Visual Place Recognition (VPR) aims to retrieve frames from a geotagged database that are located at the same place as the query frame. To improve the robustness of VPR in perceptually aliasing scenarios, sequence-bas...
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To promote the application of DSP, a fast and accurate toolchain generation method must be realized. Unlike the traditional manual method, in this paper, we propose a toolchain generation algorithm based on the archit...
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ISBN:
(纸本)9781665432078
To promote the application of DSP, a fast and accurate toolchain generation method must be realized. Unlike the traditional manual method, in this paper, we propose a toolchain generation algorithm based on the architecture description language, and migrate the toolchain to adapt to the specified processor architecture. The algorithm uses the DSP architecture description as input to generate intermediate expressions, then uses lexical analysis and syntax analysis tools to migrate the assembly toolchain. In addition, an integrated development environment is developed based on Eclipse, which creates a friendly human-computer interface. Experiments show that this toolchain generation algorithm runs fast and accurately, which solves problems of the development and application of the DSP toolchain, and creates favorable conditions for the promotion of DSP.
Accurate network traffic prediction of base station cell is very vital for the expansion and reduction of wireless devices in base station cell. The burst and uncertainty of base station cell network traffic makes the...
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The issues of both system security and safety can be dissected integrally from the perspective of behavioral appropriateness. That is, a system that is secure or safe can be judged by whether the behavior of certain a...
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The issues of both system security and safety can be dissected integrally from the perspective of behavioral appropriateness. That is, a system that is secure or safe can be judged by whether the behavior of certain agent(s) is appropriate or not. Specifically, a so-called appropriate behavior involves the right agent performing the right actions at the right time under certain conditions. Then, according to different levels of appropriateness and degrees of custodies, behavioral authentication can be graded into three levels, i.e., the authentication of behavioral Identity, Conformity, and Benignity. In a broad sense, for the security and safety issue, behavioral authentication is not only an innovative and promising method due to its inherent advantages but also a critical and fundamental problem due to the ubiquity of behavior generation and the necessity of behavior regulation in any system. By this classification, this review provides a comprehensive examination of the background and preliminaries of behavioral authentication. It further summarizes existing research based on their respective focus areas and characteristics. The challenges confronted by current behavioral authentication methods are analyzed, and potential research directions are discussed to promote the diversified and integrated development of behavioral authentication.
In the context of the ongoing global epidemic of COVID-19 and frequent virus mutations, the implementation of vaccine is the key to the prevention and control of the epidemic at this stage. In order to provide recomme...
The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit...
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Reasoning-based approaches have demonstrated their powerful ability for the task of image-text matching. In this work, two issues are addressed for image-text matching. First, for reasoning processing, conventional ap...
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Data dependency, often presented as directed acyclic graph (DAG), is a crucial application semantics for the performance of data analytic platforms such as Spark. Spark comes with two built-in schedulers, namely FIFO ...
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ISBN:
(数字)9781728168760
ISBN:
(纸本)9781728168777
Data dependency, often presented as directed acyclic graph (DAG), is a crucial application semantics for the performance of data analytic platforms such as Spark. Spark comes with two built-in schedulers, namely FIFO and Fair scheduler, which do not take advantage of data dependency structures. Recently proposed DAG-aware task scheduling approaches, notably GRAPHENE, have achieved significant performance improvements but paid little attention to cache management. The resulted data access patterns interact poorly with the built-in LRU caching, leading to significant cache misses and performance degradation. On the other hand, DAG-aware caching schemes, such as Most Reference Distance (MRD), are designed for FIFO scheduler instead of DAG-aware task *** this paper, we propose and develop a middleware Dagon, which leverages the complexity and heterogeneity of DAGs to jointly execute task scheduling and cache management. Dagon relies on three key mechanisms: DAG-aware task assignment that considers dependency structure and heterogeneous resource demands to reduce potential resource fragmentation, sensitivity-aware delay scheduling that prevents executors from long waiting for tasks insensitive to locality, and priority-aware caching that makes the cache eviction and prefetching decisions based on the stage priority determined by DAG-aware task assignment. We have implemented Dagon in Apache Spark. Evaluation on a testbed shows that Dagon improves the job completion time by up to 42% and CPU utilization by up to 46% respectively, compared to GRAPHENE plus MRD.
To improve the availability of data in the cloud and avoid vendor lock-in risk, multi-cloud storage is attracting more and more attentions. However, accessing data from the cloud usually has some disadvantages such as...
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Person search aims to locate target individuals in large image databases captured by multiple non-overlapping cameras. Existing models primarily rely on spatial feature extraction to capture fine-grained local details...
Person search aims to locate target individuals in large image databases captured by multiple non-overlapping cameras. Existing models primarily rely on spatial feature extraction to capture fine-grained local details, which is vulnerable to background clutter and occlusions and leads to unstable feature representations. To address the issues, we propose a Dynamic Frequency Selection and Spatial Interaction Fusion Network (PS-DFSI), marking the first attempt to introduce frequency decoupling and selection into person search. By integrating frequency and spatial features, PS-DFSI enhances feature expressiveness and robustness. Specifically, it comprises two core modules: the Dynamic Frequency Selection Module (DFSM) and the Spatial Frequency Interaction Module (SFIM). DFSM decouples feature maps into low-frequency and high-frequency components using learnable low-pass and high-pass filters, and a frequency selection modulator emphasizes key frequency components via channel attention. SFIM refines local details by fusing frequency-enhanced features with high-level semantic representations, leveraging multi-scale receptive fields and cross-feature attention for efficient spatial-frequency integration. Extensive experiments on CUHK-SYSU and PRW demonstrate that PS-DFSI significantly improves person search performance, validating its effectiveness and robustness.
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