Reviewerly/exHarmony huggingface dataset:
CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice

Due to the growing amount of COVID-19 research literature, medical experts, clinical scientists, and researchers frequently struggle to stay up to date on the most recent findings. There is a pressing need to assist researchers and practitioners in mining and responding to COVID-19-related questions on time.This paper introduces CoQUAD, a question-answering system that can extract answers related to COVID-19 questions in an efficient manner. There are two datasets provided in this work: a reference-standard dataset built using the CORD-19 and LitCOVID initiatives, and a gold-standard dataset prepared by the experts from a public health domain. The CoQUAD has a Retriever component trained on the BM25 algorithm that searches the reference-standard dataset for relevant documents based on a question related to COVID-19. CoQUAD also has a Reader component that consists of a Transformer-based model, namely MPNet, which is used to read the paragraphs and find the answers related to a question from the retrieved documents. In comparison to previous works, the proposed CoQUAD system can answer questions related to early, mid, and post-COVID-19 topics.Extensive experiments on CoQUAD Retriever and Reader modules show that CoQUAD can provide effective and relevant answers to any COVID-19-related questions posed in natural language, with a higher level of accuracy. When compared to state-of-the-art baselines, CoQUAD outperforms the previous models, achieving an exact match ratio score of 77.50% and an F1 score of 77.10%.CoQUAD is a question-answering system that mines COVID-19 literature using natural language processing techniques to help the research community find the most recent findings and answer any related questions.
Mixed reality environment for learning sensing technology applications in Construction: A usability study

Mixed reality is gradually becoming ubiquitous and significant in education owning to the inherent benefits of active participation and tacit knowledge development in a safe and engaging environment. However, limited studies have explored design features that facilitate its use as a pedagogical tool in construction education, particularly in equipping students with experiential skills that are otherwise challenging to obtain due to resource constraints and limited access to construction sites. By evaluating eye tracking, usability questions, and think-aloud protocol data and verbal feedback, this study investigated the usability of a mixed reality environment designed for equipping construction engineering students with competencies for deploying sensing technologies on construction projects. Results revealed features such as accuracy of represented construction activities, quality of animations, and easy access to information and resources as important for designing efficient mixed reality learning environments. While the usability data suggested that the user interface was usable, eye tracking provided profundity on encountered difficulties with the user interface. Through concise outline and sequential design of the user interface, this study revealed that knowledge scaffolding can improve task performance in a mixed reality learning environment. This study adds value to existing literature, in particular by providing insights into the affordances of mixed reality environments that address the technological gap between the construction industry and construction engineering education. The mixed reality learning environment contributes to the cognitive apprenticeship theory through the use of game objects to develop procedural knowledge for addressing construction industry challenges with sensing technologies.
Automatic defect identification in magnetic particle testing using a digital model aided De-noising method

• The proposed digital model based method can provide theoretical results of magnetic particle testing, which is potential to help the operators to design the detection scheme of MPT. • According to the calculation of the digital model, we proposed an image registration technology based method to eliminate the fake magnetic indication in the result of MPT. • Hough transform is introduced in defect identification of MPT. We have proved that Hough transform can be robust to the point noise in the result of MPT. Magnetic particle testing (MPT) has high sensitivity, which is popular in ferromagnetic material detection. However, results of MPT always mix with fake magnetic particle indication (FMPI) and point noise caused by nonrelevant magnetic particles. It mainly depends on manual work to find defects in MPT. In this paper, Hough Transform, which is robust to the point noise, is introduced to automatically identify the linear features of magnetic particle indication (MPI) caused by defects. However, the algorithm cannot distinguish between the similarly shaped FMPI and MPI. To address the problem, we proposed a digital model aided de-noising Method. Specifically, the density of normal component of magnetic flux on specimen surface is calculated by finite element analysis to evaluate the possibilities of FMPI occurrence. Then detection results are compared with the calculation to eliminate FMPI. Experiments show that scratches on hub bearing can be successfully identified by the proposed method.
GTAD: Graph and Temporal Neural Network for Multivariate Time Series Anomaly Detection

The rapid development of smart factories, combined with the increasing complexity of production equipment, has resulted in a large number of multivariate time series that can be recorded using sensors during the manufacturing process. The anomalous patterns of industrial production may be hidden by these time series. Previous LSTM-based and machine-learning-based approaches have made fruitful progress in anomaly detection. However, these multivariate time series anomaly detection algorithms do not take into account the correlation and time dependence between the sequences. In this study, we proposed a new algorithm framework, namely, graph attention network and temporal convolutional network for multivariate time series anomaly detection (GTAD), to address this problem. Specifically, we first utilized temporal convolutional networks, including causal convolution and dilated convolution, to capture temporal dependencies, and then used graph neural networks to obtain correlations between sensors. Finally, we conducted sufficient experiments on three public benchmark datasets, and the results showed that the proposed method outperformed the baseline method, achieving detection results with F1 scores higher than 95% on all datasets.
A novel end-to-end deep learning approach for cancer detection based on microscopic medical images

As a result of late diagnosis, cancer is the second leading cause of death in most countries in the world. Usually, many cases of cancer are diagnosed at an advanced stage, which reduces the chances of recovery from the disease due to the inability to provide appropriate treatment. The earlier cancer is detected, the more effective the treatment can be, especially for incurable cancers, which can result in a shorter life expectancy due to the rapid spread of the disease. The early detection of cancer also greatly reduces the financial consequences of it, as the cost of treating it in its early stages is much lower than in its other stages. Therefore, several previous studies focus on developing computer-aided cancer diagnosis systems (CACDs) that can detect cancer in its earliest stages automatically. In this paper, a novel approach is proposed for cancer detection. The proposed approach is an end-to-end deep learning approach, where the input images are fed directly to the deep model for final decision. In this research, the accuracy of a new deep convolutional neural network (CNN) for cancer detection is explored. The microscopic medical images obtained from the cancer database were used to evaluate our study, which were labelled as normal and abnormal images. The presented model achieved an accuracy of 99.99%, which is the highest accuracy compared with other deep learning models. Finally, the proposed approach would be very useful and effective, especially in low-income countries where referral systems for patients with suspected cancer are often unavailable, resulting in delayed and fragmented care.
U<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mrow /><mml:mn>2</mml:mn></mml:msup></mml:math>R-pGAN: Unpaired underwater-image recovery with polarimetric generative adversarial network

Polarimetric imaging has prominent advantages in scattering media. Emerging learning-based polarimetric technologies has succeeded but heavily relied on paired data corresponding to the same scenes. In this paper, we propose an unsupervised method for the unpaired underwater-image recovery with a polarimetric generative adversarial network, named U2R-pGAN. The method breaks the dependence on strictly paired images in traditional learning-based methods and significantly enhances the restoration performance. Besides, we merge polarization losses into the network, which has been verified beneficial for details restoration. Imaging experiments have been devised and performed on different objects and viewing conditions under varying turbidity. The results demonstrate that the proposed method improves the peak signal to noise ratio by an average of 3.4 dB. The new method can be readily applied to practical underwater applications.
Social recommendation via deep neural network-based multi-task learning

Recent years are witnessing the rapid development of social recommendation to improve the performance of recommender system, especially for cold start problem. Many of the existing social recommendation techniques utilize matrix factorization-based model, which would not essentially capture the complex nonlinear relationships for the user–item interactions and user–user interactions. Inspired by the successful application of deep neural networks on computer vision, natural language processing, and other tasks, deep learning is employed to model the social network-enhanced collaborative filtering problem. Although it has been proposed recently to model the nonlinear relationship by deep networks for collaborative filtering, it does not take the social relations between users into account. Accordingly, simultaneously modeling the social domain and item domain interactions is proposed by sharing user representation in two tasks as Social Regularized Neural Matrix Factorization (SoNeuMF). Comprehensive experiments on two real-world datasets show significant improvements of the proposed SoNeuMF framework with regard to recommendation accuracy against state-of-the-art methods. • A social recommendation model (SoNeuMF) based on deep learning is proposed. • SoNeuMF simultaneously models social and item interactions by neural networks. • User representation in two tasks is shared to enhance the social recommendation list. • Experiments show the improvement over baselines especially on cold-start users.
Deep-BERT: Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

Offensive messages on social media, have recently been frequently used to harass and criticize people.In recent studies, many promising algorithms have been developed to identify offensive texts.Most algorithms analyze text in a unidirectional manner, where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences.In addition, there are many separate models for identifying offensive texts based on monolingual and multilingual, but there are a few models that can detect both monolingual and multilingual-based offensive texts.In this study, a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers (Deep-BERT) to identify offensive posts on social media that are used to harass others.This paper explores a variety of ways to deal with multilingualism, including collaborative multilingual and translation-based approaches.Then, the Deep-BERT is tested on the Bengali and English datasets, including the different bidirectional encoder representations from transformers (BERT) pre-trained word-embedding techniques, and found that the proposed Deep-BERT's efficacy outperformed all existing offensive text classification algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.
Ameliorated Follow The Leader: Algorithm and Application to Truss Design Problem

Swarm-based models mimic the collective behavior shown in insects or animals. To date, several algorithms have been proposed by researchers to solve a wide range of complex optimization problems. This paper presents an improved version of follow the leader (iFTL) algorithm that imitates the behavioral movement of a sheep within the flock. The work presented in this paper mathematically models this foraging behavior to realize the process of optimization. The COmparing Continuous Optimisers (COCO) experimental framework is used for performance evaluation with twenty-four noiseless and thirty noisy benchmark functions. After that, it has been compared with fourteen well-presented optimization algorithms in different dimensions. The results generated show that iFTL outperformed all compared optimization algorithms and outranked in all dimensions. The iFTL algorithm is also tested on twenty-four standard benchmark function and compared with eight well-known optimization algorithms to benchmark its performance. Further, the efficacy of the proposed algorithm is tested on 10, 37, 52, 72, 120, 200, 224, and 942 bar truss design problems. Finally, the results generated by truss design problems are compared with other meta-heuristics algorithms to validate the performance of the proposed algorithm. The obtained results reveal that iFTL is efficient and stable than other state-of-the-art algorithms.
Immersive media experience: a survey of existing methods and tools for human influential factors assessment

Virtual reality (VR) applications, especially those where the user is untethered to a computer, are becoming more prevalent as new hardware is developed, computational power and artificial intelligence algorithms are available, and wireless communication networks are becoming more reliable, fast, and providing higher reliability. In fact, recent projections show that by 2022 the number of VR users will double, suggesting the sector was not negatively affected by the worldwide COVID-19 pandemic. The success of any immersive communication system is heavily dependent on the user experience it delivers, thus now more than ever has it become crucial to develop reliable models of immersive media experience (IMEx). In this paper, we survey the literature for existing methods and tools to assess human influential factors (HIFs) related to IMEx. In particular, subjective, behavioural, and psycho-physiological methods are covered. We describe tools available to monitor these HIFs, including the user's sense of presence and immersion, cybersickness, and mental/affective states, as well as their role in overall experience. Special focus is placed on psycho-physiological methods, as it was found that such in-depth evaluation was lacking from the existing literature. We conclude by touching on emerging applications involving multiple-sensorial immersive media and provide suggestions for future research directions to fill existing gaps. It is hoped that this survey will be useful for researchers interested in building new immersive (adaptive) applications that maximize user experience.
Neighbor2Neighbor: A Self-Supervised Framework for Deep Image Denoising

In recent years, image denoising has benefited a lot from deep neural networks. However, these models need large amounts of noisy-clean image pairs for supervision. Although there have been attempts in training denoising networks with only noisy images, existing self-supervised algorithms suffer from inefficient network training, heavy computational burden, or dependence on noise modeling. In this paper, we proposed a self-supervised framework named Neighbor2Neighbor for deep image denoising. We develop a theoretical motivation and prove that by designing specific samplers for training image pairs generation from only noisy images, we can train a self-supervised denoising network similar to the network trained with clean images supervision. Besides, we propose a regularizer in the perspective of optimization to narrow the optimization gap between the self-supervised denoiser and the supervised denoiser. We present a very simple yet effective self-supervised training scheme based on the theoretical understandings: training image pairs are generated by random neighbor sub-samplers, and denoising networks are trained with a regularized loss. Moreover, we propose a training strategy named BayerEnsemble to adapt the Neighbor2Neighbor framework in raw image denoising. The proposed Neighbor2Neighbor framework can enjoy the progress of state-of-the-art supervised denoising networks in network architecture design. It also avoids heavy dependence on the assumption of the noise distribution.We evaluate the Neighbor2Neighbor framework through extensive experiments, including synthetic experiments with different noise distributions and real-world experiments under various scenarios. The code is available online: https://github.com/TaoHuang2018/Neighbor2Neighbor.
Semi-supervised feature selection via adaptive structure learning and constrained graph learning

Graph-based sparse feature selection plays an important role in semi-supervised feature selection, which greatly improves the performance of feature selection. However, most existing semi-supervised methods based on graph are still limited in two main aspects. On the one hand, the quality of the similarity matrix will affect the performance of the learning model. Adaptive graph learning improves the quality of similarity matrix by learning the similarity matrix adaptively. However, most methods based on adaptive graph learning ignore the label information, which may limit the quality of the similarity matrix. On the other hand, many state-of-the-art methods only consider the local structure and neglect the global structure of samples, which will result in high redundancy in the selected features. To alleviate the impact of the above problems, in this study, a novel semi-supervised feature selection model named ASLCGLFS is proposed. In the proposed method, the adaptive graph learning is extended through label information, which aims to further improve the quality of the similarity matrix by utilizing the label information to constrain the graph learning. Moreover, adaptive structure learning is introduced, which not only considers the global structure but also facilitates feature selection. An iteration method is designed to solve the objective function and the convergence of this method is proved theoretically and experimentally. Extensive experiments conducted on common datasets verify that the proposed ASLCGLFS is better than some state-of-the-art feature selection algorithms in performance.
Deep Learning, Blockchain based Multi-layered Authentication and Security Architectures

Because of its ability to make educated judgments, deep learning has gained massive attention in recent years. Many of today's deep learning systems rely on centralized servers and lack operational transparency, traceability, dependability, security, and trustworthy data provenance. Furthermore, using centralized data to train deep learning models exposes them to the single point of failure issue. The relevance of combining blockchain technology and deep learning is highlighted in this study. Deep-learning (DL) techniques are used to authenticate and identify abnormalities and provide security for systems (in cryptographic and biometric systems). Confidentiality and effectiveness must be balanced since network sensors are energy-constrained devices, which is the most crucial idea to consider when establishing a security system that relies on deep-learning techniques and blockchain. Centralized systems have several flaws. Mostly, a network with significant demand for smart devices creates a prodigious amount of data. There is always the possibility that one or more of the centralized network's major components would fail, causing a catastrophic (or full) system failure. The data acquired by the centralized cloud storage frequently necessitates third-party modification. This may result in data breaches, jeopardizing the privacy of the end-user. This paper primarily focuses on the security models proposed in terms of authentication and security using Blockchain, Deep Learning, or the integration of both based on certain characteristics to identify and organize the literature, inclouding type, models, consensus protocols, applications, services, and deployment goals.
Improved RRT Algorithm for AUV Target Search in Unknown 3D Environment

Due to the complexity of the marine environment, underwater target search and interception is one of the biggest problems faced by an autonomous underwater vehicle (AUV). At present, there is quite a lot of research in terms of the two-dimensional environment. This paper proposes an improved rapidly exploring random trees (RRT) algorithm to solve the problem of target search and interception in an unknown three-dimensional (3D) environment. The RRT algorithm is combined with rolling planning and node screening to realize path planning in an unknown environment, and then the improved RRT algorithm is applied to the search and interception process in a 3D environment. Combined with the search decision function and the three-point numerical differential prediction method, the RRT algorithm can search for and effectively intercept the target. Numerical simulations in various situations show the superior performance, in terms of time and accuracy, of the proposed approach.
Graph convolutional networks in language and vision: A survey

Graph convolutional networks (GCNs) have a strong ability to learn graph representation and have achieved good performance in a range of applications, including social relationship analysis, biological information processing, natural language processing (NLP), computer vision (CV), and so on. In recent years, the application of GCNs in natural language processing and computer vision has attracted substantial interest from researchers, as a result of which many studies based on GCNs have emerged in the fields of natural language processing and computer vision. However, to the best of our knowledge, a comprehensive survey of GCN application in natural language processing and computer vision has not yet been conducted. Accordingly, this survey presents a comprehensive review of the principles of GCNs and its applications in these two fields. First, we summarize the principles of the two types of GCNs, namely spatial methods and spectral methods. Then we divide GCN applications into two categories: natural language processing and computer vision. Subsequently, we present multiple applications from each category in detail. Finally, we outline the limitations of GCNs and discuss possible future research directions.