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.
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.
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.
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.
45918891
Certified Electronic Mail
This paper examines certified mail delivery in postal systems and derives the essential requirements that may be met by a service called certified electronic mail. Protocols are presented to demonstrate how various flavours of certified electronic mail services may be implemented.
28799262
Cryptanalysis of Chu's DCT based watermarking scheme
In 2003, Chu proposed an oblivious watermarking algorithm by modifying the CKLS scheme proposed by Cox, Kilian, Leighton, and Shamoon in 1997, known as the CKLS scheme. In this correspondence, we report that the modification presented by Chu is susceptible to a suitably modified attack devised by Das and Maitra in 2004. In fact, the experimental results show that Chu's scheme is even weaker than the CKLS scheme in terms of our attack.
15468615
Security Remarks on a Group Signature Scheme with Member Deletion
A group signature scheme allows a group member of a given group to sign messages on behalf of the group in an anonymous and unlinkable fashion. In case of a dispute, however, a designated group manager can reveal the signer of a valid group signature. Based on the Camenisch-Michels group signature scheme [7,8], Kim, Lim and Lee proposed the first group signature scheme with a member deletion procedure at ICISC 2000 [15]. Their scheme is very efficient in both communication and computation aspects. Unfortunately, their scheme is insecure. In this paper, we first identify an effective way that allows any verifier to determine whether two valid group signatures are signed by the same group member. Secondly, we find that in their scheme a deleted group member can still update his signing key and then generate valid group signatures after he was deleted from the group. In other words, the Kim-Lim-Lee group signature scheme [15] is linkable and does not support secure group member deletion.
14293786
QoS analysis for component-based embedded software: Model and methodology
Component-based development (CBD) techniques have been widely used to enhance the productivity and reduce the cost for software systems development. However, applying CBD techniques to embedded software development faces additional challenges. For embedded systems, it is crucial to consider the quality of service (QoS) attributes, such as timeliness, memory limitations, output precision, and battery constraints. Frequently, multiple components implementing the same functionality with different QoS properties (measurements in terms of QoS attributes) can be used to compose a system. Also, software components may have parameters that can be configured to satisfy different QoS requirements. Composition analysis, which is used to determine the most suitable component selections and parameter settings to best satisfy the system QoS requirement, is very important in embedded software development process. In this paper, we present a model and the methodologies to facilitate composition analysis. We define QoS requirements as constraints and objectives. Composition analysis is performed based on the QoS properties and requirements to find solutions (component selections and parameter settings) that can optimize the QoS objectives while satisfying the QoS constraints. We use a multi-objective concept to model the composition analysis problem and use an evolutionary algorithm to determine the Pareto-optimal solutions efficiently.
557343
MAYA: Integrating hybrid network modeling to the physical world
The flourish of large-scale network applications across the Internet and or MANET has raised a challenge to network modeling environments that support experimentation and analysis of close interactions between real applications and network dynamics. To facilitate such experimentations, this paper presents MAYA, a multiparadigm network modeling framework including discrete event models, analytical models and physical network interfaces, together with its illustrative implementation using QualNet, fluid flow TCP model and physical network interface. MAYA framework allows users to interface simulated networks directly with physical networks, while attaining real-time constraints even for large-scale networks by incorporating above multiparadigm network modeling techniques. It also gives user the flexibility to emulate applications on nodes in both real and simulated networks. Experiments are conducted to validate the interoperation of QualNet and fluid flow model, to examine the performance of MAYA as well as to evaluate the optimization techniques, namely interleaved execution of fluid flow model and causality-preserve realtime synchronization relaxation. Experimental results indicate that MAYA is a scalable and extensible solution to modeling of close interactions between real application and network dynamics.