Reviewerly/exHarmony huggingface dataset:
Three-dimensional modelling of alteration zones based on geochemical exploration data: An interpretable machine-learning approach via generalized additive models

The inference of deep alterations plays an important role in the analysis of deep geological processes and the targeting of buried mineral resources. However, the construction of three-dimensional (3D) models of alteration zones based on geochemical data is challenging due to the diverse geological processes and scarcity of information related to deep alterations. In this paper, we propose an interpretable machine-learning method to infer 3D geological models of alteration zones based on geochemical data from the Dayingezhuang area, China. To expose the information hidden in geochemical data that is indirectly and weakly related to alteration zones, a generalized additive model (GAM) is used. It learns a nonlinear, intelligible model associating geochemical anomalies (extracted by PCA) with alteration features. The model learned via GAM and is then combined with a Bayesian framework to infer the thickness of alterations in a no-drilling area, thus enabling 3D modelling of deep alteration zones. With such an intelligible model, geologists are capable of inspecting the reliability of the model and better understanding the association between the distribution of geochemical elements and deep alterations. The inference results show that the relationship between geochemical anomalies and alteration depth is significant, while the relationship with alteration thickness is complex. The results of this study provide the underlying insights needed to guide the analysis of deep geological mineralization processes.
Blockchain-based Distributed Banking for Permissioned and Accountable Financial Transaction Processing

Distributed banking platforms and services forgo centralized banks to process financial transactions. For example, M-Pesa provides distributed banking service in the developing regions so that the people without a bank account can deposit, withdraw, or transfer money. The current distributed banking systems lack the transparency in monitoring and tracking of distributed banking transactions and thus do not support auditing of distributed banking transactions for accountability. To address this issue, this paper proposes a blockchain-based distributed banking (BDB) scheme, which uses blockchain technology to leverage its built-in properties to record and track immutable transactions. BDB supports distributed financial transaction processing but is significantly different from cryptocurrencies in its design properties, simplicity, and computational efficiency. We implement a prototype of BDB using smart contract and conduct experiments to show BDB’s effectiveness and performance. We further compare our prototype with the Ethereum cryptocurrency to highlight the fundamental differences and demonstrate the BDB’s superior computational efficiency.
Deep Autoencoder Ensembles for Anomaly Detection on Blockchain

Distributed Ledger technologies are becoming a standard for the management of online transactions, mainly due to their capability to ensure data privacy, trustworthiness and security. Still, they are not immune to security issues, as witnessed by recent successful cyber-attacks. Under a statistical perspective, attacks can be characterized as anomalous observations concerning the underlying activity. In this work, we propose an Ensemble Deep Learning approach to detect deviant behaviors on Blockchain where the base learner, an encoder-decoder model, is strengthened by iteratively learning and aggregating multiple instances, to compute an outlier score for each observation. Our experiments on historical logs of the Ethereum Classic network and synthetic data prove the capability of our model to effectively detect cyber-attacks.
A robust Kalman–Bucy filtering problem

A generalized Kalman–Bucy model under model uncertainty and a corresponding robust problem are studied in this paper. We find that this robust problem is equivalent to an estimated problem under a sublinear operator. By Girsanov transformation and the minimax theorem, we prove that this problem can be reformulated as a classical Kalman–Bucy filtering problem under a new probability measure. The equation which governs the optimal estimator is obtained. Moreover, the optimal estimator can be decomposed into the classical optimal estimator and a term related to the model uncertainty parameter under some condition.
Visualization of topographical internal representation of learning robots

The objective of this study is to understand the learned-strategy of neural network-controlled robots in relation to their physical learning environments by visualizing the internal layer of the neural network. During the past few years, neural network-controlled robots that are able to learn in physical environments are becoming more common. While they can autonomously acquire strategy without human supervisions, it is becoming difficult to understand their strategy, especially when the robots, their environments and their tasks are complicated. In the critical fields that involve human safety, as in self-driving vehicles or medical robots, it is important for human to understand the strategies of the robots. In this preliminary study, we propose a hierarchical neural network with a two-dimensional topographical internal representation for training robots in physical environments. The 2D representation can then be visualized and analyzed to allow us to intuitively understand the input-output strategy of the robots in the context of their learning environments. In this paper, we explain about the learning dynamics of the neural network and the visual analysis of some physical experiments.
Cookies in a Cross-site scripting: Type, Utilization, Detection, Protection and Remediation

In accordance to the annual report by the Cisco 2018, web applications are exposed to several security vulnerabilities that are exploited by hackers in various ways. It is becoming more and more frequent, specific and sophisticated. Of all the vulnerabilities, more than 40% of attempts are performed via cross-site scripting (XSS). A number of methods have been postulated to examine such vulnerabilities. Therefore, this paper attempted to address an overview of one such vulnerability: the cookies in the XSS. The objective is to present an overview of the cookies, it's type, vulnerability, policies, discovering, protecting and their mitigation via different tools/methods and via cryptography, artificial intelligence techniques etc. While some future issues, directions, challenges and future research challenges were also being discussed.
Motion Representation Using Residual Frames with 3D CNN

Recently, 3D convolutional networks (3D ConvNets) yield good performance in action recognition. However, optical flow stream is still needed to ensure better performance, the cost of which is very high. In this paper, we propose a fast but effective way to extract motion features from videos utilizing residual frames as the input data in 3D ConvNets. By replacing traditional stacked RGB frames with residual ones, 35.6% and 26.6% points improvements over top-l accuracy can be obtained on the UCF101 and HMDB51 datasets when ResNet-18 models are trained from scratch. And we achieved the state-of-the-art results in this training mode. Analysis shows that better motion features can be extracted using residual frames compared to RGB counterpart. By combining with a simple appearance path, our proposal can be even better than some methods using optical flow streams.
Distance Accuracy of Real Environments in Virtual Reality Head-Mounted Displays

Distances are consistently underestimated in virtual environments (VEs) compared to the real world. The reason behind this underestimation is still not understood. This research investigates another version of this kind of system, that to the knowledge of authors has not been explored by any previous research. Previous research used a computer-generated scene. This work uses a dual-camera video feed system through a Virtual Reality (VR) Head Mounted Display (HMD). This paper is examining distance estimation in real environments rendered to Head-Mounted Displays. Two models are evaluated; a video-based and a photo-based. In this study, we are using protocols already proven to accurately measure real-world distance estimations to compare distance judgments performance in the real world, and these two evaluated VE models. The purpose is to explore whether the misjudgment of distances in HMDs could be due to a lack of realism, or not, with the use of a realworld scene rendering system. We found that when the model was rendered as static photo-based, the underestimation was less as compared with the live video feed rendering. The real HMD model averaged 80.2% of the actual distance, the real photo-based model averaged 81.4% and the real-world estimations averaged 92.4%.
Robust Anti Windup Controller Synthesis of Multivariable Discrete Systems with Actuator Saturation

This paper aims at simultaneously synthesizing a windup compensator and controller using linear matrix inequalities (LMIs). The proposed method can be applied to any multivariable (MIMO) uncertain discrete linear system with actuator saturation and any linear control structure. The controller and windup compensator design is done iteratively. Depending on the application, one of two possible optimization problems is solved to maximize either the decay rate or the domain of attraction. The stability of the closed loop is always guaranteed by using a quadratic Lyapunov function. The approach is applied to an unstable model of the F/A 18 HARV aircraft. Different types of controllers (PID, PID with windup compensator, full state feedback) are shown, as well as the effect of system uncertainties. A second example demonstrates the effectiveness and diminution of conservativeness of the proposed method, compared to another approach using parameter dependent Lyapunov functions.
A Comparison of GANs-Based Approaches for Combustor System Fault Detection

In manufacturing industry, anomaly detection (AD) has been widely applied to monitor asset operation status and provide decision support for proactive maintenance. However, the extreme complexities of many industrial assets have raised many challenges to the classical anomaly detection approaches. For the past several years, generative adversarial networks (GANs) have achieved breakthrough in a variety of applications, such as image generation and video prediction. Some initial applications of GANs in image-related AD problems also show promising results. In this paper, we investigated the performance of three GANs-based anomaly detection approaches for a specific industrial use case – fault detection of the combustion system of gas turbines in power plants. The results show that under the framework of semi-supervised learning, the three approaches do not perform well on the one-year field data collected from a gas turbine. However, if a small portion of fault data is provided for training, we observe that the performance of GANs is significantly improved.
Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling

Data compression forms a central role in handling the bottleneck of data storage, transmission and processing. Lossless compression requires reducing the file size whilst maintaining bit-perfect decompression, which is the main target in medical applications. This paper presents a novel lossless compression method for 16-bit medical imaging volumes. The aim is to train a neural network (NN) as a 3D data predictor, which minimizes the differences with the original data values and to compress those residuals using arithmetic coding. We evaluate the compression performance of our proposed models to state-of-the-art lossless compression methods, which shows that our approach accomplishes a higher compression ratio in comparison to JPEG-LS, JPEG2000, JP3D, and HEVC and generalizes well.
Discovering underlying sensations of human emotions based on social media

Abstract Analyzing social media has become a common way for capturing and understanding people's opinions, sentiments, interests, and reactions to ongoing events. Social media has thus become a rich and real‐time source for various kinds of public opinion and sentiment studies. According to psychology and neuroscience, human emotions are known to be strongly dependent on sensory perceptions. Although sensation is the most fundamental antecedent of human emotions, prior works have not looked into their relation to emotions based on social media texts. In this paper, we report the results of our study on sensation effects that underlie human emotions as revealed in social media. We focus on the key five types of sensations: sight, hearing, touch, smell, and taste. We first establish a correlation between emotion and sensation in terms of linguistic expressions. Then, in the second part of the paper, we define novel features useful for extracting sensation information from social media. Finally, we design a method to classify texts into ones associated with different types of sensations. The sensation dataset resulting from this research is opened to the public to foster further studies.
Improved Policy Extraction via Online Q-Value Distillation

Deep neural networks are capable of solving complex control tasks in challenging environments, but their learned policies are hard to interpret. Not being able to explain or verify them limits their practical applicability. By contrast, decision trees lend themselves well to explanation and verification, but are not easy to train, especially in an online fashion. In this work we introduce Q-BSP trees and propose an Ordered Sequential Monte Carlo training algorithm that efficiently distills the Q-function from fully trained deep Q-networks into a tree structure. Q-BSP forests are used to generate the partitioning rules that transparently reconstruct an accurate value function. We explain our approach and provide results that convincingly beat earlier online policy distillation methods with respect to their own performance benchmarks.
Detection of Toxic Language in Short Text Messages

The ever-increasing online communication landscape provides circumstances for people with significant differences in their views to cross paths unlike it was ever possible before. This leads to the raise of toxicity in online comments and discussions and makes the development of means to detect instances of such phenomenon critically important. The toxic language detection problem is fairly researched and some solutions produce highly accurate predictions when significantly large datasets are available for training. However, such datasets are not always available for various languages. In this paper, we review different ways to approach the problem targeting transferring knowledge from one language to another: machine translation, multi-lingual models, and domain adaptation. We also focus on the analysis of methods for word embedding such as Word2Vec, FastText, GloVe, BERT, and methods for classification of toxic comment: Naïve Bayes, Random Forest, Logistic regression, Support Vector Machine, Majority vote, and Recurrent Neural Networks. We demonstrate that for small datasets in the Russian language, traditional machine-learning techniques produce highly competitive results on par with deep learning methods, and also that machine translation of the dataset to the English language produces more accurate results than multi-lingual models.
A Comparative Study of U-Net Topologies for Background Removal in Histopathology Images

During the last decade, the digitization of pathology has gained considerable momentum. Digital pathology offers many advantages including more efficient workflows, easier collaboration as well as a powerful venue for telepathology. At the same time, applying Computer-Aided Diagnosis (CAD) on Whole Slide Images (WSIs) has received substantial attention as a direct result of the digitization. The first step in any image analysis is to extract the tissue. Hence, background removal is an essential prerequisite for efficient and accurate results for many algorithms. In spite of the obvious discrimination for human operator, the identification of tissue regions in WSIs could be challenging for computers mainly due to the existence of color variations and artifacts. Moreover, some cases such as alveolar tissue types, fatty tissues, and tissues with poor staining are difficult to detect. In this paper, we perform experiments on U-Net architecture with different network backbones (different topologies) to remove the background as well as artifacts from WSIs in order to extract the tissue regions. We compare a wide range of backbone networks including MobileNet, VGG16, EfficientNet-B3, ResNet50, ResNext101 and DenseNet121. We trained and evaluated the network on a manually labeled subset of The Cancer Genome Atlas (TCGA) Dataset. EfficientNet-B3 and MobileNet by almost 99% sensitivity and specificity reached the best results.
Author and register as sources of variation

Abstract This paper investigates the contribution of author/idiolect vs. register/type-of-text – as the most salient factors influencing the final shape of a text – towards explaining the variation observed in Czech texts. Since it is almost impossible to explore the effect of these factors on authentic data, we used elicited letters collected in a fully crossed experimental design (representative sample of 200 authors × four elicitation scenarios serving as a proxy to register variation). The variation encompassed by the elicited texts is analyzed through the lens of a general-purpose multi-dimensional model of Czech. Using triangulation via three established statistical methods and one devised for the purpose of this study, we find that register matters a great deal, explaining 1.5 times as much variation overall as idiolect. This should be taken into account when designing research in sociolinguistics or variation studies in general.
Higher Order Function Networks for View Planning and Multi-View Reconstruction

We consider the problem of planning views for a robot to acquire images of an object for visual inspection and reconstruction. In contrast to offline methods which require a 3D model of the object as input or online methods which rely on only local measurements, our method uses a neural network which encodes shape information for a large number of objects. We build on recent deep learning methods capable of generating a complete 3D reconstruction of an object from a single image. Specifically, in this work, we extend a recent method which uses Higher Order Functions (HOF) to represent the shape of the object. We present a new generalization of this method to incorporate multiple images as input and establish a connection between visibility and reconstruction quality. This relationship forms the foundation of our view planning method where we compute viewpoints to visually cover the output of the multiview HOF network with as few images as possible. Experiments indicate that our method provides a good compromise between online and offline methods: Similar to online methods, our method does not require the true object model as input. In terms of number of views, it is much more efficient. In most cases, its performance is comparable to the optimal offline case even on object classes the network has not been trained on.
GVLD: A Fast and Accurate GPU-Based Variational Light-Field Disparity Estimation Approach

Disparity estimation is an essential task taking part in many light-field applications. Due to the complexity of algorithms and high dimensional property of light-field data, performing this task involves a significant computational effort and results in very long processing time on CPU. Graphics processing units (GPUs), which is capable of massively parallel processing, is a promising solution to cover the computation requirement and speed up the task. In this paper, we develop a GPU-accelerated approach for light-field disparity estimation using a variational computation framework (GVLD). Our algorithm combines the intrinsic sub-pixel precision of variational formulation and the effectiveness of weighted median filtering to produce a highly accurate solution. The proposed algorithm is fully parallelized and optimized for the implementation using the OpenCL framework. An intensive evaluation including a quantitative comparison to related works and a detailed analysis of the proposed approach’s performance is presented. Experimental results demonstrate our superior performance compared to state-of-the-art approaches. The proposed approach is 10+ times faster than other approaches running on a similar GPU platform and provides the most accurate solution among optimization-based approaches. Compared to the implementation running on CPU, our GPU-accelerated method achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$365\times $ </tex-math></inline-formula> speed up.
Ontology-based Interpretable Machine Learning for Textual Data

In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. Different from existing approaches, our algorithm considers contextual correlation among words, described in domain knowledge ontologies, to generate semantic explanations. To narrow down the search space for explanations, which is a major problem of long and complicated text data, we design a learnable anchor algorithm, to better extract explanations locally. A set of regulations is further introduced, regarding combining learned interpretable representations with anchors to generate comprehensible semantic explanations. An extensive experiment conducted on two real-world datasets shows that our approach generates more precise and insightful explanations compared with baseline approaches.
Deformable Quaternion Gabor Convolutional Neural Network For Color Facial Expression Recognition

In facial expression recognition (FER), convolutional neural networks (CNNs) have been shown great capability of learning features. In this paper, we propose a new CNN framework for FER in color images, which incorporates deformable Gabor filters into a quaternion CNN. Deformable Gabor filters reinforce the network's ability of extracting different orientations of facial wrinkles information. Quaternion CNNs have greater advantages over the regular CNNs in handling the coupling between color channels. The proposed deformable quaternion Gabor convolutional neural network (DQG-CNN) not only learns FER feature representation excellently, but also processes spectral correlation between color channels naturally. Moreover, it can also effectively reduce training complexity compared to other reference models. Experimental results on three benchmark color datasets Oulu-CASIA, MMI, and SFEW, demonstrate that the proposed DGQ-CNN outperforms other state-of-the-art methods clearly.