4 citations · 6 across the 8 of their papers we have counts for
8 papers
Edge-Based Predictive Data Reduction for Smart Agriculture: A Lightweight Approach to Efficient IoT Communication
Dora Krekovic, Mario Kusek, Ivana Podnar Zarko +1
The rapid growth of IoT devices has led to an enormous amount of sensor data that requires transmission to cloud servers for processing, resulting in excessive network congestion,…
SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Version
Nghiem Thanh Pham, Tung Kieu, Duc-Manh Nguyen +3
Small Language Models (SLMs) offer computational efficiency and accessibility, yet a systematic evaluation of their performance and environmental impact remains lacking. We introdu…
Collaborative Perceiver: Elevating Vision-based 3D Object Detection via Local Density-Aware Spatial Occupancy
Jicheng Yuan, Manh Nguyen Duc, Qian Liu +2
Vision-based bird's-eye-view (BEV) 3D object detection has advanced significantly in autonomous driving by offering cost-effectiveness and rich contextual information. However, exi…
Co-Learning: Towards Semi-Supervised Object Detection with Road-side Cameras
Jicheng Yuan, Anh Le-Tuan, Ali Ganbarov +2
Recently, deep learning has experienced rapid expansion, contributing significantly to the progress of supervised learning methodologies. However, acquiring labeled data in real-wo…
Towards an Implementation of the Knowledge-Based Control Plane for Intelligent Swarm Networks
Xuanchi Guo, Anh Le-Tuan, Danh Le-Phuoc
This paper proposes the possibility of integrating Dynamic Knowledge Graph (DKG) with Software-Defined Networking (SDN). This new approach aims to assist the management and control…
Experimental comparison of graph-based approximate nearest neighbor search algorithms on edge devices
Ali Ganbarov, Jicheng Yuan, Anh Le-Tuan +2
In this paper, we present an experimental comparison of various graph-based approximate nearest neighbor (ANN) search algorithms deployed on edge devices for real-time nearest neig…
Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective
Khiem Le, Nhan Luong-Ha, Manh Nguyen-Duc +3
Federated Learning (FL) is a promising paradigm that offers significant advancements in privacy-preserving, decentralized machine learning by enabling collaborative training of mod…
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization
Khiem Le, Long Ho, Cuong Do +2
Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Generalization (Fe…