OpenRaiser/Intern-Atlas huggingface dataset:
Automatic detection of craters in planetary images: an embedded framework using feature selection and boosting
Authors: Wei Ding, Tomasz F. Stepinski, Lourenço P. C. Bandeira, Ricardo Vilalta, Youxi Wu, Zhenyu Lu, Tianyu Cao
Venue: CIKM
Year: 2010

Identifying impact craters on planetary surfaces is one fundamental task in planetary science. In this paper, we present an embedded framework on auto-detection of craters, using feature selection and boosting strategies. The paradigm aims at building a universal and practical crater detector. This methodology addresses three issues that such a tool must possess: (i) it utilizes mathematical morphology to efficiently identify the regions of an image that can potentially contain craters; only those regions, defined as crater candidates, are the subjects of further processing; (ii) it selects Haar-like image texture features in combination with boosting ensemble supervised learning algorithms to accurately classify candidates into craters and non-craters; (iii) it uses transfer learning, at a minimum additional cost, to enable maintaining an accurate auto-detection of craters on new images, having morphology different from what has been captured by the original training set. All three aforementioned components of the detection methodology are discussed, and the entire framework is evaluated on a large test image of 37,500 x 56,250$ m2 on Mars, showing heavily cratered Martian terrain characterized by nonuniform surface morphology. Our study demonstrates that this methodology provides a robust and practical tool for planetary science, in terms of both detection accuracy and efficiency.
Brown dwarf: a P2P data-warehousing system
Authors: Katerina Doka, Dimitrios Tsoumakos, Nectarios Koziris
Venue: CIKM
Year: 2010

In this demonstration we present the Brown Dwarf, a distributed system designed to efficiently store, query and update multidimensional data. Deployed on any number of commodity nodes, our system manages to distribute large volumes of data over network peers on-the-fly and process queries and updates on-line through cooperating nodes that hold parts of a materialized cube. Moreover, it adapts its resources according to demand and hardware failures and is cost-effective both over the required hardware and software components. All the aforementioned functionality will be tested using various datasets and query loads.
Comparison of six aggregation strategies to compute users' trustworthiness
Authors: Pierpaolo Dondio, Stephen Barrett
Venue: CIKM
Year: 2010

The decision to grant trust in virtual societies is often an evidence based process. The evidence for such decision derives from a diverse set, where mutual relationships and contradictions might occur. This paper compares and evaluates six aggregation strategies to compute users' trustworthiness. Our evaluation performed over a large online-community, shows how a rule-based strategy based on an argumentation semantic outperforms strategies where mutual relationships among evidence are ignored.
On the selectivity of multidimensional routing indices
Authors: Christos Doulkeridis, Akrivi Vlachou, Kjetil Nørvåg, Yannis Kotidis, Michalis Vazirgiannis
Venue: CIKM
Year: 2010

Recently, the problem of efficiently supporting advanced query operators, such as nearest neighbor or range queries, over multidimensional data in widely distributed environments has attracted much attention. In unstructured peer-to-peer (P2P) networks, peers store data in an autonomous manner, thus multidimensional routing indices (MRI) are required, in order to route user queries efficiently to only those peers that may contribute to the query result set. Focusing on a hybrid unstructured P2P network, in this paper, we analyze the parameters for building MRI of high selectivity. In the case where similar data are located at different parts of the network, MRI exhibit extremely poor performance, which renders them ineffective. We present algorithms that boost the query routing performance by detecting similar peers and reassigning these peers to other parts of the hybrid network in a distributed and scalable way. The resulting MRI are able to eagerly discard routing paths during query processing. We demonstrate the advantages of our approach experimentally and show that our framework enhances a state-of-the-art approach for similarity search in terms of reduced network traffic and number of contacted peers.
Construction of a sentimental word dictionary
Authors: Eduard C. Dragut, Clement T. Yu, A. Prasad Sistla, Weiyi Meng
Venue: CIKM
Year: 2010

The Web has plenty of reviews, comments and reports about products, services, government policies, institutions, etc. The opinions expressed in these reviews influence how people regard these entities. For example, a product with consistently good reviews is likely to sell well, while a product with numerous bad reviews is likely to sell poorly. Our aim is to build a sentimental word dictionary, which is larger than existing sentimental word dictionaries and has high accuracy. We introduce rules for deduction, which take words with known polarities as input and produce synsets (a set of synonyms with a definition) with polarities. The synsets with deduced polarities can then be used to further deduce the polarities of other words. Experimental results show that for a given sentimental word dictionary with D words, approximately an additional 50 % of D words with polarities can be deduced. An experiment is conducted to find the accuracy of a random sample of the deduced words. It is found that the accuracy is about the same as that of comparing the judgment of one human with that of another.
Manifold ranking with sink points for update summarization
Authors: Pan Du, Jiafeng Guo, Jin Zhang, Xueqi Cheng
Venue: CIKM
Year: 2010

Update summarization aims to create a summary over a topic-related multi-document dataset based on the assumption that the user has already read a set of earlier documents of the same topic. Beyond the problems (i.e., topic relevance, salience, and diversity in extracted information) tackled by topic-focused multi-document summarization, the update summarization must address the novelty problem as well. In this paper, we propose a novel extractive approach based on manifold ranking with sink points for update summarization. Specifically, our approach leverages a manifold ranking process over the sentence manifold to find topic relevant and salient sentences. More important, by introducing the sink points into sentence manifold, the ranking process can further capture the novelty and diversity based on the intrinsic sentence manifold. Therefore, we are able to address the four challenging problems above for update summarization in a unified way. Experiments on benchmarks of TAC are performed and the evaluation results show that our approach can achieve comparative performance to the existing best performing systems in TAC tasks.
Adapting cost-sensitive learning for reject option
Authors: Jun Du, Eileen A. Ni, Charles X. Ling
Venue: CIKM
Year: 2010

Traditional cost-sensitive learning algorithms always deterministically predict examples as either positive or negative (in binary setting), to minimize the total misclassification cost. However, in more advanced real-world settings, the algorithms can also have another option to reject examples of high uncertainty. In this paper, we assume that cost-sensitive learning algorithms can reject the examples and obtain their true labels by paying reject cost. We therefore analyse three categories of popular cost-sensitive learning approaches, and provide generic methods to adapt them for reject option.
Temporal dynamics and information retrieval
Authors: Susan T. Dumais
Venue: CIKM
Year: 2010

Many digital resources, like the Web, are dynamic and ever-changing collections of information. However, most of the tools information retrieval and management that have been developed for interacting with Web content, such as browsers and search engines, focus on a single static snapshot of the information. In this talk, I will present analyses of how Web content changes over time, how people re-visit Web pages over time, and how re-visitation patterns are influenced by changes in user intent and content. These results have implications for many aspects of information retrieval and management including crawling, ranking and information extraction algorithms, result presentation, and evaluation. I will describe a prototype system that supports people in understanding how the information they interact with changes over time, and a new retrieval model that incorporates features about the temporal evolution of content to improve core ranking. Finally, I will conclude with an overview of some general challenges that need to be addressed to fully incorporate temporal dynamics in information retrieval and information management systems.
Data aspects in a relational database
Authors: Curtis E. Dyreson, Omar U. Florez
Venue: CIKM
Year: 2010

Data has cross-cutting concerns such as versioning, privacy, and reliability. In this paper we sketch support such concerns by adapting the aspect-oriented programming (AOP) paradigm to data. Our goal, shared by AOP, is to re-engineer applications to support cross-cutting concerns without directly modifying the application's data or queries. We propose modeling a cross-cutting data concern as a data aspect. A data aspect weaves metadata around an application's data and queries, imbuing them with additional semantics for constraint and query processing.
Multi-view clustering with constraint propagation for learning with an incomplete mapping between views
Authors: Eric Eaton, Marie desJardins, Sara Jacob
Venue: CIKM
Year: 2010

Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via the partial mapping. It uses co-EM to iteratively estimate the propagation within each view based on the current clustering model, transfer the constraints across views, and update the clustering model, thereby learning a unified model for all views. We show that this approach significantly improves clustering performance over several other methods for transferring constraints and allows multi-view clustering to be reliably applied when given a limited mapping between the views.
Web page classification on child suitability
Authors: Carsten Eickhoff, Pavel Serdyukov, Arjen P. de Vries
Venue: CIKM
Year: 2010

Children spend significant amounts of time on the Internet. Recent studies showed, that during these periods they are often not under adult supervision. This work presents an automatic approach to identifying suitable web pages for children based on topical and non-topical web page aspects. We discuss the characteristics of children's web sites with respect to recent findings in children's psychology and cognitive sciences. We finally evaluate our approach in a large-scale user study, finding, that it compares favourably to state of the art methods while approximating human performance.
Maximum normalized spacing for efficient visual clustering
Authors: Zhi-Gang Fan, Yadong Wu, Bo Wu
Venue: CIKM
Year: 2010

In this paper, for efficient clustering of visual image data that have arbitrary mixture distributions, we propose a simple distance metric learning method called Maximum Normalized Spacing (MNS) which is a generalized principle based on Maximum Spacing [12] and Minimum Spanning Tree (MST). The proposed Normalized Spacing (NS) can be viewed as a kind of adaptive distance metric for contextual dissimilarity measure which takes into account the local distribution of the data vectors. Image clustering is a difficult task because there are multiple nonlinear manifolds embedded in the data space. Many of the existing clustering methods often fail to learn the whole structure of the multiple manifolds and they are usually not very effective. Combining both the internal and external statistics of clusters to capture the density structure of manifolds, MNS is capable of efficient and effective solving the clustering problem for the complex multi-manifold datasets in arbitrary metric spaces. We apply this MNS method into the practical problem of multi-view image clustering and obtain good results which are helpful for image browsing systems. Using the COIL-20 [19] and COIL-100 [18] multi-view image databases, our experimental results demonstrate the effectiveness of the proposed MNS clustering method and this clustering method is more efficient than the traditional clustering methods.
Hypergraph-based multilevel matrix approximation for text information retrieval
Authors: Haw-ren Fang, Yousef Saad
Venue: CIKM
Year: 2010

In Latent Semantic Indexing (LSI), a collection of documents is often pre-processed to form a sparse term-document matrix, followed by a computation of a low-rank approximation to the data matrix. A multilevel framework based on hypergraph coarsening is presented which exploits the hypergraph that is canonically associated with the sparse term-document matrix representing the data. The main goal is to reduce the cost of the matrix approximation without sacrificing accuracy. Because coarsening by multilevel hypergraph techniques is a form of clustering, the proposed approach can be regarded as a hybrid of factorization-based LSI and clustering-based LSI. Experimental results indicate that our method achieves good improvement of the retrieval performance at a reduced cost
Multilevel manifold learning with application to spectral clustering
Authors: Haw-ren Fang, Sophia Sakellaridi, Yousef Saad
Venue: CIKM
Year: 2010

In the past decade, a number of nonlinear dimensionality reduction methods using an affinity graph have been developed for manifold learning. This paper explores a multilevel framework with the goal of reducing the cost of unsupervised manifold learning and preserving the embedding quality at the same time. An application to spectral clustering is also presented. Experimental results indicate that our multilevel approach is an appealing alternative to standard techniques.
Adaptive query processing in data stream management systems under limited memory resources
Authors: Fatima Farag, Moustafa A. Hammad, Reda Alhajj
Venue: CIKM
Year: 2010

Many data stream sources are prone to periods of spikes in volume as well as periods of delays and silence. Because peak load during a spike can be orders of magnitude higher than a typical load, fully provisioning data stream monitoring system with all needed resources is generally difficult to achieve. Furthermore, data stream sources are subject to network delays and congestions as they connect to a data stream monitoring system over shared communication channels. Careless management of delays and periods of silence will eventually drop system performance drastically.
Focused crawling using navigational rank
Authors: Shicong Feng, Li Zhang, Yuhong Xiong, Conglei Yao
Venue: CIKM
Year: 2010

The goal of focused crawling is to use limited resources to effectively discover web pages related to a specific topic rather than downloading all accessible web documents. The major challenge in focused crawling is how to effectively determine each hyperlink's capability of leading to target pages. To compute this capability, we present a novel approach, called Navigational Rank (NR). In general, NR is a kind of two-step and two-direction credit propagation approach. Compared to existing methods, NR mainly has three advantages. First, NR is dynamically updated during the crawling progress, which can adapt to different website structures very well. Second, when the crawling seed is far away from the target pages, and the target pages only constitute a small portion of the whole website, NR shows a significant performance advantage. Third, NR computes each link's capability of leading to target pages by considering both the target and non-target pages it leads to. This global knowledge causes a better performance than only using target pages. We have performed extensive experiments for performance evaluation of the proposed approach using two groups of large-scale, real-world datasets from two different domains. The experimental results show that our approach is domain-independent and significantly outperforms the state-of-arts.
Bringing order to your photos: event-driven classification of flickr images based on social knowledge
Authors: Claudiu S. Firan, Mihai Georgescu, Wolfgang Nejdl, Raluca Paiu
Venue: CIKM
Year: 2010

With the rapidly increasing popularity of Social Media sites, a lot of user generated content has been injected in the Web, thus resulting in a large amount of both multimedia items (music - Last.fm, MySpace.com, pictures - Flickr, Picasa, videos - YouTube) and textual data (tags and other text-based documents). As a consequence, especially for multimedia content it has become more and more difficult to find exactly the objects that best match the users' information needs. The methods we propose in this paper try to alleviate this problem and we focus on the domain of pictures, in particular on a subset of Flickr data. Many of the photos posted by users on Flickr have been shot during events and our methods aim to allow browsing and organization of picture collections in a natural way, by events. The algorithms we introduce in this paper exploit the social information produced by users in form of tags, titles and photo descriptions, for classifying pictures into different event categories. The extensive automated experiments demonstrate that our approach is very effective and opens new possibilities for multimedia retrieval, in particular image search. Moreover, the direct comparison with previous event detection algorithms confirm once more the quality of our methods.
Unsupervised public health event detection for epidemic intelligence
Authors: Marco Fisichella, Avaré Stewart, Kerstin Denecke, Wolfgang Nejdl
Venue: CIKM
Year: 2010

Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. However, state-of-the-art systems for Epidemic Intelligence have not kept the pace with the growing need for more robust public health event detection. In this paper, we propose a game-changing approach where public health events are detected in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 66% and a recall of 81% when evaluated using manually annotated, real-world data. This shows promising results for the use of such techniques in this new problem setting.
Mining rules to explain activities in videos
Authors: Omar U. Florez, Curtis E. Dyreson
Venue: CIKM
Year: 2010

We present a novel approach to mining dependency rules that explain the scenes present during a video sequence. The approach first characterizes activities based on their most important events. Next, an HMM-based approach finds the mixture components that best describe the clustering dependencies between events and activities in video data. The dependencies among activities are taken as association patterns with temporal precedence and analyzed using their co-occurrence relationships in time windows. This technique is meant to understand the multiple actions taken in a video or to predict future occurrences of certain activities.
Application of semantic annotations to predicting users' demographics
Authors: Blaz Fortuna, Dunja Mladenic, Marko Grobelnik
Venue: CIKM
Year: 2010

This paper presents an experimental study on predicting demographics for the users of a large news publishing web site. Each user is described by the articles he/she has read. The study looks at the effect of features used for article representations, such as text, automatic annotations and manually annotations on the prediction performance. It is show how different demographic dimensions depend on different combination of automatic and / or manual annotations.
Preserving location and absence privacy in geo-social networks
Authors: Dario Freni, Carmen Ruiz Vicente, Sergio Mascetti, Claudio Bettini, Christian S. Jensen
Venue: CIKM
Year: 2010

Online social networks often involve very large numbers of users who share very large volumes of content. This content is increasingly being tagged with geo-spatial and temporal coordinates that may then be used in services. For example, a service may retrieve photos taken in a certain region. The resulting geo-aware social networks (GeoSNs) pose privacy threats beyond those found in location-based services. Content published in a GeoSN is often associated with references to multiple users, without the publisher being aware of the privacy preferences of those users. Moreover, this content is often accessible to multiple users. This renders it difficult for GeoSN users to control which information about them is available and to whom it is available. This paper addresses two privacy threats that occur in GeoSNs: location privacy and absence privacy. The former concerns the availability of information about the presence of users in specific locations at given times, while the latter concerns the availability of information about the absence of an individual from specific locations during given periods of time. The challenge addressed is that of supporting privacy while still enabling useful services. We believe this is the first paper to formalize these two notions of privacy and to propose techniques for enforcing them. The techniques offer privacy guarantees, and the paper reports on empirical performance studies of the techniques.
A robust semi-supervised classification method for transfer learning
Authors: Akinori Fujino, Naonori Ueda, Masaaki Nagata
Venue: CIKM
Year: 2010

The transfer learning problem of designing good classifiers with a high generalization ability by using labeled samples whose distribution is different from that of test samples is an important and challenging research issue in the fields of machine learning and data mining. This paper focuses on designing a semi-supervised classifier trained by using unlabeled samples drawn by the same distribution as test samples, and presents a semi-supervised classification method to deal with the transfer learning problem, based on a hybrid discriminative and generative model. Although JESS-CM is one of the most successful semi-supervised classifier design frameworks and has achieved the best published results in NLP tasks, it has an overfitting problem in transfer learning settings that we consider in this paper. We expect the overfitting problem to be mitigated with the proposed method, which utilizes both labeled and unlabeled samples for the discriminative training of classifiers. We also present a refined objective that formalizes the training algorithm and classifier form. Our experimental results for text classification using three typical benchmark test collections confirmed that the proposed method outperformed the JESS-CM framework with most transfer learning settings.