Reviewerly/exHarmony huggingface dataset:
Optical see-through augmented reality can induce severe motion sickness

The aim was to investigate whether severe symptoms of visually induced motion sickness (VIMS) can occur in augmented reality (AR) optical see-through applications. VIMS has been extensively studied in virtual reality (VR), whereas it has received little attention in the context of AR technology, in which the real world is enhanced by virtual objects. AR optical see-through glasses are becoming increasingly popular as technology advances. Previous studies showed minor oculomotor symptoms of VIMS with the aforementioned technology. New applications with more dynamic simulations could alter previously observed symptom severity and patterns. In experiment 1, we exposed subjects to a traditional static AR application for pilot candidate training. In experiment 2, subjects completed tasks in a dynamic starfield simulation. We analyzed symptom profiles pre and post with the simulator sickness questionnaire (SSQ) and during exposure with the Fast Motion Sickness Scale (FMS). We also developed a new FMS-D that captures symptoms of dizziness during simulation. As expected, in experiment 1 we found low VIMS symptomatology with predominantly oculomotor symptoms. In experiment 2, on the other hand, we detected severe VIMS symptoms in some subjects, with disorientation (SSQ subscale) as the main symptom group. The present work demonstrates that VIMS can be of serious concern in modern AR applications. The FMS-D represents a new tool to measure symptoms of dizziness during exposure. VIMS symptoms need to be considered in the design and usage of future AR applications with dynamic virtual objects, e. g. for flight training or machine maintenance work.
DeepSumm: Exploiting topic models and sequence to sequence networks for extractive text summarization

In this paper, we propose DeepSumm, a novel method based on topic modeling and word embeddings for the extractive summarization of single documents. Recent summarization methods based on sequence networks fail to capture the long range semantics of the document which are encapsulated in the topic vectors of the document. In DeepSumm, our aim is to utilize the latent information in the document estimated via topic vectors and sequence networks to improve the quality and accuracy of the summarized text. Each sentence is encoded through two different recurrent neural networks based on probabilistic topic distributions and word embeddings, and then a sequence to sequence network is applied to each sentence encoding. The outputs of the encoder and the decoder in the sequence to sequence networks are combined after weighting using an attention mechanism and converted into a score through a multi-layer perceptron network. We refer to the score obtained through the topic model as Sentence Topic Score (STS) and to the score generated through word embeddings as Sentence Content Score (SCS). In addition, we propose Sentence Novelty Score (SNS) and Sentence Position Score (SPS) and perform a weighted fusion of the four scores for each sentence in the document to compute a Final Sentence Score (FSS). The proposed DeepSumm framework was evaluated on the standard DUC 2002 benchmark and CNN/DailyMail datasets. Experimentally, it was demonstrated that our method captures both the global and the local semantic information of the document and essentially outperforms existing state-of-the-art approaches for extractive text summarization with ROUGE-1, ROUGE-2, and ROUGE-L scores of 53.2, 28.7 and 49.2 on DUC 2002 and 43.3, 19.0 and 38.9 on CNN/DailyMail dataset.
Evaluation of Cryptocurrencies for Investment Decisions in the Era of Industry 4.0: A Borda Count-Based Intuitionistic Fuzzy Set Extensions EDAS-MAIRCA-MARCOS Multi-Criteria Methodology

Blockchain technologies, which form the basis of Industry 4.0, paved the way for cryptocurrencies to emerge as technological innovation in the technology age. Recently, investors worldwide have been interested in cryptocurrencies with increasing acceleration due to high earning expectations though they have no backing and intrinsic value. As such, this paper seeks to identify the most proper cryptocurrencies from an investment standpoint in our technological era. Fifteen well-known cryptocurrencies with the highest market capitalization are evaluated as per sixteen factors. An intuitionistic fuzzy set-driven methodology incorporating Evaluation Based on Distance from Average Solution (EDAS), Multi-Attributive Ideal Real Comparative Analysis (MAIRCA, and Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS), which is the study’s prominent novelty, has been applied to provide a strong group decision vehicle for cryptocurrency selection. Notwithstanding, although the results obtained with the three approaches are highly consistent, investors would not like to doubt the instrument they will invest in. The Borda count is then applied to obtain a compromise for the rankings obtained from each approach. As per our findings, Ethereum, Tether, and Bitcoin are the most suitable cryptocurrencies, whereas reliable software, ease of inclusion in the wallet, and stability are the foremost factors to consider when investing in cryptocurrencies. The findings are further discussed in detail from a financial perspective. The proposed approach could be employed to select different investment instruments in future studies.
Video-based reflection in teacher education: Comparing virtual reality and real classroom videos

While previous studies have examined the use of real-world classroom videos to support the development of student teachers’ reflective skills, there has been little research to date on the use of virtual reality (VR) videos in teacher education to provide opportunities for authentic reflection. This mixed-methods study investigated changes in reflection-related self-efficacy and differences in written reflection processes using a quasi-experimental design with two types of reflection stimuli. One group of 46 student teachers used a VR-based video to reflect on instruction while another group of 23 student teachers used a real classroom video. We found an increase in reflection-related self-efficacy over time among participants in the VR group only. We also found that VR videos triggered similar reflection processes to real classroom videos. This study shows, for the first time, that video-based reflection on VR classroom videos produced comparable results to reflection on real classroom videos. This indicates that VR can be used successfully in teacher education and that it offers a useful learning tool for teacher education programs. • We designed video-based reflection activities for student teachers. • We compared reflections using VR versus reflections using real classroom videos. • Reflection-related self-efficacy beliefs increased in the VR group only. • VR recordings stimulate the reflection process.
The Role of Chatbots in Academic Libraries: An Experience-based Perspective

Today, with the rapid advancement of technology, Artificial Intelligence (AI)-powered chatbots have become omnipresent across sectors like e-commerce, travel, hospitality, banking, EdTech, etc.; but they still haven't been predominantly adopted by academic libraries. This paper reports on a qualitative study aimed to explore the perceived risks and challenges behind the adoption of chatbots in libraries and how chatbots could help deliver superior experience to all library users as well as stakeholders. We conducted interviews with multiple stakeholders of academic libraries: the library staff, doctoral students and the faculty, to understand their perspectives on the adoption of Chatbots. We used CAQDAS NVIVO 12 content analysis software to analyse the interview transcripts. Our findings suggest that the majority of the stakeholders we interviewed favour the adoption of chatbots, as they believe that integrating chatbot technology with an existing library information system could deliver diverse services, which in turn would help in research and scholarly communication. However, 'perceived risk' with respect to employing chatbots among stakeholders was high. Also, the stakeholders raised some serious concerns with respect to privacy intrusion by chatbots, and with respect to chatbots' comprehension of task complexity, which are issues that must be addressed by chatbot developers while designing them, specifically for libraries.
On Trajectory Homotopy to Explore and Penetrate Dynamically of Multi-UAV

This paper examines a trajectory homotopy optimization framework for multiple unmanned aerial vehicles (multi-UAV) to solve the problem of dynamic penetration mission planning (PMP) with hostile obstacles and perception constraints. Constrained problems are usually more challenging and difficult to solve with some practical constraints and requirements. To improve the efficiency of the solution for the penetration path, a novel variable-time mechanism has been constructed to adapt to the updated delay time of unknown target search (UTS) and dynamic trajectory planning (DTP) two stages. The occupancy grid maps are established by a Gaussian probability field (GPF) for predicting the positions of enemy UAVs. To fully consider the hostile obstacle constraint, a hybrid adaptive obstacle avoidance approach dynamic window PRM (DW-PRM) is designed to shorten the planned path. The penetration strategy algorithm (SG) is developed based on the proposed strategy set and decision tree. To improve the ability of dynamic obstacle avoidance, the multiple coupled penetration homotopy trajectory is addressed with a turning radius constraint. The simulation results indicated that the penetration homotopy framework for multi-constraints can solve the multi-UAV PMP problem.
Bidirection-Fusion-YOLOv3: An Improved Method for Insulator Defect Detection Using UAV Image

With advances in the continuous improvement and development of the power system, insulators have gradually become one of the most important components. At present, unmanned aerial vehicles(UAV) have been widely used to inspect insulators, and insulators in pictures are accurately and efficiently identified by convolutional neural networks and this method has been extensively applied. These existing methods have been widely used to identify insulators in pictures, with high accuracy and efficiency. However, they are based on Faster R-CNN and you only look once(YOLO) either require more identification time due to the complex network structure, or do not have sufficient accuracy for insulator defects. More identification time is required due to the complexity of the network structure, or there is not enough accuracy for insulator defects. Based on the YOLOv3 network, this paper proposes a new type of convolutional neural network(CNN) for target detection, which can improve enhance the efficiency while ensuring the detection speed. In addition, this paper applies the latest EIoU and loss functions to YOLOv3, which significantly improves the coincidence of the prediction frame and the annotation frame and accelerates the convergence speed. The experimental results show that the detection model proposed in this paper has an average precision(AP) of 0.94 for insulators and 0.89 for insulator defects, and its detection speed can reach 93.5ms/image. Finally, after experimental verification, the detection model proposed in this paper meets the requirements of power inspection and has good engineering application prospects.
Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks

Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a leading cause of cancer-related death, despite the fact that both traditional and modern methods are capable of comparing images that may encompass cancer regions of various sorts after looking at a significant number of colon cancer images. The fundamental problem for colon histopathologists is differentiating benign from malignant illnesses to having some complicated factors. A cancer diagnosis can be automated through artificial intelligence (AI), enabling us to appraise more patients in less time and at a decreased cost. Modern deep learning (MDL) and digital image processing (DIP) approaches are used to accomplish this. The results indicate that the proposed structure can accurately analyze cancer tissues to a maximum of 99.80%. By implementing this approach, medical practitioners will establish an automated and reliable system for detecting various forms of colon cancer. Moreover, CAD systems will be built in the near future to extract numerous aspects from colonoscopic images for use as a preprocessing module for colon cancer diagnosis.
Impacts of climate change and meteo-solar parameters on photosynthetically active radiation prediction using hybrid machine learning with Physics-based models

The photosynthetically active radiation (PAR) ratio of 0.45 has been applied in numerous ecological studies to convert global solar radiation (H) to PAR by ordinary multiplication. The ratio is essentially useful and convenient, particularly when in situ measurement of climate data sets are not readily available due to inadequate instrumentation network and experience required. Up till now, there is no general agreement on whether the ratio is 0.45 and how the solar fluxes are distributed in Nigeria or around the world. Numerous empirical ratios have been reported in literature. This study proposed five (5) machine learning models (bagging, boosting, artificial neural network (MLP), automatic regression integrated moving average (ARIMA), controlled ARIMA), and 5 Physics-based models (Physics-based Gumbel model (PGM), popularly applied ratio (PCR), observed PAR ratio (POR), empirical, and PGM-CARIMA hybrid models. The 10 models were used to predict PAR using generalized datasets in Nigeria and the PGM-CARIMA model emerged as the best performing fit based on the R2, RMSE, MAPE, and BIC error metric indicators. PGM-CARIMA reported a better estimate for predicting PAR compared to POR and PCR with a relative error ranging from 0.00002 to 0.0503% compared to POR and PCR which recorded 4.657% and 6.178%. This implies that the use of PGM-CARIMA is more suitable for predicting the PAR ratio as it introduces a lower marginal error of approximately 0.00002–0.0503% when applied for the calculation of net primary productivity (NNP) and of climate change with a significant reduction from 6.17% and 4.65% was achieved in using the ratio of PCR and POR, respectively. Using the proposed PGM-CARIMA evolutionary hybrid model, we demonstrated that the annual mean of the ratio yielded a range of 0.4377 to 0.4712 in Nigeria. The authors also proposed the Physics-based classical climate change model (PCM) to assess the impact of climate change on PAR productivity in Nigeria. The findings revealed that changes in PAR productivity as a result of the impact of climate change may accelerate food production and crop yields in Southern Nigeria and depreciate overall food production and crop yields in Northern Nigeria in 2030, 2040, and 2021–2040. The results of the climate analysis also revealed that north-central Nigeria often referred to as the nation's food basket is most negatively impacted by the impact of climate change externalities over the periods studied. The authors recommended mitigation and adaptation plans such as delayed planting dates, intercropping, routine planting of tropical forages, especially livestock that feed on plants, could be key to climate change mitigation, also, expansion of the planting area as well as the incorporation of improved seedlings that are highly resistant to extreme temperatures will accelerate food security across the country within the periods studied.
Application of reliability-based back-propagation causality-weighted neural networks to estimate air-overpressure due to mine blasting

In the present study, the air-overpressure (AOp) due to mine blasting is predicted using an uncertainty intelligence method based on the Z-number reliability and fuzzy cognitive map (FCM). Hence, the cause-and-effect relationship between effective parameters on AOp was first determined by a team of experts. But, expert opinions are accompanied by uncertainty. Therefore, Z-number concept is used to solve the uncertainty of expert opinions and improve views. Notably, the relationships dependent on expert opinions. To overcome this problem, two learning algorithms called nonlinear Hebbian (NLH) and differential evolution (DE) algorithms integrated to decrease the dependence on experts’ views. An FCM based on NLH-DE and reliability information was first designed; then inputs’ weights were extracted during several simulations in training process. The reliability inputs’ weights were imported into back-propagation causality-weighted neural networks (BPCWNNs). The coefficient of determination (R 2 ), root mean square error (RMSE), variance account for (VAF), and accuracy indices were employed as evaluations criteria to select the optimal topology of artificial neural networks (ANNs). Back propagation neural network (BPNN) and generalized feed forward neural network (GFFNN) models were also used to compare and analyze the performance of the developed BPCWNNs model. The results indicated that Z-number considerably improved the accuracy of the ANN model. Notably, the model assessment revealed that the proposed BPCWNNs model was accurate compared to the BPNN and GFFNN and selected as the most superior model. The BPCWNN is the model with high performance which can be used as an artificial intelligence technique for predicting blast-induced AOp. • An expert-based back propagation neural network is developed to simulate of air overpressure due to blasting. • Fuzzy cognitive map is designed to simulate weight of neurons. • Z-number concept is used to overcome uncertainty of expert opinions.
Segmentation of Liver Tumor in CT Scan Using ResU-Net

Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver diseases. Although many automated techniques have been developed for liver and tumor segmentation; however, segmentation of the liver is still challenging due to the fuzzy &amp; complex background of the liver position with other organs. As a result, creating a considerable automated liver and tumour division from CT scans is critical for identifying liver cancer. In this article, deeply dense-network ResU-Net architecture is implemented on CT scan using the 3D-IRCADb01 dataset. An essential feature of ResU-Net is the residual block and U-Net architecture, which extract additional information from the input data compared to the traditional U-Net network. Before being fed to the deep neural network, image pre-processing techniques are applied, including data augmentation, Hounsfield windowing unit, and histogram equalization. The ResU-Net network performance is evaluated using the dice similarity coefficient (DSC) metric. The ResU-Net system with residual connections outperformed state-of-the-art approaches for liver tumour identification, with a DSC value of 0.97% for organ recognition and 0.83% for segmentation methods.
A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing

With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. The biomedical data produced is highly confidential and private. Unfortunately, conventional health systems cannot support such a colossal amount of biomedical data. Hence, data is typically stored and shared through the cloud. The shared data is then used for different purposes, such as research and discovery of unprecedented facts. Typically, biomedical data appear in textual form (e.g., test reports, prescriptions, and diagnosis). Unfortunately, such data is prone to several security threats and attacks, for example, privacy and confidentiality breach. Although significant progress has been made on securing biomedical data, most existing approaches yield long delays and cannot accommodate real-time responses. This paper proposes a novel fog-enabled privacy-preserving model called δrsanitizer, which uses deep learning to improve the healthcare system. The proposed model is based on a Convolutional Neural Network with Bidirectional-LSTM and effectively performs Medical Entity Recognition. The experimental results show that δr sanitizer outperforms the state-of-the-art models with 91.14% recall, 92.63% in precision, and 92% F1-score. The sanitization model shows 28.77% improved utility preservation as compared to the state-of-the-art.
MLP-based Learnable Window Size for Bitcoin price prediction

Over the past few years, Bitcoin price prediction has been changed to a big challenge for investors on cryptocurrencies. In this regard, Neural Networks as a strong structure for regression analysis would play an important role to make a precise prediction. While several leading researches in this field considered the features affecting the price of bitcoin by a fixed number of past days, a new method entitled Learnable Window Size (LWS) is presented for smartening the number of days intended to predict the price of Bitcoin the next day. This paper implements a primary deep neural network, based on the observed Bitcoin price trend in the past days and its fluctuations, to predict the best window size. Then, the secondary deep neural network predicts the price of Bitcoin according to the predicted window of the first step. The dataset of this paper is included Google, Blockchain, and Bitcoin market data. Evaluations have shown that based on the Prediction Hardship Factor (PHF), a new criterion which has been proposed to describe the degree of difficulty of prediction, this method has been able to get the minimum error under a normal situation which is superior in comparison to the well-known methods such as Support Vector Regression and ARIMA.
The Promise and Pitfalls of Learning in Immersive Virtual Reality

Can immersive virtual reality (IVR) serve as an effective venue for learning and training? The promise of learning in IVR lies in its affordances for motivating learners to engage in generative processing (i.e., cognitive processing aimed at making sense of the material). The pitfall of learning in IVR is that it can distract learners so they engage in extraneous processing (i.e., cognitive processing that does not support the instructional goal). This paper reviews (1) media comparison research we have conducted on the effectiveness of learning academic content and skills in IVR versus learning with conventional media and (2) value-added research we have conducted concerning which features can improve the instructional effectiveness of learning in IVR. The paper includes implications for practice and for further work in the area. Overall, the paper focuses on the challenges associated with determining how to reduce the distracting aspects of IVR, maintain the motivating aspects of IVR, and guide the learner towards the core instructional material.