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Enhancing Security by Using GIFT and ECC Encryption Method in Multi-Tenant Datacenters

computers, Materials & Continua 2023年第5期75卷 3849-3865页

作者： Jin Wang Ying Liu Shuying Rao R.Simon Sherratt Jinbin Hu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Systems Engineering The University of ReadingRG66AYUK

Data security and user privacy have become crucial elements in multi-tenant data *** traffic types in the multi-tenant data center in the cloud environment have their characteristics and *** the data center network(DCN),short and long flows are sensitive to low latency and high throughput,*** traditional security processing approaches,however,neglect these characteristics and *** paper proposes a fine-grained security enhancement mechanism(SEM)to solve the problem of heterogeneous traffic and reduce the traffic completion time(FCT)of short flows while ensuring the security of multi-tenant traffic ***,for short flows in DCN,the lightweight GIFT encryption method is *** Intra-DCN long flows and Inter-DCN traffic,the asymmetric elliptic curve encryption algorithm(ECC)is *** NS-3 simulation results demonstrate that SEM dramatically reduces the FCT of short flows by 70%compared to several conventional encryption techniques,effectively enhancing the security and anti-attack of traffic transmission between DCNs in cloud computing ***,SEM performs better than other encryption methods under high load and in largescale cloud environments.

关键词： Multi-tenant datacenter user privacy transmission security GIFT ECC

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Unsupervised Log Anomaly Detection Method Based on Multi-Feature

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computers, Materials & Continua 2023年第7期76卷 517-541页

作者： Shiming He Tuo Deng Bowen Chen R.Simon Sherratt Jin Wang School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Systems Engineering The University of ReadingRG66AYUK

Log anomaly detection is an important paradigm for system *** log anomaly detection based on Long Short-Term Memory(LSTM)networks is time-consuming to handle long *** model is introduced to promote ***,most existing Transformer-based log anomaly detection methods convert unstructured log messages into structured templates by log parsing,which introduces parsing *** only extract simple semantic feature,which ignores other features,and are generally supervised,relying on the amount of labeled *** overcome the limitations of existing methods,this paper proposes a novel unsupervised log anomaly detection method based on multi-feature(UMFLog).UMFLog includes two sub-models to consider two kinds of features:semantic feature and statistical feature,*** applies the log original content with detailed parameters instead of templates or template IDs to avoid log parsing *** the first sub-model,UMFLog uses Bidirectional Encoder Representations from Transformers(BERT)instead of random initialization to extract effective semantic feature,and an unsupervised hypersphere-based Transformer model to learn compact log sequence representations and obtain anomaly *** the second sub-model,UMFLog exploits a statistical feature-based Variational Autoencoder(VAE)about word occurrence times to identify the final anomaly from anomaly *** experiments and evaluations are conducted on three real public log *** results show that UMFLog significantly improves F1-scores compared to the state-of-the-art(SOTA)methods because of the multi-feature.

关键词： System log anomaly detection semantic features statistical features Transformer

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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computer Modeling in engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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A High-Efficiency, Indirectly-Fed, Dual Moon-Shaped Printed Antenna for Measuring Sand Gradation 7

A High-Efficiency, Indirectly-Fed, Dual Moon-Shaped Printed ...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Abbas, Mohammed K. Hammed, Raaed T. Salim, Ali J. Sali, Aduwati University of Technology Department of Electrical Engineering Baghdad Iraq University of Technology Department of Communication Engineering Baghdad Iraq Department of Computer and Communication Systems Engineering Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350392074

In this paper, an enhanced bandwidth antenna featuring a dual moon-shaped patch design is presented. The antenna occupies an area of 153 mm by 130 mm and operates within the frequency band of 0.69 to 1.19 GHz. The structure is printed on an FR4 substrate with a dielectric constant of 4.3 and a substrate thickness of 1.5 mm. It achieves a bandwidth of 0.5 GHz (50%) and a minimum return loss of-47 dB at the resonant frequency of 1.00 GHz, with a gain of 4.02 dB. The proposed antenna consists of a dual moon-shaped patch positioned on the top of the dielectric substrate. It utilizes an indirect feeding method via a transmission line with two arms to enhance bandwidth. The ground plane is partially reduced, which allows the antenna to resonate at a lower frequency. Additionally, a triangular slot on the backside improves impedance matching from-15dB to-47dB, thus optimizing the electric size. Experimental results confirm the antenna's performance, showing excellent agreement with the predicted parameters. The antenna demonstrates robust performance with effective gain and high bandwidth, making it suitable for practical applications in infrastructure monitoring. An innovative method for measuring sand gradation on-site is being developed by voltage network analyzer Device, eliminating the need for traditional sieve analysis in the laboratory. © 2024 IEEE.

关键词： Microstrip antennas

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Sample-Efficient Multi-Agent DQNs for Scalable Multi-Domain 5G+ Inter-Slice Orchestration

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in communications and Networking 2024年 2卷 956-977页

作者： Doanis, Pavlos Spyropoulos, Thrasyvoulos Communication Systems Department Eurecom Sophia Antipolis06410 France School of Electrical and Computer Engineering Technical University of Crete Chania731 00 Greece

Data-driven network slicing has been recently explored as a major driver for beyond 5G networks. Nevertheless, we are still a long way before such solutions are practically applicable in real problems. Most solutions addressing the problem of dynamically placing virtual network function chains (‘‘slices’’) on top of a physical topology still face one or more of the following hurdles: (i) they focus on simple slicing setups (e.g. single domain, single slice, simple VNF chains and performance metrics);(ii) solutions based on modern reinforcement learning theory have to deal with astronomically high action spaces, when considering multi-VNF, multi-domain, multi-slice problems;(iii) the training of the algorithms is not particularly data-efficient, which can hinder their practical application given the scarce(r) availability of cellular network related data (as opposed to standard machine learning problems). To this end, we attempt to tackle all the above shortcomings in one common framework. For (i), we propose a generic, queuing network based model that captures the inter-slice orchestration setting, supporting complex VNF chain topologies and end-to-end performance metrics. For (ii), we explore multi-agent DQN algorithms that can reduce action space complexity by orders of magnitude compared to standard DQN. For (iii), we investigate two mechanisms to store to and select from the experience replay buffer, in order to speed up the training of DQN agents. The proposed scheme was validated to outperform both vanilla DQN (by orders of magnitude faster convergence) and static heuristics (3× cost improvement). 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.

关键词： 5G mobile communication systems

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Congestion Control Using In-Network Telemetry for Lossless Datacenters

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computers, Materials & Continua 2023年第4期75卷 1195-1212页

作者： Jin Wang Dongzhi Yuan Wangqing Luo Shuying Rao R.Simon Sherratt Jinbin Hu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Systems Engineering The University of ReadingReadingRG66AYUK

In the Ethernet lossless Data Center Networks (DCNs) deployedwith Priority-based Flow Control (PFC), the head-of-line blocking problemis still difficult to prevent due to PFC triggering under burst trafficscenarios even with the existing congestion control solutions. To addressthe head-of-line blocking problem of PFC, we propose a new congestioncontrol mechanism. The key point of Congestion Control Using In-NetworkTelemetry for Lossless Datacenters (ICC) is to use In-Network Telemetry(INT) technology to obtain comprehensive congestion information, which isthen fed back to the sender to adjust the sending rate timely and *** is possible to control congestion in time, converge to the target rate quickly,and maintain a near-zero queue length at the switch when using ICC. Weconducted Network Simulator-3 (NS-3) simulation experiments to test theICC’s performance. When compared to Congestion Control for Large-ScaleRDMA Deployments (DCQCN), TIMELY: RTT-based Congestion Controlfor the Datacenter (TIMELY), and Re-architecting Congestion Managementin Lossless Ethernet (PCN), ICC effectively reduces PFC pause messages andFlow Completion Time (FCT) by 47%, 56%, 34%, and 15.3×, 14.8×, and11.2×, respectively.

关键词： Data center lossless networks congestion control head of line blocking in-network telemetry

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Parameter Tuning in Automatic Detection Algorithm for Detecting Artificial Brain Shift 24

Parameter Tuning in Automatic Detection Algorithm for Detect...

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6th International Conference on Intelligent Medicine and Image Processing, IMIP 2024

作者： Watanabe, Kaoru Noborio, Hiroshi Koeda, Masanao Department of Computer Science Osaka Electro-Communication University Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Japan

ISBN: (纸本)9798400710032

In neurosurgery to remove brain tumors, DICOM data, a medical imaging standard, is generated preoperatively using CT and MRI. This data is used for surgical planning. However, brain deformation problems, known as brain shift, occur during surgery, resulting in discrepancies from the preoperative surgical plan. Deviations from the surgical plan due to brain shift are a life-threatening problem as they reduce the success rate of surgery. Brain shifts have not yet been elucidated, and DICOM data acquired from MRIs and CTs for surgical planning remain stored and archived in hospital information centers. Here, it is planned to extract the physical parameters of the brain model from a large amount of brain deformation data using pre- and post-operative brain DICOM pairs, which are stored in large quantities in hospitals and discarded after a particular time. This study investigates the optimization of parameters in the brain shift detection algorithm. © 2024 ACM.

关键词： Digital Imaging and communications in Medicine (DICOM)

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Comprehensive Investigation and Evaluation of an Indoor 3D System Performance Based on Visible Light communication 3

Comprehensive Investigation and Evaluation of an Indoor 3D S...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Faraj Al Khattat, Vailet Hikmat Ahmad Anas, Siti Barirah Saif, Abdu Faculty of Engineering Universiti Putra Malaysia Department of Computer and Communication Systems Engineering UPM Serdang Selangor 43400 Malaysia Taiz University Faculty of Engineering and It Department of Communication and Computer Engineering Yemen

ISBN: (纸本)9798350305333

The obvious and accelerator trend towards efficient green technology in a modern technological revolution time makes visible light communication (VLC) a solution key to meeting this growth. Besides the illumination function, VLC uses for data transmission at high speed with the lowest cost due to utilizing the infrastructure that already exists. As a result of VLC's multiple features, it had been used in the indoor positioning system (IPS) in many pieces of research to obtain a high accuracy without conflict with the Radio Frequency (RF) spectrum besides it providing high security from any penetrating. However, achieving good performance parameters is essential and fundamental in evaluating the effectiveness of any indoor system that many pieces of research neglected and concentrated more on the positioning accuracy aspect. This paper investigates and analyses the performance of the indoor system that is designed and developed based on a VLC and proves its effectiveness through a comprehensive evaluation. Signal-to-noise ratio (SNR) and path loss are the performance parameters that are investigated in this system by varying the transmitted power, incidence angles, and Lambertian mode number in the line of sight (LOS) scenario. The examined system consists of one light-emitting diode (LED) in a 3D typical room and one photodiode (PD) in different ten locations along the half-diagonal line towards the corner. Utilizing a single LED in this system is important to avoid the interference that occurs when utilizing multiple LEDs;besides, it is more convenient in the indoor environment. The obtained results by MATLAB simulation show the reliable and effective performance of the proposed developed VLC system design by achieving a good SNR with low path loss. Furthermore, the proposed system approach proves how the optimum position of PD is crucial to obtain a strong signal with the lowest ratio of noise and losses. © 2023 IEEE.

关键词： Signal to noise ratio

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EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture

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IEEE Transactions on Affective Computing 2024年第2期16卷 1-18页

作者： PETRESCU, Alexandru TRUICA, Ciprian-Octavian APOSTOL, Elena-Simona PASCHKE, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest Romania Fraunhofer Institute for Open Communication Systems Berlin Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors

关键词： Sentiment analysis

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On the Accuracy-Energy Tradeoff for Hierarchical Federated Learning via Satisfaction Equilibrium 19

On the Accuracy-Energy Tradeoff for Hierarchical Federated L...

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19th Annual International Conference on Distributed Computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Charatsaris, Panagiotis Diamanti, Maria Papavassiliou, Symeon Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9798350346497

Hierarchical Federated Learning (HFL) has recently emerged as a promising method to overcome the limitations of conventional Federated Learning (FL) in terms of communication inability of the end users with the cloud and increased backhaul network traffic when considering implementations over wireless networks. Nevertheless, to reap the benefits of HFL, proper user association with the different edge servers and wireless resource allocation is required. Unlike existing works - that in their majority treat unilaterally a single objective using centralized optimization techniques - in this paper, we particularly aim to explore the tradeoff induced between the users' local model training accuracy and personal energy consumption in a distributed manner. The joint problem of user-to-edge-server association and uplink power allocation for transmitting the users' local model parameters to the edge is formulated and solved as a game in satisfaction form. Each user autonomously seeks to achieve a target accuracy-energy ratio by selecting their edge association and uplink transmission power level, while their cumulative decisions result in a desired Satisfaction Equilibrium (SE) point. Specifically, to determine the respective SE of the formulated game, a Reinforcement Learning (RL) algorithm is utilized. Numerical results obtained via modeling and simulation demonstrate the operational and performance characteristics of the proposed framework in terms of achieving the users' personally desired tradeoff value. © 2023 IEEE.

关键词： Game theory

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