Reviewerly/exHarmony huggingface dataset:
Derepo: A Distributed Privacy-Preserving Data Repository with Decentralized Access Control for Smart Health

Smart health has attracted a huge amount of attention nowadays with the advancement of information and communications technology. Meanwhile, the medical data is imperative to support smart health techniques. However, the storage of medical data faces serious security and privacy issues from the hacktivists, cloud service providers and even medical institutions. Therefore, we propose a novel data repository named Derepo to address these issues by securing the storage with the decentralized access control mechanism and preserving privacy via the homomorphic encryption scheme. We adopt the distributed ledger technology to endow the access control mechanism with trustworthy properties such as Byzantine fault tolerance. Besides, we utilize the fully homomorphic encryption scheme to protect data privacy and preserve the computability in the meanwhile. The design of Derepo is user-centric. Only the data owner can make the access control policy and decrypt their data while the authorized third parties can enforce the data processing processes on their encrypted data without knowing the original values. Furthermore, the prototype of Derepo is evaluated by security analysis and demonstrates the feasibility.
Coverless information hiding based on the generation of anime characters

Abstract To fundamentally resist the steganalysis, coverless information hiding has been proposed, and it has become a research hotspot in the field of covert communication. However, the current methods not only require a huge image database, but also have a very low hidden capacity, making it difficult to apply practically. In order to solve the above problems, we propose a coverless information hiding method based on the generation of anime characters, which first converts the secret information into an attribute label set of the anime characters, and then uses the label set as a driver to directly generate anime characters by using the generative adversarial networks (GANs). The experimental results show that compared with the current methods, the hidden capacity of the proposed method is improved by nearly 60 times, and it also has good performance in image quality and robustness.
Recent advances in blockchain technology: a survey on applications and challenges

The rise of blockchain technology within a few years has attracted researchers across the world. The prime reason for worldwide attention is undoubtedly due to its feature of immutability along with the decentralized approach of data protection. As this technology is progressing, lots of developments in terms of identifying new applications, blockchain-based platforms, consensus mechanisms, etc are taking place. Hence, in this article, an attempt has been made to review the recent advancements in blockchain technology. Furthermore, we have also explored the available blockchain platforms, highlighted and explored future research directions and challenges.
Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.
Improving neural machine translation with sentence alignment learning

Neural machine translation (NMT) optimized by maximum likelihood estimation (MLE) usually lacks the guarantee of translation adequacy. To alleviate this problem, we propose an NMT approach that heightens the adequacy in machine translation by transferring the semantic knowledge from bilingual sentence alignment learning. Specifically, we first design a discriminator that learns to estimate sentence aligning score over translation candidates. The discriminator is constructed by gated self-attention based sentence encoders and trained with an N-pair loss for better capturing lexical evidences from bilingual sentence pairs. Then we propose an adversarial training framework as well as a sentence alignment-aware decoding method for NMT to transfer the discriminator’s learned semantic knowledge to NMT models. We conduct our experiments on Chinese → English, Uyghur → Chinese and English → German translation tasks. Experimental results show that our proposed methods outperform baseline NMT models on all these three translation tasks. Further analysis also indicates the characteristics of our approaches and details the semantic knowledge that transfered from the discriminator to the NMT model.
Hessian-based optimization of constrained quantum control

Efficient optimization of quantum systems is a necessity for reaching fault-tolerant thresholds. A standard tool for optimizing simulated quantum dynamics is the gradient-based grape algorithm, which has been successfully applied in a wide range of different branches of quantum physics. In this work, we derive and implement exact second-order analytical derivatives of the coherent dynamics and find improvements compared to the standard of optimizing with the approximate second-order Broyden-Fletcher-Goldfarb-Shanno algorithm. We demonstrate performance improvements for both the best and the average errors of constrained unitary gate synthesis on a circuit-QED system over a broad range of different gate durations.
An Ontology for Human-Robot Collaboration

The diffusion of Human-Robot Collaborative cells is prevented by some barriers. Classical control approaches seem not yet fully suitable for facing variability conveyed by the presence of human operators beside robots. Heterogeneous knowledge representation capabilities and abstract reasoning are crucial to enhance flexibility of control solutions. This work presents SOHO (Sharework Ontology for Human Robot Collaboration), a novel ontology specifically designed for Human-Robot Collaboration. The paper describes the pursued context-based approach, the novelty of the designed ontology with respect to the state of the art and shows its validity in a realistic Human-Robot Collaboration scenario.
More Than Just Good Passwords? A Study on Usability and Security Perceptions of Risk-based Authentication

Risk-based Authentication (RBA) is an adaptive security measure to strengthen password-based authentication. RBA monitors additional features during login, and when observed feature values differ significantly from previously seen ones, users have to provide additional authentication factors such as a verification code. RBA has the potential to offer more usable authentication, but the usability and the security perceptions of RBA are not studied well.
Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study

The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that contains a significant variable renewable energy component. It is furthermore important to consider the inherent uncertainty in the data when modeling such a complex power system. Gaussian process regression has the potential to address both of these concerns: the probabilistic modeling of solar radiation data could assist in managing the variability of solar power, as well as provide a mechanism to deal with uncertainty. In this paper, solar radiation data was obtained from the Southern African Universities Radiometric Network and used to train a Gaussian process regression model which was developed especially for this purpose. Attention was given to constructing an appropriate Gaussian process kernel. It was found that a carefully constructed kernel allowed for the successful interpolation of global horizontal irradiance data, with a root-mean-squared error of 82.2W/m2. Gaps in the data, due to possible meter failure, were also bridged by the Gaussian process with a root-mean-squared error of 94.1 W/m2 and accompanying confidence intervals. A root-mean-squared error of 151.1 W/m2 was found when forecasting the global horizontal irradiance with a forecasting horizon of five days. These results, achieved in modeling solar radiation data using Gaussian process regression, could open new avenues in the development of probabilistic renewable energy management systems. Such systems could aid smart grid operators and support energy trading platforms, by allowing for better-informed decisions that incorporate the inherent uncertainty of stochastic power systems.
End-to-End Named Entity Recognition from English Speech

Named entity recognition (NER) from text has been a widely studied problem and usually extracts semantic information from text. Until now, NER from speech is mostly studied in a two-step pipeline process that includes first applying an automatic speech recognition (ASR) system on an audio sample and then passing the predicted transcript to a NER tagger. In such cases, the error does not propagate from one step to another as both the tasks are not optimized in an end-to-end (E2E) fashion. Recent studies confirm that integrated approaches (e.g., E2E ASR) outperform sequential ones (e.g., phoneme based ASR). In this paper, we introduce a first publicly available NER annotated dataset for English speech and present an E2E approach, which jointly optimizes the ASR and NER tagger components. Experimental results show that the proposed E2E approach outperforms the classical two-step approach. We also discuss how NER from speech can be used to handle out of vocabulary (OOV) words in an ASR system.
Dynamically corrected gates suppressing spatiotemporal error correlations as measured by randomized benchmarking

Quantum error correction provides a path to large-scale quantum computers, but is built on challenging assumptions about the characteristics of the underlying errors. In particular, the mathematical assumption of statistically independent errors in quantum logic operations is at odds with realistic environments where error sources may exhibit strong temporal and spatial correlations. We present experiments using trapped ions to demonstrate that the use of dynamically corrected gates (DCGs), generally considered for the reduction of error magnitudes, can also suppress error correlations in space and time throughout quantum circuits. We present a first-principles analysis of the manifestation of error correlations in randomized benchmarking, and validate this model through experiments performed using engineered errors. We find that standard DCGs can reduce error correlations by $\sim50\times$, while increasing the magnitude of uncorrelated errors by a factor scaling linearly with the extended DCG duration compared to a primitive gate. We then demonstrate that the correlation characteristics of intrinsic errors in our system are modified by use of DCGs, consistent with a picture in which DCGs whiten the effective error spectrum induced by external noise.
Machine Prediction of Topological Transitions in Photonic Crystals

Neural networks based on machine learning can interpolate well within the training dataset, but their ability to extrapolate is severely limited by fundamental issues such as the bias-variance trade-off. Here we introduce the concept of an operator parameter space consisting of physical entities encoded with Maxwell's equations to improve the networks' capability to generalize beyond their training set. We illustrate the idea with photonic crystals, and show that the network trained with operator parameters yields remarkably accurate predictions of the topological transitions both within and beyond the training physical space. Such concepts can be generalized to higher-dimensional wave structures by choosing the appropriate operator parameters.
A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network

Recent work has shown the effectiveness of the plug-and-play priors (PnP) framework for regularized image reconstruction. However, the performance of PnP depends on the quality of the denoisers used as priors. In this letter, we design a novel PnP denoising prior, called multiple self-similarity net (MSSN), based on the recurrent neural network (RNN) with self-similarity matching using multi-head attention mechanism. Unlike traditional neural net denoisers, MSSN exploits different types of relationships among non-local and repeating features to remove the noise in the input image. We numerically evaluate the performance of MSSN as a module within PnP for solving magnetic resonance (MR) image reconstruction. Experimental results show the stable convergence and excellent performance of MSSN for reconstructing images from highly compressive Fourier measurements.
Multiˆ2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT

In this paper, we propose Multi 2 OIE, which performs open information extraction (open IE) by combining BERT (Devlin et al., 2019) with multi-head attention blocks (Vaswani et al., 2017).Our model is a sequence-labeling system with an efficient and effective argument extraction method.We use a query, key, and value setting inspired by the Multimodal Transformer (Tsai et al., 2019) to replace the previously used bidirectional long short-term memory architecture with multihead attention.Multi 2 OIE outperforms existing sequence-labeling systems with high computational efficiency on two benchmark evaluation datasets, Re-OIE2016 and CaRB.Additionally, we apply the proposed method to multilingual open IE using multilingual BERT.Experimental results on new benchmark datasets introduced for two languages (Spanish and Portuguese) demonstrate that our model outperforms other multilingual systems without training data for the target languages.
Mixed-Variable Bayesian Optimization

The optimization of expensive to evaluate, black-box, mixed-variable functions, i.e. functions that have continuous and discrete inputs, is a difficult and yet pervasive problem in science and engineering. In Bayesian optimization (BO), special cases of this problem that consider fully continuous or fully discrete domains have been widely studied. However, few methods exist for mixed-variable domains and none of them can handle discrete constraints that arise in many real-world applications. In this paper, we introduce MiVaBo, a novel BO algorithm for the efficient optimization of mixed-variable functions combining a linear surrogate model based on expressive feature representations with Thompson sampling. We propose an effective method to optimize its acquisition function, a challenging problem for mixed-variable domains, making MiVaBo the first BO method that can handle complex constraints over the discrete variables. Moreover, we provide the first convergence analysis of a mixed-variable BO algorithm. Finally, we show that MiVaBo is significantly more sample efficient than state-of-the-art mixed-variable BO algorithms on several hyperparameter tuning tasks, including the tuning of deep generative models.
A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback

In display advertising, predicting the conversion rate, that is, the probability that a user takes a predefined action on an advertiser’s website, such as purchasing goods is fundamental in estimating the value of displaying the advertisement. However, there is a relatively long time delay between a click and its resultant conversion. Because of the delayed feedback, some positive instances at the training period are labeled as negative because some conversions have not yet occurred when training data are gathered. As a result, the conditional label distributions differ between the training data and the production environment. This situation is referred to as a feedback shift. We address this problem by using an importance weight approach typically used for covariate shift correction. We prove its consistency for the feedback shift. Results in both offline and online experiments show that our proposed method outperforms the existing method.
Unmanned Aerial Vehicle Control through Domain-Based Automatic Speech Recognition

Currently, unmanned aerial vehicles, such as drones, are becoming a part of our lives and reaching out to many areas of society, including the industrialized world. A common alternative to control the movements and actions of the drone is through unwired tactile interfaces, for which different remote control devices can be found. However, control through such devices is not a natural, human-like communication interface, which sometimes is difficult to master for some users. In this work, we present a domain-based speech recognition architecture to effectively control an unmanned aerial vehicle such as a drone. The drone control is performed using a more natural, human-like way to communicate the instructions. Moreover, we implement an algorithm for command interpretation using both Spanish and English languages, as well as to control the movements of the drone in a simulated domestic environment. The conducted experiments involve participants giving voice commands to the drone in both languages in order to compare the effectiveness of each of them, considering the mother tongue of the participants in the experiment. Additionally, different levels of distortion have been applied to the voice commands in order to test the proposed approach when facing noisy input signals. The obtained results show that the unmanned aerial vehicle is capable of interpreting user voice instructions achieving an improvement in speech-to-action recognition for both languages when using phoneme matching in comparison to only using the cloud-based algorithm without domain-based instructions. Using raw audio inputs, the cloud-based approach achieves 74.81% and 97.04% accuracy for English and Spanish instructions respectively, whereas using our phoneme matching approach the results are improved achieving 93.33% and 100.00% accuracy for English and Spanish languages.
Joining and Splitting Models with Markov Melding

Analysing multiple evidence sources is often feasible only via a modular approach, with separate submodels specified for smaller components of the available evidence. Here we introduce a generic framework that enables fully Bayesian analysis in this setting. We propose a generic method for forming a suitable joint model when joining submodels, and a convenient computational algorithm for fitting this joint model in stages, rather than as a single, monolithic model. The approach also enables splitting of large joint models into smaller submodels, allowing inference for the original joint model to be conducted via our multi-stage algorithm. We motivate and demonstrate our approach through two examples: joining components of an evidence synthesis of A/H1N1 influenza, and splitting a large ecology model.
Dark states of quantum search cause imperfect detection

We consider a quantum walk where a detector repeatedly probes the system with fixed rate 1/τ until the walker is detected. This is a quantum version of the first-passage problem. We focus on the total probability Pdet that the particle is eventually detected in some target state, for example, on a node rd on a graph, after an arbitrary number of detection attempts. Analyzing the dark and bright states for finite graphs and more generally for systems with a discrete spectrum, we provide an explicit formula for Pdet in terms of the energy eigenstates which is generically τ independent. We find that disorder in the underlying Hamiltonian renders perfect detection, Pdet=1, and then expose the role of symmetry with respect to suboptimal detection. Specifically, we give a simple upper bound for Pdet that is controlled by the number of equivalent (with respect to the detection) states in the system. We also extend our results to infinite systems, for example, the detection probability of a quantum walk on a line, which is τ dependent and less than half, well below Polya's optimal detection for a classical random walk.1 MoreReceived 20 June 2019Revised 24 August 2020Accepted 23 September 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043107Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum measurementsQuantum theoryTechniquesFirst passage problemsQuantum InformationStatistical PhysicsGeneral Physics
The dynamic behaviors of local quantum uncertainty for three-qubit X states under decoherence channels

We derive the analytical expression of local quantum uncertainty for three-qubit X states. We give also the expressions of quantum discord and the negativity. A comparison of these three quantum correlations quantifiers is discussed in the special cases of mixed GHZ states and Bell-type states. We find that local quantum uncertainty gives the same amount of non-classical correlations as are measured by entropic quantum discord and goes beyond negativity. We also discuss the dynamics of non-classical correlations under the effect of phase damping, depolarizing and phase reversal channels. We find the local quantum uncertainty shows more robustness and exhibits, under phase reversal effect, revival and frozen phenomena. The monogamy property of local quantum uncertainty is also discussed. It is shown that it is monogamous for three-qubit states.
Manipulating entanglement sudden death in two coupled two-level atoms interacting off-resonance with a radiation field: an exact treatment

We study a model of two coupled two-level atoms (qubits) interacting off-resonance (at non-zero detuning) with a single mode radiation field.This system is of special interest in the field of quantum information processing (QIP) and can be realized in electron spin states in quantum dots or Rydberg atoms in optical cavities and superconducting qubits in linear resonators.We present an exact analytical solution for the time evolution of the system starting from any initial state.Utilizing this solution, we show how the entanglement sudden death (ESD), which represents a major threat to QIP, can be efficiently controlled by tuning atom-atom coupling and non-zero detuning.We demonstrate that while one of these two system parameters may not separately affect the ESD, combining the two can be very effective, as in the case of an initial correlated Bell state.However in other cases, such as a W-like initial state, they may have a competing impacts on ESD.Moreover, their combined effect can be used to create ESD in the system as in the case of an anti-correlated initial Bell state.A clear synchronization between the population inversion collapse-revival pattern and the entanglement dynamics is observed at all system parameter combinations.Nevertheless, only for initial states that may evolve to ESD, the population inversion revival oscillations, where exchange of energy between the atoms and the field takes place, temporally coincide with the entanglement revival peaks, whereas the population collapse periods match the ESD intervals.The variation of the radiation field intensity has a clear impact on the duration of the ESD at any combination of the other system parameters.