Reviewerly/exHarmony huggingface dataset:
A one-class Shapelet dictionary learning method for wind turbine bearing anomaly detection

• A time series data-driven approach to the detection of anomalies and early faults. • An end-to-end one-class classification algorithm with no need of prior knowledge. • A novel loss function to learn Shapelets and SVDD decision boundary jointly. • A multi-scale hyper-parameter setting strategy to avoid tuning. Detecting main shaft bearing anomaly is crucial to ensure the safe operation of wind turbines. However, existing anomaly detection methods have a limitation that anomaly samples are required for hyper-parameters tuning. Because of the scarcity of anomaly samples in the real-world scenarios, it is difficult to implement such existing methods in real-world applications. This paper proposes an end-to-end anomaly detection algorithm named one-class Shapelet dictionary learning. Firstly, the loss function of Shapelet dictionary learning is modified by integrating a soft-boundary term, so that the features and decision boundary can be learnt jointly. Then, a hyper-parameter setting strategy is introduced, so that anomaly samples are not needed in the training stage. The proposed method is validated through a case study collected from a real-world wind power farm. Results shown that the proposed method has a better F1 score than all baselines while anomaly samples are totally banded in the training stage.
The Language of Extremism on Social Media: An Examination of Posts, Comments, and Themes on Reddit

Digital media give the public a voice to discuss or share their thoughts about political and social events. However, these discussions can often include language that contributes to creating toxic or uncivil online environments. Using data from Reddit, we examine the language surrounding three major events in the United States that occurred in 2020 and early 2021 from the comments and posts of 65 communities identified for their focus on extreme content. Our results suggest that social and political events in the U.S. triggered increased hostility in discussions as well as the formation of a set of shared language for describing and articulating information about these major political/social moments. Findings further reveal shifts in language toward more negativity, incivility, and specific language surrounding non-White outgroups. Finally, these shifts in language online were found to be durable and last after the events occurred. Our project identifies that negative language is frequently present on social media and is not necessarily exclusive to one group, topic, or real-world event. We discuss the implications of language as a powerful tool to engage, recruit, and radicalize those within communities online.
End-to-end table structure recognition and extraction in heterogeneous documents

Automatically detecting and parsing tables into an indexable and searchable format is an important problem in document digitization. It relates to computer vision, machine learning, and optical character recognition. This paper presents a simple model based on a deep neural network architecture that combines recent advances in computer vision and machine learning, which can be used to detect and convert a table into a format that can be edited or searched. The motivation for this work is to develop a sound method to extract the vast data set of knowledge available in physical documents such that it can be used to develop data-driven tools that can be used to support decisions in fields such as healthcare and finance. The model uses a Yolo-based object detector trained to maximize the Intersection over Union of the detected table regions within the document image and a novel OCR-based algorithm to parse the table from each table detected in the document.Past works have all focused on documents and images containing a level and even tables. This paper aims to present our findings after the model is run on a set of skewed image datasets. Experiments on the Marmot and Publaynet datasets show that the proposed method is entirely accurate and can generalize different tables formats. At an Intersection over the Union threshold of 50%, we achieve a mean Average Precision (mAP) of 98% and an average IoU of 88.81% on the PubLayNet dataset. With the same IoU threshold, we achieve an mAP of 95.07% and an average IoU of 75.57% on the Marmot dataset.
Augmented Reality Based Human-Machine Interfaces in Healthcare Environment: Benefits, Challenges, and Future Trends

Augmented reality and virtual reality are becoming increasingly prominent in academia and industry. A technology that superimposes additional information on top of the real world is known as augmented reality. Since augmented reality is inextricably linked to the natural world, it is regarded as a partially immersive component of reality. However, unlike virtual reality, augmented reality does not provide a completely immersive experience. Virtual reality has long been depicted as a medium defined by a bevy of technological devices, such as laptops, head-mounted displays, microphones, and motion-sensing gloves. The goal of this research is to conduct a comprehensive assessment of the literature on augmented reality and virtual reality-based Human Machine Interfaces in healthcare. The concept of a smart healthcare environment is gaining traction in industry and product development, resulting in the development of new and more intelligent solutions, technologies, and architectures. Cloud computing, the Internet of Things (IoT), data analytics, artificial intelligence, machine learning, augmented reality, and virtual reality are all being used by manufacturers and developers in their manufacturing and overall operations. The research will also examine the advantages of augmented reality in the medical field, as well as the problems these technologies face and where they are heading in the future.
Local patchwise minimal and maximal values prior for single optical remote sensing image dehazing

Poor observation conditions, such as haze, fog, offgas, and dust, which result in contrast degradation and colour distortion issues, negatively affect remote sensing images (RSIs). In this study, the local patchwise minimal values prior (MinVP) and local patchwise maximal values prior (MaxVP) are proposed for single RSI dehazing. As alternatives to the classical dark channel prior (DCP) and bright channel prior (BCP), the feasibility and reliability of MinVP and MaxVP are investigated. The transmission maps from MinVP and MaxVP are approximately equivalent to those from DCP and BCP but more efficient. To address the common problems of halo, oversaturation, and overexposure phenomena in dehazing processes, a dehazing method based on MinVP (named MinVPM) is proposed. The refined atmospheric light map and compensated transmission map are derived and formulated according to the atmospheric scattering model (ASM). Moreover, to improve the contrast in the dehazed images by MinVPM, a simple enhancement method based on MaxVP (named MaxVPM) is proposed, the formulae of which are analogously derived to those of MinVPM. The upgraded dehazing version of MinVPM, named EVPM, is the combination of MinVPM and MaxVPM. Extensive experiments and corresponding evaluations demonstrate that the proposed MinVPM and EVPM can achieve satisfactory results and high computational efficiency and outperform state-of-the-art dehazing methods for remote sensing image dehazing.
Learning Facial Representations from the Cycle-consistency of Face

Faces manifest large variations in many aspects, such as identity, expression, pose, and face styling. Therefore, it is a great challenge to disentangle and extract these characteristics from facial images, especially in an unsupervised manner. In this work, we introduce cycle-consistency in facial characteristics as free supervisory signal to learn facial representations from unlabeled facial images. The learning is realized by superimposing the facial motion cycle-consistency and identity cycle-consistency constraints. The main idea of the facial motion cycle-consistency is that, given a face with expression, we can perform de-expression to a neutral face via the removal of facial motion and further perform re-expression to reconstruct back to the original face. The main idea of the identity cycle-consistency is to exploit both de-identity into mean face by depriving the given neutral face of its identity via feature re-normalization and re-identity into neutral face by adding the personal attributes to the mean face. At training time, our model learns to disentangle two distinct facial representations to be useful for performing cycle-consistent face reconstruction. At test time, we use the linear protocol scheme for evaluating facial representations on various tasks, including facial expression recognition and head pose regression. We also can directly apply the learnt facial representations to person recognition, frontalization and image-to-image translation. Our experiments show that the results of our approach is competitive with those of existing methods, demonstrating the rich and unique information embedded in the dis-entangled representations. Code is available at https://github.com/JiaRenChang/FaceCycle.
Dual Projection Generative Adversarial Networks for Conditional Image Generation

Conditional Generative Adversarial Networks (cGANs) extend the standard unconditional GAN framework to learning joint data-label distributions from samples, and have been established as powerful generative models capable of generating high-fidelity imagery. A challenge of training such a model lies in properly infusing class information into its generator and discriminator. For the discriminator, class conditioning can be achieved by either (1) directly incorporating labels as input or (2) involving labels in an auxiliary classification loss. In this paper, we show that the former directly aligns the class-conditioned fake-and-real data distributions P (image|class) (data matching), while the latter aligns data-conditioned class distributions P (class|image) (label matching). Although class separability does not directly translate to sample quality and becomes a burden if classification itself is intrinsically difficult, the discriminator cannot provide useful guidance for the generator if features of distinct classes are mapped to the same point and thus become inseparable. Motivated by this intuition, we propose a Dual Projection GAN (P2GAN) model that learns to balance between data matching and label matching. We then propose an improved cGAN model with Auxiliary Classification that directly aligns the fake and real conditionals P (class|image) by minimizing their f-divergence. Experiments on a synthetic Mixture of Gaussian (MoG) dataset and a variety of real-world datasets including CIFAR100, ImageNet, and VGGFace2 demonstrate the efficacy of our proposed models.
Unsupervised machine learning: clustering algorithms

In this chapter, various unsupervised machine learning algorithms such as k-means clustering, hierarchical clustering, density-based spatial clustering of applications with noise (DBSCAN), isolation forest, and local outlier factor (LOF) are discussed. This chapter starts with a step-by-step explanation of how k-means clustering functions, the math behind the clustering technique, and the step-by-step codes to apply k-means clustering to a geologic data set using scikit-learn in Python. Determining the number of clusters when using k-means clustering using the elbow point is also discussed and illustrated in Python. Another method for determining the effectiveness of the clusters called "silhouette coefficient" is also discussed and applied in Python. Next, the chapter describes the step-by-step process of creating a dendrogram and applying hierarchical clustering to an oil and gas–related data set. Thereafter, an application of DBSCAN for a geomechanical well log is illustrated using the scikit-learn package. Finally, various outlier detection techniques such as isolation forest and local outlier factor along with their step-by-step codes using the scikit-learn library are discussed with an oil and gas–related example problem.
Partial policy hiding attribute-based encryption in vehicular fog computing

Vehicular fog computing (VFC), combining the vehicular ad hoc network with fog computing, is an efficient vehicle communication architecture. However, the user data is often threatened since VFC is an open environment. Attribute-based encryption (ABE) is suitable for open scenarios, such as cloud and Internet of Things, because of its confidentiality and access control characteristics. However, the traditional ABE has disadvantages, such as the inability to hide the attributes in the access policy and the use of computationally inefficient composite order bilinear pairing groups to prove adaptive security. Traditional ABE is not practical in VFC. We summarized the existing schemes of full policy hiding ABE and partial policy hiding ABE and then concluded that partial policy hiding ABE is more suitable for VFC. We combine policy hiding technology and the technology of converting bilinear pairing cryptography schemes into prime-order bilinear pairing cryptography schemes and propose an efficient and partial policy hiding ciphertext-policy ABE scheme suitable for VFC. Experiments have proved that our scheme is computationally more efficient than previous policy hiding ABE schemes.
Automatic Equivalence Checking for Assembly Implementations of Cryptography Libraries

This paper presents an approach and a tool, CASM-VERIFY, to automatically check the equivalence of highly optimized assembly implementations of cryptographic algorithms. The key idea of this paper is to decompose the equivalence checking problem into several small sub-problems using a combination of concrete and symbolic evaluation. Given a reference and an optimized implementation, CASM-VERIFY concretely executes the two implementations on randomly generated inputs and identifies likely equivalent variables. Subsequently, it uses symbolic verification using an SMT solver to determine whether the identified variables are indeed equivalent. Further, it decomposes the original query into small sub-queries using a collection of optimizations for memory accesses. These techniques enable CASM-VERIFY to verify the equivalence of assembly implementations (e.g., x86 and SSE) of various algorithms such as SHA-256, ChaCha20, and AES-128 for a message block.
Classification and Regression Trees (CART)

In this chapter, we turn to recursive partitioning, which comes in various forms including decision trees and classification and regression trees (CART). We will see that the algorithmic machinery successively subsets the data. Trees are just a visualization of the data subsetting processes. We will also see that although recursive partitioning has too many problems to be an effective, stand-alone data analysis procedure, it is a crucial component of more powerful algorithms discussed in later chapters. It is important, therefore, to get into the details.
Sentiment analysis of pets using deep learning technologies in artificial intelligence of things system

This research paper proposes sentiment analysis of pets using deep learning technologies in the artificial intelligence of things system. Mask R-CNN is used to detect image objects and generate contour mask maps, pose analysis algorithms are used to obtain object posture information, and at the same time object sound signals are converted into spectrograms as features, using deep learning image recognition technology to obtain object emotion information. By using the fusion of object posture and emotional characteristics as the basis for pet emotion identification and analysis, the detected specific pet behaviour states will be actively notified to the owner for processing. Compared with traditional speech recognition, which uses mel-frequency cepstral coefficients for feature extraction, coupled with a Gaussian mixture model-hidden Markov model for voice recognition, the experimental method of this research paper effectively improves the accuracy by 70%. Prior work on the implementation of smart pet surveillance systems has used the pet's tail and mouth as features, and has combined these with sound features to analyse the pet's emotions. This research paper proposes a new method of sentiment analysis in pets, and our method is compared with previous related work. Experimental results show that our approach increases the accuracy rate by 70%.
Towards understanding and fixing upstream merge induced conflicts in divergent forks

Divergent forks are a common practice in open-source software development to perform long-term, independent and diverging development on top of a popular source repository. However, keeping such divergent downstream forks in sync with the upstream source evolution poses engineering challenges in terms of frequent merge conflicts. In this paper, we conduct the first industrial case study of the implications of frequent merges from upstream and the resulting merge conflicts, in the context of Microsoft Edge development. The study consists of two parts. First, we describe the nature of merge conflicts that arise due to merges from upstream and classify them into textual conflicts, build breaks, and test failures. Second, we investigate the feasibility of automatically fixing a class of merge conflicts related to build breaks that consume a significant amount of developer time to root-cause and fix. Towards this end, we have implemented a tool MrgBldBrkFixer and evaluate it on three months of real Microsoft Edge Beta development data, and report encouraging results.
RealitySketch: Embedding Responsive Graphics and Visualizations in AR with Dynamic Sketching

We present RealitySketch, an augmented reality interface for sketching interactive graphics and visualizations. In recent years, an increasing number of AR sketching tools enable users to draw and embed sketches in the real world. However, with the current tools, sketched contents are inherently static, floating in mid air without responding to the real world. This paper introduces a new way to embed dynamic and responsive graphics in the real world. In RealitySketch, the user draws graphical elements on a mobile AR screen and binds them with physical objects in real-time and improvisational ways, so that the sketched elements dynamically move with the corresponding physical motion. The user can also quickly visualize and analyze real-world phenomena through responsive graph plots or interactive visualizations. This paper contributes to a set of interaction techniques that enable capturing, parameterizing, and visualizing real-world motion without pre-defined programs and configurations. Finally, we demonstrate our tool with several application scenarios, including physics education, sports training, and in-situ tangible interfaces.
Data preprocessing

This chapter proposed a general framework for data curation. It covers the different phases of data preprocessing and preparation. The presented general framework fits a broad variety of datasets. Raw data prior to cleansing and curation is usually not ready for distilling correct inferences. This chapter discusses and provides a detailed overview for the most popular algorithms and techniques, which are used in the field of data curation and preparation. This chapter's framework describes techniques for data curation, imputation, feature extraction, correlation analysis, and practical application of these algorithms. We also provided techniques that have been developed from our experience in data processing. Finally, we presented a practical example showing the effect of using different imputation methods on the performance and efficiency of SVM. The chapter describes a methodology for converting raw and messy data to a well-organized data that is ready for applying high level machine learning algorithms or any advanced methods of data analysis.
CLASSICAL COUNTERPOSSIBLES

Abstract We present four classical theories of counterpossibles that combine modalities and counterfactuals. Two theories are anti-vacuist and forbid vacuously true counterfactuals, two are quasi-vacuist and allow counterfactuals to be vacuously true when their antecedent is not only impossible, but also inconceivable . The theories vary on how they restrict the interaction of modalities and counterfactuals. We provide a logical cartography with precise acceptable boundaries, illustrating to what extent nonvacuism about counterpossibles can be reconciled with classical logic.
VR-Based Gamification of Communication Training and Oral Examination in a Second Language

The authors present a novel way of oral language training by embedding the English as a foreign language (EFL) learning process into a generic 3D Cooperative Virtual Reality (VR) Game. Due to lack of time, resources and innovation, the language classroom is limited in its possibilities of promoting authentic communication. Therefore, the researchers investigated how to induce a VR setting with information gaps, for which they designed a template which intrinsically promotes communication and the students' confidence in using EFL. Thereby, VR enables the simulation of real life situations, creating both comfortable and authentic training environments. The game content is based on the internationally approved Graded Examination in Spoken English (GESE) Trinity Exam and can be adapted to the needs of the learners or the given curricula. The empirical analysis shows that the designed game trains the students' communication skills, evoking a high amount of speech and a qualitative linguistic output.
Architecture for Big Data Storage in Different Cloud Deployment Models

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.