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- item-1 hasContent toufeeq-hussain.
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- item-2 hasContent rajesh-balakrishnan.
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- item-3 hasContent amar-viswanathan.
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- 149 type InProceedings.
- 149 label "Raise Semantics at the User Level for Dynamic and Interactive SOA-based Portals".
- 149 sameAs 149.
- 149 abstract "In this paper, we describe the fully dynamic semantic portal we implemented, integrating Semantic Web technologies and Service Oriented Architecture (SOA). The goals of the portal are twofold: first it helps administrators to easily propose new features in the portal using semantics to ease the orchestration process; secondly it automatically generates a customized user interface for these scenarios. This user interface takes into account different devices and assists end-users in the use of the portal taking benefit of context awareness. All the added-value of this portal is based on a core semantics defined by an ontology. We present here the main features of this portal and how it was implemented using state-of- the-art technologies and frameworks.".
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- 149 keyword "Poster Session".
- 149 title "Raise Semantics at the User Level for Dynamic and Interactive SOA-based Portals".
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- item-1 hasContent jean-sebastien-brunner.
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- item-2 hasContent patrick-gatellier.
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- 15 type InProceedings.
- 15 label "How Opinions are Received by Online Communities: A Case Study on Amazon.com Helpfulness Votes".
- 15 sameAs 15.
- 15 abstract "There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like "26 of 32 people found the following review helpful." Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, "What did Y think of X?", we are asking, "What did Z think of Y's opinion of X?" Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We find that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review "plagiarism" to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our findings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from different countries. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications – Data Mining General Terms: Measurement, Theory Keywords: Review helpfulness, review utility, social influence, online communities, sentiment analysis, opinion mining, plagiarism.".
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- 15 keyword "Data Mining".
- 15 title "How Opinions are Received by Online Communities: A Case Study on Amazon.com Helpfulness Votes".
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- item-1 hasContent cristian-danescu-niculescu-mizil.
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- item-2 hasContent gueorgi-kossinets.
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- 150 type InProceedings.
- 150 label "Towards Lightweight and Efficient DDoS Attacks Detection for Web Server".
- 150 sameAs 150.
- 150 abstract "In this poster, based on our previous work in building a lightweight DDoS (Distributed Denial-of-Services) attacks detection mechanism for web server using TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) and genetic algorithm based instance selection methods, we further propose a more efficient and effective instance selection method, named E-FCM (Extend Fuzzy C-Means). By using this method, we can obtain much cheaper training time for TCM-KNN while ensuring high detection performance. Therefore, the optimized mechanism is more suitable for lightweight DDoS attacks detection in real network environment. In our previous work, we proposed an effective anomaly detection method based on TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) algorithm to fulfill DDoS attacks detection task towards ensuring the QoS of web server. The method is good at detecting network anomalies with high detection rate, high confidence and low false positives than traditional methods, because it combines "strangeness" with "p-values" measures to evaluate the network traffic compared to the conventional ad-hoc thresholds based detection and particular definition based detection. Secondly, we utilize the new objective measurement as the input feature spaces of TCM-KNN, to effectively detect DDoS attack against web server. Finally, we introduce Genetic Algorithm (GA) based instance selection method to boost the real-time detection performance of TCM-KNN and thus make it be an effective and lightweight mechanism for DDoS detection for web servers [4, 5]. However, we found the computational cost for GA is expensive, which results in high training time for TCM-KNN.".
- 150 hasAuthorList authorList.
- 150 isPartOf proceedings.
- 150 keyword "Poster Session".
- 150 title "Towards Lightweight and Efficient DDoS Attacks Detection for Web Server".
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- item-1 type ListItem.
- item-1 hasContent yang-li.
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- item-2 hasContent tian-bo-lu.
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- 151 type InProceedings.
- 151 label "A General Framework for Adaptive and Online Detection of Web Attacks".
- 151 sameAs 151.
- 151 abstract "Detection of web attacks is an important issue in current defense-in-depth security framework. In this paper, we pro- pose a novel general framework for adaptive and online de- tection of web attacks. The general framework can be based on any online clustering methods. A detection model based on the framework is able to learn online and deal with "con- cept drift" in web audit data streams. Str-DBSCAN that we extended DBSCAN [1] to streaming data as well as StrAP [3] are both used to validate the framework. The detec- tion model based on the framework automatically labels the web audit data and adapts to normal behavior changes while identifies attacks through dynamical clustering of the streaming data. A very large size of real HTTP Log data col- lected in our institute is used to validate the framework and the model. The preliminary testing results demonstrated its effectiveness.".