Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.     

基于系统漏洞的攻击场景构建

在分析基于攻击前提和后果关联方法的基础上，提出了一种基于系统漏洞和报警相关度的攻击场景构建方法。它不仅能够利用系统漏洞信息验证报警的可靠性，排除误报，而且能够通过报警之间的相关度关联多跳攻击过程。实验结果表明，此方法能够有效地减少误报和漏报，从而有助于构建更加真实完整的攻击场景。     

基于多移动代理的网络性能管理系统

基于移动代理的网络性能管理,利用移动代理的移动性、智能性、灵活性和强大的扩展能力,通过对整个网络进行本地和全局的性能监控和性能数据采集,提供实时数据采集、分析和可视化工具,最终为管理员完成可视化的性能报告。能够对网络实施高效、实时和准确的性能管理,而且移动代理的平台无关性又可方便地实现跨平台的网络管理,在复杂网络的管理方面有着极大的优势。根据多移动代理的设计模式,借助于UML建模工具构建了一个性能管理系统,清晰地描述了该系统的静态和动态结构,克服了传统的集中式网络性能管理的诸多缺点。     

Optimal topology control for balanced energy consumption in wireless networks

Hosts in wireless networks are usually powered by batteries, thus the lifetime of a network depends on the battery life of each individual host. One major solution to improve energy-efficiency is to minimize the total energy consumption. However, battery energy is a local resource, to save energy at each host and balance the energy consumption among all the hosts in the network appear to be more practical. In this paper, we prove that minimum weight incremental arborescence (MWIA) is the optimal solution for minimizing the maximum transmission power among a set of wireless nodes. We propose an algorithm that utilizes MWIA to construct a connected topology, called MWIA-based Topology Control(MWIA-TC) algorithm. We further apply MWIA-TC to the accumulated energy consumption. The theoretical analysis and experimental results show that MWIA-TC outperforms a well-known algorithm, Minimum Incremental Power (MIP), in both energy saving and network lifetime extension.     

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.     

基于几何先验和深度学习的点云法矢估算

法矢是三维点云曲面最基本的几何属性.为解决传统几何估算子与基于学习技术中的问题,提出基于几何先验和深度学习的点云法矢估算方法.首先,使用一个多尺度曲面块选择方法保持点云的特征和细节,以降低后续深度网络的学习难度;然后,结合局部特征和几何先验知识设计一个法矢优化神经网络,输出精确点云法矢;最后使用合成模型数据和Microsoft Kinect V1扫描得到的真实模型数据进行验证,使用平均角度误差作为度量标准与主流方法进行对比,定量和定性分析结果表明文中方法在保持模型细节和噪声鲁棒性方面均明显优于对比方法.     

代码特征自动提取方法

神经网络在软件工程中的应用极大程度上缓解了传统的人工提取代码特征的压力。已有的研究往往将代码简化为自然语言或者依赖专家的领域知识来提取代码特征,简化为自然语言的处理方法过于简单,容易造成信息丢失,而引入专家制定启发式规则的模型往往过于复杂,可拓展性以及普适性不强。鉴于以上问题,提出了一种基于卷积和循环神经网络的自动代码特征提取模型,该模型借助代码的抽象语法树(AST)来提取代码特征。为了缓解因AST过于庞大而带来的梯度消失问题,对AST进行切割,转换成一个AST序列再作为模型的输入。该模型利用卷积网络提取代码中的结构信息,利用双向循环神经网络提取代码中的序列信息。整个流程不需要专家的领域知识来指导模型的训练,只需要将标注类别的代码作为模型的输入就可以让模型自动地学习如何提取代码特征。应用训练好的分类编码器,在相似代码搜索任务上进行测试,Top1、NDCG、MRR的值分别能达到0.560、0.679和0.638,对比当下前沿的用于代码特征提取的深度学习模型以及业界常用的代码相似检测工具有显著的优势。     

一种面向边缘环境的多实例服务链在线部署算法

边缘设备的资源有限性促使部署边缘服务需要深入理解网络功能的资源消耗情况.通过无线路由器上容器化网络功能部署实验得出,除了处理业务流的计算开销外,网络功能实例间的通信也会消耗大量CPU资源.基于该发现,考虑在近距离和相对低负载的对等边缘设备上分布式地部署网络功能实例,在满足时延约束的条件下均衡流量,从而最小化边缘设备负载.为此,提出细粒度服务链负载模型,并在此基础上设计实现了一种面向边缘环境的多实例服务链在线部署算法.该算法包括基于剪枝搜索策略的时延满足路径搜索、基于嵌套Top K策略的部署路径选择和基于贪心策略的网络功能部署3个组成部分.仿真实验验证了该算法的有效性.实验结果表明,相比不考虑通信开销的网络功能链部署,该算法可以降低10％ 边缘设备C P U负载,接近理论最优部署结果.     

一种分布对象的并行程序设计框架

计算性能和合成性能对于基于工作站网的软件十分重要,但由于缺乏相应的开发环境,现在这类软件在这两方面还做得很不够,尤其是合成性能十分薄弱.该文提出并实现了一种基于分布对象的并行程序设计框架,力图使分布对象能提供高性能的并行计算服务,同时也使并行算法获得一种良好的封装和复用机制.经过一些并行算法的测试,表明该框架具有实用价值.