Reviewerly/exHarmony huggingface dataset:
GMFQP: An Ontology-mediated Gut Microbiota Federated Query Platform

The symbiotic relationship between gut microbes and their hosts has been widely investigated by scientists. Currently, there exists lots of useful data dispersed in different gut microbiota databases. However, it is not easy for biologists to obtain valuable global information directly from these multi-sources and heterogeneous data for further research. Therefore, an ontology-mediated Gut Microbiota Federated Query Platform (GMFQP) is presented in this paper to solve this problem. Firstly, a Gut Microbiota Ontology (GMO) is built to provide a unified domain view for biologists, hiding the heterogeneity of the underlying data sources. Secondly, a three-phased federated query approach including query reasoning, rewriting, and execution based on Datalog+ rules is proposed to transform users’ queries expressed over the GMO to concrete queries executed over the actual data sources. Thirdly, a federated query prototype is implemented based on the approach mentioned above, providing a user-friendly query interface and returning results in both tabular and graphical formats. Several typical biological query examples with performance analysis are illustrated to demonstrate the utility of our approach and the platform.
Reassembling Consistent-Complementary Constraints in Triplet Network for Multi-view Learning of Medical Images

Existing multi-view learning methods based on the information bottleneck principle exhibit impressing generalization by capturing inter-view consistency and complementarity. They leverage cross-view joint information (consistency) and view-specific information (complementarity) while discarding redundant information. By fusing visual features, multi-view learning methods help medical image processing to produce more reliable predictions. However, multi-views of medical images often have low consistency and high complementarity due to modal differences in imaging or different projection depths, thus challenging existing methods to balance them to the maximal extent. To mitigate such an issue, we improve the information bottleneck (IB) loss function with a balanced regularization term, termed IBB loss, reassembling the constraints of multi-view consistency and complementarity. In particular, the balanced regularization term with a unique trade-off factor in IBB loss helps minimize the mutual information on consistency and complementarity to strike a balance. In addition, we devise a triplet multi-view network named TM net to learn the consistent and complementary features from multi-view medical images. By evaluating two datasets, we demonstrate the superiority of our method against several counterparts. The extensive experiments also confirm that our IBB loss significantly improves multi-view learning in medical images.
Dual-branch body and boundary supervision network for ultrasound image segmentation

A sharp prediction for ultrasound image segmentation is critical to assist doctors in the accurate diagnosis and treatment. Although the existing methods could achieve impressive performance on biomedical image segmentation, they still have difficulty in segmenting pixels near the boundaries of lesions faithfully. To this end, we propose a body and boundary supervision network aiming to improve ultrasound image segmentation performance, especially on the boundary of the segmented objects. In contrast to existing approaches that take lesions as a whole, a novel dual-branch supervision block was developed to explore explicit modeling of the body and boundary of the object at feature level. Furthermore, we presented an adaptive feature fusion strategy to combine feature maps from dual-branch to get more representative features for final segmentation. Experimental results on three public ultrasound datasets including BUSI, HC18 and Hemangioma demonstrate that the proposed network can achieve leading segmentation performance for breast cancer, fetal head circumference and hemangioma on ultrasound images compared with the existing state-of-the-art models.
An integrated Extreme learning machine based on kernel risk-sensitive loss of q-Gaussian and voting mechanism for sample classification

Ensemble learning is to train and combine multiple learners to complete the corresponding learning tasks. It can improve the stability of the overall model, and a good ensemble method can further improve the accuracy of the model. At the same time, as one of the outstanding representatives of machine learning, Extreme Learning Machine has attracted the continuous attention of experts and scholars. to get a better representation of the feature space, we extend the Gaussian kernel in the kernel risk-sensitive loss and propose a Kernel Risk-Sensitive Loss of q-Gaussian kernel and Hyper-graph Regularized Extreme Learning Machine method. Since the contingency in the ELM training process cannot be completely avoided, the stability of most ELM methods is affected to some extent. What’s more, we introduce the voting mechanism and a new ELM classification model named Kernel Risk-Sensitive Loss of q-Gaussian kernel and Hyper-graph Regularized Integrated Extreme Learning Machine based on Voting Mechanism is proposed. It improves the stability of the model through the idea of ensemble learning. We apply the new model on six real data sets, and through observation and analysis of experimental results, we find that the new model has certain competitiveness, especially in classification accuracy and stability.
A progressive-recursive method for low-light image enhancement without paired datasets

Low-light images challenge both human perceptions and computer vision algorithms. Enlighten low-light images is the critical underlying visual task for computer vision applications such as real-time detection and segmentation. This paper proposes a new underexposed image enhancement network that is simple yet effective and trained in datasets without paired images. More concretely, we define a pixel-level enlightened factor extracted by a depthwise separable convolution enhancement network for an efficient prediction from the original low-light image. Further, we use a combination of enlightened factor and recursive enhancement network to enhance the low-light image with afford-able model size progressively. Based on this model, we introduce a loss function to improve the generalization ability of images under different illumination conditions. Finally, we conducted ablation studies and quantitative comparisons for different models on benchmark datasets. Through experimental tests, MSE, PSNR, and SSIM of our model on MEF datasets are 0.0742, 11.4898, and 0.266, on LOL datasets are 0.025, 20.58, and 0.8, respectively, and the average values of BRISQUE and UNIQUE on five different benchmark datasets are 0.803 and 20.044 respectively. In comparing different model effectiveness, the RT, Params, and FLOPs metrics of our method are 0.001, 0.011, and 0.12, respectively, which can process about 1000 pictures per second. The results demonstrate the advantages of our method over other methods from both qualitative and quantitative perspectives.
Predicting Undergraduates Stress Level Using Eye Tracking

Stress is a feeling of emotional or physical tension, this makes a serious influence among undergraduates. There is a lack of stress prediction techniques that can detect what sort of stress level undergraduates are having from time to time. This research explored the prediction of undergraduate stress levels using eye-tracking. In this study, 306 PSS-10 data sets and 30600 eye-tracking data (Images) were collected from undergraduates at the University of Ruhuna using a questionnaire and a third-party eye-tracker application. PSS-10 (Perceived Stress Scale −10) and CNN (Convolutional Neural Network) was used to predict undergraduate stress levels. Stress levels are determined by the PSS-10 analysis, and divided into three classes: High, Moderate, and Low. Eye tracking data and stress classes are correlated in data pre-processing phase. The eye tracking images takes as input for well define eye tracking classification model; the model predict the stress level of the given undergraduate eye tracking data. According to the results, it was concluded that 19.7% of the undergraduates were in very high level of stress, 71.8% were in moderate level of stress and 8.5% were in low level of stress. However, it is certain that most of the undergraduates suffer from moderate levels of stress. The research will help predict undergraduate stress levels more accurately, and aid undergraduates managing their stress levels in academic life.
Research on Medical Data Storage and Sharing Model Based on Blockchain

With the process of medical informatization, medical diagnosis results are recorded and shared in the form of electronic data in the computer. However, the security of medical data storage can not be effectively protected and the unsafe sharing of medical data among different institutions is still a hidden danger that can not be underestimated. To solve the above problems, a secure storage and sharing model of private data based on blockchain technology and homomorphic encryption is constructed. Based on the idea of blockchain decentralization, the model maintains a reliable medical alliance chain system to ensure the safe transmission of data between different institutions; A privacy data encryption and computing protocol based on homomorphic encryption is constructed to ensure the safe transmission of medical data; Using its complete anonymity to ensure the Blockchain of medical data and patient identity privacy; A strict transaction control management mechanism of medical data based on Intelligent contract automatic execution of preset instructions is proposed. After security verification, compared with the traditional medical big data storage and sharing mode, the model has better security and sharing.
Privacy-Preserving Decision-Making Over Blockchain

Many blockchain applications require democratic on-chain decision-making. In this work, we propose a community-inclusive decentralised collaborative decision-making system with privacy assurance. Its key component is a two-stage voting scheme inspired by choice architecture. Our decision-making system is compatible with most existing blockchain infrastructures. In addition, it supports liquid democracy/delegative voting for better collaborative intelligence. Namely, stake holders can either vote directly on proposals or delegate their voting power to experts. When majority of voting committee members are honest, no one can derive voters’ voting preferences or delegations with non-negligible probability. To support concurrent multiple voting events, we design a distributed batch key generation protocol that can generate multiple keys simultaneously by voting committee members with amortised communication cost of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(n)$</tex-math></inline-formula> per key, where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of participants. Besides, our system supports “evolving committee”, i.e., voting committee members can be changed during the voting period. We implemented a pilot system in Scala, benchmark results indicate that our system can support large number of participants with high efficiency.
Evaluating Stability of Feature Selectors: Adjusted Measures Considering Feature Correlations

Besides the aim of identifying a subset of useful features, the stability of feature selection algorithms is also a critical topic in increasing the confidence of selected features, where an objective stability measure with required properties is expected. To this end, we herein propose a new stability measure sims <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nJR</inf> that considers the feature correlations and possesses the desirable properties. Specifically, we first utilize the Pearson correlation coefficients and the false discovery rate control procedure to identify significantly correlated feature pairs that are not shared in two feature sets. A normalization step is then conducted to reduce the effects of the size of feature sets and of general feature correlations in the dataset. Finally, we consider two commonly used feature selectors (i.e., relief and mRMR) and conduct comparative experiments on several datasets under different hyperparameter values and the variation of train sets. Results show its effectiveness.
Robustness Evaluation of Mask R-CNN under Extreme Environments

Mask R-CNN, has been widely applied to many areas in the last decade due to its outstanding performance regarding instance segmentation. The algorithm has various performances under different situations. So, some methods are required to reinforce its performance. This work focused on its performance with noised images. The model was trained with the images containing any annotations of cars in the Coco2017 dataset and evaluated with the images with and without noises. Besides, a Denoising AutoEncoder (DAE) is trained and used to eliminate the noise in the images. The trained DAE has a loss lower than 0.2 based on the metric called Mean Square Error (MSE) between the noise-free and reconstructed images. After the training the Mask R-CNN model, it can reach an Average Precision (AP) at 35.22 and 32.41, respectively for bounding box and segmentation. The AP drops significantly to lower than one-third as before with the noised images. Despite that the DAE recovers the images from noise, the prediction results are still not acceptable. That might be caused by the entropy loss when the images are processed by the DAE.
KEP-1.0: An Automatic Pipeline to Assist a Rapid Learning of COVID-19 Publications

Toward efficient learning of massive publications during the COVID-19 pandemic, we propose a pipeline, Knowledge Extraction for COVID-19 Publications (KEP), that aims at automatic extraction and representation of key knowledge from user-interested publications. The first version, KEP-1.0, has been developed and published on the Python Package Index (PyPI) (URL: https://pypi.org/project/KEP/). In this first release, knowledge about key topics, disease discussions, and location mentions for each publication is provided. KEP-1.0 not only extracts relevant knowledge but, more importantly, emphasizes the top discussed entities and presents visualizable plots, including bar graphs and word clouds. This allows a rapid preliminary understanding of the main discussions in the publication from these three aspects. Moreover, an enhanced TF-IDF algorithm, the weighted TF-IDF, targeting the publication topic identification purpose, has been proposed and evaluated. The pipeline is fully open-sourced and customizable. KEP-1.0 is ready for use in its current form or to be embedded into existing literature platforms. This pipeline is designed for COVID-related publications, but it has the potential to benefit similar knowledge extraction tasks for other topics of interest with a rapidly increasing number of publications.
Flexible ConvNext Block Based Multi-task Learning Framework for Liver MRI Images Analysis

Liver cancer is the second leading cause of death all over the world in the 2020s’, and the incidence rate has been growing on a global scale and become a serious threat to human life. Early diagnosis of liver cancer from medical images can allow the patients to receive better treatment and achieve good outcomes. Although medical imaging approaches have made significant progress over the past decades, there are still great demands to reconstruct the network structure for the adaptation to downstream tasks. It remains a great challenge for liver tumor identification from MRI images. Recently, self-attention mechanism based transformer models can capture long-range dependencies, which make them perform well on many medical image analysis tasks. Such as Segformer and TransUNet, since lacking the translation in-variance and inductive bias of CNNs, they are still needed large-scale training to fill the gap, especially in the field of medical image analysis domain. In this study, we incorporate a novel flexible Condeathblock as a feature extractor to perform liver images analysis and propose a new analytical framework CXNet to extract discriminative multi-scale visual representations. The experiments results demonstrated our framework outperforms the state-of the-art models on three datasets, including a publicly available liver segmentation dataset as well as two in-house liver tumor classification/segmentation datasets. On the 3DIRCADb dataset, our CXNet outperforms the UNet model by 2.82%, 2.73%, and 4.46% in terms of the Jaccard similarity coefficient, Dice coefficient, and accuracy, and outperforms the TransUNet model by 8.49%, 5.35%,which 3.62%, respectively. Code is available at https://github.com/SPECTRELWF/CXNet
TSINet: Efficient Breast Lesion Segmentation with Three-Stream Interactive Neural Network on Magnetic Resonance Images

With magnetic resonance imaging (MRI), the segmentation of breast lesions is helpful for early diagnosis. Since it is subjective and time-consuming for physicians to delineate the lesions, automatic segmentation is in active demand. However, the low contrast and small size of the lesions will cause difficulties in existing segmentation methods. In this paper, we propose a three-stream interactive neural network (TSINet) for the efficient segmentation of MRI breast lesions. TSINet uses three streams for extracting boundary information, location information and predicting lesion masks, respectively. The boundary extraction stream enhances the representation of boundary pixels based on gate convolution. The internal location stream that works based on the attention mechanism identifies the location of the lesions. The lesion segmentation stream fuse the boundary and location information to accurately segment the lesion. We collect MRI data from 248 breast patients at the Second Hospital of Dalian Medical University to evaluate TSINet. The results demonstrate that the TSINet can delineate lesion boundaries close to that drawn by physicians, and outperform state-of-the-art methods.
An Offline Assistance Tool for Visually Impaired People Based on Image Captioning

Eye defects of visually impaired people bring great inconvenience to daily lives. With the rapid development of computers and artificial intelligence, some assistance tools and wearable devices have provided convenience to a certain extent, but they still possess many shortcomings, such as high prices, limited functionality, poor real-time performance, the inability to be used offline and so on. This paper designs an offline assistance tool specifically for visually impaired people that can help them perceive their surrounding environments by taking photos and reading the descriptions output by an image captioning model. Considering that the photos taken by visually impaired people may be incomplete, an improved image stitching method based on a genetic algorithm is proposed to obtain high-quality stitched images as the inputs of the image captioning model. An improved model pruning algorithm is also designed to compress and accelerate the image captioning model to meet the offline and real-time deployment requirements of portable devices. Experimental results and a practical tool show that the developed system based on an image captioning model and pruning can quickly and accurately describe environments, thus assisting visually impaired people anytime and anywhere.
Dynamic Ontology Generation using Relational Schema

The ontology is one of the best methods of defining the semantic model of the data combined with the associated domain knowledge. It can be used in formulating some data searching strategies. Ontologies can also define links between different types of semantic knowledge. However, ontology building is a time-consuming and complicated task that needs expert participation. Therefore, the automatic generation of ontology is a crucial topic among researchers. Furthermore, to take the maximum output of the semantic systems needs a dynamic update of not only the data but relationships accordingly. This paper proposes a method of automatic generation of the dynamic ontology using a relational database and expanding dynamically when a new concept is needed to add. As per the knowledge, this is the first approach to updating the ontology by dynamically using the updated relational schema. The proposed method shows higher accuracy in the evaluation when generating and updating all ontology components dynamically.
Streaming Graph Clustering for Graph Partition

Graph partition, as a mandatory step in the process of large scale graph and distributed graph analysis system, has been heavily examined in the past decade. Generally, they have been categorized into two types, offline and online. As the massive growth of the modern graph, tradition offline methods fail in achieving both effectiveness and efficiency. Hence, there is an urgent need for fast and scalable online graph partition approaches. Most state-of-art online algorithms focus on the design of streaming methods to reach the merit. In such a context, a series of novel approaches emerge recently, most of which concentrate on partitioning the original graph based on the streaming clustering results. These innovative manners have been experimentally proved to have a better performance compared with the mainstream methods. Hence, it is essential to design a high-quality streaming graph clustering algorithm in order to refine these cluster-based partitioners. In this article, we purpose a fine-grained streaming clustering algorithms specifically designed for cluster-based partitioner. Our experimental results demonstrated that the purposed clustering approach can achieve a better replication factor w.r.t the constrained balanced factor compared to the baseline methods.
Unsupervised Domain Adaptation with Dual U-DenseTransformer Generation

Unsupervised domain adaptation is to transfer knowledge from a well-annotated source domain and learn an accurate classifier for an unlabeled target domain, which is particularly useful in multimodal medical image processing. Currently available adaptation approaches strongly reduce the domain bias or inconsistency in the latent space, deteriorating inherent data structures. To appropriately leverage the reduction of the domain discrepancy and the maintenance of the intrinsic structure, this paper proposes a dual U-DenseTransformer generation domain adaptation framework to bridge the gap between source and target domains and achieve translation. Specifically, we create a DenseTransformer with multi-head attention embedded in U-shape network to establish a dual-generator strategy, which is further enhanced by a new hybrid loss function and an edge-aware mechanism that preserve inherent data structure consistent. We apply our proposed method to medical image segmentation, with the experimental results showing that it works more effective and stable than currently available approaches. Particularly, the dice similarity was improved from 79.3% to 82.8%, while the average symmetric surface distance was reduced from 2.5 to 1.9.