Arrow Research search

Author name cluster

Wendy Hall

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers
1 author row

Possible papers

11

AIIM Journal 2021 Journal Article

Fully-channel regional attention network for disease-location recognition with tongue images

  • Yang Hu
  • Guihua Wen
  • Mingnan Luo
  • Pei Yang
  • Dan Dai
  • Zhiwen Yu
  • Changjun Wang
  • Wendy Hall

Objective Using the deep learning model to realize tongue image-based disease location recognition and focus on solving two problems: 1. The ability of the general convolution network to model detailed regional tongue features is weak; 2. Ignoring the group relationship between convolution channels, which caused the high redundancy of the model. Methods To enhance the convolutional neural networks. In this paper, a stochastic region pooling method is proposed to gain detailed regional features. Also, an inner-imaging channel relationship modeling method is proposed to model multi-region relations on all channels. Moreover, we combine it with the spatial attention mechanism. Results The tongue image dataset with the clinical disease-location label is established. Abundant experiments are carried out on it. The experimental results show that the proposed method can effectively model the regional details of tongue image and improve the performance of disease location recognition. Conclusion In this paper, we construct the tongue image dataset with disease-location labels to mine the relationship between tongue images and disease locations. A novel fully-channel regional attention network is proposed to model the local detail tongue features and improve the modeling efficiency. Significance The applications of deep learning in tongue image disease-location recognition and the proposed innovative models have guiding significance for other assistant diagnostic tasks. The proposed model provides an example of efficient modeling of detailed tongue features, which is of great guiding significance for other auxiliary diagnosis applications.

Details DOI

AAAI Conference 2020 Conference Paper

PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms

  • Tiancheng Shen
  • Jia Jia
  • Yan Li
  • Yihui Ma
  • Yaohua Bu
  • Hanjie Wang
  • Bo Chen
  • Tat-Seng Chua

With the rapid expansion of digital music formats, it’s indispensable to recommend users with their favorite music. For music recommendation, users’ personality and emotion greatly affect their music preference, respectively in a longterm and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users’ long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotionoriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users’ personality and emotion. Extensive experiments on a large real-world dataset of 171, 254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0. 5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.

PDF Details

IJCAI Conference 2018 Conference Paper

Cross-Domain Depression Detection via Harvesting Social Media

  • Tiancheng Shen
  • Jia Jia
  • Guangyao Shen
  • Fuli Feng
  • Xiangnan He
  • Huanbo Luan
  • Jie Tang
  • Thanassis Tiropanis

Depression detection is a significant issue for human well-being. In previous studies, online detection has proven effective in Twitter, enabling proactive care for depressed users. Owing to cultural differences, replicating the method to other social media platforms, such as Chinese Weibo, however, might lead to poor performance because of insufficient available labeled (self-reported depression) data for model training. In this paper, we study an interesting but challenging problem of enhancing detection in a certain target domain (e. g. Weibo) with ample Twitter data as the source domain. We first systematically analyze the depression-related feature patterns across domains and summarize two major detection challenges, namely isomerism and divergency. We further propose a cross-domain Deep Neural Network model with Feature Adaptive Transformation & Combination strategy (DNN-FATC) that transfers the relevant information across heterogeneous domains. Experiments demonstrate improved performance compared to existing heterogeneous transfer methods or training directly in the target domain (over 3. 4% improvement in F1), indicating the potential of our model to enable depression detection via social media for more countries with different cultural settings.

PDF Details

AAAI Conference 2017 Conference Paper

Cross-Domain Ranking via Latent Space Learning

  • Jie Tang
  • Wendy Hall

We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we present a unified model: BayCDR for cross-domain ranking. BayCDR uses a latent space to measure the correlation between different domains, and learns the ranking functions from the interrelated domains via the latent space by a Bayesian model, where each ranking function is based on a weighted average model. An efficient learning algorithm based on variational inference and a generalization bound has been developed. To scale up to handle real large data, we also present a learning algorithm under the Map-Reduce programming model. Finally, we demonstrate the effectiveness and efficiency of BayCDR on large datasets.

PDF Details

AAAI Conference 2017 Conference Paper

StructInf: Mining Structural Influence from Social Streams

  • Jing Zhang
  • Jie Tang
  • Yuanyi Zhong
  • Yuchen Mo
  • Juanzi Li
  • Guojie Song
  • Wendy Hall
  • Jimeng Sun

Social influence is a fundamental issue in social network analysis and has attracted tremendous attention with the rapid growth of online social networks. However, existing research mainly focuses on studying peer influence. This paper introduces a novel notion of structural influence and studies how to efficiently discover structural influence patterns from social streams. We present three sampling algorithms with theoretical unbiased guarantee to speed up the discovery process. Experiments on a big microblogging dataset show that the proposed sampling algorithms can achieve a 10× speedup compared to the exact influence pattern mining algorithm, with an average error rate of only 1. 0%. The extracted structural influence patterns have many applications. We apply them to predict retweet behavior, with performance being significantly improved.

PDF Details

IS Journal 2016 Journal Article

The Role of Data Science in Web Science

  • Christopher Phethean
  • Elena Simperl
  • Thanassis Tiropanis
  • Ramine Tinati
  • Wendy Hall

Web science relies on an interdisciplinary approach that seeks to go beyond what any one subject can say about the World Wide Web. By incorporating numerous disciplinary perspectives and relying heavily on domain knowledge and expertise, data science has emerged as an important new area that integrates statistics with computational knowledge, data collection, cleaning and processing, analysis methods, and visualization to produce actionable insights from big data. As a discipline to use within Web science research, data science offers significant opportunities for uncovering trends in large Web-based datasets. A Web science observatory exemplifies this relationship by offering an online platform of tools for carrying out Web science research, allowing users to carry out data science techniques to produce insights into Web science issues such as community development, online behavior, and information propagation. The authors outline the similarities and differences of these two growing subject areas to demonstrate the important relationship developing between them.

Details DOI

IS Journal 2013 Journal Article

The Web Science Observatory

  • Thanassis Tiropanis
  • Wendy Hall
  • Nigel Shadbolt
  • David De Roure
  • Noshir Contractor
  • Jim Hendler

To understand and enable the evolution of the Web and to help address grand societal challenges, the Web must be observable at scale across space and time. That requires a globally distributed and collaborative Web Observatory.

Details DOI

IS Journal 2012 Journal Article

Linked Open Government Data: Lessons from Data.gov.uk

  • Nigel Shadbolt
  • Kieron O'Hara
  • Tim Berners-Lee
  • Nicholas Gibbins
  • Hugh Glaser
  • Wendy Hall
  • M.C. Schraefel

A project to extract value from open government data contributes to the population of the linked data Web with high-value data of good provenance.

Details DOI

IS Journal 2011 Journal Article

Society Online, Part 2 [Guest editors' introduction]

  • James Hendler
  • Wendy Hall

The Web is a critical global infrastructure. Since its emergence in the mid-1990s, it has exploded into hundreds of billions of pages that touch almost all aspects of modern life. Today the jobs of more and more people depend on the Web. Media, banking, and healthcare are being revolutionized by it, and governments are even considering how to run their countries with it.

Details DOI

IS Journal 2009 Journal Article

Guest Editors' Introduction: Society Online

  • James Hendler
  • Wendy Hall

This special issue collects some of the best contributions to the first international Web Science Conference, exploring effects of the evolving Web on human society.

Details DOI

JAAMAS Journal 2008 Journal Article

User evaluation of a market-based recommender system

  • Yan Zheng Wei
  • Nicholas R. Jennings
  • Wendy Hall

Abstract Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approaches, since no one technique is best for all users in all situations. Given this, we believe that to be effective, systems should incorporate a wide variety of such techniques and then some form of overarching framework should be put in place to coordinate them so that only the best recommendations (from whatever source) are presented to the user. To this end, in our previous work, we detailed a market-based approach in which various recommender agents competed with one another to present their recommendations to the user. We showed through theoretical analysis and empirical evaluation with simulated users that an appropriately designed marketplace should be able to provide effective coordination. Building on this, we now report on the development of this multi-agent system and its evaluation with real users. Specifically, we show that our system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommender.

Details DOI
v2026.09.13