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检索条件"丛书名=Lecture notes in computer science,"

共 300226 条记录，以下是11-20 订阅

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Friendship with AI? - Immersive science Fiction Prototyping for Responsible Innovation 17th

Friendship with AI? - Immersive Science Fiction Prototyping ...

引用

17th International Conference on Interactive Digital Storytelling

作者： Brucker-Kley, Elke Keller, Thomas Michot, Janick Borghoff, Birgitta Stutz, Isabelle Zurich Univ Appl Sci Winterthur Switzerland

ISBN: (纸本)9783031784491;9783031784507

This paper presents the experiences from a science communication project that applied Immersive science Fiction Prototyping to explore the concept of friendship with emotional AI. The aim of this paper is to introduce the relevance and key elements of a no/low-code prototyping pipeline that enables non-technical authors to collaboratively design multilinear future scenarios, which can be seamlessly imported into VR environments. Anchored in the overarching method of science Fiction Prototyping, this pipeline not only facilitates the rapid creation of interactive multilinear VR scenarios but also promotes transdisciplinary design and public engagement for responsible innovation.

关键词： Future scenario technology critique science communication Virtual Reality interactive multilinear narrative

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Leveraging Secure Social Media Crowdsourcing for Gathering Firsthand Account in Conflict Zones 16th

Leveraging Secure Social Media Crowdsourcing for Gathering F...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Orojo, Abanisenioluwa Bhagat, Pranish Wilburn, John Donahoo, Michael Vishwamitra, Nishant Baylor Univ Waco TX 76798 USA Univ Texas San Antonio San Antonio TX USA

ISBN: (纸本)9783031785375;9783031785382

The Russo-Ukrainian conflict underscores challenges in obtaining reliable firsthand accounts. Traditional methods such as satellite imagery and journalism fall short due to limited access to zones. Secure social media platforms such as Telegram offer safer communication from conflict zones but lack effective message grouping, hindering insight collection. The proposed framework aims to enhance firsthand account gathering by crowdsourcing secure social media data. We gathered 250,000 Telegram messages on the conflict and developed a language model-based framework to identify contextual groupings. Evaluation reveals 477 new groupings from 13 news sources, enriching firsthand information. This research emphasizes the significance of secure social media crowdsourcing in conflict zones, paving the way for future advancements.

关键词： Crowdsourcing Social Media Conflict Zones Firsthand Accounts Secure Communication Data Analysis Natural Language Processing

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Privacy-Preserving Anti-money Laundering Using Secure Multi-party Computation 28th

Privacy-Preserving Anti-money Laundering Using Secure Multi-...

引用

28th International Conference on Financial Cryptography and Data Security

作者： van Egmond, Marie Beth Dunning, Vincent van den Berg, Stefan Rooijakkers, Thomas Sangers, Alex Poppe, Ton Veldsink, Jan Netherlands Org Appl Sci Res TNO The Hague Netherlands ABN AMRO Amsterdam Netherlands Rabobank Utrecht Netherlands

ISBN: (纸本)9783031786785;9783031786792

Money laundering is a serious financial crime where criminals aim to conceal the illegal source of their money via a series of transactions. Although banks have an obligation to monitor transactions, it is difficult to track these illicit money flows since they typically span over multiple banks, which cannot share this information due to privacy concerns. We present secure risk propagation, a novel efficient algorithm for money laundering detection across banks without violating privacy concerns. In this algorithm, each account is assigned a risk score, which is then propagated through the transaction network. In this article we present two results. Firstly, using data from a large Dutch bank, we show that it is possible to detect unusual activity using this model, with cash ratio as the risk score. With a recall of 20%, the precision improves from 15% to 40% by propagating the risk scores, reducing the number of false positives significantly. Secondly, we present a privacy-preserving solution for securely performing risk propagation over a joint, inter-bank transaction network. To achieve this, we use Secure Multi-Party Computation (MPC) techniques, which are particularly well-suited for the risk propagation algorithm due to its structural simplicity. We also show that the running time of this secure variant scales linearly in the amount of accounts and transactions. For 200, 000 transactions, two iterations of the secure algorithm between three virtual parties, run within three hours on a consumer-grade server.

关键词： financial crime money laundering multi-party computation privacy risk propagation

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From Complexity to Clarity: Applying Systems Thinking and Mapping to the Co-creation of Learning Idns 17th

From Complexity to Clarity: Applying Systems Thinking and Ma...

引用

17th International Conference on Interactive Digital Storytelling

作者： Roth, Christian Pitt, Breanne Bender-Salazar, Rahmin Univ Arts Utrecht Lange Viestr 2BPostbox 1520 NL-3500 BM Utrecht Netherlands Univ Dublin Trinity Coll Dublin Dublin D02 PN40 Ireland Univ Galway Univ Rd Galway H91 TK33 Ireland

ISBN: (纸本)9783031784521;9783031784538

This case study examines how two areas within Interactive Digital Narratives (IDNs)-systems thinking and mapping, and digital learning and education-come together to create communicative and transformative tools for tackling today's complex challenges. These domains foster better understanding of interconnectedness among humans, other species, and machines, and these streams can combine in the form of an IDN. Systems thinking and mapping enable stakeholders to combine their expertise on complex topics, serving as an initial step in creating a collective Learning IDN. Specifically, Causal Loop Diagrams (CLD) are effective tools for preparing diverse stakeholder teams to develop an IDN that reflects their topic's foundations, interconnections, and pathways. This research investigates systems mapping as a basis for creating an IDN, drawing on a facilitated workshop on Systems Thinking and Mapping held in May 2024 in Murcia, Spain, and previous research. The workshop involved academics, researchers, entrepreneurs, designers, communicators, and technologists interested in developing IDNs. Facilitators provided a framework for stakeholders to form topic-focused interest groups and use systems thinking to interpret their respective realities and problem spaces. This paper demonstrates how a workshop using CLD mapping with diverse stakeholders can lay the foundation for a collaborative Learning IDN. This process fosters collective understanding and shared sense-making through creating a systems map, serving as both a visualization of group sense-making and an artifact representing a complex problem. The dual roles of the systems map-visualizing shared understanding and facilitating group discussion-are crucial for developing a comprehensive understanding of complex topics.

关键词： Systems Thinking Participatory Systems Mapping Causal Loop Diagrams Learning IDN Educational Technology Complex Problems Complexity Representation

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Deep Selfish Proposing in Longest-Chain Proof-of-Stake Protocols 28th

Deep Selfish Proposing in Longest-Chain Proof-of-Stake Proto...

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28th International Conference on Financial Cryptography and Data Security

作者： Sarenche, Roozbeh Nikova, Svetla Preneel, Bart Katholieke Univ Leuven COSIC Leuven Belgium

ISBN: (纸本)9783031786754;9783031786761

It has been shown that the selfish mining attack enables a miner to achieve an unfair relative revenue, posing a threat to the progress of longest-chain blockchains. Although selfish mining is a well-studied attack in the context of Proof-of-Work blockchains, its impact on the longest-chain Proof-of-Stake (LC-PoS) protocols needs yet to be addressed. This paper involves both theoretical and implementationbased approaches to analyze the selfish proposing (As there is no mining process in PoS blockchains, we refer to this attack as "selfish proposing".) attack in the LC-PoS protocols. We discuss how factors such as the nothing-at-stake phenomenon and the proposer predictability in PoS protocols can make the selfish proposing attack in LC-PoS protocols more destructive compared to selfish mining in PoW. In the first part of the paper, we use combinatorial tools to theoretically assess the selfish proposer's block ratio in simplistic LC-PoS environments and under simplified network connection. However, these theoretical tools or classical MDP-based approaches cannot be applied to analyze the selfish proposing attack in real-world and more complicated LC-PoS environments. To overcome this issue, in the second part of the paper, we employ deep reinforcement learning techniques to find the near-optimal strategy of selfish proposing in more sophisticated protocols. The tool implemented in the paper can help us analyze the selfish proposing attack across diverse blockchain protocols with different reward mechanisms, predictability levels, and network conditions.

关键词： Blockchain Proof-of-Stake Selfish mining Deep Q-learning

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Federated Learning-Based Tokenizer for Domain-Specific Language Models in Finance 16th

Federated Learning-Based Tokenizer for Domain-Specific Langu...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Damoun, Farouk Seba, Hamida State, Radu Univ Luxembourg Luxembourg Luxembourg Univ Claude Bernard Lyon 1 Lyon France CNRS UCBL INSA Lyon LIRISUMR 5205 Villeurbanne France

ISBN: (纸本)9783031785375;9783031785382

The Federated Byte-level Byte-Pair Encoding (BPE) Tokenizer (FedByteBPE) leverages a Federated Learning (FL) approach for a privacy-preserving approach to train language models tokenizer across distributed datasets. This approach enables entities to train and refine their tokenizer models locally, with vocabulary aggregation performed on a centralized server. This method ensures the creation of a robust, domain-specific tokenizer while preserving privacy. Supported by theoretical analysis and empirical results from experiments on a real-world distributed financial dataset, our findings demonstrate that the federated tokenizer significantly outperforms off-the-shelf and individual local tokenizers in vocabulary coverage. This highlights the potential of federated learning to address training language model tokenizers in a privacy-preserving setting.

关键词： Federated Learning Language Models Tokenizer

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An Analysis of Fixed-Spread Liquidation Lending in DeFi 9th

An Analysis of Fixed-Spread Liquidation Lending in DeFi

引用

28th International Conference on Financial Cryptography and Data Security

作者： Moallemi, Ciamac Patange, Utkarsh Columbia Business Sch New York NY 10027 USA

ISBN: (纸本)9783031692307;9783031692314

We model and analyze fixed spread liquidation in DeFi lending as implemented by popular pooled lending protocols such as AAVE, JustLend, and Compound. Empirically, we observe that over 70% of liquidations occur in the absence of any downward price jumps. Then, considering who monitors their loan with an exponentially distributed horizon, we compute the liquidation cost incurred in closed form as a function of the monitoring frequency. We compare this cost against liquidation data obtained from AAVE protocol V2, and observe a match with our model assuming the borrowers monitor their loans 3-4 times more often than they interact with the pool. Such borrowers must balance the financing cost against the likelihood of liquidation. We compute the optimal health factor in this situation assuming a financing rate for the collateral. Empirically, we observe that borrowers are far more conservative compared to our model predictions indicating a very low financing and opportunity cost.

关键词： DeFi Lending Liquidations

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The Emergence of Threads: The Birth of a New Social Network 16th

The Emergence of Threads: The Birth of a New Social Network

引用

16th International Conference on Social Networks Analysis and Mining

作者： Zhang, Peixian He, Yupeng Ehsan-Ul Haq He, Jiahui Tyson, Gareth Hong Kong Univ Sci & Technol GZ Guangzhou Peoples R China

ISBN: (纸本)9783031785474;9783031785481

Threads, a new microblogging platform from Meta, was launched in July 2023. In contrast to prior new platforms, Threads was born from an existing parent platform, Instagram, for which all users must already possess an account. This offers a unique opportunity to study platform evolution, to understand how one existing platform can support the "birth" of another. With this in mind, this paper provides an initial exploration of Threads, contrasting it with its parent, Instagram. Our findings reveal that Threads engages more with political and AI-related topics, compared to Instagram which focuses more on lifestyle and fashion topics. Our analysis also shows that user activities align more closely on weekends across both platforms. Engagement analysis suggests that users prefer to post about topics that garner more likes and that topic consistency is maintained when users transition from Instagram to Threads. Our research provides insights into user behaviour and offers a basis for future studies on Threads.

关键词： Instagram Threads Topic analysis

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Optimizing Airline Destinations with AIRNODE: A Graph Attention Network Approach 16th

Optimizing Airline Destinations with AIRNODE: A Graph Attent...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Shaik, Abdul Raheem Alhamadani, Abdulaziz Sarkar, Shailik Lu, Chang-Tien Virginia Tech Bradley Dept Elect & Comp Engn Falls Church VA 22043 USA Florida Polytech Univ Dept Data Sci & Business Analyt Lakeland FL 33805 USA Virginia Tech Dept Comp Sci Falls Church VA 22043 USA

ISBN: (纸本)9783031785535;9783031785542

The airline industry faces the critical challenge of meeting increasing passenger expectations amidst rapid technological advancements and intense competition. To remain competitive, airlines must gain a deeper understanding of passenger satisfaction and use this knowledge to improve service quality. This paper addresses this challenge by leveraging online customer reviews to derive actionable insights into passenger experiences. We introduce AIRNODE (Attention-based Insights for Reviewing Node Optimized Destinations and Experiences), a comprehensive two-stage model designed to analyze these reviews. AIRNODE constructs a weighted graph to aggregate review data and utilizes a Graph Attention Network (GAT) to model complex spatial relationships between destinations, achieving 84% accuracy in classifying destinations based on aggregated user satisfaction. Through advanced keyword extraction, we identify key aspects such as customer service, delays, and staff behavior, providing deep insights into the factors that drive passenger satisfaction. Case studies highlight destinations with varying levels of satisfaction, identifying positive attributes and areas needing improvement, and offering detailed insights and justifications for enhancing customer satisfaction. These insights equip airlines with a data-driven strategy to enhance service quality, meet traveler expectations, and maintain a competitive edge in a dynamic market.

关键词： Airline Industry Large Language Models Graph Neural Networks Online Customer Reviews Analysis

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Shared Sequencing and Latency Competition as a Noisy Contest 28th

Shared Sequencing and Latency Competition as a Noisy Contest

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28th International Conference on Financial Cryptography and Data Security

作者： Mamageishvili, Akaki Schlegel, Christoph Offchain Labs Zurich Switzerland Flashbots Zurich Switzerland

ISBN: (纸本)9783031786754;9783031786761

We study shared sequencing for different chains from an economic angle. We introduce a minimal non-trivial model that captures cross-domain arbitrageurs' behavior and compare the performance of shared sequencing to that of separate sequencing. While shared sequencing dominates separate sequencing trivially in the sense that it makes it more likely that cross-chain arbitrage opportunities are realized, the investment and revenue comparison is more subtle: In the simple latency competition induced by First Come First Serve ordering, shared sequencing creates more wasteful latency competition compared to separate sequencing. For bidding-based sequencing, the most surprising insight is that the revenue of shared sequencing is not always higher than that of separate sequencing and depends on the transaction ordering rule applied and the arbitrage value potentially realized.

关键词： Competition

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