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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Muhammad Kamil Hamzah NorakmarArbain Sulaiman Murizah Kassim Shuria Saaidin Suhaili Beeran Kutty School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor Malaysia

ISBN: (数字)9798350382310

ISBN: (纸本)9798350382327

The advancement of technology has brought about significant transformations in various domains, including home security systems. This study has developed a system that enhances the functionality and security of smart doors using Internet of Things (IoT) technology called IoT Smart Door System with Motion Sensing and Visual Facial Recognition. The smart door system integrates multiple access control mechanisms, including keypad entry and remote access via a mobile application called Blynk. Additionally, it incorporates a motion sensor that triggers a camera to capture images of individuals approaching the door. These images are sent as notifications to the owner’s smartphone, enabling them to visually verify the visitor’s identity and the system to inform the condition of the door access. The system enhances home security by providing real-time monitoring and control over door access. The prototype implementation of the system allows for evaluating critical parameters such as response time, security, scalability, and user experience. The results obtained from testing provide valuable insights into the strengths and limitations of the system, enabling further refinement and optimization. This method can transform home security systems, enhancing homeowners’ convenience and safety environments.

关键词： Access control Visualization Face recognition Authentication Speech recognition Transforms User experience

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Findings of shared task on Sentiment Analysis and Homophobia Detection of YouTube Comments in Code-Mixed Dravidian Languages 22

Findings of shared task on Sentiment Analysis and Homophobia...

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Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation

作者： Subalalitha Chinnaudayar Navaneethakrishnan Bharathi Raja Chakravarthi Kogilavani Shanmugavadivel Malliga Subramanian Prasanna Kumar Kumaresan Bharathi Lavanya Sambath Kumar Rahul Ponnusamy SRM Institute of Science and Technology India Insight Centre for Data Analytics National University of Galway Ireland Kongu Engineering College India Indian Institute of Information Technology and Management-Kerala India SSN College of Engineering India Techvantage Analytics India

ISBN: (纸本)9798400700231

We present an overview of sentiment analysis and homophobia detection of YouTube comments in code-mixed Dravidian languages in this paper. We provide the details of this task and the submitted systems for the tasks. We introduce two studies: task A for detecting sentiment analysis and task B on homophobia detection, which is organized by the FIRE 2022. A total of 95 participants registered for the shared task, 13 teams finally submitted their results for task-A a, and 10 teams submitted their results for task B. The teams explored tasks A and B using traditional machine learning and deep learning models. Most of the benchmark systems have been analyzed by participants capable of handling code-mixed scenarios in Dravidian languages.

关键词： datasets Homophobia Sentiment Evaluations

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Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning

arXiv

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arXiv 2023年

作者： Yan, Sen Fang, Hongyuan Li, Ji Ward, Tomas O’Connor, Noel Liu, Mingming The School of Electronic Engineering SFI Insight Centre for Data Analytics Dublin City University Dublin 9 Ireland The School of Computing SFI Insight Centre for Data Analytics Dublin City University Dublin 9 Ireland The Department of Mechanical Engineering University of Birmingham BirminghamB15 2TT United Kingdom

Battery Electric Vehicles (BEVs) are increasingly significant in modern cities due to their potential to reduce air pollution. Precise and real-time estimation of energy consumption for them is imperative for effective itinerary planning and optimizing vehicle systems, which can reduce driving range anxiety and decrease energy costs. As public awareness of data privacy increases, adopting approaches that safeguard data privacy in the context of BEV energy consumption modeling is crucial. Federated Learning (FL) is a promising solution mitigating the risk of exposing sensitive information to third parties by allowing local data to remain on devices and only sharing model updates with a central server. Our work investigates the potential of using FL methods, such as FedAvg, and FedPer, to improve BEV energy consumption prediction while maintaining user privacy. We conducted experiments using data from 10 BEVs under simulated real-world driving conditions. Our results demonstrate that the FedAvg-LSTM model achieved a reduction of up to 67.84% in the MAE value of the prediction results. Furthermore, we explored various real-world scenarios and discussed how FL methods can be employed in those cases. Our findings show that FL methods can effectively improve the performance of BEV energy consumption prediction while maintaining user privacy. © 2023, CC BY.

关键词： Learning systems

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Energy Efficient ECG Classifier Using Smart Lead Switching and Apache TVM

Energy Efficient ECG Classifier Using Smart Lead Switching a...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ahmad Ayad Mahdi Barhoush Benedikt Völker Steffen Leonhardt Anke Schmeink Chair of Information Theory and Data Analytics Chair or Medical Information Technology Helmholtz-Institute for Biomedical Engineering RWTH Aachen University Aachen Germany

The classification of Electrocardiograms (ECG) sig-nals is vital for diagnosing and monitoring cardiovascular diseases. Recently, there has been increasing interest in leveraging efficient machine learning models for ECG classification. Op-timizing computational resources in this domain is crucial for real-time analysis and reducing power consumption. This paper explores two approaches to enhance the efficiency of ECG classification on edge devices. Firstly, a smart lead-switching mechanism intelligently switches between a high-power model and a low- power model. Secondly, Apache Tensor Virtual Machine (TVM) is employed to optimize the implementation of the developed models on the Jetson Nano single-board computer. Experimental results demonstrate noticeable achievements, including up to 96% reduction in FLOPS and 95% reduction in inference time. Our results hold significant promise for improving ECG classification on edge devices, enabling early detection of abnormalities, and enhancing patient care.

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Capturing Soldier Fitness Levels in Combat Simulations 24

Capturing Soldier Fitness Levels in Combat Simulations

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Proceedings of the Winter Simulation Conference

作者： Vikram Mittal Paul Evangelista Dept. of Systems Engineering United States Military Academy West Point NY USA Office of Data and Analytics United States Military Academy West Point NY USA

ISBN: (纸本)9798331534202

Combat simulations typically depict individual soldiers as identical entities, neglecting differences in their ability to execute warfighting tasks. An essential aspect of a soldier's effectiveness is their physical fitness, which impacts their performance on the battlefield, including their mobility, survivability, and lethality. This paper develops two models to correlate soldier physical fitness to movement speeds and shooting accuracy. The first model, derived from literature, links soldier movement speeds and durations to their physical fitness levels, characterized by their VO2,max, which is correlated with their two-mile run time. The second model used data collected from soldiers (n=60) to correlate shooting accuracy, fitness, and marksmanship scores when firing from a standing position. These models are integrated into an agent-based combat simulation to assess the impact of physical fitness on soldier survivability and lethality in urban operations. The results show that increased soldier physical fitness significantly improves soldier survivability.

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Customized Movie Recommendation for Enhanced Entertainment

Customized Movie Recommendation for Enhanced Entertainment

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Cybernation and Computation (CYBERCOM), International Conference on

作者： M.S. Minu Srikar Talapalli Saahithi Chevuri Medhasri T.S. Department of Computer Science Engineering SRM Institute of Science And Technology Ramapuram Chennai India Department of Computer Science Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience.

关键词： Hands Filtering Collaborative filtering Transforms Machine learning Motion pictures User experience Libraries Hamming distances Recommender systems

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Neural Network (NN), Perturb and Observe (PO), and Hybrid NN-PO for MPPT Controller in PV System

Neural Network (NN), Perturb and Observe (PO), and Hybrid NN...

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IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Ahmad Saufi Bin Lokman Fikry Shuria Saaidin Norakmar Sulaiman Suhaili Beeran Kutty Murizah Kassim School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor Malaysia

ISBN: (数字)9798350372106

ISBN: (纸本)9798350372113

The need for renewable energy in power systems is growing exponentially. Several algorithms may be used to track the Maximum Power Point (MPP) quickly and precisely. This research provides a comparison and analysis of different control techniques for the maximum power point tracking (MPPT) of a photovoltaic system subject to varying irradiance and temperature by using three algorithms which are Perturb and Observe (PO), Artificial Neural Network (ANN), and Hybrid NN-PO. The three MPPT algorithms were created in a standalone photovoltaic system with a boost converter to maintain the maximum power point of the solar panel. Using MATLAB/SIMULINK software, the performance of these controllers is evaluated under varying irradiance and temperature conditions. Under the 100 (W/m2s) slope, PO's efficiency is the lowest, at 96.443% and the hybrid efficiency is nearly identical to the ANN algorithm at 99,996% and 99,997%, respectively. Based on the simulation that has been demonstrated, the Perturb and Observe (PO) algorithm exhibits the lowest performance in the simulation with time response. The Hybrid Neural Network and Neural Network algorithm performs better than PO. At the same time, hybrid efficiency is similar to the ANN algorithm.

关键词： Maximum power point trackers Software algorithms Artificial neural networks Power system stability Hybrid power systems Stability analysis Temperature control

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Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and Machine Learning

Intelligent E-Commerce Chatbot for Personalized Product Reco...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： M.S. Minu Adithya Kavya SreeRanjini Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system’s scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of AI-driven solutions in e-commerce.

关键词： Text mining Machine learning algorithms Scalability Systems architecture Machine learning Chatbots User experience Real-time systems Electronic commerce Time factors

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A CRITICAL ANALYSIS OF INTERNAL RELIABILITY FOR UNCERTAINTY QUANTIFICATION OF DENSE IMAGE MATCHING IN MULTI-VIEW STEREO

arXiv

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arXiv 2023年

作者： Huang, Debao Qin, Rongjun Geospatial Data Analytics Laboratory The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

Nowadays, photogrammetrically derived point clouds are widely used in many civilian applications due to their low cost and flexibility in acquisition. Typically, photogrammetric point clouds are assessed through reference data such as LiDAR point clouds. However, when reference data are not available, the assessment of photogrammetric point clouds may be challenging. Since these point clouds are algorithmically derived, their accuracies and precisions are highly varying with the camera networks, scene complexity, and dense image matching (DIM) algorithms, and there is no standard error metric to determine per-point errors. The theory of internal reliability of camera networks has been well studied through first-order error estimation of Bundle Adjustment (BA), which is used to understand the errors of 3D points assuming known measurement errors. However, the measurement errors of the DIM algorithms are intricate to an extent that every single point may have its error function determined by factors such as pixel intensity, texture entropy, and surface smoothness. Despite the complexity, there exist a few common metrics that may aid the process of estimating the posterior reliability of the derived points, especially in a multi-view stereo (MVS) setup when redundancies are present. In this paper, by using an aerial oblique photogrammetric block with LiDAR reference data, we analyze several internal matching metrics within a common MVS framework, including statistics in ray convergence, intersection angles, DIM energy, etc. We associate these metrics to the per-point errors evaluated through LiDAR reference data and discuss their potential contributions in estimating internal reliabilities of point clouds derived from DIM algorithms. The experimental results show that ray convergence and DIM energy are relevant indicators for the accuracy of the generated point clouds. Initial investigation shows that these two indicators could be further utilized to infer the measurem

关键词： Photogrammetry

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Distributed bilevel optimization with communication compression 24

Distributed bilevel optimization with communication compress...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yutong He Jie Hu Xinmeng Huang Songtao Lu Bin Wang Kun Yuan Peking University University of Pennsylvania IBM Research Zhejiang University Peking University and National Engineering Labratory for Big Data Analytics and Applications and AI for Science Institute Beijing China

Stochastic bilevel optimization tackles challenges involving nested optimization structures. Its fast-growing scale nowadays necessitates efficient distributed algorithms. In conventional distributed bilevel methods, each worker must transmit full-dimensional stochastic gradients to the server every iteration, leading to significant communication overhead and thus hindering efficiency and scalability. To resolve this issue, we introduce the first family of distributed bilevel algorithms with communication compression. The primary challenge in algorithmic development is mitigating bias in hypergradient estimation caused by the nested structure. We first propose C-SOBA, a simple yet effective approach with unbiased compression and provable linear speedup convergence. However, it relies on strong assumptions on bounded gradients. To address this limitation, we explore the use of moving average, error feedback, and multi-step compression in bilevel optimization, resulting in a series of advanced algorithms with relaxed assumptions and improved convergence properties. Numerical experiments show that our compressed bilevel algorithms can achieve 10× reduction in communication overhead without severe performance degradation.

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