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True Rays Model Integration in OMNeT++ for IIoT Network Planning in Underground Mines 1

True Rays Model Integration in OMNeT++ for IIoT Network Plan...

引用

1st IEEE Latin American Conference on Antennas and Propagation, LACAP 2024

作者： Suancha, Cristian Montanez, Oscar Medina, Fabian Fernandez, Eduardo Avendano Cespedes, Sandra School UPTC Systems and Computing Engineering Sogamoso Colombia School UPTC Electronic Engineering Sogamoso Colombia Concordia University Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9798331527587

This work develops a new module in the OMNeT++ simulator incorporating and validating the True Rays propagation model for underground environments. The integration enables the planning and evaluation of Industrial Internet of Things deployments of wireless sensor networks (WSNs) in mining scenarios. The tool can read the mine blueprints and deploy nodes at specified locations. It also defined the environment and network communication parameters for network coverage and performance analysis, allowing an understanding of how the WSN behaves in the underground mine before its deployment. The simulation predicts variables such as received power and path losses, optimizing sensing coverage and connectivity while reducing the need for site surveys. A large number of simulations using blueprints from real mines show that the signal propagation measurements are consistent with the theoretical models proposed in the state of the art. Experimental results were compared with a simulation WSN using LoRa, the maximum range obtained in simulation was 47 m, the measurements under real conditions in the underground only achieved 25 m of reach, but were limited at a threshold of -100 dBm to ensure connectivity and reliability of transmission among the nodes. As a result, the simulations and measurements performed better at the 433 MHz frequency, and the main contribution is a simulation tool simplifying planning IIoT networks in mining tunnels. © 2024 IEEE.

关键词： Sensor nodes

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LPT-One and BFD-One Search Algorithms for Load Balance and Bin-Packing of Cloud computing 9

LPT-One and BFD-One Search Algorithms for Load Balance and B...

引用

9th IEEE International Conference on Cloud computing and Intelligence systems, CCIS 2023

作者： Zhou, Guangyao Tian, Wenhong Buyya, Rajkumar University of Electronic Science and Technology of China School of Information and Software Engineering China The University of Melbourne School of Computing and Information Systems Australia

ISBN: (纸本)9798350304428

Cloud computing contains large-scale tasks and resources. Currently, the local search is a considerable choice in ensuring both computational complexity and optimization. Based on our previous research on multi-route search algorithm to reduce makespan, we apply BFDO and LPTO algorithms to address load balancing and bin-packing problems. Through abundant experiments, we validate the superiority of our proposed BFDO and LPTO. © 2023 IEEE.

关键词： Cloud computing

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Is It a Trap? A Large-scale Empirical Study And Comprehensive Assessment of Online Automated Privacy Policy Generators for Mobile Apps 33

Is It a Trap? A Large-scale Empirical Study And Comprehensiv...

引用

33rd USENIX Security Symposium, USENIX Security 2024

作者： Pan, Shidong Zhang, Dawen Staples, Mark Xing, Zhenchang Chen, Jieshan Xu, Xiwei Hoang, Thong CSIRO's Data61 ANU CSIRO's Data61 School of Computing Australian National University Software Systems Research Group CSIRO's Data61 Australia Software Systems Research Group CSIRO's Data61 Australia

ISBN: (纸本)9781939133441

Privacy regulations protect and promote the privacy of individuals by requiring mobile apps to provide a privacy policy that explains what personal information is collected and how these apps process this information. However, developers often do not have sufficient legal knowledge to create such privacy policies. Online Automated Privacy Policy Generators (APPGs) can create privacy policies, but their quality and other characteristics can vary. In this paper, we conduct the first large-scale empirical study and comprehensive assessment of APPGs for mobile apps. Specifically, we scrutinize 10 APPGs on multiple dimensions. We further perform the market penetration analysis by collecting 46, 472 Android app privacy policies from Google Play, discovering that nearly 20.1% of privacy policies could be generated by existing APPGs. Lastly, we point out that generated policies in our study do not fully comply with GDPR, CCPA, or LGPD. In summary, app developers must carefully select and use the appropriate APPGs with careful consideration to avoid potential pitfalls. © USENIX Security Symposium *** rights reserved.

关键词： Differential privacy

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Approach to the Development of Ontology-Driven Language Toolkits Based on Metamodeling 17

Approach to the Development of Ontology-Driven Language Tool...

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17th IEEE International Conference on Application of Information and Communication Technologies, AICT 2023

作者： Ermakov, Ivan Lanin, Viacheslav Lyadova, Lyudmila Proskuryakov, Kirill Perm State University Department of Computing Systems Software Perm Russia Hse University Department of Information Technologies in Business Perm Russia

ISBN: (纸本)9798350303568

The information systems are to be conforming to the requirements defined by domain experts. These requirements are formalized as models created with modeling tools. Applying these tools is complicated for domain experts. Domain specific modeling (DSM) with domain specific languages (DSL) reduces the semantic gap. However, the system development complication shifts to the creation of languages and tools for transforming models and code generation. An approach to automating DSL creation and facilitating code generation based on using multifaceted ontology is proposed. The generalized description of the multifaceted ontology is given. Tools of automating generation of new DSL metamodels based on mapping the corresponding domain ontology onto the metamodels of the selected base languages are described. Metamodels of the visual languages, grammars of the target text languages and transformation rules are also included into the ontology. The proposed approach is implemented as a research prototype of the language toolkits. Examples of metamodels and rules described in the ontology, as well as the results of their application are shown. The results of experiments confirmed practical significance of the approach to the ontology-driven language toolkits development. © 2023 IEEE.

关键词： Ontology

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Robust Auto-Scaling with Probabilistic Workload Forecasting for Cloud Databases 40

Robust Auto-Scaling with Probabilistic Workload Forecasting ...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Hang, Haitian Tang, Xiu Sun, Jianling Bao, Lingfeng Lo, David Wang, Haoye College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China School of Computing and Information Systems Singapore Management University Singapore School of Computer and Computing Science Hangzhou City University China

ISBN: (纸本)9798350317152

Auto-scaling is crucial for achieving elasticity in cloud databases as well as other cloud systems. Predictive auto-scaling, which leverages forecasting techniques to adjust resources based on predicted workload, has been widely adopted. However, the inherent inaccuracy of forecasting presents a significant challenge, potentially causing resource under-provisioning. To address this challenge, we propose robust predictive auto-scaling that considers the uncertainty in forecasts. Unlike previous predictive approaches that rely on single-valued forecasts, we leverage probabilistic forecasting techniques to generate quan-tile forecasts, providing a more comprehensive understanding of the potential future workloads. By formulating the auto-scaling problem as a robust optimization problem, we enable the implementation of auto-scaling strategies with customizable levels of robustness, which can be determined by considering various quantile levels of forecasts. Moreover, we enhance the adaptability of our strategy by incorporating different quantile levels through-out the entire decision horizon, allowing for dynamic adjustments in the conservatism of our auto-scaling decisions. This enables us to strike a balance between resource efficiency and system robustness. Through extensive experiments, we demonstrate the effectiveness of our approach in achieving robust auto-scaling in cloud databases, while maintaining reasonable resource efficiency. © 2024 IEEE.

关键词： Forecasting

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Student Placement Prediction Using Machine Learning Algorithms

Student Placement Prediction Using Machine Learning Algorith...

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2023 Innovations in Power and Advanced computing Technologies, i-PACT 2023

作者： Aruna, P. Abinayashri, V.S. Abinayaa Sree, K.S. Meghashri, A. Department of Computing Coimbatore Institute of Technology Coimbatore India M.Sc. Software Systems Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9798350325188

The student placement prediction plays a crucial role in modern educational institutions and the job market. To make informed decisions with machine learning algorithms, accurate placement prediction has become achievable. This model focuses on the application of machine learning algorithms for placement prediction, analyzing their effectiveness, and comparing their performance. Educational institutions that facilitate this process can identify areas of improvement in their curriculum and career counseling services, leading to better student outcomes and satisfaction. To save time and resources for companies, it helps optimize their hiring process. This model also focuses on the implementation of machine learning algorithms that can accurately predict whether a student will be placed or not, based on a set of input features such as academic scores, internship experience, activities, and more. This model supports other machine learning algorithms. It provides valuable insights into student employability factors and contributes to fostering a better future for both students and the job market. However, updating the model with fresh data and refinement ensures its accuracy and relevance in an ever-changing job landscape. © 2023 IEEE.

关键词： Classification Gradient Boosting Neural Network Random Forest

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From User Stories to Domain Models: Recommending Relationships between Entities

From User Stories to Domain Models: Recommending Relationshi...

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Joint of REFSQ-2023 Workshops, Doctoral Symposium, Posters and Tools Track and Journal Early Feedback, REFSQ-JP 2023

作者： Bragilovski, Maxim Dalpiaz, Fabiano Sturm, Arnon Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Department of Information and Computing Sciences Utrecht University Netherlands

User stories are a common notation for expressing requirements, especially in agile development projects. While user stories provide a detailed account of the functional requirements, they fail to deliver a holistic view of the domain. As such, they can be complemented with domain models that not only help gain this comprehensive view, but also serve as a basis for model-driven development. We focus on the task of recommending relationships between entities in a domain model, assuming that these entities were previously extracted from a user story collection either manually or through an automated tool. We investigate whether an approach based on supervised machine learning can recommend essential relationships in a domain model more accurately than state-of-the-art rule-based methods. Based on a collection of datasets that we manually labeled and a set of 32 features we engineered, we train a machine learning model by using a random forest classifier. The results indicate that our approach has higher precision and F1-score than the baseline rule-based methods. Our findings provide preliminary evidence of the suitability of using machine learning to support the development of domain models, especially in recommending relationships between related entities. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Supervised learning

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Pipelining Graph Construction and Agent-based Computation over Distributed Memory

Pipelining Graph Construction and Agent-based Computation ov...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Hong, Yan Fukuda, Munehiro University of Washington Bothell Computing and Software Systems BothellWA98011 United States

ISBN: (纸本)9781665480451

Graph streaming has received substantial attention for the past 10+ years to cope with large-scale graph computation. Two major approaches, one using conventional data-streaming tools and the other accessing graph databases, facilitate continuous analysis of endlessly flowing graphs and query-based incremental construction of huge graphs, respectively. However, some scientific graphs including biological networks need to stay in memory for repetitive but various analyses. Although a cluster system, thus distributed memory can entirely handle a big graph in memory, a challenge is substantial overhead incurred by loading graphs into memory. A solution is hiding such graph-loading and construction overheads with graph computation in a pipelined fashion. We adapted this pipelining approach for agent-based graph computing where thousands of agents traverse a graph for finding its attributes and shape. We used the multi-agent spatial simulation (MASS) library to implement the concept. A huge graph is incrementally constructed in batches, each spawning and walking agents over the corresponding subgraph, and thus all eventually completing a given computation. We coded and ran two MASS benchmark programs: triangle counting and connected components, with which we evaluated our pipelined graph processing. The best performance was obtained once the batch size shrunk enough to fit cache memory, regardless of the number of cluster nodes. For a single node execution of connected components over a 140MB graph, our graph-pipelining implementation performed 7.7 times faster than non-pipelining execution. Its parallel execution with 24 cluster nodes achieved 8.3 times speed-up as compared to the pipelined single-node execution. © 2022 IEEE.

关键词： Cache memory

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Toward Implementing an Agent-based Distributed Graph Database System

Toward Implementing an Agent-based Distributed Graph Databas...

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IEEE International Conference on Big Data

作者： Yuan Ma Michelle Dea Lilian Cao Munehiro Fukuda Computing and Software Systems University of Washington Bothell Bothell WA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Graph database (DB) systems are increasing their popularity in big-data analysis and machine learning particularly in the areas of e-commerce recommendation, fraud detection, and social-media analytics. Speed-up and spatial scalability of their DB transactions are pursued with various techniques such as index-free access to graph components in Neo4j, graph sharding over a cluster system in ArangoDB, and graph DB construction over distributed memory in AnzoGraph. However, these techniques have their respective challenges: difficulty in expanding an index-free graph over distributed memory, slow-down in accessing distributed disks, and a bottleneck incurred by repetitive master-to-worker distributions of query *** a solution to these problems, we are applying multi-agent technologies to distributed graph DB construction: multiple user processes over a cluster system maintain portion of distributed graph in their cache space; their cache contents are synchronized through a software-snooped write-back and write-update protocol; and a DB user from any cluster node dispatches an agent that handles an independent graph query through navigating over distributed graph. To follow current trends in graph DB standardization, we adopt the Cypher language whose queries are translated into agent *** paper presents a new distributed hash-map implementation and its application to our graph DB system; differentiates it from Hazelcast from the viewpoints of its memory coherency and access speed; describes our translator that generates agent code from Cypher queries; and examines graph DB creation and traversal performance of agents in comparison with Neo4j and ArangoDB.

关键词： Ciphers Sharding Translation Codes Protocols Navigation Machine learning Standardization Vectors Synchronization

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iCare - An AI - Powered Virtual Assistant for Mental Health

iCare - An AI - Powered Virtual Assistant for Mental Health

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Remya Mavila Sugam Jaiswal Raghav Naswa Weichao Yuwen Bill Erdly Dong Si Computing and Software Systems Division University of Washington Bothell USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Currently, mental health issues are increasing among individuals. Sharing feelings with someone who cares plays a key role in resolving these issues. Virtual assistants that can simulate human conversations using artificial intelligence can be effectively used to communicate with individuals facing challenges. This paper describes iCare, a conversational AI that uses machine learning to supply support to individuals suffering from mental health issues. iCare supplies a safe, private, virtual environment for users to share their feelings, and get empathetic response that improves their mental health. It combines multiple techniques to provide the best responses to the users. iCare is designed to provide support for a range of users including those who are suffering from anxiety or depression, and individuals who are unhappy and need help to improve their present feeling.

关键词： Technological innovation Virtual assistants Anxiety disorders Virtual environments Medical treatment Mental health Oral communication

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