Lucian Popa

IBM Research - Almaden

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Kun Qian

University of Southern Denmark

Phokion G. Kolaitis

IBM Research - Almaden

Mauricio A. Hernández

IBM Research - Almaden

Ronald Fagin

IBM (United States)

Yunyao Li

University of Michigan–Ann Arbor

Douglas Burdick

IBM (United States)

Prithviraj Sen

Universidad del Noreste

Sairam Gurajada

IBM Research - Almaden

Rajasekar Krishnamurthy

Western Michigan University

Yunyao Li

Chongqing University

Cooperative Institutions

IBM (United States)

IBM Research - Almaden

IBM Research - Thomas J. Watson Research Center

University of Illinois Urbana-Champaign

Rensselaer Polytechnic Institute

Universidad del Noreste

University of North Carolina Health Care

University of North Carolina at Chapel Hill

Apple (United Kingdom)

University of Toronto

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EFFORT CHARACTERISTICS OF MALE ARTISTIC GYMNASTICS - CATEGORY JUNIOR IV LEVEL 1 AND LEVEL 2

Lucian Popa

All the positive physiological and structural transformations in the athletes ‘ body by practicing a sport, is due to body adaptations to physical and mental efforts to which they are subjected during exercise and are directly proportional to its intensity and volume, as well as with the frequency of training. Achieving high performance in any area of human activity, by default in gymnastics, are conditioned to undertake great efforts, characterized by a high level of training effort parameters such as volume, intensity, density and complexity.

Human body

Intensity

Source

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Learning-Based Methods with Human-in-the-Loop for Entity Resolution

Sairam Gurajada Lucian Popa Kun Qian Prithviraj Sen

This tutorial is intended for researchers and practitioners working in the data integration area and, in particular, entity resolution (ER), which is a sub-area focused on linking entities across heterogeneous datasets. We outline the ideal requirements of modern ER systems: (1) capture domain knowledge via (minimal) human interaction, (2) provide as much automation as possible via machine learning techniques, and (3) achieve high explainability. We describe recent research trends towards bringing such ideal ER systems closer to reality. We begin with an overview of human-in-the-loop methods that are based on techniques such as crowdsourcing and active learning. We then dive into recent trends that involve deep learning techniques such as representation learning to automate feature engineering, and combinations of transfer and active learning to reduce the amount of user labels required. We also discuss how explainable AI relates to ER, and outline some of the recent advances towards explainable ER.

Crowdsourcing

Human-in-the-loop

Representation

Transfer of learning

Feature (linguistics)

10.1145/3357384.3360316

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Data Science with Human in the Loop

Eduard Dragut Yunyao Li Lucian Popa Slobodan Vučetić

The aim of this workshop is to stimulate research on human-computer interaction challenges in data science. We invite researchers and practitioners interested in understanding how to optimize the human-computer cooperation and how to minimize human effort along the data science pipeline in a wide range of data science tasks and real-life applications. One over-arching challenge is to raise the level of abstraction of human-computer interaction to more sophisticated interaction models that better reflect a human's conceptual model and understanding. This workshop will bring together the interdisciplinary researchers from academia, research labs and practice to share, exchange, learn, and develop preliminary results, new concepts, ideas, principles, and methodologies on understanding and improving human-computer interaction for cost-effective development of data science models and for knowledge discovery. We expect the workshop to help develop and grow a strong community of researchers who are interested in this topic, and yield future collaborations and scientific exchanges across the relevant areas of data mining, machine learning, data and knowledge management, human-machine interaction, and user interfaces.

Abstraction

Human-in-the-loop

10.1145/3447548.3469476

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Towards High-Precision and Reusable Entity Resolution Algorithms over Sparse Financial Datasets

Douglas Burdick Lucian Popa Rajasekar Krishnamurthy

We describe our approach to the FEIII Data Challenge, which requires matching entities across multiple financial datasets (FFIEC, SEC and LEI). By making use of a high-level language (HIL) that includes constructs for expressing both the matching logic and the policies to avoid or reduce the ambiguities among the matches, we are able to produce highly-accurate results in a sparse context, with only name and location attributes. As part of the high-level specification, we also make use of a Smart-Term Generation (STG) component, which provides us with a sophisticated subroutine for normalizing company names. The high-level specification is reusable, in the sense that the same HIL specification (modulo changing the attribute names) is uniformly applicable not only between FFIEC and SEC, but also between FFIEC and LEI, and between LEI and SEC.

Subroutine

Component (thermodynamics)

10.1145/2951894.2951909

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Who needs to know what, when?: Broadening the Explainable AI (XAI) Design Space by Looking at Explanations Across the AI Lifecycle

Designing Interactive Systems Conference (2021)

Shipi Dhanorkar Christine T. Wolf Kun Qian Anbang Xu Lucian Popa

The interpretability or explainability of AI systems (XAI) has been a topic gaining renewed attention in recent years across AI and HCI communities. Recent work has drawn attention to the emergent explainability requirements of in situ, applied projects, yet further exploratory work is needed to more fully understand this space. This paper investigates applied AI projects and reports on a qualitative interview study of individuals working on AI projects at a large technology and consulting company. Presenting an empirical understanding of the range of stakeholders in industrial AI projects, this paper also draws out the emergent explainability practices that arise as these projects unfold, highlighting the range of explanation audiences (who), as well as how their explainability needs evolve across the AI project lifecycle (when). We discuss the importance of adopting a sociotechnical lens in designing AI systems, noting how the "AI lifecycle" can serve as a design metaphor to further the XAI design field.

Sociotechnical system

Interpretability

Exploratory research

10.1145/3461778.3462131

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Citations (72)

Exploring the Use of Personalized AI for Identifying Misinformation on Social Media

Farnaz Jahanbakhsh Yannis Katsis Dakuo Wang Lucian Popa Michael Müller

This work aims to explore how human assessments and AI predictions can be combined to identify misinformation on social media. To do so, we design a personalized AI which iteratively takes as training data a single user's assessment of content and predicts how the same user would assess other content. We conduct a user study in which participants interact with a personalized AI that learns their assessments of a feed of tweets, shows its predictions of whether a user would find other tweets (in)accurate, and evolves according to the user feedback. We study how users perceive such an AI, and whether the AI predictions influence users' judgment. We find that this influence does exist and it grows larger over time, but it is reduced when users provide reasoning for their assessment. We draw from our empirical observations to identify design implications and directions for future work.

Misinformation

User-Generated Content

10.1145/3544548.3581219

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Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

Lucian Popa Serge Abiteboul Phokion G. Kolaitis

This volume contains the proceedings of the Twenty-first ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS 2002), held at Madison, Wisconsin on June 3-5, 2002 in conjunction with the 2002 ACM SIGMOD International Conference on Management of Data. It consists of a paper based on the invited talk by Rajeev Motwani, two papers based on the invited tutorials by Maurizio Lenzerini and Dennis Shasha, and 24 contributed papers that were selected by the program committee for presentation at the symposium.The contributed papers were selected from 109 submissions. Most of the papers are extended abstracts and are preliminary reports on work in progress. While they have been read by program committee members, they have not been formally refereed. It is expected that much of the research described in these papers will be publihsed in detail in computer science journals.The program committee selected Monadic Datalog over Trees and the Expressive Power of Languages for Web Information Extraction by Georg Gottlob and Christoph Koch for the PODS 2002 Best Paper Award and From Discrepancy to Declustering: Near optimal multidimensional declustering strategies for range queries by Chung-Min Chen and Christine T. Cheng for the PODS 2002 Best Newcomer Award. Warmest congratulations to the authors of these papers.

Presentation (obstetrics)

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ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation

Kashob Kumar Roy Pritom Saha Akash Kevin Chen–Chuan Chang Lucian Popa

Tree (set theory)

Open domain

10.18653/v1/2024.findings-emnlp.807

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Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains

Proceedings of the AAAI Conference on Artificial Intelligence (2024)

Yu Shrike Zhang Yunyi Zhang Yanzhen Shen Yu Deng Lucian Popa

Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-annotated data to perform entity typing. Nevertheless, collecting such data in highly specialized science and engineering domains (e.g., software engineering and security) can be time-consuming and costly, without mentioning the domain gaps between training and inference data if the model needs to be applied to confidential datasets. In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to classify new entity mentions into both seen and unseen types (i.e., those without seed entities). To solve this problem, we propose SEType which first enriches the weak supervision by finding more entities for each seen type from an unlabeled corpus using the contextualized representations of pre-trained language models. It then matches the enriched entities to unlabeled text to get pseudo-labeled samples and trains a textual entailment model that can make inferences for both seen and unseen types. Extensive experiments on two datasets covering four domains demonstrate the effectiveness of SEType in comparison with various baselines. Code and data are available at: https://github.com/yuzhimanhua/SEType.

Science and engineering

10.1609/aaai.v38i17.29933

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Interactive generation of integrated schemas

Laura Chiticariu Phokion G. Kolaitis Lucian Popa

Schema integration is the problem of creating a unified target schema based on a set of existing source schemas that relate to each other via specified correspondences. The unified schema gives a standard representation of the data, thus offering a way to deal with the heterogeneity in the sources. In this paper, we develop a method and a design tool that provide: 1) adaptive enumeration of multiple interesting integrated schemas, and 2) easy-to-use capabilities for refining the enumerated schemas via user interaction. Our method is a departure from previous approaches to schema integration, which do not offer a systematic exploration of the possible integrated schemas.The method operates at a logical level, where we recast each source schema into a graph of concepts with Has-A relationships. We then identify matching concepts in different graphs by taking into account the correspondences between their attributes. For every pair of matching concepts, we have two choices: merge them into one integrated concept or keep them as separate concepts. We develop an algorithm that can systematically output, without duplication, all possible integrated schemas resulting from the previous choices. For each integrated schema, the algorithm also generates a mapping from the source schemas to the integrated schema that has precise information-preserving properties. Furthermore, we avoid a full enumeration, by allowing users to specify constraints on the merging process, based on the schemas produced so far. These constraints are then incorporated in the enumeration of the subsequent schemas. The result is an adaptive and interactive enumeration method that significantly reduces the space of alternative schemas, and facilitates the selection of the final integrated schema.

10.1145/1376616.1376700

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Citations (56)