Coherence Metrics Research Articles

A new discrete particle swarm optimization approach for the single-machine total weighted tardiness scheduling problem with sequence-dependent setup times

In this paper we present a new Discrete Particle Swarm Optimization (DPSO) approach to face the NP-hard single machine total weighted tardiness scheduling problem in presence of sequence-dependent setup times. Differently from previous approaches the proposed DPSO uses a discrete model both for particle position and velocity and a coherent sequence metric. We tested the proposed DPSO mainly over a benchmark originally proposed by Cicirello in 2003 and available online. The results obtained show the competitiveness of our DPSO, which is able to outperform the best known results for the benchmark. In addition, we also tested the DPSO on a set of benchmark instances from ORLIB for the single machine total weighted tardiness problem, and we analysed the role of the DPSO swarm intelligence mechanisms as well as the local search intensification phase included in the algorithm.

Supplementing Entity Coherence with Local Rhetorical Relations for Information Ordering

This paper investigates whether the model of local rhetorical coherence suggested in Knott et al. (2001) can boost the performance of the Centering-based metrics of entity coherence employed by Karamanis et al. (2004) for the task of information ordering. Rhetorical coherence is integrated into the way Centering's basic data structures are derived from the annotated features of the GNOME corpus. The results indicate that (a) the simplest metric continues to perform better than its competitors even when local rhetorical coherence is taken into account, and (b) this extra coherence constraint decreases its performance.

Source-Code-Correlated Cache Coherence Characterization of OpenMP Benchmarks

Cache coherence in shared-memory multiprocessor systems has been studied mostly from an architecture viewpoint, often by means of aggregating metrics. In many cases, aggregate events provide insufficient information for programmers to understand and optimize the coherence behavior of their applications. A better understanding would be given by source code correlations of not only aggregate events, but also finer granularity metrics directly linked to high-level source code constructs, such as source lines and data structures. In this paper, we explore a novel application-centric approach to studying coherence traffic. We develop a coherence analysis framework based on incremental coherence simulation of actual reference traces. We provide tool support to extract these reference traces and synchronization information from OpenMP threads at runtime using dynamic binary rewriting of the application executable. These traces are fed to ccSIM, our cache-coherence simulator. The novelty of ccSIM lies in its ability to relate low-level cache coherence metrics (such as coherence misses and their causative invalidations) to high-level source code constructs including source code locations and data structures. We explore the degree of freedom in interleaving data traces from different processors and assess simulation accuracy in comparison to metrics obtained from hardware performance counters. Our quantitative results show that: 1) Cache coherence traffic can be simulated with a considerable degree of accuracy for SPMD programs, as the invalidation traffic closely matches the corresponding hardware performance counters. 2) Detailed, high-level coherence statistics are very useful in detecting, isolating, and understanding coherence bottlenecks. We use ccSIM with several well-known benchmarks and find coherence optimization opportunities leading to significant reductions in coherence traffic and savings in wall-clock execution time

Software Defect Process and Product Model for High Assurance Applications

One reason we are motivated to do this research is to suggest that it is not necessary to wait until software is fielded before recording and analyzing defects. Assuming there is an effective data collection process in place, we can record actual defects and predict future defects for all software development phases. Our contribution to defect analysis research is the modeling of the process attributes and the product reliability needed for safety critical systems in NASA, using a queuing process approach that we believe is the first of its kind.We feel this approach is important because defect process attributes, such as the time required to correct defects, have a direct bearing on product attributes, such as the number of old defects that remain unrepaired. The model has the capability of computing probabilities of defect prediction and repair, and repair timesThis feature allows unrepaired defects to be fed back into the model queue input, along with the new defects. The result provides a comprehensive template of defect processing that the software engineer can use to assess both the efficacy of defectprocessingandthereliabilityoftheproductthatistheoutcomeoftheprocess.Amajor result was that the module with the highest probability of defect detection corresponds to the modulewiththelowestvaluesofdefectcount,sourcelinesofcode,andcyclomaticcomplexity. Conversely,themodulewiththelowestprobabilitycorrespondstothemodulewiththehighest values of these attributes. Thus, the engineer would be encouraged by these results to use these relationships as a guide to predicting the probability of detection of defects on other softwaresystemswithsimilarattributes.Aremainingchallengeinthisresearch,anissuethat requires resolution, is that a coherent and comprehensive NASA defect and metrics database should be developed to support research.While the individual data items in the NASA IV&V Facility Metrics Data Program data repository are valuable, the defect data is not adequately correlated to the metrics data.

COGNITIVELY INSPIRED NLP-BASED KNOWLEDGE REPRESENTATIONS: FURTHER EXPLORATIONS OF LATENT SEMANTIC ANALYSIS

Natural-language based knowledge representations borrow their expressiveness from the semantics of language. One such knowledge representation technique is Latent semantic analysis (LSA), a statistical, corpus-based method for representing knowledge. It has been successfully used in a variety of applications including intelligent tutoring systems, essay grading and coherence metrics. The advantage of LSA is that it is efficient in representing world knowledge without the need for manual coding of relations and that it has in fact been considered to simulate aspects of human knowledge representation. An overview of LSA applications will be given, followed by some further explorations of the use of LSA. These explorations focus on the idea that the power of LSA can be amplified by considering semantic fields of text units instead of pairs of text units. Examples are given for semantic networks, category membership, typicality, spatiality and temporality, showing new evidence for LSA as a mechanism for knowledge representation. The results of such tests show that while the mechanism behind LSA is unique, it is flexible enough to replicate results in different corpora and languages.

Measures of international collaboration in scientific literature: Part I

Research evaluating models of scientific productivity require coherent metrics that quantify various key relations among papers as revealed by patterns of citation. This paper focuses on the various conceptual problems inherent in measuring the degree to which papers tend to cite other papers written by authors of the same nationality. We suggest that measures can be given a degree of assurance of coherence by being based on mathematical models describing the citation process. A number of such models are developed.

Augmented geophysical data interpretation through automated velocity picking in semblance velocity images

Velocity Picking is the problem of picking velocity-time pairs based on a coherence metric between multiple seismic signals. Coherence as a function of velocity and time can be expressed as a 2-D color semblance velocity image. Currently, humans pick velocities by looking at the semblance velocity image; this process can take days or even weeks to complete for a seismic survey. The problem can be posed as a geometric feature matching problem. A feature extraction algorithm can recognize islands (peaks) of maximal semblance in the semblance velocity image: a heuristic combinatorial matching process can then be used to find a subset of peaks which maximizes the coherence metric. The peaks define a polyline through the image, and coherence is measured in terms of the summed velocity under the polyline and the smoothness of the polyline. Our best algorithm includes a constraint favoring solution near the median solution for the local area under consideration. Each image is first processed independently. Then, a second pass of optimization includes proximity to the median as an additional optimization criterion. Our results are similar to those produced by human experts.