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检索条件"任意字段=5th Israel Conference on Computer Systems and Software Engineering"

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International conference on computer Safety, Reliability, and Security, SAFECOMP 2017 and 5th International Workshop on Assurance Cases for software-Intensive systems, ASSURE 2017, 12th Workshop on Dependable Embedded and Cyber-physical systems and systems-of systems, DECSoS 2017, 6th International Workshop on Next Generation of System Assurance Approaches for Safety Critical systems, SASSUR 2017, 3rd International Workshop on Technical and Legal Aspects of Data Privacy and Security, TELERISE 2017 and 2nd International Workshop on the Timing Performance in Safety engineering, TIPS 2017

International Conference on Computer Safety, Reliability, an...

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International conference on computer Safety, Reliability, and Security, SAFECOMP 2017 and 5th International Workshop on Assurance Cases for software-Intensive systems, ASSURE 2017, 12th Workshop on Dependable Embedded and Cyber-physical systems and systems-of systems, DECSoS 2017, 6th International Workshop on Next Generation of System Assurance Approaches for Safety Critical systems, SASSUR 2017, 3rd International Workshop on Technical and Legal Aspects of Data Privacy and Security, TELERISE 2017 and 2nd International Workshop on the Timing Performance in Safety engineering, TIPS 2017

ISBN: (纸本)9783319662831

the proceedings contain 39 papers. the special focus in this conference is on computer Safety, Reliability, and Security. the topics include: Making the case for safety of machine learning in highly automated driving;a thought experiment on evolution of assurance cases —from a logical aspect;a thought experiment on evolution of assurance cases from a logical aspect;uniform model interface for assurance case integration with system models;integrated model-based development of system and safety cases;a web based GSN editor for multiple stakeholders;towards combined safety and security constraints analysis;reconciling systems-theoretic and component-centric methods for safety and security co-analysis;reconciling systems-theoretic and component-centric methods for safety and security co-analysis;increasing dependability in safety critical CPSs using reflective statecharts;a survey of hardware technologies for mixed-critical integration explored in the project EMC2;a model-based design approach;GSN support of mixed-criticality systems certification;concepts for reliable communication in a software-defined network architecture and combining safety and security analysis for industrial collaborative automation systems.

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Child and Women Safety Measures using iBeacon Technology

Child and Women Safety Measures using iBeacon Technology

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Trends in Material Science and Inventive Materials (ICTMIM), International conference on

作者： Shanker Shalini Keerthana E Nishanthi P Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

A smart real-time tracking system featuring a compact wearable device is designed to enhance the safety of children and women by utilizing IoT-driven technology and a secure database for efficient movement monitoring. this system operates on four key pillars: outdoor localization, cross-platform communication, server integration, and user engagement. the tracking mechanism comprises a wearable device embedded with an iBeacon and a built-in MEMS accelerometer, seamlessly linked to a smartphone application. the system begins with sensor calibration to establish reference values, followed by continuous tracking of sensor variations, particularly accelerometer data, to detect motion patterns with precision. Once paired with the designated Android application, the wearable device analyze movement data and transmits GPS coordinates to a centralized server, ensuring real-time location tracking of both the device and the associated iBeacon. Planned future improvements include the integration of Google Maps for enhanced real-time visualization, interactive monitoring, geofencing alerts, and emergency response notifications, making the system more robust and responsive to safety concerns.

关键词： Accelerometers Tracking Web services Real-time systems threat assessment software Safety Servers Wearable devices Monitoring

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FedDAGC: Dynamic Adaptive Graph Coarsening for Federated Learning on Non-IID Graphs 18th

FedDAGC: Dynamic Adaptive Graph Coarsening for Federated Le...

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18th International conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Zhang, Chengxi Hu, Chunqiang College of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9783031714665

Federated graph learning (FGL) has demonstrated great potential in collaboratively training graph neural network (GNN) models under the federated learning (FL) framework, benefiting from its advantage of privacy preservation. However, FGL suffers from the significant challenge of non-IID problem. In the real world, cross-source or even cross-domain graph data frequently exist among clients. To tackle this challenge, we propose a novel FGL framework, FedDAGC, from the new perspective of graph coarsening and consensus. Specifically, our method inserts a dynamic adaptive graph coarsening (DAGC) module into local GNN models to generate coarsened graphs as augmented inputs for subsequent model training. By sharing the intermediate parameters in DAGC instead of traditional GNN model parameters, FedDAGC enables clients to reach a consensus on coarsening directions, for extracting crucial patterns from local graphs based on common core knowledge while protecting privacy. Extensive experiments and analyses on various non-IID settings demonstrate the efficacy and superiority of FedDAGC. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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LLMClean: Context-Aware Tabular Data Cleaning via LLM-Generated OFDs 5th

LLMClean: Context-Aware Tabular Data Cleaning via LLM-Gener...

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5th Workshop on Intelligent Data - From Data to Knowledge, DOING 2024, 3rd Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2024, 6th Workshop on Modern Approaches in Data engineering and Information System Design, MADEISD 2024 and 3rd Workshop on Personalization and Recommender systems, PERS 2024 held in conjunction with 28th European conference on Advances in Databases and Information systems, ADBIS 2024

作者： Biester, Fabian Abdelaal, Mohamed Del Gaudio, Daniel University of Stuttgart Stuttgart Germany Software AG Darmstadt Germany

ISBN: (纸本)9783031704208

Data cleaning tools, particularly those that exploit functional dependencies within ontological frameworks or context models, are instrumental in augmenting data quality. Nevertheless, crafting these context models is a demanding task, both in terms of resources and expertise, often necessitating specialized knowledge from domain experts. this paper introduces an innovative approach, called LLMClean, for the automated generation of context models, utilizing Large Language Models to analyze and understand various datasets. LLMClean encompasses a sequence of actions, starting with categorizing the dataset, extracting or mapping relevant models, and ultimately synthesizing the context model. To demonstrate its potential, we have developed and tested a prototype that applies our approach to three distinct datasets from the Internet of things, healthcare, and Industry 4.0 sectors. the results of our evaluation indicate that LLMClean can achieve data cleaning efficacy comparable with that of context models crafted by human experts and with state-of-the-art data cleaning tools. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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the Design of GBDT-based Anomaly Detection for O-RAN Near-RT RIC

The Design of GBDT-based Anomaly Detection for O-RAN Near-RT...

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International conference on Computing, Networking and Communications (ICNC)

作者： Chih-Cheng Tseng Hsin-Cheng Wu Shao-Yu Lien Jiun-Chi You Chang-Yi Liu Rui-En You Department of Electrical Engineering National Ilan University Yilan City Taiwan College of Artificial Intelligence National Yang Ming Chiao Tung University Tainan City Taiwan

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

With the increasing demand for the 5th Generation (5G) Base Stations (BSs), the global telecommunications industry is promoting technologies with open interfaces and open software and hardware designs, driving the 5G BSs to be cost-effectively developed. While such Open Radio Access Network (O-RAN) based 5G BSs are widely installed, User Equipment (UE) handovers become frequent and important. To instantly and accurately detect anomalous UE, a UE whose performance is below threshold, that needs to be handed over, Anomaly Detection (AD) becomes important. this paper applies the Gradient Boosting Decision Tree (GBDT) algorithm to detect anomalous UE. Based on the O-RAN architecture, the developed GBDT is implemented as an AD xApp. Specifically, using the RSRP and the available number of Physical Resource Blocks (PRBs) extracted from the performance metrics reported from the developed RAN emulator, this paper compares the performance of the developed GBDT-based AD xApp with the Isoforest-based AD xApp released by O-RAN software Community (SC). Simulation results show the developed GBDT-based AD xApp demonstrates superior AD performance in terms of prediction accuracy and average prediction time. Specifically, when the number of UEs is 40, the prediction accuracy is improved by 102.2% and the average prediction time is reduced by 75.67%.

关键词： Accuracy 5G mobile communication Simulation systems architecture Open RAN computer architecture Handover software Telecommunications Anomaly detection

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Customizable DDoS Attack Data Generation in SDN Environments for Enhanced Machine Learning Detection Models

Customizable DDoS Attack Data Generation in SDN Environments...

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Advanced Research in Computing (ICARC), International conference on

作者： Nadeera Gayantha Chathura Rajapakse Janaka Senanayake Dept. of Industrial Management University of Kelaniya Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen United Kingdom

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Distributed Denial of Service (DDoS) attacks are a critical threat to the security and reliability of software-Defined Networking (SDN) environments. Existing datasets for training machine learning (ML) models, such as KDDCup ’99 and CICIDS 2017, are either outdated or fail to capture SDN-specific characteristics, limiting their effectiveness in detecting modern DDoS attacks. this paper proposes a framework for generating a comprehensive, SDN-specific dataset using a virtual environment that integrates Mininet, the Ryu controller, and Python-based automation. the dataset incorporates advanced flow-level metrics, including SYN counts, queue lengths, and real-time traffic dynamics, reflecting contemporary attack scenarios such as ICMP floods, TCP SYN floods, and UDP floods. By addressing the limitations of traditional datasets, this custom dataset enhances ML model training for DDoS detection in SDN environments, providing improved accuracy and adaptability. Contributions include a scalable SDN-based dataset generation framework, enriched feature sets for ML training, and a comprehensive approach to capturing both legitimate and malicious traffic dynamics. this study highlights the potential of SDN programmability in advancing security research and offers a robust tool for the development of reliable DDoS detection mechanisms.

关键词： Training Adaptation models Accuracy Virtual environments Denial-of-service attack Vectors Robustness Real-time systems computer crime software defined networking

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Optimizing Bevy-ECS Using Predictive JSSP Approach

Optimizing Bevy-ECS Using Predictive JSSP Approach

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Power Electronics, computer Applications (ICPECA), IEEE International conference on

作者： Benchi Ma Yugen Du Software Engineering Institution East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Entity-Component-System (ECS) is a fundamental architecture of game engines. It consists of entities (identities), components (data), and systems (logical units). It schedules the system graph to drive the update loop. the most popular open-sourced game engine on ***, the Bevy engine, is designed in an ECS architecture, and Bevy-ECS is its ECS implementation. While Bevy-ECS is a flexible implementation and easy to expand, performance issues can be found with its original scheduler. the original Bevy-ECS scheduler is based on a greedy algorithm, the system's runtime is unknown to the scheduler, which leads to a poor scheduler in terms of CPU utilization and update loop time. In this paper, we found Bevy-ECS's update loop isomorphic to a traditional NP-Hard job shop scheduling problem (JSSP), then we researched JSSP solutions' approach. With reference to the predictive JSSP solution, we represented a multi-threaded ECS scheduler based on the predictive method in Bevy-ECS. We implement genetic methods for predicting system runtime and use the predicted runtime to schedule the systems. We set up test cases using Bevy-ECS's built-in samples, and then we did performance tests using the profiling tool Tracy. In our performance tests, we use CPU utilization and total runtime to evaluate the performance of the scheduler. the results showed that our optimized scheduler achieved higher CPU utilization and faster update loop time.

关键词： Schedules Adaptation models Runtime Prefetching Games Predictive models Mathematical models Power electronics Complexity theory Engines

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Deep Learning Characterization of Volatile Organic Compounds with Spectrometer-on-Card

Deep Learning Characterization of Volatile Organic Compounds...

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11th International conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Cejudo, Ander Arrojo, Markel Gutiérrez, Miriam López-Linares, Karen Haick, Hossam Macía, Iván Martín, Cristina Mikeletegi 57 Donostia-San Sebastián20009 Spain Faculty of Engineering University of Deusto Avda. Universidades 24 Bilbao48007 Spain Universidad Rey Juan Carlos Cam. del Molino 5 Fuenlabrada28942 Spain eHealth Group Donostia-San Sebastián20014 Spain Department of Chemical Engineering The Russell Berrie Nanotechnology Institute Technion Israel Institute of Technology Haifa3200003 Israel Computational Intelligence Group Computer Science Faculty University of the Basque Country UPV/EHU Spain

ISBN: (纸本)9789897587436

the exposome encompasses all environmental exposures that affect internal biological processes throughout a person’s life, influencing health outcomes. Among these exposures, volatile organic compounds (VOCs) are particularly significant, as they are closely related to respiratory issues, cardiovascular diseases, cancer, and other health conditions. Detecting some of them is therefore critical for assessing environmental impacts on health. In this study, we use a low-cost, highly portable SPectrometer-On-Card (SPOC) device designed to characterize complex mixtures by separating VOCs through its layers. the device was previously tested to detect VOCs in controlled laboratory conditions. Hereby, we explore artificial intelligence algorithms to identify patterns in the signals captured by the SPOC in closer to real-word conditions. Specifically, we focus on two different use cases including direct exposure to a VOC source and indoors versus outdoors signal recognition. Our top-performing model, a recurrent neural network, achieves accuracies of 92,4% and 97,2% for each use case, respectively, effectively identifying exposures in the first case and correctly classifying 87,5% of exposures in the second. these results demonstrate the potential of our methodology applied to SPOC data for broader health-related applications, such as detecting incomplete combustions, identifying diseases like cancer through exhaled breath, and detecting leaks from industrial plants. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Recurrent neural networks

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International conference on computer Safety, Reliability, and Security, SAFECOMP 2016 and International Workshop on Assurance Cases for software-Intensive systems, ASSURE 2016, Workshop on Dependable Embedded and Cyber-physical systems and systems-of-systems, DECSoS 2016, 5th International Workshop on Next Generation of System Assurance Approaches for Safety-Critical systems, SASSUR 2016, and 1st International Workshop on the Timing Performance in Safety engineering, TIPS 2016

International Conference on Computer Safety, Reliability, an...

引用

International conference on computer Safety, Reliability, and Security, SAFECOMP 2016 and International Workshop on Assurance Cases for software-Intensive systems, ASSURE 2016, Workshop on Dependable Embedded and Cyber-physical systems and systems-of-systems, DECSoS 2016, 5th International Workshop on Next Generation of System Assurance Approaches for Safety-Critical systems, SASSUR 2016, and 1st International Workshop on the Timing Performance in Safety engineering, TIPS 2016

ISBN: (纸本)9783319454795

the proceedings contain 30 papers. the special focus in this conference is on computer Safety, Reliability and Security. the topics include: Systematic maintenance of safety cases to reduce risk;on using results of code-level bounded model checking in assurance cases;developing SNS tool for consensus building on environmental safety using assurance cases;the 6W1H model as a basis for systems assurance argument;building assurance cases for synthetic biology;towards safety case integration with hazard analysis for medical devices;European research and innovation initiatives in the area of cyber-physical systems and systems-of-systems;testing safety properties of cyber-physical systems with non-intrusive fault injection;quantitative reliability assessment for mobile cooperative systems;an approach for systematic in-the-loop simulations for development and test of a complex mechatronic embedded system;gate-level-accurate fault-effect analysis at virtual-prototype speed;dynamic safety contracts for functional cooperation of automotive systems;time-of-flight based optical communication for safety-critical applications in autonomous driving;limitation and improvement of STPA-sec for safety and security co-analysis;security services for mixed-criticality systems based on networked multi-core chips and analysis of informed attacks and appropriate countermeasures for cyber-physical systems.

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Investigation on Existing Blockchain Based Architecture, AI/ML Driven for Boosting IoT Security and Privacy 5th

Investigation on Existing Blockchain Based Architecture, AI/...

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5th International conference on Artificial Intelligence and Speech Technology, AIST 2023

作者： Moid, Abdul Sharma, Narendra Department of Computer Science and Engineering Sri Satya Sai University of Technology and Medical Sciences Sehore MP Bhopal India

ISBN: (纸本)9783031751660

In the social world, the Internet of things (IoT) can assimilate the physical as well as computational procedures as services. the quantity of administrations at the IoT edge is rising quickly because of the predominant purposes of smart gadgets and cyber-physical systems. One of the difficulties in exploring the encouraging applications of IoT services is making the services interoperable in a decentralized environment. It has been demonstrated that the Blockchain technology (BCT) is a promising option for calling for executions and establishing data trust. It could theoretically be utilized to aid in service interoperability. BCT records a verification of info or a procedure as a operation in a distributed ledger. Like the geography to IoT, considering BCT at the edges of the organization shows the dispersed trademark. Be that as it may, presently, BCT is as yet confronting the moves for interoperability because of various factors, for example, agreement conventions, block sizes, and time frame. the current BC-related security systems for the IoT environment are discussed in this paper. this paper further makes sense of the meaning of the provision of a safeguarded communication architecture for IoT devices utilizing a BC with software defined network (SDN) empowered network. Further, this paper depicts the significance of the AI/ML driven techniques in tightening the security and confidentiality level of IoT frameworks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial Intelligence Blockchain (BC) Technology Internet of things (IoT) SDN Security smart frameworks

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