Reviewerly/exHarmony huggingface dataset:
Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data

The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity.Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site.The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review.We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions.We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.
Dual space latent representation learning for unsupervised feature selection

In real-world applications, data instances are not only related to high-dimensional features, but also interconnected with each other. However, the interconnection information has not been fully exploited for feature selection. To address this issue, we propose a novel feature selection algorithm, called dual space latent representation learning for unsupervised feature selection (DSLRL), which exploits the internal association information of data space and feature space to guide feature selection. Firstly, based on latent representation learning in data space, DSLRL produces dual space latent representation learning, which characterizes the inherent structure of data space and feature space, respectively. Secondly, in order to overcome the problem of the lack of label information, DSLRL optimizes the low-dimensional latent representation matrix of data space as a pseudo-label matrix to provide clustering indicators. Moreover, the latent representation matrix of feature space is unified with the transformation matrix to benefit the matching of the data matrix and the clustering indicator matrix. In addition, DSLRL uses non-negative and orthogonal conditions to constrain the sparse transform matrix, making it more accurate for evaluating features. Finally, an alternating method is employed to optimize the objective function. Compared with seven state-of-the-art algorithms, experimental results on twelve datasets show the effectiveness of DSLRL.
Multi-view clustering via deep concept factorization

Recent studies have shown the satisfactory results of the matrix factorization technique in Multi-view Clustering (MVC). Compared with the single-layer formed clustering models, the deep matrix factorization clustering models can better perceive the hierarchical information of the data, thereby increasing the clustering performance. Nowadays, a particular matrix factorization technique called Concept Factorization (CF) has got extensive attention in clustering research. Nevertheless, state-of-the-art CF-based clustering methods fail to simultaneously integrate MVC and deep CF into a unified framework. In this paper, a novel MVC model is proposed to tackle this challenge, which brings deep CF to MVC for learning the hierarchical information through performing multi-layer CF and derives a common consensus representation matrix to fetch the shared features among different views. Besides, we impose manifold regularization on each view for locally geometrical structure retention and employ Gaussian kernel to map the original space to a higher Hilbert space for effectively distinguishing the data points. Finally, an efficient optimization algorithm with theoretically guaranteed convergence is developed to solve the proposed model. Experiment results on several open datasets demonstrate the superior performance of the proposed model compared with baseline methods.
Structure-Based Knowledge Tracing: An Influence Propagation View

Knowledge Tracing (KT) is a fundamental but challenging task in online education that traces learners' evolving knowledge states. Much attention has been drawn to this area and several works such as Bayesian Knowledge Tracing and Deep Knowledge Tracing are proposed. Recent works have explored the value of relations among concepts and proposed to introduce knowledge structure into KT task. However, the propagated influence among concepts, which has been shown to be a key factor in human learning by the educational theories, is still under-explored. In this paper, we propose a new framework called Structure-based Knowledge Tracing (SKT), which exploits the multiple relations in knowledge structure to model the influence propagation among concepts. In the SKT framework, we not only consider the temporal effect on the exercising sequence but also take the spatial effect on the knowledge structure into account. We take advantages of two novel formulations in modeling the influence propagation on the knowledge structure with multiple relations. For undirected relations such as similarity relations, the synchronization propagation method is adopted, where the influence propagates bidirectionally between neighbor concepts. For directed relations such as prerequisite relations, the partial propagation method is applied, where the influence can only unidirectionally propagate from a predecessor to a successor. Meanwhile, we employ the gated functions to update the states of concepts temporally and spatially. Extensive experiments demonstrate the effectiveness and interpretability of SKT.
Variable Optimization in Cervical Cancer Data Using Particle Swarm Optimization

Samples of data may consist of numerous attributes and variables which are irrelevant and redundant. Some of those attributes may not be of any vital use in classification and the irrelevant attributes can decrease the efficiency. Thus, the feature reduction process can be considered as a problem in machine learning which selects less quantity of vital attributes to obtain higher accuracy rate. This process minimizes the attributes count by eliminating less relevant and noisy samples from the data set to achieve better classification accuracy. This work uses particle swarm optimization (PSO) search algorithm for feature reduction in cervical cancer data set. The experimental result shows that the irrelevant features are removed and only 17 useful features are selected, out of which 36 in the cervical cancer data set.
Communication-Efficient Federated Learning for Anomaly Detection in Industrial Internet of Things

With the rapid development of the Industrial Internet of Things (IIoT), various IoT devices and sensors generate massive industrial sensing data. Sensing big data can be analyzed for insights that lead to better decisions and strategic industrial production by using advanced machine learning technologies. However, vulnerable IoT devices are easy to be compromised thus causing IoT devices failures (i.e., anomalies). The anomalies seriously affect the production of industrial products, thereby, it is increasingly important to accurately and timely detect anomalies. To this end, we first introduce a Federated Learning (FL) framework to enable decentralized edge devices to collaboratively train a Deep Anomaly Detection (DAD) model, which can improve its generalization ability. Second, we propose a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) model to accurately detect anomalies. The CNN-LSTM model uses CNN units to capture fine-grained features and retains the advantages of LSTM unit in predicting time series data. Third, to achieve real-time and lightweight anomaly detection in the proposed framework, a gradient compression mechanism is applied to reduce communication costs and improve communication efficiency. Extensive experiment results based on realworld datasets demonstrate that the proposed framework and mechanism can accurately and timely detect anomalies, and also reduce about 50% communication overhead when compared with traditional schemes.
Monocular 3D Detection With Geometric Constraint Embedding and Semi-Supervised Training

In this work, we propose a novel one-stage and keypoint-based framework for monocular 3D object detection using only RGB images, called KM3D-Net. 2D detection only requires a deep neural network to predict 2D properties of objects, as it is a semanticity-aware task. For image-based 3D detection, we argue that the combination of a deep neural network and geometric constraints are needed to synergistically estimate appearance-related and spatial-related information. Here, we design a fully convolutional model to predict object keypoints, dimension, and orientation, and combine these with perspective geometry constraints to compute position attributes. Further, we reformulate the geometric constraints as a differentiable version and embed this in the network to reduce running time while maintaining the consistency of model outputs in an end-to-end fashion. Benefiting from this simple structure, we propose an effective semi-supervised training strategy for settings where labeled training data are scarce. In this strategy, we enforce a consensus prediction of two shared-weights KM3D-Net for the same unlabeled image under different input augmentation conditions and network regularization. In particular, we unify the coordinate-dependent augmentations as the affine transformation for the differential recovering position of objects, and propose a keypoint-dropout module for network regularization. Our model only requires RGB images, without synthetic data, instance segmentation, CAD model, or depth generator. Extensive experiments on the popular KITTI 3D detection dataset indicate that the KM3D-Net surpasses state-of-the-art methods by a large margin in both efficiency and accuracy. And also, to the best of our knowledge, this is the first application of semi-supervised learning in monocular 3D object detection. We surpass most of the previous fully supervised methods with only 13% labeled data on KITTI.
Pedestrian detection with super-resolution reconstruction for low-quality image

Pedestrian detection has emerged as a fundamental technology for autonomous cars, robotics, pedestrian search, and other applications. Although many excellent object detection algorithms can be used for pedestrian detection, it is still a challenging problem due to the complicated real-world scenarios, e.g., the detection of pedestrians in low-quality surveillance videos. In this paper, we aim to study the challenging topic of pedestrian detection in low-quality images. Low-quality images are interpreted as those taken with a low-resolution camera, heavy weather or a blurred scene, making it difficult to distinguish pedestrians from the background. To solve this problem, we first introduce a dataset called playground (PG) for low-quality image detection. Images from PG are shot using two different camera views, and pedestrian images are taken at different periods, including day and night. The dataset contains a total of 5,752 images with 31,041 annotations. The average size of the pedestrian is 87×41 and the image size is 480 × 640, indicating that these images are taken from very long distances. Then, we propose a super-resolution detection (SRD) network to enhance the resolution of low-quality images that can help distinguish pedestrians from the blurred background. Finally, based on these enhanced images, we adopt and improve the Faster R-CNN network to help relocate occluded pedestrians. Experimental results on this new dataset proved the efficiency and effectiveness of our algorithm on low-quality images.
An α–β-Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences

To quantify user-item preferences, a recommender system (RS) commonly adopts a high-dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative latent factor analysis model relying on a single latent factor (LF)-dependent, non-negative, and multiplicative update algorithm. However, existing models' representative abilities are limited due to their specialized learning objective. To address this issue, this study proposes an α- β -divergence-generalized model that enjoys fast convergence. Its ideas are three-fold: 1) generalizing its learning objective with α- β -divergence to achieve highly accurate representation of HiDS data; 2) incorporating a generalized momentum method into parameter learning for fast convergence; and 3) implementing self-adaptation of controllable hyperparameters for excellent practicability. Empirical studies on six HiDS matrices from real RSs demonstrate that compared with state-of-the-art LF models, the proposed one achieves significant accuracy and efficiency gain to estimate huge missing data in an HiDS matrix.
Unbiased Learning to Rank

How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) can be broadly categorized into two groups—the studies on unbiased learning algorithms with logged data, namely, the offline unbiased learning, and the studies on unbiased parameters estimation with real-time user interactions, namely, the online learning to rank. While their definitions of unbiasness are different, these two types of ULTR algorithms share the same goal—to find the best models that rank documents based on their intrinsic relevance or utility. However, most studies on offline and online unbiased learning to rank are carried in parallel without detailed comparisons on their background theories and empirical performance. In this article, we formalize the task of unbiased learning to rank and show that existing algorithms for offline unbiased learning and online learning to rank are just the two sides of the same coin. We evaluate eight state-of-the-art ULTR algorithms and find that many of them can be used in both offline settings and online environments with or without minor modifications. Further, we analyze how different offline and online learning paradigms would affect the theoretical foundation and empirical effectiveness of each algorithm on both synthetic and real search data. Our findings provide important insights and guidelines for choosing and deploying ULTR algorithms in practice.
GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification

Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights from biomedical literature and to perform accurate assignment of disease-specific codes. Such approaches can largely enhance the effectiveness of diverse biomedicine and bioinformatics applications. State-of-the-art computational biomedical text classification methodologies either solely leverage discrimintaive features extracted through convolution operations performed by deep convolutional neural network or contextual information extracted by recurrent neural network. However, none of the methodology takes advantage of both convolutional and recurrent neural networks. Further, existing methodologies lack to produce decent performance for the classification of different genre biomedical text such as biomedical literature or clinical notes. We, for the very first time, present a generic deep learning based hybrid multi-label classification methodology namely GHS-NET which can be utilized to accurately classify biomedical text of diverse genre. GHS-NET makes use of convolutional neural network to extract most discriminative features and bi-directional Long Short-Term Memory to acquire contextual information. GHS-NET effectiveness is evaluated for extreme multi-label biomedical literature classification and assignment of ICD-9 codes to clinical notes. For the task of extreme multi-label biomedical literature classification, performance comparison of GHS-Net and state-of-the-art deep learning based methodology reveals that GHS-Net marks the increment of 1%, 6%, and 1% for hallmarks of cancer dataset, 10%, 16%, and 11% for chemical exposure dataset in terms of precision, recall, and F1-score. For the task of clinical notes classification, GHS-Net outperforms previous best deep learning based methodology over Medical Information Mart for Intensive Care dataset (MIMIC-III) by the significant margin of 6%, 8% in terms of recall and F1-score. GHS-NET is available as a web service at1 and potentially can be used to accurately classify multi-variate disease and chemical exposure specific text.
A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters

The success factor of sentimental analysis lies in identifying the most occurring and relevant opinions among users relating to the particular topic. In this paper, we develop a framework to analyze users’ sentiments on Twitter on natural disasters using the data pre-processing techniques and a hybrid of machine learning, statistical modeling, and lexicon-based approach. We choose TF-IDF and K-means for sentiment classification among affinitive and hierarchical clustering. Latent Dirichlet Allocation, a pipeline of Doc2Vec and K-means used to capture themes, then perform multi-level polarity indices classification and its time series analysis. In our study, we draw insights from 243,746 tweets for Kerala’s 2018 natural disasters in India. The key findings of the study are the classification of sentiments based on similarity and polarity indices and identifying themes among the topics discussed on Twitter. We observe different sets of emotions and influencers, among others. Through this case example of Kerala floods, it shows how the government and other organizations could track the positive/negative sentiments concerning time and location; gain a better understanding of the topic of discussion trending among the public, and collaborate with crucial Twitter users/influencers to spread and figure out the gaps in the implementation of schemes in terms of design and execution. This research’s uniqueness is the streamlined and efficient combination of algorithms and techniques embedded in the framework used in achieving the above output, which can be integrated into a platform with GUI for further automation.
Vision-Based Fall Detection Using ST-GCN

Falls are a growing issue in society and has become a hot topic in the healthcare domain. Falls are more likely to occur to due to age or health problems such as cardiovascular issues and muscle weakness. In this work we focus on fall detection. The aftereffects of falls often lead to the use of prescription pain medications. We are motivated to help prevent suicide attempts by overdose in the Canadian correctional services. Most previous studies were based on hand-crafted features which limit the robustness and generality of the system. We therefore propose a general vision-based system, using Spatial Temporal Graph Convolutional Networks (ST-GCN). This system has proven its efficiency and robustness in the action recognition domain. Contrary to previous works, this model can be applied directly to new data without the need to retrain the model while offering good accuracy. Additionally, with the help of transfer learning we can solve the insufficient data problem. By using three public datasets: the NTU RGB-D dataset, the TST Fall detection dataset v2 and the Fallfree dataset to validate our method, we achieved a 100% accuracy, surpassing the state-of-the-art.
HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm

To fluently collaborate with people, robots need the ability to recognize human activities accurately. Although modern robots are equipped with various sensors, robust human activity recognition (HAR) still remains a challenging task for robots due to difficulties related to multimodal data fusion. To address these challenges, in this work, we introduce a deep neural network-based multimodal HAR algorithm, HAMLET. HAMLET incorporates a hierarchical architecture, where the lower layer encodes spatio-temporal features from unimodal data by adopting a multi-head self-attention mechanism. We develop a novel multimodal attention mechanism for disentangling and fusing the salient unimodal features to compute the multimodal features in the upper layer. Finally, multimodal features are used in a fully connect neural-network to recognize human activities. We evaluated our algorithm by comparing its performance to several state-of-the-art activity recognition algorithms on three human activity datasets. The results suggest that HAMLET outperformed all other evaluated baselines across all datasets and metrics tested, with the highest top-1 accuracy of 95.12% and 97.45% on the UTD-MHAD [1] and the UT-Kinect [2] datasets respectively, and F1-score of 81.52% on the UCSD-MIT [3] dataset. We further visualize the unimodal and multimodal attention maps, which provide us with a tool to interpret the impact of attention mechanisms concerning HAR.
Accurate inverse design of Fabry–Perot-cavity-based color filters far beyond sRGB via a bidirectional artificial neural network

Structural color based on Fabry–Perot (F-P) cavity enables a wide color gamut with high resolution at submicroscopic scale by varying its geometrical parameters. The ability to design such parameters that can accurately display the desired color is therefore crucial to the manufacturing of F-P cavities for practical applications. This work reports the first inverse design of F-P cavity structure using deep learning through a bidirectional artificial neural network. It enables the production of a significantly wider coverage of color space that is over 215% of sRGB with extremely high accuracy, represented by an average <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mi mathvariant="normal">Δ</mml:mi> <mml:msub> <mml:mi>E</mml:mi> <mml:mrow> <mml:mn>2000</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> value below 1.2. The superior performance of this structural color-based neural network is directly ascribed to the definition of loss function in the uniform CIE 1976-Lab color space. Over 100,000 times improvement in the design efficiency has been demonstrated by comparing the neural network to the metaheuristic optimization technique using an evolutionary algorithm when designing the famous painting of “Haystacks, end of Summer” by Claude Monet. Our results demonstrate that, with the correct selection of loss function, deep learning can be very powerful to achieve extremely accurate design of nanostructured color filters with very high efficiency.