1638969
Verification - a method of enhancing the recognizers of isolated and touching handwritten numerals
Abstract This paper investigates verification schemes and their applications to the recognition of both isolated and touching handwritten numerals. Definitions and functionality analyses of the verifiers are given. The measurement of precision rate is used to assess the system reliability in a class-specific manner. Verification-enhanced systems are proposed with extensive experiments conducted on both isolated and touching numerals. Two databases for touching numerals are built/organized to serve as standard data sets. Experimental results indicate a substantial improvement in system precision rates by the verification scheme, which proves the effectiveness of the proposed systems and justifies the important role of verifiers in OCR systems.
7237236
Unconstrained numeral pair recognition using enhanced error correcting output coding: a holistic approach
This paper describes a new approach to recognize touching numeral strings. Currently most methods for numeral string recognition require segmenting the string image into separate numerals. As a result, the recognition system heavily depends on the reliability of the segmentation module. This study explores the holistic strategy directly on the string images without segmentation. It builds the novel classifier by combining binary classifiers based on data-driven error correcting output coding (DECOC). The dimensions of input images are reduced using principal components analysis. Support vector machines are used as base learners. Experiments on NIST SD19 touching numeral pairs confirm that DECOC can achieve favorable performance compared with other multi-class holistic classifiers. The method provides the flexibility of controlling the computational complexity versus accuracy. We also discuss an implementation suitable for distributing computing by decomposing the ensemble into subtasks.
11171096
Automatic detection of premature ventricular contraction using quantum neural networks
Premature ventricular contractions (PVCs) are ectopic heart beats originating from ventricular area. It is a common form of heart arrhythmia. Electrocardiogram (ECG) recordings have been widely used to assist cardiologists to diagnose the problem. In this paper, we study the automatic detection of PVC using a fuzzy artificial neural network named Quantum Neural Network (QNN). With the quantum neurons in the network, trained QNN can model the levels of uncertainty arising from complex classification problems. This fuzzy feature is expected to enhance the reliability of the algorithm, which is critical for the applications in the biomedical domain. Experiments were conducted on ECG records in the MIT-BIH Arrhythmia Database. Results showed consistently higher or same reliability of QNN on all the available records compared to the backpropagation network. QNN, however, has a relatively higher resource requirement for training.
164269
Recognition of handwritten numerals by Quantum Neural Network with fuzzy features
Abstract. This paper describes a new kind of neural network – Quantum Neural Network (QNN) – and its application to the recognition of handwritten numerals. QNN combines the advantages of neural modelling and fuzzy theoretic principles. Novel experiments have been designed for in-depth studies of applying the QNN to both real data and confusing images synthesized by morphing. Tests on synthesized data examine QNN's fuzzy decision boundary with the intention to illustrate its mechanism and characteristics, while studies on real data prove its great potential as a handwritten numeral classifier and the special role it plays in multi-expert systems. An effective decision-fusion system is proposed and a high reliability of 99.10% has been achieved.
15424385
Data driven approach to designing minimum hamming distance polychotomizer
A polychotomous classifier assigns an observation to one of the K categories with K > = 3. Multiple binary classifiers (K = 2) such as the popular Support Vector Machines can be combined to achieve multi-class classification. Commonly used approaches include the one-vs-others scheme and the one-vs-one (pairwise coupling) scheme. While literature reported better performance from pairwise coupling than one-vs-others, the number of base learners required by pairwise coupling is quadratic in K. Alternatively, error correcting output codes (ECOC) provides a more general framework for designing polychotomizers. It associates each class with a codeword, which provides the capability to unify the traditional schemes. However, the design of an effective "coding matrix" remains an open problem. We study one kind of ECOC polychotomizer that decodes using minimum hamming distance. We propose a novel data-driven way to design the codewords based on inter-cluster distance. It provides a systematic way to extend the traditional schemes and construct effective polychotomizers. Experiments are conducted on synthetic data and real world applications including UCI repository problems and CENPARMI handwritten numerals. Experiments show that the proposed scheme can achieve competitive accuracy compared with both traditional schemes, and the number of base learners is typically much less than the requirement of the pairwise scheme.
1706470
Regularized B-spline network and its application to heart arrhythmia classification
This paper presents an effective learning scheme that combines B-spline modeling and regularized neural networks. Essential issues of structural design and learning process are discussed. Regularization theory is leveraged to design the topological structure of the network. A training algorithm is derived for the learning of both synaptic weights and B-spline coefficients. The approach is then applied to the medical problem of heart arrhythmia detection, particularly the detection of premature ventricular contraction. Promising results demonstrate the potential benefits of the proposed method.
17429132
Rule-base technique for component adaptation to support QoS-based reconfiguration
Many critical application systems require reconfigurability in order to adapt under changing environment. To achieve system reconfigurability, it is possible to use adaptation mechanisms that are external to components or to use parameterized components. This paper focuses on making components reconfigurable in terms of QoS tradeoffs. We propose a new component parameterization technique which can transform individual components to reconfigurable ones such that they can support various QoS tradeoffs to achieve overall system QoS requirements. The parameterized components can be used to develop both statically and dynamically reconfigurable systems. The proposed technique is rule based, which provides flexibility and extensibility to enhance and improve the technique. A tool set has been developed based on the proposed technique to achieve semi-automated component parameterization.
17813636
Model Generalization and Its Implications on Intrusion Detection
To make up for the incompleteness of the known behaviors of a computing resource, model generalization is utilized to infer more behaviors in the behavior model besides the known behaviors. In principle, model generalization can improve the detection rate but may also degrade the detection performance. Therefore, the relation between model generalization and detection performance is critical for intrusion detection. However, most of past research only evaluates the overall efficiency of an intrusion detection technique via detection rate and false alarm/positive rate, rather than the usefulness of model generalization for intrusion detection. In this paper, we try to do such evaluation, and then to find the implications of model generalization on intrusion detection. Within our proposed methodology, model generalization can be achieved in three levels. In this paper, we evaluate the first level model generalization. The experimental results show that the first level model generalization is useful mostly to enhance the detection performance of intrusion detection. However, its implications for intrusion detection are different with respect to different detection techniques. Our studies show that in general, though it is useful to generalize the normal behavior model so that more normal behaviors can be identified as such, the same is not advisable for the intrusive behavior model. Therefore, the intrusion signatures should be built compactly without first level generalization.
17932719
New constructions of fuzzy identity-based encryption
In this paper we construct two new fuzzy identity-based encryption (IBE) schemes in the random oracle model. Not only do our schemes provide public parameters whose size is independent of the number of attributes in each identity (used as public key) but they also have useful structures which result in more efficient key extraction and/or encryption than the random oracle version of Sahai and Water's fuzzy IBE scheme, considered recently by Pirretti et al. We prove that the confidentiality of the proposed schemes is relative to the Bilinear Decisional Bilinear Diffie-Hellman problem.
12746600
An Evenhanded Certified Email System for Contract Signing
Certified email is a system which enables a sender to prove a receiver’s receipt of email. Such a system can be used for applications related to electronic commerce on the Internet. This paper considers a situation where a sender or a receiver wants to change his/her mind due to the change of mail content value (e.g., stock, auction, gambling) during the transaction. We point out that no traditional certified email systems have been designed for such a case, thus one of the participants can be at a disadvantage. To avoid this problem, we propose an evenhanded certified email system in which each participant can change his/her choice, either cancel or finish the transaction, at any time during the transaction.
16665035
Analysis of a free roaming agent result-truncation defense scheme
Mobile agents play an important role in electronic commerce. Security in free-roaming agents is especially hard to achieve when the mobile code is executed in hosts that may behave maliciously. Some schemes have been proposed to protect agent data (or computation results). However, a known vulnerability of these techniques is the truncation attack where two visited hosts (or one revisited host) can collude to discard the partial results collected between their respective visits. Cheng and Wei proposed a scheme in IC1CS'O2 to defense against the truncation of computation results of free-roaming agents (Cheng and Wei, 2002). Cheng-Wei scheme is effective against such an attack in most cases. However, we demonstrate that it still suffers from the truncation attack when a special loop is established on the path of a free-roaming agent. We further propose two amendments to Cheng-Wei scheme to avoid such an attack.