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Detecting the elements of deception in a conversation takes years of study and experience, and it is a skill set primarily used in law-enforcement agencies. In ever-growing business opportunities, organisations employ teleoperators to provide support and services to their large customer base, which is a potential platform for fraud. With technological advancements, it is desirable to have an automated system that spots the deceptive elements in the conversation, and provides this information to the teleoperators to better support them in their interactions. We propose the Decision Engine to detect deceptive conversation based on the proximity of linguistic markers present, which produces a deception score for a conversation and highlights the potential deceptive elements of the conversation. In collaboration with behavioural experts, we have selected ten linguistic markers that potentially indicate deception. We have built a variety of models to detect the trigger terms for selected linguistic markers without ambiguity, using either regular expressions or the BERT model. The BERT model has been trained on a conversational dataset that we collated and was labelled by our behavioural experts. The proposed Decision Engine employs the BERT model and regular expressions to detect the linguistic markers and compute the proximity features to further estimate the deception score. We evaluated the proposed approach on the Columbia-SRI-Colorado (CSC) dataset and a real-world Financial Services dataset. In addition to accuracy, we have also employed the True Positive Rate metric, with a high enough threshold to avoid any false-positive cases, which we indicate as T P R F 0 . The Decision Engine achieves 69% accuracy and 46% T P R F 0 for the CSC dataset and 72% accuracy and 60% T P R F 0 for the Financial Services dataset. In contrast, a baseline model, which uses non-proximity features achieves 67% accuracy and 32% T P R F 0 for the CSC dataset and 67% accuracy and 10% T P R F 0 for the Financial Services dataset. Furthermore, using the Decision Engine, the impact of the proximity of markers on the deception score has been analysed by our behavioural experts to provide insight into linguistic behaviour in relation to deception. [ABSTRACT FROM AUTHOR]

Telephones can carry potential bacterial pathogens, posing a risk for transfer of pathogens to users' hands. This study examined 25 mouthpieces of public telephones at a large urban U.S. university located in an area of rising incidence of community-acquired staphylococcal infections. Coagulase-negative staphylococci were most commonly isolated (64% of mouthpieces). Potential pathogens isolated included Staphylococcus aureus, vancomycin-sasceptible Enterococcus, and Klebsiella ozaenae. The efficacy of disinfectants on reducing bacterial counts on telephone mouthpieces was also investigated. Staphyloccocus aureus, Pseudomonas aeruginosa, and Enterococcusfaecalis were inoculated onto mouthpieces and challenged with disinfectant wipes. Bacterial counts were reduced substantially for all three organisms by wipes containing either 70% isopropyl alcohol, 1.84% sodium hypochlorite, or quaternary ammonium compounds. The sodium hypochlorite-based cleaner demonstrated 100% efficacy at removing or killing test organisms from telephone mouthpieces. These data suggest that tested cleaners may be appropriate and needed for public telephone disinfection. [ABSTRACT FROM AUTHOR]

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects. [ABSTRACT FROM AUTHOR]