Large language models (LLMs) are increasingly used in robotics to translate natural language instructions into executable control programs via task-specific prompts. However, existing approaches often lack correctness guarantees for LLM-generated programs, leading to compilation errors and runtime failures. While some methods consider a verification mechanism, they typically assume complete prior knowledge of the environment, making them unsuitable for complex environments where such knowledge is unavailable. This paper introduces VeBot, an environment-aware verification framework designed to ensure the correctness of robot control programs generated by LLMs. Specifically, VeBot introduces: (i) an LLM-friendly robot control language (RCL) that facilitates the program generation by abstracting away the complex Python code details, (ii) a compiler that translates LLM-generated RCL programs into a control flow graph (CFG) while verifying the lexical, syntactic, and semantic correctness, and (iii) a runtime verification mechanism that checks the CFG and compiles the verified segments into executable Python code, avoiding collisions or planning failures during execution. We illustrate the VeBot framework using a household scenario, and the evaluation shows that it consistently outperforms existing methods across a range of LLMs and tasks, achieving high success rates even with lightweight LLMs.

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness. Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to be compatible with any universal zkSNARKs constructed from a polynomial interactive oracle proof (PIOP) and a polynomial commitment scheme (PCS). It enables a computationally limited delegator to outsource proof generation to several workers in a fully non-interactive and privacy-preserving manner. Compared to the most state-of-the-art frameworks (e.g., Siniel [NDSS'25]), HyperSiniel ensures that the delegator always receives a correct proof, regardless of malicious worker behavior. We implement HyperSiniel and compare the performance with Siniel across varying bandwidths and circuit sizes. Under low-bandwidth conditions (10MBps), HyperSiniel incurs only an additional 25% overhead compared with Siniel, while the total running time of HyperSiniel is almost identical to Siniel under high-bandwidth settings (1000MBps). These results show that the strong robustness guarantee of GOD in HyperSiniel comes almost for free, making it a practical and secure solution for real-world zkSNARK delegation.

The NFT ecosystem represents an interconnected, decentralized environment that encompasses the creation, distribution, and trading of Non-Fungible Tokens (NFTs), where key actors, such as marketplaces, sellers, and buyers, utilize smart contracts to facilitate secure, transparent, and trustless transactions. Scam tokens are deliberately created to mislead users and facilitate financial exploitation, posing significant risks in the NFT ecosystem. Prior work has explored the NFT ecosystem from various perspectives, including security challenges, actor behaviors, and risks from scams and wash trading, leaving a gap in understanding the semantics and interactions of smart contracts during transactions, and how the risks associated with scam tokens manifest in relation to the semantics and interactions of contracts. To bridge this gap, we conducted a large-scale empirical study on smart contract semantics and interactions in the NFT ecosystem, using a curated dataset of nearly 100 million transactions across 20 million blocks on Ethereum. We observe a limited semantic diversity among smart contracts in the NFT ecosystem, dominated by proxy, token, and DeFi contracts. Marketplace and proxy registry contracts are the most frequently involved in smart contract interactions during transactions, engaging with a broad spectrum of contracts in the ecosystem. Token contracts exhibit bytecode-level diversity, whereas scam tokens exhibit bytecode convergence. Certain interaction patterns between smart contracts are common to both risky and non-risky transactions, while others are predominantly associated with risky transactions. Based on our findings, we provide recommendations to mitigate risks in the blockchain ecosystem, and outline future research directions.

Federated learning (FL) is predicated on the provision of high-quality data by multiple clients, which is then used to train global models. A plethora of incentive mechanism studies have been conducted with the objective of promoting the provision of high-quality data by clients. These studies have focused on the distribution of benefits to clients. However, the incentives of federated learning are transactional in nature, and the issue of the atomicity of transactions has not been addressed. Furthermore, the data quality of individual clients participating in training varies, and they may participate negatively in training out of privacy leakage concerns.Consequently, we propose an inaugural atomistic incentive scheme with privacy preservation in the FL setting: πFL (privacy, atomic, incentive). This scheme establishes a more dependable training environment based on Shapley valuation, secure multi-party computation, and smart contracts. Consequently, it ensures that each client's contribution can be accurately measured and appropriately rewarded, improves the accuracy and efficiency of model training, and enhances the sustainability and reliability of the FL system. The efficacy of this mechanism has been demonstrated through comprehensive experimental analysis. It is evident that this mechanism not only protects the privacy of trainers and provides atomic training rewards but also improves the model performance of FL, with an accuracy improvement of at least 8%.

The trading of data is becoming increasingly important as it holds substantial value. A blockchain-based data marketplace can provide a secure and transparent platform for data exchange. To facilitate this, developing a fair data exchange protocol for digital goods has garnered considerable attention in recent decades. The Zero Knowledge Contingent Payment (ZKCP) protocol enables trustless fair exchanges with the aid of blockchain and zero-knowledge proofs. However, applying this protocol in a practical data marketplace is not trivial.In this paper, several potential attacks are identified when applying the ZKCP protocol in a practical public data marketplace. To address these issues, we propose SmartZKCP, an enhanced solution that offers improved security measures and increased performance. The protocol is formalized to ensure fairness and secure against potential attacks. Moreover, SmartZKCP offers efficiency optimizations and minimized communication costs. Evaluation results show that SmartZKCP is both practical and efficient, making it applicable in a data exchange marketplace.

Remote sensing instance segmentation aims to detect and assign pixel-level labels to each instance in remote sensing images, which holds critical engineering significance for both civil and military applications. While existing domain-specific methods have made progress, they still struggle with three persistent challenges: ineffective context modeling in cluttered backgrounds, information loss during multi-scale feature fusion, and blurred boundaries for densely clustered small objects. To address these limitations, we propose a novel remote sensing instance segmentation framework with three artificial intelligence (AI) methodological innovations, which comprises: a Context Perception Module (CPM) for context modeling, a Context Guided Multi-Scale Feature Aggregation (CGFA) method for multi-scale feature fusion, and a Multi-Path Region Proposal Extractor (MPRPE) with boundary-refined segmentation. The CPM leverages the selective state space model (Mamba) to capture long-range contextual information, effectively addressing the issue of cluttered backgrounds in remote sensing images. The CGFA replaces standard feature pyramid network architecture which is limited by direct summation or concatenation, preserving fine-grained spatial details with context guidance. The MPRPE and boundary-aware segmentation head mitigate the challenges of missed detection of small objects and blurred edge predictions, which arise from the clustered distribution of small objects and semantic ambiguity. Extensive experiments on the challenging iSAID and NWPU VHR-10 datasets validate the proposed method’s consistent improvements across metrics while demonstrating its practical engineering impact on remote sensing interpretation systems.

Abstract Lip‐reading deciphers speech by observing lip movements without relying on audio data. The rapid advancements in deep learning have significantly improved lip‐reading for both English and Chinese; however, research on dialects such as Cantonese remains scarce. Consequently, most Chinese lip‐reading datasets focus on Mandarin, with only a few addressing Cantonese. To bridge this gap, a sentence‐level Cantonese lip‐reading dataset, designated as Cantonese lip‐reading sentences are introduced, comprising over 500 unique speakers and more than 30,000 samples. To ensure alignment with real‐world scenarios, no restrictions are imposed on factors such as gender, age, posture, lighting conditions, or speech rate. A comprehensive description of the pipeline employed is provided for collecting and constructing the dataset and introduce an innovative visual frontend, 3D‐visual attention net. This frontend combines the advantages of convolution and self‐attention mechanisms to extract fine‐grained lip region features. These features are subsequently input into the conformer backend for temporal sequence modelling, achieving comparable performance on Chinese Mandarin lip reading dataset, lip reading sentences 2, lip reading sentences 3, and Cantonese lip‐reading sentences datasets. Benchmark tests on Cantonese lip‐reading sentences demonstrate the challenges it poses, providing a novel research foundation for dialect lip‐reading and fostering the advancement of Cantonese lip‐reading tasks.

One important problem that is insufficiently studied is finding densest lasting subgraphs in large dynamic graphs, which considers the time duration of the subgraph pattern. We propose a framework called Expectation-Maximization with Utility functions (EMU), a novel stochastic approach that nontrivially extends the conventional EM approach. EMU has the flexibility of optimizing any user-defined utility functions. We validate our EMU approach by showing that it converges to the optimum---by proving that it is a specification of the general Minorization-Maximization (MM) framework with convergence guarantees. We devise EMU algorithms for the densest lasting subgraph problem, as well as several variants by varying the utility function. Using real-world data, we evaluate the effectiveness and efficiency of our techniques, and compare them with two prior approaches on dense subgraph detection.

One important problem that is insufficiently studied is finding densest lasting-subgraphs in large dynamic graphs, which considers the time duration of the subgraph pattern. We propose a framework called Expectation-Maximization with Utility functions (EMU), a novel stochastic approach that nontrivially extends the conventional EM approach. EMU has the flexibility of optimizing any user-defined utility functions. We validate our EMU approach by showing that it converges to the optimum-by proving that it is a specification of the general Minorization-Maximization (MM) framework with convergence guarantees. We then devise EMU algorithms for the densest lasting subgraph problem. Using real-world graph data, we experimentally verify the effectiveness and efficiency of our techniques, and compare with two prior approaches on dense subgraph detection.

Many big data applications today require querying highly dynamic and large-scale data streams for top-k frequent items in the most recent window of any specified size at any time. This is a challenging problem. We show that our novel solution is not only accurate, but it also one to two orders of magnitude faster than previous approaches. Moreover, its memory footprint grows only logarithmically with the window size, rather than linearly as in previous work. Our comprehensive experiments over real-world datasets show that our solution is very effective and scalable. In addition, we devise a concise and efficient solution to a related problem of tracking the frequency of selected items, improving upon previous work by twenty to thirty times in model conciseness while providing the same accuracy and efficiency.