Human action recognition has become one of the most active research topics in human-computer interaction and artificial intelligence, and has attracted much attention. Here, we employ a low-cost optical sensor Kinect ...
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Human action recognition has become one of the most active research topics in human-computer interaction and artificial intelligence, and has attracted much attention. Here, we employ a low-cost optical sensor Kinect to capture the action information of the human skeleton. We then propose a two-level hierarchical human action recognition model with self-selection classifiers via skeleton data. Especially different optimal classifiers are selected by probability voting mechanism and 10 times 10-fold cross validation at different coarse grained levels. Extensive simulations on a well-known open dataset and results demonstrate that our proposed method is efficient in human action recognition, achieving 94.19%the average recognition rate and 95.61% the best rate.
Most of the current dynamic binary translation (DBT) systems are single-threaded and many orders of magnitude slower than native execution. Although multi-core processors are becoming more and more prevalent, the sing...
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The Delay Tolerant Network employs a store-carry-forward paradigm to enable bundle delivery in intermittent connected environments. A router relays a message only when a proper contact opportunity occurs, which result...
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One major problem in applying object-oriented (OO) methodologies is the difficulty of identifying classes for a system. Many tried to use fuzzy set theory to model the objected-oriented system and many proposed lingui...
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In an attempt to solve problems with multi-objectives, science has come to realize that natural processes will go a long way to achieve such goal. Swarm Intelligence (SI) is a population based process that is widely u...
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A Mobile Ad-hoc Network (MANET) is a self-organizing collection of mobile devices communicating in a distributed fashion across numerous hops. MANETs are an appealing technology for many applications, including rescue...
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A Mobile Ad-hoc Network (MANET) is a self-organizing collection of mobile devices communicating in a distributed fashion across numerous hops. MANETs are an appealing technology for many applications, including rescue operations, environmental monitoring, tactical operations, and so on, because they let people communicate without the usage of permanent infrastructure. This flexibility, however, creates additional security vulnerabilities. Because of its benefits and expanding demand, MANETs have attracted a lot of interest from the scientific community. They do, however, seem to be more vulnerable to numerous attacks that wreak havoc on their performance than any network. Traditional cryptography techniques cannot entirely defend MANETs in terms of fresh attacks and vulnerabilities due to the distributed architecture of MANETs;however, these issues can be overcome by using machine learning approaches-based intrusion detection systems (IDS). IDS, typically screening system processes and identifying intrusions, are commonly employed to supplement existing security methods because preventative techniques are never enough. Because MANETs are continually evolving, their highly limited nodes, and the lack of central observation stations, intrusion detection is a complex and tough process. Conventional IDSs are difficult to apply to them. Existing methodologies must be updated for MANETs or new approaches must be created. This paper aims to present a novel concept founded on deep belief networks (DBN) and long shortterm memory (LSTM) for MANET attack detection. The experimental analysis was performed on the probe, root to local, user to root, and denial of service (DoS) attacks. In the first phase of this paper, particle swarm optimization was used for feature selection, and subsequently, the DBN and LSTM were used for the classification of attacks in the MANET. The experimental results gave an accuracy reaching 99.46%, a sensitivity of 99.52%, and a recall of 99.52% for D
The simulation grid has become a very important research topic. In order to ensure the QoS of resource management, we present a control mechanism in simulation grid, which is based on the prediction methods. In the co...
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Transactional memory (TM) is a parallel programming concept. Existing consistency protocols in distributed transactional memory system consume too much bandwidth and bring high latency. In this paper, we propose our T...
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With the development of the Internet, high-quality streaming services, including Video-on-Demand, are more popular than ever with the help of P2P technologies. But peer-to-peer (P2P) on-demand streaming systems inevit...
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We propose a discriminative model for recognizing group activities. Our model jointly captures the group activity, the individual person actions, and the interactions among them. Two new types of contextual informatio...
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ISBN:
(纸本)9781617823800
We propose a discriminative model for recognizing group activities. Our model jointly captures the group activity, the individual person actions, and the interactions among them. Two new types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. Different from most of the previous latent structured models which assume a predefined structure for the hidden layer, e.g. a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. Our experimental results demonstrate that by inferring this contextual information together with adaptive structures, the proposed model can significantly improve activity recognition performance.
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