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IEEE International Smart Cities Conference (ISC2)

作者： Reham R. Mostafa Ahmed M. Khedr Research Institute of Sci. and Eng. (RISE) Sharjah University Sharjah UAE Dept. of Computer Science University of Sharjah Sharjah UAE

ISBN: (数字)9798350364316

ISBN: (纸本)9798350364323

The Dandelion Optimizer (DO) is a pioneering swarm intelligence optimization algorithm inspired by dandelion seed flight patterns. It simulates seed dispersal with rising, descending, and landing phases, exhibiting global ascent in sunlight and local dispersion in rain. While DO has shown promise across domains, it grapples with challeng.s, particularly rapid convergence in early stages and difficulty escaping local optima. This paper introduces an improved version, IDO, addressing these issues by enhancing the initial population generation method. Performance comparisons on CEC'22 test functions and eng.neering-constrained design problems demonstrate that IDO outperforms other algorithms, showcasing superior robustness.

关键词： Rain Smart cities Metaheuristics Robustness Seeds (agriculture) Particle swarm optimization Dispersion Convergence

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Enhancing Emotional Support Chatbots Using Deep Learning & NLP: A Study on Personalization and Effectiveness

Enhancing Emotional Support Chatbots Using Deep Learning & N...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Priyanka Kaushik Renuka Sanjay Parihar Md. Saifullah Khalid Umesh Chandra Sharma J. Josphin Mary Dept. of Computer Science Chandigarh University Chandigarh India Dept. of Humanities & S.Science Madhav University Pindwara India Dept. of Bus. Management & Commerce Usha Martin University Jharkhand India Dept. of Computer Eng. & Applications Mangalayatan University Aligarh India Dept. of Computer Science Meenakshi A.H.E.R Chennai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

This research focuses on utilizing deep learning and natural language processing (NLP) to design an AI-powered chatbot for emotional support. The primary objective is to establish a supportive platform for individuals dealing with emotional challeng.s. By leveraging NLP, the chatbot will identify and interpret users’ emotional states, generating tailored and empathetic responses. Deep learning techniques will enable the system to learn from past interactions, adapting its responses to meet the unique needs of each user. The platform aims to deliver personalized, scalable support while incorporating features like sharing uplifting content, such as cute cat images. This study emphasizes the potential of AI-driven solutions in enhancing mental well-being through empathetic and meaningful interactions.

关键词： Deep learning Emotion recognition Accuracy Virtual assistants Telemedicine Customer services Mental health Chatbots Artificial intelligence

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A Proposal of Speed Sensorless Control Based on IRM-ILQ Control for SPMSM Drive with Active LC Filter

A Proposal of Speed Sensorless Control Based on IRM-ILQ Cont...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Ryoki Mikami Kazuki Abe Hiroshi Takami Fuminori Ishibashi Graduate School of Electrical Eng. And Computer Science Shibaura Institute of Technology Koto-ku Tokyo Japan Dept. of Electrical Eng. and Robotics Course College of Engineering Shibaura Institute of Technology Koto-ku Tokyo Japan Dept. of Electrical Eng. Faculty of Eng. Shibaura Institute of Technology Koto-ku Tokyo Japan

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

Generally, a position detector has been used to drive the Surface Permanent Magnet Synchronous Motor (SPMSM), because the current and voltage must be controlled according to the magnetic pole position of the rotor. However, using a position detector causes problems such as more expensive and lower reliability due to position detector failure. Therefore, the speed sensor-less control, which estimates the magnetic pole position from voltage and current without using a position detector, is required. Harmonic components in the output voltage waveform of the PWM inverter used to drive the SPMSM can cause problems such as elec.romagnetic Interference (EMI). Harmonic components can be suppressed by using an LC filter. In addition, IRM-ILQ control, which combines modern control theory's Inverse Linear Quadratic (ILQ) control and Inverse Reference Model (IRM), can achieve stable and robust SPMSM drive despite with LC filter. In this paper, robustness of speed sensorless control based on IRM-ILQ current control for SPMSM with active LC filter is described. In simulation, the proposed method allows stable control even when there are parameter errors.

关键词： Maximum likelihood detection Magnetic separation elec.romagnetic interference Sensorless control Detectors Nonlinear filters Drives Harmonic analysis Voltage control Power harmonic filters

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Towards on-device face recognition in body-worn cameras

arXiv

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arXiv 2021年

作者： Almadan, Ali Rattani, Ajita Dept. of Elec. Eng. and Computer Science Wichita State University Wichita United States

Face recognition technology related to recognizing identities is widely adopted in intelligence gathering, law enforcement, surveillance, and consumer applications. Recently, this technology has been ported to smartphones and body-worn cameras (BWC). Face recognition technology in body-worn cameras is used for surveillance, situational awareness, and keeping the officer safe. Only a handful of academic studies exist in face recognition using the body-worn camera. A recent study has assembled BWCFace facial image dataset acquired using a body-worn camera and evaluated the ResNet-50 model for face identification. However, for real-time inference in resource constraint body-worn cameras and privacy concerns involving facial images, on-device face recognition is required. To this end, this study evaluates lightweight MobileNet-V2, EfficientNetB0, LightCNN-9 and LightCNN-29 models for face identification using body-worn camera. Experiments are performed on a publicly available BWCface dataset. The real-time inference is evaluated on three mobile devices. The comparative analysis is done with heavy-weight VGG-16 and ResNet-50 models along with six hand-crafted features to evaluate the trade-off between the performance and model size. Experimental results suggest the difference in maximum rank-1 accuracy of lightweight LightCNN-29 over best-performing ResNet-50 is 1.85% and the reduction in model parameters is 23.49M. Most of the deep models obtained similar performances at rank-5 and rank-10. The inference time of LightCNNs is 2.1x faster than other models on mobile devices. The least performance difference of 14% is noted between LightCNN-29 and Local Phase Quantization (LPQ) descriptor at rank-1. In most of the experimental settings, lightweight LightCNN models offered the best trade-off between accuracy and the model size in comparison to most of the models. © 2021, CC BY.

关键词： Cameras

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Spatial Reasoning via Deep Vision Models for Robotic Sequential Manipulation

Spatial Reasoning via Deep Vision Models for Robotic Sequent...

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

作者： Hongyou Zhou Ingmar Schubert Marc Toussaint Ozgur S. Oguz Technical University of Berlin Science of Intelligence Cluster of Excellence TUB Computer Eng. Dept. Bilkent University

In this paper, we propose using deep neural architectures (i.e., vision transformers and ResNet) as heuristics for sequential decision-making in robotic manipulation problems. This formulation enables predicting the subset of objects that are relevant for completing a task. Such problems are often addressed by task and motion planning (TAMP) formulations combining symbolic reasoning and continuous motion planning. In essence, the action-object relationships are resolved for discrete, symbolic decisions that are used to solve manipulation motions (e.g., via nonlinear trajectory optimization). However, solving long-horizon tasks requires consideration of all possible action-object combinations which limits the scalability of TAMP approaches. To overcome this combinatorial complexity, we introduce a visual perception module integrated with a TAMP-solver. Given a task and an initial image of the scene, the learned model outputs the relevancy of objects to accomplish the task. By incorporating the predictions of the model into a TAMP formulation as a heuristic, the size of the search space is significantly reduced. Results show that our framework finds feasible solutions more efficiently when compared to a state-of-the-art TAMP solver.

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Spatial and Temporal Attention-Based Emotion Estimation on HRI-AVC Dataset

Spatial and Temporal Attention-Based Emotion Estimation on H...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Karthik Subramanian Saurav Singh Justin Namba Jamison Heard Christopher Kanan Ferat Sahin Dept. Elect. & Microelectronic Eng. Rochester Institute of Technology Rochester USA Dept. Computer Science Rochester Institute of Technology Rochester USA Dept. Computer Science University of Rochester Rochester USA

Many attempts have been made at estimating discrete emotions (calmness, anxiety, boredom, surprise, anger) and continuous emotional measures commonly used in psychology, namely ‘valence’ (The pleasantness of the emotion being displayed) and ‘arousal’ (The intensity of the emotion being displayed). Existing methods to estimate arousal and valence rely on learning from data sets, where an expert annotator labels every image frame. Access to an expert annotator is not always possible, and the annotation can also be tedious. Hence it is more practical to obtain self-reported arousal and valence values directly from the human in a real-time Human-Robot collaborative setting. Hence this paper provides an emotion data set (HRI-AVC) obtained while conducting a human-robot interaction (HRI) task. The self-reported pair of labels in this data set is associated with a set of image frames. This paper also proposes a spatial and temporal attention-based network to estimate arousal and valence from this set of image frames. The results show that an attention-based network can estimate valence and arousal on the HRI-AVC data set even when Arousal and Valence values are unavailable per frame.

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Enhancing SMS Spam Detection Using Deep Learning Models: A Comparative Study

Enhancing SMS Spam Detection Using Deep Learning Models: A C...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Shailendra Singh Sikarwar R. Arivukkodi Dhanya Krishnane Himanshu Sharma Avinash Namdeo Kaushiki Jadon Dept. of Computer Science Jiwaji University Gwalior India Dept. of Computer Applications Meenakshi A.H. Educ. & Research Chennai India Dept. of CSE Dhanalakshmi Srinivasan Coimbatore India Dept. of Computer Eng. & Applications Mangalayatan University Aligarh India Dept. of Mechanical Engineering Lingayas Vidyapeeth University Faridabad India Dept. of Computer Science J.S. University Shikohabad India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The rise in Machine Learning algorithms for identifying SMS spam has become popular due to a significant surge in unwanted text messages. Detecting SMS spam holds crucial importance for several reasons. Firstly, it can inundate mobile phone users’ message inboxes with irrelevant and unwanted messages, causing frustration and irritation. Secondly, SMS spam serves as a conduit for phishing scams, where scammers exploit fraudulent SMS messages to deceive users into divulging personal information or downloading malicious software. These scams can result in financial losses, identity theft, or other forms of fraud. Lastly, SMS spam can propagate malware or viruses, potentially compromising the security or functionality of the user’s device.

关键词： Machine learning algorithms Phishing Identity theft Filtering algorithms Message services Mobile handsets Security Floods Surges Systematic literature review

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Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification

arXiv

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arXiv 2024年

作者： Figueiredo, Eduardo Patane, Andrea Lahijanian, Morteza Laurenti, Luca Delft Center for Systems and Control TU Delft Netherlands Dept. of Aerospace Eng. Sciences and Computer Science CU Boulder United States School of Computer Science and Statistics Trinity College Dublin Ireland

Uncertainty propagation in non-linear dynamical systems has become a key problem in various fields including control theory and machine learning. In this work, we focus on discrete-time non-linear stochastic dynamical systems. We present a novel approach to approximate the distribution of the system over a given finite time horizon with a mixture of distributions. The key novelty of our approach is that it not only provides tractable approximations for the distribution of a nonlinear stochastic system but also comes with formal guarantees of correctness. In particular, we consider the Total Variation (TV) distance to quantify the distance between two distributions and derive an upper bound on the TV between the distribution of the original system and the approximating mixture distribution derived from our framework. We show that in various cases of interest, including in the case of Gaussian noise, the resulting bound can be efficiently computed in closed form. This allows us to quantify the correctness of the approximation and to optimize the parameters of the resulting mixture distribution to minimize such distance. The effectiveness of our approach is illustrated on several benchmarks from the control community. © 2024, CC BY.

关键词： Stochastic models

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Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals

Mitigating Attacks on Artificial Intelligence-based Spectrum...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Catak, Ferhat Ozgur Kuzlu, Murat Sarp, Salih Catak, Evren Cali, Umit University of Stavanger Dept. of Electrical Eng. & Computer Science Rogaland Norway Old Dominion University Dept. of Engineering Technology NorfolkVA United States Virginia Commonwealth University Dept. of Electrical and Computer Engineering RichmondVA United States Independent Researcher Stavanger Norway Norwegian University of Science and Technology Dept. of Electric Power Engineering Trondheim Norway

ISBN: (纸本)9781665459754

Cellular networks (LTE, 5G, and beyond) are dramatically growing with high demand from consumers and more promising than the other wireless networks with advanced telec.mmunication technologies. The main goal of these networks is to connect billions of devices, systems, and users with high-speed data transmission, high cell capacity, and low latency, as well as to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, advanced manufacturing, and many more. To achieve these goals, spectrum sensing has been paid more attention, along with new approaches using artificial intelligence (AI) methods for spectrum management in cellular networks. This paper provides a vulnerability analysis of spectrum sensing approaches using AI-based semantic segmentation models for identifying cellular network signals under adversarial attacks with and without defensive distillation methods. The results showed that mitigation methods can significantly reduce the vulnerabilities of AI-based spectrum sensing models against adversarial attacks. © 2022 IEEE.

关键词： Wireless networks

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AR-Classroom: Integrating Conversational Artificial Intelligence with Augmented Reality Technology for Learning Spatial Transformations and Their Matrix Representation 54

AR-Classroom: Integrating Conversational Artificial Intellig...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Monjoree, Uttamasha Aguilar, Samantha D. Qian, Cheng.uan Van Huyck, Carl Yeh, Shu-Hao Tranbarger, Preston Duane-Tessier, Luke Solitare-Renaldo, Leo Burte, Heather Yasskin, Philip Liew, Jeffrey Song, Dezhen Quek, Francis Yan, Wei Texas A&m University Dept. Architecture College Station United States Texas A&m University Dept. Educational Psychology College Station United States Texas A&m University Dept. Computer Sci. & Eng. College Station United States Texas A&m University Dept. Visualization College Station United States Texas A&m University Dept. Mathematics College Station United States Haverford College Haverford United States Carnegie Mellon University Dept. Computer Science Pittsburgh United States Texas A&m University Dept. Teaching Learning & Culture College Station United States

ISBN: (纸本)9798350351507

This research full paper describes the AR-Classroom application that utilizes augmented reality (AR) and physical and virtual manipulatives to enable undergraduate students to build intuition about the relation between spatial transformations and their mathematical representations. To further build on the app's usability and functionality, additional features are being prototyped to continue improving the user-app interaction with the AR-Classroom. Some of the challeng.s the students faced when using AR-Classroom were recalling basic matrix operations without geometric context, basic trigonometric functions and their applications in the two-dimensional space, loss of AR registration for not understanding the AR environment, and User Interface (UI) issues. To address these issues, a conversational Artificial Intelligence (AI)-based multi-sensory and interactive assistance has been added to the AR-Classroom. Integrating sophisticated language processing and response generation of AI with immersive three-dimensional capabilities of AR can create a more eng.ging learning experience than the previous versions of the app. This integration focuses on creating a symbiosis between AR and AI. It creates an elevated user experience by offering real-time, personalized assistance to students dealing with issues related to understanding mathematical concepts and functionalities of the app. A qualitative exploratory usability study was done to assess the user's interaction with the AI implemented in the AR-Classroom, aiming to explore the AI's ability to guide students in using AR technology and aid in introductory matrix algebra learning, to effectively serve the students' learning. Based on the thematic analysis of the user experiment we found four main themes related to users' perceptions of AR-Classroom AI features usability: (1) AI chatbot ease-of-use, (2) Need for answer elaboration from AI, (3) Desire for visual information, and (4) Increased understanding of the content ar

关键词： Students

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