A speculative approach to spatial‐temporal efficiency with multi‐objective optimization in a heterogeneous cloud environment

Liu, Qi; Cai, Weidong; Shen, Jian; Fu, Zhangjie; Liu, Xiaodong; Linge, Nigel

doi:10.1002/sec.1582

Cited by 197 publications

(70 citation statements)

References 28 publications

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“…In this article, to effectively use LDA, we apply it in a package-level corpus rather than each class to extract the latent topics to simulate the functional features or concerns for a package since small (class-level) corpus is too small to generate good topics [19][20][21][22][23]. Then, we cluster the classes according to these topics and assign different classes to their corresponding topics [23].…”

Section: Latent Dirichlet Allocationmentioning

confidence: 99%

Clustering Classes in Packages for Program Comprehension

Sun

Liu

et al. 2017

Scientific Programming

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During software maintenance and evolution, one of the important tasks faced by developers is to understand a system quickly and accurately. With the increasing size and complexity of an evolving system, program comprehension becomes an increasingly difficult activity. Given a target system for comprehension, developers may first focus on the package comprehension. The packages in the system are of different sizes. For small-sized packages in the system, developers can easily comprehend them. However, for large-sized packages, they are difficult to understand. In this article, we focus on understanding these large-sized packages and propose a novel program comprehension approach for large-sized packages, which utilizes the Latent Dirichlet Allocation (LDA) model to cluster large-sized packages. Thus, these large-sized packages are separated as small-sized clusters, which are easier for developers to comprehend. Empirical studies on four real-world software projects demonstrate the effectiveness of our approach. The results show that the effectiveness of our approach is better than Latent Semantic Indexing-(LSI-) and Probabilistic Latent Semantic Analysis-(PLSA-) based clustering approaches. In addition, we find that the topic that labels each cluster is useful for program comprehension.

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Section: Latent Dirichlet Allocationmentioning

confidence: 99%

Clustering Classes in Packages for Program Comprehension

Sun

Liu

et al. 2017

Scientific Programming

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“…Unlike GP where an individual is expressed in the form of a tree, an individual in GEP is represented by the Isometric linear symbols. GEP [10] has been successfully applied in problem solving [7], combinatorial optimization [11], real parameter optimization [12], evolving and modeling the functional parameters [13], classification [14,15], event selection in high energy physics [16].…”

Section: Introductionmentioning

confidence: 99%

“…Then, select the data at first line of the matrix X as the original values of RK4, and calculate the prediction data X * from the achieved model via the RK4 method as per the (14) by adopting the increment same with that of matrix X, see (15).…”

mentioning

confidence: 99%

Improved gene expression programming to solve the inverse problem for ordinary differential equations

Chen

et al. 2018

Swarm and Evolutionary Computation

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Many complex systems in the real world evolve with time. These dynamic systems are often modeled by ordinary differential equations in mathematics. The inverse problem of ordinary differential equations is to convert the observed data of a physical system into a mathematical model in terms of ordinary differential equations. Then the model may be used to predict the future behavior of the physical system being modeled. Genetic programming has been taken as a solver of this inverse problem. Similar to genetic programming, gene expression programming could do the same job since it has a similar ability of establishing the model of ordinary differential systems. Nevertheless, such research is seldom studied before. This paper is one of the first attempts to apply gene expression programming for solving the inverse problem of ordinary differential equations. Based on a statistic observation of traditional gene expression programming, an improvement is made in our algorithm, that is, genetic operators should act more often on the dominant part of genes than on the recessive part. This may help maintain population diversity and also speed up the convergence of the algorithm. Experiments show that this improved algorithm performs much better than genetic programming and traditional gene expression programming in terms of running time and prediction precision.

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“…Fu et al also proposed an applicable and extensible search scheme, which could support multikeyword ranked search in parallel computing with privacy-preserving approach [25]. Liu et al presented an adaptive method aiming at spatial-temporal efficiency in a heterogeneous cloud environment on sensitive data [26]. Several recent experiments have shown that users' privacy decision-making often cannot fully explained by logic or statistical models in terms of the perceived benefits and risks.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Model for Supporting Users’ Decision-Making in Privacy Disclosure for Recommendation

Zhang

Cui

et al. 2018

Security and Communication Networks

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Privacy issues have become a major concern in the web of resource sharing, and users often have difficulty managing their information disclosure in the context of high-quality experiences from social media and Internet of Things. Recent studies have shown that users' disclosure decisions may be influenced by heuristics from the crowds, leading to inconsistency in the disclosure volumes and reduction of the prediction accuracy. Therefore, an analysis of why this influence occurs and how to optimize the user experience is highly important. We propose a novel heuristic model that defines the data structures of items and participants in social media, utilizes a modified decision-tree classifier that can predict participants' disclosures, and puts forward a correlation analysis for detecting disclosure inconsistences. The heuristic model is applied to real-time dataset to evaluate the behavioral effects. Decision-tree classifier and correlation analysis indeed prove that some participants' behaviors in information disclosures became decreasingly correlated during item requesting. Participants can be "persuaded" to change their disclosure behaviors, and the users' answers to the mildly sensitive items tend to be more variable and less predictable. Using this approach, recommender systems in social media can thus know the users better and provide service with higher prediction accuracy.

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A speculative approach to spatial‐temporal efficiency with multi‐objective optimization in a heterogeneous cloud environment

Cited by 197 publications

References 28 publications

Clustering Classes in Packages for Program Comprehension

Clustering Classes in Packages for Program Comprehension

Improved gene expression programming to solve the inverse problem for ordinary differential equations

A Heuristic Model for Supporting Users’ Decision-Making in Privacy Disclosure for Recommendation

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