Jiuxin Cao

A segmentation method for web page analysis using shrinking and dividing

On the basis of image processing technology and characteristics of web pages, a new web segmentation method – iterated shrinking and dividing is proposed in this paper. Dividing conditions and concept of dividing zone are introduced, based on which web page image is divided into visually consentaneous sub-images by shrinking and splitting iteratively. First, the web page is saved as image that is preprocessed by edge detection algorithm such as Canny. Then dividing zones are detected and the web image is segmented repeatedly until all blocks are indivisible. This method can be used to analyse the web pages such as detecting similar visual layout. Experiments show that the algorithm is suitable for web page segmentation, and does well in expansibility and performance.

A context-aware personalized resource recommendation for pervasive learning

As it is difficult for learners to discover and obtain the most appropriate resources from massive education resources according to traditional keyword searching method, the context-aware based resource recommendation service becomes a significant part of pervasive learning environments. At present, recommendation mechanisms are widely used in e-commerce field, where content-based or collaborative-based filter strategies are usually considered separately. However, in these existing recommendation mechanisms, the dynamic interests and preference of learners, the access pattern and the other attributes of pervasive learning environments (such as multi-modes connecting and resources distribution) are always neglected. Thus, these mechanisms can not effectively reflect learners’ actual preference and can not adapt to pervasive learning environments perfectly. To address these problems, a context-aware resource recommendation model and relevant recommendation algorithm for pervasive learning environments are proposed. Therein, with taking into account the relevant contextual information, the calculation of relevant degree between learners and resources can be divided into two main parts: logic-based RRD (resource relevant degree) and situation-based RRD. In the first part, content-based and collaborative-based recommendation mechanisms are combined together, where the individual preference tree (IPT) is introduced to take into account the multi-dimensional attributes of resources, learners’ rating matrix and the energy of access preference. Meanwhile, the learners’ historical sequential patterns of resource accessing are also considered to further improve the accuracy of recommendation. In the second part, in order to enhance the validation of recommendation, the connecting type relevance and time satisfaction degree are calculated according to other relevant contexts. Then, the candidate resources can be filtered and sorted via combining these two parts to generate (Top-N) recommendation results. The simulations show that our newly proposed method outperforms other state of-the-art algorithms on traditional and newly presented metrics and it may also be more suitable for pervasive learning environments. Finally, a prototype system is implemented based on SEU-ESP to demonstrate the relevant recommendation process further.

Location-Based Influence Maximization in Social Networks

In this paper, we aim at the product promotion in O2O model and carry out the research of location-based influence maximization on the platform of LBSN. As offline consuming behavior exists under the O2O environment, the traditional online influence diffusion model could not describe the product acceptance accurately. Moreover, the existing researches of influence maximization tend to only concern on the online network of relationships but rarely take the offline part into consideration. This paper introduces the location property into the influence maximization to accord with the characteristic of O2O model. Firstly, we propose an improved influence diffusion model called TP Model which could accurately describe the process of accepting products under the O2O environment. Meanwhile, the definition of location-based influence maximization is presented. Then the user mobility pattern is analyzed and the calculation method of offline probability is designed. Considering the influence ability, a location-based influence maximization algorithm named TPH is proposed. Experiments prove TPH algorithm has general advantage. Finally, focusing on the performance of TPH algorithm under special circumstances, MR algorithm is designed as complement and experiments also verify its high effectiveness.

QoS and Preference Based Web Service Evaluation Approach

with the advance of web services and applications, Quality of Service (QoS) has becoming a key issue in web service selection. Current discovery model and standards such as UDDI, WSDL do not give much support to QoS description and evaluation. Meanwhile, many researches only focus on QoS model and computation, without taking user individual preference into consideration. How to dynamically evaluate candidate web services based on user preference is an important problem to be solved. This paper proposes a QoS and user preference based evaluation model called Q-WSEM(QoS-based Web Service Evaluation Model) that extends the existing model by adding web service evaluation center, and establishes a three-layer framework to achieve web service evaluation based on QoS and user preference. In the third layer, information entropy method of multiple-attribute decision making theory is adopted to facilitate QoS assessment, and preference model is also introduced to provide interface for user to modify preference-based objective weight. Moreover, we have developed and implemented a Q-WSEM prototype, and measured the validity under different preferences. Experiment results demonstrate the effectiveness of this approach.

TASS: Transaction Assurance in Service Selection

As there are various risks of failure when Web Services are deployed in unreliable environment, the execution of a composite service requires the assurance of the transaction mechanism. However, existing QoS-aware composition approaches do not consider the transactional constraints during service selection. We address this issue by considering the combination of transactional and QoS requirements. Firstly, the novel construction and processing rules are proposed to guarantee the atomic consistency of the composite service and the correctness of these rules is proved subsequently. Then, on basis of these rules, an Ant Colony System based service selection algorithm is presented to guarantee the end-to-end QoS constraints on the premise of ensuring the atomic consistency during service selection. Therein, an optimization strategy is suggested to shrink the searching space of the algorithm tremendously. Finally, experimental results show the efficiency and effectiveness of the algorithm and demonstrate further the correctness of the construction and processing rules through simulations.

Pat: A P2P Based Publish/Subscribe System for QoS Information Dissemination of Web Services

A fundamental problem that confronts QoS-aware service selection and composition is the efficient and timely QoS information obtainment. Current research on this problem usually involves query-based or monitoring-based methods. However, in a dynamic and volatile service oriented Computing (SOC) environment, these solutions suffer some or all of the limitations, such as cost, timeliness guarantee and salability. This paper presents Pat, a P2P based publish/subscribe system to disseminate new revised QoS information. Pat aims at reliable and efficient QoS information dissemination in large-scale SOC environments. It exploits specialized rendezvous points (RP) and a replicas mechanism to reduce the risk of subscriptions loss and consequently improve reliability. A reverse RP ring is designed to quicken subscription delivery and QoS information publication. In addition, an optimization mechanism for composite services is built into Pat, which helps to reduce notification traffic. Simulation results show that Pat is reliable, efficient and scalable.

Entropy-Based Denial of Service Attack Detection in Cloud Data Center

Cloud data centers today usually lack network resource isolation. Meanwhile, it is easy to deploy and terminate large number of malicious virtual machines in a few seconds, while the administrator is probably difficult to identify these malicious virtual machines immediately. These features open doors for attackers to launch denial‐of‐service (DoS) attacks that target at degrading the quality of cloud service. This paper studies an attack scenario that malicious tenants use cloud resources to launch DoS attack targeting at data center subnets. Unlike traditional data flow‐based detections, which heavily depend on the pattern of data flows, we propose an approach that takes advantage of virtual machine status including CPU usage and network usage to identify the attack. We notice that malicious virtual machines exhibit similar status patterns when attack is launched. Based on this observation, information entropy is applied in monitoring the status of virtual machines to identify the attack behaviors. We conduct our experiments in the campus‐wide data center, and the results show our detection system can promptly and accurately response to DoS attacks. Copyright

Dynamic multi-resource advance reservation in grid environment

How to guarantee user’s QoS (Quality of Service) demands become increasingly important in a service-oriented grid environment. Current research on grid resource advance reservation, a well known and effective mechanism to guarantee QoS, fails to adapt to dynamic behavior of grid, and cannot solve imprecise deny of reservation request problem efficiently. For this, enabling system architecture for advance reservation is proposed. Virtual resource container (VRC) is adopted to alleviate a negative effect resulted from dynamic behavior of grid and QoS deviation distance (QDD) based logical resource selection algorithm is put forward to decrease imprecise reject ratio of reservation. At last, this new architecture is deployed to campus grid, and two illustrative experiments are conducted. Experimental results show that system architecture for advance reservation proposed in this paper can alleviate negative influence of grid resource dynamic fluctuation and avoid imprecise reject of advance reservation request effectively.

On crowd-retweeting spamming campaign in social networks

Crowdsourcing is often used to solicit contributions from an online community for ideas, evaluation and opinions. However, spamming can pollute such a system and manipulate the results of crowdsourcing. For detection of those spammers, the training data used in previous studies is often derived by experts labeling collected data and manually identifying spammers. The reliability of such training data is questionable. In this paper, we utilize two web based service providers Zhubajie (ZBJ) and Sandaha (SDH) and obtain reliable data about the spammers. We use such data to investigate the crowd-retweeting spam in Sina Weibo. We analyze profile features, social relationship and retweeting behavior of such spammers. We find that although these spammers are likely to connect more closely than legitimate users, the underlying social tie is different from the social relationship in other spam campaigns because of the unique retweeting features with the information cascade effect. Based on these findings, we propose retweeting-aware link based ranking algorithms to detect suspect spam accounts using seeds of identified spammers. Our evaluation shows that our algorithm is more effective than other link-based methods.

Semantic-based self-organizing mechanism for service registry and discovery

Service-oriented computing is the new paradigm for distributed applications. A key issue of utilizing web services is to design a scalable service discovery mechanism. Current discovery method based on centralized registries can easily suffer from performance bottleneck in the service network of large scale. To address such problem, this paper presents a dynamic and scalable mechanism for discovery and registry of semantic web services. Registry proxies are introduced to achieve the data distribution of web service. Behavior of registry proxies are carefully designed to maintain the overall structure in a self-organization way. A distributed balance-aware algorithm is designed to improve the system performance. The result of simulation experiment shows that the proposed mechanism can serve as a scalable solution for semantic web service publication and discovery.