GA-based supervised learning of Neocognitron
Supervised learning of Neocognitron is fulfilled by presenting training patterns, which map to specified features. However, the training patterns and many parameters are designed empirically and set manually in Fukushima's Neocognitron. In this paper, we use genetic algorithms (GAs) to tune the parameters of Neocognitron and search its reasonable training pattern sets. First of all, the correlation amongst the training patterns is considered as a critical factor affecting Neocognitron's performance, but it is ignored in the design of the original Neocognitron. Then, a GA-based supervised learning of the Neocognitron is proposed to tune the parameters and search training patterns. The results prove that the performance of a Neocognitron is sensitive to its training patterns, selectivity and receptive fields, and can be improved by this supervised learning on the basis of GAs and correlation analysis.

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: 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: 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: 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: A Distributed Throttling Approach for Handling High Bandwidth Aggregates
abstract: Public-access networks need to handle persistent congestion and overload caused by high bandwidth aggregates that may occur during times of flooding-based DDoS attacks or flash crowds. The often unpredictable nature of these two activities can severely degrade server performance. Legitimate user requests also suffer considerably when traffic from many different sources aggregates inside the network and causes congestion. This paper studies a family of algorithms that "proactively" protect a server from overload by installing rate throttles in a set of upstream routers. Based on an optimal control setting, we propose algorithms that achieve throttling in a distributed and fair manner by taking important performance metrics into consideration, such as minimizing overall load variations. Using ns-2 simulations, we show that our proposed algorithms 1) are highly adaptive by avoiding unnecessary parameter configuration, 2) provide max-min fairness for any number of throttling routers, 3) respond very quickly to network changes, 4) are extremely robust against extrinsic factors beyond the system control, and 5) are stable under given delay bounds.

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: Exploiting Hidden Convexity For Flexible And Robust Resource Allocation In Cellular Networks
abstract: A systematic approach to solve seemingly nonconvex resource allocation problems in wireless cellular networks is studied in this paper. By revealing and exploiting the hidden convexity in the problem formulations, we obtain solutions that can tackle a variety of objective functions, provide robustness to resource allocations such as power, and be obtained often through distributed algorithms. The advantages of such flexibility and robustness are demonstrated through comparisons with the state-of-the-art in recent research literature. First we show how to distributively solve a variety of resource allocation problems in CDMA and interference limited CDMA channels with quality of service constraints, such as meeting minimum queueing delay or energy per bit requirement. Then, for uplink transmission in a CDMA cellular network, we propose an optimal power control scheme with congestion-aware active link protection. In particular, the tradeoff between power expenditure and the protection margin of the SIR-balancing power algorithm is optimized.

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: 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.