Man Qi

A MapReduce based parallel SVM for large scale spam filtering

Spam continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) based techniques have been proposed for spam classification. However, SVM training is a computationally intensive process. This paper presents a parallel SVM algorithm for scalable spam filtering. By distributing, processing and optimizing the subsets of the training data across multiple participating nodes, the distributed SVM reduces the training time significantly. Ontology based concepts are also employed to minimize the impact of accuracy degradation when distributing the training data amongst the SVM classifiers.

A MapReduce-based distributed SVM ensemble for scalable image classification and annotation

A combination of classifiers leads to a substantial reduction of classification errors in a wide range of applications. Among them, support vector machine (SVM) ensembles with bagging have shown better performance in classification than a single SVM. However, the training process of SVM ensembles is notably computationally intensive, especially when the number of replicated training datasets is large. This paper presents MRESVM, a MapReduce-based distributed SVM ensemble algorithm for scalable image annotation which re-samples the training dataset based on bootstrapping and trains an SVM on each dataset in parallel using a cluster of computers. A balanced sampling strategy for bootstrapping is introduced to increase the classification accuracy. MRESVM is evaluated in both experimental and simulation environments, and the results show that the MRESVM algorithm reduces the training time significantly while achieving a high level of accuracy in classifications.

MapReduce based parallel neural networks in enabling large scale machine learning

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

Leveraging legacy codes to distributed problem-solving environments: a web services approach

This paper presents WSOWG, a Web‐services‐oriented wrapper generator for automatically wrapping non‐networked legacy codes as Web services for reuse in distributed problem‐solving environments. Using WSOWG, a finite element based computational fluid dynamics (CFD) legacy code has been wrapped as a Web service. A problem‐solving environment for simulating incompressible Navier–Stokes flows has also been implemented. A user makes use of the CFD service through a Web page without knowing the exact implementation of the service. In this way, a users computing environment can be extended to a heterogeneous distributed computing environment. Performance evaluation shows that the overhead to invoke the CFD Web service generated by WSOWG using Simple Object Access Protocol (SOAP) and CORBA Internet Inter‐ORB Protocol (IIOP) is reasonable compared with that of invoking another CFD Web service manually wrapped from the CFD legacy code using SOAP only. Copyright

PGGA: A predictable and grouped genetic algorithm for job scheduling

This paper presents a predictable and grouped genetic algorithm (PGGA) for job scheduling. The novelty of the PGGA is two-fold: (1) a job workload estimation algorithm is designed to estimate a job workload based on its historical execution records and (2) the divisible load theory (DLT) is employed to predict an optimal fitness value by which the PGGA speeds up the convergence process in searching a large scheduling space. Comparison with traditional scheduling methods, such as first-come-first-serve (FCFS) and random scheduling, heuristics, such as a typical genetic algorithm, Min-Min and Max-Min indicates that the PGGA is more effective and efficient in finding optimal scheduling solutions.

Fighting cybercrime: legislation in China

Computer and network development has been so rapid and dynamic these years in China. According to authorised international report, China has the most internet users in the world. However this trend has also expedited an exponential development of new crime in cyberspace. And the existing legal framework in China is insufficient to serve the changes. The internet related regulations put forth so far tend to be on a reactive mode. The paper provides an overview of cybercrime legislation in China, starting from the history of computer and network development, cybercrime development and corresponding legislation development in China. Then the detail of the legislation system is given based on a supposed classification.

Dealing With Uncertain Entities in Ontology Alignment Using Rough Sets

Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision.

MAPBOT: A web based map information retrieval system

Abstract Many types of information are geographically referenced and interactive maps provide a natural user interface to such data. However, map presentation in geographical information systems and on the Web is closed related to traditional cartography and provides a very limited interactive experience. In this paper, we present MAPBOT, an interactive Web based map information retrieval system in which Web users can easily and efficiently search geographical information with the assistance of a user interface agent (UIA). Each kind of map feature such as a building or a motorway works as an agent called a Maplet. Each Maplet has a user interface level to assist the user to find information of interest and a graphic display level that controls the presence and the appearance of the feature on the map. The semantic relationships of Maplets are defined in an Ontology Repository provided by the system which is used by the UIA to assist a user to semantically and efficiently search map information interested. An Ontology Editor with a graphic user interface has been implemented to update the Ontology Repository. Visualization on the client is based on Scalable Vector Graphics which provides a high quality Web map.

A distributed SVM ensemble for image classification and annotation

Combination of classifiers leads to a substantial reduction of classification errors in a wide range of applications. Among them SVM ensembles with bagging have shown better performance in classification than a single SVM. However, the training process of SVM ensembles is notably computationally intensive especially when the number of replicated training datasets is large. This paper presents MRESVM, a MapReduce based distributed SVM ensemble algorithm for image annotation which re-samples the training dataset based on bootstrapping and trains SVM on each dataset in parallel using a cluster of computers. MRESVM is evaluated in a experimental environment and the results show that the MRESVM algorithm reduces the training time significantly while achieves high level of accuracy in classifications.

Semantic analysis for spam filtering

Many different techniques have been employed to analyze spam emails. The paper explores two main semantic methods: Bayesian algorithms and Support Vector Machine (SVM). More recent spam filters are introduced in the paper. They all utilize semantic analysis information to determine whether a message is spam.