Cyber Physical Socio Ecology

Shi, Xiang-Dong; Zhuge, Hai

doi:10.1002/cpe.1625

Cited by 17 publications

(14 citation statements)

References 38 publications

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“…Warnings will be issued and influence will be simulated and explained when abnormal phenomena are detected. The Cyber-Physical-Socio Ecology concerns how to keep a reasonable expansion of the cyber space and the socio space so that they can harmoniously evolve with the physical space otherwise it may form negative influence on the whole environment [64].…”

Section: (2) Education Students Can Learn Natural and Socio Laws Notmentioning

confidence: 99%

Semantic linking through spaces for cyber-physical-socio intelligence: A methodology

Zhuge

2011

Artificial Intelligence

Self Cite

211

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Humans consciously and subconsciously establish various links, emerge semantic images and reason in mind, learn linking effect and rules, select linked individuals to interact, and form closed loops through links while co-experiencing in multiple spaces in lifetime. Machines are limited in these abilities although various graph-based models have been used to link resources in the cyber space. The following are fundamental limitations of machine intelligence: (1) machines know little link and rules in the physical space, physiological space, psychological space, socio space and mental space, so it is not realistic to expect machines to discover laws and solve problems in these spaces; and, (2) machines can only process pre-designed algorithms and data structures in the cyber space. They are limited in ability to go beyond the cyber space, to learn linking rules, to know the effect of linking, and to explain computing results according to physical, physiological, psychological and socio laws. Linking various spaces will create a complex space ⎯ the Cyber-Physical-Physiological-Psychological-Socio-Mental Environment CP 3 SME. Diverse spaces will emerge, evolve, compete and cooperate with each other to extend machine intelligence and human intelligence. From multidisciplinary perspective, this paper reviews previous ideas on various links, introduces the concept of cyberphysical society, proposes the ideal of the CP 3 SME including its definition, characteristics, and multidisciplinary revolution, and explores the methodology of linking through spaces for cyber-physical-socio intelligence. The methodology includes new models, principles, mechanisms, scientific issues, and philosophical explanation. The CP 3 SME aims at an ideal environment for humans to live and work. Exploration will go beyond previous ideals on intelligence and computing.

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Section: (2) Education Students Can Learn Natural and Socio Laws Notmentioning

confidence: 99%

Semantic linking through spaces for cyber-physical-socio intelligence: A methodology

Zhuge

2011

Artificial Intelligence

Self Cite

211

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“…So the network growth mechanisms are like each other at some points [13,21,25,26]. The network expansion is based on the interactions of people.…”

Section: Sln Growth Modelmentioning

confidence: 99%

User Interaction Based Network Growth Model of Semantic Link Network

Ren

Huang

Qiu

2010

2010 Sixth International Conference on Semantics, Knowledge and Grids

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The social network of the Internet is interpreted as the consequences of invisible connection between humans. In the graph based studies the nodes are human beings and the edges represent various social relationships. SLN is a loosely coupled, selforganized semantic data model that link resources semantically. The interactions among users can be interpreted via SLN formation and evolution The interactive as well as intertwined behaviours are the foundation of network itself; at the same time, they shape the way how and where the network will evolve, enrich the semantics of the network and expand the network scale. This paper proposes a network growth model of SLN based on the semantics similarity and popularity of nodes. In our model, the nodes are Twitter blogs and are with semantics, the links are subscribing hyperlinks between blogs. The probability of link establishment between two nodes then calculated from the parameters given above. The data and experiments are based on Twitter blogs, which are the continuous results of interactions by users globally. We crawled the publicly accessible user interaction on blogs, obtaining a portion of the network's links between blogs and the hierarchy of each blog may exist in the whole scenarios. Results show that the statistic properties of SLN are in close analogy with that of social network. The studied network contains a number of high-degree nodes; these nodes are the cores which small groups strongly clustered, and low-degree nodes at the fringes of the network.However, some nodes with too much semantics (especially under one category) are in decreased chances of having links from newly added nodes. The reason may lies in that the over-abundant semantics remains confusion for knowledge acquiring.

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“…Instant messaging network shares great similarities with that of social network. So the network growth mechanisms are like each other at some points [13,21,25,26]. The network expansion is based on the interactions of people.…”

Section: B Semantic Based Sina Weibo Growth Modelmentioning

confidence: 99%

Semantic Based Network Growth in Instant Messaging Environment

Ren

Qiu

2014

JCP

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The instant information exchanging network of the Internet is interpreted as the consequences of invisible connection between humans. In the graph based studies the nodes are human beings and the edges represent various social relationships. The interactions among users can be interpreted via the formation and evolution of semantics. The interactive as well as intertwined behaviors are the foundation of network itself; at the same time, they shape the way how and where the network will evolve. This paper proposes a network growth model based on the semantic similarity as well as popularity of nodes. In our model, the nodes represent Sina Weibo blogs and are with semantics, the links are subscribing hyperlinks between nodes. The probability of link establishment between two nodes then calculated from the similarity between nodes. The data and experiments are based on Sina Weibo blogs, which are the continuous results of interactions by users. We collect data using WebCrawler from Sina API, obtaining a portion of the whole network. Results show that the statistic properties of Sina Weibo are in close analogy with that of social network and also the characteristic complex network. The studied network contains a number of very high-degree nodes; these nodes are the cores which small groups strongly clustered, and low-degree nodes at the fringes of the network. However, some nodes with too much semantics (especially under one category) are in decreased chances of having links from newly added nodes. The reason may lies in that the over-abundant semantics remains confusion for knowledge acquiring.

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Cyber Physical Socio Ecology

Cited by 17 publications

References 38 publications

Semantic linking through spaces for cyber-physical-socio intelligence: A methodology

Semantic linking through spaces for cyber-physical-socio intelligence: A methodology

User Interaction Based Network Growth Model of Semantic Link Network

Semantic Based Network Growth in Instant Messaging Environment

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