The intricate properties and relevance of graph data make it difficult to collect graph statistics privately via differential privacy (DP). Traditional centralized or local DP on graph data, face challenges like third...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
The intricate properties and relevance of graph data make it difficult to collect graph statistics privately via differential privacy (DP). Traditional centralized or local DP on graph data, face challenges like third-party threats and low data utility when collecting the clustering coefficient. In this regard, we introduce GCC-SDP, a scheme for collecting distributed Graph Clustering Coefficient with Shuffled DP (SDP). GCC-SDP gathers the local wedge lists of all edges and adjacency bit vectors through SDP and random response for calculating the noisy local triangle counts. It then collects the local degree values of all users by using Laplace mechanism, followed by estimating the global clustering coefficient of the global graph data by data collector. We provide specific steps of GCC-SDP and demonstrate through theoretical analysis that GCC-SDP conforms to various DPs, with unbiased results. Empirical experiments show that GCC-SDP performs better than existing local DP-based techniques across most accuracy metrics.
Iteration over opaque, generic data structures is an important feature of many C++ libraries. Aggressive compiler optimization and inlining enables generic C++ iterators to iterate over complex data structures with pe...
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A program’s architecture—how it organizes the invocation of application-specific logic—influences important program characteristics including its scalability and security. Architecture details are usually expressed...
A program’s architecture—how it organizes the invocation of application-specific logic—influences important program characteristics including its scalability and security. Architecture details are usually expressed in the same programming language as the rest of a program, and can be difficult to distinguish from non-architecture code. And once defined, architecture is difficult and risky to change because it couples tightly with application logic over *** introduce C-Saw: an approach to express a software’s architecture using a new embedded domain-specific language (EDSL) designed for that purpose. It decouples application-specific logic from architecture, making it easier to identify architectural details of software. C-Saw leverages three ideas: (i) introducing a new, formally-specified EDSL to separate an application’s architecture description from its programming language; (ii) reducing architecture implementation to the definition and management of distributed key-value tables, and (iii) introducing an expressive state-management abstraction for distributed *** describe a prototype implementation of C-Saw for C programs and use it to build end-to-end examples of expressing and changing the architecture of widely-used, third-party software. We evaluate this on Redis, cURL, and Suricata and find that C-Saw provides expressiveness and reusability, requires fewer lines of code when compared to directly using C to express architectural patterns, and imposes low performance overhead on typical workloads.
Accurate ship tracking is one of the important means to implement accurate ship monitoring and reduce traffic accidents. The traditional ship tracking is based on the single sensor source such as VTS, AIS or CCTV to r...
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Accelerating DNN execution on resource-limited computing platforms has been a long-standing problem. Prior works utilize l(1)-based group lasso or dynamic regularization such as ADMM to perform structured pruning on D...
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ISBN:
(纸本)9781665494663
Accelerating DNN execution on resource-limited computing platforms has been a long-standing problem. Prior works utilize l(1)-based group lasso or dynamic regularization such as ADMM to perform structured pruning on DNN models to leverage the parallel computing architectures. However, both of the pruning schemes and pruning methods lack universality, which leads to degraded performance and limited applicability. Considering mobile devices are becoming an important carrier for deep learning tasks, current approaches are not ideal for fully exploiting mobile parallelism while achieving high inference accuracy. To solve the problem, we propose BLCR, a novel block-based pruning framework that comprises a general and flexible structured pruning scheme that enjoys higher flexibility while exploiting full on-device parallelism, as well as a powerful and efficient reweighted regularization method to achieve the proposed sparsity scheme. Our framework is universal, which can be applied to both CNNs and RNNs, implying complete support for the two major kinds of computation-intensive layers (i.e., CONV and FC layers). To complete all aspects of the pruning-for-acceleration task, we also integrate compiler-based code optimization into our framework that can perform DNN inference on mobile devices in real-time. To the best of our knowledge, it is the first time that the weight pruning framework achieves universal coverage for both CNNs and RNNs with real-time mobile acceleration and no accuracy compromise.
This paper proposes a real-time power system simulation framework that is capable of simulating steady state and electromechanical transients of power systems, with submillisecond time resolution. The framework can in...
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ISBN:
(纸本)9781665433266
This paper proposes a real-time power system simulation framework that is capable of simulating steady state and electromechanical transients of power systems, with submillisecond time resolution. The framework can integrate power system component models packed as reusable Functional Mockup Units (FMUs) to flexibly create power system simulations without the need to recreate new models for different power systems. The integration of individual components is based on a novel model decomposition method, which enables the FMU reuse in different system contexts, as well as a parallel simulation execution onto multi-core machines. Furthermore, the paper proposes methods to optimize the allocation of components to cores and shows that the framework can simulate a medium voltage distributed electrical grid of about 20 components in real-time on a commodity multi-core machine.
This book presents the proceedings of the 29th Reconfigurable Architectures Workshop (RAW 2022) held in Lyon in May 2022. RAW 2022 is associated with the 36th Annual International parallel & distributedprocessing...
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ISBN:
(数字)9781665497473
ISBN:
(纸本)9781665497480
This book presents the proceedings of the 29th Reconfigurable Architectures Workshop (RAW 2022) held in Lyon in May 2022. RAW 2022 is associated with the 36th Annual International parallel & distributedprocessingsymposium (IPDPS 2022) and is sponsored by the ieee Computer Society's Technical Committee on parallelprocessing. The workshop is one of the major meetings for researchers to present ideas, results, and ongoing research on both theoretical and practical advances in reconfigurable computing.
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