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.     

Open Source Software Supply Chain for Reliability Assurance of Operating Systems

Software reliability is one of the research hotspots in the field of software engineering, and failure rate analysis is a typical research method for software reliability. However, the software construction mode has evolved from a single mode to a large-scale collaborative model represented by open source software. As one of the representative products, the operating system includes open source software connected through combinations and dependencies to form a supply network of tens of thousands of nodes. Typical methods lack consideration of supply relationships and cannot accurately identify and evaluate the software reliability issues introduced as a result. This paper extends the concept of supply chain to the field of open source software and proposes a knowledge-based management method for software supply reliability in a collaborative model. The ontological body is designed for the open source software ecosystem firstly, and then the nowledge graph of open source software is constructed to achieve the extraction, storage and management of knowledge; driven by knowledge, combined with traditional supply chain management methods, a set of reliability management methods for open source software supply chain is proposed, which constitutes a management system of open source software supply chain. With the construction of a Linux operating system distribution as an example, the experiment demonstrates how the open source software supply chain supports the reliability of the operating system. Results show that the open source software supply chain will help to clarify and evaluate the reliability risk of large complex system software.     

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.     

Composite of polyaniline containing iron oxides with nanometer size

A composite of polyaniline (PANI) containing iron oxides with nanometer size was synthesized by a chemical method. The structure, electrical and magnetic properties of the resulting PANI-Fe_xO_y composites depend on the pH value of the reaction medium and temperature. For the basic preparation conditions (e.g. pH 14), the resulting PANI-Fe_xO_y, composite can be attracted by a magnet at room temperature, and its magnetization with the applied magnetic field exhibits a hysteresis loop with H_c = 0. It has been demonstrated that nanocrystalline ferromagnetic particles of Fe₃O₄ with 14.0 nm size correspond to super-paramagnetic properties observed from PANI-Fe_xy composites. For the acidic conditions (e.g. pH1), on the other hand, the electrical properties of the resulting PANI-Fe_xO_y composite show a semiconducting behavior. By comparison with PANI-Cl⁻¹, the lower conductivity (about 10⁻⁴ S/cm) for PANI-Fe_xO_y composite may be due to the presence of iron oxides in the composite, although its doping degree is the same as that of PANI-Cl⁻.     

Probability tree based passenger flow prediction and its application to the Beijing subway system

In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Although a great number of prediction methods have been presented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Beijing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination (OD) information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be predicted by gathering all the contributions. The results of experiments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passenger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.