeling of measurement-based quantum network coding on a superconducting quantum processor

Quantum network coding has been proposed to improve resource utilization to support distributed computation but has not yet been put in to practice. We investigate a particular implementation of quantum network coding using measurement-based quantum computation on IBM Q processors. We compare the performance of quantum network coding with entanglement swapping and entanglement distribution via linear cluster states. These protocols outperform quantum network coding in terms of the final Bell pair fidelities but are unsuitable for optimal resource utilization in complex networks with contention present. We demonstrate the suitability of noisy intermediate-scale quantum (NISQ) devices such as IBM Q for the study of quantum networks. We also identify the factors that limit the performance of quantum network coding on these processors and provide estimates or error rates required to boost the final Bell pair fidelities to a point where they can be used for generation of genuinely random cryptographic keys among other useful tasks. Surprisingly, the required error rates are only around a factor of 2 smaller than the current status and we expect they will be achieved in the near future.
Building an efficient OCR system for historical documents with little training data

Abstract As the number of digitized historical documents has increased rapidly during the last a few decades, it is necessary to provide efficient methods of information retrieval and knowledge extraction to make the data accessible. Such methods are dependent on optical character recognition (OCR) which converts the document images into textual representations. Nowadays, OCR methods are often not adapted to the historical domain; moreover, they usually need a significant amount of annotated documents. Therefore, this paper introduces a set of methods that allows performing an OCR on historical document images using only a small amount of real, manually annotated training data. The presented complete OCR system includes two main tasks: page layout analysis including text block and line segmentation and OCR. Our segmentation methods are based on fully convolutional networks, and the OCR approach utilizes recurrent neural networks. Both approaches are state of the art in the relevant fields. We have created a novel real dataset for OCR from Porta fontium portal. This corpus is freely available for research, and all proposed methods are evaluated on these data. We show that both the segmentation and OCR tasks are feasible with only a few annotated real data samples. The experiments aim at determining the best way how to achieve good performance with the given small set of data. We also demonstrate that obtained scores are comparable or even better than the scores of several state-of-the-art systems. To sum up, this paper shows a way how to create an efficient OCR system for historical documents with a need for only a little annotated training data.
Real time human action recognition using triggered frame extraction and a typical CNN heuristic

Recognition of human actions with optimum accuracy and with less computational overhead has always been a challenging task. A suitable framework towards this problem can provide a robust solution for numerous domains of expertise that demands automatic surveillance. In this paper, a two-fold framework has been proposed for the frame extraction and recognition of human action therein from a video input. A Fuzzy inferencing technique has been suitably used for the purpose of video frame extraction in a smarter way. Frames are extracted from a video only when an event of action is initiated and about to commit. By this the additional computational load of continuous frame extraction is eased efficiently. Subsequent to this process, we also introduce a typical convolutional neural network (CNN) that is used for the purpose of human action recognition. This typical CNN has been designed so as to suit the nature of input that is supplied to it. Experimental evaluation has been performed on the standard HMDB51 database. A total of eight distinct and prominent actions which are mostly common in daily routine of human life are considered for the purpose. Performance comparison of the proposed scheme with other state of the art schemes is also performed with three different measures. Evaluation results for the proposed scheme indicates overall rate of accuracies of 96.5% and 98% for frame extraction and action recognition respectively. The proposed scheme outperformed the others in most of the measuring aspects.
Parametric PCA for unsupervised metric learning

In pattern recognition, the problem of quantifying a suitable similarity measure between different objects in a collection is a challenging task, especially in cases where the standard Euclidean distance is not a reasonable choice. In this context, dimensionality reduction algorithms are powerful tools for unsupervised metric learning. In this paper, we propose a framework to build dimensionality reduction methods for unsupervised metric learning based on the mapping of local neighborhoods of the KNN graph to a parametric feature space, defined in terms of a statistical model. By incorporating a non-Euclidean metric based on the Bhattacharyya coefficient, we define the parametric kernel matrix, a surrogate for the covariance matrix of the parametric feature vectors. Inspired by PCA, we use the eigenvalues of the parametric kernel matrix to learn features for the original data. Numerical experiments with real datasets show that Parametric PCA is capable of producing better defined clusters and also more discriminant features in comparison to regular PCA, kernel PCA, sparse PCA, robust PCA and manifold learning algorithms, making the proposed method a promising alternative for unsupervised metric learning.
Precision vs. Power Grip: A Comparison of Pen Grip Styles for Selection in Virtual Reality

While commercial Virtual Reality (VR) controllers are mostly designed to be held in a power grip, previous research showed that using pen-like devices with a precision grip can improve user performance for selection in VR, potentially even matching that achievable with a mouse. However, it is not known if the improvement is due to the grip style. In this work, 12 subjects performed a Fitts’ task at 3 different depth conditions with a pen-like input device used in both a precision and power grip. Our results identify that the precision grip significantly improves user performance in VR through a significant reduction in error rate, but we did not observe a significant effect of the distance of targets from the user. We believe that our results are useful for designers and researchers to improve the usability of and user performance in VR systems.