Weighted-Sampling Audio Adversarial Example Attack

Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks. Despite many research on this, the efficiency and the robustness of existing works are not yet satisfactory. In this paper, we propose~\textit{weighted-sampling audio adversarial examples}, focusing on the numbers and the weights of distortion to reinforce the attack. Further, we apply a denoising method in the loss function to make the adversarial attack more imperceptible. Experiments show that our method is the first in the field to generate audio adversarial examples with low noise and high audio robustness at the minute time-consuming level.Comment: https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuXL.9260.pd

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging. The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above. Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics. We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and glossary, 51 in total. Inclusion of a large number of recent publications and expansion of the discussion accordingl

A survey of generative adversarial networks for synthesizing structured electronic health records

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly, Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to survey the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning

A hybrid Delphi-SWOT paradigm for oil and gas pipeline strategic planning in Caspian Sea basin

The Caspian Sea basin holds large quantities of both oil and natural gas that could help meet the increasing global demand for energy resources. Consequently, the oil and gas potential of the region has attracted the attention of the international oil and gas industry. The key to realizing the energy producing potential of the region is the development of transnational export routes to take oil and gas from the landlocked Caspian Sea basin to world markets. The evaluation and selection of alternative transnational export routes is a complex multi-criteria problem with conflicting objectives. The decision makers (DMs) are required to consider a vast amount of information concerning internal strengths and weaknesses of the alternative routes as well as external opportunities and threats to them. This paper presents a hybrid model that combines strength, weakness, opportunity and threat (SWOT) analysis with the Delphi metho

Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions

Synthetic data (SD) have garnered attention as a privacy enhancing technology. Unfortunately, there is no standard for quantifying their degree of privacy protection. In this paper, we discuss proposed quantification approaches. This contributes to the development of SD privacy standards; stimulates multi-disciplinary discussion; and helps SD researchers make informed modeling and evaluation decisions.Comment: 20 pages, 4 tables, 8 figures; NeurIPS 2023 Workshop on Synthetic Data Generation with Generative A

Evidence-Based Practice in Clinical Social Work

Evidence-based practice (EBP) is a major shaping influence in clinical social work practice, in relation to economic policies, and in professional education. The definition of EBP remains contested; professionals still fail to distinguish EBP as a practice decision-making process from a list of treatments that have some type of research support (which are correctly called empirically supported treatments). All mental health practitioners should understand what EBP is, what it is not, and how it shapes both client options and their own practice experiences. This book explores EBP in depth and in detail. Our focus includes case exemplars that show how the EBP decision-making process is done in practice. There are many recent books about evidence-based practice in social work and in other mental health professions. In reviewing these books, it appeared to us that most of the books on EBP have been written by researchers, bringing a particular point of view and expertise to the technicalities of EBP. These books are important to social workers and other mental health professionals because EBP involves a lot of technical details about research design, methods, and interpretation that are not always covered in other social work texts. On the other hand, the lack of a more direct practice and clinical viewpoint seemed to leave out a lot of the day-to-day realities clinical social workers confront in learning and using EBP in practice. Recent books also lacked much in the way of a broad and critical perspective on EBP as a social movement shaping policy, agency practice, and views of what constitutes “good” research. As we explored other books as resources for our students and for our own practice, we missed both a larger or meta-perspective on EBP and a lack of attention to doing it in clinical practice. This book seeks to illustrate through several cases how important clinical knowledge and expertise are in doing EBP well. We seek to introduce the core ideas and practice of EBP and then illustrate them by applying the concepts and processes to real-world cases. We also take a critical look at how EBP has been implemented in practice, education, and policy. Eight years after we wrote the first edition of this book, EBP continues to be a major influence on clinical practice. Some areas of the book, particularly the research evidence used in our case examples, needed to be updated and mad