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2024 SIGGRAPH Asia 2024 Courses, SA Courses 2024

作者： Gobbetti, Enrico Pintore, Giovanni Agus, Marco National Research Center in High-Performance Computing Big Data and Quantum Computing Cagliari Italy Visual and Data-intensive Computing Center for Advanced Studies Research and Development in Sardinia Cagliari Italy College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9798400711350

Surround-view panoramic imaging delivers extensive spatial coverage and is widely supported by professional and commodity capture devices. Research on inferring and exploring 3D indoor models from 360° images has recently flourished, resulting in highly effective solutions. Nevertheless, challenges persist due to the complexity and variability of indoor environments and issues with noisy and incomplete data. This course provides an up-to-date integrative view of the field. After introducing a characterization of input sources, we define the structure of output models, the priors exploited to bridge the gap between imperfect input and desired output, and the main characteristics of geometry reasoning and data-driven approaches. We then identify and discuss the main sub-problems in indoor reconstruction from panoramas and review and analyze state-of-the-art solutions for indoor capture, room modeling, integrated model computation, visual representation generation, and immersive exploration. Relevant examples of implemented pipelines are described, focusing on deep-learning solutions. We finally point out relevant research issues and analyze research trends. © 2024 Copyright held by the owner/author(s).

关键词： 3D reconstruction

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PVDM-YOLOv8l: a solution for reliable pedestrian and vehicle detection in autonomous vehicles under adverse weather conditions

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Multimedia Tools and Applications 2024年 1-26页

作者： Tahir, Noor Ul Ain Zhang, Zuping Asim, Muhammad Iftikhar, Sundas A. Abd El-Latif, Ahmed School of Computer Science and Engineering Central South University Changsha410083 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Gianforte School of Computing Montana State University Bozeman59717 United States Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Ensuring the safe navigation of autonomous vehicles in intelligent transportation system depends on their ability to detect pedestrians and vehicles. While transformer-based models for object detection have shown remarkable advancements, accurately identifying pedestrians and vehicles in adverse weather conditions remains a challenging task. Adverse weather introduces image quality degradation, leading to issues such as low contrast, reduced visibility, blurred edges, false detection, misdetection of tiny objects, and other impediments that further complicate the accuracy of detection. This paper introduces a novel Pedestrian and Vehicle Detection Model under adverse weather conditions, denoted as PVDM-YOLOv8l. In our proposed model, we first incorporate the Swin-Transformer method, which is designed for global extraction of feature of small objects to identify in poor visibility, into the YOLOv8l backbone structure. To enhance detection accuracy and address the impact of inaccurate features on recognition performance, CBAM is integrated between the neck and head networks of YOLOv8l, aiming to gather crucial information and obtain essential data. Finally, we adopted the loss function Wise-IOU v3. This function was implemented to mitigate the adverse effects of low-quality instances by minimizing negative gradients. Additionally, we enhanced and augmented the DAWN dataset and created a custom dataset, named DAWN2024, to cater to the specific requirements of our study. To verify the superiority of PVDM-YOLOV8l, its performance was compared against several commonly used object detectors, including YOLOv3, YOLOv3-tiny, YOLOv3-spp, YOLOv5, YOLOv6, and all the versions of YOLOv8 (n, m, s, l, and x) and some traditional models. The experimental results demonstrate that our proposed model achieved a 6.6%, 5.4%, 6%, and 5.1% improvement in precision, recall, F1-score and mean Average Precision (mAP) on the custom DAWN2024 dataset. This substantial improvement in accuracy ind

关键词： Vehicle detection

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Boosting Meaningful Dependency Mining with Clustering and Covariance Analysis 40

Boosting Meaningful Dependency Mining with Clustering and Co...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Wang, Xi Jin, Ruochun Huang, Wanrong Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology Department of Intelligent Data Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350317152

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDM?. In order to scale with large datasets, FDM ? employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ? is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost. © 2024 IEEE.

关键词： Large datasets

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Sparse Channel Estimation in Massive MIMO Systems Using Compressed Sensing and Neural Networks

Sparse Channel Estimation in Massive MIMO Systems Using Comp...

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

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this paper, based on compressed sensing and neural networks, we present an intelligent sparse channel estimation technique for Massive MIMO systems. We show that our approach improves considerably the estimation accuracy in terms of Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) with respect to classical methods. We demonstrate [Figure: ] that our approach is computationally more efficient, requires less time and resources (memory/disk) compared to the existing literature but for a robustness of performance across distal or low Signal-to-Noise Ratios (SNRs). We also confirm our method exhibits fast convergence and efficiently accommodates a variety of sparsity levels. By visualizing it using heatmaps and ROC curves we show that this method is able to effectively differentiate between signal and noise, asserting our approach as a powerful tool for the contemporary set of problems present in wireless communications. © 2024 IEEE.

关键词： Signal to noise ratio

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A Detailed Analysis of Secrecy and Performance Metrics in Hybrid RF/ FSO Communication Systems: Implications of Composite Weibull and Log-normal Fading with M Turbulent Channels

A Detailed Analysis of Secrecy and Performance Metrics in Hy...

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

ISBN: (纸本)9798350379525

In this paper, we have analyzed the secrecy performance and system operation performance for hybrid RF/FSO systems against composite Weibull-log-normal fading channel with M turbulent channels. The performance is evaluated by carrying out extensive simulations to calculate Signal-to-Noise Ratio (SNR), Bit Error Rate (BER) and outage probability, we also re-evaluate channel capacity and secrecy capacity with respect of environmental conditions. Compared with RF only system, FSO only standalone scheme and some meaningful orthogonal-modulation multicarrier IM/DD-based schemes proposed by the literatures. Similarly, the utilization of adaptive modulation schemes improved resistance to fading and roughness in systems. The above results confirm the hybridization of both RF and FSO a comprehensive solution for dependable communication in different and even hostile surrounding conditions which set this work as reliable foundation point towards forthcoming research activity or application relevance within all range telecommunication sector. © 2024 IEEE.

关键词： Bit error rate

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Transfer Learning Approaches for Channel State Prediction in Heterogeneous Fading Networks

Transfer Learning Approaches for Channel State Prediction in...

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

ISBN: (纸本)9798350379525

In this work, we leverage state-of-the-art machine learning algorithms to predict Channel State Information (CSI), for enhancing performance of wireless communication systems. The simulation and analysis stop with transfer learning to finetune, adaptive domain adaptations, multitasking (MTL) as well a few shot meta-learning models framework under different network packet loss rate scenarios. By generalizing better from one domain to another, transfer learning pushes the performance beyond what traditional methods can obtain. Further, fine-tuning also helps to align the source and target domain distributions in feature space by a partial (or full) reduction of re-stylization error. This work proposes multi-Task learning as a tool to mitigate loses in light of predicting across fading scenarios. Considering multi-task learning also provides benefits from shared learnings, so experience is pooled between the tasks which indirectly improve prediction as well and it helps in training both of these algorithms together. Moreover, the few-shot learning models prove to be highly desirable as they deliver state-of-the-art performance with minimal training data for difficult problems. These results show the potential of AI-based methods to improve CSI prediction, and have implications for improving reliability and throughput when deploying real wireless communication systems. © 2024 IEEE.

关键词： Contrastive Learning

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Efficient CNN-based disaster events classification using UAV-aided images for emergency response application

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Neural computing and Applications 2024年第18期36卷 10599-10612页

作者： Bashir, Munzir Hubiba Ahmad, Musheer Rizvi, Danish Raza El-Latif, Ahmed A. Abd Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Kom32511 Egypt

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human resource management

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Stock Market Prediction Using Machine Learning Based Techniques

Stock Market Prediction Using Machine Learning Based Techniq...

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

作者： Dubey, Aditya Sharma, Ramnaresh Bisen, Dhananjay Venu, Nookala Jaiswal, Varshali Raghuwanshi, Bhagat Singh Shakywar, Khemchand Madhav Institute of Technology & Science Centre for Internet of Things Gwalior India Madhav Institute of Technology & Science Centre for AI Gwalior India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India School of Engineering Avantika University MP Ujjain India

ISBN: (纸本)9798350379525

Many methods have been used to forecast stock market trends in the big data age, including real numbers, fuzzy time series data, fuzzy sets design, and conventional time series data. The control of semantic value data is made possible by the use of fuzzy time series data in stock market prediction, which makes accurate value predictions possible. These techniques are mostly used to forecast dynamic, non-linear datasets in a variety of industries, such as stock markets and cryptocurrencies. In order to predict stock prices, artificial intelligence (AI) methods such as neural networks (NN) have become increasingly important. Many methods have been developed to validate parametric and non-parametric theories for predicting trends and gains in the stock market using the feed-forward (FF) NN technique. A variety of soft computing techniques have been investigated in order to provide ways for decision-making based on profit and loss criteria without relying on artificial intelligence. This paper's main goal is to perform a comprehensive examination of the different prediction techniques used to estimate price and return in the stock market. The study includes a comparative review of several approaches as well as a variety of stock market forecasting strategies. © 2024 IEEE.

关键词： Marketplaces

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Generative Image Steganography Based on Latent Space Vector Coding and Diffusion Model 1

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2nd International Conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Liu, Weisong Yang, Jianfeng Huang, Jiewen El-Latif, Ahmed A. Abd Zhou, Zhili School of Artificial Intelligence Guangzhou University Guangzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

Image steganography is a technology that embed secret information within a cover image to obtain a stego-image for covert communication. The transmission of undetectable stego-images via social media can facilitate secure and efficient covert communication. Recently, the generative image steganography has achieved promising performance with the rapid development of generative models. However, existing generative image steganography suffers from issues, i.e., the low quality of the generated stego-image and the limited hiding capacity. To address these issues, this paper proposes a generative image steganography scheme based on latent space vector coding and diffusion model. It consists of an optimized diffusion denoising network, the latent space vector coding mapping network, and the information extraction network. In this scheme, the secret information is encoded as a latent space vector, which is then transformed in the input diffusion model to generate the stego-image. Experimental results demonstrate that this scheme achieves superior performance compared with existing generative image steganography methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Vectors

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Exploring the Impact of IoT data Diversity on Machine Learning Model 5

Exploring the Impact of IoT Data Diversity on Machine Learni...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Bisen, Dhananjay Venu, Nookala Dubey, Aditya Garg, Priyanka Salendra, Suresh Devi, T. Lakshmi Madhav Institute of Technology & Science Center for Internet of Things M.P Gwalior474 005 India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing M.P Gwalior474 015 India Balaji Institute of Technology & Science Dept. of Compter Science and Engineering Telangana Warangal506 331 India Siddhartha Institute of Engineering and Technology Dept.of CSE-Data Science Hyderabad501506 India

ISBN: (纸本)9798331530013

The past few years have witnessed a remarkable change by the rapid expansion of the Internet of Things (IoT). The increasing spread of networked devices in various areas of the Internet of Things has resulted in a huge amount of machine data. This data contains rich information that can be harnessed across domains such as healthcare, agriculture, smart cities, and manufacturing. To effectively analyze and capitalize this information, the integration of machine learning (ML) methods is essential. Thus, well-known machine learning techniques including Logistic Regression, Decision Tree, KNN, SVM, Random Forest, and Naive Bayes are used in paper. The performance of the algorithms is analyzed using three diverse IoT datasets and compared to one another using pre-defined measures such as accuracy, precision, recall, F1-score and execution time. Furthermore, test findings shown that the random forest algorithm consistently outperformed other algorithms for a variety of datasets with up to 98% accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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