How to perform efficient service migration in a mobile edge environment has become one of the research hotspots in the field of service computing. Most service migration approaches assume that the mobile edge network ...
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Now object detection based on deep learning tries different *** uses fewer data training networks to achieve the effect of large dataset ***,the existing methods usually do not achieve the balance between network para...
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Now object detection based on deep learning tries different *** uses fewer data training networks to achieve the effect of large dataset ***,the existing methods usually do not achieve the balance between network parameters and training *** makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection *** improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the *** and spatial attention are used to make the network focus on features that are more useful to the *** addition,the recently popular transformer is used to fuse the features of the existing *** compensates for the previous network failure by making full use of existing object *** on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.
The ticket automation provides crucial support for the normal operation of IT software systems. An essential task of ticket automation is to assign experts to solve upcoming tickets. However, facing thousands of ticke...
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For large-scale multitask wireless sensor networks (LSM-WSNs), the traditional data collection mode could suffer low energy-efficiency on data transmission, since the large-scale multitask scenarios could result in mu...
For large-scale multitask wireless sensor networks (LSM-WSNs), the traditional data collection mode could suffer low energy-efficiency on data transmission, since the large-scale multitask scenarios could result in much higher packet collision probability, especially for harsh environments. Mobile data collection is an efficient data acquisition way to prolong network lifetime for LSM-WSNs. However, the mobile collectors could suffer electricity shortage problem, since the limited battery capacity of any mobile collector could not afford the energy consumption of its long-distance movement and massive data collection in large-scale multitask scenarios. Deploying wireless chargers to supplement the energy of mobile collectors is a feasible solution to electricity shortage problem, but will incur extra charger deployment cost. In this paper, we focus on the problem that how to optimize such charger deployment cost, which is NP-hard. By transforming it into minimum-cost submodular cover problem, we devise an efficient approximation algorithm with a provable approximation ratio. The extensive simulation results reveal that our solution always outperforms the other solutions under whatever configurations.
At present, deep learning technologies have been widely used in the field of natural language process, such as text summarization. In CQA, the answer summary could help users get a complete answer quickly. There are s...
At present, deep learning technologies have been widely used in the field of natural language process, such as text summarization. In CQA, the answer summary could help users get a complete answer quickly. There are still some problems with the current answer summary scheme, such as semantic inconsistency, repetition of words, etc. In order to solve this, we propose a novel scheme Answer Summarization based on Multi-layer Attention Scheme (ASMAM). Based on the traditional Seq2Seq, we introduce self-attention and multi-head attention scheme respectively during sentence and text encoding, which could improve text representation ability of the model. In order to solve "long distance dependence" of RNN and too many parameters of LSTM, we all use GRU as the neuron at the encoder and decoder sides. Experiments over the Yahoo! Answers dataset demonstrate that the coherence and fluency of the generated summary are all superior to the benchmark model in ROUGE evaluation system.
Multi-intent spoken language understanding joint model can handle multiple intents in an utterance and is closer to complicated real-world scenarios, attracting increasing attention. However, existing research (1) usu...
Multi-intent spoken language understanding joint model can handle multiple intents in an utterance and is closer to complicated real-world scenarios, attracting increasing attention. However, existing research (1) usually focuses on identifying implicit correlations between utterances and one-hot encoding while ignoring intuitive and explicit original label characteristics; (2) only considers the token-level intent-slot interaction, which results in the limitation of the performance. In this paper, we propose a Label-Aware Graph Interaction Model (LAGIM), which captures the correlation between utterances and explicit labels’ semantics to deliver enriched priors. Then, a global graph interaction module is constructed to model the sentence-level interaction between intents and slots. Specifically, we propose a novel framework to model the global interactive graph based on the injection of the original label semantics, which can fuse explicit original label features and provide global optimization. Experimental results show that our model outperforms existing approaches, achieving a relative improvement of 11.9% and 2.1% overall accuracy over the previous state-of-the-art model on the MixATIS and MixSnips datasets, respectively.
Multi-stability and control of hyperchaotic system are researched in this paper. Firstly, a new 4D hyperchaotic system containing hidden attractors is modeled. Secondly, the coexistence of different attractors is conf...
Multi-stability and control of hyperchaotic system are researched in this paper. Firstly, a new 4D hyperchaotic system containing hidden attractors is modeled. Secondly, the coexistence of different attractors is confirmed and transient hyperchaotic phenomena is found as the parameters are changed. Then, the Hamiltonian energy function of the system is calculated and the energy feedback controller is designed to control the system. Finally, numerical simulations are performed to verify the validity of the results.
In recent years, Few-Shot Object Detection (FSOD) has gained widespread attention and made significant progress due to its ability to build models with a good generalization power using extremely limited annotated dat...
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In this paper, we consider the lifelong age progression and regression task, which requires to synthesize a person's appearance across a wide range of ages. We propose a simple yet effective learning framework to ...
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In this paper, we consider the lifelong age progression and regression task, which requires to synthesize a person's appearance across a wide range of ages. We propose a simple yet effective learning framework to achieve this by exploiting the prior knowledge of faces captured by well-trained generative adversarial networks (GANs). Specifically, we first utilize a pretrained GAN to synthesize face images with different ages, with which we then learn to model the conditional aging process in the GAN latent space. Moreover, we also introduce a cycle consistency loss in the GAN latent space to preserve a person's identity. As a result, our model can reliably predict a person's appearance for different ages by modifying both shape and texture of the head. Both qualitative and quantitative experimental results demonstrate the superiority of our method over concurrent works. Furthermore, we demonstrate that our approach can also achieve high-quality age transformation for painting portraits and cartoon characters without additional age annotations.
Serverless edge computing is emerging as an enabler to provision scalable and flexible Function-as-a-Service (FaaS) applications with lightweight function instances at network edge. In serverless edge computing, the f...
ISBN:
(数字)9798350368550
ISBN:
(纸本)9798350368567
Serverless edge computing is emerging as an enabler to provision scalable and flexible Function-as-a-Service (FaaS) applications with lightweight function instances at network edge. In serverless edge computing, the function instances with inter-dependencies are scheduled to proximate edge nodes in a distributed manner. However, the heterogeneity and unpredictability of edge networks bring significant challenge in realizing optimal scheduling decision to guarantee execution performance of applications without any prior. In view of this challenge, a Serverless Function Scheduling Method, named SFSM, is proposed in this paper for FaaS applications over edge computing. First, a long-term optimization problem is formulated to reduce completion time and decoupled into time-slot sub-problems via Lyapunov optimization. To avoid the cross-edge redundant data transmission overhead of inter-functions, a two-level graph optimization is designed with vertical and horizontal data merging. Then, SFSM incorporates an online multi-armed bandit-based scheduling algorithm that only requires the context of requests without complete information of edge networks. Finally, extensive experimental results based on real-world datasets demonstrate the effectiveness and superiority of SFSM.
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