Addressing the needs of struggling literacy learners

Think back to your teaching experiences over the past four years. Were there a number of children or adolescents in your classes who seemed to have learning difficulties? What area of their learning seemed to be of greatest concern for these students? Did most of these struggling learners have difficulty with reading? Was expressing their thoughts in writing also a challenge? Did some of these students also have difficulty in listening to and following directions? And did some of these students seem hesitant when asked to speak to the class?

Multi-faceted visualisation of worklists

Due to the popularity of modern Collaborative Virtual Environments, there has been a related increase in their size and complexity. Developers therefore need visualisations that expose usage patterns from logged data, to understand the structures and dynamics of these complex environments. This chapter presents a new framework for the process of visualising virtual environment usage data. Major components, such as an event model, designer task model and data acquisition infrastructure are described. Interface and implementation factors are also developed, along with example visualisation techniques that make use of the new task and event model. A case study is performed to illustrate a typical scenario for the framework, and its benefits to the environment development team.

High-level concept annotation using ontology and probabilistic inference

Image annotation is a significant step towards semantic based image retrieval. Ontology is a popular approach for semantic representation and has been intensively studied for multimedia analysis. However, relations among concepts are seldom used to extract higher-level semantics. Moreover, the ontology inference is often crisp. This paper aims to enable sophisticated semantic querying of images, and thus contributes to 1) an ontology framework to contain both visual and contextual knowledge, and 2) a probabilistic inference approach to reason the high-level concepts based on different sources of information. The experiment on a natural scene database from LabelMe database shows encouraging results.

Detecting and resolving redundancies in EP3P policies

Current regulatory requirements on data privacy make it increasingly important for enterprises to be able to verify and audit their compliance with their privacy policies. Traditionally, a privacy policy is written in a natural language. Such policies inherit the potential ambiguity, inconsistency and mis-interpretation of natural text. Hence, formal languages are emerging to allow a precise specification of enforceable privacy policies that can be verified. The EP3P language is one such formal language. An EP3P privacy policy of an enterprise consists of many rules. Given the semantics of the language, there may exist some rules in the ruleset which can never be used, these rules are referred to as redundant rules. Redundancies adversely affect privacy policies in several ways. Firstly, redundant rules reduce the efficiency of operations on privacy policies. Secondly, they may misdirect the policy auditor when determining the outcome of a policy. Therefore, in order to address these deficiencies it is important to identify and resolve redundancies. This thesis introduces the concept of minimal privacy policy - a policy that is free of redundancy. The essential component for maintaining the minimality of privacy policies is to determine the effects of the rules on each other. Hence, redundancy detection and resolution frameworks are proposed. Pair-wise redundancy detection is the central concept in these frameworks and it suggests a pair-wise comparison of the rules in order to detect redundancies. In addition, the thesis introduces a policy management tool that assists policy auditors in performing several operations on an EP3P privacy policy while maintaining its minimality. Formal results comparing alternative notions of redundancy, and how this would affect the tool, are also presented.

Teaching grammar: rethinking the approach

Margaret Kettle examines grammar, its image problem and some new developments aimed at improving its teaching and learning in the TESOL classroom.

Comparing "socially responsible" and "sustainable" commercial property investment

Purpose – The purpose of this paper is to set out to explore the similarities and differences between jargon used to describe future-focussed commercial building product. This is not so much an exercise in semantics as an attempt to demonstrate that responses to challenges facing the construction and property sectors may have more to do with language than is generally appreciated. Design/methodology/approach – This is a conceptual analysis which draws upon relevant literature. Findings – Social responsibility and sustainability are often held to be much the same thing, with each term presupposing the existence of the other. Clearly, however, there are incidences where sustainable commercial property investment (SCPI) may not be particularly socially responsible, despite being understood as an environmentally friendly initiative. By contrast, socially responsible assets, at least in theory, should always be more sustainable than mainstream non-ethically based investment. Put simply, the expression of social responsibility in the built environment may evoke, and thereby deliver, a more sustainable product, as defined by wider socially inclusive parameters. Practical implications – The findings show that promoting an ethic of social responsibility may well result in more SCPI. Thus, the further articulation and celebration of social responsibility concepts may well help to further advance a sustainable property investment agenda, which is arguably more concerned about demonstrability of efficiency than wider public good outcomes. Originality/value – The idea that jargon affects outcomes is not new. However, this idea has rarely, if ever, been applied to the distinctions between social responsibility and sustainability. Even a moderate re-emphasis on social responsibility in preference to sustainability may well provide significant future benefits with respect to the investment, building and refurbishment of commercial property.

Modeling and Verification of Privacy Enhancing Protocols

Privacy enhancing protocols (PEPs) are a family of protocols that allow secure exchange and management of sensitive user information. They are important in preserving users’ privacy in today’s open environment. Proof of the correctness of PEPs is necessary before they can be deployed. However, the traditional provable security approach, though well established for verifying cryptographic primitives, is not applicable to PEPs. We apply the formal method of Coloured Petri Nets (CPNs) to construct an executable specification of a representative PEP, namely the Private Information Escrow Bound to Multiple Conditions Protocol (PIEMCP). Formal semantics of the CPN specification allow us to reason about various security properties of PIEMCP using state space analysis techniques. This investigation provides us with preliminary insights for modeling and verification of PEPs in general, demonstrating the benefit of applying the CPN-based formal approach to proving the correctness of PEPs.

Semantic and context-based retrieval of digital cultural objects [Published in Chinese]

Cultural objects are increasingly generated and stored in digital form, yet effective methods for their indexing and retrieval still remain an important area of research. The main problem arises from the disconnection between the content-based indexing approach used by computer scientists and the description-based approach used by information scientists. There is also a lack of representational schemes that allow the alignment of the semantics and context with keywords and low-level features that can be automatically extracted from the content of these cultural objects. This paper presents an integrated approach to address these problems, taking advantage of both computer science and information science approaches. We firstly discuss the requirements from a number of perspectives: users, content providers, content managers and technical systems. We then present an overview of our system architecture and describe various techniques which underlie the major components of the system. These include: automatic object category detection; user-driven tagging; metadata transform and augmentation, and an expression language for digital cultural objects. In addition, we discuss our experience on testing and evaluating some existing collections, analyse the difficulties encountered and propose ways to address these problems.

Object-centric process models and the design of flexible processes

Mainstream business process modelling techniques promote a design paradigm wherein the activities to be performed within a case, together with their usual execution order, form the backbone of a process model, on top of which other aspects are anchored. This paradigm, while eective in standardised and production-oriented domains, shows some limitations when confronted with processes where case-by-case variations and exceptions are the norm. In this thesis we develop the idea that the eective design of exible process models calls for an alternative modelling paradigm, one in which process models are modularised along key business objects, rather than along activity decompositions. The research follows a design science method, starting from the formulation of a research problem expressed in terms of requirements, and culminating in a set of artifacts that have been devised to satisfy these requirements. The main contributions of the thesis are: (i) a meta-model for object-centric process modelling incorporating constructs for capturing exible processes; (ii) a transformation from this meta-model to an existing activity-centric process modelling language, namely YAWL, showing the relation between object-centric and activity-centric process modelling approaches; and (iii) a Coloured Petri Net that captures the semantics of the proposed meta-model. The meta-model has been evaluated using a framework consisting of a set of work ow patterns. Moreover, the meta-model has been embodied in a modelling tool that has been used to capture two industrial scenarios.

An Integrative framework of the factors affecting process model understanding : a learning perspective

Process models are used by information professionals to convey semantics about the business operations in a real world domain intended to be supported by an information system. The understandability of these models is vital to them actually being used. After all, what is not understood cannot be acted upon. Yet until now, understandability has primarily been defined as an intrinsic quality of the models themselves. Moreover, those studies that looked at understandability from a user perspective have mainly conceptualized users through rather arbitrary sets of variables. In this paper we advance an integrative framework to understand the role of the user in the process of understanding process models. Building on cognitive psychology, goal-setting theory and multimedia learning theory, we identify three stages of learning required to realize model understanding, these being Presage, Process, and Product. We define eight relevant user characteristics in the Presage stage of learning, three knowledge construction variables in the Process stage and three potential learning outcomes in the Product stage. To illustrate the benefits of the framework, we review existing process modeling work to identify where our framework can complement and extend existing studies.

Truth, obligation and ethics in the propagation of democracy

In this paper I present an analysis of the language used by the National Endowment for Democracy (NED) on its website (NED, 2008). The specific focus of the analysis is on the NED's high usage of the word “should” revealed in computer assisted corpus analysis using Leximancer. Typically we use the word “should” as a term to propose specific courses of action for ourselves and others. It is a marker of obligation and “oughtness”. In other words, its systematic institutional use can be read as a statement of ethics, of how the NED thinks the world ought to behave. As an ostensibly democracy-promoting institution, and one with a clear agenda of implementing American foreign policy, the ethics of NED are worth understanding. Analysis reveals a pattern of grammatical metaphor in which “should” is often deployed counter intuitively, and sometimes ambiguously, as a truth-making tool rather than one for proposing action. The effect is to present NED's imperatives for action as matters of fact rather than ethical or obligatory claims.

Calculating shades of meaning in semantic spaces

This thesis introduces the problem of conceptual ambiguity, or Shades of Meaning (SoM) that can exist around a term or entity. As an example consider President Ronald Reagan the ex-president of the USA, there are many aspects to him that are captured in text; the Russian missile deal, the Iran-contra deal and others. Simply finding documents with the word “Reagan” in them is going to return results that cover many different shades of meaning related to "Reagan". Instead it may be desirable to retrieve results around a specific shade of meaning of "Reagan", e.g., all documents relating to the Iran-contra scandal. This thesis investigates computational methods for identifying shades of meaning around a word, or concept. This problem is related to word sense ambiguity, but is more subtle and based less on the particular syntactic structures associated with or around an instance of the term and more with the semantic contexts around it. A particularly noteworthy difference from typical word sense disambiguation is that shades of a concept are not known in advance. It is up to the algorithm itself to ascertain these subtleties. It is the key hypothesis of this thesis that reducing the number of dimensions in the representation of concepts is a key part of reducing sparseness and thus also crucial in discovering their SoMwithin a given corpus.

Semantic oscillations : encoding context and structure in complex valued holographic vectors

In computational linguistics, information retrieval and applied cognition, words and concepts are often represented as vectors in high dimensional spaces computed from a corpus of text. These high dimensional spaces are often referred to as Semantic Spaces. We describe a novel and efficient approach to computing these semantic spaces via the use of complex valued vector representations. We report on the practical implementation of the proposed method and some associated experiments. We also briefly discuss how the proposed system relates to previous theoretical work in Information Retrieval and Quantum Mechanics and how the notions of probability, logic and geometry are integrated within a single Hilbert space representation. In this sense the proposed system has more general application and gives rise to a variety of opportunities for future research.

Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.

The effects of content presentation format and user characteristics on novice developers’ understanding of process models

Process models are used by information professionals to convey semantics about the business operations in a real world domain intended to be supported by an information system. The understandability of these models is vital to them being used for information systems development. In this paper, we examine two factors that we predict will influence the understanding of a business process that novice developers obtain from a corresponding process model: the content presentation form chosen to articulate the business domain, and the user characteristics of the novice developers working with the model. Our experimental study provides evidence that novice developers obtain similar levels of understanding when confronted with an unfamiliar or a familiar process model. However, previous modeling experience, the use of English as a second language, and previous work experience in BPM are important influencing factors of model understanding. Our findings suggest that education and research in process modeling should increase the focus on human factors and how they relate to content and content presentation formats for different modeling tasks. We discuss implications for practice and research.