Improving construction industry process interoperability with Industry Foundation Processes (IFP)

With massive amounts of information generated during the life cycle of large-scale construction projects, interoperability among project stakeholders’ information systems is a requirement for effective and timely communication, collaboration, and information exchange, and ultimately for project success. While data interoperability has been substantially improved by initiatives such as IFC (Industry Foundation Classes) standardizing construction industry data, emphasis on process interoperability which facilitates timely and effective exchange of information via interaction of workflow processes is in its early stage. By conforming to a reference model such as IFP (Industry Foundation Processes), project stakeholders can communicate and collaborate using workflow processes while abstracting the information exchange to essential items to preserve their privacy. This paper explores interoperability in the AEC/FM domain, reviews the main components of the IFP system, presents two IFP interoperability models, and discusses their relationships with the IFP system. The models are demonstrated with specific examples and implemented with a process customization framework based on workflow inheritance rules. Interoperability models that conform to the IFP system not only allow seamless information exchange but can also yield active interaction and communication among stakeholders.     

众测中的工作者选择方法研究

众测是一种新兴的软件测试方法,它依靠网络上的工作者帮助完成测试任务.对于某个测试任务来说,谁来执行对于发现缺陷以及覆盖测试需求关键点是至关重要的.然而众测平台上一般有大量的候选工作者,他们拥有不同的测试经验,也常常提交重复的测试报告.由于众测工作者随机地参与测试任务,同时满足较高缺陷检测率和较高测试需求关键点覆盖度是很困难的.因此,该文关注如何为新的测试任务选择一组合适的众测工作者,从而提高缺陷检测率和需求关键点覆盖度.首先设计了3个实验,试图发现选择什么样的众测工作者能够提升缺陷检测率和需求关键点覆盖度.通过实验验证,发现众测工作者的主动性、相关性和多样性从不同的角度影响测试质量,并且给出了它们的度量方法.然后,提出一种同时考虑这3个方面工作的选择方法.基于众测平台之一——百度众测上46个真实的测试任务对该方法进行了验证,结果显示,该方法能够显著提高缺陷检测率和测试需求关键点覆盖度.     

Distributed representation learning and intelligent retrieval of knowledge concepts for conceptual design

Data-driven conceptual design is rapidly emerging as a powerful approach to generate novel and meaningful ideas by leveraging external knowledge especially in the early design phase. Currently, most existing studies focus on the identification and exploration of design knowledge by either using common-sense or building specific-domain ontology databases and semantic networks. However, the overwhelming majority of engineering knowledge is published as highly unstructured and heterogeneous texts, which presents two main challenges for modern conceptual design: (a) how to capture the highly contextual and complex knowledge relationships, (b) how to efficiently retrieve of meaningful and valuable implicit knowledge associations. To this end, in this work, we propose a new data-driven conceptual design approach to represent and retrieve cross-domain knowledge concepts for enhancing design ideation. Specifically, this methodology is divided into three parts. Firstly, engineering design knowledge from the massive body of scientific literature is efficiently learned as information-dense word embeddings, which can encode complex and diverse engineering knowledge concepts into a common distributed vector space. Secondly, we develop a novel semantic association metric to effectively quantify the strength of both explicit and implicit knowledge associations, which further guides the construction of a novel large-scale design knowledge semantic network (DKSN). The resulting DKSN can structure cross-domain engineering knowledge concepts into a weighted directed graph with interconnected nodes. Thirdly, to automatically explore both explicit and implicit knowledge associations of design queries, we further establish an intelligent retrieval framework by applying pathfinding algorithms on the DKSN. Next, the validation results on three benchmarks MTURK-771, TTR and MDEH demonstrate that our constructed DKSN can represent and associate engineering knowledge concepts better than existing state-of-the-art semantic networks. Eventually, two case studies show the effectiveness and practicality of our proposed approach in the real-world engineering conceptual design.     

Efficient task scheduling for budget constrained parallel applications on heterogeneous cloud computing systems

As the cost-driven public cloud services emerge, budget constraint is one of the primary design issues in large-scale scientific applications executed on heterogeneous cloud computing systems. Minimizing the schedule length while satisfying the budget constraint of an application is one of the most important quality of service requirements for cloud providers. A directed acyclic graph (DAG) can be used to describe an application consisted of multiple tasks with precedence constrains. Previous DAG scheduling methods tried to presuppose the minimum cost assignment for each task to minimize the schedule length of budget constrained applications on heterogeneous cloud computing systems. However, our analysis revealed that the preassignment of tasks with the minimum cost does not necessarily lead to the minimization of the schedule length. In this study, we propose an efficient algorithm of minimizing the schedule length using the budget level (MSLBL) to select processors for satisfying the budget constraint and minimizing the schedule length of an application. Such problem is decomposed into two sub-problems, namely, satisfying the budget constraint and minimizing the schedule length. The first sub-problem is solved by transferring the budget constraint of the application to that of each task, and the second sub-problem is solved by heuristically scheduling each task with low-time complexity. Experimental results on several real parallel applications validate that the proposed MSLBL algorithm can obtain shorter schedule lengths while satisfying the budget constraint of an application than existing methods in various situations.     

Discriminative transfer learning with sparsity regularization for single-sample face recognition

Discriminant analysis is an important technique for face recognition because it can extract discriminative features to classify different persons. However, most existing discriminant analysis methods fail to work for single-sample face recognition (SSFR) because there is only a single training sample per person such that the within-class variation of this person cannot be estimated in such scenario. In this paper, we present a new discriminative transfer learning (DTL) approach for SSFR, where discriminant analysis is performed on a multiple-sample generic training set and then transferred into the single-sample gallery set. Specifically, our DTL learns a feature projection to minimize the intra-class variation and maximize the inter-class variation of samples in the training set, and minimize the difference between the generic training set and the gallery set, simultaneously. To make the DTL be robust to outliers and noise, we employ a sparsity regularizer to regularize the DTL and further propose a novel discriminative transfer learning with sparsity regularization (DTLSR) method. Experimental results on three face datasets including the FERET, CAS-PEAL-R1, and real-world LFW datasets are presented to show the efficacy of the proposed methods.     

X3D在E-Learning中的应用

介绍了E-Learning和X3D的概念,以及X3D技术在现代远程教育中的应用现状,从虚拟课堂与虚拟教学、虚拟实验、虚拟图书馆、虚拟研讨等多个方面阐述了X3D技术在E-Learning中的应用和对教学的影响．     

Neuro-Fuzzy Modeling and Prediction of VBR MPEG Video Sources

The work presented in this paper intends to apply neuro-fuzzy methods for the modeling and prediction on traffic intensity of digital video sources which are coded with hybrid Motion Compensation/Differential Pulse Code Modulation/Discrete Cosine Transform (MC/DPCM/DCT) algorithm. Although current coding standards recommend constant bit rate (CBR) output by means of a smoothing buffer, the hybrid algorithm inherently produces variable bit rate (VBR) output. This paper describes the novel application of a fuzzy predictor for the purposes of modeling and prediction on video sources. The computation requirement of the fuzzy predictor and its neural network implementation are also discussed. The proposed fuzzy prediction method and its neural network version can be applied to the development of connection admission control, usage parameter control and congestion control algorithms in ATM networks.     

面向大数据处理框架的JVM优化技术综述

当前,以Hadoop、Spark为代表的大数据处理框架,已经在学术界和工业界被广泛应用于大规模数据的处理和分析.这些大数据处理框架采用分布式架构,使用Java、Scala等面向对象语言编写,在集群节点上以Java虚拟机(JVM)为运行时环境执行计算任务,因此依赖JVM的自动内存管理机制来分配和回收数据对象.然而,当前的JVM并不是针对大数据处理框架的计算特征设计的,在实际运行大数据应用时经常出现垃圾回收(GC)时间长、数据对象序列化和反序列化开销大等问题.在一些大数据场景下, JVM的垃圾回收耗时甚至超过应用整体运行时间的50%,已经成为大数据处理框架的性能瓶颈和优化热点.对近年来相关领域的研究成果进行了系统性综述:(1)总结了大数据应用在JVM中运行时性能下降的原因;(2)总结了现有面向大数据处理框架的JVM优化技术,对相关优化技术进行了层次划分,并分析比较了各种方法的优化效果、适用范围、使用负担等优缺点;(3)探讨了JVM未来的优化方向,有助于进一步提升大数据处理框架的性能.     

Integrated knowledge-based system design and validation for solving problems in uncertain environments

Symbolic problem solving, specifically which knowledge-based systems (KBSs), in new and uncertain problem domains is a difficult task. An essential part of developing systems for these environments is determining whether the system is adequately and reliably solving the problem. KBSs that utilize heuristics have a development cycle not conducive to formal control and have high potential for error or incorrect characterizations of the problem they are meant to solve. A method of validating and testing such systems to increase and quantify their reliability is needed. Software engineering strategies for accessing and projecting the reliability of traditional software have been developed after years of experience with the cause and effect of errors. Since KBSs are new, methods for accessing and projecting their reliability are not as well understood. However, validation techniques from traditional software development can be applied to KBSs. Validation and testing techniques unique to KBSs can also be used to determine system reliability. In essence, tools and techniques can be used to meet the need for a legitimate, integrated approach to validation and testing of KBSs as they are developed.