15947104
Permissible Observability Relations in FSM Networks
Previous attempts to capture the phenomenon of output don't care sequences for a component in an FSM network have been incomplete. We demonstrate that output don't care sequences for a component can be expressed using a set of observability relations given that its state transition function is kept unchanged. Each observability relation is permissible in the sense that any implementation compatible with one of them is feasible. The representation for a set of permissible observability relations is not unique. We provide a method to find a set with the minimum number of permissible relations. We briefly discuss the exploitation of permissible observability relations in state minimization, circuit implementation and signal encoding. We have implemented these methods and present some preliminary results on a few small artificially constructed examples.
7539036
Multi-level logic optimization of FSM networks
Current approaches tocompute and exploit the flexibility ofa component in an FSM network are allat the symbolic level [32,42,44,43]. Conventionally, exploitation of this flexibility relies on state minimizers for incompletely specified FSM's (ISFSM's) or pseudo non-deterministic FSM's (PNDFSM's) [44]. However, state-of-the-art state minimizers cannothandle large ISFSM's orPNDFSM's [19,21,45,14, 22]. In addition, these exploitation techniques are at the symbolic level, not directly at the net-list logic level. We present a general approach to exploit exact orapproximate flexibility directly at the net-list logic level, and we demonstrate that many sequential logic optimization techniques can beapplied inexploitation. Moreover, we propose a new procedure for input don't care sequences. As a result, both computation and exploitation of input don't care sequences inlarger FSM networks can be made efficient and effective. Finally, we give preliminary results on some artificially constructed FSM networks. Preliminary results indicate that our approach can beeffective inreducing the size of a componentof an FSM network. •This projectwassupported by NSF undercontract numberMIP-8719546.
2672581
Debugging OWL-DL Ontologies: A Heuristic Approach
After becoming a W3C Recommendation, OWL is becoming increasingly widely accepted and used. However most people still find it difficult to create and use OWL ontologies. On major difficulty is “debugging” the ontologies – discovering why a reasoners has inferred that a class is “unsatisfiable” (inconsistent). Even for people who do understand OWL and the logical meaning of the underlining description logic, discovering why concepts are unsatisfiable can be difficult. Most modern tableaux reasoners do not provide any explanation as to why the classes are unsatisfiable. This paper presents a ‘black boxed' heuristic approach based on identifying common errors and inferences.
14482795
Collaborative multi-agents for workflow management
Workflow management helps organizations to organize their work on different activities in such a way that the predictable underlying processes are carried out effectively and efficiently in a distributed environment. We describe a business workflow management system with intelligent multi-agent support in a collaborative organization environment. With the help from multi-agent collaboration, our workflow management system can organize the organization's work processes and provide resource control, data mediation, semantic mapping, and knowledge visualization processing. Such systems are able to manage the organizational workflow more effectively and more efficiently.
72839
Distributing streaming media content using cooperative networking
In this paper, we discuss the problem of distributing streaming media content, both live and on-demand, to a large number of hosts in a scalable way. Our work is set in the context of the traditional client-server framework. Specifically, we consider the problem that arises when the server is overwhelmed by the volume of requests from its clients. As a solution, we propose Cooperative Networking (CoopNet), where clients cooperate to distribute content, thereby alleviating the load on the server. We discuss the proposed solution in some detail, pointing out the interesting research issues that arise, and present a preliminary evaluation using traces gathered at a busy news site during the flash crowd that occurred on September 11, 2001.
1927942
Tracking and segmenting people with occlusions by a sample consensus based method
One of the most difficult issues in visual tracking is to track people in groups, especially under occlusions. In this paper, we present a novel sample consensus based method, which utilizes both color and spatial information of human bodies, to model the appearance of people. We use this appearance model to segment and track people through occlusions. We show experimental results in several video sequences to validate the effectiveness of the proposed method.
2230972
Pruning and dynamic scheduling of cost-sensitive ensembles
Previous research has shown that averaging ensemble can scale up learning over very large cost-sensitive datasets with linear speedup independent of the learning algorithms. At the same time, it achieves the same or even better accuracy than a single model computed from the entire dataset. However, one major drawback is its inefficiency in prediction since every base model in the ensemble has to be consulted in order to produce a final prediction. In this paper, we propose several approaches to reduce the number of base classifiers. Among various methods explored, our empirical studies have shown that the benefit-based greedy approach can safely remove more than 90% of the base models while maintaining or even exceeding the prediction accuracy of the original ensemble. Assuming that each base classifier consumes one unit of prediction time, the removal of 90% of base classifiers translates to a prediction speedup of 10 times. On top of pruning, we propose a novel dynamic scheduling approach to further reduce the "expected" number of classifiers employed in prediction. It measures the confidence of a prediction by a subset of classifiers in the pruned ensemble. This confidence is used to decide if more classifiers are needed in order to produce a prediction that is the same as the original unpruned ensemble. This approach reduces the "expected" number of classifiers by another 25% to 75% without loss of accuracy.