Filtering and keyword extraction of Web news have become hot topics of research in Web intelligence, aiming for useful news contents. Because the layouts and styles of news Web pages are different from other Web pages, it is important to accurately identify Web news for correct filtering. This paper proposes an automatic recognition method by classification rules for Web news based on a combination of URL attributes, structure attributes and content attributes. After the automatic recognition and filtering, a new keyphrase extraction method from Web news content based on semantic relations is presented. Semantic relations between phrases are analyzed, and lexical chains are used to represent semantic relations. Keyphrases are extracted and a semantic link graph is built on the lexical chains. The experimental results in our news filtering and summarization (NFAS) system demonstrate that our recognition method provides a high accuracy of above 96% with the recognition of Web news page. News Web pages with core hints are selected from the “163” website to evaluate our keyphrase extraction method. The experimental results in our NFAS system show that the keyword extraction method proposed in the paper substantially outperforms the method based on term frequency and the method based on lexical chains.

Due to the ``curse of dimensionality'', it is difficult to analyze high-dimensional data effectively using traditional statistical methods. Assuming that such data is generated from intrinsic variables with lower dimensions, manifold learning reveals intrinsic structures latent in high dimensional data spaces. It has attracted research interests from many domains in statistics and artificial intelligence. In this paper, we give a tutorial survey of several key and common manifold learning algorithms. We describe their applications to tasks in high dimensional data analysis and visualization. We discuss the pros and cons of these algorithms and how to avoid those pitfalls.

In recent years, reform and development of the urban health care system (HCS) in China has attracted increasing attentions. An urban HCS includes Community Health Systems (CHS, e.g. community hospitals) and Medical Delivery Systems (MDS, e.g. general hospitals). More cooperation between these two systems could greatly improve overall health care: making medical service more convenient and cost-effective. The urban HCS is a complex system extensively influenced by human behavior. Therefore, it is important to study patient hospital preferences and related patient decision making behavior. Using Agent-Based Modeling and Simulation (ABMS) as an innovative tool, we build an artificial HCS as a platform on which to study medical cooperation, such as sharing beds, sharing doctors and cost accommodation. The results show that patient access time can be greatly reduced and the overuse of resources in MDS is relieved. We also develop the referral appointment system based on linear programming, and present its benefits and capabilities. By adopting the artificial HCS platform in practice, researchers are capable of providing valuable insights to urban health care management.

In adversarial settings, there are those who wish to conceal their existence, properties and activities from data analysis. This substantially changes the knowledge discovery process -- finding a model that best `fits' the data is unhelpful because it provides adversaries with predictable ways to hide, and ways to manipulate. We survey some of the implications for algorithms and process, and suggest some open problems.

Radio frequency identification (RFID) is a kind of electronic identification technology that is becoming widely deployed. Due to its intrinsic small size and low cost features, the RFID technology can be readily integrated into various systems for future smart environment applications, whereby vital information is retrieved by diverse types of communications networks. In order to launch a specific service in an existing RFID system, object identification is first performed to retrieve the corresponding service codes from a backend database. However, the critical gaps that may exist in the identification recognition and subsequent handover of service codes from a database to a service machine can make it challenging to offer a good quality of service. This paper introduces a Code-centric RFID System based on an agent intelligence scheme that can potentially achieve faster service response. In this system, we replace traditional ID numbers with codes that indicate the service that the RFID tag bearer needs for improved system response.

An I Room is a virtual environment for purposeful interaction. It is intended to provide support for a range of collaborative activities, especially those that involve deliberation and decision-making. The I Room acts as a space in which information can be collected, arranged and maintained, and in which participants can collaborate using a variety of communication, presentation and support tools. This concept is founded on a number of complementary principled approaches for guiding purposeful behaviour, which in turn provide a basis for calls to external intelligent systems and knowledge bases. Prototype I Rooms have been constructed using a popular virtual world platform and used for interactive work and leisure activities; several of these applications are presented here to illustrate the concept.

Decision making is the cognitive process leading to the selection of a course of action among variations; indeed, decision making is said to be a psychological construct, depending on the individual or individuals. Although being an important factor in individuals every day life, emotions are many times forgotten in the development of systems to be used by persons. In this paper we present a context aware model of emotions that can be used to design intelligent agents endowed with emotional capabilities that can be used to simulate group decision making processes. Our experiments show that agents endowed with emotional awareness are able to achieve agreements more rapidly.

An important challenge for supporting multimedia applications in heterogeneous networks is the heterogeneity of fixed and mobile access networks. In this work, we design a new and efficient context-aware middleware for facilitating diverse multimedia services in heterogeneous networks environment. Firstly, we present an adaptive service provisioning middleware for handling the heterogeneity of diverse networks and enable service provisioning to mobile users and professionals anywhere, anytime. Then, a context-aware multimedia middleware framework is presented based on the proposed adaptive service provisioning framework to support diverse multimedia services, including, multimedia content filtering, recommendation, adaptation, aggregation, learning, reasoning, and delivery. To the best of knowledge, this study is the first one to provide a general heterogeneous multimedia middleware by jointly considering the characteristics of context-multimedia service and heterogeneous networks.

Presented By: NEC
 
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Accurately predicting human error remains an ongoing problem in many industrial settings. The combined complexity of motor, perceptive, and decision-making activities leads to a vast range of human error making it difficult to devise models capable of predicting and avoiding these errors. Our research proposes a novel behavior-based approach to human performance prediction using computer vision and machine learning. Using facial features automatically extracted from short video segments of experimental participants, we created models to predict participant performance over the entire task, over each phase of the task, and at any given instant within the task (i.e., individual errors). The models successfully predicted human performance with over 90% accuracy across classification categories. We discuss both theoretical and applied implications.

Previous sentiment classification studies have adopted either the machine learning approach or the semantic orientation approach. For this study, we proposed a lexicon enhanced method for sentiment classification by combining these two approaches into one framework. Specifically, we used the words with semantic orientations as an additional dimension of features (referred to as "sentiment features") for the machine learning classifiers. We examined the performance of our proposed method through experiments using five different online product review data sets including: digital cameras, books, DVDs, electronics, and kitchen appliances, respectively. Among them, the first data set was collected by the authors and the remaining four were publicly available. Experiments on the different data sets consistently indicated that adding sentiment features significantly improved sentiment classification performance. For the four public data sets, the best classification results were achieved when all three types of features (i.e., content-free, content-specific, and sentiment features) were combined and feature selection was conducted. For the digital camera data set, the best performance was achieved when using the combined features without feature selection.

In this article we present the self-serving-asset, developed as part of a research project at the Boeing Company and the University of Cambridge. The self-serving asset is self-aware, and has the goal to maximise its life in service by contacting, selecting and procuring service providers autonomously. The result is an open, consistent service chain where complex database transactions are eliminated, and an emergent, yet rather self-capable system starts to materialise. Among various supporting technology multi-agent systems provide the backbone for the “intelligence” characteristic required from the self-serving asset. Intelligent asset agents monitor assets, contact suppliers, use multi-criteria decision making to select among proposals, and handle competition. In this paper we aim to outline the self-serving asset concept, describe the multi-agent platform designed to support the asset, and present experimental results on the preliminary agent architecture in terms of decision optimality, scalability and stability.

This paper presents a vision of how language-endowed, next- generation intelligent agents might resolve – i.e., fully interpret – references to objects and events in language input. It describes some of the more difficult reference phenomena that are not being sufficiently treated by practical systems and suggests what kinds of knowledge must be available to intelligent agents to enable them to reach human competence in reference resolution.

The historic Encyclopedia of Architecture, written in German between 1880-1943, was one of the largest projects aiming at conserving all architectural knowledge available at that time. Today, its vast amount of content is mostly lost: few complete sets are available, and its complex structure does not lend itself easily to contemporary application. We show how modern semantic technologies can be applied to make these heritage documents accessible again. In particular, we demonstrate how to go beyond classical digitization projects by transforming the historical documents into a semantic knowledge base. Using techniques from natural language processing and the Semantic Web, we show how to automatically populate an ontology that can be used for various application scenarios: Building historians can use it to navigate and query the encyclopedia, while architects can directly integrate it into contemporary construction tools. Additionally, all content is made accessible in a user-friendly Wiki interface that combines original text with NLP-derived metadata and adds annotation capabilities for collaborative use.

IEEE Intelligent Systems