Reviewerly/exHarmony huggingface dataset:
Construction of Nursing Sequential Decision Knowledge Graph from the Perspective of International Standard Terminology

This paper presents Nursing Sequential Decision Knowledge Graph (NSDKG) data set to support sequential decision making in the field of nursing. First, according to the international standard terminology system NANDA, NOC, and NIC, the knowledge graphs of nursing evaluation, nursing problem, nursing goal, nursing intervention and nursing assessment are constructed respectively. Then, these five knowledge graphs are linked based on the international standard terminology NNN, generating the ultimate knowledge graph of nursing sequential decision. The statistical analysis shows that the proposed NSDKG has the characteristics of community knowledge graph. We apply NSDKG to the relation prediction model and find that the data set can be effectively used to realize multi-hop inference. The NSDKG fills the gap of data set in the nursing field and provides a basis for the related research in the future.
A Blockchain-Based Architecture for Interoperable Healthcare Data Exchange

Patients and healthcare providers often face challenges when dealing with creating, reading, updating, sharing, and deleting operations on individual healthcare data due to incompatible electronic medical record systems and the inability to provide necessary data. These challenges can drastically prolong both a patient's diagnosis time and the time until the operation. There are many informational discrepancies when attempting to provide adequate healthcare background - especially with new providers when a full healthcare history is required. To aid in increasing interoperability, we propose a decentralized data-sharing approach. While current centralized solutions own patients’ healthcare data, we seek to put data back in the hands of the patients using blockchain as a decentralized database. In essence, our data-sharing architecture and set of rules leverage a permissioned blockchain network, Hyperledger Fabric, to give each patient on the network the ability to own and control the flow of their healthcare data.
Pathological Tissue-level Contour Genomic Profile Interpretation of Lung Adenocarcinoma via Spatial and Morphological Features Co-action Graph Neural Network

The affections from genomics to morphology can prompt the genomic profile interpretation from inexpensive pathological image data rather than highly cost genomic sequencing data. Due to the extremely large size of Whole Slide Image (WSI), directly processing the complete Lung Adenocarcinoma (LUAD) WSI with traditional deep learning methods, will lead to memory overflow. In comparison to complete WSI, the smaller size patches can be easier processed by the existing deep learning based methods. Nevertheless, the split patches severely break the potential relationships between genomic abnormalities and morphological features, and the traditional deep learning methods may be difficult to capture the information of the broken relationship. Fortunately, a recent study has shown that the graph-structure representation can feasibly demonstrate the relationships among both local and remote regions. In consideration of the obstacles of both the break of remote area relationships and the lack of tissue-level contour for genomics-to-morphology associations, we propose Spatial and Morphological Features Co-action Graph Neural Network model (SMCGNN) to achieve the pathological tissue-level contour genomic profile interpretation of LUAD. Our SMCGNN achieves better performance on the genomic profile interpretation task than those of previous researches, yielding a relative performance increment of 9.3%. To the best of our knowledge, our SMCGNN is the first model to interpret the biological tissue-level contour of the genomic abnormality-related morphological regions. In summary, our method can provide pathologists with more fine-grained hints to molecular profile. The interpreted tissue-level regions can be accessed via the link: https://github.com/xianyvxxx/tissue-level-contour-regions-associated-with-genomic-profile.
PnP-AE: A Plug-and-Play Module for Volumetric Medical Image Segmentation

In recent years, 3D volumetric medical images have been widely used in clinical diagnosis, however, the popular 2D networks were reported unsuitable for segmenting them. In this direction, we propose a plug-and-play (PnP-AE) module to improve the performance of using 2D network for 3D medical image segmentation. Our method takes advantage of the intrinsic correlation between adjacent slices, by multiple encoders and fusion components to decouple plane feature extraction and depth information integration. In addition, the proposed weight sharing and feature storage strategies make PnP-AE extremely efficient. Our method is able to conveniently incorporate with mainstream 2D networks to segment 3D volumetric medical images. Experimental results demonstrate the excellent performance of our method. The source code is available at https://github.com/qklee-lz/PnP-AE.
Detect the Unseen: An Expandable Detection Model for Stem Cell Images

Stem cell culture in vitro is essential for research in cell biology, drug toxicity and translational studies. In recent years, there has been a surge in the development of deep learning-based object detection algorithms tailored for image analysis of stem cell culture. However, many of these algorithms fall short in terms of performance and interpretability. To address these challenges, we present StemCellDet, an innovative multimodal-based method for stem cell detection. By harnessing the power of the pretrained CLIP model, StemCellDet uniquely encodes descriptions of stem cell categories into text embeddings, which are then synchronized with image embeddings. This synchronization enhances the model’s ability to identify critical features for accurate stem cell model categorization. Furthermore, by integrating knowledge distillation and introducing our proposed Semantic Fusion Module (SFM), StemCellDet can adeptly identify stem cell culture categories that were not present during its training phase using only their textual descriptions. Our experiments highlight StemCellDet’s robust detection capabilities and its advantages in terms of accuracy and generalizability.
Low-Resource Efficient Multi-Stage Tuning Strategy for Biomedical Question Answering Task

The automated question-answering system plays a crucial role in improving the accuracy and efficiency of clinical decision-making. While large-scale language models perform prominently in the general domain, even surpassing human performance in certain aspects, challenges such as data privacy and the massive training costs affect the broader adoption of LLMs in biomedical domain. This study explores the strategies of efficient fine-tuning and optimization methods in biomedical domain. We introduce a multi-stage fine-tuning strategy that improves the accuracy of medical question-answering tasks significantly. Specifically, a contrastive learning technique based on multi-prompts is proposed, and a self-consistency voting approach is used to improve the accuracy of reasoning-required tasks. Experimental results on PubMedQA dataset reveal that even fine-tuning only 0.152% of the baseline’s parameters, our method still improves its performance, making it outperform the domain-specific pre-trained models and achieve performance comparable to GPT-4.
Deep Residual Fourier and Self-Attention for Arbitrary Scale MRI Super-Resolution

The excellent inherent contrast between biological tissues afforded by MR imaging is one of the foremost characteristics of this technique, but it depends on the scan time and hardware devices. Recent studies have shown significant progress in deep learning-based single-image super-resolution (SISR) algorithms based on MR images. Many researchers have employed implicit functions for super-resolution tasks, achieving arbitrary resolution upsampling. Nevertheless, challenges like loss of generated image texture and weak high-frequency features persist. We propose an arbitrary multiple super-resolution model based on deep residual Fourier transform and a selfattention mechanism to tackle these issues. We first use the deep Fourier residual block to construct the high and low-frequency difference of the image, compensating for the spectral bias of the multiple perceptron (MLP) network. Building upon the substantial similarity in medical image tissue structures, we add a vertical and horizontal self-attention mechanism to capture the internal correlation of features in the vertical and horizontal directions. Finally, we learn a continuous functional representation to execute super-resolution tasks at any scale. Experiment results show the effectiveness of our method on the T2 sequence of public dataset SIMON and T1, T2, and Proton Density (PD) weighted scan sequences of public dataset IXI. We conduct qualitative and quantitative comparisons of the three contrasts, illustrating the superiority of our approach.
EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformer

In this study, we address the key challenges concerning the accuracy and effectiveness of depth estimation for endoscopic imaging, with a particular emphasis on real-time inference and the impact of light reflections. We propose a novel lightweight solution named EndoDepthL that integrates Convolutional Neural Networks (CNN) and Transformers to predict multi-scale depth maps. Our approach includes optimizing the network architecture, incorporating multi-scale dilated convolution, and a multi-channel attention mechanism. We also introduce a statistical confidence boundary mask to minimize the impact of reflective areas. To better evaluate the performance of monocular depth estimation in endoscopic imaging, we propose a novel complexity evaluation metric that considers network parameter size, floating-point operations, and inference frames per second. We comprehensively evaluate our proposed method and compare it with existing baseline solutions. The results demonstrate that EndoDepthL ensures depth estimation accuracy with a lightweight structure.
Named Entity Recognition in Italian Lung Cancer Clinical Reports using Transformers

The widespread adoption of electronic health records (EHRs) offers a valuable opportunity to support clinical research by containing crucial patient information, including diagnoses, symptoms, medications, lab tests, and more. Despite the success of deep learning for biomedical Named Entity Recognition (NER), the literature in this field still presents a gap regarding applications focused on lung cancer for the Italian language. Hence, this paper presents a transformer-based approach to extract named entities from Italian clinical notes related to Non-Small Cell Lung Cancer (NSCLC). We introduce a novel set of 25 clinical entities related to NSCLC building a corpus annotated for NER. We apply a state-of the-art model pre-trained on Italian biomedical texts to the manually annotated clinical reports of a cohort of 257 patients suffering from NSCLC, successfully dealing with class-imbalance problems and obtaining promising performance (average F1-score of 84.3%). We also compared our method with two other pre-trained state-of-the-art models showing that the domain specific knowledge offered by the proposed approach is necessary to achieve higher performance. These findings also showcase the feasibility of using transformers to extract biomedical information in the Italian language.
Bridging the Gap: Cross-modal Knowledge Driven Network for Radiology Report Generation

Radiology report generation aims to generate medical reports based on given medical images, which can alleviate the workload of radiologists and has attracted significant research interest in recent years. However, existing studies have struggled to bridge the gap between the two different modalities (i.e. image and text) and generate clinically accurate reports. This is primarily due to the challenges in modelling the crossmodal mappings and the inefficiency of transferring knowledge across modalities. To address these challenges, in this paper, we propose to leverage a pre-constructed knowledge graph as a shared matrix that bridges the gap between visual and textual information, facilitating cross-modal knowledge transfer. This shared knowledge matrix effectively captures cross-modal mappings and aligns information between images and texts, thereby bridging the gap between modalities. Specifically, we propose a new module for knowledge distillation and preservation that integrates relevant knowledge representations into both visual and textual inputs, facilitating intuitive cross-modal knowledge interaction and enhancing the clinical accuracy of the generated reports. Experimental results on two benchmark datasets show the effectiveness of our method, outperforming state-of-the-arts in report generation.
Evaluation of Centralized, Distributed and Federated Learning for IoT Intrusion Detection Systems

Introducing smart intrusion detection systems (IDSs) in the IoT system was a crucial requirement in the last decades to secure these systems due to the high sensitivity of the sensed data. Smart IDS has often been built based on a machine/deep learning model trained at a centralized node in the cloud. However, the centralized learning approach violates data privacy since IoT nodes are required to share their data to the cloud to train the model. Distributed and federated learning approaches were introduced to solve this issue by keeping data within the local network and letting the edge devices train the model. In this paper, a comparative study among centralized, distributed, and federated learning approaches is presented. First, a deep learning model is proposed to identify traffic behavior using CICIDS2017 dataset. The three learning approaches are then applied with the proposed model to evaluate the effect of each approach on the model accuracy. In the centralized learning approach, an F-Score of 98% can be achieved. Moreover, the dataset is split over ten simulated nodes with various data distribution and entropies to evaluate the distributed and federated learning approaches. In distributed learning, the F-Score ranges between 63% to 93% with an average of 78% over the ten nodes. In the federated learning, the F-Score ranges between 73% to 98% with an average of 89% over the ten nodes. This result confirms that IDSs based on federated learning are a promising solution for both accuracy and preserving data privacy.
Traditional and Deep Learning Techniques in Image Steganography: Recent Advances

For many decades however recently super increased; the importance of securing the private information of individuals or organizations. Therefore, numerous researchers are motivated to be involved in the challenge of building intelligent, flexible, secured, imperceptive, bumped bit embedding rates and robust models of Stenography. Image steganography is a way of concealing data, such as text, pictures, or videos, under an image that acts as a cover. Recent years have seen an increase in interest in deep learning technology, which has been proven to be a powerful mechanism in a number of uses, such as image steganography. This paper's primary goal is to investigate and clarify three main types of image steganography techniques: traditional, convolutional neural network-based, and generative adversarial network-based. An explanation of the datasets and evaluation measures is provided along with the approach. A comprehensive summary of various image steganography techniques is introduced. Finally, a discussion on the potential directions for this field's future is introduced.
Hybrid heuristics: for the repetition-free longest common subsequence problem with index-gene

The repetition-free longest common subsequence problem is an important model in sequence comparison and analyzing conserved genes within bioinformatics. Conserved genes are highly similar across species and play a crucial role in understanding the evolution of species and gene function. However, existing algorithms suffer from limitations concerning runtime efficiency and the number of conserved genes identified, thereby hindering the practical analysis of these essential genetic elements. To address these challenges, this paper proposes a novel hybrid heuristic algorithm that partitions gene sequences based on the known conserved genes. By building a linear programming model and utilizing a linear programming exact solver, the algorithm can efficiently identify the longest subsequence that satisfies the conditions imposed by the model in a short time frame. Through experiments on simulated and real genomic data, we verify the superior performance of the algorithm in solving the repetition-free longest common subsequence problem and identifying conserved genes.
Design Pattern Recommendation Using Doc2vec

Design patterns are widely recognized as valuable tools in software engineering for improving software quality and reducing development time. Selecting a suitable design pattern is crucial for creating high-quality software systems. The wrong design pattern can lead to difficult-to-maintain code. The appropriate design pattern can improve the overall quality of the software, making it easier to maintain, modify, and extend over time. However, selecting the appropriate design pattern for a specific design problem from textual descriptions is challenging and requires a deep understanding of their functionality and characteristics. In order to address this challenge, a recommendation system is developed that utilizes text classification through the application of doc2vec. This approach has not been previously explored in research and has the potential to be highly effective. This approach involves: first, preprocessing techniques such as stop word removal, tokenization, and stemming are applied to design pattern category descriptions, design patterns' descriptions, and problem scenarios. Second, doc2vec is applied for word embeddings to create the model. The study evaluates two approaches for selecting design patterns. The first approach focuses solely on making a recommendation based on the descriptions of the design patterns themselves and achieves an accuracy of 54.3%, making accurate recommendations for 25 out of 46 problems. The second approach considers the category of the design patterns before making a selection, resulting in an enhanced accuracy of 65.21% and correctly recommending a design pattern for 30 out of 46 problems. The second approach outperforms the first by leveraging the design pattern category, significantly narrowing the search space and improving recommendation accuracy. These initial results are promising and pave the way for further improvement by enhancing the technique or combining doc2vec with other techniques.
Automating the Parking Process: A Study on Motion Planning Techniques for Autonomous Vehicles

In the rapidly evolving landscape of autonomous vehicle technologies and urban planning, the development of precise motion planning algorithms for autonomous parking has emerged as a critical research endeavor. This paper undertakes a comprehensive exploration of this vital area, with a specific focus on low-speed parking maneuvers. It meticulously analyzes and implements three prominent motion planning algorithms—Hybrid A-star, Reeds and Shepp, and Rapidly-exploring random tree star (RRT)—in known environments. Through a systematic evaluation conducted via simulations, this research assesses the distinct strengths and limitations of each algorithm across a spectrum of parking scenarios. Hybrid A-star's capacity to navigate intricate situations replete with obstacles, the proficiency of Reeds and Shepp in obstacle-free environments, and the efficient parking possibility exploration by RRT are intricately examined. These insights not only highlight the unique merits of each algorithm but also shed light on their potential applications in self-parking systems. The integration of these advanced algorithms into autonomous parking systems promises to significantly enhance parking efficiency and alleviate urban congestion, thereby making substantial contributions to the field of urban parking management. This study, thus, offers valuable academic insights into the practical implementation of motion planning algorithms for autonomous parking, addressing pertinent challenges and opportunities in this context.
Real-Time Human Pose Estimation on Embedded Devices Based on Deep Learning

Human posture detection plays an important role in areas such as human behavior analysis and human-computer interaction. Deep learning based methods are one of the mainstream approaches. In practice, detection models are often deployed in resource-constrained edge devices for privacy protection, increased realtime, etc. However, most of the pose detection is for dynamic scenes, which requires high detection speed and accuracy, and thus models are mostly complex in structure and decentralized computation. Aiming at the above difficulties, this paper proposes a multi-person pose detection method, implements a lightweight detection network-RepPose, and deploys it on an embedded device. Specifically, this paper proposes a branch pruning scheme based on the reparameterized structure; and designs a high-performance post-processing operator for the post-processing part; in addition, we also propose a multi-threaded parallel accelerated inference scheme. Finally, it is deployed on Huawei Atlas200DK, and the result reaches 26FPS in the multi-target detection experiment, and achieves more than 90% classification accuracy in pedestrian state detection experiment in simple traffic scenarios.
Domain-Adaptive Algorithm for Person Re-Identification Based on Consistency and Joint-Learning

In unsupervised domain adaptation (UDA) of person re-identification, the feature consistency between source dataset and target dataset has huge impact on performance. The way to maintain feature consistency is to keep the network update stable and reduce the impact of features with excessive differences. This paper proposes Consistency Joint-Learning model (CJL) that focuses on feature consistency and joint learning between networks. By storing the features of source dataset in the memory bank, comparing the extracted features on the training with the store features can obtain the consistency of the features in the target domain. Meanwhile, the model introduces joint learning between networks to make full use of the network's capabilities. Experimental results demonstrate the advantages of the model in improving migration performance.
IDAA-NET: An Image Domain Adaptive Alignment Network for Unsupervised Liver Vessel Segmentation from CTA Images

Accurate segmentation of liver vessel from CTA image is important for the diagnosis and treatment of liver diseases. The quality of labeled data directly affects the prediction results of the segmentation model. Compared with CTA image, MRA image has clearer 3D vasculature. Therefore, in order to reduce the reliance of the labeled CTA image which may contain ambiguous vessel contours, we propose a novel unsupervised liver vessel segmentation method based on image domain adaptive alignment network (IDAA-Net) by using labeled MRA and unlabeled CTA images. The IDAA-Net mainly contains three modules: 1) A spatial alignment module (SAM) is introduced to convert MRA image slice to synthetic CTA image slice for achieving spatial alignment of the different modality data in the feature and image levels; 2) An artifact removal module (ARM) is designed to eliminate background artifacts of synthetic CTA from SAM by using the liver label in MRA; 3) An adversarial segmentation module (ASM) is proposed to obtain the optimal segmentation by jointly adversarial learning and supervised learning between the predicted segmentation and the ground-truth label of MRA image. Experiments on the public and private datasets show that our method achieves comparable performance with state-of-the-art supervised method and outperforms the existing unsupervised segmentation methods.