HTTP/3 (H3) has experienced significant growth and extensive adoption in various scenarios, especially in Content Delivery Networks (CDNs). Over the past few years, there have been numerous insightful studies on its deployment in industrial CDNs. However, these studies often separately analyze H3 and CDN, overlooking their synergistic integration. In this work, we explore the applicability of H3 in CDN from a holistic perspective. We analyze 325 websites hosted by seven CDN providers and identify three key characteristics where CDN align perfectly with H3's strengths. Firstly, CDN resources dominate the composition of webpages, where enabling H3 can amplify H3's benefits in connection acceleration. Secondly, CDN providers also exhibit a dominant characteristic, with the majority of CDN resources hosted by a few large providers. This phenomenon makes different webpages share the same provider. When browsing consecutively, H3 helps to skip the connection phase by resuming the connections to the same CDN provider across pages. Thirdly, H3 mitigates the congestion problem on webpages serving multiple CDN resources. This work provides a deeper insight into the applicability of H3 in large-scale distributed systems like CDNs, holding promise for informing the development and optimization of industrial H3.

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Despite its enormous economical and societal impact, lack of human-perceived control and safety is re-defining the design and development of emerging AI-based technologies. New regulatory requirements mandate increased human control and oversight of AI, transforming the development practices and responsibilities of individuals interacting with AI. In this paper, we present the SPATIAL architecture, a system that augments modern applications with capabilities to gauge and monitor trustworthy properties of AI inference capabilities. To design SPATIAL, we first explore the evolution of modern system architectures and how AI components and pipelines are integrated. With this information, we then develop a proof-of- concept architecture that analyzes AI models in a human-in-the- loop manner. SPATIAL provides an AI dashboard for allowing individuals interacting with applications to obtain quantifiable insights about the AI decision process. This information is then used by human operators to comprehend possible issues that influence the performance of AI models and adjust or counter them. Through rigorous benchmarks and experiments in real- world industrial applications, we demonstrate that SPATIAL can easily augment modern applications with metrics to gauge and monitor trustworthiness, however, this in turn increases the complexity of developing and maintaining systems implementing AI. Our work highlights lessons learned and experiences from augmenting modern applications with mechanisms that support regulatory compliance of AI. In addition, we also present a road map of on-going challenges that require attention to achieve robust trustworthy analysis of AI and greater engagement of human oversight.@en

Recent advancements in hardware and software systems have been driven by the deployment of emerging smart health and mobility applications. These developments have modernized the traditional approaches by replacing conventional computing systems with cyber–physical and intelligent systems combining the Internet of Things (IoT) with Edge Artificial Intelligence. Despite the many advantages and opportunities of these systems within various application domains, the scarcity of energy, extensive computing needs, and limited communication must be considered when orchestrating their deployment. Inducing savings in these directions is central to the Approximate Computing (AxC) paradigm, in which the accuracy of some operations is traded off with energy, latency, and/or communication reductions. Unfortunately, the dynamics of the environments in which AxC-equipped IoT systems operate have been paid little attention. We bridge this gap by surveying adaptive AxC techniques applied to three emerging application domains, namely autonomous driving, smart sensing and wearables, and positioning, paying special attention to hardware acceleration. We discuss the challenges of such applications, how adaptive AxC can aid their deployment, and which savings it can bring based on traits of the data and devices involved. Insights arising thereof may serve as inspiration to researchers, engineers, and students active within the considered domains.

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Organizations are increasingly reliant on third-party software products to expedite their own development cycles, often incorporating numerous components into their end systems, resulting in a lack of transparency in software dependencies. Malicious actors exploit this, leading to Software Supply Chain (SSC) attacks with substantial economic and security damages. To mitigate this threat, the Software Bill of Materials (SBOM) concept was introduced. It details software components and their supply chain relationships, thus enhancing SSC transparency. Unfortunately, SBOM adoption still remains limited. While previous studies identified some reasons behind this, they overlooked the perspectives of different business stakeholder groups involved in SBOM's lifecycle.

In this work, we address this gap by studying business stakeholder groups directly involved in SBOM production and consumption. The main goal of this work is to identify which groups can drive or inhibit SBOM adoption and the rationale behind this behavior. By conducting interviews with the group representatives, we identified stakeholder-specific risks, benefits, concerns and incentives regarding SBOM adoption. Our analysis suggests that SBOM adoption potential is higher among System Integrators and Software Vendors. At the same time, B2B customers and Individual Developers have the least motivation, inhibiting the process of SBOM adoption. Given that these are the main SBOM consuming and supplying stakeholders correspondingly, we conclude that the overall adoption potential of this technology is currently limited and requires considerable external impulse.@en

In recent years, there has been a notable increase in the size of commonly used image classification models. This growth has empowered models to recognize thousands of diverse object types. However, their computational demands pose significant challenges, especially when deploying them on resource-constrained edge devices. In many use cases where a model is deployed on an edge device, only a small subset of the classes will ever be observed by a given model instance. Our proposed test-time specialization of dynamic neural networks allows these models to become faster at recognizing the classes that are observed frequently, while maintaining the ability to recognize all other classes, albeit slightly less efficient. We benchmark our approach on a real-world edge device, obtaining significant speedups compared to the baseline model without test-time adaptation.

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Our democratic systems have been challenged by the proliferation of artificial intelligence (AI) and its pervasive usage in our society. For instance, by analyzing individuals’ social media data, AI algorithms may develop detailed user profiles that capture individuals’ specific interests and susceptibilities. These profiles are leveraged to derive personalized propaganda, with the aim of influencing individuals toward specific political opinions. To address this challenge, the value of privacy can serve as a bridge, as having a sense of privacy can create space for people to reflect on their own political stance prior to making critical decisions, such as voting for an election. In this paper, we explore a novel approach by harnessing the potential of AI to enhance the privacy of social-media data. By leveraging adversarial machine learning, i.e., “AI versus AI,” we aim to fool AI-generated user profiles to help users hold a stake in resisting political profiling and preserve the deliberative nature of their political choices. More specifically, our approach probes the conceptual possibility of infusing people’s social media data with minor alterations that can disturb user profiling, thereby reducing the efficacy of the personalized influences generated by political actors. Our study delineates the boundary of ethical and practical implications associated with this ‘AI versus AI’ approach, highlighting the factors for the AI and ethics community to consider in facilitating deliberative decision-making toward democratic elections.@en

The temporal patterns of code submissions, denoted as work rhythms, provide valuable insight into the work habits and productivity in software development. In this paper, we investigate the work rhythms in software development and their effects on technical performance by analyzing the profiles of developers and projects from 110 international organizations and their commit activities on GitHub. Using clustering, we identify four work rhythms among individual developers and three work rhythms among software projects. Strong correlations are found between work rhythms and work regions, seniority, and collaboration roles. We then define practical measures for technical performance and examine the effects of different work rhythms on them. Our findings suggest that moderate overtime is related to good technical performance, whereas fixed office hours are associated with receiving less attention. Furthermore, we survey 92 developers to understand their experience with working overtime and the reasons behind it. The survey reveals that developers often work longer than required. A positive attitude towards extended working hours is associated with situations that require addressing unexpected issues or when clear incentives are provided. In addition to the insights from our quantitative and qualitative studies, this work sheds light on tangible measures for both software companies and individual developers to improve the recruitment process, project planning, and productivity assessment.

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We demonstrate SPATIAL, a proof-of-concept system that augments modern applications with capabilities to analyze trustworthy properties of AI models. The practical analysis of trustworthy properties is key to guaranteeing the safety of users and overall society when interacting with AI -driven applications. SPATIAL implements AI dashboards to introduce human-in-the-loop capabilities for the construction of AI models. SPATIAL allows different stakeholders to obtain quantifiable insights that characterize the decision making process of AI. This information can then be used by the stakeholders to comprehend possible issues that influence the performance of AI models, such that the issues can be resolved by human operators. Through rigorous benchmarks and experiments in a real-world industrial application, we demonstrate that SPATIAL can easily augment modern applications with metrics to gauge and monitor trustworthiness. However, this, in turn, increases the complexity of developing and maintaining the systems implementing AI. Our work paves the way towards augmenting modern applications with trustworthy AI mechanisms and human oversight approaches.@en

Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the training and inference of machine learning models to the edge of the network. This paradigm offers the opportunity to significantly impact our everyday lives with new services such as autonomous driving and ubiquitous personalized health care. Nevertheless, bringing intelligence to the edge involves several major challenges, which include the need to constrain model architecture designs, the secure distribution and execution of the trained models, and the substantial network load required to distribute the models and data collected for training. In this article, we highlight key aspects in the development of edge AI in the past and connect them to current challenges. This article aims to identify research opportunities for edge AI, relevant to bring together the research in the fields of artificial intelligence and edge computing.@en

Digitalization and datafication of financial systems result in more efficiency, but might also result in the exclusions of certain groups. Governments are looking for ways to increase inclusions and leave no one behind. For this, they must govern an organizational ecosystem of public and private parties. We derive value-based requirements through a systematic research methodology and iteratively refine design principles for achieving inclusivity goals. This refinement process is enriched by interviews with field experts, leading to the formulation of key Design principles: the essential role of inclusive metrics, leveraging alternative data sources, ensuring transparency in loan processes and the ability for decision contestation, providing tailored credit solutions, and maintaining long-term system sustainability. The government's role is to ensure a level playing field where all parties have equal access to the data. Following the principles ensures that exclusion and discrimination become visible and can be avoided. This study underscores the necessity for system-level transformations, inclusion-by-design, and advocacy for a new system design complemented by regulatory updates, new data integration, inclusive AI, and organizational collaborative shifts. These principles can also be used in different data-driven governance situations.

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The smishing-based malware Flubot was taken down in mid-2022, yet there is little understanding of how it directly impacted smartphone users. We engage with customers of a partner Internet Service Provider (ISP), who have suffered a Flubot infection on their smartphones. We surveyed 87 ISP customers who had been notified of a Flubot infection, in the months around and preceding the take-down of Flubot. We found that slightly over half of respondents were unaware of the malware infection before being notified, though many others had suspicions. We also observe that just over half of respondents experienced non-technical harms from the malware, with many experiencing harms before notification and several experiencing unwanted or aggressive activity from users of other infected devices. Many respondents reported not having removed the malware, while some discarded the infected device or stopped using online services in their efforts to be more secure afterwards. We offer recommendations, including that clearer guidance be sought to help users identify a malware infection (and not a focus only on prevention), and support provided for recovery from personal harms caused by mobile malware, as the impacts are not only technical.@en

The ageing European population and the expected increasing number of medical emergencies put pressure on the medical sector and existing emergency infrastructures, which calls for new innovative digital solutions. In parallel, the increasing utilization of the Internet of Things (IoT) has enabled the collection of real-Time data, allowing for the autonomous detection of acute medical emergencies. In this context, this paper presents two distinct machine learning (ML) models that leverage electrocardiogram (ECG) sensor data to autonomously detect Myocardial Infarctions (MI), a leading cause of emergencies. These models are intended to be integrated into an IoT-enabled next-generation emergency communications system (NG112) capable of detecting emergencies, initiating emergency calls (eCalls), and providing relevant information to emergency call takers, which reduces response time. To realize this, two disparate models working on fundamentally different data structures are proposed and compared: A one-dimensional convolutional neural network (CNN) operating on the raw ECG signals and a GoogLeNet-based model trained on ECG images. The PTB-XL dataset is used to evaluate the proposed models, and the results indicate the 1D CNN exhibits a favourable trade-off between precision and recall for the eCall use case. Finally, the paper also discusses applying eXplainable AI (XAI) methods to achieve explainability for the ML models, paving the way for an accountable and reliable implementation in safety-critical systems.

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Autonomous driving services depends on active sensing from modules such as camera, LiDAR, radar, and communication units. Traditionally, these modules process the sensed data on high-performance computing units inside the vehicle, which can deploy intelligent algorithms and AI models. The sensors mentioned above can produce large volumes of data, potentially reaching up to 20 Terabytes. This data size is influenced by factors such as the duration of driving, the data rate, and the sensor specifications. Consequently, this substantial amount of data can lead to significant power consumption on the vehicle. Similarly, a substantial amount of data will be exchanged between infrastructure sensors and vehicles for collaborative vehicle applications or fully connected autonomous vehicles. This communication process generates an additional surge of energy consumption. Although the autonomous vehicle domain has seen advancements in sensory technologies, wireless communication, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate these 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 from this survey can benefit the collaborative driving service development on low-power and memory-constrained systems and the energy optimization of autonomous vehicles.@en

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 toward 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.

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The metaverse aims to blur the boundary between the physical world and digital content. To achieve this goal, the metaverse relies heavily on extended reality (XR), the Internet of Things, and communication technologies. Concurrently, connected vehicles and intelligent transportation systems (ITSs) are envisioned as the future paradigm of driving and becoming reality thanks to increasingly powerful onboard vehicular processing capacity and advanced vehicle-to-everything networking technologies.

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A machine learning model can often produce biased outputs for a familiar group or similar sets of classes during inference over an unknown dataset. The generalization of neural networks have been studied to resolve biases, which has also shown improvement in accuracy and performance metrics, such as precision and recall, and refining the dataset's validation set. Data distribution and instances included in test and validation-set play a significant role in improving the generalization of neural networks. For producing an unbiased AI model, it should not only be trained to achieve high accuracy and minimize false positives. The goal should be to prevent the dominance of one class/feature over the other class/feature while calculating weights. This paper investigates state-of-art object detection/classification on AI models using metrics such as selectivity score and cosine similarity. We focus on perception tasks for vehicular edge scenarios, which generally include collaborative tasks and model updates based on weights. The analysis is performed using cases that include the difference in data diversity, the viewpoint of the input class and combinations. Our results show the potential of using cosine similarity, selectivity score and invariance for measuring the training bias, which sheds light on developing unbiased AI models for future vehicular edge services.@en

Connected vehicular services depend heavily on communication as they frequently transmit data and AI models/weights within the vehicular ecosystem. Energy efficiency in vehicles is crucial to keep up with the fast-growing demand for vehicular data processing and communication. To tackle this rising challenge, we explore approximation and edge AI techniques for achieving energy efficiency for vehicular services. Focusing on data-intensive vehicular services, we present an experimental case study on the high-definition (HD) map using the model partition approach. Our study compares the AI model energy consumption using multiple approximation ratios over embedded edge devices. Based on experimental insights, we further discuss an envisioned approximate Edge AI pipeline for developing and deploying energy-efficient vehicular services.@en

Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on an entire social network's information. However, for privacy-conscious users or newly-registered users, such information is not easily accessible. To address this issue, we present DeepPick, a novel framework that considers both the generalization and specialization in the detection task of OUs. For generalization, we introduce deep neural networks to capture dynamic features of the users. For specialization, we leverage the traditional descriptive features to make use of public information about users. Extensive experiments based on real-world datasets demonstrate that our approach achieves a high efficacy of detection performance against the state-of-the-art.@en

With rising numbers of people living in cities leading to increasing congestion and pollution, mobile crowdsensing applications form a potential solution to make transport systems smarter and more efficient. However, sharing data comes with the risk of private information being disclosed. Therefore, a clear incentive is necessary to motivate smart device users to share data about their activities and their environment. Taking a choice modelling approach, this study aims to identify factors related to incentives and privacy that explain choice behavior of users in crowdsensing applications. We find that the effort required by users is a main factor influencing the willingness to share data. 47% of respondents (n=125) indicated to be highly concerned about their privacy. However, the risk of re-identification was found to be the least important factor to respondents, a finding which could be explained by the Privacy Paradox. Our findings imply that a trade-off has to be made by developers of crowdsensing applications between the richness of information on one hand, and the privacy risks and participation rate of users on the other hand. We propose three practical principles for designing effective and value-sensitive crowdsensing applications for smart mobility, which are 1) Tailor-made applications, 2) Transparency by design, and 3) Ensuring attractiveness of applications. Furthermore, our study provides a basis for further research on user preferences in smart mobility applications, which will become increasingly important in the light of current challenges in the field of mobility.@en