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VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
TLDR
The study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features and Experimental results show that VulDeePecker can achieve much fewer false negatives and reasonable false positives than other approaches.
Performance and energy modeling for live migration of virtual machines
TLDR
This work constructs application-oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level and evaluates the models using five representative workloads on a Xen virtualized environment.
TripleBit: a Fast and Compact System for Large Scale RDF Data
TLDR
The design of TripleBit is presented, a fast and compact system for storing and accessing RDF data that outperforms RDF-3X, MonetDB, BitMat on LUBM, UniProt and BTC 2012 benchmark queries and it offers orders of mangnitude performance improvement for some complex join queries.
A Case for Redundant Arrays of Inexpensive Disks (RAID)
TLDR
Five levels of RAIDs are introduced, giving their relative cost/performance, and a comparison to an IBM 3380 and a Fujitsu Super Eagle is compared.
An Efficient Graph Indexing Method
TLDR
This paper proposes SEGOS, an indexing and query processing framework for graph similarity search that is easy to be pipelined to support continuous graph pruning and a novel search strategy based on the index.
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
TLDR
This work proposes the first systematic framework for using deep learning to detect vulnerabilities, dubbed Syntax- based, Semantics-based, and Vector Representations (SySeVR), which focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities.
A Compromised-Time-Cost Scheduling Algorithm in SwinDeW-C for Instance-Intensive Cost-Constrained Workflows on a Cloud Computing Platform
TLDR
This paper presents a novel compromised-time-cost scheduling algorithm which considers the characteristics of cloud computing to accommodate instance-intensive cost-constrained workflows by compromising execution time and cost with user input enabled on the fly.
Active Disks: Programming Model, Algorithms and Evaluation
TLDR
This paper evaluates Active Disk architectures which integrate significant processing power and memory into a disk drive and allow application-specific code to be downloaded and executed on the data that is being read from (written to) disk.
Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications
TLDR
This article makes the first attempt to present a survey of mobile cloud computing from the perspective of its intended usages, and introduces three common mobile cloud architectures and classify comprehensive existing work into two fundamental categories: computation offloading and capability extending.
LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce in the Cloud
TLDR
A novel algorithm named LEEN for locality-aware and fairness-aware key partitioning in MapReduce, which can efficiently achieve higher locality and reduce the amount of shuffled data and guarantees fair distribution of the reduce inputs.
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