Forecasting and Analysis of Marketing Data Using Neural Networks
This study aims to incorporate Artificial Neural Networks into a Marketing Decision Support System (MDSS), specifically, by discovering important variables that influence sales performance of colour television (CTV) sets in the Singapore market using neural networks. Three kinds of variables, expert knowledge, marketing information and environmental data, are examined. The information about the effects of each of these variables has been studied and made available for decision making. However, their combined effect is unknown. This study attempts to explore the combined effect for the benefit of our collaborator, a multinational corporation (MNC) in the consumer electronics industry in Singapore. Putting these three variables together as input variables results in a neural network model. Neural network training is conducted using historical data on CTV sales in Singapore collected over the past one and a half years. Sensitivity analysis is then performed to reduce input variables of neural networks. This is done by analyzing the weights of the input node connections in the trained neural networks using two different methods. The weaker variables can be excluded, and this results in a simpler model. Further, an R-Square value of almost 1 is obtained through the inclusion of an Unknown variable when the network model consisting only of the most influential variables is trained and tested. Knowing the most influential variables, which in this case include Average Price, Screen Size, Stereo Systems, Flat-Square screen type and Seasonal Factors, marketing managers can improve sales performance by paying more attention to them.

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: 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: 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: 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: Comparison-Shopping Websites: An Empirical Investigation on the Influence of Decision Aids and Information Load on Consumer Decision-Making Behavior
abstract: Rapid advances in Internet commerce technology have brought about the emergence of comparison-shopping Websites, which act as agents to consolidate vast amounts of product information. Researchers have traditionally focused on incorporating the latest database interrogation technology and investigating the economic implications of the existence of comparison-shopping Websites. Little attention has been paid to understanding whether the decision aids provided are capable of assisting a consumer in managing a large quantity of information or, more importantly, are compatible with the decision-making behavior of the consumer. This study surveys several successful comparison-shopping Websites, enumerates two comercially successful forms of decision aids (i.e., screening and sorting), and examines their effects on decision effectiveness under different information loads. Based on a 3×2×2 factorial controlled experiment, we had three major findings. First, more sophisticated screening aids that assist a consumer in filtering a large quantity of information do not necessarily improve decision effectiveness. Surprisingly, our results even suggest that in some circumstances the opposite could be true. Second, the results indicate that the effectiveness of decision aids, to a large extent, depends on the information load. Third, decision makers are adaptive. One may supplement the absence of more sophisticated screening aids with the sorting aid.

title: An Investigation of Online Group-Buying Institution and Buyer Behavior
abstract: This study draws on interdependency theory to propose and assess the effects of two artifacts - conditional purchase and information cue - on decisional choice in the online group-buying context. Our results suggest that buyers presented with conditional purchase options (i.e., of not honoring the purchase when the expected discount price is not met) are more willing to make a purchase choice (i.e., deviate from an inactive inertia). Counter-intuitively, provision of information cues (i.e., information about procurement actions of the other buyers) appears to induce inaction. However, when a choice has to be made between a riskier and a less risky choice, the presence of information cues leads to a higher propensity for the riskier choice. Implications for theory and practice are further discussed.

title: Public-Key Encryption from ID-Based Encryption Without One-Time Signature
abstract: Design a secure public key encryption scheme and its security proof are one of the main interests in cryptography In 2004, Canetti, Halevi and Katz  constructed a public key encryption (PKE) from a selective identity-based encryption scheme with a strong one-time signature scheme In 2005, Boneh and Katz  improved Canetti-Halevi-Katz construction by replacing a strong one-time signature with a message authentication code, but it is not publicly verifiable Later, Boyen, Mei and Waters  constructed PKE scheme directly from Waters' IBE scheme  , which is only secure against direct chosen-ciphertext attack and is not secure against adaptive chosen-ciphertext attack In 2006,  further improved the efficiency of Canetti-Halevi-Katz (CHK) construction by directly from Boneh-Boyen identity based encryption (IBE) scheme  with a weak one-time signature In this paper, we construct an efficient public key encryption scheme without one-time signature, which preserves a publicly verifiable property and secure against adaptive chosen-ciphertext attack The construction of the proposed scheme is based on Boneh-Boyen identity-based encryption (IBE) scheme  and a trapdoor function We also show that the proposed scheme is more efficient than CHK construction.

title: A secure signature scheme
abstract: Digital signature is commonly used for authentication. So, it is important to design a signature with a security proof. In 1999, Gennaro et al. and Cramer et al. respectively proposed practical and provably secure signature schemes under the standard assumption without the random oracle model. Since then, some provably secure signature schemes in the standard model were constructed, for example, Camenisch-Lysyanskaya scheme in 2002; Fischlin scheme and Tan-Yi-Siew scheme in 2003. In this paper, we construct a new provably secure signature scheme based on the strong RSA assumption against existential forgery under adaptive chosen message attack in the standard model. The proposed scheme is also more efficient than other provable secure schemes in the standard model.

title: A new electronic cash model
abstract: Credit card is currently the most popular electronic payment method on the Internet, but it is not an ideal method from the security and privacy point of view. Electronic cash solves many inherent problems of credit card, such as anonymity and off-line payment, and is widely accepted as an ideal electronic payment method. However, there are issues that are yet to be satisfactorily solved, such as fair traceability and double-spending prevention of off-line schemes. In this paper, we present a customer generated electronic cash model that offers unique trade-off between credit card and traditional off-line electronic cash systems. This model provides anonymity for customers and is technically more secure than credit card systems. It eliminates the withdrawal phase of electronic cash systems, thus it is more convenient for customers to use. In addition, fair traceability is provided in case crimes take place.