11509547
Illumination invariant face recognition based on neural network ensemble
An illumination invariant face recognition method based on neural network ensemble architecture is proposed. Given a face image with an arbitrary illumination direction, it can complete recognition in a uniform way with high performance without knowing or estimating the illumination direction. Experimental result shows that the recognition ratio of the ensemble architecture is higher than the conventional approach that uses a single neural network to recognize faces of a specific illumination direction.
14135016
A Study on Illumination Invariant Face Recognition Methods Based on Multiple Eigenspaces
This paper presents two multiple illumination eigenspaces-based methods, RDEB and BPNNB, for solving the variable illumination problem of face recognition. The experiment shows that the methods have a high recognition ratio. In particular, BPNNB has outperformed the assumptive method which knows the illumination directions of faces and completes recognition in the specific eigenspace using eigenface method[2] for each face subset with a specific illumination direction.
11698669
Modeling and verifying multi-agent behaviors using predicate/transition nets
In a multi-agent system, how agents accomplish a goal task is usually specified by multi-agent plans built from basic actions (e.g. operators) of which the agents are capable. A critical problem with such an approach is how can the designer make sure the plans are reliable. To tackle this problem, this paper presents a formal approach for modeling and analyzing multi-agent behaviors using Predicate/Transition (PrT) nets, a high-level formalism of Petri nets. We construct a multi-agent model by representing agent capabilities as transitions. To verify a multi-agent PrT model, we adapt the planning graphs as a compact structure for the reachability analysis. We also demonstrate that, based on the PrT model, whether parallel actions specified in multi-agent plans can be executed in parallel and whether the plans guarantee the achievement of the goal can be verified by analyzing the dependency relations among the transitions.
41800471
Ensuring Consistent Use/Misuse Case Decomposition for Secure Systems
11130686
An Aspect-Oriented Approach to Security Requirements Analysis
This paper presents an aspect-oriented approach to integrated elicitation of functional and security requirements based on use case-driven development. We identify security threats with respect to use cases and adopt threat mitigations for preventing or reducing security threats. To capture crosscutting nature of threats and mitigations, we specify them as aspects that encapsulate pointcuts and advice. A threat (mitigation) pointcut is a collection of join points in use cases at which the use cases are threatened (secured); whereas threat/mitigation advice describes how a threat can become an attack (can be mitigated). Eliciting threats and mitigations as aspects provides a structured way for separating functional and security concerns
14562584
Integrating functional and security requirements with use case decomposition
Misuse case modeling is a viable option to depict the security requirements together with functional requirements. To investigate the interplay between functional and security requirements, this paper presents an approach to decomposing use cases, misuse cases, and mitigation use cases. We identify relationships among decomposed cases for each case type (use, misuse, mitigation use) and ensure consistency among the cases as decomposition occurs by properly modeling shared and optional cases. We also assign applicable actors to the decomposed cases. Decomposition is conducted for each case type independently and then integrated with the "threatens" and "mitigates" relationships. We provide processes for the proper use of the "threatens" relationship between misuse cases and use cases and the "mitigates" relationship between mitigation use cases and misuse cases at different levels of abstraction. Thus, a complete set of security-centric requirements can be specified from the project outset to guide subsequent software development phases
14141173
Distributed network simulations using the dynamic simulation backplane
Presents an approach for creating distributed, component-based simulations of communication networks by interconnecting models of sub-networks drawn from different network simulation packages. This approach supports the rapid construction of simulations for large networks by reusing existing models and software, and fast execution using parallel discrete event simulation techniques. A dynamic simulation backplane is proposed that provides a common format and protocol for message exchange, and services for transmitting data and synchronizing heterogeneous network simulation engines. In order to achieve plug-and-play interoperability, the backplane uses existing network communication standards and dynamically negotiates among the participant simulators to define a minimal subset of required information that each simulator must supply, as well as other optional information. The backplane then automatically creates a message format that can be understood by all participating simulators and dynamically creates the content of each message by using callbacks to the simulation engines. We describe our approach to interoperability as well as an implementation of the backplane. We present results that demonstrate the proper operation of the backplane by distributing a network simulation between two different simulation packages, ns2 and GloMoSim. Performance results show that the overhead for the creation of the dynamic messages is minimal. Although this work is specific to network simulations, we believe our methodology and approach can be used to achieve interoperability in other distributed computing applications as well.
15674460
A two-stage logarithmic goal programming method for generating weights from interval comparison matrices
A two-stage logarithmic goal programming (TLGP) method is proposed to generate weights from interval comparison matrices, which can be either consistent or inconsistent. The first stage is devised to minimize the inconsistency of interval comparison matrices and the second stage is developed to generate priorities under the condition of minimal inconsistency. The weights are assumed to be multiplicative rather than additive. In the case of hierarchical structures, a nonlinear programming method is used to aggregate local interval weights into global interval weights. A simple yet practical preference ranking method is investigated to compare the interval weights of criteria or rank alternatives in a multiplicative aggregation process. The proposed TLGP is also applicable to fuzzy comparison matrices when they are transformed into interval comparison matrices using @a-level sets and the extension principle. Six numerical examples including a group decision analysis problem with a group of comparison matrices, a hierarchical decision problem and a fuzzy decision problem using fuzzy comparison matrix are examined to show the applications of the proposed methods. Comparisons with other existing procedures are made whenever possible.
14971297
Non-uniform Differential Mesh Deformation
In this paper, we propose a novel mesh deformation approach via manipulating differential properties non-uniformly. Guided by user-specified material properties, our method can deform the surface mesh in a non-uniform way while previous deformation techniques are mainly designed for uniform materials. The non-uniform deformation is achieved by material-dependent gradient field manipulation and Poisson-based reconstruction. Comparing with previous material-oblivious deformation techniques, our method supplies finer control of the deformation process and can generate more realistic results. We propose a novel detail representation that transforms geometric details between successive surface levels as a combination of dihedral angles and barycentric coordinates. This detail representation is similarity-invariant and fully compatible with material properties. Based on these two methods, we implement a multiresolution deformation tool, which allows the user to edit a mesh inside a hierarchy in a material-aware manner. We demonstrate the effectiveness and robustness of our methods by several examples with real-world data.
887820
A computational method for assessing peptide-identification reliability in tandem mass spectrometry analysis with SEQUEST
High throughput protein identification in mass spectrometry is predominantly achieved by first identifying tryptic peptides using SEQUEST and then by combining the peptide hits for protein identification. Peptide identification is typically carried out by selecting SEQUEST hits above a specified threshold, the value of which is typically chosen empirically in an attempt to separate true identifications from the false ones. These SEQUEST scores are not normalized with respect to the composition, length and other parameters of the peptides. Furthermore, there is no rigorous reliability estimate assigned to the protein identifications derived from these scores. Hence the interpretation of SEQUEST hits generally requires human involvement, making it difficult to scale up the identification process for genome-scale applications. To overcome these limitations, we have developed a method, which combines a neural network and a statistical model, for "normalizing" SEQUEST scores, and also for providing a reliability estimate for each SEQUEST hit. This method improves the sensitivity and specificity of peptide identification compared to the standard filtering procedure used in the SEQUEST package, and provides a basis for estimating the reliability of protein identifications.
1250480
Understanding protein dispensability through machine-learning analysis of high-throughput data
MOTIVATION
Protein dispensability is fundamental to the understanding of gene function and evolution. Recent advances in generating high-throughput data such as genomic sequence data, protein-protein interaction data, gene-expression data and growth-rate data of mutants allow us to investigate protein dispensability systematically at the genome scale.


RESULTS
In our studies, protein dispensability is represented as a fitness score that is measured by the growth rate of gene-deletion mutants. By the analyses of high-throughput data in yeast Saccharomyces cerevisiae, we found that a protein's dispensability had significant correlations with its evolutionary rate and duplication rate, as well as its connectivity in protein-protein interaction network and gene-expression correlation network. Neural network and support vector machine were applied to predict protein dispensability through high-throughput data. Our studies shed some lights on global characteristics of protein dispensability and evolution.


AVAILABILITY
The original datasets for protein dispensability analysis and prediction, together with related scripts, are available at http://digbio.missouri.edu/~ychen/ProDispen/


CONTACT
xudong@missouri.edu.