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Router and gateway node placement in wireless mesh networks for emergency rescue scenarios

Autonomous intelligent systems 2021年第1期1卷 202-231页

作者： Mariusz Wzorek Cyrille Berger Patrick Doherty Department of Computer and Information Science Linköping UniversityLinköpingSweden School of Intelligent Systems and Engineering Jinan University(Zhuhai Campus)Zhuhai 519070China

The focus of this paper is on base functionalities required for UAV-based rapid deployment of an ad hoc communication infrastructure in the initial phases of rescue *** main idea is to use heterogeneous teams of UAVs to deploy communication kits that include routers,and are used in the generation of ad hoc Wireless Mesh Networks(WMN).Several fundamental problems are considered and algorithms are proposed to solve these *** Router Node Placement problem(RNP)and a generalization of it that takes into account additional constraints arising in actual field usage is considered *** RNP problem tries to determine how to optimally place routers in a WMN.A new algorithm,the RRT-WMN algorithm,is proposed to solve this *** is based in part on a novel use of the Rapidly Exploring Random Trees(RRT)algorithm used in motion planning.A comparative empirical evaluation between the RRT-WMN algorithm and existing techniques such as the Covariance Matrix Adaptation Evolution Strategy(CMA-ES)and Particle Swarm Optimization(PSO),shows that the RRT-WMN algorithm has far better performance both in amount of time taken and regional coverage as the generalized RNP problem scales to realistic *** Gateway Node Placement Problem(GNP)tries to determine how to locate a minimal number of gateway nodes in a WMN backbone network while satisfying a number of Quality of Service(QoS)*** alternatives are proposed for solving the combined RNP-GNP *** first approach combines the RRT-WMN algorithm with a preexisting graph clustering *** second approach,WMNbyAreaDecomposition,proposes a novel divide-and-conquer algorithm that recursively partitions a target deployment area into a set of disjoint regions,thus creating a number of simpler RNP problems that are then solved *** algorithms are evaluated on real-world GIS models of different size and *** is shown to outperform existing algorithms u

关键词： Robotics UAV deployed ad hoc networks Wireless mesh networks Router node placement Gateway node placement Emergency rescue

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4th International Workshop on Software Security: Challenges, Opportunities, and Lessons Learned

ACM International Conference Proceeding Series

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ACM International Conference Proceeding Series 2024年 xxxiv页

作者： Mahmood, Sajjad Alshayeb, Mohammad Niazi, Mahmood Information and Computer Science Department Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

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Toward Automatically Identifying Legally Relevant Factors 35

Toward Automatically Identifying Legally Relevant Factors

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35th International Conference on Legal Knowledge and Information systems, JURIX 2022

作者： Gray, Morgan Savelka, Jaromir Oliver, Wesley Ashley, Kevin Intelligent Systems Program University of Pittsburgh United States School of Law University of Pittsburgh United States School of Computer Science Carnegie Mellon University United States School of Law Duquesne University United States

ISBN: (纸本)9781643683645

In making legal decisions, courts apply relevant law to facts. While the law typically changes slowly over time, facts vary from case to case. Nevertheless, underlying patterns of fact may emerge. This research focuses on underlying fact patterns commonly present in cases where motorists are stopped for a traffic violation and subsequently detained while a police officer conducts a canine sniff of the vehicle for drugs. We present a set of underlying patterns of fact, that is, factors of suspicion, that police and courts apply in determining reasonable suspicion. We demonstrate how these fact patterns can be identified and annotated in legal cases and how these annotations can be employed to fine-tune a transformer model to identify the factors in previously unseen legal opinions. © 2022 The authors and IOS Press.

关键词： Law enforcement

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Overview of tourism digital model: General factors and variables 6

Overview of tourism digital model: General factors and varia...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Sari, Ratna Gaol, Ford Lumban Prabowo, Harjanto Hastiadi, Fithra Faisal Meyliana School of Information Systems Bina Nusantara University Computer Science Department BINUS Graduate Program - Computer Science Information Systems Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Computer Science Department BINUS Graduate Program - Computer Science Management Department Jakarta11480 Indonesia Faculty of Economics and Business Universitas Indonesia Depok Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Tourism continues to be developed, as one of important sectors to support foreign exchange revenue and also support the economic sectors. The purpose of this research finds the mapping factor from digital economy and its implementation in tourism sector as known as tourism digital model. The Methodology of this research used literature approach from previous research of digital economy characteristics factors and digital tourism. The results of this research were generated variable in Tourism Digital Model that can help develop and support in implementation of digital tourism. © 2021 IEEE.

关键词： Tourism

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An Ontological Analysis of Justice 16

An Ontological Analysis of Justice

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16th International Workshop on Value Modelling and Business Ontologies, VMBO 2022

作者： Johannesson, Paul Zhemchugova, Helena Hanger-Kopp, Susanne Department of Computer and Systems Sciences Stockholm University Borgarfjordsgatan 12 Kista Sweden Population and Just Societies Program International Institute for Applied Systems Analysis Laxenburg Austria Department for Environmental Systems Science ETH Zürich Zürich Switzerland

IT systems design and architecture have many similarities with the design of organisations and institutions. Both pay attention to social concepts such as rules, norms, and values. Justice is one of the key concepts that can be relevant for any institutional design from a systems perspective. This paper outlines an ontology of justice based on the Unified Foundational Ontology (UFO). We envisage that it can support designers in recognising and addressing the issues of justice during systems design and analysis, including elicitation of requirements, rules analysis, systems evaluation, and policy analysis. © 2022 Copyright for this paper by its authors.

关键词： systems analysis

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Towards NLP-driven Online-classification of Industrial Alarm Messages

Towards NLP-driven Online-classification of Industrial Alarm...

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Annual Conference of Industrial Electronics Society

作者： Yeersen Shatewakasi Birgit Vogel-Heuser Jan Wilch Victoria Hankemeier Josua Höfgen Institute of Automation and Information Systems Technical University of Munich Garching Germany Computer Science Department Autonomous Intelligent Systems Group University of Münster Münster Germany

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Industrial alarm systems facilitate the reliable and effective management of industrial process operations. However, the manufacturer’s fear of missing a critical fault or lacking process understanding may lead to the implementation of alarm systems that overburden operators by an enormous amount of alarms. Inadequate alarm settings and maintenance, coupled with a high volume of disturbance alarms, necessitate operators making crucial decisions within a limited time. Thus, an alarm management strategy that accurately anticipates the types of incoming alarms is needed, especially in brownfield systems where it is infeasible to simplify the underlying alarming functions. This alleviates the need for human interpretation of alarms upon arrival, allowing operators to address anomalous behaviors early. This study proposes an alarm classification approach, based on active learning, latent dirichlet allocation (LDA) topic modeling and transformer-based deep learning. It focuses on addressing these challenges through a systematic natural language processing (NLP) approach to classify and map fault messages. To demonstrate the validity of this approach, real-world alarm data from an industrial hybrid non-woven manufacturing process is used.

关键词： Industrial electronics Deep learning Systematics Manufacturing processes Alarm systems Transformers Natural language processing Maintenance Behavioral sciences Resource management

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE 4

Air Quality Prediction Based on Air Pollution Emissions in t...

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4th IEEE International Conference of computer science and Information Technology, ICOSNIKOM 2022

作者： Nababan, Adli A Sutarman Zarlis, Muhammad Nababan, Erna B Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Department of Mathematics Medan Indonesia Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Department of Information Systems Management Jakarta Indonesia

ISBN: (纸本)9798350399073

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction. © 2022 IEEE.

关键词： Air quality

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Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis

Adversarial Quantum Machine Learning: An Information-Theoret...

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IEEE International Symposium on Information Theory

作者： Petros Georgiou Sharu Theresa Jose Osvaldo Simeone Department of Computer Science University of Birmingham UK Department of Engineering KCLIP Lab Centre for Intelligent Information Processing Systems (CIIPS) King's College London

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In a manner analogous to their classical counterparts, quantum classifiers are vulnerable to adversarial attacks that perturb their inputs. A promising countermeasure is to train the quantum classifier by adopting an attack-aware, or adversarial, loss function. This paper studies the generalization properties of quantum classifiers that are adversarially trained against bounded-norm white-box attacks. Specifically, a quantum adversary maximizes the classifier's loss by transforming an input state $\rho(x)$ into a state $\tau$ that is $\epsilon$ -close to the original state $\rho(x)$ in p-Schatten distance. Under suitable assumptions on the quantum embedding $\rho(x)$ , we derive novel information-theoretic upper bounds on the generalization error of adversarially trained quantum classifiers for $p=1$ and $p=\infty$ . The derived upper bounds consist of two terms: the first is an exponential function of the 2-Renyi mutual information between classical data and quantum embedding, while the second term scales linearly with the adversarial perturbation size $\epsilon$ . Both terms are shown to decrease as $1/\sqrt{T}$ over the training set size $T$ . An extension is also considered in which the adversary assumed during training has different parameters $p$ and $\epsilon$ as compared to the adversary affecting the test inputs. Finally, we validate our theoretical findings with numerical experiments for a synthetic setting.

关键词： Training Upper bound Perturbation methods Quantum mechanics Machine learning Mutual information Glass box

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Pay Self-Attention to Audio-Visual Navigation 33

Pay Self-Attention to Audio-Visual Navigation

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Yu, Yinfeng Cao, Lele Sun, Fuchun Liu, Xiaohong Wang, Liejun Department of Computer Science and Technology State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing China Motherbrain EQT Stockholm Sweden College of Information Science and Engineering Xinjiang University Urumqi China

Audio-visual embodied navigation, as a hot research topic, aims training a robot to reach an audio target using egocentric visual (from the sensors mounted on the robot) and audio (emitted from the target) input. The audio-visual information fusion strategy is naturally important to the navigation performance, but the state-of-the-art methods still simply concatenate the visual and audio features, potentially ignoring the direct impact of context. Moreover, the existing approaches requires either phase-wise training or additional aid (e.g. topology graph and sound semantics). Up till this date, the work that deals with the more challenging setup with moving target(s) is still rare. As a result, we propose an end-to-end framework FSAAVN (feature self-attention audio-visual navigation) to learn chasing after a moving audio target using a context-aware audio-visual fusion strategy implemented as a self-attention module. Our thorough experiments validate the superior performance (both quantitatively and qualitatively) of FSAAVN in comparison with the state-of-the-arts, and also provide unique insights about the choice of visual modalities, visual/audio encoder backbones and fusion patterns. © 2022. The copyright of this document resides with its authors.

关键词： Robots

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The Iterative Compromise Ranking Analysis (ICRA) - The New Approach to Make Reliable Decisions

The Iterative Compromise Ranking Analysis (ICRA) - The New A...

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Special Sessions in the Advances in Information systems and Technologies Track, AIST 2022 and 17th Conference on Information systems Management, ISM 2022 held as part of the Federated Conference on computer science and Information systems, FedCSIS 2022

作者： Paradowski, Bartosz Kizielewicz, Bartlomiej Shekhovtsov, Andrii Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland National Institute of Telecommunications Szachowa 1 Warsaw04-894 Poland

ISBN: (纸本)9783031295690

In the field of multi-criteria decision-making, compromise is often sought because it is highly desirable for decision-making. However, over the years, many methods have been developed for decision-making, between which discrepancies in the final rankings are often present. For this reason, it is worth noting the possibility of a compromise between different multi-criteria decision-making methods. One such solution is the Iterative Compromise Ranking Analysis (ICRA), which, by means of an iterative evaluation of the preferences of alternatives, leads to a compromise between the methods under consideration. This work presents an example of a solution to a theoretical decision problem, for which five methods were used: TOPSIS, VIKOR, MARCOS, MABAC and EDAS. In addition, an empirical analysis of the compromise solution was carried out to check the effect of parameters on the number of iterations needed to reach a compromise and the differences between the rankings proposed by the methods and the compromise ranking. The work showed that this is an interesting tool that can find its use in the field of multi-criteria decision-making as well as can be used to analyze the behaviour of multi-criteria decision-making methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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