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Lu H.,Beijing Institute of Technology | Lu H.,CAS Institute of Automation | Niu Z.-D.,Beijing Institute of Technology | Zhang N.,Chinese Association of Automation | And 2 more authors.
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | Year: 2014

The main features of microblog include different lengths, divergence of its themes, and inclusion of special symbols. It's essential for building a comprehensive modeling method, which integrating dependency sentence analysis, domain knowledge and emotions to analyze sentiment of microblog in various aspects, such as society, entertainment, security. In this paper, a topic-oriented sentiment analysis model for Chinese microblog was proposed, this model covers data preprocessing, dependency sentence analysis, theme extension, domain knowledge and rules, dynamic adjustment of polarity of sentiment words and emotions. Through the experiments of Sina weibo data from three different domains, this method obtains high analytical accuracy under specific experimental environment.

Lu H.,CAS Institute of Automation | Lu H.,Chinese Academy of Sciences | Zheng X.,CAS Institute of Automation | Sun X.,CAS Institute of Automation | Zhang N.,Chinese Association of Automation
Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012 | Year: 2012

Currently, the research issues are becoming increasingly global and complex. In order to master more and more professional and comprehensive ability to solve problems, it is proposed in this paper that academic intelligence, journal intelligence, conference intelligence, paper intelligence and so on are integrated together to establish intelligent scientific research collaboration platform. And taking the system application of Science and Technology Review as example, the process of scientific research collaboration is carried out to verify the effectiveness of the system. In conclusion, the scientific research collaboration platform could satisfy the comprehensive needs for effectively acquiring a mass of information and launching scientific research collaboration as well as facilitating academic communication. © 2012 IEEE.

Lu H.,CAS Institute of Automation | Yang Y.,CAS Institute of Automation | Gan R.,CAS Institute of Automation | Zhang N.,Chinese Association of Automation
Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control, ICNSC 2012 | Year: 2012

The micro-blogs, as a new social media, possesses big differences with other social media on the aspect of information updating frequency, organization structure, user connection and etc, which have astonishing power of convergence and penetration. Based on this, it is proposed in this paper that Micro-Blog Public Opinion Index (MBPOI), which consists of five sub-indexes QI, II, RI, PI and CI (Quantity Index, Intensity Index, Relation Index, Polarity Index, Confidence Index) multi-dimensionally, is used to measure and evaluate the public topics and issues discussed in the micro-blogs. In the meanwhile, taking "ABB automatic world 2011" activity as example, the MBPOI prototype system is verified; it has been shown from the results that the MBPOI, which uses five sub-indexes method, owns much better effect by quantifying the topics/issues' influence in multi-dimensions and multi-levels, and provides effective micro-blogs analysis reports for monitoring and tracking the "ABB automatic world 2011" activity. © 2012 IEEE.

News Article
Site: phys.org

Go: a game of complexity and a symbol for unity of contradiction. Credit: Chinese Association of Automation On March 15, 2016, Lee Sodol, an 18-time world champion of the ancient Chinese board game Go, was defeated by AlphaGo, a computer program. The event is one of the most historic in the field of artificial intelligence since Deep Blue bested chess Grandmaster Garry Kasparov in the late 1990s. The difference is that AlphaGo may represent an even bigger turning point in AI research. As outlined in a recently published paper, AlphaGo and programs like it possess the computational architecture to handle complex problems that lie well beyond the game table. Invented over 2500 years ago in China, Go is a game in which two players battle for territory on a gridded board by strategically laying black or white stones. While the rules that govern play are simple, Go is vastly more complex than chess. In chess, the total number of possible games is on the order of 10100. But the number for Go is 10700. That level of complexity is much too high to use the same computational tricks used to make Deep Blue a chess master. And this complexity is what makes Go so attractive to AI researchers. A program that could learn to play Go well would, in some ways, approach the complexity of human intelligence. Perhaps surprisingly, the team that developed AlphaGo, Google Deep Mind, did not create any new concepts or methods of artificial intelligence. Instead, the secret to AlphaGo's success is how it integrates and implements recent data-driven AI approaches, especially deep learning. This branch of AI deals with learning how to recognize highly abstract patterns in unlabeled data sets, mainly by using computational networks that mirror how the brain processes information. According to the authors, this kind of neural network approach can be considered a specific example of a more general technique called ACP, which is short for "artificial systems," "computational experiments," and "parallel execution." ACP effectively reduces the game space AlphaGo must search through to decide on a move. Instead of wading through all possible moves, AlphaGo is trained to recognize game patterns by continuously playing games against itself and examining its game play history. In effect, AlphaGo gets a feel for what Go players call "the shape of a game." Developing this kind of intuition is what the authors believe can also advance the management of complex engineering, economic, and social problems. The idea is that any decision problem that can be solved by a human being can also be solved by any AlphaGo-like program. This proposal, which the authors advance as the AlphaGo thesis, is a decision-oriented version of the Church-Turing thesis, which states that a simple computer called a Turing machine can compute all functions computable by humans. AlphaGo's recent triumph therefore holds a lot of promise for the field of artificial intelligence. Although advances in deep learning that extend beyond the game of Go will likely be the result of decades more research, AlphaGo is a good start. Explore further: Human champion certain he'll beat AI at ancient Chinese game

Lu H.,CAS Institute of Automation | Wang F.-Y.,CAS Institute of Automation | Liu D.-R.,CAS Institute of Automation | Zhang N.,Chinese Association of Automation | Zhao X.-L.,Chinese Association of Automation
Zidonghua Xuebao/Acta Automatica Sinica | Year: 2014

Nowadays, automation science and technology based on automatic control and information processing has become an essential impetus to productive forces and human life. So a comprehensive understanding of the latest research progress in this discipline is essential for its significant reference value to scholars and research institutions. In this paper, the automation science and technology discipline is divided into five research fields, which are specifically defined as control theory and engineering, pattern recognition and intelligent systems, measurement technology and automatic equipment, navigation and guidance, and systems engineering. Each field is depicted by analyzing and mapping the data from 46 242 academic articles published on 88 journals during 2011~2013. The results show that the research interests are different between domestic and abroad, and that the domestic institutions and ethnic Chinese scholars have played an important role in promoting the development of automation science and technology. Copyright © 2014 Acta Automatica Sinica. All rights reserved.

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