OpenRaiser/Intern-Atlas huggingface dataset:
A general framework for adaptive and online detection of web attacks
Authors: Wei Wang, Florent Masseglia, Thomas Guyet, Rene Quiniou, Marie-Odile Cordier
Venue: WWW
Year: 2009

Detection of web attacks is an important issue in current defense-in-depth security framework. In this paper, we propose a novel general framework for adaptive and online detection 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 "concept drift" in web audit data streams. Str-DBSCAN that we extended DBSCAN to streaming data as well as StrAP are both used to validate the framework. The detection 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 collected in our institute is used to validate the framework and the model. The preliminary testing results demonstrated its effectiveness.
Mashroom: end-user mashup programming using nested tables
Authors: Guiling Wang, Shaohua Yang, Yanbo Han
Venue: WWW
Year: 2009

This paper presents an end-user-oriented programming environment called Mashroom. Major contributions herein include an end-user programming model with an expressive data structure as well as a set of formally-defined mashup operators. The data structure takes advantage of nested table, and maintains the intuitiveness while allowing users to express complex data objects. The mashup operators are visualized with contextual menu and formula bar and can be directly applied on the data. Experiments and case studies reveal that end users have little difficulty in effectively and efficiently using Mashroom to build mashup applications.
Advertising keyword generation using active learning
Authors: Hao Wu, Guang Qiu, Xiaofei He, Yuan Shi, Mingcheng Qu, Jing Shen, Jiajun Bu, Chun Chen
Venue: WWW
Year: 2009

This paper proposes an efficient relevance feedback based interactive model for keyword generation in sponsored search advertising. We formulate the ranking of relevant terms as a supervised learning problem and suggest new terms for the seed by leveraging user relevance feedback information. Active learning is employed to select the most informative samples from a set of candidate terms for user labeling. Experiments show our approach improves the relevance of generated terms significantly with little user effort required.
Learning to tag
Authors: Lei Wu, Linjun Yang, Nenghai Yu, Xian-Sheng Hua
Venue: WWW
Year: 2009

Social tagging provides valuable and crucial information for large-scale web image retrieval. It is ontology-free and easy to obtain; however, irrelevant tags frequently appear, and users typically will not tag all semantic objects in the image, which is also called semantic loss. To avoid noises and compensate for the semantic loss, tag recommendation is proposed in literature. However, current recommendation simply ranks the related tags based on the single modality of tag co-occurrence on the whole dataset, which ignores other modalities, such as visual correlation. This paper proposes a multi-modality recommendation based on both tag and visual correlation, and formulates the tag recommendation as a learning problem. Each modality is used to generate a ranking feature, and Rankboost algorithm is applied to learn an optimal combination of these ranking features from different modalities. Experiments on Flickr data demonstrate the effectiveness of this learning-based multi-modality recommendation strategy.
Latent space domain transfer between high dimensional overlapping distributions
Authors: Sihong Xie, Wei Fan, Jing Peng, Olivier Verscheure, Jiangtao Ren
Venue: WWW
Year: 2009

Transferring knowledge from one domain to another is challenging due to a number of reasons. Since both conditional and marginal distribution of the training data and test data are non-identical, model trained in one domain, when directly applied to a different domain, is usually low in accuracy. For many applications with large feature sets, such as text document, sequence data, medical data, image data of different resolutions, etc. two domains usually do not contain exactly the same features, thus introducing large numbers of "missing values" when considered over the union of features from both domains. In other words, its marginal distributions are at most overlapping. In the same time, these problems are usually high dimensional, such as, several thousands of features. Thus, the combination of high dimensionality and missing values make the relationship in conditional probabilities between two domains hard to measure and model. To address these challenges, we propose a framework that first brings the marginal distributions of two domains closer by "filling up" those missing values of disjoint features. Afterwards, it looks for those comparable sub-structures in the "latent-space" as mapped from the expanded feature vector, where both marginal and conditional distribution are similar. With these sub-structures in latent space, the proposed approach then find common concepts that are transferable across domains with high probability. During prediction, unlabeled instances are treated as "queries", the mostly related labeled instances from out-domain are retrieved, and the classification is made by weighted voting using retrieved out-domain examples. We formally show that importing feature values across domains and latent semantic index can jointly make the distributions of two related domains easier to measure than in original feature space, the nearest neighbor method employed to retrieve related out domain examples is bounded in error when predicting in-domain examples. Software and datasets are available for download.
Search result re-ranking based on gap between search queries and social tags
Authors: Jun Yan, Ning Liu, Elaine Qing Chang, Lei Ji, Zheng Chen
Venue: WWW
Year: 2009

Both search engine click-through log and social annotation have been utilized as user feedback for search result re-ranking. However, to our best knowledge, no previous study has explored the correlation between these two factors for the task of search result ranking. In this paper, we show that the gap between search queries and social tags of the same Web page can well reflect its user preference score. Motivated by this observation, we propose a novel algorithm, called Query-Tag-Gap (QTG), to re-rank search results for better user satisfaction. Intuitively, on one hand, the search users' intentions are generally described by their queries before they read the search results. On the other hand, the Web annotators semantically tag Web pages after they read the content of the pages. The difference between users' recognition of the same page before and after they read it is a good reflection of user satisfaction. In this extended abstract, we formally define the query set and tag set of the same page as users' pre- and post- knowledge respectively. We empirically show the strong correlation between user satisfaction and user's knowledge gap before and after reading the page. Based on this gap, experiments have shown outstanding performance of our proposed QTG algorithm in search result re-ranking.
Mining cultural differences from a large number of geotagged photos
Authors: Keiji Yanai, Bingyu Qiu
Venue: WWW
Year: 2009

We propose a novel method to detect cultural differences over the world automatically by using a large amount of geotagged images on the photo sharingWeb sites such as Flickr. We employ the state-of-the-art object recognition technique developed in the research community of computer vision to mine representative photos of the given concept for representative local regions from a large-scale unorganized collection of consumer-generated geotagged photos. The results help us understand how objects, scenes or events corresponding to the same given concept are visually different depending on local regions over the world.
Incorporating site-level knowledge to extract structured data from web forums
Authors: Jiang-Ming Yang, Rui Cai, Yida Wang, Jun Zhu, Lei Zhang, Wei-Ying Ma
Venue: WWW
Year: 2009

Web forums have become an important data resource for many web applications, but extracting structured data from unstructured web forum pages is still a challenging task due to both complex page layout designs and unrestricted user created posts. In this paper, we study the problem of structured data extraction from various web forum sites. Our target is to find a solution as general as possible to extract structured data, such as post title, post author, post time, and post content from any forum site. In contrast to most existing information extraction methods, which only leverage the knowledge inside an individual page, we incorporate both page-level and site-level knowledge and employ Markov logic networks (MLNs) to effectively integrate all useful evidence by learning their importance automatically. Site-level knowledge includes (1) the linkages among different object pages, such as list pages and post pages, and (2) the interrelationships of pages belonging to the same object. The experimental results on 20 forums show a very encouraging information extraction performance, and demonstrate the ability of the proposed approach on various forums. We also show that the performance is limited if only page-level knowledge is used, while when incorporating the site-level knowledge both precision and recall can be significantly improved.
Discovering users' specific geo intention in web search
Authors: Xing Yi, Hema Raghavan, Chris Leggetter
Venue: WWW
Year: 2009

Discovering users' specific and implicit geographic intention in web search can greatly help satisfy users' information needs. We build a geo intent analysis system that uses minimal supervision to learn a model from large amounts of web-search logs for this discovery. We build a city language model, which is a probabilistic representation of the language surrounding the mention of a city in web queries. We use several features derived from these language models to: (1) identify users' implicit geo intent and pinpoint the city corresponding to this intent, (2) determine whether the geo-intent is localized around the users' current geographic location, (3) predict cities for queries that have a mention of an entity that is located in a specific place. Experimental results demonstrate the effectiveness of using features derived from the city language model. We find that (1) the system has over 90% precision and more than 74% accuracy for the task of detecting users' implicit city level geo intent (2) the system achieves more than 96% accuracy in determining whether implicit geo queries are local geo queries, neighbor region geo queries or none-of-these (3) the city language model can effectively retrieve cities in location-specific queries with high precision (88%) and recall (74%); human evaluation shows that the language model predicts city labels for location-specific queries with high accuracy (84.5%).
Characterizing insecure javascript practices on the web
Authors: Chuan Yue, Haining Wang
Venue: WWW
Year: 2009

JavaScript is an interpreted programming language most often used for enhancing webpage interactivity and functionality. It has powerful capabilities to interact with webpage documents and browser windows, however, it has also opened the door for many browser-based security attacks. Insecure engineering practices of using JavaScript may not directly lead to security breaches, but they can create new attack vectors and greatly increase the risks of browser-based attacks. In this paper, we present the first measurement study on insecure practices of using JavaScript on the Web. Our focus is on the insecure practices of JavaScript inclusion and dynamic generation, and we examine their severity and nature on 6,805 unique websites. Our measurement results reveal that insecure JavaScript practices are common at various websites: (1) at least 66.4% of the measured websites manifest the insecure practices of including JavaScript files from external domains into the top-level documents of their webpages; (2) over 44.4% of the measured websites use the dangerous eval() function to dynamically generate and execute JavaScript code on their webpages; and (3) in JavaScript dynamic generation, using the document.write() method and the innerHTML property is much more popular than using the relatively secure technique of creating script elements via DOM methods. Our analysis indicates that safe alternatives to these insecure practices exist in common cases and ought to be adopted by website developers and administrators for reducing potential security risks.
WPBench: a benchmark for evaluating the client-side performance of web 2.0 applications
Authors: Kaimin Zhang, Lu Wang, Xiaolin Guo, Aimin Pan, Bin B. Zhu
Venue: WWW
Year: 2009

In this paper, a benchmark called WPBench is reported to evaluate the responsiveness of Web browsers for modern Web 2.0 applications. In WPBench, variations of servers and networks are removed and the benchmark result is the closest to what Web users would perceive. To achieve these, WPBench records users' interactions with typical Web 2.0 applications, and then replays Web navigations when benchmarking browsers. The replay mechanism can emulate the actual user interactions and the characteristics of the servers and the networks in a consistent way independent of browsers so that any browser compliant to the standards can be benchmarked fairly. In addition to describing the design and generation of WPBench, we also report the WPBench comparison results on the responsiveness performance for three popular Web browsers: Internet Explorer, Firefox and Chrome.
Web-scale classification with naive bayes
Authors: Congle Zhang, Gui-Rong Xue, Yong Yu, Hongyuan Zha
Venue: WWW
Year: 2009

Traditional Naive Bayes Classifier performs miserably on web-scale taxonomies. In this paper, we investigate the reasons behind such bad performance. We discover that the low performance are not completely caused by the intrinsic limitations of Naive Bayes, but mainly comes from two largely ignored problems: contradiction pair problem and discriminative evidence cancelation problem. We propose modifications that can alleviate the two problems while preserving the advantages of Naive Bayes. The experimental results show our modified Naive Bayes can significantly improve the performance on real web-scale taxonomies.
Graph based crawler seed selection
Authors: Shuyi Zheng, Pavel A. Dmitriev, C. Lee Giles
Venue: WWW
Year: 2009

This paper identifies and explores the problem of seed selection in a web-scale crawler. We argue that seed selection is not a trivial but very important problem. Selecting proper seeds can increase the number of pages a crawler will discover, and can result in a collection with more ``good" and less "bad" pages. Based on the analysis of the graph structure of the web, we propose several seed selection algorithms. Effectiveness of these algorithms is proved by our experimental results on real web data.
Detecting image spam using local invariant features and pyramid match kernel
Authors: Haiqiang Zuo, Weiming Hu, Ou Wu, Yunfei Chen, Guan Luo
Venue: WWW
Year: 2009

Image spam is a new obfuscating method which spammers invented to more effectively bypass conventional text based spam filters. In this paper, we extract local invariant features of images and run a one-class SVM classifier which uses the pyramid match kernel as the kernel function to detect image spam. Experimental results demonstrate that our algorithm is effective for fighting image spam.
What you see is what you search: adaptive visual search framework for the web
Authors: Jae-wook Ahn, Peter Brusilovsky
Venue: WWW
Year: 2010

Information retrieval is one of the most popular information access methods for overcoming the information overload problem of the Web. However, its interaction model is still utilizing the old text-based ranked lists and static interaction algorithm. In this paper, we introduce our adaptive visualization approach for searching the Web, which we call Adaptive VIBE. It is an extended version of a reference point-based spatial visualization algorithm, and is designed to serve as a user interaction module for a personalized search system. Personalized search can incorporate dynamic user interests and different contexts, improving search results. When it is combined with adaptive visualization, it can encourage users to become involved in the search process more actively by exploring the information space and learning new facts for effective searching. In this paper, we introduce the rationale and functions of our adaptive visualization approach and discuss the approaches' potential to create a better search environment for the Web.
RESTler: crawling RESTful services
Authors: Rosa Alarcón, Erik Wilde
Venue: WWW
Year: 2010

Service descriptions allow designers to document, understand, and use services, creating new useful and complex services with aggregated business value. Unlike RPC-based services, REST characteristics require a different approach to service description. We present the Resource Linking Language (ReLL) that introduces the concepts of media types, resource types, and link types as first class citizens for a service description. A proof of concept, a crawler called RESTler that crawls RESTful services based on ReLL descriptions, is also presented.
Selecting skyline services for QoS-based web service composition
Authors: Mohammad Alrifai, Dimitrios Skoutas 0001, Thomas Risse 0001
Venue: WWW
Year: 2010

Web service composition enables seamless and dynamic integration of business applications on the web. The performance of the composed application is determined by the performance of the involved web services. Therefore, non-functional, quality of service aspects are crucial for selecting the web services to take part in the composition. Identifying the best candidate web services from a set of functionally-equivalent services is a multi-criteria decision making problem. The selected services should optimize the overall QoS of the composed application, while satisfying all the constraints specified by the client on individual QoS parameters. In this paper, we propose an approach based on the notion of skyline to effectively and efficiently select services for composition, reducing the number of candidate services to be considered. We also discuss how a provider can improve its service to become more competitive and increase its potential of being included in composite applications. We evaluate our approach experimentally using both real and synthetically generated datasets.
Money, glory and cheap talk: analyzing strategic behavior of contestants in simultaneous crowdsourcing contests on TopCoder.com
Authors: Nikolay Archak
Venue: WWW
Year: 2010

Crowdsourcing is a new Web phenomenon, in which a firm takes a function once performed in-house and outsources it to a crowd, usually in the form of an open contest.Designing efficient crowdsourcing mechanisms is not possible without deep understanding of incentives and strategic choices of all participants.This paper presents an empirical analysis of determinants of individual performance in multiple simultaneous crowdsourcing contests using a unique dataset for the world's largest competitive software development portal: TopCoder.com. Special attention is given to studying the effects of the reputation system currently used by TopCoder.com on behavior of contestants. We find that individual specific traits together with the project payment and the number of project requirements are significant predictors of the final project quality. Furthermore, we find significant evidence of strategic behavior of contestants. High rated contestants face tougher competition from their opponents in the competition phase of the contest. In order to soften the competition, they move first in the registration phase of the contest, signing up early for particular projects. Although registration in TopCoder contests is non-binding, it deters entry of opponents in the same contest; our lower bound estimate shows that this strategy generates significant surplus gain to high rated contestants. We conjecture that the reputation + cheap talk mechanism employed by TopCoder has a positive effect on allocative efficiency of simultaneous all-pay contests and should be considered for adoption in other crowdsourcing platforms.
Matrix "Bit" loaded: a scalable lightweight join query processor for RDF data
Authors: Medha Atre, Vineet Chaoji, Mohammed J. Zaki, James A. Hendler
Venue: WWW
Year: 2010

The Semantic Web community, until now, has used traditional database systems for the storage and querying of RDF data. The SPARQL query language also closely follows SQL syntax. As a natural consequence, most of the SPARQL query processing techniques are based on database query processing and optimization techniques. For SPARQL join query optimization, previous works like RDF-3X and Hexastore have proposed to use 6-way indexes on the RDF data. Although these indexes speed up merge-joins by orders of magnitude, for complex join queries generating large intermediate join results, the scalability of the query processor still remains a challenge.
SourceRank: relevance and trust assessment for deep web sources based on inter-source agreement
Authors: Raju Balakrishnan, Subbarao Kambhampati
Venue: WWW
Year: 2010

One immediate challenge in searching the deep web databases is source selection - i.e. selecting the most relevant web databases for answering a given query. The existing database selection methods (both text and relational) assess the source quality based on the query-similarity-based relevance assessment. When applied to the deep web these methods have two deficiencies. First is that the methods are agnostic to the correctness (trustworthiness) of the sources. Secondly, the query based relevance does not consider the importance of the results. These two considerations are essential for the open collections like the deep web. Since a number of sources provide answers to any query, we conjuncture that the agreements between these answers are likely to be helpful in assessing the importance and the trustworthiness of the sources. We compute the agreement between the sources as the agreement of the answers returned. While computing the agreement, we also measure and compensate for possible collusion between the sources. This adjusted agreement is modeled as a graph with sources at the vertices. On this agreement graph, a quality score of a source that we call SourceRank, is calculated as the stationary visit probability of a random walk. We evaluate SourceRank in multiple domains, including sources in Google Base, with sizes up to 675 sources. We demonstrate that the SourceRank tracks source corruption. Further, our relevance evaluations show that SourceRank improves precision by 22-60% over the Google Base and the other baseline methods. SourceRank has been implemented in a system called Factal.
AdHeat: an influence-based diffusion model for propagating hints to match ads
Authors: Hongji Bao, Edward Y. Chang
Venue: WWW
Year: 2010

In this paper, we present AdHeat, a social ad model considering user influence in addition to relevance for matching ads. Traditionally, ad placement employs the relevance model. Such a model matches ads with Web page content, user interests, or both. We have observed, however, on social networks that the relevance model suffers from two shortcomings. First, influential users (users who contribute opinions) seldom click ads that are highly relevant to their expertise. Second, because influential users' contents and activities are attractive to other users, hint words summarizing their expertise and activities may be widely preferred. Therefore, we propose AdHeat, which diffuses hint words of influential users to others and then matches ads for each user with aggregated hints. We performed experiments on a large online Q&A community with half a million users. The experimental results show that AdHeat outperforms the relevance model on CTR (click through rate) by significant margins.