Publications by authors named "Wu-Ying Cheng"

Article Synopsis
  • Automating the summarization of legal texts is tough due to their complexity and specialized language.
  • The paper presents SAC-VAE, a new reinforcement learning model tailored for summarizing legal documents using a Variational Autoencoder to simplify the data input.
  • Results show that SAC-VAE outperforms existing methods, proving to be effective at generating summaries from legal texts.
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Facial recognition technology (FRT) has emerged as a powerful tool for public governance and security, but its rapid adoption has also raised significant concerns about privacy, civil liberties, and ethical implications. This paper critically examines the current rules and policies governing FRT, highlighting the tensions between state and corporate interests on one hand, and individual rights and ethical considerations on the other. The study also investigates international legal frameworks aimed at protecting individual rights and privacy, arguing that current legislative measures often fall short of robust scholarly standards and international human rights norms.

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Introduction: Algorithmic decision-making systems are widely used in various sectors, including criminal justice, employment, and education. While these systems are celebrated for their potential to enhance efficiency and objectivity, they also pose risks of perpetuating and amplifying societal biases and discrimination. This paper aims to provide an indepth analysis of the types of algorithmic discrimination, exploring both the challenges and potential solutions.

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Active Learning has emerged as a viable solution for addressing the challenge of labeling extensive amounts of data in data-intensive applications such as computer vision and neural machine translation. The main objective of Active Learning is to automatically identify a subset of unlabeled data samples for annotation. This identification process is based on an acquisition function that assesses the value of each sample for model training.

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LGBTQ+ youth experience mental health disparities and higher rates of mental disorders due to barriers to accessing care, including insufficient services and the anticipated stigma of revealing their identities. This systematic review incorporated 15 empirical studies on digital interventions' impact on LGBTQ+ youth mental health, examining their potential to address these inequities. This study innovatively categorized existing digital interventions into four streams: Structured Formal (telehealth, online programs), Structured Informal (serious games), Unstructured Formal (mobile applications), and Unstructured Informal (social media).

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Objective To explore the influencing factors of F-Fluorodeoxyglucose(F-FDG) uptake of brown adipose tissue(BAT) in PET/CT imaging by performing meta-analysis.Methods Relevant articles published between 1980 and 2016 were searched in PubMed,EMBASE,Web of Science,CBM,WanFang Data,CNKI,and VIP.The factors of gender,season and age were extracted according to the inclusive criteria.

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Objective: To characterize the brown adipose tissue (BAT) uptake of 18F-FDG on positron emission tomography (PET)/computed tomography (CT) imaging.

Methods: We retrospectively analyzed the clinical data of 1 080 patients who received all whole-body PET/CT studies in Peking Union Medica College Hospital from July 2008 to February 2009.

Results: Forty-one patients (3.

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Objective: To observe the characteristics of the physiological uptake of uterus and ovaries on 18F-fluorodeoxyglucose positron emission tomography (FDG PET).

Methods: A total of 288 PET examinations performed in 247 women (164 with malignancies, 44 with benign diseases, and 39 without remarkable abnormality) were included for analysis, and clinical follow-ups were applied for at least 10 months to exclude pelvic diseases. The menstrual statuses, menstrual cycles, and related pelvic examinations with other modalities were inquired before each PET examination.

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