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arXiv 2020年

作者： Chaplick, Steven Klute, Fabian Parada, Irene Rollin, Jonathan Ueckerdt, Torsten Department of Data Science and Knowledge Engineering Maastricht University Netherlands Utrecht University Netherlands TU Eindhoven Netherlands Department of Mathematics and Computer Science FernUniversität in Hagen Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Germany

For a class D of drawings of loopless (multi-)graphs in the plane, a drawing D ∈ D is saturated when the addition of any edge to D results in D0 ∈/ D—this is analogous to saturated graphs in a graph class as introduced by Turán (1941) and Erdös, Hajnal, and Moon (1964). We focus on k-planar drawings, that is, graphs drawn in the plane where each edge is crossed at most k times, and the classes D of all k-planar drawings obeying a number of restrictions, such as having no crossing incident edges, no pair of edges crossing more than once, or no edge crossing itself. While saturated k-planar drawings are the focus of several prior works, tight bounds on how sparse these can be are not well understood. We establish a generic framework to determine the minimum number of edges among all n-vertex saturated k-planar drawings in many natural classes. For example, when incident crossings, multicrossings and selfcrossings are all allowed, the sparsest n-vertex saturated k-planar drawings have 2/k−(k mod 2) (n − 1) edges for any k ≥ 4, while if all that is forbidden, the sparsest such drawings have 2(k + 1)/k (k-1) (n − 1) edges for any k ≥ 6. Copyright © 2020, The Authors. All rights reserved.

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Slow Heat-Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network

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Computational intelligence and neuroscience 2023年第1期2023卷 9918748页

作者： Pavan Kumar Pagadala P Lalitha Surya Kumari Deepak Thakur Vivek Bhardwaj Mohammad Shahid Abdulrajak Buradi Abdul Razak Abiot Ketema Computer Science and Engineering Koneru Lakshmaiah Educational Foundation Hyderabad Telangana India. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India. School of Computer Science and Engineering Lovely Professional University Jalandhar 144411 India. Department of Electrical Engineering Galgotias College of Engineering and Technology 1 Knowledge Park Phase II Noida 201306 UP India. Department of Mechanical Engineering Nitte Meenakshi Institute of Technology Bangalore India. Department of Mechanical Engineering P. A. College of Engineering (Affiliated to Visvesvaraya Technological University Belagavi) Mangaluru 574153 India. Department of Biosystems Engineering Institute of Technology Hawassa University Hawassa Ethiopia.

Vehicular ad hoc networks (VANETs) using reliable protocols of routing have become crucial in identifying the changes to topology on a continuous basis for a large collection of vehicles. For this purpose, it becomes important to identify an optimal configuration of these protocols. There are several possible configurations that have been preventing the configuration of efficient protocols that do not make use of automatic and intelligent design tools. It can further motivate using the techniques of metaheuristics like the tools, which are well-suited to be able to solve these problems. The glowworm swarm optimization (GSO), simulated annealing (SA), and slow heat-based SA-GSO algorithms have been proposed in this work. The SA is a method of optimization, which imitates the manner in which the thermal system has been frozen down to its lowest state of energy. In the GSO, there is guidance to the rules of feasibility, where the swarm converges to its feasible regions very fast. Additionally, for overcoming any premature convergence, there is a local search strategy that is based on the SA and is used for making a search that is near to its true optimum solutions. Finally, this sluggish temperature-based SA-GSO algorithm will be employed to solve routing problems and problems of heat transfer. There is a hybrid slow heat SA-GSO algorithm with a faster speed of convergence and higher precision of computation that is more effective in solving problems of constrained engineering.

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Flexible Co-Planar Waveguide (CPW)-Fed Y-Shaped Patch UWB Antenna for Off-Body Communication 1

Flexible Co-Planar Waveguide (CPW)-Fed Y-Shaped Patch UWB An...

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1st International Conference on Education and Technology, ICETECH 2019

作者： Kassim, S. Rahim, H.A. Soh, P.J. Abdulmalek, M. Jusoh, M. Jamaluddin, M.H. Sabli, N.S. Yassin, M.N. Sabapathy, T. Wee, F.H. Osman, M.N. Ismail, N. Kampus Pauh Putra Perlis Perlis-Arau02600 Malaysia Advanced Communication Engineering Centre COE School of Computer and Communication Engineering Universiti Malaysia Perlis Kampus Pauh Putra Perlis02600 Malaysia Department of Engineering and Information Science University of Wollongong in Dubai Block 15 Dubai Knowledge Village Dubai United Arab Emirates Johor UTM Skudai MY81310 Malaysia Pekan Pahang Malaysia Kampus Pauh Putra Perlis Perlis-Arau02600 Malaysia

This paper intends to design an Ultra-Wideband (UWB) antenna for future Internet of Things (IoT) applications for off-body Wireless Body Area Networks (WBAN) communication. An antenna based on the Y-shaped patch fed using co-planar waveguide (CPW) line, with a full ground plane is designed. It is implemented on two different substrates, namely a 5mm thick Rogers RO4350B and a 5-mm-thick felt textile. Parametric analysis of antenna is performed by changing its critical dimensions and monitoring parameters such as gain, bandwidth, efficiency, radiation pattern when using both substrates. Besides that, the bending effects towards reflection coefficient and radiation patterns are also studied. The final patch size with the Y-shaped slot is 36 × 40 mm2 for both substrates. The antenna is capable of providing coverage for the bands from 8 to 10 GHz. Finally, the antenna designed on RO4350B substrate outperforms the antenna designed on felt by about four times in terms of bandwidth, with 3.3 GHz (7.7-11 GHz). © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Slot antennas

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Application of advanced neural network approaches to investigate the heat transfer features of Powell-Eyring hybrid nanofluids

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Tribology International 2025年 211卷

作者： Wenbo Li Amjad Ali Pasha Zahoor Shah Ali Algahtani Vineet Tirth Nashwan Adnan Othman Mohamed H. Ahmed Maryam Jawaid Waqar Azeem Khan College of Science Shenyang Aerospace University Shenyang 110136 PR China Aerospace Engineering Department King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Mathematics COMSATS University Islamabad Islamabad Campus Pakistan Mechanical Engineering Department College of Engineering King Khalid University Abha Asir 61421 Saudi Arabia Research Center for Advanced Materials Science (RCAMS) King Khalid University Guraiger PO Box 9004 Abha Asir 61413 Saudi Arabia Centre for Engineering and Technology Innovations King Khalid University Abha Asir 61421 Saudi Arabia Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Mechanical Engineering Department King Abdulaziz University P.O. Box 80204 Jeddah 21589 Saudi Arabia School of Mathematics and Statistics Beijing Institute of Technology Beijing 100081 PR China

Possibilities from artificial intelligence are emerging swiftly in many fields, offering novel approaches as well as greatly enhanced ability to analyze complex situations and patterns of various facets. The focus of this paper is to extend the utilization of neural networks with the Levenberg-Marquardt Scheme (NNs-LMS) to explore entropy analysis of Powell–Eyring hybrid nanofluid, which considers the impacts of linear thermal radiation and viscous dissipation (EPNF-LTR-VD). The critical parameters like Prandtl number, suction/injection parameter, Eckert number, and material parameter are taken into consideration in order to understand the flow characteristics and heat transfer rates in this research. By employing the right similarity transformations, the set of nonlinear PDEs is reduced to set of nonlinear ODEs, thereby enabling the lowering the order of the system complexity. A similar dataset is then obtained with the help of the Adam numerical method for analyzing the impacts that these critical parameters can have on more extensive EPNF-LTR-VD scenarios. The dataset is utilized to test, train, and validate the EPNF-LTR-VD, proving its accuracy in predicting fluid system behavior. The empirical studies do not only provide evidence of effectiveness and accuracy of the proposed approach but also identify a high level of correspondence with reference data. Additional support for the model performance is provided by sophisticated performance graphs, error histograms, as well as regression evaluations. This research shows how AI can be used to advance the modeling of fluids; it helps to bring a new perspective into how fluid dynamics can be analyzed and differs from the previous works.

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Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception

arXiv

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arXiv 2021年

作者： Han, Ridong Peng, Tao Han, Jiayu Cui, Hai Liu, Lu College of Computer Science and Technology Jilin University Jilin Changchun130012 China College of Software Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China Department of Linguistics University of Washington SeattleWA98195 United States

Wrong-labeling problem and long-tail relations severely affect the performance of distantly supervised relation extraction task. Many studies mitigate the effect of wrong-labeling through selective attention mechanism and handle long-tail relations by introducing relation hierarchies to share knowledge. However, almost all existing studies ignore the fact that, in a sentence, the appearance order of two entities contributes to the understanding of its semantics. Furthermore, they only utilize each relation level of relation hierarchies separately, but do not exploit the heuristic effect between relation levels, i.e., higher-level relations can give useful information to the lower ones. Based on the above, in this paper, we design a novel Recursive Hierarchy-Interactive Attention network (RHIA) to further handle long-tail relations, which models the heuristic effect between relation levels. From the top down, it passes relation-related information layer by layer, which is the most significant difference from existing models, and generates relation-augmented sentence representations for each relation level in a recursive structure. Besides, we introduce a newfangled training objective, called Entity-Order Perception (EOP), to make the sentence encoder retain more entity appearance information. Substantial experiments on the popular New York Times (NYT) dataset are conducted. Compared to prior baselines, our RHIA-EOP achieves state-of-the-art performance in terms of precision-recall (P-R) curves, AUC, Top-N precision and other evaluation metrics. Insightful analysis also demonstrates the necessity and effectiveness of each component of RHIA-EOP. © 2021, CC BY.

关键词： Semantics

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A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis

arXiv

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arXiv 2021年

作者： Gkoumas, Dimitris Wang, Bo Tsakalidis, Adam Wolters, Maria Purver, Matthew Zubiaga, Arkaitz Liakata, Maria School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Center for Precision Psychiatry Massachusetts General Hospital Boston United States The Alan Turing Institute London United Kingdom School of informatics University of Edinburgh United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Slovenia

Dementia affects cognitive functions of adults, including memory, language, and behaviour. Standard diagnostic biomarkers such as MRI are costly, whilst neuropsychological tests suffer from sensitivity issues in detecting dementia onset. The analysis of speech and language has emerged as a promising and non-intrusive technology to diagnose and monitor dementia. Currently, most work in this direction ignores the multi-modal nature of human communication and interactive aspects of everyday conversational interaction. Moreover, most studies ignore changes in cognitive status over time due to the lack of consistent longitudinal data. Here we introduce a novel fine-grained longitudinal multi-modal corpus collected in a natural setting from healthy controls and people with dementia over two phases, each spanning 28 sessions. The corpus consists of spoken conversations, a subset of which are transcribed, as well as typed and written thoughts and associated extralinguistic information such as pen strokes and keystrokes. We present the data collection process and describe the corpus in detail. Furthermore, we establish baselines for capturing longitudinal changes in language across different modalities for two cohorts, healthy controls and people with dementia, outlining future research directions enabled by the corpus. © 2021, CC BY.

关键词： Neurodegenerative diseases

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Correction to: Network Functions Virtualization for Mobile Core and Heterogeneous Cellular Networks

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Wireless Personal Communications 2021年第3期122卷 2561-2561页

作者： Qureshi, Kashif Naseer Ahmad , Ejaz Anwar, Muhammad Ghafoor, Kayhan Zrar Jeon, Gwanggil Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science NFC Institute of Engineering and Fertilizer Research Faisalabad Pakistan Department of Computer Science Knowledge University Erbil Iraq Department of Embedded Systems Engineering Incheon National University Incheon South Korea

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Efficient Contact Tracing for pandemics using blockchain

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Informatics in Medicine Unlocked 2021年 26卷

作者： Bari, Nida Qamar, Usman Khalid, Ayesha Knowledge & Data Science Research Centre Computer & Software Engineering Department College of Electrical and Mechanical Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan The Centre for Secure Information Technologies (CSIT) ECIT Institute Queen's University of Belfast Belfast BT7 1NN United Kingdom

Blockchain technologies have been benefiting many industries by being decentralized, secure and confidential. They offer great potential in pandemic impacted scenarios as well. Contact tracing helps to mitigate the transmission of disease by alerting people who may have been exposed so they can act on time to protect themselves. Contact tracing systems face some challenges related to issues of medical privacy, data security and transparency. Multiple researches show concern that contact tracing discourages people to seek medication because of the fear of loss of data, subsequent stigma, discrimination, or abuse. In this paper, we discuss how contact tracing can be improved using blockchain technology and could be able to solve these issues. The aim of our proposed system would be to reduce the impact of pandemic, to implement this merger of the blockchains and the Contact tracing app and to ensure user privacy and avoid data misuse by incorporating a symmetric key cryptographic mechanism. To tackle the scalability related issues of Blockchain this framework uses IPFS, a distributed file storage system also known as Interplanetary File System. This blockchain based solution system will enhance contact tracing by making it more stable, secure, performant, highly useable and above all effective in the fight against any pandemic. © 2021 The Authors

关键词： And pandemic Blockchain Contact tracing COVID-19 Distributed system Interplanetary file system

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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Detection of Pulsar Candidates using Bagging Method

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Procedia computer science 2020年 170卷 1096-1101页

作者： Mourad Azhari Abdallah Abarda Altaf Alaoui Badia Ettaki Jamal Zerouaoui Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and knowledge Engineering School of Information Sciences Rabat Morocco

The pulsar classification represents a major issue in the astrophysical area. The Bagging Algorithm is an ensemble method widely used to improve the performance of classification algorithms, especially in the case of pulsar search. In this way, our paper tries to prove how the Bagging Method can improve the performance of pulsar candidate detection in connection with four basic classifiers: Core Vector Machines (CVM), the K-Nearest-Neighbors (KNN), the Artificial Neural Network (ANN), and Cart Decision Tree (CDT). The Error Rate, Area Under the Curve (AUC), and Computation Time (CT) are measured to compare the performance of different classifiers. The High Time Resolution Universe (HTRU2) dataset, collected from the UCI Machine Learning Repository, is used in the experimentation phase.

关键词： Bagging CVM ANN KNN CDT AUC Error Rate Computation Time(CT)

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