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In this paper we propose a novel personalized paper search system using the relevance among user's queried keywords and user's behaviors on a searched paper list. The proposed system builds user's individual relevance network from analyzing the appearance frequencies of keywords in the searched papers. The relevance network is personalized by providing weights to the appearance frequencies of keywords according to users' behaviors on the searched list, such as "downloading," "opening," and "no-action." In the experimental section, we demonstrate our method using 100 faculties' search information in the University of Suwon. [ABSTRACT FROM AUTHOR]

This is a summary of the papers presented at the Active Conceptual Modeling of Learning (ACM-L) Workshop, November 8, 2006, Tucson, Arizona, USA, and several invited papers. [ABSTRACT FROM AUTHOR]

When performing quality inspection of recycled paper one phenomenon of concern is the appearance of macroscopic undulations on the paper sheet surface that may emerge shortly or some time after its production. In this paper we explore the detection and measurement of this defect by means of computer vision and statistical pattern recognition techniques that may allow early detection at the production site. We propose features computed from Gabor Filter Banks (GFB) and Discrete Wavelet Transforms (DWT) for the characterization of paper sheet surface bumpiness in recycled paper images. The lack of a precise definition of the defect and the great variability of the sheet deformation shapes and scales, both within each image and between images, introduce additional difficulties to the problem. We obtain, with both proposed modeling approaches (GFB and DWT), classification accuracies are comparable to the agreement between human observers. The best performance is obtained using DWT features. [ABSTRACT FROM AUTHOR]

This paper addresses a Korean-English machine translation system for scientific papers. The MT system is supported by CL (Controlled Language)-guided source language rewriting. After analyzing the translation errors of the system, we defined Korean rewriting rules to avoid the linguistic obstacles that may affect the translation accuracy. To support this, a Korean CL-checker was implemented. We showed that this CL-guided MT system can improve the translation accuracy by about 13% by adopting CL rewriting rules. However, this improvement is still not enough for various purposes, because most of the users of the MT system may want to submit the translated texts to a conference or an academic journal. As the MT output contains erroneous expressions, a language model module was added to the pattern-based MT engine. The system automatically detects expressions with low frequency and asks the authors to examine the translation. By adopting CL rewriting rules and language model module to the existing MT engine, almost "professional" translations can be obtained. [ABSTRACT FROM AUTHOR]

This paper presents a novel application of a semantic annotation system, named Cerno, to analyze research publications in electronic format. Specifically, we address the problem of providing automatic support for authors who need to deal with large volumes of research documents. To this end, we have developed Biblio, a user-friendly tool based on Cerno. The tool directs the user's attention to the most important elements of the papers and provides assistance by generating automatically a list of references and an annotated bibliography given a collection of published research articles. The tool performance has been evaluated on a set of papers and preliminary evaluation results are promising. The backend of Biblio uses a standard relational database to store the results. [ABSTRACT FROM AUTHOR]

This paper is concerned with a multi-resolution tool for screening paper formation variations in various frequency regions on production line. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi-resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the series. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The tools developed are used for online paper formation monitoring in a paper mill. [ABSTRACT FROM AUTHOR]

In this paper we review two well-known citation methods to find relatedness between scientific papers: co-citation and bibliographic coupling. We propose a practical method to estimate the co-citation relatedness using the Google search engine. We call this method Web co-citation. We conducted experiments on a collection of scientific papers to compare the performances of different methods. The experimental results show that our approach, despite its simplicity, is efficient in discovering the relatedness between scientific papers. [ABSTRACT FROM AUTHOR]

In this tutorial paper we summarise the key features of the multi-threaded Qu-Prolog language for implementing multi-threaded communicating agent applications. Internal threads of an agent communicate using the shared dynamic database used as a generalisation of Linda tuple store. Threads in different agents, perhaps on different hosts, communicate using either a thread-to-thread store and forward communication system, or by a publish and subscribe mechanism in which messages are routed to their destinations based on content test subscriptions. We illustrate the features using an auction house application. This is fully distributed with multiple auctioneers and bidders which participate in simultaneous auctions. The application makes essential use of the three forms of inter-thread communication of Qu-Prolog. The agent bidding behaviour is specified graphically as a finite state automaton and its implementation is essentially the execution of its state transition function. The paper assumes familiarity with Prolog and the basic concepts of multi-agent systems. [ABSTRACT FROM AUTHOR]

This paper is a tutorial describing the main features of the KGP (Knowledge-Goals-Plan) model of agency and giving user guidance on how the model can be used to develop applications. The KGP model is based on computational logic. It consists of an abstract component, a computational component and an implementation. This paper concentrates on the abstract component, which consists of formal specifications of a number of different modules, including the knowledge bases, capabilities, transitions and control. For each of these we summarise what is provided by the model, and through the platform implementing the model, and what is left to the users to specify according to the requirements of the applications for which they wish to use the KGP model to design agents. [ABSTRACT FROM AUTHOR]

The Lost Cosmonaut is an interactive narrative based on digitally enhanced paper. This technology uses an electronic pen to mediate between paper and computer. Thus any actions of the pen on the paper can be captured and manipulated by a computer as well as we can map digitally controlled events onto paper. The story in this narrative environment reveals itself partially through written text and images on the paper surface just as any other printed story. However, additional information in form of digitally controlled outputs such as sound, light and projections can be accessed through interaction with pen and paper. Furthermore the audience is not only supposed to read and otherwise perceive information, we also want them to actively produce content for this environment by writing onto the paper. By doing so they also add content to the database containing the digital output at the same time. Hence we produce a complex multimedia environment that works on three levels: On paper, in a digitally controlled visual and acoustic environment and in the combination of both worlds. Last but not least this environment is an open system, which grows as a collaborative effort over time as each user adds his own entries to paper and database. We argue that using paper as an integrated part of a digital environment is a best-of-both-world approach that opens up new possibilities for producing and perceiving narrative. [ABSTRACT FROM AUTHOR]

Researchers from the same lab often spend a considerable amount of time searching for published articles relevant to their current project. Despite having similar interests, they conduct independent, time consuming searches. While they may share the results afterwards, they are unable to leverage previous search results during the search process. We propose a research paper recommender system that avoids such time consuming searches by augmenting existing search engines with recommendations based on previous searches performed by others in the lab. Most existing recommender systems were developed for commercial domains with millions of users. The research paper domain has relatively few users compared to the large number of online research papers. The two major challenges with this type of data are the large number of dimensions and the sparseness of the data. The novel contribution of the paper is a scalable subspace clustering algorithm (SCuBASCuBA: Subspace Clustering Based Analysis.) that tackles these problems. Both synthetic and benchmark datasets are used to evaluate the clustering algorithm and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers. [ABSTRACT FROM AUTHOR]

Office workers tend to produce paper piles of documents to read or to process sometime later. The information contained in these piles is often lost if it is not transferred to electronic format and connected to knowledge structures. Information that is not part of the knowledge worker's electronic information space is frequently overlooked because it is not proactively provided during actual processes or tasks he is involved in. This paper presents a novel prototype for an intelligent office appliance, which results from an integration of three state-of-the-art office applications/appliances: a workflow system, a document classification system, and a multi-functional peripheral. The resulting system allows for leveraging an office worker's papers to her personal knowledge space in order to realize a pro-active and context-sensitive information support within knowledge-intensive tasks and processes. [ABSTRACT FROM AUTHOR]

To facilitate the transition from paper-based patient record (PPR) to computer-based patient record (CPR), engineers should try to improve the usability of CPR system. From the point of view of a physician, we reviewed PPRs written by 8 Japanese physicians. We revealed the characteristics of PPR to find out about the barrier to transition from PPR to CPR. Our findings may be helpful to the engineers who are aiming to develop a CPR system. [ABSTRACT FROM AUTHOR]

In this paper we describe the design of a hybrid search that combines simple metadata search with a traditional keyword search over unstructured context data. This paradigm provides the inquirer additional options to narrow the search with some semantic aspects through the XML metadata query. We demonstrate a paper search for a case study of the hybrid search, and describe a data integration hub to integrate those data dispersed on the Net. [ABSTRACT FROM AUTHOR]

This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions. [ABSTRACT FROM AUTHOR]

Amongst several fundamental aspects in multi-agent systems design, the definition of the agent interaction space is of the utmost importance. The specification of the agent interaction has several facets: syntax, semantics, and compliance verification. In an open society, heterogenous agents can participate without showing any credentials. Accessing their internals or their knowledge bases is typically impossible, thus it is impossible to prove a priori that agents will indeed behave according to the society rules. Within the SOCS (Societies Of ComputeeS) project, a language based on abductive semantics has been proposed as a mean to define interactions in open societies. The proposed language allows the designer to define open, extensible and not over-constrained protocols. Beside the definition language, a software tool has been developed with the purpose of verifying at execution time if the agents behave correctly with respect to the defined protocols. This paper provides a tutorial overview of the theory and of the tools the SOCS project provided to design, define and test agent interaction protocols. [ABSTRACT FROM AUTHOR]

This paper is based on the tutorial given as part of the tutorial programme of CLIMA-VI. The tutorial aimed at giving an overview of the various features available in Jason, a multi-agent systems development platform that is based on an interpreter for an extended version of AgentSpeak. The BDI architecture is the best known and most studied architecture for cognitive agents, and AgentSpeak is an elegant, logic-based programming language inspired by the BDI architecture. [ABSTRACT FROM AUTHOR]

Privacy-preserving classification mining is one of the fast-growing sub-areas of data mining. How to perturb original data and then build a decision tree based on perturbed data is the key research challenge. By applying transition probability matrix this paper proposes a novel privacy-preserving classification mining algorithm which suits all data types, arbitrary probability distribution of original data, and perturbing all attributes (including label attribute). Experimental results demonstrate that decision tree built using this algorithm on perturbed data has comparable classifying accuracy to decision tree built using un-privacy-preserving algorithm on original data. [ABSTRACT FROM AUTHOR]

This paper presents the experimental aspects of a robotic system used for detection, sorting and grading of paper objects from visual features. The experimental system is used as an automated paper recycling system. The system uses simplified stereo vision to compute position estimate of unknown objects. A vector of geometrical and textural features is used for sorting the paper, according to its grade. The grading process uses additional contact sensors for refining grading from visual features. Supervised learning is used for training the decision system to separate paper from other objects. The experimental aspects of the training process are presented in detail. A special vacuum gripper was designed for gripping paper objects detected by the vision system. The experimental results prove the efficiency of the proposed techniques. [ABSTRACT FROM AUTHOR]

We study the problem of predicting the popularity of items in a dynamic environment in which authors post continuously new items and provide feedback on existing items. This problem can be applied to predict popularity of blog posts, rank photographs in a photo-sharing system, or predict the citations of a scientific article using author information and monitoring the items of interest for a short period of time after their creation. As a case study, we show how to estimate the number of citations for an academic paper using information about past articles written by the same author(s) of the paper. If we use only the citation information over a short period of time, we obtain a predicted value that has a correlation of r = 0.57 with the actual value. This is our baseline prediction. Our best-performing system can improve that prediction by adding features extracted from the past publishing history of its authors, increasing the correlation between the actual and the predicted values to r = 0.81. [ABSTRACT FROM AUTHOR]