Reviewerly/exHarmony huggingface dataset:
Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks

Combining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells circulating in blood to add objectivity, accuracy, and speed in diagnosing hematological and non-hematological diseases. However, the variability in staining protocols across different laboratories can affect the color of images and performance of automatic recognition models. The objective of this work is to develop, train and evaluate a new system for the normalization of color staining of peripheral blood cell images, so that it transforms images from different centers to map the color staining of a reference center (RC) while preserving the structural morphological features.The system has two modules, GAN1 and GAN2. GAN1 uses the PIX2PIX technique to fade original color images to an adaptive gray, while GAN2 transforms them into RGB normalized images. Both GANs have a similar structure, where the generator is a U-NET convolutional neural network with ResNet and the discriminator is a classifier with ResNet34 structure. Digitally stained images were evaluated using GAN metrics and histograms to assess the ability to modify color without altering cell morphology. The system was also evaluated as a pre-processing tool before cells undergo a classification process. For this purpose, a CNN classifier was designed for three classes: abnormal lymphocytes, blasts and reactive lymphocytes.Training of all GANs and the classifier was performed using RC images, while evaluations were conducted using images from four other centers. Classification tests were performed before and after applying the stain normalization system. The overall accuracy reached a similar value around 96% in both cases for the RC images, indicating the neutrality of the normalization model for the reference images. On the contrary, it was a significant improvement in the classification performance when applying the stain normalization to the other centers. Reactive lymphocytes were the most sensitive to stain normalization, with true positive rates (TPR) increasing from 46.3% - 66% for the original images to 81.2% - 97.2% after digital staining. Abnormal lymphocytes TPR ranged from 31.9% - 95.7% with original images to 83% - 100% with digitally stained images. Blast class showed TPR ranges of 90.3% - 94.4% and 94.4% - 100%, for original and stained images, respectively.The proposed GAN-based normalization staining approach improves the performance of classifiers with multicenter data sets by generating digitally stained images with a quality similar to the original images and adaptability to a reference staining standard. The system requires low computation cost and can help improve the performance of automatic recognition models in clinical settings.
Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer

Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H&E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H&E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n = 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H&E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials.
Extensive evaluation of transformer-based architectures for adverse drug events extraction

Adverse Drug Event (ADE) extraction is one of the core tasks in digital pharmacovigilance, especially when applied to informal texts. This task has been addressed by the Natural Language Processing community using large pre-trained language models, such as BERT. Despite the great number of Transformer-based architectures used in the literature, it is unclear which of them has better performances and why. Therefore, in this paper we perform an extensive evaluation and analysis of 19 Transformer-based models for ADE extraction on informal texts. We compare the performance of all the considered models on two datasets with increasing levels of informality (forums posts and tweets). We also combine the purely Transformer-based models with two commonly-used additional processing layers (CRF and LSTM), and analyze their effect on the models performance. Furthermore, we use a well-established feature importance technique (SHAP) to correlate the performance of the models with a set of features that describe them: model category (AutoEncoding, AutoRegressive, Text-to-Text), pre-training domain, training from scratch, and model size in number of parameters. At the end of our analyses, we identify a list of take-home messages that can be derived from the experimental data.
Automatic Front-end Code Generation from image Via Multi-Head Attention

Code generation from Graphical User Interface (GUI) screenshots is a challenging task in machine learning. Existing methods (e.g., Pix2code) can handle simple datasets well but struggle with complex datasets requiring hundreds of code tokens. This paper proposes a novel method for generating front-end code based on multi-head attention. Our method uses a special technique called multi-head attention to analyze a GUI screenshot's feature vector, generate the code tokens, and link the analysis and generation processes. This architecture gives our method a significant advantage over similar models in terms of effectiveness. We conduct experiments on two types of datasets: Pix2code datasets and our own datasets. The experimental results demonstrate that our method achieves the best performance among existing methods.
FedCL: Federated contrastive learning for multi-center medical image classification

Federated learning, which allows distributed medical institutions to train a shared deep learning model with privacy protection, has become increasingly popular recently. However, in practical application, due to data heterogeneity between different hospitals, the performance of the model will be degraded in the training process. In this paper, we propose a federated contrastive learning (FedCL) approach. FedCL integrates the idea of contrastive learning into the federated learning framework. Specifically, it combines the local model and the global model for contrastive learning, so that the local model gradually approaches the global model with the increase of communication rounds, which improves the generalization ability of the model. We validate our method on two public datasets. Extensive experiments show that our method is superior to other federated learning algorithms in medical image classification.
RDIMM: Revocable and dynamic identity-based multi-copy data auditing for multi-cloud storage

Multi-copy data possession on multi-cloud storage service provides resource-constrained users with a secure and effective way to process data maintenance. To enable integrity for the outsourced data, numerous multi-copy data auditing schemes for multi-cloud storage have been proposed. Nevertheless, existing solutions suffer from the following limitations: (i) cannot support dynamic data updates; (ii) fail to consider the user revocation feature; (iii) may leak the privacy of user identity. To address these limitations, we propose a new identity-based multi-copy data auditing scheme for multi-cloud storage, termed RDIMM. We introduce a new variant of Merkle Tree to enable fully dynamic data updates, in which all copies of a raw block are kept in the same leaf node. In addition, we design a new key generation strategy and a private key update technique, where the cost of user revocation is independent of the total number of file blocks owned by the revoked user. Moreover, we conduct a comprehensive correctness analysis and security analysis of our proposed RDIMM. In addition, we provide detailed theoretical analysis and experimental simulations, which illustrate the effective functionality and practical performance compared to state-of-the-art works.
Efficient and privacy-preserving online diagnosis scheme based on federated learning in e-healthcare system

Electronic healthcare (e-healthcare) system has brought great convenience for people to seek medical treatment. However, data security, user privacy and online diagnosis efficiency have also aroused widespread public concern. In this paper, we propose an efficient and privacy-preserving online diagnosis scheme for e-healthcare system based on federated learning mechanism (FLM). Specifically, we first transform the data owner's data sharing problem into machine learning problem through using FLM. By sharing calculated local model parameters instead of actual data, the privacy of training data sets can be well protected. Then, we use a homomorphic cryptosystem and the support vector machine (SVM) algorithm to classify patients' physiological data efficiently without leaking their privacy. Furthermore, we design a novel approach to recover decision function of SVM model, which can efficiently prevent model parameters from leaking. Security analysis shows that the proposed scheme can protect data privacy under the defined threat models. Numerical results derived from experiments show that the proposed scheme is highly efficient. Therefore, our scheme is of practical significance in the e-healthcare system.
Ordinal consistency based matrix factorization model for exploiting side information in collaborative filtering

In designing modern recommender systems, item feature information (or side information) is often ignored as most models focus on exploiting rating information. However, the side information is equally essential for capturing users' interests in items. Also, the recommender systems that use side information partially process the feature information by ignoring the locality-preserving property of item features. This study proposes an approach for collaborative filtering by applying an ordinal consistency-based matrix factorization (MF) model to maintain the locality-preserving property of item features to counter this problem. The ordinal consistency condition is implied using a loss function to the item features. Using MF removes the redundancy and inconsistency in item features, producing good results in calculating similarity information for recommendations. We have used five benchmark datasets to evaluate and compare the proposed model. Results obtained using the experiments suggest significant improvement in performance compared to related baselines.
A fusion recommendation model based on mutual information and attention learning in heterogeneous social networks

Heterogeneous social networks generate vast amounts of data due to their large user base. In response, recommendation systems have been developed as an effective tool to mitigate the problem of information overload. Nonetheless, standalone recommendation methods are often limited by their own inherent deficiencies. Consequently, deep learning has emerged as a powerful approach to address these issues. By integrating diverse recommendation methods, the shortcomings of individual methods can be partially compensated for and improved recommendation outcomes can be achieved. This paper presents a novel user interest fusion recommendation model called MIAL, which leverages mutual information and attention-based learning mechanisms. Specifically, a user's posts are first transformed into word sequences via a Convolution Neural Network (CNN) based interest recommendation model for pre-processing. To enhance the multi-label prediction model, this study adopts a label selection method based on the label maximum mutual information spanning tree (LMMIST) to determine the final recommended label set. To identify reliable user similarity, the concept of pseudo-similar friends is introduced to construct heterogeneous networks. During the network embedding phase, a new random walk algorithm termed MRWalk and a pseudo-similar propagation mechanism are proposed. Experimental results using the Twitter dataset and public Amazon product dataset demonstrate that the proposed MIAL algorithm outperforms various state-of-the-art algorithms. The comparative analysis shows that MIAL has significantly improved performance.
MCANet: Hierarchical cross-fusion lightweight transformer based on multi-ConvHead attention for object detection

The visual Transformer model based on self-attention has achieved better performance than convolutional neural networks in object detection tasks. However, existing visual Transformer models are typically heavy-weight to extract global features. In contrast, CNNs can extract features with fewer parameters and computational costs. To combine the advantages of convolutional processing at the local level with the advantages of the Transformer's global interaction, this paper proposes MCANet, a Hierarchical Cross-Fusion Lightweight Transformer Based on Multi-ConvHead Attention for Object Detection. To bi-directionally fuse local and global features, MCANet adds two improved transformers (MCA-Former) for global interaction and two novel feature fusion modules MCA-CSP. MCA-Former uses a novel self-attention computation method named Multi-ConvHead Attention(MCA) based on multi-scale depth-separable convolution, which reduces the computational cost by 2/3. Meanwhile, the number of model parameters is reduced to 9.49 M by using channel segmentation and multi-layer cross- fusion strategies. On the Pascal VOC and COCO datasets, the proposed model outperforms YOLOv4-Tiny in terms of AP by 2.43% and 1.8%, respectively. Additionally, MCANet is also superior to many latest lightweight object detection models. Results of various ablation experiments also verify the effectiveness of the proposed method.
An Autonomous Surgical Instrument Tracking Framework With a Binocular Camera for a Robotic Flexible Laparoscope

In minimally invasive surgery (MIS), the field of view (FOV) plays a vital role. To enhance the stability of FOV and lighten the burden on surgeons, robot-assisted laparoscope systems have been developed and introduced into surgery. However, most of the existing automatic surgical tool tracking schemes with robotic laparoscopes either do not consider tracking of the depth direction or the remote center of motion (RCM) constraints. To tackle these problems, this article presents an autonomous three-dimensional surgical instrument tracking framework for a robotic laparoscope based on a binocular camera. A 3-degree-of-freedom visual tracking scheme is derived from the kinematics of the binocular robotic laparoscope. A programmable RCM constraint is introduced due to the MIS requirement. To precisely locate the surgical instruments in FOV, a surgical instrument tip segmentation dataset with 5471 images is built and a modified real-time instance segmentation network is trained based on the dataset. Then, a semi-global matching (SGM) algorithm is utilized with the binocular camera to estimate the depth in FOV. To meet the joint limit constraint and maintain the dexterity of the robotic system, a task-priority-based control method is adopted. The simulation and experiment results demonstrate the feasibility of the proposed framework for instrument tracking with the robotic laparoscope.
TARGAT: A Time-Aware Relational Graph Attention Model for Temporal Knowledge Graph Embedding

Temporal knowledge graph embedding (TKGE) aims to learn the embedding of entities and relations in a temporal knowledge graph (TKG). Although the previous graph neural networks (GNN) based models have achieved promising results, they cannot directly capture the interactions of multi-facts at different timestamps. To address the above limitation, we propose a time-aware relational graph attention model (TARGAT), which takes the multi-facts at different timestamps as a unified graph. First, we develop a relational generator to dynamically generate a series of time-aware relational message transformation matrices, which jointly models the relations and the timestamp information into a unified way. Then, we apply the generated message transformation matrices to project the neighborhood features into different time-aware spaces and aggregate these neighborhood features to explicitly capture the interactions of multi-facts. Finally, a temporal transformer classifier is applied to learn the representation of the query quadruples and predict the missing entities. The experimental results show that our TARGAT model beats the GNN-based models by a large margin and achieves new state-of-the-art results on four popular benchmark datasets.
Close encounters with the virtual kind: Defining a human-virtual agent coexistence framework

Virtual agent research has evolved into a substantial body of work, albeit one with a fragmented structure and overlapping, and at times inconsistent, definitions and results. The current paper presents a computational literature review of 1865 academic journal publications and conference proceedings from 1995 to 2022 using Latent Dirichlet Allocation to understand the publication trends in the field, its intellectual structure, and how topics within virtual agent research have evolved and relate to each other. Our results point to a model of 16 topics as best representing the current state of the research landscape. We present descriptions of these topics, as well as topic dynamics and networks, in order to provide a clear picture of the current state of the field. We then organise these topics into a Human-Virtual Agent Coexistence Framework, identifying current trends and opportunities for future research.
Sparse Online Learning With Kernels Using Random Features for Estimating Nonlinear Dynamic Graphs

Online topology estimation of graph-connected time series is challenging in practice, particularly because the dependencies between the time series in many real-world scenarios are nonlinear. To address this challenge, we introduce a novel kernel-based algorithm for online graph topology estimation. Our proposed algorithm also performs a Fourier-based random feature approximation to tackle the curse of dimensionality associated with kernel representations. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group-Lasso based optimization framework, which is solved using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. We provide theoretical guarantees for our algorithm and prove that it can achieve sublinear dynamic regret under certain reasonable assumptions. In experiments conducted on both real and synthetic data, our method outperforms existing state-of-the-art competitors.
A study on managerial leadership in education: A systematic literature review

Articles on managerial leadership (ML) have been published from year to year, since 1950s. The use of ML theory in previous research is common, but some inconsistencies are found regarding the terms commonly used. In other words, there is a mismatch between the use of the term ML in article paper and structures. This will certainly have an impact on bias and ambiguity for future research literatures.Theoretical review on this topic is rarely carried out, specifically in ML theory. The novelty of this research lies in the classification results of articles that used the term ML in accordance with the theory.This theoretical review was conducted to examine the classification of accuracy of articles which were using the term ML in title with four consistency and accuracy indicators on the article structures starting from the problem, aim, literature, results and discussion, as well as conclusion sections.This review as a qualitative literature research used a language and historical approach, as well as a ML theory. This study Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The technique and instrument used was bibliographic instruments, comprehensive list of keywords and mixed search terms to search the articles online with the help of Google Chrome and Mozilla Firefox browsers. A total of 68 articles published from 1959 to 2022 as a final reviewed. They were obtained from several well-known digital journal content such as Jstor, Proquest, Oxford University Press, Google Scholar, and National Library, as well as various journals under major publishers such as Elsevier, Taylor & Francis, SAGE, Emerald, Brill, and Wiley. The data collected were analyzed using content analysis with 4 indicators of consistent (accurate & additional) and inconsistent (difference & additional), and 4 accuracy category of accuracy, appropriate, bias, and error for determaining the articles classification, and validated them by using triangulation and grounded theory.The results showed that in 1959 the first article appeared using the word ML, in 2012 the first and only article appeared that only used ML, and the last is in 2022. Then, the consistency of the title with other article sections is 17 articles (25% of 68) according to the accurate term indicator. Last, the accuracy of articles was divided into four categories: 10 articles (15% of 68) in the accuracy category.This systematic review contributes the article classification that can become a more established scientific roadmap of references and reasoning of studying ML.
A comprehensive review of graph convolutional networks: approaches and applications

&lt;abstract&gt; &lt;p&gt;Convolutional neural networks (CNNs) utilize local translation invariance in the Euclidean domain and have remarkable achievements in computer vision tasks. However, there are many data types with non-Euclidean structures, such as social networks, chemical molecules, knowledge graphs, etc., which are crucial to real-world applications. The graph convolutional neural network (GCN), as a derivative of CNNs for non-Euclidean data, was established for non-Euclidean graph data. In this paper, we mainly survey the progress of GCNs and introduce in detail several basic models based on GCNs. First, we review the challenges in building GCNs, including large-scale graph data, directed graphs and multi-scale graph tasks. Also, we briefly discuss some applications of GCNs, including computer vision, transportation networks and other fields. Furthermore, we point out some open issues and highlight some future research trends for GCNs.&lt;/p&gt; &lt;/abstract&gt;