Evaluation of Peer Robot Communications using CryptoROS

Rahman, Abdul Hadi Abd; Sulaiman, Riza; Sani, Nor Samsiah; Adam, Afzan; Amini, Roham

doi:10.14569/ijacsa.2019.0100786

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Robotics is another rapidly growing area of technology that is increasingly being used in education [ 4 , 5 ]. Robotics can be used to teach students important skills in STEM fields [ 6 ] and critical thinking and problem-solving skills [ 7 , 8 ]. Robotics also allows students to engage in hands-on learning experiences and build and programme robots to perform specific tasks [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi–Analytic Hierarchy Process

Alshammari,

Abd Rahman,

Arshad

et al. 2023

Sensors

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Existing methods for scoring student presentations predominantly rely on computer-based implementations and do not incorporate a robotic multi-classification model. This limitation can result in potential misclassification issues as these approaches lack active feature learning capabilities due to fixed camera positions. Moreover, these scoring methods often solely focus on facial expressions and neglect other crucial factors, such as eye contact, hand gestures and body movements, thereby leading to potential biases or inaccuracies in scoring. To address these limitations, this study introduces Robotics-based Presentation Skill Scoring (RPSS), which employs a multi-model analysis. RPSS captures and analyses four key presentation parameters in real time, namely facial expressions, eye contact, hand gestures and body movements, and applies the fuzzy Delphi method for criteria selection and the analytic hierarchy process for weighting, thereby enabling decision makers or managers to assign varying weights to each criterion based on its relative importance. RPSS identifies five academic facial expressions and evaluates eye contact to achieve a comprehensive assessment and enhance its scoring accuracy. Specific sub-models are employed for each presentation parameter, namely EfficientNet for facial emotions, DeepEC for eye contact and an integrated Kalman and heuristic approach for hand and body movements. The scores are determined based on predefined rules. RPSS is implemented on a robot, and the results highlight its practical applicability. Each sub-model is rigorously evaluated offline and compared against benchmarks for selection. Real-world evaluations are also conducted by incorporating a novel active learning approach to improve performance by leveraging the robot’s mobility. In a comparative evaluation with human tutors, RPSS achieves a remarkable average agreement of 99%, showcasing its effectiveness in assessing students’ presentation skills.

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Section: Introductionmentioning

confidence: 99%

Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi–Analytic Hierarchy Process

Alshammari,

Abd Rahman,

Arshad

et al. 2023

Sensors

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show abstract

“…Similarly, in [15], Tiku et al introduced a methodology for overcoming security vulnerabilities in a deep learning localization method by making use of adversarial training samples. In distributed and multi-robot systems, most efforts focus on the analysis and mitigation of security issues from a networking perspective [16]. All of these approaches take the point of view of data security in information systems and do not explicitly involve the cyber-physical nature of autonomous robots and their interaction with their environment, which is the objective of this paper.…”

Section: Introductionmentioning

confidence: 99%

Secure Encoded Instruction Graphs for End-to-End Data Validation in Autonomous Robots

Queralta

Ferrer

et al. 2022

IEEE Internet Things J.

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As autonomous robots are becoming more widespread, more attention is being paid to the security of robotic operation. Autonomous robots can be seen as cyberphysical systems: they can operate in virtual, physical, and human realms. Therefore, securing the operations of autonomous robots requires not only securing their data (e.g., sensor inputs and mission instructions) but securing their interactions with their environment. There is currently a deficiency of methods that would allow robots to securely ensure their sensors and actuators are operating correctly without external feedback. This paper introduces an encoding method and end-to-end validation framework for the missions of autonomous robots. In particular, we present a proof of concept of a map encoding method, which allows robots to navigate realistic environments and validate operational instructions with almost zero a priori knowledge. We demonstrate our framework using two different encoded maps in experiments with simulated and real robots. Our encoded maps have the same advantages as typical landmark-based navigation, but with the added benefit of cryptographic hashes that enable end-to-end information validation. Our method is applicable to any aspect of robotic operation in which there is a predefined set of actions or instructions given to the robot.

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“…of view of data security in information systems and do not explicitly involve the cyber-physical nature of autonomous robots and their interaction with the environment, which is the objective of this paper. In terms of distributed and multirobot systems, most efforts have been directed towards the analysis and mitigation of security issues from a networking perspective [17].…”

Section: Introductionmentioning

confidence: 99%

Secure Encoded Instruction Graphs for End-to-End Data Validation in Autonomous Robots

Queralta,

Qingqing,

Ferrer

et al. 2020

Preprint

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As autonomous robots become increasingly ubiquitous, more attention is being paid to the security of robotic operation. Autonomous robots can be seen as cyber-physical systems that transverse the virtual realm and operate in the human dimension. As a consequence, securing the operation of autonomous robots goes beyond securing data, from sensor input to mission instructions, towards securing the interaction with their environment. There is a lack of research towards methods that would allow a robot to ensure that both its sensors and actuators are operating correctly without external feedback. This paper introduces a robotic mission encoding method that serves as an end-to-end validation framework for autonomous robots. In particular, we put our framework into practice with a proof of concept describing a novel map encoding method that allows robots to navigate an objective environment with almost-zero a priori knowledge of it, and to validate operational instructions. We also demonstrate the applicability of our framework through experiments with real robots for two different map encoding methods. The encoded maps inherit all the advantages of traditional landmark-based navigation, with the addition of cryptographic hashes that enable end-to-end information validation. This end-to-end validation can be applied to virtually any aspect of robotic operation where there is a predefined set of operations or instructions given to the robot.

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Evaluation of Peer Robot Communications using CryptoROS

Cited by 4 publications

References 22 publications

Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi–Analytic Hierarchy Process

Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi–Analytic Hierarchy Process

Secure Encoded Instruction Graphs for End-to-End Data Validation in Autonomous Robots

Secure Encoded Instruction Graphs for End-to-End Data Validation in Autonomous Robots

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