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Personality traits assessment using p.A.D. Emotional space in human-robot interaction 16

Personality traits assessment using p.A.D. Emotional space i...

16th International Joint Conference on computer Vision, Imaging and computer Graphics Theory and Applications, VISIGRAPP 2021

作者： Zafar, Zuhair Ashok, Ashita Berns, Karsten Robotics Research Lab. Department of Computer Science Technische Universität Kaiserslautern Germany

ISBN: (纸本)9789897584886

Cognitive social robotics is the field of research that is committed to building social robots that facilitate to draw parallels with human beings. Humans assess the behavior and personality of their counterparts to adapt their behavior and show empathy to flourish human-human interaction. Similarly, assessment of human personality is highly critical in realizing natural and intelligent human-robot interaction. Numerous personality traits assessment systems have been reported in the literature;however, most of them target the big five personality traits. From only visual information, this work proposes to use pleasure, arousal, and dominance emotional space for the assessment of personality traits based on the work of Mehrabian. To validate the system, three different scenarios have been developed to assess 12 different personality traits on a social humanoid robot. Experimental results show that the system can assess human personality traits with 84% accuracy in real-time and, hence, it can adapt its behavior according to the perceived personality of the interaction partner. Copyright © 2021 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Human robot interaction

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Surgical robotics Environment in NVIDIA Isaac Sim for Robot-Assisted Suturing

Surgical Robotics Environment in NVIDIA Isaac Sim for Robot-...

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International Symposium on Medical robotics (ISMR)

作者： Tae Wan Kim Haoying Zhou Juan Antonio Barragan Hisashi Ishida Peter Kazanzides Adnan Munawar Department of Computer Science Johns Hopkins University Baltimore MD USA Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

Virtual simulations have served as important tools to design and test robotic frameworks in a variety of domains. Increasingly, they have been used to train and experiment on surgical robotics applications. Having seen the success of an open simulation environment for robot-assisted surgical suturing in the Asynchronous Multi-Body Framework (AMBF), we sought to reproduce the surgical suturing environment from AMBF in NVIDIA Isaac Sim, a new simulator with impressive visual fidelity and GPU-accelerated computation capabilities. We present a virtual scene for robotic suturing comprising two robotic manipulators, an endoscope, two surgical phantoms, and a needle attached to a thread. The thread is modeled using three different techniques. This work also provides interactive manipulation of the virtual scene via haptic input devices using the Robot Operating System interface. We measured performance speeds, ranging between 13 to 60 frames per second depending on the task, and approximately 0.5 seconds of delay between a haptic input device and the simulator. In this environment, we hope to provide a descriptive overview of the capabilities and limitations of NVIDIA Isaac Sim for surgical simulation applications.

关键词： Performance evaluation Visualization Medical robotics Instruction sets computational modeling Operating systems Input devices Phantoms Needles Haptic interfaces

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PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving

PhysORD: A Neuro-Symbolic Approach for Physics-infused Motio...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zhipeng Zhao Bowen Li Yi Du Taimeng Fu Chen Wang Department of Computer Science and Engineering Spatial AI & Robotics (SAIR) Lab Institute for Artificial Intelligence and Data Science University at Buffalo Buffalo NY USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Motion prediction is critical for autonomous off-road driving, however, it presents significantly more challenges than on-road driving because of the complex interaction between the vehicle and the terrain. Traditional physics-based approaches encounter difficulties in accurately modeling dynamic systems and external disturbance. In contrast, data-driven neural networks require extensive datasets and struggle with explicitly capturing the fundamental physical laws, which can easily lead to poor generalization. By merging the advantages of both methods, neuro-symbolic approaches present a promising direction. These methods embed physical laws into neural models, potentially significantly improving generalization capabilities. However, no prior works were evaluated in real-world settings for off-road driving. To bridge this gap, we present PhysORD, a neural-symbolic approach integrating the conservation law, i.e., the Euler-Lagrange equation, into data-driven neural models for motion prediction in off-road driving. Our experiments showed that PhysORD can accurately predict vehicle motion and tolerate external disturbance by modeling uncertainties. It outperforms existing methods both in accuracy and efficiency and demonstrates data-efficient learning and generalization ability in long-term prediction.

关键词： Bridges Uncertainty Accuracy Neural networks Merging Predictive models Mathematical models Dynamical systems Intelligent robots

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Massive Dimensions Reduction and Hybridization with Meta-heuristics in Deep Learning

Massive Dimensions Reduction and Hybridization with Meta-heu...

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Rasa Khosrowshahli Shahryar Rahnamayan Beatrice Ombuki-Berman Department of Computer Science Nature-Inspired Computational Intelligence (NICI) Lab Bio-Inspired Computational Intelligence Research Group (BICIG) Brock University St. Catharines ON Canada Department of Engineering Nature-Inspired Computational Intelligence (NICI) Lab Brock University St. Catharines ON Canada Department of Computer Science Bio-Inspired Computational Intelligence Research Group (BICIG) Brock University St. Catharines ON Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Deep learning is mainly based on utilizing gradient-based optimization for training Deep Neural Network (DNN) models. Although robust and widely used, gradient-based optimization algorithms are prone to getting stuck in local minima. In this modern deep learning era, the SOTA DNN models have millions and billions of parameters, including weights and biases, making them huge-scale optimization problems in terms of search space. Tuning a huge number of parameters is a challenging task that causes vanishing/exploding gradients and overfitting; likewise, utilized loss functions do not exactly represent our targeted performance metrics. A practical solution to exploring large and complex solution space is meta-heuristic algorithms. Since DNNs exceed thousands and millions of parameters, even robust meta-heuristic algorithms, such as Differential Evolution, struggle to efficiently explore and converge in such huge-dimensional search spaces, leading to very slow convergence and high memory demand. To tackle the mentioned curse of dimensionality, the concept of blocking was recently proposed as a technique that reduces the search space dimensions by grouping them into blocks. In this study, we aim to introduce Histogram-based Blocking Differential Evolution (HBDE), a novel approach that hybridizes gradient-based and gradient-free algorithms to optimize parameters. Experimental results demonstrated that the HBDE could reduce the parameters in the ResNet-18 model from 11M to 3K during the training/optimizing phase by meta-heuristics, namely, the proposed HBDE, which outperforms baseline gradient-based and parent gradient-free DE algorithms evaluated on CIFAR10 and CIFAR100 datasets showcasing its effectiveness with reduced computational demands for the very first time.

关键词： Deep learning Training Measurement Dimensionality reduction Metaheuristics Memory management Artificial neural networks

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DEFVERIFY: Do Hate Speech Models Reflect Their Dataset’s Definition?

arXiv

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arXiv 2024年

作者： Khurana, Urja Nalisnick, Eric Fokkens, Antske Computational Linguistics and Text Mining Lab Vrije Universiteit Amsterdam Netherlands Department of Computer Science Johns Hopkins University United States

Warning: Due to the nature of the topic, this paper contains offensive content. When building a predictive model, it is often difficult to ensure that application-specific requirements are encoded by the model that will eventually be deployed. Consider researchers working on hate speech detection. They will have an idea of what is considered hate speech, but building a model that reflects their view accurately requires preserving those ideals throughout the workflow of data set construction and model training. Complications such as sampling bias, annotation bias, and model misspecification almost always arise, possibly resulting in a gap between the application specification and the model’s actual behavior upon deployment. To address this issue for hate speech detection, we propose DEFVERIFY: a 3-step procedure that (i) encodes a user-specified definition of hate speech, (ii) quantifies to what extent the model reflects the intended definition, and (iii) tries to identify the point of failure in the workflow. We use DEFVERIFY to find gaps between definition and model behavior when applied to six popular hate speech benchmark datasets. © 2024, CC BY.

关键词： Speech recognition

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Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection 2

Applying Pre-trained Multilingual BERT in Embeddings for Imp...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Rahman, Md Abdur Shahriar, Hossain Wu, Fan Cuzzocrea, Alfredo University of West Florida Dept. of Intelligent Systems and Robotics PensacolaFL United States University of West Florida Center for Cybersecurity PensacolaFL United States Tuskegee University Department of Computer Science TuskegeeAL United States University of Calabria Idea Lab Rende Italy

ISBN: (纸本)9798331529529

Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approaches may not adequately address the complexity and evolving nature of these vulnerabilities in real-world applications. Therefore, this work focuses the impact of malicious prompt injection attacks which is one of most dangerous vulnerability on real LLMs applications. It examines to apply various BERT (Bidirectional Encoder Representations from Transformers) like multilingual BERT, DistilBert for classifying malicious prompts from legitimate prompts. Also, we observed how tokenizing the prompt texts and generating embeddings using multilingual BERT contributes to improve the performance of various machine learning methods: Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The performance of each model is rigorously analyzed with various parameters to improve the binary classification to discover malicious prompts. Multilingual BERT approach to embed the prompts significantly improved and outperformed the existing works and achieves an outstanding accuracy of 96.55% by Logistic regression. Additionally, we investigated the incorrect predictions of the model to gain insights into its limitations. The findings can guide researchers in tuning various BERT for finding the most suitable model for diverse LLMs vulnerabilities. © 2024 IEEE.

关键词： Embeddings

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Toward Process Controlled Medical Robotic System

arXiv

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arXiv 2023年

作者： Liu, Yihao Kheradmand, Amir Armand, Mehran The Biomechanical- and Image-Guided Surgical Systems Laboratory Laboratory for Computational Sensing & Robotics Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States The Department of Neurology Department of Neuroscience Laboratory for Computational Sensing & Robotics Johns Hopkins University BaltimoreMD21218 United States The Biomechanical- and Image-Guided Surgical Systems Laboratory Laboratory for Computational Sensing & Robotics Department of Orthopaedic Surgery Department of Mechanical Engineering Department of Computer Science Applied Physics Laboratory Johns Hopkins University BaltimoreMD21218 United States

Medical errors, defined as unintended acts either of omission or commission that cause the failure of medical actions, are the third leading cause of death in the United States. Medical errors can include communication breakdowns, diagnostic errors, poor judgment, and inadequate skills. The application of autonomy and robotics can alleviate some causes of medical errors by improving accuracy and providing means to accurately follow planned procedures. However, for the robotic applications to improve safety, they must maintain constant operating conditions in the presence of disturbances, and provide reliable measurements, evaluation, and control for each state of the procedure. This article addresses the need for process control in medical robotic systems, and proposes a standardized design cycle toward its automation. Monitoring and controlling the changing conditions in a medical or surgical environment necessitates a clear definition of workflows and their procedural dependencies. We propose integrating process control into medical robotic workflows using hierarchical Finite State Machines (hFSM) to identify changes in states of the system and environment, and execute possible operations or transitions to new states. Therefore, the system translates clinician experiences and procedure workflows into machine-interpretable languages. The design cycle using hFSM formulation can be a deterministic process, which opens up possibilities for higher-level automation in medical robotics. Shown in our work, with a standardized design cycle and software paradigm, we pave the way toward controlled workflows that can be automatically generated. Additionally, a modular design for a robotic system architecture that integrates hFSM can provide easy software and hardware integration. This article discusses the system design, software implementation, and example application to Robot-Assisted Transcranial Magnetic Stimulation (RATMS) and robot-assisted femoroplasty. We also provide

关键词： Navigation systems

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ToP-ToM: Trust-aware Robot Policy with Theory of Mind

ToP-ToM: Trust-aware Robot Policy with Theory of Mind

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IEEE International Conference on robotics and Automation (ICRA)

作者： Chuang Yu Baris Serhan Angelo Cangelosi Computer Science Department UCL Interaction Centre University College London London United Kingdom Cognitive Robotics Lab Manchester Centre for Robotics and AI The University of Manchester Manchester United Kingdom

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Theory of Mind (ToM) is a fundamental cognitive architecture that endows humans with the ability to attribute mental states to others. Humans infer the desires, beliefs, and intentions of others by observing their behavior and, in turn, adjust their actions to facilitate better interpersonal communication and team collaboration. In this paper, we investigated trust-aware robot policy with the theory of mind in a multi-agent setting where a human collaborates with a robot against another human opponent. We show that by only focusing on team performance, the robot may resort to the reverse psychology trick, which poses a significant threat to trust maintenance. The human’s trust in the robot will collapse when they discover deceptive behavior by the robot. To mitigate this problem, we adopt the robot theory of mind model to infer the human’s trust beliefs, including true belief and false belief (an essential element of ToM). We designed a dynamic trust-aware reward function based on different trust beliefs to guide the robot policy learning, which aims to balance between avoiding human trust collapse due to robot reverse psychology and leveraging its potential to boost team performance. The experimental results demonstrate the importance of the ToM-based robot policy for human-robot trust and the effectiveness of our robot ToM-based robot policy in multiagent interaction settings.

关键词： Psychology Focusing Collaboration Maintenance Robots

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Molecular bases of morphologically diffused tumors across multiple cancer types

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National science Review 2022年第11期9卷 65-67页

作者： Dingyun Liu Feiyang Xing Yueying Wang Jun Xiao Zheng An Ying Xu College of Computer Science and Technology Jilin University Center for Cancer Systems Biology China-Japan Union Hospital of Jilin University School of Life Sciences Jilin University China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of Georgia

Diffuse gastric cancer (DGC) and intestinal gastric cancer (IGC) are the two main gastric cancer subtypes [1]. Morphologically, DGC consists of numerous disjoint tiny tumors while IGC forms one or a few solid tumors (Supplementary Supplementary Fig. S1)[1,2]. In-depth studies have discovered that DGCs generally have an ill-defined or missing glandular structure and reduced cell–cell adhesion,

关键词： identification enantioselective semisynthesis (−)-anisomelic acid oral agent anti-SARS-CoV-2

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Applications of Transformer Attention Mechanisms in Information Security: Current Trends and Prospects 2

Applications of Transformer Attention Mechanisms in Informat...

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2nd IEEE International Conference on Artificial Intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Vubangsi, M. Abidemi, Sarumi Usman Akanni, Olukayode Mubarak, Auwalu Saleh Al-Turjman, Fadi Artificial Intelligence Engineering Dept. Ai and Robotics Institute Near East University Mersin 10 Nicosia Cyprus Research Center for Ai and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Cyprus Computational Materials Science Lab Department of Computer Science Htttc Bambili University of Bamenda NWR Cameroon

ISBN: (纸本)9798350334104

In this work, we present a comprehensive survey on applications of the most recent transformer architecture based on attention in information security. Our review reveals three primary areas of application: Intrusion detection, Anomaly Detection and Malware Detection. We have presented an overview of attention-based mechanisms and their application in each cybersecurity use case, and discussed open grounds for future trends in Artificial Intelligence enabled information security. © 2022 IEEE.

关键词： Anomaly detection

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