Activity recognition is a key function for many context-aware applications in a smart environment. However, data collection and annotation for activity recognition is both time-consuming and costly. This paper proposes the hierarchical activity representation to enhance data reusability and introduces Personal Activity Logger (PAL), a computer aided tool with it, to reduce annotation efforts. We experimented with PAL in annotating activities within a personal space from power meters and a webcam in the office. Preliminary results show that PAL is effective in reducing the annotation efforts with only a slight loss in quality. In addition, we indicate the potential possibility to identify users from the distribution of events in their activities through the data analysis.

Despite recent impressive progress in automated planning and navigation tools, artifacts still lack robustness and flexibility of biological systems. In order to mimic biology, it is necessary to use principles of dynamics and architecture found in the brain. Here we translate our biologically inspired model of spatial learning and navigation (Samsonovich and Ascoli, L&M 2005) into a model suitable for implementation in spiking networks with STDP synapses, based on soon to become available hardware. Simulation studies of the model prove its robustness and scalability. The approach naturally extends to various types of action planning beyond the spatial domain. The architecture can be used in autonomous intelligent agents of various nature.

We present our efforts to deploy mobile robots in office environments, focusing in particular on the challenge of planning a schedule for a robot to accomplish user-requested actions. We concretely aim to make our CoBot mobile robots available to execute navigational tasks requested by users, such as telepresence, and picking up and delivering messages or objects at different locations. We contribute an efficient web-based approach in which users can request and schedule the execution of specific tasks. The scheduling problem is converted to a mixed integer programming problem. The robot executes the scheduled tasks using a synthetic speech and touch-screen interface to interact with users, while allowing users to follow the task execution online. Our robot uses a robust Kinect-based safe navigation algorithm, moves fully autonomously without the need to be chaperoned by anyone, and is robust to the presence of moving humans, as well as non-trivial obstacles, such as legged chairs and tables. Our robots have already performed 15km of autonomous service tasks.

When a new-born animal (agent) opens its eyes, what it sees is a patchwork of light and dark patterns, the natural scene.What is perceived by the agent at this moment is based on the patternof neural spikes in its brain. Life-long learning begins with such a flood of spikes in the brain. All knowledge and skills learned by the agent are mediated by such spikes, thus it is critical to understand what information these spikes convey and how they can be used to generate meaningful behavior. Here, we consider how agents can autonomously understand the meaning of these spikes without direct reference to the stimulus. We find that this problem, the problem of grounding, is unsolvable if the agent is passively perceiving, and that it can be solved only through self-initiated action. Furthermore, we show that a simple criterion, combined with standard reinforcement learning, can help solve this problem. We will present simulation results and discuss the implications of these results on life-long learning.

Rather, the goal is to Microtext media (Ellen, 2011), such as SMS, IM, Twitter, gather relevant messages, organize them, and extract some and text chat, have in common that they use short strings other kind of useful information from them, such as how for immediate communication or broadcast. Microtext can well a team is performing or what people are talking about be construed as one form of micro-messaging (e.g., and when. However, micro-messages do not exist in a Milstein, et al., 2008) which we extend here to include any vacuum; they are contextually oriented and may be part of of a number of other modalities (e.g., telephone calls, a larger network of communications which includes email, face-to-face interaction) used for short, immediate and telephone and other media, including "macro-text." Given (potentially) persistent message passing among this, we have found that natural language processing of the coordinating agents. In this paper, we describe several microtext must be paired with temporal or network recent attempts to study micro-messaging military and analysis of the context. To demonstrate this process, we related organizational contexts.

Robot factors such as motions and utterances have a possibility of interaction effects with generation and other human factors, and these effects influence robotics design in elder care. Some psychological experiments conducted in our research group found these interaction effects between generation and other factors based on directly comparison between younger and elder persons in interaction with a small-sized humanoid robot. The paper firstly reviews the previous two studies, reports results of the current experiment, and then discusses about their implications from the perspective of robotics design for elder care.

For each utterance of interest, the set of points corresponding to the location of people at the time of that The Human Speechome Project corpus (Roy 2009), (Roy et utterance are added to the appropriate bin(s) of the histogram al. 2006) is a typical large, unstructured dataset.

How widespread is the phenomenon of commenting or editing a tweet in the practice of retweeting by members of political communities in Twitter? What is the nature of comments(agree/disagree), or of edits (change audience, change meaning, curate content). Being able to answer these questions will provide knowledge that will help answering other questions such as: what are the topics, events, people that attract more discussion (in forms of commenting) or controversy (agree/disagree)? Who are the users who engage in the processing of curating content by inserting hashtags or adding links? Which political community shows more enthusiasm for an issue and how broad is the base of engaged users? How can detection of agreement/disagreement in conversations inform sentiment analysis - the technique used to make predictions (who will win an election) or support insightful analytics (which policy issue resonates more with constituents). We argue that is necessary to go beyond the much-adopted aggregate text analysis of the volume of tweets, in order to discover and understand phenomena at the level of single tweets. This becomes important in the light of the increase in the number of human-mimicking bots in Twitter. Genuine interaction and engagement can be better measured by analyzing tweets that display signs of human intervention. Editing the text of an original tweet before it is retweeted, could reveal mindful user engagement with the content, and therefore, would allow us to perform sampling among real human users. This paper presents work in progress that deals with the challenges of discovering retweets that contain comments or edits, and outlines a machine-learning based strategy for classifying the nature of such comments.

We argue, reason, cajole, and persuade -- we deploy the overtly purposive use of figures. The traditional literary rhetoric -- because we are social animals endowed with a purpose, generating aesthetic pleasure, is best known (poetry symbolic mode of thought and communication who seek and fiction, myths and prayers, songs and jokes, are highly to shape our social environment, to compete, and to cooperate. But mnemonic formulas, As rhetoricians, philosophers, and semiologists have proverbs, oral traditions, children's literature, marketing regularly noticed, some patterns of argumentation and cajolery - in short, any linguistic configuration serving purposes are more successful than others. These patterns of usage in which mental characteristics like attention, learnability, -- collectively known as rhetorical figures -- include both and recollection are at a premium - follows one syntactic and semantic patterns, but it is the schemes (e.g., or several grooves that rhetorical theorists in the classical alliteration (word-initial consonant repetition), assonance and early-modern periods identified with rhetorical figures. The repetition, incrementation, and the like), the insight that importance of rhetorical figuration in modelling aspects of motivates this project is unmistakeable. We believe incorporating rhetorical figuration rhetoric-based metrics for text summarization) into natural language systems will have profound implications.

Previous work has suggested the use of lexicalized grammars for probabilistic plan recognition. Such grammars allow the domain builder to delay commitment to hypothesizing high level goals in order to reduce computational costs. However this delay has limitations. In the case of only partial observation traces, delaying commitment can prevent such algorithms from forming correct conclusions about some goals. This paper presents a heuristic metric to address this limitation. It advocates computing the maximum change in conditional probability across all the computed explanations given the observations explicitly considering a goal of interest.