大規模網路社群偵測的分散式偽似然估計方法
本文提出了一種名為分散式偽似然估計方法 (DPL) 的新型演算法，用於有效地在大規模網路中進行社群偵測。該方法利用區塊式分割技術將大型網路資料劃分到多個工作節點上，並透過迭代優化局部偽似然函數來識別社群結構，最終在主節點彙總結果。DPL 方法有效降低了傳統偽似然估計方法的計算複雜度，並具有良好的理論基礎和實驗驗證。
대규모 네트워크에서 커뮤니티 탐지를 위한 분산 의사 우도 방법
이 논문은 대규모 네트워크에서 커뮤니티 구조를 효율적으로 식별하기 위해 계산 및 저장 측면에서 효율적인 분산 의사 우도 (DPL) 방법을 제안합니다.
Distributed Pseudo-Likelihood Method for Efficient Community Detection in Large Networks
This paper introduces DPL, a distributed algorithm designed to efficiently detect community structures within large-scale networks by leveraging a block-wise splitting method and pseudo-likelihood estimation, significantly reducing computational complexity while maintaining accuracy.
ストリーミング技術とシリアライズプロトコル：異なるデータセットにおけるパフォーマンスの比較分析
データストリーミング技術とシリアライズプロトコルは、データセットの特性とアプリケーションの要件に応じてパフォーマンスが大きく異なるため、最適な組み合わせを選択することが重要である。
Streaming Technologies and Serialization Protocols for Scientific Data: An Empirical Performance Comparison
Protocol-based serialization methods coupled with brokerless streaming technologies offer the best performance for streaming scientific data.
Optimal Estimation in Spatially Distributed Systems: Analyzing the Range of Measurement Sharing
This paper investigates the spatial localization of Kalman filters in spatially distributed systems, specifically focusing on how the interplay between system dynamics, noise characteristics, and measurement range influences the optimal sharing of measurements for state estimation.
Swiper: 가중 분산 프로토콜을 위한 효율적인 새로운 패러다임 (Sybil 공격 방어 및 실제 가중치 분포 고려)
본 논문에서는 Sybil 공격에 취약한 기존 분산 시스템의 한계를 극복하기 위해, 실제 환경의 가중치 분포를 고려한 효율적인 가중 분산 프로토콜 설계를 위한 새로운 패러다임인 Swiper를 제시합니다.
Swiper: Efficiently Transforming Nominal Distributed Protocols for Weighted Settings
This paper introduces Swiper, a novel approach for transforming distributed protocols designed for traditional "one-party, one-vote" (nominal) settings to operate efficiently in weighted settings where participants have varying levels of influence.
Real-time and Downtime-tolerant Fault Diagnosis System for Railway Turnout Machines Using Cloud-Edge Collaboration
This paper proposes a novel cloud-edge collaborative framework, CEC-PA, for real-time and downtime-tolerant fault diagnosis of Railway Turnout Machines (RTMs) to improve railway safety.
Minder: An Automated System for Detecting Faulty Machines in Large-Scale Distributed Model Training
Large-scale distributed model training is susceptible to frequent machine failures, leading to significant downtime and economic losses. Minder, an automated faulty machine detection system, leverages machine-level similarity and continuity patterns in monitoring metrics to quickly and accurately identify faulty machines, minimizing manual effort and downtime.
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