Neural Information Processing Systems http://nips.cc/

The growing complexity of cyber-physical systems, particularly in automotive applications, has increased the demand for efficient modeling and cross-domain co-simulation techniques. While SystemC Transaction-Level Modeling (TLM) enables effective hardware/software co-design, its limited interoperability with models from other engineering domains poses integration challenges. This paper presents a fully open-source methodology for integrating SystemC TLM models into Functional Mock-up Interface (FMI)-based co-simulation workflows. By encapsulating SystemC TLM components as FMI 3.0 Co Simulation Functional Mock-up Units (FMUs), the proposed approach facilitates seamless, standardized integration across heterogeneous simulation environments. We introduce a lightweight open-source toolchain, address key technical challenges such as time synchronization and data exchange, and demonstrate the feasibility and effectiveness of the integration through representative case studies.

Multi-agent AI systems can be used for simulating collective decision-making in scientific and practical applications. They can also be used to introduce a diverse group discussion step in chatbot pipelines, enhancing the cultural sensitivity of the chatbot's responses. These applications, however, are predicated on the ability of AI agents to reliably adopt assigned personas and mimic human interactions. To evaluate the ability of LLM agents to satisfy these requirements, we examine AI agent ensembles engaged in cultural collaboration and debate by analyzing their private responses and chat transcripts. Our findings suggest that multi-agent discussions can encourage collective decisions that reflect diverse perspectives, yet this benefit is tempered by the agents' susceptibility to conformity due to perceived peer pressure and challenges in maintaining consistent personas and opinions. Instructions that encourage debate in support of one's opinions rather than collaboration increase the rate of inconstancy. Without addressing the factors we identify, the full potential of multi-agent frameworks for producing more culturally diverse AI outputs or more realistic simulations of group decision-making will remain untapped.

We introduce a new on-policy algorithm called Rewarded Region Replay (R3), which significantly improves on PPO in solving environments with discrete action spaces. R3 improves sample efficiency by using a replay buffer which contains past successful trajectories with reward above a certain threshold, which are used to update a PPO agent with importance sampling. Crucially, we discard the importance sampling factors which are above a certain ratio to reduce variance and stabilize training. We found that R3 significantly outperforms PPO in Minigrid environments with sparse rewards and discrete action space, such as DoorKeyEnv and CrossingEnv, and moreover we found that the improvement margin of our method versus baseline PPO increases with the complexity of the environment. We also benchmarked the performance of R3 against DDQN (Double Deep Q-Network), which is a standard baseline in off-policy methods for discrete actions, and found that R3 also outperforms DDQN agent in DoorKeyEnv. Lastly, we adapt the idea of R3 to dense reward setting to obtain the Dense R3 algorithm (or DR3) and benchmarked it against PPO on Cartpole-V1 environment. We found that DR3 outperforms PPO significantly on this dense reward environment.

Abstract--Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Application of homomorphic encryption (HE) in secure FL framework prevents the central server from accessing plaintext models. Thus, it is no longer feasible to embed the watermark at the central server using existing watermarking schemes. To our best knowledge, it is the first scheme to embed the watermark to models under the Secure FL environment. We design a black-box watermarking scheme based on client-side backdooring to embed a pre-designed trigger set into an FL model by a gradient-enhanced embedding method. Additionally, we propose a trigger set construction mechanism to ensure the watermark cannot be forged. Experimental results demonstrate that our proposed scheme delivers outstanding protection performance and robustness against various watermark removal attacks and ambiguity attack.

Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data. In this paper, we proposed PPPML-HMI, an open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis. To our best knowledge, personalization and privacy protection were achieved simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing our novel cyclic secure aggregation with the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans. For the real-world task, PPPML-HMI achieved $\sim$5\% higher Dice score on average compared to conventional FL under the heterogeneous scenario. Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the solid privacy-preserving capability of PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we further demonstrated the strong robustness of PPPML-HMI.

Blockchain technology has been successfully exploited for deploying new economic applications. However, it has started arousing the interest of malicious users who deliver scams to deceive honest users and to gain economic advantages. Among the various scams, Ponzi schemes are one of the most common. Here, we present an automatic technique for detecting smart Ponzi contracts on Ethereum. We release a reusable data set with 4422 unique real-world smart contracts. Then, we introduce a new set of features that allow us to improve the classification. Finally, we identify a small and effective set of features that ensures a good classification quality.

This paper presents a new model of cooperative behavior based on the interaction of plan recognition and automated planning. Based on observations of the actions of an "initiator" agent, a "supporter" agent uses plan recognition to hypothesize the plans and goals of the initiator. The supporter agent then proposes and plans for a set of subgoals it will achieve to help the initiator. The approach is demonstrated in an open-source, virtual robot platform.