BlobShuffle reduces shuffling costs by over 40x in Kafka Streams by using object storage for batching with notifications, achieving sub-2s 95th-percentile latency and scaling beyond 2 GiB/s.
Frankel and Krishnendu Ghosh
10 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
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7
external citations · external index
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Stylometric analysis of 23,305 YARA rules shows that repository origin, authors, and malware families can be identified with high accuracy from rule text alone.
Introduces the diagnosis-driven CE video summarization task, the VideoCAP dataset with 240 annotated videos, and the DiCE framework that outperforms prior methods by screening candidates then weaving them into diagnostic contexts.
A composite multi-proxy framework detects harmful drift in label-free risk decision systems and enables graduated governance alerts.
Matrix factorization on a literature-mined concept-object graph predicts future associations in astronomy better than neighborhood similarity or recency heuristics.
Systematic review of 80 papers shows TTP extraction shifting to transformer and LLM methods but limited by narrow datasets, single-label focus, and low reproducibility.
TwinLiteNet+ is a hybrid-encoder multi-task segmentation model with new UCB, USB, and PCAA modules that reports 92.9% mIoU on drivable area and 34.2% IoU on lane segmentation on BDD100K while using 11x fewer FLOPs than prior models.
A literature survey synthesizes 119 studies on AI-driven alert screening into a four-stage taxonomy of filtering, triage, correlation, and generative augmentation while identifying gaps in deployment realism and robustness.
An adaptive PID controller is integrated into a Network Digital Twin to enhance real-time traffic state synchronization, with results demonstrated via an interactive user interface.
citing papers explorer
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BlobShuffle: Cost-Effective Repartitioning in Stream Processing Systems via Object Storage Exemplified with Kafka Streams
BlobShuffle reduces shuffling costs by over 40x in Kafka Streams by using object storage for batching with notifications, achieving sub-2s 95th-percentile latency and scaling beyond 2 GiB/s.
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Anonymous YARA Rules Are Not Anonymous
Stylometric analysis of 23,305 YARA rules shows that repository origin, authors, and malware families can be identified with high accuracy from rule text alone.
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Divide-then-Diagnose: Weaving Clinician-Inspired Contexts for Ultra-Long Capsule Endoscopy Videos
Introduces the diagnosis-driven CE video summarization task, the VideoCAP dataset with 240 annotated videos, and the DiCE framework that outperforms prior methods by screening candidates then weaving them into diagnostic contexts.
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Label-Free Detection of Governance Evidence Degradation in Risk Decision Systems
A composite multi-proxy framework detects harmful drift in label-free risk decision systems and enables graduated governance alerts.
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Predicting New Concept-Object Associations in Astronomy by Mining the Literature
Matrix factorization on a literature-mined concept-object graph predicts future associations in astronomy better than neighborhood similarity or recency heuristics.
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What Are Adversaries Doing? Automating Tactics, Techniques, and Procedures Extraction: A Systematic Review
Systematic review of 80 papers shows TTP extraction shifting to transformer and LLM methods but limited by narrow datasets, single-label focus, and low reproducibility.
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TwinLiteNet+: An Enhanced Multi-Task Segmentation Model for Autonomous Driving
TwinLiteNet+ is a hybrid-encoder multi-task segmentation model with new UCB, USB, and PCAA modules that reports 92.9% mIoU on drivable area and 34.2% IoU on lane segmentation on BDD100K while using 11x fewer FLOPs than prior models.
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AI-Driven Security Alert Screening and Alert Fatigue Mitigation in Security Operations Centers: A Comprehensive Survey
A literature survey synthesizes 119 studies on AI-driven alert screening into a four-stage taxonomy of filtering, triage, correlation, and generative augmentation while identifying gaps in deployment realism and robustness.
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Building Network Digital Twins Part II: Real-Time Adaptive PID for Enhanced State Synchronization
An adaptive PID controller is integrated into a Network Digital Twin to enhance real-time traffic state synchronization, with results demonstrated via an interactive user interface.
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