Deyi Li

Web service discovery based on keyword clustering and ontology

One of the challenging problem that Web service technology is now facing is effective service discovery. To solve the deficiencies of Web service description, matching and choosing under WSDL language, this paper presents a web service discovery method based on keyword clustering and concept expansion, mainly from the content of Web service, reasoning of service request and service matching, through classification and subsumption of concept, this paper retrieve and extract Web service content. Meanwhile, use weighted bipartite graph to match user request and published Web service, so as to enhance Web service discovery ability.

Network thinking and network intelligence

Networks interact with one another and are recursive. Network intelligence and networked intelligence, as the way of knowledge representation, have become very active recently. What topological measures can be used to characterize properties of networks?What properties do different structures of real-world networks share, and why? How did theses properties come about? How do these properties affect the dynamics of such networks? How to use network topology to extend other dimensions? Given a real-world network with certain properties, what are the best ways to search for particular nodes? Furthermore, some specific implementations and examples of network intelligence will be given in this paper. Such as mining typical topologies, discovering sensitive links and important communities from real complex networks, networked control, and making a virtual reality of emergence phenomenon in complex systems.

Mining Frequent Composite Service Patterns

Service mining is an important technology which could help service composition. This paper mainly studied the problem of mining frequent composite services from the log of the composition-oriented service discovery system. First, a composite service representation method based on n-tuple was proposed. Second, a frequent composite service mining algorithm was put forward. Finally, an experiment was given to show the effectiveness of the mining algorithm.

Enhancing recommender systems by incorporating social information

Although recommendation techniques have achieved distinct developments over the decades, the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality. Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings. How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge. In this paper, based on a factor graph model, we formalize the problem in a semi-supervised probabilistic model, which can incorporate different user information, user relationships, and user-item ratings for learning to predict the unknown ratings. We evaluate the method in two different genres of datasets, Douban and Last.fm. Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms. Furthermore, a distributed learning algorithm is developed to scale up the approach to real large datasets.

An uncertain control framework of cloud model

The mathematical representation of a concept with uncertainty is one of the foundations of Artificial Intelligence. Uncertain Control has been the core in VSC systems and nonlinear control systems, as the representation of Uncertainty is required. Cloud Model represents the uncertainty with expectation Ex, entropy En and Hyper-entropy He by combining Fuzziness and Randomness together. Randomness and fuzziness make uncertain control be a difficult problem, hence we propose an uncertain control framework of Cloud Model called UCF-CM to solve it. UCF-CM tunes the parameters of Ex, En and He with Cloud, Cloud Controller and Cloud Adapter to generate self-adaptive control in dealing with uncertainties. Finally, an experience of a representative application with UCF-CM is implemented by controlling the growing process of artificial plants to verify the validity and feasibility.

Path planning algorithm regarding rapid parking based on static optimization

To solve problems of automatic parking during the autonomous driving, a rapid optimization algorithm regarding parking has been proposed in this paper. The biggest challenge comes from the non-holonomic characteristics of automobiles and short-distance obstacles. The algorithm in question solves the obstacles avoidance by using Minkowski sum. Geometric path planning is derived from kinematics and connected with path parameters of Runge-Kutta discretization method. Since parking paths and relevant parameters can be obtained by virtue of Runge-Kutta discretization method, the optimization method for every discrete path is to calculate parking paths that are independent from parking lots. The static optimization method can be resolved by digitization in an effective way. Effects of the above mentioned algorithm will be evaluated by different stimulation scenarios. The stimulation experiments show that path planning algorithm regarding rapid parking based on static optimization is feasible for the following three parking scenarios-parallel parking, vertical parking and angle parking. All these three parking forms can be achieved on the premise of no modification in algorithm. Path planning can be completed within several milliseconds even in a narrow space.

A scaled-down traffic system based on autonomous vehicles: A new experimental system for ITS research

In this paper, we present our recent efforts on developing a physical environment for performing traffic experiments. The two main characteristics of this environment are: (1) the whole environment is scaled down from the real traffic, (2) the traffic behaviors are performed by numbers of miniature autonomous vehicles. Performing traffic experiments in an actual environment has long been a hard problem. Moreover, modeling traffic phenomena is also a tough task, due to the complexity of the natural traffic system. However, with the rapid development of autonomous vehicle technology, we have an opportunity to improve these problems from a new perspective. That is using autonomous vehicles to perform traffic experiments. But with the limitations in cost and land availability, directly using full size autonomous vehicles also seemed unrealistic. Thus, we built a 1/10 scaled-down traffic system (SDTS) with more than 50 miniature autonomous vehicles. The environmental design, autonomous vehicles developing, agent modeling, traffic control, and real-time monitoring is considered systematically during system design. The SDTS can be used as a repeatable, appraisable, and verifiable experimental platform for traffic researches, such as testing traffic solutions, verifying key technologies in intelligent vehicles, and performing experiments about ITS. By now, the SDTS has supported a series of workshops, exchange activities and competitions in China.

Cloud computing beyond turing machines

Development of Internet technology and social network has greatly changed the traditional software engineering based on single Turing machine. Software development will be cooperated and completed on the network with collective intelligence. The interaction among human-machine and machine-machine becomes the kernel of Internet computing, while Turing model studied on Entscheidungsproblem based on an automatic computer theoretical model without interaction with people. Clusters or virtual clusters become the basic platform of cloud computing centers. And SaaS (Software as a Service), PaaS (Platform as a Service), IaaS (Infrastructure as a Service) become the common knowledge for software engineers. Furthermore, the research of network science has discovered lots of physical law about the distribution of information resources, such as the power law distribution of Web services. This paper focuses on this computing paradigm, and analyses the trend of software development style facing Internet computing. And the features of cloud computing such as virtualization, granular computing, soft computing and uncertainty are discussed too.

Comparative study of type-2 fuzzy sets and cloud model

The mathematical representation of a concept with uncertainty is one of foundations of Artificial Intelligence. Type-2 fuzzy sets study fuzziness of the membership grade to a concept. Cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. The two methods both concentrate on the essentials of uncertainty and have been applied in many fields for more than ten years. However, their mathematical foundations are quite different. The detailed comparative study will discover the relationship between each other, and provide a fundamental contribution to Artificial Intelligence with uncertainty.

From turing machine intelligence to collective intelligence

Almost all of the progress of artificial intelligence in the last 50 years has been based on the Turing model and Von Neumann architecture. Researchers have always tried to put the human intelligence into machines by the ways of algorithms, codes or symbols that could be understood and executed by machines, thus, we may be bounded to Turing model too tightly. In Internet and World Wide Web and developing cloud computing, network has changed the role from a single huge Turing machine or sum of some Turing machines to the collective intelligence, where the inputs or outputs of nodes in network are happening not only among computers, but also among people, such that Internet has been beyond Turing machine. Users in Internet who own similar interests may cluster naturally into scalable and boundless communities with uncertainty, where online interaction avoids the difficulty of common sense representation in traditional artificial intelligence. Furthermore, collective intelligence may emerge from the crowds interaction. Those would become the new research frontiers in intelligence science.