Reviewerly/exHarmony huggingface dataset:
Assessment of Virtual Reality as a Didactic Resource in Higher Education

Given that the university teachers with more experience in the use of virtual reality are those corresponding to the areas of Health Sciences and of Engineering and Architecture, this article analyzes the assessment these teachers make about virtual reality as a teaching resource in their respective disciplines. The study uses a questionnaire that assesses the technical aspects and future projection of virtual reality, its drawbacks and the perception of the different dimensions of the participants’ knowledge about virtual reality and its didactic employability. The questionnaire was answered by a sample of 423 university teachers of different genders, ages, academic level and teaching experience, whose teaching activity is developed in various Latin American universities in the area of Health Sciences or in the area of Engineering/Architecture. Their answers have been analyzed descriptively and Spearman’s r statistics and the Multifactor ANOVA test have been used to verify the existence of significant differences in their evaluations for the different variables considered, cross-referencing them with the field of knowledge. Within the main results, gaps by area, years of teaching experience and academic level in the participants’ evaluations have been identified and discussed.
Visibility graph-based path-planning algorithm with quadtree representation

In recent years, unmanned surface vehicles (USVs) have been widely used in various applications. To fulfill target missions, USVs must be able to plan their path to the destination. Although many path-planning methods have been developed, most are inappropriate for application in complex marine environments. In this study, we propose a novel path-planning algorithm, which combines the merits of quadtree representation and visibility graphs, for efficient path planning of USVs in complex marine environments. Quadtree representation reduced the computation time and memory consumption. Additionally, the wind and wave data were considered to shorten the voyage time from the departure to destination points. Furthermore, the Dijkstra algorithm was applied to the quadtree to determine the suboptimal path. We then constructed a visibility graph with path waypoints using an improved line-of-sight algorithm to perform path planning on the visibility graph. The proposed algorithm not only facilitates path planning with minimum voyage time but also maintains the USV clearance distance from the coastline. To verify the performance, the proposed algorithm was compared with other methods for South Korean case studies. Overall, the proposed algorithm was able to generate a high-quality suboptimal path for the USV within a short time span.
Preying on the poor? Opportunities and challenges for tackling the social and environmental threats of cryptocurrencies for vulnerable and low-income communities

The rate of adoption of some cryptocurrencies is triggering alarm from energy researchers and social scientists concerned about the industry’s growing environmental and social impacts. In this paper we argue that the unsustainable trajectory of some cryptocurrencies disproportionately impacts poor and vulnerable communities where cryptocurrency producers and other actors take advantage of economic instabilities, weak regulations, and access to cheap energy and other resources. Globally, over 100 million people hold cryptocurrency, mostly as a speculative asset. The digital infrastructure behind the most popular cryptocurrency, bitcoin, currently requires as much energy as the whole of Thailand, with a carbon footprint exceeding the gold mining industry. Should bitcoin’s mass adoption continue, an escalating climate crisis is inevitable, disproportionately exacerbating social and environmental challenges for communities already experiencing multiple dimensions of deprivation. In mitigating these impacts, the paper considers 4 potential regulatory pathways, including: 1) promoting voluntary private-sector commitments to using only renewable energy, 2) encouraging a system of voluntary carbon offsetting, 3) using existing financial regulations and tax frameworks, and 4) imposing national and/or international bans on cryptocurrency ‘mining’. The paper argues that effective environmental regulation of cryptocurrencies is urgently required, both to reduce the threat of catastrophic climate change, and to help the world’s poorest towards sustainable development. However, regulating cryptocurrency mining in any context is likely to require a combination of efforts and is unlikely to result in win-win outcomes for all.
Fraud detection in publicly traded U.S firms using Beetle Antennae Search: A machine learning approach

In this paper, we present a fraud detection framework for publicly traded firms using an optimization approach integrated with a meta-heuristic algorithm known as Beetle Antennae Search (BAS). Existing techniques include human resources, like financial experts and audit teams, to determine the ambiguities or financial frauds in the companies based on financial and non-financial ratios. It is a laborious task, time-consuming, and prone to errors. We designed an optimization problem to minimize the loss function based on a non-linear decision function combined with the maximization of recall (Sensitivity and Specificity). We solved the optimization problem iteratively using the BAS. It is a nature-inspired algorithm and mimics the beetle’s food-searching nature. It includes a single searching particle to find an optimal solution to the optimization problem in n-dimensional space. We used a benchmark dataset collected from SEC’s Accounting and Auditing Enforcement Releases (AAERs) for the simulation. It includes 28 raw financial variables and the data collected between 1991–2008. For the comparison, we evaluated the performance of BAS with the recently proposed approach using RUSBoost. We also compared it with some additional algorithms, i.e., Logit and SVM-FK. The results showed that BAS is comparable with these algorithms and outperformed them in time consumption.
Pyfhel

Fully Homomorphic Encryption (FHE) allows private computation over encrypted data, disclosing neither the inputs, intermediate values nor results. Thanks to recent advances, FHE has become feasible for a wide range of applications, resulting in an explosion of interest in the topic and ground-breaking real-world deployments. Given the increasing presence of FHE beyond the core academic community, there is increasing demand for easier access to FHE for wider audiences. Efficient implementations of FHE schemes are mostly written in high-performance languages like C++, posing a high entry barrier to novice users. We need to bring FHE to the (higher-level) languages and ecosystems non-experts are already familiar with, such as Python, the de-facto standard language of data science and machine learning. We achieve this through wrapping existing FHE implementations in Python, providing one-click installation and convenience in addition to a significantly higher-level API. In contrast to other similar works, Pyfhel goes beyond merely exposing the underlying API, adding a carefully designed abstraction layer that feels at home in Python. In this paper, we present Pyfhel, introduce its design and usage, and highlight how its unique support for accessing low-level features through a high-level API makes it an ideal teaching tool for lectures on FHE.
Virtual Reality as Pedagogical Tool to Enhance Experiential Learning: A Systematic Literature Review

Since half of the century, technology has dominated the modern era. The rapid advancement of technology has reached generating artificial intelligence and artificial realities. So, virtual reality is an emerging technology and is applicable in education as well. Virtual reality is a computer-generated simulation, where people can interact within an artificial environment. Moreover, in an educational setting, such an environment provides students with a chance to get experiential learning. This paper has a systematic literature review on emerging technologies, such as virtual reality as a pedagogical tool for enhancing students’ experiential learning. This review aims to explore and understand the effect of virtual reality on students’ experiential learning by reviewing twenty-six selected articles. The selected studies have followed various methodologies and are from different contexts. This review study aims to present a systematic literature review for understanding and exploring the effect of virtual reality as a pedagogical tool for enhancing students’ experiential learning. Nine themes were identified, which are (a) virtual reality as pedagogical tool, (b) virtual reality as emerging educational technology tool, (c) virtual reality as digital transformation, (d) virtual reality as teaching-learning model, (e) virtual reality as architectural pedagogy, (f) virtual reality for communication skills, (g) virtual reality for reading and writing skills, (h) virtual reality for social learning, and (i) virtual reality for experiential learning. Thus, it is found that virtual reality is used as a pedagogical tool for various subject areas for encouraging involvement. It is helpful in medical, engineering, language, and social learning, as it provides a chance to get first-hand experience of the environment. Also, it helps learners to engage in a presented virtual environment and experience the sense of presence in it and enhances students’ experiential learning. Therefore, this review found virtual reality as an essential pedagogical tool for strengthening students’ experiential learning.
Chaos in the Real World: Recent Applications to Communications, Computing, Distributed Sensing, Robotic Motion, Bio-Impedance Modelling and Encryption Systems

Most of the papers published so far in literature have focused on the theoretical phenomena underlying the formation of chaos, rather than on the investigation of potential applications of chaos to the real world. This paper aims to bridge the gap between chaos theory and chaos applications by presenting a survey of very recent applications of chaos. In particular, the manuscript covers the last three years by describing different applications of chaos as reported in the literature published during the years 2018 to 2020, including the matter related to the symmetry properties of chaotic systems. The topics covered herein include applications of chaos to communications, to distributed sensing, to robotic motion, to bio-impedance modelling, to hardware implementation of encryption systems, to computing and to random number generation.
Fuzzy ensemble of deep learning models using choquet fuzzy integral, coalition game and information theory for breast cancer histology classification

Millions of women, worldwide, suffer from breast cancer and a large number of them succumb to death. In recent years, computer-aided diagnosis (CAD) systems are being developed for the detection of Breast Cancer. A number of fusion techniques have been proposed in this domain, but none of them take into consideration the decisions taken by a subset of classifiers during fusion. Our method, which uses Choquet Integral, considers subsets of classifiers and is thus stronger than the existing methods and beat all of these existing fusion methods in terms of accuracy. This however poses a significant challenge in terms of complexity, since the calculation of the fuzzy measures is a complicated and complex task, which we have dealt with using a novel heuristic method by employing Coalition Game, Information Theory, and by defining a novel mathematical function. In the present work, we have fused VGG16, VGG19, Xception, Inception V3, and InceptionResnet V2 for the classification of breast cancer histology images using a Choquet integral, Coalition game theory, and Information theory. The dataset used for evaluating the proposed model is the ICIAR 2018 Grand Challenge on Breast Cancer Histology (popularly known as BACH) images, which consist of 2-class and 4-class problems. To the best of our knowledge, our experimental results outperform almost all the state-of-the-art methods. For the two-class problem, the best test accuracy among the five deep learning models was achieved by Xception and it was 95% while the Fusion method has a test accuracy of 96%. For the four-class problem, Xception and InceptionResnet V2 have achieved the best test accuracy and both have a test accuracy of 91% while the Fusion method has a test accuracy of 95%. Again, in the case of the two-class problem the best precision and recall by the deep learning models are 0.95 and 0.95 respectively, while the precision and recall for after fusion are 0.96 and 0.96 respectively which is an increase of .01. In the case of the four-class problem, the best precision and recall by the deep learning models are 0.91 and 0.91 respectively, while the precision and recall after fusion are 0.95 and 0.95 respectively which is a very significant increase of .04. The source code for this project can be accessed at https://github.com/subhankar01/fuzzyBACH
Multi-Scale Fine-Grained Alignments for Image and Sentence Matching

Image and sentence matching is a critical task to bridge the visual and textual discrepancy due to the heterogeneous modalities. Great progress has been made by exploring the coarse-grained relationships between images and sentences or fine-grained relationships between regions and words. However, how to fully excavate and exploit corresponding relations between these two modalities is still challenging. In this work, we propose a novel Multi-scale Fine-grained Alignments Network (MFA), which can effectively explore multi-scale visual-textual correspondences to facilitate bridging cross-modal discrepancy. Specifically, word-scale matching module is firstly utilized to mine the basic but fundamental correspondences between a single word and independent region. Then, we propose a phrase-scale matching module to explore the relations between objects with the constraint of attribute and corresponding region, which can further reserve more associated information. To cope with the complex interactions among multiple phrases and images, we design the relation-scale matching module to capture high-order semantics between two modalities. Moreover, each matching module includes visual aggregation and textual aggregations, which can ensure the bi-directional coupling of multi-scale semantics. Extensive qualitative and quantitative experiments on two challenging datasets including Flickr30 K and MSCOCO, show that the proposed method achieves superior performance compared with the existing methods.
M<sup>5</sup>L: Multi-Modal Multi-Margin Metric Learning for RGBT Tracking

Classifying hard samples in the course of RGBT tracking is a quite challenging problem. Existing methods only focus on enlarging the boundary between positive and negative samples, but ignore the relations of multilevel hard samples, which are crucial for the robustness of hard sample classification. To handle this problem, we propose a novel Multi-Modal Multi-Margin Metric Learning framework named M5L for RGBT tracking. In particular, we divided all samples into four parts including normal positive, normal negative, hard positive and hard negative ones, and aim to leverage their relations to improve the robustness of feature embeddings, e.g., normal positive samples are closer to the ground truth than hard positive ones. To this end, we design a multi-modal multi-margin structural loss to preserve the relations of multilevel hard samples in the training stage. In addition, we introduce an attention-based fusion module to achieve quality-aware integration of different source data. Extensive experiments on large-scale datasets testify that our framework clearly improves the tracking performance and performs favorably the state-of-the-art RGBT trackers.
Multimodal medical image segmentation using multi-scale context-aware network

Multimodal medical image segmentation with different imaging devices is a key but challenging task in medical image visual analysis and reasoning. Recently, U-Net based networks achieved considerable success in semantic segmentation of medical image. However, U-Net utilizes a skip-connection to connect two symmetric encoder and decoder layers. Although the single granularity information of the encoder layer is preserved through skip connection, the rich multi-scale spatial information is ignored, which greatly affects its performance in the segmentation task. In this paper, a multi-scale context-aware network (CA-Net) for multimodal medical image segmentation is proposed, which captures rich context information with dense skip connection and assigns distinct weights to different channels. CA-Net consists of four key components, namely encoder module, multi-scale context fusion (MCF) module, decoder module, and dense skip connection module. The proposed MCF module extracts multi-scale spatial information through a spatial context fusion (SCF) block, and learn to balance channel-wise features through a Squeeze-and-Excitation (SE) block. Extensive experiments demonstrate that our model achieves state-of-the-art performance on three benchmark datasets of different modalities, including skin lesion segmentation in dermoscopy, lung segmentation in CT images, and blood vessel segmentation in retina images.
Forecasting intra-hour solar photovoltaic energy by assembling wavelet based time-frequency analysis with deep learning neural networks

Due to the expected lack of fossil fuels in near future as well as climate change produced by greenhouse effect as consequence of environmental emissions, renewable energy generation, and specifically solar photovoltaic generation, has become relevant in present energy generation challenge. Photovoltaic generators have strong relationship with solar irradiation and outdoor temperature in energy generation process. These meteorological parameters are volatile and uncertain in nature so, unexpected changes on these parameters produce variations on solar photovoltaic generators' output power. While many researchers have been focused in recent years on the development of novel models for forecasting involved meteorological parameters in photovoltaic generation, they commonly do not consider an analysis step of the data before using it in the developed models. Hence, the aim of this study consists in assembling a wavelet based time-frequency analysis of the used data with deep learning neural networks to forecast solar irradiation, in next 10 min, to compute solar photovoltaic generation. Results of the validation step showed that the deviation of the proposed forecaster was lower than 4% in 90.60% of studied sample days. Final forecaster's root mean square error was 35.77 W/m2, which was an accuracy improvement of 37.52% compared against persistence benchmark model.
Unifying framework for information processing in stochastically driven dynamical systems

A dynamical system is an information processing apparatus that encodes input streams from the external environment to its state and processes them through state transitions. The information processing capacity (IPC) is an excellent tool that comprehensively evaluates these processed inputs, providing details of unknown information processing in black box systems; however, this measure can be applied only to time-invariant systems. This paper extends the applicable range to time-variant systems and further reveals that the IPC is equivalent to coefficients of polynomial chaos (PC) expansion in more general dynamical systems. To achieve this objective, we tackle three issues. First, we establish a connection between the IPC for time-invariant systems and PC expansion, which is a type of polynomial expansion using orthogonal functions of input history as bases. We prove that the IPC corresponds to the squared norm of the coefficient vector of the basis in the PC expansion. Second, we show that an input following an arbitrary distribution can be used for the IPC, removing previous restrictions to specific input distributions. Third, we extend the conventional orthogonal bases to functions of both time and input history and propose the IPC for time-variant systems. To show the significance of our approach, we demonstrate that our measure can reveal information representations in not only machine learning networks but also a real, cultured neural network. Our generalized measure paves the way for unveiling the information processing capabilities of a wide variety of physical dynamics which have been left behind in nature.
Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychology, among others. Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management. There is a need, which is still absent, to produce an analytical compilation of PHM-XAI works. In this paper, we use preferred reporting items for systematic reviews and meta-analyses (PRISMA) to present a state of the art on XAI applied to PHM of industrial assets. This work provides an overview of the trend of XAI in PHM and answers the question of accuracy versus explainability, considering the extent of human involvement, explanation assessment, and uncertainty quantification in this topic. Research articles associated with the subject, since 2015 to 2021, were selected from five databases following the PRISMA methodology, several of them related to sensors. The data were extracted from selected articles and examined obtaining diverse findings that were synthesized as follows. First, while the discipline is still young, the analysis indicates a growing acceptance of XAI in PHM. Second, XAI offers dual advantages, where it is assimilated as a tool to execute PHM tasks and explain diagnostic and anomaly detection activities, implying a real need for XAI in PHM. Third, the review shows that PHM-XAI papers provide interesting results, suggesting that the PHM performance is unaffected by the XAI. Fourth, human role, evaluation metrics, and uncertainty management are areas requiring further attention by the PHM community. Adequate assessment metrics to cater to PHM needs are requested. Finally, most case studies featured in the considered articles are based on real industrial data, and some of them are related to sensors, showing that the available PHM-XAI blends solve real-world challenges, increasing the confidence in the artificial intelligence models' adoption in the industry.