Searched for: author%3A%22Yang%2C+J.%22
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Zhu, R. (author), Van Den Abeele, Maxim (author), Beysens, Jona (author), Yang, J. (author), Wang, Q. (author)
Visible light positioning (VLP) based on the received signal strength (RSS) can leverage a dense deployment of LEDs in future lighting infrastructure to provide accurate and energy-efficient indoor positioning. However, its positioning accuracy heavily depends on the density of collected fingerprints, which is labor-intensive. In this work,...
journal article 2024
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Zhu, P. (author), Wang, Zhen (author), Okumura, Manabu (author), Yang, J. (author)
Textbook question answering is challenging as it aims to automatically answer various questions on textbook lessons with long text and complex diagrams, requiring reasoning across modalities. In this work, we propose MRHF, a novel framework that incorporates dense passage re-ranking and the mixture-of-experts architecture for TQA. MRHF...
conference paper 2024
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Demartini, Gianluca (author), Sadiq, Shazia (author), Yang, J. (author)
This Special Issue of the Journal of Data and Information Quality (JDIQ) contains novel theoretical and methodological contributions on data curation involving humans in the loop. In this editorial, we summarize the scope of the issue and briefly describe its content.
contribution to periodical 2024
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Sharifi Noorian, S. (author), Qiu, S. (author), Sayin, Burcu (author), Balayn, A.M.A. (author), Gadiraju, Ujwal (author), Yang, J. (author), Bozzon, A. (author)
High-quality data plays a vital role in developing reliable image classification models. Despite that, what makes an image difficult to classify remains an unstudied topic. This paper provides a first-of-its-kind, model-agnostic characterization of image atypicality based on human understanding. We consider the setting of image classification...
conference paper 2023
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Yang, J. (author), Bozzon, A. (author), Gadiraju, Ujwal (author), Lease, Matthew (author)
contribution to periodical 2023
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Lammerts, Philippe (author), Lippmann, P. (author), Hsu, Yen Chia (author), Casati, Fabio (author), Yang, J. (author)
Hate speech moderation remains a challenging task for social media platforms. Human-AI collaborative systems offer the potential to combine the strengths of humans' reliability and the scalability of machine learning to tackle this issue effectively. While methods for task handover in human-AI collaboration exist that consider the costs of...
conference paper 2023
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Yang, J. (author), Chen, Xiaohong (author), Sun, Yuanxi (author), Feng, Chen (author), Yang, Zheng (author), Zadpoor, A.A. (author), Mirzaali, Mohammad J. (author), Bai, Long (author)
The advent of additive manufacturing has facilitated the design and fabrication of hybrid lattice structures with multiple morphologies. These structures combine multiple distinct architectures into a single structure with an exceptional performance that far exceeds that of each constituting architecture. However, combining strut-based...
journal article 2023
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Mesbah, Sepideh (author), Arous, Ines (author), Yang, J. (author), Bozzon, A. (author)
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...
conference paper 2023
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Sun, Zhu (author), Fang, Hui (author), Yang, J. (author), Qu, Xinghua (author), Liu, Hongyang (author), Yu, Di (author), Ong, Yew Soon (author), Zhang, Jie (author)
Recently, one critical issue looms large in the field of recommender systems - there are no effective benchmarks for rigorous evaluation - which consequently leads to unreproducible evaluation and unfair comparison. We, therefore, conduct studies from the perspectives of practical theory and experiments, aiming at benchmarking recommendation...
journal article 2023
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Sayin, Burcu (author), Yang, J. (author), Passerini, Andrea (author), Casati, Fabio (author)
In this paper, we argue that the way we have been training and evaluating ML models has largely forgotten the fact that they are applied in an organization or societal context as they provide value to people. We show that with this perspective we fundamentally change how we evaluate and select machine learning models.
conference paper 2023
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Sayin, Burcu (author), Yang, J. (author), Passerini, Andrea (author), Casati, Fabio (author)
In many practical applications, machine learning models are embedded into a pipeline involving a human actor that decides whether to trust the machine prediction or take a default route (e.g., classify the example herself). Selective classifiers have the option to abstain from making a prediction on an example they do not feel confident about...
conference paper 2023
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Balayn, A.M.A. (author), Yurrita Semperena, M. (author), Yang, J. (author), Gadiraju, Ujwal (author)
Fairness toolkits are developed to support machine learning (ML) practitioners in using algorithmic fairness metrics and mitigation methods. Past studies have investigated practical challenges for toolkit usage, which are crucial to understanding how to support practitioners. However, the extent to which fairness toolkits impact practitioners’...
conference paper 2023
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Balayn, A.M.A. (author), Rikalo, N. (author), Yang, J. (author), Bozzon, A. (author)
Handling failures in computer vision systems that rely on deep learning models remains a challenge. While an increasing number of methods for bug identification and correction are proposed, little is known about how practitioners actually search for failures in these models. We perform an empirical study to understand the goals and needs of...
conference paper 2023
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Zhu, R. (author), Yang, M. (author), Yang, J. (author), Wang, Q. (author)
Federated Learning (FL) is an important privacy-preserving learning paradigm that is expected to play an essential role in the future Intelligent Internet of Things (IoT). However, model training in FL is vulnerable to noise and the statistical heterogeneity of local data across IoT clients. In this paper, we propose FedNaWi, a “Go Narrow, Then...
conference paper 2023
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Samiotis, I.P. (author), Qiu, S. (author), Lofi, C. (author), Yang, J. (author), Gadiraju, Ujwal (author), Bozzon, A. (author)
Music content annotation campaigns are common on paid crowdsourcing platforms. Crowd workers are expected to annotate complex music artifacts, a task often demanding specialized skills and expertise, thus selecting the right participants is crucial for campaign success. However, there is a general lack of deeper understanding of the distribution...
journal article 2022
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Goedemondt, K.S. (author), Yang, J. (author), Wang, Q. (author)
Touchscreens and buttons had became a medium for virus transmission during the COVID-19 pandemic. We have seen in our daily life that people use tissues and keys to press buttons inside elevators, on public screens, etc. In the post- COVID world, touch-free interaction with public touchscreens and buttons may become more popular. Motivated by...
conference paper 2022
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Bhardwaj, Akansha (author), Yang, J. (author), Cudré-Mauroux, Philippe (author)
Platforms such as Twitter are increasingly being used for real-world event detection. Recent work often leverages event-related keywords for training machine learning based event detection models. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword – referred to as the expectation – and...
journal article 2022
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Smirnova, Alisa (author), Yang, J. (author), Yang, Dingqi (author), Cudre-Mauroux, Philippe (author)
Noisy labels represent one of the key issues in supervised machine learning. Existing work for label noise reduction mainly takes a probabilistic approach that infers true labels from data distributions in low-level feature spaces. Such an approach is not only limited by its capability to learn high-quality data representations, but also by...
journal article 2022
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He, G. (author), Balayn, A.M.A. (author), Buijsman, S.N.R. (author), Yang, J. (author), Gadiraju, Ujwal (author)
With recent advances in explainable artificial intelligence (XAI), researchers have started to pay attention to concept-level explanations, which explain model predictions with a high level of abstraction. However, such explanations may be difficult to digest for laypeople due to the potential knowledge gap and the concomitant cognitive load....
conference paper 2022
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Zhu, P. (author), Wang, Z. (author), Yang, J. (author), Hauff, C. (author), Anand, A. (author)
Quality control is essential for creating extractive question answering (EQA) datasets via crowdsourcing. Aggregation across answers, i.e. word spans within passages annotated, by different crowd workers is one major focus for ensuring its quality. However, crowd workers cannot reach a consensus on a considerable portion of questions. We...
conference paper 2022
Searched for: author%3A%22Yang%2C+J.%22
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