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Time-series satellite remote sensing reveals gradually increasing war damage in the Gaza Strip

National Science Review 2024年第9期11卷 181-191页

作者： Shimaa Holail Tamer Saleh Xiongwu Xiao Jing Xiao Gui-Song Xia Zhenfeng Shao Mi Wang Jianya Gong Deren Li State Key Laboratoryof Information Engineering in Surveying Mapping and Remote SensingWuhan University Geomatics Engineering Department Faculty of Engineering at Shoubra Benha University School of Computer Science Wuhan University Collaborative Innovation Center of Geospatial Technology National Engineering Research Center for Multi-media Software Institute of Artificial IntelligenceWuhan University

War-related urban destruction is a significant global concern, impacting national security, social stability,people's survival and economic development. The effects of urban geomorphology and complex geological contexts during conflicts, characterized by different levels of structural damage, are not yet fully understood globally. Here we report how integrating deep learning with data from the independently developed Luo Jia3-01 satellite enables near real-time detection of explosions and assessment of different building damage levels in the Israel–Palestine conflict. We found that the damage continually increased from 17 October 2023 to 2 March 2024. We found 3747 missile craters with precision positions and sizes, and timing on vital infrastructure across five governorates in the Gaza Strip on 2 March 2024, providing accurate estimates of potential unexploded ordnance locations and assisting in demining and chemical decontamination. Our findings reveal a significant increase in damage to residential and educational structures, accounting for 58.4% of the total—15.4% destroyed, 18.7% severely damaged, 11.8% moderately damaged and 12.5% slightly damaged—which exacerbates the housing crisis and potential population displacement. Additionally, there is a 34.1% decline in the cultivated area of agricultural land, posing a risk to food security. The Luo Jia3-01 satellite data are crucial for impartial conflict monitoring, and our innovative methodology offers a cost-effective, scalable approach to assess future conflicts in various global *** first-time findings highlight the urgent need for an immediate ceasefire to prevent further damage and support the release of hostages and subsequent reconstruction efforts.

关键词： war damage monitoring satellite remote sensing missile crater detection type of damaged building building damage level detection Gaza Strip

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Deniable authenticated encryption for e-mail applications

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International Journal of computers and Applications 2020年第5期42卷 429-438页

作者： Jin, Chunhua Chen, Guanhua Yu, Changhui Zhao, Jianyang Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China

In this paper, we introduce a deniable electronic mail authenticated encryption service. Our design meets the security requirement of the current Pretty Good Privacy and Secure/Multipurpose Internet Mail Extensions to provide message confidentiality and message authentication without the undesired non-repudiation, which can protect the individual privacy of the sender when communication occurs in open channels. Our scheme is based on the bilinear pairings, which is provably secure in the random oracle model under the bilinear Diffie-Hellman and computational Diffie-Hellman assumptions. In addition, our scheme needs a little high communication overhead. However, this extra cost is reasonable for the properties of deniable authentication, confidentiality, and ciphertext anonymity. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Electronic mail

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Multimodal Data Fusion Based on Generative Adversarial Network in University Teaching Evaluation

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第2期10卷 845-858页

作者： Liu, Min Teng, Hua Zhang, Hong Li, Hao School of Computer Science and Software Engineering Southwest Petroleum University Nanchong637001 China Department of Computer China West Normal University Nanchong637009 China Faculty of Computer and Information Science SouthWest University Chongqing400700 China Krirk University Bangkok10220 Thailand

With the advancement of educational evaluation reform and the popularization of higher education, university teaching quality evaluation has been a research hotspot in the field of educational evaluation. In response to the issue of multimodal heterogene-ity gap in current university education evaluation data, which makes it tough to integrate evaluation data, this article suggests a Multimodal Data Fusion (MDF) method for university teaching evaluation relied on Generative Adversarial Network (GAN). Firstly, the MDF model is constructed from two aspects: establishing similarity network and designing regularization terms. By designing sparse regularization terms, the feature selection of multimodal samples is carried out to address the issue of large deviation of fusion data. Secondly, BERT pre-training model is adopted to extract the modal embedding features of teaching evaluation text, and Bi-LSTM is adopted to extract the audio and visual modal embedding features of teaching evaluation. Then, on the ground of the constructed MDF model, combined with the GAN and autoencoder, a multi-modal adversarial autoencoder learning model is established to reduce the heterogeneous gap between modes and form a unified representation. At last, the self-attention pretest network is established to achieve the automatic classification of multi-modal university teaching evaluation data. The experimental outcome implies that the accuracy, F1 value and determination coefficient R2 of the suggested method are 92.18%, 93.72% and 90.5%, respectively, which are superior to the comparison method, showing good classification performance. © 2025, J. Network Intell. All rights reserved.

关键词： Image fusion

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Depth3DSketch: Freehand Sketching Out of Arm’s Reach in Virtual Reality

Depth3DSketch: Freehand Sketching Out of Arm’s Reach in Vir...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Bashar, Mohammad Raihanul Amini, Mohammadreza Stuerzlinger, Wolfgang Sarac, Mine Pfeuffer, Ken Machuca, Mayra Donaji Barrera Batmaz, Anil Ufuk Department of Computer Science & Software Engineering Concordia University Montreal QC Canada School of Interactive Arts + Technology (SIAT) Simon Fraser University Vancouver BC Canada Kadir Has University Istanbul Turkey Department of Computer Science Aarhus University Aarhus Denmark Faculty of Computer Science Dalhousie University Halifax NS Canada

ISBN: (纸本)9798400713958

Due to the increasing availability and popularity of virtual reality (VR) systems, 3D sketching applications have also boomed. Most of these applications focus on peripersonal sketching, e.g., within arm’s reach. Yet, sketching in larger scenes requires users to walk around the virtual environment while sketching or to change the sketch scale repeatedly. This paper presents Depth3DSketch, a 3D sketching technique that allows users to sketch objects up to 2.5 m away with a freehand sketching technique. Users can select the sketching depth with three interaction methods: using the joystick on a single controller, the intersection from two controllers, or the intersection from the controller ray and the user’s gaze. We compared these interaction methods in a user study. Results show that users preferred the joystick to select visual depth, but there was no difference in user accuracy or sketching time between the three methods. © 2025 Copyright held by the owner/author(s).

关键词： 3D Sketching 3D User Interface Eye-Gaze Multimodal VR

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Unveiling Stress: A Comparative Analysis of Multimodal Sensor Fusion Techniques for Predictive Modeling

Unveiling Stress: A Comparative Analysis of Multimodal Senso...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Noha A. Saad Eldien Raghda Essam Ali Mustafa Ezzeldin Moamen Zaher Faculty of Computer Science October University for Modern Sciences and Arts (MSA) Giza Egypt Software Engineering Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Stress in professional environments is a significant concern. Medical professionals are particularly vulnerable to stress, especially during emergencies. Nurses hold a vital position in delivering care within hospital settings. It's crucial to anticipate stress levels among nurses to help them perform their duties effectively and avoid the long-term impacts of stress. This study seeks to explore how body sensors and machine learning techniques can be employed to monitor physiological signs and identify stress levels among nurses. It utilizes a benchmark dataset collected from 15 different nurses, including signals such as heart rate (HR), Electrodermal Activity (EDA), and Skin Temperature alongside location data extracted by an accelerometer. This study explores several fusion strategies, such as data, model, and prediction fusion levels to improve the accuracy and reliability of stress prediction models. Through a comparative analysis, this paper highlights the strengths and limitations of diverse fusion techniques. shedding light on their efficacy in capturing the nuanced features of stress. The findings offer valuable insights into the optimization of multimodal sensor fusion for enhanced stress prediction, creating pathways for reliable frameworks in the healthcare domain. This research conducted a comparative study between 3 different levels: data-fusion, model-fusion, and prediction-fusion. Prediction-level fusion outperformed both model-level fusion by 1.97% and data-level fusion by 1.26%.

关键词： Temperature sensors Heart rate Temperature measurement Temperature distribution Multimodal sensors Predictive models Data models

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A comparative review for question answering frameworks on the linked data

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Recent Advances in computer Science and Communications 2021年第6期14卷 1695-1705页

作者： Tasar, Ceren O. Komesli, Murat Unalir, Murat O. Department of Software Engineering Faculty of Engineering Yaşar University Izmir35100 Turkey Department of Management Information Systems School of Applied Sciences Yaşar University Izmir35100 Turkey Department of Computer Engineering Faculty of Engineering Ege University Izmir35100 Turkey

Background: One of the state-of-the-art techniques for question answering frameworks is using linked data by converting the user input into SPARQL which is the query language for linked data. Objective: The main target is to emphasize the most fundamental issues while developing a question answering frameworks that accept input in natural language and converting it into SPARQL. Methods: The trend of applying linked data as a data source is gaining popularity among the re-searchers. In this study, question answering frameworks that combine both natural language processing techniques and linked data technologies are examined. Common principles of examined question answering frameworks recognize user intention, enriching natural language input and converting it to a SPARQL query. Results: 9 studies are selected for further examination to be compared by using selection criteria de-fined in the research methodology. Conclusion: Resulting outcomes are represented and compared in detail. In addition to the comparative review of systems, a general architecture of question answering frameworks on the linked data is drawn as an outcome of this study to provide a guideline for the researchers who are studying related research fields. © 2021 Bentham Science Publishers.

关键词： Linked data

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CASolarNet: Channel Attention EfficientNet-based Model for Solar Panel Dust Detection

CASolarNet: Channel Attention EfficientNet-based Model for S...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Hussein M. A. Mohammed Abdulfattah E. Ba Alawi Energy and Cooling Systems Department Turk Telekom Ankara Turkey Mechatronics and Robotics Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen Computer Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The accumulation of dust on solar panels dramatically reduces the efficiency of solar energy systems, an issue that is often overlooked in both urban and rural settings. This work proposes a novel solution to this widespread problem of Solar Dust Detection. Utilizing EfficientNet, which is known for its balanced approach to model depth, width, and resolution, our proposed model accurately identifies dust on solar panels. The suggested model (CASolarNet) employs Channel Attention, EfficientNet, and focal loss to enhance the performance of detecting dust on solar cells. By dynamically modifying feature weights, channel attention techniques enhance dust detection by emphasizing the locations of dust in an image. This capability is crucial for maintaining optimal energy production and extending the lifespan of solar cells. Our model has been extensively tested and validated, resulting in a high accuracy rate of more than 98%. The obtained performance demonstrates the model's effectiveness in various environmental conditions, establishing a new standard in solar maintenance technology. The use of the proposed CASolarNet model has several advantages, including increased energy efficiency, lower maintenance costs, and greater sustainability of solar power systems.

关键词： Image resolution Photovoltaic cells Solar energy Production Feature extraction Maintenance Power systems

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Data Transfer for YOLOv5 Based on Selector Network and Partial Pseudo-Labels

Data Transfer for YOLOv5 Based on Selector Network and Parti...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Guoqiang Qu Jianchu Lin Haiyi Bian Haiping Zhou Muhammad Raees UL Haq Bo Jiang Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an China Faculty of Electronic Information Engineering Huaiyin Institute of Technology Huai’an China Jiangsu Eazytec Co. LTD Yixin China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

YOLOv5 is a widely used object detection method for its high-performance of balancing speed and accuracy. Data transfer for YOLOv5 is a challenging task, as it could involve domain shift between different datasets. We propose a data transfer method for YOLOv5 based on a Selector Network and Partial Pseudo-Labeling. The backbone network of YOLOv5 is used for feature extraction, a Selector Network is employed to filter source domain outlier samples to address the domain shift issue, and Partial Pseudo-Labels are leveraged to improve classification performance in the target domain. The results show that our method improves detection accuracy by an average of 2.4% on public datasets (COCO and VOC) and our datasets, with a notable improvement of 3.1% under complex backgrounds and varying lighting conditions. It demonstrates that our method can effectively enhances the accuracy and stability of YOLOv5.

关键词： YOLO Training Accuracy Filtering Lighting Data transfer Feature extraction Cognitive systems Faces Batch normalization

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The Role of YOLOv8 in Enhancing Strategic Military Equipment Detection

The Role of YOLOv8 in Enhancing Strategic Military Equipment...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdulfattah E. Ba Alawi Hussein M. A. Mohammed Computer Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen Energy and Cooling Systems Department Turk Telekom Ankara Turkey Mechatronics and Robotics Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The accurate and fast identification of military equipment on the battlefield is a crucial problem of various military missions and tasks. First, it affects the operation success of the modern armed forces. Second, decision-making is considerably affected by such a capability. In this paper, we address this issue using the potential of the powerful YOLOv8 technique. This allows us to offer an accurate model for identifying various components related to the military and armed forces. The model is tested on the publicly available dataset which contains 11,800 images by evaluating the detection task with YOLOv8-based models. This dataset consists of eleven classes on military-related equipment, which covers different types of military assets. Finally, the YOLOv8x model shows encouraging results in detecting military equipment as demonstrated by a value of 99.1% in terms of mAP50. Through the findings of this study, there has been significant improvement in using AI in military missions, decision-making, and mission readiness which are better than those obtained in the previous studies. According to the obtained findings, this study demonstrates substantial progress in the application of artificial intelligence to military missions and decision-making and operational development.

关键词： Accuracy Decision making Task analysis Artificial intelligence Military equipment

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