Road detection based on remote sensing images is of great significance to intelligent traffic management. The performances the mainstream road methods are mainly determined by their extracted features, whose richness and robustness can be enhanced fusing features different types cross-layer connections. However, in existing model frameworks often similar same layer single-task training, traditi...

Task allocation and path planning considering changes in the mobility of robots environment allows to efficiently execute tasks with smaller travel times. A lunar base construction is one situations which can more accomplish its goal by taking such into account when performing tasks. For construction, we assumed that a robot executes task building road, aisles were unusable before become usable...

Optimized task scheduling is one of the most important challenges in multiprocessor environments such as parallel and distributed systems. In such these systems, each parallel program is decomposed into the smaller segments so-called tasks. Task execution times, precedence constrains and communication costs are modeled by using a directed acyclic graph (DAG) named task graph. The goal is to min...

We present a graph-based semi-supervised learning algorithm to address the sentiment analysis task of rating inference. Given a set of documents (e.g., movie reviews) and accompanying ratings (e.g., “4 stars”), the task calls for inferring numerical ratings for unlabeled documents based on the perceived sentiment expressed by their text. In particular, we are interested in the situation where l...

In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. To efficiently execute programs in parallel on multiprocessor scheduling problem must be solved to determine the assignment of tasks to the processors and the execution order of the tasks so that the execution time is minimized. Even when the target processors is fully con...

Coherence is a crucial feature of text because it is indispensable for conveying its communication purpose and meaning to its readers. In this paper, we propose an unsupervised text coherence scoring based on graph construction in which edges are established between semantically similar sentences represented by vertices. The sentence similarity is calculated based on the cosine similarity of se...

Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natural language processing. All graph-based algorithms rely on a graph that jointly represents labeled and unlabeled data points. The problem of how to best construct this graph remains largely unsolved. In this paper we introduce a data-driven method that optimizes the represe...

Word graphs have various applications in the field of machine translation. Therefore it is important for machine translation systems to produce compact word graphs of high quality. We will describe the generation of word graphs for state of the art phrase-based statistical machine translation. We will use these word graph to provide an analysis of the search process. We will evaluate the qualit...

We consider a parsed text corpus as an instance of a labelled directed graph, where nodes represent words and weighted directed edges represent the syntactic relations between them. We show that graph walks, combined with existing techniques of supervised learning, can be used to derive a task-specific word similarity measure in this graph. We also propose a new path-constrained graph walk meth...

The Glamis methodology provides a framework for performance modelling of parallel applications, based on a combination of task graph models and queueing network models. This paper presents a new, moderately cheap algorithm which enables the analysis of arbitrary task graph models of parallel programs with general precedence relationships, given a queueing model of the underlying machine. The al...