298 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

    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

    SCENARIO DEVELOPMENT FOR URBAN WATER MANAGEMENT PLANNING FOR UNCERTAINTY

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    The urban water sector is confronted with a multitude of challenges. Rapid population growth, changing political landscapes, aging water infrastructures, and the worsening climate crisis are creating a range of uncertainties in the sector around managing water. Scenarios have been used extensively in the environmental domain to plan for and capture uncertainties to develop plausible futures, including the field of urban water management. Scenarios are key in enabling plans and creating roadmaps to attain desired futures. Despite the advantages and opportunities that scenarios offer for planning, they also have limitations; generally, and within the urban water space. Firstly, the growing uncertainty surrounding urban water management systems necessitates a focused review specifically aimed at the use of scenarios in urban water management. This thesis presents a systematic review to empirically investigate the crucial dimensions of urban water scenarios. Through this review, key knowledge gaps are highlighted, and recommendations are proposed to address these gaps. Secondly, scenarios often depict distressing, almost dystopian futures. Though negative future visions help understand the consequences of present trends and aid in anticipating imminent threats, the limited exploration of positive future visions can make it challenging to find the direction to transform. Optimistic scenarios delve into what people want for the future and capture how their aspirations shape them. Imagining positive visions encourage innovative thinking, creates agency, and creates pathways to desired futures. There is therefore a recognition to move towards more positive, desirable futures. This thesis uses a narrative, participatory scenario process, the SEEDS method, to develop positive visions of urban water futures. The Greater Sydney region in New South Wales, Australia is used as a case study to evaluate the applicability of this approach for urban water management. The urban water sector in the Greater Sydney region faces a multitude of challenges including impacts from climate change, managing diverse water supply sources, and meeting future water demand. These challenges create an increasingly uncertain future for the water sector, where the scale and nature of water services needed in the Greater Sydney region can be unclear. Hence, the Greater Sydney region is selected as the case study region to apply the SEEDS method and develop scenarios for urban water management to plan for future uncertainties. Thirdly, only a few scenario studies include surprises, the unexpected events, which make scenarios useful for planning. Challenges around capturing surprises in scenarios include a lack of structured approaches as well as a lack of evaluation of those methods that have been developed. This thesis discusses the effectiveness and suitability of various surprise methods for scenario development. These methods have been applied in the context of the SEEDS method for urban water management. Finally, there is a lack of evaluation of the tools used to cope with surprises as well as a lack of evaluation efforts of urban water management scenario studies. The assessment of the SEEDS approach for urban water management as well as the different surprise methods for scenario development requires evaluation criteria. This thesis develops and presents an evaluation criteria list based on existing literature that captures key criteria required for adequate assessment of the surprise methods and the scenario process. This thesis contributes to the fields of scenario development and urban water management, and the use of surprises within scenarios. Critical gaps in existing urban water management scenario practices are highlighted and key recommendations are proposed to fill the gaps. Through the pilot study and full-scale implementation of a positive-visioning, narrative-based scenario approach - the SEEDS method, the thesis demonstrates that the SEEDS method is applicable for urban water planning and shows potential for use at different stages of water planning. The positive visions generated through the SEEDS method highlight fundamental aspirations for the urban water sector, possible challenges, and conflicts, and discuss pathways to achieve positive future visions. By using in-situ experimentation and engaging participants with expertise in the relevant field, this thesis provides a realistic evaluation of the scenario process and surprise methods. This thesis thus fills the critical gap about the lack of evaluation in urban water management scenario processes by assessing the scenario method using selected evaluation criteria. Further, the thesis contributes towards the development of quality surprise methods through application and evaluation, thus addressing the gap about the lack of evaluation of the methods used to explore surprise events. Finally, the lack of surprises in scenarios is addressed by presenting different methods that can be used to explore surprise events. Guidance is provided to researchers working with scenario development to understand the different surprise methods available and for choosing the appropriate method(s) to plan for uncertain futures

    Resolving corporate bribery through deferred prosecution agreements:Lessons from the US, UK and France for China

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    While bribery is designated as a criminal offense in most jurisdictions, the enforcement of anti-bribery laws in the corporate context is far from satisfactory. The weak enforcement can be mainly attributed to the practical challenges of doing so. Benefiting from deferred prosecution agreements (DPAs), the U.S., UK and French authorities have significantly ramped up their anti-bribery enforcement and encouraged corporate self-policing activities. Inspired by the foreign DPA developments, China’s prosecutorial authorities have been actively promoting the compliance non-prosecution program (CNP) since 2020. Introduced amid the Covid-19 pandemic and the ever-intensive U.S.-China trade conflicts, the CNP aims to mitigate the adverse economic implications of corporate criminal enforcement and foster corporate compliance.Combining legal doctrinal research, comparative research and insights from the law and economics literature, this thesis provides an overview of the DPA regimes in the U.S., UK and France and the CNP in China. It analyzes the advantages and weakness of the DPA programs in the three jurisdictions, aiming to draw lessons for developing the Chinese version of DPA program to address corporate bribery. Meanwhile, it also identifies the reasons for the inactive role played by the corporations in China’s anti-bribery movement and the challenges caused for the authorities in the anti-bribery enforcement. It is proposed that a Chinese version of DPA program be established based on the existing CNP to resolve corporate bribery cases. When designing and applying the Chinese version of DPA program and complementary regimes, special attention should be paid to deterrence, rehabilitation, and individual accountability.<br/

    Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

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    Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets often lack practical values for large-scale real-world applications. In this paper, we describe Tenrec, a novel and publicly available data collection for RS that records various user feedback from four different recommendation scenarios. To be specific, Tenrec has the following five characteristics: (1) it is large-scale, containing around 5 million users and 140 million interactions; (2) it has not only positive user feedback, but also true negative feedback (vs. one-class recommendation); (3) it contains overlapped users and items across four different scenarios; (4) it contains various types of user positive feedback, in forms of clicks, likes, shares, and follows, etc; (5) it contains additional features beyond the user IDs and item IDs. We verify Tenrec on ten diverse recommendation tasks by running several classical baseline models per task. Tenrec has the potential to become a useful benchmark dataset for a majority of popular recommendation tasks

    Semantic Parsing in Limited Resource Conditions

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    This thesis explores challenges in semantic parsing, specifically focusing on scenarios with limited data and computational resources. It offers solutions using techniques like automatic data curation, knowledge transfer, active learning, and continual learning. For tasks with no parallel training data, the thesis proposes generating synthetic training examples from structured database schemas. When there is abundant data in a source domain but limited parallel data in a target domain, knowledge from the source is leveraged to improve parsing in the target domain. For multilingual situations with limited data in the target languages, the thesis introduces a method to adapt parsers using a limited human translation budget. Active learning is applied to select source-language samples for manual translation, maximizing parser performance in the target language. In addition, an alternative method is also proposed to utilize machine translation services, supplemented by human-translated data, to train a more effective parser. When computational resources are limited, a continual learning approach is introduced to minimize training time and computational memory. This maintains the parser's efficiency in previously learned tasks while adapting it to new tasks, mitigating the problem of catastrophic forgetting. Overall, the thesis provides a comprehensive set of methods to improve semantic parsing in resource-constrained conditions.Comment: PhD thesis, year of award 2023, 172 page

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
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