Regularizing Face Verification Nets For Pain Intensity Regression

Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is limited by small datasets with labels of patient-reported pain intensities. Fortunately, fine-tuning from a data-extensive pre-trained domain, such as face verification, can alleviate this problem. In this paper, we propose a network that fine-tunes a state-of-the-art face verification network using a regularized regression loss and additional data with expression labels. In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification. The proposed regularized deep regressor is applied to estimate the pain expression intensity and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving the state-of-the-art performance. A weighted evaluation metric is also proposed to address the imbalance issue of different pain intensities.Comment: 5 pages, 3 figure; Camera-ready version to appear at IEEE ICIP 201

Do face masks introduce bias in speech technologies? The case of automated scoring of speaking proficiency

The COVID-19 pandemic has led to a dramatic increase in the use of face masks worldwide. Face coverings can affect both acoustic properties of the signal as well as speech patterns and have unintended effects if the person wearing the mask attempts to use speech processing technologies. In this paper we explore the impact of wearing face masks on the automated assessment of English language proficiency. We use a dataset from a large-scale speaking test for which test-takers were required to wear face masks during the test administration, and we compare it to a matched control sample of test-takers who took the same test before the mask requirements were put in place. We find that the two samples differ across a range of acoustic measures and also show a small but significant difference in speech patterns. However, these differences do not lead to differences in human or automated scores of English language proficiency. Several measures of bias showed no differences in scores between the two groups

Action-based Learning Assessment Method (ALAM) in Virtual Training Environments

Specialised and high priced simulators for surgical training, chemical labs, and flight training can provide real-world simulation in a safe and risk-free environment, but they are not accessible for the broader community due to costs for technology and availability of experts. Thus, training scenarios shifted to virtual worlds providing access for everyone interested in acquiring skills and knowledge at educational or professional institutions. Even in this context, we still expect a detailed formative feedback as would have been provided by a human trainer during the face to face process. Whilst the literature is focusing on goal-oriented assessment, it neglects the performed actions. In this paper, we present the Action-based Learning Assessment Method (ALAM) that analyses the action-sequences of the learners according to reference solutions by experts and automated formative feedback

The Usage of Online Assessment Moodle LMS and Google Classroom Environment for English Language Teaching

Moodle and Google Classroom learning management systems (LMSs) generally operate in higher education and are accommodating when shifting from traditional face-to-face instruction to online courses. The study aims to examine and analyze online assessment or testing in Moodle and Google Classroom for English language learning. The study concentrated on the mixed method and the convergent parallel design. This research reveals methods to construct, adapt, and evaluate online assessments or testing in Moodle or Google Classroom. Moodle scores higher than Google Classroom in the Automated evaluation and Submission for Items evaluation aspect. On the other hand, Discussions on the Platforms and Share and Publication have a better experience with Google Classroom educators. English language lecturers or instructors exposed that Moodle comprehended Item Mean scores of 2.43, 2.42, and 2.41. it exposed those quizzes are the most common automated evaluation. Interactive multimedia applications may also be practical for online learning and assessment. However, the mean scores of 2.09, 2.16, and 2.18 revealed that comes to online learning, Moodle instructors who consider it an inadequate Google Classroom are out of touch with reality. Moodle and Google classroom scored low for the teacher aspect as implementation differences, and the finding elaborates on the opportunities and challenges of online assessment and testing. The participants noticed increased English language learners’ accomplishments and improved English lecturers’ online technical abilities. These results support the transition toward the future introduction of more online English language online course

Long Range Automated Persistent Surveillance

This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging. field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped camera’s field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales. Size preserving tracking automatically adjusts the camera’s zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the target’s 3D motion, including off-plane rotations. The effectiveness of the scale estimation and foreground segmentation algorithms is validated via both offline and real-time tracking of pedestrians at various resolution levels. Face image quality assessment and enhancement compensate for the performance degradations in face recognition rates caused by high system magnifications and long observation distances. A class of adaptive sharpness measures is proposed to evaluate and predict this degradation. A wavelet based enhancement algorithm with automated frame selection is developed and proves efficient by a considerably elevated face recognition rate for severely blurred long range face images

COREPIG, final report of WP3: Development and evaluation of a HACCP based surveillance and management system

Organic farmer repeatedly face problems with suckling piglet mortality, weaning diarrhoea, en-doparasites and farrowing/reproduction. These problems are multifactorial, they are caused by many factors whereby the key factors often differ from farm to farm. Thus, it was the aim of the 3rd work package of Corepig to develop a management tool based on the HACCP (hazard anal-ysis critical control points) principle, which can be used by farmers, advisers and veterinarians to solve health problems on organic pig farms. Several teams of experts for organic pig production including advisers and researchers created four risk assessment protocols, one each for suckling piglet mortality, weaning diarrhoea, endo-parasites and farrowing/reproduction problems. As the lists of possible risk factors are long and complex, the assessment protocols were incorporated into semi-automated MS Excel® files. The tools were tested on 32 farms in Austria, Denmark, France and Germany, where risks for the four problem areas could but reduced on 72% of farms. Farmers as well as advisers acknowledged the HACCP based management tools as valuable helps for organic pig produc-tion. The revised tools and their descriptions can be downloaded from the project homepage at http://www.coreorganic.org/research/projects/corepig/index.html (to be launched 01.09.2011)

Corepig, final report of WP3: Development and evaluation of a HACCP based surveillance and management system

Organic farmer repeatedly face problems with suckling piglet mortality, weaning diarrhoea, en-doparasites and farrowing/reproduction. These problems are multifactorial, they are caused by many factors whereby the key factors often differ from farm to farm. Thus, it was the aim of the 3rd work package of Corepig to develop a management tool based on the HACCP (hazard anal-ysis critical control points) principle, which can be used by farmers, advisers and veterinarians to solve health problems on organic pig farms. Several teams of experts for organic pig production including advisers and researchers created four risk assessment protocols, one each for suckling piglet mortality, weaning diarrhoea, endo-parasites and farrowing/reproduction problems. As the lists of possible risk factors are long and complex, the assessment protocols were incorporated into semi-automated MS Excel® files. The tools were tested on 32 farms in Austria, Denmark, France and Germany, where risks for the four problem areas could but reduced on 72% of farms. Farmers as well as advisers acknowledged the HACCP based management tools as valuable helps for organic pig produc-tion. The revised tools and their descriptions can be downloaded from the project homepage at http://www.coreorganic.org/research/projects/corepig/index.html (to be launched 01.09.2011)

Assessing a sleep interviewing chatbot to improve subjective and objective sleep: protocol for an observational feasibility study

BACKGROUND: Sleep disorders are common among the aging population and people with neurodegenerative diseases. Sleep disorders have a strong bidirectional relationship with neurodegenerative diseases, where they accelerate and worsen one another. Although one-to-one individual cognitive behavioral interventions (conducted in-person or on the internet) have shown promise for significant improvements in sleep efficiency among adults, many may experience difficulties accessing interventions with sleep specialists, psychiatrists, or psychologists. Therefore, delivering sleep intervention through an automated chatbot platform may be an effective strategy to increase the accessibility and reach of sleep disorder intervention among the aging population and people with neurodegenerative diseases. OBJECTIVE: This work aims to (1) determine the feasibility and usability of an automated chatbot (named MotivSleep) that conducts sleep interviews to encourage the aging population to report behaviors that may affect their sleep, followed by providing personalized recommendations for better sleep based on participants' self-reported behaviors; (2) assess the self-reported sleep assessment changes before, during, and after using our automated sleep disturbance intervention chatbot; (3) assess the changes in objective sleep assessment recorded by a sleep tracking device before, during, and after using the automated chatbot MotivSleep. METHODS: We will recruit 30 older adult participants from West London for this pilot study. Each participant will have a sleep analyzer installed under their mattress. This contactless sleep monitoring device passively records movements, heart rate, and breathing rate while participants are in bed. In addition, each participant will use our proposed chatbot MotivSleep, accessible on WhatsApp, to describe their sleep and behaviors related to their sleep and receive personalized recommendations for better sleep tailored to their specific reasons for disrupted sleep. We will analyze questionnaire responses before and after the study to assess their perception of our proposed chatbot; questionnaire responses before, during, and after the study to assess their subjective sleep quality changes; and sleep parameters recorded by the sleep analyzer throughout the study to assess their objective sleep quality changes. RESULTS: Recruitment will begin in May 2023 through UK Dementia Research Institute Care Research and Technology Centre organized community outreach. Data collection will run from May 2023 until December 2023. We hypothesize that participants will perceive our proposed chatbot as intelligent and trustworthy; we also hypothesize that our proposed chatbot can help improve participants' subjective and objective sleep assessment throughout the study. CONCLUSIONS: The MotivSleep automated chatbot has the potential to provide additional care to older adults who wish to improve their sleep in more accessible and less costly ways than conventional face-to-face therapy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/45752