Search Results (4)

Transformer models produce advanced text representations that have been used to break through the hard challenge of natural language understanding. Using the Transformer’s attention mechanism, which acts as a language learning memory, trained on tens of billions of words, a word sense disambiguation (WSD) algorithm can now construct a more faithful vectorial representation of the context of a word to be disambiguated. Working with a set of 34 lemmas of nouns, verbs, adjectives and adverbs selected from the National Reference Corpus of Romanian (CoRoLa), we show that using BERT’s attention heads at all hidden layers, we can devise contextual vectors of the target lemma that produce better clusters of lemma’s senses than the ones obtained with standard BERT embeddings. If we automatically translate the Romanian example sentences of the target lemma into English, we show that we can reliably infer the number of senses with which the target lemma appears in the CoRoLa. We also describe an unsupervised WSD algorithm that, using a Romanian BERT model and a few example sentences of the target lemma’s senses, can label the Romanian induced sense clusters with the appropriate sense labels, with an average accuracy of 64%. Full article

Chronic breathlessness is a multidimensional, unpleasant symptom common to many health conditions. The Common-Sense Model of Self-Regulation (CSM) was developed to help understand how individuals make sense of their illness. This model has been underused in the study of breathlessness, especially in considering how information sources are integrated within an individual’s cognitive and emotional representations of breathlessness. This descriptive qualitative study explored breathlessness beliefs, expectations, and language preferences of people experiencing chronic breathlessness using the CSM. Twenty-one community-dwelling individuals living with varying levels of breathlessness-related impairment were purposively recruited. Semi-structured interviews were conducted with questions reflecting components of the CSM. Interview transcripts were synthesized using deductive and inductive content analysis. Nineteen analytical categories emerged describing a range of cognitive and emotional breathlessness representations. Representations were developed through participants’ personal experience and information from external sources including health professionals and the internet. Specific words and phrases about breathlessness with helpful or nonhelpful connotations were identified as contributors to breathlessness representations. The CSM aligns with current multidimensional models of breathlessness and provides health professionals with a robust theoretical framework for exploring breathlessness beliefs and expectations. Full article

Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers’ personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation. Full article

This paper proposes an electric field microsensor (EFM) with mutual shielding electrodes. Based on the charge-induction principle, the EFM consists of fixed electrodes and piezoelectric-driving vertically-movable electrodes. All the fixed electrodes and movable electrodes work as both sensing electrodes and shielding electrodes. In other words, all the fixed and movable electrodes are sensing electrodes, and they are mutually shielding electrodes simultaneously. The movable electrodes are driven to periodically modulate the electric field distribution at themselves and the fixed electrodes, and the induced currents from both movable and fixed electrodes are generated simultaneously. The electrode structure adopts an interdigital structure, and the EFM has been simulated by finite element methods. Simulation results show that, since the sensing area of this EFM is doubled, the variation of induced charge is twice, and therefore the output signal of the sensor is increased. The piezoelectric material, lead zirconate titanate (PZT), is prepared by the sol–gel method, and the microsensor chip is fabricated. Full article