Random number generator (RNG) is a fundamental element in modern cryptography. If the quality of the outputs generated by RNGs is not as well as expected, the cryptographic applications which use the random number ser...
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The patients’ case files may be maliciously tampered with during network transmission. Once the patients’ medical record information is altered by criminals, serious medical accidents will occur. To solve the proble...
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This paper proposes an omni-directional printed antenna with broadband and high gain for WiFi-5 and WiFi-6 applications. In order to achieve stable omnidirectional gain, both of the ground plane and the radiating elem...
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Digitalization of shipping aims to enhance shipping efficiency, safety, and sustainability through advanced technology. However, fully digitalized shipping relies heavily on the integration of various communication de...
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Traditional prediction methods of legal judgment rarely work well on few-shot charge prediction task, which is intended to predict possible crimes, laws and terms according to a given few-shot case description. A majo...
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In 2009, Bl¨omer and Naewe proposed the Generalized Shortest Vector Problem (GSVP). We initiate the study of the promise problem (GAPSAM) for GSVP. It is a promise problem associated with estimating the subspace ...
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Nowadays, data parallelism has been widely applied to train large datasets on distributed deep learning clusters, but it has suffered from costly global parameter updates at batch barriers. Performance imbalance among...
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Nowadays, data parallelism has been widely applied to train large datasets on distributed deep learning clusters, but it has suffered from costly global parameter updates at batch barriers. Performance imbalance among worker instances, introduced by uneven workload partitioning or biased resource allocation, can cause straggly workers, which can lead to severe impacts on both training speed and result accuracy. This paper studies the issue focusing on the tradeoff between training speed and result accuracy. We propose Cooperate Grouping Parallel (CGP), a hybrid parameter update solution that allows the flexibility of both synchronous and asynchronous update schemes. We introduce a novel Cooperate Worker Grouping Problem (CWGP) that seeks a task grouping configuration that leads to maximum model accuracy and holds customized training speed guarantees. We propose an evolution-based Pareto local searching algorithm to compute efficient worker grouping configurations. Comprehensive evaluation results are presented to demonstrate the effectiveness of CGP under both persistent and fluctuant imbalances. The proposed method alleviates the imbalance impacts without introducing extra adjustment over-heads.
Concurrent search is an efficient technique for solving distributed constraint satisfaction problems (DisCSPs). dynamic variable ordering (DVO) impacts performance of search greatly. In this paper, we present a distri...
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Concurrent search is an efficient technique for solving distributed constraint satisfaction problems (DisCSPs). dynamic variable ordering (DVO) impacts performance of search greatly. In this paper, we present a distributed dynamic variable ordering based concurrent search algorithm, in which domain sizes of variables are calculated approximately during search. Experiments on randomly generated DisCSPs demonstrate that the proposed algorithm improves concurrent search drastically.
The stream processing model continuously processes online data in an on-pass fashion that can be more vulnerable to failures than other offline-data processing schemes. Checkpoint-based fault-tolerant methods have bee...
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ISBN:
(数字)9781728166070
ISBN:
(纸本)9781728166087
The stream processing model continuously processes online data in an on-pass fashion that can be more vulnerable to failures than other offline-data processing schemes. Checkpoint-based fault-tolerant methods have been widely used to enhance the reliability of stream processing systems. To ensure exact data recoveries upon failures, full-backup mechanisms are used to store a complete copy of data, which introduces substantial runtime overhead and increases output latency. In the meantime, a wide range of online processing applications prefer quick-and-dirty results with a slight degradation inaccuracy to delayed exact results. This paper introduces a novel approximate fault-tolerant problem (OAFP) with the objective of reducing the failure-free fault-tolerant overhead and ensuring user-defiled output accuracy requirement upon failure at the same time. We present an approximate fault-tolerant scheme based on sampling backup mechanism and study the trade-off between fault-tolerant overhead and output accuracy in stream processing systems. We proposed two algorithms to compute backup plans for both single-node failure and correlated failure scenarios. Extensive experiments with different types of stream topologies are conducted on our simulator to verify the correctness and effectiveness of our approach. We prove our solution guarantees the output accuracy requirement with minimum FT latency for directed acyclic graph (DAG) stream topologies with single-node failures.
We study the problem of repairing Reed-Solomon codes with low-access complexity in the rack-aware storage model that allows collective information processing in the nodes that share the same rack. Building on recent w...
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