Reviewerly/exHarmony huggingface dataset:
Meta-USR: A Unified Super-Resolution Network for Multiple Degradation Parameters

Recent research on single image super-resolution (SISR) has achieved great success due to the development of deep convolutional neural networks. However, most existing SISR methods merely focus on super-resolution of a single fixed integer scale factor. This simplified assumption does not meet the complex conditions for real-world images which often suffer from various blur kernels or various levels of noise. More importantly, previous methods lack the ability to cope with arbitrary degradation parameters (scale factors, blur kernels, and noise levels) with a single model. A few methods can handle multiple degradation factors, e.g., noninteger scale factors, blurring, and noise, simultaneously within a single SISR model. In this work, we propose a simple yet powerful method termed meta-USR which is the first unified super-resolution network for arbitrary degradation parameters with meta-learning. In Meta-USR, a meta-restoration module (MRM) is proposed to enhance the traditional upscale module with the capability to adaptively predict the weights of the convolution filters for various combinations of degradation parameters. Thus, the MRM can not only upscale the feature maps with arbitrary scale factors but also restore the SR image with different blur kernels and noise levels. Moreover, the lightweight MRM can be placed at the end of the network, which makes it very efficient for iteratively/repeatedly searching the various degradation factors. We evaluate the proposed method through extensive experiments on several widely used benchmark data sets on SISR. The qualitative and quantitative experimental results show the superiority of our Meta-USR.
A New Steganography Method for Dynamic GIF Images Based on Palette Sort

This paper proposes a new steganography method for hiding data into dynamic GIF (Graphics Interchange Format) images. When using the STC framework, we propose a new algorithm of cost assignment according to the characteristics of dynamic GIF images, including the image palette and the correlation of interframes. We also propose a payload allocation algorithm for different frames. First, we reorder the palette of GIF images to reduce the modifications on pixel values when modifying the index values. As the different modifications on index values would result in different impacts on pixel values, we assign the elements with less impact on pixel values with small embedding costs. Meanwhile, small embedding costs are also assigned for the elements in the regions that the interframe changes are large enough. Finally, we calculate an appropriate payload for each frame using the embedding probability obtained from the proposed distortion function. Experimental results show that the proposed method has a better security performance than state-of-the-art works.
Classification of Breast Cancer from Digital Mammography Using Deep Learning

Breast cancer is the most frequent in females. Mammography has proven to be the most effective method for the early detection of this type of cancer. Mammographic images are sometimes difficult to understand, due to the nature of the anomalies, the low contrast image and the composition of the mammary tissues, as well as various technological factors such as spatial resolution of the image or noise. Computer-aided diagnostic systems have been developed to increase the accuracy of mammographic examinations and be used by physicians as a second opinion in obtaining the final diagnosis, and thus reduce human errors. Convolutional neural networks are a current trend in computer vision tasks, due to the great performance they have achieved. The present investigation was based on this type of networks to classify into three classes, normal, benign and malignant tumour. Due to the fact that the miniMIAS database used has a low number of images, the transfer learning technique was applied to the Inception v3 pre-trained network. Two convolutional neural network architectures were implemented, obtaining in the architecture with three classes, 86.05% accuracy. On the other hand, in the architecture with two neural networks in series, an accuracy of 88.2% was reached.
A New Private Information Encryption Method in Internet of Things under Cloud Computing Environment

The existence of Internet of Things (IoT) facilitates the collection and transmission of urban data information. However, it can leak users’ personal privacy information in smart cities. Therefore, we propose a new private information encryption method in IoT under cloud computing environment. Under IoT, according to the properties and acquisition time, privacy information can be divided into many subspaces. Then, we analyze the private information encryption with different levels. Based on the stream cipher mechanism, we design an encryption system model of information collection. In the subspace, the privacy information is encrypted and transferred to the relay node. After encrypting, they are segmented and restructured. The long privacy information is divided into smaller slices. Then, they are reintegrated after conversion. Finally, we use stream cipher and dual-key algorithm to complete freedom nondestructive transformation between plaintext and ciphertext to ensure the integrity of the encrypted private information. Experimental results show that the proposed method takes less time in the encryption and decryption process, which has better ciphertext conversion output effect and suffers fewer network attacks in the same encryption time. The message encryption and decryption time is less than that of other methods. In terms of calculation cost, the proposed method decreases by approximately 14%. What is more, it has higher security and improves the security and integrity of the privacy information collection process.
Flexible multicamera calibration method with a rotating calibration plate

Camera calibration is necessary for accurate image measurements, particularly in multicamera systems. The calibration process involves corresponding the coordinates of 3D calibration points with a 2D image and requires the establishment of a reliable 3D world coordinate system. This paper presents a convenient multicamera calibration method that uses a rotating calibration plate and multi-view stereo vision to calculate 3D points and their relationship with the image coordinates. Despite simple implementation, the rotation of the calibration plate presents numerous calibration points from various planes, increasing the stability of the solution and the noise reduction. The relocation accuracy and reprojection error are experimentally verified.
Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization.Fake news have become a powerful tool to manipulate public opinion, especially during major events.In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation.In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting.Behavioral biometrics can be powerful tools in this fight.In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences.Our proposed system is based on Recurrent Neural Networks adapted to the context of content deanonymization.Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%.In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.
Telenursing in care, education and management in Latin America and the Caribbean: an integrative review

ABSTRACT Objective: Analyze the knowledge generated about telenursing related to the roles of nurses (care, education and management) in Latin America and the Caribbean, based on scientific evidence. Methods: Integrative review, for the years 2009 to 2019, in scientific databases from Latin America and the Caribbean. Twelve articles were selected. Results: Nine studies focused on care and three on education. The information and communication technology used in the studies was telephony directed toward non-communicable diseases. There were concerns about the remote communication process. With respect to education, two focused on educating health teams and one on educating patients. Conclusion: It is necessary to conduct studies on telenursing that generate changes in care practices, explore information and communication technology resources and provide communication training focused on this new care model.
Towards Causality Extraction from Requirements

System behavior is often based on causal relations between certain events (e.g. If event <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , then event <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). Consequently, those causal relations are also textually embedded in requirements. We want to extract this causal knowledge and utilize it to derive test cases automatically and to reason about dependencies between requirements. Existing NLP approaches fail to extract causality from natural language (NL) with reasonable performance. In this paper, we describe first steps towards building a new approach for causality extraction and contribute: (1) an NLP architecture based on Tree Recursive Neural Networks (TRNN) that we will train to identify causal relations in NL requirements and (2) an annotation scheme and a dataset that is suitable for training TRNNs. Our dataset contains 212,186 sentences from 463 publicly available requirement documents and is a first step towards a gold standard corpus for causality extraction. We encourage fellow researchers to contribute to our dataset and help us in finalizing the causality annotation process. Additionally, the dataset can also be annotated further to serve as a benchmark for other RE-relevant NLP tasks such as requirements classification.
Nonclassicality via the Superpositions of Photon Addition and Subtraction and Quantum Decoherence for Thermal Noise

Abstract Two new photon‐modulated states are introduced by operating a more general coherent superposition on two classical states (coherent and thermal) and their nonclassicality caused by the operation is investigated via examining the squeezing, sub‐Poissonian statistics, nonclassical depth, and negativity of Wigner distribution. Their density‐operator and Wigner‐distribution evolutions in the thermal channel are also explored. It is found that the high‐order operation has better performance in creating nonclassicality for certain ratios r and more robustness against decoherence of the two photon‐modulated states for thermal noise. For convenience, a new three‐variable special polynomial and its generating function are introduced via the normal ordering of operators.
FLOPS: EFficient On-Chip Learning for OPtical Neural Networks Through Stochastic Zeroth-Order Optimization

Optical neural networks (ONNs) have attracted extensive attention due to its ultra-high execution speed and low energy consumption. The traditional software-based ONN training, however, suffers the problems of expensive hardware mapping and inaccurate variation modeling while the current on-chip training methods fail to leverage the self-learning capability of ONNs due to algorithmic inefficiency and poor variation- robustness. In this work, we propose an on-chip learning method to resolve the aforementioned problems that impede ONNs' full potential for ultra-fast forward acceleration. We directly optimize optical components using stochastic zeroth-order optimization on-chip, avoiding the traditional high-overhead back-propagation, matrix decomposition, or in situ devicelevel intensity measurements. Experimental results demonstrate that the proposed on-chip learning framework provides an efficient solution to train integrated ONNs with 3~4× fewer ONN forward, higher inference accuracy, and better variation-robustness than previous works.
Three-Dimensional Mountain Complex Terrain and Heterogeneous Multi-UAV Cooperative Combat Mission Planning

Most research regarding multi-target, multi-base, and multi-unmanned aerial vehicle (UAV) coordinated combat mission planning faces the problems of ignoring heterogeneous UAVs, as well as poor task allocation and trajectory planning coupling.To solve these problems, based on air maneuver combat mission backgrounds, the present paper provided a heterogeneous multi-UAV cooperative mission planning method in the complex three-dimensional (3D) mountain environment.In the present paper, based on the Life-cycle Swarm Optimization (LSO) algorithm, varying the number of individuals in the population was utilized to improve the algorithm and further combined with the Rapidly exploring Random Tree (RRT) algorithm to obtain an optimized path.Then, an improved algorithm was utilized for task allocation and trajectory optimization, and the number and speed of drones dispatched by each base were determined regarding time coordination.Finally, a simulation experiment was conducted.Numerical simulation results showed that the following algorithm was compared with the Particle Swarm Optimization (PSO) algorithm and the Whale Optimization Algorithm (WOA) when considering radar threats and solid obstacle areas.This has good approximation and high convergence accuracy, and it was effectively utilized in the planning of UAV collaborative missions in 3D complex terrain environments.
Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of state-of-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.
A Robust and Reversible Watermarking Algorithm for a Relational Database Based on Continuous Columns in Histogram

Due to the discreteness of integer data, there are a large number of gaps and continuous columns in the histogram based on integer data. Aiming at the characteristics, this paper presents a robust and reversible watermarking algorithm for a relational database based on continuous columns in histogram. Firstly, it groups the database tuples according to the watermark length and the grouping key. Secondly, it calculates the prediction errors and uses the absolute values of the prediction errors to construct the histogram. Thirdly, it traverses the histogram to find all the continuous columns and in turn, computes the sum of the height of each continuous column and selects the group of continuous columns that has the largest sum as the positions to embed the watermarks. FCTD (Forest cover type data set) is utilized for experimental verification. A large amount of experimental data shows that the method is effective and robust. Not only does the data distortion caused by shifting histogram columns not exist, but the robustness of the watermark is also greatly improved.
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation

Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks.In this work, we extend this approach to the task of dialog state tracking for goaloriented dialogs.Due to the inherent hierarchical structure of goal-oriented dialogs over utterances and related annotations, the deep generative model must be capable of capturing the coherence among different hierarchies and types of dialog features.We propose the Variational Hierarchical Dialog Autoencoder (VHDA) for modeling the complete aspects of goal-oriented dialogs, including linguistic features and underlying structured annotations, namely speaker information, dialog acts, and goals.The proposed architecture is designed to model each aspect of goal-oriented dialogs using inter-connected latent variables and learns to generate coherent goal-oriented dialogs from the latent spaces.To overcome training issues that arise from training complex variational models, we propose appropriate training strategies.Experiments on various dialog datasets show that our model improves the downstream dialog trackers' robustness via generative data augmentation.We also discover additional benefits of our unified approach to modeling goal-oriented dialogsdialog response generation and user simulation, where our model outperforms previous strong baselines.
Lattices with exponentially large kissing numbers

We construct a sequence of lattices $\{L_{n_i}\subset \mathbb R^{n_i}\}$ for $n_i\longrightarrow\infty$, with exponentially large kissing numbers, namely, $\log_2\tau(L_{n_i})> 0.0338\cdot n_i -o(n_i)$. We also show that the maximum lattice kissing number $ \tau^l_{n}$ in $n$ dimensions verifies $\log_2\tau^l_{n}> 0.0219\cdot n -o(n)$.
Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ

High-quality and large-scale data are key to success for AI systems.However, large-scale data annotation efforts are often confronted with a set of common challenges: (1) designing a user-friendly annotation interface; (2) training enough annotators efficiently; and (3) reproducibility.To address these problems, we introduce CROWDAQ, 1 an open-source platform that standardizes the data collection pipeline with customizable user-interface components, automated annotator qualification, and saved pipelines in a re-usable format.We show that CROWDAQ simplifies data annotation significantly on a diverse set of data collection use cases and we hope it will be a convenient tool for the community.
Geometric Dataset Distances via Optimal Transport

The notion of task similarity is at the core of various machine learning paradigms, such as domain adaptation and meta-learning. Current methods to quantify it are often heuristic, make strong assumptions on the label sets across the tasks, and many are architecture-dependent, relying on task-specific optimal parameters (e. g., require training a model on each dataset). In this work we propose an alternative notion of distance between datasets that (i) is model-agnostic, (ii) does not involve training, (iii) can compare datasets even if their label sets are completely disjoint and (iv) has solid theoretical footing. This distance relies on optimal transport, which provides it with rich geometry awareness, interpretable correspondences and well-understood properties. Our results show that this novel distance provides meaningful comparison of datasets, and correlates well with transfer learning hardness across various experimental settings and datasets.
Handling Missing Data with Graph Representation Learning

Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and label prediction where downstream labels are learned directly from incomplete data. However, existing imputation models tend to have strong prior assumptions and cannot learn from downstream tasks, while models targeting label prediction often involve heuristics and can encounter scalability issues. Here we propose GRAPE, a graph-based framework for feature imputation as well as label prediction. GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges. Under the GRAPE framework, the feature imputation is formulated as an edge-level prediction task and the label prediction as a node-level prediction task. These tasks are then solved with Graph Neural Networks. Experimental results on nine benchmark datasets show that GRAPE yields 20% lower mean absolute error for imputation tasks and 10% lower for label prediction tasks, compared with existing state-of-the-art methods.
Fuzzy Logic in Business, Management and Accounting

The aim of the paper is to show the implementation of fuzzy logic in business, administration and accounting, through the research published in Scopus.The results of the document focus on the following sections: 1) Fuzzy set theory, in business, administration and accounting.2) Analysis of fuzzy logic bibliometrics in business, administration and accounting.3) Identification and characterization of the documents or seminal documents most cited in applications of fuzzy logic in business, administration and accounting.The method used in this contribution is documentary research using the Scopus database and the VOSviewer science bibliometric analysis and mapping tool.In the future, this computational practice will focus on new diffuse models and the combination of these with other artificial intelligence techniques such as neural networks, genetic algorism, forage bacteria, among others.
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation

AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text.A key challenge in this task is to efficiently learn effective graph representations.Previously, Graph Convolution Networks (GCNs) were used to encode input AMRs, however, vanilla GCNs are not able to capture non-local information and additionally, they follow a local (first-order) information aggregation scheme.To account for these issues, larger and deeper GCN models are required to capture more complex interactions.In this paper, we introduce a dynamic fusion mechanism, proposing Lightweight Dynamic Graph Convolutional Networks (LDGCNs) that capture richer non-local interactions by synthesizing higher order information from the input graphs.We further develop two novel parameter saving strategies based on the group graph convolutions and weight tied convolutions to reduce memory usage and model complexity.With the help of these strategies, we are able to train a model with fewer parameters while maintaining the model capacity.Experiments demonstrate that LDGCNs outperform stateof-the-art models on two benchmark datasets for AMR-to-text generation with significantly fewer parameters.