732 research outputs found

    Advances in automatic terminology processing: methodology and applications in focus

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    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The information and knowledge era, in which we are living, creates challenges in many fields, and terminology is not an exception. The challenges include an exponential growth in the number of specialised documents that are available, in which terms are presented, and the number of newly introduced concepts and terms, which are already beyond our (manual) capacity. A promising solution to this ‘information overload’ would be to employ automatic or semi-automatic procedures to enable individuals and/or small groups to efficiently build high quality terminologies from their own resources which closely reflect their individual objectives and viewpoints. Automatic terminology processing (ATP) techniques have already proved to be quite reliable, and can save human time in terminology processing. However, they are not without weaknesses, one of which is that these techniques often consider terms to be independent lexical units satisfying some criteria, when terms are, in fact, integral parts of a coherent system (a terminology). This observation is supported by the discussion of the notion of terms and terminology and the review of existing approaches in ATP presented in this thesis. In order to overcome the aforementioned weakness, we propose a novel methodology in ATP which is able to extract a terminology as a whole. The proposed methodology is based on knowledge patterns automatically extracted from glossaries, which we considered to be valuable, but overlooked resources. These automatically identified knowledge patterns are used to extract terms, their relations and descriptions from corpora. The extracted information can facilitate the construction of a terminology as a coherent system. The study also aims to discuss applications of ATP, and describes an experiment in which ATP is integrated into a new NLP application: multiplechoice test item generation. The successful integration of the system shows that ATP is a viable technology, and should be exploited more by other NLP applications

    Connected World:Insights from 100 academics on how to build better connections

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    Commercial and research-based wearable devices in spinal postural analysis: A systematic review

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    The widespread use of ubiquitous computing has led to people spending more time in front of screens, causing poor posture. The COVID-19 pandemic and the shift towards remote work have only worsened the situation, as many people are now working from home with inadequate ergonomics. Maintaining a healthy posture is crucial for both physical and mental health, and poor posture can result in spinal problems. Wearable systems have been developed to monitor posture and provide instant feedback. Their goal is to improve posture over time by using these devices. This article will review commercially available, and research-based wearable devices used to analyse posture. The potential of these devices in the healthcare industry, particularly in preventing, monitoring, and treating spinal and musculoskeletal conditions, will also be discussed. The findings indicate that current devices can accurately assess posture in clinical settings, but further research is needed to validate the long-term effectiveness of these technologies and to improve their practicality for commercial use

    Assessing Research for Philanthropic Funding : Innovative Approaches

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    This publication on responsible research assessment aims to explore diverse approaches taken by foundations to enhance the fairness, transparency and effectiveness of evaluating research proposals for funding. The publication delves into three distinct methodologies that challenge traditional assessment methods and offer innovative alternatives: 1. Using artificial intelligence (AI); 2. Adopting narrative curriculum vitae (CVs); and 3. Implementing randomised selection. It provides an overview of general principles of responsible research assessment, key framing documents and recommendations for implementing these principles; and offers examples of the real-world application of these methods by various foundations and organisations.While these approaches demonstrate the innovative potential within research assessment, they are by no means an exhaustive representation of all available tools and methods. Nevertheless, they serve as compelling illustrations of the ongoing efforts to revolutionise evaluation practices and foster a more inclusive and equitable research ecosystem

    Metacomprehension Accuracy of Health-Related Information

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    As part of the production of written information, patient reader panels provide judgments of their understanding to evaluate the comprehensibility of draft documents. Previous research has suggested i) that there is a limited association, on average, between judgments of understanding and the comprehension demonstrated in tests of understanding and ii) that there is considerable variability between individuals in the direction and magnitude of this association. Unfortunately, while previous research implies, critically, that reader judgments of comprehensibility have limited utility, this research itself is characterized by important limitations that prevent firm conclusions. This thesis comprises three experimental studies. The study design, method of measurement, and the approach to analysis were motivated by a critical review of previous research. The specification of participant, text and question sample sizes was determined by a novel method of prospective study design analysis, evaluating the accuracy and precision in effect estimation. The robustness of effect estimates are established through the series of empirical replications and in analytical sensitivity checks. Across the studies, a weakly positive association between perceived and assessed comprehension was found across individuals, on average. Differences in reading ability and background knowledge did not reliably influence metacomprehension accuracy. Further, metacomprehension judgements were similarly predictive of performance on comprehension questions that targeted more versus less semantically central information. In contrast, metacomprehension judgements targeting specific ideas within texts were more predictive of understanding. The findings of this thesis indicate that metacomprehension judgements are not a gold-standard method of evaluation: judgements show some predictive validity of comprehension outcomes, yet provide little insight into whether critical elements of the documents are sufficiently understood. Overall, whilst situated within an applied context, the present research contributes more widely to the metacomprehension literature, making clear the need for a shift from traditional analytical approaches, in addition to greater theoretical precision

    A framework to assess the authenticity of subjective information in the integration of blockchain technology - an application in supply chain management.

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    Blockchain technology has burgeoned due to the booming value of cryptocurrency capitalisation. It enables financial transactions to be carried out without a bank or a third party regulating them. Aspects such as privacy, trust, security, and transparency of a transaction are ensured by its immutability characteristics. These features of blockchain have resulted in it being used in other domains, such as supply chains. As the adoption of blockchain has expanded, it is currently being applied in domains where there is an equal chance of opinions, facts, and personal commitment being part of the business operation. One such area is proactive supply chain risk management (SCRM). Previous researchers have often highlighted the fraudulent behaviour of supply chain partners who do not disclose information on the risks that impact their operations. Despite this, very few researchers consider subjective information in the processing of blockchain. Those who take this into consideration acknowledge the presence of such information but do not utilize it in the processing of blockchain. Blockchain can address this problem by encoding each partner's commitment to SCRM and achieving consensus. However, before this can be achieved, a key challenge to address is the inability of existing consensus mechanisms such as Proof of Work (PoW), Proof of Authority (PoA) and Proof of Stake (PoS) to deal with information that does not have a digital footprint such as claims, opinions, promises, or communications between supply chain partners when they form a Service Level Agreement (SLA). This type of information is called subjective information. Addressing this research gap is very important if the true potential of blockchain in providing a single source of truth in a domain, irrespective of what type of information is used, is to be achieved. Thus, future research should investigate a new consensus mechanism with a unified framework that not only stores this information but determines its trustworthiness. This thesis addresses this gap by proposing the Proof of Earnestness (PoE) consensus mechanism which accounts for the authenticity, legitimacy and trustworthiness of information that does not have a digital footprint. This thesis also proposes the Subjective Information Authenticity Earnestness Framework (SIAEF) as the overarching framework that assists PoE in achieving its aim. SIAEF comprises four modules, namely the Identification module, the Mapping module, the Data collection & Impact determination module and Local consensus & Global legitimacy module. These modules provide a complete solution to identify subjective information in an SLA, detect the potential operational risk term which may potentially impact a responsible partner who commits to the subjective information, collate its real-world occurrences in the geographic region of interest, then determine the responsible partner's adherence to what it had recommitted. SIAEF assists in achieving PoE's aim of generating a digital footprint of a responsible partner’s earnestness in communicating subjective information. Once this footprint is generated, existing consensus mechanisms such as PoW, PoS or PoA are used to encode this information in blockchains. Each module is computed in the application of machine learning and natural language processing with recent techniques, metrics and evaluation. The applicability of SIAEF and PoE is tested in a real-world blockchain environment by deploying it as a Decentralized application (Dapp) and applying it in BscScan Testnet which is an official test blockchain network. The thesis will contribute to the existing literature by proposing a new consensus mechanism and its framework to assist the existing blockchain framework in verifying and validating the truthfulness of subjective information. Supply chain partners can use the SIAEF framework as a reference to choose a potential partner with whom to form an SLA, preventing opportunistic and fraudulent behaviours in supply chain management

    Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design

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    Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data

    INVESTIGATING IMPROVEMENTS TO MESH INDEXING

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    The MEDLINE database currently comprises an extensive collection of over 35 million citations, with more than 1 million records being added each year [28]. The abundance of information available in the database presents a significant challenge in identifying and locating relevant research articles on a given search topic. This has prompted the development of various techniques and approaches aimed at improving the efficiency and effectiveness of information retrieval from the MEDLINE database. A search engine devoted to the research publications on MEDLINE is called PubMed. MeSH, or Medical Subject Headings, is a restricted vocabulary used by PubMed to categorize each article. Human annotators have been used for decades, which is not only time-consuming but also expensive. Due to its enormously complex hierarchically ordered structure, MeSH indexing is a difficult problem in the machine learning domain. We propose a model which addresses all these challenges. We propose an end-to-end model that takes MeSH description into account and combines it with a Knowledge Enhanced Mask attention model to index new research papers. We also calculated the journal correlation of each MeSH term in the MeSH hierarchy
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