Reviewerly/exHarmony huggingface dataset:
Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve

This paper focuses on the Landsat 8 satellite image classification of the OLI sensor via the remote sensing software Erdas Imagine in order to calculate the land cover surface and to establish the mapping of the special reserve Kalambatritra of Madagascar for the year 2018.For this, we adopted the methodology of satellite image processing based on supervised classification algorithms.The processing was moved to spectral preparation and improvement of spatial resolution using the blue, green, red, near infrared and panchromatic channels.Then, a comparison study of the supervised classification algorithms was done to obtain a more accurate result.The validation of the classification results was performed using several reference points, a previous national processing result already validated in the field and the Google earth image of the same year.After repeating the classification several times, we obtained accuracies of 77%, 75%, 88%, 84% and 90% with Kappa indices of 0.64, 0.61, 0.80, 0.76 and 0.84 for the Spectral Angle Mapper, Spectral Correlation Mapper, Maximum Likelihood, Mahalanobis Distance and Minimum Distance.Based on these results, the minimum distance showed a higher accuracy and gave us 13462.1842ha of forest area, 16798.
Two-Stream Convolutional Neural Network for Skeleton-Based Action Recognition

Skeleton-based action recognition has attracted much attention while the skeleton contains important information related to action. How to efficiently use the temporal and spatial information in skeleton data is still an open problem. We propose in this paper a two-stream convolutional neural network which combines joints information with bone information to recognize actions. More specifically, the input of one stream is the joints position information (point information) and the other is the bone vector information (direction and length information) which is more informative in local relative change. Every stream also integrates motion information which represent temporal difference of inputs. Besides, we enhance our network using asymmetric convolution blocks (ACBlock). ACBlock can capture features efficiently and reduce the negative effects of rotation and deformation which contributes to feature extraction. Experiments on NTU-RGB+D datasets demonstrate the advantage of the proposed model on effectiveness of the novel model to recognize the action.
Coping with Label Shift via Distributionally Robust Optimisation

The label shift problem refers to the supervised learning setting where the train and test label distributions do not match. Existing work addressing label shift usually assumes access to an unlabelled test sample. This sample may be used to estimate the test label distribution, and to then train a suitably re-weighted classifier. While approaches using this idea have proven effective, their scope is limited as it is not always feasible to access the target domain; further, they require repeated retraining if the model is to be deployed in multiple test environments. Can one instead learn a single classifier that is robust to arbitrary label shifts from a broad family? In this paper, we answer this question by proposing a model that minimises an objective based on distributionally robust optimisation (DRO). We then design and analyse a gradient descent-proximal mirror ascent algorithm tailored for large-scale problems to optimise the proposed objective. Finally, through experiments on CIFAR-100 and ImageNet, we show that our technique can significantly improve performance over a number of baselines in settings where label shift is present.
Statistical Assessment of Discrimination Capabilities of a Fractional Calculus Based Image Watermarking System for Gaussian Watermarks

In this paper, we explore the advantages of a fractional calculus based watermarking system for detecting Gaussian watermarks. To reach this goal, we selected a typical watermarking scheme and replaced the detection equation set by another set of equations derived from fractional calculus principles; then, we carried out a statistical assessment of the performance of both schemes by analyzing the Receiver Operating Characteristic (ROC) curve and the False Positive Percentage (FPP) when they are used to detect Gaussian watermarks. The results show that the ROC of a fractional equation based scheme has 48.3% more Area Under the Curve (AUC) and a False Positives Percentage median of 0.2% whilst the selected typical watermarking scheme has 3%. In addition, the experimental results suggest that the target applications of fractional schemes for detecting Gaussian watermarks are as a semi-fragile image watermarking systems robust to Gaussian noise.
Classification of Noisy Free-Text Prostate Cancer Pathology Reports Using Natural Language Processing

Free-text reporting has been the main approach in clinical pathology practice for decades. Pathology reports are an essential information source to guide the treatment of cancer patients and for cancer registries, which process high volumes of free-text reports annually. Information coding and extraction are usually performed manually and it is an expensive and time-consuming process, since reports vary widely between institutions, usually contain noise and do not have a standard structure. This paper presents strategies based on natural language processing (NLP) models to classify noisy free-text pathology reports of high and low-grade prostate cancer from the open-source repository TCGA (The Cancer Genome Atlas). We used paragraph vectors to encode the reports and compared them with n-grams and TF-IDF representations. The best representation based on distributed bag of words of paragraph vectors obtained an \(f_{1}\)-score of 0.858 and an AUC of 0.854 using a logistic regression classifier. We investigate the classifier’s more relevant words in each case using the LIME interpretability tool, confirming the classifiers’ usefulness to select relevant diagnostic words. Our results show the feasibility of using paragraph embeddings to represent and classify pathology reports.
Fast Geometric Projections for Local Robustness Certification

Local robustness ensures that a model classifies all inputs within an lp-ball consistently, which precludes various forms of adversarial inputs. In this paper, we present a fast procedure for checking local robustness in feed-forward neural networks with piecewise-linear activation functions. Such networks partition the input space into a set of convex polyhedral regions in which the network’s behavior is linear; hence, a systematic search for decision boundaries within the regions around a given input is sufficient for assessing robustness. Crucially, we show how the regions around a point can be analyzed using simple geometric projections, thus admitting an efficient, highly-parallel GPU implementation that excels particularly for the l2 norm, where previous work has been less effective. Empirically we find this approach to be far more precise than many approximate verification approaches, while at the same time performing multiple orders of magnitude faster than complete verifiers, and scaling to much deeper networks.
Joint tracking of multiple quantiles through conditional quantiles

The estimation of quantiles is one of the most fundamental data mining tasks. As most real-time data streams vary dynamically over time, there is a quest for adaptive quantile estimators. The most well-known type of adaptive quantile estimators is the incremental one which documents the state-of-the art performance in tracking quantiles. However, the absolute vast majority of incremental quantile estimators fail to jointly estimate multiple quantiles in a consistent manner without violating the monotone property of quantiles. In this paper, first we introduce the concept of conditional quantiles that can be used to extend incremental estimators to jointly track multiple quantiles. Second, we resort to the concept of conditional quantiles to propose two new estimators. Extensive experimental results, based on both synthetic and real-life data, show that the proposed estimators clearly outperform legacy state-of-the-art joint quantile tracking algorithms in terms of accuracy while achieving faster adaptivity in the face of dynamically varying data streams.
On the Adaptability of Recurrent Neural Networks for Real-Time Jazz Improvisation Accompaniment

Jazz improvisation on a given lead sheet with chords is an interesting scenario for studying the behaviour of artificial agents when they collaborate with humans. Specifically in jazz improvisation, the role of the accompanist is crucial for reflecting the harmonic and metric characteristics of a jazz standard, while identifying in real-time the intentions of the soloist and adapt the accompanying performance parameters accordingly. This paper presents a study on a basic implementation of an artificial jazz accompanist, which provides accompanying chord voicings to a human soloist that is conditioned by the soloing input and the harmonic and metric information provided in a lead sheet chart. The model of the artificial agent includes a separate model for predicting the intentions of the human soloist, towards providing proper accompaniment to the human performer in real-time. Simple implementations of Recurrent Neural Networks are employed both for modeling the predictions of the artificial agent and for modeling the expectations of human intention. A publicly available dataset is modified with a probabilistic refinement process for including all the necessary information for the task at hand and test-case compositions on two jazz standards show the ability of the system to comply with the harmonic constraints within the chart. Furthermore, the system is indicated to be able to provide varying output with different soloing conditions, while there is no significant sacrifice of “musicality” in generated music, as shown in subjective evaluations. Some important limitations that need to be addressed for obtaining more informative results on the potential of the examined approach are also discussed.
Relevance of Zero Trust Network Architecture amidts and it’s rapid adoption amidts Work From Home enforced by COVID-19

As organizations evaluate different methods to improve data security, they have to shift to find the best alternative means to secure the servers. Zero Trust Network Architecture (ZTNA) has become the focus of many institutions to prevent data loss, especially from the employees working remotely due to the spread of Covid-19, requiring people to keep social distance minimize risks of an attack. Zero trust employs a multifactor authentication process that requires all users to verify their identity or the device they are using before using a network. This security procedure has been useful in eliminating the castle-and-moat concept's weaknesses that were initially used and could allow an individual to navigate freely through a network once they penetrated the firewall. The Zero trust does not allow the penetration and further segment of different materials; therefore, an individual cannot access all materials once in a network. This cybersecurity method will be advantageous post COVID-19 to deliver a simplified user experience enabling them to manage and find the contents with ease. For the customers, the architecture creates a uniform platform that can be used to amplify the security. The technology is a must for cost-benefit analysis for every organization considering its potential to help avoid potential security and financial losses
A Novel Pix2Pix Enabled Traveling Wave-Based Fault Location Method

This paper proposes a new Image-to-Image Translation (Pix2Pix) enabled deep learning method for traveling wave-based fault location. Unlike the previous methods that require a high sampling frequency of the PMU, the proposed method can translate the scale 1 detail component image provided by the low frequency PMU data to higher frequency ones via the Pix2Pix. This allows us to significantly improve the fault location accuracy. Test results via the YOLO v3 object recognition algorithm show that the images generated by pix2pix can be accurately identified. This enables to improve the estimation accuracy of the arrival time of the traveling wave head, leading to better fault location outcomes.
On the robustness of randomized classifiers to adversarial examples

Abstract This paper investigates the theory of robustness against adversarial attacks. We focus on randomized classifiers (i.e. classifiers that output random variables) and provide a thorough analysis of their behavior through the lens of statistical learning theory and information theory. To this aim, we introduce a new notion of robustness for randomized classifiers, enforcing local Lipschitzness using probability metrics. Equipped with this definition, we make two new contributions. The first one consists in devising a new upper bound on the adversarial generalization gap of randomized classifiers. More precisely, we devise bounds on the generalization gap and the adversarial gap i.e. the gap between the risk and the worst-case risk under attack) of randomized classifiers. The second contribution presents a yet simple but efficient noise injection method to design robust randomized classifiers. We show that our results are applicable to a wide range of machine learning models under mild hypotheses. We further corroborate our findings with experimental results using deep neural networks on standard image datasets, namely CIFAR-10 and CIFAR-100. On these tasks, we manage to design robust models that simultaneously achieve state-of-the-art accuracy (over 0.82 clean accuracy on CIFAR-10) and enjoy guaranteed robust accuracy bounds (0.45 against $$\ell _{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> adversaries with magnitude 0.5 on CIFAR-10).
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous Control

Trust region methods and maximum entropy methods are two state-of-the-art branches used in reinforcement learning (RL) for the benefits of stability and exploration in continuous environments, respectively. This paper proposes to integrate both branches in a unified framework, thus benefiting from both sides. We first transform the original RL objective to a constraint optimization problem and then proposes trust entropy actor-critic (TEAC), an off-policy algorithm to learn stable and sufficiently explored policies for continuous states and actions. TEAC trains the critic by minimizing the refined Bellman error and updates the actor by minimizing KL-divergence loss derived from the closed-form solution to the Lagrangian. We prove that the policy evaluation and policy improvement in TEAC is guaranteed to converge. We compare TEAC with 4 state-of-the-art solutions on 6 tasks in the MuJoCo environment. The results show that TEAC outperforms state-of-the-art solutions in terms of efficiency and effectiveness.
A Framework for Transformation Network Training in Coordination with Semi-trusted Cloud Provider for Privacy-Preserving Deep Neural Networks

We propose a framework for transformation network training in coordination with a semi-trusted cloud provider for privacy-preserving DNNs. In the framework, a user trains a transformation network using a model that a cloud provider has for transforming plain images into visually protected ones. Conventional perceptual encryption methods have a weak visualprotection performance and some accuracy degradation in image classification. In contrast, the proposed framework overcomes the two issues. In an image classification experiment, the transformation network trained under the framework is demonstrated to strongly protect the visual information of plain images, without any performance degradation under the use of two typical classification networks: ResNet and VGG. In addition, it is shown that the visually protected images are robust against a DNN-based attack.
Images of the arXiv: Reconfiguring large scientific image datasets

In an ongoing research project on the ascendancy of statistical visual forms, we have been concerned with the transformations wrought by such images and their organisation as datasets in ‘re-drawing’ knowledge about empirical phenomena. Historians and science studies researchers have long established the generative rather than simply illustrative role of images and figures within scientific practice. More recently, the deployment and generation of images by scientific research and its communication via publication has been impacted by the tools, techniques, and practices of working with large (image) datasets. Against this background, we built a dataset of 10 million-plus images drawn from all preprint articles deposited in the open access repository arXiv from 1991 (its inception) until the end of 2018. In this article, we suggest ways – including algorithms drawn from machine learning that facilitate visually ’slicing’ through the image data and metadata – for exploring large datasets of statistical scientific images. By treating all forms of visual material found in scientific publications – whether diagrams, photographs, or instrument data – as bare images, we developed methods for tracking their movements across a range of scientific research. We suggest that such methods allow us different entry points into large scientific image datasets and that they initiate a new set of questions about how scientific representation might be operating at more-than-human scale.
Consistent image processing based on co‐saliency

In a group of images, the recurrent foreground objects are considered as the key objects in the group of images. In co-saliency detection, these are described as common saliency objects. The aim is to be able to naturally guide the user's gaze to these common salient objects. By guiding the user's gaze, users can easily find these common saliency objects without interference from other information. Therefore, a method is proposed for reducing user visual attention based on co-saliency detection. Through the co-saliency detection algorithm and matting algorithm for image preprocessing, the exact position of non-common saliency objects (called Region of Interest here, i.e. ROI) in the image group can be obtained. In the attention retargeting algorithm, the internal features of the image to adjust the saliency of the ROI areas are considered. In the HSI colour space, the three components H, S, and I are adjusted separately. First, the hue distribution is constructed by the Dirac kernel function, and then the most similar hue distribution to the surrounding environment is selected as the best hue distribution of ROI areas. The S and I components can be set as the contrast difference between ROI areas and surrounding background areas according to the user's demands. Experimental results show that this method effectively reduces the ROI areas' attraction to the user's visual attention. Moreover, comparing this method with other methods, the saliency adjustment effect achieved is much better, and the processed image is more natural.
LGPD: A Formal Concept Analysis and its Evaluation

Nowadays, personal data generated through the use of smart devices are collected and stored by many companies. So, concerns about privacy issues are raised by users. In this context, the Brazilian General Law for the Protection of Personal Data (LGPD) was proposed in 2018. It regulates how any company must store and process personal data. This paper details the results of applying formal concept analysis (FCA) to the LGPD. The goal of applying FCA was to elicit key insights to support software development or re-design in compliance with LGPD. Besides, we propose an automatic manner to apply FCA.
A Continuous Sampling Method for Batch Data Auditing in Cloud Storage

Cloud storage offers online storage services to back up data and enables easy access to real-time data at any time and on the fly. The primary issue of security arises as the data is outsourced to remote servers that cause data loss and modifications to occur. Data corruptions or misbehaviours in the cloud service providers (CSP) have to be detected at low cost and promptly. The data outsourced to CSP by the data owners might suffer from cloud service provider misbehaviour, in particular unauthorized deletion of data by CSP to save storage space towards attracting potential clients. Adapting continuous sampling of random blocks as batches reduce the computation workload on the servers, thus detecting the modifications or corruptions if any without time delays. The corrupted data blocks are located with continuous sampling methods and the probability of detection is high.
Dealing with Uncertainty in Architectural Confidentiality Analysis

In a connected world, confidentiality becomes increasingly critical. To cope with confidentiality on a higher abstraction level of software systems, architectural analyses have been proposed. By explicitly modeling data in the system design, the validity of access control policies can be ensured. However, the required information for such analyses is often too imprecise due to the high degree of uncertainty at design-time which results in incomplete and inaccurate policies. In this paper, we describe three key challenges while facing uncertainty and show how software architects could be supported in enhancing confidentiality throughout software design and evolution.
Formal Basis of a Language Universal

Abstract Steedman (2020) proposes as a formal universal of natural language grammar that grammatical permutations of the kind that have given rise to transformational rules are limited to a class known to mathematicians and computer scientists as the “separable” permutations. This class of permutations is exactly the class that can be expressed in combinatory categorial grammars (CCGs). The excluded non-separable permutations do in fact seem to be absent in a number of studies of crosslinguistic variation in word order in nominal and verbal constructions. The number of permutations that are separable grows in the number n of lexical elements in the construction as the Large Schröder Number Sn−1. Because that number grows much more slowly than the n! number of all permutations, this generalization is also of considerable practical interest for computational applications such as parsing and machine translation. The present article examines the mathematical and computational origins of this restriction, and the reason it is exactly captured in CCG without the imposition of any further constraints.