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共 300228 条记录，以下是91-100 订阅

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On-Chain Timestamps are Accurate 28th

On-Chain Timestamps are Accurate

引用

28th International Conference on Financial Cryptography and Data Security

作者： Tzinas, Apostolos Sridhar, Srivatsan Zindros, Dionysis Stanford Univ Stanford CA 94305 USA Natl Tech Univ Athens Athens Greece Common Prefix Athens Greece

ISBN: (纸本)9783031786754;9783031786761

When Satoshi Nakamoto introduced Bitcoin, a central tenet was that the blockchain functions as a timestamping server. In the Ethereum era, smart contracts widely assume on-chain timestamps are mostly accurate. In this paper, we prove this is indeed the case, namely that recorded timestamps do not wildly deviate from real-world time, a property we call timeliness. Assuming a global clock, we prove that all popular mechanisms for constructing blockchains (proof-of-work, longest chain proof-of-stake, and quorum-based proof-of-stake) are timely under honest majority, but a synchronous network is a necessary condition. Next we show that all timely blockchains can be suitably modified, in a black-box fashion, such that all honest parties output exactly the same ledgers at the same round, achieving a property we call supersafety, which may be of independent interest. Conversely, we also show that supersafety implies (perfect) timeliness, completing the circle.

关键词： Blockchain

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Popcorn Movie: Dynamic Narrative Feedback for Spontaneous Live Action Video Storytelling 17th

Popcorn Movie: Dynamic Narrative Feedback for Spontaneous Li...

引用

17th International Conference on Interactive Digital Storytelling

作者： Riley, Allen Univ Calif Santa Cruz Santa Cruz CA 95064 USA

ISBN: (纸本)9783031784491;9783031784507

This paper describes Popcorn Movie, a movie-making party game demonstrating a theoretical and practical framework for generating stories using a dynamic narrative feedback loop between the improvised dialogue performed by co-located human participants and textual summaries iteratively generated by a large language model. In Popcorn Movie, human players take turns reading iterative generative summaries and then recording and uploading video clips of themselves performing responding improvised dialogue, which are iteratively edited into a video sequence available for instant playback. The feedback loop between the iteratively generated summaries and responding dialogue improvised by participants produces a spontaneous narrative video with continuity. We will detail the design of Popcorn Movie and the results of the playtests.

关键词： Collaborative Storytelling Improvisation Emergent Narrative Narrative Generation Feedback Coordination Interactive Filmmaking

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Data Composition for Continual Learning in Application of Cyberattack Detection 16th

Data Composition for Continual Learning in Application of Cy...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Lian, Jiayi Liu, Xueying Choi, Kevin Veeramani, Balaji Murli, Sathvik Hu, Alison Freeman, Laura Bowen, Edward Deng, Xinwei Virginia Tech Dept Stat Falls Church VA 22043 USA Deloitte & Touche AI Ctr Excellence New York CA USA

ISBN: (纸本)9783031785535;9783031785542

Continual learning (CL) focuses on enabling machine learning algorithms to learn from a series of tasks without forgetting previously acquired knowledge. The use of continual learning has not been widely explored in cybersecurity and network safety applications, partially due to the lack of proper datasets. Besides, the benchmark datasets used in CL methods are often relatively restrictive in terms of data distribution shift among the tasks. In this work, we present a CL benchmark framework to construct datasets for CL in cybersecurity applications. For the cybersecurity applications, the proposed framework can generate datasets for CL under distribution shifts in data inputs (e.g., features of internet traffic flow), distribution shifts in data output (e.g., intrusion types), and distribution shifts in both data inputs and outputs, respectively. Moreover, we propose several distance-based and model-based metrics tometiculously quantify the magnitude of distribution shift between datasets of the tasks. We elaborate the construction of benchmark datasets and evaluate the quality of the constructed datasets by applying several existing CL methods and investigating their performance.

关键词： Cybersecurity Continual learning Distribution shift Network safety

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Affective Sound: Developing a Critical Framework for Audio-Based Interactive Digital Narratives 17th

Affective Sound: Developing a Critical Framework for Audio-B...

引用

17th International Conference on Interactive Digital Storytelling

作者： Holloway-Attaway, Lissa Fawcus, Jamie Univ Skovde Skovde Sweden

ISBN: (纸本)9783031784491;9783031784507

In our current research project we develop a critical framework to facilitate the design and the critique of audio-based interactive narrative experiences. We foreground deep material connections to sound and affect as well as to the rhetorical and narrative properties of story/voice as a vehicle for storytelling. We draw inspiration for our framework from a range of philosophical, theoretical and aesthetic influences including posthumanism, new material feminism, affect studies, interaction design, as well as sound design and interactive and traditional narrative research. Our framework is intended to aid in the design of such audio-based artifacts to support embodied user experiences where affect is a key principle to support innovative material storytelling/deep listening with sound as the primary vehicle for delivery. To facilitate deeper understanding of the framework, we apply it to three original audio-based narratives we created to assess them preliminarily in terms of the four key areas we identify to understand affective narrative audio-based media: Temporality, Mediation, Interaction, and Embodiment/Material Experience. In future research we hope to expand and apply our model to other sound based works and to undergo more intensive user-testing.

关键词： Affect Sound Interactive Digital Narrative Audio

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Multi Objective Optimization Approach for WSN Based on Reinforcement Learning 16th

Multi Objective Optimization Approach for WSN Based on Reinf...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Hajjej, Faten Hamdi, Monia Zaied, Mourad Gabes Univ Res Team Intelligent Machines Gabes Tunisia Gabes Univ Hosp Minist Publ Hlth Gabes Tunisia

ISBN: (纸本)9783031785535;9783031785542

Wireless sensor networks (WSNs) are gradually invading our daily lives, offering us new services every day. They can be found in applications that affect us more and more. First used to monitor the environment and urban areas, they then provided support for first-aid and military surveillance activities. Now they are appearing in applications even closer to home to improve our lifestyle, such as guiding us to available parking spaces or informing us about air quality. The wide range of applications for wireless sensor networks has prompted several researchers to work towards a WSN with lower deployment costs while maximizing network lifetime and coverage. In this paper, an optimization approach-based Q-learning algorithm for optimal coverage of heterogeneous sensor networks is proposed. The findings of the simulation prove that the proposed approach maintains network coverage while using the minimum amount of energy, compared with other approaches.

关键词： WSN Coverage Energy consumption Q-Learning

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Short Paper: Onion Messages on Leash 28th

Short Paper: Onion Messages on Leash

引用

28th International Conference on Financial Cryptography and Data Security

作者： Bashiri, Amin Khabbazian, Majid Univ Alberta Edmonton AB Canada

ISBN: (纸本)9783031786785;9783031786792

Onion messages (OMs) are private messages sent between nodes in the Lightning Network (LN) using onion routing. While they are intended to enable interesting applications such as static invoices, refunds, and asynchronous payments, onion messages may also be used for unintended applications such as streaming or spam. To mitigate this, LN nodes can impose a rate limit on forwarding onion messages. However, if not carried out carefully, the rate limit can expose the network to a denial of service (DoS) attack, where an adversary may disrupt or degrade the OM service by flooding the network. This DoS threat is particularly concerning because, under current specifications, a single OM can traverse through hundreds of nodes, affecting all the nodes on its way. In addition, the adversary can hide their true identity thanks to the privacy-preserving feature of onion routing. To address this threat, we propose a simple solution with two main components. The first component limits the distance over which OMs can travel. For this purpose, we propose two methods: a hard leash and a soft leash. The hard leash imposes a strict limit on how far OMs can travel, while the soft leash makes it exponentially more difficult for OMs to traverse long distances. While the first method requires changes in the message format, the second method can easily be adopted without altering OMs. The second component of our solution consists of a set of simple yet effective forwarding and routing rules. We demonstrate that when these rules and the proposed leashes are applied, an adversary cannot degrade the onion messaging service, assuming that the adversary does not control a significant fraction of funds in the network.

关键词： Lightning Network Onion Messages Denial of Service

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Short Paper: Naysayer Proofs 28th

Short Paper: Naysayer Proofs

引用

28th International Conference on Financial Cryptography and Data Security

作者： Seres, Istvan Andras Glaeser, Noemi Bonneau, Joseph Eotvos Lorand Univ Budapest Hungary Univ Maryland College Pk MD USA Max Planck Inst Secur & Privacy Bochum Germany a16z Crypto Res New York NY USA NYU New York NY USA

ISBN: (纸本)9783031786785;9783031786792

This work introduces the notion of naysayer proofs. We observe that in numerous (zero-knowledge) proof systems, it is significantly more efficient for the verifier to be convinced by a so-called naysayer that a false proof is invalid than it is to check that a genuine proof is valid. We show that every NP language has logarithmic size and constant-time naysayer proofs. We also show practical constructions for several example proof systems, including FRI polynomial commitments, post-quantum secure digital signatures, and verifiable shuffles. Naysayer proofs enable an interesting new optimistic verification mode potentially suitable for resource-constrained verifiers, such as smart contracts.

关键词： proof system zero-knowledge soundness blockchain smart contract

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MLCDG: Multi-Level Contrastive Graph Clustering in Dynamic Graphs 16th

MLCDG: Multi-Level Contrastive Graph Clustering in Dynamic G...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Amar, Mohamed Mahmoud Bouguessa, Mohamed Diallo, Abdoulaye Banire Univ Quebec Montreal Montreal PQ Canada

ISBN: (纸本)9783031785474;9783031785481

Dynamic graphs are widespread in social networks, biological networks, and recommendation systems. Community detection in dynamic graphs presents several challenges because these graphs continually change over time. Traditional methods often tackle the problem by clustering graphs separately at each timestep and matching these communities. Unfortunately, this strategy does not account for the temporal continuity and can lead to instability in community detection. Moreover, most current methods do not consider the scenario where nodes have attributes. To address these challenges, we introduce a multi-level contrastive graph clustering approach for Dynamic Graphs (MLCDG), a novel methodology employing deep clustering in the context of dynamic graph neural networks. MLCDG innovatively incorporates contrastive learning to generate clustering-oriented latent representations that effectively capture both node-level and temporal-level community structures. Our approach stabilizes community detection by maintaining temporal coherence and minimizing clustering disruptions caused by dynamic changes. The methodology outperforms existing state-of-the-art techniques, demonstrated by our experiments on real-world and synthetic datasets, validating our approach's effectiveness in enhancing community detection in attributed dynamic graphs.

关键词： Dynamic Graphs Deep Clustering Contrastive Learning Community detection

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Anonymous Reputation Systems with Revocation, Revisited 28th

Anonymous Reputation Systems with Revocation, Revisited

引用

28th International Conference on Financial Cryptography and Data Security

作者： Hayashi, Ryuya Katsumata, Shuichi Sakai, Yusuke Univ Tokyo Tokyo Japan PQShield Ltd Oxford England AIST Tokyo Japan

ISBN: (纸本)9783031786785;9783031786792

An anonymous reputation system (ARS) was first proposed by Blomer, Juhnke, and Kolb (FC, 2015), a protocol similar to group signatures in concept, and its definition has been refined for example by El Kaafarani, Katsumata, and Solomon (FC, 2018). A representative application of an ARS is e-commerce sites, where users are allowed to anonymously write reviews on products they have purchased, while also preventing them from double reviewing. In this work, we revisit ARS. Our contributions are threefolds: First, we show that all previous definitions of ARS allow the users' purchase history to leak. While users' privacy is being guaranteed through the notion of anonymity, our findings show that this only achieves a weaker form of privacy, contrary to previously believed. Second, we formally define purchase privacy, addressing the above shortcoming, and complement previous security models. Along the way, we notice that one of the main entities, the system manager, does not play any cryptographically relevant role in the definition of ARS. Effectively, by excluding the system manager from the definition, we are able to simplify previous definitions. Lastly, we propose a generic construction and provide one concrete efficient instantiation based on pairing-based cryptography, requiring only 16 kilobits for a signature.

关键词： Anonymity

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PARALLAX: Leveraging Polarization Knowledge for Misinformation Detection 16th

PARALLAX: Leveraging Polarization Knowledge for Misinformati...

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16th International Conference on Social Networks Analysis and Mining

作者： Paschalides, Demetris Pallis, George Dikaiakos, Marios D. Univ Cyprus Dept Comp Sci Nicosia Cyprus

ISBN: (纸本)9783031785405;9783031785412

Recent techniques for the automated detection of online misinformation typically rely on ML models trained with features extracted from content analysis and/or general-purpose Knowledge Graphs (KGs). These techniques often fail to consider the interplay between misinformation and polarization. To bridge this gap, we introduce PARALLAX, a methodology that enhances misinformation detection by infusing polarization knowledge into existing classifiers. Polarization knowledge is represented in terms of Polarization Knowledge Graphs (PKG). PARALLAX constructs PKGs in an unsupervised way, and uses them to enrich articles with polarization knowledge. A Flexible Knowledge-aware Graph Neural Network (FlexKGNN) is trained on these enriched representations. We tested our methodology on three misinformation datasets, demonstrating that it achieves approximately a 15% improvement in performance over baseline classifiers and consistently outperforms other KGs, which typically reach baseline levels only.

关键词： Polarization Knowledge Graph Misinformation Detection

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