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

共 6946 条记录，以下是631-640 订阅

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An In-Kernel Inline Crypto Solution for eXpress data Path With QuickAssist Technology: Design, Prototyping and Performance Evaluation 21

An In-Kernel Inline Crypto Solution for eXpress Data Path Wi...

引用

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： Xue, Zhan Karlsson, Magnus Yu, Tao Lobakin, Aleksander Fijalkowski, MacIej Brandeburg, Jesse Intel Corporation Shanghai China Intel Corporation Stockholm Sweden Intel Corporation Gdansk Poland Intel Corporation Oregon United States

ISBN: (纸本)9798350329223

The eXpress data Path (XDP) has been part of mainline Linux kernel as an approach of high-performance programmable packet processing. It has demonstrated strong capabilities in inline DDoS prevention, routing, forwarding, load balancing, etc. However, the fast-growing encrypted data in network have undermined the XDP's capabilities since XDP is unable to parse and process the encrypted packet. The AF_XDP enables the packet redirection to the XDP socket in user space with high performance. But it also lacks cryptographic function in its path. To overcome the limitations, in this paper we propose an inline crypto approach in XDP with a hardware-based accelerator, QuickAssist Technology (QAT), by introducing a new eBPF accelerator device map framework. The ingress encrypted packets from network interface controller (NIC) driver would be redirected to QAT accelerator device for decryption. Afterwards the flexible packet processing continues based on the plaintext. We investigate the feasibility and performance of XDP chaining QAT to process IPsec packet, with kernel networking stack and kernel networking stack bypass separately. © 2023 IEEE.

关键词： Cryptography

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Real-time Equipment State Monitoring and Cycle Time Calculation Method Based on DTW-KNN 8

Real-time Equipment State Monitoring and Cycle Time Calculat...

引用

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

作者： Feng, Zhipeng Wang, Meilin He, Jiaxian Xiao, Wen Guangdong University of Technology School of Information Engineering Guangzhou China

ISBN: (纸本)9798350302455

Effective real-time monitoring of cycle time has important significance for manufacturing workshops to achieve production improvement and increase enterprise competitiveness. In this study, based on industrial big data, a workshop equipment state monitoring and cycle time calculation method based on DTW-KNN algorithm is proposed. Firstly, the characteristics of historical current data of equipment are analyzed, and the current waveform data in different equipment states are extracted and labeled with tags. Then, the DTW-KNN algorithm is used to construct the state model. Finally, based on the Spark framework, real-time current waveform is collected and pushed into the state model to monitor the state and calculate the cycle time. The application verification from the automotive engine valve production line shows that the DTW-KNN algorithm has significantly improved the efficiency of cycle time monitoring. © 2023 IEEE.

关键词： Big data

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A Word Structure Similarity Framework for Wordle Game 15

A Word Structure Similarity Framework for Wordle Game

引用

15th international conference on Signal processing Systems, ICSPS 2023

作者： Huang, Yi Rao, Wenjun Lao, Bin Lu, Wei Han, Ling Bo School of Information Science and Technology Guangdong University of Foreign Studies China School of Foreign Languages Sun Yat-sen University China School of Electronics and Information Engineering Guangdong Ocean University China

ISBN: (纸本)9781510675056

In 2022, lots of tweets consisting of emoji squares and a few words describing moods emerge on Twitter. In fact, it is the result of a word guessing game called Wordle. In order to figure out the relationship among words in Wordle, this paper tries to study the feedback data of its players and extract useful information to further improve the design of Wordle as well as other games of its kind. Specifically, from the perspective of word structure, strict and fuzzy matching algorithms were designed to simulate the data distribution of target word, while some common structure features of words were extracted and weighted by word frequency. On one hand, the data distribution of the target word is simulated with reference to the words with the most similar structures. On the other hand, the difficulty of a word in the Wordle game was quantified by analyzing the number of player’s tries, and then the difficulty coefficient of each word was calculated. Combined with the above numerical algorithms, the difficulty of the target word was evaluated objectively by the difficulty of its similar word sequences. Experiments showed that the proposed framework achieved good performance on the Wordle dataset. Our work was awarded Honorable Mention in 2023 Mathematical Contest in Modeling (MCM). © 2024 SPIE.

关键词： data mining

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SMOTE-IF: A Novel Resampling Method Based on SMOTE Using Isolation Forest Variants for Multi-Class Imbalanced data 16

SMOTE-IF: A Novel Resampling Method Based on SMOTE Using Iso...

引用

international conferences on Internet of Things (iThings) / Green computing and Communications (GreenCom) / Cyber, Physical and Social computing (CPSCom) / Smart data, Smartdata and Congress on Cybermatics (Cybermatics)

作者： Li, Ang Ma, Tingting Ye, Sen Liu, Xunyun Acad Mil Sci Beijing Peoples R China

ISBN: (纸本)9798350309461

Imbalance learning is an important branch of classification tasks in the field of machine learning, and has received increasing attention from researchers. Currently, most researches have focused on binary imbalanced problems, while there exist numerous unsolved multi-class imbalanced problems in real world. The diversity of data distributions and the poor performance of traditional multi-class classification algorithms pose significant challenges to classify multi-class imbalanced data. In this paper, we propose a resampling method named SMOTE-IF based on isolated forest to address the issue of imbalanced overlapping in multi-classification tasks. Firstly, in order to reduce the negative impact of having severely few minority classes, we first propose a SMOTE-based strategy to oversample them. Secondly, a variant of isolated forest is proposed, which can identify overlapped and noisy data in multi-class of boundaries. Numerous experiments on various real datasets have shown that the SMOTE-IF method can effectively handle imbalanced overlapping data. Compared with state-of-the-art resampling methods, SMOTE-IF has achieved significant improvements in different classification performance metrics.

关键词： Multi-class imbalance Resampling Isolation forest Class overlap

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Mobilise-D: Experiences of processing Large Medical data Sets Using Cloud computing Resources 19

Mobilise-D: Experiences of Processing Large Medical Data Set...

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19th IEEE international conference on e-Science, e-Science 2023

作者： Hiden, Hugo Watson, Paul Newcastle University Dept. Computing Science Newcastle United Kingdom

This poster describes the data analysis infrastructure used in a large EU medical project, describes our experiences and lessons learned and presents our techniques for improving the reliability of the analysis proced... 详细信息

ISBN: (纸本)9798350322231

关键词： Cloud computing

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A Survey on Machine Learning Techniques for Multimodal Biomedical Signal processing

A Survey on Machine Learning Techniques for Multimodal Biome...

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2023 IEEE international conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Prathiksha, M. Badiger, Priyanka R. Thangaraj, Varshini Varshini, D.A. Srikanth, Sahana Gurugopinath, Sanjeev Pes University Department of Electronics and Communication Engineering Bengaluru560085 India

ISBN: (纸本)9798350396195

In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented. © 2023 IEEE.

关键词： data fusion

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Construction and Analysis of Tourism Mapping Based on data Mining and Apriori Algorithm 2

Construction and Analysis of Tourism Mapping Based on Data M...

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2nd international conference on algorithms, Microchips, and Network Applications, AMNA 2023

作者： Wang, Shenghan School of Mathematics and Computer Science Yunnan University of Nationalities Kunming650031 China

ISBN: (纸本)9781510664791

This paper relies on the corpus data of UGC (user-generated content) and OTA (online tourism) in the tourism market, adopts various natural language processing techniques represented by deep learning, mines the useful information in the corpus, establishes the knowledge graph and mines the association relationship between tourism products by using data mining techniques and association rule algorithms, and calculates the potential associations in each review, each travelogue and each public article related to cultural tourism through Apriori algorithm to mine their potential associations and explain the association phenomenon reasonably, in order to play a role in scientific regulation and commercial market development for government or enterprise. © 2023 SPIE.

关键词： data mining

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Scheduling in Cloud of Things: An Overview 3

Scheduling in Cloud of Things: An Overview

引用

3rd international conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Hussein Obaid, Abdulrahman Mohammed Ali Abdo Mohammed, Awadh Pani, Santosh K. Alsabry, Ayman Ali Qasem, Hamzah 21 September University of Medical and Applied Sciences Department of Medical Information Technology Sana'a Yemen 21 September University of Medical and Applied Sciences Department of Bioedical Engineering Technology Sana'a Yemen School of Computer Engineering Kiit University Bhubaneswar India International University of Technology Twintech Department of Computer Science Sana'a Yemen International University of Technology Twintech 21 September University of Medical and Applied Sciences sana'a Yemen

ISBN: (纸本)9798350305333

computing, Internet, digital devices, smart devices, and other technologies were leading to a new terminology known as cloud of things (CoT). Cloud of Things is a powerful technology used to analyze and store massive data from thousands of distributed devices (smartphones, vehicles, home appliances, sensors, etc.). Further, Cloud of Things integrated two concepts, Internet of Things and Cloud computing. There are many issues in cloud computing that have been widely studied, such as scheduling. On the other hand, a few issues have been studied in Cloud of Things. The objective of this paper is to discuss and classify the scheduling issues in Cloud of Things. Researchers have developed algorithms for different environments such as heterogeneous/homogeneous clusters, cloud computing, and large-scale community grids. By reviewing and considering many of these articles, we provided an overview for reuse these state-of-the-art algorithms in Cloud of Things environments. In addition, this paper provides researchers with ideas and platform for finding open research and exploring Cloud of Things issues. © 2023 IEEE.

关键词： Scheduling algorithms

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Energy Efficiency and Green computing in Large-scale data Centers

Energy Efficiency and Green Computing in Large-scale Data Ce...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Gao, Jianyong Li, Fangjun Wang, Jing Wang, Qiong Jia, Najuan State Grid Gansu Electric Power Company Gansu Lanzhou China

ISBN: (纸本)9798350313413

With the development of energy efficiency measurement methods, the target demand for energy utilization in data centers is constantly improving, and the study of green energy efficiency (EE) factors in large-scale data centers based on DEA (data envelopment analysis) is becoming increasingly important. In the construction of the entire EE factor analysis model, how to improve the green energy efficiency of the improved model and improve the efficiency of system power usage is currently a key issue that needs to be urgently solved. This article used traditional research methods on factors affecting green energy efficiency, analyzed the hierarchical composition of EE in large-scale data centers (DC), and calculated the four major energy efficiency indicators in DC. Based on the data results, the following conclusions were drawn through discussion. Through simulation experiments, six data center samples from different regions were selected, and the DEA based green energy efficiency factor analysis model was applied. Compared with traditional schemes, the green energy utilization rate has increased, with a comprehensive average improvement of 11.1%. At the same time, the power utilization efficiency has also improved, with a comprehensive average decrease of about 7.7%. This indicates that the large-scale data center green energy efficiency factor analysis model based on DEA has good results in practical applications. This study provides strong guidance and reference for the sustainable development of future data centers, and innovative solutions to address the growing digital demand and environmental challenges. © 2023 IEEE.

关键词： analog simulation data center energy efficiency green energy

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A Low Power And High Performance Software Approach to Artificial Intelligence On-Board 14

A Low Power And High Performance Software Approach to Artifi...

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14th IEEE Space computing conference (SCC)

作者： Ghiglino, Pablo Harshe, Mandar Klepsydra Technol AG Zurich Switzerland

ISBN: (纸本)9798350341430

New generations of spacecrafts are required to perform tasks with an increased level of autonomy. Space exploration, Earth Observation, space robotics, etc. are all growing fields in Space that require more sensors and more computational power to perform these missions. Furthermore, new sensors in the market produce better quality data at higher rates while new processors can increase substantially the computational power. Therefore, near-future spacecrafts will be equipped with large number of sensors that will produce data at rates that has not been seen before in space, while at the same time, data processing power will be significantly increased. Use cases like guidance navigation and control applications, vision-based navigation has become increasingly important in a variety of space applications for enhancing autonomy and dependability. Future missions such as Active Debris Removal will rely on novel high-performance avionics to support image processing and Artificial Intelligence algorithms with large workloads. Similar requirements come from Earth Observation applications, where data processing on-board can be critical in order to provide real-time reliable information to Earth. This new scenario of advanced Space applications and increase in data amount and processing power, has brought new challenges with it: low determinism, excessive power needs, data losses and large response latency. In this article, a novel approach to on-board artificial intelligence (AI) is presented that is based on state-of-the-art academic research of the well known technique of data pipeline. Algorithm pipelining has seen a resurgence in the high performance computing work due its low power use and high throughput capabilities. The approach presented here provides a very sophisticated threading model combination of pipeline and parallelization techniques applied to deep neural networks (DNN), making these type of AI applications much more efficient and reliable. This new approac

关键词： AI on-board computing on-board data processing high performance computing

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