Synthesizing high-quality tabular data is essential for privacy-preserving data analysis. However, this task remains challenging due to two key factors: (1) distribution complexity : imbalanced and skewed data make it challenging to learn the data distribution accurately; and (2) semantic coherence : implicit relationships and logical dependencies among fields must be preserved to ensure valid and meaningful synthetic samples. To address these issues, we propose SynDiSC, a high-quality tabular data synthesis approach that enforces both distributional and semantic consistency. It comprises three core designs: (1) a distribution-aware encoding that effectively handles heterogeneous data types and complex distributions; (2) a multi-dimensional semantic conditioning that leverages multi-dimensional conditional dependencies to enforce semantic validity during generation; and (3) a conditional consistency controller that guides the generator to produce diverse samples satisfying multiple conditional constraints while mitigating mode collapse. Extensive experiments on datasets from various application domains demonstrate that SynDiSC significantly improves data quality, conditional controllability, and downstream task performance compared to state-of-the-art methods. Our code and data samples are open-sourced in the GitHub repository. https://github.com/Knightz9/SynDiSC.

On-device Large Language Model (LLM) inference enables private, personalized AI but faces memory constraints. Despite memory optimization efforts, scaling laws continue to increase model sizes and memory pressure. In this paper, we revisit the core memory bottlenecks in on-device LLM inference and conduct a comprehensive analysis of mainstream optimization techniques. We uncover several overlooked inefficiencies: (1) model weights, not KV caches, dominate memory usage; (2) weight sparsity remains underutilized; (3) OS-level memory behaviors cause redundancy; and (4) naive weight loading leads to excessive memory residency. To address these challenges, we propose H2O, a heterogeneity-aware hierarchical orchestration framework for memory-efficient on-device LLM inference. H2O introduces three key techniques, including hierarchical weight orchestration to reduce redundant memory retention, zero copy I/O–compute parallelism for safe and efficient memory reuse, and heterogeneity-aware inference planning to adapt to diverse mobile hardware constraints. Extensive experimental results show that H2O reduces peak memory usage by up to 60%, eliminates out-of-memory (OOM) failures for 7B–13B models, and improves inference latency by 34%–94% under tight memory budgets. We open-source our implementation at: https://github.com/ccfeiker/H2O.

The rise of vehicular edge computing (VEC) enables vehicles to offload resource-intensive tasks to roadside units (RSUs), improving efficiency and reducing latency. However, high mobility, dynamic resource availability, and heterogeneous quality-of-experience (QoE) requirements make task offloading and coordination highly challenging. In this paper, we investigate the heterogeneous task offloading problem in dynamic VEC environments by proposing a two-stage optimization framework, TOVEC. Our TOVEC decouples the spatio-temporally coupled decision space into two tractable subproblems and solves it with a two-stage design. In the first stage, we employ a TD3-based deep reinforcement learning algorithm to handle RSU-channel access decisions under dynamic network conditions. In the second stage, we formulate the interaction between vehicular users (VUs) and RSUs as a Stackelberg game, enabling joint task scheduling and dynamic pricing that balances VUs’ QoE optimization and RSUs’ revenue maximization. We theoretically prove the existence of a Stackelberg equilibrium and validate our TOVEC using real-world vehicular traces. The experimental results show that our method has improved the QoE (measured by task delay and energy cost) of VU and the benefits of RSU by 6.8%-41.3% and 2.2%-117.4%, respectively, compared with the baseline schemes.

Powerline inspection with UAVs significantly improves efficiency and safety in remote areas. However, the lack of cellular infrastructure necessitates the use of UAV-mesh networks, whose deployment presents challenges in jointly optimizing coverage, node load, robustness, and cost under complex terrain constraints. In this paper, we investigate the computational complexity of this deployment problem by formulating it as a multi-objective optimization task and proving its NP-hardness. To address this, we presentU-Mesh+, aterrain-aware, robust, and cost-efficientdeployment framework that integrates four key components: (i) identifying line-of-sight and non-line-of-sight links to model terrain-induced communication constraints; (ii)NetConsfor cost-effective coverage and connectivity network topology construction; (iii)NetOptfor network resilience and balance node-level load improvement without extra cost; and (iv)NetEnhfor service availability enhancement via targeted local refinements. We implementU-Mesh+in a real-world 270km2mountainous forest with 174 power towers and 48km of transmission lines. Extensive experiments demonstrate its efficacy in terms of deployment cost and network performance. On-site network data from the deployed wireless network further validate its effectiveness and scalability under real-world conditions.

Unmanned sanitation vehicles (USVs) promise cleaner cities, yet efficiently coordinating multiple USVs in large urban areas remains challenging due to constraints such as limited waste capacity and battery life. In this demo, we present MRTC, a multi-robot task cooperation system. First, Dynamic Task Assignment employs an Actor-Critic policy within a Markov decision framework to allocate cleaning tasks and decide the required number of USVs. Second, Single-USV Path Planning refines each route via a fast two-layer iterative search. Over an eight-month real-world deployment in three urban testbeds, our MRTC system markedly improved cleaning efficiency while lowering operating costs. Operating over a combined 10,775 km of routes per month, the system achieved average monthly savings of 20,575 kWh of energy and 2,744 labour hours. A demonstration video is available at https://llq978.github.io/Demo/.

In the federated learning (FL) process, since the data held by each participant is different, it is necessary to figure out which participant has a higher contribution to the model performance. Effective contribution assessment can help motivate data owners to participate in the FL training. Research works in this field can be divided into two directions based on whether a validation dataset is required. Validation-based methods need to use representative validation data to measure the model accuracy, which is difficult to obtain in practical FL scenarios. Existing validation-free methods assess the contribution based on the parameters and gradients of local models and the global model in a single training round, which is easily compromised by the stochasticity of model training. In this work, we propose CoAst, a practical method to assess the FL participants' contribution without access to any validation data. The core idea of CoAst involves two aspects: one is to only count the most important part of model parameters through a weights quantization, and the other is a cross-round valuation based on the similarity between the current local parameters and the global parameter updates in several subsequent communication rounds. Extensive experiments show that CoAst has comparable assessment reliability to existing validation-based methods and outperforms existing validation-free methods.

In this article, we focus on the emerging field of web-based object detection, which has gained considerable attention due to its ability to utilize large amounts of web data for training, thus eliminating the need for labor-intensive manual annotations. However, the noisy and ever-evolving nature of web data poses challenges in preparing high-quality datasets for web-based object detection. To address these challenges, we propose a fully automatic dataset preparation method in this article. Our proposed method incorporates a hierarchical clustering module that assigns multiple precise labels to each image. This module is based on our observation that web image data exhibits different distributions at varying granularities. Furthermore, an evolutionary relabeling module ensures the adaptability of both the prepared dataset and trained detection models to the ever-evolving web data. Extensive experiments demonstrate that our method outperforms other web-based methods, and achieves a comparable performance to those manually labeled benchmark datasets.

The phishing scams pose a serious threat to the ecosystem of Ethereum which is one of the largest blockchains in the world. Such a type of cyberattack recently has caused losses of millions of dollars. In this paper, we propose a Self-supervised IncrEmental deep Graph lEarning (SIEGE) model, for the phishing scam detection problem on Ethereum. To overcome the data scalability challenge, we propose splitting the original Ethereum transaction data and constructing transaction graphs for each split. Confronted with the minimal labeled data available, we resort to graph-based self-supervised learning. We design a spatial pretext task to learn high-quality node embeddings inside a single graph split, as well as an incremental learning paradigm and a temporal pretext task to facilitate information flow between different graph splits. To evaluate the effectiveness of SIEGE, we gather a real-world dataset consisting of six-month Ethereum transaction records. The results demonstrate that our model consistently outperforms baseline approaches in both transductive and inductive settings.

Automatic dataset preparation can help users avoid labor-intensive and costly manual data annotations. The difficulty in preparing a high-quality dataset for object detection involves three key aspects: relevance, naturality, and balance, which are not addressed by existing works. In this paper, we leverage information from the web, and propose a fully-automatic dataset preparation mechanism without any human annotation, which can automatically prepare a high-quality training dataset for the detection task with English text terms describing target objects. It contains three key designs, i.e., keyword expansion, data de-noising, and data balancing. Our experiments demonstrate that the object detectors trained with auto-prepared data are comparable to those trained with benchmark datasets and outperform other baselines. We also demonstrate the effectiveness of our approach in several more challenging real-world object categories that are not included in the benchmark datasets.

Abstract Research has shown that spatial perception is not only one of the essential abilities for success in science, technology, engineering, and mathematics (STEM), but is also closely related to the quality of human existence. However, for a variety of reasons, many students' spatial skills are less than ideal. In recent years, various video games are showing great potential as low‐cost but effective training tools to improve the educational level and cognitive skills. This paper presented a novel serious strategy game named Magic World. The game was designed to enhance children's spatial perception and motivation by using narrative, virtual contexts, and game mechanics that incorporate educational content with entertainment as a powerful extra‐curricular aid. A pilot study and evaluation experiment were conducted with primary school students (N = 68) and the results showed that the training had a measurable positive impact on students' spatial ability. In addition, user experience surveys of the game showed that Magic World was considered a fun, challenging, and popular game for children.

We propose VetSC, a novel UI-driven, program analysis guided model checking technique that can automatically extract contract semantics in DApps so as to enable targeted safety vetting. To facilitate model checking, we extract business model graphs from contract code that capture its intrinsic business and safety logic. To automatically determine what safety specifications to check, we retrieve textual semantics from DApp user interfaces. To exclude untrusted UI text, we also validate the UI-logic consistency and detect any discrepancies. We have implemented VetSC and applied it to 34 real-world DApps. Experiments have demonstrated that VetSC can accurately interpret smart contract code, enable autonomous safety vetting, and discover safety risks in real-world Dapps. Using our tool, we have successfully discovered 19 new safety risks in the wild, such as expired lottery tickets and double voting.