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Reports of the AAAI 2010 fall symposia

Reports of the AAAI 2010 fall symposia

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作者： Azevedo, Roger Biswas, Gautam Bohus, Dan Carmichael, Ted Finlayson, Mark A. Hadzikadic, Mirsad Havasi, Catherine Horvitz, Eric Kanda, Takayuki Koyejo, Oluwasanmi Lawless, William F. Lenat, Doug Meneguzzi, Felipe Mutlu, Bilge Oh, Jean Pirrone, Roberto Raux, Antoine Sofge, Donald A. Sukthankar, Gita Van Durme, Benjamin Yorke-Smith, Neil Department of Educational and Counseling Psychology McGill University Canada Center for Intelligent Systems Vanderbilt University United States Microsoft Research Redmond United States Department of Software and Information Systems University of North Carolina Charlotte United States Complex Systems Institute United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Media Lab. Massachusetts Institute of Technology United States ATR Intelligent Robotics and Communication Laboratories Kyoto Japan Department of Electrical and Computer Engineering University of Texas Austin United States Department of Mathematics Paine College Augusta GA United States Cycorp Austin TX United States Robotics Institute Carnegie Mellon University United States Department of Computer Sciences University of Wisconsin Madison United States University of Palermo Italy Honda Research Institute United States Navy Center for Applied Research in Artificial Intelligence Naval Research Laboratory Washington DC United States Department of Electrical Engineering and Computer Science University of Central Florida United States Department of Computer Science Johns Hopkins University United States American University of Beirut Lebanon SRI International's Artificial Intelligence Center United States

The Association for the Advancement of Artificial Intelligence (AAAI) presented the 2010 Fall Symposium Series on November 11-13, 2010. The eight symposia included Cognitive and Metacognitive Educational systems, Commonsense Knowledge, Complex Adaptive systems: Resilience, Robustness, and Evolvability, Computational Models of Narrative, Dialog with Robots, Manifold Learning and Its Applications, Proactive Assistant Agents and Quantum Informatics for Cognitive, Social, and Semantic Processes. Cognitive and Metacognitive Educational systems aimed to provide a comprehensive definition of metacognitive educational systems that is inclusive of the theoretical, architectural, and educational aspects of this field. The AAAI Commonsense Knowledge Fall Symposium had the goal of bringing together the diverse elements of this community whose work benefits from or contributes to the representation of general knowledge about the world. One of the specific goals of Proactive symposium was to gather the researchers from various projects in assistant agents to share their wisdom in retrospect.

关键词： Semantics

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Effect of Different Diffusion Maps on Registration Results

Effect of Different Diffusion Maps on Registration Results

引用

Iranian Machine Vision and Image Processing (MVIP)

作者： Maryam Afzali Emad Fatemizadeh Hamid Soltanian-Zadeh Department of Electrical Engineering Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper, we compare registration results obtained using different diffusion maps extracted from diffusion tensor imaging (DTI). Fractional Anisotropy (FA) and Ellipsoidal Area Ratio (EAR) are two diffusion maps (indices) that may be used for image registration. First, we use FA maps to find deformation matrix and register diffusion weighted images. Then, we use EAR maps and finally we use both of FA and EAR maps to register diffusion weighted images. The difference between FA values before deformation and after registration using the FA alone or EAR alone has a median of 0.57 and using both of them has a median of 0.29. Therefore, the results of registration using both of the FA and EAR indices are superior to those obtained using only one of them alone.

关键词： Ear Diffusion tensor imaging Tensile stress Registers Eigenvalues and eigenfunctions Anisotropic magnetoresistance Image registration

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A novel type of trigonometric neural network trained by Extended Kalman Filter

A novel type of trigonometric neural network trained by Exte...

引用

International Workshop on Advanced Computational Intelligence (IWACI)

作者： M. Norouzi M. Mansouri M. Teshnehlab M. Aliyari Shoorehdeli Faculty of Engineering Department of Computer Engineering Science and Research Branch Islamic Azad University Iran Intelligent System Laboratory (ISLAB) Faculty of Electrical Engineering Control Department K.N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Department of Mechatronics Engineering Advanced Process Automation &Control Laboratory (APAC) K.N. Toosi University of Technology Tehran Iran

In this study, a new type of trigonometric neural network is presented by adding frequency and phase to trigonometric activation functions. The proposed trigonometric neural network has more flexibility in comparison with conventional trigonometric neural networks and even other types of neural networks. Due to the low convergence rate and high posibility of trapping in a local minimum of backpropagation algorithm, Extended Kalman Filter algorithm is used to train the neural network's parameters which they appear in a nonlinear form. The Simulation of the suggested neural network based on the prediction of Mackey-Glass time series and identification of a nonlinear dynamic ystem reveals the efficiency of the proposed network. To show the efficiency of this method, the results are compared with the results of the others.

关键词： Biological neural networks Kalman filters Equations Neurons Vectors Covariance matrix Mathematical model

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Universal Steganalysis Based on Local Prediction Error in Wavelet Domain

Universal Steganalysis Based on Local Prediction Error in Wa...

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International Conference on intelligent Information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Anahita Shojaei-Hashemi Mostafa Mehdipour-Ghazi Shahrokh Ghaemmaghami Hamid Soltanian-Zadeh School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Electronics Research Institute Sharif University of Technology Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

A passive universal image steganalysis method is proposed that is shown to be of higher detection accuracy than existing truly blind steganalysis methods including Farid's and the WAM. This is achieved by improving some weaknesses of Farid's steganalysis scheme in feature extraction, that is, instead of deriving an over-determined equation system for each sub band of the wavelet decomposition, the sub bands are divided into overlapping blocks and an over-determined equation system is constructed for each block. To guarantee the existence of finite answers, the over-determined equation systems are solved in a way different from Farid's by using Moore-Penrose pseudo-inverse concept. Further improvement to the performance is achieved by adding diagonal directions and increasing the number of moments. The comparative evaluations confirm the superiority of the proposed method over prevalent blind steganalysis schemes.

关键词： Equations Mathematical model Payloads Transform coding Correlation Educational institutions Reliability

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A novel method for identification of COPD in inspiratory and expiratory states of CT images

A novel method for identification of COPD in inspiratory and...

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Middle East Conference on Biomedical engineering (MECBME)

作者： M. Parsa Hosseini Hamid Soltanian-Zadeh Shahram Akhlaghpoor Department of Electrical Engineering Islamic Azad University Tehran Iran Department of Radiology Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Radiology Tehran University of Medical Sciences Sina Hospital Tehran Iran

Chronic obstructive pulmonary disease (COPD) refers to a group of lung diseases that block airflow and cause a huge degree of human suffering. While there is no cure for COPD and the lung damage that results in this disease cannot be reversed, it is very important to diagnose it as early as possible. Additional to diagnosis, using a mathematical model to estimate severity of disease would be helpful for evaluation of treatment effects. This paper presents a new method for identifying COPD from three-dimensional (3-D) pulmonary X-ray CT images. The method has five main steps. First, corresponding positions of lungs in inspiration and expiration are found based on anatomical structures. Then, lung regions are segmented from the CT images by active contours. Next, the left and right lungs are separated using a sequence of morphological operations. Then, parenchyma variations in each lung are found as a relationship between inspiratory and expiratory states. Finally, a classifier is used to decide about the disease and its severity. A t-test is done to evaluate the results. Twelve patients with variable severity of COPD and twelve normal adults were included in this study. The proposed method may assist radiologists in the detection of COPD as a computer aided diagnosis (CAD) system.

关键词： Lungs Computed tomography Diseases Image segmentation Biomedical imaging Gaussian distribution

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Assessing lung volumetric variation to detect and stage COPD

Assessing lung volumetric variation to detect and stage COPD

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Middle East Conference on Biomedical engineering (MECBME)

Chronic obstructive pulmonary disease (COPD) is a devastating disease. In this paper, we propose a novel method for scoring of air trapping in the lungs for detection and evaluation of COPD. The proposed method finds volumetric changes of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared. For this comparison, the lungs parenchyma in the inspiration and expiration images are found and changes in the surface and volume of the lungs between inspiration and expiration are used to define quantitative measures (features). Using these features, the subjects are classified into two groups of normal class and COPD patients using a Bayesian classifier. In addition, t-tests are applied to evaluate discrimination powers of the features for this classification. The main advantage of the proposed method is its ability to estimate air trapping in the lungs from CT images without human intervention. The proposed method may assists radiologists in the detection of COPD as a computer aided diagnosis (CAD) system. It may also assist them with the scoring of the disease (severity of air trapping in the lungs).

关键词： Lungs Computed tomography Diseases Active contours Image segmentation Charge carrier processes

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Near-infrared based nighttime pedestrian detection by combining multiple features

Near-infrared based nighttime pedestrian detection by combin...

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International Conference on intelligent Transportation

作者： Yu-Chun Lin Yi-Ming Chan Luo-Chieh Chuang Li-Chen Fu Shih-Shinh Huang Pei-Yung Hsiao Min-Fang Luo Intelligent Robot and Automation Laboratory Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan National Taiwan University Taipei TW Department of Computer and Communication Engineering National Kaohsiung First University of Science and Technology Kaohsiung Taiwan Department of Electrical Engineering National University of Kaohsiung Kaohsiung Taiwan System development center Chung-Shan Institute of Science and Technology Longtan Shaiang Taoyuan Taiwan

Pedestrian detection is important in the computer vision field. In the nighttime, pedestrian detection is even more valuable. In this paper, we address the issue of detecting pedestrians in video streams from a moving camera at nighttime. Most nighttime human detection approaches only use single feature extracted from images. The effective image features in daytime environment may suffer from textureless, high contrast and low light problems at night. To deal with these issues, we first segment the foreground by using the proposed Smart Region Detection approach to generate candidates. Then we design a nighttime pedestrian detection system based on the AdaBoost and the support vector machine (SVM) classifiers with contour and histogram of oriented gradients (HOG) features to effectively recognize pedestrians from those candidates. Combining different type of complementary features improve the detection performance. Results show that our pedestrian detection system is promising in the nighttime environment.

关键词： Feature extraction Image segmentation Cameras Humans Databases Lighting Support vector machines

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Adaptive and Repetitive Controller for Robotic Manipulators with Slowly Updating Scheme Using B-Spline Shape Function

Adaptive and Repetitive Controller for Robotic Manipulators ...

引用

IEEE International Conference on robotics and Biomimetics

作者： Pannatee Rakprayoon Peerayot Sanposh Nattapon Chayopitak Intelligent Mechatronics Automation Robotics and Control Laboratory Department of Electrical Engineering Kasetsart University Industrial Control and Automation Laboratory National Electronics and Computer Technology Center

ISBN: (纸本)9781457721366

This paper studies the real-time adaptive and repetitive control problem using the Desired Compensation Learning Law (DCLL) that has the advantage of robustness under the presence of unknown parameters with low memory requirement. The control structure of DCLL consists of a feedforward compensator that is designed by a linear combination of shape functions that is adaptable to parameter variations;hence the selection of appropriate shape functions plays an important role in the adaptive ability and accuracy of DCLL. In this study, the B-spline shape function is proposed to be used under the slowly updating scheme and the obtained results show that the robustness and accuracy of the tracking can be greatly improved even under payload variation and torque limits.

关键词： shape functions Repetitive controllers Self tuning splines (mathematics) Scheme Robot Manipulator adaptive capacity memory requirements Control Architecture Manipulators Unknown parameter Linear Combination

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Voxel based treatment prediction using diffusion anisotropy indices and spatial information in Glioblastoma Multiform tumor

Voxel based treatment prediction using diffusion anisotropy ...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Hadi Sabahi Hamid Soltanian-Zadeh Lisa Scarpace Tom Mikkelsen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI USA Hermelin Brain Tumor Research Center Neurosurgery Department Henry Ford Health System Detroit MI USA

In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance Images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pre-treatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures.

关键词： Tumors Anisotropic magnetoresistance Diffusion tensor imaging Support vector machines Feature extraction Artificial neural networks

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Looking around with your brain in a virtual world

Looking around with your brain in a virtual world

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IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)

作者： Danny Plass-Oude Bos Matthieu Duvinage Oytun Oktay Jaime Delgado Saa Huseyin Guruler Ayhan Istanbullu Marijn van Vliet Bram van de Laar Mannes Poel Linsey Roijendijk Luca Tonin Ali Bahramisharif Boris Reuderink HMI group Faculty of EEMCS University of Twente Netherlands TCTS Laboratory Electrical Engineering University of Mons Belgium Electronics and Communications Corlu Engineering Namik Kemal Üniversitesi Turkey IEE Group of Robotics and Intelligent Systems Universidad del Norte Colombia Faculty of Engineering and Natural Sciences VPA Laboratory Sabanci University Turkey Department of Electronics and Computer Education Faculty of Technical Education Mugla University Turkey Faculty of Computer Engineering Balikesir University Turkey Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Nijmegen Netherlands CNBI École Polytechnique Fédérale de Lausanne Switzerland Institute for Computing and Information Sciences Radboud University Nijmegen Nijmegen Netherlands

Offline analysis pipelines have been developed and evaluated for the detection of covert attention from electroen-cephalography recordings, and the detection of overt attention in terms of eye movement based on electrooculographic measurements. Some additional analysis were done in order to prepare the pipelines for use in a real-time system. This real-time system and a game application in which these pipelines are to be used were implemented. The game is set in a virtual environment where player is a wildlife photographer on an uninhabited island. Overt attention is used to adjust the angle of the first person camera, when the player is tracking animals. When making a photograph, the animal will flee when it notices it is looked at directly, so covert attention is required to get a good shot. Future work will entail user tests with this system to evaluate usability, user experience, and characteristics of the signals related to overt and covert attention when used in such an immersive environment.

关键词： Pipelines Accuracy Electrooculography Electrodes Training Electroencephalography Games

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