Scientific peer review is essential for the quality of academic publications.However, the increasing number of paper submissions to conferences has strained the reviewing process.This surge poses a burden on area chairs who have to carefully read an ever-growing volume of reviews and discern each reviewer's main arguments as part of their decision process.In this paper, we introduce GLIMPSE, a summarization method designed to offer a concise yet comprehensive overview of scholarly reviews.Unlike traditional consensus-based methods, GLIMPSE extracts both common and unique opinions from the reviews.We introduce novel uniqueness scores based on the Rational Speech Act framework to identify relevant sentences in the reviews.Our method aims to provide a pragmatic glimpse into all reviews, offering a balanced perspective on their opinions.Our experimental results with both automatic metrics and human evaluation show that GLIMPSE generates more discriminative summaries than baseline methods in terms of human evaluation while achieving comparable performance with these methods in terms of automatic metrics.

Taxonomies are used to organize knowledge in many applications, including recommender systems, content browsing, or web search. With the emergence of new concepts, static taxonomies become obsolete as they fail to capture up-to-date knowledge. Several approaches have been proposed to address the problem of maintaining taxonomies automatically. These approaches typically rely on a limited set of neighbors to represent a given node in the taxonomy. However, considering distant nodes could improve the representation of some portions of the taxonomy, especially for those nodes situated in the periphery or in sparse regions of the taxonomy.

Evaluating design ideas is necessary to predict their success and assess their impact early on in the process. Existing methods rely either on metrics computed by systems that are effective but subject to errors and bias, or experts’ ratings, which are accurate but expensive and long to collect. Crowdsourcing offers a compelling way to evaluate a large number of design ideas in a short amount of time while being cost-effective. Workers’ evaluation is, however, less reliable and might substantially differ from experts’ evaluation.

Explainability is a key requirement for text classification in many application domains ranging from sentiment analysis to medical diagnosis or legal reviews. Existing methods often rely on "attention" mechanisms for explaining classification results by estimating the relative importance of input units. However, recent studies have shown that such mechanisms tend to mis-identify irrelevant input units in their explanation. In this work, we propose a hybrid human-AI approach that incorporates human rationales into attention-based text classification models to improve the explainability of classification results. Specifically, we ask workers to provide rationales for their annotation by selecting relevant pieces of text. We introduce MARTA, a Bayesian framework that jointly learns an attention-based model and the reliability of workers while injecting human rationales into model training. We derive a principled optimization algorithm based on variational inference with efficient updating rules for learning MARTA parameters. Extensive validation on real-world datasets shows that our framework significantly improves the state of the art both in terms of classification explainability and accuracy.

Scientific peer review is pivotal to maintain quality standards for academic publication. The effectiveness of the reviewing process is currently being challenged by the rapid increase of paper submissions in various conferences. Those venues need to recruit a large number of reviewers of different levels of expertise and background. The submitted reviews often do not meet the conformity standards of the conferences. Such a situation poses an ever-bigger burden on the meta-reviewers when trying to reach a final decision.

Finding companies' websites is important when building business databases. However, automatically finding a company's website based on its name or its official entry in a registry is challenging, as companies often have similar names, acronyms, or descriptions. In this context, we built a system to evaluate different features and classifiers to automatically identify a company's website from unstructured content.

Finding social influencers is a fundamental task in many online applications ranging from brand marketing to opinion mining. Existing methods heavily rely on the availability of expert labels, whose collection is usually a laborious process even for domain experts. Using open-ended questions, crowdsourcing provides a cost-effective way to find a large number of social influencers in a short time. Individual crowd workers, however, only possess fragmented knowledge that is often of low quality.

With the emergence of the Internet of Things (IoT), time series streams have become ubiquitous in our daily life. Recording such data is rarely a perfect process, as sensor failures frequently occur, yielding occasional blocks of data that go missing in multiple time series. These missing blocks do not only affect real-time monitoring but also compromise the quality of online data analyses. Effective streaming recovery (imputation) techniques either have a quadratic runtime complexity, which is infeasible for any moderately sized data, or cannot recover more than one time series at a time. In this paper, we introduce a new online recovery technique to recover multiple time series streams in linear time. Our recovery technique implements a novel incremental version of the Centroid Decomposition technique and reduces its complexity from quadratic to linear. Using this incremental technique, missing blocks are efficiently recovered in a continuous manner based on previous recoveries. We formally prove the correctness of our new incremental computation, which yields an accurate recovery. Our experimental results on real-world time series show that our recovery technique is, on average, 30% more accurate than the state of the art while being vastly more efficient.

With the emergence of the Internet of Things (IoT), time series data has become ubiquitous in our daily life. Making sense of time series is a topic of great interest in many domains. Existing time series analysis applications generally assume or even require perfect time series (i.e. regular time intervals without unknown values), but real-world time series are rarely so neat. They often contain "holes" of different sizes (i.e., single missing values, or blocks of consecutive missing values) due to some failures or irregular time intervals. Hence, missing value recovery is a prerequisite for many time series analysis applications. In this demo, we present RecovDB, a relational database system enhanced with advanced matrix decomposition technology for missing blocks recovery. This demo will show the main features of RecovDB that are important for today's time series analysis but are lacking in state-of-the-art technologies: i) recovering large missing blocks in multiple time series at once; ii) achieving high recovery accuracy by benefiting from different correlations across time series; iii) maintaining recovery accuracy under increasing size of missing blocks; iv) maintaining recovery efficiency with increasing time series' lengths and the number of time series; and iv) supporting all these features while being parameter-free. In this paper, we also compare the efficiency and accuracy of RecovDB against state-of-the-art recovery systems.