Shandong Provincial Key Laboratory of Software Engineering

Jinan, China

Shandong Provincial Key Laboratory of Software Engineering

Jinan, China
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Zhao M.,Shandong University of Science and Technology | Li Z.,Shandong University of Science and Technology | Li Z.,Shandong University of Finance and Economics | Wang Y.,Ludong University | And 2 more authors.
NBiS 2016 - 19th International Conference on Network-Based Information Systems | Year: 2016

Longest Common Sub-sequence is a basic algorithm problem. It serves as a basic component for a variety of applications in information processing and bioinformatics. It is a NP-hard problem and often manipulated using dynamic programming, which is relatively fast but involves large memory space. Fortunately, cloud computing and outsourced computing provides a practical method for overload alleviation. However, for the security and privacy concern, clients hope to encrypt their data before upload them to the cloud, meanwhile maintain the ability for the cloud to process on the data. In this paper, we propose a method to computing Longest Common Sub-sequence using somewhat homomorphic encryption. Beyond that, we show how to use our achievement into searchable encryption to achieve rich expressiveness. © 2016 IEEE.


Cui L.,Shandong University of Science and Technology | Cui L.,Shandong Provincial Key Laboratory of Software Engineering | Zhang T.,Shandong University of Science and Technology | Zhang T.,Shandong Provincial Key Laboratory of Software Engineering | And 2 more authors.
Applied Mathematics and Information Sciences | Year: 2013

As a key service model in cloud computing, SaaS applications are becoming increasingly popular. Multi-tenancy is a key characteristics of SaaS applications. Business processes play a key role in SaaS applications because of the composability and reusability of software services. This paper focuses on multi-tenants instance-intensive workflows system, in which workflows have a large number of instances belonging to multiple tenants in a SaaS environment, and further proposes a scheduling algorithm for multitenants workflow instances. This algorithm improves the quality of service (QoS) for tenants and saves the execution cost of workflows. The simulation results demonstrate that the proposed algorithm guarantees the workflow execution conforming to the deadline set by tenants, and reduces the mean execution time for tenants in high priority whilst saves the execution cost for service providers. © 2012 NSP Natural Sciences Publishing Cor.


Yang F.,Shandong University of Science and Technology | Yang F.,Shandong Provincial Key Laboratory of Software Engineering | Yu X.,Shandong University of Science and Technology | Yu X.,York University | And 5 more authors.
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | Year: 2012

The problem of gauging information credibility on social net- works has received considerable attention in recent years. Most previous work has chosen Twitter, the world's largest micro-blogging platform, as the premise of research. In this work, we shift the premise and study the problem of information credibility on Sina Weibo, China's leading micro- blogging service provider. With eight times more users than Twitter, Sina Weibo is more of a Facebook-Twitter hybrid than a pure Twitter clone, and exhibits several important characteristics that distinguish it from Twitter. We collect an extensive set of microblogs which have been confirmed to be false rumors based on information from the official rumor-busting service provided by Sina Weibo. Unlike previous studies on Twitter where the labeling of rumors is done manually by the participants of the experiments, the official nature of this service ensures the high quality of the dataset. We then examine an extensive set of features that can be extracted from the microblogs, and train a classifier to automatically detect the rumors from a mixed set of true information and false information. The experiments show that some of the new features we propose are indeed effective in the classification, and even the features considered in previous studies have different implications with Sina Weibo than with Twitter. To the best of our knowledge, this is the first study on rumor analysis and detection on Sina Weibo. Copyright © 2012 ACM.


Guo L.,Shandong University of Science and Technology | Guo L.,Shandong Provincial Key Laboratory of Software Engineering | Ma J.,Shandong University of Science and Technology | Ma J.,Shandong Provincial Key Laboratory of Software Engineering | And 3 more authors.
ACM International Conference Proceeding Series | Year: 2012

Recommender systems with social networks (RSSN) have been well studied in recent works. However, these methods ignore the relationships among items, which may affect the quality of recommendations. Motivated by the observation that related items often have similar ratings, we propose a framework integrating items' relations, users' social graph and user-item rating matrix for recommendation. Experimental results show that our approach performs better than the state-of-art algorithm and the method with only users' social graph ensemble in terms of MAP and RMSE. © 2012 ACM.


Huang S.,Shandong University of Science and Technology | Huang S.,Shandong Provincial Key Laboratory of Software Engineering | Ma J.,Shandong University of Science and Technology | Ma J.,Shandong Provincial Key Laboratory of Software Engineering
Journal of Computational Information Systems | Year: 2013

In recent years, the bipartite graph model is becoming popular because of its simplicity and efficiency in recommender systems. However, this model may be ineffective due to the data sparsity and scalability problems. Clustering techniques are effective methods to alleviate these two problems. In this paper we propose a novel method denoted as Multi-US_BG which makes recommendation on the user-item bipartite graph using multiple user subgroups. This method first uses SVD to decompose the rating matrix to get user feature vectors, then utilizes a fuzzy c-means clustering algorithm to cluster users into multiple subgroups. Finally it integrates subgroups with the recommendation method on the user-item bipartite graph. Experimental results on MovieLens show that our method can improve the top-N recommendation performance in Precision, Recall and F1-measure in comparison with the pure recommendation method on the bipartite graph (Pure_BG). Copyright © 2013 Binary Information Press.


Guo F.,Shandong University of Science and Technology | Guo F.,Shandong Provincial Key Laboratory of Software Engineering | Li S.,Shandong University of Science and Technology
2012 2nd International Conference on Consumer Electronics, Communications and Networks, CECNet 2012 - Proceedings | Year: 2012

In this paper, a new data compression method is presented. A prediction method based on quadratic curve reconstruction is used to improve the data reduction rate. The limitations of human perception with respect to a user's hand position and force feedback are considered in the method to insure that distortions introduced by the compression scheme remain imperceptible to the user. Experiments are included to prove the effectiveness of the proposed approach in data reduction rate. © 2012 IEEE.


Li W.,Shandong University of Technology | Li W.,Shandong Provincial Key Laboratory of Software Engineering | He W.,Shandong University of Technology | He W.,Shandong Provincial Key Laboratory of Software Engineering
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Collaborative filtering is one of the most successful technologies applied in recommender systems in multiple domains. With the increasing growth of users and items involved in recommender systems, some inherent weaknesses of traditional collaborating filtering such as ratings data sparsity, new user problems become more and more manifest. We believe that one of the most important sources of these problems is the deficiency of user similarities based on all users and items in authenticity and accuracy. In this paper, we propose an improved collaborative filtering method based on user ranking and item clustering, in which the users are classified and ranked in multiple item clusters by computing their rating qualities based on the previous rating records, and items are recommended for target users according to their similar users with high-ranks in different item categories. Experiments on real world data sets have demonstrated the effectiveness of our approach. © Springer-Verlag 2013.


Zhou S.,Harbin University of Science and Technology | Xia H.,Qingdao University | Xia H.,Shandong Provincial Key Laboratory of Software Engineering
International Journal of Security and its Applications | Year: 2015

There is an inherent reliance on collaboration among the participants of mobile ad hoc networks in order to achieve the aimed functionalities. Collaboration is productive only if all participants operate in an honest manner. However, this is not always the case and these networks are subjected to a variety of malicious attacks. One of the key factors to ensure high communication quality is an efficient assessment scheme for node's prediction trust, to choose potential cooperative nodes and reduce the probability of risk occurrence for next interaction. In this paper, firstly we propose a node's trust assessment model based on node's historical behaviors, in which the trust decision factors include the subjective reputation and indirect reputation. Then we try to combine an improved grey model with the Markov chain together to effectively predict the node's trust. Experiment has been conducted to evaluate the effectiveness of the proposed mechanism. © 2015 SERSC.


Yang C.,Shandong University of Finance and Economics | Yang C.,Shandong Provincial Key Laboratory of Software Engineering | Yang C.,CVIC Software Engineering Co. | Gao S.S.,Shandong University of Finance and Economics
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

The cross-enterprise business process is different from the common business process. More issues of level interoperability must be defined and differentiated. The enterprise interoperability framework has been proposed by five levels. And a novel method of cross-enterprise process collaboration is presented. In order to enhance the capability of process reengineering and change, the services composition strategy which is based on SCA components have been proposed. An example of cross-enterprise business of cloth-order in textile industry is illustrated and analyzed as well. © Springer International Publishing Switzerland 2015.


Yang C.,Shandong University of Finance and Economics | Yang C.,Shandong Provincial Key Laboratory of Software Engineering | Yang C.,CVIC Software Engineering Co. | Peng S.,Shandong University of Finance and Economics
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

With the emergence of cloud computing paradigm, it provides a promising new solution for sophisticated instance intensive applications. However, the reliability and response speed begins to be suffered because of the limitation of the Hadoop’s FIFO scheduling model. It becomes unacceptable to execute the large scale instance intensive tasks under such conditions. In order to enhance the system resource utilization, we propose a solution in this paper. We use a delay scheduling algorithm to determine the scheduling opportunity and reduce the cost. Delay scheduling can ensure that the current scheduled tasks can make full use of the physical resources, raise resource utilization, and reduce the probability of failure scheduling. The experimental evaluation illustrates that the large scale instance intensive tasks can benefit from the Min-cost delay scheduling algorithm presented in the paper. © Springer International Publishing Switzerland 2015.

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