Reviewerly/exHarmony huggingface dataset:
Assessing Vulnerabilities of Deep Learning Explainability in Medical Image Analysis Under Adversarial Settings

Deep Learning (DL) is a valuable set of techniques that improve medical decision-making based on imaging exams, such as Chest X-rays (CXR), Computed Tomography (CT), and Optical Coherence Tomography (OCT). However, DL models may be susceptible to adversarial attacks when perturbed (tam-pered) examples sneak into the data, decreasing the model's confidence. In this paper, we evaluate the vulnerabilities of DL applied to medical images and analyze the effects of attacks on the Gradient-weighted Class Activation Mapping (GRAD-CAM). Our experiments were conducted on two scenarios: (i) CXR images with binary class; (ii) OCT images with multi-class. Vulnerabilities are described by Fooling Rate (FR) and visual analysis of Grad-CAM. We show that the PGD is the most malicious deed for multi-class, reaching an FR of up to 96%, whereas DeepFool is hurtful for binary classes, reaching an FR of up to 93%. Our analysis can be used to understand the adversarial attacks over medical images and their effects on explainability. The developed code is available at GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/eriksonJAguiar/Grad-Attacks-CBMS-2023.
Beyond Annual Revisions: A Multi-Label Concept Drift Analysis of MeSH

MeSH (Medical Subject Headings) is a hierarchically structured thesaurus used for indexing biomedical information. This vocabulary contains most of the biomedical knowledge available to date. To keep up with the continuous evolution and expanding of our understanding on the medical field, yearly revisions take place in MeSH. These revisions introduce new descriptors in the thesaurus, in addition to changes in already existing ones, either directly or indirectly. This constant evolution of the thesaurus causes many older descriptors to exhibit some form of drift in their meaning, which in turn affects the performance of Machine Learning models trained on an older version of the thesaurus when used to predict data obtained from more recent versions. In this paper, we study the phenomenon of concept drift in MeSH, through evaluating the performance of a state-of-the-art text classification algorithm in articles from different years. We also investigate how changes in descriptors indirectly affect different ones that are related to them by studying the shifts in their co-occurrence, using this shift as a measure of concept drift.
A Multimodal Image Registration System for Histology Images

Histology image registration involves aligning microscopy images for various purposes, including creating 3D reconstructions from 2D, combining data from slices with different stain samples, and from multimodal registration. However, this process poses several challenges, including high resolution, non-linear elastic deformation, occlusions, missing sections, non-rigid deformation, contrast differences, and differences in appearance and local structure. Multimodal image registration is particularly challenging because different modalities may have different characteristics and require specific optimization algorithms. To address this issue, it is important to develop software that allows users to test different image registration algorithms and combine annotations made on them, in order to leverage the benefits of multiple modalities. To address this challenge, we developed a cloud-based Multimodal Image Registration system that enables developers and researchers to visually test the outcomes of various image registration algorithms. The system includes a project manager, an algorithm manager, and an image visualization system. The system was developed using the framework Django, JavaScript, and multiple libraries that facilitate the management and annotation of very high-resolution images. To demonstrate the effectiveness and flexibility of our system, we tested it using two different algorithms, SIFT and ORB, on nonlinear multimodal and brightfield images using the Hematoxylin and Eosin staining methods. The results show the system's ability to handle challenging image registration tasks while providing visualization tools to improve user experience.
The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus?

Training medical AI algorithms requires large volumes of accurately labeled datasets, which are difficult to obtain in the real world. Synthetic images generated from deep generative models can help alleviate the data scarcity problem, but their effectiveness relies on their fidelity to real-world images. Typically, researchers select synthesis models based on image quality measurements, prioritizing synthetic images that appear realistic. However, our empirical analysis shows that high-fidelity and visually appealing synthetic images are not necessarily superior. In fact, we present a case where low-fidelity synthetic images outperformed their high-fidelity counterparts in downstream tasks. Our findings highlight the importance of comprehensive analysis before incorporating synthetic data into real-world applications. We hope our results will raise awareness among the research community of the value of low-fidelity synthetic images in medical AI algorithm training.
News event prediction by trigger evolution graph and event segment

Event prediction aims to predict the most possible following event given a chain of closely related context events. Previous methods based on event pairs or the entire event chain may ignore much structural and semantic information. Current datasets for event prediction, naturally, can be used for supervised learning. Event chains are either from document-level procedural action flow, or from news sequences under the same column. This paper leverages graph structure knowledge of event triggers and event segment information for event prediction with general news corpus, and adopts the standard multiple choice narrative cloze task evaluation. The topic model is utilized to extract event chains from the news corpus to deal with training data bottleneck. Based on trigger-guided structural relations in the event chains, we construct trigger evolution graph, and trigger representations are learned through graph convolutional neural network and the novel neighbor selection strategy. Then there are features of two levels for each event, namely, text level semantic feature and trigger level structural feature. We design the attention mechanism to learn the features of event segments derived in term of event major subjects, and integrate relevance between event segments and the candidate event. The most possible next event is picked by the relevance. Experimental results on the real-world news corpus verify the effectiveness of the proposed model.
OCT Image Synthesis through Deep Generative Models

The development of accurate methods for OCT image analysis is highly dependent on the availability of large annotated datasets. As such datasets are usually expensive and hard to obtain, novel approaches based on deep generative models have been proposed for data augmentation. In this work, a flow-based network (SRFlow) and a generative adversarial network (ESRGAN) are used for synthesizing high-resolution OCT B-scans from low-resolution versions of real OCT images. The quality of the images generated by the two models is assessed using two standard fidelity-oriented metrics and a learned perceptual quality metric. The performance of two classification models trained on real and synthetic images is also evaluated. The obtained results show that the images generated by SRFlow preserve higher fidelity to the ground truth, while the outputs of ESRGAN present, on average, better perceptual quality. Independently of the architecture of the network chosen to classify the OCT B-scans, the model's performance always improves when images generated by SRFlow are included in the training set.
Towards Realistic 3D Ultrasound Synthesis: Deformable Augmentation using Conditional Variational Autoencoders

For training deep neural networks, large data sets are required. Especially in the medical 3D ultrasound (US) image domain, the amount of available data is limited. A common method to enlarge small data sets is using data augmentation. However, simple geometric augmentation techniques like rotation or sheering can lead to unrealistic US images. In this study, a novel structure-dependent deformable augmentation method for 3D US patches is proposed. The approach is based on learning realistic motion patterns from 3D liver US images using a conditional variational auto encoder (CVAE) where the condition represents the anatomical structure type. The CVAE augmentation performance is compared to a baseline variational auto encoder (VAE). It is shown that the CVAE generates deformable augmentations 18.5 percentage points more similar to realistic deformations than the VAE. Furthermore, the method is evaluated in an expert study where experienced radiologists were asked to rate the realism of US patches. The results show that the experts could not distinguish between CVAE augmented and original US patches. Applying the proposed method in a target detection application improved the performance of a neural network by 41 %. The proposed augmentation method is able to apply realistic deformation to 3D US patches which can be used to enlarge a small data set for usage in deep learning applications.
Fluorescence Image Visualization using Multi-Channel Minimax Optimization (MCMO)

Fluorescence microscope data analysis for liquid biopsy needs to be evidenced by object visualization on the display. This visualization heavily depends on the operator's tuning of the display settings, which is typically achieved through conventional linear stretching or windowing/leveling techniques. However, manual tuning can introduce variability and bias, leading to reduced throughput of analysis verifications. We propose a novel method for enhancing multi-channel fluorescence images by optimizing the visualization of assay-related objects. This problem is formulated as a variational minimax optimization of the histogram-equalizing energy with data distortion regularization. Our method seamlessly merges the resolved, enhanced objects of interest with the background, resulting in high performance and overall visual coherence.
Siamese Network to Investigate Scanner-Dependency in MRI

Magnetic resonance imaging (MRI) is an effective imaging tool that, due to its non-invasiveness and multiple-parameter nature, is frequently used in medicine. In particular, the MRI's inherent flexibility deriving from the usage of multiple parameters allows to obtain images of variable contrast and quality. However, intrinsic MRI contrast variability often comes with drawbacks in terms of differences in different scanners, thus resulting in the impossibility of standardizing the image contrast. In particular, this variability could negatively affect the automatic analysis of Deep Learning (DL) methods, both in the training phase and in the test phase. In this work, we present several results on how images collected from different MRI scanners are handled by DL methods. To this end, we trained a Siamese network (SNN), based on the EfficientNet-B0 Convolutional Neural Network (EN-CNN), to learn how to recognize the scanner that has generated a given image. The output encoding features of the SNN have been projected into a 2D space with Uniform Manifold Approximation and Projection (UMAP) and have been discussed. Regarding the training phase, the UMAP projects show that the network is capable of separating MR images encoded features from different MRI scanners. Moreover, even if the MR images of different subjects are acquired with the same scanner, the results suggest that there are considerable differences in how the SNN encoded those features. The test phase confirmed that the SNN architecture is capable of recognizing images from different MRI scanners.
Privacy Assessment of Synthetic Patient Data

In this paper, we quantify the privacy gain of synthetic patient data drawn from two generative models, MST and PrivBayes, which is based on real anonymized primary care patient data. This evaluation is implemented for two types of inference attacks, namely membership and attribute inference attacks using a new toolbox, TAPAS. The aim is to quantitatively evaluate the privacy gain of each attack where these two differentially private generators and different threat models are used with a focus on black-box knowledge. The evaluation that was carried out in this paper demonstrates that vulnerabilities of synthetic patient data depend on the different attack scenarios, threat models, and algorithms used to generate the synthetic patient data. It was shown empirically that although the synthetic patient data achieved high privacy gain in most attack scenarios, it does not behave uniformly against adversarial attacks, and some records and outliers remain vulnerable depending on the attack scenario. Moreover, it was shown that the PrivBayes generator is the more robust generator in comparison to MST in terms of the privacy-preservation of synthetic data.
Revised barrier function-based adaptive finite- and fixed-time convergence super-twisting control

This paper presents an adaptive gain, finite- and fixed-time convergence super-twisting-like algorithm based on a revised barrier function, which is robust to perturbations with unknown bounds. It is shown that this algorithm can ensure a finite- and fixed-time convergence of the sliding variable to the equilibrium, no matter what the initial conditions of the system states are, and maintain it there in a predefined vicinity of the origin without violation. Also, the proposed method avoids the problem of overestimation of the control gain that exists in the current fixed-time adaptive control. Moreover, it shows that the revised barrier function can effectively reduce the computation load by obviating the need of increasing the magnitude of sampling step compared with the conventional barrier function. This feature will be beneficial when the algorithm is implemented in practice. After that, the estimation of the fixed convergence time of the proposed method is derived and the impractical requirement of the preceding fixed-time adaptive control that the adaptive gains must be large enough to engender the sliding mode at time <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t=0$</tex> is discarded. Finally, the outperformance of the proposed method over the existing counterpart method is demonstrated with a numerical simulation.
SecureFASTA: Ensuring privacy and trust when sharing genomic data

Genomics has profoundly influenced the field of medicine, with advancements in DNA sequencing contributing to personalized medicine and a more comprehensive understanding of various diseases' genomic underpinnings. Sharing genomic data is vital for progressing the field and devising novel approaches to decipher the genome. Nevertheless, the sensitive nature of this information necessitates robust security measures for protection during storage and transfer. In this paper, we introduce SecureFASTA, a novel tool for securely encrypting and decrypting FASTA files without requiring a shared secret while minimizing the number of keys exchanged between pairs. Our approach combines symmetric and asymmetric encryption techniques, utilizing the Advanced Encryption Standard (AES) cypher and Rivest-Shamir-Adleman (RSA) encryption. Additionally, we implement a checksum function using the Secure Hash Algorithm (SHA-256) to verify the integrity of transferred FASTA files. Our evaluation demonstrates that SecureFASTA is fast, reliable, and secure, surpassing existing tools in terms of security and user-friendliness. Consequently, it offers a valuable solution for securely sharing and leveraging sensitive genomic data, marking a significant breakthrough in genomics. The tool's source code is available at https://github.com/bioinformatics-ua/SecureFASTA.
A Blood Flow Game Based on Smoothed Particles Hydrodynamics Concepts

The modality known as serious game, has been widely used in the teaching of the most varied disciplines. The health area has benefited from the emergence of digital games that support practices related to teaching activities. This type of game aims to provide greater motivation on the part of the student as it aims to turn a boring activity into a fun one. In this work, we develop a serious game inspired by concepts of the circulatory system, more specifically angioplasty and catheterization. The visual effects was based on a numerical simulation technique known as SPH (Smoothed Particles Hydrodynamics). The game was developed in Unity3D environment and has the flexibility to run on various platforms. In addition, one can control the physical parameters of the simulation in order to adapt the game to the capacity of the hardware being used. Furthermore, we submitted our system to the analysis of students in the healthy area, through a technology acceptance questionnaire
Trustworthy Machine Learning Predictions to Support Clinical Research and Decisions

Nowadays, physicians have at their hands a huge amount of data produced by a large set of diagnostic and instrumental tests integrated with data obtained by high-throughput technologies. If such data were opportunely linked and analysed, they might be used to strengthen predictions, so that to improve the prevention and the time-to-diagnosis, reduce the costs of the health system, and bring out hidden knowledge. Machine learning is the principal technique used nowadays to leverage data and gain useful information. However, it has led to various challenges, such as improving the interpretability and explainability of the employed predictive models and integrating expert knowledge into the final system. Solving those challenges is of paramount importance to enhance the trust of both clinicians and patients in the system predictions. To solve the aforementioned issues, in this paper we propose a software workflow able to cope with the trustworthiness aspects of machine learning models and considering a multitude of heterogeneous data and models.
Identifying and Categorizing Challenges in Large-Scale Agile Software Development Projects: Insights from Two Swedish Companies

We conducted a case study to examine the challenges encountered in large-scale agile development (LSAD) within two Swedish software companies. While agile methodologies have proven successful in small and medium-sized projects, their implementation in large-scale software development projects can be problematic. To identify these challenges, we employed thematic analysis, which revealed a total of 26 distinct challenges. These challenges were categorized into three main themes: Processes and practices, Teams, and Organizational-level challenges in LSAD. By recognizing and addressing these challenges, projects operating in similar contexts can synchronize their activities and harness the advantages of agile methodologies at a large scale. The article delves into comprehensive discussions on these challenges, offering valuable insights and directions for future research endeavors.
A Field of Study-Centric Approach to Evaluating Paper Similarity

Every year, numerous papers are published across various fields, with some focusing on specific areas while others explore multidisciplinary topics. The research field information of a paper can serve as a useful indicator of its direction and topic. However, many researchers tend to assess the similarity of papers through natural language processing, overlooking the significance of considering the research fields. To this end, this paper proposes a method to calculate the similarity of papers base on the research field that leverage their correlation. We represent a paper by a vector composed of research fields. We introduce a relevance matrix between the research fields, which contains the closeness of the different research fields, and finally calculate the similarity of the paper representation vector to get the results. The method in this paper shows good performance on datasets retrieved from the Open Academic Graph.
Defect Detection of Aerial Insulator Based on Improved Lightweight YOLOv5

Real-time detection of insulators plays an important role in ensuring the stability of high-voltage power transmission and timely handling faults. For this reason, this paper proposes an aerial insulator defect detection method based on light-weight improved YOLOv5. Firstly, the ghost convolution was used to reduce the computational complexity in the YOLOv5 object detection network to improve the detection speed. Secondly, the inverted residual structure was used to improve the feature extraction network to extract richer target features, and the adaptive upsampling is combined to optimize the feature fusion network and improve the model detection accuracy. Finally, the α-IoU excitation factor was added to accelerate the convergence of the target frame loss value to improve the detection efficiency. Through the comparison of the experimental results, it can be seen that the average detection accuracy of the lightweight improved YOLOv5 model proposed in this paper is 93.6%, the detection speed is 68FPS, and the parameters of the model is 3.7M, which is better than other detection algorithms under the same conditions. The method in this paper can provide a reference for the detection of insulator defects.
A Data Mining Technology of Electricity Trading Data Based on Data Sandbox

With the rapid rise of data mining technology such as machine learning, the flow of data production factors has become a key issue. Due to the high added value and high sensitivity, the mining and utilization of electricity trading data needs security protection. This paper analyzes the value increment of electricity trading data, the security risks and limitations in current applications. Furthermore, this paper proposes a new data mining scheme for electricity trading. Technically, this paper divides the mining tasks for electricity trading data into three categories, and designs a subsystem module for each category of task. Compared with the traditional scheme, this scheme can ensure that users can complete data mining tasks without touching the real data, such that it can support the opening and utilization of any electricity trading data. Compared with the traditional scheme, this scheme greatly increases the scope of data opening. Compared with the privacy protection technology such as secure multi-party computing, our scheme only requires plaintext operations on electricity trading data, and does not require complex ciphertext computation, and hence the computational and communication costs are significantly reduced.
Position Planning for Monitoring Shepherd-UAVs Using the Herd’s Location Information

A position planning scheme is proposed for the monitoring shepherd-UAV (Unmanned Aerial Vehicle) using the herd’s location information and the bare bones fireworks algorithm (BBFWA). First, we model the UAV-based herd monitoring problem by making some reasonable assumptions and simplifications. Then, under the inspiration of the multi-agent system that enhances the system robustness and efficiency, the multi-shepherd-UAV solution is introduced, and the positions of the shepherd-UAVs are optimized via BBFWA using real-time location information of the animals in the herd. Furthermore, the validity of the scheme is illustrated by numerical experiments, and some remarks are given according to the experiment result. The proposed method can be used in smart pastures and serve as a candidate solution in some other UAV-based applications.