Architecture for service profiling

Service oriented architecture is gaining momentum. However, in order to be successful, the proper and up-to-date description of services is required. Such a description may be provided by service profiling mechanisms, such as one presented in this article. Service profile can be defined as an up-to-date description of a subset of non-functional properties of a service. It allows for service comparison on the basis of non-functional parameters, and choosing the service which is most suited to the needs of a user. In this article the notion of a service profile along with service profiling mechanism is presented as well as the architecture of a profiling system. © 2006 IEEE.

Advances in continuously profiling the thermodynamic state of the boundary layer: integration of measurements and methods

This paper describes advances in ground-based thermodynamic profiling of the lower troposphere through sensor synergy. The well-documented integrated profiling technique (IPT), which uses a microwave profiler, a cloud radar, and a ceilometer to simultaneously retrieve vertical profiles of temperature, humidity, and liquid water content (LWC) of nonprecipitating clouds, is further developed toward an enhanced performance in the boundary layer and lower troposphere. For a more accurate temperature profile, this is accomplished by including an elevation scanning measurement modus of the microwave profiler. Height-dependent RMS accuracies of temperature (humidity) ranging from 0.3 to 0.9 K (0.5–0.8 g m−3) in the boundary layer are derived from retrieval simulations and confirmed experimentally with measurements at distinct heights taken during the 2005 International Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH) of the German Weather Service. Temperature inversions, especially of the lower boundary layer, are captured in a very satisfactory way by using the elevation scanning mode. To improve the quality of liquid water content measurements in clouds the authors incorporate a sophisticated target classification scheme developed within the European cloud observing network CloudNet. It allows the detailed discrimination between different types of backscatterers detected by cloud radar and ceilometer. Finally, to allow IPT application also to drizzling cases, an LWC profiling method is integrated. This technique classifies the detected hydrometeors into three different size classes using certain thresholds determined by radar reflectivity and/or ceilometer extinction profiles. By inclusion into IPT, the retrieved profiles are made consistent with the measurements of the microwave profiler and an LWC a priori profile. Results of IPT application to 13 days of the LAUNCH campaign are analyzed, and the importance of integrated profiling for model evaluation is underlined.

海洋要素垂直剖面测量系统的数据存储

长期、定点、连续测量海洋环境参数，尤其是从海洋表面到海底的垂直剖面的监测一直以来大都是由船只完成，耗费大量的人力和财力。“海浪驱动自持式海洋要素垂直剖面测量系统”（简称“海马”）利用海洋无时不在的波浪能，驱动滑行器单方向下潜运动，直达海底或程序设定的预定深度，在控制系统作用下，滑行器依靠自身的浮力匀速上浮， 并在此过程中，完成海洋要素的自动测量和存储。 当今海洋仪器大多采用“闪存”来存储数据，主要有RS－232串口和并型口的CF(Compact Flash)卡两种形式。但是两种接口都有一定的缺陷：CF卡不能和PC机直接通信，必须通过转接口才能将数据传送到PC机上；而RS-232接口虽然可以和PC机直接通信，但是由于其传输速度较慢，不利于大量数据的传输。由于上述两种接口的缺点，“海马”采用U盘来实现数据的存储。 USB接口不仅可以和PC机直接通信，而且具有使用方便，数据传输率高，又支持即插即用等特点，但由于USB接口协议复杂，涉及的方面广，特别是软件种类比较多，而且USB芯片种类繁多，使得USB设备的开发非常困难。 本文设计的USB(Universal Serial Bus)接口电路以单片机AT89C51以及CYPRESS公司生产的USB接口芯片SL811HS为核心，结合了随机存储芯片HY62256以及看门狗芯片X25165。在介绍了SL811HS控制器芯片的主要特点的基础上，重点阐述了利用这一芯片在C语言的编程控制之下如何识别U盘的插入和拔出，以及如何将单片机的数据按协议规定写入U盘，其中的协议包括FAT文件协议，USB协议和UFI命令协议。 本系统实现了单片机对U盘的数据存储，速度快，存储容量大，数据读取方便，直观明了，系统工作安全可靠，抗干扰能力强，可扩展性大，而且针对下位机设计的串口协议简单，可以满足“海马”海量数据的存储要求。

海洋要素垂直剖面测量系统控制电路研究

“海浪驱动自持式海洋要素垂直剖面测量系统”（简称“海马”）利用海洋无时不在的波浪能，驱动滑行器单方向下潜运动，直达海底或程序设定的预定深度，在控制系统作用下，滑行器依靠自身的浮力匀速上浮， 并在此过程中，完成海洋要素的自动测量和存储。 “海马”的中央控制单元是滑行器运动状态的指令官。在控制电路的作用下，“海马”在水下完成单向抓紧、阻尼锁定和自由上浮三种状态。当系统处于单向抓紧状态时，滑行器只能向下运动。当波浪提升时，抓紧缆绳的棘爪松开，滑行器因惯性滞留原位；当波浪下降时，棘爪咬紧缆绳，滑行器步进下潜。锚定缆绳在波浪不停起伏的作用下连续做升沉运动，直达海底或控制单元预置的测量深度。在到达底部之后在控制系统的作用下，棘爪机构打开，滑行器与锚定缆绳失去啮合，完全处于自由状态，滑行器是一正浮力载体，所以，滑行器将在浮力作用下沿锚定钢缆上浮。在系统自由上浮的过程中，位于滑行器内的传感器开始测量所需海洋要素。阻尼锁定即为在引导缆的固定位置（例如，在近海面（5～8米）与近海底（15～20米））上分别敷设两只阻尼器，滑行器可固定于这两个特殊水层上，完成某些特殊海洋参数的测量。 中央控制单元硬件电路的设计基于AT89C2051单片机，结合了固态继电器、电磁继电器以及I2C总线芯片等元器件。整个硬件电路为配合系统的集成要求，设计成直径为109mm的圆形版图。在设计电路的同时考虑了从硬件方面避免电磁干扰的措施。 中央控制单元的软件编写采用汇编语言。汇编语言简单明了，程序总共分为3个主要模块，分别是主程序、运动状态控制模块和系统参数设置模块。模块化设计增强了程序的可读性。为了防止程序运行时跑飞，还可将“看门狗”电路芯片引入电路中。一旦程序跑飞，“Reset”信号被出发，程序被初始化。 文章同时介绍了对中央控制单元设置及使用的步骤及流程。后期的实验总共分为2次室内模拟实验和1次近海模拟实验。实验的数据图表说明，中央控制单元能够很好的控制“海马”滑行器在水下按照预先设定的状态运动，实验得到的数据表明，“海马”系统在下降过程中不采集任何数据，只有压力传感器不停的读取深度值与预先设定的值进行比较，系统只有在自由上浮过程中才会采集数据。在此过程中，电路的硬件和软件运行正常，达到实验初期设想的目的。

User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.

Parallel user profiling based on folksonomy for Large Scaled Recommender Systems : an implimentation of Cascading MapReduce

The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users’ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Del.icio.us website.

Tensor reduction for user profiling in personalized recommender systems

User profiling is the process of constructing user models which represent personal characteristics and preferences of customers. User profiles play a central role in many recommender systems. Recommender systems recommend items to users based on user profiles, in which the items can be any objects which the users are interested in, such as documents, web pages, books, movies, etc. In recent years, multidimensional data are getting more and more attention for creating better recommender systems from both academia and industry. Additional metadata provides algorithms with more details for better understanding the interactions between users and items. However, most of the existing user/item profiling techniques for multidimensional data analyze data through splitting the multidimensional relations, which causes information loss of the multidimensionality. In this paper, we propose a user profiling approach using a tensor reduction algorithm, which we will show is based on a Tucker2 model. The proposed profiling approach incorporates latent interactions between all dimensions into user profiles, which significantly benefits the quality of neighborhood formation. We further propose to integrate the profiling approach into neighborhoodbased collaborative filtering recommender algorithms. Experimental results show significant improvements in terms of recommendation accuracy.

Load Profiling in Distributed Real-Time Systems

Load balancing is often used to ensure that nodes in a distributed systems are equally loaded. In this paper, we show that for real-time systems, load balancing is not desirable. In particular, we propose a new load-profiling strategy that allows the nodes of a distributed system to be unequally loaded. Using load profiling, the system attempts to distribute the load amongst its nodes so as to maximize the chances of finding a node that would satisfy the computational needs of incoming real-time tasks. To that end, we describe and evaluate a distributed load-profiling protocol for dynamically scheduling time-constrained tasks in a loosely-coupled distributed environment. When a task is submitted to a node, the scheduling software tries to schedule the task locally so as to meet its deadline. If that is not feasible, it tries to locate another node where this could be done with a high probability of success, while attempting to maintain an overall load profile for the system. Nodes in the system inform each other about their state using a combination of multicasting and gossiping. The performance of the proposed protocol is evaluated via simulation, and is contrasted to other dynamic scheduling protocols for real-time distributed systems. Based on our findings, we argue that keeping a diverse availability profile and using passive bidding (through gossiping) are both advantageous to distributed scheduling for real-time systems.

Expression profiling und rationale Expressionsoptimierung am Beispiel eines prokaryontischen Wirt-,Vektor-Systems

Durch globale Expressionsprofil-Analysen auf Transkriptom-, Proteom- oder Metabolom-Ebene können biotechnologische Produktionsprozesse besser verstanden und die Erkenntnisse für die zielgerichtete, rationale Optimierung von Expressionssystemen genutzt werden. In der vorliegenden Arbeit wurde die Überexpression einer Glukose-Dehydrogenase (EC 1.1.5.2), die von der Roche Diagnostics GmbH für die diagnostische Anwendung optimiert worden war, in Escherichia coli untersucht. Die Enzymvariante unterscheidet sich in sieben ihrer 455 Aminosäuren vom Wildtyp-Enzym und wird im sonst isogenen Wirt-/Vektor-System in signifikant geringeren Mengen (Faktor 5) gebildet. Das prokaryontische Expressionssystem wurde auf Proteom-Ebene charakterisiert. Die 2-dimensionale differenzielle Gelelektrophorese (DIGE) wurde zuvor unter statistischen Aspekten untersucht. Unter Berücksichtigung von technischen und biologischen Variationen, falsch-positiven (α-) und falsch-negativen (β-) Fehlern sowie einem daraus abgeleiteten Versuchsdesign konnten Expressionsunterschiede als signifikant quantifiziert werden, wenn sie um den Faktor ≥ 1,4 differierten. Durch eine Hauptkomponenten-Analyse wurde gezeigt, dass die DIGE-Technologie für die Expressionsprofil-Analyse des Modellsystems geeignet ist. Der Expressionsstamm für die Enzymvariante zeichnete sich durch eine höhere Variabilität an Enzymen für den Zuckerabbau und die Nukleinsäure-Synthese aus. Im Expressionssystem für das Wildtyp-Enzym wurde eine unerwartet erhöhte Plasmidkopienzahl nachgewiesen. Als potenzieller Engpass in der Expression der rekombinanten Glukose-Dehydrogenase wurde die Löslichkeitsvermittlung identifiziert. Im Expressionsstamm für das Wildtyp-Enzym wurden viele Proteine für die Biogenese der äußeren Membran verstärkt exprimiert. Als Folge dessen wurde ein sog. envelope stress ausgelöst und die Zellen gingen in die stationäre Wuchsphase über. Die Ergebnisse der Proteomanalyse wurden weiterführend dazu genutzt, die Produktionsleistung für die Enzymvariante zu verbessern. Durch den Austausch des Replikationsursprungs im Expressionsvektor wurde die Plasmidkopienzahl erhöht und die zelluläre Expressionsleistung für die diagnostisch interessantere Enzymvariante um Faktor 7 - 9 gesteigert. Um die Löslichkeitsvermittlung während der Expression zu verbessern, wurde die Plasmidkopienzahl gesenkt und die Coexpression von Chaperonen initiiert. Die Ausbeuten aktiver Glukose-Dehydrogenase wurden durch die Renaturierung inaktiven Produkts aus dem optimierten Expressionssystem insgesamt um einen Faktor von 4,5 erhöht. Somit führte im Rahmen dieser Arbeit eine proteombasierte Expressionsprofil-Analyse zur zielgerichteten, rationalen Expressionsoptimierung eines prokaryontischen Modellsystems.

Task profiling : A task-Based approach to measuring the integration of ICT in the classroom

The measurement of ICT (information and communication technology) integration is emerging as an area of research interest with such systems as Education Queensland including it in their recently released list of research priorities. Studies to trial differing integration measurement instruments have taken place within Australia in the last few years, particularly Western Australia (Trinidad, Clarkson, & Newhouse, 2004; Trinidad, Newhouse & Clarkson, 2005), Tasmania (Fitzallen 2005) and Queensland (Finger, Proctor, & Watson, 2005). This paper will add to these investigations by describing an alternate and original methodological approach which was trialled in a small-scale pilot study conducted jointly by Queensland Catholic Education Commission (QCEC) and the Centre of Learning Innovation, Queensland University of Technology (QUT) in late 2005. The methodology described is based on tasks which, through a process of profiling, can be seen to be artefacts which embody the internal and external factors enabling and constraining ICT integration.

Collaborative Filtering Recommender Systems based on Popular Tags

The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences for making personalized recommendations. However, the uncontrolled vocabulary causes a lot of problems to profile users accurately, such as ambiguity, synonyms, misspelling, low information sharing etc. To solve these problems, this paper proposes to use popular tags to represent the actual topics of tags, the content of items, and also the topic interests of users. A novel user profiling approach is proposed in this paper that first identifies popular tags, then represents users’ original tags using the popular tags, finally generates users’ topic interests based on the popular tags. A collaborative filtering based recommender system has been developed that builds the user profile using the proposed approach. The user profile generated using the proposed approach can represent user interests more accurately and the information sharing among users in the profile is also increased. Consequently the neighborhood of a user, which plays a crucial role in collaborative filtering based recommenders, can be much more accurately determined. The experimental results based on real world data obtained from Amazon.com show that the proposed approach outperforms other approaches.

Collaborative filtering recommender systems using tag information

Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviours such as purchase behaviour, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.

Computer profiling for forensic purposes

Computer forensics is the process of gathering and analysing evidence from computer systems to aid in the investigation of a crime. Typically, such investigations are undertaken by human forensic examiners using purpose-built software to discover evidence from a computer disk. This process is a manual one, and the time it takes for a forensic examiner to conduct such an investigation is proportional to the storage capacity of the computer's disk drives. The heterogeneity and complexity of various data formats stored on modern computer systems compounds the problems posed by the sheer volume of data. The decision to undertake a computer forensic examination of a computer system is a decision to commit significant quantities of a human examiner's time. Where there is no prior knowledge of the information contained on a computer system, this commitment of time and energy occurs with little idea of the potential benefit to the investigation. The key contribution of this research is the design and development of an automated process to describe a computer system and its activity for the purposes of a computer forensic investigation. The term proposed for this process is computer profiling. A model of a computer system and its activity has been developed over the course of this research. Using this model a computer system, which is the subj ect of investigation, can be automatically described in terms useful to a forensic investigator. The computer profiling process IS resilient to attempts to disguise malicious computer activity. This resilience is achieved by detecting inconsistencies in the information used to infer the apparent activity of the computer. The practicality of the computer profiling process has been demonstrated by a proof-of concept software implementation. The model and the prototype implementation utilising the model were tested with data from real computer systems. The resilience of the process to attempts to disguise malicious activity has also been demonstrated with practical experiments conducted with the same prototype software implementation.

Conifer defence against insects: microarray gene expression profiling of Sitka spruce (Picea sitchensis) induced by mechanical wounding or feeding by spruce budworms (Choristoneura occidentalis) or white pine weevils (Pissodes strobi) reveals large

Conifers are resistant to attack from a large number of potential herbivores or pathogens. Previous molecular and biochemical characterization of selected conifer defence systems support a model of multigenic, constitutive and induced defences that act on invading insects via physical, chemical, biochemical or ecological (multitrophic) mechanisms. However, the genomic foundation of the complex defence and resistance mechanisms of conifers is largely unknown. As part of a genomics strategy to characterize inducible defences and possible resistance mechanisms of conifers against insect herbivory, we developed a cDNA microarray building upon a new spruce (Picea spp.) expressed sequence tag resource. This first-generation spruce cDNA microarray contains 9720 cDNA elements representing c. 5500 unique genes. We used this array to monitor gene expression in Sitka spruce (Picea sitchensis) bark in response to herbivory by white pine weevils (Pissodes strobi, Curculionidae) or wounding, and in young shoot tips in response to western spruce budworm (Choristoneura occidentalis, Lepidopterae) feeding. Weevils are stem-boring insects that feed on phloem, while budworms are foliage feeding larvae that consume needles and young shoot tips. Both insect species and wounding treatment caused substantial changes of the host plant transcriptome detected in each case by differential gene expression of several thousand array elements at 1 or 2 d after the onset of treatment. Overall, there was considerable overlap among differentially expressed gene sets from these three stress treatments. Functional classification of the induced transcripts revealed genes with roles in general plant defence, octadecanoid and ethylene signalling, transport, secondary metabolism, and transcriptional regulation. Several genes involved in primary metabolic processes such as photosynthesis were down-regulated upon insect feeding or wounding, fitting with the concept of dynamic resource allocation in plant defence. Refined expression analysis using gene-specific primers and real-time PCR for selected transcripts was in agreement with microarray results for most genes tested. This study provides the first large-scale survey of insect-induced defence transcripts in a gymnosperm and provides a platform for functional investigation of plant-insect interactions in spruce. Induction of spruce genes of octadecanoid and ethylene signalling, terpenoid biosynthesis, and phenolic secondary metabolism are discussed in more detail.