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End-to-end security in embedded system for modern mobile communication technologies

Measurement: Sensors 2022年 23卷

作者： Venu, D. J, Babu Saravanakumar, R. Cosio Borda, Ricardo Fernando Abd Algani, Yousef Methkal Kiran Bala, B. Department of ECE Kakatiya Institute of Technology and Science Warangal India Department Electronics and Communication Engineering Dadi Institute of Engineering & Technology Anakapalle India Department of Wireless Communication Institute of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Universidad Autónoma del Perú Lima Peru Department of Mathematics Sakhnin College Israel Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel Head of the Department Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India

Modern mobile electronic devices such as smartphones or cell phones can now be used for distant devices such as technical systems to monitor and control. While surveillance systems do not require high standards navigating about the time of transfer of the displayed data. More real-time data are needed for a remote mobile robot transfer. Therefore, it has investigated and measured not only the possibilities of employing mobile devices. But also, the supported data transmission channels, such as UMTS, GSM, Wireless LAN, and Bluetooth. The remote-control system is used in many applications such as smart homes, cities, smart hospitals, etc., but it must be today updated to ensure fast-changing technology. Extensive coverage, remote control, and reliable operation in real-time in the deployment of wireless security knowledge. The home automation control system delivers significant features together with a user-friendly interface. A secure remote-based end-to-end security system NTMobile, a technique that enables NAT to provide transverse and encrypted communication from end to end. This confirmed that evaluating the performance of the system in the ECHONET lite compatible smartphone ecosystem. This gives flexibility in configuring time-sensitive industrial networks and enables them to be secured. A safe and reliable remote-control system is also conceivable under the privacy of the user. © 2022 The Authors

关键词： Smartphones

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DamFormer: Generalizing Morphologies in Dam Break Simulations Using Transformer Model

arXiv

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arXiv 2024年

作者： Mu, Zhaoyang Liang, Aoming Ge, Mingming Chen, Dashuai Fan, Dixia Xu, Minyi College of Artificial Intelligence Dalian Maritime University Dalian China Zhejiang University-Westlake University Joint Training Zhejiang University Hangzhou China School of Engineering Westlake University Hangzhou China Faculty of Science and Technology Beijing Normal Univerisity-Hong Kong Baptist University United International College Zhuhai China Key Laboratory of Coastal Environment and Resources of Zhejiang Province School of Engineering Westlake University Hangzhou China Research Center for Industries of the Future Westlake University Hangzhou China State Key Laboratory of Maritime Technology and Safety Marine Engineering College Dalian Maritime University Dalian China

The interaction of waves with structural barriers such as dams breaking plays a critical role in flood defense and tsunami disasters. In this work, we explore the dynamic changes in wave surfaces impacting various structural shapes-circle, triangle, and square-using deep learning techniques. We introduce the "DamFormer" a novel transformer-based model designed to learn and simulate these complex interactions. The model was trained and tested on simulated data representing the three structural forms. Additionally, we conducted zero-shot experiments to evaluate the model's ability to generalize across different domains. This approach enhances our understanding of fluid dynamics in marine engineering and opens new avenues for advancing computational methods in the field. Our findings demonstrate the potential of deep learning models like the DamFormer to provide significant insights and predictive capabilities in ocean engineering and fluid mechanics. © 2024, CC BY.

关键词： Ocean engineering

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Efficient VM Placement Policy for data Centre in Cloud Environment 3rd

Efficient VM Placement Policy for Data Centre in Cloud Envir...

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3rd International Conference on Soft Computing: Theories and Applications, SoCTA 2018

作者： Dubey, Kalka Nasr, Aida A. Sharma, S.C. El-Bahnasawy, Nirmeen Attiya, Gamal El-Sayed, Ayman Cloud Computing and Wireless sensor Lab Indian Institute of Technology Roorkee RoorkeeUttarakhand247664 India Faculty of Artificial Intelligence Kafrelsheikh University Kafr Elsheikh Egypt Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9789811507502

Cloud computing now becomes the accomplished infrastructure model for delivering the cloud service to its user over the Internet. Virtualization is one of the key concepts, which makes the dynamic sharing of physical resources of cloud more effective. In virtualization, virtual machine is placed over the physical machine in data centre and VM placement is one of the challenging issues in cloud computing, so cloud service provider needs good VM placement policy to increase the utilization of physical resources as well as reduce the load-balancing problem. In this paper, we proposed a new VM placement policy called Efficient of service Best Fit Decreasing (EBFD). From the simulation experiments result, we see that the proposed policy performs much better performance compared with first-fit and best-fit policy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Morphosyntactic Probing of Multilingual BERT Models

arXiv

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arXiv 2023年

作者： Acs, Judit Hamerlik, Endre Schwartz, Roy Smith, Noah A. Kornai, Andras Budapest Hungary Budapest University of Technology and Economics Budapest Hungary Department of Applied Informatics Comenius University in Bratislava Faculty of Mathematics Physics and Informatics Bratislava Slovakia Hebrew University of Jerusalem Jerusalem Israel Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States Allen Institute for Artificial Intelligence SeattleWA United States

We introduce an extensive dataset for multilingual probing of morphological information in language models (247 tasks across 42 languages from 10 families), each consisting of a sentence with a target word and a morphological tag as the desired label, derived from the Universal Dependencies treebanks. We find that pre-trained Transformer models (mBERT and XLM-RoBERTa) learn features that attain strong performance across these tasks. We then apply two methods to locate, for each probing task, where the disambiguating information resides in the input. The first is a new perturbation method that "masks" various parts of context;the second is the classical method of Shapley values. The most intriguing finding that emerges is a strong tendency for the preceding context to hold more information relevant to the prediction than the following context. © 2023, CC BY.

关键词： Perturbation techniques

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Temporal networks provide a unifying understanding of the evolution of cooperation

arXiv

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arXiv 2023年

作者： Li, Aming Meng, Yao Zhou, Lei Masuda, Naoki Wang, Long Center for Systems and Control College of Engineering Peking University Beijing100871 China Center for Multi-Agent Research Institute for Artificial Intelligence Peking University Beijing100871 China School of Automation Beijing Institute of Technology Beijing100081 China Department of Mathematics State University of New York at Buffalo BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260 United States

Understanding the evolution of cooperation in structured populations represented by networks is a problem of long research interest, and a most fundamental and widespread property of social networks related to cooperation phenomena is that the node’s degree (i.e., number of edges connected to the node) is heterogeneously distributed. Previous results indicate that static heterogeneous (i.e., degree-heterogeneous) networks promote cooperation in stationarity compared to static regular (i.e., degree-homogeneous) networks if equilibrium dynamics starting from many cooperators and defectors is employed. However, the above conclusion reverses if we employ non-equilibrium stochastic processes to measure the fixation probability for cooperation, i.e., the probability that a single cooperator successfully invades a population. Here we resolve this conundrum by analyzing the fixation of cooperation on temporal (i.e., time-varying) networks. We theoretically prove and numerically confirm that on both synthetic and empirical networks, contrary to the case of static networks, temporal heterogeneous networks can promote cooperation more than temporal regular networks in terms of the fixation probability of cooperation. Given that the same conclusion is known for the equilibrium fraction of cooperators on temporal networks, the present results provide a unified understanding of the effect of temporal degree heterogeneity on promoting cooperation across two main analytical frameworks, i.e., equilibrium and non-equilibrium ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Heterogeneous networks

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Learning Memory Augmented Cascading Network for Compressed Sensing of Images 16th

Learning Memory Augmented Cascading Network for Compressed S...

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16th European Conference on Computer Vision, ECCV 2020

作者： Chen, Jiwei Sun, Yubao Liu, Qingshan Huang, Rui Jiangsu Key Laboratory of Big Data Analysis Technology Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030585419

In this paper, we propose a cascading network for compressed sensing of images with progressive reconstruction. Specifically, we decompose the complex reconstruction mapping into the cascade of incremental detail reconstruction (IDR) modules and measurement residual updating (MRU) modules. The IDR module is designed to reconstruct the remaining details from the residual measurement vector, and MRU is employed to update the residual measurement vector and feed it into the next IDR module. The contextual memory module is introduced to augment the capacity of IDR modules, therefore facilitating the information interaction among all the IDR modules. The final reconstruction is calculated by accumulating the outputs of all the IDR modules. Extensive experiments on natural images and magnetic resonance images demonstrate the proposed method achieves better performance against the state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Compressed sensing

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Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Spatial-Temporal Graph Information

arXiv

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arXiv 2021年

作者： Li, Jinhao Zhang, Ruichang Wang, Hao Liu, Zhi Lai, Hongyang Zhang, Yanru The School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Shenzhen Institute of Advanced Study University of Electronic Science and Technology of China China The Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University MelbourneVIC3800 Australia Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan

Renewable energy resources (RERs) have been increasingly integrated into large-scale power systems. Considering uncertainties and voltage fluctuation issues introduced by RERs, in this paper, we propose a deep reinforcement learning (DRL)based strategy leveraging spatial-temporal (ST) graphical information of power systems, to dynamically search for the optimal power flow (OPF), of power systems with a high uptake of RERs. We formulate the OPF problem as a multi-objective optimization problem considering generation cost, voltage fluctuation, and transmission loss, and employ deep deterministic policy gradient (DDPG) to learn an optimal strategy for OPF. Moreover, given that the nodes in power systems are self-correlated and interrelated in temporal and spatial views, we develop a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN) for extracting ST graphical correlations and features, aiming to provide graphical knowledge of power systems for its sequential DDPG in our DRL algorithm to more effectively solve OPF. We validate our algorithm on modified IEEE 33, 69, and 118-bus radial distribution systems and demonstrate that our algorithm outperforms other benchmark algorithms. Our experimental results also reveal that our MG-ASTGCN can significantly accelerate DDPG’s training process and performance in solving OPF. The proposed DRL-based strategy also improves power systems’ robustness in the presence of node faults, especially for large-scale power systems. © 2021, CC BY.

关键词： Renewable energy resources

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EFFICIENT LIVER SEGMENTATION WITH 3D CNN USING COMPUTED TOMOGRAPHY SCANS

arXiv

引用

arXiv 2022年

作者： Humady, Khaled Al-Saeed, Yasmeen Eladawi, Nabila Elgarayhi, Ahmed Elmogy, Mohammed Sallah, Mohammed Applied Mathematical Physics Research Group Physics Department Faculty of Science Mansoura University Mansoura35516 Egypt Information Technology Department Faculty of Computers and Information Mansoura University Mansoura35516 Egypt Information Technology Department Faculty of Computers and Artificial Intelligence South Valley University Hurghada84511 Egypt Information Systems Department Faculty of Computers and Information Mansoura University Mansoura35516 Egypt Higher Institute of Engineering and Technology New Damietta Egypt

The liver is one of the most critical metabolic organs in vertebrates due to its vital functions in the human body, such as detoxification of the blood from waste products and medications. Liver diseases due to liver tumors are one of the most common mortality reasons around the globe. Hence, detecting liver tumors in the early stages of tumor development is highly required as a critical part of medical treatment. Many imaging modalities can be used as aiding tools to detect liver tumors. Computed tomography (CT) is the most used imaging modality for soft tissue organs such as the liver. This is because it is an invasive modality that can be captured relatively quickly. This paper proposed an efficient automatic liver segmentation framework to detect and segment the liver out of CT abdomen scans using the 3D CNN DeepMedic network model. Segmenting the liver region accurately and then using the segmented liver region as input to tumors segmentation method is adopted by many studies as it reduces the false rates resulted from segmenting abdomen organs as tumors. The proposed 3D CNN DeepMedic model has two pathways of input rather than one pathway, as in the original 3D CNN model. In this paper, the network was supplied with multiple abdomen CT versions, which helped improve the segmentation quality. The proposed model achieved 94.36%, 94.57%, 91.86%, and 93.14% for accuracy, sensitivity, specificity, and Dice similarity score, respectively. The experimental results indicate the applicability of the proposed method. © 2022, CC BY.

关键词： Tumors

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Discrete Wavelet Transform-Based Capsule Network for Hyperspectral Image Classification

arXiv

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arXiv 2025年

作者： Gao, Zhiqiang Wang, Jiaqi Shen, Hangchi Dou, Zhihao Zhang, Xiangbo Huang, Kaizhu College of Science Mathematics and Technology Wenzhou-Kean University 88 Daxue Road Zhejiang Province Wenzhou325060 China College of Primary Education Hainan Normal University No. 99 Longkun South Road Hainan Province Haikou571158 China College of Artificial Intelligence Southwest University No. 2 Tiansheng Road Sichuan Province Chongqing400715 China Pratt School of Engineering Duke University 305 Teer Engineering Building DurhamNC27708 United States Data Science Research Center Division of Natural and Applied Sciences Duke Kunshan University No. 8 Duke Avenue Jiangsu Province Suzhou215316 China

Hyperspectral image (HSI) classification is a crucial technique for remote sensing to build large-scale earth monitoring systems. HSI contains much more information than traditional visual images for identifying the categories of land covers. One recent feasible solution for HSI is to leverage CapsNets for capturing spectral-spatial information. However, these methods require high computational requirements due to the full connection architecture between stacked capsule layers. To solve this problem, a DWT-CapsNet is proposed to identify partial but important connections in CapsNet for a effective and efficient HSI classification. Specifically, we integrate a tailored attention mechanism into a Discrete Wavelet Transform (DWT)-based downsampling layer, alleviating the information loss problem of conventional downsampling operation in feature extractors. Moreover, we propose a novel multi-scale routing algorithm that prunes a large proportion of connections in CapsNet. A capsule pyramid fusion mechanism is designed to aggregate the spectral-spatial relationships in multiple levels of granularity, and then a self-attention mechanism is further conducted in a partially and locally connected architecture to emphasize the meaningful relationships. As shown in the experimental results, our method achieves state-of-the-art accuracy while keeping lower computational demand regarding running time, flops, and the number of parameters, rendering it an appealing choice for practical implementation in HSI classification. © 2025, CC BY.

关键词： Discrete wavelet transforms

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Lightweight binary convolutional-transformers fusion network for facial expression recognition

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engineering Applications of artificial intelligence 2025年 158卷

作者： Xing Jin Xiyin Wu Libo Weng Qiaolin Ye College of Information Science and Technology & Artificial Intelligence Nanjing Forestry University Nanjing 210037 Jiangsu China College of Big Data and Information Engineering Guizhou University Guiyang 550025 Guizhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou 310023 Zhejiang China

Deep learning-based methods exploit local facial regions for facial expression recognition (FER) but overlook long-range dependencies of facial muscle movements. Moreover, deploying existing large-scale deep networks on mobile devices remains a huge challenge. To this end, this paper designs a mini-binary transformer (MiniBTR) for FER. Specifically, we first construct image patches by regions of interest (ROIs) which are associated with facial action units (AUs) and represent these patches by the histogram of oriented gradient (HOG) feature. Then, by integrating the complementary advantages of convolutional layers (Convs) and multi-head self-attention mechanism (MSA), an effective and efficient mini-transformer (MiniTR) architecture with 69K parameters is proposed to follow the human–machine collaborative strategy. Finally, we design the binary model MiniBTR by extending MiniTR with a binary operation. Extensive experimental results on four publicly available datasets demonstrate that the MiniBTR yields comparable results in terms of recognition accuracy, model size and inference speed while offering a more deployable yet high-performing alternative.

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