Reviewerly/exHarmony huggingface dataset:
A Secret Image Sharing Method Based on Shared Matrix and Variational Hyperprior Network

At present people can easily share multimedia information on Internet, which leads to serious data security issues. Especially in medical, military and financial fields, images always contain a lot of sensitive information. To safely transmit images among people, many secret image sharing methods are proposed. However, the existing methods can not solve the problems of pixel expansion and high computational complexity of shadow images at the same time. In this paper, we propose an image sharing method by combining sharing matrix and variational hyperprior network, to reduce the pixel expansion and computational complexity of secret image sharing methods. The method uses the variational hyperprior network to encode images. It introduces the hyperprior to effectively catch spatial dependencies in the latent representation, which can compress image with high efficiency. The experimental results show that our method has low computational complexity and high security performance compared with the state-of-the-art approaches. In addition, the proposed method can effectively reduce the pixel expansion when using the sharing matrix to generate shadow images.
Unregistered Firms, Financial Access and Innovation

The purpose of this article is to examine the relationship between a firm starting operation informally and its future innovation and whether this relation is moderated by institutional support (having access to finance from financial institutions to run their business). Data from the World Bank Enterprise Survey on 30 Eastern European and Central Asian countries were analysed using probit regression analysis. The findings show that there is a positive significant relationship between firms that start operation informally and the firm’s innovation and that such effect persists overtime. The study found that this relationship is stronger if the firms can gain access to finance to expand their business activities. Finally, the results show that such a relationship is based on the type of innovation being pursued by the firms. By examining the moderation effect of access to finance on starting a business informally, the study provides an alternative explanation to policymakers on how to deal with informal firms to benefit from their contribution to growth.
A novel variable exponent non-convex TV<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e247" altimg="si230.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>q</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>x</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:msup></mml:math> model in image restoration

Most of the existing image restoration methods either fail to preserve edges or produce a staircase effect and over-smooth phenomenon. To overcome these drawbacks, this paper proposes a novel variable exponent non-convex TVp,q(x) model in image restoration. The proposed method combines a TVp regularization and a TVq(x) regularization and inherits the advantages of TVp model and TVq(x) model well. The proposed model avoids the staircase effect while preserving good edges. The iterative support shrinking algorithm with proximal linearization (ISSAPL) is employed to solve the proposed variable exponent model. Experimental results illustrate the effectiveness of the proposed model in image restoration.
No-reference blurred image quality assessment method based on structure of structure features

The deep structure in the image contains certain information of the image, which is helpful to perceive the quality of the image. Inspired by deep level image features extracted via deep learning methods, we propose a no-reference blurred image quality evaluation model based on the structure of structure features. In spatial domain, the novel weighted local binary patterns are proposed which leverage maximum local variation maps to extract structural features from multi-resolution images. In spectral domain, gradient information of multi-scale Log-Gabor filtered images is extracted as the structure of structure features, and combined with entropy features. Then, the features extracted from both domains are fused to form a quality perception feature vector and mapped into the quality score via support vector regression (SVR). Experiments are conducted to evaluate the performance of the proposed method on various IQA databases, including the LIVE, CSIQ, TID2008, TID2013, CID2013, CLIVE, and BID. The experimental results show that compared with some state-of-the-art methods, our proposed method achieves better evaluation results and is more in line with the human visual system. The source code will be released at https://github.com/JamesC0321/s2s_features/.
Human detection on antistatic floors

Nowadays, a correct detection of people is very important for different purposes. Most applications use images as sources of information. However, an image may contain more information than is necessary for the detection task. For this reason, raw video images can end up being used for malicious purposes or privacy concerns. We present baseline results for a new human detection task. We evaluate long short-term memory (LSTM)-based deep learning models for detecting people using electrical signals from electrostatic discharge (ESD) floors as a source of information. Statistical features were provided to the models every second and four classification problems were studied. The first model discriminates between motion and non-motion. A second model classifies the action of the person between: no person, walking or standing. A third model classifies between walking and standing. And a last model predicts whether there is someone or no one on the ESD floor. Mattews Correlation Coefficient (MCC) was used as the main metric to evaluate the performance of the models. The LSTM models obtained a MCC between 0.94 and 0.99.
Local stereo matching with side window

Although local stereo matching methods have attracted much attention for their promising performance in disparity estimation, the issue of edge-blurring remains a challenge for existing local methods. To address this issue, we propose a novel local stereo matching method based on side window filtering technique, namely LSMSW. In the proposed method, the multiple-window aggregation strategy is adopted, which can adaptively select the window type according to the different texture information. Compared to traditional local methods, our cost aggregation model shows the significant performance in terms of accuracy and computational efficiency, especially for images with complex edges. The experimental results on the Middlebury benchmark confirm our achievements.
The dual PC algorithm and the role of Gaussianity for structure learning of Bayesian networks

Learning the graphical structure of Bayesian networks is key to describing data-generating mechanisms in many complex applications but poses considerable computational challenges. Observational data can only identify the equivalence class of the directed acyclic graph underlying a Bayesian network model, and a variety of methods exist to tackle the problem. Under certain assumptions, the popular PC algorithm can consistently recover the correct equivalence class by reverse-engineering the conditional independence (CI) relationships holding in the variable distribution. The dual PC algorithm is a novel scheme to carry out the CI tests within the PC algorithm by leveraging the inverse relationship between covariance and precision matrices. By exploiting block matrix inversions we can also perform tests on partial correlations of complementary (or dual) conditioning sets. The multiple CI tests of the dual PC algorithm proceed by first considering marginal and full-order CI relationships and progressively moving to central-order ones. Simulation studies show that the dual PC algorithm outperforms the classic PC algorithm both in terms of run time and in recovering the underlying network structure, even in the presence of deviations from Gaussianity. Additionally, we show that the dual PC algorithm applies for Gaussian copula models, and demonstrate its performance in that setting.
Kernel-mask knowledge distillation for efficient and accurate arbitrary-shaped text detection

Abstract Recently, segmentation-based approaches have been proposed to tackle arbitrary-shaped text detection. The trade-off between speed and accuracy is still a challenge that hinders its deployment in practical applications. Previous methods adopt complex pipelines to improve accuracy while ignoring inference speed. Moreover, the performance of most efficient scene text detectors often suffers from weak feature extraction when equipping lightweight networks. In this paper, we propose a novel distillation method for efficient and accurate arbitrary-shaped text detection, termed kernel-mask knowledge distillation. Our approach equips a low computational-cost visual transformer module (VTM) and a feature adaptation layer to make full use of feature-based and response-based knowledge in distillation. More specifically, first, the text features are obtained by aggregating the multi-level information extracted in the respective backbones of the teacher and student networks. Second, the text features are respectively sent to the VTM to enhance the feature representation ability. Then, we distill the feature-based and response-based kernel knowledge of the teacher network to obtain an efficient and accurate arbitrary-shaped text detection model. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method. It is worth noting that our method can achieve a competitive F -measure of 86.92% at 34.5 FPS on Total-text. Code is available at https://github.com/giganticpower/KKDnet .
Magic state distillation and cost analysis in fault-tolerant universal quantum computation

Abstract Magic states have been widely studied in recent years as resource states that help quantum computers achieve fault-tolerant universal quantum computing. The fault-tolerant quantum computing requires fault-tolerant implementation of a set of universal logical gates. Stabilizer code, as a commonly used error correcting code with good properties, can apply the Clifford gates transversally which is fault tolerant. But only Clifford gates cannot realize universal computation. Magic states are introduced to construct non-Clifford gates that combine with Clifford operations to achieve universal quantum computing. Since the preparation of quantum states is inevitably accompanied by noise, preparing the magic state with high fidelity and low overhead is the crucial problem to realizing universal quantum computation. In this paper, we survey the related literature in the past 20 years and introduce the common types of magic states, the protocols to obtain high-fidelity magic states, and overhead analysis for these protocols. Finally, we discuss the future directions of this field.
Dynamic Data Visualizations to Enhance Insight and Communication Across the Life Cycle of a Scientific Project

In scientific communication, figures are typically rendered as static displays. This often prevents active exploration of the underlying data, for example, to gauge the influence of particular data points or of particular analytic choices. Yet modern data-visualization tools, from animated plots to interactive notebooks and reactive web applications, allow psychologists to share and present their findings in dynamic and transparent ways. In this tutorial, we present a number of recent developments to build interactivity and animations into scientific communication and publications using examples and illustrations in the R language (basic knowledge of R is assumed). In particular, we discuss when and how to build dynamic figures, with step-by-step reproducible code that can easily be extended to the reader’s own projects. We illustrate how interactivity and animations can facilitate insight and communication across a project life cycle—from initial exchanges and discussions in a team to peer review and final publication—and provide a number of recommendations to use dynamic visualizations effectively. We close with a reflection on how the scientific-publishing model is currently evolving and consider the challenges and opportunities this shift might bring for data visualization.
MPENAS: Multi-fidelity Predictor-guided Evolutionary Neural Architecture Search with Zero-cost Proxies

Neural architecture search (NAS) aims to automatically design suitable architectures of artificial neural networks (ANNs) under various situations. Recently, NAS based on zero-cost proxies can predict the performance of ANNs with the cost of a single forward/backward propagation pass at most. While zero-cost proxies can speed up NAS by orders of magnitude, the gap between the predicted and actual performance of ANNs prevents zero-cost proxies from identifying ANNs with top performance.
Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization

Genetic algorithms constitute a family of black-box optimization algorithms, which take inspiration from the principles of biological evolution. While they provide a general-purpose tool for optimization, their particular instantiations can be heuristic and motivated by loose biological intuition. In this work we explore a fundamentally different approach: Given a sufficiently flexible parametrization of the genetic operators, we discover entirely new genetic algorithms in a data-driven fashion. More specifically, we parametrize selection and mutation rate adaptation as cross- and self-attention modules and use Meta-Black-Box-Optimization to evolve their parameters on a set of diverse optimization tasks. The resulting Learned Genetic Algorithm outperforms state-of-the-art adaptive baseline genetic algorithms and generalizes far beyond its meta-training settings. The learned algorithm can be applied to previously unseen optimization problems, search dimensions & evaluation budgets. We conduct extensive analysis of the discovered operators and provide ablation experiments, which highlight the benefits of flexible module parametrization and the ability to transfer ('plug-in') the learned operators to conventional genetic algorithms.
Channel Configuration for Neural Architecture: Insights from the Search Space

We consider search spaces associated with neural network channel configuration. Architectures and their accuracy are visualised using low-dimensional Euclidean embedding (LDEE). Optimisation dynamics are captured using local optima networks (LONs). LONs are a compression of a fitness landscape: the nodes are local optima and the edges are search transitions between them. Several neural architecture search algorithms are tested on the search space and we discover that iterated local search (ILS) is a competitive algorithm for neural channel configuration. We additionally implement a landscape-aware ILS which performs well. Observations from the search and landscape space analyses bring visual clarity and insight to the science of neural network channel design: the results indicate that a high number of channels, kept constant throughout the network, is beneficial.
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning

The synergies between Quality-Diversity (QD) and Deep Reinforcement Learning (RL) have led to powerful hybrid QD-RL algorithms that have shown tremendous potential, and bring the best of both fields. However, only a single deep RL algorithm (TD3) has been used in prior hybrid methods despite notable progress made by other RL algorithms. Additionally, there are fundamental differences in the optimization procedures between QD and RL which would benefit from a more principled approach. We propose Generalized Actor-Critic QD-RL, a unified modular framework for actor-critic deep RL methods in the QD-RL setting. This framework provides a path to study insights from Deep RL in the QD-RL setting, which is an important and efficient way to make progress in QD-RL. We introduce two new algorithms, PGA-ME (SAC) and PGA-ME (DroQ) which apply recent advancements in Deep RL to the QD-RL setting, and solve the humanoid environment which was not possible using existing QD-RL algorithms. However, we also find that not all insights from Deep RL can be effectively translated to QD-RL. Critically, this work also demonstrates that the actor-critic models in QD-RL are generally insuficiently trained and performance gains can be achieved without any additional environment evaluations.
On an Interpretation of ResNets via Gate-Network Control

This letter first constructs a typical solution of ResNets for multicategory classifications based on the idea of the gate control of LSTMs, from which a general interpretation of the ResNet architecture is given and the performance mechanism is explained. We also use more solutions to further demonstrate the generality of that interpretation. The classification result is then extended to the universal-approximation capability of the type of ResNet with two-layer gate networks, an architecture that was proposed in an original paper of ResNets and has both theoretical and practical significance.
First Improvement Hill Climber with Linkage Learning -- on Introducing Dark Gray-Box Optimization into Statistical Linkage Learning Genetic Algorithms

Gray-box optimization requires user-supported information about inter-variable dependencies to propose more effective optimizers for hard combinatorial problems. In Black-box optimization, such information is unavailable. Therefore, the Gray-box operators are only usable in Black-box scenarios if an optimizer can discover the inter-variable dependencies independently. Empirical Linkage Learning (ELL) techniques are guaranteed to discover only the true dependencies, which led to proposing the Dark Gray-box optimizers class. Such optimizers use ELL to construct Empirical Variable Interaction Graph (eVIG), which may miss some dependencies but contains only the true ones. eVIG allows using Gray-box operators in Black-box scenarios. ELL techniques are computationally expensive. Therefore, the recently proposed Local Search with Linkage Learning (LSwLL) is promising because it makes ELL a no-cost technique. However, LSwLL has some disadvantages. First, it can decompose only the problems of additive nature. Second, LSwLL removes ELL costs, but in some optimization scenarios, it may be expensive itself. Therefore, we propose the First Improvement Hill Climber with Linkage Learning (FIHCwLL). FIHCwLL decomposes additive and non-additive problems, and its overall costs are frequently lower than LSwLL (although ELL is not no-cost anymore). We introduce FIHCwLL into two state-of-the-art model-building optimizers, creating two new Dark Gray-box optimizers of significantly improved effectiveness.
Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling

Genetic programming (GP) has been successfully introduced to learn scheduling heuristics for dynamic flexible job shop scheduling (DFJSS) automatically. However, the evaluations of GP individuals are normally time-consuming, especially with long DFJSS simulations. Taking k-nearest neighbour with phenotypic characterisations of GP individuals as a surrogate approach, has been successfully used to preselect GP offspring to the next generation for effectiveness improvement. However, this approach is not straightforward to improve the training efficiency, which is normally the primary goal of surrogate. In addition, there is no study on which GP individuals (samples) are good for building surrogate models. To this end, first, this paper proposes a surrogate-assisted GP algorithm to reduce the training time of learning scheduling heuristics for DFJSS. Second, this paper further proposes an effective sampling strategy for surrogate-assisted GP. The results show that our proposed algorithm can achieve comparable performance with only about a third of training time of traditional GP. With the same training time, the proposed algorithm can significantly improve the quality of learned scheduling heuristics in all examined scenarios. Furthermore, the evolved scheduling heuristics by the proposed sample-aware surrogate-assisted GP are more interpretable with smaller rule sizes than traditional GP.
Doubly Stochastic Matrix Models for Estimation of Distribution Algorithms

Problems with solutions represented by permutations are very prominent in combinatorial optimization. Thus, in recent decades, a number of evolutionary algorithms have been proposed to solve them, and among them, those based on probability models have received much attention. In that sense, most efforts have focused on introducing algorithms that are suited for solving ordering/ranking nature problems. However, when it comes to proposing probability-based evolutionary algorithms for assignment problems, the works have not gone beyond proposing simple and in most cases univariate models. In this paper, we explore the use of Doubly Stochastic Matrices (DSM) for optimizing matching and assignment nature permutation problems. To that end, we explore some learning and sampling methods to efficiently incorporate DSMs within the picture of evolutionary algorithms. Specifically, we adopt the framework of estimation of distribution algorithms and compare DSMs to some existing proposals for permutation problems. Conducted preliminary experiments on instances of the quadratic assignment problem validate this line of research and show that DSMs may obtain very competitive results, while computational cost issues still need to be further investigated.
MOREA: a GPU-accelerated Evolutionary Algorithm for Multi-Objective Deformable Registration of 3D Medical Images

Finding a realistic deformation that transforms one image into another, in case large deformations are required, is considered a key challenge in medical image analysis. Having a proper image registration approach to achieve this could unleash a number of applications requiring information to be transferred between images. Clinical adoption is currently hampered by many existing methods requiring extensive configuration effort before each use, or not being able to (realistically) capture large deformations. A recent multi-objective approach that uses the Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) and a dual-dynamic mesh transformation model has shown promise, exposing the trade-offs inherent to image registration problems and modeling large deformations in 2D. This work builds on this promise and introduces MOREA: the first evolutionary algorithm-based multi-objective approach to deformable registration of 3D images capable of tackling large deformations. MOREA includes a 3D biomechanical mesh model for physical plausibility and is fully GPU-accelerated. We compare MOREA to two state-of-the-art approaches on abdominal CT scans of 4 cervical cancer patients, with the latter two approaches configured for the best results per patient. Without requiring per-patient configuration, MOREA significantly outperforms these approaches on 3 of the 4 patients that represent the most difficult cases.