Large language models (LLMs) augmented with knowledge graphs (KGs) have exhibited great potential for complex reasoning tasks. However, existing approaches often struggle with incomplete subgraph retrieval and inaccurate semantic alignment, which hinder reasoning performance and answer quality. In this paper, we present Faico, a KG-enhanced reasoning framework designed to achieve both semantic faithfulness and structural completeness. Faico decouples model inference from graph traversal by integrating a fine-tuned LLM-based relation type generator for accurate semantic mapping and a KG retriever for reasoning subgraph search. Based on the predicted relation types, we model the reasoning subgraph (RS) as a k-bounded edge type (k-BET) subgraph, where k constrains the recurrence of relation types within paths, and devise a budget-dominance-based algorithm to efficiently identify the maximal k-BET subgraph. Our framework ensures comprehensive coverage of relevant multi-hop relations while reducing computational overhead. Through extensive experiments on multiple KGQA benchmarks, Faico demonstrates improvements in both effectiveness and efficiency over LLM-native and state-of-the-art KG-augmented reasoning baselines, delivering more accurate, complete answers and lower inference latency.

Federated recommender systems (FedRec) have emerged as a promising approach to provide personalized recommendations while protecting user privacy. However, recent studies have demonstrated their vulnerability to poisoning attacks, wherein malicious clients can inject carefully crafted gradients to prompt target items to benign users. Existing attacks typically target the full user group, which compromises stealth and increases the risk of detection. In contrast, real-world adversaries may prefer to target specific user subgroup, such as promoting health supplements to older individual, to maximize attack success while preserving stealth to evade detection. Motivated by this gap, we introduce Spattack, the first poisoning attack designed to manipulate recommendations for specific user subgroups in federated setting. Specifically, Spattack adopts an approximate-and-promote paradigm, which first approximate user embeddings of target/non-target subgroups and then prompts target items to the target subgroups. We further reveal a trade-off in achieving strong attack performance on the target group while keeping the non-target group largely unaffected. To achieve a better trade-off, we propose enhanced approximation and promotion strategies. For the approximation, we push the embeddings of different subgroup away based on contrastive learning and augment the target group's relevant item set via clustering. For the promotion, we align target and relevant item embeddings to strengthen their semantic connections. An adaptive weighting strategy is further proposed to balance promotion effects between target and non-target subgroups. Experiments on three real-world datasets demonstrate that Spattack consistently achieves strong attack performance on the target subgroup with minimal impact on non-target users, even when only 0.1% of users are malicious. Moreover, Spattack maintains competitive recommendation performance and exhibits strong resilience against mainstream defenses.

Edge computing brings computational resources to network edge, enabling mobile devices (MDs) to offload computing-intensive tasks to nearby edge servers. This significantly reduces the energy consumption of MDs and supports latency-sensitive applications. Meanwhile, the advancement of full-duplex (FD) cell-free massive multiple-input multiple-output (MIMO) technology provides a promising opportunity to enhance end-edge communication efficiency, particularly in scenarios with coexisting uplink (UL) and downlink (DL) users. In this paper, we investigate the joint task offloading and resource scheduling problem in FD cell-free massive MIMO-enabled edge computing networks. The problem is formulated as a two-stage optimization framework. In the first stage, we develop a hybrid simulated annealing–particle swarm optimization (SA-PSO) algorithm, which incorporates the Metropolis criterion to enhance global search capability, aiming to maximize spectral efficiency. In the second stage, we propose a diffusion-augmented prioritized deep deterministic policy gradient (DAP-DDPG) algorithm. This algorithm integrates prioritized experience replay with diffusion models to minimize the total energy consumption of MDs while satisfying stringent latency constraints. Simulation results demonstrate that, compared with benchmark schemes, the proposed SA-PSO algorithm achieves a 13.4% to 146% improvement in spectral efficiency, while the DAP-DDPG algorithm reduces the energy consumption of MDs by 12.5% to 33.1%.

Although a large amount of valuable knowledge can be obtained from the weighted graph snapshots modeled over time, it may cause privacy issues. Local differential privacy (LDP) provides a strong solution for private graph data publishing in decentralized networks. However, most existing LDP studies over graphs are only applicable to static unweighted graphs. This paper investigates the problem of continuous publication of weighted graph snapshots and proposes a graph publication framework, WGT-LDP, under w -event edge weight LDP, which can protect the privacy of edges and weights over any w consecutive time steps. WGT-LDP consists of four key components: population division-based sampling that overcomes the problem of over-segmentation of the privacy budget, data range estimation that mitigates noise on edge weights, aggregate information collection that obtains important information about the graph structure and edge weights, and graph snapshot generation that reconstructs weighted graph snapshot at each time step. We provide theoretical guarantees on privacy and utility, and perform extensive experiments on three real-world and two synthetic datasets, using four commonly used metrics. Our experiments show that WGT-LDP produces high-quality synthetic weighted graphs and significantly outperforms baseline methods.

Federated learning (FL) has become an emerging paradigm via cooperative training models among distributed clients without leaking data privacy. The performance degradation of F1 on heterogeneous data has driven the development of personalized FL (PFL) solutions, where different models are built for individual clients. However, existing PFL approaches often have limited personalization in terms of modeling capability and training strategy. In this paper, we propose a novel PFL solution, Feed, that employs an enhanced shared-private model architecture and equips with a hybrid federated training strategy. Specifically, to model heterogeneous data for different clients, we design an ensemble-based shared encoder that generates an ensemble of embeddings, and a private decoder that adaptively aggregates these embeddings for personalized prediction. In addition, we propose a server-side hybrid federated aggregation strategy to enable effective training of the heterogeneous shared-private model. To prevent personalization degradation in local model updates, we further optimize the personalized local training on the client-side by smoothing the historical encoders. Extensive experiments on MNIST/FEMNIST, CIFARIO/CIFARIOO, and YELP datasets demonstrate that Feed consistently outperforms state-of-the-art approaches.

Distributed key-value storage and computation are essential components of cloud services. As the demand for high-performance systems has increased significantly, a new architecture has been motivated to separate computing and storage nodes and connect them using RDMA-enabled networks. Existing RDMA-enabled systems use client-side cached indexes to reduce communication overhead and improve performance. However, such approaches could result in high server CPU contention due to heavy dynamic workloads (i.e.,inserts), and cause a large accuracy gap because of the different indexes between client-side and server-side. These drawbacks limit the performance of RDMA-enabled systems. In this paper, to deal with these issues, we introduce AStore to achieve high performance with low memory footprint. AStore employs a new uniformed architecture, utilizing an adaptive learned index as both the server-side learned index and the client-side cached index, to handle dynamic and static workloads. We propose several optimization techniques to optimize dynamic and static workload procedures and design the leaf node lock mechanism to support high concurrent access. Extensive evaluations on YCSB, LGN, and OSM datasets demonstrate that AStore achieves competitive performance on read-only workloads by up to 75.2%, 107.3% and 57.7%, as well as improving performance on write-read workloads by up to 65.7%, 108.7% and 74.3% than XStore.

Sparse Mobile CrowdSensing (MCS) aims to reduce sensing cost while ensuring high task quality by intelligently selecting small regions for sensing and accurately inferring the remaining areas. Data inference and cell selection are crucial components in Sparse MCS. However, cell division, which is a prerequisite for cell selection, has received insufficient attention. The existing uniform division method disregards the correlation of the sensing area. In addition, the impact of sparse noise on both data inference and cell selection has been ignored, potentially undermining the effectiveness of Sparse MCS. To address these issues, we propose a novel scheme termed Robust data Inference and Cost-Effective cell Selection for Sparse MCS (Rices). Specifically, we first design an adaptive region division strategy that captures the correlation of sensing regions. Subsequently, we tackle the robust data inference problem in the presence of sparse noise by formulating it as a dual-objective optimization. Furthermore, we optimize the cell selection strategy to dynamically adjust the set of sampled cells under the constraints of data inference quality. Extensive experiments on large-scale real-world datesets are conducted to evaluate the proposed scheme. The results demonstrate that Rices can accurately recover missing data with 20% sparse noise and significantly reduce sensing costs compared to baseline models.

Recommender systems across many Internet services have become a critical part of online businesses, as consumers would refer to them before making decisions. However, the lack of explicit ratings for items on many services makes it challenging to capture user preferences and item characteristics. Both academia and the industry have drawn attention to rating predications as a fundamental problem in recommendation systems. With the emergence of social networks, social recommender systems have been proposed to utilize the relationship between users and items to alleviate the data sparsity problem for rating predictions. However, they either concentrate on the opinion mining for each user and item, or consider the connections between users only. In this paper, we present an effective framework, Triple-hierarchical Attention Graph-based social rating prediction (TAG), to exploit the social relationships between users, the user-item interest relationships, the correlation relationships between items, and reviews for rating predictions. In order to consider opinions from reviews and these complex relationships, we first employ two triple-hierarchical attention to extract user and item features from reviews. We then design an inductive GNN, which generates effective embedding for users and items. Experiments over Yelp show that TAG outperforms state-of-the-art methods across RMSE, MAE, and NDCG metrics.

In the era of big data, recommender systems have become the key part of many Internet applications. One successful recommendation strategy is to jointly recommend items from different domains where the system can model an accurate portrait of user behaviors. However, it is still challenging to identify the correlation among various domains and make efficient utilization of features from each domain. In this paper, we propose a novel framework, called Domain Adversarial Cross-Domain Recommendation (DACDR), to learn the implicit transitive semantic features among various information relevant domains. The framework automatically retrieves semantic features from both the source and the target domains, and adaptively learns the transitive latent factors to connect the two domains. The user behaviors are then modelled by the learnt latent factors, based on which DACDR can provide an accurate recommendation. Evaluation over real-world dataset verifies that the proposed framework outperforms the state-of-the-art algorithms in terms of F1, NDCG and MRR metrics.

Mobile sensing is challenging in 3D space, as there are many inaccessible places where people rarely venture. Unmanned aerial vehicle (UAV), commonly known as drone, has greatly extended the scope of mobile sensing in 3D space, and pushed forward a variety of 3D mobile sensing applications, such as aerial photo- or video-graphy, 3D wireless signal survey, and air quality monitoring. However, the short battery life of drones has largely restricted the wide adoption of these applications. In this paper, we study the trajectory planning problem for optimizing the flight route in a given sensing space. We first divide the 3D space into an infinite three-dimensional network of observation locations (OLs), and model the sensing scope as a finite subgraph of 3D OL network. We formulate the problem as finding the optimal trajectory in the sensing scope. We propose an algorithm that finds trajectory in each divided 3D grid of the sensing scope by generating a nearly optimal dominating path, and finding the minimum dominating set in the dominating path. Then, we concatenate obtained trajectories in 3D grids to a nearly optimal trajectory in the sensing scope. Experimental results show that the proposed algorithm takes 24% less time to complete sensing the given space, and during the battery life it can cover 19% more sensing scope, than existing solutions.