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Enhancing Micro-Expression Recognition with Broadbent Attention Mechanism: A High-Performance Approach for Emotion Detection 21

Enhancing Micro-Expression Recognition with Broadbent Attent...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ashraf, Imran Kehkashan, Tanzila Zaman, Muhammad Akhunzada, Adnan Talib, Hafiz Khizer Bin Cao, Yanlong University of Lahore Faculty of Computer Science Lahore Pakistan Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia COMSATS University Islamabad Department of Computer Science Islamabad Pakistan University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350378078

Micro-expression recognition (MER) is a critical task in affective computing, yet it remains challenging due to the subtle, brief, and involuntary nature of micro-expressions. Existing methods often find it difficult to spot these quick micro facial expressions (MFEs), especially when there are obstacles, background noise, or differences between people. In this study, we propose a novel approach that integrates the Broadbent Attention Mechanism into a Convolutional Neural Network (CNN) to enhance MER. This attention mechanism is designed to focus on the most informative regions of facial images, improving the model's ability to extract relevant features and discard irrelevant information. The model is evaluated on the SAMM dataset, covering various emotions. Extensive experiments are conducted using both K-fold and Stratified K-fold cross-validation techniques to ensure a robust evaluation process. The proposed method achieves high accuracy 99.72%, precision 99.73%, Recall 99.72% and f1-Score 99.72% with Stratified K-fold & accuracy 99.65%, precision 99.65%, Recall 99.66% and f'1-Score 99.65% with K-fold, outperforming baseline models and demonstrating its effectiveness in recognizing subtle micro-expressions. These results indicate that the Broadbent Attention Mechanism significantly enhances accuracy and robustness of MER systems, offering a promising solution for applications in human-computer interaction (HCI) and emotion analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparison of the SOLA model and ARIMA for Cloud computing Resources Allocations

Comparison of the SOLA model and ARIMA for Cloud Computing R...

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International Multidisciplinary Information Technology and Engineering Conference (IMITEC)

作者： Sello Prince Sekwatlakwatla Vusumuzi Malele Department of Computer Science and Information Systems Unit for Data Science and Computing North-West University South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

Cloud computing solutions are becoming more and more popular as a way for organizations to improve productivity, save costs, and simplify procedures. The advantage of cloud services is that they enable users to store data remotely and access on-demand applications and services from a shared pool of configurable computing resources. Cloud computing resource allocation faces challenges in load balancing, effective resource management, and compliance with regulatory and legal requirements, requiring complex tools and techniques for optimal use. Most organizations are moving to cloud services and encouraging their clients to access services online. However, effective resource allocation is critical for improving performance and lowering costs in this area. Due to unpredictable network traffic in cloud computing, resource allocation is challenging, which causes customers to complain about application timeouts, delayed system times, and higher bandwidth use during peak hours. This entails allocating resources to various users and programs, such as memory, processing power, storage, and network bandwidth. In this regard, this study compares the ensemble method, which is stepwise Gaussian Linear Autoregressive (SGLA) and the individual method, which is autoregressive integrated moving average (ARIMA). The results show SGLA prediction accuracy increased with an average of 97.9%, and ARIMA prediction showed an accuracy of 71.5%. In this regard the ensemble method performed better than individual methods using the same datasets. The study recommends the ensemble method for the prediction and allocation of resources in cloud computing.

关键词： Productivity Cloud computing Accuracy Costs Organizations Bandwidth Telecommunication traffic Resource management Ensemble learning Servers

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments 13

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kirubhakaran Karthikeswaran, D. Arun, M. Chakravarthy, R. Arun Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Surya Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A∗ algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry. © 2024 IEEE.

关键词： Motion planning

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Sustainable Learning of Computer Programming Languages Using Mind Mapping

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Intelligent Automation & Soft computing 2023年第5期36卷 1687-1697页

作者： Shahla Gul Muhammad Asif Zubair Nawaz Muhammad Haris Aziz Shahzada Khurram Muhammad Qaiser Saleem Elturabi Osman Ahmed Habib Muhammad Shafiq Osama E.Sheta Department of Computer Science National Textile University Faisalabad37610Pakistan Department of Data Science University of the PunjabLahore54590Pakistan Department of Mechanical Engineering University of SargodhaSargodha40100Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan College of Computer Science and Information Technology Al Baha UniversityAl Baha1988Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea College of Science Zagazig UniversityZagazig44511Egypt

In the current era of information technology,students need to learn modern programming languages efﬁ*** art of teaching/learning program-ming requires many logical and conceptual *** it’s a challenging task for the instructors/learners to teach/learn these programming languages effectively and efﬁ*** mapping is a useful visual tool for establishing ideas and connecting them to solve *** research proposed an effective way to teach programming languages through visual *** experimental study uses a mind mapping tool to teach two programming environments:Text-based Programming and Blocks-based *** performed the experiments with one hundred and sixty undergraduate students of two public sector universities in the Asia Paciﬁc *** different instructional approaches,including block-based language(BBL),text-based languages(TBL),mind map with text-based language(MMTBL)and mind mapping with block-based(MMBBL)are used for this *** results show that instructional approaches using a mind mapping tool to help students solve given tasks in their critical thinking are more effective than other instructional techniques.

关键词： Text programming blocks programming novice programmer

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Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation 22

Securing Consumer IoT Swarms Using Graph Transformers and SR...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Kohli, Varun Kohli, Bhavya Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln United States

ISBN: (纸本)9798331508050

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a ∼ 10-4 second inference latency on a laptop CPU. © 2025 IEEE.

关键词： Computer viruses

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Beyond Normal: Learning Spatial Density Models of Node Mobility

arXiv

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arXiv 2024年

作者： Gao, Wanxin Nikolaidis, Ioanis Harms, Janelle Faculty of Data Science City University of Macau China Department of Computing Science University of Alberta Edmonton Canada

Learning models of complex spatial density functions, representing the steady-state density of mobile nodes moving on a two-dimensional terrain, can assist in network design and optimization problems, e.g., by accelerating the computation of the density function during a parameter sweep. We address the question of applicability for off-the-shelf mixture density network models for the description of mobile node density over a disk. We propose the use of Möbius distributions to retain symmetric spatial relations, yet be flexible enough to capture changes as one radially traverses the disk. The mixture models for Möbius versus Gaussian distributions are compared and the benefits of choosing Möbius distributions become evident, yet we also observe that learning mixtures of Möbius distributions is a fragile process, when using current tools, compared to learning mixtures of Gaussians. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Tomato Disease Classification Using CNN 4th

Tomato Disease Classification Using CNN

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4th International Conference on Smart Systems: Innovations in computing, SSIC 2023

作者： Archanaa, N. Daniel, V. Vifert Jenuben Divya, S. Raja, Kalpana Oviya, I.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Sciences Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

ISBN: (纸本)9789819736898

Plant disease detection using various machine learning techniques is a very popular research area. Many promising results have been obtained, but only a few specific applications can still make farmers’ lives easier. This research focuses on the important task of identifying tomato diseases at an early stage to prevent their spread and protect crop yields more broadly, emphasizing the idea that disease prevention will more important than treatment. Leveraging the capabilities of convolutional neural networks (CNN), we developed a model that diagnoses eight distinct tomato diseases using only their images. Diseases listed include bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, and tomato yellow leaf curl virus. An integral part of our approach involves sophisticated image prepossessing and enhancement techniques, ensuring data quality and model robustness. Feature extraction plays a key role in transforming raw image data into recognizable patterns. The architecture of the CNN has been meticulously designed, consisting of multiple layers with a notable focus on the ReLU activation function and other relevant hyper-parameters. Our main dataset comes from PlantVillage, known for synthesizing rich and diverse plant images. After rigorous training and validation, the CNN model showed exceptional accuracy of 96.88% in tomato disease identification. However, challenges such as overfitting, image quality variability, and class imbalances were encountered but were resolved through strategic data augmentation, careful CNN overlay. In summary, our research presents a practical solution for farmers and agricultural entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fruits

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Leveraging Deep Learning for the Identification and Categorization of Fruit Diseases

Leveraging Deep Learning for the Identification and Categori...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Solanki, Shubhangi Singh Chouhan, Siddharth Dwivedi, Abhishek Singh, Uday Pratap Patel, Rajneesh Kumar SIT Lonavala SPPU Department of Computer Engineering 410401 India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Sehore Kothrikalan466114 India Manipal University Jaipur Department of Data Science and Engineering Rajasthan Jaipur303007 India Central University of Jammu Department of Mathematics 181143 India

ISBN: (纸本)9798350379525

The precise assessment of fruit quality and the detection of illnesses are critical tasks in the agriculture sector. One important component that has a direct impact on consumers' physical well-being and inclination to buy is the freshness of fruits and vegetables. Analyzing the degree of freshness in fruits and vegetables is important since it has a big impact on how market prices are set. Fruit and vegetable classification and identification by machine vision is difficult because of their comparable colors, textures, and external environmental elements including illumination, reflections, and complicated backdrops. This study presents a comprehensive overview of methods for detecting and classifying the quality of fruits and plants using several strategies for feature extraction and classification. Several academics have suggested various ways including binarization, histogram-based approaches, Gray Level Co-occurrence matrix, machine learning techniques, and deep learning. The outcome of our study showcases the results obtained from different methodologies, together with their respective benefits and challenges. In conclusion, we will discuss potential areas for improvement and future directions for additional research. © 2024 IEEE.

关键词： Fruits

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Securing P4-SDN data Plane against Flow Table Modification Attack

Securing P4-SDN Data Plane against Flow Table Modification A...

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IEEE Symposium on Network Operations and Management

作者： Buchammagari Avinash Reddy Kshira Sagar Sahoo Monowar Bhuyan Department of Data Science & Artificial Intelligence HIT Naya Raipur India Department of Computing Science Umeå University Umeå Sweden

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Security in Software Defined Network (SDN) architecture is becoming the most substantial challenge. This paper introduces a novel threat model focused on flow table modification in the P4-programmable SDN data plane, outlining an attacker’s stochastic manipulation of flow rules from a compromised switch. A detection framework is proposed to identify the malicious switch within the network by utilizing the thrift port. Moreover, a fuzzy-rule-based mitigation strategy has been proposed to identify the severity of attacks. The feasibility and effectiveness of the methodology are evaluated using a developed testbed setup by employing Facebook datacenter fabric topology in a Mininet emulator and BMv2 switch.

关键词： Threat modeling Social networking (online) Network topology Stochastic processes Switches Computer architecture Fabrics

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Facial Recognition for Humanitarian Efforts: A Deep Learning based Solution for Missing Person Identification

Facial Recognition for Humanitarian Efforts: A Deep Learning...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Dhruv Limbani Abhishek Barhate D Hemavathi Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Our work offers a novel method for identifying missing persons that makes use of recent developments in face recognition technology. Conventional techniques depend on converting images into embeddings and then figuring out how far apart picture embeddings are from one another by calculating distances between them, making it computationally demanding, particularly while dealing with huge databases. Our process involves first extracting faces from images using OpenCV’s face extractor module, and then using VGGFace to turn those faces into embeddings. However, we employ Principal Component Analysis (PCA) to further reduce their dimensionality to 128-dimensional vectors rather than directly comparing embeddings. On the basis of these reduced embeddings, a Dense Neural Network is then trained. With this method, the traditional O(n) time for classification is greatly reduced to O(1). Our tests show that our methodology outperforms numerous other methods and reaches an excellent accuracy of 98.75%. This simplified method gives encouraging outcomes in situations involving missing persons identification in addition to increasing efficiency.

关键词： Deep learning Adaptation models Accuracy Face recognition Computational modeling Predictive models Vectors

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