Reviewerly/exHarmony huggingface dataset:
Quantum walks in weak electric fields and Bloch oscillations

Bloch oscillations appear when an electric field is superimposed on a quantum particle that evolves on a lattice with a tight-binding Hamiltonian (TBH), i.e., evolves via what we call an electric TBH; this phenomenon will be referred to as TBH Bloch oscillations. A similar phenomenon is known to show up in so-called electric discrete-time quantum walks (DQWs) [C. Cedzich et al., Phys. Rev. Lett. 111, 160601 (2013);] this phenomenon will be referred to as DQW Bloch oscillations. This similarity is particularly salient when the electric field of the DQW is weak. For a wide, i.e., spatially extended, initial condition, one numerically observes semiclassical oscillations, i.e., oscillations of a localized particle, for both the electric TBH and the electric DQW. More precisely, the numerical simulations strongly suggest that the semiclassical DQW Bloch oscillations correspond to two counterpropagating semiclassical TBH Bloch oscillations. In this work it is shown that, under certain assumptions, the solution of the electric DQW for a weak electric field and a wide initial condition is well approximated by the superposition of two continuous-time expressions, which are counterpropagating solutions of an electric TBH whose hopping amplitude is the cosine of the arbitrary coin-operator mixing angle. In contrast, if one wishes the continuous-time approximation to hold for spatially localized initial conditions, one needs at least the DQW to be lazy, as suggested by numerical simulations and by the fact that this has been proven in the case of a vanishing electric field [F. W. Strauch, Phys. Rev. A 74, 030301(R) (2006)].
Multi-Colony Ant Algorithm Using Both Generative Adversarial Nets and Adaptive Stagnation Avoidance Strategy

Aiming at Travel Salesman Problem (TSP) that ant colony algorithm is easy to fall into local optima and slow convergence, a multi-colony ant algorithm using both generative adversarial nets (GAN) and adaptive stagnation avoidance strategy (GAACO) is proposed. First, to improve the convergence speed of the algorithm, we introduce a GAN model based on the game between convergence speed and solution quality. Then, to overcome premature convergence, an adaptive stagnation avoidance strategy is proposed. The strategy consists of two parts: (1) information entropy. It is used to measure the diversity of GAACO; (2) a cooperative game model. When the value of information entropy is less than threshold value, the cooperative game model will be used to select the appropriate pheromone matrix for different colonies to improve the accuracy. Finally, to further accelerate the convergence of the algorithm, the initial pheromone matrix is preprocessed to increase the pheromone of the optimal path for each iteration in the early stage. And according to reinforcement learning method, each colony increases the pheromone of the global optimal path at the end of each iteration. Extensive experiments with numerous instances in the TSPLIB standard library show that the proposed methods significantly outperform the state-of-the-art multi-colony ant colony optimization algorithms, especially in the large-scale TSPs.
Learning Disentangled Representations via Independent Subspaces

Image generating neural networks are mostly viewed as black boxes, where any change in the input can have a number of globally effective changes on the output. In this work, we propose a method for learning disentangled representations to allow for localized image manipulations. We use face images as our example of choice. Depending on the image region, identity and other facial attributes can be modified. The proposed network can transfer parts of a face such as shape and color of eyes, hair, mouth, etc.directly between persons while all other parts of the face remain unchanged. The network allows to generate modified images which appear like realistic images. Our model learns disentangled representations by weak supervision. We propose a localized resnet autoencoder optimized using several loss functions including a loss based on the semantic segmentation, which we interpret as masks, and a loss which enforces disentanglement by decomposition of the latent space into statistically independent subspaces. We evaluate the proposed solution w.r.t. disentanglement and generated image quality. Convincing results are demonstrated using the CelebA dataset.
MARBLE: Mining for Boilerplate Code to Identify API Usability Problems

Designing usable APIs is critical to developers' productivity and software quality, but is quite difficult. One of the challenges is that anticipating API usability barriers and real-world usage is difficult, due to a lack of automated approaches to mine usability data at scale. In this paper, we focus on one particular grievance that developers repeatedly express in online discussions about APIs: "boilerplate code." We investigate what properties make code count as boilerplate, the reasons for boilerplate, and how programmers can reduce the need for it. We then present MARBLE, a novel approach to automatically mine boilerplate code candidates from API client code repositories. MARBLE adapts existing techniques, including an API usage mining algorithm, an AST comparison algorithm, and a graph partitioning algorithm. We evaluate MARBLE with 13 Java APIs, and show that our approach successfully identifies both already-known and new API-related boilerplate code instances.
Characterising Seasonality of Solar Radiation and Solar Farm Output

With the recent rapid increase in the use of roof top photovoltaic solar systems worldwide, and also, more recently, the dramatic escalation in building grid connected solar farms, especially in Australia, the need for more accurate methods of very short-term forecasting has become a focus of research. The International Energy Agency Tasks 46 and 16 have brought together groups of experts to further this research. In Australia, the Australian Renewable Energy Agency is funding consortia to improve the five minute forecasting of solar farm output, as this is the time scale of the electricity market. The first step in forecasting of either solar radiation or output from solar farms requires the representation of the inherent seasonality. One can characterise the seasonality in climate variables by using either a multiplicative or additive modelling approach. The multiplicative approach with respect to solar radiation can be done by calculating the clearness index, or alternatively estimating the clear sky index. The clearness index is defined as the division of the global solar radiation by the extraterrestrial radiation, a quantity determined only via astronomical formulae. To form the clear sky index one divides the global radiation by a clear sky model. For additive de-seasoning, one subtracts some form of a mean function from the solar radiation. That function could be simply the long term average at the time steps involved, or more formally the addition of terms involving a basis of the function space. An appropriate way to perform this operation is by using a Fourier series set of basis functions. This article will show that for various reasons the additive approach is superior. Also, the differences between the representation for solar energy versus solar farm output will be demonstrated. Finally, there is a short description of the subsequent steps in short-term forecasting.
Learning a confidence measure in the disparity domain from O(1) features

Depth sensing is of paramount importance for countless applications and stereo represents a popular, effective and cheap solution for this purpose. As highlighted by recent works concerned with stereo, uncertainty estimation can be a powerful cue to improve accuracy in stereo. Most confidence measures rely on features, mainly extracted from the cost volume, fed to a random forest or a convolutional neural network trained to estimate match uncertainty. In contrast, we propose a novel strategy for confidence estimation based on features computed in the disparity domain, making our proposal suited for any stereo system including COTS devices, and in constant time. We exhaustively assess the performance of our proposals, referred to as O1 and O2, on KITTI and Middlebury datasets with three popular and different stereo algorithms (CENSUS, MC-CNN and SGM), as well as a deep stereo network (PSM-Net). We also evaluate how well confidence measures generalize to different environments/datasets.
Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility

When self-adaptive systems encounter changes withintheir surrounding environments, they enacttacticsto performnecessary adaptations. For example, a self-adaptive cloud-basedsystem may have a tactic that initiates additional computingresources when response time thresholds are surpassed, or theremay be a tactic to activate a specific security measure when anintrusion is detected. In real-world environments, these tacticsfrequently experiencetactic volatilitywhich is variable behaviorduring the execution of the tactic.Unfortunately, current self-adaptive approaches do not accountfor tactic volatility in their decision-making processes, and merelyassume that tactics do not experience volatility. This limitationcreates uncertainty in the decision-making process and mayadversely impact the system's ability to effectively and efficientlyadapt. Additionally, many processes do not properly account forvolatility that may effect the system's Service Level Agreement(SLA). This can limit the system's ability to act proactively, especially when utilizing tactics that contain latency.To address the challenge of sufficiently accounting for tacticvolatility, we propose aTactic Volatility Aware(TVA) solution.Using Multiple Regression Analysis (MRA), TVA enables self-adaptive systems to accurately estimate the cost and timerequired to execute tactics. TVA also utilizesAutoregressiveIntegrated Moving Average(ARIMA) for time series forecasting, allowing the system to proactively maintain specifications.
A Rational Polynomial Tracking Control Approach to a Common System Representation for Unmanned Aerial Vehicles

This article presents a rational polynomial tracking control approach to a common system representation for unmanned aerial vehicles (UAVs). First, we newly provide a common system representation of kinematic models for straight and orbit paths. A polynomial representation is introduced to describe the common system. To stabilize the polynomial system, we design a rational polynomial controller using a sum-of-squares (SOS)-based design framework. A set of stabilization conditions considering a real actuator saturation is represented in terms of SOS, where a relaxation for the SOS design conditions is brought by considering practical operation domains. The SOS-based design framework is applied to path stabilization for a real UAV platform (a parafoil wing-type UAV). Experimental results demonstrate the capabilities of the proposed SOS-based design framework in path stabilization of real-world complex UAVs.
Comprehensive reliability evaluation of multistate complex electromechanical systems based on similarity of cloud models

Abstract High reliability and security have become the hallmarks of complex electromechanical systems. Owing to the difficulties in fault data collection, ambiguity and uncertainty have been inevitably associated with complex electromechanical systems. Thus, the ability to perform reliability evaluation using scarce fault data is of immense significance to these machines and is the focus of this study. A similarity based cloud model is proposed to evaluate the running state of complex electromechanical systems. By combining objective and subjective factors, the reliability of complex electromechanical systems is evaluated by calculating the similarity between the cloud models of actual and standardised states. Next, the inverter of an offshore wind turbine is used to verify the accuracy and effectiveness of the proposed approach. The cloud model based framework for reliability evaluation inherits the preponderance of the uncertainty problem, overcomes the drawbacks of the current reliability approaches, and provides a theoretical basis, as well as a practical approach for the maintenance and repair of complex electromechanical systems with missing fault data. Additionally, it also provides a new methodology for solving the uncertainty problems caused by paucity of data.
The Impact of Structure on Software Merging: Semistructured Versus Structured Merge

Merge conflicts often occur when developers concurrently change the same code artifacts. While state of practice unstructured merge tools (e.g Git merge) try to automatically resolve merge conflicts based on textual similarity, semistructured and structured merge tools try to go further by exploiting the syntactic structure and semantics of the artifacts involved. Although there is evidence that semistructured merge has significant advantages over unstructured merge, and that structured merge reports significantly fewer conflicts than unstructured merge, it is unknown how semistructured merge compares with structured merge. To help developers decide which kind of tool to use, we compare semistructured and structured merge in an empirical study by reproducing more than 40,000 merge scenarios from more than 500 projects. In particular, we assess how often the two merge strategies report different results, we identify conflicts incorrectly reported by one but not by the other (false positives), and conflicts correctly reported by one but missed by the other (false negatives). Our results show that semistructured and structured merge differ in 24% of the scenarios with conflicts. Semistructured merge reports more false positives, whereas structured merge has more false negatives. Finally, we found that adapting a semistructured merge tool to resolve a particular kind of conflict makes semistructured and structured merge even closer.
Latent correlation embedded discriminative multi-modal data fusion

Ever-growing volume of multi-modal data has garnered considerable research enthusiasm in many fields, such as biomedical image analysis and biometrics. It remains a challenge how to effectively fuse multi-modal data for improving the learning performance. Many data fusion methods have been proposed, such as multi-kernel learning (MKL) and canonical component analysis (CCA). Most of the previous fusion methods focus on concatenating the multi-modal data, which fail to preserve the structure information across different modalities. In addition, they treat the samples equally in fusion and ignore the difference of the contributions of samples to the fusion model. In this paper, we proposed a discriminative multi-modal dimensionality reduction method, which can seamlessly fuse the multi-modal data and explore the latent correlation among different modalities for robust representation learning. In optimization, self-paced learning is adopted to dynamically estimate the contribution of each sample to the overall fusion model. Following the easy-to-hard learning sequence, our model can adaptively and sequentially enhance the robustness of the learning system in a knowledge discovery manner. Extensive experimental results on multi-modal brain disease diagnosis and multi-spectral palmprint classification tasks demonstrate the proposed method outperforms the previous multi-modal classification methods.
Towards a maturity model for health-care cloud security (M<sup>2</sup>HCS)

Purpose The purpose of this paper is to propose a novel maturity model for health-care cloud security (M 2 HCS), which focuses on assessing cyber security in cloud-based health-care environments by incorporating the sub-domains of health-care cyber security practices and introducing health-care-specific cyber security metrics. This study aims to expand the domain of health-care cyber security maturity model by including cloud-specific aspects than is usually seen in the literature. Design/methodology/approach The intended use of the proposed model was demonstrated using the evaluation method – “construct validity test” as the paper’s aim was to assess the final model and the output of the valuation. The study involved a literature-based case study of a national health-care foundation trust with an overall view because the model is assessed for the entire organisation. The data were complemented by examination of hospitals’ cyber security internal processes through web-accessible documents, and identified relevant literature. Findings The paper provides awareness about how organisational-related challenges have been identified as a main inhibiting factor for the adoption of cloud computing in health care. Regardless of the remunerations of cloud computing, its security maturity and levels of adoption varies, especially in health care. Maturity models provide a structure towards improving an organisation’s capabilities. It suggests that although several cyber security maturity models and standards resolving specific threats exist, there is a lack of maturity models for cloud-based health-care security. Research limitations/implications Due to the selected research method, the research results may lack generalizability. Therefore, future research studies can investigate the propositions further. Another is that the current thresholds were determined empirically, although it worked for the case study assessment. However, to establish more realistic threshold levels, there is a need for more validation of the model using more case studies. Practical implications The paper includes maturity model for the assessment management and improvement of the security posture of a health-care organisation actively using cloud. For executives, it provides a detailed security assessment of the eHealth cloud to aid in decision making. For security experts, its quantitative metrics support proactive and reactive processes. Originality/value The paper fulfils a recognised requirement for security maturity model focussed on health-care cloud. It could be extended to resolve evolving cyber settings.
A cockpit of multiple measures for assessing film restoration quality

In machine vision, the idea of expressing the quality of a films by a single value is very popular. Usually this value is computed by processing a set of image features with the aim of resembling as much as possible a kind of human judgment of the film quality. Since human quality assessment is a complex mechanism involving many different perceptual aspects, we believe that such approach may scarcely provide a comprehensive analysis. Especially in the field of digital movie restoration, a single score can hardly provide reliable information about the effects of the various restoring operations. For this reason we introduce an alternative approach, where a set of measures, describing over time basic global and local visual properties of the film frames, is computed in an unsupervised way and delivered to expert evaluators for checking the restoration pipeline and results. The proposed framework can be viewed as a car or airplane cockpit, whose parameters (i.e. the computed measures) are necessary to control the machine status and performance. This cockpit, which is publicly available online, would like to support the digital restoration process and its assessment.
A Decision Support Framework for AutoML Systems: A Meta-Learning Approach

In general, the process of building a high-quality machine learning model is an iterative, complex and timeconsuming process that involves exploring the performance of various machine learning algorithms in addition to having a good understanding and experience with effectively tuning their hyper-parameters. In practice, conducting this process efficiently requires solid knowledge and experience with the various techniques that can be employed. With the continuous and vast increase of the amount of data in our digital world, it has been acknowledged that the number of knowledgeable data scientists can not scale to address these challenges. Thus, there is a crucial need for automating the process of Combined Algorithm Selection and Hyper-parameter tuning (CASH) in the machine learning domain. Recently, several systems (e.g., AutoWeka, AutoSKLearn, SmartML) have been introduced to tackle this challenge with the main aim of reducing the role of human in the loop and filling the gap for non-expert machine learning users by playing the role of the data scientist. Meta-Learning is described as the process of learning from previous experience gained during applying various learning algorithms on different types of data, and hence reducing the needed time to learn new tasks. In the context of the Automated Machine Learning (AutoML) process, one main advantage of meta-learning techniques is that they allow hand-engineered algorithms to be replaced with novel automated methods which are designed in a data-driven way. In this paper, we present a methodology and framework for using Meta-Learning techniques to develop new methods that serve as an effective decision support for the AutoML process. In particular, we use Meta-Learning techniques to answer several crucial questions for the AutoML process including: 1) Which classifiers are expected to be the best performing on a given dataset? 2) Can we predict the training time of a classifier? 3) Which classifiers are worth investing a larger portion of the time budget to improve their performance by tuning them? In our Meta-Learning process, we used 200 datasets with different characteristics on a wide set of metafeatures. In addition, we used 30 classifiers from two popular machine learning libraries, namely, Weka and Scikit-learn. Our results and Meta-Models have been obtained in a fully automated way. The methodology and results of our framework can be easily embeded/utilized by any AutoML system.
Remote-Sensing Image Superresolution Based on Visual Saliency Analysis and Unequal Reconstruction Networks

Remote-sensing images (RSIs) generally have strong spatial characteristics for surface features. Various ground objects, such as residential areas, roads, forests, and rivers, differ substantially. According to this visual attention characteristic, regions with complicated texture features require more realistic details to reflect a better description of the topography, while regions such as farmlands should be smooth and have less noise. However, most existing single-image superresolution (SISR) methods fail to fully utilize these properties and therefore apply a uniform reconstruction strategy to the whole image. In this article, we propose a novel saliency-driven unequal single-image reconstruction network in which the demands of various regions in the superresolution (SR) process are distinguished by saliency maps. First, we design a new gradient-based saliency analysis method to produce more accurate saliency maps with imagewise annotations. The method utilizes the superiority of a multireception field to extract both high-level features and low-level features. Second, we propose a novel saliency-driven gate conditional generative adversarial network, where the saliency map is regarded as a medium during the training procedure of the whole network. The saliency map is regarded as a pixelwise condition in a generator to enhance the training capability of the network. Additionally, we design a new loss function that combines normalized content loss, saliency-driven perceptual loss, and gate-control adversarial loss to further refine details of texture-complex areas for RSIs. We evaluate the performance of our algorithm and compare it with many other state-of-the-art SR methods using a remote-sensing data set. The experimental results show that our approach achieves the optimal outcome in salient areas. Our method attains the best effect on global quality and visual performance.