Recently the analysis of remotely sensed images has played a vital role in various aspects of research. The current researches ignore the unique prior knowledge in remote sensing images and do not consider exploring t...
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Quantum algorithms are raising concerns in the field of cryptography all over the world. A growing number of symmetric cryptography algorithms have been attacked in the quantum setting. Type-3 generalized Feistel sche...
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Quantum algorithms are raising concerns in the field of cryptography all over the world. A growing number of symmetric cryptography algorithms have been attacked in the quantum setting. Type-3 generalized Feistel scheme(GFS) and unbalanced Feistel scheme with expanding functions(UFS-E) are common symmetric cryptography schemes, which are often used in cryptographic analysis and design. We propose quantum distinguishing attacks on Type-3 GFS and UFS-E in the quantum chosen plaintext attack setting. The results of key recovery are better than those based on exhaustive search in the quantum setting.
For medical images, domain shift is a very common phenomenon. To address this issue, researchers have proposed unsupervised domain adaptation and multi-source domain generalization. However, these methods are sometime...
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software-defined networking (SDN) offers enhanced control over network infrastructure through a central controller, but it remains vulnerable to security threats like DDoS attacks. This paper proposes an automatic att...
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ChatGPT can improve software engineering (SE) research practices by offering efficient, accessible information analysis, and synthesis based on natural language interactions. However, ChatGPT could bring ethical chall...
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Wireless Sensor Networks (WSN) can be considered as a self-organizing system that might be a good alternative to wired systems due to their ease of deployment in distant places. The primary goal of this proj...
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Large-scale point cloud semantic segmentation is a challenging task in 3D computer vision. A key challenge is how to resolve ambiguities arising from locally high inter-class similarity. In this study, we introduce a ...
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Distinguishing identity-unrelated background information from discriminative identity information poses a challenge in unsupervised vehicle re-identification(Re-ID).Re-ID models suffer from varying degrees of backgrou...
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Distinguishing identity-unrelated background information from discriminative identity information poses a challenge in unsupervised vehicle re-identification(Re-ID).Re-ID models suffer from varying degrees of background interference caused by continuous scene *** recently proposed segment anything model(SAM)has demonstrated exceptional performance in zero-shot segmentation *** combination of SAM and vehicle Re-ID models can achieve efficient separation of vehicle identity and background *** paper proposes a method that combines SAM-driven mask autoencoder(MAE)pre-training and backgroundaware meta-learning for unsupervised vehicle *** method consists of three ***,the segmentation capacity of SAM is utilized to separate the vehicle identity region from the *** cannot be robustly employed in exceptional situations,such as those with ambiguity or ***,in vehicle Re-ID downstream tasks,a spatiallyconstrained vehicle background segmentation method is presented to obtain accurate background segmentation ***,SAM-driven MAE pre-training utilizes the aforementioned segmentation results to select patches belonging to the vehicle and to mask other patches,allowing MAE to learn identity-sensitive features in a self-supervised ***,we present a background-aware meta-learning method to fit varying degrees of background interference in different scenarios by combining different background region *** experiments demonstrate that the proposed method has state-of-the-art performance in reducing background interference variations.
Community smells are sub-optimal developer community structures that hinder *** studies performed smell prediction and provided refactoring guidelines from a top-down aspect to help community ***,refactoring smells al...
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Community smells are sub-optimal developer community structures that hinder *** studies performed smell prediction and provided refactoring guidelines from a top-down aspect to help community ***,refactoring smells also requires bottom-up effort from every ***,supportive measures and guidelines for them are not available at a fine-grained *** recent work revealed developers'personalities and working states could influence community smells'emergence and variation,we build prediction models with experience,sentiment,and development process features of developers considering three smells including Organizational Silo,Lone Wolf,and Bottleneck,as well as two related classes including smelly developer and smelly *** predict the five classes in the individual granularity,and we also generate forecasts for the number of smelly developers in the community *** proposed models achieve F-measures ranging from 0.73 to 0.92 in individual-wide within-project,time-wise,and cross-project prediction,and mean R2 performance of 0.68 in community-wide Smelly Developer *** also exploit SHAP(SHapley Additive exPlanations)to assess feature importance to explain our *** conclusion,we suggest developers with heavy workload should foster more frequent communication in a straightforward and polite way to build healthier communities,and we recommend community shepherds to use the forecasting model for refactoring planning.
High-dimensional parametric models and large-scale mathematical computations limit the efficiency of IoT devices. The emergence of serverless edge computing provides a solution where users can deploy models as serverl...
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