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2021 ACM computer science in Cars Symposium, CSCS 2021

作者： Lautenbach, Aljoscha Almgren, Magnus Olovsson, Tomas Department of Computer Science and Engineering Chalmers University of Technology Sweden

ISBN: (纸本)9781450391399

Risk-based security models have seen a steady rise in popularity over the last decades, and several security risk assessment models have been proposed for the automotive industry. The new UN vehicle regulation 155 on cybersecurity provisions for vehicle type approval, as part of the 1958 agreement on vehicle harmonization, mandates the use of risk assessment to mitigate cybersecurity risks and is expected to be adopted into national laws in 54 countries within 1 to 3 years. This new legislation will also apply to autonomous vehicles. The automotive cybersecurity engineering standard ISO/SAE 21434 is seen as a way to fulfill the new UN legislation, so we can expect quick and wide industry adoption. One risk assessment model that has gained some popularity and is in active use in several companies is the HEAVENS model, but since ISO/SAE 21434 introduces additional requirements on the risk assessment process, the original HEAVENS model does not fulfill the standard. In this paper, we investigate the gap between the HEAVENS risk assessment model and ISO/SAE 21434, and we identify and propose 12 model updates to HEAVENS to close this gap. We also discuss identified weaknesses of the HEAVENS risk assessment model and propose 5 additional model updates to overcome them. In accordance with these 17 identified model updates, we propose HEAVENS 2.0, a new risk assessment model based on HEAVENS which is fully compliant with ISO/SAE 21434. © 2021 ACM.

关键词： Risk assessment

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Qualifying and Quantifying the Benefits of Mindfulness Practices for IT Workers

Qualifying and Quantifying the Benefits of Mindfulness Pract...

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ICT for Sustainability (ICT4S), International Conference on

作者： Cristina Martinez Montes Fredrik Sjögren Adam Klevfors Birgit Penzenstadler Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg Sweden Lappeenranta University of Technology Lappeenranta Finland

ISBN: (数字)9798331505288

ISBN: (纸本)9798331505295

The well-being and productivity of IT workers are crucial for both individual success and the overall prosperity of the organisations they serve. This study proposes mindfulness to alleviate stress and improve mental well-being for IT workers. During an 8-week program, IT workers learn about mindfulness, coupled with breathing practices. This study investigates the potential effects of these practices by analysing participants' reflections through thematic analysis and daily well-being ratings. The analysis showcased an increase in mental well-being and perceived productivity. It also indicated a change in the participants' perception, which showed increased self-awareness. The study recommends continuing the program in the industry to see its impact on work outputs.

关键词： Productivity Industries Human factors Software Reflection Sustainable development

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Experimentation in Software Ecosystems: a Systematic Literature Review

Experimentation in Software Ecosystems: a Systematic Literat...

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Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Shady Hegazy Christoph Elsner Jan Bosch Helena Holmström Olsson Siemens Technology Munich Germany Department of Computer Science and Engineering Chalmers University of Technology Goteborg Sweden Department of Computer Science and Media Technology Malmo University Malmo Sweden

ISBN: (数字)9798350380262

ISBN: (纸本)9798350380279

Context: Software ecosystems have transformed many industries, redefining collaboration and value co-creation. The success of such ecosystems depends on the dynamism of the network of users on its different sides. Consequently, decision-making in such multifaceted and interconnected environments is more complex than in conventional software products. On-line controlled experiments are considered the gold standard for aiding decision-making in software engineering processes. Experiments are extensively used to reduce bias and estimation noise for design, engineering, and business decisions. However, experimentation in software ecosystems is inherently more com-plex as it deals with atypical sources of bias and technical complications. Primary studies of experimentation approaches in software ecosystems are scattered across multiple domains and disciplines, and secondary research on the topic is scarce as highlighted in different tertiary studies. Hence, we conducted this study. Objectives: To explore primary research on experimentation in software ecosystems; Summarize current approaches, toolboxes, and solutions that practitioners and researchers, facing similar problems, can use to inform their approaches; To outline underexplored research areas and provide recommendations for practitioners. Method: We conducted a systematic literature review. The search strategy, application of exclusion and inclusion criteria, and subsequent quality assessment resulted in 63 relevant studies. Data extraction process was designed and carried out to collect data relevant to the study objectives. The extracted data under-went descriptive and thematic syntheses and analyses, in addition to cross-analysis on relevant axes. Contributions: The study resulted in four contributions. First, a distillation of the themes and patterns in the available research on the topic. Second, a practical summary of the experimental designs specific to each software ecosystem type. Third, an actionable r

关键词： Roads Ecosystems Decision making Process control Search problems Software Quality assessment Data mining Systematic literature review Software engineering

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A Membrane Computing Approach to the Generalized Nash Equilibrium

arXiv

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arXiv 2024年

作者： Luque-Cerpa, Alejandro Gutiérrez-Naranjo, Miguel A. Department of Computer Science and Engineering Chalmers University of Technology Sweden Department of Computer Sciences and Artificial Intelligence University of Seville Spain

In Evolutionary Game Theory (EGT), a population reaches a Nash equilibrium when none of the agents can improve its objective by solely changing its strategy on its own. Roughly speaking, this equilibrium is a protection against betrayal. Generalized Nash Equilibrium (GNE) is a more complex version of this idea with important implications in real-life problems in economics, wireless communication, the electricity market, or engineering among other areas. In this paper, we propose a first approach to GNE with Membrane Computing techniques and show how GNE problems can be modeled with P systems, bridging both areas and opening a door for a flow of problems and solutions in both directions. © 2024, CC BY-NC-ND.

关键词： Game theory

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Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves

Comparing Optical Flow and Deep Learning to Enable Computati...

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International Conference on Intelligent Transportation

作者： Tayssir Bouraffa Elias Kjellberg Carlson Erik Wessman Ali Nourir Pierre Lamart Christian Berger Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Volvo Cars Gothenburg Sweden Department of Computer Science and Engineering University of Gothenburg Gothenburg Sweden

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Gathering data and identifying events in various traffic situations remain an essential challenge for the systematic evaluation of a perception system's performance. Analyzing large-scale, typically unstructured, multi- modal, time-series data obtained from video, radar, and LiDAR is computationally demanding, particularly when meta-information or annotations are missing. We compare Optical Flow (OF) and Deep Learning (DL) to feed computationally efficient event detection via space-filling curves on video data from a forward-facing, in-vehicle camera. Our first approach leverages unexpected disturbances in the OF field from vehicle surroundings; the second approach is a DL model trained on human visual attention to predict a driver's gaze to spot potential event locations. We feed these results to a space-filling curve to reduce dimensionality and achieve computationally efficient event retrieval. We systematically evaluate our concept by obtaining characteristic patterns for both approaches from a large-scale virtual dataset (SMIRK) and applied our findings to the Zenseact Open Dataset (ZOD), a large multi-modal, real-world dataset, collected over two years in 14 different European countries. Our results yield that the OF approach excels in specificity and reduces false positives, while the DL approach demonstrates superior sensitivity. Both approaches offer comparable processing speed, making them suitable for real-time applications.

关键词： Deep learning Visualization Systematics Sensitivity Event detection System performance Real-time systems Computational efficiency Feeds Optical flow

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Exploring Trade-Offs in MLOps Adoption

Exploring Trade-Offs in MLOps Adoption

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Asia-Pacific Conference on Software engineering

作者： Meenu Mary John Helena Holmström Olsson Jan Bosch Daniel Gillblad Computer Science and Media Technology Malmö University Malmö Sweden Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Chalmers University of Technology & AI Sweden Gothenburg Sweden

Machine Learning Operations (MLOps) play a crucial role in the success of data science projects in companies. However, despite its obvious benefits, several companies struggle to adopt MLOps practices and face difficulty in deciding how to deploy and evolve ML models. To gain a deeper understanding of these challenges, we conduct a multi-case study involving nine practitioners from seven companies. Based on our empirical results, we identify the key trade-offs we see companies make when adopting MLOps. We categorise these trade-offs into four concerns of the BAPO model: Business, Architecture, Process, and Organisation. Finally, we provide suggestions to mitigate the identified trade-offs. By identifying and detailing these trade-offs and the implications of these, this research helps companies to ensure the successful adoption of MLOps.

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Advancing MLOps from Ad hoc to Kaizen

Advancing MLOps from Ad hoc to Kaizen

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Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Meenu Mary John Daniel Gillblad Helena Holmström Olsson Jan Bosch Computer Science and Media Technology Malmö University Malmö Sweden Chalmers University of Technology & AI Sweden Gothenburg Sweden Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

Companies across various domains increasingly adopt Machine Learning Operations (MLOps) as they recognise the significance of operationalising ML models. Despite growing interest from practitioners and ongoing research, MLOps adoption in practice is still in its initial stages. To explore the adoption of MLOps, we employ a multi-case study in seven companies. Based on empirical findings, we propose a maturity model outlining the typical stages companies undergo when adopting MLOps, ranging from Ad hoc to Kaizen. We identify five dimensions associated with each stage of the maturity model as part of our MLOps framework. We also map these seven companies to the identified stages in the maturity model. Our study serves as a roadmap for companies to assess their current state of MLOps, identify gaps and overcome obstacles to successfully adopting MLOps.

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Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms

arXiv

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arXiv 2024年

作者： Wanner, Marc Lewis, Laura Bhattacharyya, Chiranjib Dubhashi, Devdatt Gheorghiu, Alexandru Department of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

A fundamental problem in quantum many-body physics is that of finding ground states of local Hamiltonians. A number of recent works gave provably efficient machine learning (ML) algorithms for learning ground states. Specifically, Huang et al. in [1], introduced an approach for learning properties of the ground state of an n-qubit gapped local Hamiltonian H from only nO(1) data points sampled from Hamiltonians in the same phase of matter. This was subsequently improved by Lewis et al. in [2], to O(log n) samples when the geometry of the n-qubit system is known. In this work, we introduce two approaches that achieve a constant sample complexity, independent of system size n, for learning ground state properties. Our first algorithm consists of a simple modification of the ML model used by Lewis et al. and applies to a property of interest known in advance. Our second algorithm, which applies even if a description of the property is not known, is a deep neural network model. While empirical results showing the performance of neural networks have been demonstrated, to our knowledge, this is the first rigorous sample complexity bound on a neural network model for predicting ground state properties. We also perform numerical experiments on systems of up to 45 qubits that confirm the improved scaling of our approach compared to [1, 2]. © 2024, CC BY.

关键词： Qubits

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All data is equal or is some data more equal? On strategic data collection and use in the embedded systems domain

All data is equal or is some data more equal? On strategic d...

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Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Helena Holmström Olsson Jan Bosch Computer Science and Media Technology Malmö University Malmö Sweden Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

Effective collection and use of data is key for companies across domains and it is only increasing in importance. For companies in the embedded systems domain, data constitutes the basis not only for quality assurance and diagnostics of their systems but also for new service development and innovation. For these companies, data is an enabler for continuous delivery of customer value and hence, a key asset for entirely new and recurring revenue streams. However, effective use of data requires careful collection of different kinds of data depending on the purpose and context for which it is intended to be used. In this paper, we identify the challenges that companies experience in their contemporary data practices and we outline the kinds of data that companies need to collect as they evolve through different maturity stages. In addition, we provide concrete guidance on the specific data to collect during each maturity stage.

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Tapping into Privacy: A Study of User Preferences and Concerns on Trigger-Action Platforms

arXiv

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arXiv 2023年

作者： Romare, Piero Karegar, Farzaneh Morel, Victor Fischer-Hübner, Simone Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Department of Computer Science and Engineering Chalmers University of Technology Karlstad University Gothenburg Karlstad Sweden

The Internet of Things (IoT) devices are rapidly increasing in popularity, with more individuals using Internet-connected devices that continuously monitor their activities. This work explores privacy concerns and expectations of end-users related to Trigger-Action platforms (TAPs) in the context of the Internet of Things (IoT). TAPs allow users to customize their smart environments by creating rules that trigger actions based on specific events or conditions. As personal data flows between different entities, there is a potential for privacy concerns. In this study, we aimed to identify the privacy factors that impact users’ concerns and preferences for using IoT TAPs. To address this research objective, we conducted three focus groups with 15 participants and we extracted nine themes related to privacy factors using thematic analysis. Our participants particularly prefer to have control and transparency over the automation and are concerned about unexpected data inferences, risks and unforeseen consequences for themselves and for bystanders that are caused by the automation. The identified privacy factors can help researchers derive predefined and selectable profiles of privacy permission settings for IoT TAPs that represent the privacy preferences of different types of users as a basis for designing usable privacy controls for IoT TAPs. © 2023, CC BY.

关键词： Internet of things

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