2,907 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 β€” 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

    Configuration Management of Distributed Systems over Unreliable and Hostile Networks

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    Economic incentives of large criminal profits and the threat of legal consequences have pushed criminals to continuously improve their malware, especially command and control channels. This thesis applied concepts from successful malware command and control to explore the survivability and resilience of benign configuration management systems. This work expands on existing stage models of malware life cycle to contribute a new model for identifying malware concepts applicable to benign configuration management. The Hidden Master architecture is a contribution to master-agent network communication. In the Hidden Master architecture, communication between master and agent is asynchronous and can operate trough intermediate nodes. This protects the master secret key, which gives full control of all computers participating in configuration management. Multiple improvements to idempotent configuration were proposed, including the definition of the minimal base resource dependency model, simplified resource revalidation and the use of imperative general purpose language for defining idempotent configuration. Following the constructive research approach, the improvements to configuration management were designed into two prototypes. This allowed validation in laboratory testing, in two case studies and in expert interviews. In laboratory testing, the Hidden Master prototype was more resilient than leading configuration management tools in high load and low memory conditions, and against packet loss and corruption. Only the research prototype was adaptable to a network without stable topology due to the asynchronous nature of the Hidden Master architecture. The main case study used the research prototype in a complex environment to deploy a multi-room, authenticated audiovisual system for a client of an organization deploying the configuration. The case studies indicated that imperative general purpose language can be used for idempotent configuration in real life, for defining new configurations in unexpected situations using the base resources, and abstracting those using standard language features; and that such a system seems easy to learn. Potential business benefits were identified and evaluated using individual semistructured expert interviews. Respondents agreed that the models and the Hidden Master architecture could reduce costs and risks, improve developer productivity and allow faster time-to-market. Protection of master secret keys and the reduced need for incident response were seen as key drivers for improved security. Low-cost geographic scaling and leveraging file serving capabilities of commodity servers were seen to improve scaling and resiliency. Respondents identified jurisdictional legal limitations to encryption and requirements for cloud operator auditing as factors potentially limiting the full use of some concepts

    Pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inΕΎenjerstvu voΔ‘enom modelima

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    In this thesis, we present an approach to the production process specification and generation based on the model-driven paradigm, with the goal to increase the flexibility of factories and respond to the challenges that emerged in the era of Industry 4.0 more efficiently. To formally specify production processes and their variations in the Industry 4.0 environment, we created a novel domain-specific modeling language, whose models are machine-readable. The created language can be used to model production processes that can be independent of any production system, enabling process models to be used in different production systems, and process models used for the specific production system. To automatically transform production process models dependent on the specific production system into instructions that are to be executed by production system resources, we created an instruction generator. Also, we created generators for different manufacturing documentation, which automatically transform production process models into manufacturing documents of different types. The proposed approach, domain-specific modeling language, and software solution contribute to introducing factories into the digital transformation process. As factories must rapidly adapt to new products and their variations in the era of Industry 4.0, production must be dynamically led and instructions must be automatically sent to factory resources, depending on products that are to be created on the shop floor. The proposed approach contributes to the creation of such a dynamic environment in contemporary factories, as it allows to automatically generate instructions from process models and send them to resources for execution. Additionally, as there are numerous different products and their variations, keeping the required manufacturing documentation up to date becomes challenging, which can be done automatically by using the proposed approach and thus significantly lower process designers' time.Π£ овој Π΄ΠΈΡΠ΅Ρ€Ρ‚Π°Ρ†ΠΈΡ˜ΠΈ прСдстављСн јС приступ ΡΠΏΠ΅Ρ†ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΡ˜ΠΈ ΠΈ Π³Π΅Π½Π΅Ρ€ΠΈΡΠ°ΡšΡƒ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса заснован Π½Π° ΠΈΠ½ΠΆΠ΅ΡšΠ΅Ρ€ΡΡ‚Π²Ρƒ Π²ΠΎΡ’Π΅Π½ΠΎΠΌ ΠΌΠΎΠ΄Π΅Π»ΠΈΠΌΠ°, Ρƒ Ρ†ΠΈΡ™Ρƒ ΠΏΠΎΠ²Π΅Ρ›Π°ΡšΠ° флСксибилности ΠΏΠΎΡΡ‚Ρ€ΠΎΡ˜Π΅ΡšΠ° Ρƒ Ρ„Π°Π±Ρ€ΠΈΠΊΠ°ΠΌΠ° ΠΈ Π΅Ρ„ΠΈΠΊΠ°ΡΠ½ΠΈΡ˜Π΅Π³ Ρ€Π°Π·Ρ€Π΅ΡˆΠ°Π²Π°ΡšΠ° ΠΈΠ·Π°Π·ΠΎΠ²Π° који сС ΠΏΠΎΡ˜Π°Π²Ρ™ΡƒΡ˜Ρƒ Ρƒ Π΅Ρ€ΠΈ Π˜Π½Π΄ΡƒΡΡ‚Ρ€ΠΈΡ˜Π΅ 4.0. Π—Π° ΠΏΠΎΡ‚Ρ€Π΅Π±Π΅ Ρ„ΠΎΡ€ΠΌΠ°Π»Π½Π΅ ΡΠΏΠ΅Ρ†ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΡ˜Π΅ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса ΠΈ ΡšΠΈΡ…ΠΎΠ²ΠΈΡ… Π²Π°Ρ€ΠΈΡ˜Π°Ρ†ΠΈΡ˜Π° Ρƒ Π°ΠΌΠ±ΠΈΡ˜Π΅Π½Ρ‚Ρƒ Π˜Π½Π΄ΡƒΡΡ‚Ρ€ΠΈΡ˜Π΅ 4.0, ΠΊΡ€Π΅ΠΈΡ€Π°Π½ јС Π½ΠΎΠ²ΠΈ намСнски јСзик, Ρ‡ΠΈΡ˜Π΅ ΠΌΠΎΠ΄Π΅Π»Π΅ Ρ€Π°Ρ‡ΡƒΠ½Π°Ρ€ ΠΌΠΎΠΆΠ΅ Π΄Π° ΠΎΠ±Ρ€Π°Π΄ΠΈ Π½Π° Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°Ρ‡ΠΈΠ½. ΠšΡ€Π΅ΠΈΡ€Π°Π½ΠΈ јСзик ΠΈΠΌΠ° могућност модСловања ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса који ΠΌΠΎΠ³Ρƒ Π±ΠΈΡ‚ΠΈ нСзависни ΠΎΠ΄ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… систСма ΠΈ Ρ‚ΠΈΠΌΠ΅ ΡƒΠΏΠΎΡ‚Ρ€Π΅Π±Ρ™Π΅Π½ΠΈ Ρƒ Ρ€Π°Π·Π»ΠΈΡ‡ΠΈΡ‚ΠΈΠΌ ΠΏΠΎΡΡ‚Ρ€ΠΎΡ˜Π΅ΡšΠΈΠΌΠ° ΠΈΠ»ΠΈ Ρ„Π°Π±Ρ€ΠΈΠΊΠ°ΠΌΠ°, Π°Π»ΠΈ ΠΈ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса који су спСцифични Π·Π° ΠΎΠ΄Ρ€Π΅Ρ’Π΅Π½ΠΈ систСм. Како Π±ΠΈ ΠΌΠΎΠ΄Π΅Π»Π΅ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса зависних ΠΎΠ΄ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠ³ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎΠ³ систСма Π±ΠΈΠ»ΠΎ ΠΌΠΎΠ³ΡƒΡ›Π΅ Π½Π° Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°Ρ‡ΠΈΠ½ трансформисати Ρƒ ΠΈΠ½ΡΡ‚Ρ€ΡƒΠΊΡ†ΠΈΡ˜Π΅ којС рСсурси ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎΠ³ систСма ΠΈΠ·Π²Ρ€ΡˆΠ°Π²Π°Ρ˜Ρƒ, ΠΊΡ€Π΅ΠΈΡ€Π°Π½ јС Π³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€ ΠΈΠ½ΡΡ‚Ρ€ΡƒΠΊΡ†ΠΈΡ˜Π°. Π’Π°ΠΊΠΎΡ’Π΅ су ΠΊΡ€Π΅ΠΈΡ€Π°Π½ΠΈ ΠΈ Π³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€ΠΈ Ρ‚Π΅Ρ…Π½ΠΈΡ‡ΠΊΠ΅ Π΄ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚Π°Ρ†ΠΈΡ˜Π΅, који Π½Π° Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°Ρ‡ΠΈΠ½ Ρ‚Ρ€Π°Π½ΡΡ„ΠΎΡ€ΠΌΠΈΡˆΡƒ ΠΌΠΎΠ΄Π΅Π»Π΅ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ… процСса Ρƒ Π΄ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚Π΅ Ρ€Π°Π·Π»ΠΈΡ‡ΠΈΡ‚ΠΈΡ… Ρ‚ΠΈΠΏΠΎΠ²Π°. Π£ΠΏΠΎΡ‚Ρ€Π΅Π±ΠΎΠΌ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠ³ приступа, намСнског јСзика ΠΈ софтвСрског Ρ€Π΅ΡˆΠ΅ΡšΠ° доприноси сС ΡƒΠ²ΠΎΡ’Π΅ΡšΡƒ Ρ„Π°Π±Ρ€ΠΈΠΊΠ° Ρƒ процСс Π΄ΠΈΠ³ΠΈΡ‚Π°Π»Π½Π΅ Ρ‚Ρ€Π°Π½ΡΡ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΡ˜Π΅. Како Ρ„Π°Π±Ρ€ΠΈΠΊΠ΅ Ρƒ Π΅Ρ€ΠΈ Π˜Π½Π΄ΡƒΡΡ‚Ρ€ΠΈΡ˜Π΅ 4.0 ΠΌΠΎΡ€Π°Ρ˜Ρƒ Π±Ρ€Π·ΠΎ Π΄Π° сС ΠΏΡ€ΠΈΠ»Π°Π³ΠΎΠ΄Π΅ Π½ΠΎΠ²ΠΈΠΌ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΠ° ΠΈ ΡšΠΈΡ…ΠΎΠ²ΠΈΠΌ Π²Π°Ρ€ΠΈΡ˜Π°Ρ†ΠΈΡ˜Π°ΠΌΠ°, Π½Π΅ΠΎΠΏΡ…ΠΎΠ΄Π½ΠΎ јС Π΄ΠΈΠ½Π°ΠΌΠΈΡ‡ΠΊΠΈ Π²ΠΎΠ΄ΠΈΡ‚ΠΈ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄ΡšΡƒ ΠΈ Π½Π° Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°Ρ‡ΠΈΠ½ слати ΠΈΠ½ΡΡ‚Ρ€ΡƒΠΊΡ†ΠΈΡ˜Π΅ рСсурсима Ρƒ Ρ„Π°Π±Ρ€ΠΈΡ†ΠΈ, Ρƒ зависности ΠΎΠ΄ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π° који сС ΠΊΡ€Π΅ΠΈΡ€Π°Ρ˜Ρƒ Ρƒ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠΌ ΠΏΠΎΡΡ‚Ρ€ΠΎΡ˜Π΅ΡšΡƒ. Π’ΠΈΠΌΠ΅ ΡˆΡ‚ΠΎ јС Ρƒ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠΌ приступу ΠΌΠΎΠ³ΡƒΡ›Π΅ ΠΈΠ· ΠΌΠΎΠ΄Π΅Π»Π° процСса Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ΠΎ гСнСрисати ΠΈΠ½ΡΡ‚Ρ€ΡƒΠΊΡ†ΠΈΡ˜Π΅ ΠΈ послати ΠΈΡ… рСсурсима, доприноси сС ΠΊΡ€Π΅ΠΈΡ€Π°ΡšΡƒ јСдног Π΄ΠΈΠ½Π°ΠΌΠΈΡ‡ΠΊΠΎΠ³ ΠΎΠΊΡ€ΡƒΠΆΠ΅ΡšΠ° Ρƒ саврСмСним Ρ„Π°Π±Ρ€ΠΈΠΊΠ°ΠΌΠ°. Π”ΠΎΠ΄Π°Ρ‚Π½ΠΎ, услСд Π²Π΅Π»ΠΈΠΊΠΎΠ³ Π±Ρ€ΠΎΡ˜Π° Ρ€Π°Π·Π»ΠΈΡ‡ΠΈΡ‚ΠΈΡ… ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄Π° ΠΈ ΡšΠΈΡ…ΠΎΠ²ΠΈΡ… Π²Π°Ρ€ΠΈΡ˜Π°Ρ†ΠΈΡ˜Π°, ΠΏΠΎΡΡ‚Π°Ρ˜Π΅ ΠΈΠ·Π°Π·ΠΎΠ²Π½ΠΎ ΠΎΠ΄Ρ€ΠΆΠ°Π²Π°Ρ‚ΠΈ Π½Π΅ΠΎΠΏΡ…ΠΎΠ΄Π½Ρƒ Ρ‚Π΅Ρ…Π½ΠΈΡ‡ΠΊΡƒ Π΄ΠΎΠΊΡƒΠΌΠ΅Π½Ρ‚Π°Ρ†ΠΈΡ˜Ρƒ, ΡˆΡ‚ΠΎ јС Ρƒ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠΌ приступу ΠΌΠΎΠ³ΡƒΡ›Π΅ ΡƒΡ€Π°Π΄ΠΈΡ‚ΠΈ Π½Π° Π°ΡƒΡ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°Ρ‡ΠΈΠ½ ΠΈ Ρ‚ΠΈΠΌΠ΅ Π·Π½Π°Ρ‡Π°Ρ˜Π½ΠΎ ΡƒΡˆΡ‚Π΅Π΄Π΅Ρ‚ΠΈ Π²Ρ€Π΅ΠΌΠ΅ ΠΏΡ€ΠΎΡ˜Π΅ΠΊΡ‚Π°Π½Π°Ρ‚Π° процСса.U ovoj disertaciji predstavljen je pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inΕΎenjerstvu voΔ‘enom modelima, u cilju poveΔ‡anja fleksibilnosti postrojenja u fabrikama i efikasnijeg razreΕ‘avanja izazova koji se pojavljuju u eri Industrije 4.0. Za potrebe formalne specifikacije proizvodnih procesa i njihovih varijacija u ambijentu Industrije 4.0, kreiran je novi namenski jezik, čije modele računar moΕΎe da obradi na automatizovan način. Kreirani jezik ima moguΔ‡nost modelovanja proizvodnih procesa koji mogu biti nezavisni od proizvodnih sistema i time upotrebljeni u različitim postrojenjima ili fabrikama, ali i proizvodnih procesa koji su specifični za odreΔ‘eni sistem. Kako bi modele proizvodnih procesa zavisnih od konkretnog proizvodnog sistema bilo moguΔ‡e na automatizovan način transformisati u instrukcije koje resursi proizvodnog sistema izvrΕ‘avaju, kreiran je generator instrukcija. TakoΔ‘e su kreirani i generatori tehničke dokumentacije, koji na automatizovan način transformiΕ‘u modele proizvodnih procesa u dokumente različitih tipova. Upotrebom predloΕΎenog pristupa, namenskog jezika i softverskog reΕ‘enja doprinosi se uvoΔ‘enju fabrika u proces digitalne transformacije. Kako fabrike u eri Industrije 4.0 moraju brzo da se prilagode novim proizvodima i njihovim varijacijama, neophodno je dinamički voditi proizvodnju i na automatizovan način slati instrukcije resursima u fabrici, u zavisnosti od proizvoda koji se kreiraju u konkretnom postrojenju. Time Ε‘to je u predloΕΎenom pristupu moguΔ‡e iz modela procesa automatizovano generisati instrukcije i poslati ih resursima, doprinosi se kreiranju jednog dinamičkog okruΕΎenja u savremenim fabrikama. Dodatno, usled velikog broja različitih proizvoda i njihovih varijacija, postaje izazovno odrΕΎavati neophodnu tehničku dokumentaciju, Ε‘to je u predloΕΎenom pristupu moguΔ‡e uraditi na automatizovan način i time značajno uΕ‘tedeti vreme projektanata procesa

    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 (β€˜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 CatΓ³lica 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

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    2017 GREAT Day Program

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    SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp

    Systemic Circular Economy Solutions for Fiber Reinforced Composites

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    This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials
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