Artificial Emotional Intelligence in Socially Assistive Robots

Artificial Emotional Intelligence (AEI) bridges the gap between humans and machines by demonstrating empathy and affection towards each other. This is achieved by evaluating the emotional state of human users, adapting the machine’s behavior to them, and hence giving an appropriate response to those emotions. AEI is part of a larger field of studies called Affective Computing. Affective computing is the integration of artificial intelligence, psychology, robotics, biometrics, and many more fields of study. The main component in AEI and affective computing is emotion, and how we can utilize emotion to create a more natural and productive relationship between humans and machines. An area in which AEI can be particularly beneficial is in building machines and robots for healthcare applications. Socially Assistive Robotics (SAR) is a subfield in robotics that aims at developing robots that can provide companionship to assist people with social interaction and companionship. For example, residents living in housing designed for older adults often feel lonely, isolated, and depressed; therefore, having social interaction and mental stimulation is critical to improve their well-being. Socially Assistive Robots are designed to address these needs by monitoring and improving the quality of life of patients with depression and dementia. Nevertheless, developing robots with AEI that understand users’ emotions and can reply to them naturally and effectively is in early infancy, and much more research needs to be carried out in this field. This dissertation presents the results of my work in developing a social robot, called Ryan, equipped with AEI for effective and engaging dialogue with older adults with depression and dementia. Over the course of this research there has been three versions of Ryan. Each new version of Ryan is created using the lessons learned after conducting the studies presented in this dissertation. First, two human-robot-interaction studies were conducted showing validity of using a rear-projected robot to convey emotion and intent. Then, the feasibility of using Ryan to interact with older adults is studied. This study investigated the possible improvement of the quality of life of older adults. Ryan the Companionbot used in this project is a rear-projected lifelike conversational robot. Ryan is equipped with many features such as games, music, video, reminders, and general conversation. Ryan engages users in cognitive games and reminiscence activities. A pilot study was conducted with six older adults with early-stage dementia and/or depression living in a senior living facility. Each individual had 24/7 access to a Ryan in his/her room for a period of 4-6 weeks. The observations of these individuals, interviews with them and their caregivers, and analysis of their interactions during this period revealed that they established rapport with the robot and greatly valued and enjoyed having a companionbot in their room. A multi-modal emotion recognition algorithm was developed as well as a multi-modal emotion expression system. These algorithms were then integrated into Ryan. To engage the subjects in a more empathic interaction with Ryan, a corpus of dialogues on different topics were created by English major students. An emotion recognition algorithm was designed and implemented and then integrated into the dialogue management system to empathize with users based on their perceived emotion. This study investigates the effects of this emotionally intelligent robot on older adults in the early stage of depression and dementia. The results of this study suggest that Ryan equipped with AEI is more engaging, likable, and attractive to users than Ryan without AEI. The long-term effect of the last version of Ryan (Ryan V3.0) was studied in a study involving 17 subjects from 5 different senior care facilities. The participants in this study experienced a general improvement in their cognitive and depression scores

Prospectus, May 14, 1980

GRADUATES, OR ALMOST GRADUATES, ARE LOOKING TO THE YEARS AHEAD; Intellectual Freedom Essay winner: Maybe they know something in the 2nd grade that we don\u27t; My momma was cumulus; Reporter blows it, Twist blues it; Stars highlight Sullivan; Staerkel bids farewell; Attention; Doomsday schedule; Letters to the Editor; It takes a week, but you can try out your water bed before you buy; Dates to live by; Classifieds; The softball nine win conference; Bench Warmer: LaBadie\u27s enthusiasm rubs off on athletes; Cobras beat Danville, 23-7; Complete Parkland Baseball Statisticshttps://spark.parkland.edu/prospectus_1980/1026/thumbnail.jp

Designing an Information-Experience Using Creativity Science Theory and Tools

An “information-experience” encapsulated by a technological/digital audio-visual tool presents data and potentially meaningful information to prompt actionable knowledge concerning: “unspoken creative process elements;” their profound impacts on both how well our “physiology of creativity” functions; but also on how well foundational creative thinking and behavioral prerequisites (energy, motivation, imagination, and ownership) are leveraged. The product: 1) introduces the user to one component of the CPS (Creative Problem Solving) Facilitation Process - Exploring the Challenge; 2) features a content specific component which prompts exploration of the many correlations between societal, organizational / community, human physiological / behavioral data, and the direct relationships of these to creative/productive capacities and capabilities; while also 3) establishing an overview and resources to delve further into experiences or information concerning the domain of Creativity Science, Innovation, Change Leadership, or wellness/health-driving productivity factors, behaviors, and tools

The Media Inequality, Uncanny Mountain, and the Singularity is Far from Near: Iwaa and Sophia Robot versus a Real Human Being

Design of Artificial Intelligence and robotics habitually assumes that adding more humanlike features improves the user experience, mainly kept in check by suspicion of uncanny effects. Three strands of theorizing are brought together for the first time and empirically put to the test: Media Equation (and in its wake, Computers Are Social Actors), Uncanny Valley theory, and as an extreme of human-likeness assumptions, the Singularity. We measured the user experience of real-life visitors of a number of seminars who were checked in either by Smart Dynamics' Iwaa, Hanson's Sophia robot, Sophia's on-screen avatar, or a human assistant. Results showed that human-likeness was not in appearance or behavior but in attributed qualities of being alive. Media Equation, Singularity, and Uncanny hypotheses were not confirmed. We discuss the imprecision in theorizing about human-likeness and rather opt for machines that 'function adequately.

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience

Nam-Shub versus the Big Other: Revising the Language that Binds Us in Philip K. Dick, Neal Stephenson, Samuel R. Delany, and Chuck Palahniuk

Within the science fiction genre, utopian as well as dystopian experiments have found equal representation. This balanced treatment of two diametrically opposed social constructs results from a focus on the future for which this particular genre is well known. Philip K. Dick’s VALIS, Neal Stephenson’s Snow Crash, Samuel R. Delany’s Babel-17, and Chuck Palahniuk’s Lullaby, more aptly characterized as speculative fiction because of its use of magic against scientific social subjugation, each tackle dystopian qualities of contemporary society by analyzing the power that language possesses in the formation of the self and propagation of ideology. The utopian goals of these texts advocate for a return to the modernist metanarrative and a revision of postmodern cynicism because the authors look to the future for hopeful solutions to the social and ideological problems of today. Using Slavoj Žižek’s readings of Jacques Lacan and Theodor Adorno’s readings of Karl Marx for critical insight, I argue these four novels imagine language as the key to personal empowerment and social change. While not all of the novels achieve their utopian goals, they each evince a belief that the attempt belies a return to the modernist metanarrative and a rejection of postmodern helplessness. Thus, each novel imagines the revision of Žižek’s big Other through the remainders of Adorno’s inevitably failed revolutions, injecting hope in a literary period that had long since lost it

Brain-Inspired Computing

This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures

Filmes sobre a escola & educação: uma investigação artístico-cultural

Mestrado em Línguas, Literaturas e CulturasEsta dissertação visa questionar as representações da escola através da cultura fílmica anglófona produzida entre 1960 e 2006. Os filmes selecionados são o ponto de partida para uma reflexão sobre educação, pedagogia e cultura através das lentes críticas de alguns cineastas britânicos e americanos. Deste modo, os filmes apresentados foram desconstruídos e analisados através de um hipotético diálogo entre a ficção e a realidade e o meta-diálogo ficção-ficção, tendo como estratégia o contraste e a comparação dos textos fílmicos de épocas díspares, contudo transtemporais e transculturais. Sem deixarmos de valorizar o contexto histórico-cultural de cada texto fílmico em particular, tentámos extrair deste as lições mais universais quanto ao fenómeno educativo, estabelecendo através do cinema uma ligação pedagógica e cultural entre as lições do passado, do presente e as possíveis num futuro. Desta feita, este trabalho pretende valorizar o contributo dos filmes sobre a escola para o debate educativo e pedagógico, enquanto documentos culturais que registam idiossincrasias e estereótipos.This dissertation questions the Anglophone film school representations portrayed in cinema since 1960 until 2006. The set of films chosen aim to open the debate around education, pedagogy and culture via the fictional realities captured by the lenses of a few British and American filmmakers. The films studied were deconstructed and analyzed through an hypothetical dialogue between fiction and reality and the meta-dialogue fiction-fiction, relying on the strategy of contrast and comparison of different transtemporal and transcultural epochs. Despite not overlooking the particular historical and cultural hic et nunc of the filmic products, I’ve tried to extract the most universal lessons as far as the educational phenomenon is concerned, using cinema to establish bridges among the lessons of the past, the lessons of the present and the lessons of the future-to-be. Hence, this dissertation wishes to value the role of the school film subgenre within the educational and pedagogic debate, considering it valuable cultural documentation about school idiosyncrasies and stereotypes

The potential of DNA structure to provide a resource for the creation of art

Art's search for new subjects and methods and science's need for effective communication have led to the creation of what is known as Sci-Art. It is the central argument of this thesis that collaboration between creative and scientific disciplines can play a useful role in society, but that this potential is held back by misunderstanding of the roles of art and science. The main purpose of this practice-based research project, which is also supported by a written thesis, is to determine the relationship between artists and scientists, focusing on the visualisation of DNA. The project will identify their shared approaches to its representation, and will explore the history of DNA as an iconic form. An additional purpose of this study is to analyse the importance of the role of collaboration between scientists and artists including its application to education. My method is to review Sci-Art work and analyze the benefit of collaboration between science and art. Part of this research will focus on the benefits of Sci-Art collaboration for education. This part of the research involved a case study at Trinity Catholic School, with a project called Laboratories. Collaborative artworks and exhibitions are the final outcome of this project; they explore the ways in which Sci-Art can be developed as a useful form of interdisciplinary practice. These creative methods provide a route to a deeper understanding of the relationship between art and science. The thesis demonstrates through a combination of theoretical argument and creative practice that Sci-Art has the potential to: Act as an aid to understanding difficult scientific concepts; add to debate about the ethical issues surrounding science and increase the effectiveness of education