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检索条件"任意字段=23rd International Conference on Computer Systems and Technologies, CompSysTech 2022"

共 801 条记录，以下是191-200 订阅

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A Case-Based Reasoning Solution for Urban Drinking Water Quality Control 23

A Case-Based Reasoning Solution for Urban Drinking Water Qua...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wu, Di Seidu, Razak Wang, Hao Ban, Xiaojuan Norwegian University of Science and Technology Department of Ict and Natural Science Ålesund Norway Norwegian University of Science and Technology Department of Ocean Operations and Civil Engineering Ålesund Norway Norwegian University of Science and Technology Department of Computer Science Gjφvik Norway University of Science and Technology Beijing Department of Computer Science and Technology Beijing China

ISBN: (纸本)9781665494571

The Urban Drinking Water Supply (UDWS) systems play a vital role nowadays to support global city management, especially in the context of the fast growth of urbanization. In Europe, there is a natural conflict between urban population growth and the aging drinking water supply infrastructure. In particular, according to European standards, water should be drinkable from the taps. The requirements from these standards not only bring easement in our modern daily life but also are examining the robustness of the risk warning and control response in the UDWS system. Water quality as the key factor in the urban water supply is by default strictly regulated in the municipality management. Currently, the most common water quality control is relying on periodic tests for various quality indicators. In addition, further processing in the treatment plant, including measures and doses, is relatively fixed and weak to respond to the risk in UDWS. In order to address these problems, we propose a Case-Based Reasoning (CBR) approach for water quality risk control in UDWS systems. First, we build a case-based analysis computational scheme for the risk control in the UDWS system. Then we provide our methods in feature selection and similarity evaluation between different cases. Finally, the results of an industrial case study are presented. This case is based on our data collection from the UDWS system in Oslo city in Norway. The discussion and analysis show the feasibility of this approach and practically provide decision support for water treatment actions. © 2021 IEEE.

关键词： Smart cities Sociology Europe Water quality Control systems Cognition Robustness

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Cloud Ubiquitous Learning Approach Based on Multi-agents System 23

Cloud Ubiquitous Learning Approach Based on Multi-agents Sys...

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23rd international Arab conference on Information Technology, ACIT 2022

作者： Guettala, Manel Harous, Saad Bourekkache, Samir Athamena, Belkacem Kazar, Okba Houhamdi, Zina University of Biskra LINFI Laboratory Department of Computer Science Biskra07000 Algeria University of Sharjah College of Computing and Informatics Department of Computer Science United Arab Emirates College of Business Al Ain University Al Ain United Arab Emirates College of Information Technology UAE University Department of Information Systems and Security United Arab Emirates

ISBN: (纸本)9798350320244

Information technology (IT) plays an important role in the education field, with ubiquitous computing technology the ubiquitous learning (U-Iearning) is now a practical possibility. Cloud computing can offer many benefits to the learning field, as it provides all resources such as software (SaaS), platforms (PaaS), and infrastructure (IaaS) as a service. The multi-agent systems approach is an excellent way to implement flexible systems whose structure evolves dynamically. Based on the discussion of the benefits of cloud computing and the multi-agent systems approach to support adaptive and personalized learning content, the paper presents a hybrid approach based on cloud computing that uses the multi-tenant architecture where tenants share the system software. Multi-agent system is used in this approach to support a ubiquitous learning system. A multi-tenant SaaS application addresses the unique requirements of each tenant by enabling customization at the tenant level with the adoption of the concept ofvirtualization (virtual machines). This paper explores the challenges of achieving performance isolation in the context of multi-tenant SaaS applications. As an output of this study, U- learning on the cloud-based multi-agent system architecture has been proposed. To achieve lower cost of resources and server allocation and provide isolation among learners in the ubiquitous learning environment. © 2022 IEEE.

关键词： Multi agent systems

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A Data-Driven Password Strength Meter for Cybersecurity Assessment and Enhancement 23

A Data-Driven Password Strength Meter for Cybersecurity Asse...

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作者： Algharibeh, Moath M. Husari, Ghaith Jaf, Sardar Oakland University Department of Electrical and Computer Engineering Rochester United States East Tennessee State University Department of Computer Science Johnson City United States University of Sunderland Department of Computer Science Sunderland United Kingdom

ISBN: (纸本)9781665494571

Password-based authentication is the most popular authentication mechanism over insecure networks due to its simplicity and convenience. To ensure the security of this authentication mechanism, measuring the strength of users' passwords is a crucial task not only to guide users to create strong passwords but to protect systems from unauthorized access. Password strength meters are usually used for measuring the strength of passwords, often in real-time. However, password strength meters are only helpful if they are accurate. Passwords meters that do not accurately reflect the actual passwords strengths, e.g., providing a high score for a weak password, may misinform users and hinder the overall security of password-based authentication mechanisms. Also, a user-friendly password strength meter is important in guiding users to create a strong password for further authentication. While many password strength meters were proposed in the literature, using the most appropriate password meter remains a difficult process. In this paper, we propose a data-driven password meter. Our system scrapes and collects large datasets to be used to measure the strength of user passwords based on proven password strength policies and we offer a user-friendly mechanism to help users create a strong password. We evaluate our system by measuring the influence of the proposed meter at guiding users to create stronger passwords by tracking their eye movements. We conducted our experiment with a pilot study on a testing web service and monitored the eye movements of 240 participants using an eye-tracking tool. Our results exhibited a significant improvement by influencing users to create stronger passwords, with an average of 110.97 years for password cracking time. © 2021 IEEE.

关键词： Meters Visualization Tracking Atmospheric measurements Web services Authentication Passwords

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Deep Learning for Single-Channel EEG-Based Driver Drowsiness: Comparative Study 23

Deep Learning for Single-Channel EEG-Based Driver Drowsiness...

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23rd international Arab conference on Information Technology, ACIT 2022

作者： Latreche, Imene Slatnia, Sihem Kazar, Okba Harous, Saad Athamena, Belkacem Houhamdi, Zina University of Biskra Department of Computer Science Biskra Algeria University of Biskra UAE University Department of Computer Science Department of Information Systems and Security Biskra Algeria Al Ain United Arab Emirates University of Sharjah College of Computing and Informatics Departement of Computer Science Sharjah United Arab Emirates College of Business Al Ain University Al Ain United Arab Emirates

ISBN: (纸本)9798350320244

To prevent car accidents caused by drowsiness, a transitional state between alertness and sleepiness, numerous authors have conducted studies employing various monitoring approaches and detecting techniques. However, the Electroencephalogram (EEG) was the most used monitoring approach due to its advantages compared to other techniques. Also, Deep Learning (DL) techniques have been widely used in this context and have given prominent results. In this paper, we have established a comparative study between four deep learning methods. The aim of this comparison is to determine the optimal DL model for detecting EEG-Based Drowsiness using a small balanced dataset. We evaluated the performance of the models using the hold-out method and the Leave-One-Subject-Out Cross-Validation (LOSO CV) techniques. With an accuracy of 77% with the Hold-Out method and 70.31% with the LOSO CV method, the GRU model outperforms other deep learning and machine learning models, followed by the BI-LSTM with 74.88% and 68.07, respectively. © 2022 IEEE.

关键词： Electroencephalography

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Classification of CXR Chest Diseases by Ensembling Deep Learning Models 23

Classification of CXR Chest Diseases by Ensembling Deep Lear...

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23rd IEEE international conference on Information Reuse and Integration for Data Science (IEEE IRI)

作者： Nasser, Adnane Ait Akhloufi, Moulay A. Univ Moncton Percept Robot & Intelligent Machines Res Grp PRIM Moncton NB Canada

ISBN: (数字)9781665466035

ISBN: (纸本)9781665466035

Chest related diseases are the most frequent health issues worldwide. They are mainly diagnosed by radiologists using a visual inspection of a chest X-ray (CXR) radiography. This task is challenging and error-prone because of the similarity between signs of the diseases, which occur as opacities around the infected organs. An early diagnosis of chest abnormalities is an essential step in the treatment process. computer-aided detection (CAD) systems can be useful as decision support tools for radiologists. In recent years, chest disease detection using deep learning (DL) and CXR images has become one of the most promising topics in healthcare. In this work, we propose an approach using an ensemble of deep convolutional neural networks (DCNN) for a two-steps classification of CXR images. In the first step, a CXR image is taken as input of our model. This latter determines whether it is a heart or a lung abnormality. Then, it performs a binary classification for diseases of the same organ as a second step. A dataset of 26,316 CXR images was consolidated by merging images from two open-access datasets (VinDr-CXR and CheXpert). Our approach showed impressive results, obtaining an AUC of 0.9489 for infected organ classification (multi-class classification) and a mean AUC of 0.9957 for specific diseases' classification (binary classification) surpassing state-of-the-art DCNN models.

关键词： Chest X-ray Radiography computer-Aided Detection Deep Learning Deep Convolutional Neural Network Ensemble Learning

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Behavioural Monitoring and Security Profiling in the Internet of Things (IoT) 23

Behavioural Monitoring and Security Profiling in the Interne...

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作者： Safi, Miraqa Kaur, Barjinder Dadkhah, Sajjad Shoeleh, Farzaneh Lashkari, Arash Habibi Molyneaux, Heather Ghorbani, Ali A. Unb Faculty of Computer Science Fredericton Canada National Research Council Canada Fredericton Canada

ISBN: (纸本)9781665494571

In recent years the number of IoT devices in businesses has significantly increased, which creates an undeniable challenge for network administrators and industries to identify and monitor the heterogeneous IoT environment. Also, identifying IoT devices helps organizations resolve several security dilemmas, such as determining if connected IoT devices' behaviour is compromised or not. In this work, we propose a distributive profiling mode to distinguish between IoT and non-IoT devices, map devices, identify the network that devices are attached to, and then continuously update the profile. Using a hybrid set of features and 23 new proposed features for identifying IoT devices, our method improves accuracy and shorter training time compared to other existing methods. Our proposed approach experiments with 18 Machine Learning (ML) classifiers on three publicly available datasets, i.e., IoTSentinel, UNSW, LSIF. Our study shows that Random Forest (RF) and Decision Tree (DT) classifiers outperformed the other ML classifiers by achieving an average accuracy, precision, recall, and f1-score above 90% with a shorter training time. This shows that our proposed system is suitable for providing a more generalized solution for profiling IoT devices. © 2021 IEEE.

关键词： Training Radio frequency Industries Organizations Internet of Things Object recognition Security

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23rd international conference on Distributed computer and Communication Networks, DCCN 2020

23rd International Conference on Distributed Computer and Co...

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23rd international conference on Distributed computer and Communication Networks, DCCN 2020

ISBN: (纸本)9783030664701

The proceedings contain 55 papers. The special focus in this conference is on Distributed computer and Communication Networks. The topics include: Architectural ML Framework for IoT Services Delivery Based on Microservices;applying Machine Learning to Data from a Structured Database in a Research Institute to Support Decision Making;generation of Metadata for Network Control;IoT Traffic Prediction with Neural Networks Learning Based on SDN Infrastructure;Transmission Latency Analysis in Cloud-RAN;Resource Queuing System with Preemptive Priority for Performance Analysis of 5G NR systems;redundant Servicing of a Flow of Heterogeneous Requests Critical to the Total Waiting Time During the Multi-path Passage of a Sequence of Info-Communication Nodes;preface;Power Domain NOMA Without SIC in Downlink CSS-Based LoRa Networks;agriculture Management Based on LoRa Edge Computing System;dynamic Algorithm for Building Future Networks Based on Intelligent Core Network;Methods and Models for Using Heterogeneous Gateways in the Mesh LPWANs;Research on Using the AODV Protocol for a LoRa Mesh Network;on the Algebraic Theory of Loop Free Routing;queueing System with Two Unreliable Servers and Backup Server as a Model of Hybrid Communication System;flexible Random Early Detection Algorithm for Queue Management in Routers;architecture and Functionality of the Collective Operations Subnet of the Angara Interconnect;The Model of WBAN Data Acquisition Network Based on UFP;Development and Investigation of Model Network IMT2020 with the Use of MEC and Voice Assistant technologies;Simulation-Based Analysis of Mobility Models for Wireless UAV-to-X Networks;queues with Markovian Arrivals, Phase Type Services, Breakdowns, and Repairs.

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FIT: Fairness-Aware Intelligent Traffic Signal Control with Deep Reinforcement Learning 23

FIT: Fairness-Aware Intelligent Traffic Signal Control with ...

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作者： Chen, Jian Zhang, Zhengwei Feng, Jianhang Zhu, Kun The Key Lab. of Information System Requirement of China Electronics Technology Group Corporation Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781665494571

The fast development of learning techniques make it possible to intelligently adjust the traffic signal, also the promotion of the future network(e.g. 6G) provides the possibility to adjust the signal in real time. Several studies have proposed learning-based methods for signal control achieving superior performance compared with traditional methods. However, these methods mainly focus on improving the total efficiency for passing the intersection. To this end, a higher priority is usually given to the lane with larger traffic, which could cause the lane with few vehicles to be starved for a long time. Although traditional methods have better performance in preventing starvation, the efficiency still needs to be greatly improved. In this paper, we consider both the fairness and efficiency in intelligent traffic signal control and try to relieve such starvation while improving the total efficiency. Specifically, a deep reinforcement learning based approach is proposed to dynamically control the traffic signal according to real-time traffic information. Inspired by the proportional fair scheduling (PFS) in wireless networks, a new fairness-aware traffic signal control model is designed to maintain a good trade-off between efficiency and fairness. Extensive experiments are conducted to demonstrate that our method achieves better fairness while also provides a good efficiency guarantee. © 2021 IEEE.

关键词： 6G mobile communication Learning systems Smart cities Wireless networks Reinforcement learning Telecommunication traffic Real-time systems

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The Parallelized Cuckoo Filter for Cold Data Representation 23

The Parallelized Cuckoo Filter for Cold Data Representation

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作者： Sun, Bowen Luo, Lailong Li, Shangsen Chen, Yingwen Guo, Deke College of Computer National University of Defense Technology China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China College of Computer National University of Defense Technology National Lab for Parallel and Distributed Processing China

ISBN: (纸本)9781665494571

Cold data contributes a large portion of the big data today and is usually stored in secondary storage. Various sketch data structures are implemented to represent the stored elements and provide constant-time membership queries. Considering the large volume and infrequent access of cold data, we envision three rationales of cold data representation: 1) parallelized insertion, 2) accurate representation, and 3) constant-time query/deletion. The existing sketches, however, fail to achieve these rationales simultaneously. To this end, we propose a novel sketch named Parallelized cuckoo Filter (PCF) in this paper. PCF divides the standard cuckoo filter into multiple homogenous segments. An additional hash function is introduced to assign an element to a segment. The element will be inserted into the selected segment. With such a design, element insertions in PCF can be naturally parallelized. Besides, as a variant of the cuckoo filter, PCF naturally realizes the accurate representation and constant-time query/deletion requirements. Comprehensive experiments further indicate that PCF achieves the three rationales simultaneously. © 2021 IEEE.

关键词： Hash functions Smart cities Big Data Data structures Standards

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PAL-FD: an Efficient and Accurate Multi-Scene Human Face Detection Tool for Windows 23

PAL-FD: an Efficient and Accurate Multi-Scene Human Face Det...

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作者： Xu, Shuai Huang, Zhelong Qian, Pinyi Cen, Jiacheng Guan, Donghai Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China China

ISBN: (纸本)9781665494571

Face detection serves as a fundamental component for facial analytics such as face recognition at edge devices, which has been extensively studied in recent years. However, previous studies focus on the architecture design of face detection models based on deep convolutional neural networks (DCNN), ignoring the rapid deployment and intuitive use of human face detection models. In this paper, we develop an easy-to-use but accurate face detection tool called PAL-FD under Windows for public use. First, we release a toy graphical user interface (GUI) library called PAL written in C++ for rapid visual software development. Second, we simplify Faster R-CNN based on benchmark datasets to obtain an efficient face detector. Third, we build the user-friendly tool PAL-FD for human face detection based on effective fusion of the face detector and PAL GUI library. Quantitative and qualitative evaluations on real world images show that PAL-FD can efficiently detect human faces from multiple types of images with large variations in scale, pose, blurry, occlusion, illumination and makeup of human faces. © 2021 IEEE.

关键词： Visualization Face recognition Operating systems Education Toy manufacturing industry Lighting Detectors

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