Reviewerly/exHarmony huggingface dataset:
Potential bias when creating a differential-vector movement algorithm

Simulating natural biological processes has been used widely to create new algorithms to optimize complex problems. Hundreds of new algorithms have been proposed in this growing area of research, with many of these using differential-vector movement to generate new solutions. However, the relationship between the natural inspiration for an algorithm and its algorithmic behavior is sometimes tenuous. Unfortunately, high levels of solution quality and speed on narrowly defined benchmarks are often prioritized over general theoretical understanding. Several algorithms, including teaching–learning-based optimization, symbiotic organisms search, sine cosine algorithm, forensic-based investigation optimization, and grey wolf optimizer were examined to explore the search regions achieved by their suggested strategies. Interestingly, all of the five algorithms were found to frequently consider the origin of coordinates when generating new solutions. Accordingly, these algorithms sometimes obtained extraordinary results when applied to benchmark functions where the optimum solutions reside at the origin. The results highlight the importance of properly checking how and from which regions points are sampled when developing new metaheuristics. Furthermore, providing clear descriptions of the underlying designs of strategies and parameter settings is encouraged.
Reversible Data Hiding Scheme for Images Using Gray Code Pixel Value Optimization

Steganography is a technique that allows confidential data to be delivered securely by embedding the data into cover digital media. In this paper, we propose a simple steganography method for cover gray images with high embedding capacity. The secret pixels are not directly embedded into the cover image by a k-bit LSB substitution method with k in the range [1, 4], but can be extracted from stego image by calculating the Canonical Gray Code (CGC) for the correct number of LSBs known only to sender and receiver. The bit embedding process is performed to minimize the influence of the embedded bits on the pixel intensity of the stego-image and limit the possibility of human perception to follow the distortions of the original image.This effect was achieved by considering virtual embedding of secret bits in canonical Gray code (CGC) calculated for the desired number of n bits (k < n ≤ 8) and optimally adjusting the CGC code to the embedded secret bits, and to the original pixel value of the cover image. The paper, in its experimental part, compares the effectiveness of the proposed method with other techniques. For the results verification, peak signal-to-noise ratio (PSNR), mean square error (MSE), and structural similarity index (SSIM) are calculated.
Unsupervised Segmentation incorporating Shape Prior via Generative Adversarial Networks

We present an image segmentation algorithm that is developed in an unsupervised deep learning framework. The delineation of object boundaries often fails due to the nuisance factors such as illumination changes and occlusions. Thus, we initially propose an unsupervised image decomposition algorithm to obtain an intrinsic representation that is robust with respect to undesirable bias fields based on a multiplicative image model. The obtained intrinsic image is subsequently provided to an unsupervised segmentation procedure that is developed based on a piecewise smooth model. The segmentation model is further designed to incorporate a geometric constraint imposed in the generative adversarial network framework where the discrepancy between the distribution of partitioning functions and the distribution of prior shapes is minimized. We demonstrate the effectiveness and robustness of the proposed algorithm in particular with bias fields and occlusions using simple yet illustrative synthetic examples and a benchmark dataset for image segmentation.
Deep learning-based LPI radar signals analysis and identification using a Nyquist Folding Receiver architecture

Nyquist Folding Receiver (NYFR) is a perceptron structure that realizes a low probability of intercept (LPI) signal analog to information. Aiming at the problem of LPI radar signal receiving, the time domain, frequency domain, and time-frequency domain problems of signals intercepted by NYFR structure are studied. Combined with the time-frequency analysis (TFA) method, a radar recognition scheme based on deep learning (DL) is introduced, which can reliably classify common LPI radar signals. First, the structure of NYFR and its characteristics in the time domain, frequency domain, and time and frequency domain are analyzed. Then, the received signal is then converted into a time-frequency image (TFI). Finally, four kinds of DL algorithms are used to classify LPI radar signals. Simulation results demonstrate the correctness of the NYFR structure, and the effectiveness of the proposed recognition method is verified by comparison experiments.
Virtual Reality-driven serious communication: Through VR-Dialogue towards VR-Participation

e-Participation depends on a community of users-citizens who constructively engage and collaborate with governments and decision-makers on key democratic and social matters. Effective serious communication requires meaningful social interactions supported by relevant community-building efforts. We argue that achieving that is more visible by engaging dialogue with a constructive shared viewpoint rather than thorough discussions or argumentation. The emerging social Immersive Virtual Reality technologies supply a novel mode of digital communication that brings an opportunity to overcome some of the challenges hindering e-Participation. In this paper, we present the key concepts and explore the principles of Dialogue in the context of serious communication. We link those principles with specific Immersive VR affordances and propose a Framework for Virtual-Reality-Mediated Serious Communication – VR-Dialogue. Finally, we discuss the implications of employing that framework to support e-Participation through an additional component: VR-Participation.
Security and Scalability in Private Permissionless Blockchain: Problems and Solutions Leading to Creating Consent-as-a-Service (CaaS) Deployment

The purpose of this paper is to analyze the security and scalability problems occurring in private permissionless blockchain systems. The consent management system (CMS) based upon Hyperledger Fabric (HLF), was implemented in the selected blockchain-as-a-service (BaaS), and therefore led to consent-as-a-service (CaaS) deployment. The experiments results assessed to what level the network transaction throughput is affected by changing the world state size, which indicates scalability of chosen blockchain system implementation. Additional experiments with the IBM Blockchain Platform and the FastFabric framework (a HLF modification) were performed to prove the possibility to achieve transaction throughput comparable to the Ethereum blockchain network.
Two-colour high-purity Einstein-Podolsky-Rosen photonic state

We report a high-purity Einstein-Podolsky-Rosen (EPR) state between light modes with the wavelengths separated by more than 200 nm. We demonstrate highly efficient EPR-steering between the modes with the product of conditional variances [Formula: see text]. The modes display - 7.7 ± 0.5 dB of two-mode squeezing and an overall state purity of 0.63 ± 0.16. EPR-steering is observed over five octaves of sideband frequencies from RF down to audio-band. The demonstrated combination of high state purity, strong quantum correlations, and extended frequency range enables new matter-light quantum protocols.
Beyond Vanilla Convolution: Random Pixel Difference Convolution for Face Perception

Face perception is an essential and significant problem in pattern recognition, concretely including Face Recognition (FR), Facial Expression Recognition (FER), and Race Categorization (RC). Though handcrafted features perform well on face images, Deep Convolutional Neural Networks (DCNNs) have brought new vitality to this field recently. Vanilla DCNNs are powerful at learning high-level semantic features, but are weak in capturing low-level image characteristic changes in illumination, intensity,and texture regarded as key traits in facial processing and feature extraction, which is alternatively the strength of human-designed feature descriptors. To integrate the best of both worlds, we proposed novel Random Pixel Difference Convolution (RPDC) which is efficient alternatives to vanilla convolutional layers in standard CNNs and can promote to extract discriminative and diverse facial features. By means of searched RPDC of high efficiency, we build S-RaPiDiNet, and achieve promising and extensive experiment results in FR (≈0.5% improvement), FER (over 1% growth), and RC (0.25%-3% increase) than baseline network in vanilla convolution, showing strong generalization of RPDC.
LipBaB: Computing Exact Lipschitz Constant of ReLU Networks

The Lipschitz constant of neural networks plays an important role in several contexts of deep learning ranging from robustness certification and regularization to stability analysis of systems with neural network controllers. Obtaining tight bounds of the Lipschitz constant is therefore important. We introduce LipBaB, a branch and bound framework to compute certified bounds of the local Lipschitz constant of deep neural networks with ReLU activation functions up to any desired precision. It is based on iteratively upper-bounding the norm of the Jacobians, corresponding to different activation patterns of the network caused within the input domain. Our algorithm can provide provably exact computation of the Lipschitz constant for any p-norm.
Smart prediction for refactorings in the software test code

Test smells are bad practices to either design or implement a test code. Their presence may reduce the test code quality, harming the software testing activities, primarily from a maintenance perspective. Therefore, defining strategies and tools to handle test smells and improve the test code quality is necessary. State-of-the-art strategies encompass automated support mainly based on hard thresholds of rules, static and dynamic metrics to identify the test smells. Such thresholds are subjective to interpretation and may not consider the complexity of the software projects. Moreover, they are limited as they do not automate test refactoring but only count on developers' expertise and intuition. In this context, a technique that uses historical implicit or tacit data to generate knowledge could assist the identification and refactoring of test smells. This study aims to establish a novel approach based on machine learning techniques to suggest developers refactoring strategies for test smells. As an expected result, we could understand the applicability of the machine learning techniques to handle test smells and a framework proposal that helps developers in decision-making regarding the refactoring of test smells.
Segmentation of Left Atrial MR Images via Self-supervised Semi-supervised Meta-learning

Deep learning algorithms for cardiac MRI segmentation depend heavily upon abundant, labelled data located at a single medical centre. Clinical settings, however, contain abundant, unlabelled and scarce, labelled data located across distinct medical centres. To account for this, we propose a unified pre-training framework, entitled self-supervised semi-supervised meta-learning (\(\text {S}^{4}\)ML), that exploits distributed labelled and unlabelled data to quickly and reliably perform cardiac MRI segmentation given scarce, labelled data from a potentially different distribution. We show that \(\text {S}^{4}\)ML outperforms baseline methods when adapting to data from a novel medical centre, cardiac chamber, and MR sequence. We also show that this behaviour holds even in extremely low-data regimes.
On the convergence of a randomized block coordinate descent algorithm for a matrix least squares problem

Randomized block coordinate descent type methods have been demonstrated to be efficient for solving large-scale optimization problems. Linear convergence to the unique solution is established if the objective function is strongly convex. In this paper we propose a randomized block coordinate descent algorithm for solving the matrix least squares problem minX∈Rm×n‖C − AXB‖F with A∈Rp×m, B∈Rn×q, and C∈Rp×q. We prove that the proposed algorithm converges linearly to the unique minimum norm least squares solution (i.e., A†CB†) without the strong convexity assumption. Instead, we only need that B has full row rank. Numerical experiments are given to illustrate the theoretical results.
Sensor Fusion of Camera and Lidar Using Kalman Filter

Self-driving cars are the next milestone of the automation industry. To achieve the level of autonomy expected in a self-driving car, the vehicle needs to be mounted with an assortment of sensors that can help the vehicle to perceive its 3D environment better which leads to better decision making and control of the vehicle. To complement the advantages of different sensors, sensor fusion is done, to enhance the accuracy of the overall information. In real-time implementations, uncertainty in factors that affect the vehicle's motion can lead to overshoot in parameters. To avoid that, an estimation filter is used to predict and update the fused values. This paper focuses on sensor fusion of Lidar and Camera followed by estimation using Kalman filter. It can be seen how the use of an estimation filter can significantly improve the accuracy in tracking the path of an obstacle.
Thallo – Scheduling for High-Performance Large-Scale Non-Linear Least-Squares Solvers

Large-scale optimization problems at the core of many graphics, vision, and imaging applications are often implemented by hand in tedious and error-prone processes in order to achieve high performance (in particular on GPUs), despite recent developments in libraries and DSLs. At the same time, these hand-crafted solver implementations reveal that the key for high performance is a problem-specific schedule that enables efficient usage of the underlying hardware. In this work, we incorporate this insight into Thallo, a domain-specific language for large-scale non-linear least squares optimization problems. We observe various code reorganizations performed by implementers of high-performance solvers in the literature, and then define a set of basic operations that span these scheduling choices, thereby defining a large scheduling space. Users can either specify code transformations in a scheduling language or use an autoscheduler. Thallo takes as input a compact, shader-like representation of an energy function and a (potentially auto-generated) schedule, translating the combination into high-performance GPU solvers. Since Thallo can generate solvers from a large scheduling space, it can handle a large set of large-scale non-linear and non-smooth problems with various degrees of non-locality and compute-to-memory ratios, including diverse applications such as bundle adjustment, face blendshape fitting, and spatially-varying Poisson deconvolution, as seen in Figure 1. Abstracting schedules from the optimization, we outperform state-of-the-art GPU-based optimization DSLs by an average of 16× across all applications introduced in this work, and even some published hand-written GPU solvers by 30%+.
CNN-Based Handwritten Mathematical Symbol Recognition Model

Mathematical expression or formula recognition system is evolving field in pattern recognition. Various methods are implemented by different researchers on wide areas. Handwritten mathematical expression field evolved in last decades rapidly. This paper aims to target and represent CNN as an application of the deep learning method for recognizing the handwritten mathematical symbols. The current trend of the deep learning architectures witnesses CNN is one of the most prominent and widely used techniques that have been successfully implemented in NLP, computer vision, and pattern recognition. The kinds of deep learning models have proven to produce a recognizable state of the art. In this paper, we recognize the handwritten mathematical symbols using CNN. This paper comprises stages involved in recognition process, challenges and proposed methodology used by the author to conduct their experiment and further result and future scope is discussed. Dataset used for this experiment is downloaded from the public available platform (HasyV2 Dataset 2018. A dataset of size 369 classes comprising 168,223 images has been used for experimentation, and the results relieve the accuracy of 76.17%. For further improvement, an extra dense net (fully connected layer) has been used. The DuosdenseNet model has been designed; the accuracy of recognizable results shows a decent work for consideration.
ReDUCE: Reformulation of Mixed Integer Programs Using Data from Unsupervised Clusters for Learning Efficient Strategies

Mixed integer convex and nonlinear programs, MICP and MINLP, are expressive but require long solving times. Recent work that combines learning methods on solver heuristics has shown potential to overcome this issue allowing for applications on larger scale practical problems. Gathering sufficient training data to employ these methods still present a challenge since getting data from traditional solvers are slow and newer learning approaches still require large amounts of data. In order to scale up and make these hybrid learning approaches more manageable we propose ReDUCE, a method that exploits structure within small to medium size datasets. We also introduce the bookshelf organization problem as an MINLP as a way to measure performance of solvers with ReDUCE. Results show that existing algorithms with ReDUCE can solve this problem within a few seconds, a significant improvement over the original formulation. ReDUCE is demonstrated as a high level planner for a robotic arm for the bookshelf problem.
Weakly-Supervised Single-view Dense 3D Point Cloud Reconstruction via Differentiable Renderer

Abstract In recent years, addressing ill-posed problems by leveraging prior knowledge contained in databases on learning techniques has gained much attention. In this paper, we focus on complete three-dimensional (3D) point cloud reconstruction based on a single red-green-blue (RGB) image, a task that cannot be approached using classical reconstruction techniques. For this purpose, we used an encoder-decoder framework to encode the RGB information in latent space, and to predict the 3D structure of the considered object from different viewpoints. The individual predictions are combined to yield a common representation that is used in a module combining camera pose estimation and rendering, thereby achieving differentiability with respect to imaging process and the camera pose, and optimization of the two-dimensional prediction error of novel viewpoints. Thus, our method allows end-to-end training and does not require supervision based on additional ground-truth (GT) mask annotations or ground-truth camera pose annotations. Our evaluation of synthetic and real-world data demonstrates the robustness of our approach to appearance changes and self-occlusions, through outperformance of current state-of-the-art methods in terms of accuracy, density, and model completeness.
Chaotic dynamics of an atomic Bose–Einstein condensate in a frequency-modulated cavity QED

Nonlinear systems, including atom–field interaction, are investigated due to their fundamental applications in quantum mechanics and rapidly growing fields of quantum communication, especially secure communication with chaotic dynamics. In this paper, we study the chaotic dynamics of a system consisting of an atomic Bose–Einstein condensate interacting with a quantized radiation field in a high-quality cavity with a periodically modulated length. The frequency modulation is adapted by a periodic time-dependent atom–field coupling strength. We use a semiclassical approach to decouple the atoms and field variables and then numerically solve the corresponding nonlinear dynamical equations of the system. Generally, the dynamics of the system sensitively depends on its initial conditions, thereby long-term prediction is impossible. We show that the system demonstrates the emergence of classical dynamical chaos from quantum electrodynamics. The chaotic behavior of energy transfer in the system can be enhanced by increasing the depth of frequency modulation. The strange attractor clearly illustrates that the system presents extremely exotic dynamics over a wide range of parameters. This implies that the dynamical quantities oscillate irregularly, never exactly repeating but always remaining in a bounded region of the phase space.
Semantic diversity is best measured with unscaled vectors: Reply to Cevoli, Watkins and Rastle (2020)

Semantic diversity refers to the degree of semantic variability in the contexts in which a particular word is used. We have previously proposed a method for measuring semantic diversity based on latent semantic analysis (LSA). In a recent paper, Cevoli et al. (2020) attempted to replicate our method and obtained different semantic diversity values. They suggested that this discrepancy occurred because they scaled their LSA vectors by their singular values, while we did not. Using their new results, they argued that semantic diversity is not related to ambiguity in word meaning, as we originally proposed. In this reply, we demonstrate that the use of unscaled vectors provides better fits to human semantic judgements than scaled ones. Thus we argue that our original semantic diversity measure should be preferred over the Cevoli et al. version. We replicate Cevoli et al.'s analysis using the original semantic diversity measure and find (a) our original measure is a better predictor of word recognition latencies than the Cevoli et al. equivalent and (b) that, unlike Cevoli et al.'s measure, our semantic diversity is reliably associated with a measure of polysemy based on dictionary definitions. We conclude that the Hoffman et al. semantic diversity measure is better-suited to capturing the contextual variability among words and that words appearing in a more diverse set of contexts have more variable semantic representations. However, we found that homonyms did not have higher semantic diversity values than non-homonyms, suggesting that the measure does not capture this special case of ambiguity.
STROBE-Voting: Send Two, Receive One Ballot Encoding

Numerous designs for end-to-end verifiable voting systems have been proposed in recent years to accommodate in-person voting scenarios and Internet voting scenarios, but very few have been offered to support vote-by-mail and none that are practical with current equipment and processes. This work describes a simple approach to end-to-end verifiable vote-by-mail that can be implemented with little change to existing processes. A specific architecture is described in this work, but the basic technique can also be used to enable many existing end-to-end verifiable systems to support vote-by-mail.