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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

引用

2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Alehdaghi, Mahdi Shamsolmoali, Pourya Cruz, Rafael M. O. Granger, Eric Livia Ills Dept. of Systems Engineering Ets Montreal Canada University of York Dept. of Computer Science United Kingdom

ISBN: (纸本)9798331510831

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments11Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance. © 2025 IEEE.

关键词： Multi-task learning

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Dual Auction Peer-to-Peer Control Scheme for Empowering Prosumers Participation in the Local Energy Markets 4

Dual Auction Peer-to-Peer Control Scheme for Empowering Pros...

引用

4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Ikram, Muhammad Ahmed, Salman Khan Marwat, Safdar Nawaz University of Engineering and Technology Dept. of Computer Systems Engineering Peshawar Pakistan

ISBN: (纸本)9798350335316

The emerging trend of distributed and renewable energy sources encourages energy trading in the electricity market. Various forms of energy markets are evolving for successful energy trading. The local energy market is one of them in which various participants are actively coordinating for optimal utilization of power. Such type of local energy markets provides individual prosumers to trade their surplus energy in the neighbourhood in peer-to-peer topology. The complexity of such type of local energy market is increasing in terms of structure, control, coordination, and cost estimation. For the cooperative behaviour of the local energy markets, a dual auction coordination control scheme is important to encourage more prosumers for energy trading. We proposed a novel scheme that performs dual auctions for prosumers in a decentralized fashion. We assessed dual auction matching by coordinating prosumers and consumers to sell and purchase energy in the local energy market. The aggregator performed balancing in the proposed dual auction scheme as an autonomous entity. The results were simulated in MATLAB and JADE for the complex prosumer peer-to-peer network. Our proposed scheme shows each peer dual auction matching improves from 0.95 to 2.03 for optimal hierarchal energy trading in energy markets. For peer participation, the participation ratio increased from 0.684 to 1.74 and optimal aggregator coordination from 9.18% to 19% in the local energy markets. © 2023 IEEE.

关键词： Smart power grids

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LightNN: An Optimized Deep Learning Framework for Enhanced Fruit Classification

LightNN: An Optimized Deep Learning Framework for Enhanced F...

引用

2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Afroj, Maharin Jahan, Israt Islam, Md Polash Saifur Rahman, Md. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Stockholm University Dept. of Computer and Systems Sciences Stockholm Sweden

ISBN: (纸本)9798331529765

The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and sorting of various fruit types, these technologies enhance efficiency, accuracy, and scalability, benefiting processes from farm production to market distribution. The integration of sophisticated algorithms and neural networks not only improves the speed and reliability of fruit classification but also offers the potential for real-time quality control and inventory management, revolutionizing traditional methods and paving the way for more innovative, data-driven approaches in these sectors. This study introduces LightNN, a novel and effective paradigm with a lightweight architecture that tackles the complex problems related to computational efficiency. Combining a small classifier with a simplified feature extractor, LightNN achieves the ideal balance between simplicity and accuracy. The feature extractor is capable of extracting hierarchical features from RGB images by using convolutional layers with ReLU activations and max-pooling procedures. The flattened output is then processed by the classifier, which consists of fully connected layers with ReLU activations and dropout regularisation. Remarkably, LightNN, with just 298,835 parameters, manages to get a respectable 96.37% test accuracy in fruit categorization tasks. The results highlight LightNN's promise as an effective and efficient fruit classifying method, particularly in situations with constrained computational resources. © 2024 IEEE.

关键词： Fruits

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Augmented Karuta: Interactive Playing Cards on the Floor for Learning Local Culture 7

Augmented Karuta: Interactive Playing Cards on the Floor for...

引用

7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Umezu, Nobuyuki Bando, Hideki Ibaraki University Dept. of Computer and Information Sciences Hitachi Japan Ibaraki University Dept. of Mechanical Systems Engineering Hitachi Japan

ISBN: (纸本)9798331521578

This paper introduces Augmented Karuta, an interactive floor projection experience inspired by traditional Japanese playing cards, known as 'Karuta'. Designed to engage users and stimulate interest in local cultures, history, and traditions, the system utilizes three projectors. Two projectors project 46 'grabbing' cards onto the floor screen of 4.1 x 3.7 (m), while the third displays a corresponding 'reading' card on a wall screen. Each matching pair of cards shares a poem or sentence that begins with one of the Japanese phonograms. During a session, users actively engage by stepping onto the projected 'grabbing' card that matches the 'reading' card displayed on the wall, effectively blending physical movement with cognitive skills to find the correct match. By integrating a two-dimensional LiDAR sensor, the system tracks the user's position on the interactive floor. This tracking is vital for the proposed system to immediately respond to the user's actions, ensuring an immersive and seamless interaction with the digital karutas on the floor. We conducted evaluation experiments at two museums, gathering positive feedback from 57 participants. Future work will focus on quantitatively evaluating the system's impact on learning effectiveness. © 2025 IEEE.

关键词： Tropics

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

引用

2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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Enhancing Train Transportation in Sri Lanka: A Smart IOT Based Multi-Subsystem Approach Using MQTT

Enhancing Train Transportation in Sri Lanka: A Smart IOT Bas...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Bandara, Anuththara Arachchige, Gayantha Godakanda Gamage, Narmada Wijayaraja, Jaliya L. Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Sri Lanka

ISBN: (纸本)9798350350821

This research proposes a system as a solution for the challenges faced by Sri Lanka's historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study's data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka's railway infrastructure. © 2024 IEEE.

关键词： Global positioning system

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Explainable Long-Term Forecasting of Air Pollution

Explainable Long-Term Forecasting of Air Pollution

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Mutakabbir, Abdul Ajila, Samuel A. Carleton University Dept. of Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350324457

Long-term air pollution forecasting is essential for making public policies and issuing warnings. This will reduce the impact of pollution on the environment and human health. This research focuses on achieving long-term forecasting of air pollution attributes $(PM 2.5, PM_{10}, SO_{2}$, $NO_{2}, CO$, and $O_{3})$ by providing minimal historical data as input to the model. The best-performing models in this research produced between a 1% increase in RMSE for certain pollutants to a 50% decrease in RMSE for other air pollution attributes (except $CO)$ compared to the initial research on compositional learning, while being able to forecast the trend and seasonality accurately for more than one year into the future. The training time for each pollution attribute model dropped to 4 seconds compared to 1800 seconds in the case of the compositional learning method. This paper also delves into the challenges with long-term forecasting for the Beijing air quality dataset and discusses approaches to overcome these challenges. © 2023 IEEE.

关键词： Air Quality Index ARIMA Bagging Big Data Analytics Boosting Deep Learning Explainable AI Forecasting Machine Learning NeuralProphet Random Committee Random Forest Timeseries Forecasting

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Synergizing CXL with Unified Memory for Scalable GPU Memory Expansion

Synergizing CXL with Unified Memory for Scalable GPU Memory ...

引用

2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Lee, Junseung Kim, Jungrae Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Semiconductor Systems Engineering Suwon Korea Republic of

ISBN: (纸本)9798350371888

Heterogeneous computing, which combines the power of CPUs and GPUs, is increasingly important in today's data-centric era. However, it also presents significant challenges, particularly in terms of GPU memory management. Compute eXpress Link (CXL) technology, which is supported by industry leaders, offers enhanced memory expansion capabilities. Alongside this, NVIDIA's Unified Memory (UM) system simplifies the process of sharing data between CPUs and GPUs. We explore the role of CXL memory in various scenarios within the UM framework while simultaneously highlighting issues such as the handling of page faults at the software level. © 2024 IEEE.

关键词： Graphics processing unit

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Data Augmentation Framework for Improving Image Recognition Using cycleGAN 18

Data Augmentation Framework for Improving Image Recognition ...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kim, Sangmin Deribe, Selome Tesfaye Byun, Gyurin Joo, Kyeongjin Pyo, Jeongwon Kuc, Taeyong Choo, Hyunseung Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of AI Systems Engineering Suwon Korea Republic of

ISBN: (纸本)9798350331011

This paper proposes a novel framework that significantly enhances the performance of semantic segmentation models in recognizing specific objects. Leveraging the capabilities of Generative Adversarial Networks (GANs), particularly Cycle-GAN, this framework focuses on augmenting high-quality data to improve object recognition in autonomous driving and other applications. The study utilizes a dataset of 5,005 road images, enriched with polygon labels for precise object recognition. Key advancements in this research include the implementation of feature matching and fact forcing techniques to stabilize and integrate GAN performance, thereby overcoming common challenges like mode collapse, slow training, and overfitting. In the performance-enhanced GAN model, we improved the Discriminator Loss from the original 1.0517 to 0.0001, achieving convergence to zero 66.67% faster. © 2024 IEEE.

关键词： Semantic Segmentation

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Activity Recognition from Channel State Information for Few-Sampled Scenarios

Activity Recognition from Channel State Information for Few-...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Diaz, Guillermo Sobron, Iker Eizmendi, Iñaki Landa, Iratxe Velez, Manuel Dept. of Communications Engineering Bilbao Spain Dept. of Computer Languages and Systems Bilbao Spain

ISBN: (纸本)9798350370218

Human Activity Recognition (HAR) using channel state information (CSI) from wireless systems has been a field in progress for the last few years. In particular, amplitudes of WiFi-based CSI have been widely used for HAR in indoor scenarios. In contrast, CSI phases are less exploited due to higher sensitivity to timing errors in channel estimation. This paper further explores a recent phase processing method to obtain environmental information from CSI and improves classification using a Convolutional Neural Network in few-sampled scenarios. For this purpose, a new dataset has been generated with measurements of three receivers on different days and with different people, for activity recognition and people counting. In addition, we incorporate a transfer learning strategy using fine-tuning along with a data fusion model from different receivers to improve accuracy metrics by training the model and validating it on data taken on different days. Promising results with accuracies higher than 80 % have been obtained with transfer learning between days using 5 % of data for fine-tuning. © 2023 IEEE.

关键词： activity recognition CSI few-shot learning fine-tuning people counting phase

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