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International Conference on Computing, Networking and Communications (ICNC)

作者： Jiachen Yang Jiabao Wen Bin Jiang Hoibing Song Fanhii Kong Zhizhio Zhen School of Electrical and Information Engineering Tianjin University Tianjin China Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona Beach USA School of Management Tianjin University of Technology Tianiin China School of Artificial Intelligence Hebei University of Technology Tianiin China

ISBN: (数字)9781728149059

ISBN: (纸本)9781728149066

In Ocean of Things, information prediction is an important part of marine data processing. Due to a variety of marine acquisition devices, the distribution of marine information is discrete. The marine area is wide, and the resolution of the collected data is small, which causes the IoT device to accurately calculate the information of a small sea area. Processing discrete and sparse data is the main job of marine information processing equipment. The main purpose of this paper is to predict the information of the blind area based on the collected data. In order to increase the resolution of sea surface information, the availability of marine data in the Internet of Things is improved. This paper proposes a method for predicting information on marine blind spots. This method is based on the improved machine learning algorithm FCM. This method is used to predict sea surface temperature and improve the resolution of sea surface data. Finally, this paper has done a number of simulation experiments, based on different methods to predict the sea surface temperature. The experimental results are discussed and the performance of the proposed method is analyzed.

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Development of a fuzzy authentication system for the automated line devices of a distributed internet of things using a hash-resistant algorithm

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AIP Conference Proceedings 2023年第1期2700卷

作者： A. L. Zolkin T. G. Aygumov A. N. Losev E. V. Alexandrova 1Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara 443010 Russia 2Department of computer engineering software and automated systems Dagestan State Technical University Makhachkala 367000 Russia 3Department of Applied Informatics Russian State Agrarian University - Moscow Timiryazev Agricultural Academy Moscow 127550 Russia 4Department of Information Technologies and Mathematics Orel State Agrarian University named after N.V. Parakhin Orel 302019 Russia

The cybernetic essence of the data extraction process at the level of end nodes (intermediate links of the information exchange path) is considered in the article. Authors consider and generalize the context-oriented problem of control action transferring to a specific activation point (transfer perceptron (response node)).The authors consider the subject area - distributed response networks. The role of addictive technologies in the process of formation of a complex control action or command and their transmitting is indicated. A simulation balance model is being developed for a routing model in the spatial field of an industrial computer network. It implements the method of filtering and disaggregation of automated technological process control lines according to a group criterion (by the method of sets determination).The introduction of hardware control of the model of the authentication system of automated line devices using the method of data multiplexing and the apparatus of fuzzy mathematics is given, formalized and considered, taking into account the use of a cryptographic flow limiter (unidirectional hash function, which has an integrated address controlled impact).Authors generalize a probabilistic model for calculation of the efficiency and expediency of the functioning of this system for a four-loop motion control line. Calculation of the standard deviation and variance for the emulating output of the considered perceptron is given. In conclusion, a block diagram of the developed device is presented.

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Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

arXiv

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arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

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An ontology for threat modeling and simulation of small unmanned aerial vehicles 9

An ontology for threat modeling and simulation of small unma...

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9th International Defense and Homeland Security Simulation Workshop, DHSS 2019

作者： Chhaya, Bharvi Jafer, Shafagh Proietti, Paolo Marco, Bruno Di Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona BeachFL United States Leonardo S.p.A. Land and Naval Defence Electronics Roma Italy Consorzio S3log Roma Italy

ISBN: (纸本)9788885741348

Low, Slow and Small Unmanned Aerial Vehicles (LSS UAVs) are one of the fastest-growing threats for national defense, security and privacy. A NATO task group performed a study to identify the elements necessary to define LSS models applicable for the development of necessary countermeasure to potential threats in the future. The goal of this project is to utilize this data collected by the NMSG-154 study to generate a Web Ontology Language (OWL) ontology for LSS threat modeling. The LSS ontology will form the basis for a metamodel for a domain-specific language (DSL) based on the parameters identified. This DSL will eventually be used to generate specific simulation scenarios to model potential threats caused by small drones. © 9th International Defense and Homeland Security Simulation Workshop, DHSS 2019. All rights reserved.

关键词： Ontology

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Multi-sensor information fusion in ocean of things based on improved adaptive dempster-shafer evidence theory 2

Multi-sensor information fusion in ocean of things based on ...

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2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Yang, Jiachen Wen, Jiabao Jiang, Bin Song, Houbing Kong, Fanhui Zhen, Zhizhuo School of Electrical and Information Engineering Tianjin University Tianjin China Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona Beach United States School of Management Tianjin University of Technology Tianjin China School of Artificial Intelligence Hebei University of Technology Tianjin China

ISBN: (纸本)9781728129778

The Internet of Things has been used in a variety of industries, including the marine information industry. The Ocean of Things includes measurements of marine environmental information. Due to the variety of sensors and the duplication of measurement tasks, the marine environment data is numerous and redundant. Achieving information fusion computing for multiple measurement devices is an important task for IoT devices. In the absence of human participation, IoT devices need to intelligently calculate the credibility of each sensor information to enable multi-sensor information fusion. This paper presents an improved adaptive DS evidence fusion algorithm. The method calculates the reliability of the sensor data by using multiple sets of sensor measurement data. It enables data level fusion of multisensor information. Ocean temperature measurements are used as experimental examples. The fusion results and the reliability of each temperature sensor are obtained through simulation calculation. By analyzing the experimental results, the reliability and accuracy of the proposed method are proved. © 2019 IEEE.

关键词： Reliability

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ASD: Emotions and behavioural-based interventions through social sensor networks

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International Journal of Networking and Virtual Organisations 2018年第2-4期19卷 133-152页

作者： Faustin, Uwizeyimana Kovalan, A. Kannan, Rajkumar School of Computer Sciences and Application Periyar Maniyammai University Thanjavur India Department of Software Engineering Periyar Maniyammai University Thanjavur India Department of Information Systems College of Computer Sciences and IT King Faisal University Saudi Arabia

In India, nowadays one per 88 children is born with autism spectrum disorder (ASD) contrary to a ratio of one per 110 children few years back. In this paper, we propose a social sensor network for individuals with ASD to offer medical promotions as intervention and first aid. They should be in care and medical surveillances. We are monitoring enjoyment, frustration, boredom, and stress by using body-worn sensors and video streaming. This sensor collects the information from the human body and transmits it to their smart phones then to the external server. We introduce an algorithm named as spectrum clustering algorithm (SCA) which clusters people based on activities of the child. Communication is provided between the clusters of people by using context recommendation algorithm. We use prerogative response algorithm (PRA) for load balancing on server while processing the requests. Our processes are simulated using Omnet++ simulator and provide effective results. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Sensor networks

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Business model canvas should pay more attention to the software startup team

arXiv

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arXiv 2021年

作者： Kemell, Kai-Kristian Elonen, Atte Suoranta, Mari Nguyen-Duc, Anh Garbajosa, Juan Chanin, Rafael Melegati, Jorge Rafiq, Usman Aldaeej, Abdullah Assyne, Nana Sales, Afonso Hyrynsalmi, Sami Risku, Juhani Edison, Henry Abrahamsson, Pekka Faculty of Information Technology University of Jyvaskyla Jyvaskyla Finland School of Business and Economics University of Jyväskylä Jyväskylä Finland Department of Business and IT University of Southeast Norway Bø Norway Universidad Politécnica de Madrid Madrid Spain School of Technology PUCRS Porto Alegre Brazil Faculty of Computer Science Free University of Bozen-Bolzano Bolzano Italy Department of Information Systems University of Maryland Baltimore Country Baltimore United States Department of Software Engineering LUT University Lahti Finland Lero NUI Galway Galway Ireland

Business Model Canvas (BMC) is a tool widely used to describe startup business models. Despite the various business aspects described, BMC pays a little emphasis on team-related factors. The importance of team-related factors in software development has been acknowledged widely in literature. While not as extensively studied, the importance of teams in software startups is also known in both literature and among practitioners. In this paper, we propose potential changes to BMC to have the tool better reflect the importance of the team, especially in a software startup environment. Based on a literature review, we identify various components related to the team, which we then further support with empirical data. We do so by means of a qualitative case study of five startups. Copyright © 2021, The Authors. All rights reserved.

关键词： software design

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Consumer product preference prediction towards online shopping

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IOP Conference Series: Materials Science and engineering 2021年第1期1055卷

作者： S Deepa R Suguna P Sathishkumar C Jamunadevi E N Vidhya Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai India

Nowadays E-commerce plays a major role in a business organization. People prefer online shopping rather than offline shopping which helps them to purchase their product from anywhere around the world through mobile phones and laptop. Online shopping websites help people by saving time and product order can be done easily by clicking the product. Online shopping websites are built using the Single Page Application (SPA) framework and the objective of this research is to find the customer preference product prediction by tracking the frequent clicks of the product by the customer. By tracking the clicks of customer we can find their product choice and helps retailer to add the products according to user preference.

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Self-paced and self-consistent co-training for semi-supervised image segmentation

arXiv

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arXiv 2020年

作者： Wang, Ping Peng, Jizong Pedersoli, Marco Zhou, Yuanfeng Zhang, Caiming Desrosiers, Christian Department of Software IT Engineering Ecole de technologie supérieure MontrealH3C1K3 Canada Department of Systems Engineering Ecole de technologie supérieure MontrealH3C1K3 Canada School of Computer Science and Technology Shandong University Jinan250101 China

Deep co-training has recently been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches for semi-supervised segmentation with a self-paced and self-consistent co-training method. To help distillate information from unlabeled images, we first design a self-paced learning strategy for co-training that lets jointly-trained neural networks focus on easier-to-segment regions first, and then gradually consider harder ones. This is achieved via an end-to-end differentiable loss in the form of a generalized Jensen Shannon Divergence (JSD). Moreover, to encourage predictions from different networks to be both consistent and confident, we enhance this generalized JSD loss with an uncertainty regularizer based on entropy. The robustness of individual models is further improved using a self-ensembling loss that enforces their prediction to be consistent across different training iterations. We demonstrate the potential of our method on three challenging image segmentation problems with different image modalities, using small fraction of labeled data. Results show clear advantages in terms of performance compared to the standard co-training baselines and recently proposed state-of-the-art approaches for semi-supervised segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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Hybrid Filter for Attributes Reduction in Soft Set 1

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Mohammed, Mohammed Adam Taheir Mohd, Wan Maseri Wan Arshah, Ruzaini Abdullah Mungad, M. Sutoyo, Edi Chiroma, Haruna Faculty of Computer System & Software Engineering Universiti Malaysia Pahang Lebuhraya TunRazak KuantanPahang Malaysia Department of Information Systems University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Department of Artificial Intelligence University of Malaya Pantai Valley Kuala Lumpur50603 Malaysia Gombe Nigeria

ISBN: (数字)9789811317996

ISBN: (纸本)9789811317972

The purpose of this research is to overcome parameterization reduction limitation that focuses only on individual parameter reduction, whereas in some cases the individual parameter reduction is not sufficient even implies reduction. It was found that the dimensions sometimes are not able to reduce the number of data in the case of big data;hence, for this reason it became necessary to look for an alternative technique that can significantly reduce the parameters. This paper proposed an alternative decision partition order method based on rough set indiscernibility to select attributes reductions in soft set using decompositions. For significant candidates, the method decomposition partition order used R supp checking to confirm the correctness of the reduction. Comparison of the reduction methods shows that the proposed method provides better result than the parameterization reduction in enhancing reduction. The false candidates were filtered in the huge candidate reduction by the Min supp. The proposed method can be used to maintain object before attribute reduction as well as to reduce parameter size drastically while maintaining consistency in decision making. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Decision making

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