Technical writing in professional environments, such as user manual authoring for new products, is a task that relies heavily on reuse of content. Therefore, technical content is typically created following a strategy where modular units of text have references to each other. One of the main challenges faced by technical authors is to avoid duplicating existing content, as this adds unnecessary effort, generates undesirable inconsistencies, and dramatically increases maintenance and translation costs. However, there are few computational tools available to support this activity. This paper investigates the use of different similarity methods for the task of identification of reuse opportunities in technical writing. We evaluated our results using existing ground truth as well as feedback from technical authors. Finally, we also propose a tool that combines text similarity algorithms with interactive visualizations to aid authors in understanding differences in a collection of topics and identifying reuse opportunities.
This paper presents a processor architecture for elliptic curve cryptography computations over GF(p). The speed to compute the Elliptic-curve point multiplication over the prime fields GF(p) is increased by using the maximum degree of parallelism, and by carefully selecting the most appropriate coordinates system. The proposed Elliptic Curve processor is implemented using FPGAs. The time, area and throughput results are obtained, analyzed, and compared with previously proposed designs showing interesting performance and features.
This paper presents a contactless method for monitoring infant sleep apnoea. The method uses a remote infrared (IR) sensor to monitor the motion of the infant's abdomen. This method has important clinical advantages in comparison with conventional methods. First, it improves the comfort and compliance of the infants. Second, it eliminates the effects of motion artefacts and skin irritation. Third, it enhances infant safety. Fourth, it does not require frequent calibration and thus enables a continuous monitoring of sleep apnoea. Finally, it is suitable for home applications. Experimental evaluation of this method showed that it has 85% accuracy, 85.71% specificity and 84.62% sensitivity, which imply that it is a promising technique for the detection of infant sleep apnoea.
This paper presents P-GTM, a privacy-preserving text similarity algorithm that extends the Google Tri-gram Method (GTM). The Google Tri-gram Method is a high-performance unsupervised semantic text similarity method based on the use of context from the Google Web 1T n-gram dataset. P-GTM computes the semantic similarity between two input bag-of-words documents on public cloud hardware, without disclosing the documents' contents. Like the GTM, P-GTM requires the uni-gram and tri-gram lists from the Google Web 1T n-gram dataset as additional inputs. The need for these additional lists makes private computation of GTM text similarities a challenging problem. P-GTM uses a combination of pre-computation, encryption, and randomized preprocessing to enable private computation of text similarities using the GTM. We discuss the security of the algorithm and quantify its privacy using standard and real life corpora.
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