Neural Logic Network Learning Using Genetic Programming
Neural Logic Networks or Neulonets are hybrids of neural networks and expert systems capable of representing complex human logic in decision making. Each neulonet is composed of rudimentary net rules which themselves depict a wide variety of fundamental human logic rules. An early methodology employed in neulonet learning for pattern classification involved weight adjustments during back-propagation training which ultimately rendered the net rules incomprehensible. A new technique is now developed that allows the neulonet to learn by composing the net rules using genetic programming without the need to impose weight modifications, thereby maintaining the inherent logic of the net rules. Experimental results are presented to illustrate this new and exciting capability in capturing human decision logic from examples. The extraction and analysis of human logic net rules from an evolved neulonet will be discussed. These extracted net rules will be shown to provide an alternate perspective to the greater extent of knowledge that can be expressed and discovered. Comparisons will also be made to demonstrate the added advantage of using net rules, against the use of standard boolean logic of negation, disjunction and conjunction, in the realm of evolutionary computation.

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: 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: An Empirical Investigation of the Auction Buyer's Choice to Buy Out or Bid: Cry of Regret or Laugh of Satisfaction?
abstract: Online auction buyers often face the predicament of choosing between the buyout strategy and the bidding strategy to acquire an auctioned product. A buyer who chooses to buy out will obtain the item immediately at the posted price while the one who chooses to bid will have to monitor the bidding process in the hope of acquiring the product at a price lower than the posted one. Despite the wealth of auction literature, relatively few studies empirically assess the buyout option. This study seeks to bridge the gap by investigating how the buyout option (permanent or temporary) and price parameters (starting price and buyout price) affect the formation of two emotions, namely anticipated satisfaction of choice and anticipated regret of outcome, which in turn affect the buying strategy (i.e., buy out or bid). We employ laboratory experiments in our study and our results indicate two interesting findings. First, when the permanent buyout option is available, a buyer is more likely to exhibit loss aversion characteristics and prefer to adopt the buyout strategy. Second, when the temporary buyout option is present, the thought that one could avoid premature ending of auction by exercising the bidding option at the start decreases one’s preference for adopting the buyout strategy. Implications of these results for further research and practice are discussed.

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: Functional Principles of Registry-based Service Discovery
abstract: As service discovery protocols (SDP) are becoming increasingly important for ubiquitous computing, they must behave according to predefined principles. We present the functional principles of service discovery for robust, registry-based service discovery. A methodology to guarantee adherence to these principles is provided and illustrated by formal verification of the principles against FRODO, an SDP built for the home environment. We show that, to make behavioral guarantees, an SDP has to be robust against network disturbances, and cannot rely only on the network layer

title: Low-power group key agreement for heterogeneous wireless networks
abstract: Heterogeneous wireless networks are gaining popularity as users can be connected to these networks without any cables and even when they are mobile. The high power nodes in heterogeneous wireless networks do not have energy constraints but the user nodes, which can be large in numbers, are usually low power energy constrained devices. Therefore, the GKA protocol used to secure group communications in these networks has to take into consideration both the low power nature of the user nodes and the network size. In this paper, we present an energy efficient and scalable GKA protocol, which uses our proposed Contributory Ring-Centralized (ContRi-Central) group model. Besides providing complexity analysis, we also show the computational and communication energy consumption costs analysis of all nodes running our proposed scheme and four other efficient GKA protocols. Both the complexity analysis and energy consumption costs analysis indicate that our proposed scheme is more efficient and suitable for heterogeneous wireless networks.

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.