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Secure digital databases using watermarking based on english-character attributes

Recent Advances in computer Science and Communications 2020年第3期13卷 477-481页

作者： Khatatneh, Khalaf Odeh, Ashraf Mashaleh, Ashraf Hamadeen, Hind Department of Computer Science Al Balqa Applied University As-Salt Jordan Department of Science and Technology Computer Quality Society Jordan Computer Center Al Balqa Applied University As-Salt Jordan Department of Software Engineering Al-Balqa Applied University As-Salt Jordan

Introduction: The single space and the double space (DS). In this procedure, an image is used to watermark a digital database, where the image bytes are divided into binary strings that block the text attributes of the selected database, we proposed an algorithm to defend against four common database attacks. Objective: Perform the watermark is Embedding and makes extraction of the watermark. We also describe the principal of the Embedding and extraction the watermark. Methods: The procedure to extract the watermark does not require knowledge of the original database that does not carry the same watermark. This feature is extremely important because it allows the discovery of a watermark in a copy of the original database, regardless of the subsequent updates to the asset. The extraction procedure is a direct reflection of the procedure used to embed the watermark is six steps. Results: Using new algorithm ability to develop a database watermark that would make it difficult for an attacker to remove or change the watermark without discovering the value of the object. To be judged effective, the database algorithm had to be able to create a strong enough watermark that could sustain the security of the database in the face of the following four types of attack: deletion of a sub-dataset, addition of a sub-dataset. Conclusion: The performance of the proposed algorithm was assessed in respect of its ability to defend the database against four common attacks for all tuples selection. © 2020 Bentham Science Publishers.

关键词： Database systems

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Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe

arXiv

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arXiv 2023年

作者： Lew, Hah Min Kim, Jae Seong Lee, Moon Hwan Park, Jaegeun Youn, Sangyeon Kim, Hee Man Kim, Jihun Hwang, Jae Youn Department of Electrical Engineering and Computer Science DGIST Daegu42988 Korea Republic of Deep Learning Research Team KLleon R&D Center Seoul04637 Korea Republic of Department of Information and Communication Engineering DGIST Daegu42988 Korea Republic of Software Team ALPINION MEDICAL SYSTEMS Anyang-si14117 Korea Republic of Department of Health Promotion Center Yonsei University College of Medicine Gangnam Severance Hospital Seoul06273 Korea Republic of Department of Electrical Engineering and Computer Science The Interdisciplinary Studies of Artificial Intelligence DGIST Daegu42988 Korea Republic of

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues. Copyright © 2023, The Authors. All rights reserved.

关键词： Phantoms

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MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence

arXiv

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arXiv 2024年

作者： Tian, Hongduan Liu, Feng Liu, Tongliang Du, Bo Cheung, Yiu-Ming Han, Bo TMLR Group Hong Kong Baptist University Hong Kong Department of Computer Science Hong Kong Baptist University Hong Kong TMLR Group University of Melbourne Australia Sydney AI Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan Univeristy China

In cross-domain few-shot classification, nearest centroid classifier (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class. An intuition behind NCC is that each sample is pulled closer to the class centroid it belongs to while pushed away from those of other classes. However, in this paper, we find that there exist high similarities between NCC-learned representations of two samples from different classes. In order to address this problem, we propose a bi-level optimization framework, maximizing optimized kernel dependence (MOKD) to learn a set of class-specific representations that match the cluster structures indicated by labeled data of the given task. Specifically, MOKD first optimizes the kernel adopted in Hilbert-Schmidt independence criterion (HSIC) to obtain the optimized kernel HSIC (opt-HSIC) that can capture the dependence more precisely. Then, an optimization problem regarding the opt-HSIC is addressed to simultaneously maximize the dependence between representations and labels and minimize the dependence among all samples. Extensive experiments on Meta-Dataset demonstrate that MOKD can not only achieve better generalization performance on unseen domains in most cases but also learn better data representation clusters. The project repository of MOKD is available at: https://***/tmlr-group/MOKD. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

arXiv

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arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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An Adaptive Handover Scheme to support UE with various movement speeds in 5G network

An Adaptive Handover Scheme to support UE with various movem...

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2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022

作者： Yoon, Mahnsuk Park, Jeaseok Park, Taeuk Seo, Jihun Yun, Jang-Kyu Cho, Keuchul Future Mobile Communications Research Center Gumi-si Korea Republic of Korea Transport Institute Center for Connected and Automated Driving Research Department of the Fourth Industrial Revolution and Transport Sejong-si Korea Republic of Gyeongbuk Institute of It Convergence Industry Technology Vehicle Research Team Gyeongsan-si Korea Republic of Kyungpook National University School of Computer Secience and Engineering Software Education Center Daegu-si Korea Republic of

ISBN: (数字)9781665464345

ISBN: (纸本)9781665464345

The 5G network is infrastructure that can be supported by extending the mobile speed of UE supported from 350 km/h in the existing 4G network environment to 500 km/h. The 5G network has to support the fast movement speed of UE. 3GPP-based handover has a problem in that it cannot guarantee QoS for the mobility of some UEs as it provides UE based handover in a single environment that does not consider the network service environment with various movement speeds of UE. In this paper, an adaptive handover scheme is proposed to the cell located to the moving direction by determining and triggering the handover time according to the individual speed of UE to ensure the QoS according to the UE's seamless service and movement between cells. In simulation test to verify the proposed scheme compared with existing handover scheme based on 3GPP, the proposed scheme shows excellent performance in handover failure and data transmission for UE with a fast movement speed. © 2022 IEEE.

关键词： 5G mobile communication systems

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The Development of a Visual Novel Role-Playing Game [VN RPG] as an Open Educational Resource [OER] for Philippine Literature Educators Administering the 'Noli Me Tangere' Module 13

The Development of a Visual Novel Role-Playing Game [VN RPG]...

引用

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021

作者： Sales, Nina Ana Marie Jocelyn Villanueva, Nathaniel Evangelista, Matthew Robert Cestina, Joshua Luigi Emmanuel Camba, Jhona De La Salle University College of Computer Studies Software Technology Department Manila Philippines Gokongwei College of Engineering de la Salle University Industrial and Systems Engineering Department Manila Philippines De La Salle University International Center Office of the Vice President for External Relations and Internationalization Manila Philippines

ISBN: (纸本)9781665401678

Many scholars saw opportunities of using digital interactive video games as a learning tool during the pandemic. However, it was indeed complex for educators, especially those who teach history-driven novels to prepare lesson plans for classes not only to get the learning outcomes but also to produce high technology materials such as video games. Given the predicament, this study aims to measure the effectiveness of the developed video game named 'Touch Me Not' under the genre Visual Novel Role-Playing Game (VN RPG) as an Open Education Resource (OER) to educators who teach the novel, Noli Me Tangere or Touch Me Not. To accomplish the proposed objective, the Understanding by Design (UbD) approach was analyzed through the administered survey questionnaire, pre-test, and post-test. The findings show that using the 'Touch Me Not' VN RPG to complement the standard class modules had a more significant effect on the educators' performance. Thus, it proved that using VN RPG is an effective complementary pedagogical tool in teaching history-driven classes. © 2021 IEEE.

关键词： Serious games

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FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

引用

Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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State of Security Awareness in the Additive Manufacturing Industry: 2020 Survey 6

State of Security Awareness in the Additive Manufacturing In...

引用

6th International Conference on Additive Manufacturing, ICAM 2021

作者： Yampolskiy, Mark Bates, Paul Seifi, Mohsen Shamsaei, Nima Dept. of Computer Science and Software Engineering Auburn Cyber Research Center 3127B Shelby Center 345 W. Magnolia Ave. AuburnAL36849 United States ASTM International 1850 M St. NW Suite 1030 WashingtonDC20036 United States Dept. of Mechanical Engineering Auburn University 1418 Wiggins Hall 354 War Eagle Way AuburnAL36849 United States National Center for Additive Manufacturing Excellence Auburn University 311 W. Magnolia Ave. AuburnAL36849 United States

ISBN: (纸本)9780803177352

The security of additive manufacturing (AM) gets increased attention due to the growing proliferation and adoption of AM in a variety of applications and business models. However, there is a significant disconnect between the AM community focused on manufacturing and the AM security community focused on securing this highly computerized manufacturing technology. To bridge this gap, we surveyed the America Makes AM community, asking in total 11 AM security-related questions aiming to discover the existing concerns, posture, and expectations. The first set of questions aimed to discover how many of these organizations use AM, outsource AM, or provide AM as a service. Then we asked about biggest security concerns as well as about assessment of who the potential adversaries might be and their motivation for attack. We then proceeded with questions on any experienced security incidents, if any security risk assessment was conducted, and if the participants' organizations were partnering with external experts to secure AM. Last, we asked whether security measures are implemented at all and, if so, whether they fall under the general cybersecurity category. Out of 69 participants affiliated with commercial industry, agencies, and academia, 53 have completed the entire survey. This paper presents the results of this survey, as well as our assessment of the AM security posture. The answers are a mixture of what we could label as expected, shocking but not surprising, and completely unexpected. Assuming that the provided answers are somewhat representative of the current state of the AM industry, we conclude that the industry is not ready to prevent or detect AM-specific attacks that have been demonstrated in the research literature. Copyright © 2022 by ASTM International.

关键词： 3D printing

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Deep Reinforcement Learning for Sleep Control in 5G and Beyond Radio Access Networks: An Overview

Deep Reinforcement Learning for Sleep Control in 5G and Beyo...

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Nessrine Trabelsi Rihab Maaloul Lamia Chaari Fourati Wael Jaafar Laboratory of Signals systeMs aRtificial intelligence and neTworkS – Digital Research Center of Sfax Sfax Tunisia Higher Institute of Computer Science of Mahdia (ISIMa) University of Monastir Tunisia Higher Institute of Computer Science and Multimedia in Sfax (ISIMS) University of Sfax Tunisia Department of Software and IT Engineering École de technologies supérieure (ÉTS) Montreal Canada

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The advent of 5G and beyond networks is envisioned to support lower latency, higher data rates, and wider connectivity than previous cellular network generations. However, given the denser deployment of base stations (BSs) to accommodate such improvements, this results inevitably in a significant and unsustainable increase in the network’s energy consumption. Sleep Control (SC), which allows switching off some BS hardware components during light-traffic time, is considered a viable solution for greener and more energy-efficient Radio Access Networks (RAN). However, the optimization of SC is a highly challenging large-scale network combinatorial problem that depends on dynamic wireless channel conditions and varying traffic demands with stringent Quality-of-Service (QoS) requirements. Driven by the benefits and efficiency of Deep Reinforcement Learning (DRL), which has been successfully applied to multiple wireless network optimization problems, this paper investigates DRL approaches addressing sleep control in 5G and beyond RAN. To this end, we propose a taxonomy to classify the related literature. Then, we provide an overview of the different components of the Markov Decision Process (MDP) modeling the sequential decision-making of sleep control and the applied DRL algorithms. Finally, we highlight the main challenges in existing works and suggest novel strategies to address them.

关键词： 5G mobile communication Wireless networks Taxonomy Decision making Quality of service Switches Deep reinforcement learning

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Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model 1

Elucidating Spatial Complex Structures from Mass Spectrometr...

引用

1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Cui, Jiangnan Xia, Junjie Li, Xuan Wang, Yan Qiu, Fufang Xu, Yaochen Xu, Keren Zuo, Chunman School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Huashan Hospital Department of Neurosurgery Shanghai China Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Cas Key Laboratory of Systems Biology Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9798350358780

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms. © 2023 IEEE.

关键词： Data integration

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