Software development cost estimation approaches — A survey

This paper summarizes several classes of software cost estimation models and techniques: parametric models, expertise‐based techniques, learning‐oriented techniques, dynamics‐based models, regression‐based models, and composite‐Bayesian techniques for integrating expertise‐based and regression‐based models. Experience to date indicates that neural‐net and dynamics‐based techniques are less mature than the other classes of techniques, but that all classes of techniques are challenged by the rapid pace of change in software technology. The primary conclusion is that no single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates. This revised version was published online in June 2006 with corrections to the Cover Date.     

高级计算机图形学建模技术

本文综述了几种不同类型的高级计算机图形学建模技术,然后介绍了计算机图形学高级建模技术的发展方向。     

Feature Model Visualization

Feature modelling is now the predominant way of modelling products. Feature visualization is an important aspect here that can still be considerably improved. In this paper, an integrated way of visualizing feature models is presented, using new techniques for both the geometry and the structure of models. For the geometry of feature models, techniques are presented to visualize a selected subset of form features in a way that clearly distinguishes them from the rest of the model, as well as functional information such as closure faces of subtractive form features. For the structure of features models, techniques are presented to visualize several types of graphs. The different visualization techniques are used in an integrated way. Implementation of some of the techniques requires a non‐manifold representation of the geometry of the feature model. This representation, and some other implementation aspects, are briefly described. Throughout the paper, numerous examples of images of feature models are given which show that the new visualization techniques can indeed improve the effectiveness of feature modelling. ACM CSS: I.3.7 Three‐Dimensional Graphics and Realism—visible line/surface algorithms, J.6 Computer‐AidedEngineering—feature modelling     

Acquiring user models

Existing machine techniques for acquiring user models are characterized along five orthogonal dimensions: passive/active, user-initiated/automatic, logical/plausible, direct/indirect, and on-line/off-line. Passive techniques observe users whereas active techniques query users. User-initiated techniques require that users volunteer information; automatic techniques do not. The logical/plausible dimension measures the accuracy of derived user model data. Indirect techniques build upon data gathered by more direct methods. On-line techniques acquire user models in real-time during user interaction, while off-line techniques work after the user interaction is finished. Commonalities and differences in capabilities and features of different user model acquisition techniques are analyzed along the above dimensions, and the relationship of these techniques to similar techniques in other areas of artificial intelligence are discussed.     

A comparison of linear and nonlinear statistical techniques inperformance attribution

Performance attribution is usually conducted under the linear framework of multifactor models. Although commonly used by practitioners in finance, linear multifactor models are known to be less than satisfactory in many situations. After a brief survey of nonlinear methods, nonlinear statistical techniques are applied to performance attribution of a portfolio constructed from a fixed universe of stocks using factors derived from some commonly used cross sectional linear multifactor models. By rebalancing this portfolio monthly, the cumulative returns for procedures based on standard linear multifactor model and three nonlinear techniques-model selection, additive models, and neural networks-are calculated and compared. It is found that the first two nonlinear techniques, especially in combination, outperform the standard linear model. The results in the neural-network case are inconclusive because of the great variety of possible models. Although these methods are more complicated and may require some tuning, toolboxes are developed and suggestions on calibration are proposed. This paper demonstrates the usefulness of modern nonlinear statistical techniques in performance attribution.     

Modeling nonlinear behavior of Buckling-Restrained Braces via different artificial intelligence methods

Five AI models are presented to model the dynamic nonlinear behavior of Buckling-Restrained Braces (BRBs). The AI techniques utilized in the models are: Time-Delayed Neural Networks (TDNN), Nonlinear Auto-Regressive eXogenous (NARX) neural networks, Gaussian-Mixture Models Regression (GMMR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Polynomial Classifier Regression (PCR). The models are developed using time-delayed brace displacements inputs and brace force outputs to predict updated brace forces during load reversals. The training and testing of the AI models are performed using experimental data from BRB specimens tested at the Pacific Earthquake Engineering Research (PEER) Center. The training stage for every method makes use of the experimental data from one specimen. In order to assess the models’ learning and generalization capabilities, three sets of experimental data for different specimens are used. To arrive at an optimized architecture that best models the phenomenon, the model performance with different parameters is evaluated. The brace force predicted by the proposed model shows excellent resemblance to the experimental results for the training sample, for all techniques. The predicted behavior of the testing samples shows noticeable accuracy and further demonstrates the generalization and prediction capability of the proposed modeling techniques. The various techniques are compared on the basis of selected performance criteria. It is found that the performance of two AI techniques standout among the others: the NARX and the PCR. Although the NARX demonstrates a slight advantage in the prediction accuracy over the PCR, the latter is far more superior in terms of computational efficiency. Thus, the PCR would be recommended for scenarios where online training is needed. The BRB design and performance investigation processes can be facilitated by the developed modeling techniques thus minimizing the need for, and extent of, experimental testing.     

Developing empirical models from observational data using artificial neural networks

Computer-integrated manufacturing requires models of manufacturing processes to be implemented on the computer. Process models are required for designing adaptive control systems and selecting optimal parameters during process planning. Mechanistic models developed from the principles of machining science are useful for implementing on a computer. However, in spite of the progress being made in mechanistic process modeling, accurate models are not yet available for many manufacturing processes. Empirical models derived from experimental data still play a major role in manufacturing process modeling. Generally, statistical regression techniques are used for developing such models. However, these techniques suffer from several disadvantages. The structure (the significant terms) of the regression model needs to be decided a priori. These techniques cannot be used for incrementally improving models as new data becomes available. This limitation is particularly crucial in light of the advances in sensor technology that allow economical on-line collection of manufacturing data. In this paper, we explore the use of artificial neural networks (ANN) for developing empirical models from experimental data for a machining process. These models are compared with polynomial regression models to assess the applicability of ANN as a model-building tool for computer-integrated manufacturing.Operated for the United States Department of Energy under contract No. DE-AC04-76-DP00613.     

航天器任务调度模型、算法与通用求解技术综述

针对航天器任务调度大规模、复杂化的新常态和灵活组网、快速响应的新要求, 综述了航天器任务调度模型、算法与通用求解技术的发展现状. 首先, 基于遥感卫星、中继通信卫星、导航卫星和航天测控等航天器任务, 从任务排序模型和时间窗口分配模型两个角度出发, 揭示了不同航天器任务调度模型的决策形式和共性特征, 阐明提升模型兼容性、适用性的必要性. 其次, 基于启发式算法、精确求解算法和元启发式算法, 探讨了航天器任务调度算法的适用模型与编码特色, 指明“算法?模型”解耦、算法深度融合的重要性. 在此基础上, 介绍了CPLEX、STK/Scheduler、Europa2和“高景一号”任务调度分系统等航天器任务调度通用求解技术的模型、算法与主要功能, 说明我国自主研发通用求解技术的必要性和新的应用思路. 最后, 指出了开发航天器任务调度统一化建模语言、打造算法库与测试集等未来航天器任务调度研究的新方向.     

Models and technologies for the extraction of aggregated knowledge to control processes of the retrieval of non-structured information

To control the retrieval and to arrange the retrieval of non-structured information in large-scale collections of texts in natural languages, crucially new models of aggregated representation of knowledge about their topic and content (semiotic and semantic characteristics of the text) are proposed. Based on the proposed models, techniques of computational extraction and use of knowledge about the topic and content of text collections are developed. The proposed models and techniques are based on formation of secondary information resources characterizing the topic and content of particular texts in natural languages.     

BPMN Miner: Automated discovery of BPMN process models with hierarchical structure

Existing techniques for automated discovery of process models from event logs generally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique, namely BPMN Miner, for automated discovery of hierarchical BPMN models containing interrupting and non-interrupting boundary events and activity markers. The technique employs approximate functional and inclusion dependency discovery techniques in order to elicit a process–subprocess hierarchy from the event log. Given this hierarchy and the projected logs associated to each node in the hierarchy, parent process and subprocess models are discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. By employing approximate dependency discovery techniques, BPMN Miner is able to detect and filter out noise in the event log arising for example from data entry errors, missing event records or infrequent behavior. Noise is detected during the construction of the subprocess hierarchy and filtered out via heuristics at the lowest possible level of granularity in the hierarchy. A validation with one synthetic and two real-life logs shows that process models derived by the proposed technique are more accurate and less complex than those derived with flat process discovery techniques. Meanwhile, a validation on a family of synthetically generated logs shows that the technique is resilient to varying levels of noise.     

On the potential applications of data mining for information security provision of cloud-based environments

An overview of several applications of techniques and models of data mining (DM) in applied information security systems is presented. Special focus is put on the new and actively developed area of cloud-based computing environments. Both the available and future applicabilities of models and techniques of artificial intelligence to IS problem solving are discussed.     

A review of 3D reconstruction techniques in civil engineering and their applications

Three-dimensional (3D) reconstruction techniques have been used to obtain the 3D representations of objects in civil engineering in the form of point cloud models, mesh models and geometric models more often than ever, among which, point cloud models are the basis. In order to clarify the status quo of the research and application of the techniques in civil engineering, literature retrieval is implemented by using major literature databases in the world and the result is summarized by analyzing the abstracts or the full papers when required. First, the research methodology is introduced, and the framework of 3D reconstruction techniques is established. Second, 3D reconstruction techniques for generating point clouds and processing point clouds along with the corresponding algorithms and methods are reviewed respectively. Third, their applications in reconstructing and managing construction sites and reconstructing pipelines of Mechanical, Electrical and Plumbing (MEP) systems, are presented as typical examples, and the achievements are highlighted. Finally, the challenges are discussed and the key research directions to be addressed in the future are proposed. This paper contributes to the knowledge body of 3D reconstruction in two aspects, i.e. summarizing systematically the up-to-date achievements and challenges for the applications of 3D reconstruction techniques in civil engineering, and proposing key future research directions to be addressed in the field.     

Modeling Stochastic Dynamical Systems for Interactive Simulation

We present techniques for constructing approximate stochastic models of complicated dynamical systems for applications in interactive computer graphics. The models are designed to produce realistic interaction at low cost.
We describe two kinds of stochastic models: continuous state (ARX) models and discrete state (Markov chains) models. System identi cation techniques are used for learning the input-output dynamics automatically, from either measurements of a real system or from an accurate simulation. The synthesis of behavior in this manner is several orders of magnitude faster than physical simulation.We demonstrate the techniques with two examples: (1) the dynamics of candle ame in the wind, modeled using data from a real candle and (2) the motion of a falling leaf, modeled using data from a complex simulation. We have implemented an interactive Java program which demonstrates real-time interaction with a realistically behaving simulation of a cartoon candle ame. The user makes the ame animation icker by blowing into a microphone.     

Ratio Selection for Classification Models

This paper is concerned with the selection of inputs for classification models based on ratios of measured quantities. For this purpose, all possible ratios are built from the quantities involved and variable selection techniques are used to choose a convenient subset of ratios. In this context, two selection techniques are proposed: one based on a pre-selection procedure and another based on a genetic algorithm. In an example involving the financial distress prediction of companies, the models obtained from ratios selected by the proposed techniques compare favorably to a model using ratios usually found in the financial distress literature.     

语言发音模型研究综述

对各种语言发音模型进行了综述,分别讨论了言语声音模型和言语动作模型。言语声音模型研究语言发音的声学原理,利用声音信号处理技术重构语音信号波形,由于对声源和共鸣之间的关系的认识不同,以及对共鸣的分析方法的不同,产生了3种不同的语言发音模型,第一种是频谱分析模型,第二种是共振峰模型,第三种是生理发音模型。言语动作模型研究发音器官的运动过程,利用图像信号处理技术重构发音器官的发音动作,根据建模方法的不同,言语动作模型可以分为3类：生理机能模型、几何特征模型、统计参数模型。     

语音识别及端到端技术现状及展望

通过对语音识别技术的发展梳理,简单介绍了语音识别的历史和应用现状,并将传统语音识别的技术和当前的研究进展进行描述.传统语音识别采用基于统计的方法,采用声谱特征,在GMM-HMM混合结构上进行训练和匹配.当前的语音识别模型主要基于深度学习的方法,采用CNN、RNN都可以有效的进行特征提取从而建立声学模型.进一步的研究采用了端到端的技术,避免了多个模型间的误差传导.端到端技术主要有CTC技术和attention技术,最新的模型和方法着重研究了attention技术,并在尝试进行与CTC的融合以达到更好的效果.最后结合作者自身的理解,概括了语音识别当前所面临问题和未来发展方向.     

情景感知驱动的移动对象多模式轨迹预测技术综述

如何利用多源异构时空数据进行准确的轨迹预测并且反映移动对象的移动特性是轨迹预测领域的核心问题.现有的大多数轨迹预测方法是长序列轨迹模式预测模型,根据历史轨迹的特点进行预测,或将当前移动对象的轨迹位置放入时空语义场景根据历史移动对象轨迹预测位置.综述当前常用的轨迹预测模型和算法,涉及不同的研究领域.首先,阐述了多模式轨迹预测的主流工作,轨迹预测的基本模型类;其次,对不同类的预测模型进行总结,包括数学统计类、机器学习类、滤波算法,以及上述领域具有代表性的算法;再次,对情景感知技术进行了介绍,描述了不同领域的学者对情景感知的定义,阐述了情景感知技术所包含的关键技术点,诸如情景感知计算、情景获取和情景推理的不同类模型,分析了情景感知的不同分类、过滤、存储和融合以及它们的实现方法等.详细介绍了情景感知驱动的轨迹预测模型技术路线及各阶段任务的工作原理.给出了情景感知技术在真实场景中的应用,包括位置推荐,兴趣点推荐等,通过与传统算法对比,分析情景感知技术在此类应用中的优劣.详细介绍了情景感知结合LSTM (long short-term memory)技术应用于行人轨迹预测领域的新方法.最后,总结了...     

Controlled automated discovery of collections of business process models

Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models – each one representing a variant of the business process – as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.     

虚拟智能体情感研究综述

分别从合成情感模型、个性模型以及合成情感具体行为表现等方面,介绍并分析了各类模型和方法的基本思想和关键技术;进而给出了若干具有代表性的虚拟智能体情感应用系统,并对如何将上述情感技术应用于虚拟智能体进行了总结;最后提出了未来虚拟智能体情感研究需解决的若干问题.