Reviewerly/exHarmony huggingface dataset:
Self-supervised Few-Shot Learning for Semantic Segmentation: An Annotation-Free Approach

Few-shot semantic segmentation (FSS) offers immense potential in the field of medical image analysis, enabling accurate object segmentation with limited training data. However, existing FSS techniques heavily rely on annotated semantic classes, rendering them unsuitable for medical images due to the scarcity of annotations. To address this challenge, multiple contributions are proposed: First, inspired by spectral decomposition methods, the problem of image decomposition is reframed as a graph partitioning task. The eigenvectors of the Laplacian matrix, derived from the feature affinity matrix of self-supervised networks, are analyzed to estimate the distribution of the objects of interest from the support images. Secondly, we propose a novel self-supervised FSS framework that does not rely on any annotation. Instead, it adaptively estimates the query mask by leveraging the eigenvectors obtained from the support images. This approach eliminates the need for manual annotation, making it particularly suitable for medical images with limited annotated data. Thirdly, to further enhance the decoding of the query image based on the information provided by the support image, we introduce a multi-scale large kernel attention module. By selectively emphasizing relevant features and details, this module improves the segmentation process and contributes to better object delineation. Evaluations on both natural and medical image datasets demonstrate the efficiency and effectiveness of our method. Moreover, the proposed approach is characterized by its generality and model-agnostic nature, allowing for seamless integration with various deep architectures. The code is publicly available at GitHub .
Dynamic Depth-Supervised NeRF for Multi-view RGB-D Operating Room Videos

The operating room (OR) is an environment of interest for the development of sensing systems, enabling the detection of people, objects, and their semantic relations. Due to frequent occlusions in the OR, these systems often rely on input from multiple cameras. While increasing the number of cameras generally increases algorithm performance, there are hard limitations to the number and locations of cameras in the OR. Neural Radiance Fields (NeRF) can be used to render synthetic views from arbitrary camera positions, virtually enlarging the number of cameras in the dataset. In this work, we explore the use of NeRF for view synthesis of dynamic scenes in the OR, and we show that regularisation with depth supervision from RGB-D sensor data results in higher image quality. We optimise a dynamic depth-supervised NeRF with up to six synchronised cameras that capture the surgical field in five distinct phases before and during a knee replacement surgery. We qualitatively inspect views rendered by a virtual camera that moves 180 $$^\circ $$ around the surgical field at differing time values. Quantitatively, we evaluate view synthesis from an unseen camera position in terms of PSNR, SSIM and LPIPS for the colour channels and in MAE and error percentage for the estimated depth. We find that NeRFs can be used to generate geometrically consistent views, also from interpolated camera positions and at interpolated time intervals. Views are generated from an unseen camera pose with an average PSNR of 18.2 and a depth estimation error of 2.0%. Our results show the potential of a dynamic NeRF for view synthesis in the OR and stress the relevance of depth supervision in a clinical setting.
Probabilistic Scoring Lists for Interpretable Machine Learning

A scoring system is a simple decision model that checks a set of features, adds a certain number of points to a total score for each feature that is satisfied, and finally makes a decision by comparing the total score to a threshold. Scoring systems have a long history of active use in safety-critical domains such as healthcare and justice, where they provide guidance for making objective and accurate decisions. Given their genuine interpretability, the idea of learning scoring systems from data is obviously appealing from the perspective of explainable AI. In this paper, we propose a practically motivated extension of scoring systems called probabilistic scoring lists (PSL), as well as a method for learning PSLs from data. Instead of making a deterministic decision, a PSL represents uncertainty in the form of probability distributions. Moreover, in the spirit of decision lists, a PSL evaluates features one by one and stops as soon as a decision can be made with enough confidence. To evaluate our approach, we conduct a case study in the medical domain.
Divide-and-conquer the NAS puzzle in resource-constrained federated learning systems

Federated Learning (FL) is a privacy-preserving distributed machine learning approach geared towards applications in edge devices. However, the problem of designing custom neural architectures in federated environments is not tackled from the perspective of overall system efficiency. In this paper, we propose DC-NAS-a divide-and-conquer approach that performs supernet-based Neural Architecture Search (NAS) in a federated system by systematically sampling the search space. We propose a novel diversified sampling strategy that balances exploration and exploitation of the search space by initially maximizing the distance between the samples and progressively shrinking this distance as the training progresses. We then perform channel pruning to reduce the training complexity at the devices further. We show that our approach outperforms several sampling strategies including Hadamard sampling, where the samples are maximally separated. We evaluate our method on the CIFAR10, CIFAR100, EMNIST, and TinyImagenet benchmarks and show a comprehensive analysis of different aspects of federated learning such as scalability, and non-IID data. DC-NAS achieves near iso-accuracy as compared to full-scale federated NAS with 50% fewer resources.
Evaluation of A Computer-Aided Detection Software for Prostate Cancer Prediction: Excellent Diagnostic Accuracy Independent of Preanalytical Factors

Prostate cancer (PCa) is the most common noncutaneous cancer in men in the Western world. In addition to accurate diagnosis, Gleason grading and tumor volume estimates are critical for patient management. Computer-aided detection (CADe) software can be used to facilitate the diagnosis and improve the diagnostic accuracy and reporting consistency. However, preanalytical factors such as fixation and staining of prostate biopsy specimens and whole slide images (WSI) on scanners can vary significantly between pathology laboratories and may, therefore, impact the quality of WSI and utility of CADe algorithms. We evaluated the performance of a CADe software in predicting PCa on WSIs of prostate biopsy specimens and focused on whether there were any significant differences in image quality between WSIs obtained on different scanners and specimens from different histopathology laboratories. Thirty prostate biopsy specimens from 2 histopathology laboratories in the United States were included in this study. The hematoxylin and eosin slides of the biopsy specimens were scanned on 3 scanners, generating 90 WSIs. These WSIs were then analyzed using a CADe software (INIFY Prostate, Algorithm), which identified and annotated all areas suspicious for PCa and calculated the tumor volume (percentage area of the biopsy core involved). Study pathologists then reviewed the Algorithm's annotations and tumor volume calculation to confirm the diagnosis and identify benign glands that were misclassified as cancer (false positive) and cancer glands that were misclassified as benign (false negative). The CADe software worked equally well on WSIs from all 3 scanners and from both laboratories, with similar sensitivity and specificity. The overall sensitivity was 99.4%, and specificity was 97%. The percentage of suspicious cancer areas calculated by the Algorithm was similar for all 3 scanners. For WSIs with small foci of cancer (<1 mm), the Algorithm identified all cancer glands (sensitivity, 100%). Preanalytical factors had no significant impact on whole slide imaging and subsequent application of a CADe software. Prediction accuracy could potentially be further improved by processing biopsy specimens in a centralized histology laboratory and training the Algorithm on WSIs from the same laboratory in order to minimize variations in preanalytical factors and optimize the diagnostic performance of the Algorithm.
Readability and content gaps in online epilepsy surgery materials as potential health literacy and <scp>shared‐decision</scp>‐making barriers

Epilepsy surgery is an effective albeit underused treatment for refractory epilepsy, and online materials are vital to patient understanding of the complex process. Our goal is to analyze the readability and content inclusion of online patient health education materials designed for epilepsy surgery.A private browser setting was used on Google and Bing to identify the top 100 search results for the terms "epilepsy+surgery". Scientific papers, insurance pages, pay-wall access sites, and non-text content were excluded. The website text was reformatted to exclude graphics, contact information, links, and headers. Readability metrics were calculated using an online tool. Text content was analyzed for inclusion of important concepts (pre-surgical evaluation, complications, risks of continued seizures, types of surgery, complimentary diagrams/audiovisual material). Comparison of readability and content inclusion was performed as a function of organization types (epilepsy center, community health organization, pediatric-specific) and location (region, country).Browser search yielded 82 distinct websites with information regarding epilepsy surgery, with 98.7% of websites exceeding the recommended 6th-grade reading level for health information. Epilepsy centers had significantly worse readability (Flesch-Kincaid Grade Level (FKGL) P < 0.01 and Flesch Reading Ease (FRE) P < 0.05). Content analysis showed that only 37% of websites discuss surgical side effects and only 23% mention the risks of continued seizures. Epilepsy centers were less likely to report information on surgical side effects (P < 0.001). UK-based websites had better readability (FKGL P < 0.01 and FRE P < 0.01) and were more likely to discuss side effects (P = 0.01) compared to US-based websites.The majority of online health content is overly complex and relatively incomplete in multiple key areas important to health literacy and understanding of surgical candidacy. Our findings suggest academic organizations, including level 4 epilepsy centers, need to simplify and broaden online education resources. More comprehensive, publicly accessible, and readable information may lead to better-shared decision-making.
“I knew it was a scam”: Understanding the triggers for recognizing romance fraud

Abstract Research Summary This article investigates the contributing factors (or triggers) to a realization of romance fraud victimization, based on 1015 reports lodged with Scamwatch (Australian online reporting portal for fraud) between July 2018 and July 2019 (inclusive). The article examines the free text narrative of each report to propose five discernible trigger categories: further requests for money; characteristics of communications; verification checks; an offender's action(s); and being told by a third party. Policy Implications Based on a comprehensive understanding of these five categories, the article advocates for broader messaging approaches to encompass financial literacy and well‐being, cyberliteracy and critical thinking skills, cybersecurity practices, and respectful and healthy relationships. Leveraging the use of these wider education and awareness campaigns could improve recognition of romance fraud. Importantly, it is suggested that banks and other financial institutions are in a strong position to focus their efforts on some of these broader messages to effect positive change.
Vision-based investigation of road traffic and violations at urban roundabout in India using UAV video: A case study

In the past decade, the number of vehicles in India has increased exponentially; however, road infrastructure has not scaled proportionately. As a result, road traffic problems such as congestion on urban roads, dangerous traffic violations, and road accidents have increased significantly. Due to limited road infrastructure, traffic violations (human errors) have intensified in densely populated urban areas. This paper presents a case study (at a multi-lane urban roundabout in Ahmedabad city, India) and the methodology based on computer vision to investigate road traffic and violations using drone/UAV-based aerial video. You Only Look Once-YOLOv7 is used for vehicle detection, and Simple Online and Real Time Tracking-SORT for tracking vehicles. Our methodology divides the road scene (roundabout) into certain zones. We then formulated the dictionary, which maps the traffic violations under Motor Vehicle Driving Regulations - MVDR/ Motor Vehicle Act - MVA and the movement of the vehicle (zone traversal sequences). Using the zone-based methodology, we could also probe other road traffic data such as the count of vehicles, speed of vehicles, rate of traffic flow, and congestion. Based on our results, we also infer some of the possible causes of traffic violations in terms of problems/limitations of road infrastructure. As per our analysis, around 23.26% of vehicles committed traffic violations. We detected traffic violations related to lane indiscipline, driving against the authorized flow of traffic, parking violations, and over-speeding within the roundabout. Our methodology of investigating road traffic and violations can be used for road infrastructure improvement, law enforcement drives, and policy making, for road traffic safety, in developing and densely populated countries.
An Effective Method for Underwater Biological Multi-Target Detection Using Mask Region-Based Convolutional Neural Network

Underwater creatures play a vital role in maintaining the delicate balance of the ocean ecosystem. In recent years, machine learning methods have been developed to identify underwater biologicals in the complex underwater environment. However, the scarcity and poor quality of underwater biological images present significant challenges to the recognition of underwater biological targets, especially multi-target recognition. To solve these problems, this paper proposed an ensemble method for underwater biological multi-target recognition. First, the CutMix method was improved for underwater biological image augmentation. Second, the white balance, multiscale retinal, and dark channel prior algorithms were combined to enhance the underwater biological image quality, which could largely improve the performance of underwater biological target recognition. Finally, an improved model was proposed for underwater biological multi-target recognition by using a mask region-based convolutional neural network (Mask-RCNN), which was optimized by the soft non-maximum suppression and attention-guided context feature pyramid network algorithms. We achieved 4.97 FPS, the mAP was 0.828, and the proposed methods could adapt well to underwater biological multi-target recognition. The recognition effectiveness of the proposed method was verified on the URPC2018 dataset by comparing it with current state-of-the-art recognition methods including you-only-look-once version 5 (YOLOv5) and the original Mask-RCNN model, where the mAP of the YOLOv5 model was lower. Compared with the original Mask-RCNN model, the mAP of the improved model increased by 3.2% to 82.8% when the FPS was reduced by only 0.38.
How to Train Accurate BNNs for Embedded Systems?

A key enabler of deploying convolutional neural networks on resource-constrained embedded systems is the binary neural network (BNN). BNNs save on memory and simplify computation by binarizing both features and weights. Unfortunately, binarization is accompanied by a severe decrease in accuracy. To reduce the accuracy gap between binary and full-precision networks, many repair methods have been proposed in the recent past, which we have classified and put into a single overview. The repair methods are divided into two main branches, training techniques and network topology changes, which are further split into smaller categories. We observe progress in reducing the accuracy gap, but BNN papers are not aligned on what repair methods should be used. Therefore, we conduct an empirical review that evaluates the benefits of repair methods in isolation on two benchmarks: CIFAR10 on ResNet-20 and CIFAR100 on ResNet-18. We found three repair categories most beneficial: feature binarizer, feature normalization, and double residual. Based on this review, we discuss future directions and research opportunities. We sketch the benefit and costs associated with BNNs on embedded systems because it remains to be seen whether BNNs will be able to close the accuracy gap while staying highly energy-efficient on resource-constrained embedded systems.
Co-embedding of edges and nodes with deep graph convolutional neural networks

Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing framework exhibit a shallow structure, which hampers their ability to efficiently transmit information between distant nodes. To address this, we aim to propose a novel message-passing framework, enabling the construction of GNN models with deep architectures akin to convolutional neural networks (CNNs), potentially comprising dozens or even hundreds of layers. (2) Existing models often approach the learning of edge and node features as separate tasks. To overcome this limitation, we aspire to develop a deep graph convolutional neural network learning framework capable of simultaneously acquiring edge embeddings and node embeddings. By utilizing the learned multi-dimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features. To address these challenges, we propose the Co-embedding of Edges and Nodes with Deep Graph Convolutional Neural Networks (CEN-DGCNN). In our approach, we propose a novel message-passing framework that can fully integrate and utilize both node features and multi-dimensional edge features. Based on this framework, we develop a deep graph convolutional neural network model that prevents over-smoothing and obtains node non-local structural features and refined high-order node features by extracting long-distance dependencies between nodes and utilizing multi-dimensional edge features. Moreover, we propose a novel graph convolutional layer that can learn node embeddings and multi-dimensional edge embeddings simultaneously. The layer updates multi-dimensional edge embeddings across layers based on node features and an attention mechanism, which enables efficient utilization and fusion of both node and edge features. Additionally, we propose a multi-dimensional edge feature encoding method based on directed edges, and use the resulting multi-dimensional edge feature matrix to construct a multi-channel filter to filter the node information. Lastly, extensive experiments show that CEN-DGCNN outperforms a large number of graph neural network baseline methods, demonstrating the effectiveness of our proposed method.
A Co-Evolutionary Genetic Algorithm Based on Improved K-Means Clustering

In this paper, we propose a co-evolutionary genetic algorithm based on k-means clustering improvement. In this algorithm, the initial populations are subjected to different operations and the two evolve collaboratively; firstly, the generated population individuals are k-means clustered separately for genetic operations, while the population individuals are combined with roulette selection to make the population undergo adaptive genetic operations, and secondly, after performing the operations separately, they are integrated in a certain proportion, and the two evolve collaboratively so that the overall performance of the genetic algorithm is improved. Finally, in this paper, a typical test function is used to test and analyze the algorithm, and the performance of the algorithm is significantly improved.
Path Planning of Service Robot Based on Improved Particle Swarm Optimization Algorithm

In recent years, people’s lives have become more and more intelligent, and service robots have appeared in all aspects of people’s lives. It brings a lot of convenience for people’s quick life. The service robot must first ensure that it can accurately reach its destination. As one of the core contents of robot technology, path planning has important practical value and research significance. Therefore, the design of efficient algorithm to solve the path planning problem plays a key role in improving the robot technology. In this paper, an improved particle swarm optimization (PSO) algorithm is applied to the path planning of service robots. First of all, the irregular obstacles are handled by using an external circle to avoid the situation that the robot can not walk out of the deadlock area. For the particle swarm optimization (PSO), the linear decline strategy is used to deal with the inertia weight w, which makes the PSO have strong global convergence ability in the early stage and strong local convergence ability in the later stage. The shrinkage factor is introduced to ensure the convergence of the algorithm. At the same time, the selection mechanism and variation mechanism of genetic algorithm are integrated into PSO algorithm, and the PSO algorithm can jump out of the local optimal and accurately find the global optimal.
Study of UAV Path Planning Problem Based on DQN and Artificial Potential Field Method

UAV path planning has become increasingly important, and reinforcement learning is one of the most rapidly developing fields of artificial intelligence, and studies have been conducted to apply reinforcement learning to UAV path planning. This paper firstly introduces the basic principle of DQN algorithm, and then improves the reward function of the DQN algorithm by combining with the artificial potential field method, and introduces direction reward to solve the problem of sparse reward in the DQN algorithm, and finally further improves the network performance. The article compares the performance of the improved DQN algorithm with that of the DQN algorithm through experimental simulations and finally finds that the DQN network combined with the artificial potential field method has a significant performance improvement compared with the conventional DQN network, which further verifies the effectiveness of the proposed model.