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Social media platforms such as Twitter, Reddit, and Sina Weibo play a crucial role in global communication but often encounter strict regulations in geopolitically sensitive regions. This situation has prompted users to ingeniously modify their way of communicating, frequently resorting to coded language in these regulated social media environments. This shift in communication is not merely a strategy to counteract regulation, but a vivid manifestation of language evolution, demonstrating how language naturally evolves under societal and technological pressures. Studying the evolution of language in regulated social media contexts is of significant importance for ensuring freedom of speech, optimizing content moderation, and advancing linguistic research. This paper proposes a multi-agent simulation framework using Large Language Models (LLMs) to explore the evolution of user language in regulated social media environments. The framework employs LLM-driven agents: supervisory agent who enforce dialogue supervision and participant agents who evolve their language strategies while engaging in conversation, simulating the evolution of communication styles under strict regulations aimed at evading social media regulation. The study evaluates the framework's effectiveness through a range of scenarios from abstract scenarios to real-world situations. Key findings indicate that LLMs are capable of simulating nuanced language dynamics and interactions in constrained settings, showing improvement in both evading supervision and information accuracy as evolution progresses. Furthermore, it was found that LLM agents adopt different strategies for different scenarios., Comment: Accepted by IEEE WCCI 2024

In the realm of autonomous vehicles, dynamic user preferences are critical yet challenging to accommodate. Existing methods often misrepresent these preferences, either by overlooking their dynamism or overburdening users as humans often find it challenging to express their objectives mathematically. The previously introduced framework, which interprets dynamic preferences as inherent uncertainty and includes a ``human-on-the-loop'' mechanism enabling users to give feedback when dissatisfied with system behaviors, addresses this gap. In this study, we further enhance the approach with a user study of 20 participants, focusing on aligning system behavior with user expectations through feedback-driven adaptation. The findings affirm the approach's ability to effectively merge algorithm-driven adjustments with user complaints, leading to improved participants' subjective satisfaction in autonomous systems., Comment: accepted by CHI LBW 2024

Graph learning has a wide range of applications in many scenarios, which require more need for data privacy. Federated learning is an emerging distributed machine learning approach that leverages data from individual devices or data centers to improve the accuracy and generalization of the model, while also protecting the privacy of user data. Graph-federated learning is mainly based on the classical federated learning framework i.e., the Client-Server framework. However, the Client-Server framework faces problems such as a single point of failure of the central server and poor scalability of network topology. First, we introduce the decentralized framework to graph-federated learning. Second, determine the confidence among nodes based on the similarity of data among nodes, subsequently, the gradient information is then aggregated by linear weighting based on confidence. Finally, the proposed method is compared with FedAvg, Fedprox, GCFL, and GCFL+ to verify the effectiveness of the proposed method. Experiments demonstrate that the proposed method outperforms other methods., Comment: 12 pages, 4 figures, 4 tables

In this paper, we address the challenges faced by Value Iteration Networks (VIN) in handling larger input maps and mitigating the impact of accumulated errors caused by increased iterations. We propose a novel approach, Value Iteration Networks with Gated Summarization Module (GS-VIN), which incorporates two main improvements: (1) employing an Adaptive Iteration Strategy in the Value Iteration module to reduce the number of iterations, and (2) introducing a Gated Summarization module to summarize the iterative process. The adaptive iteration strategy uses larger convolution kernels with fewer iteration times, reducing network depth and increasing training stability while maintaining the accuracy of the planning process. The gated summarization module enables the network to emphasize the entire planning process, rather than solely relying on the final global planning outcome, by temporally and spatially resampling the entire planning process within the VI module. We conduct experiments on 2D grid world path-finding problems and the Atari Mr. Pac-man environment, demonstrating that GS-VIN outperforms the baseline in terms of single-step accuracy, planning success rate, and overall performance across different map sizes. Additionally, we provide an analysis of the relationship between input size, kernel size, and the number of iterations in VI-based models, which is applicable to a majority of VI-based models and offers valuable insights for researchers and industrial deployment., Comment: 13 pages,6 figures,submitted to IEEE ACCESS

The increasing prevalence of security attacks on software-intensive systems calls for new, effective methods for detecting and responding to these attacks. As one promising approach, game theory provides analytical tools for modeling the interaction between the system and the adversarial environment and designing reliable defense. In this paper, we propose an approach for securing software-intensive systems using a rigorous game-theoretical framework. First, a self-adaptation framework is deployed on a component-based software intensive system, which periodically monitors the system for anomalous behaviors. A learning-based method is proposed to detect possible on-going attacks on the system components and predict potential threats to components. Then, an algorithm is designed to automatically build a \emph{Bayesian game} based on the system architecture (of which some components might have been compromised) once an attack is detected, in which the system components are modeled as independent players in the game. Finally, an optimal defensive policy is computed by solving the Bayesian game to achieve the best system utility, which amounts to minimizing the impact of the attack. We conduct two sets of experiments on two general benchmark tasks for security domain. Moreover, we systematically present a case study on a real-world water treatment testbed, i.e. the Secure Water Treatment System. Experiment results show the applicability and the effectiveness of our approach.

LLR measures the distance between observatories on Earth and retro-reflectors on Moon since 1969. In this paper, we estimate the Earth Rotation Parameters (ERP; terrestrial pole offsets, $x_p$ and $y_p$, and Earth rotation phase, $\Delta$UT) using LLR data. We estimate the values of $\Delta$UT, and the pole offsets separately. For the pole offsets, we estimate the values of $x_p$ and $y_p$ together and separately. Overall, the uncertainties of ERP from the new LLR data (after 2000.0) have significantly improved, staying less than 20 $\mu$s for $\Delta$UT, less than 2.5 mas for $x_p$, and less than 3 mas for $y_p$ for nights selected from subsets of the LLR time series which have 10 and 15 normal points obtained per night. Furthermore, we add the non-tidal loading effect provided by the IMLS, as observation level corrections of the LLR observatories in the analysis. This effect causes deformations of the Earth surface up to the centimetre level. Its addition in the Institute of Geodesy (IfE) LLR model, leads to a marginal improvement in the uncertainties (3-$\sigma$ values) of about 1% for both, $\Delta$UT and the pole offsets., Comment: 15 pages

In this paper, we develop a novel method for fast geodesic distance queries. The key idea is to embed the mesh into a high-dimensional space, such that the Euclidean distance in the high-dimensional space can induce the geodesic distance in the original manifold surface. However, directly solving the high-dimensional embedding problem is not feasible due to the large number of variables and the fact that the embedding problem is highly nonlinear. We overcome the challenges with two novel ideas. First, instead of taking all vertices as variables, we embed only the saddle vertices, which greatly reduces the problem complexity. We then compute a local embedding for each non-saddle vertex. Second, to reduce the large approximation error resulting from the purely Euclidean embedding, we propose a cascaded optimization approach that repeatedly introduces additional embedding coordinates with a non-Euclidean function to reduce the approximation residual. Using the precomputation data, our approach can determine the geodesic distance between any two vertices in near-constant time. Computational testing results show that our method is more desirable than previous geodesic distance queries methods., Comment: Presented at Computational Visual Media 2021; to be published in IEEE Transactions on Visualization and Computer Graphics