基于软件行为轨迹的可信性评价模型

软件动态可信性评价已经成为信息安全领域研究的一个热点问题.为了提高评价的精确性,在充分考虑了软件的运行流程和运行背景的基础上,提出了基于软件行为轨迹的可信性评价模型(CEMSBT).该模型引入软件行为轨迹描述软件行为,软件行为轨迹由运行轨迹和功能轨迹构成,运行轨迹表示软件运行时的有序操作,表征为有序的检查点向量;功能轨迹则由能够表征软件功能的一系列场景来刻画.为了减少可信性评价的时间和空间开销,给出了软件行为轨迹的化简规则.模型应用检查点的标识评价规则和场景评价规则对实际的软件行为进行评价.考虑到分支给程序带来的随机性很可能被入侵者利用,分支处的检查很必要.模型通过场景确定分支的走向,从而降低了分支处异常情况的漏报率.仿真实验表明CEMSBT具有较高的精确性和效率.     

Simulating the behavior of software modules by trace rewriting

The trace assertion method is a module interface specification method based on the finite state machine model. To support this method, we plan to develop a specification simulation tool, a trace simulator, that symbolically interprets trace assertions of trace specifications and simulates the externally observable behavior of the modules specified. We first present the trace assertion method. Then we formally define trace rewriting systems and show how trace rewriting, a technique similar to term rewriting, can be applied to implement trace simulation     

用图像处理技术提取交通车辆移动轨迹

交通仿真的不断发展要求能为其提供现实中的车辆运动过程,从而使得模拟更加逼真,同时交通仿真模型评估也需要用到现实的车辆运动轨迹来比对仿真模型模拟出来的车辆运动轨迹。该文提出了一种用图像处理技术提取交通车辆移动轨迹的方法。该文介绍的这一方法采用了动态目标区域检测的技术来追踪运动车辆,所谓动态目标区域检测就是指被检测区域是动态更新的,是随着被检测对象位置的变化而改变的。这样一种思路有效地提高了系统运行的速度和系统检测的精度。避免了采用全局扫描思路所引起的系统复杂、运算量大以及精度不高的缺点。同时又有别于静态的重点区域检测的思路,使得系统更加灵活,运算更加快捷。通过实验验证,该系统可以快速准确地提取交通车辆的移动轨迹。     

Accurately modeling superscalar processor performance with reduced trace

Trace-driven simulation of out-of-order superscalar processors is far from straightforward. The dynamic nature of out-of-order superscalar processors combined with the static nature of traces can lead to large inaccuracies in the results when the traces contain only a subset of executed instructions for trace reduction. In this paper, we describe and comprehensively evaluate the pairwise dependent cache miss model (PDCM), a framework for fast and accurate trace-driven simulation of out-of-order superscalar processors. The model determines how to treat a cache miss with respect to other cache misses recorded in the trace by dynamically reconstructing the reorder buffer state during simulation and honoring the dependencies between the trace items. Our experimental results demonstrate that a PDCM-based simulator produces highly accurate simulation results (less than 3% error) with fast simulation speeds (62.5× on average) compared with an execution-driven simulator. Moreover, we observed that the proposed simulation method is capable of preserving a processor’s dynamic off-core memory access behavior and accurately predicting the relative performance change when a processor’s low-level memory hierarchy parameters are changed.     

Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool

This paper focuses on the creation and presentation of a user-friendly experience for developing computational models of human behavior. Although computational models of human behavior have enjoyed a rich history in cognitive psychology, they have lacked widespread impact, partly due to the technical knowledge and programming required in addition to the complexities of the modeling process. We describe a modeling tool called IBLTool that is a computational implementation of the Instance-based Learning Theory (IBLT). IBLT is a theory that represents how decisions are made from experience in dynamic tasks. The IBLTool makes IBLT usable and understandable to a wider community of cognitive and behavioral scientists. The tool uses graphical user interfaces that take a modeler step-by-step through several IBLT processes and help the modeler derive predictions of human behavior in a particular task. A task would connect and interact with the IBLTool and store the decision-making data while the tool collects statistical data from the execution of a model for the task. We explain the functioning of the IBLTool and demonstrate a concrete example of the design and execution of a model for the Iowa Gambling task. The example is intended to provide a concrete demonstration of the capabilities of the IBLTool.     

Dynamic project performance estimation by combining static estimation models with system dynamics

Changes in user requirements or project personnel occur frequently during project execution particularly in long-term and large-size projects. We need a tool which can estimate the effects of changing conditions to effectively manage the project.This paper proposes a simulation method for dynamic project performance in terms of effort, schedule, and defect density changes in a dynamic project environment by combining COCOMO II with system dynamics. We apply expert judgment technique to overcome the lack of empirical data on the effects of dynamic project environment. We develop a simulation tool (available on the authors’ website) which has model adjustment parameters to reflect experts’ estimation on project characteristics. The simulation experiment on a military application development project demonstrates that the developed model can show the behavioral characteristics of a project suffering unanticipated and uncontrolled requirements creep. This helps project managers understand interactions between project factors and proactively evaluate and control the effects of dynamic project environment.     

The design and implementation of a Bayesian CAD modeler for robotic applications

We present a Bayesian CAD modeler for robotic applications. We address the problem of taking into account the propagation of geometric uncertainties when solving inverse geometric problems. The proposed method may be seen as a generalization of constraint-based approaches in which we explicitly model geometric uncertainties. Using our methodology, a geometric constraint is expressed as a probability distribution on the system parameters and the sensor measurements, instead of a simple equality or inequality. Tosolve geometric problems in this framework, we propose an original resolution method able to adapt to problem complexity. Using two examples, we show how to apply our approach by providing simulation results using our modeler.     

Interactive 3-D indoor modeler for virtualizing service fields

This paper describes an interactive 3-D indoor modeler that effectively creates photo-realistic 3-D indoor models from multiple photographs. This modeler supports the creation of 3-D models from photographs by implementing interaction techniques that use geometric constraints estimated from photographs and visualization techniques that help to easily understand shapes of 3-D models. We evaluated the availability and usability by applying the modeler to model service fields where actual workers provide services and an experience-based exhibit. Our results confirmed that the modeler enables the creation of large-scale indoor environments such as hot-spring inns and event sites at a relatively modest cost. We also confirmed that school children could learn modeling operations and create 3-D models from a photograph for approximately 20 min because of the easy operations. In addition, we describe additional functions that increase the effectiveness of 3-D modeling based on knowledge from service-field modeling. We present applications for behavior analysis of service workers and for 3-D indoor navigation using augmented virtuality (AV)-based visualization realized by photo-realistic 3-D models.     

Real-time deformable models of non-linear tissues by model reduction techniques

In this paper we introduce a new technique for the real-time simulation of non-linear tissue behavior based on a model reduction technique known as proper orthogonal (POD) or Karhunen-Loève decompositions. The technique is based upon the construction of a complete model (using finite element modelling or other numerical technique, for instance, but possibly from experimental data) and the extraction and storage of the relevant information in order to construct a model with very few degrees of freedom, but that takes into account the highly non-linear response of most living tissues. We present its application to the simulation of palpation a human cornea and study the limitations and future needs of the proposed technique.     

Defining requirements for a standard simulation environment

A simulation environment combines the many elements of simulation into one powerful tool. A standard based on a reference model would benefit users and researchers alike. We define a reference model for general-purpose discrete-event simulation environments, as well as the associated requirements for the model's functional layers, to be used as the basis for a future standard. We characterize environments by highlighting and grouping different tasks with common traits. Each group can then be related to the other groups in a logical, structured manner to create a model for a standard environment. Functionally, the standard environment would reside between the modeler (user) and the hardware platform's operating system     

Dynamics modeling and culling

The tools described, permit including large numbers of complex dynamic models in a VRML world easily and efficiently while maintaining high frame rates. We describe three tools that together provide an environment for authoring cullable, dynamic, rigid-body objects in VRML and Java: a code transformation tool that exploits approximations to dynamical systems to enable culling; a runtime layer generator, which defines a simple standard interface between a VRML browser and dynamical systems described in Java; a rigid-body modeler, which allows users to interactively design the runtime layer and preview the dynamic behavior. The article describes these tools, including some example systems, and discusses the runtime performance improvements obtained. Our tools are applicable if the spatial range of the dynamic model can be bounded by a static volume, the model is closed to outside influence, the underlying equations are continuous, and the dimension (number of degrees of freedom) of the system is small. Note that while the article focuses on VRML and Java as the target environment, the underlying techniques apply to any rendering and language environment     

Monte Carlo simulation-compatible stochastic field for application to expansion-based stochastic finite element method

In order to endow the expansion-based stochastic formulation with the capability of representing the characteristic behavior of stochastic systems, i.e., the non-linear dependence of the response variability on the coefficient of variation of the stochastic field, a Monte Carlo simulation-compatible stochastic field is suggested. Through a theoretical comparison of displacement vectors in the Monte Carlo method and an expansion-based scheme, it is found that the stochastic field adopted in the expansion-based scheme is not compatible with that appearing in the Monte Carlo simulation. The Monte Carlo simulation-compatible stochastic field is established by means of enforcing the compatibility between the stochastic fields in the expansion-based scheme and the Monte Carlo simulation. Employing the stochastic field suggested in this study, the response variability is reproduced with high precision even for uncertain fields with a moderately large coefficient of variation. Furthermore, the formulation proposed here can be used as an indirect Monte Carlo scheme by directly substituting the numerically simulated random fields into the covariance formula. This yields a pronounced reduction in the computation cost while resulting in virtually the same response variability as the Monte Carlo technique.     

Symmetry and partial order reduction techniques in model checking Rebeca

Rebeca is an actor-based language with formal semantics which is suitable for modeling concurrent and distributed systems and protocols. Due to its object model, partial order and symmetry detection and reduction techniques can be efficiently applied to dynamic Rebeca models. We present two approaches for detecting symmetry in Rebeca models: One that detects symmetry in the topology of inter-connections among objects and another one which exploits specific data structures to reflect internal symmetry in the internal structure of an object. The former approach is novel in that it does not require any input from the modeler and can deal with the dynamic changes of topology. This approach is potentially applicable to a wide range of modeling languages for distributed and reactive systems. We have also developed a model checking tool that implements all of the above-mentioned techniques. The evaluation results show significant improvements in model size and model-checking time.     

混合选别浓密过程双速率智能切换控制

赤铁矿混合选别浓密过程是以底流矿浆泵频率为输入,以底流矿浆流量为内环输出,以底流矿浆浓度为外环输出的强非线性串级工业过程.由于受到频繁的浮选过程产生的中矿矿浆和污水的随机干扰,底流矿浆浓度外环和流量内环始终处于动态变化之中,控制器积分作用失效,内外环相互影响,使被控系统的动态性能变坏,底流矿浆浓度与流量超出工艺规定的控制目标的范围,甚至产生谐振.本文针对上述问题利用提升技术建立基于内环流量闭环动态模型的浓度外环动态模型,将基于未建模动态补偿驱动的一步最优PI控制和基于模糊推理与规则推理的切换控制相结合,提出了由浓度外环控制和流量内环控制组成的混合选别浓密过程的双速率智能切换控制算法,建立了由机理主模型和神经网络补偿模型组成的混合选别浓密过程动态模型.所提算法通过混合选别浓密过程的半实物仿真实验结果表明本文所提控制方法的有效性.     

From dynamic influence nets to dynamic Bayesian networks: A transformation algorithm

This paper presents an algorithm to transform a dynamic influence net (DIN) into a dynamic Bayesian network (DBN). The transformation aims to bring the best of both probabilistic reasoning paradigms. The advantages of DINs lie in their ability to represent causal and time‐varying information in a compact and easy‐to‐understand manner. They facilitate a system modeler in connecting a set of desired effects and a set of actionable events through a series of dynamically changing cause and effect relationships. The resultant probabilistic model is then used to analyze different courses of action in terms of their effectiveness to achieve the desired effect(s). The major drawback of DINs is their inability to incorporate evidence that arrive during the execution of a course of action (COA). Several belief‐updating algorithms, on the other hand, have been developed for DBNs that enable a system modeler to insert evidence in dynamic probabilistic models. Dynamic Bayesian networks, however, suffer from the intractability of knowledge acquisition. The presented transformation algorithm combines the advantages of both DINs and DBNs. It enables a system analyst to capture a complex situation using a DIN and pick the best (or close‐to‐best) COA that maximizes the likelihood of achieving the desired effect. During the execution, if evidence becomes available, the DIN is converted into an equivalent DBN and beliefs of other nodes in the network are updated. If required, the selected COA can be revised on the basis of the recently received evidence. The presented methodology is applicable in domains requiring strategic level decision making in highly complex situations, such as war games, real‐time strategy video games, and business simulation games. © 2009 Wiley Periodicals, Inc.     

Development of real-time sketch-based on-the-spot process modeling and analysis system

Contemporary manufacturing processes require faster real-time controls against dynamic and volatile production environments. While a corresponding simulation model is considered a prerequisite system for the real-time control of a contemporary manufacturing process, simulation modeling and relevant analysis have supported these real-time features comparatively less. These issues might cause procrastination of the simulation modeling, and result in wrong decisions and inaccurate controls. In order to overcome these issues, a new real-time simulation modeling and analysis system is proposed. The proposed system supports sketch-based simulation modeling. The simulation model is constructed using modelers’ sketches of predefined simulation symbols. The sketches are converted automatically into a corresponding stochastic queueing network using Self-organizing Map, a type of neural network. Then, the model is simulated and analyzed using the embedded stochastic queueing analyses. The effectiveness of the proposed system is proven with the modeling, simulation and analyses of several real-time manufacturing cases.     

Stochastic communication for application-specific Networks-on-Chip

In this paper, we have developed analytical stochastic communication technique for inter and intra-Networks-on-Chip (NoC) communication. It not only separates the computation and communication in Networks-in-Package (NiP) but also predicts the communication performance. Moreover, it will help in tracking of the lost data packets and their exact location during the communication. Further, the proposed technique helps in building the Closed Donor Controlled Based Compartmental Model, which helps in building Stochastic Model of NoC and NiP. This model helps in computing the transition probabilities, latency, and data flow from one IP to other IP in a NoC and among NoCs in NiP. From the simulation results, it is observed that the transient and steady state response of transition probabilities give state of data flow latencies among the different IPs in NoC and among the compartments of NoCs in NiP. Furthermore, the proposed technique produces low latency as compared to the latencies being produced by the existing topologies.     

Disturbance-adaptive stochastic optimal control of energy harvesters,with application to ocean wave energy conversion

This paper proposes a technique for optimizing the power generated from stationary stochastic vibratory disturbances, using a resonant energy harvester. Although the theory is general, the target application of the paper concerns ocean wave energy harvesting. The control technique involves the use of a causal discrete-time feedback algorithm to dynamically optimize the power extracted from the waves. The theory assumes that the input impedance of the harvester is known precisely, but that a priori models are unavailable for the characterization of the stochastic behavior of the incident waves as well as the transfer functions characterizing their hydrodynamic excitation of the system. For these assumptions, we develop an adaptive control technique, which adapts the feedback law at each time step based on updated estimates for the stochastic disturbance model, obtained through a subspace-based system identification algorithm. The technique is demonstrated on a simulation example pertaining to a cylindrical surface-floating wave energy converter in heave.     

Pseudo-fuzzy discrete-event simulation for on-line production control

In this paper we propose an alternative use of commercial object-oriented discrete-event simulators. We attempt to bridge the intrinsic inaccuracy of simulators in modelling real systems affected by fuzziness. The strategy adopted, called pseudo-fuzzy discrete event simulation, models fuzziness through a set of several classic simulation runs to trace an output fuzzy performance function. The idea behind the approach proposed is to use the simulator as a fuzzy operator, which embeds some stochastic functions.A benchmark industrial setting has been used to build a reference simulation model and perform evaluations of the simulation strategy proposed for a specific working case.