Reviewerly/exHarmony huggingface dataset:
Lesson Learned from COVID-19 Retrospective Study: An Entropy-Based Clinical-Interpretable Scorecard for Mortality Risk Control at ICU Admission

With severe acute respiratory syndrome coronavirus 2 spreading globally and causing 2019 coronavirus disease (COVID-19), a challenge that we unprepared for was about how to optimally plan and distribute limited top-medical resources for patients in need of urgent care. To address this challenge, physicians desperately needed a scientific tool to methodically differentiate between cases with varying severity. In this study, the unique data of COVID-19 intensive care unit (ICU) patients provided by the national medical team in Wuhan were classified into discrete and continuous variable types. All continuous data were discretized using an entropy-based method and transformed into serial information margins, in which each information margin is related to a specific symptom or clinical meaning. Finally, all these native and processed discrete data were used to configure a readable scorecard through logistic regression, which is the desired scientific tool aforementioned. A total of 322 ICU patients (age: [median: 64, interquartile range: 54–75], males: 178 [55.28%], and death: 72 [22.36%]) were included in the study. Probabilities of mortality in COVID-19 patients can be evaluated using a scorecard model (calibration slope: 1.343, Brier: 0.048, Dxy = 0.972, and population stability index = 0.071), with desired model performances (accuracy = 0.948, area under curve = 0.99, sensitivity = 1, and specificity = 0.939). This new model can interpret clinical meanings from complex data, and compare it with existing machine learning methods through a black-box mechanism. This new data-information model answers a critical question of how a computing algorithm produces clinically meaningful results that will help physicians logically allocate medical resources for COVID-19 patients. Notably, this tool has limitations, giving that this research is a retrospective study. Hopefully, this tool will be tested further and optimized for adaptation to similar clinical cases in the future.
ISFB-GAN: Interpretable semantic face beautification with generative adversarial network

Face beautification is an emerging application widely used in the entertainment industry. Previous studies have proved that Generative Adversarial Networks (GANs) can successfully build the mapping from a general face to a target face. However, their facial style code in the latent space is taken as a black box with a lack of semantics interpretation. They usually fail to consider the geometric semantic information, and cannot focus on the facial beauty-related regions. Moreover, most of them change the background information. To overcome these problems, we propose a novel Interpretable Semantic Generative Adversarial Network (ISFB-GAN) for face beautification. First, we design a new dual-branch facial style encoder to factorize the abundant semantic information of the reference face into the facial geometry style codes and the appearance style codes in the latent space. Then, a novelty facial attention generator is proposed to preserve more background information in the generated images. In the attention generator, the facial style attention is designed to focus on the most discriminative facial region; the background attention is designed to retain the background information. Finally, we propose a hybrid loss function including geometric loss, orthogonality loss, segmentation loss, style reconstruction loss, perceptual loss, etc., to enhance the learning capacity and improve the visual quality. The combination of the geometric loss and the orthogonality loss is deployed to guarantee the disentanglement between the semantic information. Extensive experiments conducted on the CelebAMask-HQ dataset and the SCUT-FBP5500 dataset, demonstrate that ISFB-GAN performs better than the state-of-the-art methods in facial attractiveness enhancement.
Adaptive stepsize estimation based accelerated gradient descent algorithm for fully complex-valued neural networks

Nesterov accelerated gradient (NAG) method is an efficient first-order algorithm for optimization problems. To ensure the convergence, it usually takes a relatively conservative constant as the stepsize. However, the choice of stepsize has a great impact on the optimization process. In this paper, two adaptive stepsize estimation methods are proposed for complex-valued NAG algorithm for efficient training of fully complex-valued neural networks. The basic idea of the first one is to adaptively determine suitable stepsize by estimating the local smoothness constant of the loss function with the norm of approximate complex Hessian matrix. Its validity is theoretically analyzed by means of the decomposition of complex matrix. Furthermore, by introducing a new parameter design method for multi-step quasi-Newton condition, an improved stepsize estimation is presented. Experimental results on pattern recognition, channel equalization, wind forecasting and synthetic aperture radar (SAR) target classification demonstrate the effectiveness of the proposed methods.
Graph isomorphism U-Net

Graph embedding learning is a fundamental task when dealing with diverse datasets. While encoder–decoder architectures, such as U-Nets, have shown great success in image pixel-wise prediction tasks, applying similar methods to graph data poses challenges due to the lack of natural pooling and up-sampling operations for graphs. Recent methods leverage learnable parameters to extract structural information from neural networks and extend pooling and unpooling to graphs using node features and graph structural information. This paper proposes a novel model called GIUNet (Graph Isomorphism U-Net) for the graph classification task. The proposed Graph U-Net structure is based on graph isomorphism convolution while using a comprehensive pqPooling layer. The pqPooling layer in our approach effectively combines node features and graph structure information during the graph down-sampling stage. To incorporate graph structure information, we utilize both the spectral representation and node centrality measurements. Node centrality measurements capture various structural aspects of nodes in the graph, while the spectral representation helps us focus on the informative low-frequency components of the graph structure. Through ablation studies, we have demonstrated that leveraging the GIUNet model leads to significant improvements compared to state-of-the-art methods across multiple benchmark datasets.
Collaborative Learning of Different Types of Healthcare Data From Heterogeneous IoT Devices

In the realm of healthcare data analysis, privacy concerns have been tackled by the Federated Learning (FL) framework. However, in the situation that heterogeneous healthcare Internet of Things (IoT) devices collect different types of data, applying FL becomes difficult. To train a model leveraging diverse healthcare IoT devices, we propose an advanced collaborative learning framework to fill the gap. With the proposed collaborative learning framework, individual IoT devices project their sensed features into a carefully developed latent space, which are transmitted to a central server. For privacy preservation, the latent local features are encoded within this space, while the samples’ labels remain securely stored in the individual IoT devices. Collaboratively, the deep neural network model is trained by both the central server and the diverse IoT devices. The central server handles the computationally intensive training processes, while the individual IoT devices evaluate the model’s performance and initiate back-propagation based on their locally stored labels. Experimental results demonstrate that the proposed collaborative learning framework achieves performance similar to centralized training and significantly outperforms individual training while preserving data privacy.
FDTR: Weakening feature disparity transformer for accurate multicategory computed tomography image segmentation

Accurately segmenting object structures in computed tomography (CT) images is crucial for computer-aided surgery, diagnosis, and other interdisciplinary applications. Most state-of-the-art CT segmentation methods employ a classical jump structure to integrate shallow and deep feature information. However, the effectiveness of the current CT image segmentation and related subtasks must still be improved due to feature disparity between different layers and information transfer loss. To alleviate the problem, we propose a novel three-dimensional multicategory segmentation model, the weakening feature disparity transformer (FDTR), based on the transformer structure for CT imaging. Firstly, to effectively capture global features, we design a vision transformer-based encoder. Secondly, to enhance the model's information representation in a lightweight manner, we devise a nested structure of dense compression connections. Lastly, to mitigate the disparity in semantic features at shallow layers, we propose supervised semantic signals. By employing the vision transformer-based encoder and the dense connection structure, we enhance the detailed information of deep features. Additionally, the semantic supervisory signal introduces deep semantic information to the shallow features, enriching the overall feature representation. We conducted extensive experiments on the KIPA2022 dataset for multiorgan segmentation and the YC2022 dataset for volumetric trait segmentation in broiler breeding. The proposed method, FDTR, effectively enhances feature transfer between shallow and deep features, achieving optimal results compared to other advanced models and demonstrating broad prospects for application.
Consistent penalizing field loss for zero-shot image retrieval

Zero-shot image retrieval involves retrieving images of unseen classes using a query image of the same class. To determine whether a given image is of the same class as the query image, a universal threshold of similarity measures is needed, as class-specific thresholds are not feasible for unseen classes. However, existing methods for zero-shot image retrieval focus on pushing a margin between intra-class and inter-class similarities for each class during the training phase. This approach can result in varying decision boundaries between intra- and inter-class similarities across classes, which could compromise performance when a universal threshold is used in the inference stage. Additionally, for classes with low intra-class variances or inter-class correlations, the pushing force of the margin-pushing approach might be too weak to learn high-quality embeddings. To address these issues, we propose a novel Consistent Penalizing Field (CPF) Loss for zero-shot image retrieval. The proposed method has a single consistent penalizing field for all classes, resulting in similar decision boundaries across classes. By penalizing samples outside the penalizing field, CPF Loss can better utilize the information of samples with highly unbalanced intra-class and inter-class correlations, and improve the discriminative power of DML learning for zero-shot image retrieval. Extensive experiments are conducted on the challenging Shopee Product Matching dataset and other established benchmarks, and the results demonstrate that the proposed method consistently outperforms the state-of-the-art methods. The code is available at https://github.com/cloudlc/CPF.
Explainable subgradient tree boosting for prescriptive analytics in operations management

Motivated by the success of gradient boosting approaches in machine learning and driven by the need for explainable prescriptive analytics approaches in operations management (OM), we propose subgradient tree boosting (STB) as an explainable prescriptive analytics approach to solving convex stochastic optimization problems that frequently arise in OM. The STB approach combines the well-known method of subgradient descent in function space with sample average approximation, and prescribes decisions from a problem-specific loss function, historical demand observations, and prescriptive features. The approach provides a decision-maker with detailed explanations for the prescribed decisions, such as a breakdown of individual features' impact. These explanations are particularly valuable in practice when the decision-maker has the discretion to adjust the recommendations made by a decision support system. We show how subgradients can be derived for common single-stage and two-stage stochastic optimization problems; demonstrate the STB approach's applicability to two real-world, complex capacity-planning problems in the service industry; benchmark the STB approach's performance against those of two prescriptive approaches—weighted sample average approximation (wSAA) and kernelized empirical risk minimization (kERM); and show how the STB approach's prescriptions can be explained by estimating the impact of individual features. The results suggest that the quality of the STB approach's prescriptions is comparable to that of wSAA's and kERM's prescriptions while also providing explanations.
Multi-Level Medical Image Segmentation Network Based on Multi-Scale and Context Information Fusion Strategy

Accurate segmentation of human tissue structure from medical images is one of the critical links in medical image diagnosis. However, due to the medical image scale of different tissues varying significantly and being structurally complex, the low contrast between tissues and background in some medical imaging makes it challenging to identify. The existing models are difficult to extract representative multi-scale features of medical images that cannot accurately segment the organizational structure from the background in low-contrast medical images. To solve these problems, this study presents a scale and context information fusion network structure based on multi-type medical image segmentation (SCIF-Net), which contains three modules: feature pyramid encoder (FPE), multi-scale feature dynamic aggregation (MFDA), and adaptive spatial information fusion (ASIF). We build the FPE module to further enhance the representational ability of the network encoder output feature map at each stage. The MFDA module is used to effectively extract multi-scale information from the encoder output feature map and aggregate multi-scale features. The constructed ASIF module enables the network to selectively concentrate on the vital spatial information in the encoder feature map and merge the decoder feature map semantic information, minimizing background noise influence. Extensive experiments on the retinal segmentation task, gland segmentation task, and femur segmentation task, show that the SCIF-Net network outperforms other advanced methods.
FMAP: Learning robust and accurate local feature matching with anchor points

Local feature matching involves the task of establishing the pixel-wise correspondences between a pair of images. As an integral component of plentiful computer vision applications (e.g., visual localization), this task has been successfully performed using Transformer-based methods. However, these methods typically extract numerous keypoints from sparse texture regions to construct a densely-connected graph neural network (GNN) for long-range feature aggregation, which inevitably triggers redundant message exchange and hampers the learning process. Furthermore, they employ transformer encoder layers that consider images as 1D sequences, leaving them incapable of extracting multiscale local structural information from the images, which is critical for establishing correspondence in image pairs with significantly scales shifts. In this study, we develop FMAP, an innovative detector-free approach that enables accurate local feature matching. For the first issue, FMAP employs an anchor points feature aggregation module (APAM) that captures representative keypoints and discards the extraneous keypoints to build a sparsified GNN for compact yet clean message exchange, with the key insight that the keypoints containing abundant visual information are distinguishable from their neighbors. For the second issue, FMAP proposes a global–local multiscale perception module (GMPM), which incorporates abundant multiscale local context information into global feature representation by employing multiple depth-wise convolutions with varying kernel sizes, thereby generating discriminative features that are robust to scale shifts. In addition, the depth-wise convolutions are utilized in the feed-forward network of the GMPM to further fuse the global context information and local feature representation. Extensive experiments on several standard benchmarks demonstrate that the proposed FMAP method significantly outperforms state-of-the-art methods. Compared to the cutting-edge methods MatchFormer, QuadTree, and TopicFM in relative pose estimation task, FMAP surpasses them by 2.27%, 0.58%, and 1.08% in terms of AUC@5°. Besides, FMAP noticeably outperforms the baseline LoFTR by (2.38%,1.89%,1.45%) in terms of AUC@(5°, 10°, 20°). Moreover, we integrate FMAP into an official visual localization framework and conduct a visual localization experiment, with the results showing that FMAP exceeds LoFTR by 2.3% in terms of AP.
SufRec, an algorithm for mining association rules: Recursivity and task parallelism

The mining of frequent itemsets and association rules is a core problem in data mining and an essential task in data analysis. In this paper, we present SufRec, a new algorithm for finding frequent itemsets and association rules. We give two versions. In both versions, the mining of the frequent itemsets is decomposed as a sequence of tasks. The first version (the SufRecDep algorithm) proceeds the tasks successively, each one using the results of the previous ones. The second version (the SufRecInd algorithm) performs the tasks independently of each other. Both versions are recursive with respect to the items and have thus the advantage to be particularly efficient for updating the mining process when new items are added to the database or when others are excluded. Moreover, the task-independent processing of SufRecInd makes it very easy to build parallel versions of SufRec. We present two parallel SufRec algorithms. With the first one, P-FIFO-SufRec, the processors perform the tasks according to the order in which the items appear in the database, preserving the recursive nature of SufRec. For second one, P-PAST-SufRec, the set of items is fixed and the tasks are pre-assigned to each processor. In order to evaluate the performance of SufRec, we carry out a consequent experimental study. In particular, we compare its running times with those of well-known parallel and non-parallel frequent itemsets mining algorithms.
Computing recommendations from free-form text

While searching for consumer goods, users frequently ask for suggestions from their peers by writing short free-form textual requests. For example, when searching for movies users may ask for “Drama movies with a mind-bending story and a surprise ending, such as Fight Club” in one of the many online discussion boards. Despite the recent developments in large language models (LLMs) and natural language processing (NLP), modern recommender systems still struggle to process such requests. Therefore, in this paper we evaluate several approaches for annotating structured information from such short, free-form natural language user texts to calculate recommendations. We set up this evaluation as a two phase processes including (a) identification of the best NLP approach to identify key elements of users’ requests, and (b) assessment of the quality of recommendations computed with such elements. For our evaluation, we use a gold-standard reddit movie recommendation dataset consisting of annotations, manually created by crowdworkers who extracted keywords, actor names and movie titles. Using this dataset we evaluate a collection of more than 30 NLP and five recommender approaches. In addition, we perform an ablation study to assess relative annotation importance for movie recommendations. We find that domain-specific deep learning models, trained on a subset of data as well as embedding-based recommendation approaches are able to match the recommendation performance of recommendations computed from manual annotations. These promising results warrant further investigation in automatic processing of short free-form texts for computation of recommendations. Specifically, we provide insights into which NLP models and configurations work best for automatically annotating free text to compute (movie) recommendations, hence substantially reducing the search space for combinations of NLP and recommendation algorithms in the movie and potentially other domains.
MLIBT: A multi-level improvised binarization technique for Tamizhi inscriptions

The Tamizhi inscriptions, one of the earliest ever discovered, is predominantly found on memorial stones and caves which dates 5th century BCE to 3rd century CE. Today's generations need ways to interpret the script in order to know the historical figures and events because the Tamizhi script evolved into the modern Tamil script over time. Currently, only few epigraphists are available to manually decode the inscriptions into modern Tamil. Hence, there is a need for an alternate way to preserve this cultural heritage. Image processing is one such digital technology that enables binarization on inscription images, and the retrieved text may then be utilized to convert them to the needed target language. Nevertheless, binarization of Tamizhi inscription images are highly complex due to aging, environmental factors, handwritten, similar foreground & background and uneven size and shapes of the stones. Also, due to the small dataset, deep learning techniques are inapplicable. Furthermore, existing approaches produce poor results for Tamizhi inscriptions since they can only be used on flat stone backgrounds and require sufficient light illumination for effective binarization. This research suggests a multi-level improvised binarization solution for Tamizhi inscription images to address these challenges. It achieves this utilizing post-processing with shrink and swell filters together with an improved median filter with modified adaptive thresholding. Outperforming the current binarization techniques, which achieved a maximum accuracy of about 74%, MLIBT produced an accuracy of around 92.19%.
Temporal-Spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit

Recent advancements in medical information technology have enabled electronic health records (EHRs) to store comprehensive clinical data which has ushered healthcare into the era of "big data". However, medical data are rather complicated, making problem-solving in healthcare be limited in scope and comprehensiveness. The rapid development of deep learning in recent years has opened up opportunities for leveraging big data in healthcare. In this article we introduce a temporal-spatial correlation attention network (TSCAN) to address various clinical characteristic prediction problems, including mortality prediction, length of stay prediction, physiologic decline detection, and phenotype classification. Leveraging the attention mechanism model's design, our approach efficiently identifies relevant items in clinical data and temporally correlated nodes based on specific tasks, resulting in improved prediction accuracy. Additionally, our method identifies crucial clinical indicators associated with significant outcomes, which can inform and enhance treatment options. Our experiments utilize data from the publicly accessible Medical Information Mart for Intensive Care (MIMIC-IV) database. Finally, our approach demonstrates notable performance improvements of 2.0% (metric) compared to other SOTA prediction methods. Specifically, we achieved an impressive 90.7% mortality rate prediction accuracy and 45.1% accuracy in length of stay prediction.