In this paper we introduce the core results of the project on software development for social robotics systems. The usability of maintenance and control features is crucial for many kinds of systems, but in the case of social robotics we also have to take into account that (1) the humanoid robot physically interacts with humans, (2) the conversation with children might have different requirements in comparison to the conversation with adults. The results of our work were implement for the humanoid PAL REEM robot, but their core ideas can be applied for other types of humanoid robots. We developed a web-based solution that supports the management of robot-guided tours, provides recommendations for the users as well as allows for a visual analysis of the data on previous tours.
There were also a number of surveys and literature reviews on the related topics. A survey on social robots for long-term interaction was presented in @cite_2 . A systematic review on application of social robotics in the Autism Spectrum Disorders treatment was presented in @cite_6 . @cite_1 presented a survey on the roles and benefits of social robots in the therapy of children with autism.
@cite_1 @cite_6 @cite_2
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Social robotics could be a promising method for Autism Spectrum Disorders (ASD) treatment. The aim of this article is to carry out a systematic literature review of the studies on this topic that were published in the last 10 years. We tried to address the following questions: can social robots be a useful tool in autism therapy? We followed the PRISMA guidelines, and the protocol was registered within PROSPERO database (CRD42015016158). We found many positive implications in the use of social robots in therapy as for example: ASD subjects often performed better with a robot partner rather than a human partner; sometimes, ASD patients had, toward robots, behaviors that TD patients had toward human agents; ASDs had a lot of social behaviors toward robots; during robotic sessions, ASDs showed reduced repetitive and stereotyped behaviors and, social robots manage to improve spontaneous language during therapy sessions. Therefore, robots provide therapists and researchers a means to connect with autistic subjects in an easier way, but studies in this area are still insufficient. It is necessary to clarify whether sex, intelligence quotient, and age of participants affect the outcome of therapy and whether any beneficial effects only occur during the robotic session or if they are still observable outside the clinical experimental context. Autism Res 2016, 9: 165–183. © 2015 International Society for Autism Research, Wiley Periodicals, Inc. Virtual reality appears to be a promising and motivating platform to safely practice and rehearse social skills for children with Autism Spectrum Disorders (ASD). However, the literature to date is subject to limitations in elucidating the effectiveness of these virtual reality interventions. This study investigated the impact of a Virtual Reality Social Cognition Training to enhance social skills in children with ASD. Thirty children between the ages of 7-16 diagnosed with ASD completed 10, 1-h sessions across 5 weeks. Three primary domains were measured pre-post: emotion recognition, social attribution, attention and executive function. Results revealed improvements on measures of emotion recognition, social attribution, and executive function of analogical reasoning. These preliminary findings suggest that the use of a virtual reality platform offers an effective treatment option for improving social impairments commonly found in ASD. The VR-SCT provides an interactive and stimulating approach for children with ASD.The VR-SCT can improve social cognitive skills in children with ASD.Training improved emotion recognition, social attribution, and executive function.The training is a safe and socially non-threatening platform. As a great number of robotic products are entering people’s lives, the question of how can they behave in order to sustain long-term interactions with users becomes increasingly more relevant. In this paper, we present an empathic model for social robots that aim to interact with children for extended periods of time. The application of this model to a scenario where a social robot plays chess with children is described. To evaluate the proposed model, we conducted a long-term study in an elementary school and measured children’s perception of social presence, engagement and social support. Investigation into robot-assisted intervention for children with autism spectrum disorder (ASD) has gained momentum in recent years. Therapists involved in interventions must overcome the communication impairments generally exhibited by children with ASD by adeptly inferring the affective cues of the children to adjust the intervention accordingly. Similarly, a robot must also be able to understand the affective needs of these children-an ability that the current robot-assisted ASD intervention systems lack-to achieve effective interaction that addresses the role of affective states in human-robot interaction and intervention practice. In this paper, we present a physiology-based affect-inference mechanism for robot-assisted intervention where the robot can detect the affective states of a child with ASD as discerned by a therapist and adapt its behaviors accordingly. This paper is the first step toward developing ldquounderstandingrdquo robots for use in future ASD intervention. Experimental results with six children with ASD from a proof-of-concept experiment (i.e., a robot-based basketball game) are presented. The robot learned the individual liking level of each child with regard to the game configuration and selected appropriate behaviors to present the task at his her preferred liking level. Results show that the robot automatically predicted individual liking level in real time with 81.1 accuracy. This is the first time, to our knowledge, that the affective states of children with ASD have been detected via a physiology-based affect recognition technique in real time. This is also the first time that the impact of affect-sensitive closed-loop interaction between a robot and a child with ASD has been demonstrated experimentally. We introduce a new problem domain for activity recognition: the analysis of children's social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1-2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new publicly-available dataset containing over 160 sessions of a 3-5 minute child-adult interaction. In each session, the adult examiner followed a semi-structured play interaction protocol which was designed to elicit a broad range of social behaviors. We identify the key technical challenges in analyzing these behaviors, and describe methods for decoding the interactions. We present experimental results that demonstrate the potential of the dataset to drive interesting research questions, and show preliminary results for multi-modal activity recognition. Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are more likely to inspire comments from new social media audiences if they create “cultural bridges,” or produce messages that combine conversational themes within an advocacy field that are seldom discussed together. I use natural language processing, network analysis, and a social media application to analyze how cultural bridges shaped public discourse about autism spectrum disorders on Facebook over the course of 1.5 years, controlling for various characteristics of advocacy organizations, their social media audiences, and the broader social context in which they interact. I show that organizations that create substantial cultural bridges provoke 2.52 times more comments about their messages from new social media users than those that do not, controlling for these factors. This study thus offers a theory of cultural messaging and public deliberation and computational techniques for text analysis and application-based survey research. We introduce new, fine-grained action and emotion recognition tasks defined on non-staged videos, recorded during robot-assisted therapy sessions of children with autism. The tasks present several challenges: a large dataset with long videos, a large number of highly variable actions, children that are only partially visible, have different ages and may show unpredictable behaviour, as well as non-standard camera viewpoints. We investigate how state-of-the-art 3d human pose reconstruction methods perform on the newly introduced tasks and propose extensions to adapt them to deal with these challenges. We also analyze multiple approaches in action and emotion recognition from 3d human pose data, establish several baselines, and discuss results and their implications in the context of child-robot interaction. As the field of HRI evolves, it is important to understand how users interact with robots over long periods. This paper reviews the current research on long-term interaction between users and social robots. We describe the main features of these robots and highlight the main findings of the existing long-term studies. We also present a set of directions for future research and discuss some open issues that should be addressed in this field. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Touch sensing can help robots understand their surrounding environment, and in particular the objects they interact with. To this end, roboticists have, in the last few decades, developed several tactile sensing solutions, extensively reported in the literature. Research into interpreting the conveyed tactile information has also started to attract increasing attention in recent years. However, a comprehensive study on this topic is yet to be reported. In an effort to collect and summarize the major scientific achievements in the area, this survey extensively reviews current trends in robot tactile perception of object properties. Available tactile sensing technologies are briefly presented before an extensive review on tactile recognition of object properties. The object properties that are targeted by this review are shape, surface material and object pose. The role of touch sensing in combination with other sensing sources is also discussed. In this review, open issues are identified and future directions for applying tactile sensing in different tasks are suggested. In search for better technological solutions for education, we adapted a principle from economic game theory, namely that giving a help will promote collaboration and eventually long-term relations between a robot and a child. This principle has been shown to be effective in games between humans and between humans and computer agents. We compared the social and cognitive engagement of children when playing checkers game combined with a social strategy against a robot or against a computer. We found that by combining the social and game strategy the children (average age of 8.3 years) had more empathy and social engagement with the robot since the children did not want to necessarily win against it. This finding is promising for using social strategies for the creation of long-term relations between robots and children and making educational tasks more engaging. An additional outcome of the study was the significant difference in the perception of the children about the difficulty of the game – the game with the robot was seen as more challenging and the robot – as a smarter opponent. This finding might be due to the higher perceived or expected intelligence from the robot, or because of the higher complexity of seeing patterns in three-dimensional world. We propose a survey of soft computing techniques applied to financial market.We surveyed several primary studies proposed in the literature.A framework for building an intelligent trading system was proposed.Future directions of this research field are discussed. Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field. Preface 1. Introduction 2. Some context for Context Analysis: a view of the origins of structural studies of face-to-face interaction 3. Some functions of gaze direction in two-person conversation 4. Movement co-ordination in social interaction 5. Some functions of the face in a kissing round 6. A description of some human greetings 7. Spatial organisation in social encounters: the F-formation system 8. Behavioural foundations for the process of frame-attunement in face-to-face interaction List of films cited References Index. Swarm robotics is a novel approach to the coordination of large numbers of relatively simple robots which takes its inspiration from social insects. This paper proposes a definition to this newly emerging approach by 1) describing the desirable properties of swarm robotic systems, as observed in the system-level functioning of social insects, 2) proposing a definition for the term swarm robotics, and putting forward a set of criteria that can be used to distinguish swarm robotics research from other multi-robot studies, 3) providing a review of some studies which can act as sources of inspiration, and a list of promising domains for the utilization of swarm robotic systems. Virtual reality (VR) refers to an advanced technological communication interface in which the user is actively participating in a computer-generated 3-dimensional virtual world that includes computer sensory input devices used to simulate real-world interactive experiences. VR has been used within psychiatric treatment for anxiety disorders, particularly specific phobias and post-traumatic stress disorder, given several advantages that VR provides for use within treatment for these disorders. Exposure therapy for anxiety disorder is grounded in fear-conditioning models, in which extinction learning involves the process through which conditioned fear responses decrease or are inhibited. The present review will provide an overview of extinction training and anxiety disorder treatment, advantages for using VR within extinction training, a review of the literature regarding the effectiveness of VR within exposure therapy for specific phobias and post-traumatic stress disorder, and limitations and future directions of the extant empirical literature. This paper presents an overview of our work towards building socially intelligent, cooperative humanoid robots that can work and learn in partnership with people. People understand each other in social terms, allowing them to engage others in a variety of complex social interactions including communication, social learning, and cooperation. We present our theoretical framework that is a novel combination of Joint Intention Theory and Situated Learning Theory and demonstrate how this framework can be applied to develop our sociable humanoid robot, Leonardo. We demonstrate the robot's ability to learn quickly and effectively from natural human instruction using gesture and dialog, and then cooperate to perform a learned task jointly with a person. Such issues must be addressed to enable many new and exciting applications for robots that require them to play a long-term role in people's daily lives. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain. In this paper we present the results of a qualitative study with therapists to inform social robotics and human robot interaction (HRI) for engagement in rehabilitative therapies. Our results add to growing evidence that socially assistive robots (SARs) could play a role in addressing patients' low engagement with self-directed exercise programmes. Specifically, we propose how SARs might augment or offer more pro-active assistance over existing technologies such as smartphone applications, computer software and fitness trackers also designed to tackle this issue. In addition, we present a series of design implications for such SARs based on therapists' expert knowledge and best practices extracted from our results. This includes an initial set of SAR requirements and key considerations concerning personalised and adaptive interaction strategies. In this video, we present the human robot interaction generated by applying the DAC cognitive architecture [1] on the iCub robot. We demonstrate how the robot reacts and adapts to its environment within the context a continuous interactive scenario including different games. We emphasize as well that the artificial agent is maintaining a self-model in terms of emotions and drives and how those are expressed in order affect the social interaction. Categories and Subject Descriptors I.2.9 [Commercial robots and applications]: Layered Cognitive Architecture for Distributed and Adaptive Control of a social robot. General Terms Experimentation, Human Factors, Theory