This paper proposes a simple yet effective method to re-move shadows from text document images. It mainly includes several parts. Firstly, we propose a text elimination-based background extraction strategy to estimate...
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Collaboration between healthcare institutions can significantly lessen the imbalance in medical resources across various geographic areas. However, directly sharing diagnostic information between institutions is typic...
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An Opportunistic Network (OppNet), as opposed to a ubiquitous centralized network, relies on sporadic and opportunistic encounters between nodes to facilitate communication. The uncertainty about the node's nature...
Peer code review is a key practice in professional software development, and its integration into computer science education can provide valuable learning experiences for students. However, few reports compare differe...
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ISBN:
(纸本)9798400705328
Peer code review is a key practice in professional software development, and its integration into computer science education can provide valuable learning experiences for students. However, few reports compare different peer code review methods within a single educational context. This experience report shares insights from implementing various review types-individual, team, and pair code reviews-in a first-year Data Structures and Algorithms course in a bachelor's degree program. Throughout the semester, students took an active role in their learning by completing three programming assignments, each followed by a different peer review method. Feedback was collected through questionnaires to capture the students' perceptions of their data structure knowledge, programming skills, and overall learning experience. Our report outlines the design of the different review learning activities, provides insights into the students' opinions on the review techniques, and reflects on the challenges and successes we encountered. As each method offers unique benefits, we believe that incorporating a variety of peer code review methods can enhance the overall learning experience in computer science courses.
tRNAs play a pivotal role in protein synthesis by transporting amino acids to the ribosome according to mRNA instructions. These molecules are essential regulators in various biological processes, and their dysregulat...
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Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant by...
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In this article, we utilize the advantages of an event camera to tackle the visual place recognition (VPR) problem. The event camera's high measurement rate, low latency, and high dynamic range make it well-suited...
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In this article, we utilize the advantages of an event camera to tackle the visual place recognition (VPR) problem. The event camera's high measurement rate, low latency, and high dynamic range make it well-suited to overcome the limitations of conventional vision sensors. However, to apply the existing convolutional neural network (CNN)-based algorithms such as NetVLAD, the asynchronous event stream should be converted to a synchronous image frame, which causes a loss in temporal information. To address this problem, this article proposes a method that employs the asynchronous characteristic of spiking neural networks (SNNs) to leverage the temporal nature of event streams. The event stream is converted to event images and tensors in our preprocessing module. The SNN-based reconstruction networks, which are converted from CNNs, reconstruct edge images from event tensors regardless of external environment changes. VPR is conducted by matching features of the database and those from NetVLAD, which we used as a feature extraction network in this study. To evaluate the performance of VPR by comparing the previous methods for DDD17 and the Brisbane-Event-VPR dataset, experimental results demonstrate that the matching accuracy of the proposed method is better than previous methods, especially for datasets with adverse weather conditions. We also verify that the performance and energy efficiency are improved with SNNs over CNNs. Our code is available for download on https://***/AIRLABkhu/EvReconNet.
Additive manufacturing(AM)has made significant progress in recent years and has been successfully applied in various fields owing to its ability to manufacture complex *** method efficiently expands the design space,a...
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Additive manufacturing(AM)has made significant progress in recent years and has been successfully applied in various fields owing to its ability to manufacture complex *** method efficiently expands the design space,allowing for the creation of products with better performance than ever *** the emergence of new manufacturing technologies,new design methods are required to efficiently utilize the expanded design ***,topology optimization methods have attracted the attention of researchers because of their ability to generate new and optimized designs without requiring prior *** combination of AM and topology optimization has proven to be a powerful tool for structural innovation in design and ***,it is important to note that AM does not eliminate all manufacturing restrictions but instead replaces them with a different set of design considerations that designers must consider for the successful implementation of these *** has motivated research on topology optimization methods that incorporate manufacturable constraints for AM *** this paper,we present a survey of the latest studies in this research area,with a particular focus on developments in ***,we discuss the existing research gaps and future development trends.
Graph neural networks (GNNs), a class of deep learning models designed for performing information interaction on non-Euclidean graph data, have been successfully applied to node classification tasks in various applica...
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Graph neural networks (GNNs), a class of deep learning models designed for performing information interaction on non-Euclidean graph data, have been successfully applied to node classification tasks in various applications such as citation networks, recommender systems, and natural language processing. Graph node classification is an important research field for node-level tasks in graph data mining. Recently, due to the limitations of shallow GNNs, many researchers have focused on designing deep graph learning models. Previous GNN architecture search works only solve shallow networks (e.g., less than four layers). It is challenging and nonefficient to manually design deep GNNs for challenges like over-smoothing and information squeezing, which greatly limits their capabilities on large-scale graph data. In this article, we propose a novel neural architecture search (NAS) method for designing deep GNNs automatically and further exploit the application potential on various node classification tasks. Our innovations lie in two aspects, where we first redesign the deep GNNs search space for architecture search with a decoupled mode based on propagation and transformation processes, and we then formulate and solve the problem as a multiobjective optimization to balance accuracy and computational efficiency. Experiments on benchmark graph datasets show that our method performs very well on various node classification tasks, and exploiting large-scale graph datasets further validates that our proposed method is scalable.
The use of virtual reality as a tool for professional training enables access to knowledge in a more active way. Applying it in high-risk situations training encourages finding the most effective way to share knowledg...
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