Reviewerly/exHarmony huggingface dataset:
Oversampling based Classifiers for Categorization of Radar Returns from the Ionosphere

Study of the ionosphere is important for research in various domains. Especially in communication systems, this study holds a great importance. In ionospheric research, there is a need to delineate useful and non-useful radar returns from the ionosphere. The useful radar returns can be used for further analysis and non-useful radar returns can be discarded. When the usefulness of radar returns is analyzed by humans, it is simply time-consuming and is prone to more human errors. Thus, some machine learning methods are needed to delineate useful and non-useful radar returns. The various machine learning algorithms (classical learning algorithms and ensemble learners) as well as deep learning models are tested in this study. After 10-fold cross validation, the highest accuracy by Random Forest was 94.22% and the accuracy for a single layer neural network was 99.43%.
Embodied Agentic STEM Education: Effects of 3D VR Compared to 2D PC

Embodiment is expected to play a key role in learning in VR. This randomized control trial determined how the two factors of embodiment (low versus high) and platform (2D PC versus 3D Virtual Reality (VR)) affected STEM learning. A total of 214 undergraduate participants were randomly assigned to four conditions in a 2 x 2 design with pretests and posttests. The low embodied groups did not control the virtual net in the natural selection game, they observed a playback of butterflies being captured. All groups showed significant content knowledge gains by posttest. There was a main effect of embodiment; the active groups that controlled the virtual net learned more content. There was not a main effect of platform on learning. However, there was an intriguing interaction of embodiment by platform by test time revealing that the low embodied VR group learned the least (ES= .38), while the high embodied VR group learned the most (ES=1.07). The low embodied VR players may have expected to have more agency and control over the game mechanics, and playing in the observational mode was infelicitous for learning. Interestingly, the PC condition was not significantly affected when in the more passive/observational mode. Players in the 2D PC condition may be more accustomed to low embodied learning, e.g., watching playback videos on monitors. Several guidelines are recommended for learning design in immersive VR. Non-interactive objects and videos in VR should be used judiciously and designers are encouraged to make the 3D VR content manipulatable and interactive.
Comparison of Planned Path and Travelled Path Using ROS Navigation Stack

The main aim of this paper is to study the differences between the planned path and travelled path of a virtual differential drive robot in Gazebo-ROS simulator while using ROS navigation stack. A virtual environment is created in GAZEBO for the simulation. For the path comparison experiment the differential drive robot was autonomously navigated through 4 fixed destinations in the environment using a python program. Another python code running alongside will record the coordinates of the planned and actual paths and the coordinates were plotted to get the path. Multiple scenarios are tested with dynamic obstacles placed in different positions. Travelled time through these destinations using manual control for each case was also recorded for references.
Conceptualising Embodiment through Virtual Reality for Education

The concept of embodiment has been central to the design of XR (extended reality) technologies and is key to the deployment of and research on immersive learning, especially through virtual reality. Despite the important place that the notion holds in the immersive learning field, there have been few conceptual frameworks developed to assist developers, practitioners and educators understand how they might consider various aspects of embodiment and their implications for learning. The purpose of this paper is to present a set of sociological theoretical and empirical ways through which to reflect on how the body and human virtuality intertwine and how this occurs in educational endeavours. Specifically, the paper reviews how embodiment has been treated in the immersive learning literature, with a focus on virtual reality, and presents a conceptual framework, derived from the sociology of the body, intended to facilitate nuanced exploration of the topic in immersive learning. The paper concludes by demonstrating the utility of the conceptual framework by applying it to findings from the VR School Study, research that embedded immersive virtual reality into school classrooms.
Clothing Image Recognition Based on Multiple Features Using Deep Neural Networks

For many online customers, clothing image recognition is used mainly in computer vision for fashion applications. Recognition of the clothing image and identification of their style and presentation make the problem difficult for the fashion item. Image recognition technologies allow consumers to scan a picture taken from a fashion magazine or print ad and automatically land on the product page where they can purchase the exact item. Retailers can propose similar looking items in different price ranges with visual search solutions running in the app or website, so the customer can buy a look-alike product at a lower price. The main focus is on classifying the MNIST fashion dataset using multilayer perceptron, convolutional neural network and extreme learning machine. Extracting the F-mnist dataset using these deep neural networks that are most common in computer vision for the implementation of image recognition and even more effective for cloth prediction evaluation. Fashion-MNIS T enhances cultural diversity by attracting more young women students, collectors, artists and designers. Selecting the right deep learning technique for extracting features and choosing the best model of classification remains a major challenge in achieving good quality accuracy. The experimental results, however, show that this Fashion-MNIST dataset has impressive results.
De‐anonymizing Ethereum blockchain smart contracts through code attribution

Summary Blockchain users are identified by addresses (public keys), which cannot be easily linked back to them without out‐of‐network information. This provides pseudo‐anonymity, which is amplified when the user generates a new address for each transaction. Since all transaction history is visible to all users in public blockchains, finding affiliation between related addresses undermines pseudo‐anonymity. Such affiliation information can be used to discriminate against addresses linked with undesired activities or can lead to de‐anonymization if out‐of‐network information becomes available. In this work, we propose an approach to undermine pseudo‐anonymity of blockchain transactions by linking together addresses that were used to deploy smart contracts, which were produced by the same authors. In our approach, we leverage stylometry techniques, widely used in the social science field for attribution of literary texts to their corresponding authors. The assumption underlying authorship attribution is the existence of a distinctive writing style, unique to an author and easily distinguishable from others. Drawing an analogy between literary text and smart contracts' source code, we explore the extent to which unique features of source code and byte code of Ethereum smart contracts can represent the coding style of smart contract developers. We show that even a small number of representative features leads to a sufficiently high accuracy in attributing smart contracts' code to its deployer's address. We further validate our approach on real‐world scammers' data and Ponzi scheme‐related contracts. Additionally, we provide an algorithm to extract distinctly contributing features per an entire dataset or per specific authors. We use this algorithm to extract and explore such features in our dataset and in the Ponzi scheme‐related dataset.
A Cramér-Rao Lower Bound Derivation for Passive Sonar Track-Before-Detect Algorithms

Track-before-detect (TkBD) algorithms have been shown to greatly abate measurement-to-track association (MTA) challenges. These simplifications are aptly relevant for reducing operator workload in deployed sonar systems that require a human “in-the-loop.” In a prior manuscript a case study of a passive bearings-only target motion analysis TkBD algorithm was demonstrated in complex sonar scenarios relevant to advanced fielded sonar systems. In this manuscript, a Cramer-Rao Lower Bound (CRLB) is derived for the algorithm previously developed. The approximations used in developing the CRLB are validated with a real data set. The CRLB itself, as a predictor of state estimation error performance, is validated with single- and multi-contact simulated data scenarios. The prior algorithm and CRLB derived herein is applicable to passive sonar, active sonar, radar, and optical applications through a change of point spread functions and Jacobians. The CRLB derived is simple to implement, requires minimal statistical assumptions, and is applicable to similarly implemented TkBD algorithms.
XHAR: Deep Domain Adaptation for Human Activity Recognition with Smart Devices

To further improve the convenience and effectiveness of human computer interaction (HCI) with smart devices, human activity recognition (HAR) has been widely studied from various aspects. Unfortunately, deep learning based methods often suffer from either expensive labeling efforts or weak generalization ability. Inspired by recently developed domain adaptation strategies, we propose XHAR, a novel adversarial deep domain adaptation framework for HAR using smart devices, providing better device and user adaptation. XHAR first selects the most similar source dataset (with label), then extracts device and user independent spatial-temporal features through the combinations of Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) feature extractors. Moreover, it removes the distribution discrepancy using multiple domain discriminators, and finally performs adaptation on the target dataset (without label) to obtain the predicted labels. We conduct extensive experiments on 50 users (i.e., of different ages, genders, and body shapes) and 4 smart devices with two kinds of datasets (i.e., gesture activities and sport activities). We compare our method with the source-only model and several state-of-the-art domain adaptation models. The results show that XHAR increases the classification accuracy by at least 4.81% (to 74.50%) on the adaptation between different users, and accordingly by at least 9.25% (to 69.23%) between different devices.
Deep Learning Approaches for Open Set Wireless Transmitter Authorization

Wireless signals contain transmitter specific features, which can be used to verify the identity of transmitters and assist in implementing an authentication and authorization system. Most recently, there has been a wide interest in using deep learning for transmitter identification. However, the existing deep learning work has posed the problem as closed set classification, where a neural network classifies among a finite set of known transmitters. No matter how large this set is, it will not include all transmitters that exist. Malicious transmitters outside this closed set, once within communications range, can jeopardize the system security. In this paper, we propose a deep learning approach for transmitter authorization based on open set recognition. Our proposed approach identifies a set of authorized transmitters, while rejecting any other unseen transmitters by recognizing their signals as outliers. We propose three approaches for this problem and show their ability to reject signals from unauthorized transmitters on a dataset of WiFi captures. We consider the structure of training data needed, and we show that the accuracy improves by having signals from known unauthorized transmitters in the training set.
Deep Single Image Deraining via Modeling Haze-Like Effect

Removing rain from images is of a great importance to various applications such as autonomous driving, drone piloting, and photo editing. Conventional methods rely on some heuristics to handcraft various priors to remove or separate rain from images. Recently, deep learning models are proposed to learn various end-to-end methods to complete this task. However, these methods might fail in obtaining satisfactory results in some real-world scenarios, especially when the captured images suffer from heavy rain that brings not only rain streaks but also a haze-like effect (caused by the accumulation of tiny raindrops). Different from most of the existing deep learning deraining methods that focus on handling rain streaks, we add a new variable to model the haze-like effect in a general model for rain, based on which a deep neural network is designed accordingly. Specifically, in our method, two branches are designed to handle rain streaks and the haze-like effect, respectively. The output of such branch structure is fed to an additional module to further enhance the performance. Three modules are trained jointly, leading to an end-to-end network, which supports a adjustment to the strength of removing the haze-like effect. Extensive experiments on several datasets show that our method outperforms several state-of-the-art methods in both objective assessment and visual quality.
Knowledge-guided unsupervised rhetorical parsing for text summarization

Automatic text summarization (ATS) has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale corpora. However, there is still no guarantee that the generated summaries are grammatical, concise, and convey all salient information as the original documents have. To make the summarization results more faithful, this paper presents an unsupervised approach that combines rhetorical structure theory, deep neural model, and domain knowledge concern for ATS. This architecture mainly contains three components: domain knowledge base construction based on representation learning, the attentional encoder–decoder model for rhetorical parsing, and subroutine-based model for text summarization. Domain knowledge can be effectively used for unsupervised rhetorical parsing thus rhetorical structure trees for each document can be derived. In the unsupervised rhetorical parsing module, the idea of translation was adopted to alleviate the problem of data scarcity. The subroutine-based summarization model purely depends on the derived rhetorical structure trees and can generate content-balanced results. To evaluate the summary results without golden standard, we proposed an unsupervised evaluation metric, whose hyper-parameters were tuned by supervised learning. Experimental results show that, on a large-scale Chinese dataset, our proposed approach can obtain comparable performances compared with existing methods.
Transfer processes of potential toxic elements (PTE) between rock-soil systems and soil risk evaluation in the Baoshan area, Yunnan Province, Southwest China

Geochemical fluxes in rock-soil systems are important tools in the evaluation of soil environmental risk, especially where the geological background (parent materials) are enriched in potential toxic elements (PTE). In the Baoshan area, the presence of mineral resources along with carbonate and basalt parent materials often leads to PTE being elevated in soils. In this study, we compared rock and soil geochemical compositions by collecting rock samples from the host formations and ore samples from two mineral deposits (Cu–Pb–Zn and Fe–Cu–Pb–Zn) and their overlying soils. This research demonstrates enrichment of PTE (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn) in soils relative to their parent materials. The enrichment in soils may be due to the removal of mobile elements (e.g., Ca, Na, Mg, and Sr), and coprecipitation (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn) and/or adsorption by Fe-oxy-hydroxides (Cr and Ni) under the influence of high pH values during soil formation. The results point to a parent lithological origin and natural enrichment of PTE in Baoshan soils. However, anthropogenic contamination of soils near mining areas is also possible. Soils forming from carbonate rocks, basalts, and especially from mineralized formations have higher PTE concentrations, and in places, levels can exceed the risk screening values for soil contamination of agricultural land of China. Where the latter is the case, it is suggested that ecological monitoring be implemented in the study area. The conventional use of the geo-accumulation index (Igeo) and enrichment factor (EF) methods is challenged in this research and found to underestimate the PTE contamination. A compositional data analysis approach (using clr biplots) is presented which shows that comparison with regional-scale background datasets is essential to provide a more informative approach to evaluate the risk of soil contamination, especially in regions with elevated levels of PTE in parent materials.
A Mobile Clinical DSS based on Augmented Reality and Deep Learning for the home cares of patients afflicted by bedsores

A bedsore, also known as pressure sore, pressure ulcer or decubitus ulcer, is the result of constant pressure on skin occurring in bedridden patients and paraplegics continuously sitting in chair. All patients who are immobile for a long time due to any cause are likely to get bedsores. Effective and efficient management of processes related to the treatment of bedsores is an important issue for healthcare organizations as it heavily affects the quality of life of patients and the costs for such organizations. Therefore organizations need and look for more and more to provide their field workforce with smart mobile tools able to support such processes. In such a context, this paper proposes a mobile app implementing a Clinical Decision Support System (CDSS) to help field operators to measure the bedsore, classify its status, trace its evolution along the timeline and making correct decisions about the course of actions to effectively treat it. The mobile app is mostly based on Augmented Reality supported by Deep Learning, thus it requires an adequate system architecture to be effectively deployed, adopted and used. From the conceptual viewpoint, the defined CDSS model lays on three important considerations: providing automatic support to classify the status of a bedsore does not do all the work but help operators to improve the quality of their decisions, augmented reality allows to build a situated environment for decision-making supporting the operators' cognitive processes, operators should use only one tool to execute all their tasks in order to be more focused on the real problem which is to improve the quality of life of their patients.
Iterative Refinement for Multi-source Visual Domain Adaptation

One of the main challenges in multi-source domain adaptation is how to reduce the domain discrepancy between each source domain and a target domain, and then evaluate the domain relevance to determine how much knowledge should be transferred from different source domains to the target domain. However, most prior approaches barely consider both discrepancies and relevance among domains. In this paper, we propose an algorithm, called Iterative Refinement based on Feature Selection and the Wasserstein distance (IRFSW), to solve semi-supervised domain adaptation with multiple sources. Specifically, IRFSW aims to explore both the discrepancies and relevance among domains in an iterative learning procedure, which gradually refines the learning performance until the algorithm stops. In each iteration, for each source domain and the target domain, we develop a sparse model to select features in which the domain discrepancy and training loss are reduced simultaneously. Then a classifier is constructed with the selected features of the source and labeled target data. After that, we exploit optimal transport over the selected features to calculate the transferred weights. The weight values are taken as the ensemble weights to combine the learned classifiers to control the amount of knowledge transferred from source domains to the target domain. Experimental results validate the effectiveness of the proposed method.
Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI)

In recent year, there has been a growing need for intelligent systems that not only are able to provide reliable classifications but can also produce explanations for the decisions they make. The demand for increased explainability has led to the emergence of explainable artificial intelligence (XAI) as a specific research field. In this context, fuzzy logic systems represent a promising tool thanks to their inherently interpretable structure. The use of a rule-base and linguistic terms, in fact, have allowed researchers to create models that are able to produce explanations in natural language for each of the classifications they make. So far, however, designing systems that make use of interval type-2 (IT2) fuzzy logic and also give explanations for their outputs has been very challenging, partially due to the presence of the type-reduction step. In this paper, it will be shown how constrained interval type-2 (CIT2) fuzzy sets represent a valid alternative to conventional interval type-2 sets in order to address this issue. Through the analysis of two case studies from the medical domain, it is shown how explainable CIT2 classifiers are produced. These systems can explain which rules contributed to the creation of each of the endpoints of the output interval centroid, while showing (in these examples) the same level of accuracy as their IT2 counterpart.
Visual Question Answering via Combining Inferential Attention and Semantic Space Mapping

Visual Question Answering (VQA) has emerged and aroused widespread interest in recent years. Its purpose is to explore the close correlations between the image and question for answer inference. We have two observations about the VQA task: (1) the number of newly defined answers is ever-growing, which means that answer prediction on pre-defined labeled answers may lead to errors, as some unlabeled answers may be the right choice to the question–image pairs; (2) in the process of answering visual questions, the gradual change of human attention has an important guiding role in exploring the correlations between images and questions. Based on these observations, we propose a novel model for VQA, i.e., combining Inferential Attention and Semantic Space Mapping (IASSM). Specifically, our model has two salient aspects: (1) a semantic space shared by both the labeled and unlabeled answers is constructed to learn new answers, where the joint embedding of a question and the corresponding image is mapped and clustered around the answer exemplar; (2) a novel inferential attention model is designed to simulate the learning process of human attention to explore the correlations between the image and question. It focuses on the more important question words and image regions associated with the question. Both the inferential attention and the semantic space mapping modules are integrated into an end-to-end framework to infer the answer. Experiments performed on two public VQA datasets and our newly constructed dataset show the superiority of IASSM compared with existing methods.