Reviewerly/exHarmony huggingface dataset:
Simple and maximally robust processes with no classical common-cause or direct-cause explanation

Guided by the intuition of coherent superposition of causal relations, recent works presented quantum processes without classical common-cause and direct-cause explanation, that is, processes which cannot be written as probabilistic mixtures of quantum common-cause and quantum direct-cause relations (CCDC). In this work, we analyze the minimum requirements for a quantum process to fail to admit a CCDC explanation and present "simple" processes, which we prove to be the most robust ones against general noise. These simple processes can be realized by preparing a maximally entangled state and applying the identity quantum channel, thus not requiring an explicit coherent mixture of common-cause and direct-cause, exploiting the possibility of a process to have both relations simultaneously. We then prove that, although all bipartite direct-cause processes are bipartite separable operators, there exist bipartite separable processes which are not direct-cause. This shows that the problem of deciding weather a process is direct-cause process<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">is not</mml:mtext></mml:mrow></mml:math>equivalent to entanglement certification and points out the limitations of entanglement methods to detect non-classical CCDC processes. We also present a semi-definite programming hierarchy that can detect and quantify the non-classical CCDC robustnesses of every non-classical CCDC process. Among other results, our numerical methods allow us to show that the simple processes presented here are likely to be also the maximally robust against white noise. Finally, we explore the equivalence between bipartite direct-cause processes and bipartite processes without quantum memory, to present a separable process which cannot be realized as a process without quantum memory.
An Action-Selection Policy Generator for Reinforcement Learning Hardware Accelerators

We propose the first hardware architecture for an action-selection Policy Generator feasible for Reinforcement Learning hardware accelerators. The system is capable of producing outputs for random, greedy and ε-greedy action-selection policies within the same circuit. It requires a very moderate number of hardware resources, shows a limited power dissipation, and can be integrated in the state of the art of Reinforcement Learning hardware accelerators due to its high computational speed. Our architecture is meant to work with Q-Matrix based Reinforcement Learning algorithms such as Q-Learning and SARSA.
Blur2Sharp: A GAN-Based Model for Document Image Deblurring

GAN) Cycle-consistent generative adversarial network (CycleGAN) Document deblurring Blind deconvolution Motion blur Out-of-focus blur A B S T R A C TThe advances in mobile technology and portable cameras have facilitated enormously the acquisition of text images.However, the blur caused by camera shake or out-of-focus problems may affect the quality of acquired images and their use as input for optical character recognition (OCR) or other types of document processing.This work proposes an end-to-end model for document deblurring using cycle-consistent adversarial networks.The main novelty of this work is to achieve blind document deblurring, i.e., deblurring without knowledge of the blur kernel.Our method, named "Blur2Sharp CycleGAN, " generates a sharp image from a blurry one and shows how cycle-consistent generative adversarial networks (CycleGAN) can be used in document deblurring.Using only a blurred image as input, we try to generate the sharp image.Thus, no information about the blur kernel is required.In the evaluation part, we use peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to compare the deblurring images.The experiments demonstrate a clear improvement in visual quality with respect to the state-of-the-art using a dataset of text images.
Multi-source material image optimized selection based multi-option composition

Image composition aims to composite a material region into a target image. Using this technique, more images could be interactive, as there are millions of images created daily in modern life. However, it is difficult for the majority of traditional composition methods to retrieve relatively realistic semantically valid material images. Also, even if the minority of traditional composition methods yield superior results, it remains difficult to retrieve adequate material images due to the limitation of existing semantically valid images. Artificial traces also significantly reduce the visual esthetic of composited results. Based on these problems, we propose a multi-source material image optimized selection based multi-option composition method. Firstly, a robust sparse coding based retrieval model is used to retrieve material images effectively. On this basis, the attention mechanism based circular SA-GAN model could generate many semantically valid material images that guarantee the adequacy of material images. Secondly, the pixel-level optimization based multi-scale composition model minimizes artificial traces and reduces computational burden. Finally, an experimental database using many images was built. Based on this model, adequate comparative experiments using multiple evaluation criteria fully show the proposed method's effectiveness and robustness.
An Atomic Cross-Chain Swap-Based Management System in Vehicular Ad Hoc Networks

The blockchain-based management system has been regarded as a novel way to improve the efficiency and safety of Vehicular Ad Hoc Networks (VANETs). A blockchain-based scheme’s performance depends on blockchain nodes’ computing power composed from the road-side unit (RSU). However, the throughput of blockchain-based application in VANETs is limited by the network bandwidth. A single blockchain cannot record large-scale VANETs’ data. In this paper, we design an atomic cross-chain swap-based management system (ACSMS) to boost the scalability of blockchain-based application in VANETs. The blockchain-based public-key encryption with keyword search is further introduced to protect user privacy. The analysis shows that ACSMS achieves cross-chain swap without loss of CAV security privacy. The simulation results show that our method can realize multiple blockchain-based applications in VANETs.
Interactive Attention Networks for Semantic Text Matching

Semantic text matching, which matches target texts to source texts, is a general problem in many areas, such as information retrieval, question answering, and recommendation. The challenges to existing research on this topic include 1) out-of-vocabulary and low-frequency keywords and 2) direct utilization of sparse matching matrix of source and target. The out-of-vocabulary and low-frequency keywords could lead to the mismatch of similar keywords in source and target texts. The sparse matching matrix cannot provide enough clues to match the source with the target. To address these challenges, we propose a novel deep neural semantic text matching model. Our model adopts an interactive attention network to achieve information exchange between the source text and the target text, and dynamically explores the matching matrix and learns new representations of source and target texts. Experimental results on three different text matching datasets demonstrate that our model can significantly outperform competitive baselines. Furthermore, our model demonstrates great advantage in alleviating the sparse matching problem and learning out-of-vocabulary words with the local context, which widely exists in a broad spectrum of NLP applications.
Isometric Transformation Invariant and Equivariant Graph Convolutional Networks

Graphs are one of the most important data structures for representing pairwise relations between objects. Specifically, a graph embedded in a Euclidean space is essential to solving real problems, such as physical simulations. A crucial requirement for applying graphs in Euclidean spaces to physical simulations is learning and inferring the isometric transformation invariant and equivariant features in a computationally efficient manner. In this paper, we propose a set of transformation invariant and equivariant models based on graph convolutional networks, called IsoGCNs. We demonstrate that the proposed model has a competitive performance compared to state-of-the-art methods on tasks related to geometrical and physical simulation data. Moreover, the proposed model can scale up to graphs with 1M vertices and conduct an inference faster than a conventional finite element analysis, which the existing equivariant models cannot achieve.
Permutation Based EDM: An Inverse Free BBB Secure PRF

In CRYPTO 2019, Chen et al. have initiated an interesting research direction in designing PRF based on public permutations. They have proposed two beyond the birthday bound secure n-bit to n-bit PRF constructions, i.e., SoEM22 and SoKAC21, which are built on public permutations, where n is the size of the permutation. However, both of their constructions require two independent instances of public permutations. In FSE 2020, Chakraborti et al. have proposed a single public permutation based n-bit to n-bit beyond the birthday bound secure PRF, which they refer to as PDMMAC. Although the construction is minimal in the number of permutations, it requires the inverse call of its underlying permutation in their design. Coming up with a beyond the birthday bound secure public permutation based n-bit to n-bit PRF with a single permutation and two forward calls was left as an open problem in their paper. In this work, we propose pEDM, a single permutation based n-bit to n-bit PRF with two calls that do not require invertibility of the permutation. We have shown that our construction is secured against all adaptive information-theoretic distinguishers that make roughly up to 22n/3 construction and primitive queries. Moreover, we have also shown a matching attack with similar query complexity that establishes the tightness of our security bound.
FineHand: Learning Hand Shapes for American Sign Language Recognition

American Sign Language recognition is a difficult gesture recognition problem, characterized by fast, highly articulate gestures. These are comprised of arm movements with different hand shapes, facial expression and head movements. Among these components, hand shape is the vital, often the most discriminative part of a gesture. In this work, we present an approach for effective learning of hand shape embeddings, which are discriminative for ASL gestures. For hand shape recognition our method uses a mix of manually labelled hand shapes and high confidence predictions to train deep convolutional neural network (CNN). The sequential gesture component is captured by recursive neural network (RNN) trained on the embeddings learned in the first stage. We will demonstrate that higher quality hand shape models can significantly improve the accuracy of final video gesture classification in challenging conditions with variety of speakers, different illumination and significant motion blurr. We compare our model to alternative approaches exploiting different modalities and representations of the data and show improved video gesture recognition accuracy on GMU-ASL51 benchmark dataset.
Proposal for a new layer for energy cloud management: The regulatory layer

The global energy matrix has been transforming in recent years and today it is moving toward a decentralized form of management for the generation of clean energy worldwide. With this, new ways of management for the energy environment has been emerging, being the Energy Cloud (EC), through cloud computing, one of these trends that seek to optimize the process of generation, distribution, storage and energy consumption, making the most flexible and dynamic energy market. Given these motivations, the objective of this article is to discuss the proposal of a series of technical, economic, and environmental standards for energy management in a cloud computing environment. For this, a systematic review was carried out to identify these regulatory suggestions, where through the reading and analysis of 121 articles, 72 suggestions were extracted and classified according to the layers and management support blocks of from EC management environment. Besides, these policies were grouped according to their approach into technical, economic, and environmental factors to identify what was already proposed by the academy's authors and what are the regulatory deficiencies for EC. These regulatory suggestions can be used by policy-makers, researchers, and managers of this innovative energy management environment, which is the EC. Novelty Statement This study consists of a regulatory proposal for Energy Cloud (EC), an innovative energy management environment. As there is still no regulatory body responsible for the regulation of EC, the main contribution of this article is the proposal of a regulation layer. In this sense, 72 suggestions for standards were raised, presenting a new layer for EC.
Hyperparameter Optimization of Deep Learning Networks for Classification of Breast Histopathology Images

After tumor detection in their breasts, women typically fear mastectomy; this affects curative care outcomes.Most tumors are benign.After resection and pathological examination, because of advances in medicine and treatment, the success rate of early breast cancer treatment can reach 60 to 90%.An accurate assessment of tumor extent is essential.In this study, a novel method of hyperparameter optimization of deep learning networks was proposed to classify tumors as malignant or benign.When setting hyperparameters in deep learning networks, most users use trial and error to determine them.In our experiments, the Taguchi method was used to select the impact factors.The orthogonal table design was used to conduct experiments.Then, the best combination of parameters was determined and significant impact factors were analyzed.The Breast Cancer Histopathological Database was used for analysis.This database was built in collaboration with the P&D Laboratory and contains 2480 benign and 5429 malignant samples.The experimental results showed that the proposed method obtained a high accuracy of 83.19%.
Query-by-example HDR image retrieval based on CNN

Due to the expension of High Dynamic Range (HDR) imaging applications into various aspects of daily life, an efficient retrieval system, tailored to this type of data, has become a pressing challenge. In this paper, the reliability of Convolutional Neural Networks (CNN) descriptor and its investigation for HDR image retrieval are studied. The main idea consists in exploring the use of CNN to determine HDR image descriptor. Specifically, a Perceptually Uniform (PU) encoding is initially applied to the HDR content to map the luminance values in a perceptually uniform scale. Afterward, the CNN features, using Fully Connected (FC) layer activation, are extracted and classified by applying the Support Vector Machines (SVM) algorithm. Experimental evaluation demonstrates that the CNN descriptor, using the VGG19 network, achieves satisfactory results for describing HDR images on public available datasets such as PascalVoc2007, Cifar-10 and Wang. The experimental results also show that the features, after a PU processing, are more descriptive than those directly extracted from HDR contents. Finally, we show the superior performance of the proposed method against a recent state-of-the-art technique.
FULLY CONVOLUTIONAL SPATIO-TEMPORAL MODELS FOR REPRESENTATION LEARNING IN PLASMA SCIENCE

We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of major disruptions is a critically important fusion energy science issue that must be resolved for advanced tokamak plasmas such as the $25B burning plasma international thermonuclear experimental reactor (ITER) experiment. While a variety of statistical methods have been used to address the problem of tokamak disruption prediction and control, recent approaches based on deep learning have proven particularly compelling. In the present paper, we introduce further improvements to the fusion recurrent neural network (FRNN) software suite, which delivered cross-machine disruption predictions with unprecedented accuracy using a large database of experimental signals from two major tokamaks. Up to now, FRNN was based on the long short-term memory (LSTM) variant of recurrent neural networks to leverage the temporal information in the data. Here, we implement and apply the "temporal convolutional neural network (TCN)" architecture to the time-dependent input signals. This allows highly optimized convolution operations to carry the majority of the computational load of training, thus enabling a reduction in training time, and the effective use of high-performance computing resources for hyperparameter tuning. At the same time, the TCN-based architecture achieves better predictive performance when compared with the LSTM architecture for various tasks for a representative fusion database.
Dynamics of local quantum uncertainty and local quantum fisher information for a two-qubit system driven by classical phase noisy laser

In this paper, the dynamics of quantum correlation quantified by local quantum uncertainty (LQU) and local quantum Fisher information (LQFI) for two uncoupled qubits driven by classical phase noisy laser have been investigated. The results show that, both LQU and LQFI exhibit similar behaviours, either display exponential decay or oscillatory decay with revivals depending on the ratios of classical phase noisy laser rate and system-environment coupling strength. Beside, our results also confirm the amount of quantum correlation measured by LQU is strictly bounded by LQFI in an open system. Finally, we propose a scheme to protect quantum correlation against classical phase noisy laser with the technique of weak measurement and reversal.
Image Encryption Scheme Based on Block Scrambling, Closed-Loop Diffusion, and DNA Molecular Mutation

A new image encryption scheme is proposed with a combination of block scrambling, closed-loop diffusion, and DNA molecular mutation. The new chaotic block scrambling mechanism is put forward to replace the traditional swapping rule by combining the rectangular-ambulatory-plane cyclic shift with the bidirectional random disorganization. The closed-loop diffusion strategy is designed to form a feedback system, which improves the anti-interference capacity of the algorithm. To further destroy the blocks characteristics and eliminate the correlations among adjacent blocks, two efficient methods of DNA molecular mutation are adopted in the mutation stage. Moreover, the proposed algorithm possesses a large key space and the keys are highly related with the plaintext image. Experimental results demonstrate that the suggested image encryption strategy is practicable and has strong ability against a variety of common attacks.
Getting a CLUE: A Method for Explaining Uncertainty Estimates

Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems. However, there is little work at the intersection of these two areas. We address this gap by proposing a novel method for interpreting uncertainty estimates from differentiable probabilistic models, like Bayesian Neural Networks (BNNs). Our method, Counterfactual Latent Uncertainty Explanations (CLUE), indicates how to change an input, while keeping it on the data manifold, such that a BNN becomes more confident about the input's prediction. We validate CLUE through 1) a novel framework for evaluating counterfactual explanations of uncertainty, 2) a series of ablation experiments, and 3) a user study. Our experiments show that CLUE outperforms baselines and enables practitioners to better understand which input patterns are responsible for predictive uncertainty.
Detection on sarcasm using machine learning classifiers and rule based approach

Abstract Sentiment Analysis has been mainly used to understand the judgment of the text. It has been undergoing major provocation and irony detection is considered as one among the most provocations in it. Irony is the unusual way of narrating an information which disagrees the concept which leads to uncertainty. One primary task included by most developers is data preprocessing which includes many techniques like lemmatization, tokenization and stemming. Many researches are done under irony detection which includes many feature extraction techniques. Machine learning classifiers used for these researches are Support Vector Machine (SVM), linear regression, Naïve Bayes, Random Forest and many more. Results of these research works includes accuracy, precision, recall, F-score which can be used to predict the best suited model. In this paper various methodology used in irony text detection for Sentiment Analysis is discussed.
Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams

Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.
Design of low-density-generator-matrix–based quantum codes for asymmetric quantum channels

Quantum low-density-generator-matrix (QLDGM) codes are known to exhibit great error correction capabilities, surpassing existing quantum low-density-parity-check (QLDPC) codes and other sparse-graph schemes over the depolarizing channel. Most of the research on QLDPC codes and quantum error correction (QEC) is conducted for the symmetric instance of the generic Pauli channel, which incurs bit flips, phase flips, or a combination of both with the same probability. However, due to the behavior of the materials they are built from, some quantum devices must be modelled using a different channel model capable of accurately representing asymmetric scenarios in which the likelihood of a phase flip is higher than that of a bit flip. In this work, we study the design of QLDGM CSS codes for such Pauli channels. We show how codes tailored to the depolarizing channel are not well suited to these asymmetric environments and we derive methods to aptly design QLDGM CSS codes for this paradigm.