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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6952 条记录，以下是4911-4920 订阅

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Acconotate: Exploiting Acoustic Changes for Automatic Annotation of Inertial data at the Source

Acconotate: Exploiting Acoustic Changes for Automatic Annota...

引用

international conference on Distributed computing in Sensor Systems (DCOSS)

作者： Soumyajit Chatterjee Arun Singh Bivas Mitra Sandip Chakraborty Department of CSE IIT Kharagpur India

Smart infrastructures often intend to provide personalized context-aware services for their residents. These context-aware services, in turn, often rely on sophisticated machine learning algorithms which need vast volumes of costly annotated sensor data. State-of-the-art automated annotation frameworks try to solve this problem by generating annotated sensor data obtained from personal wearables. However, most of these approaches - (a) either need visual data from the environment or (b) can only work for environments with a single resident. This paper discusses the design of a first-of-its-kind framework Acconotate which can automatically generate annotated data from dual resident smart environments without requiring any visual information. Acconotate achieves this by exploiting the typical transitions present in complex human activities first to solve the critical problem of the user-to-activity association and then use that to annotate the sensor stream available from both the users. Rigorous evaluation with two real-life datasets collected in two diverse scenarios shows that Acconotate can successfully generate annotated sensor data over the edge without human intervention.

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An Automatic Scoring System for Wushu Movements based on Real-time Action Image Recognition and Matching Algorithm

An Automatic Scoring System for Wushu Movements based on Rea...

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Pervasive computing and Social Networking (ICPCSN), international conference on

作者： Li Deng Hengru Liu Polus International College ChengDu SiChuan China Chengdu Sport University Martial Arts Institute Sichuan chengdu China

Existing engineering practices show that it is a complex task to require automatic detection and matching of corresponding features, usually without any prior knowledge of the scene and image. As the detailed application, this research study considered the scheme of automatic scoring system for Wushu movements based on real-time action image recognition and matching algorithm. Starting from the review of the state-of-the-art study results, the questions for the efficient image recognition is firstly presented. Then, efficient effective moment invariants are designed to serve as the pre-processing of the images and the quadtree method is used for the modelling. The new roles and methods are designed to conduct the task of the efficient image recognition. Furthermore, the SIFT based matching algorithm is designed. Combining these 2 algorithms, the efficient scoring system is implemented and comparison experiment is conducted. After testing, it is reflected that the proposed model has the better recognition accuracy and the scoring performance is good.

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A Comprehensive Study Based on Routing Protocols and data Transmission in M-WSN Environment

A Comprehensive Study Based on Routing Protocols and Data Tr...

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international conference on computing, Communication, and Intelligent Systems (ICCCIS)

作者： Kamalinder Kaur Sandeep Kang Department of CSE Chandigarh University Mohali Gharuan CEC Landran Mohali India

With the advancements in technology, various kinds of physical actions may now be digitally operated with the click of a button. This has given rise to the concept of IoT to strengthen the MANET-WSN (M-WSN) in a creative perspective. To find the appropriate path, several routing methods transmit control packets and transmitting unwanted network information consume the battery and raise the issue of packet overhead which reduces then network lifespan. Among various issues, high latency, energy consumption, packet drop, network lifetime and insecure data delivery were to name a few. Moreover, a highly reliable, and adaptive routing is lacking that could provide efficient data transmission in mobile node environment. Therefore, in this research, important protocols for routing supported by M-WSN and game theory methodology, with various route discovery algorithms and secure data transmission from source to destination are discussed. This systematic review considers the last 15 years' studies which were found from highly credible sources. This paper presents a comprehensive study of routing algorithms in mobile and static WSN architectures with an emphasis on using game theory as a decision-making tool. The paper begins with a summary of the modern facilities in routing algorithms and then delves into the many game theory-based strategies that have been put forth for this field. The paper concludes with a discussion of the advantages and challenges of using game theory in the context of routing algorithms, and provides recommendations for future research directions. The idea is expanded to include the performance metrics, security measures, and privacy considerations that surround the secure transmission of data packets between different nodes. The comprehensive analysis provides potential solutions proposed by different researchers that can help for future studies aimed at enhancing network information sharing. A number of routing designs were studied from an analytica

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High-Speed data Transfer using AXI Protocol for Image Detection

High-Speed Data Transfer using AXI Protocol for Image Detect...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Rajat Raj Sharat Kumar Miskin Naman Bhatia Electronics and Communication Engineering RV College of Engineering Bengaluru India

Convolutional neural networks (CNN) due to their excellent accuracy, have emerged as the leading machine learning method. However, it becomes difficult to deploy CNN algorithms on hardware platforms because of their high memory bandwidth, computational complexity, and power consumption. Hardware accelerators such as Field Programmable Gate Arrays (FPGA), Graphics processing Unit (GPU), etc. are excellent platforms for modelling CNN algorithms. The purpose of the paper is to run a standard software process in hardware and exploit natural parallelism in hardware to speed up neural network prediction. The paper presents a rule-based synthesizable Advanced Microcontroller Bus Architecture (AMBA) Advanced eXtensible Interface (AXI) protocol for high-speed data transmission. The paper develops a computer vision system on an FPGA that could recognize the numbers 0 through 9 written by hand for the capstone design project. The findings show that 91% accuracy is achieved through Rectified linear activation unit (ReLU) which can be increased to 98% by implementing the Sigmoid activation function with a depth of 10 bits. The method provides good accuracy and clock performance while using few resources.

关键词： Protocols Machine learning algorithms Computational modeling Software algorithms Neural networks Graphics processing units Parallel processing

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Restoration of Time-Varying Graph Signals using Deep Algorithm Unrolling

Restoration of Time-Varying Graph Signals using Deep Algorit...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hayate Kojima Hikari Noguchi Koki Yamada Yuichi Tanaka Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Department of Electrical Engineering Tokyo University of Science Graduate School of Engineering Osaka University

In this paper, we propose a restoration method of time-varying graph signals, i.e., signals on a graph whose signal values change over time, using deep algorithm unrolling. Deep algorithm unrolling is a method that learns parameters in an iterative optimization algorithm with deep learning techniques. It is expected to improve convergence speed and accuracy while the iterative steps are still interpretable. In the proposed method, the minimization problem is formulated so that the time-varying graph signal is smooth both in time and spatial domains. The internal parameters, i.e., time domain FIR filters and regularization parameters, are learned from training data. Experimental results using synthetic data and real sea surface temperature data show that the proposed method improves signal reconstruction accuracy compared to several existing time-varying graph signal re- construction methods.

关键词： Temperature distribution Sea surface Smoothing methods Finite impulse response filters Signal processing algorithms Training data Time-domain analysis

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Efficient Node Selection for Coding-based Timely Computation over Heterogeneous Systems

Efficient Node Selection for Coding-based Timely Computation...

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IEEE international conference on Big data and Cloud computing (BdCloud)

作者： Yuxuan Lin Bin Tang Siyuan Zhou Zaipeng Xie Baoliu Ye Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

In large-scale distributed systems, computational nodes often experience random slowdown which can degrade the performance of timely computation tasks significantly. Recently, coded computing has emerged as a promising approach where coded subtasks are generated for parallel execution over different nodes. In this paper, we focus on exploring the tradeoff between the performance of coding-based timely computation tasks and the computation cost in distributed systems with heterogeneous nodes. We introduce a node selection problem (NSP), which aims at minimizing the task failure probability by selecting proper participating nodes under a total cost constraint. We show that NSP is NP-hard due to the total cost constraint, and is more challenging since the task failure probability is a complicated multivariate polynomial. By approximating the task failure probability using central limit theorem and establishing relationships with certain integer linear programming problems, we then propose a linear programming relaxation based algorithm with performance guarantee, as well as an efficient Lagrangian relaxation inspired heuristic algorithm. The effectiveness of the proposed node selection algorithms is further demonstrated via both trace-driven and model-driven simulations.

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Towards Industry data Governance: Construction of An Industrial data Decentralized Distributed Symbiotic Sharing Space Based on Tensor

Towards Industry Data Governance: Construction of An Industr...

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international conference on Automation and computing (ICAC)

作者： Gang Chen Kai Zhao Minghao Wang Xufei Wang Yi Shen Shanghai Open Source Big Data Institute Shanghai China ZyllionData Technology (Shanghai) Co. Ltd. shanghai China

One of the focuses of smart manufacturing technology is data management and processing. However, there are issues which is difficult to integrate the data within the enterprise, share industry chain data and obtain open data in the management and processing of existing industrial data. To address this challenge, this paper proposes a construction method of industrial data decentralized distributed symbiotic sharing space (iDS3) based on tensor. The proposed method consists of three core steps. First, metadata is extracted from multi-source heterogeneous raw data; Second, a knowledge graph is constructed using industrial mechanism to enrich the metadata; Finally, tensors are used to store metadata and its corresponding unique identifier ID. A metadata encoding rule based on industrial mechanisms is adopted in this work, which can support tensor queries in different application scenarios, thereby achieving ubiquitous storage of data by solving the distributed symbiotic sharing of multi-source heterogeneous raw data. At present, the industrial data governance platform (iDGP) developed based on iDS3 has been applied to the data integration of industrial enterprises such as ships, integrated circuits and new energy vehicles, the integration development with various the third-party data processing tools, and application services of industry dataspaces and so on, which shows that iDS3 has been a successful solution for the development of technology-driven Industry 4.0 to value-driven Industry 5.0.

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ICCVAE: Item Concept Causal Variational Auto-Encoder for top-n recommendation

ICCVAE: Item Concept Causal Variational Auto-Encoder for top...

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6th international conference on Intelligent computing and Signal processing (ICSP)

作者： Jingyun Feng Qianqian Wang Zhejun Huang Lili Yang Department of Statistics and Data Science Southern University of Science and Technology Shenzhen China

Recently, recommendation systems, especially interpretable ones, have become increasingly popular. The recommendation system provides personalized recommendations to users based on their previous behaviour data. Existing approaches often transfer methods in disentangle learning, especially Variational Auto-Encoder (VAE) framework, into the recommendation system. However, VAE is proved sub-optimal due to the independence factor assumption. Unfortunately, few research focuses on the VAE framework without an independent assumption for recommendation system. To escape from sub-optimality from independence assumption and provide more interpretability, in this paper, we propose a new method for top-n recommendation tasks called Item Concept Causal Variational Auto-Encoder (ICCVAE), enabling to build up causal structure for factors, i.e., item concepts in recommendation system. We conduct experiments to prove superiority for ICCVAE. Our framework can reach up to 5.3%, 3.4%, and 6.1% definite improvement over baselines in terms of Recall@20, Recall@50 and NDCG@100.

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Congestion-aware Optimal data rate Selection for Efficient Routing in Wireless Rechargeable Sensor Network

Congestion-aware Optimal Data rate Selection for Efficient R...

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Advances in computing, Communication, Embedded and Secure Systems (ACCESS), international conference on

作者： Siron Anita Susan T B Nithya Abdulla Ameen Sarvesh Rajkumar Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Wireless Rechargeable Sensor Nodes (WRSNs) are widely used to sense, collect, and process data or signals transmitted among people, the environment, and computer systems. However, it faces severe threats such as congestion, resource deprivation, security risk, limited communication speed, high cost, limited bandwidth, and so on. To address some of these issues and to improve the QoS, this paper proposes an Adaptive Rate Determined Congestion Management (ARDCM) approach. It adopts a feedback-based system in which the incoming data rate is managed to increase when congestion is low and decrease when congestion is high. It guarantees congestion free data flow between upstream and downstream in data communication by controlling the data rate and buffer occupancy. The proposed approach is simulated on NS2 with varying numbers of active nodes in a simulated environment. The proposed model’s performance is evaluated by comparing the Packet Delivery Ratio(PDR), Packet Loss Ratio(PLR), delay, and throughput to the existing algorithms.

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Leveraging Content Based Image Retrieval Using data Mining for Efficient Image Exploration 1

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4th international conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Kaur, Jaspreet Gupta, Divya Singh, Amrinder Shah, Syed Hassan Ahmed Department of Computer Science and Engineering Chandigarh University Mohali 140413 India University Institute of Teachers Training and Research ChandigarhUniversity Punjab Mohali India California State University FullertonCA United States

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

A content-based image Retrieval (CBIR) has become an essential tool for managing and searching large-scale images. However, the accuracy and performance of CBIR systems can be improved by combining data mining techniques. Content-based retrieval (CBR) uses the properties and characteristics of the content itself to search for and retrieve information from a big database instead of depending on text or metadata. CBR is very helpful in research, where it is necessary to swiftly and effectively examine vast amounts of data. According to the results, data mining techniques can considerably increase the retrieval process accuracy and effectiveness. Similar photos can be grouped together using clustering algorithms, common patterns of visual features can be found using association rule mining, and images can be classified using classification techniques. In order to create a system for content-based image retrieval and processing, we studied the retrieval of images from huge databases using a variety of feature extraction and matching techniques. The demand for CBIR development came as a result of the sharp rise in image database volumes and their widespread use in several applications. The description of basic feature extraction methods including texture, color, and form is provided in this study. Once these features are retrieved and then used for comparing photos based on similarity. This study suggests a cutting-edge system design for CBIR system that integrates content-based picture and color analysis with data mining methods. This work is intended to develop a segmentation module for the CBIR system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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