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5th IEEE International conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Yue, Wei Yang, Lei Zhao, Xiaofang Dongguan University of Technology School of Computer Science and Technology Dongguan China Dongguan University of Technology School of Electronic Engineering and Intelligence Dongguan China

ISBN: (纸本)9798350310603

The carbon residue formed from calcination of flame retardant materials is crucial to evaluate their performance and ensure quality control. The complex shape and texture variations of the charcoal dross make traditional segmentation methods difficult to perform accurately. We propose an enhanced SwinUNet method to achieve more precise segmentation of the charcoal residue region in flame retardant materials. Improvements involve adapting the original encoder to utilize a convolutional approach and adding a transformer to the skip connection for processing the feature map. Annotate images are fed into an encoder based on a convolutional neural network to extract features, which are then up-sampled in a Swin Transformer-based decoder and connected with shallow feature maps of different scales produced during the encoding phase. Finally, the improved SwinUNet method is compared to several classical image segmentation methods. The results demonstrate significant outperformance of the remaining methods with about a 2% improvement over SwinUnet on Dice. These findings offer an effective tool and guidance for evaluating the performance of flame retardant materials. © 2023 IEEE.

关键词： Image segmentation

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Alchemist: A Unified Accelerator Architecture for Cross-Scheme Fully Homomorphic Encryption 24

Alchemist: A Unified Accelerator Architecture for Cross-Sche...

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61st ACM/IEEE Design Automation conference, DAC 2024

作者： Mu, Jianan Han, Husheng Shi, Shangyi Ye, Jing Liu, Zizhen Liang, Shengwen Li, Meng Zhang, Mingzhe Bian, Song Hu, Xing Li, Huaiwei Li, Xiaowei SKLP ICT CAS China University of Chinese Academy of Sciences China CASTEST China Peking University China IIE CAS China Beihang University China ZGC LAB China Cambricon Technologies China

ISBN: (纸本)9798400706011

The use of cross-scheme fully homomorphic encryption (FHE) in privacy-preserving applications present to be a new challenge to hardware accelerator design. Existing accelerator architectures with customized polynomial-level operator abstraction fail to efficiently handle hybrid FHE schemes due to the mismatch between computational demands and available hardware resources under various parameter settings. In this work, we propose a new accelerator architecture that consists of a novel finer-grained low-level operator, i.e., Meta-OP, that not only mathematically supports a diverse range of polynomial operations, but is also hardware-friendly for accelerator design without complex topological logic. We then design a new slot-based data management scheme to efficiently handle the distinct memory access patterns over the Meta-OP. With a slot-based data management approach, Alchemist can accelerate both arithmetic and logic FHE workloads with high hardware utilization rates. In the experiment, we show that Alchemist is up to 24,829× faster than CPU. For arithmetic FHE, compared with the SOTA ASIC accelerators, Alchemist achieves a 29.4× performance per area improvement on average. For logic FHE, compared with the SOTA ASIC accelerators, Alchemist achieves a 7.0× overall speed up on average. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： computer circuits

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Line-Start Permanent Magnet Machines for Low-Power Applications

Line-Start Permanent Magnet Machines for Low-Power Applicati...

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2024 International conference on Electrical Machines, ICEM 2024

作者： Barta, Jan Toman, Marek Losak, Petr Aarniovuori, Lassi Vitek, Ondrej Brno University of Technology Department of Power Electrical and Electronic Engineering Brno Czech Republic Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic Electrical Engineering LUT University Lahti Finland

ISBN: (纸本)9798350370607

The improvement of efficiency is one of the main areas of research in electrical machines today. Three-phase induction machines are widely used in industry, motivating researchers to explore methods to increase their efficiency or replace them with more efficient alternatives. A line-start synchronous machine is a promising machine design for achieving high-efficiency classes even for low-power applications. The main contribution of this paper is to show the achievable performance of the LSPMSM by simply replacing the rotor of an induction machine. In this work, two different geometries suitable for low-power machines are optimized and studied in terms of electromagnetic parameters. Finally, the results of the start-up simulations for both machine designs are presented. The presented method shows that by replacing the induction machine rotor with a line-start permanent magnet rotor, it is possible to significantly enhance machine efficiency, even for low-power applications. © 2024 IEEE.

关键词： Permanent magnets

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Stress Detection of Philippine Rice Variety in Lubang Occidental Mindoro Using Deep Learning

Stress Detection of Philippine Rice Variety in Lubang Occide...

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2024 IEEE International conference on Agrosystem Engineering, Technology and Applications, AGRETA 2024

作者： De Lara, Karren Vitalicio Sultana, Maksuda School of Graduate Studies Ama University Quezon City Philippines Dean of Computer Science Ama University Quezon City Philippines

ISBN: (纸本)9798350373660

The present study deals with rice varieties from the Philippines that were grown in Lubang, Occidental Mindoro using deep learning. The aim of the research is to tackle the problem of rice plant stress, which greatly affects yield and quality. This research uses sophisticated deep learning techniques to measure different types of stress, such as discoloration, leaf wilting, and growth anomalies, as shown in high resolution images taken in rice fields. A complete data set was created through field surveys and remote sensing technology. The developed model showed a better performance than traditional methods in terms of accuracy when identifying and classifying stresses. Such an approach promises to be an effective tool for farmers as well as other agricultural stakeholders who wish to have better monitoring and management of crop health, resulting into increased productivity and sustainability within paddy farming. The results highlight how artificial intelligence can be used in agriculture to achieve food safety amidst environmental challenges. © 2024 IEEE.

关键词： Deep learning

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Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged Photonic-Electrical Silicon Transceiver for high-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged P...

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2024 conference on Lasers and Electro-Optics, CLEO 2024

作者： Qin, Jun Yang, Qipeng Han, Changhao Zhou, Yan Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Liu, Siming Li, Yueqin Sun, Jian Min, Miao Hu, Weiwei He, Zhixue Wang, Lei Yu, Shaohua Shu, Haowen Wang, Xingjun Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100192 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China Peng Cheng Laboratory Shenzhen518055 China State Key Laboratory of Information Photonics and Optical Communications BUPT Beijing100876 China

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical interconnects

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CFS: Scaling Metadata Service for Distributed File System via Pruned Scope of Critical Sections 23

CFS: Scaling Metadata Service for Distributed File System vi...

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18th European conference on computer Systems (EuroSys)

作者： Wang, Yiduo Wu, Yufei Li, Cheng Zheng, Pengfei Cao, Biao Sun, Yan Zhou, Fei Xu, Yinlong Wang, Yao Xie, Guangjun Univ Sci & Technol China Hefei Peoples R China Baidu China Co Ltd Beijing Peoples R China Anhui Prov Key Lab High Performance Comp Hefei Peoples R China

ISBN: (纸本)9781450394871

There is a fundamental tension between metadata scalability and POSIX semantics within distributed file systems. The bottleneck lies in the coordination, mainly locking, used for ensuring strong metadata consistency, namely, atomicity and isolation. CFS is a scalable, fully POSIX-compliant distributed file system that eliminates the metadata management bottleneck via pruning the scope of critical sections for reduced locking overhead. First, CFS adopts a tiered metadata organization to scale file attributes and the remaining namespace hierarchies independently with appropriate partitioning and indexing methods, eliminating cross-shard distributed coordination. Second, it further scales up the single metadata shard performance by single-shard atomic primitives, shortening the metadata requests' lifespan and removing spurious conflicts. Third, CFS drops the metadata proxy layer but employs the light-weight, scalable client-side metadata resolving. CFS has been running in the production environment of Baidu AI Cloud for three years. Our evaluation with a 50-node cluster and microbenchmarks shows that CFS simultaneously improves the throughput of baselines like HopsFS and InfiniFS by 1.76-75.82x and 1.22-4.10x, and reduces their average latency by up to 91.71% and 54.54%, respectively. Under cases with higher contention and larger directories, CFS' throughput benefits expand by one order of magnitude. For three real-world workloads with data accesses, CFS introduces 1.62-2.55x end-to-end throughput speedups and 35.06-62.47% tail latency reductions over InfiniFS.

关键词： Distributed file system Metadata management

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TSDDISCOVER: Discovering Data Dependency for Time Series Data 40

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

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40th IEEE International conference on Data Engineering, ICDE 2024

作者： Ding, Xiaoou Li, Yingze Wang, Hongzhi Wang, Chen Liu, Yida Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Research Center for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods. © 2024 IEEE.

关键词： Semantics

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QSR: Q-Learning Based Algorithm of Support Recovery for Sparse Signals 100

QSR: Q-Learning Based Algorithm of Support Recovery for Spar...

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100th IEEE Vehicular Technology conference, VTC 2024-Fall

作者： Zhou, Tianzhi Wang, Yibo Wu, Zhangyan Liang, Hengwei Qi, Yuan Qian, Rongrong Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Beijing University of Posts and Telecommunications School of Intelligent Engineering and Automation Beijing China

ISBN: (纸本)9798331517786

Within a number of compressed sensing (CS) problems, support recovery of sparse signals is the primary task that needs to be performed. In this paper, we propose a Q-learning based algorithm of support recovery (QSR) for sparse signals, which treats the atomic selection in traditional greedy CS algorithms as Markov Decision Process (MDP) in reinforcement learning (RL). We adopt parallel computing to address "the curse of dimensionality"in Q-learning, dividing the overall RL problem into multiple subproblems. The computational complexity can thus significantly decrease, which grows linearly with the increase of signal length. Experiments show that the proposed algorithm performs closely to other CS algorithms when dealing with high sparsity level signals and it has better performance when dealing with low sparsity level signals. © 2024 IEEE.

关键词： Markov processes

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Cultural Transcreation with LLMs as a new product 25

Cultural Transcreation with LLMs as a new product

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25th Annual conference of the European Association for Machine Translation, EAMT 2024

作者： Silva, Beatriz Wu, Helena Jingxuan, Yan Cabarrão, Vera Moniz, Helena de Sousa, SaraGuerreiro Almeida, João Søholm, Malene Sjørslev Farinha, Catarina Dimas, Paulo Unbabel Lisbon Portugal University of Lisbon Portugal INESC-ID Lisbon Portugal

ISBN: (纸本)9781068690716

We present how at Unbabel we have been using large language models (LLMs) to apply a cultural transcreation product on customer support emails and how we have been testing the quality and potential of this product. We discuss our preliminary evaluation of the performance of different MT models in the task of translating rephrased content and the quality of the translation outputs. Furthermore, we introduce the live pilot programme and the corresponding relevant findings, showing that transcreated content is not only culturally adequate but it is also of high rephrasing and translation quality. © 2024 The authors.

关键词： computer aided language translation

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ISAV 2023: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV 2023)

ACM International Conference Proceeding Series

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ACM International conference Proceeding Series 2023年 855-855页

作者： Ziegeler, Sean Larsen, Matt Huebl, Axel Usher, Will Us Department of Defense Hpc Modernization Program/GDIT United States Luminary Cloud United States Lawrence Berkeley National Laboratory United States Intel Corporation United States

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