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Found 12 papers, 2 papers with code
Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset
However, the hidden traits of bias inheritance in the AI models, class imbalances and disparities in the datasets are often overlooked.
A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services
The insights and vision from this survey can be beneficial for the collaborative driving service development on low-power and memory-constrained systems and also for the energy optimization of autonomous vehicles.
Design Guidelines for Inclusive Speaker Verification Evaluation Datasets
We propose a schema for grading the difficulty of utterance pairs, and present an algorithm for generating inclusive SV datasets.
Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows
Billions of distributed, heterogeneous and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast and offline inference on personal data.
Roadmap for Edge AI: A Dagstuhl Perspective
Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI.
SVEva Fair: A Framework for Evaluating Fairness in Speaker Verification
Despite the success of deep neural networks (DNNs) in enabling on-device voice assistants, increasing evidence of bias and discrimination in machine learning is raising the urgency of investigating the fairness of these systems.
Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence
Machine learning systems (MLSys) are emerging in the Internet of Things (IoT) to provision edge intelligence, which is paving our way towards the vision of ubiquitous intelligence.
Machine Learning Systems for Intelligent Services in the IoT: A Survey
Machine learning (ML) technologies are emerging in the Internet of Things (IoT) to provision intelligent services.
Enabling Seamless Device Association with DevLoc using Light Bulb Networks for Indoor IoT Environments
To enable serendipitous interaction for indoor IoT environments, spontaneous device associations are of particular interest so that users set up a connection in an ad-hoc manner.
Transfer Learning-Based Outdoor Position Recovery with Telco Data
In this paper, by leveraging the recently developed transfer learning techniques, we design a novel Telco position recovery framework, called TLoc, to transfer good models in the carefully selected source domains (those fine-grained small subareas) to a target one which originally suffers from poor localization accuracy.
Practical Prediction of Human Movements Across Device Types and Spatiotemporal Granularities
The goal of this study is to investigate practical prediction mechanisms to quantify predictability as an aspect of human mobility modeling, across time, space and device types.
Networking and Internet Architecture I.6.4; C.2.5; J.1
IoT-KEEPER: Securing IoT Communications in Edge Networks
The increased popularity of IoT devices have made them lucrative targets for attackers.
Cryptography and Security
