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This paper presents the results from a tertiary entrance test that was delivered to two groups of candidates, one as a paper based test and the other as a computer based test. Item level differential reveals a pattern that appears related to item type: questions based on diagrammatic stimulus show a pattern of increased difficulty when delivered on computer. Differential in performance was not present in other sections of the test and it would appear unlikely to be explained by demographic differences between the groups. It is suggested this differential is due to the inability of the candidates to freely annotate on the stimulus when delivered on computer screen. More work needs to be done on considering the role of annotation as a problem solving strategy in high-stakes testing, in particular with certain kinds of stimulus, such as diagrams.

The purpose of paper wrapping is to protect goods from external shocks, decorate goods beautifully, carry out materials/goods safely, etc. Also, the paper wrapping is intelligent and creative work. The knowledge about paper wrapping is dependent on the features of target-objects, paper sheets and wrapping purposes. This article addresses a method to design the wrapping process. We introduce the knowledge about paper wrapping and then construct a stage tree, which represents various kinds of wrapping means successfully. We propose a framework for designing the wrapping process appropriate to target-objects, and also describe an interactive support interface in the wrapping process.

This position paper presents the main problems in classical and modern Fuzzy Theory and gives solutions in Formal Concept Analysis for many of these problems. To support the successful cooperation between scientists from the communities of Fuzzy Theory and Formal Concept Analysis the author starts with this position paper an initiative, called "Pragmatics in Fuzzy Theory".

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.

SVM is a new general learning method based on the statistic learning system which can be used as an effective means to process small sample, nonlinear and high dimensional pattern recognition. This paper did research on the learning algorithm of support vector machine, extracted characteristic data of banknote which is on account of PCA according to the characteristics of the support vector machine (SVM), and proposed to put support vector machine (SVM) into banknotes denomination recognition by combining SMO training algorithm with one-to-many multi-value classification algorithm. Besides, this article used genetic algorithm in parameters optimization such as the punishment coefficient C of soft margin SVM and the width parameter of Gaussian kernel function. The ultimate purpose is to recognize the denomination of banknote efficiently and accurately. The experimental results verified that this kind of recognition method increases the recognition accuracy up to 90% or more.

Automatic test paper generation is highly helpful in teaching and learning. In order to generate a test paper that covers as many knowledge points as possible, it is needed to discover knowledge points from exam questions. However, the problem of automatically finding knowledge points is seldom investigated in existing work. To fill this gap, this paper proposes an ontology-based method to discover knowledge points from mathematical exam questions. Accordingly, a system for automatically generating mathematical test papers is also proposed. It composes a test paper by solving a pseudo-Boolean optimization problem. Its practicality is demonstrated by a task of generating mathematical test papers from hundreds of postgraduate entrance exam questions.

This paper analyzes what stylistic characteristics differentiate different styles of writing, and specifically types of different A-level computer science articles. To do so, we compared various full papers using stylistic feature sets and a supervised machine learning method. We report on the success of this approach in identifying papers from the last 6 years of the following three conferences: SIGIR, ACL, and AAMAS. This approach achieves high accuracy results of 95.86%, 97.04%, 93.22%, and 92.14% for the following four classification experiments: (1) SIGIR / ACL, (2) SIGIR / AAMAS, (3) ACL / AAMAS, and (4) SIGIR / ACL / AAMAS, respectively. The Part of Speech (PoS) and the Orthographic sets were superior to all others and have been found as key components in different types of writing.

The goal of this research is to design a multi-label classification model which determines the research topics of a given technical paper. Based on the idea that papers are well organized and some parts of papers are more important than others for text classification, segments such as title, abstract, introduction and conclusion are intensively used in text representation. In addition, new features called Title Bi-Gram and Title SigNoun are used to improve the performance. The results of the experiments indicate that feature selection based on text segmentation and these two features are effective. Furthermore, we proposed a new model for text classification based on the structure of papers, called Back-off model, which achieves 60.45% Exact Match Ratio and 68.75% F-measure. It was also shown that Back-off model outperformed two existing methods, ML-kNN and Binary Approach.

In this paper we describe a machine vision approach to recognize the denomination classes of the Mexican paper currency by extracting their color features. A banknote's color is characterized by summing all the color vectors of the image's pixels to obtain a resultant vector, the banknote's denomination is classified by knowing the orientation of the resulting vector within the RGB space. In order to obtain a more precise characterization of paper currency, the less discriminative colors of each denomination are eliminated from the images; the color selection is applied in the RGB and HSV spaces, separately. Experimental results with the current Mexican banknotes are presented.

With the increasing of the amount of the scientific papers, it is very important and difficult for paper-sharing platforms to recommend related papers accurately for users. This paper tackles the problem by proposing a method that models user historical behavior. Through collecting the operations on scientific papers of online users and carrying on the detailed analysis, we build preference model for each user. The personalized recommendation model is constructed based on content-based filtering model and statistical language model.. Experimental results show that users’ historical behavior plays an important role in user preference modeling and the proposed method improves the final predication performance in the field of technical papers recommendation.

This paper presents paper retrieval using the specific paper features chain and laid lines. Paper features are detected in digitized paper images and they are represented such that they could be used for retrieval. Optimal retrieval performance is achieved by means of a trainable similarity measure for a given set of paper features. By means of these methods a retrieval system is developed that art experts could use real-time in order to speed up their paper research.

We report on advances in deep linguistic parsing of the full textual content of 8200 papers from the ACL Anthology, a collection of electronically available scientific papers in the fields of Computational Linguistics and Language Technology. We describe how - by incorporating new techniques - we increase both speed and robustness of deep analysis, specifically on long sentences where deep parsing often failed in former approaches. With the current open source HPSG (Head-driven phrase structure grammar) for English (ERG), we obtain deep parses for more than 85% of the sentences in the 1.5 million sentences corpus, while the former approaches achieved only approx. 65% coverage. The resulting sentence-wise semantic representations are used in the Scientist's Workbench, a platform demonstrating the use and benefit of natural language processing (NLP) to support scientists or other knowledge workers in fast and better access to digital document content. With the generated NLP annotations, we are able to implement important, novel applications such as robust semantic search, citation classification, and (in the future) question answering and definition exploration.

This paper presents COMPENDIUM, a text summarization system, which has achieved good results in extractive summarization. Therefore, our main goal in this research is to extend it, suggesting a new approach for generating abstractive-oriented summaries of research papers. We conduct a preliminary analysis where we compare the extractive version of COMPENDIUM (COMPENDIUME) with the new abstractiveoriented approach (COMPENDIUME-A). The final summaries are evaluated according to three criteria (content, topic, and user satisfaction) and, from the results obtained, we can conclude that the use of COMPENDIUM is appropriate for producing summaries of research papers automatically, going beyond the simple selection of sentences.

In this paper, we report recent work of the data-embedding pen, which adds an ink-dot sequence along a hand written pattern during writing. The ink-dot sequence represents some information, such as writer's name, date of writing, and URL. This information drastically increases the value of hand writing on a paper. The embedded information can be extracted from the hand written pattern by image processing techniques and a stroke recovery technique. Consequently, we can augment the hand written pattern by the data-embedding pen to carry arbitrary information.

Online Test Paper Generation (Online-TPG) is a promising approach for Web-based testing and intelligent tutoring. It generates a test paper automatically online according to user specification based on multiple assessment criteria, and the generated test paper can then be attempted over the Web by user for self-assessment. Online-TPG is challenging as it is a multi-objective optimization problem on constraint satisfaction that is NP-hard, and it is also required to satisfy the online runtime requirement. The current techniques such as dynamic programming, tabu search, swarm intelligence and biologically inspired algorithms are ineffective for Online-TPG as these techniques generally require long runtime for generating good quality test papers. In this paper, we propose an efficient approach, called DAC-TS, which is based on the principle of constraint-based divide-and-conquer (DAC) and tabu search (TS) for constraint decomposition and multi-objective optimization for Online-TPG. Our empirical performance results have shown that the proposed DAC-TS approach has outperformed other techniques in terms of runtime and paper quality.

Conference management that requires proper coordination and international communication, is a complex task. There are many Conference Management Systems (CMS), which can be used to carry out the conference. A convenient to use, free and effective module for automatic paper-reviewer assignment is still not available. Searching for the best assignments relying only on common paper-reviewer topics not always will give good solutions. This paper proposes an approach, that uses the reviewers responses information, to tune the solution up. Proposed algorithm combines genetic algorithm (GA) and ant colony optimization (ACO), to quickly find good solutions. The experiment results confirm the superiority of proposed algorithm.

It is important for researchers/investigators to read and understand scientific papers effectually and effectively. However, it takes much time and many efforts to read and understand many papers related directly to their researches, even if they could refer to necessary papers timely. In this paper, we address a function for supporting the scientific paper understanding process successfully. We focus on figures which can usually explain the important topics along a series of successive paragraphs, and develop an intellectual tool which collects the mutually related sentences, attended interdependently to the focused figure, and supports a paper understanding ability through the focused figure. In this paper, we introduce the propagation mechanism of important words over the corresponding sentences. This propagation mechanism can select candidate sentences appropriate to explain the focused figure.

International audience; All modellers have come across, one day, one of these popular toy agent-based models (ABMs), like "Ants", for instance, which depicts the appearance of pheromone trails built by simulated ants. They are simple, but representative of the way "real", more complex, ABMs are designed: in addition to explicitly describe the individual entities used to represent the system, modellers make implicit references to abstractions corresponding to the emerging structures they are tracking in the simulations. Yet, these abstractions are not represented in the models themselves as first-class entities: they are either hidden in ex-post computations or only part of visualization tasks, as if an explicit representation could somehow damage the processes at work in their emergence. This clearly constitutes an obstacle to the development of multi-level models, where emergence is likely to occur at different levels of abstraction of the system: if some of these levels are not represented in the models, the emergence of higher-level structures is not likely to be observed. This paper describes a modelling language that allows a modeller to represent and specify emerging structures in agent-based models. Firstly, to ease the description, we present these structures and their properties in four toy ABMs: Schelling, Boids, Collective Sort and Ants. Then we define the operations that are needed to represent and specify them without sacrificing the properties of the original model. An implementation of these operations in the GAML modelling language (part of the GAMA agent-based platform) is then presented. Finally, two simulations of the Boids model are used to illustrate the expressivity of this language and the multiple advantages it brings in terms of analysis, visualization and modeling of multi-level ABMs.

Most printed material is produced by printing halftone dot patterns. One of the key issues that determine the attainable print quality is the structure of the paper surface but the relation is non-deterministic in nature. We examine the halftone print quality and study the statistical dependence between the defects in printed dots and the topography measurement of the unprinted paper. The work concerns SC paper samples printed by an IGT gravure test printer. We have small-scale 2D measurements of the unprinted paper surface topography and the reflectance of the print result. The measurements before and after printing are aligned with subpixel resolution and individual printed dots are detected. First, the quality of the printed dots is studied using Self Organizing Map and clustering and the properties of the corresponding areas in the unprinted topography are examined. The printed dots are divided into high and low print quality. Features from the unprinted paper surface topography are then used to classify the corresponding paper areas using Support Vector Machine classification. The results show that the topography of the paper can explain some of the print defects. However, there are many other factors that affect the print quality and the topography alone is not adequate to predict the print quality.

This paper is concerned with data mining techniques for identifying the main parameters of the printing press, the printing process and paper affecting the occurrence of paper web breaks in a pressroom. Two approaches are explored. The first one treats the problem as a task of data classification into "break " and "non break " classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The search process results in a set of input variables providing the lowest average loss incurred in taking decisions. The second approach, also based on genetic search, combines procedures of input variable selection and data mapping into a low dimensional space. The tests have shown that the web tension parameters are amongst the most important ones. It was also found that, provided the basic off-line paper parameters are in an acceptable range, the paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the off-line ones. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of the break cases was equal to 76.7%.