Adversarial attacks have threatened the credibility of machine learning models and cast doubts over the integrity of data. The attacks have created much harm in the fields of computer vision, and natural language processing. In this paper, we focus on the adversarial attack, in particular the poisoning attack, against the network intrusion detection system (NIDS), which is often viewed as the first line of defense against cyber threats. We develop an Anomaly-based intrusion on IoT networks Generative Adversarial Network (AIGAN), which uses deep learning techniques to generate adversarial data and to conduct an anomaly attack on IoT networks. To evaluate the effectiveness of our generator, we measure the similarities between real and fake data using the Jaccard similarity index, in addition comparing the F1-scores from four generic algorithms: multilayer perception, logistic regression, decision tree, random forest. We contrast the performance of ten machine learning classifiers experimented on two real IoT datasets and their fake adversarial samples. Our work highlights a vulnerable side of NIDS created by machine learning when attacked with adversarial perturbation.

Background: Depressive disorder is a common affective disorder, also known as depression, which is characterized by sadness, loss of interest, feelings of guilt or low self-worth and poor concentration. As speech is easy to obtain non-offensively with low-cost, many researchers explore the possibility of depression prediction through speech. Adopting speech signals to recognize depression has important practical significance. Aiming at the problem of the complex structure of the deep neural network method used in the recognition of speech depression and the traditional machine learning methods need to manually extract the features and the low recognition rate. Methods: This paper proposes a model that combines residual thinking and attention mechanism. First, depression corpus is designed based on the classic psychological experimental paradigm self-reference effect (SRE), and the speech dataset is labeled; then the attention module is introduced into the residual, and the channel attention is used to learn the features of the channel dimension, the spatial attention feedback the features of the spatial dimension, and the combination of the two to obtain the attention residual unit; finally the stacking unit constructs a speech depression recognition model based on the attention residual network. Results: Experimental results show that compared with traditional machine learning methods, this model obtains better results in the recognition of depression, which can meet the need for actual recognition application of depression. Conclusions: In this study, we not only predict whether person is depressed, but also estimate the severity of depression. In the designed corpus, the depression binary classification of an individual is given based on the severity of depression which is measured using BDI-II scores. Experimental results show that spontaneous speech can obtain better results than automatic speech, and the classification of speech features corresponding to negative questions is better than other tasks under negative emotions. Besides, the recognition accuracy rate of both male and female subjects is higher than that under other emotions.

Audio sentiment analysis is a growing area of research, however little attention has been paid to the fairness of machine learning models in this field. Whilst the current literature covers research on machine learning models’ reliability and fairness in various demographic groups, fairness in audio sentiment analysis with respect to gender is still an uninvestigated field. To fill this knowledge gap, we conducted experiments aimed at assessing the fairness of machine learning algorithms concerning gender within the context of audio sentiment analysis. In this research, we used 442 audio files of happiness and sadness—representing equal samples of male and female subjects—and generated spectrograms for each file. Then we performed feature extraction using bag-of-visual-words method followed by building classifiers using Random Forest, Support Vector Machines, and K-nearest Neighbors algorithms. We investigated whether the machine learning models for audio sentiment analysis are fair across female and male genders. We found the need for gender-specific models for audio sentiment analysis instead of a gender-agnostic-model. Our results provided three pieces of evidence to back up our claim that gender-specific models demonstrate bias in terms of overall accuracy equality when tested using audio samples representing the other gender, as well as combination of both genders. Furthermore, gender-agnostic-model performs poorly in comparison to gender-specific models in classifying sentiments of both male and female audio samples. These findings emphasize the importance of employing an appropriate gender-specific model for an audio sentiment analysis task to ensure fairness and accuracy. The best performance is achieved when using a female-model (78% accuracy) and a male-model (74% accuracy), significantly outperforming the 66% accuracy of the gender-agnostic model.

Purpose: This study aims to understand the impact of the COVID-19 pandemic on social determinants of health (SDOH) of marginalized racial/ethnic US population groups, specifically African Americans and Asians, by leveraging natural language processing (NLP) and machine learning (ML) techniques on race-related spatiotemporal social media text data. Specifically, this study establishes the extent to which Latent Dirichlet Allocation (LDA) and Gibbs Sampling Dirichlet Multinomial Mixture (GSDMM)-based topic modeling determines social determinants of health (SDOH) categories, and how adequately custom named-entity recognition (NER) detects key SDOH factors from a race/ethnicity-related Reddit data corpus. Methods: In this study, we collected race/ethnicity-specific data from 5 location subreddits including New York City, NY; Los Angeles, CA; Chicago, IL; Philadelphia, PA; and Houston, TX from March to December 2019 (before COVID-19 pandemic) and from March to December 2020 (during COVID-19 pandemic). Next, we applied methods from natural language processing and machine learning to analyze SDOH issues from extracted Reddit comments and conversation threads using feature engineering, topic modeling, and custom named-entity recognition (NER). Results: Topic modeling identified 35 SDOH-related topics. The SDOH-based custom NER analyses revealed that the COVID-19 pandemic significantly impacted SDOH issues of marginalized Black and Asian communities. On average, the Social and Community Context (SCC) category of SDOH had the highest percent increase (366%) from the pre-pandemic period to the pandemic period across all locations and population groups. Some of the detected SCC issues were racism, protests, arrests, immigration, police brutality, hate crime, white supremacy, and discrimination. Conclusion: Reddit social media platform can be an alternative source to assess the SDOH issues of marginalized Black and Asian communities during the COVID-19 pandemic. By employing NLP/ML techniques such as LDA/GSDMM-based topic modeling and custom NER on a race/ethnicity-specific Reddit corpus, we uncovered various SDOH issues affecting marginalized Black and Asian communities that were significantly worsened during the COVID-19 pandemic. As a result of conducting this research, we recommend that researchers, healthcare providers, and governments utilize social media and collaboratively formulate responses and policies that will address SDOH issues during public health crises.

Many people struggle with mood disorders and breakdowns, such as depression, anxiety, and stress. These mood disorders spill over into the tasks that are completed throughout the day. For example, how a person interacts with a computer (e.g., how fast they type on a keyboard, move a mouse, and correct mistakes) can show the type of mood they are experiencing. In this study, we aim to contribute to the development of machine learning models to detect a person's mood in an unobtrusive manner. The detection of low/poor mood can contribute to the intervention of improving mental health. In particular, we collected more than 16,000 keystroke and mouse dynamics data. Next, we built two type-based (i.e., keystroke-based and mouse-based) models and evaluated six classification algorithms on our models. The results show that the mouse-based models performed the best results. The random forest (RF) and extreme gradient boosting (XGBoost) provided the highest overall scores for both keystroke and mouse dynamics. The classification models offer better accuracy for the pleasant mood than that of the unpleasant mood. Our findings indicate that mental states spill over into regular computer interactions. With the models we have developed, we contribute to the detection of human mood.