7 research outputs found
Trauma lurking in the shadows: A Reddit case study of mental health issues in online posts about Childhood Sexual Abuse
Childhood Sexual Abuse (CSA) is a menace to society and has long-lasting
effects on the mental health of the survivors. From time to time CSA survivors
are haunted by various mental health issues in their lifetime. Proper care and
attention towards CSA survivors facing mental health issues can drastically
improve the mental health conditions of CSA survivors. Previous works
leveraging online social media (OSM) data for understanding mental health
issues haven't focused on mental health issues in individuals with CSA
background. Our work fills this gap by studying Reddit posts related to CSA to
understand their mental health issues. Mental health issues such as depression,
anxiety, and Post-Traumatic Stress Disorder (PTSD) are most commonly observed
in posts with CSA background. Observable differences exist between posts
related to mental health issues with and without CSA background. Keeping this
difference in mind, for identifying mental health issues in posts with CSA
exposure we develop a two-stage framework. The first stage involves classifying
posts with and without CSA background and the second stage involves recognizing
mental health issues in posts that are classified as belonging to CSA
background. The top model in the first stage is able to achieve accuracy and
f1-score (macro) of 96.26% and 96.24%. and in the second stage, the top model
reports hamming score of 67.09%. Content Warning: Reader discretion is
recommended as our study tackles topics such as child sexual abuse,
molestation, etc
Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks
Speech emotion recognition (SER) is a field that has drawn a lot of attention
due to its applications in diverse fields. A current trend in methods used for
SER is to leverage embeddings from pre-trained models (PTMs) as input features
to downstream models. However, the use of embeddings from speaker recognition
PTMs hasn't garnered much focus in comparison to other PTM embeddings. To fill
this gap and in order to understand the efficacy of speaker recognition PTM
embeddings, we perform a comparative analysis of five PTM embeddings. Among
all, x-vector embeddings performed the best possibly due to its training for
speaker recognition leading to capturing various components of speech such as
tone, pitch, etc. Our modeling approach which utilizes x-vector embeddings and
mel-frequency cepstral coefficients (MFCC) as input features is the most
lightweight approach while achieving comparable accuracy to previous
state-of-the-art (SOTA) methods in the CREMA-D benchmark.Comment: Accepted to Interspeech 202
A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting
Crowd counting finds direct applications in real-world situations, making
computational efficiency and performance crucial. However, most of the previous
methods rely on a heavy backbone and a complex downstream architecture that
restricts the deployment. To address this challenge and enhance the versatility
of crowd-counting models, we introduce two lightweight models. These models
maintain the same downstream architecture while incorporating two distinct
backbones: MobileNet and MobileViT. We leverage Adjacent Feature Fusion to
extract diverse scale features from a Pre-Trained Model (PTM) and subsequently
combine these features seamlessly. This approach empowers our models to achieve
improved performance while maintaining a compact and efficient design. With the
comparison of our proposed models with previously available state-of-the-art
(SOTA) methods on ShanghaiTech-A ShanghaiTech-B and UCF-CC-50 dataset, it
achieves comparable results while being the most computationally efficient
model. Finally, we present a comparative study, an extensive ablation study,
along with pruning to show the effectiveness of our models
Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows
The well-known Vehicle Routing Problem with Time Windows (VRPTW) aims to
reduce the cost of moving goods between several destinations while
accommodating constraints like set time windows for certain locations and
vehicle capacity. Applications of the VRPTW problem in the real world include
Supply Chain Management (SCM) and logistic dispatching, both of which are
crucial to the economy and are expanding quickly as work habits change.
Therefore, to solve the VRPTW problem, metaheuristic algorithms i.e. Particle
Swarm Optimization (PSO) have been found to work effectively, however, they can
experience premature convergence. To lower the risk of PSO's premature
convergence, the authors have solved VRPTW in this paper utilising a novel form
of the PSO methodology that uses the Roulette Wheel Method (RWPSO). Computing
experiments using the Solomon VRPTW benchmark datasets on the RWPSO demonstrate
that RWPSO is competitive with other state-of-the-art algorithms from the
literature. Also, comparisons with two cutting-edge algorithms from the
literature show how competitive the suggested algorithm is
From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue
In this study, we present a novel hybrid algorithm, combining Levy Flight
(LF) and Particle Swarm Optimization (PSO) (LF-PSO), tailored for efficient
multi-robot exploration in unknown environments with limited communication and
no global positioning information. The research addresses the growing interest
in employing multiple autonomous robots for exploration tasks, particularly in
scenarios such as Urban Search and Rescue (USAR) operations. Multiple robots
offer advantages like increased task coverage, robustness, flexibility, and
scalability. However, existing approaches often make assumptions such as search
area, robot positioning, communication restrictions, and target information
that may not hold in real-world situations. The hybrid algorithm leverages LF,
known for its effectiveness in large space exploration with sparse targets, and
incorporates inter-robot repulsion as a social component through PSO. This
combination enhances area exploration efficiency. We redefine the local best
and global best positions to suit scenarios without continuous target
information. Experimental simulations in a controlled environment demonstrate
the algorithm's effectiveness, showcasing improved area coverage compared to
traditional methods. In the process of refining our approach and testing it in
complex, obstacle-rich environments, the presented work holds promise for
enhancing multi-robot exploration in scenarios with limited information and
communication capabilities
Detecting Substance Use Disorder using Social Media Data and Dark Web: Time and Knowledge aware Study
Opioid and substance misuse is rampant in the United States today, with the phenomenon known as the opioid crisis . The relationship between substance use and mental health has been extensively studied, with one possible relationship being: substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means. This study analyzes the substance use posts on social media with opioids being sold through crypto market listings. We use the Drug Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based models to generate sentiment and emotion for the social media posts to understand users\u27 perceptions on social media by investigating questions such as: which synthetic opioids people are optimistic, neutral, or negative about? or what kind of drugs induced fear and sorrow? or what kind of drugs people love or are thankful about? or which drugs people think negatively about? or which opioids cause little to no sentimental reaction. We discuss how we crawled crypto market data and its use in extracting posts for fentanyl, fentanyl analogs, and other novel synthetic opioids. We also perform topic analysis associated with the generated sentiments and emotions to understand which topics correlate with people\u27s responses to various drugs. Additionally, we analyze time-aware neural models built on these features while considering historical sentiment and emotional activity of posts related to a drug. The most effective model performs well (statistically significant) with (macroF1=82.12, recall =83.58) to identify substance use disorder
An Automated Stress Recognition for Digital Healthcare: Towards E-Governance
Mental health is of utmost importance in present times as mental health problems can have a negative impact on an individual. Stress recognition is an important part of the digital healthcare system as stress may act as a catalyst and lead to mental health problems or further amplify them. With the advancement of technology, the presence of smart wearable devices is seen and it can be used to automate stress recognition for digital healthcare. These smart wearable devices have physiological sensors embedded into them. The data collected from these physiological sensors have paved an efficient way for stress recognition in the user. Most of the previous work related to stress recognition was done using classical machine learning approaches. One of the major drawbacks related to these approaches is that they require manually extracting important features that will be helpful in stress recognition. Extracting these features requires human domain expertise. Another drawback of previous works was that it only caters to specific groups of individuals such as stress among youths, stress due to the workplace, etc. and fails to generalize. To overcome the issues related to previous works done, this study proposes a transformer-based deep learning approach for automating the feature extraction phase and classifying a user’s state into three classes baseline, stress, and amusement