Design of a model-generated repository as a service for USDL

SAP and its research partners have been developing a lan- guage for describing details of Services from various view- points called the Unified Service Description Language (USDL). At the time of writing, version 3.0 describes technical implementation aspects of services, as well as stakeholders, pricing, lifecycle, and availability. Work is also underway to address other business and legal aspects of services. This language is designed to be used in service portfolio management, with a repository of service descriptions being available to various stakeholders in an organisation to allow for service prioritisation, development, deployment and lifecycle management. The structure of the USDL metadata is specified using an object-oriented metamodel that conforms to UML, MOF and EMF Ecore. As such it is amenable to code gener-ation for implementations of repositories that store service description instances. Although Web services toolkits can be used to make these programming language objects available as a set of Web services, the practicalities of writing dis- tributed clients against over one hundred class definitions, containing several hundred attributes, will make for very large WSDL interfaces and highly inefficient “chatty” implementations. This paper gives the high-level design for a completely model-generated repository for any version of USDL (or any other data-only metamodel), which uses the Eclipse Modelling Framework’s Java code generation, along with several open source plugins to create a robust, transactional repository running in a Java application with a relational datastore. However, the repository exposes a generated WSDL interface at a coarse granularity, suitable for distributed client code and user-interface creation. It uses heuristics to drive code generation to bridge between the Web service and EMF granularities.

Graphcut-based interactive segmentation using colour and depth cues

Segmentation of novel or dynamic objects in a scene, often referred to as background sub- traction or foreground segmentation, is critical for robust high level computer vision applica- tions such as object tracking, object classifca- tion and recognition. However, automatic real- time segmentation for robotics still poses chal- lenges including global illumination changes, shadows, inter-re ections, colour similarity of foreground to background, and cluttered back- grounds. This paper introduces depth cues provided by structure from motion (SFM) for interactive segmentation to alleviate some of these challenges. In this paper, two prevailing interactive segmentation algorithms are com- pared; Lazysnapping [Li et al., 2004] and Grab- cut [Rother et al., 2004], both based on graph- cut optimisation [Boykov and Jolly, 2001]. The algorithms are extended to include depth cues rather than colour only as in the original pa- pers. Results show interactive segmentation based on colour and depth cues enhances the performance of segmentation with a lower er- ror with respect to ground truth.

Configurable multi-perspective business process models

A configurable process model provides a consolidated view of a family of business processes. It promotes the reuse of proven practices by providing analysts with a generic modelling artifact from which to derive individual process models. Unfortunately, the scope of existing notations for configurable process modelling is restricted, thus hindering their applicability. Specifically, these notations focus on capturing tasks and control-flow dependencies, neglecting equally important ingredients of business processes such as data and resources. This research fills this gap by proposing a configurable process modelling notation incorporating features for capturing resources, data and physical objects involved in the performance of tasks. The proposal has been implemented in a toolset that assists analysts during the configuration phase and guarantees the correctness of the resulting process models. The approach has been validated by means of a case study from the film industry.

Semi-supervised intelligent surveillance system for secure environments

This paper proposes a semi-supervised intelligent visual surveillance system to exploit the information from multi-camera networks for the monitoring of people and vehicles. Modules are proposed to perform critical surveillance tasks including: the management and calibration of cameras within a multi-camera network; tracking of objects across multiple views; recognition of people utilising biometrics and in particular soft-biometrics; the monitoring of crowds; and activity recognition. Recent advances in these computer vision modules and capability gaps in surveillance technology are also highlighted.

Distributed object tracking with robot and disjoint camera networks

We describe a novel two stage approach to object localization and tracking using a network of wireless cameras and a mobile robot. In the first stage, a robot travels through the camera network while updating its position in a global coordinate frame which it broadcasts to the cameras. The cameras use this information, along with image plane location of the robot, to compute a mapping from their image planes to the global coordinate frame. This is combined with an occupancy map generated by the robot during the mapping process to track the objects. We present results with a nine node indoor camera network to demonstrate that this approach is feasible and offers acceptable level of accuracy in terms of object locations.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.

A crash avoidance framework for heavy equipment control systems using 3D imaging sensors

This paper presents a preliminary crash avoidance framework for heavy equipment control systems. Safe equipment operation is a major concern on construction sites since fatal on-site injuries are an industry-wide problem. The proposed framework has potential for effecting active safety for equipment operation. The framework contains algorithms for spatial modeling, object tracking, and path planning. Beyond generating spatial models in fractions of seconds, these algorithms can successfully track objects in an environment and produce a collision-free 3D motion trajectory for equipment.

A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques

Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

Object identification based on 3D spatial models of construction sites

On obstacle-cluttered construction sites, understanding the motion characteristics of objects is important for anticipating collisions and preventing accidents. This study investigates algorithms for object identification applications that can be used by heavy equipment operators to effectively monitor congested local environment. The proposed framework contains algorithms for three-dimensional spatial modeling and image matching that are based on 3D images scanned by a high-frame rate range sensor. The preliminary results show that an occupancy grid spatial modeling algorithm can successfully build the most pertinent spatial information, and that an image matching algorithm is best able to identify which objects are in the scanned scene.

The effectiveness of literalism-symbolism in business-to-business advertising

A basic element in advertising strategy is the choice of an appeal. In business-to-business (B2B) marketing communication, a long-standing approach relies on literal and factual, benefit-laden messages. Given the highly complex, costly and involved processes of business purchases, such approaches are certainly understandable. This project challenges the traditional B2B approach and asks if an alternative approach—using symbolic messages that operate at a more intrinsic or emotional level—is effective in the B2B arena. As an alternative to literal (factual) messages, there is an emerging body of literature that asserts stronger, more enduring results can be achieved through symbolic messages (imagery or text) in an advertisement. The present study contributes to this stream of research. From a theoretical standpoint, the study explores differences in literal-symbolic message content in B2B advertisements. There has been much discussion—mainly in the consumer literature—on the ability of symbolic messages to motivate a prospect to process advertising information by necessitating more elaborate processing and comprehension. Business buyers are regarded as less receptive to indirect or implicit appeals because their purchase decisions are based on direct evidence of product superiority. It is argued here, that these same buyers may be equally influenced by advertising that stimulates internally-directed motivation, feelings and cognitions about the brand. Thus far, studies on the effect of literalism and symbolism are fragmented, and few focus on the B2B market. While there have been many studies about the effects of symbolism no adequate scale exists to measure the continuum of literalism-symbolism. Therefore, a first task for this study was to develop such a scale. Following scale development, content analysis of 748 B2B print advertisements was undertaken to investigate whether differences in literalism-symbolism led to higher advertising performance. Variations of time and industry were also measured. From a practical perspective, the results challenge the prevailing B2B practice of relying on literal messages. While definitive support was not established for the use of symbolic message content, literal messages also failed to predict advertising performance. If the ‘fact, benefit laden’ assumption within B2B advertising cannot be supported, then other approaches used in the business-to-consumer (B2C) sector, such as symbolic messages may be also appropriate in business markets. Further research will need to test the potential effects of such messages, thereby building a revised foundation that can help drive advances in B2B advertising. Finally, the study offers a contribution to the growing body of knowledge on symbolism in advertising. While the specific focus of the study relates to B2B advertising, the Literalism-Symbolism scale developed here provides a reliable measure to evaluate literal and symbolic message content in all print advertisements. The value of this scale to advance our understanding about message strategy may be significant in future consumer and business advertising research.

Textures of optical flow for real-time anomaly detection in crowds

Automated visual surveillance of crowds is a rapidly growing area of research. In this paper we focus on motion representation for the purpose of abnormality detection in crowded scenes. We propose a novel visual representation called textures of optical flow. The proposed representation measures the uniformity of a flow field in order to detect anomalous objects such as bicycles, vehicles and skateboarders; and can be combined with spatial information to detect other forms of abnormality. We demonstrate that the proposed approach outperforms state-of-the-art anomaly detection algorithms on a large, publicly-available dataset.

A hierarchical security assessment model for object-oriented programs

We present a hierarchical model for assessing an object-oriented program's security. Security is quantified using structural properties of the program code to identify the ways in which `classified' data values may be transferred between objects. The model begins with a set of low-level security metrics based on traditional design characteristics of object-oriented classes, such as data encapsulation, cohesion and coupling. These metrics are then used to characterise higher-level properties concerning the overall readability and writability of classified data throughout the program. In turn, these metrics are then mapped to well-known security design principles such as `assigning the least privilege' and `reducing the size of the attack surface'. Finally, the entire program's security is summarised as a single security index value. These metrics allow different versions of the same program, or different programs intended to perform the same task, to be compared for their relative security at a number of different abstraction levels. The model is validated via an experiment involving five open source Java programs, using a static analysis tool we have developed to automatically extract the security metrics from compiled Java bytecode.

Business service management : understanding business models

One of the prominent topics in Business Service Management is business models for (new) services. Business models are useful for service management and engineering as they provide a broader and more holistic perspective on services. Business models are particularly relevant for service innovation as this requires paying attention to the business models that make new services viable and business model innovation can drive the innovation of new and established services. Before we can have a look at business models for services, we first need to understand what business models are. This is not straight-forward as business models are still not well comprehended and the knowledge about business models is fragmented over different disciplines, such as information systems, strategy, innovation, and entrepreneurship. This whitepaper, ‘Understanding business models,’ introduces readers to business models. This whitepaper contributes to enhancing the understanding of business models, in particular the conceptualisation of business models by discussing and integrating business model definitions, frameworks and archetypes from different disciplines. After reading this whitepaper, the reader will have a well-developed understanding about what business models are and how the concept is sometimes interpreted and used in different ways. It will help the reader in assessing their own understanding of business models and that and of others. This will contribute to a better and more beneficial use of business models, an increase in shared understanding, and making it easier to work with business model techniques and tools.

Integrating information literacy into higher education curricula : an IL curricular integration model

This study investigates a way to systematically integrate information literacy (IL) into an undergraduate academic programme and develops a model for integrating information literacy across higher education curricula. Curricular integration of information literacy in this study means weaving information literacy into an academic curriculum. In the associated literature, it is also referred to as the information literacy embedding approach or the intra-curricular approach. The key findings identified from this study are presented in 4 categories: the characteristics of IL integration; the key stakeholders in IL integration; IL curricular design strategies; and the process of IL curricular integration. Three key characteristics of the curricular integration of IL are identified: collaboration and negotiation, contextualisation and ongoing interaction with information. The key stakeholders in the curricular integration of IL are recognised as the librarians, the course coordinators and lecturers, the heads of faculties or departments, and the students. Some strategies for IL curricular design include: the use of IL policies and standards in IL curricular design; the combination of face to face and online teaching as an emerging trend; the use of IL assessment tools which play an important role in IL integration. IL can be integrated into the intended curriculum (what an institution expects its students to learn), the offered curriculum (what the teachers teach) and the received curriculum (what students actually learn). IL integration is a process of negotiation, collaboration and the implementation of the intended curriculum. IL can be integrated at different levels of curricula such as: institutional, faculty, departmental, course and class curriculum levels. Based on these key findings, an IL curricular integration model is developed. The model integrates curriculum, pedagogy and learning theories, IL theories, IL guidelines and the collaboration of multiple partners. The model provides a practical approach to integrating IL into multiple courses across an academic degree. The development of the model was based on the IL integration experiences of various disciplines in three universities and the implementation experience of an engineering programme at another university; thus it may be of interest to other disciplines. The model has the potential to enhance IL teaching and learning, curricular development and to implement graduate attributes in higher education. Sociocultural theories are applied to the research process and IL curricular design of this study. Sociocultural theories describe learning as being embedded within social events and occurring as learners interact with other people, objects, and events in a collaborative environment. Sociocultural theories are applied to explore how academic staff and librarians experience the curricular integration of IL; they also support collaboration in the curricular integration of IL and the development of an IL integration model. This study consists of two phases. Phase I (2007) was the interview phase where both academic staff and librarians at three IL active universities were interviewed. During this phase, attention was paid specifically to the practical process of curricular integration of IL and IL activity design. Phase II, the development phase (2007-2008), was conducted at a fourth university. This phase explores the systematic integration of IL into an engineering degree from Year 1 to Year 4. Learning theories such as sociocultural theories, Bloom’s Taxonomy and IL theories are used in IL curricular development. Based on the findings from both phases, an IL integration model was developed. The findings and the model contribute to IL education, research and curricular development in higher education. The sociocultural approach adopted in this study also extends the application of sociocultural theories to the IL integration process and curricular design in higher education.

Automated object identification using optical video cameras on construction sites

Visual recording devices such as video cameras, CCTVs, or webcams have been broadly used to facilitate work progress or safety monitoring on construction sites. Without human intervention, however, both real-time reasoning about captured scenes and interpretation of recorded images are challenging tasks. This article presents an exploratory method for automated object identification using standard video cameras on construction sites. The proposed method supports real-time detection and classification of mobile heavy equipment and workers. The background subtraction algorithm extracts motion pixels from an image sequence, the pixels are then grouped into regions to represent moving objects, and finally the regions are identified as a certain object using classifiers. For evaluating the method, the formulated computer-aided process was implemented on actual construction sites, and promising results were obtained. This article is expected to contribute to future applications of automated monitoring systems of work zone safety or productivity.