Real, complex, and binary semantic vectors

This paper presents a combined structure for using real, complex, and binary valued vectors for semantic representation. The theory, implementation, and application of this structure are all significant. For the theory underlying quantum interaction, it is important to develop a core set of mathematical operators that describe systems of information, just as core mathematical operators in quantum mechanics are used to describe the behavior of physical systems. The system described in this paper enables us to compare more traditional quantum mechanical models (which use complex state vectors), alongside more generalized quantum models that use real and binary vectors. The implementation of such a system presents fundamental computational challenges. For large and sometimes sparse datasets, the demands on time and space are different for real, complex, and binary vectors. To accommodate these demands, the Semantic Vectors package has been carefully adapted and can now switch between different number types comparatively seamlessly. This paper describes the key abstract operations in our semantic vector models, and describes the implementations for real, complex, and binary vectors. We also discuss some of the key questions that arise in the field of quantum interaction and informatics, explaining how the wide availability of modelling options for different number fields will help to investigate some of these questions.

Semantic judgement of medical concepts : combining syntagmatic and paradigmatic information with the tensor encoding model

This paper outlines a novel approach for modelling semantic relationships within medical documents. Medical terminologies contain a rich source of semantic information critical to a number of techniques in medical informatics, including medical information retrieval. Recent research suggests that corpus-driven approaches are effective at automatically capturing semantic similarities between medical concepts, thus making them an attractive option for accessing semantic information. Most previous corpus-driven methods only considered syntagmatic associations. In this paper, we adapt a recent approach that explicitly models both syntagmatic and paradigmatic associations. We show that the implicit similarity between certain medical concepts can only be modelled using paradigmatic associations. In addition, the inclusion of both types of associations overcomes the sensitivity to the training corpus experienced by previous approaches, making our method both more effective and more robust. This finding may have implications for researchers in the area of medical information retrieval.

Accountable-eHealth systems : the next step forward for privacy

EHealth systems promise enviable benefits and capabilities for healthcare. But, the technologies that make these capabilities possible brings with them undesirable drawback such as information security related threats which need to be appropriately addressed. Lurking in these threats are patient privacy concerns. Fulfilling these privacy concerns have proven to be difficult since they often conflict with information requirements of care providers. It is important to achieve a proper balance between these requirements. We believe that information accountability can achieve this balance. In this paper we introduce accountable-eHealth systems. We will discuss how our designed protocols can successfully address the aforementioned requirement. We will also compare characteristics of AeH systems with Australia’s PCEHR system and identify similarities and highlight the differences and the impact those differences would have to the eHealth domain.

Legal issues related to Accountable-eHealth systems in Australia

Information privacy requirements of patients and information requirements of healthcare providers (HCP) are competing concerns. Reaching a balance between these requirements have proven difficult but is crucial for the success of eHealth systems. The traditional approaches to information management have been preventive measures which either allow or deny access to information. We believe that this approach is inappropriate for a domain such as healthcare. We contend that introducing information accountability (IA) to eHealth systems can reach the aforementioned balance without the need for rigid information control. IA is a fairly new concept to computer science, hence; there are no unambiguously accepted principles as yet. But the concept delivers promising advantages to information management in a robust manner. Accountable-eHealth (AeH) systems are eHealth systems which use IA principles as the measure for privacy and information management. AeH systems face three main impediments; technological, social and ethical and legal. In this paper, we present the AeH model and focus on the legal aspects of AeH systems in Australia. We investigate current legislation available in Australia regarding health information management and identify future legal requirements if AeH systems are to be implemented in Australia.

Empirical analysis of the effect of dimension reduction and word order on semantic vectors

The aim of this paper is to provide a comparison of various algorithms and parameters to build reduced semantic spaces. The effect of dimension reduction, the stability of the representation and the effect of word order are examined in the context of the five algorithms bearing on semantic vectors: Random projection (RP), singular value decom- position (SVD), non-negative matrix factorization (NMF), permutations and holographic reduced representations (HRR). The quality of semantic representation was tested by means of synonym finding task using the TOEFL test on the TASA corpus. Dimension reduction was found to improve the quality of semantic representation but it is hard to find the optimal parameter settings. Even though dimension reduction by RP was found to be more generally applicable than SVD, the semantic vectors produced by RP are somewhat unstable. The effect of encoding word order into the semantic vector representation via HRR did not lead to any increase in scores over vectors constructed from word co-occurrence in context information. In this regard, very small context windows resulted in better semantic vectors for the TOEFL test.

Finding additional semantic entity information for search engines

Entity-oriented search has become an essential component of modern search engines. It focuses on retrieving a list of entities or information about the specific entities instead of documents. In this paper, we study the problem of finding entity related information, referred to as attribute-value pairs, that play a significant role in searching target entities. We propose a novel decomposition framework combining reduced relations and the discriminative model, Conditional Random Field (CRF), for automatically finding entity-related attribute-value pairs from free text documents. This decomposition framework allows us to locate potential text fragments and identify the hidden semantics, in the form of attribute-value pairs for user queries. Empirical analysis shows that the decomposition framework outperforms pattern-based approaches due to its capability of effective integration of syntactic and semantic features.

How activation, entanglement, and searching a semantic network contribute to event memory

Free association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist cuing, primed free association, intralist cuing, and single-item recognition tasks. The findings also show that when a related word is presented to cue the recall of a studied word, the cue activates it in an array of related words that distract and reduce the probability of its selection. The activation of the semantic network produces priming benefits during encoding and search costs during retrieval. In extralist cuing recall is a negative function of cue-to-distracter strength and a positive function of neighborhood density, cue-to-target strength, and target-to cue strength. We show how four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks indicating that the contribution of the semantic network varies with the context provided by the task. We evaluate spreading activation and quantum-like entanglement explanations for the priming effect produced by neighborhood density.

Semantic labelling for document feature patterns using ontological subjects

Finding and labelling semantic features patterns of documents in a large, spatial corpus is a challenging problem. Text documents have characteristics that make semantic labelling difficult; the rapidly increasing volume of online documents makes a bottleneck in finding meaningful textual patterns. Aiming to deal with these issues, we propose an unsupervised documnent labelling approach based on semantic content and feature patterns. A world ontology with extensive topic coverage is exploited to supply controlled, structured subjects for labelling. An algorithm is also introduced to reduce dimensionality based on the study of ontological structure. The proposed approach was promisingly evaluated by compared with typical machine learning methods including SVMs, Rocchio, and kNN.

Designing an information accountability framework for eHealth

Information privacy is a crucial aspect of eHealth. Appropriate privacy management measures are therefore essential for its success. However, traditional measures for privacy preservation such as rigid access controls (i.e., preventive measures) are not suitable to eHealth because of the specialised and information - intensive nature of healthcare itself, and the nature of the information. Healthcare professionals (HCP) require easy, unrestricted access to as much information as possible towards making well - informed decisions. On the other end of the scale however, consumers (i.e., patients) demand control over their health information and raise concerns for privacy arising from internal activities (i.e., information use by HCPs). A proper balance of these competing concerns is vital for the implementation of successful eHealth systems. Towards reaching this balance, we propose an information accountability framework (IAF) for eHealth systems.

The role of human factors when evaluating information accountability for eHealth systems

The availability of health information is rapidly increasing; its expansion and proliferation is inevitable. At the same time, breeding of health information silos is an unstoppable and relentless exercise. Information security and privacy concerns are therefore major barriers in the eHealth socio-eco system. We proposed Information Accountability as a measurable human factor that should eliminate and mitigate security concerns. Information accountability measures would be practicable and feasible if legislative requirements are also embedded. In this context, information accountability constitutes a key component for the development of effective information technology requirements for health information system. Our conceptual approach to measuring human factors related to information accountability in eHealth is presented in this paper with some limitations.

eHealth in Australia and elsewhere : a comparison and lessons for the near future

Meticulous planning and preparation do not always guarantee that eHealth programs unfold as predicted. eHealth entails interdependent social interactions which are difficult to predict without past experience or reference to lessons learned. Judicious insight into past case studies and eventualities, therefore, is essential towards building a successful eHealth solution. Australia’s eHealth program is at a crucial stage where appropriate policy considerations and operational changes are in order. In this paper, we present an initial exploration of prominent eHealth initiatives of other countries to identify similarities, differences and to seek lessons towards making Australia’s eHealth initiative a better journey.

Modelling word activation in semantic networks : three scaled entanglement models compared

Modelling how a word is activated in human memory is an important requirement for determining the probability of recall of a word in an extra-list cueing experiment. Previous research assumed a quantum-like model in which the semantic network was modelled as entangled qubits, however the level of activation was clearly being over-estimated. This paper explores three variations of this model, each of which are distinguished by a scaling factor designed to compensate the overestimation.

Designing an information accountability framework for eHealth

This tutorial is primarily based on the IEEE eHealth technical committee Newsletter published in March 2013. Its main focus is on information privacy management in eHealth through information accountability. The tutorial consists of three main aspects of a proposed information accountability framework for eHealth, namely, social aspects, technical aspects and legal aspects. Following a brief introduction of the problem domain and context, we present the tutorial in these three main components. The length of the tutorial is intended to be half a day.

Identifying and using ehealth phobias to implement communication protocols and change cultural and social behaviours in ehealth

Standardisation of validated communication protocols that aid in the adoption of policies, methods and tools in a secure eHealth setting require a significant cultural shift among clinicians

Practical issues when designing an information accountability framework for eHealth systems

This thesis is the result of an investigation into information privacy management in eHealth. It explores the applicability of accountability measures as a means of protection of eHealth consumer privacy. The thesis presented a new concept of Accountable eHealth Systems for achieving a balance between the information privacy concerns of eHealth consumers and the information access requirements of healthcare professionals and explored the social, technological and implementation aspects involved in such a system.