Dissymmetric warfare versus asymmetric warfare

This paper presents two concepts: asymmetric warfare and dissymmetric warfare. NATO and US Military operations in recent wars in Kosovo and Afghanistan are analysed by means of comparison with old Chinese strategic thinking. No‐Loss‐Strategy and virtual warfare as well as the transformation from asymmetric to dissymmetric and from dissymmetric to asymmetric warfare are reflected upon.     

Hierarchical models of elastic shells in curvilinear coordinates

In the present paper, static and dynamical problems for linearly elastic shells in curvilinear coordinates are considered. Hierarchies of two-dimensional models for corresponding boundary and initial boundary value problems are constructed within the variational settings. The existence and uniqueness of solutions of the reduced problems are investigated in suitable spaces. Under the conditions of solvability of the original static or dynamical problem, convergence of the sequence of vector functions of three variables restored from the solutions of the constructed two-dimensional problems to the solution of the three-dimensional problem is proved and approximation error is estimated.     

Classification using Hierarchical Naïve Bayes models

Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe an instance are conditionally independent given the class of that instance. When this assumption is violated (which is often the case in practice) it can reduce classification accuracy due to “information double-counting” and interaction omission. In this paper we focus on a relatively new set of models, termed Hierarchical Naïve Bayes models. Hierarchical Naïve Bayes models extend the modeling flexibility of Naïve Bayes models by introducing latent variables to relax some of the independence statements in these models. We propose a simple algorithm for learning Hierarchical Naïve Bayes models in the context of classification. Experimental results show that the learned models can significantly improve classification accuracy as compared to other frameworks.     

Hierarchical bayesian models for regularisation in sequential learning

We show that a hierarchical Bayesian modeling approach allows us to perform regularization in sequential learning. We identify three inference levels within this hierarchy: model selection, parameter estimation, and noise estimation. In environments where data arrive sequentially, techniques such as cross validation to achieve regularization or model selection are not possible. The Bayesian approach, with extended Kalman filtering at the parameter estimation level, allows for regularization within a minimum variance framework. A multilayer perceptron is used to generate the extended Kalman filter nonlinear measurements mapping. We describe several algorithms at the noise estimation level that allow us to implement on-line regularization. We also show the theoretical links between adaptive noise estimation in extended Kalman filtering, multiple adaptive learning rates, and multiple smoothing regularization coefficients.     

Hierarchical models for analysis and recognition of handwritten characters

Different hierarchical models in pattern analysis and recognition are proposed, based on occurrence probability of patterns. As an important application of recognizing handprinted characters, three typical kinds of hierarchical models such asM _89-89,M _89-36 andM _36-36 have been presented, accompanied by the computer algorithms for computing recognition rates of pattern parts. Moreover, a comparative study of their recognition rates has been conducted theoretically; and numerical experiments have been carried out to verify the analytical conclusions made. Various hierarchical models deliberated in this paper can provide users more or better choices of pattern models in practical application, and lead to a uniform computational scheme (or code). The recognition rates of parts can be improved remarkably by a suitable hierarchical model. For the modelM _89-36 in which case some of the Canadian standard handprinted characters have multiple occurrence probabilities, the total mean recognition rates of the given sample may reach 120% of that by the model proposed by Li et al., and 156% of that obtained from the subjective experiments reported by Suen.     

Hierarchical attentive multiple models for execution and recognition of actions

According to the motor theories of perception, the motor systems of an observer are actively involved in the perception of actions when these are performed by a demonstrator. In this paper we review our computational architecture, HAMMER (Hierarchical Attentive Multiple Models for Execution and Recognition), where the motor control systems of a robot are organised in a hierarchical, distributed manner, and can be used in the dual role of (a) competitively selecting and executing an action, and (b) perceiving it when performed by a demonstrator. We subsequently demonstrate that such an arrangement can provide a principled method for the top-down control of attention during action perception, resulting in significant performance gains. We assess these performance gains under a variety of resource allocation strategies.     

Hierarchical multi-channel hidden semi Markov graphical models for activity recognition

Recognizing human actions from a stream of unsegmented sensory observations is important for a number of applications such as surveillance and human-computer interaction. A wide range of graphical models have been proposed for these tasks, and are typically extensions of the generative hidden Markov models (HMMs) or their discriminative counterpart, conditional random fields (CRFs). These extensions typically address one of three key limitations in the basic HMM/CRF formalism – unrealistic models for the duration of a sub-event, not encoding interactions among multiple agents directly and not modeling the inherent hierarchical organization of activities. In our work, we present a family of graphical models that generalize such extensions and simultaneously model event duration, multi agent interactions and hierarchical structure. We also present general algorithms for efficient learning and inference in such models based on local variational approximations. We demonstrate the effectiveness of our framework by developing graphical models for applications in automatic sign language (ASL) recognition, and for gesture and action recognition in videos. Our methods show results comparable to state-of-the-art in the datasets we consider, while requiring far fewer training examples compared to low-level feature based methods.     

Hierarchical syntactic models for human activity recognition through mobility traces

Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for     

Hierarchical fuzzy relational models: linguistic interpretation and universal approximation

Hierarchical fuzzy structures composed of a series of sub-models connected in cascade have been found to be effective tools for dealing with the dimensionality problem in fuzzy systems. This paper addresses both the issues of linguistic interpretation and universal approximation of systems using hierarchical fuzzy models. Fuzzy relational equations are used to implement the sub-models of a hierarchical structure that has two very important properties: i) it can be converted into a completely equivalent nonhierarchical model, which in turn allows the extraction of linguistic knowledge in the form of consistent fuzzy If-Then rules; and ii) it is a universal approximator. These properties are analytically derived and the proposed model is illustrated by means of an example.     

Hierarchical optimization of personalized experiences for e-Learning systems through evolutionary models

Recent researches in e-Learning area highlight the need to define novel and advanced support mechanism for commercial and academic organizations in order to enhance the skills of employees and students and, consequently, to increase the overall competitiveness in the new economy world. This is due to the unbelievable velocity and volatility of modern knowledge that require novel learning methods which are able to offer additional support features as efficiency, task relevance and personalization. This paper tries to deal with these features by proposing an adaptive e-Learning framework based on Computational Intelligence methodologies by supporting e-Learning systems’ designers in two different aspects: (1) they represent the most suitable solution, able to support learning content and activities, personalized to specific needs and influenced by specific preferences of the learner and (2) they assist designers with computationally efficient methods to develop “in time” e-Learning environments. Our work attempts to achieve both results by exploiting an ontological representation of learning environment and a hierarchical memetic approach of optimization. In detail, our approach takes advantage of a collection of ontological models and processes for adapting an e-Learning system to the learner expectations by efficiently solving a well-defined optimization problem, through a hierarchical multi-cores memetic approach.     

Models of information warfare

The simplest mathematical models describing the spreading of information warfare are considered. New mathematical models which describe the process of information warfare are created. In particular, complete conditions are obtained for one of the participants to win in this warfare.     

网络中心战初探

随着信息技术的快速发展，20世纪末开始酝酿一场新的军事革命——网络中心战（NCW，network centric warfare）。信息化战争将从“平台中心战”转向“网络中心战”，网络中心战将成为未来信息化作战的主要作战方式。论述了网络中心战的概念、网络中心战的网络结构和作战结构，介绍了美国网络中心战现状，分析了当前制约美国网络中心战发展的因索，筒述了计算机在网络中心战中的地位和作用。     

Surviving information warfare attacks

The past few years have seen governmental, military, and commercial organizations widely adopt Web-based commercial technologies because of their convenience, ease of use, and ability to take advantage of rapid advances in the commercial market. With this increasing reliance on internetworked computer resources comes an increasing vulnerability to information warfare. In today's heavily networked environment, safety demands protection from both obvious and subtle intrusions that can delete or corrupt vital data. Traditionally, information systems security focuses primarily on prevention: putting controls and mechanisms in place that protect confidentiality, integrity, and availability by stopping users from doing bad things. Moreover, most mechanisms are powerless against misbehavior by legitimate users who perform functions for which they are authorized. The paper discusses traditional approaches and their limitations