Elastic SI-Cache: consistent and scalable caching in multi-tier architectures

The new vision of cloud computing demands scalable, available and autonomic software platforms in order to deploy applications and services accessible anywhere and anytime. Multi-tier architectures are an important building block for many applications that are deployed in the cloud. This paper presents a novel caching and replication infrastructure that facilitates the scalable and elastic deployment of multi-tier architectures. Our Elastic SI-Cache is a novel multi-version cache that attains high performance and consistency in multi-tier systems. In contrast to most existing caches, Elastic SI-Cache provides snapshot isolation coherently across all tiers. Furthermore, Elastic SI-Cache supports scalable replication of the different tiers where replicas can be added or removed dynamically as needed, making the cache amenable for cloud computing environments. Elastic SI-Cache has been implemented and integrated into an open source JEE application server and its performance evaluated with the industrial benchmark SPECjAppServer.     

A multi-tier phishing detection and filtering approach

Phishing attacks continue to pose serious risks for consumers and businesses as well as threatening global security and the economy. Therefore, developing countermeasures against such attacks is an important step towards defending critical infrastructures such as banking. Although different types of classification algorithms for filtering phishing have been proposed in the literature, the scale and sophistication of phishing attacks have continued to increase steadily. In this paper, we propose a new approach called multi-tier classification model for phishing email filtering. We also propose an innovative method for extracting the features of phishing email based on weighting of message content and message header and select the features according to priority ranking. We will also examine the impact of rescheduling the classifier algorithms in a multi-tier classification process to find out the optimum scheduling. A detailed empirical performance and analysis of the proposed algorithm is present. The results of the experiments show that the proposed algorithm reduces the false positive problems substantially with lower complexity.     

Pattern recognition techniques for automatic detection of suspicious-looking anomalies in mammograms

We have employed two pattern recognition methods used commonly for face recognition in order to analyse digital mammograms. The methods are based on novel classification schemes, the AdaBoost and the support vector machines (SVM). A number of tests have been carried out to evaluate the accuracy of these two algorithms under different circumstances. Results for the AdaBoost classifier method are promising, especially for classifying mass-type lesions. In the best case the algorithm achieved accuracy of 76% for all lesion types and 90% for masses only. The SVM based algorithm did not perform as well. In order to achieve a higher accuracy for this method, we should choose image features that are better suited for analysing digital mammograms than the currently used ones.     

基于多层架构的机电资源库自动整合系统设计

为了提高机电资源库自动整合能力,构建机电资源库自动整合系统,提出一种基于多层架构的机电资源库自动整合系统设计方法。采用多层次的大数据融合方法进行机电资源库自动整合的算法设计,提取机电资源库的统计特征量,采用自适应有限元网格划分方法进行机电资源库的分类整合,建立机电资源库的自动整合多层构架模型,在B/S结构体系下进行机电资源库自动整合系统的底层数据库构造,采用交叉编译方法进行机电资源库自动整合系统的程序加载和自动控制,在嵌入式多层次架构体系下,实现机电资源库自动整合系统的软件开发设计。测试结果表明,该系统能有效实现机电资源库自动整合,信息融合度较高,资源信息召回性较好。     

RX architectures for real-time anomaly detection in hyperspectral images

In the field of hyperspectral image processing, anomaly detection (AD) is a deeply investigated task whose goal is to find objects in the image that are anomalous with respect to the background. In many operational scenarios, detection, classification and identification of anomalous spectral pixels have to be performed in real time to quickly furnish information for decision-making. In this framework, many studies concern the design of computationally efficient AD algorithms for hyperspectral images in order to assure real-time or nearly real-time processing. In this work, a sub-class of anomaly detection algorithms is considered, i.e., those algorithms aimed at detecting small rare objects that are anomalous with respect to their local background. Among such techniques, one of the most established is the Reed–Xiaoli (RX) algorithm, which is based on a local Gaussian assumption for background clutter and locally estimates its parameters by means of the pixels inside a window around the pixel under test (PUT). In the literature, the RX decision rule has been employed to develop computationally efficient algorithms tested in real-time systems. Initially, a recursive block-based parameter estimation procedure was adopted that makes the RX processing and the detection performance differ from those of the original RX. More recently, an update strategy has been proposed which relies on a line-by-line processing without altering the RX detection statistic. In this work, the above-mentioned RX real-time oriented techniques have been improved using a linear algebra-based strategy to efficiently update the inverse covariance matrix thus avoiding its computation and inversion for each pixel of the hyperspectral image. The proposed strategy has been deeply discussed pointing out the benefits introduced on the two analyzed architectures in terms of overall number of elementary operations required. The results show the benefits of the new strategy with respect to the original architectures.     

Tracking error in digitized analog video: automatic detection and correction

In the last half century the most used video storage devices have been the magnetic tapes, where the information are stored in analog format based on the electromagnetism principles. When the digital technique has become the most used, it was necessary to convert analog information in digital format in order to preserve these data. Unfortunately, analog videos may be affected by drops that produce some visual defect which could be acquired during the digitization process. Despite there are many hardware to perform the digitization, just few implement the automatic correction of these defects. In some cases, drop removal is possible through the analog device. However, when a damaged already-converted video is owned, a correction based on image processing technique is the unique way to enhance the videos. In this paper, the drop, also known as “Tracking Error” or “Mistracking,” is analyzed. We propose an algorithm to detect the drops’ visual artifacts in the converted videos, as well as a digital restoration method.     

Scope Consistency: A Bridge between Release Consistency and Entry Consistency

Systems that maintain coherence at large granularity, such as shared virtual memory systems, suffer from false sharing and extra communication. Relaxed memory consistency models have been used to alleviate these problems, but at a cost in programming complexity. Release Consistency (RC) and Lazy Release Consistency (LRC) are accepted to offer a reasonable tradeoff between performance and programming complexity. Entry Consistency (EC) offers a more relaxed consistency model, but it requires explicit association of shared data objects with synchronization variables. The programming burden of providing such associations can be substantial. This paper proposes a new consistency model for such systems, called Scope Consistency (ScC), which offers most of the performance advantages of the EC model without requiring explicit bindings between data and synchronization variables. Instead, ScC dynamically detects the associations implied by the programmer, using a programming interface similar to that of RC or LRC. We propose two ScC protocols: one that uses hardware support for fine-grained remote writes (automatic updates or AU) and the other, an all-software protocol. We compare the AU-based ScC protocol with Automatic Update Release Consistency (AURC), a modified LRC protocol that also takes advantage of automatic update support. AURC already improves performance substantially over an all-software LRC protocol. For three of the five applications we used, ScC further improves the speedups achieved by AURC by about 10%. Received October 1996, and in final form July 1997.     

位置自动检测与数据处理

介绍一种以位置敏感器件 (positionsensitivedetector,PSD )为核心构成的位置自动检测系统 ,分析了输出信号与位置参数的关系 ,论述了微机位置数据处理系统的硬件组成与软件功能。最后给出系统的主要参数。     

Shot boundary detection using Zernike moments in multi-GPU multi-CPU architectures

This paper presents an analysis of a Multi-GPU Multi-CPU environment, along with the different possible hybrid combinations. The analysis has been performed for a shot boundary detection application, based on Zernike moments, although it is general enough to be applied to many different application areas. A deep study of the performance, bottlenecks and design challenges is carried out showing the validity of this approach and achieving very high frame per second rates. In this paper, Zernike calculations are carried out on GPUs, taking advantage of a packing strategy proposed to minimize host-device communication time.     

Traffic violation detection and deterrence: implications for automatic policing

Current efforts directed at enforcing traffic laws are beset by a dilemma. On the one hand enforcement programmes are limited in duration and intensity, thereby producing weak halo effects on drivers' behaviour. On the other hand, public attitudes towards compliance are largely conditioned by the visibility of enforcement programmes. This paper reviews the impact of the semi-automatic monitoring systems in improving detection in traffic law programmes. The effects of the technology on drivers' behaviour are assessed and implications for the development of a fully automatic traffic policing system are teased out. Secondly, the paper attempts to put into perspective any consequences that an automatic monitoring system may have for driver behaviour. The fact that such systems do not yet exist poses problems for an ergonomics evaluation of their projected impact. However, taking into account the existence of core technology, tentative conclusions can be drawn as to the likely benefits and disadvantages of such systems.     

Soldering defect detection in automatic optical inspection

This paper proposes an integrated detection framework of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Both localization and classifications tasks were considered. For the localization part, in contrast to the existing methods that are highly specified for particular PCBs, we used a generic deep learning method which can be easily ported to different configurations of PCBs and soldering technologies and also gives real-time speed and high accuracy. For the classification part, an active learning method was proposed to reduce the labeling workload when a large labeled training database is not easily available because it requires domain-specified knowledge. The experiments show that the localization method is fast and accurate. In addition, high accuracy with only minimal user input was achieved in the classification framework on two different datasets. The results also demonstrated that our method outperforms three other active learning benchmarks.     

LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins

LIGSITE is a new program for the automatic and time-efficient detection of pockets on the surface of proteins that may act as binding sites for small molecule ligands. Pockets are identified with a series of simple operations on a cubic grid. Using a set of receptor–ligand complexes we show that LIGSITE is able to identify the binding sites of small molecule ligands with high precision. The main advantage of LIGSITE is its speed. Typical search times are in the range of 5 to 20 s for medium-sized proteins. LIGSITE is therefore well suited for identification of pockets in large sets of proteins (e.g., protein families) for comparative studies. For graphical display LIGSITE produces VRML representations of the protein–ligand complex and the binding site for display with a VRML viewer such as WebSpace from SGI.     

Fuzzy neural networks for classification and detection of anomalies

A new learning algorithm for the Simpson fuzzy min-max neural network is presented. It overcomes some undesired properties of the Simpson model. Our new algorithm improves the network performance; the classification result does not depend on the presentation order of the patterns in the training set, and at each step, the classification error in the training set cannot increase. The new neural model is particularly useful in classification problems. Tests were executed on three different classification problems: 1) with two-dimensional synthetic data; 2) with realistic data generated by a simulator to find anomalies in the cooling system of a blast furnace; and 3) with real data for industrial diagnosis. The experiments were made following some recent evaluation criteria known in the literature and by using Microsoft Visual C++ development environment on personal computers.     

High performance motion detection: some trends toward new embedded architectures for vision systems

The goal of this article is to compare some optimised implementations on current high performance platforms in order to highlight architectural trends in the field of embedded architectures and to get an estimation of what should be the components of a next generation vision system. We present some implementations of robust motion detection algorithms on three architectures: a general purpose RISC processor—the PowerPC G4—a parallel artificial retina dedicated to low level image processing—Pvlsar34—and the Associative Mesh, a specialized architecture based on associative net. To handle the different aspects and constraints of embedded systems, execution time and power consumption of these architectures are compared.

基于SDN构架的DoS/DDoS攻击检测与防御体系

针对Do S/DDo S的攻击检测算法大多应用于攻击的目的端,只能实现检测效果、并不能缓解攻击的问题,提出利用SDN架构的集中控制等特点,在攻击的源头实现流量实时监控,使用源IP防伪、接入层异常检测、链路流量异常检测形成多重防御体系,尽可能早地发现攻击,逐渐过滤异常流量,实现网络层DDo S攻击在源端的检测和防御。提出防御体系概念,便于应用更先进的检测算法完善防御体系。     

乳腺超声图像肿瘤全自动定位方法研究*

乳腺超声图像肿瘤的定位是计算机辅助诊断(CAD)系统进行肿瘤分割和良恶性分类处理的前提,为此提出了一种全自动定位肿瘤位置的方法.该方法不依赖初始的固定参考位置和强制性后处理规则,能够较大限度地适应肿瘤在超声图像中相对位置的变化.与目前最好的几种自动定位方法相比,该方法具有更高的定位准确率.     

基于视频图像的火灾自动检测

首先分析了火焰的基本特性,给出了火焰图像的特征描述,然后利用火焰图像序列的边缘不稳定和相似性等可识别特征以及面积大小和颜色等信息,实现了对视频序列图像中火焰的自动检测。实验证明,所实现的系统检测效果好、误判率低。     

An integrated automatic face detection and recognition system

This paper proposes an integrated system for unconstrained face recognition in complex scenes. The scale and orientation tolerant system comprises a face detector followed by a recognizer. Given a color input image of a person, the face detector encloses the face from the complex scene within a circular boundary, and locates the position of the nose. A radial grid mapping centered on the nose is then performed to extract a feature vector within the boundary. The feature vector is input to a radial basis function neural network classifier for face identification. The proposed face detector achieved an average detection rate of 95.8% while the face recognizer achieved an average recognition rate of 97.5% on a database of 21 persons with variations in scale, orientation, natural illumination and background. The two modules were combined to form an automatic face recognition system that was evaluated in the context of a security system using a video database of 21 users and 10 intruders, acquired in an unconstrained environment. A recognition rate of 93.5% with 0% false acceptance rate was achieved.     

Towards automatic conflict detection in home and building automation systems

Home and Building Automation Systems (HBAS) are becoming of widespread adoption. When distinct users interact with such systems, their intentions are likely to be different, often resulting in conflicting situations, which the systems ought to recognize and resolve automatically. This work aims at investigating conflict in HBAS and creating a solution to detect and resolve them. Herein, we review the literature concerning conflict detection and resolution, and propose a formal framework based on constraint solving that enables detecting and solving conflict situations automatically.     

ISMS insider intrusion prevention and detection

A wide variety of different techniques and technologies are potentially applicable for ISMS insider intrusion prevention and detection. In this report we examine three approaches that have not been reviewed in any great detail recently, namely: simulation and modelling, scenario gaming and game theory, and artificial learning technologies. We show how each of these diverse approaches might be applicable to particular corporate scenarios that may eventuate as a result of potential insider intrusions into an ISMS.