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High Dynamic Range Imaging for Dynamic Scenes Based on Multi-Level Spike Camera

IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5394-5406页

作者： Zhu, Zhenkun Xiong, Ruiqin Zhao, Jing Zhao, Rui Fan, Xiaopeng Zhu, Shuyuan Huang, Tiejun Peking University School of Computer Science Beijing100871 China Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

Spike camera is a retina-inspired neuromorphic camera which can capture dynamic scenes of high-speed motion by firing a continuous stream of spikes at an extremely high temporal resolution. The limitation in the current design is that each spike only represents the arrival of a fixed amount of photons. It can not deal with strong light areas in which the amount of accumulated photons reaches the pre-specified threshold multiple times within a single readout interval. In this paper, we propose a new spike camera model of high-speed imaging for high dynamic range scenarios. In this scheme, each pixel accumulates the incoming photons persistently and generates a new type of spike stream in which each spike symbol may be associated with different levels, indicating the arrival of different amounts of photons since the last readout. This enables the camera to support dynamic scenes with wider dynamic range. To achieve this, we propose a two-level buffer mechanism, one for photon accumulation and one for spike-firing encoding. We use a register to hold the number of spike-firings which has not been read out yet. At each readout time, the major part in the counter is read out via a carefully designed exponential encoding and the counter is updated. Such encoding and readout strategy enables a very efficient expansion of the dynamic range using a small number of encoding bits. Furthermore, we propose an image reconstruction scheme for the proposed camera, utilizing both spike intervals and spike levels to recover the light intensity. We incorporate Mamba and propose a temporal-spatial selective scan mechanism to extract temporal-spatial correlation within spike streams. We employ a pyramid adaptive filtering and alignment module to achieve coarse-to-fine feature alignment. Experimental results show that the proposed scheme can achieve better imaging quality and outperform the existing spike camera in high dynamic range scenarios. © 1991-2012 IEEE.

关键词： Adaptive filtering

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How graph convolutions amplify popularity bias for recommendation?

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Frontiers of computer science 2024年第5期18卷 121-132页

作者： Jiajia CHEN Jiancan WU Jiawei CHEN Xin XIN Yong LI Xiangnan HE School of Information Science and Technology University of Science and Technology of ChinaHefei 230026China School of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computer Science and Technology Shandong UniversityQingdao 250100China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

Graphconvolutional networks(GCNs)have become prevalent in recommender system(RS)due to their superiority in modeling collaborative *** improving the overall accuracy,GCNs unfortunately amplify popularity bias-tail items are less likely to be *** effect prevents the GCN-based RS from making precise and fair recommendations,decreasing the effectiveness of recommender systems in the long *** this paper,we investigate how graph convolutions amplify the popularity bias in *** theoretical analyses,we identify two fundamental factors:(1)with graph convolution(i.e.,neighborhood aggregation),popular items exert larger influence than tail items on neighbor users,making the users move towards popular items in the representation space;(2)after multiple times of graph convolution,popular items would affect more high-order neighbors and become more *** two points make popular items get closer to almost users and thus being recommended more *** rectify this,we propose to estimate the amplified effect of popular nodes on each node's representation,and intervene the effect after each graph ***,we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node,then remove the effect from the node embeddings at each graph convolution *** method is simple and generic-it can be used in the inference stage to correct existing models rather than training a new model from scratch,and can be applied to various GCN *** demonstrate our method on two representative GCN backbones LightGCN and UltraGCN,verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular *** are open-sourced^(1)).

关键词： recommendation graph convolution networks popularity bias

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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A Survey of Edge Caching:Key Issues and Challenges

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Tsinghua science and technology 2024年第3期29卷 818-842页

作者： Hanwen Li Mingtao Sun Fan Xia Xiaolong Xu Muhammad Bilal School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 262700China Reading Academy Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China Department of Computer Engineering Hankuk University of Foreign StudiesYongin-si 17035Republic of Korea

With the rapid development of mobile communication technology and intelligent applications,the quantity of mobile devices and data traffic in networks have been growing exponentially,which poses a great burden to networks and brings huge challenge to servicing user *** caching,which utilizes the storage and computation resources of the edge to bring resources closer to end users,is a promising way to relieve network burden and enhance user *** this paper,we aim to survey the edge caching techniques from a comprehensive and systematic *** first present an overview of edge caching,summarizing the three key issues regarding edge caching,i.e.,where,what,and how to cache,and then introducing several significant caching *** then carry out a detailed and in-depth elaboration on these three issues,which correspond to caching locations,caching objects,and caching strategies,*** particular,we innovate on the issue“what to cache”,interpreting it as the classification of the“caching objects”,which can be further classified into content cache,data cache,and service ***,we discuss several open issues and challenges of edge caching to inspire future investigations in this research area.

关键词： edge caching edge computing caching location caching object caching strategy 5G networkarchitecture Internet of Things(IoT)

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Detecting Fake Reviews in E-Commerce using Deep Learning Techniques 1

Detecting Fake Reviews in E-Commerce using Deep Learning Tec...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Abd, Maysam Jalal Hussein, Mohsin Hasan University of Kerbala College of Computer Science and Information Technology Department of Computer Science Karbala Iraq

ISBN: (纸本)9798331529246

Users may now create and use large amounts of data saved online thanks to e-commerce systems. Modern shoppers examine online reviews before making purchases. Evaluations are essential for both people and organizations. People and organizations need evaluations. Customers use this information to buy. Spammers produce fake reviews to mislead consumers about internet products and services. No system controls reviews, thus anybody may post anonymously. This causes fake reviews. Consumers need ways to differentiate fake reviews from real ones. Long Short-Term Memory (LSTM) network and Convolutional Neural Network (CNN) are used alongside Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) in this study. We use the Deceptive Opinion Spam Corpus and Yelp Labeled datasets to do this. Experimental results showed that the Deceptive Opinion Spam Corpus LSTM classifier beat earlier approaches by 1.14% to 3.49%. The Yelp-annotated dataset showed 44.83% maximum recall. © 2025 IEEE.

关键词： Convolutional neural networks

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Hybrid CatBoost and SVR Model for Earthquake Prediction Using the LANL Earthquake Dataset

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 93-110页

作者： Kaushal, Arush Gupta, Ashok Kumar Sehgal, Vivek Kumar Department of Computer Science and Information Technology Jaypee University of Information Technology Solan171234 India Department of Civil Engineering Jaypee University of Information Technology Solan171234 India

Earthquakes have the potential to cause catastrophic structural and economic damage. This research explores the application of machine learning for earthquake prediction using LANL (Los Alamos National Laboratory) dataset. The data, obtained from a laboratory stick-slip friction experiment, simulate real earthquakes through digitized acoustic signals recorded against the time to failure of a granular layer. We introduced a hybrid model combining CatBoost and Support Vector Regression (SVR) to predict the time of the next earthquake, evaluating its performance against individual CatBoost and SVR models. The hybrid model demonstrated superior accuracy with a Mean Absolute Error (MAE) of 0.0825, outperforming the individual models. We implemented feature engineering to optimize the predictive capability of the models. Additionally, we compared our hybrid model's performance with previous studies to validate its efficacy. Our findings underscore the potential of machine learning, particularly hybrid models, in enhancing earthquake prediction accuracy. This study highlights the robustness and effectiveness of the hybrid CatBoost-SVR model, paving the way for advanced AI algorithms in seismology and contributing to improved disaster preparedness and mitigation strategies. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Stick-slip

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Prediction of Heart Disease using an Ensemble Learning Approach

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International Journal of Advanced computer science and Applications 2023年第8期14卷 1089-1097页

作者： Alshehri, Ghalia A. Alharbi, Hajar M. Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The ability to predict diseases early is essential for improving healthcare quality and can assist patients in avoiding potentially dangerous health conditions before it is too late. Various machine learning techniques are used in the medical field. Nonetheless, machine learning is critical in determining the future of pharmaceuticals and patients’ health. This is because the various classification techniques provide a high level of accuracy. However, because so much data are being gathered from patients, it becomes harder to find meaningful cardiac disease predictions. A vital research task is to identify these characteristics. Individual classification algorithms in this situation cannot generate flawless models capable of reliably predicting heart disease. As a result, higher performance might be achieved by using ensemble learning approaches (ELA), producing accurate cardiac disease predictions. In the present research work, we utilized an ELA for the early prediction of heart disease, using a new combination including four machine learning algorithms—adaptive boosting, support vector machine, decision tree, and random forest—to increase the accuracy of the prediction results. We used two wrapper methods for feature selection: forward selection and backward elimination. We used the proposed model with three datasets: the StatLog UCI dataset, the Z-Alizadeh Sani dataset, and the Cardiovascular Disease (CVD) dataset. We obtained the highest accuracy when using our proposed model with the Z-Alizadeh Sani dataset, where it was 0.91, while the StatLog UCI dataset was 0.83. The CVD dataset obtained the lowest accuracy, 0.73. © (2023), (science and information Organization). All Rights Reserved.

关键词： Forecasting

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A Detailed Review of Current AI Solutions for Enhancing Security in Internet of Things Applications

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computers, Materials & Continua 2025年第6期83卷 3713-3752页

作者： Arshiya Sajid Ansari Ghadir Altuwaijri Fahad Alodhyani Moulay Ibrahim El-Khalil Ghembaza Shahabas Manakunnath Devasam Paramb Mohammad Sajid Mohammadi Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Engineering and Information TechnologyOnaizah CollegesQassim56447Saudi Arabia

IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication,processing,and real-time monitoring across diverse *** to their heterogeneous nature and constrained resources,as well as the growing trend of using smart gadgets,there are privacy and security issues that are not adequately managed by conventional *** review offers a thorough analysis of contemporary AI solutions designed to enhance security within IoT *** intersection of AI technologies,including ML,and blockchain,with IoT privacy and security is systematically examined,focusing on their efficacy in addressing core security *** methodology involves a detailed exploration of existing literature and research on AI-driven privacy-preserving security mechanisms in *** reviewed solutions are categorized based on their ability to tackle specific security *** review highlights key advancements,evaluates their practical applications,and identifies prevailing research gaps and *** findings indicate that AI solutions,particularly those leveraging ML and blockchain,offerpromising enhancements to IoT privacy and security by improving threat detection capabilities and ensuring data *** paper highlights how AI technologies might strengthen IoT privacy and security and offer suggestions for upcoming studies intended to address enduring problems and improve the robustness of IoT networks.

关键词： Security in IoT applications privacy-preserving blockchain AI-driven security mechanisms

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Optimizing photo-to-anime translation with prestyled paired datasets

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Multimedia Tools and Applications 2024年第41期83卷 89393-89414页

作者： Chang, Chuan-Wang Dharmawan, Pratamagusta Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan

Animation is a widespread artistic expression that holds a special place in people's hearts. Traditionally, animation creation has relied heavily on manual techniques, demanding skilled drawing abilities and a significant amount of time. For instance, many Japanese anime films draw inspiration from real-world settings, requiring access to relevant references and artists capable of translating them into anime visuals. Consequently, the development of technology that automatically converts images into anime holds great significance. Numerous methods for style transfer have been developed using unsupervised learning and have achieved impressive results. However, unsupervised learning methods suffer when the image contains multiple styles within itself because they learn the style of the image globally. To solve this problem, we propose splitting these styles within the image into multiple classes: sky, buildings, greenery, water, and other objects. Then, we style these separated classes using existing image-to-image translation models. Finally, we train a pix2pix model to learn image style transfer in a paired manner. The experimental results show that the images are effectively styled into the resulting anime-styled image domain with comparable results to existing unsupervised learning GAN-based methods. The proposed method can effectively transfer the style from real-world photos into the anime-styled image domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Unsupervised learning

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Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

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computer Modeling in Engineering & sciences 2024年第2期138卷 1427-1467页

作者： Mohammed Abdalsalam Chunlin Li Abdelghani Dahou Natalia Kryvinska School of Computer Science and Technology Wuhan University of TechnologyWuhan430070China LDDI Laboratory Faculty of Science and TechnologyUniversity of Ahmed DRAIAAdrar01000Algeria Information Systems Department Faculty of ManagementComenius UniversityBratislava82005Slovakia

One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

关键词： Artificial intelligence machine learning natural language processing data analytic DistilBERT feature extraction terrorism classification GTD dataset

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