Semantic data modelling and visualisation using Noetica

Noetica is a tool for structuring knowledge about concepts and the relationships between them. It differs from typical information systems in that the knowledge it represents is abstract, highly connected, and includes meta-knowledge (knowledge about knowledge). Noetica represents knowledge using a strongly typed graph data model. By providing a rich type system it is possible to represent conceptual information using formalised structures. A class hierarchy provides a basic classification for all objects. This allows for a consistency of representation that is not often found in “free” semantic networks, and gives the ability to easily extend a knowledge model while retaining its semantics.

Visualisation and query tools are provided for this data model. Visualisation can be used to explore complete sets of link-classes, show paths while navigating through the database, or visualise the results of queries. Noetica supports goal-directed queries (a series of user-supplied goals that the system attempts to satisfy in sequence) and pathfinding queries (where the system finds relationships between objects in the database by following links).     

Discovering and understanding android sensor usage behaviors with data flow analysis

Today’s Android-powered smartphones have various embedded sensors that measure the acceleration, orientation, light and other environmental conditions. Many functions in the third-party applications (apps) need to use these sensors. However, embedded sensors may lead to security issues, as the third-party apps can read data from these sensors without claiming any permissions. It has been proven that embedded sensors can be exploited by well designed malicious apps, resulting in leaking users’ privacy. In this work, we are motivated to provide an overview of sensor usage patterns in current apps by investigating what, why and how embedded sensors are used in the apps collected from both a Chinese app. market called “AppChina” and the official market called “Google Play”. To fulfill this goal, We develop a tool called “SDFDroid” to identify the used sensors’ types and to generate the sensor data propagation graphs in each app. We then cluster the apps to find out their sensor usage patterns based on their sensor data propagation graphs. We apply our method on 22,010 apps collected from AppChina and 7,601 apps from Google Play. Extensive experiments are conducted and the experimental results show that most apps implement their sensor related functions by using the third-party libraries. We further study the sensor usage behaviors in the third-party libraries. Our results show that the accelerometer is the most frequently used sensor. Though many third-party libraries use no more than four types of sensors, there are still some third-party libraries registering all the types of sensors recklessly. These results call for more attentions on better regulating the sensor usage in Android apps.     

基于数据中台架构的数据管理研究

为了解决数据问题,数据管理技术的价值得到了多家公司的认可,并出现了许多关于数据管理的想法和研究。然而,在实际的业务数据管理中,仍然存在一些问题。因此,文章基于对数据中台架构的数据管理进行了深入分析。     

Structural analysis and detection of android botnets using machine learning techniques

Nowadays, smartphone devices are an integral part of our lives since they enable us to access a large variety of services from personal to banking. The worldwide popularity and adoption of smartphone devices continue to approach the capabilities of traditional computing environments. The computer malware like botnets is becoming an emerging threat to users and network operators, especially on popular platform such as android. Due to the rapid growth of botnet applications, there is a pressing need to develop an effective solution to detect them. Most of the existing detection techniques can detect only malicious android applications, but it cannot detect android botnet applications. In this paper, we propose a structural analysis-based learning framework, which adopts machine learning techniques to classify botnets and benign applications using the botnet characteristics-related unique patterns of requested permissions and used features. The experimental evaluation based on real-world benchmark datasets shows that the selected patterns can achieve high detection accuracy with low false positive rate. The experimental and statistical tests show that the support vector machine classifier performs well compared to other classification algorithms.     

Service-oriented middleware for distributed data mining on the grid

Distribution of data and computation allows for solving larger problems and executing applications that are distributed in nature. The grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. The grid extends the distributed and parallel computing paradigms allowing for resource negotiation and dynamical allocation, heterogeneity, open protocols, and services. Grid environments can be used both for compute-intensive tasks and data intensive applications by exploiting their resources, services, and data access mechanisms. Data mining algorithms and knowledge discovery processes are both compute and data intensive, therefore the grid can offer a computing and data management infrastructure for supporting decentralized and parallel data analysis. This paper discusses how grid computing can be used to support distributed data mining. Research activities in grid-based data mining and some challenges in this area are presented along with some promising future directions for developing grid-based distributed data mining.     

基于设计模式构建数据访问中间件

设计了一种两层的数据访问结构,第一层是介于数据访问层和用户之间的中间层,主要是组织数据和调用数据访问中间件;第二层是数据访问中间件,主要是同数据库交互.在设计数据访问中间件时,根据设计模式的概念,对访问数据层模式进行了比较,指出了各个类的作用、相互的关系以及如何对这个类进行扩展,最后构建出中间件.     

探索基于DDS模型的数据分发中间件

介绍了对象管理组织发布的数据分发服务(DDS)新规范,描述了DDS规范两个层次的接口DCPS和DLRL,阐述了DDS数据分发的基本思想,分析了QoS策略及其特征,探讨了DDS适用的场合和未来的发展。     

Wearable sensor activity analysis using semi-Markov models with a grammar

Detailed monitoring of training sessions of elite athletes is an important component of their training. In this paper we describe an application that performs a precise segmentation and labeling of swimming sessions. This allows a comprehensive breakdown of the training session, including lap times, detailed statistics of strokes, and turns. To this end we use semi-Markov models (SMM), a formalism for labeling and segmenting sequential data, trained in a max-margin setting. To reduce the computational complexity of the task and at the same time enforce sensible output, we introduce a grammar into the SMM framework. Using the trained model on test swimming sessions of different swimmers provides highly accurate segmentation as well as perfect labeling of individual segments. The results are significantly better than those achieved by discriminative hidden Markov models.     

基于XML的数据交换中间件的研究与实现

使用XML分别表示数据的结构信息和值信息,通过对数据结构文件和用户定义的规则文件进行分析生成映射文件,从而解决数据冲突.在此基础上,设计实现了一个基于XML的数据交换中间件(XDEM)系统.系统将数据交换处理流程与处理方法相分离,用户可以定制开发不同的处理插件,以满足交换过程的动态配置.这些极大地增强了系统的适应性和扩展性.     

Discovering meaning on the go in large heterogenous data

The world is increasingly full of data. Organisations, governments and individuals are creating increasingly large data sources, and in many cases making them publicly available. This offers massive potential for interaction and mutual collaboration. But using this data often creates problems. Those creating the data will use their own terminology, structure and formats for the data, meaning that data from one source will be incompatible with data from another source. When presented with a large, unknown data source, it is very difficult to ascribe meaning to the terms of that data source, and to understand what is being conveyed. Much effort has been invested in data interpretation prior to run-time, with large data sources being matched against each other off-line. But data is often used dynamically, and so to maximise the value of the data it is necessary to extract meaning from it dynamically. We therefore postulate that an essential competent of utilising the world of data in which we increasingly live is the development of the ability to discover meaning on the go in large, heterogenous data.This paper provides an overview of the current state-of-the-art, reviewing the aims and achievements in different fields which can be applied to this problem. We take a brief look at cutting edge research in this field, summarising four papers published in the special issue of the AI Review on Discovering Meaning on the go in Large Heterogenous Data, and conclude with our thoughts about where research in this field is going, and what our priorities must be to enable us to move closer to achieving this goal.     

通信层数据交换中间件设计实现

针对企业级信息系统通信层数据交换量大,数据帧编码协议不统一,难于利用和管理的问题,设计通用数据交换中间件解决方案,将系统的数据通信层独立封装,利用DLL、ACE(自适配通信环境)、XML等技术为上层应用提供数据交换服务.设计了基于XML的数据帧结构、TCP粘包处理算法、数据流压缩、节点故障检测技术等.适配器便于数据共享与集成利用,并提供节点故障发现能力.     

面向大规模数据集成消息中间件系统设计实现

在分布式网络环境中,构造基于消息通信中间件的数据传输系统,能有效地满足数据传输的可靠性、安全性和异步性等企业级需求.然而,随着企业经营规模的扩大,对数据传输系统提出了参与方多、交互数据量大等新的需求,现有的基于点对点(PTP)和发布/订阅(pub/Sub)通信模式的消息中间件难以很好的支持这些新的需求.根据大规模数据传输系统的新特点,结合 PTP 和 Pub/Sub 通信模式,提出了一种基于点对点的发布/订阅消息中间件通信模型(PBPS),并在消息中间件ISMQ基础上提供了实现.实际应用表明,该方法能够有效满足大规模数据传输的要求.     

Identification of malicious android app using manifest and opcode features

In this paper, we propose a statistical approach for smartphone malware detection. A set of features such as hardware, permission, application components, filtered intents, opcodes and strings are extracted from the samples to form a vector space model. Feature selection methods such as Entropy based Category Coverage Difference (ECCD) and Weighted Mutual Information (WI) are used to choose the prominent features. The performance of the system is analyzed using classifiers, Random Forest, Rotation Forest and Support Vector Machine (SVM). The system was evaluated on individual models as well as Meta feature space model for both malware and benign features. It was observed that the meta feature space model with malware features provide the best results for both feature selection. For ECCD, Random Forest classifier performs better [Dataset 1—0.972, Dataset 2—0.976 and Dataset 3—0.969] whereas in the case of WI, SVM gives highest F-measure [Dataset 1—0.993, Dataset 2—0.994 and Dataset 3—0.992]. From the overall analysis on the system, we conclude that the malware model outperforms it’s benign counterpart and also that WI is a better feature selection technique compared to ECCD.     

The analysis and visualisation of metal microstructures

Micromechanics is the design and manufacture of very small three-dimensional parts. It combines electronic, mechanical, optical and other technologies into a single system and employs miniaturisation to achieve high complexity in a small volume. Micro engineered devices and components have major dimensions measured in micro- or nanometres. An overview of microengineering will be presented together with actual examples of micromachined devices.

The analysis of microstructures is essentially the same as the analysis of larger components, however the tiny dimensions of these components does pose problems to some of the ‘off the shelf’ analysis systems. Some of these problems are discussed together with some simple guidelines for successful analysis. A case study example is presented detailing the finite element analysis (FEA) of a novel metal micro-accelerometer manufactured using the LIGA technique (Maner, A., Harsch, S. and Ehrfeld, W., Plating and Surface Finishing, 1988, March, 60–65). The finite element analysis software ALGOR has been used in this assessment. Special attention is given to the accuracy of results.     

Facial landmarks detection using improved active shape model on android platform

Detection of facial feature is fundamental for applications such as security, biometrics, 3D face modeling and personal authentication. Active Shape Model (ASM) is one of the most popular local texture models for face detection. This paper presents an issue related to face detection based on ASM, and proposes an efficient extraction algorithm for facial landmarks suitable for use on mobile devices. We modifies the original ASM to improve its performance with three changes; (1) Improving the initialization model using the center of the eyes by using a feature map of color information, (2) Constructing modified model definition and fitting more landmarks than the classical ASM, and (3) Extending and building a 2-D profile model for detecting faces in input image. The proposed method is evaluated on dataset containing over 700 images of faces, and experimental results reveal that the proposed algorithm exhibited a significant improvement of over 10.2 % in average success ratio, compared to the classic ASM, clearly outperforming on success rate and computing time.     

Agents, grids, and middleware

Agents, grids, and middleware are more closely related. We develop software architecture for two software projects: one involves infrastructure for distributed, pervasive computing; the other involves partitioning huge data sets across data grids consisting of thousands of PCs. On the surface, these projects seem very different, but there are similarities and lessons we can learn from comparing them that have implications for agents, grids, Web services, pervasive computing, and middleware.     

基于 BIoom fiIter 的 RFID 中间件数据过滤算法研究

有效地减少 RFID 系统中冗余阅读器或天线采集到的大量重复数据,可以降低系统能耗和提高处理效率。经研究,提出采用改进的布隆过滤器（Bloom filter）对 RFID 采集数据进行去重过滤,并运用到中间件系统中。改进的 Bloom filter 主要将两个标准的 Bloom filter 组成二维并行 Bloom filter,对 RFID 采集数据所包含的两个属性值 tagID 和 readerID 进行并行过滤。经实验可见,标准 Bloom filter 与哈希过滤（hash filter）相比具有明显的优势,对其改进后,采用二维并行 Bloom filter 在误判率、吞吐率和存储空间上具有更高的系统性能。     

DiffGram结合中间件的网络应用方法

文章利用XML DiffGram及中间件技术,通过网络实现了对SQL数据库表的删除、插入和更新等操作。与一般操作数据库方法不同,该方法直接使用XML文件对SQL Server2000的数据进行操作,是操作数据库的一种新方法。