Moving towards personalising translation technology

Technology has had an important impact on the work of translators and represents a shift in the boundaries of translation work over time. Improvements in machine translation have brought about further boundary shifts in some translation work and are likely to continue having an impact. Yet translators sometimes feel frustrated with the tools they use. This chapter looks to the field of personalisation in information technology and proposes that personalising translation technology may be a way of improving translator-computer interaction. Personalisation of translation technology is considered from the perspectives of context, user modelling, trust, motivation and well-being

Does Explanation Matter? An Exploratory Study on the Effects of Covid 19 Misinformation Warning Flags on Social Media

We investigate whether adding specific explanations from fact checking websites enhances trust in these flags. We experimented with 348 American participants, exposing them to a randomised order of true and false news headlines related to COVID 19, with and without warning flags and explanation text. Our findings suggest that warning flags, whether alone or accompanied by explanatory text, effectively reduce the perceived accuracy of fake news and the intent to share such headlines. Interestingly, our study also suggests that incorporating explanatory text in misinformation warning systems could significantly enhance their trustworthiness, emphasising the importance of transparency and user comprehension in combating fake news on social media

Epitaxial graphene immunosensor for human chorionic gonadotropin

Human chorionic gonadotropin (hCG), a 37 kDa glycoprotein hormone, is a key diagnostic marker of pregnancy and has been cited as an important biomarker in relation to cancerous tumors found in the prostate, ovaries and bladder.A novel chemically-modified epitaxial graphene diagnostic sensor has been developed for ultrasensitive detection of the biomarker hCG. Multi-layer epitaxial graphene (MEG), grown on silicon carbide substrates, was patterned using electron beam lithography to produce channel based devices. The MEG channels have been amine terminated using 3-Aminopropyl-triethoxysilane (APTES) in order to attach the anti-hCG antibody to the channel.Detection of binding of hCG with its graphene-bound antibody was monitored by measuring reduction of the channel current of the graphene biosensor. The sensitivity of the sensor device was investigated using varying concentrations of hCG, with changes in the channel resistance of the sensor observed upon exposure to hCG. The detection limit of the sensor was 0.62 ng/mL and the sensor showed a linear response to hCG in the range 0.62–5.62 ng/mL with a response of 142 Ω/ng/mL. At concentrations above 5.62 ng/mL the sensor begins to saturate

Long-term growth patterns of vestibular schwannomas after stereotactic radiotherapy: delayed re-growth

Purpose: To determine the long-term outcomes of patients with vestibular schwannomas (VS) after stereotactic radiosurgery (SRS) who experience delayed tumour regrowth. Methods: We carried out a retrospective case series in tertiary university settings. We included patients with VS with initial response to SRS and delayed regrowth, assessing a database of 735 patients with VS and 159 patients who had SRS as sole treatment. Following SRS, all patients had clinical follow-up and serial magnetic resonance imaging (MRI). We documented the post-SRS clinical assessment, pre- and post-SRS VS size as per MRI in predetermined time periods, response to treatment and rate of (re-) growth and the final outcome in each case. Results: We identified six patients with good initial response but delayed VS regrowth at a faster rate than pre-SRS. The mean growth rate for these VS was 0.347 mm/month (range 0.04–0.78 mm/month) prior to treatment; the mean growth rate at the time of delayed re-growth was 0.48 mm/month (range 0.17–0.75 mm/month); this did not reach the level of statistical significance (p = 0.08). This regrowth occurred at a mean time of 42 months (range 36–66 months) post-SRS and stopped 22 months (mean, range 12–36 months) post regrowth detection in all cases. Conclusions: Given that delayed post-SRS VS regrowth can occur in approximately 4% of the treated cases, it is important to continue close clinical and radiological follow-up. Despite this abnormal behaviour, VS do stop growing again; still, patients should be made aware of the possibility of this uncommon VS behaviour following SRS

A User Modeling Shared Challenge Proposal

Comparative evaluation in the areas of User Modeling, Adaptation and Personalization (UMAP) is significantly challenging. It has always been difficult to rigorously compare different approaches to personalization, as the function of the resulting systems is, by their nature, heavily influenced by the behavior of the users involved in trialing the systems. Developing comparative evaluations in this space would be a huge advancement as it would enable shared comparison across research. Here we present a proposal for a shared challenge generation in UMAP, focusing on user model generation using logged mobile phone data, with an assumed purpose of supporting mobile phone notification suggestion. The dataset, evaluation metrics, and challenge operation are described

A Big Data Smart Agricultural System: Recommending Optimum Fertilisers For Crops

Nutrients are important to promote plant growth and nutrient deficiency is the primary factor limiting crop production. However, excess fertilisers can also have a negative impact on crop quality and yield, cause an increase in pollution and decrease producer profit. Hence, determining the suitable quantities of fertiliser for every crop is very useful. Currently, the agricultural systems with internet of things make very large data volumes. Exploiting agricultural Big Data will help to extract valuable information. However, designing and implementing a large scale agricultural data warehouse are very challenging. The data warehouse is a key module to build a smart crop system to make proficient agronomy recommendations. In our paper, an electronic agricultural record (EAR) is proposed to integrate many separate datasets into a unified dataset. Then, to store and manage the agricultural Big Data, we built an agricultural data warehouse based on Hive and Elasticsearch. Finally, we applied some statistical methods based on our data warehouse to extract fertiliser information such as a case study. These statistical methods propose the recommended quantities of fertiliser components across a wide range of environmental and crop management conditions, such as nitrogen (N), phosphorus (P) and potassium (K) for the top ten most popular crops in EU

Dataset creation framework for personalized type-based facet ranking tasks evaluation

Faceted Search Systems (FSS) have gained prominence in many existing vertical search systems. They provide facets to assist users in allocating their desired search target quickly. In this paper, we present a framework to generate datasets appropriate for simulation-based evaluation of these systems. We focus on the task of personalized type-based facet ranking. Type-based facets (t-facets) represent the categories of the resources being searched in the FSS. They are usually organized in a large multilevel taxonomy. Personalized t-facet ranking methods aim at identifying and ranking the parts of the taxonomy which reflects query relevance as well as user interests. While evaluation protocols have been developed for facet ranking, the problem of personalising the facet rank based on user profiles has lagged behind due to the lack of appropriate datasets. To fill this gap, this paper introduces a framework to reuse and customise existing real-life data collections. The framework outlines the eligibility criteria and the data structure requirements needed for this task. It also details the process to transform the data into a ground-truth dataset. We apply this framework to two existing data collections in the domain of Point-of-Interest (POI) suggestion. The generated datasets are analysed with respect to the taxonomy richness (variety of types) and user profile diversity and length. In order to experiment with the generated datasets, we combine this framework with a widely adopted simulated user-facet interaction model to evaluate a number of existing personalized t-facet ranking baselines