OpenRaiser/Intern-Atlas huggingface dataset:
Birdspotter: A Tool for Analyzing and Labeling Twitter Users
Authors: Rohit Ram, Quyu Kong, Marian-Andrei Rizoiu
Venue: Conference WSDM 2021
Year: 2021

The impact of online social media on societal events and institutions is profound; and with the rapid increases in user uptake, we are just starting to understand its ramifications. Social scientists and practitioners who model online discourse as a proxy for real-world behavior, often curate large social media datasets. A lack of available tooling aimed at non-data science experts frequently leaves this data (and the insights it holds) underutilized. Here, we propose birdspotter -- a tool to analyze and label Twitter users --, and birdspotter.ml -- an exploratory visualizer for the computed metrics. birdspotter provides an end-to-end analysis pipeline, from the processing of pre-collected Twitter data, to general-purpose labeling of users, and estimating their social influence, within a few lines of code. The package features tutorials and detailed documentation. We also illustrate how to train birdspotter into a fully-fledged bot detector that achieves better than state-of-the-art performances without making any Twitter API online calls, and we showcase its usage in an exploratory analysis of a topical COVID-19 dataset.
Efficient Exploration of the Rashomon Set of Rule-Set Models
Authors: Martino Ciaperoni, Han Xiao 0009, Aristides Gionis
Venue: Conference KDD 2024
Year: 2024

Today, as increasingly complex predictive models are developed, simple rule sets remain a crucial tool to obtain interpretable predictions and drive high-stakes decision making. However, a single rule set provides a partial representation of a learning task. An emerging paradigm in interpretable machine learning aims at exploring the Rashomon set of all models exhibiting near-optimal performance. Existing work on Rashomon-set exploration focuses on exhaustive search of the Rashomon set for particular classes of models, which can be a computationally challenging task. On the other hand, exhaustive enumeration leads to redundancy that often is not necessary, and a representative sample or an estimate of the size of the Rashomon set is sufficient for many applications. In this work, we propose, for the first time, efficient methods to explore the Rashomon set of rule-set models with or without exhaustive search. Extensive experiments demonstrate the effectiveness of the proposed methods in a variety of scenarios.
Newsjunkie: Providing personalized newsfeeds via analysis of information novelty
Authors: Evgeniy Gabrilovich, Susan T. Dumais, Eric Horvitz
Venue: Conference WWW 2004
Year: 2004

We present a principled methodology for filtering news stories by formal measures of information novelty, and show how the techniques can be usedto custom-tailor news feeds based on information that a user has already reviewed. We review methods for analyzing novelty and then describe Newsjunkie, a system that personalizes news for users by identifying the novelty of stories in the context of stories they have already reviewed. Newsjunkie employs novelty-analysis algorithms that represent articles as words and named entities. The algorithms analyze inter-andintra-document dynamics by considering how information evolves over timefrom article to article, as well as within individual articles. We review the results of a user study undertaken to gauge the value of the approachover legacy time-based review of newsfeeds, and also to compare the performance of alternate distance metrics that are used to estimate the dissimilarity between candidate new articles and sets of previously reviewed articles.
A model for the detection of motion over time
Authors: Michael J. Black, P. Anandan 0001
Venue: Conference ICCV 1990
Year: 1990

A model is proposed for the incremental estimation of visual motion fields from image sequences. The authors' model exploits three standard constraints on image motion within an optimization framework: (1) data conservation-the intensity structure of a surface patch changes gradually over time; (2) spatial coherence-neighboring points have similar motions; and (3) temporal coherence-the image velocity of a surface patch changes gradually. The authors' formulation takes into account the possibility of multiple motions at a particular location. They present an incremental scheme for the minimization of the objective function, based on simulated annealing. All computations are parallel, local, and incremental, and occlusion and disocclusion boundaries are estimated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Text Mining: Finding Nuggets in Mountains of Textual Data
Authors: Jochen Dörre, Peter Gerstl, Roland Seiffert
Venue: Conference KDD 1999
Year: 1999

Text mining applies the same analytical functions of data mining to the domain of textual information, relying on sophisticated text analysis techniques that distill information from free-text documents. IBM's Intelligent Miner for Text provides the necessary tools to unlock the business information that is "trapped" in email, insurance claims, news feeds, or other document repositories. It has been successfully applied in analyzing patent portfolios, customer complaint letters, and even competitors' Web pages. After defining our notion of "text mining", we focus on the differences between text and data mining and describe in some more detail the unique technologies that are key to successful text mining.
Qualitative probabilities for image interpretation
Authors: Allan D. Jepson, Richard Mann
Venue: Conference ICCV 1999
Year: 1999

Two basic problems in image interpretation are: a) determining which interpretations are the most plausible amongst many possibilities; and b) controlling the search for plausible interpretations. We address these issues using a Bayesian approach, with the plausibility ordering and search pruning based on the posterior probabilities of interpretations. However, due to the need for detailed quantitative prior probabilities and the need to evaluate complex integrals over various conditional distributions, a full Bayesian approach is currently impractical except in tightly constrained domains. To circumvent these difficulties we introduce the notion of qualitative probabilistic analysis. In particular, given spatial and contrast resolution parameters, we consider only the asymptotic order of the posterior probability for any interpretation as these resolutions are made finer. We introduce this approach for a simple card-world domain, and present computational results for blocks-world images.
A state-based technique for the summarization and recognition of gesture
Authors: Andrew D. Wilson, Aaron F. Bobick
Venue: Conference ICCV 1995
Year: 1995

We define a gesture to be a sequence of states in a measurement or configuration space. For a given gesture, these states are used to capture both the repeatability and variability evidenced in a training set of example trajectories. The states are positioned along a prototype of the gesture, and shaped such that they are narrow in the directions in which the ensemble of examples is tightly constrained, and wide in directions in which a great deal of variability is observed. We develop techniques for computing a prototype trajectory of an ensemble of trajectories, for defining configuration states along the prototype, and for recognizing gestures from an unsegmented, continuous stream of sensor data. The approach is illustrated by application to a range of gesture-related sensory data: the two-dimensional movements of a mouse input device, the movement of the hand measured by a magnetic spatial position and orientation sensor, and, lastly, the changing eigenvector projection coefficients computed from an image sequence.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
A service creation environment based on end to end composition of web services
Authors: Vikas Agarwal, Koustuv Dasgupta, Neeran M. Karnik, Arun Kumar, Ashish Kundu, Sumit Mittal, Biplav Srivastava
Venue: Conference WWW 2005
Year: 2005

The demand for quickly delivering new applications is increasingly becoming a business imperative today. Application development is often done in an ad hoc manner, without standard frameworks or libraries, thus resulting in poor reuse of software assets. Web services have received much interest in industry due to their potential in facilitating seamless business-to-business or enterprise application integration. A web services composition tool can help automate the process, from creating business process functionality, to developing executable workflows, to deploying them on an execution environment. However, we find that the main approaches taken thus far to standardize and compose web services are piecemeal and insufficient. The business world has adopted a (distributed) programming approach in which web service instances are described using WSDL, composed into flows with a language like BPEL and invoked with the SOAP protocol. Academia has propounded the AI approach of formally representing web service capabilities in ontologies, and reasoning about their composition using goal-oriented inferencing techniques from planning. We present the first integrated work in composing web services end to end from specification to deployment by synergistically combining the strengths of the above approaches. We describe a prototype service creation environment along with a use-case scenario, and demonstrate how it can significantly speed up the time-to-market for new services.
Decentralized Orchestration of Composite Web Services
Authors: Girish Chafle, Sunil Chandra, Vijay Mann, Mangala Gowri Nanda
Venue: Conference WWW 2004
Year: 2004

Web services make information and software available programmatically via the Internet and may be used as building blocks for applications. A composite web service is one that is built using multiple component web services and is typically specified using a language such as BPEL4WS or WSIPL. Once its specification has been developed, the composite service may be orchestrated either in a centralized or in a decentralized fashion. Decentralized orchestration offers performance improvements in terms of increased throughput and scalability and lower response time. However, decentralized orchestration also brings additional complexity to the system in terms of error recovery and fault handling. Further, incorrect design of a decentralized system can lead to potential deadlock or non-optimal usage of system resources. This paper investigates build time and runtime issues related to decentralized orchestration of composite web services. We support our design decisions with performance results obtained on a decentralized setup using BPEL4WS to describe the composite web services and BPWS4J as the underlying runtime environment to orchestrate them.
Probabilistic models for discovering e-communities
Authors: Ding Zhou, Eren Manavoglu, Jia Li, C. Lee Giles, Hongyuan Zha
Venue: Conference WWW 2006
Year: 2006

The increasing amount of communication between individuals in e-formats (e.g. email, Instant messaging and the Web) has motivated computational research in social network analysis (SNA). Previous work in SNA has emphasized the social network (SN) topology measured by communication frequencies while ignoring the semantic information in SNs. In this paper, we propose two generative Bayesian models for semantic community discovery in SNs, combining probabilistic modeling with community detection in SNs. To simulate the generative models, an EnF-Gibbs sampling algorithm is proposed to address the efficiency and performance problems of traditional methods. Experimental studies on Enron email corpus show that our approach successfully detects the communities of individuals and in addition provides semantic topic descriptions of these communities.
Sequential update of adtrees
Authors: Josep Roure, Andrew W. Moore 0001
Venue: Conference ICML 2006
Year: 2006

Ingcreasingly, data-mining algorithms must deal with databases that continuously grow over time. These algorithms must avoid repeatedly scanning their databases. When database attributes are symbolic, ADtrees have already shown to be efficient structures to store sufficient statistics in main memory and to accelerate the mining process in batch environments. Here we present an efficient method to sequentially update ADtrees that is suitable for incremental environments.
Collaborative Ordinal Regression
Authors: Shipeng Yu, Kai Yu 0001, Volker Tresp, Hans-Peter Kriegel
Venue: Conference ICML 2006
Year: 2006

Ordinal regression has become an effective way of learning user preferences, but most research focuses on single regression problems. In this paper we introduce collaborative ordinal regression, where multiple ordinal regression tasks are handled simultaneously. Rather than modeling each task individually, we explore the dependency between ranking functions through a hierarchical Bayesian model and assign a common Gaussian Process (GP) prior to all individual functions. Empirical studies show that our collaborative model outperforms the individual counterpart in preference learning applications.
Joint recognition of complex events and track matching
Authors: Michael T. Chan, Anthony Hoogs, Rahul Bhotika, A. G. Amitha Perera, John Schmiederer, Gianfranco Doretto
Venue: Conference CVPR 2006
Year: 2006

We present a novel method for jointly performing recognition of complex events and linking fragmented tracks into coherent, long-duration tracks. Many event recognition methods require highly accurate tracking, and may fail when tracks corresponding to event actors are fragmented or partially missing. However, these conditions occur frequently from occlusions, traffic and tracking errors. Recently, methods have been proposed for linking track fragments from multiple objects under these difficult conditions. Here, we develop a method for solving these two problems jointly. A hypothesized event model, represented as a Dynamic Bayes Net, supplies data-driven constraints on the likelihood of proposed track fragment matches. These event-guided constraints are combined with appearance and kinematic constraints used in the previous track linking formulation. The result is the most likely track linking solution given the event model, and the highest event score given all of the track fragments. The event model with the highest score is determined to have occurred, if the score exceeds a threshold. Results demonstrated on a busy scene of airplane servicing activities, where many non-event movers and long fragmented tracks are present, show the promise of the approach to solving the joint problem.
Recognition of group activities using dynamic probabilistic networks
Authors: Shaogang Gong, Tao Xiang 0002
Venue: Conference ICCV 2003
Year: 2003

Dynamic Probabilistic Networks (DPNs) are exploited for modeling the temporal relationships among a set of different object temporal events in the scene for a coherent and robust scene-level behaviour interpretation. In particular, we develop a Dynamically Multi-Linked Hidden Markov Model (DML-HMM) to interpret group activities involving multiple objects captured in an outdoor scene. The model is based on the discovery of salient dynamic interlinks among multiple temporal events using DPNs. Object temporal events are detected and labeled using Gaussian Mixture Models with automatic model order selection. A DML-HMM is built using Schwarz's Bayesian Information Criterion based factorisation resulting in its topology being intrinsically determined by the underlying causality and temporal order among different object events. Our experiments demonstrate that its performance on modelling group activities in a noisy outdoor scene is superior compared to that of a Multi-Observation Hidden Markov Model (MOHMM), a Parallel Hidden Markov Model (PaHMM) and a Coupled Hidden Markov Model (CHMM).
MISSL: Multiple instance semi-supervised learning
Authors: Rouhollah Rahmani, Sally A. Goldman
Venue: Conference ICML 2006
Year: 2006

There has been much work on applying multiple-instance (MI) learning to content-based image retrieval (CBIR) where the goal is to rank all images in a known repository using a small labeled data set. Most existing MI learning algorithms are non-transductive in that the images in the repository serve only as test data and are not used in the learning process. We present MISSL (Multiple-Instance Semi-Supervised Learning) that transforms any MI problem into an input for a graph-based single-instance semi-supervised learning method that encodes the MI aspects of the problem simultaneously working at both the bag and point levels. Unlike most prior MI learning algorithms, MISSL makes use of the unlabeled data.
Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning
Authors: Matthew R. Rudary, Satinder Singh 0001, Martha E. Pollack
Venue: Conference ICML 2004
Year: 2004

Reminder systems support people with impaired prospective memory and/or executive function, by providing them with reminders of their functional daily activities. We integrate temporal constraint reasoning with reinforcement learning (RL) to build an adaptive reminder system and in a simulated environment demonstrate that it can personalize to a user and adapt to both short- and long-term changes. In addition to advancing the application domain, our integrated algorithm contributes to research on temporal constraint reasoning by showing how RL can select an optimal policy from amongst a set of temporally consistent ones, and it contributes to the work on RL by showing how temporal constraint reasoning can be used to dramatically reduce the space of actions from which an RL agent needs to learn.
Measuring article quality in wikipedia: models and evaluation
Authors: Meiqun Hu, Ee-Peng Lim, Aixin Sun, Hady Wirawan Lauw, Ba-Quy Vuong
Venue: Conference CIKM 2007
Year: 2007

Wikipedia has grown to be the world largest and busiest free encyclopedia, in which articles are collaboratively written and maintained by volunteers online. Despite its success as a means of knowledge sharing and collaboration, the public has never stopped criticizing the quality of Wikipedia articles edited by non-experts and inexperienced contributors. In this paper, we investigate the problem of assessing the quality of articles in collaborative authoring of Wikipedia. We propose three article quality measurement models that make use of the interaction data between articles and their contributors derived from the article edit history. Our B<scp>asic</scp> model is designed based on the mutual dependency between article quality and their author authority. The P<scp>eer</scp>R<scp>eview</scp> model introduces the review behavior into measuring article quality. Finally, our P<scp>rob</scp>R<scp>eview</scp> models extend P<scp>eer</scp>R<scp>eview</scp> with partial reviewership of contributors as they edit various portions of the articles. We conduct experiments on a set of well-labeled Wikipedia articles to evaluate the effectiveness of our quality measurement models in resembling human judgement.
Redundant feature elimination for multi-class problems
Authors: Annalisa Appice, Michelangelo Ceci, Simon Alan Rawles, Peter A. Flach
Venue: Conference ICML 2004
Year: 2004

We consider the problem of eliminating redundant Boolean features for a given data set, where a feature is redundant if it separates the classes less well than another feature or set of features. Lavrač et al. proposed the algorithm REDUCE that works by pairwise comparison of features, i.e., it eliminates a feature if it is redundant with respect to another feature. Their algorithm operates in an ILP setting and is restricted to two-class problems. In this paper we improve their method and extend it to multiple classes. Central to our approach is the notion of a neighbourhood of examples: a set of examples of the same class where the number of different features between examples is relatively small. Redundant features are eliminated by applying a revised version of the REDUCE method to each pair of neighbourhoods of different class. We analyse the performance of our method on a range of data sets.
A least squares formulation for a class of generalized eigenvalue problems in machine learning
Authors: Liang Sun 0001, Shuiwang Ji, Jieping Ye
Venue: Conference ICML 2009
Year: 2009

Many machine learning algorithms can be formulated as a generalized eigenvalue problem. One major limitation of such formulation is that the generalized eigenvalue problem is computationally expensive to solve especially for large-scale problems. In this paper, we show that under a mild condition, a class of generalized eigenvalue problems in machine learning can be formulated as a least squares problem. This class of problems include classical techniques such as Canonical Correlation Analysis (CCA), Partial Least Squares (PLS), and Linear Discriminant Analysis (LDA), as well as Hypergraph Spectral Learning (HSL). As a result, various regularization techniques can be readily incorporated into the formulation to improve model sparsity and generalization ability. In addition, the least squares formulation leads to efficient and scalable implementations based on the iterative conjugate gradient type algorithms. We report experimental results that confirm the established equivalence relationship. Results also demonstrate the efficiency and effectiveness of the equivalent least squares formulations on large-scale problems.
Learning Similarity Metrics for Event Identification in Social Media
Authors: Hila Becker, Mor Naaman, Luis Gravano
Venue: Conference WSDM 2010
Year: 2010

Social media sites (e.g., Flickr, YouTube, and Facebook) are a popular distribution outlet for users looking to share their experiences and interests on the Web. These sites host substantial amounts of user-contributed materials (e.g., photographs, videos, and textual content) for a wide variety of real-world events of different type and scale. By automatically identifying these events and their associated user-contributed social media documents, which is the focus of this paper, we can enable event browsing and search in state-of-the-art search engines. To address this problem, we exploit the rich "context" associated with social media content, including user-provided annotations (e.g., title, tags) and automatically generated information (e.g., content creation time). Using this rich context, which includes both textual and non-textual features, we can define appropriate document similarity metrics to enable online clustering of media to events. As a key contribution of this paper, we explore a variety of techniques for learning multi-feature similarity metrics for social media documents in a principled manner. We evaluate our techniques on large-scale, real-world datasets of event images from Flickr. Our evaluation results suggest that our approach identifies events, and their associated social media documents, more effectively than the state-of-the-art strategies on which we build.