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Third international computing Imaging conference, CITA 2023

Third International Computing Imaging Conference, CITA 2023

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3rd international computing Imaging conference, CITA 2023

ISBN: (纸本)9781510671409

The proceedings contain 199 papers. The topics discussed include: lensless light-field imaging with flexible perspective selection;comparison of line-structured light stripe segmentation algorithms;imaging-based methods for lunar dust flux and velocity measurement;a multi-level encoded and interleaved data format for holographic data storage with high reliability and high recoverability;intelligent information processing of fiber Bragg grating biosensor for stress monitoring in microfluidics system;LCVR-based Stokes polarimeter: Principles and evaluation;A high energy end pumped Q-switched Nd:YAG laser;effect of resonant cavity parameters on the self-excited oscillation for end-pumped Nd:YAG Q-switched laser;and influence of comprehensive resolution on sampled response of staring photoelectric imaging system.

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IoV data processing algorithms for Automatic Real-Time Object Detection - Literature Review 6

IoV Data Processing Algorithms for Automatic Real-Time Objec...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Zineb, Seghir Lyamine, Guezouli Kamel, Barka Rezki, Assem Computer Science Department LaSTIC Laboratory Batna 2 University Batna Algeria Renewable Energies and New Technologies Department LEREESI Laboratory Higher National School of Renewable Energies Environment & Sustainable Development Batna Algeria

ISBN: (纸本)9798350398496

With the advent of the Internet of Vehicles (IoV), there is an increasing demand for real-time and accurate object detection algorithms for intelligent transportation systems. This literature review aims to provide a comprehensive analysis of the latest research in IoV data processing algorithms for automatic real-time object detection. By analyzing and comparing various state-of-the-art algorithms such as Faster R-CNN, Mask R-CNN, and YOLOv4, the strengths and limitations of each approach are identified. Additionally, the challenges associated with real-world implementation are discussed and provide recommendations for future research directions. This research study highlights the importance of efficient and accurate object detection algorithms in the context of the IoV and emphasizes the need for ongoing research in this field. © 2023 IEEE.

关键词： Autonomous vehicles

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Intelligent processing of data Streams on the Edge Using Reinforcement Learning

Intelligent Processing of Data Streams on the Edge Using Rei...

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IEEE international conference on Communications (IEEE ICC)

作者： Vaishnav, Shubham Magnusson, Sindri Stockholm Univ Dept Comp & Syst Sci Stockholm Sweden

ISBN: (纸本)9798350333077

A key challenge in many IoT applications is to ensure energy efficiency while processing large amounts of streaming data at the edge. Nodes often need to process time-sensitive data using limited computing and communication resources. To that end, we design a novel R-Learning based Offloading framework, RLO, that allows edge nodes to learn energy optimal decisions from experience regarding processing incoming data streams. In particular, when should the node process data locally? When should it transmit data to be processed by a fog node? And when should it store data for later processing? We validate our results on both real and simulated data streams. Simulation results show that RLO learns with time to achieve better overall-rewards with respect to three existing baseline schemes. Moreover, the proposed algorithm excels the existing baseline schemes when different priorities were set on the two objectives. We also illustrate how to adjust the priorities of the two objectives based on the application requirements and network constraints.

关键词： Reinforcement learning data stream processing Offloading Edge IoT

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Partitioning-based Active Trust Mechanism in Cloud-Edge-Device Collaborative data Collection 21

Partitioning-based Active Trust Mechanism in Cloud-Edge-Devi...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xia, Zhuoqun Wang, Ziyu Liu, Xiao Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China Guangxi University School of Computer Electronics and Information Nanning China

ISBN: (纸本)9798350329223

Since the open character of the Internet of Things (IoT), IoT devices are vulnerable to various attacks. Compromised devices may maliciously provide unreliable data to the cloud. Furthermore, existing trust evaluation models struggle to balance data security needs across different regions. To address this issue, the paper proposes a partition-based active trust mechanism (PATM). This PATM scheme mainly includes the following parts: First, the region is divided into subregions, and evaluations are conducted for the indicators of each subregion. Mechanism modeling and fuzzy analytic hierarchy process are used to analyze the impact of each indicator on the sensitivity of subregions. Second, the direct estimation method is utilized to estimate the demand for active detection routing based on the sensitivity of subregions. Then, the number of operations of malicious nodes is increased through the high-frequency transmission of detection routes required by each subregion. Additionally, a trust calculation method based on node operations is proposed to evaluate the reliability of the device. The experimental results show that the PATM scheme proposed in this article can quickly identify the trust degree of nodes while balancing the needs of different regions, ensuring the credibility of data collection. © 2023 IEEE.

关键词： Internet of things

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Advancing Singular Value Decomposition Techniques for Enhanced data Mining in Recommender Systems 22nd

Advancing Singular Value Decomposition Techniques for Enhanc...

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22nd international conference on Artificial Intelligence and Soft computing (ICAISC)

作者： Beshley, Mykola Hordiichuk-Bublivska, Olena Beshley, Halyna Ivanochko, Iryna Lviv Polytech Natl Univ Dept Telecommun UA-79013 Lvov Ukraine Comenius Univ Dept Informat Syst Fac Management Bratislava 82005 Slovakia

ISBN: (纸本)9783031425073;9783031425080

The development of effective algorithms for processing large volumes of information is a very urgent problem for modern information systems. The work examines the problems of content analysis and data mining by recommender systems. Features of automatic systems management using machine learning algorithms are presented. The importance of using recommender systems for more accurate prediction of goods or services of interest to users, determination of interrelationships between various production factors, etc. have been determined. The operation of the FunkSVD algorithm, which allows accurate and fast processing of sparse data sets of a large volume, has been studied. A modification of FunkSVD is proposed to use fewer data about users and items when forming recommendations. The simulation of the proposed algorithm was carried out and it was determined that it works faster than the usual one, while maintaining fairly high accuracy, so it can be used in the processing of large data. It is also proposed to increase the privacy and accuracy of FunkSVD recommendations in distributed systems by using the Fed SVD algorithm. The results of the experiments showed the high accuracy of the proposed algorithm but at the expense of a certain increase in the duration of calculations. According to the conducted research, it was concluded that both modifications can be used in systems with different requirements for data mining and distributed architecture.

关键词： Big data Recommender Systems data Mining Funk SVD Fed SVD

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Dimensionality Reduction-Integrated Analog-to-Digital Converters for Efficient data Acquisition in High-Dimensional Signal processing

Dimensionality Reduction-Integrated Analog-to-Digital Conver...

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2023 international conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Kiruthika, R. Umamaheswari, H. Karthikeyan, A. Sns College of Technology Dept of Ece Coimbatore India

ISBN: (纸本)9798350313987

Humans struggle to comprehend and visualize data in more than three dimensions. Dimensionality reduction techniques allow us to project high-dimensional data into lower-dimensional spaces (such as 2D or 3D), enabling easier visualization and interpretation of patterns and relationships in the data. This research study proposes a novel approach to revolutionize high-dimensional signal processing by seamlessly integrating adaptive dimensionality reduction algorithms with analog-to-digital converters (ADCs). The focal point is the development of a system that not only efficiently acquires high-dimensional data but dynamically tailors its processing strategy based on the inherent characteristics of incoming signals. The adaptive dimensionality reduction algorithms embedded in the system facilitate real-time adjustments to the processing pipeline, ensuring optimal feature selection in response to varying signal patterns. This adaptability is especially crucial in dynamic environments where signal characteristics may change dynamically. Emphasizing low-power design, the integrated ADCs are optimized to operate efficiently in energy-constrained settings, making the system well-suited for deployment in resource-sensitive applications, including IoT devices and wearables. © 2023 IEEE.

关键词： Adaptive algorithms data Acquisition Dimensionality Reduction Feature Selection High dimension Low-Power Design

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Towards Building Edge-side Common data processing Services on The computing Continuum 23

Towards Building Edge-side Common Data Processing Services o...

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24th international Middleware conference Demos, Posters and Doctoral Symposium, Middleware 2023

作者： Tanimura, Yusuke Nakada, Hidemoto Dong, Yunzhi National Institute of Advanced Industrial Science and Technology Japan University of Tsukuba Japan

ISBN: (纸本)9798400704291

In order to respond data growth from edge to cloud and efficiently use various real data for advanced digital services, we advocate edge-side common data processing services on the computing continuum. This poster presents our background motivation of the concept, summarizes requirements and assumptions, and introduces an overview design of our proposed depot service, which would be implemented as our future work to show a proof of the concept. © 2023 ACM.

关键词： data handling

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MapReduce: A Big data-Maintained Algorithm Empowering Big data processing for Enhanced Business Insights 8th

MapReduce: A Big Data-Maintained Algorithm Empowering Big Da...

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8th international conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Uprety, Deepak Chandra Banarjee, Dyuti Kumar, Nitish Dhiman, Abhimanyu IEC University Himachal Pradesh Baddi India Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada522302 India LPU University Punjab Phagwara144001 India

ISBN: (纸本)9789819702091

A method for displaying huge amounts of data is known as a big data algorithm. Security, storage, searching, sharing, exposure, and transferring are among the difficulties. Due to its straightforward user interface, high scalability, and fault-tolerance capability, MapReduce is currently required for data server applications (Niemenmaa et al. in Bioinformatics 28:876–877, 2012). It results in a fresh indication of caution regarding sophisticated algorithms for data analysis. The easiest way to manage the enormous amount of data while speeding up processing is to use MapReduce. This paper discusses MapReduce applications and optimization techniques, as well as their similarities and differences, and offers some recommendations for future research projects. Hadoop is a well-known MapReduce success, and MapReduce is currently the Hadoop programming style for massive data processing (Uma Maheswara Rao, S., & Lakshmanan, L. (2023). Security and scalability issues in big data analytics in heterogeneous networks. Soft computing, 1–7.). We suggest a MapReduce programming methodology for managing enormous data sets in Distributed Systems to address the aforementioned issues. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： MapReduce

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Exascale programing models for extreme data processing 23

Exascale programing models for extreme data processing

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20th ACM international conference on computing Frontiers (CF)

作者： Garcia-Blas, Javier Carretero, Jesus Univ Carlos III Madrid Madrid Spain

ISBN: (纸本)9798400701405

Extreme data is an incarnation of Big data concept distinguished by the massive amounts of data that must be queried, communicated and analysed in (near) real-time by using a very large number of memory/storage elements and Exascale computing systems. Immediate examples are the scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analysed, the millions of images per day that must be mined/analyzed in parallel, the one billion of social data posts queried in real-time on an in-memory components database. Traditional disks or commercial storage cannot handle nowadays the extreme scale of such application data. Following the need of improvement of current concepts and technologies, ASPIDE's activities focus on data-intensive applications running on systems composed of up to millions of computing elements (Exascale systems). Practical results will include the methodology and software prototypes that will be designed and used to implement Exascale applications. The ASPIDE project contributed with the definition of a new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on Exascale systems, which can pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, promoting high performance and efficiency, and offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real-time. This work is continued now in the ADMIRE EuroHPC project.

关键词： High Performance data Analysis Programming models Monitoring Auto-tuning

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2023 8th international conference on Intelligent computing and Signal processing, ICSP 2023

2023 8th International Conference on Intelligent Computing a...

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8th international conference on Intelligent computing and Signal processing, ICSP 2023

ISBN: (纸本)9798350302455

The proceedings contain 432 papers. The topics discussed include: consistent sensing algorithm for distributed PV station area line loss anomaly fluctuation based on spectral clustering;video anomaly detection based on multiple instance 3D channel attention;transformer-based Siamese neural network face verification method;a network structure for rehabilitation training evaluation based on transformer;intelligent evaluation model of fetal health based on PCA and stacking algorithm;attention feature selection based on prior knowledge injection;analysis and prediction of heart disease based on machine learning algorithms;point cloud simplification method based on two-fold neighboring feature preservation;multi-strategy fusion improved Archimedes optimization algorithm;a video captioning method based on visual-text semantic association;and an Improved DVB-S2 phase coarse synchronization algorithm and Its FPGA implementation.

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