OpenRaiser/Intern-Atlas huggingface dataset:
Fake news, disinformation, propaganda, media bias, and flattening the curve of the COVID-19 infodemic
Authors: Preslav Nakov, Giovanni Da San Martino
Venue: Conference KDD 2021
Year: 2021

The rise of social media has democratized content creation and has made it easy for anybody to share and to spread information online. On the positive side, this has given rise to citizen journalism, thus enabling much faster dissemination of information compared to what was possible with newspapers, radio, and TV. On the negative side, stripping traditional media from their gate-keeping role has left the public unprotected against the spread of disinformation, which could now travel at breaking-news speed over the same democratic channel. This situation gave rise to the proliferation of false information, specifically created to affect individual people's beliefs, and ultimately to influence major events such as political elections; it also set the dawn of the Post-Truth Era, where appeal to emotions has become more important than the truth. More recently, with the emergence of the COVID-19 pandemic, a new blending of medical and political misinformation and disinformation has given rise to the first global infodemic. Limiting the impact of these negative developments has become a major focus for journalists, social media companies, and regulatory authorities. We offer an overview of the emerging and inter-connected research areas of fact-checking, misinformation, disinformation, "fake news'', propaganda, and media bias detection, with focus on text and computational approaches. We explore the general fact-checking pipeline and important elements thereof such as check-worthiness estimation, spotting previously fact-checked claims, stance detection, source reliability estimation, detection of persuasion/propaganda techniques in text and memes, and detecting malicious users in social media. We further cover large-scale pre-trained language models, and the challenges and opportunities they offer for generating and for defending against neural fake news. Finally, we explore some recent efforts towards flattening the curve of the COVID-19 infodemic.
Putting the human in the time series analytics loop
Authors: Shima Imani, Sara Alaee, Eamonn J. Keogh
Venue: Conference WWW 2019
Year: 2019

Time series are one of the most common data types in nature. Given this fact, there are dozens of query-by-sketching/ query-by-example/ query-algebra systems proposed to allow users to search large time series collections. However, none of these systems have seen widespread adoption. We argue that there are two reasons why this is so. The first reason is that these systems are often complex and unintuitive, requiring the user to understand complex syntax/interfaces to construct high-quality queries. The second reason is less well appreciated. The expressiveness of most query-by-content systems is surprisingly limited. There are well defined, simple queries that cannot be answered by any current query-by-content system, even if it uses a state-of-the-art distance measure such as Dynamic Time Warping. In this work, we propose a natural language search mechanism for searching time series. We show that our system is expressive, intuitive, and requires little space and time overhead. Because our system is text-based, it can leverage decades of research text retrieval, including ideas such as relevance feedback. Moreover, we show that our system subsumes both motif/discord discovery and most existing query-by-content systems in the literature. We demonstrate the utility of our system with case studies in domains as diverse as animal motion studies, medicine and industry.
Fedgtp: Exploiting inter-client spatial dependency in federated graph-based traffic prediction
Authors: Linghua Yang, Wantong Chen, Xiaoxi He, Shuyue Wei 0001, Yi Xu 0013, Zimu Zhou, Yongxin Tong
Venue: Conference KDD 2024
Year: 2024

Graph-based methods have witnessed tremendous success in traffic prediction, largely attributed to their superior ability in capturing and modeling spatial dependencies. However, urban-scale traffic data are usually distributed among various owners, limited in sharing due to privacy restrictions. This fragmentation of data severely hinders interaction across clients, impeding the utilization of inter-client spatial dependencies. Existing studies have yet to address this non-trivial issue, thereby leading to sub-optimal performance. To fill this gap, we propose FedGTP, a new federated graph-based traffic prediction framework that promotes adaptive exploitation of inter-client spatial dependencies to recover close-to-optimal performance complying with privacy regulations like GDPR. We validate FedGTP via large-scale application-driven experiments on real-world datasets. Extensive baseline comparison, ablation study and case study demonstrate that FedGTP indeed surpasses existing methods through fully recovering inter-client spatial dependencies, achieving 21.08%, 13.48%, 19.90% decrease on RMSE, MAE and MAPE, respectively. Our code is available at https://github.com/LarryHawkingYoung/KDD2024_FedGTP
Form 10-q itemization
Authors: Yanci Zhang, Tianming Du, Yujie Sun, Lawrence Donohue, Rui Dai
Venue: Conference CIKM 2021
Year: 2021

The quarterly financial statement, or Form 10-Q, is one of the most frequently required filings for US public companies to disclose financial and other important business information. Due to the massive volume of 10-Q filings and the enormous variations in the reporting format, it has been a long-standing challenge to retrieve item-specific information from 10-Q filings that lack machine-readable hierarchy. This paper presents a solution for itemizing 10-Q files by complementing a rule-based algorithm with a Convolutional Neural Network (CNN) image classifier. This solution demonstrates a pipeline that can be generalized to a rapid data retrieval solution among a large volume of textual data using only typographic items. The extracted textual data can be used as unlabeled content-specific data to train transformer models (e.g., BERT) or fit into various field-focus natural language processing (NLP) applications.
Unsupervised search-based structured prediction
Authors: Hal Daumé III
Venue: Conference ICML 2009
Year: 2009

We describe an adaptation and application of a search-based structured prediction algorithm “Searn” to unsupervised learning problems. We show that it is possible to reduce unsupervised learning to supervised learning and demonstrate a high-quality unsupervised shift-reduce parsing model. We additionally show a close connection between unsupervised Searn and expectation maximization. Finally, we demonstrate the efficacy of a semi-supervised extension. The key idea that enables this is an application of the predict-self idea for unsupervised learning.
Probabilistic Discovery of Time Series Motifs
Authors: Bill Yuan-chi Chiu, Eamonn J. Keogh, Stefano Lonardi
Venue: Conference KDD 2003
Year: 2003

Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs. Two limitations of this work were the poor scalability of the motif discovery algorithm, and the inability to discover motifs in the presence of noise.Here we address these limitations by introducing a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences. Our algorithm is probabilistic in nature, but as we show empirically and theoretically, it can find time series motifs with very high probability even in the presence of noise or "don't care" symbols. Not only is the algorithm fast, but it is an anytime algorithm, producing likely candidate motifs almost immediately, and gradually improving the quality of results over time.
Continuous Tracking Within and Across Camera Streams
Authors: Jinman Kang, Isaac Cohen, Gérard G. Medioni
Venue: Conference CVPR 2003
Year: 2003

This paper presents a new approach for continuous tracking of moving objects observed by multiple, heterogeneous cameras. Our approach simultaneously processes video streams from stationary and pan-tilt-zoom cameras. The detection of moving objects from moving camera streams is performed by defining an adaptive background model that takes into account the camera motion approximated by an affine transformation. We address the tracking problem by separately modeling motion and appearance of the moving objects using two probabilistic models. For the appearance model, multiple color distribution components are proposed for ensuring a more detailed description of the object being tracked. The motion model is obtained using a Kalman filter (KF) process, which predicts the position of the moving object. The tracking is performed by the maximization of a joint probability model. The novelty of our approach consists in modeling the multiple trajectories observed by the moving and stationary cameras in the same KF framework. It allows deriving a more accurate motion measurement for objects simultaneously viewed by the two cameras and an automatic handling of occlusions, errors in the detection and camera handoff. We demonstrate the performances of the system on several video surveillance sequences.
Man-Made Structure Detection in Natural Images using a Causal Multiscale Random Field
Authors: Sanjiv Kumar, Martial Hebert
Venue: Conference CVPR 2003
Year: 2003

This paper presents a generative model based approach to man-made structure detection in 2D (two-dimensional) natural images. The proposed approach uses a causal multiscale random field suggested by Bouman and Shapiro (1994) as a prior model on the class labels on the image sites. However, instead of assuming the conditional independence of the observed data, we propose to capture the local dependencies in the data using a multiscale feature vector. The distribution of the multiscale feature vectors is modeled as mixture of Gaussians. A set of robust multi-scale features is presented that captures the general statistical properties of man-made structures at multiple scales without relying on explicit edge detection. The proposed approach was validated on real-world images from the Corel data set, and a performance comparison with other techniques is presented.
Scale-hierarchical 3D object recognition in cluttered scenes
Authors: Prabin Bariya, Ko Nishino
Venue: Conference CVPR 2010
Year: 2010

3D object recognition in scenes with occlusion and clutter is a difficult task. In this paper, we introduce a method that exploits the geometric scale-variability to aid in this task. Our key insight is to leverage the rich discriminative information provided by the scale variation of local geometric structures to constrain the massive search space of potential correspondences between model and scene points. In particular, we exploit the geometric scale variability in the form of the intrinsic geometric scale of each computed feature, the hierarchy induced within the set of these intrinsic geometric scales, and the discriminative power of the local scale-dependent/invariant 3D shape descriptors. The method exploits the added information in a hierarchical coarse-to-fine manner that lets it cull the space of all potential correspondences effectively. We experimentally evaluate the accuracy of our method on an extensive set of real scenes with varying amounts of partial occlusion and achieve recognition rates higher than the state-of-the-art. Furthermore, for the first time we systematically demonstrate the method's ability to accurately localize objects despite changes in their global scales.
Structure learning in random fields for heart motion abnormality detection
Authors: Mark Schmidt 0001, Kevin P. Murphy, Glenn Fung, Rómer Rosales
Venue: Conference CVPR 2008
Year: 2008

Coronary Heart Disease can be diagnosed by assessing the regional motion of the heart walls in ultrasound images of the left ventricle. Even for experts, ultrasound images are difficult to interpret leading to high intra-observer variability. Previous work indicates that in order to approach this problem, the interactions between the different heart regions and their overall influence on the clinical condition of the heart need to be considered. To do this, we propose a method for jointly learning the structure and parameters of conditional random fields, formulating these tasks as a convex optimization problem. We consider block-L1 regularization for each set of features associated with an edge, and formalize an efficient projection method to find the globally optimal penalized maximum likelihood solution. We perform extensive numerical experiments comparing the presented method with related methods that approach the structure learning problem differently. We verify the robustness of our method on echocardiograms collected in routine clinical practice at one hospital.
Using specularities for recognition
Authors: Margarita Osadchy, David W. Jacobs, Ravi Ramamoorthi
Venue: Conference ICCV 2003
Year: 2003

Recognition systems have generally treated specular highlights as noise. We show how to use these highlights as a positive source of information that improves recognition of shiny objects. This also enables us to recognize very challenging shiny transparent objects, such as wine glasses. Specifically, we show how to find highlights that are consistent with a hypothesized pose of an object of known 3D shape. We do this using only a qualitative description of highlight formation that is consistent with most models of specular reflection, so no specific knowledge of an object's reflectance properties is needed. We first present a method that finds highlights produced by a dominant compact light source, whose position is roughly known. We then show how to estimate the lighting automatically for objects whose reflection is part specular and part Lambertian. We demonstrate this method for two classes of objects. First, we show that specular information alone can suffice to identify objects with no Lambertian reflectance, such as transparent wine glasses. Second, we use our complete system to recognize shiny objects, such as pottery.
Learning compact hashing codes for efficient tag completion and prediction
Authors: Qifan Wang, Lingyun Ruan, Zhiwei Zhang, Luo Si
Venue: Conference CIKM 2013
Year: 2013

Tags have been popularly utilized in many applications with image and text data for better managing, organizing and searching for useful information. Tag completion provides missing tag information for a set of existing images or text documents while tag prediction recommends tag information for any new image or text document. Valuable prior research has focused on improving the accuracy of tag completion and prediction, but limited research has been conducted for the efficiency issue in tag completion and prediction, which is a critical problem in many large scale real world applications.
Independent factor topic models
Authors: Duangmanee Putthividhya, Hagai Thomas Attias, Srikantan S. Nagarajan
Venue: Conference ICML 2009
Year: 2009

Topic models such as Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) have recently emerged as powerful statistical tools for text document modeling. In this paper, we improve upon CTM and propose Independent Factor Topic Models (IFTM) which use linear latent variable models to uncover the hidden sources of correlation between topics. There are 2 main contributions of this work. First, by using a sparse source prior model, we can directly visualize sparse patterns of topic correlations. Secondly, the conditional independence assumption implied in the use of latent source variables allows the objective function to factorize, leading to a fast Newton-Raphson based variational inference algorithm. Experimental results on synthetic and real data show that IFTM runs on average 3--5 times faster than CTM, while giving competitive performance as measured by perplexity and loglikelihood of held-out data.
Scalable kNN search on vertically stored time series
Authors: Shrikant Kashyap, Panagiotis Karras
Venue: Conference KDD 2011
Year: 2011

Nearest-neighbor search over time series has received vast research attention as a basic data mining task. Still, none of the hitherto proposed methods scales well with increasing time-series length. This is due to the fact that all methods provide an one-off pruning capacity only. In particular, traditional methods utilize an index to search in a reduced-dimensionality feature space; however, for high time-series length, search with such an index yields many false hits that need to be eliminated by accessing the full records. An attempt to reduce false hits by indexing more features exacerbates the curse of dimensionality, and vice versa. A recently proposed alternative, iSAX, uses symbolic approximate representations accessed by a simple file-system directory as an index. Still, iSAX also encounters false hits, which are again eliminated by accessing records in full: once a false hit is generated by the index, there is no second chance to prune it; thus, the pruning capacity iSAX provides is also one-off. This paper proposes an alternative approach to time series kNN search, following a nontraditional pruning style. Instead of navigating through candidate records via an index, we access their features, obtained by a multi-resolution transform, in a stepwise sequential-scan manner, one level of resolution at a time, over a vertical representation. Most candidates are progressively eliminated after a few of their terms are accessed, using pre-computed information and an unprecedentedly tight double-bounding scheme, involving not only lower, but also upper distance bounds. Our experimental study with large, high-length time-series data confirms the advantage of our approach over both the current state-of-the-art method, iSAX, and classical index-based methods.
Bid generation for advanced match in sponsored search
Authors: Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josifovski, George Mavromatis, Alexander J. Smola
Venue: Conference WSDM 2011
Year: 2011

Sponsored search is a three-way interaction between advertisers, users, and the search engine. The basic ad selection in sponsored search, lets the advertiser choose the exact queries where the ad is to be shown. To increase advertising volume, many advertisers opt into advanced match, where the search engine can select additional queries that are deemed relevant for the advertiser's ad. In advanced match, the search engine is effectively bidding on the behalf of the advertisers. While advanced match has been extensively studied in the literature from the ad relevance perspective there is little work that discusses how to infer the appropriate bid value for a given advanced match. The bid value is crucial as it affects both the ad placement in revenue reordering and the amount advertisers are charged in case of a click.
Bid optimization for broad match ad auctions
Authors: Eyal Even-Dar, Vahab S. Mirrokni, S. Muthukrishnan, Yishay Mansour, Uri Nadav
Venue: Conference WWW 2009
Year: 2009

Ad auctions in sponsored search support "broad match" that allows an advertiser to target a large number of queries while bidding only on a limited number. While giving more expressiveness to advertisers, this feature makes it challenging to optimize bids to maximize their returns: choosing to bid on a query as a broad match because it provides high profit results in one bidding for related queries which may yield low or even negative profits. We abstract and study the complexity of the {\em bid optimization problem} which is to determine an advertiser's bids on a subset of keywords (possibly using broad match) so that her profit is maximized. In the query language model when the advertiser is allowed to bid on all queries as broad match, we present a linear programming (LP)-based polynomial-time algorithm that gets the optimal profit. In the model in which an advertiser can only bid on keywords, ie., a subset of keywords as an exact or broad match, we show that this problem is not approximable within any reasonable approximation factor unless P=NP. To deal with this hardness result, we present a constant-factor approximation when the optimal profit significantly exceeds the cost. This algorithm is based on rounding a natural LP formulation of the problem. Finally, we study a budgeted variant of the problem, and show that in the query language model, one can find two budget constrained ad campaigns in polynomial time that implement the optimal bidding strategy. Our results are the first to address bid optimization under the broad match feature which is common in ad auctions.
An Approach to Spacecraft Anomaly Detection Problem Using Kernel Feature Space
Authors: Ryohei Fujimaki, Takehisa Yairi, Kazuo Machida
Venue: Conference KDD 2005
Year: 2005

Development of advanced anomaly detection and failure diagnosis technologies for spacecraft is a quite significant issue in the space industry, because the space environment is harsh, distant and uncertain. While several modern approaches based on qualitative reasoning, expert systems, and probabilistic reasoning have been developed recently for this purpose, any of them has a common difficulty in obtaining accurate and complete a priori knowledge on the space systems from human experts. A reasonable alternative to this conventional anomaly detection method is to reuse a vast amount of telemetry data which is multi-dimensional time-series continuously produced from a number of system components in the spacecraft.This paper proposes a novel "knowledge-free" anomaly detection method for spacecraft based on Kernel Feature Space and directional distribution, which constructs a system behavior model from the past normal telemetry data from a set of telemetry data in normal operation and monitors the current system status by checking incoming data with the model.In this method, we regard anomaly phenomena as unexpected changes of causal associations in the spacecraft system, and hypothesize that the significant causal associations inside the system will appear in the form of principal component directions in a high-dimensional non-linear feature space which is constructed by a kernel function and a set of data.We have confirmed the effectiveness of the proposed anomaly detection method by applying it to the telemetry data obtained from a simulator of an orbital transfer vehicle designed to make a rendezvous maneuver with the International Space Station.
Automated snippet generation for online advertising
Authors: Stamatina Thomaidou, Ismini Lourentzou, Panagiotis Katsivelis-Perakis, Michalis Vazirgiannis
Venue: Conference CIKM 2013
Year: 2013

Products, services or brands can be advertised alongside the search results in major search engines, while recently smaller displays on devices like tablets and smartphones have imposed the need for smaller ad texts. In this paper, we propose a method that produces in an automated manner compact text ads (promotional text snippets), given as input a product description webpage (landing page). The challenge is to produce a small comprehensive ad while maintaining at the same time relevance, clarity, and attractiveness. Our method includes the following phases. Initially, it extracts relevant and important n-grams (keywords) given the landing page. The keywords reserved must have a positive meaning in order to have a call-to-action style, thus we attempt sentiment analysis on them. Next, we build an Advertising Language Model to evaluate phrases in terms of their marketing appeal. We experiment with two variations of our method and we show that they outperform all the baseline approaches.