Aaron Yi Ding

Dewant Katare, Diego Perino, Jari Nurmi et al.

Autonomous driving services rely heavily on sensors such as cameras, LiDAR, radar, and communication modules. A common practice of processing the sensed data is using a high-performance computing unit placed inside the vehicle, which deploys AI models and algorithms to act as the brain or administrator of the vehicle. The vehicular data generated from average hours of driving can be up to 20 Terabytes depending on the data rate and specification of the sensors. Given the scale and fast growth of services for autonomous driving, it is essential to improve the overall energy and environmental efficiency, especially in the trend towards vehicular electrification (e.g., battery-powered). Although the areas have seen significant advancements in sensor technologies, wireless communications, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate those technology innovations to achieve energy efficiency. This survey reviews and compares the connected vehicular applications, vehicular communications, approximation and Edge AI techniques. The focus is on energy efficiency by covering newly proposed approximation and enabling frameworks. To the best of our knowledge, this survey is the first to review the latest approximate Edge AI frameworks and publicly available datasets in energy-efficient autonomous driving. 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.

Wiebke Toussaint Hutiri, Lauriane Gorce, Aaron Yi Ding

Speaker verification (SV) provides billions of voice-enabled devices with access control, and ensures the security of voice-driven technologies. As a type of biometrics, it is necessary that SV is unbiased, with consistent and reliable performance across speakers irrespective of their demographic, social and economic attributes. Current SV evaluation practices are insufficient for evaluating bias: they are over-simplified and aggregate users, not representative of real-life usage scenarios, and consequences of errors are not accounted for. This paper proposes design guidelines for constructing SV evaluation datasets that address these short-comings. We propose a schema for grading the difficulty of utterance pairs, and present an algorithm for generating inclusive SV datasets. We empirically validate our proposed method in a set of experiments on the VoxCeleb1 dataset. Our results confirm that the count of utterance pairs/speaker, and the difficulty grading of utterance pairs have a significant effect on evaluation performance and variability. Our work contributes to the development of SV evaluation practices that are inclusive and fair.

Wiebke Toussaint, Aaron Yi Ding

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. Speaker verification is a form of biometric identification that gives access to voice assistants. Due to a lack of fairness metrics and evaluation frameworks that are appropriate for testing the fairness of speaker verification components, little is known about how model performance varies across subgroups, and what factors influence performance variation. To tackle this emerging challenge, we design and develop SVEva Fair, an accessible, actionable and model-agnostic framework for evaluating the fairness of speaker verification components. The framework provides evaluation measures and visualisations to interrogate model performance across speaker subgroups and compare fairness between models. We demonstrate SVEva Fair in a case study with end-to-end DNNs trained on the VoxCeleb datasets to reveal potential bias in existing embedded speech recognition systems based on the demographic attributes of speakers. Our evaluation shows that publicly accessible benchmark models are not fair and consistently produce worse predictions for some nationalities, and for female speakers of most nationalities. To pave the way for fair and reliable embedded speaker verification, SVEva Fair has been implemented as an open-source python library and can be integrated into the embedded ML development pipeline to facilitate developers and researchers in troubleshooting unreliable speaker verification performance, and selecting high impact approaches for mitigating fairness challenges

Tanvina Patel, Wiebke Hutiri, Aaron Yi Ding et al.

There is increasingly more evidence that automatic speech recognition (ASR) systems are biased against different speakers and speaker groups, e.g., due to gender, age, or accent. Research on bias in ASR has so far primarily focused on detecting and quantifying bias, and developing mitigation approaches. Despite this progress, the open question is how to measure the performance and bias of a system. In this study, we compare different performance and bias measures, from literature and proposed, to evaluate state-of-the-art end-to-end ASR systems for Dutch. Our experiments use several bias mitigation strategies to address bias against different speaker groups. The findings reveal that averaged error rates, a standard in ASR research, alone is not sufficient and should be supplemented by other measures. The paper ends with recommendations for reporting ASR performance and bias to better represent a system's performance for diverse speaker groups, and overall system bias.

Thanh Linh Nguyen, Marcela Tuler de Oliveira, An Braeken et al.

Federated unlearning (FUL) enables removing the data influence from the model trained across distributed clients, upholding the right to be forgotten as mandated by privacy regulations. FUL facilitates a value exchange where clients gain privacy-preserving control over their data contributions, while service providers leverage decentralized computing and data freshness. However, this entire proposition is undermined because clients have no reliable way to verify that their data influence has been provably removed, as current metrics and simple notifications offer insufficient assurance. We envision unlearning verification becoming a pivotal and trust-by-design part of the FUL life-cycle development, essential for highly regulated and data-sensitive services and applications like healthcare. This article introduces veriFUL, a reference framework for verifiable FUL that formalizes verification entities, goals, approaches, and metrics. Specifically, we consolidate existing efforts and contribute new insights, concepts, and metrics to this domain. Finally, we highlight research challenges and identify potential applications and developments for verifiable FUL and veriFUL.

Prachi Bagave, Marcus Westberg, Marijn Janssen et al.

AI is transforming the healthcare domain and is increasingly helping practitioners to make health-related decisions. Therefore, accountability becomes a crucial concern for critical AI-driven decisions. Although regulatory bodies, such as the EU commission, provide guidelines, they are highlevel and focus on the ''what'' that should be done and less on the ''how'', creating a knowledge gap for actors. Through an extensive analysis, we found that the term accountability is perceived and dealt with in many different ways, depending on the actor's expertise and domain of work. With increasing concerns about AI accountability issues and the ambiguity around this term, this paper bridges the gap between the ''what'' and ''how'' of AI accountability, specifically for AI systems in healthcare. We do this by analysing the concept of accountability, formulating an accountability framework, and providing a three-tier structure for handling various accountability mechanisms. Our accountability framework positions the regulations of healthcare AI systems and the mechanisms adopted by the actors under a consistent accountability regime. Moreover, the three-tier structure guides the actors of the healthcare AI system to categorise the mechanisms based on their conduct. Through our framework, we advocate that decision-making in healthcare AI holds shared dependencies, where accountability should be dealt with jointly and should foster collaborations. We highlight the role of explainability in instigating communication and information sharing between the actors to further facilitate the collaborative process.

Dewant Katare, David Solans Noguero, Souneil Park et al.

The accuracy and fairness of perception systems in autonomous driving are essential, especially for vulnerable road users such as cyclists, pedestrians, and motorcyclists who face significant risks in urban driving environments. While mainstream research primarily enhances class performance metrics, the hidden traits of bias inheritance in the AI models, class imbalances and disparities within the datasets are often overlooked. Our research addresses these issues by investigating class imbalances among vulnerable road users, with a focus on analyzing class distribution, evaluating performance, and assessing bias impact. Utilizing popular CNN models and Vision Transformers (ViTs) with the nuScenes dataset, our performance evaluation indicates detection disparities for underrepresented classes. Compared to related work, we focus on metric-specific and Cost-Sensitive learning for model optimization and bias mitigation, which includes data augmentation and resampling. Using the proposed mitigation approaches, we see improvement in IoU(\%) and NDS(\%) metrics from 71.3 to 75.6 and 80.6 to 83.7 for the CNN model. Similarly, for ViT, we observe improvement in IoU and NDS metrics from 74.9 to 79.2 and 83.8 to 87.1. This research contributes to developing reliable models while enhancing inclusiveness for minority classes in datasets.

Wiebke Toussaint, Aaron Yi Ding, Fahim Kawsar et al.

Billions of distributed, heterogeneous and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast and offline inference on personal data. On-device ML is highly context dependent, and sensitive to user, usage, hardware and environment attributes. This sensitivity and the propensity towards bias in ML makes it important to study bias in on-device settings. Our study is one of the first investigations of bias in this emerging domain, and lays important foundations for building fairer on-device ML. We apply a software engineering lens, investigating the propagation of bias through design choices in on-device ML workflows. We first identify reliability bias as a source of unfairness and propose a measure to quantify it. We then conduct empirical experiments for a keyword spotting task to show how complex and interacting technical design choices amplify and propagate reliability bias. Our results validate that design choices made during model training, like the sample rate and input feature type, and choices made to optimize models, like light-weight architectures, the pruning learning rate and pruning sparsity, can result in disparate predictive performance across male and female groups. Based on our findings we suggest low effort strategies for engineers to mitigate bias in on-device ML.

Aaron Yi Ding, Ella Peltonen, Tobias Meuser et al.

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. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimization, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.

Wiebke Toussaint, Alejandra Gomez Ortega, Jered Vroon et al.

Mobile applications and online service providers track our virtual and physical behaviour more actively and with a broader scope than ever before. This has given rise to growing concerns about ethical personal data management. Even though regulation and awareness around data ethics are increasing, end-users are seldom engaged when defining and designing what a future with ethical personal data management should look like. We explore a participatory process that uses design futures, the Future workshop method and design fictions to envision ethical personal data management with end-users and designers. To engage participants effectively, we needed to bridge their differential expertise and make the abstract concepts of data and ethics tangible. By concretely presenting personal data management and control as fictitious entities called Data Daemons, we created a shared understanding of these abstract concepts, and empowered non-expert end-users and designers to become actively engaged in the design process.

Wiebke Toussaint, Aaron Yi Ding

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. However, despite the maturity of machine learning systems and the IoT, we are facing severe challenges when integrating MLSys and IoT in practical context. For instance, many machine learning systems have been developed for large-scale production (e.g., cloud environments), but IoT introduces additional demands due to heterogeneous and resource-constrained devices and decentralized operation environment. To shed light on this convergence of MLSys and IoT, this paper analyzes the trade-offs by covering the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. We position machine learning systems as a component of the IoT, and edge intelligence as a socio-technical system. On the challenges of designing trustworthy edge intelligence, we advocate a holistic design approach that takes multi-stakeholder concerns, design requirements and trade-offs into consideration, and highlight the future research opportunities in edge intelligence.

Wiebke Toussaint, Aaron Yi Ding

Machine learning (ML) technologies are emerging in the Internet of Things (IoT) to provision intelligent services. This survey moves beyond existing ML algorithms and cloud-driven design to investigate the less-explored systems, scaling and socio-technical aspects for consolidating ML and IoT. It covers the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. With a multi-layered framework to classify and illuminate system design choices, this survey exposes fundamental concerns of developing and deploying ML systems in the rising cloud-edge-device continuum in terms of functionality, stakeholder alignment and trustworthiness.

Michael Haus, Jörg Ott, Aaron Yi Ding

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. Based on the similarity of light signals, our system named DevLoc takes advantage of ubiquitous light sources around us to perform continuous and seamless device grouping. We provide a configuration framework to control the spatial granularity of user's proximity by managing the lighting infrastructure through customized visible light communication. To realize either proximity-based or location-based services, we support two modes of device associations between different entities: device-to-device and device-to-area. Regarding the best performing method for device grouping, machine learning-based signal similarity performs in general best compared to distance and correlation metrics. Furthermore, we analyze patterns of device associations to improve the data privacy by recognizing semantic device groups, such as personal and stranger's devices, allowing automated data sharing policies.

Yige Zhang, Aaron Yi Ding, Jorg Ott et al.

Telecommunication (Telco) outdoor position recovery aims to localize outdoor mobile devices by leveraging measurement report (MR) data. Unfortunately, Telco position recovery requires sufficient amount of MR samples across different areas and suffers from high data collection cost. For an area with scarce MR samples, it is hard to achieve good accuracy. 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. Specifically, TLoc introduces three dedicated components: 1) a new coordinate space to divide an area of interest into smaller domains, 2) a similarity measurement to select best source domains, and 3) an adaptation of an existing transfer learning approach. To the best of our knowledge, TLoc is the first framework that demonstrates the efficacy of applying transfer learning in the Telco outdoor position recovery. To exemplify, on the 2G GSM and 4G LTE MR datasets in Shanghai, TLoc outperforms a nontransfer approach by 27.58% and 26.12% less median errors, and further leads to 47.77% and 49.22% less median errors than a recent fingerprinting approach NBL.

Aaron Yi Ding, Gianluca Limon De Jesus, Marijn Janssen

The security of the Internet of Things (IoT) has attracted much attention due to the growing number of IoT-oriented security incidents. IoT hardware and software security vulnerabilities are exploited affecting many companies and persons. Since the causes of vulnerabilities go beyond pure technical measures, there is a pressing demand nowadays to demystify IoT "security complex" and develop practical guidelines for both companies, consumers, and regulators. In this paper, we present an initial study targeting an unexplored sphere in IoT by illuminating the potential of crowdsource ethical hacking approaches for enhancing IoT vulnerability management. We focus on Bug Bounty Programs (BBP) and Responsible Disclosure (RD), which stimulate hackers to report vulnerability in exchange for monetary rewards. We carried out a qualitative investigation supported by literature survey and expert interviews to explore how BBP and RD can facilitate the practice of identifying, classifying, prioritizing, remediating, and mitigating IoT vulnerabilities in an effective and cost-efficient manner. Besides deriving tangible guidelines for IoT stakeholders, our study also sheds light on a systematic integration path to combine BBP and RD with existing security practices (e.g., penetration test) to further boost overall IoT security.

Ibbad Hafeez, Markku Antikainen, Aaron Yi Ding et al.

The increased popularity of IoT devices have made them lucrative targets for attackers. Due to insecure product development practices, these devices are often vulnerable even to very trivial attacks and can be easily compromised. Due to the sheer number and heterogeneity of IoT devices, it is not possible to secure the IoT ecosystem using traditional endpoint and network security solutions. To address the challenges and requirements of securing IoT devices in edge networks, we present IoT-Keeper, which is a novel system capable of securing the network against any malicious activity, in real time. The proposed system uses a lightweight anomaly detection technique, to secure both device-to-device and device-to-infrastructure communications, while using limited resources available on the gateway. It uses unlabeled network data to distinguish between benign and malicious traffic patterns observed in the network. A detailed evaluation, done with real world testbed, shows that IoT-Keeper detects any device generating malicious traffic with high accuracy (0.982) and low false positive rate (0.01). The results demonstrate that IoT-Keeper is lightweight, responsive and can effectively handle complex D2D interactions without requiring explicit attack signatures or sophisticated hardware.

Ibbad Hafeez, Aaron Yi Ding, Sasu Tarkoma

The number of mobile and IoT devices connected to home and enterprise networks is growing fast. These devices offer new services and experiences for the users; however, they also present new classes of security threats pertaining to data and device safety and user privacy. In this article, we first analyze the potential threats presented by these devices connected to edge networks. We then propose Securebox: a new cloud-driven, low cost Security-as-a-Service solution that applies Software-Defined Networking (SDN) to improve network monitoring, security and management. Securebox enables remote management of networks through a cloud security service (CSS) with minimal user intervention required. To reduce costs and improve the scalability, Securebox is based on virtualized middleboxes provided by CSS. Our proposal differs from the existing solutions by integrating the SDN and cloud into a unified edge security solution, and by offering a collaborative protection mechanism that enables rapid security policy dissemination across all connected networks in mitigating new threats or attacks detected by the system. We have implemented two Securebox prototypes, using a low-cost Raspberry-PI and off-the-shelf fanless PC. Our system evaluation has shown that Securebox can achieve automatic network security and be deployed incrementally to the infrastructure with low management overhead.

Ibbad Hafeez, Aaron Yi Ding, Markku Antikainen et al.

The growing popularity of Internet-of-Things (IoT) has created the need for network-based traffic anomaly detection systems that could identify misbehaving devices. In this work, we propose a lightweight technique, IoT-guard, for identifying malicious traffic flows. IoT-guard uses semi-supervised learning to distinguish between malicious and benign device behaviours using the network traffic generated by devices. In order to achieve this, we extracted 39 features from network logs and discard any features containing redundant information. After feature selection, fuzzy C-Mean (FCM) algorithm was trained to obtain clusters discriminating benign traffic from malicious traffic. We studied the feature scores in these clusters and use this information to predict the type of new traffic flows. IoT-guard was evaluated using a real-world testbed with more than 30 devices. The results show that IoTguard achieves high accuracy (98%), in differentiating various types of malicious and benign traffic, with low false positive rates. Furthermore, it has low resource footprint and can operate on OpenWRT enabled access points and COTS computing boards.