The classical cake cutting problem studies how to find fair allocations of a heterogeneous and divisible resource among multiple agents. Two of the most commonly studied fairness concepts in cake cutting are proportio...
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Generative Adversarial network(GAN) provides a good generative framework to produce realistic samples, but suffers from two recognized issues as mode collapse and unstable training. In this work, we propose to employ ...
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Event detection is an important information extraction task in nature language processing. Recently, the method based on syntactic information and graph convolution network has been wildly used in event detection task...
Event detection is an important information extraction task in nature language processing. Recently, the method based on syntactic information and graph convolution network has been wildly used in event detection task and achieved good performance. For event detection, graph convolution network (GCN) based on dependency arcs can capture the sentence syntactic representations and the syntactic information, which is from candidate triggers to arguments. However, existing methods based on GCN with dependency arcs suffer from imbalance and redundant information in graph. To capture important and refined information in graph, we propose Multi-graph Convolution network with Jump Connection (MGJ-ED). The multi-graph convolution network module adds a core subgraph splitted from dependency graph which selects important one-hop neighbors' syntactic information in breadth via GCN. Also the jump connection architecture aggregate GCN layers' representation with different attention score, which learns the importance of neighbors' syntactic information of different hops away in depth. The experimental results on the widely used ACE 2005 dataset shows the superiority of the other state-of-the-art methods.
For increasingly complex communication demands of large-scale AI communication systems, the Space-Air-Ground Integrated network (SAGIN) better caters to demands but also raises concerns about resource scarcity and div...
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For increasingly complex communication demands of large-scale AI communication systems, the Space-Air-Ground Integrated network (SAGIN) better caters to demands but also raises concerns about resource scarcity and diversity. This paper innovatively combines Graph Pointer Neural networks (GPNN) and Reinforcement Learning (RL) to enhance resource allocation efficiency. The method leverages the advantages of GPNN in handling graph data and RL in optimizing decisions in dynamic environments. It also targets the optimization goal of maximizing resource allocation while minimizing deployment latency. This paper begins by modeling SAGIN and elucidating the SAGIN logical architecture based on Software-defined networking (SDN). Subsequently, it introduces an SFC deployment algorithm aimed at joint optimization of resource allocation and latency. The algorithm leverages GPNN and RL to deploy virtual nodes and links, with the goal of optimizing resource allocation and deployment latency. Experiment findings conclusively demonstrate that the efficacy of proposed algorithm in effectively weighing limited heterogeneous resources and minimum mapping delay. Notably, when compared to three other SFC mapping algorithms MLRL, NFVdeep, and RL, the proposed algorithm consistently outperforms them, with an average improvement of 10.17% in long-term average reward/cost, 11.21% in link resource utilization ratio, 15.34% in node resource utilization ratio, and 16.38% in acceptance ratio.
In gesture recognition,static gestures,dynamic gestures and trajectory gestures are collectively known as multi-modal *** solve the existing problem in different recognition methods for different modal gestures,a unif...
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In gesture recognition,static gestures,dynamic gestures and trajectory gestures are collectively known as multi-modal *** solve the existing problem in different recognition methods for different modal gestures,a unified recognition algorithm is *** angle change data of the finger joints and the movement of the centroid of the hand were acquired respectively by data glove and *** the preprocessing of the multi-source heterogeneous data,all hand gestures were considered as curves while solving hand shaking,and a uniform hand gesture recognition algorithm was established to calculate the Pearson correlation coefficient between hand gestures for gesture *** this way,complex gesture recognition was transformed into the problem of a simple comparison of curves *** main innovations:1) Aiming at solving the problem of multi-modal gesture recognition,an unified recognition model and a new algorithm is proposed;2) The Pearson correlation coefficient for the first time to construct the gesture similarity operator is *** testing 50 kinds of gestures,the experimental results showed that the method presented could cope with intricate gesture interaction with the 97.7% recognition rate.
Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing....
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ISBN:
(数字)9781665403924
ISBN:
(纸本)9781665403931
Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.
In Big data Era, it is becoming more and more important to timely and efficient processing of massive videos, and mining of the value information contained in them. This paper studies chaotic compressed sensing theory...
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With the advancement of processor technology, general-purpose GPUs have become popular parallel computing accelerators in the cloud. However, designed for graphics rendering and high-performance computing, GPUs are bo...
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ISBN:
(数字)9781728198293
ISBN:
(纸本)9781728198309
With the advancement of processor technology, general-purpose GPUs have become popular parallel computing accelerators in the cloud. However, designed for graphics rendering and high-performance computing, GPUs are born without sound security mechanisms. Consequently, the GPU-based service in the cloud is vulnerable to attacks from the potentially compromised guest OS as large amounts of sensitive code and data are offloaded directly to the unprotected *** this paper, we propose SEGIVE, a practical framework of secure GPU execution in the virtualization environment, which protects offloaded device code and data from disclosure or tampering by malicious guest OSes through the full life cycle of security-critical GPU applications. First, SEGIVE secures all the traffic transferred to GPUs with Intel SGX technology, including the users' sensitive data and GPU binaries. Second, with various memory isolation mechanisms, SEGIVE enhances security in multi-user execution scenarios by sharing a GPU among multiple workloads, which avoids underutilization of device resources. Besides, SEGIVE requires no modifications to application source codes, the GPU architecture, or I/O interconnection to fulfill security principles, and thus almost all prevailing GPU-based applications can easily benefit from SEGIVE with little porting effort. We have implemented SEGIVE with KVM-QEMU on off-the-shelf NVIDIA GPUs and CPUs. Evaluation results show that with security-enhances, the performance of SEGIVE prototype is still competitive to the native execution on compute-intensive applications, especially for the public-key cryptography algorithm.
A coupled-channel approach is applied to the charged tetraquark state Tcc+ recently discovered by the LHCb Collaboration. The parameters of the interaction are fixed by a fit to the observed line shape in the three-bo...
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A coupled-channel approach is applied to the charged tetraquark state Tcc+ recently discovered by the LHCb Collaboration. The parameters of the interaction are fixed by a fit to the observed line shape in the three-body D0D0π+ channel. Special attention is paid to the three-body dynamics in the Tcc+ due to the finite life time of the D*. An approach to the Tcc+ is argued to be self-consistent only if both manifestations of the three-body dynamics, the pion exchange between the D and D* mesons and the finite D* width, are taken into account simultaneously to ensure that three-body unitarity is preserved. This is especially important to precisely extract the pole position in the complex energy plane whose imaginary part is very sensitive to the details of the coupled-channel scheme employed. The D0D0 and D0D+ invariant mass distributions, predicted based on this analysis, are in good agreement with the LHCb data. The low-energy expansion of the D*D scattering amplitude is performed and the low-energy constants (the scattering length and effective range) are extracted. The compositeness parameter of the Tcc+ is found to be close to unity, which implies that the Tcc+ is a hadronic molecule generated by the interactions in the D*+D0 and D*0D+ channels. Employing heavy-quark spin symmetry, an isoscalar D*D* molecular partner of the Tcc+ with JP=1+ is predicted under the assumption that the DD*−D*D* coupled-channel effects can be neglected.
Composed of ultralight bosons, fuzzy dark matter provides an intriguing solution to challenges that the standard cold dark matter model encounters on sub-galactic scales. The ultralight dark matter with mass m ∼ 10−2...
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