24959035
A Novel Constraint-Based Approach to Online Graphics Recognition
Online graphics recognition has become the key problem for pen-based user interface on small screen devices, such as PDA and Tablet PC. In this paper, a novel constraint-based approach to online graphics recognition is proposed. The key idea of our approach is that when the user is drawing a graphic object, the system can extract the constraints between primitives and basic shapes from the object and use these constraints to retrieve similar graphic objects from the database at run time. The user can then choose the standard object from the ranked list of results to replace his sketches before he finishes drawing all strokes of the object. For this purpose, we summarize three types of primitives and several types of basic shapes as the basic components of a graphic object. We also define a set of constraints between primitives and basic shapes to represent their structural relations. The algorithms for online constraint extraction and graphics recognition are also presented. Experimental results show that our approach is efficient for online graphics recognition and effective for improving the user’s productivity.
23025365
A structural approach to recognizing incomplete graphic objects
We propose a structural approach to recognizing incomplete graphic objects, which aims to recognize a graphic object before the user finishes drawing all its strokes. In our approach, we first create a spatial division tree (SDT) for the input object and then search for similar objects in the database of graphic object models based on this representation. Along with the SDT, an intersection-based relationship model is also proposed in our approach to describe the relationships among strokes. The experiment result has shown that our approach can significantly save user's input time for complex graphic objects. Moreover, it also proves that our approach can response to user's input in real-time and is independent of the user's drawing style.
140108792
Online Segmentation of Freehand Stroke by Dynamic Programming
619505
Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions
We describe an approach to extract attribute-value pairs from product descriptions. This allows us to represent products as sets of such attribute-value pairs to augment product databases. Such a representation is useful for a variety of tasks where treating a product as a set of attribute-value pairs is more useful than as an atomic entity. Examples of such applications include product recommendations, product comparison, and demand forecasting. We formulate the extraction as a classification problem and use a semi-supervised algorithm (co-EM) along with (Naive Bayes). The extraction system requires very little initial user supervision: using unlabeled data, we automatically extract an initial seed list that serves as training data for the supervised and semi-supervised classification algorithms. Finally, the extracted attributes and values are linked to form pairs using dependency information and co-location scores. We present promising results on product descriptions in two categories of sporting goods.
1073181
Keeping Secrets within a Family: Rediscovering Parnas
David Parnas wrote several papers in the 1970’s and 1980’s that are now considered classics. The concepts he advocated such as information hiding and use of abstract interfaces are generally accepted as the appropriate way to design nontrivial software systems. However, not all of what he proposed has been fully appreciated and assimilated into our practices. Many of his simple, elegant ideas have been lost amongst the hype surrounding the technologies and methods that have arisen in the past two decades. This paper examines Parnas’s ideas, especially his emphasis on program families, and proposes that college-level computing science and software engineering curricula should renew their attention to these very important principles and techniques and present them in the context of contemporary software development.
13098192
On the impact of concurrent downloads
Concurrent downloads accelerate information access speed for individual Web users. The speed-up comes from multiple connections launched for one subject download, which leads to unfairness at user level. We study the impact of concurrent downloads on the network. Particularly, we investigate the fairness between users who employ different downloading concurrency. We also discuss how concurrent downloads affect the transient behavior of the network.
20456243
Energy-Efficient Cooperative Image Processing in Video Sensor Networks
Different from conventional sensor networks, video sensor networks are distinctly characterized by their immense information and directional sensing models. In this paper, we propose an innovative, systematic method for image processing in video sensor networks in order to reduce the workload for individual sensors. Given the severe resource constraints on individual sensor nodes, our approach is to employ the redundancy among sensor nodes by partitioning the sensing task among highly correlated sensors. For an object of interest, each sensor only needs to capture and deliver a fraction of the scene of interests and these partial images can be fused at the sink to reconstruct a composite image. In particular, we detail how the sensing task can be partitioned among the sensors and propose an image fusion algorithms based on epipolar line constraint to fuse the received partial images at the sink. The experimental results show that our approach can achieve satisfactory results and we give detailed discussions on the effects of different system parameters.
2123278
Program optimization using indexed and recursive data structures
This paper describes a systematic method for optimizing recursive functions using both indexed and recursive data structures. The method is based on two critical ideas: first, determining a minimal input increment operation so as to compute a function on repeatedly incremented input; second, determining appropriate additional values to maintain in appropriate data structures, based on what values are needed in computation on an incremented input and how these values can be established and accessed. Once these two are determined, the method extends the original program to return the additional values, derives an incremental version of the extended program, and forms an optimized program that repeatedly calls the incremental program. The method can derive all dynamic programming algorithms found in standard algorithm textbooks. There are many previous methods for deriving efficient algorithms, but none is as simple, general, and systematic as ours.