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检索条件"任意字段=International Symposium on Computer Networks and Distributed Systems"

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Chaotic Water Wave Optimization and Support Vector Machine of Role-Based Access Control Policies in Enterprise IAM

Chaotic Water Wave Optimization and Support Vector Machine o...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international Conference on

作者： Priyanka K Chikka Harshini Srinivas Aluvala Haider Mohammed Abbas Ramaswamy Padmanathan Ram Kumar Department of ISE Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of Computer Science and Engineering SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The access control state of a system is complex, and Machine Learning (ML) based access control decision engines have demonstrated advantages of policies. However, struggle to effectively implement Role-Based Access Control (RBAC) policies because require large enterprises Identity and Access Management (IAM) system with a high volume of users and access logs. This paper, proposed Chaotic Water Wave Optimization (CWWO) algorithm combined with a Support Vector Machine (SVM) to classify users based on selected features, leading to more accurate and secure role to assignment. The dataset used is obtained from amazon and pre-processing is done using Min-max normalization, which scales the feature to a specific range, ensuring that different features are on same scale. Feature selection is performed using the CWWO algorithm, which helps in selecting relevant feature and reduce dimensionality. The classification using SVM for binary classification, which assigns relevant feature and handle large amounts of data, achieving better accuracy. The proposed ensemble model technique for ship motion detection is achieving better outcomes such as high accuracy of 0.98, precision of 0.97, F1-score of 0.96 and recall of 0.98 and respectively. The existing technique such Random Forest (RF) and Decision Tree (DT) are evaluated of proposed method.

关键词： Support vector machines Access control Radio frequency Accuracy Feature extraction Motion detection Classification algorithms Marine vehicles Optimization Random forests

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Drowsy Driver Detection System using computer Vision and Transfer Learning for Preventing Road Accidents

Drowsy Driver Detection System using Computer Vision and Tra...

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Blockchain and distributed systems Security (ICBDS), IEEE international Conference on

作者： Naisargi Bagal Kashish Dingreja Atharva Atterkar Prasad Bachhav Nilesh P. Sable Anuradha Yenkikar Ritesh Alkunte Department of Computer Science and Engineering - Artificial Intelligence Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Major traffic accidents are attributed to driver fatigue, according to study on the topic. Driver drowsiness is a state in which the driver of a car is on the verge of falling asleep or losing consciousness. It can be brought on by a number of variables, including biological, physical, psychological, and other factors, all of which can impede and inhibit safe driving. The several methods that can be applied to the implementation of a system for the detection of driver sleepiness will be reviewed in this paper. Improving the ability to identify drowsiness in real time through computer vision is the main objective of all technologies. Thus, our work also pertains to the identification of driver drowsiness, whereby a motorist's tiredness can be ascertained by first recognizing their face, followed by eye tracking. The system compares the extracted eye image with the dataset. The system used the dataset and predicts using transfer learning with our base model as InceptonV3 to identify that it could alert the driver with an alarm if the driver's eyes were closed for a predetermined amount of time, and it could resume monitoring if the driver's eyes were open following the alarm alert. We established a score that grew if the eyes were closed and dropped if they were open. This study aims to reduce traffic accidents by solving the sleepiness detection problem with InceptionV3 with an accuracy of 95.46% compare to 92.11% of ***, driving drowsiness detection—which assesses a driver's degree of fatigue using facial recognition and eye tracking becomes the main focus of our research.

关键词： computer vision Accuracy Sleep Face recognition Transfer learning Neural networks Real-time systems Safety Security Vehicles

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HSDP: Accelerating Large-scale Model Training via Efficient Sharded Data Parallelism

HSDP: Accelerating Large-scale Model Training via Efficient ...

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international symposium on Parallel and distributed Processing with Applications, ISPA

作者： Yanqi Hao Zhiquan Lai Wei Wang Shengwei Li Weijie Liu Keshi Ge Dongsheng Li National Key Laboratory of Parallel and Distributed Computing(PDL) College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Large deep neural network (DNN) models have demonstrated exceptional performance across diverse downstream tasks. Sharded data parallelism (SDP) has been widely used to reduce the memory footprint of model states. In a DNN training cluster, a device usually has multiple inter-device links that connect to other devices, like NVLink and InfiniBand. However, existing SDP approaches employ a single link at any given time, encountering challenges in efficient training due to significant communication overheads. We observe that the inter-device links can work independently without affecting each other. To reduce the fatal communication overhead of distributed training of large DNNs, this paper introduces HSDP, an efficient SDP training approach that enables the simultaneous utilization of multiple inter-device links. HSDP partitions models in a novel fine-grained manner and orchestrates the communication processes of partitioned parameters while considering inter-device links. This design enables concurrent communication execution and reduces communication overhead. To further optimize the training performance of HSDP, we propose a HSDP planner. The HSDP planner first abstracts the model partition and execution of HSDP into a communication parallel strategy, and builds a cost model to estimate the performance of each strategy. We then formulate the strategy searching as an optimization problem and solve it with an off-the-shelf solver. Evaluations on representative DNN workloads demonstrate that HSDP achieves up to 1.30× speedup compared to the state-of-the-art SDP training approaches.

关键词： Training Performance evaluation Costs distributed databases Artificial neural networks Parallel processing Throughput Search problems Data models Optimization

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Design and Implementation of a Wireless Water Quality Monitoring System

Design and Implementation of a Wireless Water Quality Monito...

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computer Technology and Information Science (ISCTIS), international symposium on

作者： Jiayue Zhao Zhijie Hu Shenrui Bing Xiaoyue Yang School of Information Engineering Xi’an Eurasia University Xi’an China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

With the development of technology and the economy, people's living standards have significantly improved. However, environmental pollution has become increasingly severe, especially water quality pollution, which is closely related to people's daily lives. The current water quality monitoring systems have drawbacks such as single monitoring indicators, mostly wired monitoring, and long monitoring cycles. Based on this, this study designs and implements a wireless water quality monitoring system that can monitor multiple water quality parameters in real-time and achieve remote data viewing and alarming through wireless communication. Firstly, the system collects data such as pH, oxidation-reduction potential (ORP), and temperature through sensors. Then, the collected data is processed by the STM32F103C8T6 microcontroller and transmitted to the gateway node via the LoRa module. Finally, the gateway node uploads the data to a cloud platform for remote viewing on an upper computer. Additionally, the upper computer can modify the threshold values in manual mode. When the relevant parameters exceed the set thresholds, the system will issue an alarm to promptly alert users to changes in water quality. The establishment of a real-time water quality monitoring system not only helps in promptly detecting abnormal water quality conditions but also provides strong data support for relevant departments, aiding them in formulating more scientific and targeted improvement measures.

关键词： Temperature sensors Temperature measurement Wireless communication Wireless sensor networks Microcontrollers Water quality Real-time systems Sensor systems Sensors Monitoring

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Efficient Probabilistic Packet Marking for AS Traceback

Efficient Probabilistic Packet Marking for AS Traceback

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2021 international symposium on networks, computers and Communications, ISNCC 2021

作者： Nur, Abdullah Yasin University Of New Orleans Department Of Computer Science New OrleansLA70148 United States

ISBN: (纸本)9780738113166

distributed Denial of Service (DDoS) attacks are among the most perilous attack types in the Internet. In addition to the significant harms for the victim site, intermediate Autonomous systems (AS) are also unintended victims in DDoS attacks. The main goal of a DDoS defense mechanism is to reduce the attack's effect as close as possible to their sources to prevent malicious traffic in the Internet. In this work, we proposed an AS traceback scheme to infer AS level forward paths from attacker sites to a victim site. We utilize the 16-bit IP ID field and 6-bit from the ToS field in the IPv4 protocol. In our method, only the ingress routers of ASes probabilistically mark the packet with their AS numbers. We propose an encoding technique to reduce the number of required packets significantly. The destination site can construct the path after receiving enough packets. Our results show that a victim site can construct the forward path from an attacker site after receiving 10.14 packets on the average. Compared to the other techniques, our approach requires fewer packets to construct the paths from attacker sites to a victim site. © 2021 IEEE.

关键词： Denial-of-service attack

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Electronically Steerable Mobile Optical-Wireless Mesh-Network

Electronically Steerable Mobile Optical-Wireless Mesh-Networ...

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Annual IEEE systems Conference

作者： Suman Bhunia Yicheng Qian Mahmudur Khan Department of Computer Science and Software Engineering Miami University Oxford Ohio USA Department of Electrical & Computer Engineering and Computer Science York College of Pennsylvania USA

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

Free Space Optical (FSO) Communication provides better communication coverage and higher data rate. However, FSO requires neighboring nodes to schedule their transceiver towards each other for communication, which makes multi-hop communication challenging. In this paper, we present an FSO mesh network system that enables data transfer among multiple nodes. Each node is equipped with a multi-transceiver FSO communication module and a low-bitrate long-range (LoRa) omnidirectional radio frequency (RF) communication module. Scheduling remains a significant challenge for wireless communication using highly directional transceivers such as FSO. Hence, we propose a distributed coordination and communication method that utilizes a supplementary omnidirectional LoRa channel for distributed coordination among the nodes in the mesh network. In the proposed scheme, a per-packet beam scheduling is proposed where the assisting low bandwidth omnidirectional channel helps the higher data rate directional channel to coordinate and face the line of sight. Our proposed method enables communication via both single-hop and multi-hop FSO links. We also present the implementation of a proof-of-concept prototype of an RF-FSO communication module comprising multiple FSO transceivers and a LoRa transceiver using commercial off-the-shelf devices (COTS). We demonstrate the effectiveness of our proposed FSO mesh network system through real test-bed experiments using the developed prototype.

关键词： Wireless communication Radio frequency Mesh networks Schedules Free-space optical communication Protocols Wireless mesh networks

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Identification of Abnormal Behavior in Activities of Daily Life Using Novelty Detection 19th

Identification of Abnormal Behavior in Activities of Daily L...

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19th European-Alliance-for-Innovation (EAI) international Conference on Mobile and Ubiquitous systems - Computing, Networking and Services (MobiQuitous)

作者： Freitas, Mauricio Piai, Vinicius de Aquino Dazzi, Rudimar Teive, Raimundo Parreira, Wemerson Fernandes, Anita Pires, Ivan Miguel Quietinho Leithardt, Valderi Reis Univ Vale Itajai Artificial Intelligence Lab BR-88302901 Itajai Brazil Univ Beira Interior Inst Telecomunicacoes P-6200001 Covilha Portugal Inst Politecn Portalegre Res Ctr Endogenous Resources Valorizat VALORIZA P-7300555 Portalegre Portugal Univ Lusofona Humanidades & Tecnol COPELABS P-1749024 Lisbon Portugal Univ Vale Itajai Lab Embedded & Distributed Syst BR-88302901 Itajai Brazil

ISBN: (纸本)9783031347757;9783031347764

The world population is aging at a rapid pace. According to the WHO (World Health Organization), from 2015 to 2050, the proportion of elderly people will practically double, from 12 to 22%, representing 2.1 billion people. From the individual's point of view, aging brings a series of challenges, mainly related to health conditions. Although, seniors can experience opposing health profiles. With advancing age, cognitive functions tend to degrade, and conditions that affect the physical and mental health of the elderly are disabilities or deficiencies that affect Activities of Daily Living (ADL). The difficulty of carrying out these activities within the domestic context prevents the individual from living independently in their home. Abnormal behaviors in these activities may represent a decline in health status and the need for intervention by family members or caregivers. This work proposes the identification of anomalies in the ADL of the elderly in the domestic context through Machine Learning algorithms using the Novelty Detection method. The focus is on using available ADL data to create a baseline of behavior and using new data to classify them as normal or abnormal daily. The results obtained using the E-Health Monitoring database, using different Novelty Detection algorithms, have an accuracy of 91% and an F1-Score of 90%.

关键词： Novelty Detection Anomaly Detection Activities of Daily Living Machine Learning One-Class Support Vector Machine (OC-SVM) Local Outlier Factor (LOF)

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(POSTER) Towards Data Dissemination Policy Prediction for Constrained Environments Using Analytics

(POSTER) Towards Data Dissemination Policy Prediction for Co...

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international Conference on distributed Computing in Sensor systems (DCOSS)

作者： Loic Guegan Issam Raïs Otto Anshus Department of Computer Science UiT The Arctic University of Norway Tromso Norway

In Cyber-Physical systems (CPS) such as Wireless Sensors networks (WSN), disseminating data is crucial. Under energy constraints with limited communications capabilities, performing data dissemination is challenging. In such contexts, common data dissemination methods cannot be used. Nodes must rely on device-to-device communications policies to mitigate the impact of communications on the nodes energy consumption. However, depending on nodes configuration (up-times duration, wireless technology capabilities and energy consumption), choosing a suitable communication policy is challenging. This work exposes the problem statement for using analytic algorithms to predict the most suitable device-to-device communication policy, for a given node configuration, to match a given coverage and energy consumption target in a constrained environment.

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Analysis of the impact of visual attacks on the characteristics of neural networks in image recognition 14

Analysis of the impact of visual attacks on the characterist...

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14th international symposium on Intelligent systems

作者： Andriyanov, N. A. Dementiev, V. E. Kargashin, Yu D. Financial Univ Govt Russian Federat Leningradsky AveH 49 Moscow 125993 Russia Ulyanovsk State Tech Univ Severny Venets StH 32 Ulyanovsk 432027 Russia JSC RPC Istok Vokzalnaya StH 2 Fryazino 141190 Moscow Region Russia

The article discusses various methods of visual attacks on neural networks such as the introducing of additive white Gaussian noise, pixel maximization and minimization of brightness, maximization and minimization of brightness in the area. For well-known datasets (MNIST, Kaggle dataset called as Dogs_vs_Cats), the dependence of the proportion of correct recognitions on the proportion of distorted images in the test sample is studied. networks such as VGG-16, Inception_v3, networks without training transfer are analyzed. The characteristics of the accuracy of such networks from the noise level in the image, from the size of the attack areas are obtained. The probabilities of belonging to a given class for various images in various attacks are analyzed. As a method for combating visual attacks, it is proposed to single out a new class during training: "poor image". By selecting valid images by dumping "bad images" by the neural network, accuracy is improved even in the case of a large volume of distorted images in the test sample. The accuracy of such a network is compared for N classes and for (N + 1) classes of images. Ways of further research and ideas of alternative methods of combating visual attacks on neural networks are formulated. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks pattern recognition visual attacks VGG-16 Inception_v3 image processing

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A Digital Twin Framework for Edge Server Placement in Mobile Edge Computing 4

A Digital Twin Framework for Edge Server Placement in Mobile...

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4th international Informatics and Software Engineering Conference, IISEC 2023

作者： Bozkaya, Elif National Defence University Turkish Naval Academy Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350318036

Mobile edge computing is considered a key technology for upcoming 6G networks, which allows real-Time processing of large amounts of data at the network edge. Specifically, considering the strict requirements of new services and Internet of Things (IoT) applications in different domains, such as healthcare, transportation, smart city, smart grid and more, increased mobile data traffic can be handled with the benefits of this technology. As an enabler for these interesting applications for 6G, mobile edge computing deploys edge servers at the network edge, enabling the offloading of computation tasks to reduce processing latency. However, achieving cost-effective placement of edge servers presents a challenge in effectively distributing the workload among them while simultaneously minimizing overall system cost. To address this issue, we propose a digital twin framework, where digital twin technology is used to model virtual replicas of edge servers in the real world and simulate their behavior. Then, the proposed edge server placement framework can place the edge servers and balance the workload of the edge servers. The objective of the proposed strategy is to minimize the system cost in terms of processing time and access delay. The edge server placement is formulated as an optimization problem in mobile edge computing systems and a genetic-based algorithm is introduced to address this issue. Simulation results demonstrate the efficiency of our proposed digital twin framework achieving less system cost compared to other benchmark methods. © 2023 IEEE.

关键词： Genetic algorithms

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