Document clustering based on cluster validation
This paper presents a cluster validation based document clustering algorithm, which is capable of identifying both important feature words and true model order (cluster number). Important feature subset is selected by optimizing a cluster validity criterion subject to some constraint. For achieving model order identification capability, this feature selection procedure is conducted for each possible value of cluster number. The feature subset and cluster number which maximize the cluster validity criterion are chosen as our answer. We have applied our algorithm to several datasets from 20Newsgroup corpus. Experimental results show that our algorithm can find important feature subset, estimate the model order and yield higher micro-averaged precision than other four document clustering algorithms which require cluster number to be provided.

title: Correcting document image warping based on regression of curved text lines
abstract: Image warping is a common problem when one scans or photocopies a document page from a thick bound volume, resulting in shading and curved text lines in the spine area of the bound volume. This will not only impair readability, but will also reduce the OCR accuracy. Further to our earlier attempt to correct such images, this paper proposes a simpler connected component analysis and regression technique. Compared to our earlier method, the present system is computationally less expensive and is resolution independent too. The implementation of the new system and improvement of OCR accuracy are presented in this paper.

title: Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information
abstract: In this paper we focus on how to improve pronoun resolution using the statistics-based semantic compatibility information. We investigate two unexplored issues that influence the effectiveness of such information: statistics source and learning framework. Specifically, we for the first time propose to utilize the web and the twin-candidate model, in addition to the previous combination of the corpus and the single-candidate model, to compute and apply the semantic information. Our study shows that the semantic compatibility obtained from the web can be effectively incorporated in the twin-candidate learning model and significantly improve the resolution of neutral pronouns.

title: Chart analysis and recognition in document images
abstract: Hidden Markov models are a probabilistic modeling tool for time series data. It has been successfully applied to many areas, such as speech recognition, hand-written character recognition, etc. In this paper, we present a novel statistical approach using ergodic hidden Markov models to recognize scientific charts. We also present a newly developed feature extraction method for chart images. Unlike traditional primitive-based diagram recognition method, our approach need not recognize the graphic primitives in charts thus bypassing the recognition error problem caused by the inaccurate primitive extraction that is also a major obstacle to the construction of a general chart recognition system.

title: Document Flattening through Grid Modeling and Regularization
abstract: For document images captured by a digital camera, perspective and geometric distortions make it hard to recognize the document content properly. In this paper, we propose an integrated document restoration technique, which is capable of removing perspective and geometric distortions, and producing a flattened and fronto-parallel text image that is friendly to the generic OCR systems. The proposed document restoration is accomplished through grid modeling, which divides camera images into multiple quadrilateral grids using vertical text directions and the x lines and base lines. The global distortions are then removed through grid regularization that transforms the quadrilateral grids together with the pixel contents to the regular square grids. Experimental results show the proposed method is fast and easy for implementation

title: Motion deblurring for optical character recognition
abstract: This paper investigates the problem of blurring caused by motion during image capture of text documents. Motion blurring prevents proper optical character recognition of the document text contents. One area of such applications is to deblur name card images obtained from handheld cameras. In this paper, a complete motion deblurring procedure for document images has been proposed. The method handles both uniform linear motion blur and uniform acceleration motion blur. Experiments on synthetic and real-life blurred images prove the feasibility and reliability of this algorithm provided that the motion is not too irregular. The restoration procedure consumes only small amount of computation time.

title: Signature Schemes Based on 3rd Order Shift Registers
abstract: In this paper, we propose two digital signature schemes based on a third order linear feedback shift register. One of them is a normal signature scheme for signing document and the other is with encryption for intended reciever. These two signature schemes are different from most of the signature schemes which are based on discrete logarithm problem, elliptic curves discrete logarithm problem, RSA or quadratic residues. The efficient computational algorithm for computing kth term of a sequence is also presented. The advantage of these two schemes is that the computation is carried out in the ground field and not in an extension field. We also show that the security of these two signature schemes is equivalent to that of Schnorr signature scheme and Signed-ElGamal encryption scheme respectively.

title: Authenticated Broadcast Encryption Scheme
abstract: With the advancement of wireless networks and mobile access devices such as cell phones and PDAs, multimedia services could be offered through wireless broadcast networks to these mobile devices. Therefore, there is a need to securely deliver multimedia content/data to many users over an insecure channel. Broadcast encryption scheme could provide this solution. The first formal study of broadcast encryption scheme is by Fiat and Noar in 1993. Since then, many broadcast encryption schemes have been proposed to improve the efficiency which is measured by three parameters, that is, length of transmission messages, users' storage and computational overhead at a user device. Some broadcast encryption schemes require less users storage, but the length of transmission messages or computation overhead depends on the number of users. In this paper, we proposed an authenticated broadcast encryption scheme which has an additional property than current proposed broadcast encryption schemes, that is, all users can verify who the sender is. The proposed scheme is proved to be secure against chosen ciphertext attacks based on the decision l-bilinear Diffie-Hellman exponent assumption. Furthermore, the simulation results, transmission messages nearly independent of the number of users and reasonable user storage show that the proposed authenticated broadcast encryption scheme is efficient and suitable for mobile devices.

title: User Response to Free Trial Restrictions: A Coping Perspective
abstract: Software vendors often provide software for free download but with restrictions (e.g., time and/or functionality restrictions). The question that arises is to what extent the restrictions should be set to induce users to procure the full version. This study seeks to answer this question by looking from two perspectives: expectation-disconfirmation and coping behavior. Building on these perspectives, we present a research model of user's coping reactions toward software restrictions. We seek to understand user reactions (i.e., derivation of coping strategy) when their expectation toward trial restrictions is negatively disconfirmed. It is further posited that situational control could moderate the relationship between expectation disconfirmation and coping responses. We believe this research will contribute to enrich the current IS field and benefit market practitioners.

title: The use of bigrams to enhance text categorization
abstract: In this paper, we present an efficient text categorization algorithm that generates bigrams selectively by looking for ones that have an especially good chance of being useful. The algorithm uses the information gain metric, combined with various frequency thresholds. The bigrams, along with unigrams, are then given as features to two different classifiers: Naive Bayes and maximum entropy. The experimental results suggest that the bigrams can substantially raise the quality of feature sets, showing increases in the break-even points and F1 measures. The McNemar test shows that in most categories the increases are very significant. Upon close examination of the algorithm, we concluded that the algorithm is most successful in correctly classifying more positive documents, but may cause more negative documents to be classified incorrectly.

title: A Human Capital Perspective of Organizational Intention to Adopt Open Source Software
abstract: This study adopts the human capital perspective as a theoretical lens to understand the factors that influence organizational intention to adopt open source software (OSS). We posit that availability of internal human capital and accessibility to external human capital significantly affect an organization’s intention to adopt OSS either directly or indirectly through switching cost. We analyze survey responses from 81 chief information officers or information systems managers and the results indicate that organizational availability of internal human capital has an indirect effect on an organization’s intention to adopt OSS through switching cost whereas accessibility to external human capital has a direct effect on an organization’s intention to adopt OSS. This study has both theoretical and practical implications. Theoretically, the unique approach from the human capital perspective adds to the extant literature on organizational adoption of innovation and calls for more attention to be directed at understanding the influence of human capital in an increasingly technologically complex environment. Practically, our findings suggest that potential OSS adopters and proponents should develop the human capital necessary for effective exploitation of OSS.