OpenRaiser/Intern-Atlas huggingface dataset:
Managing trust in a peer-2-peer information system
Authors: Karl Aberer, Zoran Despotovic
Venue: Conference CIKM 2001
Year: 2001

Managing trust is a problem of particular importance in peer-to-peer environments where one frequently encounters unknown agents. Existing methods for trust management, that are based on reputation, focus on the semantic properties of the trust model. They do not scale as they either rely on a central database or require to maintain global knowledge at each agent to provide data on earlier interactions. In this paper we present an approach that addresses the problem of reputation-based trust management at both the data management and the semantic level. We employ at both levels scalable data structures and algorithms that require no central control and allow to assess trust by computing an agents reputation from its former interactions with other agents. Thus the meethod can be implemented in a peer-to-peer environment and scales well for very large numbers of participants. We expect that scalable methods for trust management are an important factor, if fully decentralized peer-to-peer systems should become the platform for more serious applications than simple file exchange.
Mining Models of Human Activities from the Web
Authors: Mike Perkowitz, Matthai Philipose, Kenneth P. Fishkin, Donald J. Patterson
Venue: Conference WWW 2004
Year: 2004

The ability to determine what day-to-day activity (such as cooking pasta, taking a pill, or watching a video) a person is performing is of interest in many application domains. A system that can do this requires models of the activities of interest, but model construction does not scale well: humans must specify low-level details, such as segmentation and feature selection of sensor data, and high-level structure, such as spatio-temporal relations between states of the model, for each and every activity. As a result, previous practical activity recognition systems have been content to model a tiny fraction of the thousands of human activities that are potentially useful to detect. In this paper, we present an approach to sensing and modeling activities that scales to a much larger class of activities than before. We show how a new class of sensors, based on Radio Frequency Identification (RFID) tags, can directly yield semantic terms that describe the state of the physical world. These sensors allow us to formulate activity models by translating labeled activities, such as 'cooking pasta', into probabilistic collections of object terms, such as 'pot'. Given this view of activity models as text translations, we show how to mine definitions of activities in an unsupervised manner from the web. We have used our technique to mine definitions for over 20,000 activities. We experimentally validate our approach using data gathered from actual human activity as well as simulated data.
MedSearch: A specialized search engine for medical information
Authors: Gang Luo, Chunqiang Tang, Hao Yang, Xing Wei
Venue: Conference WWW 2007
Year: 2007

People are thirsty for medical information. Existing Web search engines cannot handle medical search well because they do not consider its special requirements. Often a medical information searcher is uncertain about his exact questions and unfamiliar with medical terminology. Therefore, he prefers to pose long queries, describing his symptoms and situation in plain English, and receive comprehensive, relevant information from search results. This paper presents MedSearch, a specialized medical Web search engine, to address these challenges. MedSearch can assist ordinary Internet users to search for medical information, by accepting queries of extended length, providing diversified search results, and suggesting related medical phrases.
Document quality models for web ad hoc retrieval
Authors: Yun Zhou, W. Bruce Croft
Venue: Conference CIKM 2005
Year: 2005

The quality of document content, which is an issue that is usually ignored for the traditional ad hoc retrieval task, is a critical issue for Web search. Web pages have a huge variation in quality relative to, for example, newswire articles. To address this problem, we propose a document quality language model approach that is incorporated into the basic query likelihood retrieval model in the form of a prior probability. Our results demonstrate that, on average, the new model is significantly better than the baseline (query likelihood model) in terms of precision at the top ranks.
An unsupervised framework for extracting and normalizing product attributes from multiple web sites
Authors: Tak-Lam Wong, Wai Lam, Tik-Shun Wong
Venue: Conference SIGIR 2008
Year: 2008

We have developed an unsupervised framework for simultaneously extracting and normalizing attributes of products from multiple Web pages originated from different sites. Our framework is designed based on a probabilistic graphical model that can model the page-independent content information and the page-dependent layout information of the text fragments in Web pages. One characteristic of our framework is that previously unseen attributes can be discovered from the clue contained in the layout format of the text fragments. Our framework tackles both extraction and normalization tasks by jointly considering the relationship between the content and layout information. Dirichlet process prior is employed leading to another advantage that the number of discovered product attributes is unlimited. An unsupervised inference algorithm based on variational method is presented. The semantics of the normalized attributes can be visualized by examining the term weights in the model. Our framework can be applied to a wide range of Web mining applications such as product matching and retrieval. We have conducted extensive experiments from four different domains consisting of over 300 Web pages from over 150 different Web sites, demonstrating the robustness and effectiveness of our framework.
Mining templates from search result records of search engines
Authors: Hongkun Zhao, Weiyi Meng, Clement T. Yu
Venue: Conference KDD 2007
Year: 2007

Metasearch engine, Comparison-shopping and Deep Web crawling applications need to extract search result records enwrapped in result pages returned from search engines in response to user queries. The search result records from a given search engine are usually formatted based on a template. Precisely identifying this template can greatly help extract and annotate the data units within each record correctly. In this paper, we propose a graph model to represent record template and develop a domain independent statistical method to automatically mine the record template for any search engine using sample search result records. Our approach can identify both template tags (HTML tags) and template texts (non-tag texts), and it also explicitly addresses the mismatches between the tag structures and the data structures of search result records. Our experimental results indicate that this approach is very effective.
Application and evaluation of spatiotemporal enhancement of live aerial video using temporally local mosaics
Authors: Bryan S. Morse, Damon Gerhardt, Cameron H. Engh, Michael A. Goodrich, Nathan D. Rasmussen, Daniel R. Thornton, Dennis Eggett
Venue: Conference CVPR 2008
Year: 2008

Camera-equipped mini-UAVs are popular for many applications, including search and surveillance, but video from them is commonly plagued with distracting jittery motions and disorienting rotations that make it difficult for human viewers to detect objects of interest and infer spatial relationships. For time-critical search situations there are also inherent tradeoffs between detection and search speed. These problems make the use of dynamic mosaics to expand the spatiotemporal properties of the video appealing. However, for many applications it may not be necessary to maintain full mosaics of all of the video but to mosaic and retain only a number of recent (temporally local) frames, still providing a larger field of view and effectively longer temporal view as well as natural stabilization and consistent orientation. This paper presents and evaluates a real-time system for displaying live video to human observers in search situations by using temporally local mosaics while avoiding masking effects from dropped or noisy frames. Its primary contribution is an empirical study of the effectiveness of using such methods for enhancing human detection of objects of interest, which shows that temporally local mosaics increase task performance and are easier for humans to use than non-mosaiced methods, including stabilized video.
A statistical comparison of tag and query logs
Authors: Mark James Carman, Mark Baillie, Robert Gwadera, Fabio Crestani
Venue: Conference SIGIR 2009
Year: 2009

We investigate tag and query logs to see if the terms people use to annotate websites are similar to the ones they use to query for them. Over a set of URLs, we compare the distribution of tags used to annotate each URL with the distribution of query terms for clicks on the same URL. Understanding the relationship between the distributions is important to determine how useful tag data may be for improving search results and conversely, query data for improving tag prediction. In our study, we compare both term frequency distributions using vocabulary overlap and relative entropy. We also test statistically whether the term counts come from the same underlying distribution. Our results indicate that the vocabulary used for tagging and searching for content are similar but not identical. We further investigate the content of the websites to see which of the two distributions (tag or query) is most similar to the content of the annotated/searched URL. Finally, we analyze the similarity for different categories of URLs in our sample to see if the similarity between distributions is dependent on the topic of the website or the popularity of the URL.
Visualizing tags over time
Authors: Micah Dubinko, Ravi Kumar, Joseph Magnani, Jasmine Novak, Prabhakar Raghavan, Andrew Tomkins
Venue: Conference WWW 2006
Year: 2006

We consider the problem of visualizing the evolution of tags within the Flickr (flickr.com) online image sharing community. Any user of the Flickr service may append a tag to any photo in the system. Over the past year, users have on average added over a million tags each week. Understanding the evolution of these tags over time is therefore a challenging task. We present a new approach based on a characterization of the most interesting tags associated with a sliding interval of time. An animation provided via Flash in a web browser allows the user to observe and interact with the interesting tags as they evolve over time.New algorithms and data structures are required to support the efficient generation of this visualization. We combine a novel solution to an interval covering problem with extensions to previous work on score aggregation in order to create an efficient backend system capable of producing visualizations at arbitrary scales on this large dataset in real time.
Telling experts from spammers: expertise ranking in folksonomies
Authors: Michael G. Noll, Ching-man Au Yeung, Nicholas Gibbins, Christoph Meinel, Nigel Shadbolt
Venue: Conference SIGIR 2009
Year: 2009

With a suitable algorithm for ranking the expertise of a user in a collaborative tagging system, we will be able to identify experts and discover useful and relevant resources through them. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. Firstly, an expert should possess a high quality collection of resources, while the quality of a Web resource depends on the expertise of the users who have assigned tags to it. Secondly, an expert should be one who tends to identify interesting or useful resources before other users do. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements these ideas for ranking users in a folksonomy. We evaluate our method with experiments on data sets collected from Delicious.com comprising over 71,000 Web documents, 0.5 million users and 2 million shared bookmarks. We also show that the algorithm is more resistant to spammers than other methods such as the original HITS algorithm and simple statistical measures.
Identifying regional sensitive queries in web search
Authors: Srinivas Vadrevu, Ya Zhang, Belle L. Tseng, Gordon Sun, Xin Li
Venue: Conference WWW 2008
Year: 2008

In Web search ranking, the expected results for some queries could vary greatly depending upon location of the user. We name such queries regional sensitive queries. Identifying regional sensitivity of queries is important to meet users' needs. The objective of this work is to identify whether a user expects only regional results for a query. We present three novel features generated from search logs and build a meta query classifier to identify regional sensitive query. Experimental results show that the proposed method achieves high accuracy in identifying regional sensitive queries.
Web taxonomy integration using support vector machines
Authors: Dell Zhang, Wee Sun Lee
Venue: Conference WWW 2004
Year: 2004

We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to train a classifier for each category in the master taxonomy, and then classify objects from the source taxonomy into these categories. In this paper we attempt to use a powerful classification method, Support Vector Machine (SVM), to attack this problem. Our key insight is that the availability of the source taxonomy data could be helpful to build better classifiers in this scenario, therefore it would be beneficial to do transductive learning rather than inductive learning, i.e., learning to optimize classification performance on a particular set of test examples. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose a method, Cluster Shrinkage (CS), to further enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
Exploiting bidirectional links: making spamming detection easier
Authors: Yan Zhang, Qiancheng Jiang, Lei Zhang, Yizhen Zhu
Venue: Conference CIKM 2009
Year: 2009

Previous anti-spamming algorithms based on link structure suffer from either the weakness of the page value metric or the vagueness of the seed selection. In this paper, we propose two page value metrics, AVRank and HVRank. These two "values" of all the web pages can be well assessed by using the bidirectional links' information. Moreover, with the help of bidirectional links, it becomes easier to enlarge the propagation coverage of seed sets. We further discuss the effectiveness of the combination of these two metrics, such as the quadratic mean of them. Our experimental results show that with such two metrics, our method can filter out spam sites and identify reputable ones more effectively than previous algorithms such as TrustRank.
Exploiting session-like behaviors in tag prediction
Authors: Dawei Yin, Liangjie Hong, Brian D. Davison
Venue: Conference WWW 2011
Year: 2011

In social bookmarking systems, existing methods in tag prediction have shown that the performance of prediction can be significantly improved by modeling users' preferences. However, these preferences are usually treated as constant over time, neglecting the temporal factor within users' behaviors. In this paper, we study the problem of session-like behavior in social tagging systems and demonstrate that the predictive performance can be improved by considering sessions. Experiments, conducted on three public datasets, show that our session-based method can outperform baselines and two state-of-the-art algorithms significantly.
The recurrence dynamics of social tagging
Authors: Dell Zhang, Robert Mao, Wei Li
Venue: Conference WWW 2009
Year: 2009

How often do tags recur? How hard is predicting tag recurrence? What tags are likely to recur? We try to answer these questions by analysing the RSDC08 dataset, in both individual and collective settings. Our findings provide useful insights for the development of tag suggestion techniques etc.
Low-order tensor decompositions for social tagging recommendation
Authors: Yuanzhe Cai, Miao Zhang, Dijun Luo, Chris H. Q. Ding, Sharma Chakravarthy
Venue: Conference WSDM 2011
Year: 2011

Social tagging recommendation is an urgent and useful enabling technology for Web 2.0. In this paper, we present a systematic study of low-order tensor decomposition approach that are specifically targeted at the very sparse data problem in tagging recommendation problem. Low-order polynomials have low functional complexity, are uniquely capable of enhancing statistics and also avoids over-fitting than traditional tensor decompositions such as Tucker and Parafac decompositions. We perform extensive experiments on several datasets and compared with 6 existing methods. Experimental results demonstrate that our approach outperforms existing approaches.
Folks in folksonomies: Social link prediction from shared metadata
Authors: Rossano Schifanella, Alain Barrat, Ciro Cattuto, Benjamin Markines, Filippo Menczer
Venue: Conference WSDM 2010
Year: 2010

Web 2.0 applications have attracted a considerable amount of attention because their open-ended nature allows users to create light-weight semantic scaffolding to organize and share content. To date, the interplay of the social and semantic components of social media has been only partially explored. Here we focus on Flickr and Last.fm, two social media systems in which we can relate the tagging activity of the users with an explicit representation of their social network. We show that a substantial level of local lexical and topical alignment is observable among users who lie close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local alignment between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar topical interests are more likely to be friends, and therefore semantic similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on the Last.fm data set, confirming that the social network constructed from semantic similarity captures actual friendship more accurately than Last.fm's suggestions based on listening patterns.
Illumination invariant face recognition using thermal infrared imagery
Authors: Diego A. Socolinsky, Lawrence B. Wolff, Joshua D. Neuheisel, Christopher K. Eveland
Venue: Conference CVPR 2001
Year: 2001

A key problem for face recognition has been accurate identification under variable illumination conditions. Conventional video cameras sense reflected light so that image grayvalues are a product of both intrinsic skin reflectivity and external incident illumination, thus obfuscating the intrinsic reflectivity of skin. Thermal emission from skin, on the other hand, is an intrinsic measurement that can be isolated from external illumination. We examine the invariance of Long-Wave InfraRed (LWIR) imagery with respect to different illumination conditions from the viewpoint of performance comparisons of two well-known face recognition algorithms applied to LWIR and visible imagery. We develop rigourous data collection protocols that formalize face recognition analysis for computer vision in the thermal IR.
Tower of Babel: A crowdsourcing game building sentiment lexicons for resource-scarce languages
Authors: Yoonsung Hong, Haewoon Kwak, Youngmin Baek, Sue B. Moon
Venue: Conference WWW 2013
Year: 2013

With the growing amount of textual data produced by online social media today, the demands for sentiment analysis are also rapidly increasing; and, this is true for worldwide. However, non-English languages often lack sentiment lexicons, a core resource in performing sentiment analysis. Our solution, Tower of Babel (ToB), is a language-independent sentiment-lexicon-generating crowdsourcing game. We conducted an experiment with 135 participants to explore the difference between our solution and a conventional manual annotation method. We evaluated ToB in terms of effectiveness, efficiency, and satisfactions. Based on the result of the evaluation, we conclude that sentiment classification via ToB is accurate, productive and enjoyable.
Actions speak as loud as words: Predicting relationships from social behavior data
Authors: Sibel Adali, Fred Sisenda, Malik Magdon-Ismail
Venue: Conference WWW 2012
Year: 2012

In recent years, new studies concentrating on analyzing user personality and finding credible content in social media have become quite popular. Most such work augments features from textual content with features representing the user's social ties and the tie strength. Social ties are crucial in understanding the network the people are a part of. However, textual content is extremely useful in understanding topics discussed and the personality of the individual. We bring a new dimension to this type of analysis with methods to compute the type of ties individuals have and the strength of the ties in each dimension. We present a new genre of behavioral features that are able to capture the "function" of a specific relationship without the help of textual features. Our novel features are based on the statistical properties of communication patterns between individuals such as reciprocity, assortativity, attention and latency. We introduce a new methodology for determining how such features can be compared to textual features, and show, using Twitter data, that our features can be used to capture contextual information present in textual features very accurately. Conversely, we also demonstrate how textual features can be used to determine social attributes related to an individual.
Scalable robust covariance and correlation estimates for data mining
Authors: Fatemah A. Alqallaf, Kjell P. Konis, R. Douglas Martin, Ruben H. Zamar
Venue: Conference KDD 2002
Year: 2002

Covariance and correlation estimates have important applications in data mining. In the presence of outliers, classical estimates of covariance and correlation matrices are not reliable. A small fraction of outliers, in some cases even a single outlier, can distort the classical covariance and correlation estimates making them virtually useless. That is, correlations for the vast majority of the data can be very erroneously reported; principal components transformations can be misleading; and multidimensional outlier detection via Mahalanobis distances can fail to detect outliers. There is plenty of statistical literature on robust covariance and correlation matrix estimates with an emphasis on affine-equivariant estimators that possess high breakdown points and small worst case biases. All such estimators have unacceptable exponential complexity in the number of variables and quadratic complexity in the number of observations. In this paper we focus on several variants of robust covariance and correlation matrix estimates with quadratic complexity in the number of variables and linear complexity in the number of observations. These estimators are based on several forms of pairwise robust covariance and correlation estimates. The estimators studied include two fast estimators based on coordinate-wise robust transformations embedded in an overall procedure recently proposed by [14]. We show that the estimators have attractive robustness properties, and give an example that uses one of the estimators in the new Insightful Miner data mining product.