15019092
Proposing a New Term Weighting Scheme for Text Categorization
In text categorization, term weighting methods assign appropriate weights to the terms to improve the classification performance. In this study, we propose an effective term weighting scheme, i.e. tf.rf, and investigate several widely-used unsupervised and supervised term weighting methods on two popular data collections in combination with SVM and kNN algorithms. From our controlled experimental results, not all supervised term weighting methods have a consistent superiority over unsupervised term weighting methods. Specifically, the three supervised methods based on the information theory, i.e. tf.χ2, tf.ig and tf.or, perform rather poorly in all experiments. On the other hand, our proposed tf.rf achieves the best performance consistently and outperforms other methods substantially and significantly. The popularly-used tf.idf method has not shown a uniformly good performance with respect to different data corpora.
38236882
Chart analysis and recognition in document images
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.
2536406
Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information
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.
15710341
Motion deblurring for optical character recognition
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.
16665932
Document Flattening through Grid Modeling and Regularization
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
14312797
Correcting document image warping based on regression of curved text lines
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.
29337903
User Response to Free Trial Restrictions: A Coping Perspective
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.
34324862
Signature Schemes Based on 3rd Order Shift Registers
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.
5965390
The use of bigrams to enhance text categorization
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.
1033402
Energy-efficient ID-based group key agreement protocols for wireless networks
One useful application of wireless networks is for secure group communication, which can be achieved by running a group key agreement (GKA) protocol. One well-known method of providing authentication in GKA protocols is through the use of digital signatures. Traditional certificate-based signature schemes require users to receive and verify digital certificates before verifying the signatures but this process is not required in ID-based signature schemes. In this paper, we present an energy-efficient ID-based authenticated GKA protocol and four energy-efficient ID-based authenticated dynamic protocols, namely join, leave, merge and partition protocol, to handle dynamic group membership events, which are frequent in wireless networks. We provide complexity and energy cost analysis of our protocols and show that our protocols are more energy-efficient and suitable for wireless networks.
32509548
Chosen Ciphertext Security from Identity-Based Encryption Without Strong Condition
Recently, Canetti et al [11] gave a generic construction (called CHK construction) of public key encryption (PKE) from a selective identity-based encryption scheme combined with a strong one-time signature scheme. Later, few schemes were proposed to improve the efficiency of CHK construction [11], for example, Boneh-Katz scheme [8] replaced a strong one-time signature with a message authentication code and Boyen-Mei-Waters scheme [9] was constructed directly from Waters' IBE scheme. But, both constructions have either trade-off the publicly verifiable property or security against adaptive chosen-ciphertext attack. We ask a question whether it is possible to construct an efficient and publicly verifiable PKE scheme from a selective IBE scheme with a weak one-time signature scheme. In this paper, we provide an affirmative answer and construct a public key encryption scheme which preserves the publicly verifiable property and is secure against adaptive chosen-ciphertext attack. The construction of the proposed scheme is based on Boneh-Boyen identity-based encryption (IBE) scheme [5] and a weak one-time signature scheme (using Waters' signature scheme [24]) built within Boneh-Boyen IBE scheme. In this construction, one-time signature scheme is not required to be strongly existential unforgeable as Waters' signature scheme is not a strongly existential unforgeability. We also show that the proposed scheme is ”almost” as efficient as the original Boneh-Boyen IBE scheme.