Reviewerly/exHarmony huggingface dataset:
Approximating the Gradient of Cross-Entropy Loss Function

A loss function has two crucial roles in training a conventional discriminant deep neural network (DNN): (i) it measures the goodness of classification and (ii) generates the gradients that drive the training of the network.In this paper, we approximate the gradients of cross-entropy loss which is the most often used loss function in the classification DNNs.The proposed approximations are noise-free, which means they depend only on the labels of the training set.They have a fixed length to avoid the vanishing gradient problem of the cross-entropy loss.By skipping the forward pass, the computational complexities of the proposed approximations are reduced to O(n) where n is the batch size.Two claims are established based on the experiments of training DNNs using the proposed approximations: (i) It is possible to train a discriminant network without explicitly defining a loss function.(ii) The success of training does not imply the convergence of network parameters to fixed values.The experiments show that the proposed gradient approximations achieve comparable classification accuracy to the conventional loss functions and can accelerate the training process on multiple datasets.
Digital Competence and University Teachers’ Conceptions about Teaching. A Structural Causal Model

Introduction: University teachers, in their ongoing scientific and pedagogical updating, must master the new methodologies and trends in their professional field. ICT’s importance for any person requires ethical responsibility, and it is incumbent on the educational professionals to preserve, improve and update their level of digital competence, and so improve learning and teaching. Method: This study uses a representative sample of 186 university teachers and follows the quantitative descriptive survey method. Two questionnaires were applied (CEE-ACUTIC). The first identified the approach for the transmission of information or the construction of knowledge, and the second measured attitude, knowledge and use of ICT. Results: A causal model of structural equations of maximum likelihood was run. We highlight the positive and significant relationship between the teaching approach oriented to the construction of knowledge and the use of ICTs (β = 0.17, p &lt; 0.01) and, in another sense, a statistically significant but negative relationship between the use of ICTs and the teaching approach focused on the transmission of information (β = −0.16, p &lt; 0.05). Discussion: It is empirically confirmed that the teaching approach conditions the use of technologies for teaching. There is no research in other contexts showing a causal relationship between teaching approaches and digital teaching competence.
Defending Against Model Stealing Attacks With Adaptive Misinformation

Deep Neural Networks (DNNs) are susceptible to model stealing attacks, which allows a data-limited adversary with no knowledge of the training dataset to clone the functionality of a target model, just by using black-box query access. Such attacks are typically carried out by querying the target model using inputs that are synthetically generated or sampled from a surrogate dataset to construct a labeled dataset. The adversary can use this labeled dataset to train a clone model, which achieves a classification accuracy comparable to that of the target model. We propose "Adaptive Misinformation" to defend against such model stealing attacks. We identify that all existing model stealing attacks invariably query the target model with Out-Of-Distribution (OOD) inputs. By selectively sending incorrect predictions for OOD queries, our defense substantially degrades the accuracy of the attacker's clone model (by up to 40%), while minimally impacting the accuracy (<; 0.5%) for benign users. Compared to existing defenses, our defense has a significantly better security vs accuracy trade-off and incurs minimal computational overhead.
A comprehensive review on water cycle algorithm and its applications

In recent years, significant attentions have been devoted to design of metaheuristic optimization algorithms in order to solve optimization problems. Metaheuristic optimizers are methods which are inspired by observing the phenomena occurring in nature. In this paper, a comprehensive and exhaustive review has been carried out on water cycle algorithm (WCA) and its applications in a wide variety of study fields. The WCA is one of the novel metaheuristic optimization algorithms which is inspired by water cycle process in nature and how streams and rivers flow into the sea. Good exploitation and exploration capabilities have made the WCA a good alternative for solving large-scale optimization problems. Due to its capabilities and strengths, the WCA has been utilized in many and various majors including mechanical engineering, electrical and electronic engineering, civil engineering, industrial engineering, water resources and hydropower engineering, computer engineering, mathematics, and so forth. A variety of articles based on WCA have been published in different international journals such as Elsevier, Springer, IEEE Transactions, Wiley, Taylor & Francis, and in the proceedings of international conferences as well, since 2012 to the present. Thus, it is highly believed that this paper can be appropriate, beneficial and practical for students, academic researchers, professionals, and engineers. Also, it can be an innovative and comprehensive reference for subsequent academic papers and books relevant to the WCA, optimization methods, and metaheuristic optimization algorithms.
Infrared head pose estimation with multi-scales feature fusion on the IRHP database for human attention recognition

Head pose estimation (HPE) has been widely applied in human attention recognition, robot vision and assistant driving. Infrared (IR) images bear unique advantages of being still effective under visible scenarios, which are resistance to illumination changing and strong penetration. However, the lack of public IR database hinders the research progress in the low illumination environment. In this paper, we establish a first-of-its-kind infrared head pose (IRHP) database and propose a novel convolutional neural network architecture IRHP-Net on the IRHP database. The IRHP database contains 145 kinds of IR head pose images of subjects, and benchmark evaluations are conducted on our database by the facial features-based standard HPE classification methods to prove the usability and effectiveness of IRHP database. To extract the adaptive features for the IR images, a novel multi-scale feature fusion descriptor is developed in the proposed IRHP-Net model. Quantitative assessments of the proposed method on the IRHP images demonstrate the significant improvements over the traditional methods. The new proposed IRHP-Net model can be utilized in human attention recognition and intelligent driving assistant system.
Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysis

The diagnosis of cancer is mainly performed by visual analysis of the pathologists, through examining the morphology of the tissue slices and the spatial arrangement of the cells. If the microscopic image of a specimen is not stained, it will look colorless and textured. Therefore, chemical staining is required to create contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, cutting thicknesses, and laboratory protocols, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to Interpretive disparity among pathologists more is one of the main challenges in designing robust and flexible systems for automated analysis. To address the staining color variations, several methods for normalizing stain have been proposed. In our proposed method, a Stain-to-Stain Translation (STST) approach is used to stain normalization for Hematoxylin and Eosin (H&E) stained histopathology images, which learns not only the specific color distribution but also the preserves corresponding histopathological pattern. We perform the process of translation based on the "pix2pix" framework, which uses the conditional generator adversarial networks (cGANs). Our approach showed excellent results, both mathematically and experimentally against the state of the art methods. We have made the source code publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Detecting CNN-Generated Facial Images in Real-World Scenarios

Artificial, CNN-generated images are now of such high quality that humans have trouble distinguishing them from real images. Several algorithmic detection methods have been proposed, but these appear to generalize poorly to data from unknown sources, making them infeasible for real-world scenarios. In this work, we present a framework for evaluating detection methods under real-world conditions, consisting of cross-model, cross-data, and post-processing evaluation, and we evaluate state-of-the-art detection methods using the proposed framework. Furthermore, we examine the usefulness of commonly used image pre-processing methods. Lastly, we evaluate human performance on detecting CNN-generated images, along with factors that influence this performance, by conducting an online survey. Our results suggest that CNN-based detection methods are not yet robust enough to be used in real-world scenarios.
Mechanism of decoherence-free coupling between giant atoms

Giant atoms are a new paradigm of quantum optics going beyond the usual local coupling. Building on this, a new type of decoherence-free (DF) many-body Hamiltonians was shown in a broadband waveguide. Here these are incorporated in a general framework (not relying on master equations) and contrasted to dispersive DF Hamiltonians with normal atoms: the two schemes are shown to correspond to qualitatively different ways to match the same general condition for suppressing decoherence. Next, we map the giant atoms dynamics into a cascaded collision model (CM), providing an intuitive interpretation of the connection between nontrivial DF Hamiltonians and coupling points topology. The braided configuration is shown to implement a scheme where a shuttling system subject to periodic phase kicks mediates a DF coupling between the atoms. From the viewpoint of CMs theory, this shows a collision model where ancillas effectively implement a dissipationless, maximally entangling two-qubit gate on the system.2 MoreReceived 27 June 2020Accepted 22 September 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043184Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum information with atoms & lightQuantum opticsQuantum optics with artificial atomsPhysical SystemsWaveguidesTechniquesRotating wave approximationAtomic, Molecular & OpticalQuantum Information
Deep learning-based fringe modulation-enhancing method for accurate fringe projection profilometry

Fringe projection profilometry (i.e., FPP) has been one of the most popular 3-D measurement techniques. The phase error due to system random noise becomes non-ignorable when fringes captured by a camera have a low fringe modulation, which are inevitable for objects’ surface with un-uniform reflectivity. The phase calculated from these low-modulation fringes may have a non-ignorable phase error and generate 3-D measurement error. Traditional methods reduce the phase error with losing details of 3-D shapes or sacrificing the measurement speed. In this paper, a deep learning-based fringe modulation-enhancing method (i.e., FMEM) is proposed, that transforms two low-modulation fringes with different phase shifts into a set of three phase-shifted high-modulation fringes. FMEM enables to calculate the desired phase from the transformed set of high-modulation fringes, and result in accurate 3-D FPP without sacrificing the speed. Experimental analysis verifies its effectiveness and accurateness.
Deep learning in steganography and steganalysis

For almost 10 years, the detection of a hidden message in an image has been mainly carried out by the computation of rich models (RMs), followed by classification using an ensemble classifier (EC). In 2015 the first study using a convolutional neural network (CNN) obtained the first results of steganalysis by deep learning approaching the performances of the two-step approach (EC + RM). During 2015–2018, numerous publications have shown that it is possible to obtain improved performances, notably in spatial steganalysis, JPEG steganalysis, selection-channel-aware steganalysis, and quantitative steganalysis. This chapter deals with deep learning in steganalysis from the point of view of current methods, by presenting different neural networks from the period 2015–2018, evaluated with a methodology specific to the discipline of steganalysis. We do not intend to repeat the basic concepts of machine learning or deep learning. So we will present the structure of a deep neural network in a generic way and present the networks proposed in the existing literature for different scenarios of steganalysis, and finally, we will discuss steganography by deep learning.
Robust Feature Matching for Remote Sensing Image Registration via Linear Adaptive Filtering

As a fundamental and critical task in feature-based remote sensing image registration, feature matching refers to establishing reliable point correspondences from two images of the same scene. In this article, we propose a simple yet efficient method termed linear adaptive filtering (LAF) for both rigid and nonrigid feature matching of remote sensing images and apply it to the image registration task. Our algorithm starts with establishing putative feature correspondences based on local descriptors and then focuses on removing outliers using geometrical consistency priori together with filtering and denoising theory. Specifically, we first grid the correspondence space into several nonoverlapping cells and calculate a typical motion vector for each one. Subsequently, we remove false matches by checking the consistency between each putative match and the typical motion vector in the corresponding cell, which is achieved by a Gaussian kernel convolution operation. By refining the typical motion vector in an iterative manner, we further introduce a progressive strategy based on the coarse-to-fine theory to promote the matching accuracy gradually. In addition, an adaptive parameter setting strategy and posterior probability estimation based on the expectation-maximization algorithm enhance the robustness of our method to different data. Most importantly, our method is quite efficient where the gridding strategy enables it to achieve linear time complexity. Consequently, some sparse point-based tasks may inspire from our method when they are achieved by deep learning techniques. Extensive feature matching and image registration experiments on several remote sensing data sets demonstrate the superiority of our approach over the state of the art.
FindBounce: Package for multi-field bounce actions

We are launching FindBounce, a Mathematica package for the evaluation of the Euclidean bounce action that enters the decay rate of metastable states in quantum and thermal field theories. It is based on the idea of polygonal bounces, which is a semi-analytical approach to solving the bounce equation by discretizing the potential into piecewise linear segments. This allows for a fast and robust evaluation of arbitrary potentials with specified precision and any number of scalar fields. Time cost grows linearly with the number of fields and/or the number of segments. Computation with 20 fields takes ∼2 s with 0.5% accuracy of the action. The FindBounce function is simple to use with the native Mathematica look and feel, it is easy to install, and comes with detailed documentation and physical examples, such as the calculation of the nucleation temperature. We also provide timing benchmarks with comparisons to existing tools, where applicable. Program title: FindBounce CPC Library link to program files: http://dx.doi.org/10.17632/tysw84skx3.1 Developer's repository link: https://github.com/vguada/FindBounce Licensing provisions: GNU General Public License 3 Programming language: Mathematica Nature of problem: Evaluation of the Euclidean bounce action that controls the decay rate of metastable local minima in thermal and quantum field theories. Solution method: Semi-analytical solution of a system of coupled differential equations, based on the polygonal bounce idea (Guada et al., 2019). Restrictions: Mathematica version 10 or above, works in D=3,4.
Artificial intelligence and crime: A primer for criminologists

This article introduces the concept of Artificial Intelligence (AI) to a criminological audience. After a general review of the phenomenon (including brief explanations of important cognate fields such as ‘machine learning’, ‘deep learning’, and ‘reinforcement learning’), the paper then turns to the potential application of AI by criminals, including what we term here ‘crimes with AI’, ‘crimes against AI’, and ‘crimes by AI’. In these sections, our aim is to highlight AI’s potential as a criminogenic phenomenon, both in terms of scaling up existing crimes and facilitating new digital transgressions. In the third part of the article, we turn our attention to the main ways the AI paradigm is transforming policing, surveillance, and criminal justice practices via diffuse monitoring modalities based on prediction and prevention. Throughout the paper, we deploy an array of programmatic examples which, collectively, we hope will serve as a useful AI primer for criminologists interested in the ‘tech-crime nexus’.
Intelligent algorithms for cold chain logistics distribution optimization based on big data cloud computing analysis

Abstract In recent years, the rapid development of fresh food e-commerce in China has brought about more development opportunities for the cold chain logistics industry but has also presented new challenges. With the development of cloud computing and big data technology, it is increasingly important to study the application of big data and cloud computing technology in cold chain logistics. The purpose of this paper is to study the intelligent algorithm of cold chain logistics distribution optimization based on big data cloud computing analysis. Based on the constituent elements of the cold chain distribution problem and using cloud computing technology to obtain real-time traffic information in the transportation system through a unified access interface, this article analyses the distribution time and cost of refrigerated vehicles, thereby establishing a cold chain distribution vehicle path optimization model. By analysing the parallel programming mode of cloud computing, the parallel design and analysis of a coarse-grained genetic algorithm are used to solve the simulation model of the established optimization model. The experimental results show that the method of optimizing cold chain logistics vehicle routing using cloud computing is effective. When comparing 1, 2, 4, and 8 processors, the execution times are 19.89, 14.52, 8.12, and 6.41, respectively. It can be seen that the more processors there are, the shorter the calculation time.
Representation Learning of Histopathology Images using Graph Neural Networks

Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. We propose a two-stage framework for WSI representation learning. We sample relevant patches using a color-based method and use graph neural networks to learn relations among sampled patches to aggregate the image information into a single vector representation. We introduce attention via graph pooling to automatically infer patches with higher relevance. We demonstrate the performance of our approach for discriminating two sub-types of lung cancers, Lung Adenocarcinoma (LUAD) & Lung Squamous Cell Carcinoma (LUSC). We collected 1,026 lung cancer WSIs with the 40× magnification from The Cancer Genome Atlas (TCGA) dataset, the largest public repository of histopathology images and achieved state-of-the-art accuracy of 88.8% and AUC of 0.89 on lung cancer sub-type classification by extracting features from a pre-trained DenseNet model.
Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks Optimization Algorithm

In this research, we propose to utilize the newly introduced Multi-population differential evolution-assisted Harris Hawks Optimization Algorithm (CMDHHO) in the optimization process for satellite image denoising in the wavelet domain.This optimization algorithm is the improved version of the previous HHO algorithm which consists of chaos, multi-population, and differential evolution strategies.In this study, we applied several optimization algorithms in the optimization procedure and we compared the de-noising results with CMDHHO based noise suppression as well as with the Thresholding Neural Network (TNN) approaches.It is observed that applying the CMDHHO algorithm provides us with better qualitative and quantitative results comparing with other optimized and TNN based noise removal techniques.In addition to the quality and quantity improvement, this method is computationally efficient and improves the processing time.Based on the experimental analysis, optimized based noise suppression performs better than TNN based image de-noising.Peak Signal to Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) are used to evaluate and measure the performance of different de-noising methods.Experimental results indicate the superiority of the proposed CMDHHO based satellite image de-noising over other available approaches in the literature.
Efficient list based mining of high average utility patterns with maximum average pruning strategies

High average utility pattern mining is the concept proposed to complement drawbacks of high utility pattern mining by considering lengths of patterns along with the utilities of the patterns. High average utility pattern mining should be able to gratify the anti-monotone property like other pattern mining techniques. Many high average utility pattern mining studies to satisfy the anti-monotone property have been proposed in order to improve various upper-bounds because the performance of pattern mining can be improved efficiently by satisfying the anti-monotone property. Although those upper-bounds can effectively reduce the search space, they still take a lot of cost to calculate all unpromising patterns or cannot find them in advance. Therefore, in this paper, a novel high average utility pattern mining approach is proposed by employing two novel upper-bounds called tight maximum average utility upper-bound and maximum remaining average utility upper-bound. Moreover, a newly suggested list-based structure, TA-List, is designed to adopt two pruning strategies. The proposed technique can efficiently extract high average utility patterns by reducing search space. To evaluate the performance of the proposed method, various experiments using real and synthetic datasets are conducted in terms of runtime, memory usage and scalability and the proposed approach is compared with the state-of-the-art high average utility pattern mining algorithms. The results of experiments show that the suggested algorithm has better performance with regard to runtime, memory usage and scalability.