Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models.

Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper.

Correction: The Effect of Robot Attentional Behaviors on User Perceptions and Behaviors in a Simulated Health Care Interaction: Randomized Controlled Trial.

[This corrects the article DOI: 10.2196/13667.].

The Effect of Robot Attentional Behaviors on User Perceptions and Behaviors in a Simulated Health Care Interaction: Randomized Controlled Trial.

BACKGROUND: For robots to be effectively used in health applications, they need to display appropriate social behaviors. A fundamental requirement in all social interactions is the ability to engage, maintain, and demonstrate attention. Attentional behaviors include leaning forward, self-disclosure, and changes in voice pitch. OBJECTIVE: This study aimed to examine the effect of robot attentional behaviors on user perceptions and behaviors in a simulated health care interaction. METHODS: A parallel randomized controlled trial with a 1:1:1:1 allocation ratio was conducted. We randomized participants to 1 of 4 experimental conditions before engaging in a scripted face-to-face interaction with a fully automated medical receptionist robot. Experimental conditions included a self-disclosure condition, voice pitch change condition, forward lean condition, and neutral condition. Participants completed paper-based postinteraction measures relating to engagement, perceived robot attention, and perceived robot empathy. We video recorded interactions and coded for participant attentional behaviors. RESULTS: A total of 181 participants were recruited from the University of Auckland. Participants who interacted with the robot in the forward lean and self-disclosure conditions found the robot to be significantly more stimulating than those who interacted with the robot in the voice pitch or neutral conditions (P=.03). Participants in the forward lean, self-disclosure, and neutral conditions found the robot to be significantly more interesting than those in the voice pitch condition (P<.001). Participants in the forward lean and self-disclosure conditions spent significantly more time looking at the robot than participants in the neutral condition (P<.001). Significantly, more participants in the self-disclosure condition laughed during the interaction (P=.01), whereas significantly more participants in the forward lean condition leant toward the robot during the interaction (P<.001). CONCLUSIONS: The use of self-disclosure and forward lean by a health care robot can increase human engagement and attentional behaviors. Voice pitch changes did not increase attention or engagement. The small effects with regard to participant perceptions are potentially because of the limitations in self-report measures or a lack of comparison for most participants who had never interacted with a robot before. Further research could explore the use of self-disclosure and forward lean using a within-subjects design and in real health care settings.

Transformer transfer learning emotion detection model: synchronizing socially agreed and self-reported emotions in big data.

Tactics to determine the emotions of authors of texts such as Twitter messages often rely on multiple annotators who label relatively small data sets of text passages. An alternative method gathers large text databases that contain the authors' self-reported emotions, to which artificial intelligence, machine learning, and natural language processing tools can be applied. Both approaches have strength and weaknesses. Emotions evaluated by a few human annotators are susceptible to idiosyncratic biases that reflect the characteristics of the annotators. But models based on large, self-reported emotion data sets may overlook subtle, social emotions that human annotators can recognize. In seeking to establish a means to train emotion detection models so that they can achieve good performance in different contexts, the current study proposes a novel transformer transfer learning approach that parallels human development stages: (1) detect emotions reported by the texts' authors and (2) synchronize the model with social emotions identified in annotator-rated emotion data sets. The analysis, based on a large, novel, self-reported emotion data set (n = 3,654,544) and applied to 10 previously published data sets, shows that the transfer learning emotion model achieves relatively strong performance.

Older adults' experiences and perceptions of living with Bomy, an assistive dailycare robot: a qualitative study.

An aging global population and preference for aging-in-place pose the opportunity for home-based robots to assist older adults with their daily routines. However, there is limited research into the experiences of older adults using robots in their own homes. In this descriptive qualitative feasibility study, older self-supporting and community-dwelling adults with various age-related health needs used Bomy, a dailycare robot in their homes for up to one week. The study explored the usefulness of the robot and participants' perceptions and experiences of using it. Bomy reminded them of daily activities and delivered cognitive stimulation games. Semi-structured in-person interviews were conducted, and data were analyzed thematically. Findings revealed an acceptance toward robots and the value of assistive dailycare robots. Participants perceived Bomy as a companion and made suggestions for improvement, including resolving technical issues associated with long-term use. Future functions should be personalizable, to accommodate each user's health needs and could also include smoke detection and reading aloud functions. Dailycare robots show promising potential in elderly care, especially in providing reminders for medication, health and wellbeing. This study highlights the importance of co-design and testing robotics in the environments for which they have been developed. Widespread implementation of Bomy might be feasible in the future, with some further adjustments.

Spectral domain optical coherence tomography in a patient with adult-onset vitelliform dystrophy treated with intravitreal bevacizumab.

A patient with adult-onset vitelliform dystrophy received intravitreal injections of bevacizumab in one eye. During 4 months of follow-up, spectral domain optical coherence tomography (SD-OCT) showed a progressive decrease of yellowish deposits and complete resolution of subretinal fluid at the last followup. Despite the good anatomic outcome, the patient reported a mild decrease in visual acuity and persistent metamorphopsia. The clinical course and the correlation between the fundus findings and the morphologic information from SD-OCT data are described.

Case report of Acremonium intraocular infection after cataract extraction.

A 64-year-old woman was referred to our clinic for the treatment of chronic uveitis in her left eye, which had started two weeks after an uncomplicated cataract extraction. She was treated with topical steroids with an initially good response, yet she subsequently developed severe inflammation and plaque-like material around the intraocular lens, despite continuous steroid therapy. She underwent pars plana vitrectomy, smear and culture of the aqueous and vitreous fluids, and intravitreal antibiotic injection under the impression of Propionibacterium acne (P. acne) endophthalmitis. As a result of the smear and culture of the vitreous fluid identified as an Acremonium species, she was treated with intravenous amphotericin B injections for five days, followed by oral voriconazole administration. During the post-operative 18-month follow-up, she was stable without significant relapse of uveitis. In this case, the best correction of visual acuity was an improvement from 20/40 to 20/20.

Neuroprotective effect of KR-31378 via KATP channel opening against ischemic insult.

The opening of the adenosine triphosphate (ATP)-sensitive potassium (KATP) channel has been proposed as a therapeutic approach for ischemia. Here we examined the opening effect of KR-31378 on the KATP channel using patch clamp recording in neuroblastoma 2a (N2a) cells and investigated the neuroprotective effect of KR-31378 in organotypic hippocampal slice cultures exposed to oxygen/glucose deprivation. The treatment with KR-31378 (10 microM) to N2a cells seemed to induce KATP channel opening in a dose dependent manner. The opening effect of KR-31378 was more significant than that of other known KATP channel openers. Pretreatment with KR-31378 (10 microM) showed a neuroprotective effect in both CA1 and CA3 regions and its effect was attenuated by glibenclamide in a dose dependent manner in both areas. This remarkable neuroprotective effect of KR-31378 seemed to be mediated by the opening of the KATP channel. These results suggest that KR-31378 could be a possible neuroprotective agent against cerebral ischemia.