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This paper deals with modeling human behavior routines during driving. We propose a new vision of the maximum causal entropy framework for inverse reinforcement learning to predict actions to be triggered in particular situation (lane change). We designed a plugin to enhance functionalities of the vCar platform which is presents an open source solution for the analysis and visualization of data from...
With the development of the Internet, it is vital for the security of the Internet to detect web-based anomalies. Clustering based on feature extraction by manually has been verified as a significant way to detect new anomalies. But the presentations of these features can't express semantic information of the URLs. In addition, few studies try to cluster the anomalies into specific types like SQL-injection...
This paper discusses a spoken language acquisition system for a command-and-control interface. The proposed system learns a set of words through coupled commands and demonstrations. The user can teach the system a new command by demonstrating the uttered command through an alternative interface. With these coupled commands and demonstrations, the system can learn the acoustic representations of the...
Similar cases recommendation is more and more popular in the internet inquiry. There have been lots of cases which have been solved perfectly, and recommending them to similar inquiries can not only save the patients' waiting time, but also giving more good references. However, the inquiry platform cannot understand the diversity of description, i.e. the same meaning with different description. This...
In this paper, we propose a multi-layered Probabilistic Latent Semantic Analysis (PLSA) model for personalized video summarization problem based on time synchronous comments offered by multiple users. Preliminary evaluations performed on an animation series of 624 minutes long with 12212 users show that the proposed model is able to captures the relationships among the preference of each individual...
During the past few years, there has been a massive explosion of multimedia content such as un-annotated images on the web. Automatic image annotation is an important task for multimedia retrieval. By automatically allocating semantic concepts to un-annotated images, image retrieval can be performed over annotation concepts. In this work, we address the problem of automatic image annotation, namely...
With a growing number of web services, discovering services that can match with a user's query becomes a challenging task. It's very tedious for a service consumer to select the appropriate one according to her/his needs. In this paper, we propose a non-logic-based matchmaking approach that uses the Correlated Topic Model (CTM) to extract topic from semantic service descriptions and model the correlation...
We propose an error learning model for image classification. Motivated by the observation that classifiers trained using local grid regions of the images are often biased, i.e., contain many classification error, we present a two-level combined model to learn useful classification information from these errors, based on Bayes rule. We give theoretical analysis and explanation to show that this error...
Ontology matching finds correspondences between similar entities of different ontologies. Two ontologies may be similar in some aspects such as structure, semantic etc. Most ontology matching systems integrate multiple matchers to extract all the similarities that two ontologies may have. Thus, we face a major problem to aggregate different similarities. Some matching systems use experimental weights...
Personalized recommendation is hot issue in information management system nowadays. With the technology, it can improve the QOS of information service. In this paper, we present a new user profile model based on semantic meta-model of digital resource, using implicit feedback, the users' profiles can be adjusted in time. Comparing the 'like' query in the standard SQL in relational databases, which...
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