Xiaoping Che

Joint semantic similarity assessment with raw corpus and structured ontology for semantic-oriented service discovery

Semantic-oriented service matching is one of the challenges in automatic Web service discovery. Service users may search for Web services using keywords and receive the matching services in terms of their functional profiles. A number of approaches to computing the semantic similarity between words have been developed to enhance the precision of matchmaking, which can be classified into ontology-based and corpus-based approaches. The ontology-based approaches commonly use the differentiated concept information provided by a large ontology for measuring lexical similarity with word sense disambiguation. Nevertheless, most of the ontologies are domain-special and limited to lexical coverage, which have a limited applicability. On the other hand, corpus-based approaches rely on the distributional statistics of context to represent per word as a vector and measure the distance of word vectors. However, the polysemous problem may lead to a low computational accuracy. In this paper, in order to augment the semantic information content in word vectors, we propose a multiple semantic fusion (MSF) model to generate sense-specific vector per word. In this model, various semantic properties of the general-purpose ontology WordNet are integrated to fine-tune the distributed word representations learned from corpus, in terms of vector combination strategies. The retrofitted word vectors are modeled as semantic vectors for estimating semantic similarity. The MSF model-based similarity measure is validated against other similarity measures on multiple benchmark datasets. Experimental results of word similarity evaluation indicate that our computational method can obtain higher correlation coefficient with human judgment in most cases. Moreover, the proposed similarity measure is demonstrated to improve the performance of Web service matchmaking based on a single semantic resource. Accordingly, our findings provide a new method and perspective to understand and represent lexical semantics.

A hybrid approach for measuring semantic similarity based on IC-weighted path distance in WordNet

As a valuable tool for text understanding, semantic similarity measurement enables discriminative semantic-based applications in the fields of natural language processing, information retrieval, computational linguistics and artificial intelligence. Most of the existing studies have used structured taxonomies such as WordNet to explore the lexical semantic relationship, however, the improvement of computation accuracy is still a challenge for them. To address this problem, in this paper, we propose a hybrid WordNet-based approach CSSM-ICSP to measuring concept semantic similarity, which leverage the information content(IC) of concepts to weight the shortest path distance between concepts. To improve the performance of IC computation, we also develop a novel model of the intrinsic IC of concepts, where a variety of semantic properties involved in the structure of WordNet are taken into consideration. In addition, we summarize and classify the technical characteristics of previous WordNet-based approaches, as well as evaluate our approach against these approaches on various benchmarks. The experimental results of the proposed approaches are more correlated with human judgment of similarity in term of the correlation coefficient, which indicates that our IC model and similarity detection approach are comparable or even better for semantic similarity measurement as compared to others.

MRSIM: Mitigating Reducer Skew In MapReduce

MapReduce has emerged as a popular programming model in the field of data-intensive computing. This is due to its simplistic design, which provides ease of use for programmers, and its framework implementations such as Hadoop, which have been adopted by large business and technology companies. One significant issue in practical MapReduce applications is data skew: the imbalance in the amount of data assigned to each task. This causes some tasks to take much longer to finish than others and can significantly impact performance. Existing solutions for the data skew in reduce side increase the overhead that the users need to customize a novel partitioner for the specific application, or perform additional sampling processes before the map function begins. To mitigate the data skew in reduce side, which is called Reducer skew in this paper, we proposed a load balancing strategy based on load statistics, namely MRSIM. To gets the input data distribution in reduce stage, MRSIM computed the statistics while preparing data, which makes full use of the shuffle stage in MapReduce. To balance the load of entire cluster, MRSIM reallocated reduce tasks on the heavy nodes to idle ones according to the data distribution. In addition, by introducing the load feedback mechanism, MRSIM further improved the clusters performance when running complex applications. We evaluated MRSIM in YARN (Hadoop 2.2.0), the experimental results show that our MRSIM outperformed the default strategy in native Hadoop greatly, the improvement in execution time reached 17%.

A leader election based deadlock detection algorithm in distributed systems

Deadlock detection is an important and challenge work in distributed systems. Thing becomes more complex when multiple deadlock detection algorithm instances executing currently in the system. In this paper, we propose a leader election based deadlock detection algorithm in distributed system. Our algorithm aims to improve the performance in the condition of concurrent execution. In addition, our algorithm can provide a certain extent of fault tolerance after a current leader fails. We have proved the liveness and safety property of our algorithm. Simulation results show that our algorithm obtains an order of magnitude performance improvement on message complexity.

A Novel Concurrent Generalized Deadlock Detection Algorithm in Distributed Systems

Detecting deadlocks has been considered an important problem in distributed systems. Many approaches are proposed to handle this issue; however, little attention has been paid on coordinating concurrent execution of distributed deadlock detection algorithms. Previous approaches may report incorrect results false negatives, and they are inefficient due to lack of proper coordination of concurrent execution. In this paper, we present a novel concurrent coordination algorithm for distributed generalized deadlock detection. The proposed algorithm aims to avoid false negatives and improve the performance when concurrently executing deadlock detection in a distributed system. Our algorithm adopts diffusion computation to distribute probe messages and employs priority-based method to coordinate concurrent algorithm instances. Priority carried in the received probe messages will be locally recorded by each initiator. Instead of being suspended by higher priority algorithm instances, lower priority algorithm instances can accomplish deadlock detection locally. The initiator with the highest priority will receive and collect all related resource requests information from lower priority instances in a hierarchical manner and perform global deadlock detection at last. We evaluate our algorithm on a bunch of event-driven simulations. The experimental results show that our approach can achieve better accuracy and efficiency compared to previous approaches.

Differential Evolutionary Algorithm Based on Multiple Vector Metrics for Semantic Similarity Assessment in Continuous Vector Space.

Automatic service discovery in heterogeneous environment is becoming one of the challenging problems for applications in semantic web, wireless sensor networks, etc. It is mainly due to the lack of accurate semantic similarity assessment between profile attributes of user request and web services. Generally, lexical semantic resources consist of corpus and domain knowledge. To improve similarity measures in terms of accuracy, various hybrid methods have been proposed to either integrate different semantic resources or combine various similarity methods based on a single resource. In this work, we propose a novel approach which combines vector similarity metrics in a continuous vector space to evaluate semantic similarity between concepts. This approach takes advantage of both corpus and knowledge base by constructing diverse vector space models. Specifically, we use differential evolutionary (DE) algorithm which is an powerful population-based stochastic search strategy for obtaining optimal value of the combination. Our approach has been validated against a variety of vector-based similarity approaches on multiple benchmark datasets. The empirical results demonstrate that our approach outperforms the state-of-the-art approaches. The results also indicate the continuous vectors are efficient for evaluating semantic similarity, since they have outstanding expressiveness to latent semantic features of words. Moreover, the robustness of our approach is presented by the steady measure results under different hyper-parameters of neural network. Keywords-differential evolutionary; semantic similarity; continuous vector space; vector similarity metrics

Semantic Similarity Assessment Using Differential Evolution Algorithm in Continuous Vector Space

The assessment of semantic similarity between terms is one of the challenging tasks in knowledge-based applications, such as multimedia retrieval, automatic service discovery and emotion mining. By means of similarity estimation, the comprehension of textual resources can become more feasible and accurate. Some studies have proposed the integration of various assessment methods for taking advantage of different semantic resources, but most of them simply employ average operation or regression training. In this paper, we address this problem by combining the corpus-based similarity methods with the WordNet-based methods based on a differential evolution (DE) algorithm. Specifically, this DE-based approach conducts similarity assessment in a continuous vector space. It is validated against a variety of similarity approaches on multiple benchmark datasets. Empirical results demonstrate that our approach outperforms existing works and more conforms to the human judgement of similarity. The results also prove the expressiveness of continuous vectors learned from neural network on latent lexical semantics. HighlightsWe combine corpus-based and WordNet-based similarity methods based on differential evolution (DE) algorithm.We assess semantic similarity between terms in a continuous vector space to improve similarity computation in terms of accuracy.Empirical results demonstrate that our approach outperforms related works and conforms more to the human judgement of similarity.The robustness of our approach is presented by the steady results.Our findings provide a new perspective to estimate lexical semantics.

A fault tolerant election-based deadlock detection algorithm in distributed systems

Deadlock detection in a distributed system without shared memory is important to ensure the reliability of the system. It becomes more complex when multiple deadlock detection algorithm instances execute concurrently in the system. In addition, the problem of communication disconnection between computing nodes or processes makes deadlock detection more difficult. Existing centralized algorithms suffer from single point failure of the central controller (due to communication disconnection), and they are performance-inefficient in the case of concurrent execution. In this paper, we extend our previous work (Lu et al. 2016) and propose a fault tolerant deadlock detection algorithm in distributed systems. The extended proposed algorithm can tolerate a certain extent of communication disconnection between computing nodes or processes. A central controller is used to collect requesting conditions, construct a wait-for graph, and detect deadlocks. The proposed algorithm can select a new central controller if the current central leader fails due to communication disconnections. The liveness and safety properties of the proposed algorithm are proved in this paper. Experimental results show that the proposed algorithm provides better performance than most of existing algorithms in terms of message number, data traffic, and execution time. In addition, the proposed algorithm provides additional fault tolerance compared to existing deadlock detection algorithms in the case of communication disconnection.

Detecting and resolving deadlocks in mobile agent systems

Abstract Mobile agents environment is a new application paradigm with unique features such as mobility and autonomy. Traditional deadlock detection algorithms in distributed computing systems do not work well in mobile agent systems due to the unique feature property of the mobile agent. Existing deadlock detection and resolution algorithms in mobile agent systems have limitations such as performance inefficiency and duplicate detection/resolution when multiple mobile agents simultaneously detect/resolve the same deadlock. To address these problems, we propose an improved deadlock detection and resolution algorithm that adopts priority-based technique and lazy reaction strategy. The priority-based technique aims to ensure that there is only one instance of deadlock detection and resolution, and it also helps reduce mobile agent movement and data traffic together with the lazy reaction strategy. The liveness and safety properties of the proposed algorithm are proved in this paper. Theoretical analysis and experimental results show that the proposed algorithm provides better performance in terms of agent movement, data traffic, and execution time.

A Novel Priority-based Deadlock Detection and Resolution Algorithm in Mobile Agent Systems.

Deadlock detection and resolution is one of the challenges in mobile agent systems, especially, when concurrent execution (i.e., more than one algorithm instances executing simultaneously) of algorithm instances. In this paper, we propose a deadlock detection and resolution algorithm in mobile agent systems. Priority-based approach is adopted in our algorithm to coordinate concurrent execution of algorithm instances. The liveness and safety properties of our algorithm are proved. Analysis and simulation results indicate that our algorithm can provide better performance and avoid duplicate detection and resolutions of the same deadlock in condition of concurrent execution. Deadlock detection; Deadlock resolution; Mobile-Agent system; Distributed system; Concurrent coordination