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IEEE International Conference on Multimedia and Expo (ICME)

作者： Qin Lei Rui Yang Jiang Zhong Rongzhen Li Muyang He Mianxiong Dong Kaoru Ota College of Computer Science Chongqing University Chongqing China School of Computer Science Shaanxi Normal University Xi’an China The School of Advanced Technology Xi’an Jiaotong-Liverpool University Xi’an China Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In this paper, we address the significant challenge of data scarcity in the field of crack segmentation, a key aspect of structural health monitoring. To tackle this, we introduce the CrackGrowDiff framework, an innovative approach for expanding crack datasets. Utilizing a two-stage controllable generation process that combines a random walk algorithm and semantic diffusion models, our framework minimizes discrepancy of misalignment between synthetic data and original data while enhancing data informativeness. We further ensure the quality and informativeness of synthetic data through feature space diversity, employing a pre-trained Variational Autoencoder (VAE) for selection based on Kullback-Leibler (KL) divergence. Comparative experiments demonstrate CrackGrowDiff’s superiority over traditional data augmentation and GANs-based methods, making it a substantial advancement in addressing the data scarcity in crack segmentation tasks. A DEMO and related code will be made public: https://***/spaces/QinLei086/Two-stage-SDM-for-crack-dataset-expending

关键词： Spatial diversity Image edge detection Semantics Process control Diffusion models Data augmentation Data models

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Mqtt Routing Optimizing Based Intrusion Detection for Internet of Things Using Hybrid Machine Learning

Mqtt Routing Optimizing Based Intrusion Detection for Intern...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Hoomod, Haider K. Al-Hamami, Mohammad Alaa Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Niaf, Jolan Rokan College of Education Mustansiriyah University Baghdad Iraq Applied Science University Manama Department of Management Information Systems Manam Bahrain Imam Al-Kadhim College Computer Techniques Engineering Department Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq Informatics Institute for Postgraduate Studies University of Information Technology and Communications Baghdad Iraq

ISBN: (纸本)9798350369106

With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined communication protocol. Other software programs can be linked to operate together using Message Queue Telemetry Transport Protocol (MQTT) or MQSeries components. Business integration software and middleware are common terms for this category of applications. With its suggested lightweight, easy-to-implement, and lowbandwidth system for billions of smart items, the MQTT protocol offers a wide range of potential applications, including home automation. In order to enhance the power savings in MQservice routing, we presented an intelligent assistant for optimizing routing model. For Internet of Things (IoT) purposes, it introduces an optimal routing model based on optimized hybrid machine learning-based anomaly detection (OHMLA). In order to extend the reach of a network, the offered approach allows Internet of Things devices to make efficient use of energy. To further enhance Intrusion Detection identification and get optimum network selection routes, the moth Salp Swarm technique (SSA) technique is used for the optimized hyperparameters. The model is capable of accurately detecting attacks that result in network congestion, causing delays in connection response and data transfer in IoT network. © 2024 IEEE.

关键词： Middleware

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Edge Intelligence Based Collaborative Learning System for IoT Edge 12

Edge Intelligence Based Collaborative Learning System for Io...

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12th IEEE Annual Information technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Welagedara, Lahiru Harischandra, Janani Jayawardene, Nuwan Informatics Institute of Technology Computing Department Colombo Sri Lanka University of Moratuwa Department of Computer Science and Engineering Sri Lanka

ISBN: (纸本)9781665400664

Edge Intelligence based collaborative learning systems have been developed to perform collaborative learning among multiple devices in a distributed environment. Majority of the collaborative learning systems have been designed using resources containing high computational power. It was identified that a system could be implemented to facilitate collaborative learning in resource constrained Internet of Things (IoT) devices. The existing collaborative learning systems were critically reviewed and analyzed to identify the ideal collaborative learning approach for resource constrained IoT edge. During the initial stages of the research, partitioned model training was identified as the most ideal approach. The research paved the way to design and implement two training architectures based on partitioned model training approach to facilitate environments with adequate and limited access to edge infrastructure. The proposed system utilized a hybrid deep learning model in partitioned model training approach for the first time. Furthermore, the research utilized a lightweight containerization mechanism to deploy the proposed collaborative learning system. The testing and evaluation phases of the research proved that the system was able to significantly reduce the resource consumption of the devices while achieving high model accuracy. The experimental setup reached up to 97% in model accuracy while consuming a significantly lower CPU consumption of 6.33%. The proposed system also proved to function efficiently by reducing energy consumption and reducing operational temperature by up to 4°C. © 2021 IEEE.

关键词： Internet of things

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Analysis of data from UAVs for surveillance and threat identification in maritime areas

Analysis of data from UAVs for surveillance and threat ident...

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Big Data, Cloud Computing, Data Science & Engineering (BCD)

作者： Fotis Lampropoulos Ioannis Daramouskas Isidoros Perikos Michael Paraskevas Electrical and Computer Engineering Dept. University of Peloponnese Patras Greece Hellenic Coast Guard Patras Greece Computer Engineering and Informatics Department Computer Technology Institute and Press "Diophantus" University of Patras Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press "Diophantus" University of Peloponnese Patras Greece

In this paper, we present a methodology for drones for recognizing different types of objects in maritime areas. The concept and the aim is to assist the national maritime surveillance authorities in the identification of treats and the recognition of the exact type of the treat. The methodology relies on the use of deep learning networks and YOLO framework as well as regression models. The YOLO model detects where each object is and which label should be applied. In this way, object detection is subject to the analysis of Machine Learning-based approaches and Deep Learning-based approaches providing more information about the video or an image than traditional approaches to recognition. These approaches are used to identify groups of pixels that may individually belong to an object. This then feeds a regression model with the help of a convolutional neural network and more specifically, R-CNN, Fast R-CNN, Faster R-CNN and after that object detection algorithm approximates the location of the object and gives its label at the same time. The purpose of the application is to detect objects with an emphasis on identifying one or more targets of interest from data of a video or an image capture. An experimental study was conducted in real-world conditions and revealed quite interesting findings.

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AutoRoto: Automated Rotoscoping with Refined Deep Masks

AutoRoto: Automated Rotoscoping with Refined Deep Masks

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2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

作者： Dissanayake, Sajitha Ayoob, Mohamed Vekneswaran, Prathieshna University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9781665425032

The legendary movie Jurassic Park (1993) would have been nearly impossible without computer Generated Imaginary(CGI). The movie's ground-breaking visual effects was able to smoothly integrate virtual characters with real-life footage. This rapidly increased the use of visual effects in modern movies. Creating high-end visual effects is a complex workflow where each shot is subjected to artistic operations. The key step in this workflow is the process called digital rotoscoping. It is the process of manually outlining and cutting out the foreground characters in a raw video frame via software. This is a time-consuming process and the average number of frames a professional artist can accomplished per day is 15. Due to this, it has become a temporal bottleneck of the VFX *** order to address the above mentioned issue, an automated rotoscoping approach with instance segmentation is presented in this paper. The novel method is efficient, accurate and will redeem the time of rotoscoping artists. Our model was able to extract foregrounds of multiple instances of classes and to the best of our knowledge our rotoscoping tool is the most generalized to extract foregrounds. © 2021 IEEE.

关键词： Object detection

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Wide-range Angle-sensitive Plasmonic Color Printing with a Lossy Resonator

Wide-range Angle-sensitive Plasmonic Color Printing with a L...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chowdhury, Sarah N. Simon, Jeffrey Nowak, Michal P. Prokopeva, Ludmila J. Pagadala, Karthik Fruhling, Colton Nyga, Piotr Bravo, Esteban Garcia Kildishev, Alexander V. Shalaev, Vladimir M. School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University 1205 W State St West LafayetteIN47906 United States Institute of Optoelectronics Military University of Technology 2 Kaliskiego St Warsaw00-908 Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń87-100 Poland Department of Computer Graphics Technology Purdue University 401 N. Grant St Knoy Hall West LafayetteIN47907 United States

We demonstrate a wide gamut of color generation by large-scale, lithography-free, and environment-friendly plasmonic structures with a resolution of 100 urn for macroscopic color printing by utilizing femtosecond lase... 详细信息

ISBN: (纸本)9781957171258

关键词： Color

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A Scalable Sparse Transformer Model for Singing Melody Extraction

A Scalable Sparse Transformer Model for Singing Melody Extra...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shuai Yu Jun Liu Yi Yu Wei Li School of Computer Science and Technology Donghua University Shanghai China National Institute of Informatics (NII) Tokyo Japan School of Computer Science and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

Extracting the melody of a singing voice is an essential task within the realm of music information retrieval (MIR). Recently, transformer based models have drawn great attention in the field of MIR. However, due to the expensive computation cost and extensive parameters, it is difficult to train and deploy a transformer-based model for practical singing melody extraction. In this paper, we propose a simple yet effective scalable sparse transformer for singing melody extraction. To be specific, we first propose to employ a sparse transformer to reduce computation cost and the amount of parameters. Then, we proposed to scale the self-attention region of the sparse transformer in the spectrogram to obtain more accurate performance. Moreover, we propose to combine a scalable sparse transformer (S 2 Former) with CNN-based model to extract global and local features in the spectrogram. The proposed scalable transformer model can achieve a better balance between a standard transformer and a sparse transformer. To better fuse the features from transformer and CNN, we further propose a transformer-CNN fusion (TCF) module to combine significant features from transformer and CNN. The proposed model obtains state-of-the-art results on several public datasets. The conducted experiments confirm the effectiveness of the model we proposed.

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Requirement Identification for Blockchain-based Secure Transmission of Oil and Gas

Requirement Identification for Blockchain-based Secure Trans...

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informatics (ICI), International Conference on

作者： Mukta Goyal Nitin Kumar Tyagi Dinesh Kumar Saini Computer Science Engineering & Information Technology Jaypee Institute of Information Technology Noida India National Informatics Centre NIC (MeitY) New Delhi India Computer and Communication Engineering Manipal University Jaipur India

Currently, blockchain is using different areas of life such as finance, education, health, government sector, banking, property chain, etc. As digital technology advances, more and more companies are integrating blockchain to improve security, privacy and data transparency for both small businesses and large enterprises. Hence, there is a need to define a software requirement clearly, and precisely so that developer develops the application efficiently. This paper discusses the requirement analysis of one of the blockchain-based applications for the secure transmission of Oil and Gas.

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Robust Transmission Design in Multiobjective RIS-Aided SWIPT IoT Communications

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IEEE Internet of Things Journal 2024年第10期11卷 18605-18618页

作者： Sharma, Vaibhav Allu, Raviteja Singh, Sandeep Kumar Singh, Keshav Duong, Trung Q. Tsiftsis, Theodoros A. Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Motilal Nehru National Institute of Technology Allahabad Electronics and Communication Engineering Department Prayagraj211004 India Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided Internet of Things (IoT) communications under imperfect channel state information (CSI). We formulate a multiobjective optimization problem (MOOP) to design a transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that jointly maximizes energy efficiency (EE) and harvested power (HP) under the norm-bounded CSI error model. Due to the conflicting objective functions and nonconvex nature of the above optimization problem, the MOOP is simplified using the ϵ-constraint method and subsequently adopting advanced optimization tools, such as the Dinkelbach method, S-procedure, general sign-definiteness, semidefinite programming and convex-concave procedure. Thereafter, we propose an alternating optimization-based algorithm which determines optimal TPV and PSM iteratively that jointly maximizes the EE and HP of the considered system. Through numerical simulations, we validate the robustness, optimality, convergence, accuracy, and effectiveness of our proposed algorithm. Furthermore, we assess the impact of several key parameters, such as the number of RIS elements, available transmit power at BS, and the minimum HP on the performance of the considered system. © 2014 IEEE.

关键词： Energy efficiency

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