On the Correctness of Goscinski′s Algorithm

In this paper, the correctness of the mutual exclusion algorithm proposed by Goscinski (J. Parallel Distribut. Comput.9(7), 77-82 (1990)), hereafter , is discussed and its features are compared with other token-based algorithms already published. In particular, we show that works correctly only using a communication system that guarantees a total ordering of messages, otherwise it is incorrect. We further give a modified version of , hereafter , and show that is actually a simple modification of the Suzuki-Kasami algorithm (ACM Trans. Comput. Systems3(5), 344-349 (1985)).     

Extension of some MAP results to transient MAPs and Markovian binary trees

In this work we extend previous results on moment-based characterization and minimal representation of stationary Markovian arrival processes (MAPs) and rational arrival processes (RAPs) to transient Markovian arrival processes (TMAPs) and Markovian binary trees (MBTs).     

A New Model of Learning

Abstract

Most learning machines are of a single type. Input patterns are transformed into some other patterns by a processor then classified by a discriminant function. Such machines are considered to offer poor information processing capability. This paper proposes a new and unusual model of learning. In conventional machines only the classification of input patterns is learned; however, the new model can memorize the input patterns themselves in the learning, that is, an input pattern appears as an output if it has been seen sufficiently often. Basic to the formulation of the new model are the multiplexing of local processing and the use of connections which operate on a manner similar to presynaptic inhibition.     

A Nondeterministic Extension over Finitely Recursive Process Model

This paper extends the Finitely Recursive Process framework introduced by Inan and Varaiya for modelling Discrete Event Systems to encompass nondeterministic processes. Nondeterminism has been captured as a set of possible deterministic futures instead of using the standard failure model of Communicating Sequential Processes. In the beginning a general structure of finitely recursive process space is provided with some important modifications. Next, the nondeterministic process space has been introduced as a special case of the general algebraic process space. A collection of operators has been defined over this nondeterministic process space that enables its characterisation in a finitely recursive manner. Finally, the advantages and disadvantages of the proposed model vis-a-vis other nondeterministic models of discrete event systems are discussed.     

KFUR:一个新型内核扩展安全模型

保障内核扩展的安全性对操作系统具有重要意义.当前存在大量针对内核函数使用规则的攻击,内核扩展中也存在大量违反内核函数使用规则的错误,因此针对内核函数使用规则的安全性检测十分必要.虽然存在多种提高内核扩展安全性的方法,但很少有方法对内核函数的使用规则进行安全性检测.文中设计了KFUR( Kernel Function Usage Rule)内核扩展安全模型系统,用于在运行时检测内核扩展调用内核函数是否遵守内核函数使用规则.如果内核扩展调用内核函数满足模型安全运行条件,则允许对该内核函数进行调用,否则将错误报告给操作系统内核并终止该内核扩展的运行.文中所述研究在Iinux操作系统上对KFUR安全模型系统进行实现,并将其运用于e1000网卡驱动、SATA硬盘驱动和HDA声卡驱动内核扩展.安全性评测表明安全模型系统能够对内核函数使用规则进行安全性检测,性能评测表明安全模型系统带来的开销很小.     

A Stable,Multivariate Extension of the Log-Normal Survival Model

When applied to survival data from a population of cancer patients, the log-normal model provides estimates of three important parameters; cured fraction, mean log survival time, and standard deviation log survival time. In the original model, however, these parameters are unrelated to prognostic covariates. Furthermore, the original algorithm is computationally unstable and highly dependent on initial parameter estimates. We have developed an extension of the log-normal model that stabilizes computation and expresses survival parameters as functions of prognostic covariates. We have also developed an ancillary algorithm that provides reliable initial estimates.     

个性化的自主学习模型研究

自主学习系统是现代远程教育系统的重要内容。本文通过分析自主学习的特点,构造个性化的自主学习模型,并对学习材料的组织、推荐模型进行研究。目的是为学习者推荐有针对性的学习材料,提高学习者的学习效率。     

Model reduction of discrete Markovian jump systems with time‐weighted H2 performance

This paper is concerned with the optimal time‐weighted H₂ model reduction problem for discrete Markovian jump linear systems (MJLSs). The purpose is to find a mean square stable MJLS of lower order such that the time‐weighted H₂ norm of the corresponding error system is minimized for a given mean square stable discrete MJLSs. The notation of time‐weighted H₂ norm of discrete MJLS is defined for the first time, and then a computational formula of this norm is given, which requires the solution of two sets of recursive discrete Markovian jump Lyapunov‐type linear matrix equations. Based on the time‐weighted H₂ norm formula, we propose a gradient flow method to solve the optimal time‐weighted H₂ model reduction problem. A necessary condition for minimality is derived, which generalizes the standard result for systems when Markov jumps and the time‐weighting term do not appear. Finally, numerical examples are used to illustrate the effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

一种组合型的深度学习模型学习率策略

一个设计良好的学习率策略可以显著提高深度学习模型的收敛速度, 减少模型的训练时间. 本文针对AdaGrad和AdaDec学习策略只对模型所有参数提供单一学习率方式的问题, 根据模型参数的特点, 提出了一种组合型学习策略: AdaMix. 该策略为连接权重设计了一个仅与当前梯度有关的学习率, 为偏置设计使用了幂指数型学习率.利用深度学习模型Autoencoder对图像数据库MNIST进行重构, 以模型反向微调过程中测试阶段的重构误差作为评价指标, 验证几种学习策略对模型收敛性的影响.实验结果表明, AdaMix比AdaGrad和AdaDec的重构误差小并且计算量也低, 具有更快的收敛速度.     

基于XPath数据模型的双时态扩展

XML作为Web上新的数据发布语言,将成为Web下一代"数据表达"和"数据交换"的统一标准.然而XML文档很少是静止的,它经常会被修改,引入"时态表达"后时态XML文档能够记录一系列的修改痕迹,提供了一种高效可行的XML版本管理方案.另一方面XML表达时态信息比关系模型更自然,时态数据库的研究人员开始将部分焦点转向研究如何利用XML实现时态信息处理.提出了一个双时态XML数据模型并给出了形式化定义,该模型将一个双时态XML文档建模成一个带有"双时态元素"边标记的有向树形图.还讨论了该模型映射到双时态XML文档的两种映射方法;并采用扩展DOM API的方法实现了模型以及映射.最后通过实验对比了扩展DOM实现模型和不扩展DOM实现模型两种实现方法,两种模型映射到双时态XML文档的映射方法.     

在不完备信息系统中的一种粗集扩充模型

对已有的基于不完备信息系统的粗集拓展模型进行分析比较,进而提出一种改进的限制容差关系模型。主要思想是考虑到人的主观要求,在容差类的划分中加入一个插值,再利用这些类得到上下近似集,最后通过实例说明该模型是符合客观实际的。     

A Learning Model to Detect Android C&C Applications Using Hybrid Analysis

Smartphone devices particularly Android devices are in use by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks launched the Distributed denial of services (DDoS) that causes to steal of sensitive data, remote access, and spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic analysis. In this paper, a novel hybrid model, the combination of static and dynamic methods that relies on machine learning to detect android botnet applications is proposed. Furthermore, results are evaluated using machine learning classifiers. The Random Forest (RF) classifier outperform as compared to other ML techniques i.e., Naïve Bayes (NB), Support Vector Machine (SVM), and Simple Logistic (SL). Our proposed framework achieved 97.48% accuracy in the detection of botnet applications. Finally, some future research directions are highlighted regarding botnet attacks detection for the entire community.     

一个基于Bayesian学习的协商模型

在Multi-Agent系统(MAS)中,每一个Agent都有不同的目标。通常只拥有对方的不完全信息。Agent需要具有解决在实现各自目标过程中所产生的各种矛盾的能力。协商是解决这些矛盾的一种有效途径。本文提出了一个基于Bayesian学习的协商模型NMBL：在每一轮协商中,Agent通过Bayesian学习获取协商对手的信息,更新对协商对手的信念,然后根据基于冲突点和不妥协度的协商策略提出下一轮的协商提议。NMBL把整个协商过程看成一个动态的交互过程,体现了Multi-Agent系统的动态特性,同时NMBL具有较强的学习能力。试验证明,该模型具有较好的协商性能。     

A Neural Model for Context-dependent Sequence Learning

A novel neural network model is described that implements context-dependent learning of complex sequences. The model utilises leaky integrate-and-fire neurons to extract timing information from its input and modifies its weights using a learning rule with synaptic noise. Learning and recall phases are seamlessly integrated so that the network can gradually shift from learning to predicting its input. Experimental results using data from the real-world problem domain demonstrate that the use of context has three important benefits: (a) it prevents catastrophic interference during learning of multiple overlapping sequences, (b) it enables the completion of sequences from missing or noisy patterns, and (c) it provides a mechanism to selectively explore the space of learned sequences during free recall.     

一种支持纸的折叠、剪切与展开的计算模型

为了满足儿童剪纸方案的交互设计以及向儿童展示典型的儿童剪纸过程的需要,设计了一个支持纸的折叠、剪切与展开的计算模型,它包括当前纸态的几何与拓扑数据结构、操作过程的记录方法以及各相关功能的实现算法。以此计算模型为核心所开发的原型软件(命名为Virtual Paper)验证了该模型的可行性。     

粗糙集的泛系化扩展模型

粗糙集与泛系理论相结合已成为一个新兴的研究领域,基于泛系理论中的泛权场/网等理论,对粗糙集理论的基本概念进行了基本的概括和扩展,将粗糙集理论泛系化扩展加以研究,进而构建了粗糙集的泛系化扩展模型,并通过实例给予解释,为粗糙集的进一步完善和扩展找到了一条新路。     

一个基于XCS的同质团队学习模型

同质团队学习是实现多Agent协作的一种方法。但是,传统方法仅在系统运行的前后对目标Agent进行修改,系统运行过程没有直接对Agent的改进做出贡献。本文利用合作策略,在学习分类器系统XCS的基础上提出了一种同质团队学习模型,弥补了传统方法的上述不足。文中还在模型的基础上实验分析了相关因素,如规则积累、通信以及发现新规则等对多Agent协作效率的影响。     

个性化的远程学习模型

随着互联网应用的发展,人们越来越多地把学习过程放到网络上来,而不再局限于传统的教室学习,因此基于Ｗｅｂ的远程学习得到了迅速推广。但是由于受教育对象之间存在着鲜明的个性特征差异,而目前存在的远程教育系统模式单一,学生只能被动地接受完全相同的学习内容,而不能主动地根据自己的需要选择、调度、控制自己的学习过程,这样,再丰富的教育资源也未能得到充分利用。为了从根本上解决这一矛盾,提高教育信息资源的利用度,使学生在学习过程中变被动为主动,实现因材施教,文章结合个性化服务的思想,提出个性化学习的概念,并系统地叙述了个性化的远程学习模型ＰＤＬＭ的原理和构成,给出了实现ＰＤＬＭ系统的具体思路和解决方案。个性化的远程学习将成为远程教育应用的新模式。     

一个基于增强学习算法的路由模型

由于Internet的不断发展，现有的路由算法为适应不同的网络要求，从一开始的RIP、OSPF、BGP等几种，衍生出很多新的适用于特殊网络的路由协议。本文提出一种基于增强学习算法的路由模型。将每个路由节点看作一个Agent，利用增强学习算法的思想使得每个节点在不了解网络拓扑结构的情况下从向邻居转发的概率获得网络的信息，这样路由节点可以选择一个较优的转发方向。同时，节点能对网络的拥塞等情况作出调整。该模型为一些具体网络的路由协议，特别是QoS类路由算法提出了一个新的路由思想。