Beyond Tracking: Modelling Activity and Understanding Behaviour

In this work, we present a unified bottom-up and top-down automatic model selection based approach for modelling complex activities of multiple objects in cluttered scenes. An activity of multiple objects is represented based on discrete scene events and their behaviours are modelled by reasoning about the temporal and causal correlations among different events. This is significantly different from the majority of the existing techniques that are centred on object tracking followed by trajectory matching. In our approach, object-independent events are detected and classified by unsupervised clustering using Expectation-Maximisation (EM) and classified using automatic model selection based on Schwarz's Bayesian Information Criterion (BIC). Dynamic Probabilistic Networks (DPNs) are formulated for modelling the temporal and causal correlations among discrete events for robust and holistic scene-level behaviour interpretation. In particular, we developed a Dynamically Multi-Linked Hidden Markov Model (DML-HMM) based on the discovery of salient dynamic interlinks among multiple temporal processes corresponding to multiple event classes. A DML-HMM is built using BIC based factorisation resulting in its topology being intrinsically determined by the underlying causality and temporal order among events. Extensive experiments are conducted on modelling activities captured in different indoor and outdoor scenes. Our experimental results demonstrate that the performance of a DML-HMM on modelling group activities in a noisy and cluttered scene is superior compared to those of other comparable dynamic probabilistic networks including a Multi-Observation Hidden Markov Model (MOHMM), a Parallel Hidden Markov Model (PaHMM) and a Coupled Hidden Markov Model (CHMM). First online version published in February, 2006     

复杂性和意义含糊问题的定性仿真和控制

许多系统是高阶、时变、非线性，传统的模型分析方法不容易应用。定性推理能避免复杂的数学计算，定性推理有吸引力是因为它涉及到物理系统一般原理的描述，这意味着一个特殊的定性模型能用于描述一个大范围的起作用的条件。在工程上定性推理用于监控和诊断。本文阐述如何用定性推理技术去仿真和控制液位系统的问题。     

Modelling the Appearance and Behaviour of Urban Spaces

Urban spaces consist of a complex collection of buildings, parcels, blocks and neighbourhoods interconnected by streets. Accurately modelling both the appearance and the behaviour of dense urban spaces is a significant challenge. The recent surge in urban data and its availability via the Internet has fomented a significant amount of research in computer graphics and in a number of applications in urban planning, emergency management and visualization. In this paper, we seek to provide an overview of methods spanning computer graphics and related fields involved in this goal. Our paper reports the most prominent methods in urban modelling and rendering, urban visualization and urban simulation models. A reader will be well versed in the key problems and current solution methods.     

Classification of process trends based on fuzzified symbolic representation and hidden Markov models

This paper presents a strategy to represent and classify process data for detection of abnormal operating conditions. In representing the data, a wavelet-based smoothing algorithm is used to filter the high frequency noise. A shape analysis technique called triangular episodes then converts the smoothed data into a semi-qualitative form. Two membership functions are implemented to transform the quantitative information in the triangular episodes to a purely symbolic representation. The symbolic data is classified with a set of sequence matching hidden Markov models (HMMs), and the classification is improved by utilizing a time correlated HMM after the sequence matching HMM. The method is tested on simulations with a non-isothermal CSTR and compared with methods that use a back-propagation neural network with and without an ARX model.     

定性立体形状表达和建模方法

概念设计在整个产品开发过程中的地位越来越重要,计算机辅助概念设计是计算机辅助设计的发展方向之一。传统的产品建模技术由于不能在产品开发的初期阶段很好地为产品的设计服务,因此有必要研究和开发面向产品概念设计的新的产品建模方法。本文介绍了一种基于领域和领域关系的定性立体形状表达方法和建模方法。该方法满足了概念设计对形状表达的要求,并能简化定性立体构筑过程。     

基于混合因子分析的隐马尔可夫模型

经典隐马尔可夫模型用于语音识别存在的两个主要缺陷是“离散状态假设”和“独立分布假设”。前者忽略了语音信号的非平稳性,后者忽略了语音信号的相关性。文章将混合因子分析方法用于语音建模,提出了基于混合因子分析的隐马尔可夫模型框架,并用动态贝叶斯网络形象地表示。该模型框架不仅从理论上解决了上述问题,而且给出许多语音建模的选择。目前广泛使用的统计声学模型均可视为该模型的特例。     

Learning Qualitative Models of Dynamic Systems

The automated construction of dynamic system models is an important application area for ILP. We describe a method that learns qualitative models from time-varying physiological signals. The goal is to understand the complexity of the learning task when faced with numerical data, what signal processing techniques are required, and how this affects learning. The qualitative representation is based on Kuipers' QSIM. The learning algorithm for model construction is based on Coiera's GENMODEL. We show that QSIM models are efficiently PAC learnable from positive examples only, and that GENMODEL is an ILP algorithm for efficiently constructing a QSIM model. We describe both GENMOEL which performs RLGG on qualitative states to learn a QSIM model, and the front-end processing and segmenting stages that transform a signal into a set of qualitative states. Next we describe results of experiments on data from six cardiac bypass patients. Useful models were obtained, representing both normal and abnormal physiological states. Model variation across time and across different levels of temporal abstraction and fault tolerance is explored. The assumption made by many previous workers that the abstraction of examples from data can be separated from the learning task is not supported by this study. Firstly, the effects of noise in the numerical data manifest themselves in the qualitative examples. Secondly, the models learned are directly dependent on the initial qualitative abstraction chosen.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

Modelling the student in Pitagora 2.0

With the aim to individualise human-computer interaction, an Intelligent Tutoring System (ITS) has to keep track of what and how the student has learned. Hence, it is necessary to maintain a Student Model (SM) dealing with complex knowledge representation, such as incomplete and inconsistent knowledge and belief revision. With this in view, the main objective of this paper is to present and discuss the student modelling approach we have adopted to implement Pitagora 2.0, an ITS based on a co-operative learning model, and designed to support teaching-learning activities in a Euclidean Geometry context. In particular, this approach has led us to develop two distinct modules that cooperate to implement the SM of Pitagora 2.0. The first module resembles a classical student model, in the sense that it maintains a representation of the current student knowledge level, which can be used by the teacher in order to tune its teaching strategies to the specific student needs. In addition, our system contains a second module that implements a virtual partner, called companion. This module consists of a computational model of an average student which cooperates with the student during the learning process. The above mentioned module calls for the use of machine learning algorithms that allow the companion to improve in parallel with the real student. Computational results obtained when testing this module in simulation experiments are also presented.     

Test Data Sets and Evaluation of Gene Prediction Programs on the Rice Genome

With several rice genome projects approaching completion gene prediction/finding by computer algorithms has become an urgent task. Two test sets were constructed by mapping the newly published 28,469 full-length KOME rice cDNA to the RGP BAC clone sequences of Oryza sativa ssp. japonica: a single-gene set of 550 sequences and a multi-gene set of 62 sequences with 271 genes. These data sets were used to evaluate five ab initio gene prediction programs: RiceHMM, GlimmerR, GeneMark, FGENSH and BGF. The predictions were compared on nucleotide, exon and whole gene structure levels using commonly accepted measures and several new measures. The test results show a progress in performance in chronological order. At the same time complementarity of the programs hints on the possibility of further improvement and on the feasibility of reaching better performance by combining several gene-finders.     

Statistical Translation of Text and Speech: First Results with the RWTH System

In this paper, we describe a first version of a system for statisticaltranslation and present experimental results. The statistical translationapproach uses two types of information: a translation model and a languagemodel. The language model used is a standard bigram model. The translationmodel is decomposed into lexical and alignment models. After presenting the details of the alignment model, we describe the search problem and present a dynamic programming-based solution for the special case of monotone alignments.So far, the system has been tested on two limited-domain tasks for which abilingual corpus is available: the EuTrans traveller task (Spanish–English,500-word vocabulary) and the Verbmobil task (German–English, 3000-wordvocabulary). We present experimental results on these tasks. In addition to the translation of text input, we also address the problem of speech translation and suitable integration of the acoustic recognition process and the translation process.     

浅析在数字媒体艺术下的绘画造型语言

绘画造型语言是一种使用色彩、线条、形体等艺术语言,通过构图、明暗等艺术手段在平面空间(即二维空间)中创造能够表现作者追求的静态视觉形象的艺术。本文主要是从绘画造型语言的特点出发,来分析在数字媒体艺术下的绘画造型语言的一些新的特性。     

A New Method of Systemological Analysis Coordinated with the Object-Oriented Design Procedure. I

A formal concept of system as a functional object is proposed. A new method of systemological analysis of complex dynamic systems, conforming to requirements of object-oriented design and based on a formal semantic normative system with an adaptive alphabet, is considered. The base class hierarchy is presented for construction of conceptual models during the process of object-oriented analysis and design.     

Qualitative representation of spatial knowledge in two-dimensional space

Various relation-based systems, concerned with the qualitative representation and processing of spatial knowledge, have been developed in numerous application domains. In this article, we identify the common concepts underlying qualitative spatial knowledge representation, we compare the representational properties of the different systems, and we outline the computational tasks involved in relation-based spatial information processing. We also describesymbolic spatial indexes, relation-based structures that combine several ideas in spatial knowledge representation. A symbolic spatial index is an array that preserves only a set of spatial relations among distinct objects in an image, called the modeling space; the index array discards information, such as shape and size of objects, and irrelevant spatial relations. The construction of a symbolic spatial index from an input image can be thought of as a transformation that keeps only a set of representative points needed to define the relations of the modeling space. By keeping the relative arrangements of the representative points in symbolic spatial indexes and discarding all other points, we maintain enough information to answer queries regarding the spatial relations of the modeling space without the need to access the initial image or an object database. Symbolic spatial indexes can be used to solve problems involving route planning, composition of spatial relations, and update operations.     

定性与定量相结合的任务调度方法

针对在复杂产品虚拟样机协同设计阶段的任务调度中存在的问题,建立了定性与定量相结合的调度模型,提出了基于遗传算法的模型求解策略.调度模型通过引入调度主、客体的隐性知识,综合考虑定性指标与定量指标对协同设计的影响;模型求解策略能在满足任务约束的前提下快速求得模型的最优解.实例结果表明,该调度模型及模型求解策略是有效的,能大幅缩短协同设计时间.     

属于不同集合的定性信任度的序比较及应用

在定性不确定推理中,定性信任度集并不是惟一的。Akdag等人提出的不同定性信任度集中元素之间的序比较方法存在缺陷,与定性运算并不协调,这将会导致不确定性的定性表达和操作上的矛盾。提出了新的属于不同集的定性信任度的序比较方法,并定义了不同定性信任度集中元素之间的定性混合运算。这种序比较方法不仅更加符合直观,而且与定性运算保持一致,有利于在不确定推理中对属于不同集的定性信任度进行操作。     

物相定性定量分析模拟实验教学

简述了计算机辅助教学（CAI）软件在物相定性、定量分析实验教学中的意义。详细介绍CAI在物相定性、定量分析应用中,谱图数据与处理方法。     

非串行生产线的建模,分析及设计

本文利用等效工作站理论和工件流平衡原理，推导出非串行离散事件生产线可以等价于串联系统，并归结为装配和拆卸两条定理，解决了系统的建模、分析与设计问题。     

人员紧急疏散系统动力学模型及其分析

在大型的体育赛事中,观众是一个非常复杂的能量系统,群体内人与人之间存在着相互影响、相互渗透的交互作用。针对能量的汇聚、冲突、平衡与失衡以及群体行为的趋向和拒斥等现象的研究,对系统问题进行分析,明确系统的界限,确定系统的各种变量,建立系统的因果关系图和流图,构建出系统模型。利用定性描述的方法,分析系统整体与局部的反馈关系,写出SD方程,并根据数据进行仿真试验和计算,最终得到一个与实际较为相符的预测结果。     

Modelling and simulation of fabrication and assembly plants: an object-driven approach

This paper presents the technical characteristics and methodologies underlying the computer-aided modelling and simulation of fabrication and assembly plants through an object-driven approach. The paper is organized in four main sections. The first and second sections discuss the needs for model integration and then outline the modelling and simulation approach used. The third and fourth sections illustrate an application of the approach for modelling and simulating a specific fabrication and assembly plant.