Special tracks are a vital part of the FLAIRS Thanks go to the authors of both accepted and rejected conferences, with 11 held at FLAIRS-25. Over 90 papers; the special track coordinator Chutima percent of the papers were reviewed by four or Boonthum-Denecke and all the special track organizers; more reviewers, and all papers were reviewed by at the program committees and their reviewers; least three. These were coordinated by the program the invited speakers; Chad Lane for organizing committees of the general conference and the special the conference; Jean Gerber for administering the tracks. The accepted submissions include 74 conference; the Florida Artificial International Research full papers (19 from the general conference and 55 Society for maintaining the conference series; from the special tracks), 27 short papers presented the Association for the Advancement of Artificial as posters (6 from the general conference and 21 Intelligence for its cooperation with the conference; from the special tracks), and 20 poster abstracts Mike Hamilton for organizing the publication that appear in these proceedings. of the proceedings; and EasyChair for hosting the review process. The program included five invited talks: Bill Swartout, the Director of Technology and Research Professor at the University of Southern California's

Most of preference representation languages developed in the literature are based on comparative preference statements. The latter offer a simple and intuitive way for expressing preferences. They can be interpreted following different semantics. This paper presents a postulate-based analysis of the different semantics describing their behavior w.r.t. three criteria: coherence, syntax independence and inference.

The dynamic between speaker intent and listener perception is played out in the variation of acoustical cues by the speaker that must be interpreted by the listener to determine in an appropriate way. Emotion speech research must rely on either acted intent (i.e., an actor attempting to express an emotion) or listener perception (i.e., listening tests to assign emotional categories to non-acted data) to define ground truth labels for analysis. The emotion labels are described either using emotion dimension or emotion category. This study examines the two emotion characterization strategies dimension and category in communication of emotion embedded in speech as expressed through acted intent and the perception of emotion determined by a group of listeners. The results reveal that, without context information, intended emotion categories could be perceived by listeners with the averaged accuracy rate five times of chance in category. Also, the trend of listener ratings between emotion dimensions (valence/arousal) and emotional word categories was shown to be well correlated. Furthermore, while listeners confused the specific identity of certain emotional expressions, they were generally very accurate at identifying the intended affective space of the actor as determined by intended valence and arousal.

Intelligent Tutoring Systems (ITSs) have begun to incorporate game-based components in an attempt to balance the learning benefits of ITSs with the motivational benefits of games. iSTART-ME (Motivationally Enhanced) is a new game-based learning environment that was developed on top of an existing ITS (iSTART). In a multi-session lab-based efficacy study with 125 high school students, those students with a low prior reading ability who were trained by a game-based tutoring system (iSTART-ME) or a traditional intelligent tutoring system (iSTART-Regular) performed significantly better on posttest measures than students assigned to a time-delayed control condition. Additionally, the low reading ability students who interacted with the game-based system had a tendency to gain more than students in the traditional ITS system.

This paper addresses questions of the transition related to conscious processes and unconscious processes, namely aims to substantiating a primary framework to the following open question: The vast majority of brain activity is non-conscious. What is the criterion to distinguish the non-conscious activities from conscious ones? To support our answers in a principled way, we present a general framework for the study of mental processes resting on two main principles: firstly, we endorse Matte Blanco’s principle of symmetry by giving central stage to the concept of unconscious processes. Secondly, to structure and combine the notions of infinity and part-whole equivalence in a mathematical logic method, moreover we base our work on modern non-classical logics in the disposition of context-dependency, as forcefully put forward by CJS Clarke. In particular, we employ the paraconsistent logic as the underlying logical system for defining the general framework for mental processes, highly structural and formal representation, called bi-logic framework.

Our motivation believe our results will be of special interest to researchers for this work comes from the fact that with the large from the field of medical image analysis, as these images amounts of data that the SDO mission started transmitting, seem to be the closest to our dataset [21. We also identify hand labeling (commonly used by solar physicist in the last some interesting combinations of dimensionality reduction decades) of these images is simply impossible. There have methods and classifiers that behave differently across the been several successful CBIR systems for medical images presented datasets. Our research problem in Solar physics is [2] as well as in other domains [3]; none of them, however, of great practical relevance for Earth's climate since solar have dealt with the volume of data that the SDO mission flares endanger the lives of passengers on commercial airline generates. This NASA mission, only with its Atmospheric routes going over the poles, interrupt radio communications Imaging Assembly (AIA), generates eight 4096 pixels x in bands the military uses, can (and have) knocked 4096 pixels images every 10 seconds. This leads to a data down power grids. The systematic feature recognition and transmission rate of approximately 700 gigabytes per day the study of the metadata, is a key component of the ultimate only from the AIA component (the entire mission is expected prediction of solar activity (space weather).

Web 2.0 has changed the ways people communicate, collaborate, and express their opinions and sentiments. But despite social data on the Web being perfectly suitable for human consumption, they remain hardly accessible to machines. To bridge the cognitive and affective gap between word-level natural language data and the concept-level sentiments conveyed by them, we developed SenticNet 2, a publicly available semantic and affective resource for opinion mining and sentiment analysis. SenticNet 2 is built by means of sentic computing, a new paradigm that exploits both AI and Semantic Web techniques to better recognize, interpret, and process natural language opinions. By providing the semantics and sentics (that is, the cognitive and affective information) associated with over 14,000 concepts, SenticNet 2 represents one of the most comprehensive semantic resources for the development of affect-sensitive applications in fields such as social data mining, multimodal affective HCI, and social media marketing.

This project focuses on artificial social interactions where things get nasty and mean. The purpose is training in social 'facework' -- managing the situation so that participants maintain their social dignity or 'face'. This can be especially delicate in cross-cultural contexts, where assumptions about social protocols and the emotional associations of utterances and gestures may differ. The purpose of this project is two-fold. First, it is intended as a training system, so that users might learn the do's and don'ts of social interactions in different cultures and different situations. The knowledge base draws from existing theories of diplomacy, facework, and (im)politeness theory. The other goal is to provide a platform for observation and experimentation of social interaction in an artificial, virtual setting in order to improve these theories.

Officers of the Florida AI Research Society.

We evaluated an affect-sensitive version of AutoTutor, a dialogue based ITS that simulates human tutors. While the original AutoTutor is sensitive to students’ cognitive states, the affect-sensitive tutor (Supportive tutor) also responds to students’ affective states (boredom, confusion, and frustration) with empathetic, encouraging, and motivational dialogue moves that are accompanied by appropriate emotional expressions. We conducted an experiment that compared the Supportive and Regular (non-affective) tutors over two 30-minute learning sessions with respect to perceived effectiveness, fidelity of cognitive and emotional feedback, engagement, and enjoyment. The results indicated that, irrespective of tutor, students’ ratings of engagement, enjoyment, and perceived learning decreased across sessions, but these ratings were not correlated with actual learning gains. In contrast, students’ perceptions of how closely the computer tutors resembled human tutors increased across learning sessions, was related to the quality of tutor feedback, the increase was greater for the Supportive tutor, and was a powerful predictor of learning. Implications of our findings for the design of affect-sensitive ITSs are discussed.