Phan-Thuan Do

An ant colony optimization algorithm for solving Group Steiner Problem

Group Steiner Problem (GSP) is an important generalization of some basic NP-hard problems. Many complex real-world applications require solving the GSP in graphs modeling the topology of the given problem, such as: the design of Very Large Scale Integration (VLSI) circuits, the design of a minimal length irrigation network, and routing problems for wireless sensor networks. We show our design of a new algorithm based on an Ant Colony Optimization model to solve the GSP in general graphs. Our experimental results show that our method strongly outperforms the best other heuristic methods for GSP.

An improvement of the overlap complexity in the spaced seed searching problem between genomic DNAs

In homology search, finding optimal multiple spaced seeds in genomic DNA sequences is NP-hard but even finding good ones is very difficult. The exponential-time algorithm PatternHunter use optimal spaced seeds to increase both the sensitivity and the speed of homology search. The overlap complexity measure based on the overlaps between hits of a multiple seed are well correlated with sensitivity but is computable in polynomial time. Based on overlap complexity, we have improved polynomial-time algorithms to provide better multiple seeds. Our experimental results shows that these improvements significantly run faster and make better quality of spaced seeds than previous algorithms in almost all test cases.

A practical dynamic share-a-ride problem with speed windows for Tokyo city

This paper deals with a new time-dependent model of public transportation system in the urban context that allows sharing a taxi between a passenger and parcels with speed widows consideration. We solve a realistic dynamic scenario in which requests are accepted or declined at the time of their calls. We classify vehicle speeds by different time windows during a day. Different speed windows induce the dynamic graph model for road networks and make the problem much more difficult to solve. Because of the complex model, the preprocessing steps on data as well as on dynamic graphs are very important. We use a greedy algorithm to initiate the solution and then use some local search techniques to improve the solution quality. The experimental data set is recorded by Tokyo-Musen Taxi company. The data set includes more than 20,000 requests per day, more than 4,500 served taxis per day and more than 130,000 crossing points on the Tokyo map. Experimental results are analyzed on various factors such as the total benefit, the accumulating traveling time during the day, the number of used taxis and the number of shared requests. We expect that our solution has chances to apply directly to real-life situations.

An information content based partitioning method for the anatomical ontology matching task

Anatomy ontology matching has been attracting a lot of interest and attention of researchers, especially, biologists, medics and geneticists. This is a very difficult task due to the huge size of anatomy ontologies. Despite the fact that many ontology matching tools have been proposed so far, most of them achieve good results only for small size ontologies. In a recent survey [22], the authors pointed out that the large scale ontology matching problem still presents a real challenge because it is a time consuming and memory intensive process. According to state of the art works, the authors also state that partitioning large scale ontology is a promising solution to deal with this issue. Therefore, in this paper, we propose a partitioning approach to break up the large matching problem into smaller matching subproblems. At first, we propose a method to semantically split anatomy ontology into groups called clusters. It relies on a specific method for computing semantic similarities between concepts based on both their information content on anatomy ontology, and a scalable agglomerative hierarchical clustering algorithm. We then propose a filtering method to select the possible similar partitions in order to reduce the computation time. The experimental analysis demonstrates that our approach is capable of solving the scalability ontology matching problem and encourages us to the future works.

Towards State-of-the-art English-Vietnamese Neural Machine Translation

Machine translation is one of the most challenging topics in natural language processing. The common approaches to machine translation base on either statistical or rule-based methods. Rule-based translation analyzes sentence structures, requires extensive lexicons with morphological, syntactic, semantic information, and large sets of manually created rules. Statistics-based translation faces the challenge of collecting bilingual text corpora, which is particularly difficult for low resource language pairs as English-Vietnamese. This research aims at building state-of-the-art English-Vietnamese machine translation. Our contribution includes: (1) an enormous effort in collecting training dataset, (2) adaptation of current neural machine for English-Vietnamese translation, (3) an experimental result suggested the unnecessary of Vietnamese word segmentation as a common pre-processing step. Our model achieves a highest BLEU score in comparison with other researches.

A time-dependent model with speed windows for share-a-ride problems: A case study for Tokyo transportation

Abstract This paper introduces a new fully time-dependent model of a public transportation system in the urban context that allows sharing a taxi between one passenger and parcels with speed widows consideration. The model contains many real-life case features and is presented by a mathematical formulation. We study both static and dynamic scenarios in comparison to traditional strategies, i.e., the direct delivery model. Moreover, we classify speed windows by different zones and congestion levels during a day in the urban context. Different speed windows induce the dynamic graph model for road networks and make the problem much more difficult to solve. Because of the complex model, the preprocessing steps on data as well as on dynamic graphs are very important. We use a greedy algorithm to initiate the solution and then use some local search techniques to improve the solution quality. The experimental data set is recorded by Tokyo-Musen Taxi company. The data set includes more than 20000 requests per day, more than 4500 used taxis per day and more than 130000 crossing points on the Tokyo map. Experimental results are analyzed on various factors such as the total benefit, the accumulating traveling time during the day, the number of used taxis and the number of shared requests.

Efficient maximum matching algorithms for trapezoid graphs

Trapezoid graphs are intersection graphs of trapezoids between two horizontal lines. Many NP-hard problems can be solved in polynomial time if they are restricted on trapezoid graphs. A matching in a graph is a set of pairwise disjoint edges, and a maximum matching is a matching of maximum size. In this paper, we first propose an

Joint source-channel coding with adaptation

O(n(\log n)^3)

An O(n(log n) 3 ) algorithm for maximum matching in trapezoid graphs

algorithm for finding a maximum matching in trapezoid graphs, then improve the complexity to

An efficient exact algorithm for finding two link-disjoint paths with related path cost and QoS constraint

O(n(\log n)^2)