Semantic role labeling (SRL) is a technique of semantic interpretation of text on the sentence level. In this paper, we present a corpus that is labeled with semantic roles for biological transport events. The corpus was built using domain knowledge provided by ontologies. We also report on a word-chunking approach for identifying semantic roles of biomedical predicates describing transport events. We trained a first-order Conditional Random Fields (CRF) for chunking applications with the traditional role labeling features and also domain-specific features. The results show that the system performance varies between different roles and the performance was not improved for all roles by introducing domain specific features.

The ever-growing literature in biomedicine makes it virtually impossible for individuals to grasp all the information relevant to their interests. Since even experts' knowledge is likely to be incomplete, important associations among key biomedical concepts may remain unnoticed in the flood of information. Discovering those implicit, hidden knowledge is called hypothesis discovery. This paper reports our preliminary work on hypothesis discovery, which takes advantage of a syllogistic chain of relations extracted from existing knowledge (i.e., published literature). We consider such chains of relations as implicit patterns or rules to generate potential hypotheses. The generated hypotheses are then ranked according to their plausibility judged from the reliability of the rule which generated the hypothesis and the analogical resemblance between new and existing knowledge. We discuss the validity of the proposed approach on the entire Medline database.

Emotional intelligence is the key for acceptance of intelligent agents by humans as equal partners, e.g., in ad hoc teams. At the same time, its existing implementations in intelligent agents are mostly limited to basic affects. Currently, there is no consensus in the understanding of complex and social emotions at the level of functional and computational models. The approach of this work is based on the mental state formalism, originally developed as a part of the cognitive architecture GMU BICA and recently extended to include affective building blocks (A.V. Samsonovich, AAAI Technical Report WS-12-06: 109-116, 2012). In the present work, complex social emotions like humor, jealousy, compassion, shame, pride, etc. are identified as emergent patterns of appraisals represented by schemas, that capture the cognitive nature of these emotions and enable their modeling. A general model of complex emotions and emotional relationships is constructed that can be validated by simulations of emotionally biased interactions and emergent relationships in small groups of agents. The framework will be useful in cognitive architectures for designing human-like-intelligent social agents possessing a sense of humor and other human-like emotionally intelligent capabilities.

Higher life expectancies lead to an increased prevalenceof dementia in older adults, which is projected torise dramatically in the future. The link between malnutritionand dementia highlights the need to closelymonitor nutrition as early as possible. However, currentself-report assessment methods are labor-intensive,time-consuming and inaccurate. Technology has the potentialof assisting in nutritional analysis by alleviatingthe cognitive load of recording food intake and lesseningthe burden of care for the elderly. Therefore, we proposean intelligent nutritional assessment system thatwill monitor the dietary patterns of older adults with dementiaat their homes. Our computer vision-based systemconsists of food recognition and portion estimationalgorithms that, together, provide nutritional analysisof an image of a meal. We create a novel food imagedataset on which we achieve an 87.2% recognition accuracy.We apply several well-known segmentation andrecognition algorithms and analyze their suitability tothe food recognition problem.

Although decision processes are often described at the individual level of cognition (e.g. Tversky and Kahnemann The particular beliefs instantiated within the model are (1981)), they are subject to social and cultural influences based on a combination of results from empirical studies at both the interpersonal and societal levels. The adoption of technology adoption by Venkatesh et al. (2003). of new technology depends on various factors, such The UTAUT model combines eight of the most prominent as the type of technology, the context or culture in which technology-acceptance models observed in the literature and the technology is introduced, and the individual decisions provides a definitive list of variables that are critically relevant by people within that culture, as most individuals evaluate to an individual's Behavioral Intention (BI) and Use Behavior an innovation from the subjective evaluations of peers who (UB) for adopting a new technology, including Performance have adopted an innovation (see Watts and Dodds (2007) Expectancy (PE), Effort Expectancy (EE), Social for a discussion of network-diffused influence). These influences Influence (SI), Facilitating Conditions (FC), and Voluntariness propagate through the social network as a function of Use (VoU). of agent interactions.

Many strategic actions carry a "contagious" component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block the spread of support for an insurgency, such operations lack the manpower to defend the entire population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done, we propose using game theory to develop such policies and model the interconnected network of leaders as a graph. Unlike this past work in security games, actions in these domains possess a probabilistic, non-local impact. To address this new class of security games, recent research has used novel heuristic oracles in a double oracle formulation to generate mixed strategies. However, these heuristic oracles were evaluated only on runtime and quality scaling with the graphsize. Given the complexity of the problem, numerous other problem features and metrics must be considered to better inform practical application of such techniques. Thus, this work provides a thorough experimental analysis including variations of the contagion probability average and standard deviation. We extend the previous analysis to also examine the size of the action set constructed in the algorithms and the final mixed strategies themselves. Our results indicate that game instances featuring smaller graphs and low contagion probabilities converge slowly while games with larger graphs and medium contagion probabilities converge most quickly.

There has been a rapid proliferation of location-based social networks (LBSNs) during the last years. The spatial component of these systems provides a rich source of information that can be exploited by a number of novel services. However, to better design such services, it is important to understand the way people make use of these platforms and how this usage changes over time. While there exist studies that examine the motivations of people for adopting the usage of LBSNs and the temporal dynamics of these motivations, they are based on interviews and are mostly qualitative. Motivations can further only indirectly reveal or help us infer user behavior. In this paper, we analyze data from two commercial LBSNs to examine the temporal evolution of usage patterns to see what the data on their own reveal. We nd that users of two social networks that we examined increase their level of activity as they use the system. However, depending on the main purpose of the underlying LBSN, users may exhibit dierent behaviors over time. We believe that our ndings can open new directions and stimulate further research on areas such as location prediction and its applications (e.g., urban and transportation planning and location-based advertisment).

Network analysis is a growing field across many domains, including computer vision, social media marketing, transportation networks, and intelligence analysis. The growing use of digital communication devices and platforms, as well as persistent surveillance sensors, has resulted in explosion of the quantity of data and stretched the abilities of current technologies to process this data and draw meaningful conclusions. Current tools either require significant levels of manual intervention (e.g., to prepare the data, to define patterns, or to draw conclusions from data) or are unable to generalize to new data sources and analysis needs. In this paper, we present automated solutions to two major problems in network analysis: (a) finding patterns in the network data that contains high levels of noise and irrelevant information; and (b) learning repetitive patterns and dependencies between entities and attributes. Our modeling framework represents network data using multi-attributed graphs that can encode various discrete and continuous features and relationships between network entities. The pattern search and learning model is based on probabilistic multi-attributed graph matching, and implemented using distributed message passing algorithms. Our algorithms achieved high accuracy rates in learning and finding patterns in the data, are flexible to new domains and data types, and scale to large datasets using the Map-Reduce framework.

In the real world we observe a proliferation of regional dialects and jargons. Most of the research on naming conventions focuses on how to explain the process that allows a single naming convention to establish itself. This paper presents a different approach that aims to investigate why different conventions may emerge and coexist for a certain amount of time. The naming game is an abstraction of lexical acquisition dynamics, in which n agents try to find an agreement on the names to give to objects. To understand how different heterogeneous conventions emerge, I discuss a naming game model that takes into account experimental data on human and animal learning.

There is growing interest in automating the detection of interesting new developments in science and technology. BAE Systems is pursuing ARBITER (Abductive Reasoning Based on Indicators and Topics of EmeRgence), a multi-disciplinary study and development effort to analyze full- text and metadata for indicators of emergent technologies and scientific fields. To define these indicators, our team has applied the primary insights of actant network theory developed within the disciplines of Science and Technology Studies and the history of technology and science to create a pragmatic theory of technoscientific emergence. Specifically, this practical theory articulates emergence in terms of the robustness of actant networks. This applied actant-network theory currently guides our definition of indicators and indicator patterns for the ARBITER system, and represents a novel contribution to the discussion of emergent technologies and fields. Several elements of our theory were validated with 15 case studies and 25 example technologies.