OpenRaiser/Intern-Atlas huggingface dataset:
Relative Pose Problem for Non-Overlapping Surveillance Cameras with Known Gravity Vector
Authors: Branislav Micusík
Venue: Conference CVPR 2011
Year: 2011

We present a method for estimating the relative pose of two calibrated or uncalibrated non-overlapping surveillance cameras from observing a moving object. We show how to tackle the problem of missing point correspondences heavily required by SfM pipelines and how to go beyond this basic paradigm. We relax the non-linear nature of the problem by accepting two assumptions which surveillance scenarios offer, i.e. the presence of a moving object and easily estimable gravity vector. By those assumptions we cast the problem as a Quadratic Eigenvalue Problem offering an elegant way of treating nonlinear monomials and delivering a quasi closed-form solution as a reliable starting point for a further bundle adjustment. We are the first to bring the closed form solution to such a very practical problem arising in video surveillance. Results in different camera setups demonstrate the feasibility of the approach.
Instruction-based hypergraph pretraining
Authors: Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu
Venue: Conference SIGIR 2024
Year: 2024

Pretraining has been widely explored to augment the adaptability of graph learning models to transfer knowledge from large datasets to a downstream task, such as link prediction or classification. However, the gap between training objectives and the discrepancy between data distributions in pretraining and downstream tasks hinders the transfer of the pretrained knowledge. Inspired by instruction-based prompts widely used in pretrained language models, we introduce instructions into graph pretraining. In this paper, we propose a novel pretraining framework named Instruction-based Hypergraph Pretraining. To overcome the discrepancy between pretraining and downstream tasks, text-based instructions are applied to provide explicit guidance on specific tasks for representation learning. Compared to learnable prompts, whose effectiveness depends on the quality and the diversity of training data, text-based instructions intrinsically encapsulate task information and support the model to generalize beyond the structure seen during pretraining. To capture high-order relations with task information in a context-aware manner, a novel prompting hypergraph convolution layer is devised to integrate instructions into information propagation in hypergraphs. Extensive experiments conducted on three public datasets verify the superiority of IHP in various scenarios.
Ranking-based name matching for author disambiguation in bibliographic data
Authors: Jialu Liu, Kin Hou Lei, Jeffery Yufei Liu, Chi Wang, Jiawei Han
Venue: Conference KDD 2013
Year: 2013

Author name ambiguity is a frequently encountered problem in digital publication libraries such as Microsoft Academic Search. The cause of this problem mostly is that different authors may publish under the same name, while the same author could publish under various names due to abbreviations, nicknames, etc. Author disambiguation is exactly the goal of the Track II of KDD Cup Data Mining Contest 2013. In this paper we introduce our ranking-based name matching algorithm and system called RankMatch. One important feature of our solution is using heterogeneous meta-paths to evaluate the similarity between two potential duplicate authors whose names are compatible. We participated under team name "SmallData" and our final solution achieved a Mean F1 score of 99.157%, ranking in the second place in the contest.
Using relational knowledge discovery to prevent securities fraud
Authors: Jennifer Neville, Özgür Simsek, David D. Jensen, John Komoroske, Kelly Palmer, Henry G. Goldberg
Venue: Conference KDD 2005
Year: 2005

We describe an application of relational knowledge discovery to a key regulatory mission of the National Association of Securities Dealers (NASD). NASD is the world&apos;s largest privatesector securities regulator, with responsibility for preventing and discovering misconduct among securities brokers. Our goal was to help focus NASD’s limited regulatory resources on the brokers who are most likely to engage in securities violations. Using statistical relational learning algorithms, we developed models that rank brokers with respect to the probability that they would commit a serious violation of securities regulations in the near future. Our models incorporate organizational relationships among brokers (e.g., past coworker), which domain experts consider important but cannot easily use otherwise. The learned models were subjected to an extensive evaluation using more than 18 months of data unseen by the model developers and comprising over two person weeks of effort by NASD staff. Model predictions were found to correlate highly with the subjective evaluations of experienced NASD examiners. Furthermore, in all performance measures, our models performed as well as or better than the handcrafted rules that are currently in use at NASD. 1.
Performance analysis of a privacy constrained kNN recommendation using data sketches
Authors: Armita Afsharinejad, Neil Hurley
Venue: Conference WSDM 2018
Year: 2018

This paper evaluates two algorithms, BLIP and JLT, for creating differentially private data sketches of user profiles, in terms of their ability to protect a kNN collaborative filtering algorithm from an inference attack by third-parties. The transformed user profiles are employed in a user-based top-N collaborative filtering system. For the first time, a theoretical analysis of the BLIP is carried out, to derive expressions that relate its parameters to its performance. This allows the two techniques to be fairly compared. The impact of deploying these approaches on the utility of the system---its ability to make good recommendations, and on its privacy level---the ability of third-parties to make inferences about the underlying user preferences, is examined. An active inference attack is evaluated, that consists of the injection of a number of tailored sybil profiles into the system database. User profile data of targeted users is then inferred from the recommendations made to the sybils. Although the differentially private sketches are designed to allow the transformed user profiles to be published without compromising privacy, the attack we examine does not use such information and depends only on some pre-existing knowledge of some user preferences as well as the neighbourhood size of the kNN algorithm. Our analysis therefore assesses in practical terms a relatively weak privacy attack, which is extremely simple to apply in systems that allow low-cost generation of sybils. We find that, for a given differential privacy level, the BLIP injects less noise into the system, but for a given level of noise, the JLT offers a more compact representation.
Optimal point correspondence through the use of rank constraints
Authors: Ricardo Oliveira 0002, João Paulo Costeira, João Manuel Freitas Xavier
Venue: Conference CVPR 2005
Year: 2005

We propose a solution to the n-frame correspondence problem under the factorization framework. During the matching process, our algorithm takes explicitly into account the geometrical constraints associated to the reconstruction process. To this end, a rank constraint is imposed on the measurement matrix. Since our method relies solely on geometric constraints, it is not dependent on corners as image features and can consequently match generic points (e.g. contours). Outlier rejection is integrated as a part of the actual matching process. In general the problem is formulated as combinatorial, but we develop a method, which can provide a solution with a low computational complexity. Because of this, our algorithm is able to handle high-dimensional matching problems that are common in real-life applications.
IPOC: An adaptive interval prediction model based on online chasing and conformal inference for large-scale systems
Authors: Jiadong Chen, Yang Luo 0004, Xiuqi Huang, Fuxin Jiang, Yangguang Shi, Tieying Zhang, Xiaofeng Gao 0001
Venue: Conference KDD 2023
Year: 2023

In large-scale systems, due to system complexity and demand volatility, diverse and dynamic workloads make accurate predictions difficult. In this work, we address an online interval prediction problem (OnPred-Int) and adopt ensemble learning to solve it. We depict that the ensemble learning for OnPred-Int is a dynamic deterministic Markov Decision Process (Dd-MDP) and convert it into a stateful online learning task. Then we propose IPOC, a lightweight and flexible model able to produce effective confidence intervals, adapting the dynamics of real-time workload streams. At each time, IPOC selects a target model and executes chasing for it by a designed chasing oracle, during which process IPOC produces accurate confidence intervals. The effectiveness of IPOCis theoretically validated through sublinear regret analysis and satisfaction of confidence interval requirements. Besides, we conduct extensive experiments on 4 real-world datasets comparing with 19 baselines. To the best of our knowledge, we are the first to apply the frontier theory of online learning to time series prediction tasks.
Independent subspace analysis using geodesic spanning trees
Authors: Barnabás Póczos, András Lörincz
Venue: Conference ICML 2005
Year: 2005

A novel algorithm for performing Independent Subspace Analysis, the estimation of hidden independent subspaces is introduced. This task is a generalization of Independent Component Analysis. The algorithm works by estimating the multi-dimensional differential entropy. The estimation utilizes minimal geodesic spanning trees matched to the sample points. Numerical studies include (i) illustrative examples, (ii) a generalization of the cocktail-party problem to songs played by bands, and (iii) an example on mixed independent subspaces, where subspaces have dependent sources, which are pairwise independent.
Finding interesting rules from large sets of discovered association rules
Authors: Mika Klemettinen, Heikki Mannila, Pirjo Ronkainen, Hannu Toivonen, A. Inkeri Verkamo
Venue: Conference CIKM 1994
Year: 1994

Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form &quot;for 90 % of the rows of the relation, if the row has value 1 in the columns in set W , then it has 1 also in column B&quot;. Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates. 1 Introduction Data mining (knowledge discovery in databases) is a field of increasing interest combining databases, artificial intelligence, and machine learning. The purpose of data mining is to facilitate understanding large amounts of data by discovering interesting regularities or exceptions (see e...
Cross-training: Learning probabilistic mappings between topics
Authors: Sunita Sarawagi, Soumen Chakrabarti, Shantanu Godbole
Venue: Conference KDD 2003
Year: 2003

Classification is a well-established operation in text mining. Given a set of labels A and a set DA of training documents tagged with these labels, a classifier learns to assign labels to unlabeled test documents. Suppose we also had available a different set of labels B, together with a set of documents DB marked with labels from B. If A and B have some semantic overlap, can the availability of DB help us build a better classifier for A, and vice versa? We answer this question in the affirmative by proposing cross-training: a new approach to semi-supervised learning in presence of multiple label sets. We give distributional and discriminative algorithms for cross-training and show, through extensive experiments, that cross-training can discover and exploit probabilistic relations between two taxonomies for more accurate classification.
Joint deduplication of multiple record types in relational data
Authors: Aron Culotta, Andrew McCallum
Venue: Conference CIKM 2005
Year: 2005

Record deduplication is the task of merging database records that refer to the same underlying entity. In relational data-bases, accurate deduplication for records of one type is often dependent on the decisions made for records of other types. Whereas nearly all previous approaches have merged records of different types independently, this work models these inter-dependencies explicitly to collectively deduplicate records of multiple types. We construct a conditional random field model of deduplication that captures these relational dependencies, and then employ a novel relational partitioning algorithm to jointly deduplicate records. For two citation matching datasets, we show that collectively deduplicating paper and venue records results in up to a 30% error reduction in venue deduplication, and up to a 20% error reduction in paper deduplication.
Feature value acquisition in testing: a sequential batch test algorithm
Authors: Victor S. Sheng, Charles X. Ling
Venue: Conference ICML 2006
Year: 2006

In medical diagnosis, doctors often have to order sets of medical tests in sequence in order to make an accurate diagnosis of patient diseases. While doing so they have to make a trade-off between the cost of the tests and possible misdiagnosis. In this paper, we use cost-sensitive learning to model this process. We assume that test examples (new patients) may contain missing values, and their actual values can be acquired at cost (similar to doing medical tests) in order to reduce misclassification errors (misdiagnosis). We propose a novel Sequential Batch Test algorithm that can acquire sets of attribute values in sequence, similar to sets of medical tests ordered by doctors in sequence. The goal of our algorithm is to minimize the total cost (i.e., the trade-off) of acquiring attribute values and misclassifications. We demonstrate the effectiveness of our algorithm, and show that it outperforms previous methods significantly. Our algorithm can be readily applied in real-world diagnosis tasks. A case study on the heart disease is given in the paper.
Applying neighborhood consistency for fast clustering and kernel density estimation
Authors: Kai Zhang 0001, Ming Tang 0001, James T. Kwok
Venue: Conference CVPR 2005
Year: 2005

Nearest neighborhood consistency is an important concept in statistical pattern recognition, which underlies the well-known k-nearest neighbor method. In this paper, we combine this idea with kernel density estimation based clustering, and derive the fast mean shift algorithm (FMS). FMS greatly reduces the complexity of feature space analysis, resulting satisfactory precision of classification. More importantly, we show that with FMS algorithm, we are in fact relying on a conceptually novel approach of density estimation, the fast kernel density estimation (FKDE) for clustering. The FKDE combines smooth and non-smooth estimators and thus inherits advantages from both. Asymptotic analysis reveals the approximation of the FKDE to standard kernel density estimator. Data clustering and image segmentation experiments demonstrate the efficiency of FMS.
Optimizing scoring functions and indexes for proximity search in type-annotated corpora
Authors: Soumen Chakrabarti, Kriti Puniyani, Sujatha Das
Venue: Conference WWW 2006
Year: 2006

We introduce a new, powerful class of text proximity queries: find an instance of a given "answer type" (person, place, distance) near "selector" tokens matching given literals or satisfying given ground predicates. An example query is type=distance NEAR Hamburg Munich. Nearness is defined as a flexible, trainable parameterized aggregation function of the selectors, their frequency in the corpus, and their distance from the candidate answer. Such queries provide a key data reduction step for information extraction, data integration, question answering, and other text-processing applications. We describe the architecture of a next-generation information retrieval engine for such applications, and investigate two key technical problems faced in building it. First, we propose a new algorithm that estimates a scoring function from past logs of queries and answer spans. Plugging the scoring function into the query processor gives high accuracy: typically, an answer is found at rank 2-4. Second, we exploit the skew in the distribution over types seen in query logs to optimize the space required by the new index structures required by our system. Extensive performance studies with a 10GB, 2-million document TREC corpus and several hundred TREC queries show both the accuracy and the efficiency of our system. From an initial 4.3GB index using 18,000 types from WordNet, we can discard 88% of the space, while inflating query times by a factor of only 1.9. Our final index overhead is only 20% of the total index space needed.
Using annotations in enterprise search
Authors: Pavel A. Dmitriev, Nadav Eiron, Marcus Fontoura, Eugene J. Shekita
Venue: Conference WWW 2006
Year: 2006

A major difference between corporate intranets and the Internet is that in intranets the barrier for users to create web pages is much higher. This limits the amount and quality of anchor text, one of the major factors used by Internet search engines, making intranet search more difficult. The social phenomenon at play also means that spam is relatively rare. Both on the Internet and in intranets, users are often willing to cooperate with the search engine in improving the search experience. These characteristics naturally lead to considering using user feedback to improve search quality in intranets. In this paper we show how a particular form of feedback, namely user annotations, can be used to improve the quality of intranet search. An annotation is a short description of the contents of a web page, which can be considered a substitute for anchor text. We propose two ways to obtain user annotations, using explicit and implicit feedback, and show how they can be integrated into a search engine. Preliminary experiments on the IBM intranet demonstrate that using annotations improves the search quality.
Using historical data to enhance rank aggregation
Authors: Miriam Fernández, David Vallet, Pablo Castells
Venue: Conference SIGIR 2006
Year: 2006

Rank aggregation is a pervading operation in IR technology. We hypothesize that the performance of score-based aggregation may be affected by artificial, usually meaningless deviations consis-tently occurring in the input score distributions, which distort the combined result when the individual biases differ from each other. We propose a score-based rank aggregation model where the source scores are normalized to a common distribution before being combined. Early experiments on available data from several TREC collections are shown to support our proposal.
A system for principled matchmaking in an electronic marketplace
Authors: Tommaso Di Noia, Eugenio Di Sciascio, Francesco M. Donini, Marina Mongiello
Venue: Conference WWW 2003
Year: 2003

More and more resources are becoming available on the Web, and there is a growing need for infrastructures that, based on advertised descriptions, are able to semantically match demands with supplies.We formalize general properties a matchmaker should have, then we present a matchmaking facilitator, compliant with desired properties.The system embeds a NeoClassic reasoner, whose structural subsumption algorithm has been modified to allow match categorization into potential and partial, and ranking of matches within categories. Experiments carried out show the good correspondence between users and system rankings.
Eigen-trend: trend analysis in the blogosphere based on singular value decompositions
Authors: Yun Chi, Belle L. Tseng, Jun&apos, ichi Tatemura
Venue: Conference CIKM 2006
Year: 2006

The blogosphere - the totality of blog-related Web sites - has become a great source of trend analysis in areas such as product survey, customer relationship, and marketing. Existing approaches are based on simple counts, such as the number of entries or the number of links. In this paper, we introduce a novel concept, coined eigen-trend, to represent the temporal trend in a group of blogs with common interests and propose two new techniques for extracting eigen-trends in blogs. First, we propose a trend analysis technique based on the singular value decomposition. Extracted eigen-trends provide new insights into multiple trends on the same keyword. Second, we propose another trend analysis technique based on a higher-order singular value decomposition. This analyzes the blogosphere as a dynamic graph structure and extracts eigen-trends that reflect the structural changes of the blogosphere over time. Experimental studies based on synthetic data sets and a real blog data set show that our new techniques can reveal a lot of interesting trend information and insights in the blogosphere that are not obtainable from traditional count-based methods.
Mining blog stories using community-based and temporal clustering
Authors: Arun Qamra, Belle L. Tseng, Edward Y. Chang
Venue: Conference CIKM 2006
Year: 2006

In recent years, weblogs, or blogs for short, have become an important form of online content. The personal nature of blogs, online interactions between bloggers, and the temporal nature of blog entries, differentiate blogs from other kinds of Web content. Bloggers interact with each other by linking to each other's posts, thus forming online communities. Within these communities, bloggers engage in discussions of certain issues, through entries in their blogs. Since these discussions are often initiated in response to online or offline events, a discussion typically lasts for a limited time duration. We wish to extract such temporal discussions, or stories, occurring within blogger communities, based on some query keywords. We propose a Content-Community-Time model that can leverage the content of entries, their timestamps, and the community structure of the blogs, to automatically discover stories. Doing so also allows us to discover hot stories. We demonstrate the effectiveness of our model through several case studies using real-world data collected from the blogosphere.
Query expansion using associated queries
Authors: Bodo Billerbeck, Falk Scholer, Hugh E. Williams, Justin Zobel
Venue: Conference CIKM 2003
Year: 2003

Hundreds of millions of users each day use web search engines to meet their information needs. Advances in web search effectiveness are therefore perhaps the most significant public outcomes of IR research. Query expansion is one such method for improving the effectiveness of ranked retrieval by adding additional terms to a query. In previous approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. We propose a new method of obtaining expansion terms, based on selecting terms from past user queries that are associated with documents in the collection. Our scheme is effective for query expansion for web retrieval: our results show relative improvements over unexpanded full text retrieval of 26%--29%, and 18%--20% over an optimised, conventional expansion approach.