Reviewerly/exHarmony huggingface dataset:
Concept-based reasoning in medical imaging

As concept-based reasoning for improving model interpretability becomes promising, the question of how to define good concepts becomes more pertinent. In domains like medical, it is not always feasible to access instances clearly representing good concepts. In this work, we propose an approach to use organically mined concepts from unlabeled data to explain classifier predictions.A Concept Mapping Module (CMM) is central to this approach. Given a capsule endoscopy image predicted as abnormal, the CMM's main task is to identify which concept explains the abnormality. It consists of two parts, namely a convolutional encoder and a similarity block. The encoder maps the incoming image into the latent vector, while the similarity block retrieves the closest aligning concept as explanation.Abnormal images can be explained in terms of five pathology-related concepts retrieved from the latent space given by inflammation (mild and severe), vascularity, ulcer and polyp. Other non-pathological concepts found include anatomy, debris, intestinal fluid and capsule modality.This method outlines an approach through which concept-based explanations can be generated. Exploiting the latent space of styleGAN to look for variations and using task-relevant variations for defining concepts is a powerful way through which an initial concept dictionary can be created which can subsequently be iteratively refined with much less time and resource.
A Machine Learning Framework for Breast Cancer Detection and Classification

Breast cancer is a very frequently seen type of cancer. It is one of the biggest contributors to new cancer cases and death as shown by the world dataset, making it a major medical concern. This makes the research and work on breast cancer detection a necessary course of action. During this paper, we have shown an outline of the development of huge data within the health system, here we have relied on the support vector machine algorithm since in many cases it has proven to give better results. In this paper, we aim to predict the risks related to breast cancer based on the tumor cell biopsy. Breast cancer causes a lot of deaths, with the help of prevention or early diagnosis the probability of dying will dramatically narrow down. We have tried cross validation and hyperparameter tuning for getting better results with this algorithm. We have compared the model we built with five different machine learning models. The best model came out to be SVM with a 97% accuracy.
A Deep Learning Approach for Gaussian Noise-Level Quantification

Image noise and its reduction have been of active interest over the last few years due to the increasing application of Image processing in various domains such as medical, video processing, robotics, etc. This paper presents a Deep Learning (Convolutional Neural Network) approach to classify an image into different levels of Gaussian noise it has. Most of the image-denoising models are based on the assumption that the images to be processed have some level of noise in them. The proposed model evaluates whether the image has noise present in it and also quantifies the amount of noise the image has. The MSRA 10K dataset has been used to train, test, and validate the model. Ten levels of Gaussian noise have been introduced to the dataset which represent the ten classes for the model and one class with a clean image. The model has achieved an accuracy of 96%. The proposed model has proven to be very effective in identifying the level of Gaussian Noise present in the image.
Facemask Detection and Maintaining Safe Distance Using AI and ML to Prevent COVID-19—A Study

In this paper, we present a set of studies of detecting facemask to prevent COVID-19 spread using machine learning, deep learning, and artificial intelligence. It also explains some recent studies based on the hybrid concepts of deep learning and image processing techniques in this domain. This paper is mainly focused on the set of studies that analyze the machine learning capabilities to detect facemasks through the scanning of people’s images. Besides that, this paper also describes some popular techniques to analyze and detect the safe distance among people using image processing with machine learning techniques. However, it describes a brief comparison among these recent popular studies in terms of technology used, model testing and usage, and accuracy prediction. Readers can find some useful references at the end of this paper.
A Novel AUC Maximization Imbalanced Learning Approach for Predicting Composite Outcomes in COVID-19 Hospitalized Patients

The COVID-19 patient data for composite outcome prediction often comes with class imbalance issues, i.e., only a small group of patients develop severe composite events after hospital admission, while the rest do not. An ideal COVID-19 composite outcome prediction model should possess strong imbalanced learning capability. The model also should have fewer tuning hyperparameters to ensure good usability and exhibit potential for fast incremental learning. Towards this goal, this study proposes a novel imbalanced learning approach called Imbalanced maximizing-Area Under the Curve (AUC) Proximal Support Vector Machine (ImAUC-PSVM) by the means of classical PSVM to predict the composite outcomes of hospitalized COVID-19 patients within 30 days of hospitalization. ImAUC-PSVM offers the following merits: (1) it incorporates straightforward AUC maximization into the objective function, resulting in fewer parameters to tune. This makes it suitable for handling imbalanced COVID-19 data with a simplified training process. (2) Theoretical derivations reveal that ImAUC-PSVM has the same analytical solution form as PSVM, thus inheriting the advantages of PSVM for handling incremental COVID-19 cases through fast incremental updating. We built and internally and externally validated our proposed classifier using real COVID-19 patient data obtained from three separate sites of Mayo Clinic in the United States. Additionally, we validated it on public datasets using various performance metrics. Experimental results demonstrate that ImAUC-PSVM outperforms other methods in most cases, showcasing its potential to assist clinicians in triaging COVID-19 patients at an early stage in hospital settings, as well as in other prediction applications.
Automated Reusable Tests for Mitigating Secure Pattern Interpretation Errors

The importance of software security has increased along with the number and severity of incidents in recent years. Security is a multidisciplinary aspect of the software development lifecycle, operation, and user utilization. Being a complex and specialized area of software engineering, it is often sidestepped in software development methodologies and processes. We address software security at the design level by adopting design patterns that encapsulate reusable solutions for recurring security problems. Design patterns can help development teams implement the best-proven solutions for a specialized problem domain. However, from the analysis of three secure pattern implementations by 70 junior programmers, we detected several structural errors resulting from their interpretation. We propose reusable unit testing test cases based on annotations to avoid secure pattern interpretation errors and provide an example for one popular secure pattern. Providing these test cases to the same group of programmers, they implemented the pattern without errors. The reason is annotations build a framework that disciplines programmers to incorporate secure patterns in their applications and ensure automatic testing.
Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables

Vertical federated learning (VFL) has recently emerged as an appealing distributed paradigm empowering multi-party collaboration for training high-quality models over vertically partitioned datasets. Gradient boosting has been popularly adopted in VFL, which builds an ensemble of weak learners (typically decision trees) to achieve promising prediction performance. Recently there have been growing interests in using decision table as an intriguing alternative weak learner in gradient boosting, due to its simpler structure, good interpretability, and promising performance. In the literature, there have been works on privacy-preserving VFL for gradient boosted decision trees, but no prior work has been devoted to the emerging case of decision tables. Training and inference on decision tables are different from that in the case of generic decision trees, not to mention gradient boosting with decision tables in VFL. In light of this, we design, implement, and evaluate Privet, the first system framework enabling privacy-preserving VFL service for gradient boosted decision tables. Privet delicately builds on lightweight cryptography and allows an arbitrary number of participants holding vertically partitioned datasets to securely train gradient boosted decision tables. Extensive experiments over several real-world datasets and synthetic datasets demonstrate that Privet achieves promising performance, with utility comparable to plaintext centralized learning.
An Improved Path Planning Algorithm With Adaptive Parameters and Predictions

An improved path planning algorithm based on dynamic window approach (DWA) is proposed to optimize real-time paths for agents moving in complex environments with plenty of static and/or dynamic obstacles, path constraints, and performance constraints. In order to improve the accuracy of the evaluation function and the adaptability of the agents to different obstacle environments, the weight of the evaluation function is ameliorated by designing adaptive laws in this article. Meanwhile, aiming at the defect of local minima in DWA algorithm, a low-speed steering strategy is presented to help the robot escape the U-shaped trap. Especially, in the case of dynamic obstacle environment, a predictive scheme is carried out for the agents to reach the destination safely by fitting the trajectory of the dynamic obstacles to calculate their possible future positions, and then setting these positions as virtual obstacles. By comparison, some simulations and experiments are illustrated to show that the proposed algorithms are superior to the DWA algorithm in terms of running time, path length, and security to a large extent.
Survey on Decentralized Auctioning Systems

An electronic auction (e-auction) is an efficient negotiation model that allows multiple sellers or buyers to compete for assets or rights.Such systems have become increasingly popular with the evolution of the internet for commerce.In centralized auctioning systems, the presence of a governing third party has been a major trust concern, as such a party may not always be trustworthy or create transaction fees for the hosted auctions.Distributed and decentralized systems based on blockchain for auctions of nonphysical assets have been suggested as a means to distribute and establish trust among peers, and manage disputes and concurrent entries.Although a blockchain system provides attractive features such as decentralized trust management and fraud prevention, it cannot alone support dispute resolutions and adjudications for physical assets.In this paper, we compare blockchain and non-blockchain decentralized auctioning systems based on the identified functional needs and quality attributes.We contrast these needs and attributes with the stateof-the-art models and other implementations of auctioning systems, and discuss the associated trade-offs.We further analyze the gaps in the existing decentralized approaches and propose design approaches for decentralized auctioning systems, for both physical and nonphysical assets, that support dispute resolution and adjudication based on collected evidence, and dispute prevention based on distributed consensus algorithms.
Pre-Impact Firefighter Fall Detection Using Machine Learning on the Edge

Falling is one of the primary causes of firefighter casualties. Therefore, early fall detection, followed by reliable and timely notification, is vital to ensure the survival of firefighters. However, due to complex and diverse firefighting scenarios, current fall detection systems (FDSs) are inadequate, especially in terms of accuracy and real-time applications. Hence, in this study, a wearable pre-impact FDS (PIFDS) for firefighters is proposed. We also evaluated the feasibility of using machine learning (ML) and ensemble learning (EL) methods deployed on the edge node to simultaneously improve the real-time performance and accuracy. Moving thresholding method is introduced to overcome the class-imbalanced issue due to fewer samples acquired during the pre-impact (PI) phase. Based on 14 firefighters’ data collection, the experimental results revealed that a Decision Tree classifier with optimized parameters (DT-ED4) outperformed other ML and EL methods in a tradeoff between processing time and accuracy. The results also showed that our system can detect falls before the impact with an average lead time of 447.9 ms, sensitivity of 95.10%, and specificity of 97.99%. The system’s heterogeneous IoT network architecture facilitated remote monitoring and alerts that enabled the incident commander (IC) to execute the rescue mission immediately upon notification. This study enhances the practicability of PIFDS and extends the application range of PIFDS from healthcare to firefighting.
Anomaly Detection Using Ensemble Classification and Evidence Theory

Multi-class ensemble classification remains a popular focus of investigation within the research community. The popularization of cloud services has sped up their adoption due to the ease of deploying large-scale machine-learning models. It has also drawn the attention of the industrial sector because of its ability to identify common problems in production. However, there are challenges to conform an ensemble classifier, namely a proper selection and effective training of the pool of classifiers, the definition of a proper architecture for multi-class classification, and uncertainty quantification of the ensemble classifier. The robustness and effectiveness of the ensemble classifier lie in the selection of the pool of classifiers, as well as in the learning process. Hence, the selection and the training procedure of the pool of classifiers play a crucial role. An (ensemble) classifier learns to detect the classes that were used during the supervised training. However, when injecting data with unknown conditions, the trained classifier will intend to predict the classes learned during the training. To this end, the uncertainty of the individual and ensemble classifier could be used to assess the learning capability. We present a novel approach for anomaly detection using ensemble classification and evidence theory. A pool selection strategy is presented to build a solid ensemble classifier. We present an architecture for multi-class ensemble classification and an approach to quantify the uncertainty of the base classifiers and the ensemble classifier. We address the problem of detecting unknown conditions (while feeding out-of-distribution data), presenting a novel approach that monitors the uncertainty of the ensemble classifier using evidence theory. Finally, we use the benchmark Tennessee Eastman to perform experiments to test the ensemble classifier’s prediction and anomaly detection capabilities.
Ipsilateral Lesion Detection Refinement for Tomosynthesis

Computer-aided detection (CAD) frameworks for breast cancer screening have been researched for several decades. Early adoption of deep-learning models in CAD frameworks has shown greatly improved detection performance compared to traditional CAD on single-view images. Recently, studies have improved performance by merging information from multiple views within each screening exam. Clinically, the integration of lesion correspondence during screening is a complicated decision process that depends on the correct execution of several referencing steps. However, most multi-view CAD frameworks are deep-learning-based black-box techniques. Fully end-to-end designs make it very difficult to analyze model behaviors and fine-tune performance. More importantly, the black-box nature of the techniques discourages clinical adoption due to the lack of explicit reasoning for each multi-view referencing step. Therefore, there is a need for a multi-view detection framework that can not only detect cancers accurately but also provide step-by-step, multi-view reasoning. In this work, we present Ipsilateral-Matching-Refinement Networks (IMR-Net) for digital breast tomosynthesis (DBT) lesion detection across multiple views. Our proposed framework adaptively refines the single-view detection scores based on explicit ipsilateral lesion matching. IMR-Net is built on a robust, single-view detection CAD pipeline with a commercial development DBT dataset of 24675 DBT volumetric views from 8034 exams. Performance is measured using location-based, case-level receiver operating characteristic (ROC) and case-level free-response ROC (FROC) analysis.
YOLOD: A Task Decoupled Network Based on YOLOv5

Object detection includes three subtasks of predicting target position, classification, and confidence. In the mainstream object detection model, the model pursues internal structure refinement, and each subtask shares almost the same structure, which is a task-coupled structure. The task-coupled structure of the model reduces the training parameters, but it cannot be tuned on the network structure for each task separately, which can limit the model performance. We designed a task decoupled object detection network (YOLOD) based on YOLOv5, where YOLOD is decoupled immediately after the backbone network. By observing the loss convergence of each subtask, three network structures are designed separately and the branch size is controlled so that the model has fewer training parameters. At the same time, some experimental adjustments were made to YOLOD to accelerate the convergence speeds of the model. In addition, we add image contour information to the original three-channel image to assist model training and improve detection accuracy. The experiments demonstrate that the modified model is smaller in size and has the largest accuracy improvement on the small-scale model. without introducing any attention-based modules, YOLOD-S achieves a mAP improvement of 1.1% on the MS COCO dataset and 2.29% on the VOC dataset, and the larger model YOLOD-L achieves an accuracy of 48.8% on the COCO dataset.
Reconciliation Efficiency Impact on Discrete Modulated CV-QKD Systems Key Rates

Continuous variable quantum key distribution (CV-QKD) allows the distribution of symmetric keys in a secure manner. CV-QKD systems can extract keys at its maximum rate when using Gaussian modulation (GM). Nonetheless, GM demands high-capacity random number sources and tends to be very hard to approach in practice. To circumvent these disadvantages, higher-order probabilistic -shaped discrete modulation (DM) can be used. State-of-the-art works compute the key rates of DM-CV-QKD systems considering a fixed value for the reconciliation efficiency and do not take into account the frame error rate (FER) of the system, thus over or under estimating the key rates. In this work, we study the security bounds of CV-QKD systems considering probabilistic shaped DM formats with 256-symbols in the finite-size regime. This accounting for the true value of the reconciliation efficiency, and the FER of the information reconciliation step. Both conventional and hexagonal 256-quadrature amplitude modulation (QAM) constellations yield higher key rates than 256-amplitude and phase shift keying (APSK) constellations, with 256-QAM constellations being indistinguishable in performance with GM for high transmission distances. Minimum signal-to-noise ratio (SNR) values were fixed from a FER analysis through a CV-QKD simulation allowing for key extraction considering different FER levels. Lower FER values are associated with higher SNRs in the system and thus lower achievable transmission distances. Nonetheless, the FER maximizing the extraction key rate is not null. Our results show that the extraction key rate is maximized by SNR adjustment which should have in account both the reconciliation efficiency and the FER.
A Novel Classification Method Based on Stacking Ensemble for Imbalanced Problems

Imbalanced problems are a significant challenge in data-driven diagnosis technology, as the data collected from the scenario of engineering is usually imbalanced. Imbalanced data poses a negative effect on classification algorithms and reduces the recognition performance of the minority samples. To address this challenge, ensemble classifiers have gained significant attention due to their superior performance compared to the individual classifiers. In this paper, we provide improvements at both the data and algorithm levels by using the stacking ensemble. At the data level, a Data-Space balanced Partition (DSP) method based on under-sampling is proposed, which recursively partitions the imbalanced data-space into relatively balanced data-subspaces. At the algorithm level, we propose a cost-sensitive stacking approach, which adjusts the weights of different categories to enhance the algorithm’s attention to the minority class. Based on these two improvements, a novel classification method for imbalanced problems, Data-Space balanced Partition based on Cost-sensitive Stacking learning (DPCStacking), is proposed. The proposed method is evaluated on 38 imbalanced datasets from KEEL. The experimental results demonstrate the effectiveness of the proposed improvements. Compared to the other six algorithms specifically designed to address imbalanced problems, the DPCStacking algorithm achieves the highest Average Accuracy (AAcc) and True Positive Rate (TPR) of 0.876 and 0.876, respectively.
Model Poisoning Attack on Neural Network Without Reference Data

Due to the substantial computational cost of neural network training, adopting third-party models has become increasingly popular. However, recent works demonstrate that third-party models can be poisoned. Nonetheless, most model poisoning attacks require reference data, e.g., training dataset or data belonging to the target label, making them difficult to launch in practice. In this paper, we propose a reference data independent model poisoning attack that can (1) directly search for sensitive features with respect to the target label, (2) quantify the positive and negative effects of the model parameters on sensitive features, and (3) accomplish the training of poisoned model by our parameter selective update strategy. The extensive evaluation on datasets with a few classes and numerous classes show that the attack is (I) effective: the trigger input can be labeled as a deliberate class by the poisoned model with high probability; (II) covert: the performance of the poisoned model is almost indistinguishable from the intact model on non-trigger inputs; and (III) straightforward: an adversary only needs a little background knowledge to launch the attack. Overall, the evaluation results show that our attack achieves 95%, 100%, 81%, 96%, and 96% success rates on Cifar10, Cifar100, ISIC2018, FaceScrub, and ImageNet datasets, respectively.