Jove
Visualize
Contact Us

Related Concept Videos

Guidelines and Strategies for Safe Computer Charting01:18

Guidelines and Strategies for Safe Computer Charting

2.7K
The guidelines and strategies provided by the American Nurses Association (ANA) and the Canadian Nurses Association (CNA) offer essential principles for ensuring safe and secure computer charting systems in healthcare settings. Let's break down each recommendation:
Maintain Confidentiality and Security:
2.7K
Masking and Demasking Agents01:19

Masking and Demasking Agents

3.7K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.7K
Classification of Systems-I01:26

Classification of Systems-I

609
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
609
Classification of Systems-II01:31

Classification of Systems-II

520
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
520
Introduction to Learning01:18

Introduction to Learning

1.2K
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
1.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Achieving Efficient and Privacy-Preserving k-NN Query for Outsourced eHealthcare Data.

Journal of medical systems·2019
Same journal

How students use generative AI for software testing: An observational study.

Empirical software engineering·2026
Same journal

Is common sense all you need? Using expert defined rules to identify vulnerability patches instead of machine learning.

Empirical software engineering·2026
Same journal

Less is more: usefulness of data flow diagrams and large language models for security threat validation.

Empirical software engineering·2026
Same journal

Tools and benchmarks evolve: what is their impact on parameter tuning in SBSE experiments?

Empirical software engineering·2025
Same journal

AI support for data scientists: An empirical study on workflow and alternative code recommendations.

Empirical software engineering·2025
Same journal

Analyzing and mitigating (with LLMs) the security misconfigurations of Helm charts from Artifact Hub.

Empirical software engineering·2025
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 26, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System
10:09

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

SecMLOps: A comprehensive framework for integrating security throughout the machine learning operations lifecycle.

Xinrui Zhang1,2, Pincan Zhao3, Jason Jaskolka1

  • 1Department of Systems and Computer Engineering, Carleton University, Ottawa, ON Canada.

Empirical Software Engineering
|February 16, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Secure Machine Learning Operations (SecMLOps), a framework to embed security into the ML lifecycle, enhancing system resilience against sophisticated attacks. It balances security needs with performance for reliable ML deployments.

Keywords:
MLOpsMachine learning securitySecMLOps

More Related Videos

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Related Experiment Videos

Last Updated: Jun 26, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System
10:09

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Machine Learning (ML) is crucial for complex systems but faces security challenges like adversarial attacks.
  • Current ML Operations (MLOps) lack comprehensive security integration, risking system integrity.
  • Securing ML deployments is vital for trustworthy autonomous vehicles, healthcare, and finance.

Purpose of the Study:

  • To introduce Secure Machine Learning Operations (SecMLOps), a framework for integrating security throughout the MLOps lifecycle.
  • To safeguard ML applications against sophisticated attacks targeting various MLOps stages.
  • To provide practical guidance on balancing security and performance in ML deployments.

Main Methods:

  • Developed a comprehensive SecMLOps framework integrating security into the MLOps lifecycle.
  • Applied SecMLOps to an advanced Pedestrian Detection System (PDS) use case.
  • Conducted empirical evaluations to analyze security-performance trade-offs.

Main Results:

  • The SecMLOps framework effectively enhances the resilience and trustworthiness of ML applications.
  • Empirical evaluations demonstrated the practical application and impact of SecMLOps.
  • Identified critical trade-offs between security measures and system performance.

Conclusions:

  • SecMLOps provides a robust approach to secure the entire ML lifecycle.
  • A balanced approach is essential for optimizing security without compromising operational efficiency.
  • The framework offers valuable guidance for practitioners deploying secure ML systems.