Reviewerly/exHarmony huggingface dataset:
The Opportunities of Blockchain in Health 4.0

The next definitive step into the future is expected to involve a shift towards a fully automated environment that utilizes exponential technology such as Artificial Intelligence (AI), Internet of Things (IoT), Sensor Networks and Blockchain paradigms. We are immersed in the Fourth Industrial Revolution, that is fundamentally changing the way we live, work and interact with one another, and with technology. Industry 4.0 is an emerging era of connectivity and interaction among parts, machines and humans that have the potential to create enormous production and efficiency dividends, in addition to improvements in the quality of life and sustainable environmental outcomes. The Healthcare Industry is in the process of adopting technologies that allow digitization of health records and automation of various plausible clinical procedures. The field has undergone an evolution of its own, and is now standing at Healthcare Version 3.0, the extension of Web 3.0, that involves greater transparency of healthcare data to individuals, personalized to optimize their experience with the interface. The amalgamation of the two revolutions to realise the new era of Health 4.0, is where the emerging Blockchain Technology could play a major role. The need for interoperability across clinical departments within the same hospital, the necessity of updated set of records for reference across multiple healthcare facilities, and the exigency of trusted and transparent documentation of individual health data, urge the use of Blockchain concepts in our attempt to transform healthcare services. This chapter explores the transition of the Healthcare Industry, the drawbacks of previous versions, and the opportunities of Blockchain concepts that would aid the medical field in keeping up with Industry 4.0.
Encryption scheme based on expanded Reed-Solomon codes

We present a code-based public-key cryptosystem, in which we use Reed-Solomon codes over an extension field as secret codes and disguise it by considering its shortened expanded code over the base field. Considering shortened expanded codes provides a safeguard against distinguisher attacks based on the Schur product. Moreover, without using a cyclic or a quasi-cyclic structure we obtain a key size reduction of nearly $45 \%$ compared to the classic McEliece cryptosystem proposed by Bernstein et al.
Verbal patterns in Palestinian Arabic

Abstract The study examines the distribution of verbal patterns and their semantic-syntactic functions as they are used in spoken narrative text production by adult native speakers of Palestinian Arabic. 30 native Palestinian Arabic adult speakers from Kufur Qareʕ, a village in Central Israel, were shown a clip demonstrating conflicts and were asked to produce an oral narrative text based on it. The verbs used in these narratives were examined according to root, pattern, transitivity and semantic class. The results revealed strong tendencies with regard to the distribution of the patterns that were used. CaCaC was the most productive pattern by type and token counts. This stands in contradiction to the results for verb innovation, where the CaCCaC and tCaCCaC patterns are selected almost exclusively, and it highlights the gap between productivity based on new formations and productivty based on basic forms in use. In addition, some verbal patterns were extermely rarely used. The results also show that there was no transparent form-function relation with respect to the semantic functions of verbal patterns. Most semantic functions were delivered in a small number of patterns (between 1–3) and the majority of them were in were found in one pattern, CaCaC . The results shed light on the actual usage of Arabic verbal patterns in text production and their semantic and syntactic features.
GANKIN: generating Kin faces using disentangled GAN

Kin image generation from parents’ images is a high-level prediction and generation problem. This study presents a new method to predict and generate a kin face using parents’ faces, i.e. Tri-subject prediction or two-to-one prediction. Training one end-to-end conditional GAN from scratch can run into mode-collapse and may not converge, we overcome this by using a modular pipeline of unconditional smaller models. We start by extracting father and mother features using a pre-trained model FaceNet, then we use extracted features to predict kin features. After that, we use a linear regression model to convert the predicted features to a latent vector that can be passed to an unconditioned generative adversarial network (GAN) which generates the required kin face. Also, we present a disentangling method to help choosing the age and gender of the generated kin. The modular nature of the proposed model allows validating and improving each part of the pipeline separately. The model achieves promising results compared to the state-of-the-art.
Uncomputability and complexity of quantum control

Abstract In laboratory and numerical experiments, physical quantities are known with a finite precision and described by rational numbers. Based on this, we deduce that quantum control problems both for open and closed systems are in general not algorithmically solvable, i.e., there is no algorithm that can decide whether dynamics of an arbitrary quantum system can be manipulated by accessible external interactions (coherent or dissipative) such that a chosen target reaches a desired value. This conclusion holds even for the relaxed requirement of the target only approximately attaining the desired value. These findings do not preclude an algorithmic solvability for a particular class of quantum control problems. Moreover, any quantum control problem can be made algorithmically solvable if the set of accessible interactions (i.e., controls) is rich enough. To arrive at these results, we develop a technique based on establishing the equivalence between quantum control problems and Diophantine equations, which are polynomial equations with integer coefficients and integer unknowns. In addition to proving uncomputability, this technique allows to construct quantum control problems belonging to different complexity classes. In particular, an example of the control problem involving a two-mode coherent field is shown to be NP-hard, contradicting a widely held believe that two-body problems are easy.
Empirical wavelet transform‐based fog removal via dark channel prior

Haze and fog removing from videos and images has got massive concentration in the field of video and image processing because videos and images are severely affected by fog in tracking and surveillance system, object detection. Different defogging techniques proposed so far are based on polarisation, colour-line model, anisotropic diffusion, dark channel prior (DCP) etc. However, these methods are unable to produce output image with desirable quality in the presence of dense fog and sky region. In this study, the authors have proposed a novel fog removal technique where DCP is applied on the low-frequency component of empirical wavelet transformation coefficients of the foggy input image. They apply unsharp masking on wavelet coefficients of the embedded wavelet transformed image for improving the sharpness of the output image. Later contrast limited adaptive histogram equalisation technique is used as a post-processing task to the inverse transformed image for producing the sharp and high contrast output. Finally, the colour and intensity of the contrast-enhanced image are uplifted through S-channel and V-channel gain adjustment. The proposed method provides significant improvement to the overall quality of the output image compared to contemporary techniques. The quantitative and qualitative measurements confirm the claims.
The Supersingular Isogeny Problem in Genus 2 and Beyond

Let $$A/\overline{\mathbb {F}}_p$$ and $$A'/\overline{\mathbb {F}}_p$$ be superspecial principally polarized abelian varieties of dimension $$g>1$$. For any prime $$\ell \ne p$$, we give an algorithm that finds a path $$\phi :A \rightarrow A'$$ in the $$(\ell , \dots , \ell )$$-isogeny graph in $$\widetilde{O}(p^{g-1})$$ group operations on a classical computer, and $$\widetilde{O}(\sqrt{p^{g-1}})$$ calls to the Grover oracle on a quantum computer. The idea is to find paths from A and $$A'$$ to nodes that correspond to products of lower dimensional abelian varieties, and to recurse down in dimension until an elliptic path-finding algorithm (such as Delfs–Galbraith) can be invoked to connect the paths in dimension $$g=1$$. In the general case where A and $$A'$$ are any two nodes in the graph, this algorithm presents an asymptotic improvement over all of the algorithms in the current literature. In the special case where A and $$A'$$ are a known and relatively small number of steps away from each other (as is the case in higher dimensional analogues of SIDH), it gives an asymptotic improvement over the quantum claw finding algorithms and an asymptotic improvement over the classical van Oorschot–Wiener algorithm.
Exploring public wearable display of wellness tracker data

We investigate wearable presentation of tracked wellness data, and people’s perceptions and motivations for sharing it through a wearable display. Whilst online sharing is a common feature in wellness tracking solutions, the motivations and experiences of users to share tracked data in situ has not been widely studied. We created two functional prototypes – the hat tracker and the tracker badge – which we used as probes in two focus groups to elicit opinions on the content and format of wearable tracker displays. Complementing this, a study where participants used the hat tracker prototype in public locations provides insights on sharing in everyday life use contexts. We report that users appreciate the motivating nature of such displays, but favor the display of positive information. Leveraging prior work, we present a model describing the factors affecting users’ willingness to share tracked data via wearable displays, and highlight such displays’ potential for supporting behavior change.
A multicriteria optimization model for cloud service provider selection in multicloud environments

Summary Multicloud computing is a strategy that helps customers to reduce reliance on any single cloud provider (known as the vendor lock‐in problem). The value of such strategy increases with proper selection of qualified service providers. In this paper, a constrained multicriteria multicloud provider selection mathematical model is proposed. Three metaheuristics algorithms (simulated annealing [SA], genetic algorithm [GA], and particle swarm optimization algorithm [PSO]) were implemented to solve the model, and their performance was studied and compared using a hypothetical case study. For the sake of comparison, Taguchi's robust design method was used to select the algorithms' parameters values, an initial feasible solution was generated using analytic hierarchy process (AHP)—as the most used method to solve the cloud provider selection problem in the literature, all three algorithms used that solution and, in order to avoid AHP limitations, another initial solution was generated randomly and used by the three algorithm in a second set of performance experiments. Results showed that SA, GA, PSO improved the AHP solution by 53.75%, 60.41%, and 60.02%, respectively, SA and PSO are robust because of reaching the same best solution in spite of the initial solution.
Four-Dimensional Trajectory Generation for UAVs Based on Multi-Agent Q Learning

A distributed four-dimensional (4D) trajectory generation method based on multi-agent Q learning is presented for multiple unmanned aerial vehicles (UAVs). Based on this method, each vehicle can intelligently generate collision-free 4D trajectories for time-constrained cooperative flight tasks. For a single UAV, the 4D trajectory is generated by the bionic improved tau gravity guidance strategy, which can synchronously guide the position and velocity to the desired values at the arrival time. Furthermore, to optimise trajectory parameters, the continuous state and action wire fitting neural network Q (WFNNQ) learning method is applied. For multi-UAV applications, the learning is organised by the win or learn fast-policy hill climbing (WoLF-PHC) algorithm. Dynamic simulation results show that the proposed method can efficiently provide 4D trajectories for the multi-UAV system in challenging simultaneous arrival tasks, and the fully trained method can be used in similar trajectory generation scenarios.
Solving Optimization Problems Through Fully Convolutional Networks: An Application to the Traveling Salesman Problem

In the new wave of artificial intelligence, deep learning is impacting various industries. As a closely related area, optimization algorithms greatly contribute to the development of deep learning. But the reverse applications are still insufficient. Is there any efficient way to solve certain optimization problem through deep learning? The key is to convert the optimization to a representation suitable for deep learning. In this article, a traveling salesman problem (TSP) is studied. Considering that deep learning is good at image processing, an image representation method is proposed to transfer a TSP to an image. Based on samples of a ten city TSP, a fully convolutional network (FCN) is used to learn the mapping from a feasible region to an optimal solution. The training process is analyzed and interpreted through stages. A visualization method is presented to show how an FCN can understand the training task of a TSP. Once the training is completed, no significant effort is required to solve a new TSP and the prediction is obtained on the scale of milliseconds. The results show good performance in finding the global optimal solution. Moreover, the developed FCN model has been demonstrated on TSP&#x2019;s with different city numbers, proving excellent generalization performance.
Nanophotonic quantum network node with neutral atoms and an integrated telecom interface

Abstract The realization of a long-distance, distributed quantum network based on quantum memory nodes that are linked by photonic channels remains an outstanding challenge. We propose a quantum network node based on neutral alkali atoms coupled to nanophotonic crystal cavities that combines a long-lived memory qubit with a photonic interface at the telecom range, thereby enabling the long-distance distribution of entanglement over low loss optical fibers. We present a novel protocol for the generation of an atom–photon entangled state which uses telecom transitions between excited states of the alkali atoms. We analyze the realistic implementation of this protocol using rubidium and cesium atoms taking into account the full atomic level structure and properties of the nanophotonic crystal cavity. We find that a high fidelity entangled state can be generated with current technologies.
At first sight: robots’ subtle eye movement parameters affect human attentional engagement, spontaneous attunement and perceived human-likeness

Abstract Human-robot interaction research could benefit from knowing how various parameters of robotic eye movement control affect specific cognitive mechanisms of the user, such as attention or perception. In the present study, we systematically teased apart control parameters of Trajectory Time of robot eye movements (rTT) between two joint positions and Fixation Duration (rFD) on each of these positions of the iCub robot. We showed recordings of these behaviors to participants and asked them to rate each video on how human-like the robot’s behavior appeared. Additionally, we recorded participants’ eye movements to examine whether the different control parameters evoked different effects on cognition and attention. We found that slow but variable robot eye movements yielded relatively higher human-likeness ratings. On the other hand, the eye-tracking data suggest that the human range of rTT is most engaging and evoked spontaneous involvement in joint attention. The pattern observed in subjective ratings was paralleled only by one measure in the implicit objective metrics, namely the frequency of spontaneous attentional following. These findings provide significant clues for controller design to improve the interaction between humans and artificial agents.
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows

We show that normalising flows become pathological when used to model targets whose supports have complicated topologies. In this scenario, we prove that a flow must become arbitrarily numerically noninvertible in order to approximate the target closely. This result has implications for all flow-based models, and especially Residual Flows (ResFlows), which explicitly control the Lipschitz constant of the bijection used. To address this, we propose Continuously Indexed Flows (CIFs), which replace the single bijection used by normalising flows with a continuously indexed family of bijections, and which can intuitively "clean up" mass that would otherwise be misplaced by a single bijection. We show theoretically that CIFs are not subject to the same topological limitations as normalising flows, and obtain better empirical performance on a variety of models and benchmarks.
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data

Deep neural networks have attained remarkable performance when applied to data that comes from the same distribution as that of the training set, but can significantly degrade otherwise. Therefore, detecting whether an example is out-of-distribution (OoD) is crucial to enable a system that can reject such samples or alert users. Recent works have made significant progress on OoD benchmarks consisting of small image datasets. However, many recent methods based on neural networks rely on training or tuning with both in-distribution and out-of-distribution data. The latter is generally hard to define a-priori, and its selection can easily bias the learning. We base our work on a popular method ODIN, proposing two strategies for freeing it from the needs of tuning with OoD data, while improving its OoD detection performance. We specifically propose to decompose confidence scoring as well as a modified input pre-processing method. We show that both of these significantly help in detection performance. Our further analysis on a larger scale image dataset shows that the two types of distribution shifts, specifically semantic shift and non-semantic shift, present a significant difference in the difficulty of the problem, providing an analysis of when ODIN-like strategies do or do not work.
Blockchain Technology in Accounting and Auditing

Over the past few years the popularity of blockchain technology has grown significantly. It is able to make change as the economic environment, as also identifying new approaches to business management. The paper is devoted to innovative aspects that appear and have a tendency to develop in the field of accounting and audit due to the blockchain technologies development. This technology can affect entire industries, and especially the financial sector. The decision to use blockchain in combination with appropriate data analytics can help in accounting processes, as well as in conducting daily transactions involved in the audit. The transition to a financial system with a significant element of the blockchain opens up many opportunities for the profession of an accountant and the skills of the auditor will be focused more on concerning issues of a higher level. The methodological base of the study includes the theory of accounting and auditing, as well as the principles of operation of blockchain technologies in the aspect of their application in finance. The study overviews the main trends in the development of blockchain technology in accounting and auditing based on foreign scientific studies, as well as reviews of blockchain practices in articles by various scientists. The result of this research is an analysis of blockchain technologies in the finance field. The study depicts conclusions about the degree of applicability of this technology in the Russian Federation, reflects the main problems, risks and benefits from the introduction of this technology. This paper may promote an interest to the current professionals in the accounting and auditing field, as well as to professionals working in the range of building data exchange systems between employees involved in working with finances.
Self-Supervised Graph Representation Learning via Global Context Prediction

To take full advantage of fast-growing unlabeled networked data, this paper introduces a novel self-supervised strategy for graph representation learning by exploiting natural supervision provided by the data itself. Inspired by human social behavior, we assume that the global context of each node is composed of all nodes in the graph since two arbitrary entities in a connected network could interact with each other via paths of varying length. Based on this, we investigate whether the global context can be a source of free and effective supervisory signals for learning useful node representations. Specifically, we randomly select pairs of nodes in a graph and train a well-designed neural net to predict the contextual position of one node relative to the other. Our underlying hypothesis is that the representations learned from such within-graph context would capture the global topology of the graph and finely characterize the similarity and differentiation between nodes, which is conducive to various downstream learning tasks. Extensive benchmark experiments including node classification, clustering, and link prediction demonstrate that our approach outperforms many state-of-the-art unsupervised methods and sometimes even exceeds the performance of supervised counterparts.
Graphon Filters: Graph Signal Processing in the Limit

Graph signal processing is an emerging field which aims to model processes that exist on the nodes of a network and are explained through diffusion over this structure. Graph signal processing works have heretofore assumed knowledge of the graph shift operator. Our approach is to investigate the question of graph filtering on a graph about which we only know a model. To do this we leverage the theory of graphons proposed by L. Lovasz and B. Szegedy. We make three key contributions to the emerging field of graph signal processing. We show first that filters defined over the scaled adjacency matrix of a random graph drawn from a graphon converge to filters defined over the Fredholm integral operator with the graphon as its kernel. Second, leveraging classical findings from the theory of the numerical solution of Fredholm integral equations, we define the Fourier-Galerkin shift operator. Lastly, using the Fourier-Galerkin shift operator, we derive a graph filter design algorithm which only depends on the graphon, and thus depends only on the probabilistic structure of the graph instead of the particular graph itself. The derived graphon filtering algorithm is verified through simulations on a variety of random graph models.