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Software is a critical component of brain–computer interfaces (BCIs). While BCI hardware enables the retrieval of brain signals, BCI software is required to analyze these signals, produce output, and provide feedback. Users from multiple research areas have adopted BCI software platforms to investigate various concepts. Recently, interest in web-based BCI software has also emerged. The system design and control signal techniques of state-of-the-art BCI software platforms have been previously investigated. However, there is limited literature discussing user adoption of BCI software platforms. Additionally, there is a lack of work discussing the recent emergence of web tools relevant to BCI applications. This article aims to address these gaps by presenting a bibliometric review of the state-of-the-art BCI software. Furthermore, we discuss web-based BCIs and present tools that may be used to develop future web-based BCI applications.

Our future urban environments are smart. Sensors and actuators are embedded in these environments and their inhabitants. We have an Internet of Things, where the ‘Things’ include objects, cars, tools, buildings, street furniture, and whatever can be equipped with sensors and actuators, including human and non-human animals. Augmented humans and augmented animals have their senses extended with digital technology. Their smart wearables connected with the smart environment make humans and animals smarter. Rather than on living animals, in this survey paper we focus on non-living virtual and augmented reality non-human animals that will inhabit our smart and playable urban environments. They will co-exist with robotic animals and (digitally augmented) humans and non-human animals. We include observations on augmented humans interacting with virtual and augmented reality animals. The paper is meant to raise awareness for the possibilities of augmented reality to introduce virtual animals for social, entertainment, and educational reasons.

Artists have been using BCIs for artistic expression since the 1960s. Their interest and creativity is now increasing because of the availability of affordable BCI devices and software that does not require them to invest extensive time in getting the BCI to work or tuning it to their application. Designers of artistic BCIs are often ahead of more traditional BCI researchers in ideas on using BCIs in multimodal and multiparty contexts, where multiple users are involved, and where robustness and efficiency are not the main matters of concern. The aim of this workshop is to look at current (research) activities in BCIs for artistic expression and to identify research areas that are of interest for both BCI and HCI researchers as well as artists/designers of BCI applications.

Science, Technology, Engineering, and Mathematics (STEM) is rapidly transitioning to STEAM, which is the integration of the arts and design into the sciences. This transition is due to the need of adapting creativity in the sciences and engineering fields. This demonstrates the growing importance of the arts contribution towards the sciences in various ways. An area, where art shows promise is in Brain-Computer Interfaces. In this paper, we propose a definition for Artistic Brain-Computer Interfaces (Artistic BCI) from a Passive BCI perspective. We defined its composition in four fields: Human-Computer Interaction, Neurophysiology, Art, and Computing. This definition is based on the user’s state stimulation and expression and how art could help us and the end user understand the user’s affective state. We also discuss its applicability towards different areas and how these areas can benefit from Artistic BCI.

This book constitutes the refereed proceedings of the Third International Conference on Digital Economy, ICDEc 2018, held in Brest, France in May 2018. The conference was founded in 2016 to discuss innovative research and projects related to the support role of Information System Technologies in the digital transformation process, business innovation and e-commerce. The 15 papers presented in this volume were carefully reviewed and selected from 41 submissions. The theme of ICDEc 2018 was “Digital Economy: Emerging Technologies and Business Innovation”. The papers were organized in topical sections named: digital marketing; e-banking and competitive intelligence; information system technologies; and e-learning, e-government and e-health.

Brain-computer interfaces (BCIs) are often evaluated in terms of performance and seldom for usability. However in some application domains, such as entertainment computing, user experience evaluation is vital. User experience evaluation in BCI systems, especially in entertainment applications such as games, can be biased due to the novelty of the interface. However, as the novelty will eventually vanish, what matters is the user experience related to the unique features offered by BCI. Therefore it is a viable approach to compare BCI to other novel modalities, such as a speech or motion recognizer, rather than the traditional mouse and keyboard. In the study that we present in this article, our participants played a computer game with a BCI and an automatic speech recognizer (ASR), and they rated their expectations and experiences for both modalities. Our analysis on subjective ratings revealed that both ASR and BCI were successful in satisfying participants' expectations in general. Participants found speech control easier to learn than BCI control. They indicated that BCI control induced more fatigue than they expected.

This book constitutes the refereed proceedings of the Second International Conference on Digital Economy, ICDEc 2017, held in Sidi Bou Said, Tunisia, in May 2017. The 18 papers presented in this volume were carefully reviewed and selected from 46 submissions. The theme of ICDEc 2017 was “Digital Economy: Emerging Technologies and Business Innovation”. The conference offered a number of sessions discussing innovative research focusing on emerging technologies which support the digital transformation of business and the economy: digital marketing, digital economy and e-learning, data science and security, and uncertainty in Web data.

In this paper we draw attention to the emerging field of smart material interfaces. These novel composites, that in some cases are already celebrated as the answer for the 21st century's technological needs, are generally referred to as materials that are capable of sensing the environment and actively responding to environmental changes by changing their physical properties. Smart materials have physical properties that can be changed or controlled by external stimuli such as electric or magnetic fields, light, temperature or stress. Shape, size and color are among the properties that can be changed. Smart material interfaces are physical interfaces that utilize these materials to sense the environment and display responses by changing their physical properties. Common smart materials appear in the form of polymers, ceramics, memory shape alloys or hydro-gels. This paper aims at stimulating research and development in interfaces that make novel use of such smart materials.

In this paper, we draw attention to the emerging field of smart material interfaces. These novel composites that in some cases are already celebrated as the answer for the 21stcentury's technological needs, are generally referred to as materials that are capable of sensing the environment and actively responding to environmental changes by changing their physical properties. Smart materials have physical properties that can be changed or controlled by external stimuli such as electric or magnetic fields, light, temperature or stress. Shape, size and color are among the properties that can be changed. Smart material interfaces are physical interfaces that utilize these materials to sense the environment and display responses by changing their physical properties. Common smart materials appear in the form of polymers, ceramics, memory shape alloys or hydro-gels. This paper aims at stimulating research and development in interfaces that make novel use of such smart materials.

Everyday eating and drinking experiences involve multiple, interrelated, sensory inputs. However, when it comes to human-food interaction design (HFI) research, certain senses have received more attention than others have. Here, we focus on audition, a sense that has received limited attention in such context. In particular, we highlight the role of food/drink-related eating sounds, as a potential input for human-food interaction design. We review some of the few systems that have built on such sounds within food and drink contexts. We also present a multisensory design framework and discuss how the systematic connections that exist between the senses may provide some guidelines for the integration of eating sounds in HFI design. Finally, we present some key prospects that we foresee for research in technology design in HFI.

Many applications require knowledge about how to deceive, including those related to safety, security, and warfare. Speech and text analysis can help detect deception, as can cameras, microphones, physiological sensors, and intelligent software. Models of deception and noncooperation can make a virtual or mixed-reality training environment more realistic, improve immersion, and thus make it more suitable for training military or security personnel. Robots might need to operate in physical and nontraining environments where they must perform military activity, including misleading the enemy. The contributions to this installment of Trends a#x0026; Controversies present state-of-the-art research approaches to the analysis and generation of noncooperative and deceptive behavior in virtual humans, agents, and robots; the analysis of multiparty interaction in the context of deceptive behavior; and methods to detect misleading information in texts and computer-mediated communication. Articles include: "Computational Deception and Noncooperation," by Anton Nijholt; "Robots that Need to Mislead: Biologically-Inspired Machine Deception," by Ronald C. Arkin; "Deception in Sports Using Immersive Environments," by Sa#x00E9;bastien Brault, Richard Kulpa, Franck Multon, and Benoit Bideau; "Non-Cooperative and Deceptive Virtual Agents," by David Traum; "Deception Detection in Multiparty Contexts,"by Hayley Hung; "Deception Detection, Human Reasoning, and Deception Intent," by Eugene Santos Jr., Deqing Li, and Fei Yu; and "Automatic Deception Detection in Computer-Mediated Communication," by Lina Zhou and Dongsong Zhang.

Item does not contain fulltext A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Traditional approaches to BCIs require the user to train for weeks or even months to learn to control the BCI. In contrast, BCIs based on machine learning only require a calibration session of less than an hour before the system can be used, since the machine adapts to the user's existing brain signals. However, this calibration session has to be repeated before each use of the BCI due to inter-session variability, which makes using a BCI still a time-consuming and an error-prone enterprise. In this work, we present a second-order baselining procedure that reduces these variations, and enables the creation of a BCI that can be applied to new subjects without such a calibration session. The method was validated with a motor-imagery classification task performed by 109 subjects. Results showed that our subject-independent BCI without calibration performs as well as the popular common spatial patterns (CSP)-based BCI that does use a calibration session.

We propose a novel approach to developing a tractable affective dialogue model for probabilistic frame-based dialogue systems. The affective dialogue model, based on Partially Observable Markov Decision Process (POMDP) and Dynamic Decision Network (DDN) techniques, is composed of two main parts: the slot-level dialogue manager and the global dialogue manager. It has two new features: (1) being able to deal with a large number of slots and (2) being able to take into account some aspects of the user's affective state in deriving the adaptive dialogue strategies. Our implemented prototype dialogue manager can handle hundreds of slots, where each individual slot might have hundreds of values. Our approach is illustrated through a route navigation example in the crisis management domain. We conducted various experiments to evaluate our approach and to compare it with approximate POMDP techniques and handcrafted policies. The experimental results showed that the DDN–POMDP policy outperforms three handcrafted policies when the user's action error is induced by stress as well as when the observation error increases. Further, performance of the one-step look-ahead DDN–POMDP policy after optimizing its internal reward is close to state-of-the-art approximate POMDP counterparts.

The seven articles in this special issue focus on human computing. Most focus on two challenging issues in human computing, namely, machine analysis of human behavior in group interactions and context-sensitive modeling.

Human---computer interaction requires modeling of the user. A user profile typically contains preferences, interests, characteristics, and interaction behavior. However, in its multimodal interaction with a smart environment the user displays characteristics that show how the user, not necessarily consciously, verbally and nonverbally provides the smart environment with useful input and feedback. Especially in ambient intelligence environments we encounter situations where the environment supports interaction between the environment, smart objects (e.g., mobile robots, smart furniture) and human participants in the environment. Therefore it is useful for the profile to contain a physical representation of the user obtained by multi-modal capturing techniques. We discuss the modeling and simulation of interacting participants in a virtual meeting room, we discuss how remote meeting participants can take part in meeting activities and they have some observations on translating research results to smart home environments.

Currently the field of brain–computer interfacing is increasingly focused on developing usable brain–computer interfaces (BCIs) to better ensure technology transfer and acceptance. Many studies have investigated the usability of BCI applications as a whole. Here we aim to investigate one specific component of an electroencephalogram (EEG)-based BCI system: the acquisition component. This study compares on the usability of three different EEG headsets in the context of a P300-based BCI application for communication. Thirteen participants took part in a within-subject experiment. Participants were randomly given a Biosemi, Emotiv EPOC or g.Sahara headset. After every session offline classification accuracy (efficacy) was calculated and usability factors (perceived efficiency and user satisfaction) were measured using questionnaires. The 32-channel Biosemi headset offered the highest accuracy (88.5%) compared with the 8-channel g.Sahara (62.7%) and the 14-channel Emotiv (61.7%). There was no difference in accuracy between the Biosemi and the g.Sahara when comparing the same 8 channels. The Biosemi and g.Sahara were rated as more comfortable than the Emotiv. The Emotiv was rated as best for aesthetics. System setup time was highest for the Biosemi headset when compared with the g.Sahara and the Emotiv. Without information about the effectiveness, participants preferred the Emotiv. We recommend the use of a gelled headset for applications which require high accuracy and efficiency and water-based or dry headsets when aesthetics, easy setup and fun are important.

This book covers the latest advances in playful user interfaces – interfaces that invite social and physical interaction. These new developments include the use of audio, visual, tactile and physiological sensors to monitor, provide feedback and anticipate the behavior of human users. The decreasing cost of sensor and actuator technology makes it possible to integrate physical behavior information in human-computer interactions. This leads to many new entertainment and game applications that allow or require social and physical interaction in sensor- and actuator-equipped smart environments. The topics discussed include: human-nature interaction, human-animal interaction and the interaction with tangibles that are naturally integrated in our smart environments. Digitally supported remote audience participation in artistic or sport events is also discussed. One important theme that emerges throughout the book is the involvement of users in the digital-entertainment design process or even design and implementation of interactive entertainment by users themselves, including children doing so in educational settings.

The brain is involved in the registration, evaluation, and representation of emotional events and in the subsequent planning and execution of appropriate actions. Novel interface technologies - so-called affective brain-computer interfaces (aBCI) - can use this rich neural information, occurring in response to affective stimulation, for the detection of the user’s affective state. This chapter gives an overview of the promises and challenges that arise from the possibility of neurophysiology-based affect detection, with a special focus on electrophysiological signals. After outlining the potential of aBCI relative to other sensing modalities, the reader is introduced to the neurophysiological and neurotechnological background of this interface technology. Potential application scenarios are situated in a general framework of brain-computer interfaces. Finally, the main scientific and technological challenges that have yet to be solved on the way toward reliable affective brain-computer interfaces are discussed.

Human-Media Interaction research is devoted to methods and situations where humans individually or collectively interact with digital media, systems, devices and environments. Novel forms of interaction paradigms have been enabled by new sensor and actuator technology in the last decades, combining with advances in our knowledge of human-human interaction and human behavior in general when designing user interfaces.

It is not easy for a growing artist to find his poetry. Smart materials could be an answer for those who are looking for new forms of art. Smart Material Interfaces (SMI) define a new interaction paradigm based on dynamic modifications of the innovative materials' properties. SMI can be applied in different domains and used for different purposes; functional, communicative and creative. In this paper we focus on experimenting in the art and creative communication domain. In particular we describe the results of a workshop held with 15 students of the Fine Arts Academy in Venice who learned how to make and program SMI and took advantage of their new skills to design a variety of interesting creative artifacts.

Over the last several years, brain-computer interface (BCI) research has grown well beyond initial efforts to provide basic communication for people with severe disabilities that prevent them from communicating otherwise. Since BCIs rely on direct measures of brain activity, users do not have to move in any way to convey information. During the early years of BCI research, BCI systems had little to offer healthy users. Since most users can communicate quickly and easily by speaking or typing, why would healthy people use a BCI? Several different answers to this question have been presented. For example, when healthy users’ hands and/or voice are not available in certain situations, they may experience ‘situational disability’. In that situation, healthy users may need a low-bandwidth interface. Healthy people may use a BCI just for fun; the idea of communicating just through brainwaves is new and exciting to many people. BCIs may also detect emotion, arousal, or other characteristics that would otherwise be burdensome, irritating, or impossible to convey through other means. The last category of BCIs presented includes ‘affective BCIs’ or ‘aBCIs’, which are BCIs that detect affect. Information about users’ affect could be used to modify the user’s interaction with software and people in real time. For example, the alpha WoW system can change a user’s World of Warcraft avatar if the user’s EEG indicates an increased level of stress. Users could always press a button to send this information, but doing so creates some distraction and burden just when the user exhibits stress, and may need hands free to deal with ingame challenges. Similarly, games or other software applications might automatically adapt to states when users seem overburdened or bored. Again, users could choose to press a button when they want to increase or decrease their workload, but might prefer to keep focused on their work while the environment adapts automatically.

Brain–computer interfaces (BCI) provide a valuable new input modality within human–computer interaction systems. However, like other body-based inputs such as gesture or gaze based systems, the system recognition of input commands is still far from perfect. This raises important questions, such as what level of control should such an interface be able to provide. What is the relationship between actual and perceived control? And in the case of applications for entertainment in which fun is an important part of user experience, should we even aim for the highest level of control, or is the optimum elsewhere? In this paper, we evaluate whether we can modulate the amount of control and if a game can be fun with less than perfect control. In the experiment users (n = 158) played a simple game in which a hamster has to be guided to the exit of a maze. The amount of control the user has over the hamster is varied. The variation of control through confusion matrices makes it possible to simulate the experience of using a BCI, while using the traditional keyboard for input. After each session the user completed a short questionnaire on user experience and perceived control. Analysis of the data showed that the perceived control of the user could largely be explained by the amount of control in the respective session. As expected, user frustration decreases with increasing control. Moreover, the results indicate that the relation between fun and control is not linear. Although at lower levels of control fun does increase with improved control, the level of fun drops just before perfect control is reached (with an optimum around 96%). This poses new insights for developers of games who want to incorporate some form of BCI or other modality with unreliable input in their game: for creating a fun game, unreliable input can be used to create a challenge for the user.

These are the proceedings of the 10th International Conference on Advances in Computer Entertainment (ACE 2013), hosted by the Human Media Interaction research group of the Centre for Telematics and Information Technology at the University of Twente, The Netherlands. The ACE series of conferences, held yearly since 2004, has always been lively and interactive events. There are not just mainly paper presentations, but also many creative showcases, demonstrations, workshops, and often a game competition as well. For ten years now, ACE has shown itself to be a strong and vibrant community. Throughout the years, there has been a common element that ties together many of the different types of work presented at ACE. In their contributions, authors not only present solutions to known problems, or observe and describe aspects of the technological reality that is out there, but also actively explore what new things they can make, and why these new things might be important or interestingly different.

In this paper we will present the design process and development of "Follow the Grass", our smart material interactive pervasive display, with related technical detailed explanation. We will present the design steps and prototypes with instructions for the use of smart materials (NiTiNOL) to create interaction.

In the first part of this talk we survey our and others research efforts on human-computer interaction: natural, affective and social interactions. The assumption is that sensor-equipped environments are able to detect, interpret and anticipate our intentions and feelings. This allows more natural interaction between humans and intelligent environments that support human activity. However, it also allows these environments to collect more information about their human partners than these human partners may find desirable. Environments collect our lives, environments process our lives. Humans are becoming part of the 'Network of Things'. Their actions and activities are detected, anticipated and interpreted. The environment may provide alerts and suggestions to the user, it may also take decisions in the interest of the owner of the environment or in the interest of the user; and, it may be the case that the user does not fully understand the decisions that are made or simply does not agree with them. In the second part of our presentation we look at situations where it is quite acceptable or even desirable that part of the intentions and feelings of an interacting partner remains hidden for the other. The 'other' can be a human partner, but also a virtual human partner, a social robot, or a socially intelligent networked environment. Hiding information, not giving away yourselves, not always telling what you think, is natural behavior. It occurs in everyday life, but also in sports and entertainment situations. Non-cooperative behavior is often more natural than cooperative behavior. In this talk we will also discuss the many useful uses of non-cooperative behavior - for example in training and simulation environments - both from the point of view of a smart environment and from the point of view of human partners, users, or inhabitants of smart environments.

Mimicry occurs in conversations both when people agree with each other and when they do not. However, it has been reported that there is more mimicry when people agree than when they disagree: when people want to express shared opinions and attitudes, they do so by displaying behavior that is similar to their interlocutors' behavior. In a conversation, mimicry occurs in order to gain acceptance from an interaction partner by conforming to that person's attitudes, opinions, and behavior. In this paper we describe how visual behavioral information expressed between two interlocutors can be used to detect and identify visual mimicry. We extract and encode visual features that are expected to represent mimicry in a useful way. In order to show that mimicry has indeed occurred, we calculate correlations between visual features extracted from the interactants and compare these against each other and against a baseline. We show that it is possible to visualize the occurrence of visual mimicry during the progress of a conversation which allows us to research in which situations and to what extent mimicry occurs.

Very big displays are now commonplace but interactions with them are limited, even poorly understood. Recently, understanding touch-based interactions have received a great deal of attention due to the popularity and low costs of these displays. The direct extension of such interactions, touch less interactions, has not. In this paper we evaluated gesture-based interactions with very big interactive screens to learn which gestures are suited and why. In other words, did ‘Minority Report’ get it right? We aim to discover to which extend these gesture interfaces are technology-driven and influenced by prototyped, commercial and fictive interfaces. A qualitative evaluation of a gesture interface for wall sized displays is presented in which subjects experienced the interface while completing several simple puzzle tasks. We found that simple gestures based on the act of pressing buttons was the most intuitive.

Visual search is a task that is performed in various application areas. Search can be aided by an automatic warning system, which highlights the sections that may contain targets and require the user's attention. The effect of imperfect automatic warnings on overall performance ultimately depends on the interplay between the user and the automatic warning system. While various user studies exist, the different studies differ in several experimental variables including the nature of the visualisation itself. Studies in the medical area remain relatively rare, even though there is a growing interest in medical screening systems. We describe an experiment where users had to perform a visual search on a vascular structure, traversing a particular vessel linearly in search of possible errors made in an automatic segmentation. We find that only the case in which the warning system generates only false positives improves user time and error performance. We discuss this finding in relation to the findings of other studies.

For generations, humans have fantasized about the ability to create devices that can see into a person’s mind and thoughts, or to communicate and interact with machines through thought alone. Such ideas have long captured the imagination of humankind in the form of ancient myths and modern science fiction stories. Recent advances in cognitive neuroscience and brain imaging technologies have started to turn these myths into a reality, and are providing us with the ability to interface directly with the human brain. This ability is made possible through the use of sensors that monitor physical processes within the brain which correspond with certain forms of thought. Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction broadly surveys research in the Brain-Computer Interface domain. More specifically, each chapter articulates some of the challenges and opportunities for using brain sensing in Human-Computer Interaction work, as well as applying Human-Computer Interaction solutions to brain sensing work. For researchers with little or no expertise in neuroscience or brain sensing, the book provides background information to equip them to not only appreciate the state-of-the-art, but also ideally to engage in novel research. For expert Brain-Computer Interface researchers, the book introduces ideas that can help in the quest to interpret intentional brain control and develop the ultimate input device. It challenges researchers to further explore passive brain sensing to evaluate interfaces and feed into adaptive computing systems. Most importantly, the book will connect multiple communities allowing research to leverage their work and expertise and blaze into the future.

Recent efforts in defining ambient intelligence applications based on user-centric concepts, the advent of technology in different sensing modalities as well as the expanding interest in multi-modal information fusion and situation-aware and dynamic vision processing algorithms have created a common motivation across different research disciplines to utilize context as a key enabler of application-oriented vision systems design. Improved robustness, efficient use of sensing and computing resources, dynamic task assignment to different operating modules as well as adaptation to event and user behavior models are among the benefits a vision processing system can gain through the utilization of contextual information. The Workshop on Use of Context in Vision Processing (UCVP) aims to address the opportunities in incorporating contextual information in algorithm design for single or multi-camera vision systems, as well as systems in which vision is complemented with other sensing modalities, such as audio, motion, proximity, occupancy, and others.

These are the proceedings of ABCI 2009, Affective Brain Computer Interfaces, a workshop that was organized in conjunction with ACII 2009, the International Conference on Affective Computation and Intelligent Interaction, held in Amsterdam, The Netherlands, September 2009. The workshop took place on September 9, one day before the main conference in the Keizerzaal at De Rode Hoed, Amsterdam. The workshop explored the advantages and limitations of using neurophysiological signals as a modality for the automatic recognition of affective and cognitive states, and the possibilities of using this information about the user state in innovative and adaptive applications. Recent research in brain-computer interfaces (BCI) has shown that brain activity can be used as an active/voluntary, or passive/involuntary control modality in man-machine interaction. While active BCI paradigms have received a lot of attention in recent years, research on passive approaches to BCI still desperately needs concerted activity. More than once it has been shown that brain activations can carry information about the affective and cognitive state of a subject, and that the interaction between humans and machines can be aided by the recognition of those user states. To achieve robust passive BCIs, efforts from applied and basic sciences have to be combined. On the one hand, applied fields such as affective computing aim at the development of applications that adapt to changes in the user states and thereby enrich the interaction, leading to a more natural and effective usability. On the other hand, basic research in neuroscience advances our understanding of the neural processes associated with emotions. Furthermore, similar advancements are being made for more cognitive mental states, for example, attention, fatigue, and work load, which strongly interact with affective states. The topics we have explored in this particular workshop are: * emotion elicitation and data collection for affective BCI * detection of affect and mental state via BCI and other modalities * adaptive interfaces and affective BCI In this workshop researchers from the communities of brain computer interfacing, affective computing, neuro-ergonomics, affective and cognitive neuroscience have been asked to present state-of-the-art progress and visions on the various overlaps between those disciplines. In addition to the paper presentations there were demonstrations by the company g.tec (Guger Technologies, Graz) and by the Frauenhofer Institute FIRST (Berlin).

This chapter addresses awareness support to enhance teamwork in co-located collaborative environments. In particular, the authors focus on the concept of situational awareness which is essential for successful team collaboration. Mutual situational awareness leads to informal social interactions, development of shared working cultures which are essential aspects of maintaining working relationships. First, an overview of the studies on team coordination and situational awareness support is presented. Second, a collaborative working environment is described for scientific teams in a molecular biology omics experimentation domain. Then, the results of practical case studies are discussed, as well as situational awareness support for scientific teams in collaborative environments. Finally, the authors discuss practical challenges in design and evaluation of group support systems for collaborative working environments and our multi-level analysis approach. The chapter gives new insights into how shared displays support group awareness, and how to design and evaluate interactive systems and visualisations that afford awareness in order to stimulate existing and new forms of collaboration in advanced working environments.

This book constitutes the thoroughly refereed post-proceedings of two events discussing AI for Human Computing: one Special Session during the Eighth International ACM Conference on Multimodal Interfaces (ICMI 2006), held in Banff, Canada, in November 2006, and a Workshop organized in conjunction with the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), held in Hyderabad, India, in January 2007. A large number of the contributions in this state-of-the-art survey are updated and extended versions of the papers presented during these two events. In order to obtain a more complete overview of research efforts in the field of human computing, a number of additional invited contributions are also included in this book on AI for human computing. The 17 revised papers presented were carefully selected from numerous submissions to and presentations made at the two events and include invited articles to round off coverage of all relevant topics of the emerging topic. The papers are organized in three parts: a part on foundational issues of human computing, a part on sensing humans and their activities, and a part on anthropocentric interaction models.

Hybrid recommender systems are capable of providing better rec-ommendations than non-hybrid ones. Our approach to hybrid recommenders is the use of prediction strategies that determine which prediction technique(s) should be used at the moment an actual prediction is required. In this paper, we determine whether case-based reasoning can provide more accurate prediction strategies than rule-based predictions strategies created manually by experts. Experiments show that case-based reasoning can indeed be used to create pre-diction strategies; it can even increase the accuracy of the recommender in sys-tems where the accuracy of the used prediction techniques is highly spread.

Emotions and personality have received quite a lot of attention the last few years in research on embodied conversational agents. Attention is also increasingly being paid to matters of social psychology and interpersonal aspects, for work of our group). Given the nature of an embodied conversational agent�s main activity: 'conversation', we feel that interpersonal issues should be central in their design. We consider the case of tutoring systems in more depth and examine some of the interpersonal factors that need to be accounted for when building emotionally intelligent tutors.

List of Figures. List of Tables. Acknowledgements. 1. New Parsing Technologies H. Bunt, A. Nijholt. 2. Encoding Frequency Information in Lexicalized Grammars J. Carroll, D. Weir. 3. Bilexical Grammars and Their Cubic-Time Parsing Algorithms J. Eisner. 4. Probabilistic Feature Grammars J. Goodman. 5. Probabilistic GLR Parsing K. Inui, et al. 6. Probabilistic Parsing Using Left Corner Language Models C. Manning, B. Carpenter. 7. A New Parsing Method Using a Global Association Table J. Yoon, et al. 8. Towards a Reduced Commitment, D-Theory Style TAG Parser J. Chen, K. Vijay-Shanker. 9. Probabilistic Parse Selection Based on Semantic Co-occurrences E. Hektoen. 10. Message-Passing Protocols for Object-Oriented Parsing U. Hahn, et al. 11. SuperTagging for Partial Parsing. 12. Regular Approximation of CFLs: A Grammatical View M.-J. Nederhof. 13. Parsing By Successive Approximation H. Schmid. Index.

Attention is paid to structure preserving properties of transformations from a non-leftrecursive context-free grammar to a Greibach normal form grammar. It is demonstrated that such a transformation cannot only be ambiguity preserving, but also both cover and functor relations between grammars or their associated syntax-categories can be obtained from such a transformation.

An overview is given of cover results for normal forms of context-free grammars. The emphasis in this paper is on the possibility of constructing ?-free grammars, non-left-recursive grammars and grammars in Greibach normal form. Among others it is proved that any ?-free context-free grammar can be right covered with a context-free grammar in Greibach normal form. All the cover results concerning the ?-free grammars, the non-left-recursive grammars and the grammars in Greibach normal form are listed, with respect to several types of covers, in a cover-table.

The notion of a parsable grammar is introduced. A definition of cover is provided which is a generalization of a well-known definition of cover. With this new definition of cover we prove that every parsable grammar is covered by an LR(1) grammar or, if the language is prefic-free, by a strict-deterministic grammar. A consequence of this result is that every LR(k) grammar is covered by an LR(1) or a strict deterministic grammar.

Each context-free grammar can be transformed to a context-free grammar in Greibach normal form, that is, a context-free grammar where each right-hand side of a prorfuction begins with a terminal symbol and the remainder of the right-hand side consists of nonterminal symbols. In this short paper we show that for a left-regular grammar G we can obtain a right-regular grammar G’ (which is by definition in Greibach normal form) which left-to-right covers G (in this case left parses of G’ can be mapped by a homomorphism on right parses of G. Moreover, it is possible to obtain a context-free grammar G��? in Greibach normal form which right covers the left-regular grammar G (in this case right parses of G��? are mapped on right parses of G).

We will be concerned with grammar covers, The first part of this paper presents a general framework for covers. The second part introduces a transformation from nonleft-recursive grammars to grammars in Greibach normal form. An investigation of the structure preserving properties of this transformation, which serves also as an illustration of our framework for covers, is presented.

Parsing is the process of assigning structure to sentences. The structure is obtained from the grammatical description of the language. Both in Computer Science and in Computational Linguistics, context-free grammars and associated parsing algorithms are among the most useful tools. Numerous parsing algorithms have been developed. Special subclasses of the context-free grammars have been introduced in order to allow and induce efficient parsing algorithms. Special superclasses of the context-free grammars have been introduced in order to allow use of variants of efficient parsing methods that had been developed for context-free grammars. At first sight many parsing algorithms seem to be different, but nevertheless related. Some unifying approaches have been attempted in the past, but none survived the changing field. This report introduces a unifying approach at a level between grammars and algorithms, introducing so-called parsing schemata. In the parsing schemata framework the essentials of different parsing algorithms can be compared and it can be shown how to derive an algorithm from another one. The insight that is obtained this way also allows the derivation of new algorithms and it allows less tedious observations about correctness than usual. The framework can also be applied to grammar formalisms beyond the context-free grammars.

Sokolowski's criterion is a condition on languages which can be used to show that a language is not context-free. The question has been raised whether the criterion is not only a necessary, but also a sufficient one. In this paper we show that the answer is negative.

International audience; There are numerous possibilities and motivations for an adaptive BCI, which may not be easy to clarify and organize for a newcomer to the field. To our knowledge, there has not been any work done in classifying the literature on adaptive BCI in a comprehensive and structured way. We propose a conceptual framework, a taxonomy of adaptive BCI methods which encompasses most important approaches to fit them in such a way that a reader can clearly visualize which elements are being adapted and for what reason. In the interest of having a clear review of existing adaptive BCIs, this framework considers adaptation approaches for both the user and the machine, i.e., using instructional design observations as well as the usual machine learning techniques. This framework not only provides a coherent review of such extensive literature but also enables the reader to perceive gaps and flaws in the current BCI systems, which would hopefully bring novel solutions for an overall improvement.