Graph Theory

Debugging parallel and concurrent applications is well-recognized as a time-consuming task, which often requires a significant part of the application development process. In the context of embedded systems, Multi-Processor-System-on-Chip(MPSoC) architectures feature numerous multicore processors which may be coupled with heterogeneous processors like Digital Signal Processors (DSPs) and/or application-specific accelerators. In this situation, it is important that developers are provided with high-level programming environments able to efficiently exploit these architectures, as well as suitable debugging tools. Dataflow programming models were explicitly designed to program parallel architectures and they have the ability to abstract away heterogeneous computing complexity. In addition, the stream-processing aspect of multimedia algorithms naturally exhibits data-dependency graphs, which simplifies application design and implementation. In this paper, we propose a new approach for interactive debugging of dataflow applications. Going beyond the long-established ability of interactive debuggers to support sequential programming languages, we describe the functionalities they should be able to provide to debug embedded and parallel dataflow applications. Then we demonstrate our solution to this problem with a proof-of-concept debugger targeting the dataflow framework used on an industrial MPSoC platform. We also explain the development challenges we faced during the implementation of this GDB-based debugger and illustrate its efficiency through a case study of a video decoder debugging session.

A technique for the design rule verification of layouts composed of library cells is presented. Instead of directly manipulating mask geometries, DRC correctness is tied to a set of user defined patterns of allowed cell interactions called templates. Design rule verification is achieved by covering the layout with these templates. Both layouts and templates are defined in terms of graphs and all operations are performed in the graph domain. The verification procedure is incremental and because the number of cell instances is much smaller than the number of mask geometries it is much faster than techniques that directly manipulate mask geometries.<<ETX>>

We propose a case-based reasoning (CBR) approach to answer validation/answer scoring and reranking in question answering (QA) systems, where annotated answer candidates for known questions provide evidence for validating answer candidates for new questions. The use of CBR promises a continuous increase in answer quality, given user feedback that extends the case base. In the paper, we present the complete approach, emphasizing the use of CBR techniques, namely the structural case base, built with annotated MultiNet graphs, and corresponding graph similarity measures. We cover a priori relations to experienced answer candidates for former questions. We describe the adequate structuring of the case base and develop appropriate similarity measures. Finally we integrate CBR into an existing framework for answer validation and reranking that also includes logical answer validation and a shallow linguistic validation, using a learning-to-rank approach for the final answer ranking based on...

The paper analyses international migration flows from the network perspective by the evaluation of centrality indices. In order to find the most influential countries in the international migration network classical centrality indices and new centrality indices are evaluated. New centrality indices consider short (SRIC) and long-range (LRIC) indirect interactions and the node attribute – population of the destination country. The model is applied to the annual data on international migration flows from 1970 to 2013 provided by United Nations Organization. The analysis is made for one year of each decade and indices’ dynamics is described. It is shown that countries with huge migration flows are outlined by both classical and SRIC, LRIC indices, and SRIC and LRIC indices point out countries with considerable outflows of migrants to countries highly involved in international migration and the most interconnected countries.

This paper deals with a novel technique that utilizes a weighted undirected graph for each color, called GHSG, and applied in landscape images including three steps: Calculating the similarity between query image and all database images based on global color histogram method. Making a set of images includes all the images that are filtered from the image database. The similarity

ABSTRACT Web rapid development has provided new learning environments, bringing online education as a necessity in many sectors of the society. In such an environment, selecting supervisor is a critical decision that graduate students as well as professors are involved with, which could benefit from e-learning tools. In this paper we have proposed a solution for student-supervisor assignment based on Genetic Algorithm (GA), so that the task of student-supervisor assignment is mapped to an optimization problem that could be solved with GA approaches. In our GA approach, search space is the set of all bipartite graphs that are transformed to arrays of integer numbers as chromosomes representations. Assigning supervisors to students requires some information about professors and students. For this purpose, we have profiled students and professors through deriving their decision parameters and other required information, using data obtained from various sources including Learning Management System (LMS), Community of Practice (CoP), as well as our question-answering user interface. This system is implemented at the University of Tehran, using different profiles of students and professors. Delivery results suggest that our new method provides good precision in student-supervisor compatibility.

The advance of social computing study is grown very fast to the extents that influence many aspects of our daily life. One of those aspects is implementation in marketing /advertising. We are always looking for the effective way to understand our market, but the absent of powerful metrics has cause some problem. The availibility of Big Data all around us has triggered a new perspective on how we approach those problems. Online social network disseminate information much faster than before, supporting highly exposure of brand awareness. The network behavior of the information spreading can be explained by the study of complex network. In this paper, we propose the social network modelling approach using graph theoritic to understand on how brand information travels in online social network and how it can benefit for business. In prior study in marketing, it is uncommon to approach phenomenon using social network model and online social data, they are mostly using questionnaire from p...