Reviewerly/exHarmony huggingface dataset:
Machine and human interpretable patient visualizations

A growing amount of data is stored in electronic health records, which are crucial for the clinical decision-making process. A large part of these data has a tabular form, consisting of numerical and categorical values originating from various laboratory examinations and sensors. Unlike medical images and clinical notes, tabular data lack higher semantics and, combined with the high dimensionality and heterogeneity, their interpretation by a human is challenging. On the other hand, we have witnessed superior performance of deep convolutional neural network (DCNN) models in the visual medical domain. In this paper, we propose visual representations of complex tabular medical data readable simultaneously by humans and machines. To show that these representations can encode the patient’s data semantics effectively, we use them to fine-tune a DCNN to predict the disability level of patients suffering from multiple sclerosis. Our experiments show that the visual models could match the performance of non-visual models. Moreover, the visual representations add the benefit o f s ummarizing complex information about the patient’s state to a human.
A Multipurpose Video Immersion Laboratory

In the pandemic context of the last year and a half, there has been an increasing emphasis on the need to digitize teaching activities. This paper proposes a new concept of visual education based on visual immersion. A laboratory containing eleven video projectors commanded by a single computer is developed to create the immersion effect. While in this case, the advantages of the design are presented in bio-engineering class, it can be easily extended to any other discipline.
Comparison of anomaly detection algorithms for near-fall detection with video surveillance

Falls are a major problem for seniors living in the community. To prevent their negative consequences, we propose a video surveillance system to detect early signs of mobility decline based on the frequency of near-fall occurrences (e.g., stumble, slip, misstep, loss of balance) for more efficient intervention before a fall occurs. Velocity features were extracted from a simplified skeleton fitted to the body based on the keypoint RCNN (Region-based Convolutional Neural Network) algorithm. Then, three anomaly detection algorithms (One-Class SVM, Gaussian Mixture Model (GMM) and Isolation Forest (IF)) were tested to identify abnormal events in a dataset of 55 videos (> 16000 frames) of simulated normal and abnormal activities in a realistic apartment-laboratory. Our experimental results showed that all three algorithms could successfully distinguish anomalies (near-falls) with an EER (Equal Error Rate) of 10% (AUC = 87%) for the One-Class SVM, EER = 20% (AUC = 85%) for the GMM, and EER = 10% (AUC = 92%) for the Isolation Forest. This study demonstrates the potential of a smart video surveillance system to identify near-falls associated with mobility decline for the benefit of older adults living alone at home.
Hand Segmentation for Chest Pain Behaviour of A Car Driver in Vehicle by Using Fusion Watershed and Blob Analysis

Hand segmentation is an important identification of chest pain behaviours among drivers. Chest pain is an early detection of heart-related diseases. Among the many methods in hand segmentation, YCbCr is determined as the most effective colour space to detect the colour of human skin. The threshold applied technique has great robustness in skin colour detection and segmentation of human body objects such as the face, arm, hands and neck through the undertaking of blob analysis. However, one of several obstacles in hand segmentation which resulted in its low accuracy rate of 62% is identified to be an existing connection between the hands and face regions. This connection problem is specifically overcome by the introduction of a fusion watershed technique with blob analysis. As an effective technique for image segmentation, watershed transform would separate the connected area into multiple regions. The conducted analysis then confirmed the hand as the smallest area across other separated regions. Enhanced execution of hand segmentation was subsequently achieved through extraction of the hand region via the output of the fusion watershed by the employment of blob analysis. Experimental results ultimately confirmed a higher accuracy rate of 75% through the employment of the fusion technique.
A Systematic Review of Control Strategies for Solar Tracking Systems

In this paper it is shown two control strategies which are commonly found for active solar tracking systems. The type of solar tracking mechanisms that will be analyzed in this research are basically one, a 2-axis solar tracking system. For this strategy the dynamics of the actuator are considered in order that in this case is a DC motor in order that the dynamics of the real physical systems will be obtained as real as possible in order to facilitate the controller design and the implementation in a real setup. Then after obtaining the dynamics of the solar tracking system it is important to mention that two control strategies will be implemented for decentralized control. For the 2-axis control design it will be tested by decentralized control; in order to provide a novel contribution to the field. Among these strategies, two strategies are tested and compared. A proportional-integral-derivative PID controller and a second order sliding mode controller in order to make the respective comparative analysis and provide the conclusions of this research study.
Cuneiform Symbols Identification System using Correlation Technique

Cuneiform is one of the oldest forms of writing used in the ancient Middle East. It provides information about the ancient Sumerian history and the history of humanity as a whole. This project is aimed to develop an algorithm to read and identify cuneiform characters. In order to achieve this objective, the cuneiform image is initially loaded into the program memory to apply some preprocessing operations including binarizing the image and removing noise and unwanted elements. Then, characters are extracted through the segmentation phase which uses vertical and horizontal projections. The characters are then passed to the correlation function to compare each character with the utilized database. This proposed algorithm can be further applied to more complex cases of cuneiform symbols and can be utilized in historical museums.
Curriculum Contrastive Learning for COVID-19 FAQ Retrieval

Medical Frequently Asked Question (FAQ) retrieval aims to find the most relevant question-answer pairs for a given user query, which is of great significance for enhancing people medical health awareness and knowledge. However, due to medical data privacy and labor-intensive labeling, there is a lack of large-scale question-matching training datasets. Previous methods directly use the collected question-answer pairs on search engines to train retrieval models, which achieved poor performance. Inspired by recent advances in contrastive learning, we propose a novel contrastive curriculum learning framework for modeling user medical queries. First, we design different data augmentation methods to generate positive samples and different types of negative samples. Second, we propose a curriculum learning strategy that associates difficulty levels with negative samples. Through a contrastive learning process from easy to hard, our method achieves excellent results on two medical datasets.
A Regional Multiple Instance Learning Network for Whole Slide Image Segmentation

Whole slide image (WSI) analysis represents the current gold standard for cancer diagnosis. To date many fully supervised learning methods have been proposed for WSI classification and segmentation. However, these methods are substantially limited by accurate pixel-level labels, which are labor-intensive to obtain. To solve this problem, we developed an end-to-end multiple instance learning (MIL)-based network for WSI segmentation using coarse-grained labels only. Our network consists of two main components. First, we introduce a hybrid transformer architecture, which uses a fusion mechanism to fuse the feature maps of the convolutional neural network (CNN) and transformer. Second, a novel regional MIL aggregator is proposed, which is used to identify the key instances and address the problem of data imbalance. Unlike the current MIL methods that treat each instance as being independent, our method gathers the information from neighborhood pixels of each instance and captures the correlation between instances. We evaluated our network on CAMELYON16. The benchmarking experiments and ablation studies show that the performance of our method is competitive with those of fully supervised methods and is also better than those of previous MIL segmentation methods.
Based on the Wavelet Transform and the Half-Wave Fourier Transform Study of Intelligent Decoupler Algorithms

The previous intelligent decoupler uses the Fourier algorithm to calculate the effective value of the fundamental component of the fault signal and determine whether its effective value is greater than the trigger threshold to determine the working state of the intelligent decoupler. Fourier algorithm, although with high accuracy but can not respond in a short period of time, seriously reducing the efficiency of the intelligent decoupler. When the circuit has a significant fluctuation, is challenging to do in time to trip, which will cause great losses. So this paper proposes the use of a wavelet algorithm and half-wave Fourier algorithm combination method, the signal for noise reduction at the same time, but also can relatively reduce the action response time of the decoupler, the optimization of the decoupler algorithm.
MIR: A Benchmark for Molecular Image Retrival with a Cross-modal Pretraining Framework

Molecular image retrieval is one of the crucial steps in automatic mining and utilization of biochemistry-related literatures, which is also a relatively open and challenging task in cross fields of biochemistry and artificial intelligence. The challenges come from two aspects: 1) there is a lack of open datasets and evaluation criteria for molecular image retrieval. 2) Common retrieval methods always ignore that molecular image retrieval has cross-modal information of both images and SMILES texts. To address the first challenge, we firstly construct a new molecular image retrieval benchmark, named MIR, including 130770 molecular images, labeled structural similarity, and reasonable evaluation metrics. Faced with the second challenge, we propose an effective cross-modal pre-training framework for molecular image retrieval following CLIP. Experimental results reflect the effectiveness of our proposed benchmark MIR and cross-modal pre-training framework.
Unsupervised continual learning of single-cell clustering based on novelty detection and memory replay

Unsupervised clustering of single-cell RNA sequencing (scRNA-seq) data is essential because it allows us to identify putative cell types. With the rapid growth of scRNA-seq data, it is difficult for traditional learning-based single-cell analysis methods to efficiently and continuously deal with them due to catastrophic forgetting. Inspired by how the human brain learns and remembers, we propose a novel unsupervised continual learning method for single-cell analysis, namely Continual Unsupervised Memory Replay (CUMR). We first employ a novelty detection algorithm to learn and assign pseudo-labels to unlabeled scRNA-seq data. Then, we apply memory-replay-based continual learning networks to achieve unsupervised continual learning of single-cell analysis. Experiments on real datasets show that CUMR has superior performance over other state-of-the-art continual learning methods in single-cell analysis tasks such as cell typing.
Analysis of Text Emotion Based on Logistic Regression Model

In recent years, text clustering technology, as an unsupervised learning method in the field of machine learning, has become one of the most concerned technologies in the field of data mining. To some extent, it is easy to group small-scale text data into several categories. However, when faced with a large number of high-dimensional Chinese text data, the text clustering in this case will face high-dimensional and sparse data. Under the condition of ensuring the clustering quality, improving the speed of clustering and ensuring the effectiveness and accuracy of sentiment analysis become one of the topics of clustering research. In this paper, 99452 lines of film review data collected from web pages are used as experimental objects, and TFID+LSA is used to extract text features. The extracted features are analyzed with logical regression and Bayesian model, and then the accuracy is verified with the AUC of the reserve method and 10 fold cross test method. The experimental results show that the logical regression model is more suitable for text emotion analysis than Bayesian model.
A dynamic multi-modal fusion network for ovarian tumor differentiation

Accurate ovarian tumor differentiation is a challenging task where the benign and malignant tumors share similar T1C and T2WI MRI appearances. Therefore, it is necessary to leverage additional multi-modal data, e.g., the age, CA125level, and other clinical information, which are helpful but rarely exploited. In this paper, we propose a dynamic fusion network that can adaptively make full use of multi-modal data, including MRI and clinical information, to realize precise ovarian tumor differentiation. Specifically, we design a dynamic nonlinear module (D-Non-L module) on the top of the image representation. The D-Non-L module is formulated as an iterative nonlinear projection parameterized by the learned features of the patient-wise clinical information. With the help of this module, the interaction between clinical features and image features could be achieved to adaptively improve the discrimination of visual representations. Moreover, we construct a dual-path-based architecture to fully exploit the complementary information from T1C and T2WI MRIs. Extensive experimental results on the locally organized ovarian tumor dataset demonstrate that our methods are superior to the single-modal and single-path-based methods. And the proposed dynamic non-linear module obtains the best performance compared with other multi-modal fusion strategies.
Research on Question Answering of Lung Cancer Based on Knowledge Graph

With the influence of various comprehensive factors such as people’s diet and living habits and the external environment, lung cancer has become a high-incidence and high-risk disease in the world. In order to prevent and treat lung cancer, a lung cancer question and answer system based on knowledge graph is built to provide intelligent auxiliary diagnosis and treatment, which can quickly and accurately answer the questions raised by patients. In this article, the lung cancer medical case data is used to construct the knowledge graph of lung cancer, and the problem data set is constructed from the template and enhanced to increase the data volume and diversity of the data. Then build a multi-task learning model, and perform question sentence intent recognition and question sentence entity recognition at the same time, which can learn the relationship between tasks, improve the effect of the two types of tasks, and shorten the training and inference time, which can effectively reduce training cost. Finally, by analyzing the questions through the model, the entity and relationship are obtained, and the answer is obtained by querying from the knowledge graph.
A Reliability-constrained Association Rule Mining Method for Explaining Machine Learning Predictions on Continuity of Asthma Care

Continuity of care has been shown to possess numerous health benefits for asthma. However, the use of methodology to predetermine the level of care continuity has been underinvestigated. We recently built a machine learning model to predict the continuity of care for diagnosed asthma patients at the University of Washington Medicine. As there is a consensus on the un-explainable featured nowadays by black-box machine learning, which impedes the clinical deployment of our model. To tackle this issue, we proposed a reliability-constrained association rule mining method, called RC-ARM, to automatically explain the predictions of any machine learning model. First, we introduced the belief function to build a reliability-constrained rule framework. Then, the two-step reliable association rule mining algorithms were developed to generate reliable explanation rules for the machine learning model’s predictions. At last, clinical intervention suggestions regarding each mined rule were embedded for further understanding. The results showed that the proposed method could explain all (110/110) predictions of our machine learning model for asthma patients with low levels of continuity of care. This semantic-fused method sheds light on black-box models and encourages clinical experts to embrace the benefits of machine learning without any prior concern about its lack of explainability.
A Deep Active Learning Framework with Information Guided Label Generation for Medical Image Segmentation

Acquiring medical images and their segmentation labels is often time-consuming and labor-intensive. Compared to natural images, medical images need to be screened and annotated by professional doctors in segmentation tasks, especially those containing multiple organ tissues. To reduce the workload of doctors, we propose a deep active learning-based framework for medical image segmentation. Instead of all, the proposed framework can select an optimal number of medical images for label generation by doctors. The selected images are enough to train a good segmentation model because they are diversified, informational, and unique to represent the whole dataset. The proposed method consists of three steps: (1) Using an auto-encoder to extract features from unlabeled images and applying feature clustering and image entropy to select an initial subset of images for segmentation label generation; (2) Employing the mean-teacher method to train a segmentation model in a semi-supervised manner; (3) Updating the labeled subset and reducing redundancy by a deduplication uncertainty query strategy. Extensive experiments are conducted on a breast ultrasound dataset and a knee cartilage ultrasound dataset to evaluate the performance of the proposed method. Experimental results prove that our deep active learning framework can significantly reduce the number of labeled samples while achieving comparable segmentation results to fully-labeled supervision.
Image Recognition Based on Enhanced-Conformer

Convolutional Neural Networks (CNNs) has always dominated visual recognition tasks, and it is difficult to link distant information in images due to the size limitation of each convolution filter. Vision Transformer (ViT) can capture features at a distance in an image, but lacks the details of local features. Conformer combines the advantages of both using Convolutional Neural Networks (CNNs) and Attention mechanisms in parallel, but it does not take into account the relationship between different samples. Therefore, we propose a new attention calculation method, Extra-Attention, which can effectively learn intra-sample and inter-sample relationships. In order to combine the advantages of CNNs and Attention, in our work, we proposed a new network Enhanced-Conformer (ENC) based on Conformer, in which the attention mechanism adopts a more efficient computational module Inside And Outside Transformer (IAOT), which contains three parallel Attention: Extra-Attention, Self-Attention, External-Attention. Enhanced-Conformer (ENC) can fuse local features, global features and external features at the same time. We conduct experiments on the commonly used image recognition datasets, the recognition accuracies reach 93.29%, 68.70 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and 57.63% on Tiny- ImageNet, CIFAR-10 and CIFAR-100, respectively.
EpiMCBN: A Kind of Epistasis Mining Approach Using MCMC Sampling Optimizing Bayesian Network

Proposing a more effective and accurate epistatic loci detection method is of great significance in improving crop quality, disease treatment, etc. Due to the characteristics of high accuracy and processing non-linear relationship, Bayesian network (BN) has been widely used in constructing the network of SNPs and phenotypes and thus to mine epistasis. However, the shortcoming of BN is that the search space is too large and unable to process large-scale SNPs. In this work, we propose a kind of epistasis mining method using Markov Chain Monte Carlo (MCMC) sampling optimizing Bayesian network (EpiMCBN). Firstly, we use the space of node order composed of SNPs and phenotype to replace the space of network structure. Then MCMC algorithm is used to do sampling to generate multiple different initial orders in linear space or partial space. We use Markov state transition matrix to transfer the initial samples along the Markov chain, thus obtaining multiple order samples. Then we use the $\alpha$-BICBN scoring function to score the Bayesian networks corresponding to these node orders. Through estimating the probability of edge occurrence in the Bayesian networks, we get an approximate Bayesian network of SNPs and phenotype, then obtain the epistatic loci affecting phenotype. Finally, we compare EpiMCBN with the current popular epistasis mining algorithms using both simulated and real age-related macular disease (AMD) datasets. Experiment results show that EpiMCBN has better epistasis detection accuracy, lower false positive rate, and higher F1-score compared to other methods. Availability and implementation: Source code and dataset are available at: http://122.205.95.139/EpiMCBN/.
S<sup>3</sup>AAL: Support Set Selection based on Adversarial Active Learning for Medical Few-Shot Relation Extraction

Support set is one of the most important components of Few-Shot Learning (FSL) methods that greatly affects the performance of these methods. Most existing studies mainly focus on how to effectively utilize the support set sampled randomly, but ignoring the representative of the support set, leading to that the performance of the few-shot learning methods using different support sets randomly sampled varies greatly. In this paper, we focus on how to select a representative support set for FSL methods for medical few-shot relation extraction (FSRE), and propose a novel approach for Support Set Selection based on Adversarial Active Learning $(\text{S}^{3}$ AAL). The adversarial active learning does not only keeps the features shared by source and target, but also guarantees the diversity of the support set. We create three benchmark datasets for medical FSRE based on four public medical RE datasets. The experimental results on the three benchmark datasets demonstrate the effectiveness of our approach when it is plugged into state-of-the-art (SOTA) few-shot learning methods.
Robot Ground Recognition and Prediction Based on Feature Fusion and Bi-LSTM

The ground perception and recognition which have a wide range of application prospects are good for the robot to improve the efficiency of the navigation process. This paper proposed a CNN-Bi-LSTM algorithm based on feature fusion, which effectively combined local features, global features and reverse features in time series data. First, a one-dimensional convolutional layer was used to fuse adjacent local features. After the processing of the max-pooling layer and the Relu function, a bidirectional LSTM was used to extract global time series features and reverse features. Finally, the Softmax function was used for activation, and the classification of nine different road surfaces was realized successfully. The experimental results show that the accuracy of the CNN-Bi- LSTM algorithm is 83.2%, which is higher than that of LSTM, CNN-LSTM, and CNN-GRU-LSTM models.