Framework investigations for harmonizing global taxation of internet commerce

Identifies and evaluates contemporary global approaches to taxation in internet commerce. Adapts the principles of evidence-based medicine to develop a working model for evidence-based taxation. A collaborative extranet structure is used to validate the axioms underpinning the critical success factors for a modern knowledge management solution.

Accounting : a framework for decision making

Thoroughly updated and re-sequenced in response to market feedback, Accounting: A Framework for Decision Making is an ideal first year accounting principles text. Presented in a straightforward style, the third edition focuses on key concepts and illustrates each concept with clear graphical or textual explanations and examples. The latest AASB standards and accounting techniques underpin the content throughout and new and revised questions have been added to the end of chapter material. With an accessible technical focus, Accounting: A Framework for Decision Making has a clear and broad content structure that means the book lends itself to both majors and non-majors courses.

Australia's venture capital industry : past performance, present structure and future prospects

A brief history of the Australian venture capital (VC) industry to April 2001 precedes critique of the current industry framework. This focuses on two questions: What has history taught us? and How well does the Australian VC industry enable entrepreneurship in the Australian economy? The answers embrace suggestions for better encouragement of entrepreneurship through research-based VC industry enhancement. A positive prognosis is based on a case history. A knowledgeable private citizen significantly influenced the passage of legislation vital to the industry. This success indicates the practical importance that a future program of structured research might have for the development of venture capital and entrepreneurship in Australia.

Mobility, aspiration, voice : a new structure of feeling for student equity in higher education

There is a changed ‘structure of feeling’ emerging in higher education systems, particularly in OECD nations, in response to changed social, cultural and economic arrangements. Taking a student equity perspective, the paper names this change in terms of ‘mobility’, ‘aspiration’ and ‘voice’. It argues that (1) new kinds and degrees of mobility are now a significant factor in sustaining unequal access to and experience of higher education for different student groups, (2) despite government and institutional aspirations to expand higher education, students' desires for university are not a given among new target populations and (3) while universities are seeking to enroll different students in greater numbers, the challenge now is how to give greater voice to this difference. Drawing on these themes of mobility, aspiration and voice and taking recent changes to higher education policy in Australia as the case, the paper presents a new conceptual framework for thinking about student equity in HE. The framework extends from established approaches that focus on barriers to accessing higher education in order to focus on people's capacities in relation to higher education participation.

Developing an ethics education framework for accounting

The purpose of this paper is to propose a framework of ethics education that promotes the structured learning of ethics in the accounting discipline. The Ethics Education Framework (EEF) is based on three key inter-related components that include: Rest’s (1986) Four-Component Model of ethical decision-making and behaviour; the key cognitive and behavioural objectives of ethics education; and the discrete and pervasive approaches to delivering content. The EEF provides university students and professional accountants a structure to learn to identify, analyse and resolve ethical issues, to the point of action. The EEF is a four-stage learning continuum represented as a set of building blocks which introduces ethical concepts and then reinforces and develops new levels of understanding with progressive stages. This paper describes the EEF, and includes a discussion of how it compares with other ethics education models, and an analysis of the support through responses by professional organisations (based on an Exposure Draft issued by the International Federation of Accountants (IFAC), as the initial International Education Practice Statement). The IFAC has now revised its International Education Standard (IES 4) in relation to ethics, with a commentary period till July 2011.

A probabilistic framework for extracting narrative act boundaries and semantics in motion pictures

This work constitutes the first attempt to extract the important narrative structure, the 3-Act storytelling paradigm in film. Widely prevalent in the domain of film, it forms the foundation and framework in which a film can be made to function as an effective tool for story telling, and its extraction is a vital step in automatic content management for film data. The identification of act boundaries allows for structuralizing film at a level far higher than existing segmentation frameworks, which include shot detection and scene identification, and provides a basis for inferences about the semantic content of dramatic events in film. A novel act boundary likelihood function for Act 1 and 2 is derived using a Bayesian formulation under guidance from film grammar, tested under many configurations and the results are reported for experiments involving 25 full-length movies. The result proves to be a useful tool in both the automatic and semi-interactive setting for semantic analysis of film, with potential application to analogues occuring in many other domains, including news, training video, sitcoms.

Event extraction using behaviors of sentiment signals and burst structure in social media

Significant world events often cause the behavioral convergence of the expression of shared sentiment. This paper examines the use of the blogosphere as a framework to study user psychological behaviors, using their sentiment responses as a form of ‘sensor’ to infer real-world events of importance automatically. We formulate a novel temporal sentiment index function using quantitative measure of the valence value of bearing words in blog posts in which the set of affective bearing words is inspired from psychological research in emotion structure. The annual local minimum and maximum of the proposed sentiment signal function are utilized to extract significant events of the year and corresponding blog posts are further analyzed using topic modeling tools to understand their content. The paper then examines the correlation of topics discovered in relation to world news events reported by the mainstream news service provider, Cable News Network, and by using the Google search engine. Next, aiming at understanding sentiment at a finer granularity over time, we propose a stochastic burst detection model, extended from the work of Kleinberg, to work incrementally with stream data. The proposed model is then used to extract sentimental bursts occurring within a specific mood label (for example, a burst of observing ‘shocked’). The blog posts at those time indices are analyzed to extract topics, and these are compared to real-world news events. Our comprehensive set of experiments conducted on a large-scale set of 12 million posts from Livejournal shows that the proposed sentiment index function coincides well with significant world events while bursts in sentiment allow us to locate finer-grain external world events.

Active data-centric framework for data protection in cloud environment

Cloud computing is an emerging evolutionary computing model that provides highly scalable services over highspeed Internet on a pay-as-usage model. However, cloud-based solutions still have not been widely deployed in some sensitive areas, such as banking and healthcare. The lack of widespread development is related to users’ concern that their confidential data or privacy would leak out in the cloud’s outsourced environment. To address this problem, we propose a novel active data-centric framework to ultimately improve the transparency and accountability of actual usage of the users’ data in cloud. Our data-centric framework emphasizes “active” feature which packages the raw data with active properties that enforce data usage with active defending and protection capability. To achieve the active scheme, we devise the Triggerable Data File Structure (TDFS). Moreover, we employ the zero-knowledge proof scheme to verify the request’s identification without revealing any vital information. Our experimental outcomes demonstrate the efficiency, dependability, and scalability of our framework.

Framework for the relational analysis of texts : theory and practice.

This workshop is useful for academic researchers and postgraduate students who wish to explore relational approaches to the examination of the organisation of academic texts.
The first part of the workshop introduces the theory and premises of the framework for the rhetorical analysis of the structure of texts (FARS). FARS provides a functional account of the structure of text in terms of the strategies employed by writers to achieve their communicative purposes. Its coherence relations obtain from the level of text as a whole to the clausal level. The discourse parts at all levels except the bottom level constitute relational schemata. FARS relational taxonomy includes the following relational clusters: Elaboration, List, Causal, Adversative, Facilitation, Assessing and Digression. The second part of the workshopl provides opportunity to practice relational analyses of selected texts within this framework.

Automatic rule induction in Arabic to English machine translation framework

This chapter addresses the exploitation of a supervised machine learning technique to automatically induce Arabic-to-English transfer rules from chunks of parallel aligned linguistic resources. The induced structural transfer rules encode the linguistic translation knowledge for converting an Arabic syntactic structure into a target English syntactic structure. These rules are going to be an integral part of an Arabic-English transfer-based machine translation. Nevertheless, a novel morphological rule induction method is employed for learning Arabic morphological rules that are applied in our Arabic morphological analyzer. To demonstrate the capability of the automated rule induction technique, we conducted rule-based translation experiments that use induced rules from a relatively small data set. The translation quality of the hybrid translation experiments achieved good results in terms of WER.

Mind change optimal learning of Bayes net structure

This paper analyzes the problem of learning the structure of a Bayes net (BN) in the theoretical framework of Gold’s learning paradigm. Bayes nets are one of the most prominent formalisms for knowledge representation and probabilistic and causal reasoning. We follow constraint-based approaches to learning Bayes net structure, where learning is based on observed conditional dependencies between variables of interest (e.g., “X is dependent on Y given any assignment to variable Z”). Applying learning criteria in this model leads to the following results. (1) The mind change complexity of identifying a Bayes net graph over variables V from dependency data is |V| 2 , the maximum number of edges. (2) There is a unique fastest mind-change optimal Bayes net learner; convergence speed is evaluated using Gold’s dominance notion of “uniformly faster convergence”. This learner conjectures a graph if it is the unique Bayes net pattern that satisfies the observed dependencies with a minimum number of edges, and outputs “no guess” otherwise. Therefore we are using standard learning criteria to define a natural and novel Bayes net learning algorithm. We investigate the complexity of computing the output of the fastest mind-change optimal learner, and show that this problem is NP-hard (assuming P = RP). To our knowledge this is the first NP-hardness result concerning the existence of a uniquely optimal Bayes net structure.

A helical conotoxin from Conus imperialis has a novel cysteine framework and defines a new superfamily

Cone snail venoms are a rich source of peptides, many of which are potent and selective modulators of ion channels and receptors. Here we report the isolation and characterization of two novel conotoxins from the venom of Conus imperialis. These two toxins contain a novel cysteine framework, C-C-C-CC-C, which has not been found in other conotoxins described to date. We name it framework XXIII and designate the two toxins im23a and im23b; cDNAs of these toxins exhibit a novel signal peptide sequence, which defines a new K-superfamily. The disulfide connectivity of im23a has been mapped by chemical mapping of partially reduced intermediates and by NMR structure calculations, both of which establish a I-II, III-IV, V-VI pattern of disulfide bridges. This pattern was also confirmed by synthesis of im23a with orthogonal protection of individual cysteine residues. The solution structure of im23a reveals that im23a adopts a novel helical hairpin fold. A cluster of acidic residues on the surface of the molecule is able to bind calcium. The biological activity of the native and recombinant peptides was tested by injection into mice intracranially and intravenously to assess the effects on the central and peripheral nervous systems, respectively. Intracranial injection of im23a or im23b into mice induced excitatory symptoms; however, the biological target of these new toxins has yet to be identified.

[Al4(OH)2(OCH3)4(H2N-bdc)3]⋅x H2O: A 12-connected porous metal–organic framework with an unprecedented aluminum-containing brick

A new stable aluminum aminoterephthalate system contains octameric building blocks that are connected by organic linkers to form a 12-connected net (see picture). The structure adopts a cubic centered packing motive in which octameric units replace individual atoms, thus forming distorted octahedral (red sphere) and tetrahedral cages (green spheres) with effective accessible diameters of 1 and 0.45 nm, respectively

Scalable RFID security framework and protocol supporting Internet of Things

Radio-frequency identification (RFID) is seen as one of the requirements for the implementation of the Internet-of-Things (IoT). However, an RFID system has to be equipped with a holistic security framework for a secure and scalable operation. Although much work has been done to provide privacy and anonymity, little focus has been given to performance, scalability and customizability issues to support robust implementation of IoT. Also, existing protocols suffer from a number of deficiencies such as insecure or inefficient identification techniques, throughput delay and inadaptability. In this paper, we propose a novel identification technique based on a hybrid approach (group-based approach and collaborative approach) and security check handoff (SCH) for RFID systems with mobility. The proposed protocol provides customizability and adaptability as well as ensuring the secure and scalable deployment of an RFID system to support a robust distributed structure such as the IoT. The protocol has an extra fold of protection against malware using an incorporated malware detection technique. We evaluated the protocol using a randomness battery test and the results show that the protocol offers better security, scalability and customizability than the existing protocols. © 2014 Elsevier B.V. All rights reserved.

A secure cloud computing based framework for big data information management of smart grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call 'Smart-Frame.' The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.