6309018
Knowledge-based learning integrating acquisition and learning
Empirical learning algorithms are hampered by their inability to use domain knowledge to guide the induction of new rules. This paper describes knowledge-based learning, an approach to learning that selects the examples and relevant attributes for an empirical algorithm. Knowledge-based learning can be used for developing rules for engineering expert systems. Engineers often have some rules for problem solving, but also many experiences (examples) that facilitate solving problems. Knowledge-based learning systems are able to use both forms of knowledge.
12858200
Building Robust Learning Systems by Combining Induction and Optimization
Each concept description language and search strategy has an inherent inductive bias, a preference for some hypotheses over others. No single inductive bias performs optimally on all problems. This paper describes a system that couples induction with optimization to carry out an efficient search of large regions of inductive bias space. Experimental results are reported demonstrating the system's capacity to choose optimal biases even for complex and noisy problems.
35812356
A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems
Abstract A knowledge-based learning system is developed to demonstrate that intelligently selecting a subset of examples based on domain knowledge for rule induction can be more productive than using all provided examples. Knowledge-based learning uses an incomplete domain theory and examples to induce knowledge missing in the theory. Knowledge-based learning systems determine where knowledge is missing in a domain theory, select a relevant subset of examples to use in induction, and induce new rules or modify existing rules.
5343942
Knowledge-Based Equation Discovery in Engineering Domains
Machine learning techniques that can compress datasets into relationships that underlie the data have great utility for engineers. Most model discovery systems are not well suited to discovering comprehensible models in engineering domains. In this paper we present KEDS, a Knowledge-based Equation Discovery System, which uses a model-driven approach to discover equations and thereby build models from engineering data.
37627056
SWIFT: system workbench for integrating and facilitating teams
There have been many R&D efforts in recent years to develop computer environments that directly enhance group problem-solving productivity. An example of such efforts is the computer-based virtual teaming technology (VTT) developed at the knowledge-based Engineering Sytems Research Laboratory (KESRL). The core software environment for KESRL's VTT is called SWIFT and is based on the knowledge processing technology (KPT) previously developed at KESRL to support various engineering design tasks. SWIFT is a flexible environment which enables the rapid development of collaborative applications, linking workers in distributed environments, of many domains. The paper presents the overall system architecture of SWIFT, and introduces the current and future R&D efforts at KESRL in realizing SWIFT for significant industrial applications.<<ETX>>
12297349
Creating and using models for engineering design: a machine-learning approach
An adaptive and interactive modeling system (AIMS) that integrates simulation, optimization and machine learning to help engineers make design decisions is described. AIMS views engineering decision making as a two-phase process of creating and then using models. The competitive relation learner and the induce-and-select optimizer, AIMS's two main components, and their roles in both phases of decision-making are discussed. AIMS's role in supporting the design of a diesel engine that outputs power within the 440- to 460-kW range and consumes the least amount of fuel is also discussed.<<ETX>>
30892527
On Design and Implementation of Adaptive Data Classification Scheme for DSM Systems
Distributed Shared Memory (DSM) environment is built by using specific softwares, to combine a number of computer hardware resources into one computing environment. Such environment not only provides an easy way to execute parallel applications, but also combines resources to speedup execution of these applications. DSM systems need to maintain data consistency in memory, what usually leads to communication overhead. Therefore, there exist a number of strategies that can be used to overcome this overhead and improve overall performance. Prefetching strategies have been proven to show great performance in DSM systems, since they can reduce data access communication latencies from remote nodes. However, these strategies also transfer unnecessary prefetching pages to remote nodes. In this research paper, we focus on the analysis of data access pattern during execution of parallel applications. We propose an Adaptive Data Classification scheme to improve prefetching strategy, with the goal to improve overall performance. Adaptive Data Classification scheme classifies data according to the access behavior of pages, so that home node uses past history access patterns of remote nodes to decide whether it needs to transfer related pages to remote nodes. From experimental results, our method can improve the performance of prefetching strategies in DSM systems.
12432910
HOURS: achieving DoS resilience in an open service hierarchy
Hierarchical systems have been widely used to provide scalable distributed services in the Internet. Unfortunately, such a service hierarchy is vulnerable to DoS attacks. This paper presents HOURS that achieves DoS resilience in an open service hierarchy. HOURS ensures high degree of service accessibility for each surviving node by: 1) augmenting the service hierarchy with hierarchical overlay networks with rich connectivity; 2) making the connectivity of each overlay highly unpredictable; and 3) recovering the overlay when its normal operations are disrupted. We analyze an HOURS-protected open service hierarchy, and demonstrate its high degree of resilience to even large-scale, topology-aware DoS attacks.
14016627
Semantic conditions for correctness at different isolation levels
Many transaction processing applications execute at isolation levels lower than serializable in order to increase throughput and reduce response time. The problem is that non-serializable schedules are not guaranteed to be correct for all applications. The semantics of a particular application determines whether that application will run correctly at a lower isolation level, and in practice it appears that many applications do. Unfortunately, we know of an analysis technique that has been developed to test an application for its correctness at a particular level. Apparently decisions of this nature are made on an informal basis. In this paper we describe such a technique in a formal way. We use a new definition of correctness, semantic correctness, which is weaker than serializability, to investigate the correctness of such executions. For each isolation level, we prove a condition under which transactions that execute at that level will be semantically correct. In addition to the ANSI/ISO isolation levels of read uncommitted, read committed, and repeatable read, we also prove a condition for correct execution at the read committed with first-committer-wins (a variation of read committed) and at the snapshot isolation level. We assume that different transactions can be executing at different isolation levels, but that each transaction is executing at least at the read uncommitted level.
6486168
A Dataflow-Oriented Atomicity and Provenance System for Pipelined Scientific Workflows
Scientific workflows have gained great momentum in recent years due to their critical roles in e-Science and cyberinfrastructure applications. However, some tasks of a scientific workflow might fail during execution. A domain scientist might require a region of a scientific workflow to be "atomic". Data provenance, which determines the source data that are used to produce a data item, is also essential to scientific workflows. In this paper, we propose: (i) an architecture for scientific workflow management systems that supports both provenance and atomicity; (ii) a dataflow-oriented atomicity model that supports the notions of commit and abort; and (iii) a dataflow-oriented provenance model that, in addition to supporting existing provenance graphs and queries, also supports queries related to atomicity and failure.
33554044
PEAS: a robust energy conserving protocol for long-lived sensor networks
In this paper we present PEAS, a robust energy-conserving protocol that can build long-lived, resilient sensor networks using a very large number of small sensors with short battery lifetime. PEAS extends the network lifetime by maintaining a necessary set of working nodes and turning off redundant ones. PEAS operations are based on individual node's observation of the local environment and do not require any node to maintain per neighbor node state. PEAS performance possesses a high degree of robustness in the presence of both node power depletions and unexpected failures. Our simulations and analysis show that PEAS can maintain an adequate working node density in the face of up to 38% node failures, and it can maintain roughly a constant overhead level under various deployment conditions ranging from sparse to very dense node deployment by using less than 1% of total energy consumption. As a result, PEAS can extend a sensor network's functioning time in linear proportion to the deployed sensor population.