Digital Persuasion: Understanding the Impact of Online Influencers on Public Opinion
研究使用Friedkin-Johnsen模型框架,通过操纵初始意见实验,评估了网络影响者对社区整体意见的影响,验证了其在意见动态中的重要作用。
研究使用Friedkin-Johnsen模型框架,通过操纵初始意见实验,评估了网络影响者对社区整体意见的影响,验证了其在意见动态中的重要作用。
本文提出BPFence框架,通过eBPF技术在内核中实现状态化安全策略的执行,解决多租户系统上基于历史依赖的多步骤攻击问题。
In the digital online ecosystem, we are surrounded by distinct forms of information pollution, posing significant threats to both individuals and society. Fake news, for instance, wields power to sway public opinion on matters of politics and finance. Deceptive reviews can either bolster or tarnish the reputation of businesses, while unverified medical advice may steer people toward harmful health practices. In light of this challenging landscape, it has become imperative to ensure that users have access to both topically relevant and factually accurate information that does not warp their perception of reality, and there has been a surge of interest in various strategies to combat misinformation through different contexts and multiple tasks. The purpose of the ROMCIR Workshop, for some years now, is precisely that of engaging the Information Retrieval community to explore potential solutions that extend beyond conventional misinformation detection approaches. Key objectives include identifying subjective and objective factors associated with information credibility and truthfulness, respectively, and integrating such factors as fundamental dimensions of relevance within IR Systems (IRSs), achieving early detection of misinformation, and ensuring that the search results retrieved are not only truthful but also explainable to the users of IRSs. Moreover, it is essential to evaluate the role of generative models such as Large Language Models (LLMs) in inadvertently amplifying misinformation problems, and how they can be used to support IRSs, together with the contribution that the human-in-the-loop paradigm can have in this context.
本文通过算法稳定性建立样本外边界,利用耗散性论点为学习动力系统提供了一种系统理论解释,并通过优化算法依赖的动力增益来认证和比较学习动态的泛化能力。
This work addresses the challenge of implicit information leakage in cloud-based IoT access control policies, where unintended inter-device information flows can bypass conventional permission checks. To tackle this issue, the authors propose a novel approach that integrates information flow analysis with SMT solving. By formally modeling AWS IoT Core components, they construct policy-driven information flow graphs and leverage an SMT solver to generate finite graph representations, enabling automated verification of cross-device information flows. The resulting tool, IOT:POKER, demonstrates both effectiveness and practicality by successfully uncovering previously unknown security vulnerabilities in real-world deployment scenarios and multiple production-grade policies.
研究使用Friedkin-Johnsen模型框架,通过操纵初始意见实验,评估了网络影响者对社区整体意见的影响,验证了其在意见动态中的重要作用。
本文提出BPFence框架,通过eBPF技术在内核中实现状态化安全策略的执行,解决多租户系统上基于历史依赖的多步骤攻击问题。
In the digital online ecosystem, we are surrounded by distinct forms of information pollution, posing significant threats to both individuals and society. Fake news, for instance, wields power to sway public opinion on matters of politics and finance. Deceptive reviews can either bolster or tarnish the reputation of businesses, while unverified medical advice may steer people toward harmful health practices. In light of this challenging landscape, it has become imperative to ensure that users have access to both topically relevant and factually accurate information that does not warp their perception of reality, and there has been a surge of interest in various strategies to combat misinformation through different contexts and multiple tasks. The purpose of the ROMCIR Workshop, for some years now, is precisely that of engaging the Information Retrieval community to explore potential solutions that extend beyond conventional misinformation detection approaches. Key objectives include identifying subjective and objective factors associated with information credibility and truthfulness, respectively, and integrating such factors as fundamental dimensions of relevance within IR Systems (IRSs), achieving early detection of misinformation, and ensuring that the search results retrieved are not only truthful but also explainable to the users of IRSs. Moreover, it is essential to evaluate the role of generative models such as Large Language Models (LLMs) in inadvertently amplifying misinformation problems, and how they can be used to support IRSs, together with the contribution that the human-in-the-loop paradigm can have in this context.
本文通过算法稳定性建立样本外边界,利用耗散性论点为学习动力系统提供了一种系统理论解释,并通过优化算法依赖的动力增益来认证和比较学习动态的泛化能力。
This work addresses the challenge of implicit information leakage in cloud-based IoT access control policies, where unintended inter-device information flows can bypass conventional permission checks. To tackle this issue, the authors propose a novel approach that integrates information flow analysis with SMT solving. By formally modeling AWS IoT Core components, they construct policy-driven information flow graphs and leverage an SMT solver to generate finite graph representations, enabling automated verification of cross-device information flows. The resulting tool, IOT:POKER, demonstrates both effectiveness and practicality by successfully uncovering previously unknown security vulnerabilities in real-world deployment scenarios and multiple production-grade policies.