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Networked and Multimodal 3D Modeling of Cities for Collaborative Virtual Environments

Networked and Multimodal 3D Modeling of Cities for Collabora...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Benjamin Hall Joseph Kessler Osayamen Edo-Ohanba Jaired Collins Haoxiang Zhang Nick Allegreti Ye Duan Songjie Wang Kannappan Palaniappan Prasad Calyam Dept. of Engineering Physics Murray State University Murray KY Computer Science Dept Truman State University Kirksville MO Dept. of Computer Science Electrical Engineering University of Missouri-Kansas City Kansas City MO Dept. of Electrical Engineering and Computer Science University of Missouri Columbia MO

3D city-scale models are useful in a number of applications, including education, city planning, navigation systems, artificial intelligence training, and simulations. However, final models need to be immersive and interactive, which requires a mixed reality (XR) environment design that combines e.g., a Cave Automatic Virtual Environment (CAVE) VR system with the Microsoft Hololens2 in a networked and multimodal setting. In this paper, we propose a pipeline to convert a city-scale point cloud into a finalized city-scale textured mesh in which, a number of XR devices can share the same environment and co-exist in a shared space for model interactions. Specifically, we use input point clouds obtained from wide area motion imagery systems or off-the-shelf drones pertaining to Albuquerque, New Mexico, but the pipeline is generalized so that other input can be used. Using four different traditional algorithms and an additional deep learning method, we create meshes for the model interactions. For each mesh produced, we map high-resolution textures onto them, producing a more accurate city, which is then passed into the shared/networked Unity environment. Ten participants provided their assessment of mesh quality and interactivity of the networked environment during exploration of different city reconstructions with the CAVE and laptop device modalities. Results on the perceptual immersive quality of the Point2Mesh deep learning meshes highlights the need for improvements to handle large city scale point clouds.

关键词： Point cloud compression Deep learning Solid modeling Surface reconstruction Three-dimensional displays Software algorithms Pipelines

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Prioritization of Benchmarking Framework for Social Media Content Based on Integration of AHP and Group TOPSIS: A Case Study of Instagram Travel

Prioritization of Benchmarking Framework for Social Media Co...

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Computer technologies (ICCTech), international conference on

作者： Nurul Akhmal Mohd Zulkefli Mukesh Madanan Muhamad Hariz Muhamad Adnan Department of Computer Science CAAS Dhofar University Salalah Sultanate of Oman Computing Department FSKIK Universiti Pendidikan Sultan Idris Tanjong Malim Perak Malaysia

ISBN: (纸本)9781665499194

Prioritization of social media content is a difficult process due to the variability of data, different assessment criteria, criterion significance, and alternative members. In the present research, there is no precise approach for ranking social media content based on Multi-criteria decision making (MCDM). This study suggests a revolutionary approach to social media content prioritization based on the integration of AHP and TOPSIS. We present the benchmarking framework in this paper. The approach began with the extraction of the dataset, followed by the determination of the criteria and the construction of the TOPSIS decision matrix. 75 Instagram accounts were included in the data. In the meanwhile, AHP is utilized to determine the weighting based on expert judgments. A decision matrix is constructed using alternative criteria. The results indicate that expert weighing is appropriate, with a consistency rate of less than 10%. Additionally, the AHP and TOPSIS integration findings show that the prioritizing benchmark framework provides the similarity priority rating for the top 10 Instagram, based on the different individual expert weighting.

关键词： Social networking (online) Multimedia Web sites Decision making Benchmark testing Media data mining

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Fake Product Identification System Using Blockchain

Fake Product Identification System Using Blockchain

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international conference on Circuits, Communication, Control and computing

作者： Mallegowda M Anita Kanavalli M.N. Thippeswamy Kushagra Gupta Lakshya Khandelwal Vishal Bhattad Himanshu Vaswani Department of Computer Science Ramaiah Institute of Technology Bengaluru Karnataka Department of Computer Science(AI & ML) Ramaiah Institute of Technology Bengaluru Karnataka

The immutable decentralised and fast nature of blockchain has given rise to numerous applications based on this technology. The integrity of its data is one of the primary features of blockchain technology. By utilising blockchain technology, the system becomes decentralised, enabling the consumer to independently check the accuracy of the data and the product. We suggest a decentralised Blockchain system to stop the fabrication of fake products so that both the supplier and the consumer may use it to trade real goods without having to supervise directly owned stores, which can significantly reduce the cost of product quality assurance. In this project, Quick Response (QR) codes offer a powerful method to tackle the practice of product counterfeiting thanks to new developments in wireless and mobile technologies. Counterfeit goods may be found using a QR code scanner, which connects the product's QR code to a Blockchain. Therefore, this technique might be used to construct blocks in the database using the product's unique codes and record the product's details. It gathers the user's individual code and runs it against records in the Blockchain database. The consumer will be informed if the code matches, or if the product is fake, in which case they will receive the message.

关键词： Wireless communication Fabrication Codes databases Manufactured products QR codes Product design

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Investigating Opthalmic images to Diagnose Eye diseases using Deep Learning Techniques

Investigating Opthalmic images to Diagnose Eye diseases usin...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Shalini Agarwal Aruna Bhat Dept. of Computer Science & Engineering Delhi Technological University Delhi India

In Ophthalmology, several eye image related diagnostic methodologies started offering meaningful insight about eye disease based on huge set of data points and morphological datasets. The common causes of various eye abnormalities are any trauma, aging and diseases, particularly diabetes. Glaucoma, retinal detachment, Cataract, macular degeneration, diabetic retinopathy, hypertensive Retinopathy, and diabetic macular edema are the most common causes of blindness all around the world. All these diseases are manually suspected and diagnosed by ophthalmologists with clinical practice. Lack of eye care centers and scarcity of trained ophthalmologists are very common in rural and remote areas in developing countries like India. Detection at early stage, followed by appropriate and timely clinical treatment for a varieties of eye diseases, can solve problem of blindness to a large extent. Ophthаlmоlоgist adopt a computerized analysis of ophthalmic imaging modalities for faster clinical screening and accurate diagnosis of these diseases. Latest deep learning models, when compared with traditional machine learning algorithms, speed up the diagnosis of disease and improve accuracy with less false positive rate. This paper investigates all latest deep learning methodologies used in automatic retinal disease diagnosis process concerning different image modalities. This paper also lists some of the most common eye diseases, limitations with the latest developments and potential challenges for future enhancements.

关键词： Deep learning Cataracts Retinopathy Process control Blindness Retina Nonhomogeneous media

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Traffic Congestion Management based on Vehicle Density Using Image Processing Techniques

Traffic Congestion Management based on Vehicle Density Using...

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international conference on Futuristic technologies (INCOFT)

作者： V Sadrisjain B B Anushree T N Manohar Department of Computer Science Mysuru Campus Amrita Vishwa Vidyapeetham Amrita School of Computing India

India is one of the world’s most densely populated country, with more vehicles being used in urban areas. The ability to follow or track individual vehicles as well as public transport frameworks is useful. We can address complex challenges with effective results using computer vision and machine learning. The major purpose of our project is to deal with the concept of real-time-vehicle tracking is based on video stream by a surveillance camera to track vehicles. We would focus on the type of vehicle i.e., car or bike, Firstly, the software would slice the frames from the streaming video so that would be easier to detect the attributes precisely. Secondly, slice the area of the required part, for instance if we take the frame, the car in it would be cropped for focusing on the required output as correctly as possible. Lastly, the software would store the required data in the database for future usage.

关键词： Systematics Image processing Surveillance Urban areas Switches Streaming media Traffic control

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MSMA-Net: A Multi-scale Multidirectional Adaptation Network for Polyp Segmentation

MSMA-Net: A Multi-scale Multidirectional Adaptation Network ...

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IEEE international conference on Research, Innovation and Vision for the Future

作者： Trung-Kien Le Hoang-Minh-Quang Le Thi-Thao Tran Van-Truong Pham Department of Automation Engineering School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam

ISBN: (纸本)9781665461672

Polyps are mostly noncancerous tumors that occur in several locations in the digestive tract but are most com-monly found in the colon. But over time, some colon polyps can develop into cancer, especially Adenomas that need to be detected to remove as soon as possible. In recent years, with the variety of modern techniques for polyps detection, image segmentation using deep learning has always been an appreciated method. However, polyp segmentation in this way also has some trouble such as long dependencies, complexity computation, poor local and global context, and lack of multi-scale context. There have been many researches and techniques proposed to overcome these problems, such as attention mechanisms, atrous spatial pyramid pooling (ASPP), Receptive Field Block(RFB), etc. Inspired by those advances in deep learning, in this work, we inherited and proposed an Efficient Attention Receptive Field Block (EA-RFB Block) and Local Global Fusion (LGF) that ensures the network's multi-scale representation, capturing enough information of both local and global context. Our proposed network, namely MSMA-Net has demonstrated improved performance through two metrics of Intersection over Union (IoU) and Dice Coefficient when compared with other state-of-the-art models in Polyp datasets.

关键词： Deep learning Measurement Image segmentation Computer vision Convolution Computational modeling Colonoscopy

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Knowledge Graphs and interoperability techniques for hybrid-cloud deployment of FaaS applications

Knowledge Graphs and interoperability techniques for hybrid-...

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IEEE international conference on Cloud computing Technology and science (CloudCom)

作者： Georgios Fatouros Yannis Poulakis Ariana Polyviou Stylianos Tsarsitalidis Georgios Makridis John Soldatos Georgios Kousiouris Michael Filippakis Dimosthenis Kyriazis Dept. of Digital Systems Greece Innov-Acts Ltd University of Piraeus Piraeus Nicosia Cyprus Dept. of Digital Systems University of Piraeus Piraeus Greece Dept. of Management Nicosia Cyprus Innov-Acts Ltd University of Nicosia Nicosia Cyprus Dept. of Informatics and Telematics Harokopio University Athens Greece Innov-Acts Ltd Nicosia Cyprus

ISBN: (纸本)9781665463683

Towards enabling the automated and optimized FaaS deployment of applications in a hybrid-cloud setting, the application requirements should be met by comparing them to the capabilities of the available resources of available clusters. To this end, semantic matching between the application characteristics and the individual descriptions of available compute clusters (e.g. from public or private cloud or edge facilities available) is required. In this work, such a system is proposed, namely the Reasoning Framework, which performs semantic matching between application and resource (meta)data and facilitates information sharing among the FaaS platform components leveraging Knowledge Graphs, ontology technologies, and semantic reasoning. The proposed system harvests information from the application function workflow, provided as a graph by the function editor specification (based on Node-RED), including developer-inserted annotations during the design process, and maps them to the dynamic information retrieved from the available clusters. The Reasoning Framework interprets these data as graphs and automatically applies several semantic rules that enable filtering of the available resources and efficient information retrieval through a RESTfull interface. The paper also discusses experimental results to further showcase the advantages of the proposed approach.

关键词： Knowledge engineering Computers Cloud computing Filtering Semantics Information sharing Ontologies

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Adoption of Blockchain-Fog-IoMT Framework in Healthcare 4.0 Digital Revolution

Adoption of Blockchain-Fog-IoMT Framework in Healthcare 4.0 ...

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Information Technology (OCIT), OITS international conference on

作者： Soubhagya Ranjan Mallick Suraj Sharma Pradyumna Kumar Tripathy Niranjan Kumar Ray Computer Science and Engineering International Institute of Information Technology Bhubaneswar India Computer Science and Engineering Silicon Institute of Technology Bhubaneswar India

Healthcare is the fastest growing sector that implements the latest technologies for providing healthcare services. Most of the IoMT healthcare systems are using cloud-based systems for providing smart healthcare services. Fog computing is introduced in loT-based healthcare environments to reduce the additional computing and storage required by cloud servers. Healthcare 4.0 focuses on a smart environment that guarantees security, privacy, reliability, integrity, easy access, trust, transparency, faster processing, etc. Blockchain-integrated IoT-fog systems are used for providing security, and privacy and avoiding single-point failure in healthcare systems. In this paper, we have proposed a Blockchain-Fog-IoMT framework for faster processing of healthcare data and reducing the storage and processing overhead on the cloud server. We have also minimized the latency, resource utilization and response time with the implementation of fog computing at the nearest network of the user devices. Ethereum Blockchain and smart contract has been integrated with our system to address the security, privacy, scalability, transparency, reliability, and interoperability challenges. Block validation and transaction validation are done with the use of proof-of-work. Moreover, this model helps to create a decentralized platform for providing healthcare services, facilitates remote-patient tracking and monitoring, and is cost-effective.

关键词： Privacy Cloud computing Scalability Medical services Telecommunication traffic Organizations Security

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Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer

Detection of Uncertainty in Exceedance of Threshold (DUET): ...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Terence Jie Chua Wenhan Yu Chang Liu Jun Zhao Graduate College Nanyang Technological University (NTU) School of Computer Science and Engineering Nanyang Technological University (NTU)

Development of defenses against physical world attacks such as adversarial patches is gaining traction within the research community. We contribute to the field of adversarial patch detection by introducing an uncertainty-based adversarial patch localizer which localizes adversarial patch on an image, permitting post-processing patch-avoidance or patch-reconstruction. We quantify our prediction uncertainties with the development of Detection of Uncertainties in the Exceedance of Threshold (DUET) algorithm. This algorithm provides a framework to ascertain confidence in the adversarial patch localization, which is essential for safety-sensitive applications such as self-driving cars and medical imaging. We conducted experiments on localizing adversarial patches and found our proposed DUET model outperforms baseline models. We then conduct further analyses on our choice of model priors and the adoption of Bayesian Neural Networks in different layers within our model architecture. We found that isometric gaussian priors in Bayesian Neural Networks are suitable for patch localization tasks and the presence of Bayesian layers in the earlier neural network blocks facilitates top-end localization performance, while Bayesian layers added in the later neural network blocks contribute to better model generalization. We then propose two different well-performing models to tackle different use cases.

关键词： Location awareness Analytical models Uncertainty Computational modeling Neural networks Computer architecture Prediction algorithms

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A Novel Framework for Distributed Stream Processing and Analysis of Twitter data 3rd

A Novel Framework for Distributed Stream Processing and Anal...

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3rd international conference on Innovative computing and Communication, ICICC 2020

作者： Arora, Shruti Rani, Rinkle Department of Computer Science and Technology Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9789811551123

data is ubiquitously generated each microsecond from heterogeneous sources. These sources are connected devices in the form of mobiles, telemetry machines, sensors, clickstreams, weather forecasting devices, and many more. The social media platform is not less a firehose of streaming data. Every microsecond, millions of tweets on Twitter, posts, and uploads on Facebook contribute to the volume of data. Twitter’s most important characteristic is its ability for users to tweet about events, situations, feelings, opinions, or even something totally new, in real-time. The motivation of the work arises from the Twitter’s easily available API to capture the tweets and earlier research work on data analytics of streaming social data. There are many workflows developed to handle Twitter data streams nowadays, but still, the research area of streaming data is still in its infancy stage. This study attempts to develop a real-time framework for processing, transformation, storage, and visualization of Twitter data streams. The framework integrates some open-source technologies for execution of the proposed architecture such as Apache Kafka for ingestion of tweets, Apache Spark Streaming for data stream processing, and Cassandra NoSQL database for storage of tweets. The work aims to analyze the ingestion and processing time of the tweets in real time using the proposed integrated framework. The proposed framework shows the minimum average scheduling delay and processing time for different Twitter data modules such as print tweets, average tweet length, and most trending hashtag. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Weather forecasting

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