Institution
Beijing University of Posts and Telecommunications
Education•Beijing, Beijing, China•
About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.
Papers published on a yearly basis
Papers
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TL;DR: This paper provides a comprehensive survey on the literature involving machine learning algorithms applied to SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security.
Abstract: In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.
436 citations
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TL;DR: A model to predict social status of individuals with 93% accuracy is developed and it is shown that high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another.
Abstract: Social status, defined as the relative rank or position that an individual holds in a social hierarchy, is known to be among the most important motivating forces in social behaviors. In this paper, we consider the notion of status from the perspective of a position or title held by a person in an enterprise. We study the intersection of social status and social networks in an enterprise. We study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks. To that end, we use two enterprise datasets with three communication channels — voice call, short message, and email — to demonstrate the social-behavioral differences among individuals with different status. We have several interesting findings and based on these findings we also develop a model to predict social status. On the individual level, high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another. On the community level, the principle of homophily, social balance and clique theory generally indicate a “rich club” maintained by high-status individuals, in the sense that this community is much more connected, balanced and dense. Our model can predict social status of individuals with 93% accuracy.
436 citations
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TL;DR: A new deep locality-preserving convolutional neural network (DLP-CNN) method that aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatter is proposed.
Abstract: Facial expression is central to human experience, but most previous databases and studies are limited to posed facial behavior under controlled conditions In this paper, we present a novel facial expression database, Real-world Affective Face Database (RAF-DB), which contains approximately 30 000 facial images with uncontrolled poses and illumination from thousands of individuals of diverse ages and races During the crowdsourcing annotation, each image is independently labeled by approximately 40 annotators An expectation–maximization algorithm is developed to reliably estimate the emotion labels, which reveals that real-world faces often express compound or even mixture emotions A cross-database study between RAF-DB and CK+ database further indicates that the action units of real-world emotions are much more diverse than, or even deviate from, those of laboratory-controlled emotions To address the recognition of multi-modal expressions in the wild, we propose a new deep locality-preserving convolutional neural network (DLP-CNN) method that aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatter Benchmark experiments on 7-class basic expressions and 11-class compound expressions, as well as additional experiments on CK+, MMI, and SFEW 20 databases, show that the proposed DLP-CNN outperforms the state-of-the-art handcrafted features and deep learning-based methods for expression recognition in the wild To promote further study, we have made the RAF database, benchmarks, and descriptor encodings publicly available to the research community
429 citations
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TL;DR: An abstract network model of the IoV is proposed, the technologies required to create the IoVs are discussed, different applications based on certain currently existing technologies are presented, and essential future research is described in the area of IoV.
Abstract: The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. This paper proposes an abstract network model of the IoV, discusses the technologies required to create the IoV, presents different applications based on certain currently existing technologies, provides several open research challenges and describes essential future research in the area of IoV.
423 citations
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TL;DR: The structural principle, the characteristics, and some kinds of classic models of deep learning, such as stacked auto encoder, deep belief network, deep Boltzmann machine, and convolutional neural network are described.
408 citations
Authors
Showing all 39925 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jie Zhang | 178 | 4857 | 221720 |
Jian Li | 133 | 2863 | 87131 |
Ming Li | 103 | 1669 | 62672 |
Kang G. Shin | 98 | 885 | 38572 |
Lei Liu | 98 | 2041 | 51163 |
Muhammad Shoaib | 97 | 1333 | 47617 |
Stan Z. Li | 97 | 532 | 41793 |
Qi Tian | 96 | 1030 | 41010 |
Xiaodong Xu | 94 | 1122 | 50817 |
Qi-Kun Xue | 84 | 589 | 30908 |
Long Wang | 84 | 835 | 30926 |
Jing Zhou | 84 | 533 | 37101 |
Hao Yu | 81 | 981 | 27765 |
Mohsen Guizani | 79 | 1110 | 31282 |
Muhammad Iqbal | 77 | 961 | 23821 |