Mapping FoodHCI Futures

Recognizing the significant potential impact that HCI has on food practices and experiences, researchers and practitioners are undertaking a growing number of explorations of novel computing technology and food combinations. These explorations have so far primarily emphasized technology-driven systems and taken a human-centric perspective. We propose a Special Interest Group (SIG) in "foodHCI futures"that creates a space for researchers to discuss the boundaries of food incorporating HCI, and with the simultaneous aims of reconciling food with technology and extending our visions for human-food interactions towards anthropocentrism. Specifically, the SIG will be a beginning of developing a structured conceptual map of the possibilities for future technology interventions in food systems. In developing this map, we hope to encourage democratized debate, provoke new and divergent thoughts on the opportunities for foodHCI, and ultimately gain unique insights that contribute to preferable food futures

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data

Systematic literature review of hand gestures used in human computer interaction interfaces

Gestures, widely accepted as a humans' natural mode of interaction with their surroundings, have been considered for use in human-computer based interfaces since the early 1980s. They have been explored and implemented, with a range of success and maturity levels, in a variety of fields, facilitated by a multitude of technologies. Underpinning gesture theory however focuses on gestures performed simultaneously with speech, and majority of gesture based interfaces are supported by other modes of interaction. This article reports the results of a systematic review undertaken to identify characteristics of touchless/in-air hand gestures used in interaction interfaces. 148 articles were reviewed reporting on gesture-based interaction interfaces, identified through searching engineering and science databases (Engineering Village, Pro Quest, Science Direct, Scopus and Web of Science). The goal of the review was to map the field of gesture-based interfaces, investigate the patterns in gesture use, and identify common combinations of gestures for different combinations of applications and technologies. From the review, the community seems disparate with little evidence of building upon prior work and a fundamental framework of gesture-based interaction is not evident. However, the findings can help inform future developments and provide valuable information about the benefits and drawbacks of different approaches. It was further found that the nature and appropriateness of gestures used was not a primary factor in gesture elicitation when designing gesture based systems, and that ease of technology implementation often took precedence

Reflections on different learning analytics indicators for supporting study success

Common factors, which are related to study success include students’ sociodemographic factors, cognitive capacity, or prior academic performance, and individual attributes as well as course related factors such as active learning and attention or environmental factors related to supportive academic and social embeddedness. In addition, there are various stages of a learner’s learning journey from the beginning when commencing learning until its completion, as well as different indicators or variables that can be examined to gauge or predict how successfully that journey can or will be at different points during that journey, or how successful learners may complete the study and thereby acquiring the intended learning outcomes. The aim of this research is to gain a deeper understanding of not only if learning analytics can support study success, but which aspects of a learner’s learning journey can benefit from the utilisation of learning analytics. We, therefore, examined different learning analytics indicators to show which aspect of the learning journey they were successfully supporting. Key indicators may include GPA, learning history, and clickstream data. Depending on the type of higher education institution, and the mode of education (face-to-face and/or distance), the chosen indicators may be different due to them having different importance in predicting the learning outcomes and study success

Improving Facial Emotion Recognition with Image processing and Deep Learning

Humans often use facial expressions along with words in order to communicate effectively. There has been extensive study of how we can classify facial emotion with computer vision methodologies. These have had varying levels of success given challenges and the limitations of databases, such as static data or facial capture in non-real environments. Given this, we believe that new preprocessing techniques are required to improve the accuracy of facial detection models. In this paper, we propose a new yet simple method for facial expression recognition that enhances accuracy. We conducted our experiments on the FER-2013 dataset that contains static facial images. We utilized Unsharp Mask and Histogram equalization to emphasize texture and details of the images. We implemented Convolution Neural Networks [CNNs] to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. We also employed pre-trained models such as Resnet-50, Senet-50, VGG16, and FaceNet, and applied transfer learning to achieve an accuracy of 76.01% using an ensemble of seven models

Hi YouTube! Personality Impressions and Verbal Content in Social Video

Despite the evidence that social video conveys rich human personality information, research investigating the automatic prediction of personality impressions in vlogging has shown that, amongst the Big-Five traits, automatic nonverbal be- havioral cues are useful to predict mainly the Extraversion trait. This finding, also reported in other conversational settings, indicates that personality information may be coded in other behavioral dimensions like the verbal channel, which has been less studied in multimodal interaction research. In this paper, we address the task of predicting personality impressions from vloggers based on what they say in their YouTube videos. First, we use manual transcripts of vlogs and verbal content analysis techniques to understand the ability of verbal content for the prediction of crowdsourced Big-Five personality impressions. Second, we explore the feasibility of a fully-automatic framework in which transcripts are obtained using automatic speech recognition (ASR). Our results show that the analysis of error-free verbal content is useful to predict four of the Big-Five traits, three of them better than using nonverbal cues, and that the errors caused by the ASR system decrease the performance signicantly

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has an imbalanced nature. Moreover, we designed a deep learning model that combines a convolution-based feature extractor and deep neural network blocks, the LSTM block, and the transformer encoder block, followed by a position-wise feedforward layer. We found that combining the input sequence with the convolution-learned features of different kernels tends to increase the performance of the fall-detection model. Last, we analyzed that the sensor signals collected by both accelerometer and gyroscope sensors can be leveraged to develop an effective classifier that can accurately detect falls, especially differentiating falls from near-falls. Furthermore, we also used data from sixteen different body parts and compared them to determine the better sensor position for fall-detection methods. We found that the shank is the optimal position for placing our sensors, with an F1 score of 0.97, and this could help other researchers collect high-quality fall datasets

Exploring quality teaching of information and communication technology in New South Wales and Yenbai high schools : a comparative study

This study compares ICT policy and curriculum and assessment practices between Australian and Vietnamese secondary schools, and investigates differences between these two school systems. Document analyses and case studies were used to examine the key differences in ICT curriculum and policy and assessment practices between Australian and Vietnamese secondary schools. The document analyses focused on the intended ICT policy and curriculum and assessment, as presented in official documents in both countries. Using a case study approach for in-depth examination, two secondary schools were selected (one from Yenbai province, Vietnam and one from Sydney, New South Wales, Australia). Two principals and three teachers were interviewed. Classroom teaching and assessment practices were observed, and principals and teachers‟ views were obtained through semi-structured interviews and extensive discussions. Findings from the two case studies were compared with the findings from the document analysis. This study explored and analysed differences in ICT teaching, learning, assessment, and achievement between Vietnamese and Australian secondary students. It was found that that Australian ICT school curricula and assessment differed markedly from the Vietnamese system. Student ICT achievement in these Australian and Vietnamese schools could not only be attributed to higher standards of intended ICT curricula and assessment, or teacher knowledge or classroom practices. These differences are better explained by economic and cultural factors, ICT policies and their degrees of implementation, and extra ICT curricula. In order to bridge the gap and implement adequate ICT curricula and policies, rigorous professional training in teaching and assessment is essential for both Australian and Vietnamese teachers. In order to improve Australian students‟ ICT achievement, achievement motivation must be addressed. Many challenging aspects were found in ICT policies and classrooms in the Vietnamese educational system that calls for immediate change and improvement. In order to implement reforms in Vietnamese education, the impact of cultural influence must be considered more seriously. In particular, this study highlights the need to integrate case study with large-scale study in international comparative studies