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Found 22 papers, 0 papers with code
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model
The results show that our meta-trained model performs significantly better than standard fine-tuning methods, outperforming the baseline fine-tuning in macro F1 score with 18\% and 0. 8\% over XLM-R and mBERT.
HARMamba: Efficient Wearable Sensor Human Activity Recognition Based on Bidirectional Selective SSM
Recently, an efficient hardware-aware state space model (SSM) called Mamba has emerged as a promising alternative.
MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer
Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features.
P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer
Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data.
Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations
Following that, We introduce a novel optimization strategy for UAV fog node placement in dynamic networks during evacuation scenarios, with a primary focus on ensuring robust connectivity and maximal coverage for mobile users, while extending the network's lifespan.
Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems
This problem is shown to be an instance of the unbounded multidimensional knapsack problem which is considered a strongly NP-hard problem.
Task Offloading for Smart Glasses in Healthcare: Enhancing Detection of Elevated Body Temperature
Wearable devices like smart glasses have gained popularity across various applications.
Context-Aware Service Recommendation System for the Social Internet of Things
Finally, we propose a service recommendation framework for SIoT based on review aggregation and feature learning processes.
Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning
However, the typical FL structure relies on the client-server, which leads to the single-point-of-failure (SPoF) attack, and the random selection of clients for model training compromised the model accuracy.
A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things
The Social Internet of Things (SIoT), is revolutionizing how we interact with our everyday lives.
Chatbots to ChatGPT in a Cybersecurity Space: Evolution, Vulnerabilities, Attacks, Challenges, and Future Recommendations
Subsequently, we explored the cybersecurity attacks and vulnerabilities in chatbots.
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation
Sensor-based human activity segmentation and recognition are two important and challenging problems in many real-world applications and they have drawn increasing attention from the deep learning community in recent years.
Dynamic Interactional And Cooperative Network For Shield Machine
Then, according to the relationship, models were established for the control terminal, including SM rate prediction and SM anomaly detection.
Edge-enabled Metaverse: The Convergence of Metaverse and Mobile Edge Computing
The Metaverse is a virtual environment where users are represented by avatars to navigate a virtual world, which has strong links with the physical one.
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition
Compared with SimCLR, it redefines the negative pairs in the contrastive loss function by using unsupervised clustering methods to generate soft labels that mask other samples of the same cluster to avoid regarding them as negative samples.
Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review
Death by suicide is the seventh leading death cause worldwide.
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition
The experiment results show that the resampling augmentation method outperforms all state-of-the-art methods under a small amount of labeled data, on SimCLRHAR and MoCoHAR, with mean F1-score as the evaluation metric.
Big-Five, MPTI, Eysenck or HEXACO: The Ideal Personality Model for Personality-aware Recommendation Systems
However, from personality computing perspective, the choice of the most suitable personality model that satisfy the requirements of the recommendation application and the recommended content type still needs further investigation.
Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic
Due to the restrictions imposed by COVID-19 people are increasingly using online social networks to express their feelings.
A Survey of Hybrid Human-Artificial Intelligence for Social Computing
In particular with the boom of artificial intelligence (AI), social computing is significantly influenced by AI.
IoT-Enabled Social Relationships Meet Artificial Social Intelligence
With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade.
A Survey on Personality-Aware Recommendation Systems
With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems.
