3D LiDAR scene completion from point clouds is a fundamental component of perception systems in autonomous vehicles. Previous methods have predominantly employed diffusion models for high‑fidelity reconstruction. However, their multi-step iterative sampling incurs significant computational overhead, limiting its real-time applicability. To address this, we propose LiNeXt: a lightweight, non‐diffusion network optimized for rapid and accurate point cloud completion. Specifically, LiNeXt first applies the Noise‑to‑Coarse (N2C) Module to denoise the input noisy point cloud in a single pass, thereby obviating the multi‑step iterative sampling of diffusion‑based methods. The Refine Module then takes the coarse point cloud and its intermediate features from the N2C Module to perform more precise refinement, further enhancing structural completeness. Furthermore, we observe that LiDAR point clouds exhibit a distance-dependent spatial distribution, being densely sampled at proximal ranges and sparsely sampled at distal ranges. Accordingly, we propose the Distance‑aware Selected Repeat strategy to generate a more uniformly distributed noisy point cloud. On the SemanticKITTI dataset, LiNeXt achieves a 199.8 times speedup in inference, reduces Chamfer Distance by 50.7 percent, and uses only 6.1 percent of the parameters compared with LiDiff. These results demonstrate the superior efficiency and effectiveness of LiNeXt for real-time scene completion.

Federated learning (FL) enables collaborative training of a global model while preserving participants' local data privacy, making it ideal for data-sensitive fields like Industrial Internet of Things (IIoT), finance, and healthcare. However, Non-IID data among participants and the presence of malicious participants pose significant challenges to the model's performance and convergence. The global model is difficult to achieve consistent performance across all participants. Therefore, this paper proposes personalized and robust federated learning (PRFL) to handle non-independently and identically distributed (Non-IID) data with malicious participants. First, to enhance the robustness and convergence, a model similarity-based division mechanism is employed. It groups participants with similar data and removes both independent and colluding malicious participants. Second, we propose a three-stage knowledge sharing personalized federated learning framework. Each participant undergoes inner-loop knowledge sharing, outer-loop knowledge sharing, and personalized knowledge distillation, incorporating performance-driven dynamic weighted sharing mechanism. Moreover, extensive experiments demonstrate that PRFL outper forms other advanced personalized federated learning methods across various benchmark datasets, particularly in scenarios with Non-IID data and malicious participants.

Graph-structured data has been widely applied in transportation, molecular, and e-commerce networks, etc. Graph Convolutional Network (GCN) has emerged as an efficient approach to processing non-Euclidean graph data. However, the varying sizes and sparsity of graph datasets, coupled with the dependency of the dataflow patterns in GCN computation on the graph data, have rendered the acceleration of GCN inference increasingly challenging. This paper proposes a GCN inference accelerator based on multi-dataflow and high bandwidth memory (HBM), named DAHBM-GCN. Firstly, we designed a computing engine that supports multiple dataflows, aggregation-first, and combination-first orders. Furthermore, an adaptive selector for the multi-dataflow computing engine based on the decision tree is proposed to select the optimal dataflow computing engine. Secondly, an efficient mapping of pseudo channels (PCs) for multi-channel HBM is devised to enhance bandwidth, effectively alleviating memory latency and bandwidth bottlenecks. Thirdly, a hybrid fixed-point quantization strategy for GCN is introduced, which reduces the GCN model's computation complexity and parameter count with almost no loss of accuracy. Finally, extensive performance evaluation experiments demonstrate that across various datasets, DAHBM-GCN achieved average speedups of 52.5–129.3× and 4.9–7.9× compared to PyG-GCN and DGL-GCN on CPU, respectively. Compared to the AWB-GCN, HyGCN, HLS-GCN, and GCNAX accelerators FPGA-based, DAHBM-GCN also exhibits average speedups of 1.21–2.21×, 1.25–1.98×, 1.65–2.68×, and 1.18–1.56× respectively, on various datasets. Additionally, DAHBM-GCN possesses the advantages of high flexibility and low energy consumption.

Sparsity is widely prevalent in real-world applications, yet existing compiler optimizations and code generation techniques for sparse computations remain underdeveloped. Sparse matrix-matrix multiplication (SpMM) is a representative operator in sparse computations, whose performance is often limited by the design of sparse formats and the extent of hardware architecture optimization. Most existing solutions achieve highperformance SpMM through two approaches: (1) meticulously designed kernels and specialized sparse formats, which require extensive manual effort, or (2) tensor compilers that support code generation, though these typically offer limited support for sparse patterns, making it challenging to adapt to complex sparsity patterns in practical applications. This paper presents SpMMTC, an input-aware sparse tensor compiler. Given a sparse matrix as input, SpMMTC analyzes its non-zero distribution and generates a vectorized kernel optimized for SpMM on the specific matrix. We evaluated SpMMTC on various workloads. It achieves speedups of 1.21 x to 2.97 x over state-of-the-art methods such as TACO, TVM, and ASpT on different multi-core processors. It also provides a speedup of up to $\mathbf{1. 5 2 x}$ for sparse MobileNetV1 inference on the edge device.

Entity alignment has emerged as a powerful technique for integrating knowledge graphs, facilitating the fusion of heterogeneous knowledge into a unified graph. The state-of-the-art methods combine both graph structures and side information for effective entity alignment. However, they neglect low-quality issues in data. Specifically, the emerging knowledge graphs in diverse fields amass a wealth of entities that lack not only adequate descriptions but also annotated alignments. These two limitations lead to the overfitting problem and degrade the alignment performance. To tackle these challenges, we propose DEBT, an innovative approach that systematically enhances entity alignment. It first enriches the descriptions of entities by aggregating their neighbors and attributes. Then, a bootstrap strategy is utilized to expand the training set by incorporating entity pairs with similarity scores exceeding a dynamically decreasing threshold. Experimental results demonstrate that our method achieves the state-of-the-art accuracy while reducing the number of annotated entity alignment pairs.

With the advancement of deep learning, network communication has become the most critical factor in model training. Especially, the all-reduce operation can comprise over 70% of the cumulative training duration as a pivotal component within data parallelism. However, existing all-reduce algorithms often perform poorly in complex network topologies, and there is currently no universal and straightforward all-reduce algorithm that can effectively adapt to diverse topological structures.In this work, we propose a topology decoupled all-reduce algorithm. We decouple the network into multiple tree substructures, select the trees with the smallest heights, and then split the data to perform aggregate communication within these selected structures. This approach significantly reduces the number of communications and enhances efficiency. Experimental results show that our topology decoupled all-reduce algorithm reduces communication time compared to NCCL’s by 42.9% and enhances end-to-end training efficiency by 11.7%.

Secure aggregation has recently gained popularity in federated learning to defend against inference attacks by malicious aggregators or eavesdroppers. However, existing methods usually bring additional communication overhead and possibly impede the convergence rate of the global model. The challenge becomes acute in wireless network environments where bandwidth is severely constrained. Hence, the attainment of effective communication compression while ensuring secure aggregation has been a profoundly demanding and valuable quandary.In this work, we propose a novel uplink communication compression method for federated learning, named FedMPQ. It guarantees both high security and efficiency without compromising accuracy. Specifically, we introduce multiple optional codebooks for clients to ensure almost lossless information under a high compression rate. In addition, FedMPQ achieves high security with a combination of secure aggregation paradigm and differential privacy, preventing data leakage both on server and during communication. The experiments conducted on the LEAF dataset demonstrate that FedMPQ reduces the uplink communications by 90-95% with no sacrifice on accuracy.

As the network data in real life become multi-modal and multi-relational, multi-view attributed graphs have garnered significant attention. Numerous methods have achieved excellent performance in multi-view attributed graph clustering; however, they cannot efficiently handle incomplete attribute scenarios, which are prevalent in many real-life applications. Inspired by this, we investigate the problem of multi-view incomplete attributed graph clustering for the first time. In particular, the TOTF (Train Once Then Freeze) framework is designed to train missing-aware encoders that capture view-specific information while ignoring the impact of incomplete attributes, and then employs frozen encoders to uncover common information driven by clustering. After that, we propose a correlation strength-aware graph neural network on the basis of the inherent relationships among attributes to enhance accuracy. It is proven theoretically that traditional Generative Adversarial Networks (GANs) are unable to generate the unique real distribution. To address this issue, we further introduce the missing-position reminder mechanism into our intra-view adversarial games for better clustering results. Extensive experimental results demonstrate that our method achieves up to a 17% improvement in accuracy over the state-of-the-art methods. The source code is available at https://anonymous.4open.science/r/TOTF-main.

The performance of computer vision models in certain real-world applications, such as medical diagnosis, is often limited by the scarcity of available images. Expanding datasets using pre-trained generative models is an effective solution. However, due to the uncontrollable generation process and the ambiguity of natural language, noisy images may be generated. Re-weighting is an effective way to address this issue by assigning low weights to such noisy images. We first theoretically analyze three types of supervision for the generated images. Based on the theoretical analysis, we develop TriReWeight, a triplet-connection-based sample re-weighting method to enhance generative data augmentation. Theoretically, TriReWeight can be integrated with any generative data augmentation methods and never downgrade their performance. Moreover, its generalization approaches the optimal in the order O(√d ln (n)/n). Our experiments validate the correctness of the theoretical analysis and demonstrate that our method outperforms the existing SOTA methods by 7.9% on average over six natural image datasets and by 3.4% on average over three medical datasets. We also experimentally validate that our method can enhance the performance of different generative data augmentation methods.

Abstract Distributed computing frameworks play a crucial role in supporting compute-intensive applications in the era of big data. The growing demand for computing resources has spurred the interconnection of data centers, leading to the formation of supercomputing Internet. MapReduce is a popular distributed computing framework designed for large independent clusters. The original MapReduce framework deployed on supercomputing Internet performs inefficiently due to redundant geo-distributed reduce operations. Nonetheless, its abstraction remains significant potential. This paper proposes an enhanced MapReduce framework for geo-distributed supercomputing Internet to minimize the necessity for data transmission across data centers. Leveraging hierarchical scheduling techniques, the framework optimizes data locality to mitigate network latency and bandwidth consumption during reduce operations, thereby reducing overall job execution times. The paper introduces a mathematical model for task scheduling within supercomputing Internet and formally describes the data transmission process among data centers. In the job scheduling phase, our framework facilitates efficient overlap of transferring and computing through pre-selected data centers. Meanwhile, in the data transmission phase, the framework aggregate data to reduce the frequency of transmission, thus alleviating the adverse effects on transmission of hierarchical network architecture. Comparative analysis with existing methods demonstrates the efficacy of the proposed framework in addressing similar computational challenges. Empirical evaluations underscore the effectiveness of our method in practice.

The proliferation of vulnerable code poses a significant threat to software system security and user privacy. Given the inefficiency inherent in manual vulnerability analysis, there has been a pronounced surge of interest in automating vulnerability management using machine learning techniques. However, the scarcity of publicly accessible and large-scale datasets in the vulnerability domain impedes the advancement of automated methodologies. The advent of federated learning has introduced the potential utilization of private data for learning, while ensuring privacy and security within this paradigm presents a novel challenge. To solve this problem, we introduce a new approach called vulnerability solution with abstract syntax tree (AST), SOEHash, and clustering (V-ASC). We first obtain the AST of the vulnerability code to obtain the underlying pattern of the vulnerability. To protect data privacy as well as to extract vector features of the, we use the SOEHash algorithm to process the AST. Finally, to speed up the process of similarity comparison between vectors, we use an unsupervised clustering algorithm to transform the set of vectors into individual vulnerability clusters. Experiments on a recent vulnerability code dataset validate the effectiveness and efficiency of V-ASC.

Ring all-reduce is currently the most commonly used collective communication technique in the fields of data parallel and distributed computing. It consists of three phases: communication establishment, data transmission, and data processing at each step. However, this method may suffer from increased communication latency as the number of computation nodes increases, excessive communication steps and data processing procedures can lead to insufficient bandwidth utilization. To address this issue, this article proposes an Interleaved Bidirectional Ring (IBing) all-reduce method, which uses specially crafted communication operations to improve communication efficiency by reducing the effects of both communication establishment and data processing time. IBing reduces the number of communication steps by half compared to the Ring all-reduce. The results of extensive experiments indicate that the proposed IBing design can reduce total communication consumption by an average of 8.49% and up to 49.73%.

Stream processing is integral to edge computing due to its low-latency attributes. Nevertheless, variability in user group sizes and disparate computing capabilities of edge devices necessitate frequent operator migrations within the stream. Moreover, intricate dependencies among stream operators often obscure the detection of potential bottleneck operators until an identified bottleneck is migrated in the stream. To address this, we propose a Cost-Aware Operator Migration (CAOM) scheme. The CAOM scheme incorporates a bottleneck operator detection mechanism that directly identifies all bottleneck operators based on task running metrics. This approach avoids multiple consecutive operator migrations in complex tasks, reducing the number of task interruptions caused by operator migration. Moreover, CAOM takes into account the temporal variance in operator migration costs. By factoring in the fluctuating data generation rate from data sources at different time intervals, CAOM selects the optimal start time for operator migration to minimize the amount of accumulated data during task interruptions. Finally, we implemented CAOM on Apache Flink and evaluated its performance using the WordCount and Nexmark applications. Our experiments show that CAOM effectively reduces the number of necessary operator migrations in tasks with complex topologies and decreases the latency overhead associated with operator migration compared to state-of-the-art schemes.

Both truth discovery and pattern analysis are effective methods for extracting valuable insights from data streams in mobile crowdsensing. However, existing privacy-preserving schemes either suffer from low data utility or provide high utility at the cost of weak privacy protection. To address this challenge, we introduce a robust privacy-preserving scheme that facilitates high-utility truth discovery and pattern analysis over mobile crowdsensing data streams. Concretely, we leverage the Square Wave mechanism, a randomized reporting technique, to perturb the data to prevent privacy breaches. To reduce the utility loss caused by perturbation, we design a budget allocation algorithm. This algorithm ensures that adjacent timestamps with approximate data share a perturbed value derived from their accumulated budgets. Furthermore, to facilitate robust pattern analysis, we propose a data splitting method that divides the perturbed data into two parts: one part records patterns randomly, while the other part recovers the perturbed values. Theoretical analysis confirms that our scheme satisfies ω-event ϵ-differential privacy level. Extensive experiments conducted on four real-world datasets demonstrate that our scheme outperforms existing schemes, delivering more accurate results for both truth discovery and pattern analysis under the same privacy constraints.

In the context of distributed deep learning, computational clusters place greater emphasis on static all-reduce communication latency while also needing to support large-scale networking. However, the communication efficacy of current all-reduce algorithms within specialized network topologies requires enhancement. Existing all-reduce communication algorithms inadequately exploit cluster bandwidth, leading to considerable bandwidth idleness. The optimization of communication algorithms becomes imperative to fully utilize the available bandwidth. Addressing this concern, we propose an innovative approach: a topology-aware interleaved all-reduce algorithm for Dragonfly networks (TIAD). Leveraging the inherent characteristics of the Dragonfly network, TIAD employs an interleaved communication mechanism for both intra- and inter-group data collection, significantly augmenting communication efficiency. Moreover, we refine the Dragonfly network with minimal adjustments, aligning it with the theoretical structure of interleaved communication. We also proposed an all-reduce communication method to complement the TIAD algorithm, specifically for scenarios where only a subset of nodes in the Dragonfly network participate in the communication task. Our experiments demonstrate that TIAD exhibits the shortest communication time across diverse node sizes and bandwidth conditions. Notably, our algorithm reduces communication time by up to 23.4% during the collection communication phase in comparison to the PAARD algorithm.

A crucial privacy-driven issue nowadays is re-identifying anonymized social networks by mapping them to correlated cross-domain auxiliary networks. Prior works are typically based on modeling social networks as random graphs representing users and their relations, and subsequently quantify the quality of mappings through varied cost functions. However, many cost functions are empirically proposed without sufficient theoretical support. For some other works probing the theoretical bound, it remains unknown how to algorithmically meet the demand of such quantifications, i.e., to minimize the cost functions. Besides, only few prior works have discussed the de-anonymization of social networks with communities. We address those concerns in a social network modeling parameterized by community structures that can be leveraged as side information for de-anonymization. Based on the Maximum A Posteriori (MAP) estimation, our first contribution is a series of MAP-based cost functions, which, when minimized, enjoy superiority to previous ones in finding the correct mapping with the highest probability. The feasibility of the cost functions is then for the first time algorithmically characterized. We prove the general multiplicative inapproximability and thus propose two heuristics, which, respectively, enjoy an$\epsilon$-additive approximation and a conditional optimality in carrying out successful user re-identification. Our theoretical findings are also empirically validated under classical synthetic and real-wrold social networks. Both theoretical and empirical observations manifest the importance of community in enhancing privacy inferencing.

As Internet users interacting with their different friends in different social networks, the de-anonymization problem has been raising improving concern. Since the assailants may de-anonymize a social network by matching it with a correlated sanitized network and identifying anonymized user identities, multifarious arts study on the theoretical conditions or practical algorithms for correctly de-anonymizing a social network. Except for the structural information of these social networks, there has also been bounteous works taking advantage of some pre-identified seed nodes for reference in the anonymized network. In this paper, we systematically probe the theoretical conditions and algorithmic approaches for correctly matching two different-sized social networks by leveraging the multi-hop neighborhood relationships. A limited number of seeds are also taken into consideration as auxiliary information. To this end, we introduce the de-anonymization problem with the aid of the collectiveness and the collective adjacency disagreements, which are the collection of disagreements of different multi-hop adjacency matrices. We theoretically demonstrate that minimizing the collective adjacency disagreements can help match two social networks even in a very sparse circumstance, as it significantly enlarges the difference between the mismatched node pairs and the correctly matched pairs. Besides, the seeds is proved to bring positive influence in improving the de-anonymization accuracy. Algorithmically, we relax the domain of the matching function to continuum and adopt the conditional gradient descending method on the collective-form objective, to efficiently minimize the collective adjacency disagreements of two networks. We conduct tremendous experiments on different networks with or without seeds, the results of which return desirable de-anonymization accuracies and reveal the advantages of the collectiveness: the collectiveness manifests rich structural information, thereby most nodes can be correctly matched with their correspondences even in some sparse networks, where merely utilizing the 1-hop adjacency relationships might fail to work.

Anonymization techniques have tranquilized current social network users in terms of privacy leakage, however, it does not radically prevent adversaries from de-anonymizing users, as they may map the users to an un-anonymized network. Till now, researchers share a common thread in such de-anonymization attack: unveiling conditions leading to successful de-anonymization under the chosen network model. However, it has not yet been well understand how the structural property in different network models intrinsically determines de-anonymizability. We address the above issue in this paper by making the two contributions: (i) We discover that the automorphic degree and homomorphic degree of social networks determine their de-anonymizability universally. The automorphic degree characterizes the distinguishability of the users in a network, while the homomorphic degree models the similarities of users between two networks. We conclude that a smaller automorphic degree and a larger homomorphic degree conduce to a higher de-anonymizability. Such model-independent phenomenon refreshes us with a latitudinal study as it generalizes the essential commonness of de-anonymization in different network models. (ii) We derive explicit parametric bounds of the de-anonymizability for three classic network models, showing that such bounds correspond well to our conclusion about morphism property. We then algorithmically and experimentally show that such theoretical results literally make sense to adversaries. Such longitudinal study, including welding theory, algorithm and validation, promises applicability of our results on morphism property in real cases.

This paper studies social network de-anonymization problem, which aims to identify users of an anonymized network by matching its user set with that of another auxiliary sanitized network. Prior arts primarily assume that both networks share exactly the same set of users, as opposed to many real situations of partially shared users in between. Different from the full matching case that only needs to take care of increasing the number of correctly matched pairs, the case of partial overlapping imposes additional demand on avoiding the wrong matches of those who do not have accounts across networks.To this end, we establish a new cost function, which we call the structural F-score to incorporate both the structural commonness and difference across networks. Intrinsically, the structural F-score computes the ratio of link agreements and disagreements, thus serving as the harmonic mean of precision and recall for any given matching function. Theoretically, we show that for networks parameterized by node overlap t2and link overlap s2, as long as the mean degree of networks grows as Ω(t-2s-3log n), maximizing the structural F-score provably ensures the perfect matching, where the nodal precision and recall are both maximized to 1. Algorithmically, for small-scale networks, we propose a two-step heuristic of F-score based de-anonymization, which firstly finds the optimal full matching between networks and then removes those pairs hindering structural F-score maximization. Due to the universal adaptability of the structural F-score, we further extend the algorithm to large-scale networks via a progressive matching process. Empirical results also validate the effectiveness of our methods in terms of improving the nodal F-score.

The interaction among users in different social networks raises deep concern on user privacy, as it may facilitate the assailants to identify user identities by matching the anonymized networks with a correlated sanitized one. Prior arts regarding such de-anonymization problem can be primarily divided into a seeded case or a seedless one, depending on whether or not there are a subset of pre-identified nodes. The seedless case is much more complicated since the adjacency matrix representation of one-hop user relations delivers limited structural information. To address this issue, we, for the first time, integrate the multi-hop neighborhood relationships, which exhibit more structural commonness between the anonymized and the sanitized networks, into seedless de-anonymization process. Our aim is to sufficiently leverage these multi-hop neighbors of all nodes and minimize the total disagreements of these multi-hop adjacency matrices, which we call collective adjacency disagreements (CADs), between two networks of different sizes. Theoretically, we demonstrate that CAD enlarges the difference between wrongly matched node pairs and correctly matched pairs, whereby two networks can be correctly matched with high probability even when the network density is below logn. Algorithmically, we adopt the conditional gradient descending method on a collective-form objective, which can efficiently find the minimal CADs for networks with broad degree distributions. Experiments on both synthetic and realworld networks return desirable de-anonymization accuracies thanks to the rich structural information manifested by such collectiveness, since most nodes can be correctly matched with their correspondences, especially in sparse networks where merely utilizing adjacency relations might fail to work.

The advent of social networks poses severe threats on user privacy as adversaries can de-anonymize users' identities by mapping them to correlated cross-domain networks. Without ground-truth mapping, prior literature proposes various cost functions in hope of measuring the quality of mappings. However, their cost functions, whose minimizers may remain algorithmically unknown, usually bring imponderable mapping errors when the true mapping cannot minimize these cost functions. We jointly tackle above concerns under a more practical social network model parameterized by overlapping communities, which, neglected by prior art, can serve as side information for de-anonymization. Regarding the unavailability of ground-truth mapping to adversaries, by virtue of the Minimum Mean Square Error (MMSE), our first contribution is a well-justified cost function minimizing the expected number of mismatched users over all possible true mappings. While proving the NP-hardness of minimizing MMSE, we validly transform it into the weighted-edge matching problem (WEMP), which, as disclosed theoretically, resolves the tension between optimality and complexity: 1) WEMP asymptotically returns a negligible mapping error in large network size under mild conditions facilitated by higher overlapping strength; 2) WEMP can be algorithmically characterized via the convex-concave based de-anonymization algorithm (CBDA), effectively finding the optimum of WEMP. Extensive experiments further confirm the effectiveness of CBDA under overlapping communities: 90% users are re-identified averagely in a series of networks when communities overlap densely, and the re-identification ratio is enhanced about 70% compared to non-overlapping cases.