As in-vehicle automation becomes increasingly prevalent and capable, there will be more opportunity for vehicle drivers to delegate control to automated systems. as well as increased ability for automated systems to intervene to increase road safety. With the decline in how much a driver must be engaged, two problems arise: driver disengagement and reduced ability to act when necessary; and also a likely decrease in active driving, which may reduce the engagement a driver can have for the purpose of enjoyment. As vehicles become more intelligent, they need to work collaboratively with human drivers, in the frame of a joint-cognitive system in order to both extend and backstop human capabilities to optimize safety, comfort, and engagement.

This paper first sketches a living lab infrastructure installed in an alternative housing unit built to host 10 people with traumatic brain injury. It then presents the first research project in progress within this living lab. This interdisciplinary project aims at designing, implementing, deploying, and assessing a personalized assistive technology (PAT). Based on the needs and expectations expressed by the residents, their caregivers and their families, a cooking assistant appeared as one of the best suited PAT to foster residents autonomy and social participation. The resulting PAT will rely on pervasive computing and ambient intelligence. It will then be personalized according to each participant's capacities and specific cognitive impairments. The impact of the assistant on autonomy and quality of life will then be measured. The overall organizational impact of such assistive technology will be also documented and evaluated.

We describe TickTock, a conversational agent designed to engage humans on topics of its choosing and to carry on an interaction for as long as possible. Our prototype uses a database of talk show transcripts featuring guests from the film industry. To be an interesting companion Tick Tock uses immediate context from the last two turns to formulate queries into a database of utterances. The process is automatic. TickTock monitors user engagement and performs certain moves, such as topic shifts, based on its assessment of user state. Initially we used utterance content for monitoring and subsequently we begun to investigate non-language cues, such as prosody and visual cues to create a more robust engagement model based on multiple human communication channels.

A method for determining whether given text is a rumor or disinformation is proposed, based on web mining and linguistic technology comparing two paragraphs of text. We hypothesize about a family of content generation algorithms which are capable of producing disinformation from a portion of genuine, original text. We then propose a disinformation detection algorithm which finds a candidate source of text on the web and compares it with the given text, applying parse thicket technology. Parse thicket is graph combined from a sequence of parse trees augmented with inter-sentence relations for anaphora and rhetoric structures. We evaluate our algorithm in the domain of customer reviews, considering a product review as an instance of possible disinformation. It is confirmed as a plausible way to detect rumor and disinformation in a web document. Linguistic approach presented here complements social network structure-based described on a corpus of research on disinformation detection.

Since the occurrence of the first `flash mob' organized by Bill Wasik (senior editor of the Harper's Magazine) in Manhattan in 2003, flash mob phenomenon has become widespread. Recent journalistic accounts have reported that this form of public engagement can pose significant threats to civil, political, social, and economic stability of a region. Gaps in the scientific understanding of such phenomenon and the imminent security risks posed by such acts call for a need to systematically study them. In this ongoing research, we shed light on the social dynamics of the flash mob phenomenon and build a conceptual model examining the necessary factors for the formation of flash mob and predicting its success or failure. Grounded in the sociological theories of collective action and collective identity formation, we evaluate the motivations of a flash mob practitioner and logically analyze the choices he/she would face with regards to acting or withdrawing from the flash mob. More broadly, this work is an attempt to bridge social and computational sciences that would help clarify and explain manifestations of emerging sociotechnical behaviors such as parkour, campaigns, and social movements that are widely observed.

We explore how children with autism form teams and what kind of difficulties they experience. Autistic reasoning is an adequate means to explore team formation because it is rather simple compared to the reasoning of controls and software systems on one hand, and allows exploration of human behavior in real-world environment on the other hand. We discover that reasoning about mental world, impaired in various degrees in autistic patients, is the key parameter of limiting the capability to form teams and cooperate. While teams of humans, robots and software agents have a manifold of limitations to form teams, including resources, conflicting desires, uncertainty, environment constraints, children with autism have only single limitation which is reduced reasoning about mental world. We correlate the complexity of the expressions for mental states children are capable of operating with their ability to form teams. Reasoning rehabilitation methodology is described, as well as its implications for children behavior in real world including cooperation and team formation.

There is not much evaluation technique of coimagination method, which is one of the group conversation techniques have been proposed for the purpose of cognitive function training. As one of the indicator of usefulness of cognitive function training, episodic memory is usable. Therefore we have proposed an analytical method for measuring the utilization of episodic memory in coimaginaiton method. Thereafter, We conducted the experiment of group conversation base on walking around in order to give the common experience to the participants, and analyzed the results by the proposed method. In consequence, it is revealed the occurrence of past episodic memory. Furthermore, it indicates individual difference of episodic memory utilization quantitatively in terms of memory taxonomy.

We introduce the concept of Hypoelliptic Diffusion Maps (HDM), a framework generalizing Diffusion Maps in the context of manifold learning and dimensionality reduction. Standard non-linear dimensionality reduction methods (e.g., LLE, ISOMAP, Laplacian Eigenmaps, Diffusion Maps) focus on mining massive data sets using weighted affinity graphs; Orientable Diffusion Maps and Vector Diffusion Maps enrich these graphs by attaching to each node also some local geometry. HDM likewise considers a scenario where each node possesses additional structure, which is now itself of interest to investigate. Virtually, HDM augments the original data set with attached structures, and provides tools for studying and organizing the augmented ensemble. The goal is to obtain information on individual structures attached to the nodes and on the relationship between structures attached to nearby nodes, so as to study the underlying manifold from which the nodes are sampled. In this paper, we analyze HDM on tangent bundles, revealing its intimate connection with sub-Riemannian geometry and a family of hypoelliptic differential operators. In a later paper, we shall consider more general fibre bundles.

Latent tree graphical models are a popular class of latent variable models, where a probability distribution involving observed and hidden variables are Markovian on a tree. Due to the fact that structure of (observable and hidden) variable interactions are approximated as a tree, inference on latent trees can be carried out exactly through a simple belief propagation [Pea88]. Therefore, latent tree graphical models present a good tradeoff between model accuracy and computational complexity. They are applicable in many domains, where it is natural to expect hierarchical or sequential relationships among the variables (through a hidden-Markov model). For instance, latent tree models have been employed for phylogenetic reconstruction [DEKM99], object recognition [CTW12a, CTW12b] and human pose estimation [WL13]. In this paper, we use latent tree model for discovering a hierarchy among diseases based on comorbidities exhibited in patients' health records, i.e. co-occurrences of diseases in patients. In particular, two large healthcare datasets of 30K and 1.6M patients are used to build the latent disease trees, where clinically meaningful disease clusters are identified as shown in fig 3 and 4. The task of learning a latent tree models consists of two parts: learning the tree structure, and learning the parameters of the tree. There exist many challenges which prohibit efficient or guaranteed learning of the latent tree graphical model, which will be addressed in this paper: 1. The location and the number of latent variables are hidden and the marginalized graph over the observable variables no longer conforms to a tree structure.

Complex operational environments require improved tactical mission command capabilities with a high level of interoperability among coalition control and command (C2) systems. This paper focuses on two areas of interest: decision support based on automated planning and Service Oriented Architecture (SOA) for rapid service development. Previous experiments were performed bilaterally by US, France and Germany to focus on collaborative mission planning using Web Services (WSs). The results reported herein were obtained from a unified experiment performed by US, France and Germany involving a common scenario. The operational benefit from the experimentation has been to improve mutual understanding among allied forces, to dynamically plan for assistance among ground support troops (logistics, MEDE- VAC, and other areas) as well as to improve their coordination. The effort addressed system design, and integration within an experimental framework. It enabled the evolution of the CERDEC Mission Command Gateway (MCG) architecture as well as a constraint based planner ”ORTAC”, developed by French DGA and Sagem. It takes into account near real-time multimodal Situation Awareness and readiness status from tactical edge units. The trilateral experiment, entitled From Data to Decision included Net-Centric manned and unmanned assets from all three nations (France - Germany - US) operating as a cohesive coalition force while preserving command and support relationships as required through their respective chains of command.