In real-time domains such as video games, a planning algo- rithm has a strictly bounded time before it must return the next action for the agent to execute. We introduce a realistic video game benchmark domain that is useful for evaluating real-time heuristic search algorithms. Unlike previous bench- marks such as grid pathfinding and the sliding tile puzzle, this new domain includes dynamics and induces a directed graph. Using both the previous and new domains, we investigate sev- eral enhancements to a leading real-time search algorithm, LSS-LRTA*. We show experimentally that 1) it is not dif- ficult to outperform A* when optimizing goal achievement time, 2) it is better to plan after each action than to commit to multiple actions or to use a dynamically sized lookahead, 3) A*-based lookahead can cause undesirable actions to be selected, and 4) on-line de-biasing of the heuristic can lead to improved performance. We hope that this new domain and results will stimulate further research on applying real-time search to dynamic real-time domains.

According to Daniel Kahneman, there are two systems that drive the human decision making process: The intuitive system that performs the fast thinking, and the deliberative system that does more logical and slower thinking. Inspired by this model, we propose a framework for implementing human activity recognition on mobile devices. In this area, the mobile app is usually always-on and the general challenge is how to balance accuracy and energy consumption. However, among existing approaches, those based on cellular IDs consume little power but are less accurate; those based on GPS/WiFi sampling are accurate often at the costs of battery drainage; moreover, previous methods in general do not improve over time. To address these challenges, our framework consists of two modes: In the deliberation mode, the system learns cell ID patterns that are trained by existing GPS/WiFi based methods; in the intuition mode, only the learned cell ID patterns are used for activity recognition, which is both accurate and energy-efficient; system parameters are learned to control the transition from deliberation to intuition, when sufficient confidence is gained, and the transition from intuition to deliberation, when more training is needed. For the scope of this paper, we first elaborate our framework in a subproblem in activity recognition, trip detection, which recognizes significant places and trips between them. For evaluation, we collected real-life traces of six participants over five months. Our experiments demonstrated consistent results across different participants in terms of accuracy and energy efficiency, and, more importantly, its fast improvement on energy efficiency over time due to regularities in human daily activities.

The recent explosive growth of smart phones and social media creates a unique opportunity to view events from various unique perspectives. Unfortunately, this relatively new form of communication lacks the structural integrity, accuracy, and reduced noise of other forms of communication. Nevertheless, social media increasingly plays a vita role in the observation of societal actions before, during, and after significant events. In October 2012, Hurricane Sandy making landfall on the northeastern coasts of the United States demonstrated this role. This work provides a preliminary view into how social media could be used to monitor and gauge community resilience to such natural disasters. We observe, evaluate, and visualize how Twitter data evolves over time before, during, and after a natural disaster such as Hurricane Sandy and what opportunities there may be to leverage social media for situational awareness and emergency response.

Beyond being facilitators of human interactions, social networks have become an interesting target of research, providing rich information for studying and modeling user’s behavior. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in our current research efforts. This paper explores the feasibility of modeling user personality based on a proposed set of features extracted from the Facebook data. The encouraging results of our study, exploring the suitability and performance of several classification techniques, will also be presented.

The way people communicate — be it verbally, visually, or via text– is indicative of personality traits. In social media the concept of the status update is used for individuals to communicate to their social networks in an always-on fashion. In doing so individuals utilize various kinds of speech acts that, while primarily communicating their content, also leave traces of their personality dimensions behind. We human-coded a set of Facebook status updates from the myPersonality dataset in terms of speech acts label and then experimented with surface level linguistic features including lexical, syntactic, and simple sentiment detection to automatically label status updates as their appropriate speech act. We apply supervised learning to the dataset and using our features are able to classify with high accuracy two dominant kinds of acts that have been found to occur in social media. At the same time we used the coded data to perform a regression analysis to determine which speech acts are significant of certain personality dimensions. The implications of our work allow for automatic large-scale personality identification through social media status updates.

For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.

Current travel recommendation systems are helpful in addressing a traveler's information needs to certain extent, however, most of them fail to factor in the user in their recommendations. TripEneer proposes travel recommendations to a traveler by keeping the user preferences and constraints as first class citizens. We present an intuitive UI for helping users plan their travel trips quickly and easily. In the current demo we present various global and user-specific ranking models used for recommending travel destinations. Our preliminary evaluation showed that the users found the personalized recommendations, based on the user model, most useful.

Two main approaches to using social network information in recommendation have emerged: augmenting collaborative filtering with social data and algorithms that use only ego-centric data. We compare the two approaches using movie and music data from Facebook, and hashtag data from Twitter. We find that recommendation algorithms based only on friends perform no worse than those based on the full network, even though they require much less data and computational resources. Further, our evidence suggests that locality of preference, or the non-random distribution of item preferences in a social network, is a driving force behind the value of incorporating social network information into recommender algorithms. When locality is high, as in Twitter data, simple k-nn recommenders do better based only on friends than they do if they draw from the entire network. These results help us understand when, and why, social network information is likely to support recommendation systems, and show that systems that see ego-centric slices of a complete network (such as websites that use Facebook logins) or have computational limitations (such as mobile devices) may profitably use ego-centric recommendation algorithms.

Providing a convenient mechanism for accessing the Internet, smartphones have led to the rapid growth of Social Networking Services (SNSs) such as Twitter and have served as a major platform for SNSs. Nowadays, people are able to check conveniently the SNS messages posted by their friends and followers via their smartphones. As a consequence, people are exposed to spoilers of TV programs that they follow. So far, there are two previous works that explored the detection of spoilers in texts, not SNS: (1) keyword matching method and (2) machine-learning method based on Latent Dirichlet Allocation (LDA). The keyword matching method evaluates most tweets as spoilers; hence its poor recall performance. The other method based on LDA, although successful on large text, works poorly on short segments of text such as those found on Twitter and evaluates most tweets as non-spoilers. This paper presents four features that are significant in the classification of spoiler tweets. Using those features, we classified spoiler tweets pertaining to a reality TV show (“Dancing with the Stars”). We experimentally compared our method with previous methods, with our method achieving substantially higher precision compared to the keyword matching and LDA-based methods while maintaining comparable recalls.

Social media provides rich sources of personal information and community interaction which can be linked to aspect of mental health. In this paper we investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and authors' mood, of a large corpus of blog posts, to analyze the aspect of social capital in social media communities. Using data collected from Live Journal, we find that bloggers with lower social capital have fewer positive moods and more negative moods than those with higher social capital. It is also found that people with low social capital have more random mood swings over time than the people with high social capital. Significant differences are found between low and high social capital groups when characterized by a set of latent topics and psycholinguistic features derived from blogposts, suggesting discriminative features, proved to be useful for classification tasks. Good prediction is achieved when classifying among social capital groups using topic and linguistic features, with linguistic features are found to have greater predictive power than latent topics. The significance of our work lies in the importance of online social capital to potential construction of automatic healthcare monitoring systems. We further establish the link between mood and social capital in online communities, suggesting the foundation of new systems to monitor online mental well-being.