Early detection of melanoma and prompt treatment are key approaches to reducing melanoma-related deaths. In order to improve the ability of early detection of melanoma, this paper introduces a set of hyperspectral ima...
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In many areas of computer science, we are given an unsatisfiable Boolean formula F in CNF, i.e. a set of clauses, with the goal to analyse the unsatisfiability. Examination of minimal unsatisfiable subsets (MUSes) of ...
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In many areas of computer science, we are given an unsatisfiable Boolean formula F in CNF, i.e. a set of clauses, with the goal to analyse the unsatisfiability. Examination of minimal unsatisfiable subsets (MUSes) of F is a kind of such analysis. While researchers in the past two decades focused mainly on techniques for explicit identification of MUSes, there have recently emerged various applications that do not require the explicit identification of MUSes. For instance, in the domain of diagnosis, it is often sufficient to count the number of MUSes. While in theory, one can simply count all MUSes by explicitly enumerating them, in practice, the complete explicit enumeration is often not possible for instances with a reasonably large number of MUSes. In this work, we describe our approximate MUS counting procedure called AMUSIC. Our approach avoids exhaustive MUS enumeration by combining the classical technique of universal hashing with advances in QBF solvers along with usage of union and intersection of MUSes to achieve runtime efficiency. Our prototype implementation of AMUSIC is shown to scale to instances that were clearly beyond the realm of enumeration-based approaches.
- BACKGROUND: Ventriculostomy, one of the most common neurosurgical procedures, involves inserting a draining catheter into the brain's ventricular system to alleviate excessive cerebrospinal fluid accumulation. T...
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- BACKGROUND: Ventriculostomy, one of the most common neurosurgical procedures, involves inserting a draining catheter into the brain's ventricular system to alleviate excessive cerebrospinal fluid accumulation. Traditionally, this procedure has relied on freehand techniques guided by anatomical landmarks, which have shown a high rate of misplacement. Recent advancements in virtual reality (VR) and augmented reality (AR) technologies have opened up new possibilities in the field. This comprehensive review aims to analyze the existing literature, examine the diverse applications of VR and AR in ventriculostomy procedures, address their limitations, and propose potential future directions. METHODS: A systematic search was conducted in Web of science and PubMed databases to identify studies employing VR and AR technologies in ventriculostomy procedures. Review papers, non-English records, studies unrelated to VR/AR technologies in ventriculostomy, and supplementary documents were excluded. In total 29 papers were included in the review. RESULTS: The development of various VR and AR systems aimed at enhancing the ventriculostomy procedure are categorized according to the Data, Visualization and View taxonomy. The study investigates the data utilized by these systems, the visualizations employed, and the virtual or augmented environments created. Furthermore, the surgical scenarios and applications of each method, as well as the validation and evaluation metrics used, are discussed. DISCUSSION: The review delves into the fundamental challenges encountered in the implementation of VR and AR systems in ventriculostomy. Additionally, potential future directions and areas for improvement are proposed, addressing the identified limitations and paving the way for further advancements in the field.
In the medical field, medical images generally do not have corresponding accurate annotations. The reason is that doctors do not have enough time to finely annotate a large number of medical images. Therefore, this pa...
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Image inpainting is a crucial research area in computer vision. Despite significant advancements with deep learning methods, challenges such as information loss and weak adaptability remain. This paper introduces a Tr...
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Multiview spectral clustering has received considerable attention in the past decades and still has great potential due to its unsupervised integration manner. It is well known that pairwise constraints boost the clus...
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Multiview spectral clustering has received considerable attention in the past decades and still has great potential due to its unsupervised integration manner. It is well known that pairwise constraints boost the clustering process to a great extent. Nevertheless, the constraints are usually marked by human beings. To ameliorate the performance of multiview spectral clustering and alleviate the consumption of human resources, we propose self-supervised multiview spectral clustering with a small number of automatically retrieved pairwise constraints. First, the fused multiple autoencoders are used to extract the latent consistent feature of multiple views. Second, the pairwise constraints are achieved based on the commonality among multiple views. Then, the pairwise constraints are propagated through the neural network with historical memory. Finally, the propagated constraints are used to optimize the fused affinity matrix of spectral clustering. Our experiments on four benchmark datasets show the effectiveness of our proposed approach.
Polyp segmentation has recently garnered significant attention, and multiple methods have been formulated to achieve commendable outcomes. However, these techniques often confront difficulty when working with the comp...
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Knowledge distillation (KD) is an effective method for compressing models in object detection tasks. Due to limited computational capability, unmanned aerial vehicle-based object detection (UAV-OD) widely adopt the KD...
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Knowledge distillation (KD) is an effective method for compressing models in object detection tasks. Due to limited computational capability, unmanned aerial vehicle-based object detection (UAV-OD) widely adopt the KD technique to obtain lightweight detectors. Existing methods often overlook the significant differences in feature space caused by the large gap in scale between the teacher and student models. This limitation hampers the efficiency of knowledge transfer during the distillation process. Furthermore, the complex backgrounds in aerial images make it challenging for the student model to efficiently learn the object features. In this letter, we propose a novel KD framework for UAV-OD. Specifically, a progressive distillation approach is designed to alleviate the feature gap between teacher and student models. Then, a new feature alignment method is provided to extract object-related features for enhancing the student model's knowledge reception efficiency. Finally, extensive experiments are conducted to validate the effectiveness of our proposed approach. The results demonstrate that our proposed method achieves state-of-the-art performance on two datasets.
Bug reporting is a mechanism that is used by software companies to audit and track product quality. The developers and the users can log bug reports via a dedicated system. The quality of the bug reports affects the d...
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Crowdsourced testing has gained prominence in the field of software testing due to its ability to effectively address the challenges posed by the fragmentation problem in mobile app testing. The inherent openness of c...
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Crowdsourced testing has gained prominence in the field of software testing due to its ability to effectively address the challenges posed by the fragmentation problem in mobile app testing. The inherent openness of crowdsourced testing brings diversity to the testing outcome. However, it also presents challenges for app developers in inspecting a substantial quantity of test reports. To help app developers inspect the bugs in crowdsourced test reports as early as possible, crowdsourced test report prioritization has emerged as an effective technology by establishing a systematic optimal report inspecting sequence. Nevertheless, crowdsourced test reports consist of app screenshots and textual descriptions, but current prioritization approaches mostly rely on textual descriptions, and some may add vectorized image features at the image-as-a-whole level or widget level. They still lack precision in accurately characterizing the distinctive features of crowdsourced test reports. In terms of prioritization strategy, prevailing approaches adopt simple prioritization based on features combined merely using weighted coefficients, without adequately considering the semantics, which may result in biased and ineffective outcomes. In this paper, we propose EncrePrior, an enhanced crowdsourced test report prioritization approach via image-and-text semantic understanding and feature integration. EncrePrior extracts distinctive features from crowdsourced test reports. For app screenshots, EncrePrior considers the structure (i.e., GUI layout) and the contents (i.e., GUI widgets), viewing the app screenshot from the macroscopic and microscopic perspectives, respectively. For textual descriptions, EncrePrior considers the Bug Description and Reproduction Step as the bug context. During the prioritization, we do not directly merge the features with weights to guide the prioritization. Instead, in order to comprehensively consider the semantics, we adopt a prioritize-reprioritize stra
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