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In this paper, we present a unified statistical framework for modeling both saccadic eye movements and visual saliency. By analyzing the statistical properties of human eye fixations on natural images, we found that human attention is sparsely distributed and usually deployed to locations with abundant structural information. This new observations inspired us to model saccadic behavior and visual...
To investigate fast human reaching movements in 3D, we asked 11 right-handed persons to catch a tennis ball while we tracked the movements of their arms. To ensure consistent trajectories of the ball, we used a catapult to throw the ball from three different positions. Tangential velocity profiles of the hand were in general bell-shaped and hand movements in 3D coincided with well known results for...
Natural selection acts as a culling force that favors highly fit organisms. There are numerous examples ranging from bacteria through humans showing fitness can sometimes improve if individuals cooperate. Yet despite the widespread evidence of cooperation throughout nature, we still do not fully understand how it evolves. Over the past 10–15 years a large number of theoretical (computer) models have...
In this article, a novel platform is addressed for modeling and simulating cause and effect relationships of strategy map. Our work is constructed from two components. First, we identify and model the causal relationships among strategic objectives using adaptive fuzzy cognitive maps. Then, we will recognize and simulate the interrelatedness of causalities using fuzzy inference system. In a real case...
Random forests have been successfully applied to various high level computer vision tasks such as human pose estimation and object segmentation. These models are extremely efficient but work under the assumption that the output variables (such as body part locations or pixel labels) are independent. In this paper, we present a conditional regression forest model for human pose estimation that incorporates...
We explore recently proposed Bayesian nonparametric models of image partitions, based on spatially dependent Pitman-Yor processes. These models are attractive because they adapt to images of varying complexity, successfully modeling uncertainty in the structure and scale of human segmentations of natural scenes. By developing substantially improved inference and learning algorithms, we achieve performance...
Mining patterns of human behavior from large-scale mobile phone data has potential to understand certain phenomena in society. The study of such human-centric massive datasets requires new mathematical models. In this paper, we propose a probabilistic topic model that we call the distant n-gram topic model (DNTM) to address the problem of learning long duration human location sequences. The DNTM is...
Simple rule based Multi Agent Systems are widely used in the fields of social simulations and game artificial intelligence in order to incorporate the complexity and richness of action and interaction into the characters in the virtual environments while keeping computational cost low. This paper presents an approach to synthesize the spatio-temporal dynamics of groups in standing conversation: four...
Individuals make commitments towards others in order to influence others to behave in certain ways. Most commitments may depend on some incentive that is required to ensure that the action is in the agent's best interest and thus, should be carried out to avoid eventual penalties. Similarly, individuals may ground their decision on an accurate assessment of the intentions of others. Hence, both commitments...
Mathematical description and modeling of dynamic systems is challenging due to their high level of complexity, their nonlinear and chaotic behaviors, the presence of uncertainties and interference of human behavior in their outputs, and their time-variant nature. Because of such characteristics and the importance of dynamic systems modeling, high-performance modeling tools are required to analyze,...
We propose a method that relies on markerless visual observations to track the full articulation of two hands that interact with each-other in a complex, unconstrained manner. We formulate this as an optimization problem whose 54-dimensional parameter space represents all possible configurations of two hands, each represented as a kinematic structure with 26 Degrees of Freedom (DoFs). To solve this...
Despite significant recent progress, the best available visual saliency models still lag behind human performance in predicting eye fixations in free-viewing of natural scenes. Majority of models are based on low-level visual features and the importance of top-down factors has not yet been fully explored or modeled. Here, we combine low-level features such as orientation, color, intensity, saliency...
Inside a computer network, virus brings damage and cost money. On the other hand, anti-virus software brings protection and cost money as well. The trade-off between being attacked by virus and paying money to get protection is of interest to study. To study this, we present a model to simulate the decision-making process of each computer user within a network of computers. Similar models that simulate...
In service-oriented enterprise architecture, provisioning business services is made on top of IT processes, which should be elastic amid the availability of computing resources and the variation of user demand. In addition, the provisioning depends on human resources utilized and is constrained by the business objectives (e.g. a goal) plus coarse-grained constraints (e.g. an order in which business...
This paper investigates basic semantic aspects of evaluation logic. We propose a verbalized approach to the design and use of the Generalized Conjunction/disjunction (GCD) aggregators. The main goals of verbalized approach are to help in specifying semantic components of GCD, and to facilitate the use of soft computing evaluation logic and corresponding evaluation methods (such as LSP), making those...
This paper addresses the problem of learning human behavior models from sensor information in a smart home environment. Any smart home is provided with many devices that can determine the state of the environment at any moment, as well as the user interaction with the environment. This information is used by our approach to learn a flexible and reliable human behavior representation, extracting the...
Argumentation plays an important role in promoting deep learning, fostering conceptual change and supporting problem solving. The new “learning by arguing” paradigm leads to new learning opportunities. However, due to the difficulties in modeling human cognition, there are few learning systems that can facilitate argumentation dialogues between systems and learners. Fuzzy Cognitive Map (FCM) is an...
We introduce a saliency model based on two key ideas. The first one is considering local and global image patch rarities as two complementary processes. The second one is based on our observation that for different images, one of the RGB and Lab color spaces outperforms the other in saliency detection. We propose a framework that measures patch rarities in each color space and combines them in a final...
We present a machine learning framework that automatically generates a model set of landmarks for some class of registered 3D objects: here we use human faces. The aim is to replace heuristically-designed landmark models by something that is learned from training data. The value of this automatically generated model is an expected improvement in robustness and precision of learning-based 3D landmarking...
In this paper, we propose an effective method to recognize human actions from 3D positions of body joints. With the release of RGBD sensors and associated SDK, human body joints can be extracted in real time with reasonable accuracy. In our method, we propose a new type of features based on position differences of joints, EigenJoints, which combine action information including static posture, motion,...
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