Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

In this article, an algorithm is proposed for creating an ensemble classifier. The name of the algorithm is the F-score subspace method (FsBoost). According to this method, the features are selected with the F-score and classified with different or the same classifiers. In the next step, the ensemble classifier is created. Two versions that are named FsBoost.V1 and FsBoost.V2 have been developed based on classification by the same or different classifiers. According to the results obtained, the results are consistent with the literature. Besides, a higher accuracy rate is obtained compared with many algorithms in the literature. The algorithm is fast because it has a few steps. It is thought that the algorithm will be successful due to these advantages.
Building User Trust in Recommendations via Fairness and Explanations

Modern Artificial Intelligence (AI) techniques, based on the statistical analysis of big volumes of data, are quickly gaining traction across various domains. Recommender Systems are a class of AI techniques that extract preference patterns from large traces of human behavior. Recommenders assist people in taking decisions that range from harmless everyday life dilemmas, e.g., what shoes to buy, to seemingly innocuous choices but with long-term, hidden consequences, e.g., what news article to read, up to more critical decisions, e.g., which person to hire.
Feature Driven Combination of Animated Vector Field Visualizations

Abstract Animated visualizations are one of the methods for finding and understanding complex structures of time‐dependent vector fields. Many visualization designs can be used to this end, such as streamlines, vector glyphs, and image‐based techniques. While all such designs can depict any vector field, their effectiveness in highlighting particular field aspects has not been fully explored. To fill this gap, we compare three animated vector field visualization techniques, OLIC, IBFV, and particles, for a critical point detection‐and‐classification task through a user study. Our results show that the effectiveness of the studied techniques depends on the nature of the critical points. We use these results to design a new flow visualization technique that combines all studied techniques in a single view by locally using the most effective technique for the patterns present in the flow data at that location. A second user study shows that our technique is more efficient and less error prone than the three other techniques used individually for the critical point detection task.
RETRACTED: Application of virtual reality technology in Wushu education

With the theoretical and technical progress of Wushu education, the traditional teaching method of oral instruction and demonstration can no longer satisfy the needs of current Wushu education. To address this issue, the concept and basic characteristics of virtual reality technology were interpreted, and the effective path and challenges of applying virtual reality technology in Wushu education. The results of this study show that virtual reality technology contributes to the concrete presentation of implicit knowledge in Wushu, the construction of virtual scenes in Wushu education and the realization of a simulated self-learning system in Wushu. In the meanwhile, the application of virtual reality technology in Wushu education faces the challenges of high virtual reality equipment investment, three-dimensional modeling difficulties, huge data volume of Wushu movements and side effects after the experience of Wushu, etc.
Sharing a Virtual World with People Living with Dementia

Designing Virtual Reality (VR) experiences have become a growing interest in Human–Computer Interaction (HCI) with recreational VR applications coming to the market. However, consideration for how these experiences may be designed for people living with dementia to provide both comfort and enrichment has rarely been explored. With HCI design shifting toward an emphasis on designing with people living with dementia rather than for, this chapter focuses on the people living with dementia and their family members who have been rarely recognized as worthy of design intervention. Drawing on two recent studies, this chapter presents a detailed understanding in co-creating meaningful experiences for people living with dementia and their families in VR. Results provide insights into their shared social experiences and place personalization as an overarching narrative for designing evocative, meaningful experiences. This chapter concludes with future directions for work in VR focusing on designing for contested realities, shared experiences, the personhood of carers, and representing an individual in the virtual world.
Enabling Interaction with Arbitrary 2D Applications in Virtual Environments

Virtual environments (VE) provide immersive experiences that allow users to perceive and interact with three dimensional (3D) content. Yet, the 3D applications are often tailored for specific tasks and hardware setups such as a virtual or augmented reality (VR/AR) head mounted display (HMD). This limits the range of available content, because not every standard application (e.g. web browser, calender, office) that is available on a 2D desktop PC can be easily accessed using VR or AR. As commonly used by other systems, we capture arbitrary 2D windows from a desktop PC and display them as a 2D surface in the VE. Furthermore, we provide different techniques that aim to allow easy and fast interaction with the 2D applications in VR. For example, the work bench tool allows to copy and paste subsections of a 2D window. The copied sections are fully interactable and can be moved around and scaled to provide easy access to small menus. With the macro tool, users can create shortcuts to frequently used functions that are executed by cursor presses or hotkeys. The provided tools yield new ways of interacting with 2D interfaces. By allowing the user to easily interact with already available 2D applications, the productivity of VEs increases strongly.
A Formal Approach to Prove Compatibility in Transformation Networks

The increasing complexity of software and cyberphysical systems is handled by dividing the description of the system under construction into different models or views, each with an appropriate abstraction for the needs of specific roles. Since all such models describe the same system, they usually share an overlap of information, which can lead to inconsistencies if overlapping information is not modified uniformly in all models. A well-researched approach to make these overlaps explicit and resolve inconsistencies are incremental, bidirectional model transformations. They specify the constraints between two metamodels and the restoration of consistency between their instances. Relating more than two metamodels can be achieved by combining bidirectional transformations to a network. However, such a network may contain cycles of transformations, whose consistency constraints can be contradictory if they are not aligned with each other and thus cannot be fulfilled at the same time. Such transformations are considered incompatible. In this article, we provide a formal definition of consistency and compatibility of transformations and propose an inductive approach to prove compatibility of a given network of transformations. We prove correctness of the approach based on these formal definitions. Furthermore, we present an operationalization of the approach at the example of QVT-R. It detects contradictions between relations by transforming them into first-order logic formulae and evaluating them with an SMT solver. The approach operates conservatively, i.e., it is not able to prove compatibility in all cases, but it identifies transformations as compatible only if they actually are. We applied the approach to different evaluation networks and found that it operates conservatively and is able to properly prove compatibility in 80% of the cases, indicating its practical applicability. Its limitations especially arise from restricted capabilities of the used SMT solver, but not from conceptual shortcomings. Our approach enables multiple domain experts to define transformations independently and to check their compatibility when combining them to a network, relieving them from the necessity to align the transformations with each other a priori and to ensure compatibility manually
Sunspot Plots: Model‐based Structure Enhancement for Dense Scatter Plots

Abstract Scatter plots are a powerful and well‐established technique for visualizing the relationships between two variables as a collection of discrete points. However, especially when dealing with large and dense data, scatter plots often exhibit problems such as overplotting, making the data interpretation arduous. Density plots are able to overcome these limitations in highly populated regions, but fail to provide accurate information of individual data points. This is particularly problematic in sparse regions where the density estimate may not provide a good representation of the underlying data. In this paper, we present sunspot plots, a visualization technique that communicates dense data as a continuous data distribution, while preserving the discrete nature of data samples in sparsely populated areas. We furthermore demonstrate the advantages of our approach on typical failure cases of scatter plots within synthetic and real‐world data sets and validate its effectiveness in a user study.
A Blockchain-Based Secure Data Sharing Framework for Healthcare

In the healthcare setting, electronic databases are extensively used, specifically for storing and recalling data for improved medical interventions. However, the currently employed systems in healthcare organizations, such as hospitals and laboratories, have major security issues such as data breaches, unauthorized access, and loss of personal information. Blockchain has been regarded as an effective alternative for providing a secure data sharing for healthcare and other institutions. This is due to the cryptographic features associated with the blockchain technology that can make data breaching difficult and improve data access and sharing. However, it requires a robust and secure framework for effective operations. In order to understand the needs and requirements for a blockchain-based framework for data sharing in the healthcare, an extensive overview of the literature has been carried out, and a proposal is drafted to understand the concepts behind the data sharing framework. This is performed to delineate the requirements of an effective framework for data sharing based on blockchain. This will serve as a cornerstone for the actual research, where the associated requirements would be further researched and utilized in proposing a general data sharing framework. Through a survey of the literature, it has been identified that blockchain-based data sharing can provide better security on access control. Open research issues are also discussed, along with the existing literature.
Distributed Ledger Technology for Smart Mobility: Variable Delay Models

Recently, Directed Acyclic Graph (DAG) based Distributed Ledgers have been proposed for various applications in the smart mobility domain [1]. While many application studies have been described in the literature, an open problem in the DLT community concerns the lack of mathematical models describing their behaviour, and their validation. Building on a previous work in [1], we present, in this paper, a fluid based approximation for the IOTA Foundation's DAG-based DLT that incorporates varying transaction delays. This extension, namely the inclusion of varying delays, is important for feedback control applications (such as transactive control [2]). Extensive simulations are presented to illustrate the efficacy of our approach.
Ultrasound Speckle Reduction Based on Histogram Curve Matching and Region Growing

The quality of ultrasound scanning images is usually damaged by speckle noise. This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm. Unlike single statistical moment-based speckle reduction algorithms, this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability. The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance. Then, according to the similarity value and tissue characteristics, the entire image is divided into several levels of speckle-content regions, and adaptive smoothing is performed based on these classification characteristics and the corresponding window size determined by the proposed region growing technique. Tests conducted from phantoms and in vivo images have shown very promising results after a quantitative and qualitative comparison with existing work.
Meta-Learning Applications in Digital Image Processing

In recent decades, advances in capture devices and the increase of available digital image data have stimulated the creation of methodologies for data processing that produce various forms of valuable models, such as descriptors, classifiers, approximations and visualizations. These models are often developed in the field of machine learning, which is characterized by a large number of available algorithms. These algorithms often do not have guidelines to identify the most appropriate one based on specific data to which they will be applied and the nature of the problem under analysis. There is a knowledge that allows to relate the features of the algorithms and data that present a good performance to fulfill a specific task, known as Meta-Knowledge, which can include information on algorithms, evaluation metrics to calculate similarity of datasets or relation of tasks. Being Meta-Learning the study of methods based on principles that explore the Meta-Knowledge to obtain efficient models and solutions, adapting the processes of Machine Learning and Data Mining. The research carried out in this work analyzes the applications and advantages offered by Meta-Learning in the field of digital image processing. To carry out this task, different types of images, characterizers, and feature analysis techniques are used. The results obtained show that methodology based on Meta-Learning is efficient when applied in the processing of digital images for identification and storage of experience generated by developing methodologies for classification of different types of images, obtaining a high performance with respect to an evaluation metrics. This statement means that Meta-Learning allows recommending the most appropriate methodology to perform the processing of a specific type of image based on features of the dataset under analysis and the type of specific task to be performed.
Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection

Stance detection is the task of attitude identification toward a standpoint. Previous work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during higher-level abstracting. Moreover, because the target is not always mentioned in the text, most methods have ignored target information. In order to solve these problems, we propose a neural network ensemble method that combines the timing dependence bases on long short-term memory (LSTM) and the excellent extracting performance of convolutional neural networks (CNNs). The method can obtain multi-level features that consider both local and global features. We also introduce attention mechanisms to magnify target information-related features. Furthermore, we employ sparse coding to remove noise to obtain characteristic features. Performance was improved by using sparse coding on the basis of attention employment and feature extraction. We evaluate our approach on the SemEval-2016Task 6-A public dataset, achieving a performance that exceeds the benchmark and those of participating teams.
Evaluation of Machine Learning-Driven Automatic Modulation Classifiers Under Various Signal Models

Abstract Automatic Modulation Classification (AMC) is becoming an essential component in receiver designs for next-generation communication systems, such as Cognitive Radios (CR). AMC enables receivers to classify an intercepted signal’s modulation scheme without any prior information about the signal. This is becoming increasingly vital due to the combination of congested frequency bands and geographically disparate frequency licensing for the railroad industry across North America. Thus, a radio technology is needed that allows train systems to adapt automatically and intelligently to changing locations and corresponding RF environment fluctuations. Three AMC approaches have been proposed in the scientific literature. The performance of these approaches depends especially on the particular environment where the classifiers are employed. In this work, the authors present a performance evaluation of the Feature-based AMC approach, as this is the most promising approach for railroads in real-time AMC operations under various different wireless channel environments. This study is done as the first one for railroads application where it considers different environments models including Non-Gaussian Class A noise, Multipath fast fading, and their combination. The evaluation is conducted for signals using a series of QAM modulation schemes. The authors selected the signal’s Cumulant statistical features for the feature extraction stage in this study, coupled with three different machine learning classifiers: Support Vector Machine (SVM), Deep Neural Network (DNN) and Recurrent Neural Network (RNN) utilizing long-short term memory (LSTM), in order to maintain control over the classifiers’ accuracy and computational complexity, especially for the non-linear cases. Our results indicate that when the signal model noise shows higher non-linear behavior, the RNN classifier on average achieves higher classification accuracy than the other classifiers.
An Opinion Spam Detection Method Based on Multi-Filters Convolutional Neural Network

With the continuous development of e-commerce, consumers show increasing interest in posting comments on consumption experience and quality of commodities. Meanwhile, people make purchasing decisions relying on other comments much more than ever before. So the reliability of commodity comments has a significant impact on ensuring consumers’ equity and building a fair internet-trade-environment. However, some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their profits. Those improper ways of self-profiting have severely ruined the entire online shopping industry. Aiming to detect and prevent these deceptive comments effectively, we construct a model of Multi-Filters Convolutional Neural Network (MFCNN) for opinion spam detection. MFCNN is designed with a fixed-length sequence input and an improved activation function to avoid the gradient vanishing problem in spam opinion detection. Moreover, convolution filters with different widths are used in MFCNN to represent the sentences and documents. Our experimental results show that MFCNN outperforms current stateof-the-art methods on standard spam detection benchmarks.
Digital therapeutic culture

This chapter sets out from the questions: What happens when therapeutic culture, popular psychology, and the happiness industry go digital? Does digitalisation lead to a radical transformation of the phenomena of (neuro)psychologisation and its related forms of subjectivity or not? First, a short introduction is given on what (neuro)psychologisation is (and what it is not), sketching out the central interpellative mechanism that hails the layperson to adopt the theoretical perspective of the psy-sciences to look at itself, the others, and the world. Then it is probed whether this dynamic is still operative in digital therapeutic culture or whether the latter entails the outsourcing of auto-disciplination to technologies. Taking digital technologies in education and schooling as a prime example the chapter proceeds in laying out the (new?) subject formations involved in digital culture. The central issue thus becomes, not only whether something changes or not when the digital screen becomes the key element in today’s (inter)subjectivities, but also, what will happen when digital technologies even make the screen redundant, connecting everybody directly (bypassing the screen) with everybody and the (virtual) world itself.
Maintaining Quality in FEVER Annotation

We propose two measures for measuring the quality of constructed claims in the FEVER task.Annotating data for this task involves the creation of supporting and refuting claims over a set of evidence.Automatic annotation processes often leave superficial patterns in data, which learning systems can detect instead of performing the underlying task.Humans also can leave these superficial patterns, either voluntarily or involuntarily (due to e.g.fatigue).The two measures introduced attempt to detect the impact of these superficial patterns.One is a new information-theoretic and distributionality based measure, DCI; and the other an extension of neural probing work over the ARCT task, utility.We demonstrate these measures over a recent major dataset, that from the English FEVER task in 2019.
Building Systems for Object Recognition by Multichannel Sensing Systems Based on Neural Networks and Fractal Signatures

An approach to building a multichannel pattern-recognition system by sensing systems is proposed. A list of problems solved by the sensing systems in the construction of object-recognition systems is specified. The problem of image preprocessing is formulated in the form of an ill-posed problem. The value of the fractal dimension D (the fractal signature) of images of contours of objects is used to create an additional characteristic. The methods for solving the problems of building modern object-recognition systems are evaluated.
An Efficient Multi-Stage Approach for Identifying Domain Shadowing

Domain shadowing is the introduction of an illegitimate subdomain under a preexisting legitimate domain. Attackers benefit not only from the inconspicuous nature of these subdomains, but also from the trust associated with the legitimate domain. Classifiers have been used to identify shadowed domains within the DNS namespace; however, most approaches rely on features created from a variety of sources, such as DNS data, Javascript inspection, and HTTP source. Unfortunately, the generation of these features is often highly time-consuming and the features themselves are not always effective in distinguishing current shadowing approaches.This paper introduces a new domain shadowing detection approach that leverages machine learning techniques (classifiers) distributed across multiple stages. Domain names are processed by later stages only if earlier stage findings are inconclusive; therefore, only domain names that require additional scrutiny undergo supplementary processing. Furthermore, features that can be quickly synthesized are located in earlier stages to further reduce detection time. Experimental results using the multi-stage detection system with data from recent domain shadowing campaigns results in 97.7% accuracy and 0.04% false positive rate, with an average classification time of 0.83 seconds per name.