Smart Remote Classroom: Creating a Revolutionary Real-Time Interactive Distance Learning System
Real-time interactive virtual classroom is an important type of distance learning. However currently available systems are insufficient in the support of large-scale user access, and neither can they accommodate accessing with heterogeneous devices and networks. Furthermore, these systems are usually desktop-based, which results that the teachers' experience is completely different from teaching in a real classroom. The Smart Remote Classroom project tackles these difficulties through the development of the following technologies: 1) A hybrid application-layer Multicast protocol called TORM and an Adaptive Content Delivery scheme called AMTM. Together they enable large-scale users to access a virtual classroom with different devices and networks at the same time. 2) A dedicated software called SameView, which take use of the proposed TORM and AMTM technology and provide a rich set of functions for teachers and students to efficiently carry out the real-time interactive distance learning. Moreover, SameView can record the whole process of a class into a compound multimedia document for later retrieval and review of the class. 3) An augmented classroom called Smart Classroom, where the user interface of the SameView for the teacher are distributed in and fused with the room space. Thus the teacher can instruct the remote students just like teaching face-to-face in a conventional classroom. All these technologies has been successfully integrated and demonstrated in the prototype system.

title: Statistical modeling and conceptualization of natural images
abstract: Multi-level annotation of images is a promising solution to enable semantic image retrieval by using various keywords at different semantic levels. In this paper, we propose a multi-level approach to interpret and annotate the semantics of natural images by using both the dominant image components and the relevant semantic image concepts. In contrast to the well-known image-based and region-based approaches, we use the concept-sensitive salient objects as the dominant image components to achieve automatic image annotation at the content level. By using the concept-sensitive salient objects for image content representation and feature extraction, a novel image classification technique is developed to achieve automatic image annotation at the concept level. To detect the concept-sensitive salient objects automatically, a set of detection functions are learned from the labeled image regions by using support vector machine (SVM) classifiers with an automatic scheme for searching the optimal model parameters. To generate the semantic image concepts, the finite mixture models are used to approximate the class distributions of the relevant concept-sensitive salient objects. An adaptive EM algorithm has been proposed to determine the optimal model structure and model parameters simultaneously. In addition, a large number of unlabeled samples have been integrated with a limited number of labeled samples to achieve more effective classifier training and knowledge discovery. We have also demonstrated that our algorithms are very effective to enable multi-level interpretation and annotation of natural images.

title: Learning-Based Tracking of Complex Non-Rigid Motion
abstract: This paper describes a novel method for tracking complex non-rigid motions by learning the intrinsic object structure.The approach builds on and extends the studies on non-linear dimensionality reduction for object representation,object dynamics modeling and particle filter style tracking.First,the dimensionality reduction and density estimation algorithm is derived for unsupervised learning of object intrinsic representation,and the obtained non-rigid part of object state reduces even to 2-3 dimensions.Secondly the dynamical model is derived and trained based on this intrinsic representation.Thirdly the learned intrinsic object structure is integrated into a particle filter style tracker.It is shown that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle filter style tracker more robust and reliable.Extensive experiments are done on the tracking of challenging non-rigid motions such as fish twisting with selfocclusion,large inter-frame lip motion and facial expressions with global head rotation.Quantitative results are given to make comparisons between the newly proposed tracker and the existing tracker.The proposed method also has the potential to solve other type of tracking problems.

title: Error analysis of pure rotation-based self-calibration
abstract: Self-calibration using pure rotation is a well-known technique and has been shown to be a reliable means for recovering intrinsic camera parameters. However, in practice, it is virtually impossible to ensure that the camera motion for this type of self-calibration is a pure rotation. In this paper, we present an error analysis of recovered intrinsic camera parameters due to the presence of translation. We derived closed-form error expressions for a single pair of images with nondegenerate motion; for multiple rotations for which there are no closed-form solutions, analysis was done through repeated experiments. Among others, we show that translation-independent solutions do exist under certain practical conditions. Our analysis can be used to help choose the least error-prone approach (if multiple approaches exist) for a given set of conditions.

title: Three-dimensional animated face modeling from stereo video
abstract: 3D modeling of human face has been an active research topic in computer vision and computer graphics communities. In this paper we develop a face-modeling system from calibrated stereo video input. The stereo configuration is chosen according to the characteristics of the face structure. For data capturing the subject sits in front of the calibrated stereo video cameras and rotates his/her head from front view to side view. Then we can create his/her 3D face model with realistic texture mapped, which is ready for MPEG-4 compatible facial animation, by adapting a generic wireframe model to the input. In deciding 3D face geometry we utilize two kinds of 2D information. One is the correspondence between stereo pair; the other is self-occluded contour. After some 3D points in the specific face are obtained from both image clues in different frames, RBF (Radius Basis Function) data interpolation is performed to decide its shape. Afterwards texture image is generated from the face geometry and video. Experiments show that satisfactory results can be reached with our system.

title: Building the Software Infrastructure for Smart Classroom: From Open Agent Architecture (OAA) to Smart Platform
abstract: Smart Classroom is a Smart Space developed to enhance tele-education practice. This paper describes the considerations of bringing forward Smart Platform as the software infrastructure of Smart Classroom system, abandoning the former implementation based on OAA. As a multi-agent system for Smart Space, Smart Platform encompasses following features: spontaneous discovery of runtime environment, automatic management and resolving of agent dependencies, combination of delegated communication and peer-to-peer communication, etc. In addition, a surrogate agent on Smart Platform and Metaglue from MIT is now being developed to inter-connect Smart Classroom with MIT's Intelligent Room to extend their functionality.

title: Discrete Laplace-Beltrami operators and their convergence
abstract: The convergence property of the discrete Laplace-Beltrami operators is the foundation of convergence analysis of the numerical simulation process of some geometric partial differential equations which involve the operator. In this paper we propose several simple discretization schemes of Laplace-Beltrami operators over triangulated surfaces. Convergence results for these discrete Laplace-Beltrami operators are established under various conditions. Numerical results that support the theoretical analysis are given. Application examples of the proposed discrete Laplace-Beltrami operators in surface processing and modelling are also presented.

title: Acyclic domination on bipartite permutation graphs
abstract: For a graph G = (V, E), a subset S ⊆ V is called an acyclic dominating set of G if every vertex in V - S is adjacent to at least one vertex in S and the subgraph induced by S contains no cycles. In this paper, we present a linear time algorithm for transforming a minimum dominating set in a bipartite permutation graph into a minimum acyclic dominating set. Chao et al. gave a linear time algorithm to find a minimum dominating set of a permutation graph. Thus, we show that the acyclic domination problem is linear time solvable for bipartite permutation graphs.

title: An Improved Approximation Algorithm for Uncapacitated Facility Location Problem with Penalties
abstract: In this paper, we consider an interesting variant of the facility location problem called uncapacitated facility location problem with penalties (UFLWP for short) in which each client is either assigned to an opened facility or rejected by paying a penalty. We present a 1.8526-approximation algorithm for the UFLWP problem. Our algorithm first enhances the primal-dual method for the UFLWP problem  so that outliers can be recognized more efficiently, and then applies a local search heuristic to further reduce the cost for serving those non-rejected clients.

title: An LP rounding algorithm for approximating uncapacitated facility location problem with penalties
abstract: In this paper, we consider an interesting variant of the facility location problem called uncapacitated facility location problem with penalties (UFLWP, for short) in which each client can be either assigned to some opened facility or rejected by paying a penalty. Existing approaches [M. Charikar, S. Khuller, D. Mount, G. Narasimhan, Algorithms for facility location problems with outliers,  2001, p. 642] and [K. Jain, M. Mahdian, E. Markakis, A. Saberi, V. Vazirani, Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP,  (2003) 795] for this variant of facility location problem are all based on primal-dual method. In this paper, we present an efficient linear programming (LP) rounding based approach to show that LP rounding techniques are equally capable of solving this variant of facility location problem. Our algorithm uses a two-phase filtering technique (generalized from Lin and Vitter's [@?-approximation with minimum packing constraint violation, ) to identify those to-be-rejected clients and open facilities for the remaining ones. Our approach achieves an approximation ratio of 2+2/e (~2.736) which is worse than the best approximation ratio of 2 achieved by the much more sophisticated dual fitting technique [K. Jain, M. Mahdian, E. Markakis, A. Saberi, V. Vazirani, Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP,  (2003) 795], but better than the approximation ratio of 3 achieved by the primal-dual method [M. Charikar, S. Khuller, D. Mount, G. Narasimhan, Algorithms for facility location problems with outliers,  2001, p. 642]. Our algorithm is simple, natural, and can be easily integrated into existing LP rounding based algorithms for facility location problem to deal with outliers. problem to deal with outliers.

title: Acoustic scattering on arbitrary manifold surfaces
abstract: We propose the use of surface subdivision as adaptive and higher-order boundary elements for solving a Helmholtz partial differential equation to calculate accurate acoustic scattering on arbitrary manifolds. Such acoustic transfer functions prove useful for designing and tuning hearing aid devices for hearing impaired individuals. The number of unknowns of the discretized linear system is the same as that in a linear element approach. Our results show that the accuracy of the subdivision approach is much better than that of the linear element approach.