1,850 research outputs found

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    On the real world practice of Behaviour Driven Development

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    Surveys of industry practice over the last decade suggest that Behaviour Driven Development is a popular Agile practice. For example, 19% of respondents to the 14th State of Agile annual survey reported using BDD, placing it in the top 13 practices reported. As well as potential benefits, the adoption of BDD necessarily involves an additional cost of writing and maintaining Gherkin features and scenarios, and (if used for acceptance testing,) the associated step functions. Yet there is a lack of published literature exploring how BDD is used in practice and the challenges experienced by real world software development efforts. This gap is significant because without understanding current real world practice, it is hard to identify opportunities to address and mitigate challenges. In order to address this research gap concerning the challenges of using BDD, this thesis reports on a research project which explored: (a) the challenges of applying agile and undertaking requirements engineering in a real world context; (b) the challenges of applying BDD specifically and (c) the application of BDD in open-source projects to understand challenges in this different context. For this purpose, we progressively conducted two case studies, two series of interviews, four iterations of action research, and an empirical study. The first case study was conducted in an avionics company to discover the challenges of using an agile process in a large scale safety critical project environment. Since requirements management was found to be one of the biggest challenges during the case study, we decided to investigate BDD because of its reputation for requirements management. The second case study was conducted in the company with an aim to discover the challenges of using BDD in real life. The case study was complemented with an empirical study of the practice of BDD in open source projects, taking a study sample from the GitHub open source collaboration site. As a result of this Ph.D research, we were able to discover: (i) challenges of using an agile process in a large scale safety-critical organisation, (ii) current state of BDD in practice, (iii) technical limitations of Gherkin (i.e., the language for writing requirements in BDD), (iv) challenges of using BDD in a real project, (v) bad smells in the Gherkin specifications of open source projects on GitHub. We also presented a brief comparison between the theoretical description of BDD and BDD in practice. This research, therefore, presents the results of lessons learned from BDD in practice, and serves as a guide for software practitioners planning on using BDD in their projects

    Impact of Imaging and Distance Perception in VR Immersive Visual Experience

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    Virtual reality (VR) headsets have evolved to include unprecedented viewing quality. Meanwhile, they have become lightweight, wireless, and low-cost, which has opened to new applications and a much wider audience. VR headsets can now provide users with greater understanding of events and accuracy of observation, making decision-making faster and more effective. However, the spread of immersive technologies has shown a slow take-up, with the adoption of virtual reality limited to a few applications, typically related to entertainment. This reluctance appears to be due to the often-necessary change of operating paradigm and some scepticism towards the "VR advantage". The need therefore arises to evaluate the contribution that a VR system can make to user performance, for example to monitoring and decision-making. This will help system designers understand when immersive technologies can be proposed to replace or complement standard display systems such as a desktop monitor. In parallel to the VR headsets evolution there has been that of 360 cameras, which are now capable to instantly acquire photographs and videos in stereoscopic 3D (S3D) modality, with very high resolutions. 360° images are innately suited to VR headsets, where the captured view can be observed and explored through the natural rotation of the head. Acquired views can even be experienced and navigated from the inside as they are captured. The combination of omnidirectional images and VR headsets has opened to a new way of creating immersive visual representations. We call it: photo-based VR. This represents a new methodology that combines traditional model-based rendering with high-quality omnidirectional texture-mapping. Photo-based VR is particularly suitable for applications related to remote visits and realistic scene reconstruction, useful for monitoring and surveillance systems, control panels and operator training. The presented PhD study investigates the potential of photo-based VR representations. It starts by evaluating the role of immersion and user’s performance in today's graphical visual experience, to then use it as a reference to develop and evaluate new photo-based VR solutions. With the current literature on photo-based VR experience and associated user performance being very limited, this study builds new knowledge from the proposed assessments. We conduct five user studies on a few representative applications examining how visual representations can be affected by system factors (camera and display related) and how it can influence human factors (such as realism, presence, and emotions). Particular attention is paid to realistic depth perception, to support which we develop target solutions for photo-based VR. They are intended to provide users with a correct perception of space dimension and objects size. We call it: true-dimensional visualization. The presented work contributes to unexplored fields including photo-based VR and true-dimensional visualization, offering immersive system designers a thorough comprehension of the benefits, potential, and type of applications in which these new methods can make the difference. This thesis manuscript and its findings have been partly presented in scientific publications. In particular, five conference papers on Springer and the IEEE symposia, [1], [2], [3], [4], [5], and one journal article in an IEEE periodical [6], have been published

    Driving venture capital funding efficiencies through data driven models. Why is this important and what are its implications for the startup ecosystem?

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    This thesis aims to test whether data models can fit the venture capital funding process better, and if they do fit, can they help improve the venture capital funding efficiency? Based on the reported results, venture capitalists can only see returns in 20% of their investments. The thesis argues that it is essential to help venture capital investment as it can help drive economic growth through investments in innovation. The thesis considers four startup scenarios and the related investment factors. The scenarios are a funded artificial intelligence startup seeking follow-on funding, a new startup seeking first funding, the survivability of a sustainability-focused startup, and the importance of patents for exit. Patents are a proxy for innovation in this thesis. Through quantitative analysis using generalized linear models, logit regressions, and t-tests, the thesis can establish that data models can identify the relative significance of funding factors. Once the factor significance is established, it can be deployed in a model. Building the machine learning model has been considered outside the scope of this thesis. A mix of academic and real-world research has been used for the data analysis of this thesis. Accelerators and venture capitalists also used some of the results to improve their own processes. Many of the models have shifted from a prediction to factor significance. This thesis implies that it could help venture capitalists plan for a 10% efficiency improvement. From an academic perspective, this study focuses on the entire life of a startup, from the first funding stage to the exit. It also links the startup ecosystem with economic development. Two additional factors from the study are the regional perspective of funding differences between Asia, Europe, and the US and that this study would include the recent economic sentiment. The impact of the funding slowdown has been measured through a focus on first funding and longitudinal validations of the data decision before the slowdown. Based on the results of the thesis, data models are a credible alternative and show significant correlations between returns and factors. It is advisable for a venture capitalist to consider these

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Digitalization and Development

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    This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents. The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term. This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    DECEPTION BASED TECHNIQUES AGAINST RANSOMWARES: A SYSTEMATIC REVIEW

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    Ransomware is the most prevalent emerging business risk nowadays. It seriously affects business continuity and operations. According to Deloitte Cyber Security Landscape 2022, up to 4000 ransomware attacks occur daily, while the average number of days an organization takes to identify a breach is 191. Sophisticated cyber-attacks such as ransomware typically must go through multiple consecutive phases (initial foothold, network propagation, and action on objectives) before accomplishing its final objective. This study analyzed decoy-based solutions as an approach (detection, prevention, or mitigation) to overcome ransomware. A systematic literature review was conducted, in which the result has shown that deception-based techniques have given effective and significant performance against ransomware with minimal resources. It is also identified that contrary to general belief, deception techniques mainly involved in passive approaches (i.e., prevention, detection) possess other active capabilities such as ransomware traceback and obstruction (thwarting), file decryption, and decryption key recovery. Based on the literature review, several evaluation methods are also analyzed to measure the effectiveness of these deception-based techniques during the implementation process

    Design and Implementation of a Portable Framework for Application Decomposition and Deployment in Edge-Cloud Systems

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    The emergence of cyber-physical systems has brought about a significant increase in complexity and heterogeneity in the infrastructure on which these systems are deployed. One particular example of this complexity is the interplay between cloud, fog, and edge computing. However, the complexity of these systems can pose challenges when it comes to implementing self-organizing mechanisms, which are often designed to work on flat networks. Therefore, it is essential to separate the application logic from the specific deployment aspects to promote reusability and flexibility in infrastructure exploitation. To address this issue, a novel approach called "pulverization" has been proposed. This approach involves breaking down the system into smaller computational units, which can then be deployed on the available infrastructure. In this thesis, the design and implementation of a portable framework that enables the "pulverization" of cyber-physical systems are presented. The main objective of the framework is to pave the way for the deployment of cyber-physical systems in the edge-cloud continuum by reducing the complexity of the infrastructure and exploit opportunistically the heterogeneous resources available on it. Different scenarios are presented to highlight the effectiveness of the framework in different heterogeneous infrastructures and devices. Current limitations and future work are examined to identify improvement areas for the framework

    Untersuchung von Performanzveränderungen auf Quelltextebene

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    Änderungen am Quelltext einer Software können zu veränderter Performanz führen. Um das Auftreten von Regressionen zu verhindern und die Effekte von Quelltextänderungen, von denen eine Verbesserung erwartet wird, zu überprüfen, ist die Messung der Auswirkungen von Quelltextänderungen auf die Performanz sowie das tiefgehende Verständnis des Laufzeitverhaltens der beteiligten Quelltextkonstrukte notwendig. Die Spezifikation von Benchmarks oder Lasttests, um Regressionen zu erkennen, erfordert immensen manuellen Aufwand. Für das Verständnis der Änderungen sind anschließend oft weitere Experimente notwendig. In der vorliegenden Arbeit wird der Ansatz Performanzanalyse von Softwaresystemen (Peass) entwickelt. Peass beruht auf der Annahme, dass Performanzänderungen durch Messung der Performanz von Unittests erkennbar ist. Peass besteht aus (1) einer Methode zur Regressionstestselektion, d. h. zur Bestimmung, zwischen welchen Commits sich die Performanz geändert haben kann basierend auf statischer Quelltextanalyse und Analyse des Laufzeitverhaltens, (2) einer Methode zur Umwandlung von Unittests in Performanztests und zur statistisch zuverlässigen und reproduzierbaren Messung der Performanz und (3) einer Methode zur Unterstützung des Verstehens von Ursachen von Performanzänderungen. Der Peass-Ansatzes ermöglicht es somit, durch den Workload von Unittests messbare Performanzänderungen automatisiert zu untersuchen. Die Validität des Ansatzes wird geprüft, indem gezeigt wird, dass (1) typische Performanzprobleme in künstlichen Testfällen und (2) reale, durch Entwickler markierte Performanzänderungen durch Peass gefunden werden können. Durch eine Fallstudie in einem laufenden Softwareentwicklungsprojekt wird darüber hinaus gezeigt, dass Peass in der Lage ist, relevante Performanzänderungen zu erkennen.:1 Einleitung 1.1 Motivation 1.2 Ansatz 1.3 Forschungsfragen 1.4 Beiträge 1.5 Aufbau der Arbeit 2 Grundlagen 2.1 Software Performance Engineering 2.2 Modellbasierter Ansatz 2.2.1 Überblick 2.2.2 Performanzantipattern 2.3 Messbasierter Ansatz 2.3.1 Messprozess 2.3.2 Messwertanalyse 2.4 Messung in künstlichen Umgebungen 2.4.1 Benchmarking 2.4.2 Lasttests 2.4.3 Performanztests 2.5 Messung in realen Umgebungen: Monitoring 2.5.1 Überblick 2.5.2 Umsetzung 2.5.3 Werkzeuge 3 Regressionstestselektion 3.1 Ansatz 3.1.1 Grundidee 3.1.2 Voraussetzungen 3.1.3 Zweistufiger Prozess 3.2 Statische Testselektion 3.2.1 Selektierte Änderungen 3.2.2 Prozess 3.2.3 Implementierung 3.3 Tracevergleich 3.3.1 Selektierte Änderungen 3.3.2 Prozess 3.3.3 Implementierung 3.3.4 Kombination mit statischer Analyse 3.4 Evaluation 3.4.1 Implementierung 3.4.2 Exaktheit 3.4.3 Korrektheit 3.4.4 Diskussion der Validität 3.5 Verwandte Arbeiten 3.5.1 Funktionale Regressionstestbestimmung 3.5.2 Regressionstestbestimmung für Performanztests 4 Messprozess 4.1 Vergleich von Mess- und Analysemethoden 4.1.1 Vorgehen 4.1.2 Fehlerbetrachtung 4.1.3 Workloadgröße der künstlichen Unittestpaare 4.2 Messmethode 4.2.1 Aufbau einer Iteration 4.2.2 Beenden von Messungen 4.2.3 Garbage Collection je Iteration 4.2.4 Umgang mit Standardausgabe 4.2.5 Zusammenfassung der Messmethode 4.3 Analysemethode 4.3.1 Auswahl des statistischen Tests 4.3.2 Ausreißerentfernung 4.3.3 Parallelisierung 4.4 Evaluierung 4.4.1 Vergleich mit JMH 4.4.2 Reproduzierbarkeit der Ergebnisse 4.4.3 Fazit 4.5 Verwandte Arbeiten 4.5.1 Beenden von Messungen 4.5.2 Änderungserkennung 4.5.3 Anomalieerkennung 5 Ursachenanalyse 5.1 Reduktion des Overheads der Messung einzelner Methoden 5.1.1 Generierung von Beispielprojekten 5.1.2 Messung von Methodenausführungsdauern 5.1.3 Optionen zur Overheadreduktion 5.1.4 Messergebnisse 5.1.5 Überprüfung mit MooBench 5.2 Messkonfiguration der Ursachenanalyse 5.2.1 Grundlagen 5.2.2 Fehlerbetrachtung 5.2.3 Ansatz 5.2.4 Messergebnisse 5.3 Verwandte Arbeiten 5.3.1 Monitoringoverhead 5.3.2 Ursachenanalyse für Performanzänderungen 5.3.3 Ursachenanalyse für Performanzprobleme 6 Evaluation 6.1 Validierung durch künstliche Performanzprobleme 6.1.1 Reproduktion durch Benchmarks 6.1.2 Umwandlung der Benchmarks 6.1.3 Überprüfen von Problemen mit Peass 6.2 Evaluation durch reale Performanzprobleme 6.2.1 Untersuchung dokumentierter Performanzänderungen offenen Projekten 6.2.2 Untersuchung der Performanzänderungen in GeoMap 7 Zusammenfassung und Ausblick 7.1 Zusammenfassung 7.2 AusblickChanges to the source code of a software may result in varied performance. In order to prevent the occurance of regressions and check the effect of source changes, which are expected to result in performance improvements, both the measurement of the impact of source code changes and a deep understanding of the runtime behaviour of the used source code elements are necessary. The specification of benchmarks and load tests, which are able to detect performance regressions, requires immense manual effort. To understand the changes, often additional experiments are necessary. This thesis develops the Peass approach (Performance analysis of software systems). Peass is based on the assumption, that performance changes can be identified by unit tests. Therefore, Peass consists of (1) a method for regression test selection, which determines between which commits the performance may have changed based on static code analysis and analysis of the runtime behavior, (2) a method for transforming unit tests into performance tests and for statistically reliable and reproducible measurement of the performance and (3) a method for aiding the diagnosis of root causes of performance changes. The Peass approach thereby allows to automatically examine performance changes that are measurable by the workload of unit tests. The validity of the approach is evaluated by showing that (1) typical performance problems in artificial test cases and (2) real, developer-tagged performance changes can be found by Peass. Furthermore, a case study in an ongoing software development project shows that Peass is able to detect relevant performance changes.:1 Einleitung 1.1 Motivation 1.2 Ansatz 1.3 Forschungsfragen 1.4 Beiträge 1.5 Aufbau der Arbeit 2 Grundlagen 2.1 Software Performance Engineering 2.2 Modellbasierter Ansatz 2.2.1 Überblick 2.2.2 Performanzantipattern 2.3 Messbasierter Ansatz 2.3.1 Messprozess 2.3.2 Messwertanalyse 2.4 Messung in künstlichen Umgebungen 2.4.1 Benchmarking 2.4.2 Lasttests 2.4.3 Performanztests 2.5 Messung in realen Umgebungen: Monitoring 2.5.1 Überblick 2.5.2 Umsetzung 2.5.3 Werkzeuge 3 Regressionstestselektion 3.1 Ansatz 3.1.1 Grundidee 3.1.2 Voraussetzungen 3.1.3 Zweistufiger Prozess 3.2 Statische Testselektion 3.2.1 Selektierte Änderungen 3.2.2 Prozess 3.2.3 Implementierung 3.3 Tracevergleich 3.3.1 Selektierte Änderungen 3.3.2 Prozess 3.3.3 Implementierung 3.3.4 Kombination mit statischer Analyse 3.4 Evaluation 3.4.1 Implementierung 3.4.2 Exaktheit 3.4.3 Korrektheit 3.4.4 Diskussion der Validität 3.5 Verwandte Arbeiten 3.5.1 Funktionale Regressionstestbestimmung 3.5.2 Regressionstestbestimmung für Performanztests 4 Messprozess 4.1 Vergleich von Mess- und Analysemethoden 4.1.1 Vorgehen 4.1.2 Fehlerbetrachtung 4.1.3 Workloadgröße der künstlichen Unittestpaare 4.2 Messmethode 4.2.1 Aufbau einer Iteration 4.2.2 Beenden von Messungen 4.2.3 Garbage Collection je Iteration 4.2.4 Umgang mit Standardausgabe 4.2.5 Zusammenfassung der Messmethode 4.3 Analysemethode 4.3.1 Auswahl des statistischen Tests 4.3.2 Ausreißerentfernung 4.3.3 Parallelisierung 4.4 Evaluierung 4.4.1 Vergleich mit JMH 4.4.2 Reproduzierbarkeit der Ergebnisse 4.4.3 Fazit 4.5 Verwandte Arbeiten 4.5.1 Beenden von Messungen 4.5.2 Änderungserkennung 4.5.3 Anomalieerkennung 5 Ursachenanalyse 5.1 Reduktion des Overheads der Messung einzelner Methoden 5.1.1 Generierung von Beispielprojekten 5.1.2 Messung von Methodenausführungsdauern 5.1.3 Optionen zur Overheadreduktion 5.1.4 Messergebnisse 5.1.5 Überprüfung mit MooBench 5.2 Messkonfiguration der Ursachenanalyse 5.2.1 Grundlagen 5.2.2 Fehlerbetrachtung 5.2.3 Ansatz 5.2.4 Messergebnisse 5.3 Verwandte Arbeiten 5.3.1 Monitoringoverhead 5.3.2 Ursachenanalyse für Performanzänderungen 5.3.3 Ursachenanalyse für Performanzprobleme 6 Evaluation 6.1 Validierung durch künstliche Performanzprobleme 6.1.1 Reproduktion durch Benchmarks 6.1.2 Umwandlung der Benchmarks 6.1.3 Überprüfen von Problemen mit Peass 6.2 Evaluation durch reale Performanzprobleme 6.2.1 Untersuchung dokumentierter Performanzänderungen offenen Projekten 6.2.2 Untersuchung der Performanzänderungen in GeoMap 7 Zusammenfassung und Ausblick 7.1 Zusammenfassung 7.2 Ausblic
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