Reviewerly/exHarmony huggingface dataset:
Synergy of Granular Computing, Shadowed Sets, and Three-way Decisions

Evolutionary algorithms (EAs) that integrate niching techniques are among the most effective methods for multimodal optimization problems. However, most algorithmic contributions are based on empirical performance observations rather than rigorous mathematical convergence support; this makes most existing methods parameter sensitive. Inspired by a recently proposed deterministic global optimization method, granular sieving (GrS), an extended global optimization method named collaborative GrS (Co-GrS) and a novel deterministic multi-EA design framework are proposed in this paper. The innovations are threefold. (1) Existing EAs are stochastic methods, and this paper introduces the principle of deterministic global optimization into EA for the first time in the literature. (2) A deterministic multi-EA framework is designed and implemented in the paper; from the perspective of population evolution, an easy-to-operate survival-of-the-fittest strategy based on mathematical principles is established in Co-GrS. (3) Unlike existing stochastic EAs, where the reproducibility of optimal solutions is achieved in a statistical sense, Co-GrS does not involve random parameters, and it automatically runs the algorithm only once with pre-set fixed parameters to find all optimal solutions. The experimental results demonstrate the effectiveness and competitiveness of our method compared to 16 state-of-the-art multimodal algorithms on the CEC’2013 benchmark suite.
Exposing Los Angeles’s Shaky Geologic Underbelly

Current calculations might underestimate the susceptibility of Los Angeles to earthquake shaking, so researchers and volunteers are deploying seismic networks near the city to remedy a data shortage.
Visual appearance based person retrieval in unconstrained environment videos

Visual appearance-based person retrieval is a challenging problem in surveillance. It uses attributes like height, cloth color, cloth type and gender to describe a human. Such attributes are known as soft biometrics. This paper proposes person retrieval from surveillance video using height, torso cloth type, torso cloth color and gender. The approach introduces an adaptive torso patch extraction and bounding box regression to improve the retrieval. The algorithm uses fine-tuned Mask R-CNN and DenseNet-169 for person detection and attribute classification respectively. The performance is analyzed on AVSS 2018 challenge II dataset and it achieves 11.35% improvement over state-of-the-art based on average Intersection over Union measure.
Spatiotemporal graph states from a single optical parametric oscillator

An experimental scheme is proposed for building massively multipartite entangled states using both the spatial and the frequency modes of an optical parametric oscillator. We provide analytical forms of the entangled states using the squeezed eigenmodes of Heisenberg equations, also known as the nullifiers of the corresponding graph state. This scheme can generate, in parallel, several cluster states described by sparsely connected, bicolorable graphs, usable for one-way quantum computing.
Continuous-time quantum walks on planar lattices and the role of the magnetic field

We address the dynamics of continuous-time quantum walk (CTQW) on planar two-dimensional (2D) lattice graphs, i.e., those forming a regular tessellation of the Euclidean plane (triangular, square, and honeycomb lattice graphs). We first consider the free particle: On square and triangular lattice graphs we observe the well-known ballistic behavior, whereas on the honeycomb lattice graph we obtain a sub-ballistic one, although still faster than the classical diffusive one. We impute this difference to the different amount of coherence generated by the evolution and, in turn, to the fact that, in 2D, the square and the triangular lattices are Bravais lattices, whereas the honeycomb one is non-Bravais. From the physical point of view, this means that CTQWs are not universally characterized by the ballistic spreading. We then address the dynamics in the presence of a perpendicular uniform magnetic field and study the effects of the field by two approaches: (i) introducing the Peierls phase factors, according to which the tunneling matrix element of the free particle becomes complex or (ii) spatially discretizing the Hamiltonian of a spinless charged particle in the presence of a magnetic field. Either way, the dynamics of an initially localized walker is characterized by a lower spread compared to the free particle case; larger fields correlate to more localized stays of the walker. Remarkably, upon analyzing the dynamics by spatial discretization of the Hamiltonian (vector potential in the symmetric gauge), we obtain that the variance of the space coordinate is characterized by pseudo-oscillations, a reminiscence of the harmonic oscillator behind the Hamiltonian in the continuum, whose energy levels are the well-known Landau levels.
Physics-Based Deep Neural Network for Real-Time Lesion Tracking in Ultrasound-Guided Breast Biopsy

In the context of ultrasound (US) guided breast biopsy, image fusion techniques can be employed to track the position of US-invisible lesions previously identified on a pre-operative image. Such methods have to account for the large anatomical deformations resulting from probe pressure during US scanning within the real-time constraint. Although biomechanical models based on the finite element (FE) method represent the preferred approach to model breast behavior, they cannot achieve real-time performances. In this paper we propose to use deep neural networks to learn large deformations occurring in ultrasound-guided breast biopsy and then to provide accurate prediction of lesion displacement in real-time. We train a U-Net architecture on a relatively small amount of synthetic data generated in an offline phase from FE simulations of probe-induced deformations on the breast anatomy of interest. Overall, both training data generation and network training are performed in less than 5 h, which is clinically acceptable considering that the biopsy can be performed at most the day after the pre-operative scan. The method is tested both on synthetic and on real data acquired on a realistic breast phantom. Results show that our method correctly learns the deformable behavior modelled via FE simulations and is able to generalize to real data, achieving a target registration error comparable to that of FE models, while being about a hundred times faster.
Unsupervised Deep Features for Privacy Image Classification

Sharing images online poses security threats to a wide range of users due to the unawareness of privacy information. Deep features have been demonstrated to be a powerful representation for images. However, deep features usually suffer from the issues of a large size and requiring a huge amount of data for fine-tuning. In contrast to normal images (e.g., scene images), privacy images are often limited because of sensitive information. In this paper, we propose a novel approach that can work on limited data and generate deep features of smaller size. For training images, we first extract the initial deep features from the pre-trained model and then employ the K-means clustering algorithm to learn the centroids of these initial deep features. We use the learned centroids from training features to extract the final features for each testing image and encode our final features with the triangle encoding. To improve the discriminability of the features, we further perform the fusion of two proposed unsupervised deep features obtained from different layers. Experimental results show that the proposed features outperform state-of-the-art deep features, in terms of both classification accuracy and testing time.
STNet: A Style Transformation Network for Deep Image Steganography

Image steganography is the technique of hiding information within images in plain sight. With the rapid development of deep learning in the field of steganalysis, it becomes a tremendous challenge to design a secure steganographic algorithm. To this end, we propose a novel steganographic network based on style transfer, named STNet. This network accepts the content and style images as input to synthesize art image with content from the former and style from the latter and embeds the secret information in style features. It can effectively resist most steganalysis tools. Steganalysis can identify stego images from cover images, but they cannot distinguish our stego images from other art images. Meanwhile, our method produces stego images of arbitrary size with 0.06 bit per pixel, improving over other deep steganographic models which only can embed fixed-length secret. Experiment results demonstrate that our STNet can achieve great visual effect, security, and reliability.
Local Proximity for Enhanced Visibility in Haze

Atmospheric medium often constrains the visibility of outdoor scenes due to scattering of light rays. This causes attenuation in the irradiance reaching the imaging device along with an additive component to render a hazy effect in the image. The visibility is further reduced for poorly illuminated scenes. The attenuation becomes wavelength dependent in underwater scenario, causing undesired color cast along with hazy effect. In order to suppress the effect of different atmospheric/underwater conditions such as haze and to enhance the contrast of such images, we reformulate local haziness in a generalized manner. The parameters are estimated by harnessing the similarity of patches within a local neighborhood. Unlike existing methods, our approach is developed based on the assumption that for outdoor scenes the depth of patches changes gradually in a local neighborhood surrounding the patch. This change in depth can be approximated by patch similarity in that neighborhood. As the attenuation in irradiance of an image in presence of atmospheric medium relies on the depth of the scene, the coefficients related to the attenuation are estimated from the weights of patch similarity. The additive haze effect is deduced using non-local mean of the patch. Our experimental results demonstrate the effectiveness of our approach in reducing the haze component as well as in enhancing the image under different conditions of haze (daytime, nighttime, and underwater).
Characterising port-based teleportation as universal simulator of qubit channels

Port-based teleportation (PBT) is a teleportation protocol that employs a number of Bell pairs and a joint measurement to enact an approximate input-output identity channel. Replacing the Bell pairs with a different multi-qubit resource state changes the enacted channel and allows the PBT protocol to simulate qubit channels beyond the identity. The channel resulting from PBT using a general resource state is consequently of interest. In this work, we fully characterise the Choi matrix of the qubit channel simulated by the PBT protocol in terms of its resource state. We also characterise the PBT protocol itself, by finding a description of the map from the resource state to the Choi matrix of the channel that is simulated by using that resource state. Finally, we exploit our expressions to show improved simulations of the amplitude damping channel by means of PBT with a finite number of ports.
High Quality Light Field Extraction and Post-Processing for Raw Plenoptic Data

Light field technology has reached a certain level of maturity in recent years, and its applications in both computer vision research and industry are offering new perspectives for cinematography and virtual reality. Several methods of capture exist, each with its own advantages and drawbacks. One of these methods involves the use of handheld plenoptic cameras. While these cameras offer freedom and ease of use, they also suffer from various visual artefacts and inconsistencies. We propose in this paper an advanced pipeline that enhances their output. After extracting sub-aperture images from the RAW images with our demultiplexing method, we perform three correction steps. We first remove hot pixel artefacts, then correct colour inconsistencies between views using a colour transfer method, and finally we apply a state of the art light field denoising technique to ensure a high image quality. An in-depth analysis is provided for every step of the pipeline, as well as their interaction within the system. We compare our approach to existing state of the art sub-aperture image extracting algorithms, using a number of metrics as well as a subjective experiment. Finally, we showcase the positive impact of our system on a number of relevant light field applications.
Dynamic parameter allocation in parameter servers

To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks. Parameter servers ease the implementation of distributed parameter management---a key concern in distributed training---, but can induce severe communication overhead. To reduce communication overhead, distributed machine learning algorithms use techniques to increase parameter access locality (PAL), achieving up to linear speed-ups. We found that existing parameter servers provide only limited support for PAL techniques, however, and therefore prevent efficient training. In this paper, we explore whether and to what extent PAL techniques can be supported, and whether such support is beneficial. We propose to integrate dynamic parameter allocation into parameter servers, describe an efficient implementation of such a parameter server called Lapse, and experimentally compare its performance to existing parameter servers across a number of machine learning tasks. We found that Lapse provides near-linear scaling and can be orders of magnitude faster than existing parameter servers.
Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping

This paper presents a global and local tensor sparse approximation (GLTSA) model for removing the stripes in hyperspectral images (HSIs). HSIs can easily be degraded by unwanted stripes. Two intrinsic characteristics of the stripes are (1) global sparse distribution and (2) local smoothness along the stripe direction. Stripe-free hyperspectral images are smooth in spatial domain, with strong spectral correlation. Existing destriping approaches often do not fully investigate such intrinsic characteristics of the stripes in spatial and spectral domains simultaneously. Those methods may generate new artifacts in extreme areas, causing spectral distortion. The proposed GLTSA model applies two ℓ 0 -norm regularizers to the stripe components and along-stripe gradient to improve the destriping performance. Two ℓ 1 -norm regularizers are applied to the gradients of clean image in spatial and spectral domains. The double non-convex functions in GLTSA are converted to single non-convex function by mathematical program with equilibrium constraints (MPEC). Experiment results demonstrate that GLTSA is effective and outperforms existing competitive matrix-based and tensor-based destriping methods in visual, as well as quantitative, evaluation measures.
A Novel Image Encryption Algorithm Based on Parameter-Control Scroll Chaotic Attractors

This paper describes a novel image encryption algorithm based on a parameter-control scroll chaotic attractor. Firstly, a novel chaotic system is constructed, and some basic dynamic characteristics of the chaotic system are analyzed. Secondly, the chaotic sequence is decomposed using the Schur decomposition method to obtain N orthogonal matrices, which are multiplied by the plaintext matrix. Finally, the six high-order bit planes are subjected to disturbance and “same OR” operations, and the scrambled image matrix is combined with the low two-bit matrix to obtain an eight-bit matrix. At the same time, the same OR operation is applied to the disturbed image matrix and the chaotic sequence to obtain the final encrypted image. Theoretical analysis shows that the key space of the algorithm can be up to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">135</sup> . Additionally, simulation results demonstrate that the proposed method achieves high key sensitivity and can effectively resist statistical analysis and gray value analysis attacks. With the exception of exhaustive attacks, all attack methods based on the determined plaintext–ciphertext mapping relationship will be invalid.
An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity

With the emergence of network security issues, various security devices that generate a large number of logs and alerts are widely used. This paper proposes an alert aggregation scheme that is based on conditional rough entropy and knowledge granularity to solve the problem of repetitive and redundant alert information in network security devices. Firstly, we use conditional rough entropy and knowledge granularity to determine the attribute weights. This method can determine the different important attributes and their weights for different types of attacks. We can calculate the similarity value of two alerts by weighting based on the results of attribute weighting. Subsequently, the sliding time window method is used to aggregate the alerts whose similarity value is larger than a threshold, which is set to reduce the redundant alerts. Finally, the proposed scheme is applied to the CIC-IDS 2018 dataset and the DARPA 98 dataset. The experimental results show that this method can effectively reduce the redundant alerts and improve the efficiency of data processing, thus providing accurate and concise data for the next stage of alert fusion and analysis.
Tourism demonstration system for large-scale museums based on 3D virtual simulation technology

Purpose The purpose of this study is to enhance audience experience in museum by using three-dimensional (3D) virtual simulation technology. Design/methodology/approach In this study, a large space museum building tourism demonstration system based on 3D virtual simulation technology was proposed. Starting from the concept of virtual reality (VR), the characteristics of VR and the classification of VR systems were introduced, and the research status of VR technology at home and abroad and the application of 3D virtual simulation were discussed. Then the key technologies of 3D modeling, 3D scene optimization and 3D simulation driving of 3D virtual simulation were expounded, and the characteristics and application scope of different technical methods were analyzed. Finally, an example of the Hongzhou Kiln 3D network museum was listed. Findings The research results showed that 3D virtual simulation has a wide range of applications in the field of VR. Different elements need to be considered for different types of applications, and different contents need to be integrated to achieve the corresponding interaction modes. Originality/value Virtual image; multimedia; large space museum; tourism demonstration system; 3D virtual simulation technology.
Improving Performance Estimation for FPGA-Based Accelerators for Convolutional Neural Networks

Field-programmable gate array (FPGA) based accelerators are being widely used for acceleration of convolutional neural networks (CNNs) due to their potential in improving the performance and reconfigurability for specific application instances. To determine the optimal configuration of an FPGA-based accelerator, it is necessary to explore the design space and an accurate performance prediction plays an important role during the exploration. This work introduces a novel method for fast and accurate estimation of latency based on a Gaussian process parametrised by an analytic approximation and coupled with runtime data. The experiments conducted on three different CNNs on an FPGA-based accelerator on Intel Arria 10 GX 1150 demonstrated a 30.7% improvement in accuracy with respect to the mean absolute error in comparison to a standard analytic method in leave-one-out cross-validation.
Quantum coherence measure based on affinity

Quantification of quantum coherence is a fundamental and key issue in the field of resource theory and quantum information processing. Exploiting an affinity-based metric, in this article, we define a faithful measure of quantum coherence. It is shown that the affinity-based coherence is bounded by that based on fidelity and trace distance. The measure of quantumness in terms of the difference between bipartite coherence and corresponding product state coherence is also identified. Finally, we interpret the operational meaning of the affinity-based coherence as a lower bound of interferometric power of the quantum state.
Advanced Alicki–Fannes–Winter method for energy-constrained quantum systems and its use

We describe an universal method for quantitative continuity analysis of entropic characteristics of energy-constrained quantum systems and channels. It gives asymptotically tight continuity bounds for basic characteristics of quantum systems of wide class (including multi-mode quantum oscillators) and channels between such systems under the energy constraint. The main application of the proposed method is the advanced version of the uniform finite-dimensional approximation theorem for basic capacities of energy-constrained quantum channels.
Exploring EOSIO via Graph Characterization

Designed for commercial decentralized applications (DApps), EOSIO is a Delegated Proof-of-Stake (DPoS) based blockchain system. It has overcome some shortages of the traditional blockchain systems like Bitcoin and Ethereum with its outstanding features (e.g., free for usage, high throughput and eco-friendly), and thus becomes one of the mainstream blockchain systems. Though there exist billions of transactions in EOSIO, the ecosystem of EOSIO is still relatively unexplored. To fill this gap, we conduct a systematic graph analysis on the early EOSIO by investigating its four major activities, namely account creation, account vote, money transfer and contract authorization. We obtain some novel observations via graph metric analysis, and our results reveal some abnormal phenomenons like voting gangs and sham transactions.
Using word embeddings to improve the privacy of clinical notes

Abstract Objective In this work, we introduce a privacy technique for anonymizing clinical notes that guarantees all private health information is secured (including sensitive data, such as family history, that are not adequately covered by current techniques). Materials and Methods We employ a new “random replacement” paradigm (replacing each token in clinical notes with neighboring word vectors from the embedding space) to achieve 100% recall on the removal of sensitive information, unachievable with current “search-and-secure” paradigms. We demonstrate the utility of this paradigm on multiple corpora in a diverse set of classification tasks. Results We empirically evaluate the effect of our anonymization technique both on upstream and downstream natural language processing tasks to show that our perturbations, while increasing security (ie, achieving 100% recall on any dataset), do not greatly impact the results of end-to-end machine learning approaches. Discussion As long as current approaches utilize precision and recall to evaluate deidentification algorithms, there will remain a risk of overlooking sensitive information. Inspired by differential privacy, we sought to make it statistically infeasible to recreate the original data, although at the cost of readability. We hope that the work will serve as a catalyst to further research into alternative deidentification methods that can address current weaknesses. Conclusion Our proposed technique can secure clinical texts at a low cost and extremely high recall with a readability trade-off while remaining useful for natural language processing classification tasks. We hope that our work can be used by risk-averse data holders to release clinical texts to researchers.