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Question Answering (QA) systems play an important role in today's human–computer interaction systems. QA performance can be significantly improved using effective answer passage retrieval and ranking techniques. Our focus in this paper is on both non machine learning-based and deep learning-based passage retrieval and ranking systems for QA to leverage linguistic features within the text of questions and passages and improve passage ranking effectiveness. We propose a decoupled linguistic and linear programming-based approach for passage ranking using the Data Envelopment Analysis (DEA) technique to improve over well-established answer passage retrieval techniques. Our method scores passages using information retrieval and deep learning relevance metrics, represents retrieved passages using their relevance scores and several linguistic features, and finally makes use of DEA to re-rank the retrieved list of passages. The high effectiveness and significance of our proposed passage ranking method is demonstrated based on several experiments that we have conducted on a standard benchmark data set. [ABSTRACT FROM AUTHOR]

Evaluating human dialogue is a complex task, as our conversation are never structured. There are, however, cases where there is some structure in our conversation, e.g., in a typical call centre, dialogue between a call centre agent and customer revolves around certain topics of conversation. These dialogues can be evaluated based on some pre-specified criteria as well as sub-criteria. This evaluation is typically done manually, which can be time-consuming, motivating the need for an automated system that employs Artificial Intelligence (AI) algorithms to evaluate dialogues efficiently. In this paper, we have proposed a novel dialogue-evaluation framework that leverages recent advancements in deep learning research. The contributions of this work are two fold. Firstly, we introduce a straightforward end-to-end framework – CallAI, for evaluating dialogues in any domain, based on some predefined hierarchical criteria. Secondly, we present a novel algorithm – TAABLM, utilizing a novel combination of aspect-based learning along with traditional TF-IDF features for text. We show in this paper, that TAABLM outperforms conventional baselines such as BERT, LSTM, etc, delivering improved performance in automated dialogue evaluation, whereas CallAI offers a simple yet elegant framework for an AI-based solution to hierarchical dialogue evaluation. We demonstrate the efficacy of our proposed framework and proposed algorithm on three datasets, where we quantify performance in terms of an aggregated dialogue-score as well as in terms of either accuracy or AuROC metrics. [ABSTRACT FROM AUTHOR]

With the continued growth of Internet of Things (IoT) and its convergence with the cloud, numerous interoperable software are being developed for cloud. Therefore, there is a growing demand to maintain a better quality of software in the cloud for improved service. This is more crucial as the cloud environment is growing fast towards a hybrid model; a combination of public and private cloud model. Considering the high volume of the available software as a service (SaaS) in the cloud, identification of non-standard software and measuring their quality in the SaaS is an urgent issue. Manual testing and determination of the quality of the software is very expensive and impossible to accomplish it to some extent. An automated software defect detection model that is capable to measure the relative quality of software and identify their faulty components can significantly reduce both the software development effort and can improve the cloud service. In this paper, we propose a software defect detection model that can be used to identify faulty components in big software metric data. The novelty of our proposed approach is that it can identify significant metrics using a combination of different filters and wrapper techniques. One of the important contributions of the proposed approach is that we designed and evaluated a parallel framework of a hybrid software defect predictor in order to deal with big software metric data in a computationally efficient way for cloud environment. Two different hybrids have been developed using Fisher and Maximum Relevance (MR) filters with a Artificial Neural Network (ANN) based wrapper in the parallel framework. The evaluations are performed with real defect-prone software datasets for all parallel versions. Experimental results show that the proposed parallel hybrid framework achieves a significant computational speedup on a computer cluster with a higher defect prediction accuracy and smaller number of software metrics compared to the independent filter or wrapper approaches. [ABSTRACT FROM AUTHOR]

Plant diseases are one of the dominant factors that threaten sustainable agriculture, leading to economic losses. Developing an accurate mobile-based plant disease detection methodology is important for enabling rapid identification of emerging diseases directly from the farms. The deep learning methods have limited usage in mobile-based applications as they require larger memory and processing power to operate directly on smartphones or internet connectivity when used with a client–server computing model. To address this challenge, we propose a mobile-based lightweight deep learning-based model, which requires only a small footprint and processing power while maintaining higher detection accuracy. With around 0.088 billion multiply–accumulation operations, 0.26 million parameters, and 1 MB storage space, this framework achieved 97%, 97.1% and 96.4% accuracies on apple, citrus and tomato leaves datasets, respectively. One of our tiny models achieved 93.33% accuracy on a custom sourced in-the-wild apple leaves images dataset, which affirms the in-field applicability of the proposed framework. The superiority of the proposed model is further demonstrated through a comparative study with equivalent lightweight models. • A lightweight and accurate deep learning-based solution, P2OP, is proposed. • P2OP has less computational overhead and suitable for mobile deployment. • Deep metric learning concepts are systematically exploited to develop P2OP. • P2OP has achieved 97%, 97.1% and 96.4% accuracies on three datasets. • A proof-of-concept mobile solution supporting multiple crop types is developed. [ABSTRACT FROM AUTHOR]

Research in the field of micro-expressions has gained significance in recent years. Many researchers have concentrated on classifying micro-expressions in different discrete emotion classes, while detecting the presence of micro-expression in the video frames is considered as a pre-requisite step in the recognition process. Hence, there is a need to introduce more advanced detection models for micro-expressions. In order to address this, we propose a dual attention network based micro-expression detection architecture called LGAttNet. LGAttNet is one of the first to utilize a dual attention network grouped with 2-dimensional convolutional neural network to perform frame-wise automatic micro-expression detection. This method divides the feature extraction and enhancement task into two different convolutional neural network modules; sparse module and feature enhancement module. One of the key modules in our approach is the attention network which extracts local and global facial features, namely local attention module and global attention module. The attention mechanism adopts the human characteristic of focusing on the specific regions of micro-movements, which enables the LGAttNet to concentrate on particular facial regions along with the full facial features to identify the micro-expressions in the frames. Experiments performed on widely used publicly available databases demonstrate the robustness and superiority of our LGAttNet when compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Background: Regression-based research has successfully identified independent predictors of smoking cessation, both its initiation and maintenance. However, it is unclear how these various independent predictors interact with each other and conjointly influence smoking behaviour. As a proof-of-concept, this study used decision tree analysis (DTA) to identify the characteristics of smoker subgroups with high versus low smoking cessation initiation probability based on the conjoint effects of four predictor variables, and determine any variations by socio-economic status (SES).Methods: Data come from the Australian arm of the ITC project, a longitudinal cohort study of adult smokers followed up approximately annually. Reported wanting to quit smoking, worries about smoking negative health impact, quitting self-efficacy and quit intentions assessed in 2005 were used as predictors and reported quit attempts at the 2006 follow-up survey were used as the outcome for the initial model calibration and validation analyses (n = 1475), and further cross-validated using the 2012-2013 data (n = 787).Results: DTA revealed that while all four predictor variables conjointly contributed to the identification of subgroups with high versus low smoking cessation initiation probability, quit intention was the most important predictor common across all SES strata. The relative importance of the other predictors showed differences by SES.Conclusions: Modifiable characteristics of smoker subgroups associated with making a quit attempt and any variations by SES can be successfully identified using a decision tree analysis approach, to provide insights as to who might benefit from targeted intervention, thus, underscoring the value of this approach to complement the conventional regression-based approach. [ABSTRACT FROM AUTHOR]

Deep-learning-based QRS-detection algorithms often require essential post-processing to refine the output prediction-stream for R-peak localisation. The post-processing involves basic signal-processing tasks including the removal of random noise in the model's prediction stream using a basic Salt and Pepper filter, as well as, tasks that use domain-specific thresholds, including a minimum QRS size, and a minimum or maximum R-R distance. These thresholds were found to vary among QRS-detection studies and empirically determined for the target dataset, which may have implications if the target dataset differs such as the drop of performance in unknown test datasets. Moreover, these studies, in general, fail to identify the relative strengths of deep-learning models and the post-processing to weigh them appropriately. This study identifies the domain-specific post-processing, as found in the QRS-detection literature, as three steps based on the required domain knowledge. It was found that the use of minimal domain-specific post-processing is often sufficient for most of the cases and the use of additional domain-specific refinement ensures superior performance, however, it makes the process biased towards the training data and lacks generalisability. As a remedy, a domain-agnostic automated post-processing is introduced where a separate recurrent neural network (RNN)-based model learns required post-processing from the output generated from a QRS-segmenting deep learning model, which is, to the best of our knowledge, the first of its kind. The RNN-based post-processing shows superiority over the domain-specific post-processing for most of the cases (with shallow variants of the QRS-segmenting model and datasets like TWADB) and lags behind for others but with a small margin ( ≤ 2%). The consistency of the RNN-based post-processor is an important characteristic which can be utilised in designing a stable and domain agnostic QRS detector.

Automatic signal analysis using artificial intelligence is getting popular in digital healthcare, such as ECG rhythm analysis, where ECG signals are collected from traditional ECG machines or wearable ECG sensors. However, the risk of using an automated system for ECG analysis when noise is present can lead to incorrect diagnosis or treatment decisions. A noise detector is crucial to minimise the risk of incorrect diagnosis. Machine learning (ML) models are used in ECG noise detection before clinical decision-making systems to mitigate false alarms. However, it is essential to prove the generalisation capability of the ML model in different situations. ML models performance is 50% lesser when the model is trained with synthetic and tested with physiologic ECG datasets compared to trained and tested with physiologic ECG datasets. This suggests that the ML model must be trained with physiologic ECG datasets rather than synthetic ones or add more various types of noise in synthetic ECG datasets that can mimic physiologic ECG.Clinical relevance- ML model trained with synthetic noisy ECG can increase the 50% misclassification rate in ECG noise detection compared to training with physiologic ECG datasets. The wrong classification of noise-free and noisy ECG will lead to misdiagnosis regarding the patient's condition, which could be a cause of death.

Automatic sleep stage classification is vital for evaluating the quality of sleep. Conventionally, sleep is monitored using multiple physiological sensors that are uncomfortable for long-term monitoring and require expert intervention. In this study, we propose an automatic technique for multi-stage sleep classification using photoplethysmographic (PPG) signal. We have proposed a convolutional neural network (CNN) that learns directly from the PPG signal and classifies multiple sleep stages. We developed models for two- (Wake-Sleep), three- (Wake-NREM-REM) and four- (Wake-Light sleep-Deep sleep-REM) stages of sleep classification. Our proposed approach shows an average classification accuracy of 94.4%, 94.2%, and 92.9% for two, three, and four stages, respectively. Experimental results show that the proposed CNN model outperforms existing state-of-the-art models (classical and deep learning) in the literature.

Interpretability often seeks domain-specific facts, which is understandable to human, from deep-learning (DL) or other machine-learning (ML) models of black-box nature. This is particularly important to establish transparency in ML model's inner-working and decision-making, so that a certain level of trust is achieved when a model is deployed in a sensitive and mission-critical context, such as health-care. Model-level transparency can be achieved when its components are transparent and are capable of explaining reason of a decision, for a given input, which can be linked to domain-knowledge. This article used convolutional neural network (CNN), with sinc-convolution as its constrained first-layer, to explore if such a model's decision-making can be explained, for a given task, by observing the sinc-convolution's sinc-kernels. These kernels work like band-pass filters, having only two parameters per kernel - lower and upper cutoff frequencies, and optimised through back-propagation. The optimised frequency-bands of sinc-kernels may provide domain-specific insights for a given task. For a given input instance, the effects of sinc-kernels was visualised by means of explanation vector, which may help to identify comparatively significant frequency-bands, that may provide domain-specific interpretation, for the given task. In addition, a CNN model was further optimised by considering the identified subset of prominent sinc frequency-bands as the constrained first-layer, which yielded comparable or better performance, as compared to its all sinc-bands counterpart, as well as, a classical CNN. A minimal CNN structure, achieved through such an optimisation process, may help design task-specific interpretable models. To the best of our knowledge, the idea of sinc-convolution layer's task-specific significant sinc-kernel-based network optimisation is the first of its kind. Additionally, the idea of explanation-vector-based joint time-frequency representation to analyse time-series signals is rare in the literature. The above concept was validated for two tasks, ECG beat-classification (five-class classification task), and R-peak localisation (sample-wise segmentation task).

Precision psychiatry is attracting increasing attention lately as a recognized priority. One of the goals of precision psychiatry is to develop tools capable of aiding a clinically informed psychiatric diagnosis objectively. Cognitive, inflammatory and immunological factors are altered in both bipolar disorder (BD) and schizophrenia (SZ), however, most of these alterations do not respect diagnostic boundaries from a phenomenological perspective and possess great variability in different individuals with the same phenotypic diagnosis and, consequently, none so far has proven to have the ability of reliably aiding in the differential diagnosis of BD and SZ. We developed a probabilistic multi-domain data integration model consisting of immune and inflammatory biomarkers in peripheral blood and cognitive biomarkers using machine learning to predict diagnosis of BD and SZ. A total of 416 participants, being 323, 372, and 279 subjects for blood, cognition and combined biomarkers analysis, respectively. Our multi-domain model performances for the BD vs. control (sensitivity 80% and specificity 71%) and for the SZ vs. control (sensitivity 84% and specificity 81%) pairs were high in general, however, our multi-domain model had only moderate performance for the differential diagnosis of BD and SZ (sensitivity 71% and specificity 73%). In conclusion, our results show that the diagnosis of BD and of SZ, and that the differential diagnosis of BD and SZ can be predicted with possible clinical utility by a computational machine learning algorithm employing blood and cognitive biomarkers, and that their integration in a multi-domain outperforms algorithms based in only one domain. Independent studies are needed to validate these findings.