36378256
Type-theoretical Semantics of Some Declarative Languages
30871480
Optimized Encodings of Fragments of Type Theory in First-Order Logic
7567156
Optimized Encodings of Fragments of Type Theory in First Order Logic
The paper presents sound and complete translations of several fragments of Martin-Lof's monomorphic type theory to first order predicate calculus. The translations are optimised for the purpose of automated theorem proving in the mentioned fragments. The implementation of the theorem prover Gandalf and several experimental results are described.
43104862
Program Derivation in Type Theory: A Partitioning Problem
Abstract Martin-Lof's type theory is a theory in which one can write both specifications and programs. By interpreting propositions as types, predicate logic is available when formulating a specification. The rules of type theory are formulated as tactics which makes a “top down” construction of programs possible. These ideas are illustrated by a formal derivation of a program for a partitioning problem.
31236386
Speeding Up Navigational Requests in a Parallel Object Database System
In data intensive applications, both programming and declarative query languages have attractions, the former in comprehensiveness and the latter for ease of use. Databases sometimes support the calling of side-effect free user defined functions from within declarative queries. As well as enabling more efficient coding of computationally intensive functions, this provision not only moves computation to data in a client-server setting, but also enables speedup through data parallel execution if the server is parallel. There has been little work on the combined use of query and program based database access in the context of parallel servers. We believe Polar is the first parallel object-oriented database which supports this arbitrary navigation both in a client application and in functions (operations) which may be called from within declarative queries. This work introduces Polar's support for navigation and for calling operations from within parallel queries and presents performance results for example navigational requests for the large OO7 benchmark.
11001529
A Stochastic Approach to Measuring the Robustness of Resource Allocations in Distributed Systems
Often, parallel and distributed computing systems must operate in an environment replete with uncertainty. Determining a resource allocation that accounts for this uncertainty in a way that can provide a probabilistic guarantee that a given level of quality of service (QoS) is achieved is an important research problem. This paper defines a stochastic methodology for quantifiably determining a resource allocation's ability to satisfy QoS constraints in the midst of uncertainty in system parameters. Uncertainty in system parameters and its impact on system performance are modeled stochastically. This stochastic model is then used to derive a quantitative expression for the robustness of a resource allocation. The paper investigates the utility of the proposed stochastic robustness metric by applying the metric to resource allocations in a simulated distributed system. The simulation results are then compared with deterministically defined metrics from the literature
13961662
Improving Nearest Neighbor Classifier Using Tabu Search and Ensemble Distance Metrics
The nearest-neighbor (NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results with this technique is the choice of distance function, and correspondingly which features to consider when computing distances between samples. In this paper, a new ensemble technique is proposed to improve the performance of NN classifier. The proposed approach combines multiple NN classifiers, where each classifier uses a different distance function and potentially a different set of features (feature vector). These feature vectors are determined for each distance metric using Simple Voting Scheme incorporated in Tabu Search (TS). The proposed ensemble classifier with different distance metrics and different feature vectors (TS-DF/NN) is evaluated using various benchmark data sets from UCI Machine Learning Repository. Results have indicated a significant increase in the performance when compared with various well-known classifiers. Furthermore, the proposed ensemble method is also compared with ensemble classifier using different distance metrics but with same feature vector (with or without Feature Selection (FS)).
14200244
A multi-modal system for the retrieval of semantic video events
A framework for event detection is proposed where events, objects, and other semantic concepts are detected from video using trained classifiers. These classifiers are used to automatically annotate video with semantic labels, which in turn are used to search for new, untrained types of events and semantic concepts. The novelty of the approach lies in the: (1) semi-automatic construction of models of events from feature descriptors and (2) integration of content-based and concept-based querying in the search process. Speech retrieval is independently applied and combined results are produced. Results of applying these to the Search benchmark of the NIST TREC Video track 2001 are reported, and the gained experience and future work are discussed.
15945659
Distributed Query Processing on the Grid
Distributed query processing (DQP) has been widely used in data intensive applications where data of relevance to users is stored in multiple locations. This paper argues: (i) that DQP can be important in the Grid, as a means of providing high-level, declarative languages for integrating data access and analysis; and (ii) that the Grid provides resource management facilities that are useful to developers of DQP systems. As well as discussing and illustrating how DQP technologies can be deployed within the Grid, the paper describes a prototype implementation of a DQP system running over Globus.