An Emotion Recognition Embedded System using a Lightweight Deep Learning Model.

Background: Diagnosing emotional states would improve human-computer interaction (HCI) systems to be more effective in practice. Correlations between Electroencephalography (EEG) signals and emotions have been shown in various research; therefore, EEG signal-based methods are the most accurate and informative.

Methods: In this study, three Convolutional Neural Network (CNN) models, EEGNet, ShallowConvNet and DeepConvNet, which are appropriate for processing EEG signals, are applied to diagnose emotions.

Public injecting and its association with mental health and other drug-related outcomes among people who inject drugs in Iran.

Background: Injecting in public places may increase the risk of drug and health-related harms among people who inject drugs (PWID). We examined the prevalence of public injecting and associations with non-fatal overdose, needle/syringe sharing, sexual health, and mental health among PWID in Iran.

Methods: Using respondent-driven sampling, we recruited 2684 PWID from 11 major cities between July 2019 and March 2020.

A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis.

Identification and re-identification are two major security and privacy threats to medical imaging data. De-identification in DICOM medical data is essential to preserve the privacy of patients' Personally Identifiable Information (PII) and requires a systematic approach. However, there is a lack of sufficient detail regarding the de-identification process of DICOM attributes, for example, what needs to be considered before removing a DICOM attribute.

When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization.

This article investigates the performance of multistart next ascent hillclimbing and well-known evolutionary algorithms incorporating diversity preservation techniques on instances of the multimodal problem generator. This generator induces a class of problems in the bitstring domain which is interesting to study from a theoretical perspective in the context of multimodal optimization, as it is a generalization of the classical OneMax and TwoMax functions for an arbitrary number of peaks. An average-case runtime analysis for multistart next ascent hillclimbing is presented for uniformly distributed equal-height instances of this class of problems.