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195 results on '"Bagheri, Ebrahim"'

1. Early Maladaptive Schemas, Cognitive Fusion, and Anabolic-Androgenic Steroid Use Attitudes: The Mediating Role of Muscle Dysmorphia in Iran.

Author

Ebrahimi, Mehdi, Zamani, Zahra, and Bagheri, Ebrahim

Subjects
SUBSTANCE abuse prevention, MUSCLE dysmorphia, BODY schema, PHYSICAL fitness centers, COGNITIVE training, BODY image
Abstract

In recent decades, the interest in having an ideal body in men has caused a pathological tendency to be muscular, followed by a tendency to use anabolic-androgenic steroids (AASs). This study was designed to evaluate the mediating role of muscle dysmorphia in the relationship between early maladaptive schemas and body image-related cognitive fusion with the tendency to use AAS in male athletes. Out of the total number of men referring to fitness clubs in Isfahan, Iran, 474 men were evaluated using a multistage random cluster sampling method. The questionnaires used in this research included the Muscle Dysmorphic Disorder Inventory, Cognitive Fusion Questionnaire—Body Image, Young Schema Questionnaire—Short Form, and Prototype Willingness Model Questionnaire. The results demonstrated that the relationship of AAS use with body image-related cognitive fusion and the three maladaptive schemas with the mediation of muscle dysmorphia is significant. The present study provides significant implications in the discussion of prevention and treatment of AAS addiction. [ABSTRACT FROM AUTHOR]

Published
2024
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26. A Variational Neural Architecture for Skill-based Team Formation.

Author

RAD, RADIN HAMIDI, FANI, HOSSEIN, BAGHERI, EBRAHIM, KARGAR, MEHDI, SRIVASTAVA, DIVESH, and SZLICHTA, JAROSLAW

Abstract

The article focuses on skill-based team formation and proposes a variational Bayesian neural network architecture to create optimal teams by maximizing the coverage of required skills and the effectiveness of past collaborations. Topics include the challenges of team formation, the limitations of existing graph-based methods, and the introduction of a novel neural network approach for team formation.

Published
2024
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45. A Latent Model for Ad Hoc Table Retrieval

Author

Bagheri, Ebrahim and Al-Obeidat, Feras

Subjects
InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Article
Abstract

The ad hoc table retrieval task is concerned with satisfying a query with a ranked list of tables. While there are strong baselines in the literature that exploit learning to rank and semantic matching techniques, there are still a set of hard queries that are difficult for these baseline methods to address. We find that such hard queries are those whose constituting tokens (i.e., terms or entities) are not fully or partially observed in the relevant tables. We focus on proposing a latent factor model to address such hard queries. Our proposed model factorizes the token-table co-occurrence matrix into two low dimensional latent factor matrices that can be used for measuring table and query similarity even if no shared tokens exist between them. We find that the variation of our proposed model that considers keywords provides statistically significant improvement over three strong baselines in terms of NDCG and ERR.

Published
2020

46. Temporal Latent Space Modeling for Community Prediction

Author

Fani, Hossein, Bagheri, Ebrahim, and Du, Weichang

Subjects
Article
Abstract

We propose a temporal latent space model for user community prediction in social networks, whose goal is to predict future emerging user communities based on past history of users’ topics of interest. Our model assumes that each user lies within an unobserved latent space, and similar users in the latent space representation are more likely to be members of the same user community. The model allows each user to adjust its location in the latent space as her topics of interest evolve over time. Empirically, we demonstrate that our model, when evaluated on a Twitter dataset, outperforms existing approaches under two application scenarios, namely news recommendation and user prediction on a host of metrics such as mrr, ndcg as well as precision and f-measure.

Published
2020

47. Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction

Author

Arabzadeh, Negar, Zarrinkalam, Fattane, Jovanovic, Jelena, and Bagheri, Ebrahim

Subjects
InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Neural embeddings, Specificity, Query Performance Prediction, Article
Abstract

Query Performance Prediction (QPP) is concerned with estimating the effectiveness of a query within the context of a retrieval model. It allows for operations such as query routing and segmentation, leading to improved retrieval performance. Pre-retrieval QPP methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the set of documents retrieved for the query. Since neural embedding-based models are showing wider adoption in the Information Retrieval (IR) community, we propose a set of pre-retrieval QPP metrics based on the properties of pre-trained neural embeddings and show that such metrics are more effective for query performance prediction compared to the widely known QPP metrics such as SCQ, PMI and SCS. We report our findings based on Robust04, ClueWeb09 and Gov2 corpora and their associated TREC topics.

Published
2020

48. Report on the 8th International Workshop on Mining Actionable Insights from Social Networks (MAISoN'22) - Special Edition on Mental Health and Social Media at TheWebConf 2022.

Author

Bagheri, Ebrahim, Inkpen, Diana, Yang, Christopher C., and Zarrinkalam, Fattane

Subjects
SOCIAL media, SOCIAL networks, COVID-19 pandemic, MENTAL health, RESEARCH personnel
Abstract

The eighth edition of the workshop on Mining Actionable Insights from Social Networks (MAISoN 2022) took place virtually on April 26th, 2022, co-located with the ACM Web Conference 2022 (TheWebConf 2022). This year, we organized a special edition with focus on mental health and social media. The aim of this edition was to bring together researchers from different disciplines to discuss research that goes beyond descriptive analysis of social media data and instead investigate different techniques that use social media data for building diagnostic, predictive and prescriptive analysis models for mental health applications. This topic attracted a lot of interest from the community especially because of all the considerations surrounding the impact of social media during the COVID-19 pandemic which has impacted on people's mental health issues. Date: 26 April 2022. Website: https://2022.maisonworkshop.org/. [ABSTRACT FROM AUTHOR]

Published
2023
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