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Found 12 papers, 3 papers with code
Medication Error Detection Using Contextual Language Models
Medication errors most commonly occur at the ordering or prescribing stage, potentially leading to medical complications and poor health outcomes.
Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method
With the rapidly growing number of security-sensitive systems that use voice as the primary input, it becomes increasingly important to address these systems' potential vulnerability to replay attacks.
Second-order Non-local Attention Networks for Person Re-identification
Recent efforts have shown promising results for person re-identification by designing part-based architectures to allow a neural network to learn discriminative representations from semantically coherent parts.
The power of dynamic social networks to predict individuals' mental health
Precision medicine has received attention both in and outside the clinic.
Heterogeneous network approach to predict individuals' mental health
To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source.
Real-Time Adversarial Attacks
In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on the input can make them fail.
ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems
This paper introduces a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs).
Towards Learning Fine-Grained Disentangled Representations from Speech
Learning disentangled representations of high-dimensional data is currently an active research area.
Topic Modeling Based Multi-modal Depression Detection
Major depressive disorder is a common mental disorder that affects almost 7% of the adult U. S. population.
An Overview of Vulnerabilities of Voice Controlled Systems
These systems have been shown to be vulnerable to various types of voice spoofing attacks.
How do deep convolutional neural networks learn from raw audio waveforms?
Prior work on speech and audio processing has demonstrated the ability to obtain excellent performance when learning directly from raw audio waveforms using convolutional neural networks (CNNs).
Crafting Adversarial Examples For Speech Paralinguistics Applications
Computational paralinguistic analysis is increasingly being used in a wide range of cyber applications, including security-sensitive applications such as speaker verification, deceptive speech detection, and medical diagnostics.
