Reviewerly/exHarmony huggingface dataset:
Deep 3D Vessel Segmentation based on Cross Transformer Network

The coronary microvascular disease poses a great threat to human health. Computer-aided analysis/diagnosis systems help physicians intervene in the disease at early stages, where 3D vessel segmentation is a fundamental step. However, there is a lack of carefully annotated dataset to support algorithm development and evaluation. On the other hand, the commonly-used U-Net structures often yield disconnected and inaccurate segmentation results, especially for small vessel structures. In this paper, motivated by the data scarcity, we first construct two large-scale vessel segmentation datasets consisting of 100 and 500 computed tomography (CT) volumes with pixel-level annotations by experienced radiologists. To enhance the U-Net, we further propose the cross transformer network (CTN) for fine-grained vessel segmentation. In CTN, a transformer module is constructed in parallel to a U-Net to learn long-distance dependencies between different anatomical regions; and these dependencies are communicated to the U-Net at multiple stages to endow it with global awareness. Experimental results on the two in-house datasets indicate that this hybrid model alleviates unexpected disconnections by considering topological information across regions. Our codes, together with the trained models are made publicly available at https://github.com/qibaolian/ctn.
Domain Adaptation for Medical Image Classification without Source Data

Although deep learning has achieved promising results on medical image classification, the domain shift between training and testing datasets leads to a low prediction accuracy. Domain adaptation is a effective solution. However, due to privacy issues and the lack of annotated data, it’s hard for conventional domain adaptation methods to access source images and labeled target images. To tackle this issue, we propose a novel framework that only requires unlabeled target domain data. This framework has two modules, one is based on class conditional generative adversarial net for source domain generation and another is for classification m odel t raining. S pecifically, the generator can generate target-style data as the pseudo-source data using random noise and a given label to improve the classifier. The increased a ccuracy of the classifier also can guide the generator. Besides, we introduce weight regularization and clustering-based regularization to keep the training process stable and fully explore the discriminative information. We take diabetic retinopathy grade classification as our task and conduct experiments on three datasets which are EysPACS, MESSIDOR and IDRiD. The experimental results show that our method performs well on only unlabeled target data, which proves that it is a general method and can be widely used in the field of medical image classification.
Knowledge-Enhanced Dual Graph Neural Network for Robust Medicine Recommendation

Medicine recommendation assists physicians in automatically providing medicine combinations, which is critical in health care. Existing efforts focus on making medicine recommendations based on the patient’s electronic health record(EHR). However, they ignore external medicine knowledge and are vulnerable to the missing EHR. In this paper, a knowledge-enhanced dual graph neural network (KDGN) is proposed to recommend medicine sets. KDGN combines diagnosis-level and procedure-level attention mechanisms to encode multiple types of medical codes. In order to mine medicine from medical knowledge, KDGN further designs a dual-graph neural network, which constructs a medicine co-occurrence graph and molecular connection graph, and retrieves potential therapeutic drugs. Furthermore, during the training phase, we introduce the automatic correction loss based on maximum likelihood estimation to mitigate the impact of missing EHR and enhance the robustness of KDGN. We evaluate the proposed model on the public MIMIC-III dataset, and experimental results show that KDGN outperforms the state-of-the-art model in 4 out of 5 evaluation metrics. Our dataset and code are available at: https://github.com/Benjamin-cell/KDGN.
Unpaired Speckle Extraction for SAR Despeckling

Speckle suppression is a critical step in synthetic aperture radar (SAR) imaging. Since speckle-free SAR images are inaccessible, supervised denoising methods are not suitable for this task. To exploit the strong capabilities of convolutional neural networks (CNNs), we propose Unpaired Speckle Extraction (SAR-USE), an unsupervised method for SAR despeckling. Our method utilizes unpaired SAR and clean optical images to extract “real” speckle for learning despeckling. First, a CNN that has never seen clean SAR images is employed to extract speckle from the SAR image. Then, the extracted speckle is multiplied with a random optical image to synthesize paired data for learning speckle removal. Through a Siamese network, speckle extraction and learning despeckling are performed alternately and promote each other. To make the extracted speckle more visually and statistically realistic, it is constrained by a noise correction module to be unit mean while maintaining spatial correlation. After convergence, the CNN is a good denoiser that can effectively extract speckle from SAR images. Experiments on synthetic datasets show that the denoising ability of the proposed method is as good as its supervised counterpart. More importantly, SAR-USE is very efficient for removing the spatially correlated speckle in real data that supervised learning methods cannot.
Design of an Automated Blockchain-Enabled Vehicle Data Management System

The Internet of Vehicles (IoV) has a tremendous prospect for numerous vehicular applications. IoV enables vehicles to transmit data to improve roadway safety and efficiency. Data security is essential for increasing the security and privacy of vehicle and roadway infrastructures in IoV systems. Several researchers proposed numerous solutions to address security and privacy issues in IoV systems. However, these issues are not proper solutions that lack data authentication and verification protocols. In this paper, a blockchain-enabled automated data management system for vehicles has been proposed and demonstrated. This work enables automated data verification and authentication using smart contracts. Certified organizations can only access vehicle data uploaded by the vehicle user to the Interplanetary File System (IPFS) server through that vehicle user’s consent. The proposed system increases the security of vehicles and data. Vehicle privacy is also maintained here by increasing data privacy.
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare

Researchers have conduct adversarial attacks against deep neural networks (DNNs) for health risk prediction in the white/gray-box setting to evaluate their robustness. However, since most real-world solutions are trained by private data and released as black-box services on the cloud, we should investigate their robustness in the black-box setting. Unfortunately, existing work ignores to consider the uniqueness of electronic health records (EHRs). To fill this gap, we propose the first black-box adversarial attack method against health risk prediction models named MedAttacker to investigate their vulnerability. It addresses the challenges brought by EHRs via two steps: hierarchical position selection which selects the attacked positions in a reinforcement learning (RL) framework and substitute selection which identifies substitutes with a score-based principle. Particularly, by considering the temporal context inside EHRs, MedAttacker initializes its RL position selection policy by using the contribution score of each visit and the saliency score of each code, which can be well integrated with the deterministic substitute selection process decided by the score changes. We evaluate MedAttacker by attacking three advanced risk prediction models in the black-box setting across multiple real-world datasets, and MedAttacker consistently achieves the highest average success rate and even outperforms a recent white-box EHR adversarial attack technique in certain cases.
Unsupervised extraction, labelling and clustering of segments from clinical notes

This work is motivated by the scarcity of tools for accurate, unsupervised information extraction from unstructured clinical notes in computationally underrepresented languages, such as Czech.We introduce a stepping stone to a broad array of downstream tasks such as summarisation or integration of individual patient records, extraction of structured information for national cancer registry reporting or building of semistructured semantic patient representations for computing patient embeddings.More specifically, we present a method for unsupervised extraction of semantically-labelled textual segments from clinical notes and test it out on a dataset of Czech breast cancer patients, provided by Masaryk Memorial Cancer Institute (the largest Czech hospital specialising in oncology).Our goal was to extract, classify (i.e.label) and cluster segments of the freetext notes that correspond to specific clinical features (e.g., family background, comorbidities or toxicities).The presented results demonstrate the practical relevance of the proposed approach for building more sophisticated extraction and analytical pipelines deployed on Czech clinical notes.
Energy efficient and optimized genetic algorithm for software effort estimator using double hidden layer bi-directional associative memory

In software development, it's important to have an accurate assessment of effort, cost, energy, and time in order to plan and allocate resources in the best way possible. This makes it more likely that the software will work and lowers the risk that it won't. Bi-directional associative memory is used in the suggested method to figure out how long it will take to finish a project. This effort estimator was built in MATLAB and tested, verified, and trained on a large project dataset. In this work, a genetic algorithm called Double Hidden Layers Bi-directional Associative Memory is used to make a unique model for a software estimator (DHBAM). After doing several simulations, we found that the DHBAM architecture works better than the Single Hidden Layer Feed-Forward Neural Net (SHFNN) model for getting the best results. It has also been proven with the root mean square error (RMSE) method. In a previous study, the RMSE for the network design SHFNN 16-19-1 with a learning rate of 1.01 and a momentum of 0.70 after 1,000,000 iterations was 1.49074 × 10^-3. With a learning rate parameter of 1.05 and a momentum parameter of 0.6, the RMSE for the network design DHBAM 16-8-6-1 is now 1.241703 × 10^-3, which drops to 1.2238 × 10^-3 after 100 generations in 10,000 populations using the optimized based genetic algorithm (GA). Based on the results, it's clear that the proposed effort estimator does a better job than the ones that are already in use. Experiments show that the newly proposed optimized based genetic algorithm will help researchers, scientists, and businesses predict important traits more accurately and efficiently early on in the planning process.
Digital pathology implementation in a private laboratory: The CEDAP experience

The transition to digital pathology has been carried out by several laboratories across the globe, with some cases described in Portugal. In this article, we describe the transition to digital pathology in a high-volume private laboratory, considering the main challenges and opportunities.Our process started in 2020, with laboratory workflow adaptation and we are currently using a high-capacity scanner (Aperio GT450DX) to digitize slides at 20×. The visualization system, Aperio eSlide Manager WebViewer, is integrated into the Laboratory System. The validation process followed the Royal College of Pathologists Guidelines.Regarding validation, the first phase detected an error rate of 6.8%, mostly due to digitization errors. Phase optimization and collaboration with technical services led to improvements in this process. In the second validation phase, most of the slides had the desired quality for evaluation, with only an error rate of 0.6%, corrected with a new scan. The interpathologist correlation had a total agreement rate of 96.87% and 3.13% partial agreement.The implementation and validation of digital pathology was a success, being ready for prime time. The total integration of all laboratory systems and the acquisition of new equipment will maximize their use, especially with the application of artificial intelligence algorithms.
Fully privacy-preserving location recommendation in outsourced environments

Currently, location-based services (LBS) have been widely used in real-world settings, including restaurant and travel recommendations. To reduce workload and improve query efficiency, a service provider usually outsources its services to a powerful cloud server. However, the service provider’s database and users’ queries always contain sensitive information, so their leakage to the cloud may raise serious privacy concerns. Although some existing schemes have been proposed to address the privacy problems, they are impractical in real-world LBS due to some issues in privacy, accuracy, or heavy computation costs for query users. In order to overcome these problems, we propose a fully privacy-preserving location recommendation scheme that supports multi-attribute queries and returns accurate results based on the recommendation condition. Specifically, based on the Paillier cryptosystem, we first propose a secure equal test (SET) protocol to check whether two encrypted values are equal. Second, with our proposed protocols, we develop a privacy-preserving location recommendation scheme without revealing anything about the service provider or query users. Finally, we analyze the security of our scheme in the semi-honest model and show that the privacy of the service provider and query users is well protected. Meanwhile, we evaluate the performance of our scheme using synthetic datasets. The experimental results demonstrate that our proposed scheme is practical in real-world applications.
A Novel Reverse Engineering Approach for Gene Regulatory Networks

Capturing the rules that govern a particular system can be useful in any field where the causes of its effects are unknown. Indeed, discovering the causes that produced a particular effect is extremely useful in fields such as biology. In this paper, a reverse engineering method based on machine learning is proposed. This method was used to replicate real world behaviour and use this knowledge to generate the relative Gene Regulatory Network. The datasets from the DREAM4 Challenge were used to validate this method.
Effect of connection induced upper body movements on embodiment towards a limb controlled by another during virtual co-embodiment

Even if we cannot control them, or when we receive no tactile or proprioceptive feedback from them, limbs attached to our bodies can still provide indirect proprioceptive and haptic stimulations to the body parts they are attached to simply due to the physical connections. In this study we investigated whether such indirect movement and haptic feedbacks from a limb contribute to a feeling of embodiment towards it. To investigate this issue, we developed a 'Joint Avatar' setup in which two individuals were given full control over the limbs in different sides (left and right) of an avatar during a reaching task. The backs of the two individuals were connected with a pair of solid braces through which they could exchange forces and match the upper body postures with one another. Coupled with the first-person view, this simulated an experience of the upper body being synchronously dragged by the partner-controlled virtual arm when it moved. We observed that this passive synchronized upper-body movement significantly reduced the feeling of the partner-controlled limb being owned or controlled by another. In summary, our results suggest that even in total absence of control, connection induced upper body movements synchronized with the visible limb movements can positively affect the sense of embodiment towards partner-controlled or autonomous limbs.
Multi-Label Feature Selection via Label Enhancement and Analytic Hierarchy Process

Multi-label feature selection can effectively resolve the challenges of high or even ultra-high dimensionality in multi-label data. However, most existing multi-label feature selection algorithms can only handle a single data type, assume all labels are equally significant and utilize heuristic search strategies, which results in inefficient and relatively unsatisfactory classification accuracy. In view of the above shortcomings, this paper proposes a new multi-label feature selection algorithm that effectively resolves existing algorithms' issues through three innovative procedures. First, a new similarity relation metric is proposed to deal with hybrid data types effectively. Second, a label enhancement algorithm is designed to enhance and transform the logical labels into a label distribution by fully considering the analytic hierarchy process (AHP) embedded with label correlation, which can automatically identify the significance of different labels. Third, a feature weighting evaluation is redesigned in the feature selection process to obtain the optimal feature subset through feature ranking directly. Under these proposed procedures, multi-label feature selection can effectively utilize the abundant semantic information of the label significance and can significantly improve the operating accuracy and efficiency simultaneously. Comparative experiments are conducted on 20 real multi-label datasets with seven state-of-the-art multi-label feature selection algorithms. Experimental results show that the proposed multi-label feature selection algorithm in this paper is about 5–10% better than the algorithms in 80% of the compared datasets.
Prototype-Guided Multitask Adversarial Network for Cross-Domain LiDAR Point Clouds Semantic Segmentation

Unsupervised domain adaptation (UDA) segmentation aims to leverage labeled source data to make accurate predictions on unlabeled target data. The key is to make the segmentation network learn domain-invariant representations. In this work, we propose a prototype-guided multitask adversarial network (PMAN) to achieve this. First, we propose an intensity-aware segmentation network (IAS-Net) that leverages the private intensity information of target data to substantially facilitate feature learning of the target domain. Second, the category-level cross-domain feature alignment strategy is introduced to flee the side effects of global feature alignment. It employs the prototype (class centroid) and includes two essential operations: 1) build an auxiliary nonparametric classifier to evaluate the semantic alignment degree of each point based on the prediction consistency between the main and auxiliary classifiers and 2) introduce two class-conditional point-to-prototype learning objectives for better alignment. One is to explicitly perform category-level feature alignment in a progressive manner, and the other aims to shape the source feature representation to be discriminative. Extensive experiments reveal that our PMAN outperforms state-of-the-art results on two benchmark datasets.
Mapping the use of Google Scholar in evaluative bibliometric or scientometric studies: A bibliometric review

Abstract Google Scholar (GS) has aroused a good deal of interest among the bibliometric and scientometric community, owing to its capacity for gathering publication data, tracking citations, and creating metrics. This has led to reflections on its potential value as a means of enhancing evaluative procedures. However, despite being a useful tool because of its wide coverage, it has been monitored by specialists. For this reason, we aimed to map out the publications in the areas of Information Science &amp; Library Science and/or Computer Science that make use of GS through a bibliometric review. Comprising data retrieved from the WoS and Dimensions, the results drew the attention of the bibliometric and scientometric community to the range of research problems in studies using GS. They also made it possible to identify the most prolific countries and authors and their preferred sources for publication. The presence of non-Anglophone countries and those from Latin America highlights the importance of alternative information sources to bibliometric and scientometric studies.
Secure Decentralized Image Classification With Multiparty Homomorphic Encryption

Decentralized image classification plays a key role in various scenarios due to its attractive properties, including tolerating high network latency and less prone to single-point failures. Unfortunately, training such a decentralized image classification model is more vulnerable to data privacy leaks compared to other distributed training frameworks. Existing efforts exclusively use differential privacy as the cornerstone to alleviate the threat to data privacy. However, differential privacy is implemented at the expense of accuracy, which goes against our motivation for designing an image classification model without loss of accuracy. To address this problem, we propose D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -MHE, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> secure and efficient decentralized training framework with lossless precision. Inspired by the latest developments in the homomorphic encryption technology, we design a multiparty version of Brakerski-Fan-Vercauteren (BFV), one of the most advanced cryptosystems, and use it to implement private gradient updates of users’ local models. D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -MHE can reduce the communication complexity of general Secure Multiparty Computation (MPC) tasks from quadratic to linear in the number of users, making it very suitable and scalable for large-scale decentralized learning systems. Moreover, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -MHE provides strict semantic security protection even if the majority of users are dishonest with collusion. We conduct extensive experiments on MNIST, CIFAR-10, and ImageNet to demonstrate the superiority of D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -MHE. Experimental results show that D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -MHE achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.5\times $ </tex-math></inline-formula> reduction in computation overhead, and at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12\times $ </tex-math></inline-formula> reduction in communication overhead compared to existing schemes.
Ten simple rules for using public biological data for your research

With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public biological data are: (1) use public data purposefully in your research; (2) evaluate data for your use case; (3) check data reuse requirements and embargoes; (4) be aware of ethics for data reuse; (5) plan for data storage and compute requirements; (6) know what you are downloading; (7) download programmatically and verify integrity; (8) properly cite data; (9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share; and (10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.