Reviewerly/exHarmony huggingface dataset:
Risk Detection in Wireless Body Sensor Networks for Health Monitoring Using Hybrid Deep Learning

The Internet of Things (IoT) idea has arisen as interconnected components of the healthcare tracking facilities of smart linked healthcare networks. Hard sensor-based data aggregation with the help of devices in the form of wearables or intrusive samples attached with the acquisition of soft sensors like crowd sensor results in the aggregated sensor data being concealed in patterns. This problem is tackled through several secret stages of interpretation of deep learning techniques. In this research work, we proposed hybrid deep learning (HDL) techniques to develop estimation and enhance quality of smart health services on health monitoring data. We also showed a detailed comparison of methods on the basis of health surveillance types. Hence, our proposed models work for risk detection in health information which will help us to increase the efficiency of existing healthcare industry.
AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection

Automated hate speech detection in social media is a challenging task that has recently gained significant traction in the data mining and Natural Language Processing community. However, most of the existing methods adopt a supervised approach that depended heavily on the annotated hate speech datasets, which are imbalanced and often lack training samples for hateful content. This paper addresses the research gaps by proposing a novel multitask learning-based model, AngryBERT, which jointly learns hate speech detection with sentiment classification and target identification as secondary relevant tasks. We conduct extensive experiments to augment three commonly-used hate speech detection datasets. Our experiment results show that AngryBERT outperforms state-of-the-art single-task-learning and multitask learning baselines. We conduct ablation studies and case studies to empirically examine the strengths and characteristics of our AngryBERT model and show that the secondary tasks are able to improve hate speech detection.
Road Detection Using Semantic Segmentation-Based Convolutional Neural Network for Intelligent Vehicle System

Road scene perception of driving environment is an important yet stimulating task for an intelligent vehicle system (IVS). Advancement of deep learning techniques has enabled the image sensors to understand the scene more accurately for the intended object, especially in segmentation domain. In context of computer vision approach, segmentation of road scene from colour images is challenging, due to the varying illumination circumstances, the non-uniform road shapes and the fuzzy boundaries between the road and other objects. However, existing studies have limited performance in context of standard measures, therefore fails to incorporate the mentioned issues. With this aim and to clearly distinguish road and non-road portion, we have utilized U-Net, Seg-Net and fully convolutional network (FCN) models. From the conducted experiments, U-Net has achieved 94% score for mIoU and dice coefficient which is better than the scores achieved by other models Seg-Net and FCN-32. In our experiment, images from Camvid dataset have been used to train and validate the performance.
A fast instance segmentation with one-stage multi-task deep neural network for autonomous driving

An accurate real-time instance segmentation, which can perform both object detection and semantic segmentation at the same time with a multi-task neural network, is important for autonomous driving. This paper proposes a fast one-stage multi-task neural network for instance segmentation, which can meet the requirements of real-time processing with sufficient accuracy and that is more desirable for self-driving applications. With a one-stage strategy, it can perform object detection and segmentation concurrently. This paper conducts the related experiments with two public datasets. The cross-validation was carried out with set of variables to determine the optimal combination of each model and compared with the mainstream instance segmentation algorithms. According to our experiment, the proposed algorithm has five times the performance compared to the previous algorithms, which can meet the real-time requirement for autonomous driving applications.
Detection of Acute Lymphoblastic Leukemia Using Machine Learning Techniques

Automatic detection of acute lymphoblastic leukemia (ALL) is essential, as well as a challenging job. Recently, machine learning and deep learning-based classification have emerged as an esteemed approach in medical image analysis. In the presence of small medical datasets, transfer learning dominates over traditional deep learning methods. In this work, we have presented an effective and computationally efficient ALL detection technique. We have presented three models by introducing fully connected layers and/ or dropout layers in ResNet50 architecture. Out of these three models, the model that demonstrates the best training performance is selected to extract features efficiently. Finally, we have applied logistic regression, support vector machine (SVM), and random forest to classify ALL and compare their performances.
Inconsistency-Aware Wavelet Dual-Branch Network for Face Forgery Detection

Current face forgery techniques can generate high-fidelity fake faces with extremely low labor and time costs. As a result, face forgery detection becomes an important research topic to prevent technology abuse. In this paper, we present an inconsistency-aware wavelet dual-branch network for face forgery detection. This model is mainly based on two kinds of forgery clues called inter-image and intra-image inconsistencies. To fully utilize them, we firstly enhance the forgery features by using additional inputs based on stationary wavelet decomposition (SWD). Then, considering the different properties of the two inconsistencies, we design a dual-branch network that predicts image-level and pixel-level forgery labels respectively. The segmentation branch aims to recognize real and fake local regions, which is crucial for discovering intra-image inconsistency. The classification branch learns to discriminate the real and fake images globally, thus can extract inter-image inconsistency. Finally, bilinear pooling is employed to fuse the features from the two branches. We find that the bilinear pooling is a kind of spatial attentive pooling. It effectively utilizes the rich spatial features learned by the segmentation branch. Experimental results show that the proposed method surpasses the state-of-the-art face forgery detection methods.
A robust integrated Bayesian multi-model uncertainty estimation framework (IBMUEF) for quantifying the uncertainty of hybrid meta-heuristic in global horizontal irradiation predictions

Accurate and stable prediction of global horizontal irradiation (GHI) is vital for managing energy systems, irrigation planning, and also decision making for future investment. Existing artificial intelligence (AI) models for prediction GHI generally have high performance, but suffer from instability and uncertainty, especially in different climate areas. Therefore, the aim of this study is to implement an ensemble strategy based on the Integrated Bayesian Multi-model Uncertainty Estimation Framework (IBMUEF) for simultaneous input parameters and model structure uncertainty quantification in AIs. In this study, robust prediction techniques of meta-heuristic optimization algorithms (Multiverse optimization (MVO), Sine-cosine algorithm (SCA), Salp swarm algorithm (SSA)) are hybridized with machine learning models of Adaptive Neuro Fuzzy Inference System (ANFIS) and Extreme Learning Machine (ELM) for GHI predictions. The ensemble IBMUEF combines the advantages of developed models to improve the predicting performance. Comparing results of IBMUEF against the developed individual models show the predictive skill and strength of IBMUEF for four meteorological stations in two climate areas (arid and semi-arid) located in Iran. The results of developed individual models showed that all six hybrid models (ANFIS-MVO, ANFIS-SCA, ANFIS-SSA, ELM-MVO, ELM-SCA, ELM-SSA) performed well in four stations. The ranking of models carried out by Multi-Criteria Decision-Making (MCDM) method of Weighted Aggregated Sum Product Assessment (WASPAS). The ANFIS-MVO model with the highest rank was selected as the best model that R2, RMSE, MAE, and NSE values were ranged from 0.9929 to 0.9989, from 16.23 to 54.78 Wh/m2/day, from 4.21 to 10.39 Wh/m2/day, and from 0.993 to 0.999, respectively. The accuracy of ensemble IBMUEF based on the average RMSE was 23.7% and 32.2% better than the ANFIS-MVO model in arid and semi-arid climates, respectively. Evaluation results related to input parameters and model structure uncertainties proved the superiority of ensemble IBMUEF over the individual models, while 95% confidence interval covered almost 96% of observation data. The results highlighted the significance of combining individual models in IBMUEF for more accurate prediction of GHI with a suitable level of uncertainty. In conclusion, simultaneous considering of model input uncertainty and model parameter uncertainty is crucial for obtaining realistic and certain GHI predictions and correct quantification of the uncertainty bounds.
Matrix product state pre-training for quantum machine learning

Abstract Hybrid quantum–classical algorithms are a promising candidate for developing uses for NISQ devices. In particular, parametrised quantum circuits (PQCs) paired with classical optimizers have been used as a basis for quantum chemistry and quantum optimization problems. Tensor network methods are being increasingly used as a classical machine learning tool, as well as a tool for studying quantum systems. We introduce a circuit pre-training method based on matrix product state machine learning methods, and demonstrate that it accelerates training of PQCs for both supervised learning, energy minimization, and combinatorial optimization.
Attentive Heterogeneous Graph Embedding for Job Mobility Prediction

Job mobility prediction is an emerging research topic that can benefit both organizations and talents in various ways, such as job recommendation, talent recruitment, and career planning. Nevertheless, most existing studies only focus on modeling the individual-level career trajectories of talents, while the impact of macro-level job transition relationships (e.g., talent flow among companies and job positions) has been largely neglected. To this end, in this paper we propose an enhanced approach to job mobility prediction based on a heterogeneous company-position network constructed from the massive career trajectory data. Specifically, we design an Attentive heterogeneous graph embedding for sequential prediction (Ahead) framework to predict the next career move of talents, which contains two components, namely an attentive heterogeneous graph embedding (AHGN) model and a Dual-GRU model for career path mining. In particular, the AHGN model is used to learn the comprehensive representation for company and position on the heterogeneous network, in which two kinds of aggregators are employed to aggregate the information from external and internal neighbors for a node. Afterwards, a novel type-attention mechanism is designed to automatically fuse the information of the two aggregators for updating node representations. Moreover, the Dual-GRU model is devised to model the parallel sequences that appear in pair, which can be used to capture the sequential interactive information between companies and positions. Finally, we conduct extensive experiments on a real-world dataset for evaluating our Ahead framework. The experimental results clearly validate the effectiveness of our approach compared with the state-of-the-art baselines in terms of job mobility prediction.
Real-Time Detection of River Surface Floating Object Based on Improved RefineDet

Object detection is widely used in robot navigation, intelligent security and industrial detection, but it is rarely used in water conservancy industry. In the aspect of river and lake health management, it is important to clean out the floating objects in time, which prevents water pollution caused by the accumulation of the floating objects. The early methods of water surface object detection, such as background subtraction and frame difference, are greatly affected by the change of object shape and background, resulting in poor robustness. Therefore, we propose a real-time detection method of surface floating objects based on improved RefineDet model, which includes three modules: the anchor refinement module, the transfer connection block and the object detection module. We improve anchor refinement module by adding convolution layers to obtain higher-level semantic, and fuse high-level features with low-level features to improve detection accuracy. Moreover, we adjust the parameters setting of anchors according to the scale and aspect ratio distribution to match the multi-scale object better. Aiming at the foreground-background class imbalance caused by dense anchors sampling, we introduce focal loss function to solve it and make our model more efficient by adjusting the parameters of the function. We verify the performance of the proposed method on different floating object datasets we constructed. The detection accuracies on these different datasets are 83.8%, 88.0%, and 82.3% respectively, and the detection speed is 28 FPS. This shows that the improved RefineDet realizes high-precision and real-time detection.
CNN-based Mask Detection System Using OpenCV and MobileNetV2

this paper establishes a `Safety system for mask detection during this COVID-19 pandemic'. Face mask detection has seen an overwhelming growth in the realm of Computer vision and deep learning, since the unprecedented COVID-19 global pandemic that has mandated wearing masks in public places. To tackle the situation, machine learning engineers have come up with several algorithms and techniques to identify unmasked individuals using various mask detection models. The proposed approach in this paper adopts frameworks of deep learning, TensorFlow, Keras, and OpenCV libraries to detect face masks in real time. The trained MobileNet model, presented in this paper, yielded an accuracy score of 0.99 and an F1 score of 0.99 in the training data. This user-friendly model can be incorporated with several existing technologies such as face detection, biometric authentication and facial expression detection for further advancements in the future.
Seeing Like a State, Enacting Like an Algorithm: (Re)assembling Contact Tracing and Risk Assessment during the COVID-19 Pandemic

As states increasingly use algorithms to improve the legibility of society, particularly during the COVID-19 pandemic, it is common for concerns about the expanding power of the algorithm or the state to be raised in a deterministic manner. However, how are the algorithms for states’ legibility projects enacted, contested, and reconfigured? Drawing on interviews and media data, this study fills this gap by examining Health Code ( jiankangma), the Chinese contact tracing and risk assessment algorithmic system that serves as the COVID-19 health passport. I first explore the intensive and invisible work and infrastructures that enact and stabilize Health Code’s sociotechnical assemblage. I then show how this assemblage is frequently challenged and destabilized by errors, breakdowns, and exclusions. Facing unintended engagements from heterogeneous social actors, local interests, and power hierarchies, Health Code reassembles into multiple and contradictory assemblages at different periods and social localities. Finally, I examine how people game and bypass the algorithm’s surveillance with their agencies. Recognizing this messiness and heterogeneity contributes to a more nuanced and realistic understanding of states’ use of algorithms, including the risks. Doing so also urges us to rethink the politics of citizenship and inequality in the digital age beyond inclusion.
EU27 Higher Education Institutions and COVID-19, Year 2020

COVID-19 forced higher education institutions to reinvent themselves. The (usually) face-to-face education has swapped to distance contingency education. This change brought about numerous challenges that impose adjustments in several dimensions, such as pedagogical strategies and the dependence on teaching platforms and computer systems—and, above all, the new relationship between the various actors (students, teachers, and management staff). All the sudden changes, combined with uncertainty concerning what was happening, created several strategies and options. This paper has the main purpose of analyzing the scientific production on higher education of EU27 academic institutions during the 2020 COVID-19 pandemic in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. The sample is composed of 22 articles in total. The results show that the articles were published in 19 journals; their main focuses are Higher Education, COVID-19, and distance learning. In our database, we find several types of concerns, which shows that HEIs have a wide range of dimensions. We intend this article to be an instrument, not only to identify what was done in 2020, but to point out clues for the future.
VFIRM: Verifiable Fine-Grained Encrypted Image Retrieval in Multi-Owner Multi-User Settings

To ensure the security of images outsourced to the malicious cloud without affecting searchability on such outsourced (typically encrypted) images, one could use privacy-preserving Content-Based Image Retrieval (CBIR) primitive. However, conventional privacy-preserving CBIR schemes based on Searchable Symmetric Encryption (SSE) are not capable of supporting efficient fine-grained access control and result verification simultaneously. Therefore, in this article, we propose a <u>V</u> erifiable <u>F</u> ine-grained encrypted <u>I</u> mage <u>R</u> etrieval scheme in the <u>M</u> ulti-owner multi-user settings (VFIRM). VFIRM first utilizes a novel polynomial-based access strategy to provide efficient fine-grained access control. Then, it employs the dual secure <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbor technique to distribute distinct keys to different data owners and data users, and finally implements an adapted homomorphic MAC technique to check the correctness of search results. Our formal security analysis shows that VFIRM is non-adaptive semantic secure if the client's search key is generated randomly and keeps in secret. Our empirical experiments using two real-world datasets (i.e., Caltech101 and Corel5k) demonstrate the practicality of VFIRM.
Evolving data-adaptive support vector machines for binary classification

Support vector machines (SVMs) have been exploited in a plethora of real-life classification and regression tasks, and are one of the most researched supervised learners. However, their generalization abilities strongly depend on the pivotal hyperparameters of the classifier, alongside its training dataset. Also, the training process is computationally and memory expensive, hence learning multiple SVMs to grid-search the hyperparameter space is infeasible in practice. In this paper, we address the problem of optimizing SVMs for binary classification of difficult datasets, including very large and extremely imbalanced cases. We propose an evolutionary technique that simultaneously optimizes the critical SVM aspects, including the training sample, kernel functions, and features. Also, we introduce a co-evolutionary scheme that allows us to guide the search in a competitive way to the highest-quality solutions. Our extensive experimental study performed over more than 120 benchmarks showed that the proposed algorithm outperforms popular supervised learners, as well as other techniques for optimizing SVMs reported in the literature.
Graph Transformer Networks: Learning meta-path graphs to improve GNNs

Graph Neural Networks (GNNs) have been widely applied to various fields due to their powerful representations of graph-structured data. Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs. The limitations especially become problematic when learning representations on a misspecified graph or a heterogeneous graph that consists of various types of nodes and edges. To address these limitations, we propose Graph Transformer Networks (GTNs) that are capable of generating new graph structures, which preclude noisy connections and include useful connections (e.g., meta-paths) for tasks, while learning effective node representations on the new graphs in an end-to-end fashion. We further propose enhanced version of GTNs, Fast Graph Transformer Networks (FastGTNs), that improve scalability of graph transformations. Compared to GTNs, FastGTNs are up to 230× and 150× faster in inference and training, and use up to 100× and 148× less memory while allowing the identical graph transformations as GTNs. In addition, we extend graph transformations to the semantic proximity of nodes allowing non-local operations beyond meta-paths. Extensive experiments on both homogeneous graphs and heterogeneous graphs show that GTNs and FastGTNs with non-local operations achieve the state-of-the-art performance for node classification tasks. The code is available: https://github.com/seongjunyun/Graph_Transformer_Networks.
Crisis-centric distance learning model in Jordanian higher education sector: factors influencing the continuous use of distance learning platforms during COVID-19 pandemic

Purpose This paper aims to investigate the factors that affect the acceptance of distance learning systems by university academic staff and students in Jordan. To achieve this objective, it has been proposed to examine the distance learning experience of Jordanian Higher Education Institutions (HEIs) after the universities institution suspended face-to-face (traditional) courses delivery owing to novel Coronavirus’ (COVID-19) fears. Design/methodology/approach This study expands upon unified theory of acceptance and use of technology 2 by incorporating contextual variables such as trust (TR), autonomy (AUT) and compatibility (CMP). Data collection has been carried out through an online survey, which targeted participants at public and private universities during the crisis time of coronavirus. Structural equation modelling has been used to validate the proposed research model. Findings The outcomes revealed that performance expectancy, facilitating conditions, TR and AUT were the significant predictors of distance learning acceptance in both samples. By identifying the factors affecting the acceptance of distance learning systems, it will be more useful to offer better services of distance learning. This will also help to demonstrate that distance learning will be capable of delivering the educational aims of HEIs to areas where a pandemic outbreak in the Middle East. Originality/value Distance learning provides university students with quality education, engaging platforms and most significantly a safe teaching environment. The results and implications to both practice and theory are described.