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BackgroundThe prevalence of child and adolescent mental health issues is increasing faster than the number of services available, leading to a shortfall. Mental health chatbots are a highly scalable method to address this gap. Manage Your Life Online (MYLO) is an artificially intelligent chatbot that emulates the method of levels therapy. Method of levels is a therapy that uses curious questioning to support the sustained awareness and exploration of current problems. ObjectiveThis study aimed to assess the feasibility and acceptability of a co-designed interface for MYLO in young people aged 16 to 24 years with mental health problems. MethodsAn iterative co-design phase occurred over 4 months, in which feedback was elicited from a group of young people (n=7) with lived experiences of mental health issues. This resulted in the development of a progressive web application version of MYLO that could be used on mobile phones. We conducted a case series to assess the feasibility and acceptability of MYLO in 13 young people over 2 weeks. During this time, the participants tested MYLO and completed surveys including clinical outcomes and acceptability measures. We then conducted focus groups and interviews and used thematic analysis to obtain feedback on MYLO and identify recommendations for further improvements. ResultsMost participants were positive about their experience of using MYLO and would recommend MYLO to others. The participants enjoyed the simplicity of the interface, found it easy to use, and rated it as acceptable using the System Usability Scale. Inspection of the use data found evidence that MYLO can learn and adapt its questioning in response to user input. We found a large effect size for the decrease in participants’ problem-related distress and a medium effect size for the increase in their self-reported tendency to resolve goal conflicts (the proposed mechanism of change) in the testing phase. Some patients also experienced a reliable change in their clinical outcome measures over the 2 weeks. ConclusionsWe established the feasibility and acceptability of MYLO. The initial outcomes suggest that MYLO has the potential to support the mental health of young people and help them resolve their own problems. We aim to establish whether the use of MYLO leads to a meaningful reduction in participants’ symptoms of depression and anxiety and whether these are maintained over time by conducting a randomized controlled evaluation trial.

The prevalent convolutional neural network (CNN)-based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy. However, these methods may ignore the underlying distribution of clean images, inducing distortions or artifacts in denoising results. This paper proposes a new perspective to treat image denoising as a distribution learning and disentangling task. Since the noisy image distribution can be viewed as a joint distribution of clean images and noise, the denoised images can be obtained via manipulating the latent representations to the clean counterpart. This paper also provides a distribution-learning-based denoising framework. Following this framework, we present an invertible denoising network, FDN, without any assumptions on either clean or noise distributions, as well as a distribution disentanglement method. FDN learns the distribution of noisy images, which is different from the previous CNN-based discriminative mapping. Experimental results demonstrate FDN’s capacity to remove synthetic additive white Gaussian noise (AWGN) on both category-specific and remote sensing images. Furthermore, the performance of FDN surpasses that of previously published methods in real image denoising with fewer parameters and faster speed.

Music elicits strong emotional reactions in people, regardless of their gender, age or cultural background. Understanding the effects of music on brain activity can enhance existing music therapy techniques and lead to improvements in various medical and affective computing research. We explore the effects of three different music genres on people’s cerebral hemodynamic responses. Functional near-infrared spectroscopy (fNIRS) signals were collected from 27 participants while they listened to 12 different pieces of music. The signals were pre-processed to reflect oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) concentrations in the brain. K-nearest neighbor (KNN), random forest (RF) and a one-dimensional (1D) convolutional neural network (CNN) were used to classify the signals using music genre and subjective responses provided by the participants as labels. Results from this study show that the highest accuracy in distinguishing three music genres was achieved by deep learning models (73.4% accuracy in music genre classification and 80.5% accuracy when predicting participants’ subjective rating of emotional content of music). This study validates a strong motivation for using fNIRS signals to detect people’s emotional state while listening to music. It could also be beneficial in giving personalised music recommendations based on people’s brain activity to improve their emotional well-being.

Facial thermal imaging is a non-contact technology which can be useful for ubiquitous deceptive anxiety recognition. To date, studies investigating this technology have produced equivocal results in classification accuracy and finding the most correlated regions on the face. This study was conducted using our dataset with 41 subjects using two different protocols and three modalities (thermal, GSR and PPG). We selected and tracked five regions of interest (ROI) on each facial thermal imprint including periorbital, forehead, cheek, perinasal and chin that were mostly used in previous papers. By employing six statistical features, four feature reduction techniques and three classifiers, we attempted to identify the ROIs which are mostly associated with activation of the sympathetic nervous system to increase the final classification accuracy rate. The results of linear classification models show significant improvement of classification accuracy by using ROC feature selection method. We achieved 90.1% and 74.7% accuracy rate for thermal features in mock crime and best friend scenarios, respectively. Our experimental results show that perinasal and cheek areas have greater discriminatory power in comparison with other ROIs on the face.

Information foraging connects optimal foraging theory in ecology withhow humans search for information. The theory suggests that, followingan information scent, the information seeker must optimize the tradeoffbetween exploration by repeated steps in the search space vs.exploitation, using the resources encountered. We conjecture that thistradeoff characterizes how a user deals with uncertainty and its twoaspects, risk and ambiguity in economic theory. Risk is related to theperceived quality of the actually visited patch of information, and canbe reduced by exploiting and understanding the patch to a better extent.Ambiguity, on the other hand, is the opportunity cost of having higherquality patches elsewhere in the search space. The aforementionedtradeoff depends on many attributes, including traits of the user: atthe two extreme ends of the spectrum, analytic and wholistic searchersemploy entirely different strategies. The former type focuses onexploitation first, interspersed with bouts of exploration, whereas thelatter type prefers to explore the search space first and consume later.Our findings from an eye-tracking study of experts' interactions withnovel search interfaces in the biomedical domain suggest that usertraits of cognitive styles and perceived search task difficultyare significantly correlated with eye gaze and search behaviour. Wealso demonstrate that perceived risk shifts the balance betweenexploration and exploitation in either type of users, tilting it againstvs. in favour of ambiguity minimization. Since the pattern of behaviourin information foraging is quintessentially sequential, risk andambiguity minimization cannot happen simultaneously, leading to afundamental limit on how good such a tradeoff can be. This in turnconnects information seeking with the emergent field of quantum decisiontheory.