相关论文: denet, A lightweight command-line tool for process…
This paper presents HeNet, a hierarchical ensemble neural network, applied to classify hardware-generated control flow traces for malware detection. Deep learning-based malware detection has so far focused on analyzing executable files and…
This paper presents the Container Profiler, a software tool that measures and records the resource usage of any containerized task. Our tool profiles the CPU, memory, disk, and network utilization of containerized tasks collecting over…
Compendium Manager is a command-line tool written in Python to automate the provisioning, launch, and evaluation of bioinformatics pipelines. Although workflow management tools such as Snakemake and Nextflow enable users to automate the…
A computational workflow, also known as workflow, consists of tasks that must be executed in a specific order to attain a specific goal. Often, in fields such as biology, chemistry, physics, and data science, among others, these workflows…
DCentNet is a novel decentralized multistage signal classification approach designed for biomedical data from IoT wearable sensors, integrating early exit points (EEP) to enhance energy efficiency and processing speed. Unlike traditional…
Networked robotic systems balance compute, power, and latency constraints in applications such as self-driving vehicles, drone swarms, and teleoperated surgery. A core problem in this domain is deciding when to offload a computationally…
We present MiniTensor, an open source tensor operations library that focuses on minimalism, correctness, and performance. MiniTensor exposes a familiar PyTorch-like Python API while it executes performance critical code in a Rust engine.…
We discuss the design and ongoing development of the Monitoring Extreme-scale Lustre Toolkit (MELT), a unified Lustre performance monitoring and analysis infrastructure that provides continuous, low-overhead summary information on the…
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a…
We present nanoBench, a tool for evaluating small microbenchmarks using hardware performance counters on Intel and AMD x86 systems. Most existing tools and libraries are intended to either benchmark entire programs, or program segments in…
Edge computing provides resources for IoT workloads at the network edge. Monitoring systems are vital for efficiently managing resources and application workloads by collecting, storing, and providing relevant information about the state of…
The capability of process mining techniques in providing extensive knowledge and insights into business processes has been widely acknowledged. Process mining techniques support discovering process models as well as analyzing process…
In this paper, we present the modular design and implementation of DONUT, a novel tool for identifying software running on a device. Our tool uses a rule-based approach to detect software-specific DNS fingerprints (stored in an easily…
This paper introduces BioAgent Bench, a benchmark dataset and an evaluation suite designed for measuring the performance and robustness of AI agents in common bioinformatics tasks. The benchmark contains curated end-to-end tasks (e.g.,…
Process mining techniques including process discovery, conformance checking, and process enhancement provide extensive knowledge about processes. Discovering running processes and deviations as well as detecting performance problems and…
Static data-flow analysis has proven its effectiveness in assessing security of applications. One major challenge it faces is scalability to large software. This issue is even exacerbated when additional limitations on computing and storage…
The ROOT based Offline and Online Analysis (ROAn) framework was developed to perform data analysis on data from Depleted P-channel Field Effect Transistor (DePFET) detectors, a type of active pixel sensors developed at the MPI…
Kernel Density Estimation (KDE) is a nonparametric method for estimating the shape of a density function, given a set of samples from the distribution. Recently, locality-sensitive hashing, originally proposed as a tool for nearest neighbor…
Accurately estimate performance of currently available processors is becoming a key activity, particularly in HENP environment, where high computing power is crucial. This document describes the methods and programs, opensource or freeware,…
Over the lifetime of a computing task, determining the maximum usage of random-access memory (RAM) on both the motherboard and on a graphical processing unit (GPU), as well as the utilization percentage of the central processing unit (CPU)…