Existing AI agents typically execute multi-step tasks autonomously and only allow user confirmation at the end. During execution, users have little control, making the confirm-at-end approach brittle: a single error can cascade and force a complete restart. Confirming every step avoids such failures, but imposes tedious overhead. Balancing excessive interruptions against costly rollbacks remains an open challenge. We address this problem by modeling confirmation as a minimum time scheduling problem. We conducted a formative study with eight participants, which revealed a recurring Confirmation-Diagnosis-Correction-Redo (CDCR) pattern in how users monitor errors. Based on this pattern, we developed a decision-theoretic model to determine time-efficient confirmation point placement. We then evaluated our approach using a within-subjects study where 48 participants monitored AI agents and repaired their mistakes while executing tasks. Results show that 81 percent of participants preferred our intermediate confirmation approach over the confirm-at-end approach used by existing systems, and task completion time was reduced by 13.54 percent.

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.

As governments increasingly adopt digital tools, public service chatbots have emerged as a growing communication channel.This paper explores the design considerations and engagement opportunities of public service chatbots, using a 311 chatbot from a metropolitan city as a case study.Our qualitative study consisted of official survey data and 16 interviews examining stakeholder experiences and design preferences for the chatbot.We found two key areas of concern regarding these public chatbots: individual-level and community-level.At the individual level, citizens experience three key challenges: interpretation, transparency, and social contextualization.Moreover, the current chatbot design prioritizes the efficient completion of individual tasks but neglects the broader community perspective.It overlooks how individuals interact and discuss problems collectively within their communities.To address these concerns, we offer design opportunities for creating more intelligent, transparent, community-oriented chatbots that better engage individuals and their communities.

UAV-assisted task execution is a promising approach to powerline inspections in remote areas where no cellular network infrastructure exists for inspection data transmission. In this paper, we investigate UAV-mesh network deployment in remote areas to empower UAV-assisted powerline inspection, which is challenging considering a mountainous environment with no power supply. Particularly, given the locations of a set of power towers, we first formulate the UAV-mesh network deployment problem with connectivity and coverage constraints, which is NP-hard. Then, we propose U-Mesh, which is a cost-effective and load-balanced deployment scheme. To be specific, U-Mesh integrates three components, i.e., link identification: identifying the link conditions between power towers based on geographical barriers, NetCons: conducting local search to gradually obtain a cost-effective initial mesh nodes with connectivity and coverage constraints, and NetOpti: optimizing the initial mesh node positions to improve the mesh load and robustness from the perspectives of overall network structure. Finally, we implement U-Mesh in a 270 km2mountain forest area, and demonstrate its efficacy in terms of both deployment cost and network performance via extensive evaluations.

The deployment of high-accuracy low-distinction and multi-scale object detection models on resource-constrained edge devices is essential for ubiquitous intelligent applications, from autonomous obstacle avoidance to anomaly object recognition. However, these models' computational burden, energy consumption, and memory footprint pose significant challenges for distributed and pervasive systems. In this paper, we propose HiPOD, a hierarchical pruning framework designed to prune low-distinction and multi-scale object detection models by jointly learning layer-wise and path-wise pruning strategies. HiPOD balances the accuracy-efficiency trade-off through two novel components: Layer-Adaptive Ratio Learning, named AdaLR, which leverages network structural characteristics and a reinforcement learning-based action-feedback mechanism to adaptively generate balanced layer-wise pruning ratios; and Genetic Path Optimization, named GenPath, which employs crossover and mutation operations to optimize inter-layer kernel pruning paths, preserving critical semantic and spatial information. Extensive experiments on three benchmark datasets demonstrate the effectiveness of HiPOD, with only a 3 % drop in mAP50 and a 2 % drop in mAP50:95 compared to the full models. The ablation studies and impact analysis further validate the effectiveness of each module and highlight the robustness of our framework. Furthermore, evaluations on an edge device demonstrate the practicality of the proposed solution for powerline inspection.

UAV-assisted inspection is critical for modern power grid maintenance, enhancing efficiency and safety in remote areas. However, automatically designing UAV inspection trajectories is challenging due to the cluttered inspection environments, small inspection targets, and pervasive obstacles. We propose Auto-UIT, a novel method for generating inspection trajectories in noisy, sparse, and complex 3D point cloud. Auto-UIT has three core techniques: (1) A local structure-enhanced pylon segmentation, which accurately segments pylons, power lines, and surroundings in noisy point cloud for effective inspection target identification and trajectory planning. (2) A 3D fingerprint-based pylon type recognition that compensates for point cloud sparsity to complete missing inspection targets based on the pylon type. (3) An adaptive trajectory generation that samples positions in response to diverse pylon orientations and pervasive environmental obstacles, ensuring UAV operational safety. Our experiments on a real-world dataset across four distinct areas demonstrate that Auto-UIT outperforms existing baseline methods in all three tasks. Furthermore, a four-month deployment in a power grid inspection system—covering a 270 km2 primary mountainous area—yielded an expert first-review acceptance rate of 91.86% for the generated trajectories, and reduced design time by an average of 88.19% compared to manual methods, significantly improving inspection efficiency.

UAV-assisted inspection is critical for modern power grid maintenance, enhancing efficiency and safety in remote areas. However, automatically designing UAV inspection trajectories is challenging due to the cluttered inspection environments, small inspection targets, and pervasive obstacles. We propose a novel method for generating inspection trajectories in noisy, sparse, and complex 3D point cloud. It has three core techniques: (1) A local structure-enhanced pylon segmentation, which accurately segments pylons, power lines, and surroundings in noisy point cloud for effective inspection target identification and trajectory planning. (2) A 3D fingerprint-based pylon type recognition that compensates for point cloud sparsity to complete missing inspection targets based on the pylon type. (3) An adaptive trajectory generation that samples positions in response to diverse pylon orientations and pervasive environmental obstacles, ensuring UAV operational safety. A four-month deployment in a power grid inspection system—covering a 270 km2 primary mountainous area—yielded an expert first-review acceptance rate of 91.86% for the generated trajectories, and reduced design time by an average of 88.19% compared to manual methods, significantly improving inspection efficiency. Demo video and dataset are available at https://ljhe006.github.io/autouit/.

Cellular operators face significant challenges in cutting operating expenses while maintaining the quality of service (QoS) for users due to growing network traffic and dynamic user connections. These challenges are addressed by the cloud radio access network (C-RAN) architecture, which includes a centralized pool of baseband units (BBUs) and distributes them from remote radio heads (RRHs). The key to improving C-RAN performance is to dynamically allocate large-scale RRHs to different BBUs in real time. In this paper, we propose a user behavior-aware RRH-BBU mapping framework to improve the performance of large-scale C-RANs by predicting RRH traffic and users in advance. First, we propose a Multivariate RRH time series Prediction Model (MRPM) that captures the spatio-temporal patterns in the data to predict the traffic volume and the number of users of RRHs, which represents key indicators of RRH connection states. Second, we formulate the RRH-BBU mapping as a Markov decision process problem to optimize cost and QoS by considering BBU utilization, BBU energy consumption, RRH migration frequency, and BBU load balancing. Third, we propose a prediction-based RRH-BBU mapping scheme (PB-RBM) to find the optimal RRH-BBU mapping strategy by leveraging the prediction information of MRPM. In the PB-RBM algorithm, we employ an A3C algorithm to learn the mapping policy and group the RRHs based on a defined popularity metric to reduce the state and action space of the reinforcement learning algorithm. Finally, extensive experiments are conducted on a real-world dataset, and our algorithm is compared with several matching algorithms, such as ACKTR, heuristic, etc., to demonstrate its superiority, especially reducing 17.5% in RMSE compared to the best-performing baseline.

Large language models (LLMs) have demonstrated proficiency in various language tasks but encounter difficulties in specific domain or scenario. These challenges are mitigated through prompt engineering techniques such as retrieval-augmented generation, which improves performance by integrating contextual information. However, concerns regarding the privacy implications of context-aware reasoning architectures persist, particularly regarding the transmission of sensitive data to LLMs service providers, potentially compromising personal privacy. To mitigate these challenges, this paper introduces Tex-tual Differential Privacy, a novel paradigm aimed at safeguarding user privacy in LLMs-based context-aware reasoning. The proposed Differential Embedding Hash algorithm anonymizes sensitive information while maintaining the reasoning capability of LLMs. Additionally, a quantification scheme for privacy loss is proposed to better understand the trade-off between privacy protection and loss. Through rigorous analysis and experimentation, the effectiveness and robustness of the proposed paradigm in mitigating privacy risks associated with context-aware reasoning tasks are demonstrated. This paradigm addresses privacy concerns in context-aware reasoning architectures, enhancing the trust and utility of LLMs in various applications.

As critical infrastructure, smart grids require periodic power inspections to remain in good condition. Due to the complexities and variations in the geographical environment especially in remote areas, power inspections can be challenging. In this paper, we investigate efficient large-scale power inspections in remote areas with limited cellular network coverage by providing network support to inspection unmanned aerial vehicle (UAV) via wireless mesh networks. Specifically, we first formulate a network construction problem with the objective of balancing deployment cost and transmission delay, which is an NP-hard problem. Then, we propose U-Auto, i.e., UAV-Assisted Air-ground Network Construction (U-Auto) scheme, to construct a wireless network for UAV-assisted power inspection, which includes a group-coverage maximization selection (GMS) algorithm and two air-ground link handover rules. GMS generates a wireless network topology based on a structure termed group and a utility function. Based on the network topology constructed by GMS, two air-ground link handover rules are employed to optimize transmission delay and connection stability of the air-ground link, called minDelay and minSwitch. Finally, extensive simulations are conducted based on a real world environment of a forest in Hubei, China, and demonstrate the effectiveness of the proposed algorithm.