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.
457518
Automatic recognition and annotation of gene expression patterns of fly embryos
MOTIVATION
Gene expression patterns obtained by in situ mRNA hybridization provide important information about different genes during Drosophila embryogenesis. So far, annotations of these images are done by manually assigning a subset of anatomy ontology terms to an image. This time-consuming process depends heavily on the consistency of experts.


RESULTS
We develop a system to automatically annotate a fruitfly's embryonic tissue in which a gene has expression. We formulate the task as an image pattern recognition problem. For a new fly embryo image, our system answers two questions: (1) Which stage range does an image belong to? (2) Which annotations should be assigned to an image? We propose to identify the wavelet embryo features by multi-resolution 2D wavelet discrete transform, followed by min-redundancy max-relevance feature selection, which yields optimal distinguishing features for an annotation. We then construct a series of parallel bi-class predictors to solve the multi-objective annotation problem since each image may correspond to multiple annotations.


SUPPLEMENTARY INFORMATION
The complete annotation prediction results are available at: http://www.cs.niu.edu/~jzhou/papers/fruitfly and http://research.janelia.org/peng/proj/fly_embryo_annotation/. The datasets used in experiments will be available upon request to the correspondence author.
17829598
Maya: a Multi-Paradigm Network Modeling Framework
This paper presents Maya, a multi-paradigm, scalableand extensible network modeling framework for emulatingdistributed applications. A novel three-tier architecture isproposed to integrate three disparate modeling paradigms,namely, discrete event models, analytical models and physicalnetwork interfaces into one unified framework of Maya.As the first effort to integrate all three paradigms intoone framework, this paper discusses the implementationsof Maya using Qualnet, fluid flow based TCP model andphysical network interface. It addresses the performanceissues involved in attaining the real time constraints imposedby distributed applications and demonstrates the effectivenessof using analytical models in Maya. Furthermore, it identifies the negative impact on real time performance through the computation intensive ordinary differential equation (ODE) solver in the fluid flow model. A new approach to interleaved executions of the fluid flow model is proposed to hide ODE solver turnaround time. As a result, the percentage of packets missing their deadlines has been reduced from more than 6% to less than 0.2%.
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.
14147992
On the Security of a Multi-party Certified Email Protocol
As a value-added service to deliver important data over the Internet with guaranteed receipt for each successful delivery, certified email has been discussed for years and a number of research papers appeared in the literature. But most of them deal with the two-party scenarios, i.e., there are only one sender and one recipient. In some applications, however, the same certified message may need to be sent to a set of recipients. In ISC’02, Ferrer-Gomila et. al presented a multi-party certified email protocol [5]. It has two major features. A sender could notify multiple recipients of the same information while only those recipients who acknowledged are able to get the information. In addition, its exchange protocol is optimized, which has only three steps. In this paper, we demonstrate some flaws and weaknesses in that protocol, and propose an improved version which is robust against the identified attacks while preserving the features of the original protocol.
26863874
Efficient Signature Validation Based on a New PKI
Digital signatures usually serve as irrefutable cryptographic evidence to support dispute resolution in electronic transactions. Trusted time-stamping and certificate revocation services, although very costly in practice, must be available to prevent big loss due to compromising of the signing key. In this paper, we employ a revocation-free public-key framework to improve the efficiency in maintaining the validity of digital signatures as non-repudiation evidence. The new PKI allows an end user to control the validity of his own public-key certificate and enables certificate verification without retrieving the revocation information from the CA. Based on this new PKI, we could validate generic digital signatures using a TSA only. Moreover, we could validate forward-secure digital signatures without the TTP’s involvement.
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.