1 citations · 3 across the 6 of their papers we have counts for
8 papers
GraphProfiler: Source-Linked Sensitive Attribute Inference via Personal Knowledge Graphs
Ahmed Sohair Khan, Estrid He, Chenglong Ma +2
Sensitive attributes such as age, income, and occupation can be inferred from user-generated content by aggregating indirect cues across many ordinary posts. LLM-based profilers ca…
I Am No One: Style-Aware Paraphrasing for Text Anonymization
Ahmed Sohair Khan, Estrid He, Monica Wachowicz +1
Authorship attribution models can re-identify users from seemingly anonymized text by exploiting stable stylistic fingerprints, even after explicit identifiers are removed, posing…
Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation
Seyed Alireza Rahimi Azghadi, Truong-Thanh-Hung Nguyen, Helene Fournier +4
The aging population is growing rapidly, and so is the danger of falls in older adults. A major cause of injury is falling, and detection in time can greatly save medical expenses…
MACeIP: A Multimodal Ambient Context-enriched Intelligence Platform in Smart Cities
Truong Thanh Hung Nguyen, Phuc Truong Loc Nguyen, Monica Wachowicz +1
This paper presents a Multimodal Ambient Context-enriched Intelligence Platform (MACeIP) for Smart Cities, a comprehensive system designed to enhance urban management and citizen e…
An Adaptive Indoor Localization Approach Using WiFi RSSI Fingerprinting with SLAM-Enabled Robotic Platform and Deep Neural Networks
Seyed Alireza Rahimi Azghadi, Atah Nuh Mih, Asfia Kawnine +3
Indoor localization plays a vital role in the era of the IoT and robotics, with WiFi technology being a prominent choice due to its ubiquity. We present a method for creating WiFi…
Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment
Atah Nuh Mih, Alireza Rahimi, Asfia Kawnine +4
This paper proposes an optimization of an existing Deep Neural Network (DNN) that improves its hardware utilization and facilitates on-device training for resource-constrained edge…
Developing a Resource-Constraint EdgeAI model for Surface Defect Detection
Atah Nuh Mih, Hung Cao, Asfia Kawnine +1
Resource constraints have restricted several EdgeAI applications to machine learning inference approaches, where models are trained on the cloud and deployed to the edge device. Th…
Evaluating Multi-Global Server Architecture for Federated Learning
Asfia Kawnine, Hung Cao, Atah Nuh Mih +1
Federated learning (FL) with a single global server framework is currently a popular approach for training machine learning models on decentralized environment, such as mobile devi…