Reviewerly/exHarmony huggingface dataset:
A hybrid deep-learning approach for complex biochemical named entity recognition

Named entity recognition (NER) of chemicals and drugs is a critical domain of information extraction in biochemical research. NER provides support for text mining in biochemical reactions, including entity relation extraction, attribute extraction, and metabolic response relationship extraction. However, the existence of complex naming characteristics in the biomedical field, such as polysemy and special characters, make the NER task very challenging. Here, we propose a hybrid deep learning approach to improve the recognition accuracy of NER. Specifically, our approach applies the Bidirectional Encoder Representations from Transformers (BERT) model to extract the underlying features of the text, learns a representation of the context of the text through Bi-directional Long Short-Term Memory (BILSTM), and incorporates the multi-head attention (MHATT) mechanism to extract chapter-level features. In this approach, the MHATT mechanism aims to improve the recognition accuracy of abbreviations to efficiently deal with the problem of inconsistency in full-text labels. Moreover, conditional random field (CRF) is used to label sequence tags because this probabilistic method does not need strict independence assumptions and can accommodate arbitrary context information. The experimental evaluation on a publicly-available dataset shows that the proposed hybrid approach achieves the best recognition performance; in particular, it substantially improves performance in recognizing abbreviations, polysemes, and low-frequency entities, compared with the state-of-the-art approaches. For instance, compared with the recognition accuracies for low-frequency entities produced by the BILSTM-CRF algorithm, those produced by the hybrid approach on two entity datasets (MULTIPLE and IDENTIFIER) have been increased by 80% and 21.69%, respectively.
A personalized paper recommendation method considering diverse user preferences

Prior studies of paper recommendation methods that consider historical user preferences rarely adequately address the complexity of user preferences and interests. We propose a method to recommend personalized papers based on a heterogeneous network that includes papers, venues, authors, terms, and users as well as the relations among these entities. We investigate meta-paths in the network to capture user preferences and apply random walks on these meta-paths to measure recommendation scores of candidate papers to target users. We employ a personalized weight learning process to discover a user's personalized weights on different meta-paths using Bayesian Personalized Ranking as the objective function. A global recommendation score is calculated by combining recommendation scores on different meta-paths with personalized weights. We conducted experiments using two different datasets and the results showed that the proposed method performed better than other baseline methods.
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling

We develop FedCluster - a novel federated learning framework with improved optimization efficiency, and investigate its theoretical convergence properties. The FedCluster groups the devices into multiple clusters that perform federated learning cyclically in each learning round. Therefore, each learning round of FedCluster consists of multiple cycles of meta-update that boost the overall convergence. In nonconvex optimization, we show that FedCluster with the devices implementing the local stochastic gradient descent (SGD) algorithm achieves a faster convergence rate than the conventional federated averaging (Fe-dAvg) algorithm in the presence of device-level data heterogeneity. We conduct experiments on deep learning applications and demonstrate that FedCluster converges significantly faster than the conventional federated learning under diverse levels of device-level data heterogeneity for a variety of local optimizers.
Retinex model based stain normalization technique for whole slide image analysis

Medical imaging provides the means for diagnosing many of the medical phenomena currently studied in clinical medicine and pathology. The variations of color and intensity in stained histological slides affect the quantitative analysis of the histopathological images. Moreover, stain normalization utilizing color for the classification of pixels into different stain components is challenging. The staining also suffers from variability, which complicates the automatization of tissue area segmentation with different staining and the analysis of whole slide images. We have developed a Retinex model based stain normalization technique in terms of area segmentation from stained tissue images to quantify the individual stain components of the histochemical stains for the ideal removal of variability. The performance was experimentally compared to reference methods and tested on organotypic carcinoma model based on myoma tissue and our method consistently has the smallest standard deviation, skewness value, and coefficient of variation in normalized median intensity measurements. Our method also achieved better quality performance in terms of Quaternion Structure Similarity Index Metric (QSSIM), Structural Similarity Index Metric (SSIM), and Pearson Correlation Coefficient (PCC) by improving robustness against variability and reproducibility. The proposed method could potentially be used in the development of novel research as well as diagnostic tools with the potential improvement of accuracy and consistency in computer aided diagnosis in biobank applications.
Floor-vibration VR: Mitigating Cybersickness Using Whole-body Tactile Stimuli in Highly Realistic Vehicle Driving Experiences

This work addresses cybersickness, a major barrier to successful long-exposure immersive virtual reality (VR) experiences since user discomfort frequently leads to prematurely ending such experiences. Starting from sensory conflict theory, we posit that if a vibrating floor delivers vestibular stimuli that minimally match the vibration characteristics of a scenario, the size of the conflict between the visual and vestibular senses will be reduced and, thus, the incidence and/or severity of cybersickness will also be reduced. We integrated a custom-built, computer-controlled vibrating floor in our VR system. To evaluate the system, we implemented a realistic off-road vehicle driving simulator in which participants rode multiple laps as passengers on an off-road course. We programmed the floor to generate vertical vibrations similar to those experienced in real off-road vehicle travel. The scenario and driving conditions were designed to be cybersickness-inducing for users in both the Vibration and No-vibration conditions. We collected subjective and objective data for variables previously shown to be related to levels of cybersickness or presence. These included presence and simulator sickness questionnaires (SSQ), self-rated discomfort levels, and the physiological signals of heart rate, galvanic skin response (GSR), and pupil size. Comparing data between participants in the Vibration group (N=11) to the No-Vibration group (N=11), we found that Delta-SSQ Oculomotor response and the GSR physiological signal, both known to be positively correlated with cybersickness, were significantly lower (with large effect sizes) for the Vibration group. Other variables differed between groups in the same direction, but with trivial or small effect sizes. The results indicate that the floor vibration significantly reduced some measures of cybersickness.
Multilevel Graph Matching Networks for Deep Graph Similarity Learning

While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning. Recent work on graph similarity learning has considered either global-level graph-graph interactions or low-level node-node interactions, however ignoring the rich cross-level interactions (e.g., between each node of one graph and the other whole graph). In this paper, we propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs. Furthermore, to compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks with different sizes in order to evaluate the effectiveness and robustness of our models. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks. Compared with previous work, MGMN also exhibits stronger robustness as the sizes of the two input graphs increase.
A Secure Trust Method for Multi-Agent System in Smart Grids Using Blockchain

This paper proposes a blockchain based trust management method for agents in a multi-agent system (MAS). In this work, three objectives are achieved: trust, cooperation and privacy. The trust of agents depends on the credibility of trust evaluators, which is verified using the proposed methods of trust distortion, consistency and reliability. To enhance the cooperation between agents, a tit-3-for-tat (T3FT) repeated game strategy is developed. The strategy is more forgiving than the existing tit-for-tat (TFT) strategy. It encourages cheating agents to re-establish their trust by cooperating for three consecutive rounds of play. Also, a proof-of-cooperation consensus protocol is proposed to improve agents' cooperation while creating and validating blocks. The privacy of agents is preserved in this work using the publicly verifiable secret sharing mechanism. The proposed methods are implemented using MATLAB R2018a while the MAS is simulated using Java Agent DEvelopment framework (JADE). Simulation results validate the effectiveness of the proposed work. From the simulation results, the proposed trust method outperforms an existing fuzzy logic trust method in terms of detecting the cheating behavior of agents in the system. Besides, the proposed T3FT strategy is effective as compared to the existing tit-for-2-tat and TFT strategies in the literature. Moreover, the security analysis of the proposed method is performed. The analysis shows that the proposed work is safe from bad-mouthing and on-off trust related attacks.
Adaptive dimensionality reduction for neural network-based online principal component analysis

“Principal Component Analysis” (PCA) is an established linear technique for dimensionality reduction. It performs an orthonormal transformation to replace possibly correlated variables with a smaller set of linearly independent variables, the so-called principal components, which capture a large portion of the data variance. The problem of finding the optimal number of principal components has been widely studied for offline PCA. However, when working with streaming data, the optimal number changes continuously. This requires to update both the principal components and the dimensionality in every timestep. While the continuous update of the principal components is widely studied, the available algorithms for dimensionality adjustment are limited to an increment of one in neural network-based and incremental PCA. Therefore, existing approaches cannot account for abrupt changes in the presented data. The contribution of this work is to enable in neural network-based PCA the continuous dimensionality adjustment by an arbitrary number without the necessity to learn all principal components. A novel algorithm is presented that utilizes several PCA characteristics to adaptivly update the optimal number of principal components for neural network-based PCA. A precise estimation of the required dimensionality reduces the computational effort while ensuring that the desired amount of variance is kept. The computational complexity of the proposed algorithm is investigated and it is benchmarked in an experimental study against other neural network-based and incremental PCA approaches where it produces highly competitive results.
The Impact of Gender and Years of Teaching Experience on College Teachers’ Digital Competence: An Empirical Study on Teachers in Gansu Agricultural University

With the development of Information and Communication Technologies (ICT) and the emergence of the Corona virus (COVID-19,) our way of life and even our education have been affected. The education approach has changed from traditional tutorials to online education. As a result, it has been a necessary for not only students but also teachers to improve their digital competence. The aim of this study was to describe in-service teachers’ self-perceptions of digital competence in the context of higher education and analyze the impact of gender and years of teaching experience on college teachers’ digital competence. For this purpose, a quantitative methodology has been used. A sample of 536 in-service teachers from Gansu Agricultural University, China, completed a questionnaire on digital competence. The data were collected in the academic year of 2019–2020. The results show that the sample considered themselves positively in information and data literacy, communication and collaboration, security and problem solving, while they self-evaluated their digital content creation negatively. Regarding the variables studied, significant differences were found in favor of male college teachers in the perception of digital competence. In relation to the teaching experience, teachers with less teaching experience thought themselves better in the areas of communication and collaboration, digital content creation, security and problem solving.
Zero-shot Cross-modal Retrieval by Assembling AutoEncoder and Generative Adversarial Network

Conventional cross-modal retrieval models mainly assume the same scope of the classes for both the training set and the testing set. This assumption limits their extensibility on zero-shot cross-modal retrieval (ZS-CMR), where the testing set consists of unseen classes that are disjoint with seen classes in the training set. The ZS-CMR task is more challenging due to the heterogeneous distributions of different modalities and the semantic inconsistency between seen and unseen classes. A few of recently proposed approaches are inspired by zero-shot learning to estimate the distribution underlying multimodal data by generative models and make the knowledge transfer from seen classes to unseen classes by leveraging class embeddings. However, directly borrowing the idea from zero-shot learning (ZSL) is not fully adaptive to the retrieval task, since the core of the retrieval task is learning the common space. To address the above issues, we propose a novel approach named Assembling AutoEncoder and Generative Adversarial Network (AAEGAN), which combines the strength of AutoEncoder (AE) and Generative Adversarial Network (GAN), to jointly incorporate common latent space learning, knowledge transfer, and feature synthesis for ZS-CMR. Besides, instead of utilizing class embeddings as common space, the AAEGAN approach maps all multimodal data into a learned latent space with the distribution alignment via three coupled AEs. We empirically show the remarkable improvement for ZS-CMR task and establish the state-of-the-art or competitive performance on four image-text retrieval datasets.
Rear-Seat Productivity in Virtual Reality: Investigating VR Interaction in the Confined Space of a Car

Ubiquitous technology lets us work in flexible and decentralised ways. Passengers can already use travel time to be productive, and we envision even better performance and experience in vehicles with emerging technologies, such as virtual reality (VR) headsets. However, the confined physical space constrains interactions while the virtual space may be conceptually borderless. We therefore conducted a VR study (N = 33) to examine the influence of physical restraints and virtual working environments on performance, presence, and the feeling of safety. Our findings show that virtual borders make passengers touch the car interior less, while performance and presence are comparable across conditions. Although passengers prefer a secluded and unlimited virtual environment (nature), they are more productive in a shared and limited one (office). We further discuss choices for virtual borders and environments, social experience, and safety responsiveness. Our work highlights opportunities and challenges for future research and design of rear-seat VR interaction.
Augmented reality technology in science education for students with specific learning difficulties: its effect on students’ learning and views

(2021). Augmented reality technology in science education for students with specific learning difficulties: its effect on students' learning and views. Research in Science & Technological Education. Ahead of Print.
A systematic review on machine learning for fall detection system

Abstract Fall is a major threat to the health and life of the elders. A Fall Detection System (FDS) assist the elders by identifying the fall and save their life. Machine Learning‐ (ML) based FDS has turned into a major research area due to its capability to assist the elders automatically. The efficiency of a FDS depends on its strength to identify the fall from nonfall accurately. The initial fall detection scheme depends on the threshold‐based classification to classify the fall from the Activity of Daily Living (ADL) but this classification method has failed to reduce the false alarm rate, which raises a question on the efficiency of the technique. This review work identifies the problems in threshold‐based classification from existing works and finds the need for an efficient ML‐based classification technique to accurately identify the fall. Then, presents a comprehensive literature review on various ML‐based classification in fall detection. Moreover, the scrutiny investigates the shortcomings associated with the ML‐based techniques for future research. This study finds that present ML‐based FDS has not addressed problems like data preprocessing and data dimensionality reduction techniques even though ML‐based techniques are far superior to threshold‐based techniques. The study concludes that Self‐Adaptive‐based FDS, as well as the complexity reduction of ML‐based models, should be concentrated in future research.
B-ReST: Blockchain-Enabled Resource Sharing and Transactions in Fog Computing

Driven by the extensively emerging applications requiring big data processing, a series of heterogeneous network architectures have been proposed to meet user experience requirements. Among them, the concept of fog computing facilitates the effective integration and utilization of ubiquitous computing resources. In fog computing scenarios, willingness and service billing issues become significant to computing resource sharing and transactions. In this article, the recently developed blockchain technology characterized by successfully enabling consensus in an untrustworthy environment is introduced. Based on the blockchain technology, we propose a new architecture for resource sharing and transactions in fog computing networks, named Blockchain-Enabled Resource Sharing and Transactions in Fog Computing (B-ReST). The physical architecture, functional architecture, and workflow in B-ReST are defined. We also discuss the key technologies in B-ReST such as the smart contracts, the consensus mechanism and the requester and provider matching (RPM). The wireless characteristics of fog computing and blockchain technology are closely combined to make full and efficient use of ubiquitous computing resources. To prove the feasibility of the proposed architecture, the RPM problem is solved by a deep reinforcement learning (DRL) based method. Simulation results show the advantages of B-ReST to realize resource sharing and transactions, and the performance of B-ReST based on the DRL method has been enhanced. Challenges and future research directions are summarized as well.
Key Factors in the Implementation of the Internet of Things in the Hotel Sector

Many factors can influence decision-making, and if you wish to know which are the most influential factors in a decision, they must be classified by their degrees of influence. This study seeks to determine the most influential factors in the decision of hotels to accept and implement the Internet of Things in their services through a literary review and a causal study carried out on experts in technology and hotels. The methodology involves the use of fuzzy cognitive maps and the FCMappers tool. The results obtained show that the following factors are among the most influential (in order of relevance): the perceived reliability of the technology, the relative advantage it gives, the level of top management support, compatibility, customer pressure, information systems provider support, security, business partner pressure, characteristics of the leader or manager, government pressure or incentives, pressure from competitors, technological organizational readiness, complexity, size of the company, and the perceived cost.
Fixing Defect of Photometric Loss for Self-Supervised Monocular Depth Estimation

View-synthesis-based methods have shown very promising results for the task of unsupervised depth estimation in single images. Most existing approaches synthesize a new image and employ it as the supervision signal for depth and pose prediction. There are two problems in these approaches: 1) There are many combinations of pose and depth that can synthesize a certain new image; therefore, reconstructing the depth and pose based on the view-synthesis method from only two images is an inherently ill-posed problem; 2) The model is trained under the photometric consistency assumption that the brightness or gradient is constant when applied to the video sequences. However, this assumption is easily violated in realistic scenes due to light changes, reflective surfaces and occlusions. To overcome the first drawback, we exploit the point cloud consistency constraint to eliminate ambiguity. To overcome the second drawback, we use threshold masks to filter dynamic and occluded points and introduce matching point constraints that implicitly encode the geometry relationship between two matched points to improve the precision of depth prediction. In addition, we employ epipolar constraints to compensate for the instability of the photometric error in textureless regions and varying illumination conditions. The experimental results on the KITTI, Cityscapes and NYUv2 datasets show that the method can improve the accuracy of depth prediction and enhance the robustness of the model in handling textureless regions and illumination changes. The code and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XTUPRLAB/FixUnDepth</uri> .