{"title":"Editorial: MAPPING: MAnagement and Processing of Images for Population ImagiNG","authors":"M. Dojat, D. Kennedy, W. Niessen","doi":"10.3389/fict.2017.00018","DOIUrl":"https://doi.org/10.3389/fict.2017.00018","url":null,"abstract":"","PeriodicalId":37157,"journal":{"name":"Frontiers in ICT","volume":"71 1","pages":"18"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75628223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Animallike robot companions such as robotic seal Paro are increasingly used in dementia care due to the positive effects that interaction with these robots can have on the well-being of these patients. Touch is one of the most important interaction modalities for patients with dementia and can be a natural way to interact with animallike robots. To advance the development of animallike robots we explored in what ways people with dementia could benefit from interaction with an animallike robot with more advanced touch recognition capabilities and which touch gestures would be important in their interaction with Paro. In addition we explored which other target groups might benefit from interaction with animallike robots with more advanced interaction capabilties. In this study we administered a questionnaire and conducted interviews with two groups of health care providers who all worked in a geriatric psychiatry department. One group used Paro in their work (i.e., the expert group; $n=5$) while the other group had no experience with the use of animallike robot (i.e., the layman group; $n=4$). The results showed that health care providers perceived Paro as an effective intervention to improve the well-being of people with dementia. Examples of usages for Paro that were mentioned were providing distraction, interrupting problematic behaviors and stimulating communication. Furthermore, the care providers indicated that people with dementia (would) use mostly positive forms of touch and speech to interact with Paro. Paro's auditory responses were criticized because they can overstimulate the patients. Additionally, the care providers argued that social interactions with Paro are currently limited and therefore the robot does not meet the needs of a broader audience such as healthy elderly people that still live in their own homes. The development of robot pets with more advanced social capabilities such as touch and speech recognition might result in more intelligent interactions which could help to better adapt to the needs of people with dementia and could make interactions more interesting for a broader audience. Moreover, the robot's response modalities and its appearance should match the needs of to the target group.
{"title":"An Exploration of the Benefits of an Animallike Robot Companion with More Advanced Touch Interaction Capabilities for Dementia Care","authors":"Merel M. Jung, L. V. D. Leij, S. Kelders","doi":"10.3389/fict.2017.00016","DOIUrl":"https://doi.org/10.3389/fict.2017.00016","url":null,"abstract":"Animallike robot companions such as robotic seal Paro are increasingly used in dementia care due to the positive effects that interaction with these robots can have on the well-being of these patients. Touch is one of the most important interaction modalities for patients with dementia and can be a natural way to interact with animallike robots. To advance the development of animallike robots we explored in what ways people with dementia could benefit from interaction with an animallike robot with more advanced touch recognition capabilities and which touch gestures would be important in their interaction with Paro. In addition we explored which other target groups might benefit from interaction with animallike robots with more advanced interaction capabilties. In this study we administered a questionnaire and conducted interviews with two groups of health care providers who all worked in a geriatric psychiatry department. One group used Paro in their work (i.e., the expert group; $n=5$) while the other group had no experience with the use of animallike robot (i.e., the layman group; $n=4$). The results showed that health care providers perceived Paro as an effective intervention to improve the well-being of people with dementia. Examples of usages for Paro that were mentioned were providing distraction, interrupting problematic behaviors and stimulating communication. Furthermore, the care providers indicated that people with dementia (would) use mostly positive forms of touch and speech to interact with Paro. Paro's auditory responses were criticized because they can overstimulate the patients. Additionally, the care providers argued that social interactions with Paro are currently limited and therefore the robot does not meet the needs of a broader audience such as healthy elderly people that still live in their own homes. The development of robot pets with more advanced social capabilities such as touch and speech recognition might result in more intelligent interactions which could help to better adapt to the needs of people with dementia and could make interactions more interesting for a broader audience. Moreover, the robot's response modalities and its appearance should match the needs of to the target group.","PeriodicalId":37157,"journal":{"name":"Frontiers in ICT","volume":"68 1","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2017-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83593679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Good public speaking skills are essential in many professions as well as everyday life, but speech anxiety is a common problem. While it is established that public speaking training in Virtual Reality is effective, comprehensive studies on the underlying factors that contribute to this success are rare. The QUEST-VR framework for evaluation of VR applications is presented that includes system features, user factors, and moderating variables. Based on this framework, variables that are postulated to influence the quality of a public speaking training application were selected for a first validation study. In a cross-sectional, repeated measures laboratory study (N = 36 undergraduate students; 36% men, 64% women, mean age = 26.42 years (SD = 3.42)), the effects of task difficulty (independent variable), ability to concentrate, fear of public speaking, and social presence (covariates) on public speaking performance (dependent variable) in a virtual training scenario were analyzed, using stereoscopic visualization on a screen. The results indicate that the covariates moderate the effect of task difficulty on speech performance, turning it into a non-significant effect. Further interrelations are explored. The presenter’s reaction to the virtual agents in the audience shows a tendency of overlap of explained variance with task difficulty. This underlines the need for more studies dedicated to the interaction of contributing factors for determining the quality of VR public speaking applications.
{"title":"Virtual Reality Training for Public Speaking—A QUEST-VR Framework Validation","authors":"Sandra Poeschl","doi":"10.3389/fict.2017.00013","DOIUrl":"https://doi.org/10.3389/fict.2017.00013","url":null,"abstract":"Good public speaking skills are essential in many professions as well as everyday life, but speech anxiety is a common problem. While it is established that public speaking training in Virtual Reality is effective, comprehensive studies on the underlying factors that contribute to this success are rare. The QUEST-VR framework for evaluation of VR applications is presented that includes system features, user factors, and moderating variables. Based on this framework, variables that are postulated to influence the quality of a public speaking training application were selected for a first validation study. In a cross-sectional, repeated measures laboratory study (N = 36 undergraduate students; 36% men, 64% women, mean age = 26.42 years (SD = 3.42)), the effects of task difficulty (independent variable), ability to concentrate, fear of public speaking, and social presence (covariates) on public speaking performance (dependent variable) in a virtual training scenario were analyzed, using stereoscopic visualization on a screen. The results indicate that the covariates moderate the effect of task difficulty on speech performance, turning it into a non-significant effect. Further interrelations are explored. The presenter’s reaction to the virtual agents in the audience shows a tendency of overlap of explained variance with task difficulty. This underlines the need for more studies dedicated to the interaction of contributing factors for determining the quality of VR public speaking applications.","PeriodicalId":37157,"journal":{"name":"Frontiers in ICT","volume":"67 1","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81600752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Theodoros Kostoulas, G. Chanel, Michal Muszynski, Patrizia Lombardo, T. Pun
Over the last years affective computing has been strengthening its ties with the humanities, exploring and building understanding of people's responses to specific artistic multimedia stimuli. 'Aesthetic experience' is acknowledged to be the subjective part of some artistic exposure, namely, the inner affective state of a person exposed to some artistic object. In this work we describe ongoing research activities for studying the aesthetic experience of people when exposed to movie artistic stimuli. To do so, this work is focused on the definition of emotional and aesthetic highlights in movies and studies the people responses to them using physiological and behavioural signals, in a social setting. In order to examine the suitability of multimodal signals for detecting highlights we initially evaluate a supervised highlight detection system. Further, in order to provide an insight on the reactions of people, in a social setting, during emotional and aesthetic highlights, we study two unsupervised systems. Those systems are able to (a) measure the distance among the captured signals of multiple people using the dynamic time warping algorithm and (b) create a reaction profile for a group of people that would be indicative of whether that group reacts or not at a given time. The results indicate that the proposed systems are suitable for detecting highlights in movies and capturing some form of social interactions across different movie genres. Moreover, similar social interactions during exposure to emotional and some types of aesthetic highlights, such as those corresponding to technical or lightening choices of the director, can be observed. The utilisation of electrodermal activity measurements yields in better performances than those achieved when using acceleration measurements, whereas fusion of the modalities does not appear to be beneficial for the majority of the cases.
{"title":"Films, Affective Computing and Aesthetic Experience: Identifying Emotional and Aesthetic Highlights from Multimodal Signals in a Social Setting","authors":"Theodoros Kostoulas, G. Chanel, Michal Muszynski, Patrizia Lombardo, T. Pun","doi":"10.3389/fict.2017.00011","DOIUrl":"https://doi.org/10.3389/fict.2017.00011","url":null,"abstract":"Over the last years affective computing has been strengthening its ties with the humanities, exploring and building understanding of people's responses to specific artistic multimedia stimuli. 'Aesthetic experience' is acknowledged to be the subjective part of some artistic exposure, namely, the inner affective state of a person exposed to some artistic object. In this work we describe ongoing research activities for studying the aesthetic experience of people when exposed to movie artistic stimuli. To do so, this work is focused on the definition of emotional and aesthetic highlights in movies and studies the people responses to them using physiological and behavioural signals, in a social setting. In order to examine the suitability of multimodal signals for detecting highlights we initially evaluate a supervised highlight detection system. Further, in order to provide an insight on the reactions of people, in a social setting, during emotional and aesthetic highlights, we study two unsupervised systems. Those systems are able to (a) measure the distance among the captured signals of multiple people using the dynamic time warping algorithm and (b) create a reaction profile for a group of people that would be indicative of whether that group reacts or not at a given time. The results indicate that the proposed systems are suitable for detecting highlights in movies and capturing some form of social interactions across different movie genres. Moreover, similar social interactions during exposure to emotional and some types of aesthetic highlights, such as those corresponding to technical or lightening choices of the director, can be observed. The utilisation of electrodermal activity measurements yields in better performances than those achieved when using acceleration measurements, whereas fusion of the modalities does not appear to be beneficial for the majority of the cases.","PeriodicalId":37157,"journal":{"name":"Frontiers in ICT","volume":"27 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87646643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Mouritsen, R. Edwards-Stuart, Yong-Yeol Ahn, S. Ahnert
The study of food consumption, in the broadest sense, intersects with a wide range of academic disciplines. The perception of food involves molecular biology, chemistry, soft-matter physics, neuroscience, psychology, physiology, and even machine learning. Our choices and preparation of food are of interest to anthropologists, social scientists, historians, philosophers, and linguists. The advent of information technology has enabled the accumulation and analysis of large amounts of food-related data, from the interactions of biomolecules and the chemical properties of aroma compounds to online recipes and food-related social media postings. In this perspective article, we outline several areas in which the availability and analysis of data can inform us about general principles that may underlie the perception of food and the diversity of culinary practice. One of these areas is the study of umami taste through a combination of chemical analysis and quantitative sensory evaluation for a wide range of fermented products. Another is the mapping of global culinary diversity by mining online recipes and the analysis of culinary habits recorded by individuals on social media. A third area is the study of the properties of flavour compounds, and the application of these insights in the context of high-end gastronomy. These examples illustrate that large-scale data analysis is already transforming our understanding of food perception and consumption, and that it is likely to fundamentally influence our food choices and habits in the future.
{"title":"Data-driven Methods for the Study of Food Perception, Preparation, Consumption, and Culture","authors":"O. Mouritsen, R. Edwards-Stuart, Yong-Yeol Ahn, S. Ahnert","doi":"10.3389/fict.2017.00015","DOIUrl":"https://doi.org/10.3389/fict.2017.00015","url":null,"abstract":"The study of food consumption, in the broadest sense, intersects with a wide range of academic disciplines. The perception of food involves molecular biology, chemistry, soft-matter physics, neuroscience, psychology, physiology, and even machine learning. Our choices and preparation of food are of interest to anthropologists, social scientists, historians, philosophers, and linguists. The advent of information technology has enabled the accumulation and analysis of large amounts of food-related data, from the interactions of biomolecules and the chemical properties of aroma compounds to online recipes and food-related social media postings. In this perspective article, we outline several areas in which the availability and analysis of data can inform us about general principles that may underlie the perception of food and the diversity of culinary practice. One of these areas is the study of umami taste through a combination of chemical analysis and quantitative sensory evaluation for a wide range of fermented products. Another is the mapping of global culinary diversity by mining online recipes and the analysis of culinary habits recorded by individuals on social media. A third area is the study of the properties of flavour compounds, and the application of these insights in the context of high-end gastronomy. These examples illustrate that large-scale data analysis is already transforming our understanding of food perception and consumption, and that it is likely to fundamentally influence our food choices and habits in the future.","PeriodicalId":37157,"journal":{"name":"Frontiers in ICT","volume":"40 1","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2017-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91042347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose an original method for detecting and localizing anomalous motion patterns in videos from a camera view -based motion representation perspective. Anomalous motion should be taken in a broad sense, i.e., unexpected, abnormal, singular, irregular, or unusual motion. Identifying distinctive dynamic information at any time point and at any image location in a sequence of images is a key requirement in many situations and applications. The proposed method relies on so-called labeled affine flows (LAF) involving both affine velocity vectors and affine motion classes. At every pixel a motion class is inferred from the affine motion model selected in a set of candidate models estimated over a collection of windows. Then, the image is subdivided in blocks where motion class histograms weighted by the affine motion vector magnitudes are computed. They are compared blockwise to histograms of normal behaviours with a dedicated distance. More specifically, we introduce the local outlier factor (LOF) to detect anomalous blocks. LOF is a local flexible measure of the relative density of data points in a feature space, here the space of LAF histograms. By thresholding the LOF value, we can detect an anomalous motion pattern in any block at any time instant of the video sequence. The threshold value is automatically set in each block by means of statistical arguments. We report comparative experiments on several real video datasets, demonstrating that our method is highly competitive for the intricate task of detecting different types of anomalous motion in videos. Specifically, we obtain very competitive results on all the tested datasets: 99.2 % AUC for UMN, 82.8 % AUC for UCSD, and 95.73 % accuracy for PETS 2009, at the frame level.
{"title":"Detection and Localization of Anomalous Motion in Video Sequences from Local Histograms of Labeled Affine Flows","authors":"Juan-Manuel Pérez-Rúa, A. Basset, P. Bouthemy","doi":"10.3389/fict.2017.00010","DOIUrl":"https://doi.org/10.3389/fict.2017.00010","url":null,"abstract":"We propose an original method for detecting and localizing anomalous motion patterns in videos from a camera view -based motion representation perspective. Anomalous motion should be taken in a broad sense, i.e., unexpected, abnormal, singular, irregular, or unusual motion. Identifying distinctive dynamic information at any time point and at any image location in a sequence of images is a key requirement in many situations and applications. The proposed method relies on so-called labeled affine flows (LAF) involving both affine velocity vectors and affine motion classes. At every pixel a motion class is inferred from the affine motion model selected in a set of candidate models estimated over a collection of windows. Then, the image is subdivided in blocks where motion class histograms weighted by the affine motion vector magnitudes are computed. They are compared blockwise to histograms of normal behaviours with a dedicated distance. More specifically, we introduce the local outlier factor (LOF) to detect anomalous blocks. LOF is a local flexible measure of the relative density of data points in a feature space, here the space of LAF histograms. By thresholding the LOF value, we can detect an anomalous motion pattern in any block at any time instant of the video sequence. The threshold value is automatically set in each block by means of statistical arguments. We report comparative experiments on several real video datasets, demonstrating that our method is highly competitive for the intricate task of detecting different types of anomalous motion in videos. Specifically, we obtain very competitive results on all the tested datasets: 99.2 % AUC for UMN, 82.8 % AUC for UCSD, and 95.73 % accuracy for PETS 2009, at the frame level.","PeriodicalId":37157,"journal":{"name":"Frontiers in ICT","volume":"75 0","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2017-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72628409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since the release date struck on a coin is important information of its monetary type, recognition of extracted digits may assist in identification of monetary types. However, digit images extracted from coins are challenging for conventional optical character recognition (OCR) methods because the foreground of such digits has very often the same color as their background. In addition, other noises, including the wear of coin metal, make it more difficult to obtain a correct segmentation of the character shape. To address those challenges, this paper presents the CoinNUMS database for automatic digit recognition. The database CoinNUMS, containing 3006 digit images, is divided into three subsets. The first subset CoinNUMS_geni consists of 606 digit images manually cropped from high-resolution photos of well-conserved coins from GENI coin photos; the second subset CoinNUMS_pcgs_a consists of 1200 digit images automatically extracted from a subset of the USA_Grading numismatic database containing coins in different quality; the last subset CoinNUMS_pcgs_m consists of 1200 digit images manually extracted from the same coin photos as CoinNUMS_pcgs_a. In CoinNUMS_pcgs_a and CoinNUMS_pcgs_m, the digit images are extracted from the release date. In CoinNUMS_geni, the digit images can come from the cropped date, the face value or any other legends containing digits in the coin. To show the difficulty of these databases, we have tested recognition algorithms of the literature. The database and the results of the tested algorithms will be freely available on a dedicated website .
{"title":"A New Database of Digits Extracted from Coins with Hard-to-Segment Foreground for Optical Character Recognition Evaluation","authors":"Xingyu Pan, L. Tougne","doi":"10.3389/fict.2017.00009","DOIUrl":"https://doi.org/10.3389/fict.2017.00009","url":null,"abstract":"Since the release date struck on a coin is important information of its monetary type, recognition of extracted digits may assist in identification of monetary types. However, digit images extracted from coins are challenging for conventional optical character recognition (OCR) methods because the foreground of such digits has very often the same color as their background. In addition, other noises, including the wear of coin metal, make it more difficult to obtain a correct segmentation of the character shape. To address those challenges, this paper presents the CoinNUMS database for automatic digit recognition. The database CoinNUMS, containing 3006 digit images, is divided into three subsets. The first subset CoinNUMS_geni consists of 606 digit images manually cropped from high-resolution photos of well-conserved coins from GENI coin photos; the second subset CoinNUMS_pcgs_a consists of 1200 digit images automatically extracted from a subset of the USA_Grading numismatic database containing coins in different quality; the last subset CoinNUMS_pcgs_m consists of 1200 digit images manually extracted from the same coin photos as CoinNUMS_pcgs_a. In CoinNUMS_pcgs_a and CoinNUMS_pcgs_m, the digit images are extracted from the release date. In CoinNUMS_geni, the digit images can come from the cropped date, the face value or any other legends containing digits in the coin. To show the difficulty of these databases, we have tested recognition algorithms of the literature. The database and the results of the tested algorithms will be freely available on a dedicated website .","PeriodicalId":37157,"journal":{"name":"Frontiers in ICT","volume":"13 1","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78306035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Screen use can influence the circadian phase and cause eye strain. Smart eyeglasses with an integrated colour light sensor can detect screen use. We present a screen use detection approach based on a light sensor embedded into the bridge of smart eyeglasses. By calculating the light intensity at the user’s eyes for different screens and content types we found only computer screens to have significant impact on the circadian phase. Our screen use detection is based on ratios between colour channels and used a linear support vector machine to detect screen use. We validated our detection approach in three studies. A Test bench was built to detect screen use under different ambient light sources and intensities in a controlled environment. In a Lab study, we evaluated recognition performance for different ambient light intensities. Using participant-independent models we achieved a ROC AUC above 0.9 for ambient light intensities below 200 lux. In a study of typical ADLs screen use was detected with an average ROC AUC of 0.83 assuming screen use for 30 % of the time.
{"title":"Computer Screen Use Detection Using Smart Eyeglasses","authors":"Florian Wahl, Jakob Kasbauer, O. Amft","doi":"10.3389/fict.2017.00008","DOIUrl":"https://doi.org/10.3389/fict.2017.00008","url":null,"abstract":"Screen use can influence the circadian phase and cause eye strain. Smart eyeglasses with an integrated colour light sensor can detect screen use. We present a screen use detection approach based on a light sensor embedded into the bridge of smart eyeglasses. By calculating the light intensity at the user’s eyes for different screens and content types we found only computer screens to have significant impact on the circadian phase. Our screen use detection is based on ratios between colour channels and used a linear support vector machine to detect screen use. We validated our detection approach in three studies. A Test bench was built to detect screen use under different ambient light sources and intensities in a controlled environment. In a Lab study, we evaluated recognition performance for different ambient light intensities. Using participant-independent models we achieved a ROC AUC above 0.9 for ambient light intensities below 200 lux. In a study of typical ADLs screen use was detected with an average ROC AUC of 0.83 assuming screen use for 30 % of the time.","PeriodicalId":37157,"journal":{"name":"Frontiers in ICT","volume":"5 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2017-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74267591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Several studies have indicated that interacting with social robots in educational contexts may lead to greater learning than interactions with computers or virtual agents. As such, an increasing amount of social human-robot interaction research is being conducted in the learning domain, particularly with children. However, it is unclear precisely what social behaviour a robot should employ in such interactions. Inspiration can be taken from human-human studies; this often leads to an assumption that the more social behaviour an agent utilises, the better the learning outcome will be. We apply a nonverbal behaviour metric to a series of studies in which children are taught how to identify prime numbers by a robot with various behavioural manipulations. We find a trend which generally agrees with the pedagogy literature, but also that overt nonverbal behaviour does not account for all learning differences. We discuss the impact of novelty, child expectations, and responses to social cues to further the understanding of the relationship between robot social behaviour and learning. We suggest that the combination of nonverbal behaviour and social cue congruency is necessary to facilitate learning.
{"title":"The Impact of Robot Tutor Nonverbal Social Behavior on Child Learning","authors":"James Kennedy, Paul E. Baxter, Tony Belpaeme","doi":"10.3389/fict.2017.00006","DOIUrl":"https://doi.org/10.3389/fict.2017.00006","url":null,"abstract":"Several studies have indicated that interacting with social robots in educational contexts may lead to greater learning than interactions with computers or virtual agents. As such, an increasing amount of social human-robot interaction research is being conducted in the learning domain, particularly with children. However, it is unclear precisely what social behaviour a robot should employ in such interactions. Inspiration can be taken from human-human studies; this often leads to an assumption that the more social behaviour an agent utilises, the better the learning outcome will be. We apply a nonverbal behaviour metric to a series of studies in which children are taught how to identify prime numbers by a robot with various behavioural manipulations. We find a trend which generally agrees with the pedagogy literature, but also that overt nonverbal behaviour does not account for all learning differences. We discuss the impact of novelty, child expectations, and responses to social cues to further the understanding of the relationship between robot social behaviour and learning. We suggest that the combination of nonverbal behaviour and social cue congruency is necessary to facilitate learning.","PeriodicalId":37157,"journal":{"name":"Frontiers in ICT","volume":"43 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2017-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87864530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In response to widespread looting of archaeological sites, archaeologists have used satellite imagery to enable the investigation of looting of affected archaeological sites. Such analyses often require time-consuming direct human interpretation of images, with the potential for human-induced error. We introduce a novel automated image processing mechanism applied to the analysis of very high resolution panchromatic satellite images, and demonstrate its ability to identify damage at archaeological sites with high accuracy and low false-positive rates compared to standard image classification methods. This has great potential for large scale applications whereby country-wide satellite datasets can be batch processed to find looting hotspots. Time is running out for many archaeological sites in the Middle East and elsewhere, and this mechanism fills a needed gap for locating looting damage in a cost and time efficient manner, with potential global applications.
{"title":"Algorithmic Identification of Looted Archaeological Sites from Space","authors":"E. Bowen, Brett Tofel, S. Parcak, R. Granger","doi":"10.3389/fict.2017.00004","DOIUrl":"https://doi.org/10.3389/fict.2017.00004","url":null,"abstract":"In response to widespread looting of archaeological sites, archaeologists have used satellite imagery to enable the investigation of looting of affected archaeological sites. Such analyses often require time-consuming direct human interpretation of images, with the potential for human-induced error. We introduce a novel automated image processing mechanism applied to the analysis of very high resolution panchromatic satellite images, and demonstrate its ability to identify damage at archaeological sites with high accuracy and low false-positive rates compared to standard image classification methods. This has great potential for large scale applications whereby country-wide satellite datasets can be batch processed to find looting hotspots. Time is running out for many archaeological sites in the Middle East and elsewhere, and this mechanism fills a needed gap for locating looting damage in a cost and time efficient manner, with potential global applications.","PeriodicalId":37157,"journal":{"name":"Frontiers in ICT","volume":"7 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2017-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85858599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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