Essays about: "user’s requirements of trust"

Showing result 1 - 5 of 20 essays containing the words user’s requirements of trust.

1. 1. Integrating Trust-Based Adaptive Security Framework with Risk Mitigation to enhance SaaS User Identity and Access Control based on User Behavior

   University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknik

   Author : Johnson Akpotor Scott; [2022]
   Keywords : Risk Assessment; Security countermeasure; Risk Management; Risk Mitigation; Adaptive Security Controls; Threats; Risk; Vulnerabilities; User Behavior Trust Degree; User Behavior Risk Rating; Policy Decision Point; Policy Enforcement Point; User Behavior Trust Model; Adaptive Security Architecture; SaaS; Public Cloud.;

   Abstract : In recent years, the emerging trends in cloud computing technologies have given rise to different computing services through the Internet. Organizations across the globe have seized this opportunity as a critical business driver for computing resource access and utilities that will indeed support significant business operations. READ MORE

3. 2. Analysis of the User Requirements and Product Specifications for Home-Use of the ABLE Exoskeleton

   University essay from KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

   Author : Katlin Kreamer-Tonin; [2021]
   Keywords : Exoskeleton; Lower-limb exoskeleton; Spinal cord injury; Home community; Assistive technology; Exoskelett; nedre extremitet; ryggmärgsskada; hem och samhälle; hjälpmedel;

   Abstract : Lower-limb exoskeletons are an emerging technology to provide walking assistance to people who have a spinal cord injury (SCI). Until now, exoskeletons have primarily been used in a clinical setting for a range of applications in rehabilitation, and there is potential for exoskeletons to be used by people with SCI at home. READ MORE

4. 3. Explainable Reinforcement Learning for Risk Mitigation in Human-Robot Collaboration Scenarios

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Alessandro Iucci; [2021]
   Keywords : Explainable Reinforcement Learning; Human-Robot Collaboration; Risk Mitigation; Reward Decomposition; Autonomous Policy Explanation; Collaborative Robots; Förklarbar förstärkningslärande; Mänskligt-robot-samarbete; Riskreducering; Reward Decomposition; Autonomous Policy Explanation; Samarbetsrobotar;

   Abstract : Reinforcement Learning (RL) algorithms are highly popular in the robotics field to solve complex problems, learn from dynamic environments and generate optimal outcomes. However, one of the main limitations of RL is the lack of model transparency. This includes the inability to provide explanations of why the output was generated. READ MORE

5. 4. Technology-enhanced Speech and Language Relearning for Stroke Patients- Understanding the users and their needs for technology acceptance

   University essay from Mittuniversitetet/Institutionen för data- och systemvetenskap

   Author : Awais Ahmad; [2021]
   Keywords : eHealth; Stroke Rehabilitation; Speech and Language Relearning; Adult Learning; Technology-enhanced Systems; Technology Acceptance;

   Abstract : Stroke is a rapidly increasing disease worldwide, and speech and language impairments are common in stroke patients. A patient’s ability to speak, listen, read and write is reduced after stroke which affects the patient's independently living and quality of life. READ MORE

6. 5. Explainable AI methods for credit card fraud detection : Evaluation of LIME and SHAP through a User Study

   University essay from Högskolan i Skövde/Institutionen för informationsteknologi

   Author : Yingchao Ji; [2021]
   Keywords : Explainable AI; Local Explanations; Shapley Additive Explanations; Local Interpretable Model Agnostic Explanations; Credit Card Fraud Detection;

   Abstract : In the past few years, Artificial Intelligence (AI) has evolved into a powerful tool applied in multi-disciplinary fields to resolve sophisticated problems. As AI becomes more powerful and ubiquitous, oftentimes the AI methods also become opaque, which might lead to trust issues for the users of the AI systems as well as fail to meet the legal requirements of AI transparency. READ MORE