Reviewerly/exHarmony huggingface dataset:
Employing a robust data-driven model to assess the environmental damages caused by installing grouted columns

The jet grouting process involves injecting large quantities of highly pressurized fluids into the soil, which may result in a substantial ground displacement and adverse effects on the environment around the excavation. Consequently, the ground displacement must be estimated accurately in the design phase. In this study, two machine learning models namely, extreme learning machine (ELM) and modified K-nearest neighbor (KNN) are used to estimate the ground displacements. The comparison results show that the ELM is superior to the KNN model in terms of estimation accuracy (coefficient of determination is 0.940). Moreover, the ELM model shows an enhancement by 11.43% higher accuracy in terms of reducing the mean absolute error compared to the KNN model. Overall, the results indicate that ELM has the ability to accurately assess the harmful damages caused by installing grouted columns.
Traffic Sign Recognition Using Convolutional Neural Network and Skipped Layer Architecture

This paper proposes an efficient deep learning approach for the Traffic sign recognition system (TSR). TSR plays an essential role in the intelligent transportation system (ITS). TSR can be utilised for driver assistance and, eventually, driverless vehicles to reduce accidents. Drivers usually concentrate on the road when driving; however, most traffic signs are located on the side of the road, leading to an accident. TSR enables users to observe traffic sign information without having to divert their attention away from the road. For the past four decades, TSR has been a hot topic of research. Because of the broad background, clutter, varying levels of illumination, variable sizes of traffic signs, and fluctuating weather conditions, TSR is an important but challenging procedure in ITS. Many efforts have been made to find answers to the major issues that they face. The objective of this study addresses the road traffic sign recognition using skipped convolutional layer architecture and neural network. This study differs from earlier studies in that it uses skipped layer connections and extracts information in two ways. The features are then concatenated and fed into a fully connected neural network. Finally, SoftMax is used to predict the labels of the traffic sign. The German Traffic Sign Recognition Benchmark (GTSRB) dataset was used to train our proposed Convolutional Neural Network (CNN) model. The model was tested on 43 different traffic sign classes using 12630 images from the GTSRB dataset. The same dataset had a classification accuracy of 99.4%, whereas the existing lenet-based model had an accuracy of 97.1%. Rather than a more intricate model, the effort produced a basic yet effective model for recognising traffic signs.
Learning from Imbalanced Data in Classification

Imbalanced data learning is a research area and day by day development is going on. Due to these researchers are motivated to pay attention to find efficient and adaptive methods for real-world problems. Machine learning, as well as data mining, is a field where researchers are finding different methods to solve problems related to imbalanced datasets and also the challenges faced in day to day life. The uneven class distribution in the dataset is the reason behind the degradation of performance in approaches used by data mining as well as machine learning. Continuous advancements of machine learning as well as mining data combining it with big data, a deep insight is required to understand the nature of learning imbalanced data. New challenges are emerging due to this development. Among the two approaches algorithm level and data level, the most popular approach compared to this is the hybrid approach. It is found that there is a bias for the majority class which affects the decision making task and overall accuracy of classification. The ensemble method is an efficient technique to deal with the uneven distribution of data. The aim of the paper is to presents the overview of class imbalance problems, solutions to handle it, open issues and challenges in learning imbalanced datasets. Based on the experiment conducted on one dataset it is found that ensemble technique along with other data-level methods gives good results. This hybrid method can be applied in many real-life applications like software defect prediction, behavior analysis, intrusion detection, medical diagnosis, etc. The paper further provides research directions in learning from the imbalanced dataset.
Combining Wikipedia to Identify Prerequisite Relations of Concepts in MOOCs

Many applications like the personalization recommendation system of online learning are based on prerequisite relations of concepts, which prompted us to automatically infer the prerequisite relations between the concepts in Massive Open Online Courses (MOOCs). The previous methods mostly use artificial features to identify the prerequisite relations from learning materials and Wikipedia. However, artificial features are complicated to deeply mine prerequisite information in MOOC videos and the Wikipedia-directed graph, resulting in poor performance. We propose a new and more effective method to identify prerequisite relations from the above two data resources. We first use a graph embedding algorithm to learn the vector representations of concepts from the created Wikipedia-directed graph and use the cosine similarity between the vectors to represent the semantic and structural relevance between the concepts. Second, we pre-train a Siamese network whose inputs are representations of course concepts learned by a variation of the LDA model to find more practical information of prerequisite relations from MOOC subtitles. Then, the concept similarities related to topic distribution can be represented by the pre-trained Siamese network's outputs. Finally, we add some excellent artificial features to expand the information of the prerequisite relations and input them together into a binary classifier to identify the prerequisite relations of the concepts in MOOCs. Our experiments on two MOOC datasets indicate that the proposed method achieves significant improvements comparing with existing methods.
Design Smells in Deep Learning Programs: An Empirical Study

Nowadays, we are witnessing an increasing adoption of Deep Learning (DL) based software systems in many industries. Designing a DL program requires constructing a deep neural network (DNN) and then training it on a dataset. This process requires that developers make multiple architectural (e.g., type, size, number, and order of layers) and configuration (e.g., optimizer, regularization methods, and activation functions) choices that affect the quality of the DL models, and consequently software quality. An under-specified or poorly-designed DL model may train successfully but is likely to perform poorly when deployed in production. Design smells in DL programs are poor design and-or configuration decisions taken during the development of DL components, that are likely to have a negative impact on the performance (i.e., prediction accuracy) and then quality of DL based software systems. In this paper, we present a catalogue of 8 design smells for a popular DL architecture, namely deep Feedforward Neural Networks which is widely employed in industrial applications. The design smells were identified through a review of the existing literature on DL design and a manual inspection of 659 DL programs with performance issues and design inefficiencies. The smells are specified by describing their context, consequences, and recommended refactorings. To provide empirical evidence on the relevance and perceived impact of the proposed design smells, we conducted a survey with 81 DL developers. In general, the developers perceived the proposed design smells as reflective of design or implementation problems, with agreement levels varying between 47\% and 68\%.
Misperceptions of Bitcoin Volatility

Bitcoin market capitalization recently surpassed $1 trillion. While popular belief holds that a key characteristic of bitcoin is its excessive volatility, this article provides evidence that this is largely a misperception. We show that bitcoin return fluctuations are lower than those of roughly 900 stocks in the S&amp;P1500 and 190 stocks in the S&amp;P500. Moreover, we find that bitcoin is less volatile than commodities such as oil and silver, US Treasuries, AAA-rated corporate bonds, EU carbon credits, and some of the most popular technology and media stocks, including Apple, Twitter, and Netflix. Equally important, we find that during the March 2020 stock market crash triggered by COVID-19, the volatility of bitcoin was lower than that of most of these asset classes. The significant decline in bitcoin volatility over the past decade renders it more investable for conservative investors.
Digital Image Art Style Transfer Algorithm Based on CycleGAN

With the continuous development and popularization of artificial intelligence technology in recent years, the field of deep learning has also developed relatively rapidly. The application of deep learning technology has attracted attention in image detection, image recognition, image recoloring, and image artistic style transfer. Some image art style transfer techniques with deep learning as the core are also widely used. This article intends to create an image art style transfer algorithm to quickly realize the image art style transfer based on the generation of confrontation network. The principle of generating a confrontation network is mainly to change the traditional deconvolution operation, by adjusting the image size and then convolving, using the content encoder and style encoder to encode the content and style of the selected image, and by extracting the content and style features. In order to enhance the effect of image artistic style transfer, the image is recognized by using a multi-scale discriminator. The experimental results show that this algorithm is effective and has great application and promotion value.
A Dual Model for Restoring Images Corrupted by Mixture of Additive and Multiplicative Noise

A critical challenge in image restoration is the presence of various types of noise. Meanwhile, noise detection is a crucial step in mixed noise removal. This paper tackles the challenge of restoring images corrupted by a mixture of additive Gaussian and multiplicative Gamma noise. In the proposed method, we integrate the noise detection process into a variational model using a dual formulation of a maximum a posteriori (MAP) estimator. The variational model consists of a novel adaptive fidelity term and a plugin-and-play regularization term. The fidelity term contains an adaptive weight that can automatically detect the noise types, levels, and pollution ways for each pixel. There is flexibility in choosing a plugin-and-play regularization term. For example, we can use a model-based regularizer or a deep learning-based regularizer. In addition, we present a splitting algorithm to minimize the proposed cost functional. This splitting technique enables us to transfer a mixed noise removing problem to several subproblems, including noise removal and detection. The noise detection process can be iteratively estimated by the proposed algorithm itself. Therefore, in the numerical experiments, the proposed model outperforms the existing Rudin-Osher-Fatemi (ROF), Aubert-Aujol (AA), BM3D, and deep learning-based single type denoiser. Experimental results show that the proposed model can remove noise more efficiently and better preserve details in images. Compared to the existing best-performing single type denoiser, on average, the improvements of PSNR values range from 0.33 dB to 0.81 dB under noise mixture ratios α = 0.4, 0.6.
Multi-view Fractional Deep Canonical Correlation Analysis for Subspace Clustering

Canonical correlation analysis (CCA) is a classic unsupervised dimensionality reduction approach, but it has difficulty to analyze the nonlinear relationship and learn from more than two views. In addition, real-world data sets often contain much noise, which makes the performance of machine learning algorithms degraded. This paper presents a multi-view fractional deep CCA (MFDCCA) method for representation learning and clustering tasks. The proposed MFDCCA method utilizes fractional order embedding to reduce the adverse effect of noise in multi-view data and deep neural networks to explore the nonlinear relationship. Experimental results on three popular datasets have demonstrated the effectiveness of the proposed MFDCCA.
Feature Detection and Matching

AbstractFeature detection and matching are an essential component of many computer vision applications. Consider the two pairs of images shown in Figure 7.2. For the first pair, we may wish to align the two images so that they can be seamlessly stitched into a composite mosaic (Sect. 8.2).
Evaluating Federated Learning Scenarios in a Tumor Classification Application

Federated Learning is a new area of distributed Machine Learning (ML) that emerged to deal with data privacy concerns. In this approach, each client has access to a local and private dataset. They only exchange the model weights and updates. This paper presents a Federated Learning (FL) approach to a cloud Tumor-Infiltrating Lymphocytes (TIL) application. The results show that the FL approach outperformed the centralized one in all evaluated ML metrics. It also reduced the execution time although the financial cost has increased.
Measurement data intrusion detection in industrial control systems based on unsupervised learning

&lt;abstract&gt;&lt;p&gt;Anomaly detection strategies in industrial control systems mainly investigate the transmitting network traffic called network intrusion detection system. However, The measurement intrusion detection system inspects the sensors data integrated into the supervisory control and data acquisition center to find any abnormal behavior. An approach to detect anomalies in the measurement data is training supervised learning models that can learn to classify normal and abnormal data. But, a labeled dataset consisting of abnormal behavior, such as attacks, or malfunctions is extremely hard to achieve. Therefore, the unsupervised learning strategy that does not require labeled data for being trained can be helpful to tackle this problem. This study evaluates the performance of unsupervised learning strategies in anomaly detection using measurement data in control systems. The most accurate algorithms are selected to train unsupervised learning models, and the results show an accuracy of 98% in stealthy attack detection.&lt;/p&gt;&lt;/abstract&gt;
All-optical differentiator in frequency domain

Signal processing is a focused topic with communication technologies developing rapidly nowadays. However, optical signal processing methods are not so mature compared to the digital signal processors. Here, we propose an all-optical differentiator in frequency domain designed with fundamental optical elements, which is able to give the first order differentiation of the input signal. A fully theoretical description of the differentiator and its transfer function are demonstrated and discussed. According to experiments, the differentiator is proved reliable and the results match well with simulations. Moreover, an application of the frequency domain differentiator for measuring small time delay changes is also demonstrated and discussed. We believe that this work can give different perspectives of optical signal processing in frequency domain.
More Than One-Hot: Chinese Macro Discourse Relation Recognition on Joint Relation Embedding

Discourse relation recognition is to identify the logical relations between discourse units (DUs). Previous work on discourse relation recognition only considered the loss of prediction and ground truth before back-propagation in the form of a one-hot vector, which cannot reflect the relation coherence. To remedy this deficiency, we propose a macro discourse relation recognition model based on the Joint Relation Embedding (JRE). This model contains two different relation embedding mechanisms, Relation Name Embedding (RNE) and Relation Translation Embedding (RTE), which can obtain relation encoding associated with semantic coherence. These two mechanisms are integrated through relation matching and joint learning. The experimental results on the Macro Chinese Discourse Treebank dataset show that our JRE outperforms baselines and effectively improves the performance of discourse relation recognition, especially those minority relations.
RSA, DH and DSA in the Wild

In Chapter 6, RSA, DH and DSA in the Wild, Nadia Heninger outlines the various cryptographic pitfalls one can – but really should not – make in practice. Often it is possible to bypass the 'hard' mathematical problem a cryptosystem is based upon, and instead take advantage of implementation, deployment or protocol mistakes to extract the private key. Often, the techniques used are excellent examples of the interplay of mathematics and computer science, requiring a combination of ingenuity to find the core idea and perseverance to exploit the weakness in practice. Heninger gives a wide-ranging overview of the multitude of cryptographic implementation vulnerabilities that have been found in the past decades and their impact in practice, including a fair number where she was personally involved in identifying the vulnerability. In Chapter 6, she wonders whether, after several decades of implementation chaos and catastrophic vulnerabilities, we are doomed, but concludes that there is hope yet by bringing into practice the lessons learned.
Blockchain Technology and the Contemporary Business Models; Connecting Nano Energy Storage Devices with Trade for Investors

The rapid rate of growth in nanotechnologies and their application in almost every field of life is not only set to improve the living standards of the general public around the globe but also serves as a great opportunity for business as well. In this regard, a sound business model is required, as the product and services based upon this technology may not conform to traditional business models. In this research, we have reviewed the general business models for Nanotechnology-based products and services. In this respect one of the potential opportunities in the field of Nano energy storage devices and their practical application. We have conceptualized the potential use of “Blockchain” technology in the trading aspect of energy storage contracts and the potential opportunities this presents to the investors as well as the general public who form the wider sphere of stakeholders.
Modeling Authentication Mechanisms on Social Media Accounts

Fake pages are used to exploit important categories, by using personal photos or personal information, in order to spread lies and discredit. This is illegal and threatens privacy. So, dealing with social networking sites has become a dangerous and risky thing. Hence, each important category seeks to have its pages on social networks distinctive and authenticated in order to attract real followers, in addition to removing all fake pages that use the name or pictures in order to mislead the followers. The authentication mechanisms on social media accounts differ from one site to another, but they are similar in terms of the objective, which is authenticating personal pages, in addition to showing a logo that distinguishes these pages from other pages. This mechanism requires everyone to follow specific procedures to complete authentication. Hence, obtaining a trust logo is not easy, accompanied by complicated procedures that are difficult to follow by normal users, unlike the important personalities who usually have managers who maintain their daily work and accounts.
RGB-D Depth-sensor-based Hand Gesture Recognition Using Deep Learning of Depth Images with Shadow Effect Removal for Smart Gesture Communication

Recently, compound image sensor devices have been widely used to construct many nextgeneration human-machine interaction applications, including hand gesture action recognition.Such devices, generally known as RGB-D devices, contain an RGB color camera and a depth sensor set.The depth sensor in RGB-D devices is essentially a set of a specific type of sensor and comprises one IR projector and one IR camera.This structure of the depth sensor inevitably generates an undesired shadow effect, adversely affecting hand gesture recognition.To tackle this issue and alleviate the shadow effect on hand gesture recognition, we have developed a serial binary image extraction approach.The proposed approach is essentially composed of two consecutive computation phases, phase-1 and phase-2 binary image extraction.In this work, the Kinect compound sensor device is employed to capture hand gesture depth images.The deep learning model, a visual geometry group (VGG)-type convolutional neural network (CNN), i.e., the well-known VGG-CNN, is utilized to evaluate the recognition effectiveness of improved hand gesture depth images derived from serial binary image extraction.Ten hand gestures that are common in daily life are chosen to evaluate depth-sensor-based interactive action recognition.Experimental results show that the proposed serial binary image extraction can effectively eliminate the undesired shadow region in hand gesture depth images and significantly improve the recognition accuracy of VGG-type CNN hand gesture recognition.The proposed depth-sensor-based hand gesture recognition approach can benefit people requiring interaction action recognition and further promote smart gesture communication.
Installing R

You are here to learn statistics, and the first step is installing some program. It can almost feel like a magician’s trick. You need to know some applied statistics, and somehow you need to learn how to program instead. While this can look scary, never fear! This chapter will walk you through step by step what you need to do to have a pain-free installation. Regardless of whether you are learning on your personal computer (be that a PC or Mac) or on your company/institution’s networked machine, this guide will let you know in everyday language what to do and yet stay complete enough to give to your IT department if needed (for corporate/institutional learners).
Deep Active Learning via Open-Set Recognition

In many applications, data is easy to acquire but expensive and time-consuming to label, prominent examples include medical imaging and NLP. This disparity has only grown in recent years as our ability to collect data improves. Under these constraints, it makes sense to select only the most informative instances from the unlabeled pool and request an oracle (e.g., a human expert) to provide labels for those samples. The goal of active learning is to infer the informativeness of unlabeled samples so as to minimize the number of requests to the oracle. Here, we formulate active learning as an open-set recognition problem. In this paradigm, only some of the inputs belong to known classes; the classifier must identify the rest as unknown. More specifically, we leverage variational neural networks (VNNs), which produce high-confidence (i.e., low-entropy) predictions only for inputs that closely resemble the training data. We use the inverse of this confidence measure to select the samples that the oracle should label. Intuitively, unlabeled samples that the VNN is uncertain about contain features that the network has not been exposed to; thus they are more informative for future training. We carried out an extensive evaluation of our novel, probabilistic formulation of active learning, achieving state-of-the-art results on MNIST, CIFAR-10, CIFAR-100, and FashionMNIST. Additionally, unlike current active learning methods, our algorithm can learn even in the presence of out-of-distribution outliers. As our experiments show, when the unlabeled pool consists of a mixture of samples from multiple datasets, our approach can automatically distinguish between samples from seen vs. unseen datasets. Overall, our results show that high-quality uncertainty measures are key for pool-based active learning.