Yidong Chai

Yidong Chai
Hefei University of Technology · School of Management

About

19
Publications
3,811
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127
Citations

Publications

Publications (19)
Article
In this paper, we focus on the popularity prediction for marketer-generated content (MGC), which has not been investigated by current studies. To address this problem, we propose a novel deep learning approach, namely the text-guided attention neural network (TGANN) model, to make full use of heterogeneous and multi-modal data related to MGCs (e.g....
Article
Full-text available
Artificial Intelligence (AI) has rapidly emerged as a key disruptive technology in the 21 st century. At the heart of modern AI lies Deep Learning (DL), an emerging class of algorithms that can automatically detect non-trivial patterns from petabytes of rapidly evolving "Big Data." While the Information Systems (IS) discipline has embraced DL, ther...
Article
Full-text available
Black hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticiz...
Preprint
p> Deep learning (DL) models have significantly improved the performance of text classification and text regression tasks. However, DL models are often strikingly vulnerable to adversarial attacks. Many researchers have aimed to develop adversarial attacks against DL models in realistic black-box settings (i.e., assumes no model knowledge is access...
Preprint
Full-text available
Learning predictive models in new domains with scarce training data is a growing challenge in modern supervised learning scenarios. This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution. This becomes more challenging wh...
Article
Full-text available
Learning predictive models in new domains with scarce training data is a growing challenge in modern supervised learning scenarios. This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution. This becomes more challenging wh...
Conference Paper
These are a series of online platforms that allow users to rate and comment on VR virtual reality applications. In this paper, we develop a topic model, namely the general and sparse topic model, that automatically identifies a set of features of VR applications from user reviews. In our context, we overcome two severe challenges (i.e., internal no...
Preprint
Full-text available
Deep learning models have significantly advanced various natural language processing tasks. However, they are strikingly vulnerable to adversarial text attacks, even in the black-box setting where no model knowledge is accessible to hackers. Such attacks are conducted with a two-phase framework: 1) a sensitivity estimation phase to evaluate each el...
Preprint
Deep learning models have significantly advanced various natural language processing tasks. However, they are strikingly vulnerable to adversarial text attacks, even in the black-box setting where no model knowledge is accessible to hackers. Such attacks are conducted with a two-phase framework: 1) a sensitivity estimation phase to evaluate each el...
Article
Falls are among the most life-threatening events that challenge senior citizens’ independent living. Wearable sensor technologies have emerged as a viable solution for fall detection. However, existing fall detection models either focus on manual feature engineering or lack explainability. To advance the state-of-the-art of wearable sensor-based he...
Preprint
Artificial Intelligence (AI) has rapidly emerged as a key disruptive technology in the 21st century. At the heart of modern AI lies Deep Learning (DL), an emerging class of algorithms that has enabled today's platforms and organizations to operate at unprecedented efficiency, effectiveness, and scale. Despite significant interest, IS contributions...
Preprint
Full-text available
The regularity of devastating cyber-attacks has made cybersecurity a grand societal challenge. Many cybersecurity professionals are closely examining the international Dark Web to proactively pinpoint potential cyber threats. Despite its potential, the Dark Web contains hundreds of thousands of non-English posts. While machine translation is the pr...
Conference Paper
Full-text available
The regularity of devastating cyber-attacks has made cybersecurity a grand societal challenge. Many cybersecurity professionals are closely examining the international Dark Web to proactively pinpoint potential cyber threats. Despite its potential, the Dark Web contains hundreds of thousands of non-English posts. While machine translation is the pr...
Article
Peripapillary atrophy (PPA) is a clinical finding, which reflects the atrophy of retina layer and retinal pigment epithelium. The size of PPA area is a useful medical indicator, as it is highly associated with many diseases such as glaucoma and myopia. Therefore, separating the PPA area from retinal images, which is called PPA area segmentation, is...
Article
Glaucoma is one of the leading causes of blindness in the world and there is no cure for it yet. But it is very meaningful to detect it early as earlier detection makes it possible to stop further loss of visions. Although deep learning models have proved their advantages in natural image analysis, they usually rely on large datasets to learn to ex...
Conference Paper
Glaucoma is a group of eye diseases that damage the optic nerves progressively and lead to deterioration in vision irreversibly. Diagnosing glaucoma based on retinal images automatically is meaningful both in practice and research area. While deep learning models have achieved superior performance in natural images recognition and have been also us...

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