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Automatic English Translation using Artificial Recurrent Neural network with Machine Translation

Automatic English Translation using Artificial Recurrent Neu...

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distributed Systems, Computer networks and Cybersecurity (ICDSCNC), International conference on

作者： Jianxun Guo Beibei Zheng Lei Zhang Biao Li Haolin Guo Ming Chen Tianjin University of Technology and Education Tianjin China Zhuobei Technology (Wuhan) Co. Ltd Wuhan China Tianjin Data Development Center Tianjin China Tianjin Academy of Fine Arts Tianjin China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In recent years, automated recognition technique for English translation error recognition yields poor accuracy recognition results due to its limited ability for semantic analysis. Consequently, this research creates an automatic identification approach for Machine Translation (MT) error detection based on Artificial Intelligence (AI). Thus, this research developed an intelligent identification method known as Artificial Recurrent Neural network with Machine Translation (ARNN-MT) in order to recognize English translation errors. Context-learning from previous words is used by ARNN sequential data to detect translation issues. MT uses these networks to predict accurate translations. These hybridized ARNN-MT techniques combined with rule-based process, and learned patterns to enhance error detection. Consequently, ARNN-MT improves phrase flow and able to resolve specific translation problems while providing a logical translation in context. When compared to existing algorithms, including Machine English Translation Errors-Multifeatured Fusion (METE-MF), Double-Recurrent Neural network (Double RNN), and Radio Magnetic Pronunciation Recording Devices (RMPRD), the results demonstrated that the proposed ANN-MT method performed better in terms of accuracy, of 99.96%.

关键词： Performance evaluation Translation Accuracy Recurrent neural networks Computational modeling Semantics parallel processing Prediction algorithms Machine translation Artificial intelligence

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A Transformer-Block-Wise Collaborative Training Mechanism with Hybrid parallelism Over Heterogeneous networks

A Transformer-Block-Wise Collaborative Training Mechanism wi...

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IEEE conference on Wireless Communications and networking

作者： Jiewei Chen Jingrong Wang Shaoyong Guo Jiakai Hao Xuesong Qiu Zehui Xiong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications State Grid Beijing Information & Communication Company Pillar of Information Systems Technology and Design Singapore University of Technology and Design

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the rise of AI-Generated Content (AIGC) services in wireless networks, efficient and high-quality distributed training of Large Language Models (LLMs) has become essential for enabling the large-scale application of next generation AI technologies. However, the extensive parameters of LLMs impose significant demands on memory, computing power and communication resources in heterogeneous networks. To efficiently utilize the dispersed network resources, this paper presents a First-Pipeline- Then-Federated Learning (FPTFL) approach with a hybrid parallel scheduling strategy to facilitate the training of Transformer-based LLMs. We propose a block-wise splitting mechanism to partition the Transformer's encoder into distinct segments, which are deployed cross individual devices. The encoder parameters and intermediate smashed data are uploaded to the edge server, where the whole model is updated through federated aggregation. Particularly, we develop a fine-grained computation-efficient method based on pipeline parallelism, enabling the segments to cooperatively train the entire encoder. An optimization problem is formulated to determine the LLM segments and the number of micro-batches under network resource constraints, with the goal of minimizing the total latency of LLM training services. Simulation results demonstrate that our approach enables Transformer-based model training on resource-constrained devices, preserves model performance, and reduces waiting time.

关键词： Training Performance evaluation Large language models Wireless networks Pipelines parallel processing Transformers Heterogeneous networks Classification algorithms Optimization

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Comparative evaluation of performance and scalability of convolutional neural network implementations on a multisystem HPC architecture 2

Comparative evaluation of performance and scalability of con...

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2nd International conference on Computational Intelligence and Energy Advancements, ICCIEA 2021

作者： Pandey, Sunil Nagwani, Naresh Kumar Verma, Shrish Dept of Computer Science and Engineering NIT Raipur India Dept of Electronics and Communications Engineering NIT Raipur India

The convolutional neural network training algorithm has been implemented for a central processing unit based high performance multisystem architecture machine. The multisystem or the multicomputer is a parallel machine model which is essentially an abstraction of distributed memory parallel machines. In actual practice, this model corresponds to high performance computing clusters. The proposed implementation of the convolutional neural network training algorithm is based on modeling the convolutional neural network as a computational pipeline. The various functions or tasks of the convolutional neural network pipeline have been mapped onto the multiple nodes of a central processing unit based high performance computing cluster for task parallelism. The pipeline implementation provides a first level performance gain through pipeline parallelism. Further performance gains are obtained by distributing the convolutional neural network training onto the different nodes of the compute cluster. The two gains are multiplicative. In this work, the authors have carried out a comparative evaluation of the computational performance and scalability of this pipeline implementation of the convolutional neural network training with a distributed neural network software program which is based on conventional multi-model training and makes use of a centralized server. The dataset considered for this work is the North Eastern University’s hot rolled steel strip surface defects imaging dataset. In both the cases, the convolutional neural networks have been trained to classify the different defects on hot rolled steel strips on the basis of the input image. One hundred images corresponding to each class of defects have been used for the training in order to keep the training times manageable. The hyperparameters of both the convolutional neural networks were kept identical and the programs were run on the same computational cluster to enable fair comparison. Both the convolutional neur

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Treatment and Prevention of Stuck Pipe based on Artificial Neural networks Analysis

Treatment and Prevention of Stuck Pipe Based on Artificial N...

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2022 Offshore Technology conference Asia, OTCA 2022

作者： Zhu, Qi China National Petroleum Corporation China

ISBN: (纸本)9781613998359

Oil and gas drilling is a field practice with risks and uncertainties. Uncertainty and ambiguity of formation conditions often cause downhole accidents such as borehole wall instability, stuck drilling, blowout, etc., and also pose a threat to drilling *** to the incorrect understanding of the objective environment and the wrong decision of subjective consciousness;it caused complex underground conditions and serious accidents. Collapse stuck is the worst kind of accident in stuck stuck. The procedures to deal with this kind of accident are the most complicated, the most time-consuming, the most risky, and even the whole or part of the wellbore may be scrapped, so we should try our best to avoid this accident during the drilling *** Neural networks (ANNs for short) is a mathematical model of algorithms that imitate the behavioral characteristics of animal neural networks and perform distributed parallel information processing. This kind of network depends on the complexity of the system and adjusts the interconnection relationship between a large numbers of internal nodes to achieve the purpose of processing information, and has the ability of self-learning and self-adaptation. This paper analyzes the causes of collapse stuck, the mechanical mechanism of drilling fluid wettability on the stability of mud shale formation wall.A surface wetting reversal agent added to the drilling fluid system was used to change the wettability of the shale *** mechanism analysis and research results of changing the wettability to change the mechanical properties of the shale fracture surface were applied to the actual production of the collapsed drilling *** the change of drilling parameters, the risk of stuck drilling is predicted in advance, the drilling speed is increased, the drilling time loss caused by stuck drilling is reduced, and the drilling cycle and cost are saved. Copyright © 2022, Offshore Technology conference.

关键词： Wetting

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HKTGNN: Hierarchical Knowledge Transferable Graph Neural network-based Supply Chain Risk Assessment

HKTGNN: Hierarchical Knowledge Transferable Graph Neural Net...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Zhanting Zhou Kejun Bi Yuyanzhen Zhong Chao Tang Dongfen Li Shi Ying Ruijin Wang School of Information Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Sichuan University Chengdu China Chongqing CEPREI Industrial Technology Research Institute Co. Ltd Chongqing China Sichuan Changhong Electronic Holding Group Co. Ltd Chengdu China College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China School of Computer Science Wuhan University Wuhan China

The strength of a supply chain is an important measure of a country’s or region’s technical advancement and overall competitiveness. Establishing supply chain risk assessment models for effective management and mitigation of potential risks has become increasingly crucial. As the number of businesses grows, the important relationships become more complicated and difficult to measure. This emphasizes the need of extracting relevant information from graph data. Previously, academics mostly employed knowledge inference to increase the visibility of links between nodes in the supply chain. However, they have not solved the data hunger problem of single node feature characteristics. We propose a hierarchical knowledge transferable graph neural network-based (HKTGNN 1 ) supply chain risk assessment model to address these issues. Our approach is based on current graph embedding methods for assessing corporate investment risk assessment. We embed the supply chain network corresponding to individual goods in the supply chain using the graph embedding module, resulting in a directed homogeneous graph with just product nodes. This reduces the complicated supply chain network into a basic product network. It addresses difficulties using the domain difference knowledge transferable module based on centrality, which is presented by the premise that supply chain feature characteristics may be biased in the actual world. Meanwhile, the feature complement and message passing will alleviate the data hunger problem, which is driven by domain differences. Our model outperforms in experiments on a real-world supply chain dataset. We will give an equation to prove that our comparative experiment is both effective and fair. 1 Accepted by The 21st IEEE International Symposium on parallel and distributed processing with Applications (IEEE ISPA 2023).

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Design and Implementation of Student Behavior Big Data Analysis and Prediction System based on Deep Learning 23

Design and Implementation of Student Behavior Big Data Analy...

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Proceedings of the 2023 International conference on Information Education and Artificial Intelligence

作者： Yani Liu Chongqing College Of Architecture And Technology China

ISBN: (纸本)9798400716157

With the continuous acceleration of digital campus construction, students have accumulated a lot of data and information in their daily study and life, which provides an important basis for student management. In this regard, based on the current situation and shortcomings in the practical application of colleges and universities, this paper will put forward a set of construction scheme of college students' behavior analysis and prediction system, aiming at constructing the corresponding student behavior analysis model and providing decision-making reference for multi-dimensional, multi-perspective and diversified educational programs. The whole system is designed with B/S architecture, the front end is an interactive interface, and the back end is based on Spark, Hadoop and Tensorflow to complete the construction of the server, which reduces the difficulty of use and improves the work efficiency. Practice has proved that the system supports the distributed parallel processing framework to optimize the convolutional neural network model, so as to improve the training and running speed of various analysis models, and can support users to use the student behavior analysis model to complete the analysis and processing of massive data, so as to make the school teaching and student management more accurate and scientific, and then make a useful attempt to promote the high-quality development of colleges and universities.

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Pyramid Region-based Slot Attention network for Temporal Action Proposal Generation 33

Pyramid Region-based Slot Attention Network for Temporal Act...

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33rd British Machine Vision conference Proceedings, BMVC 2022

作者： Li, Shuaicheng Zhang, Feng Zhao, Rui-Wei Yang, Kunlin Liu, Lingbo Feng, Rui Hou, Jun Sensetime Research China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China The Hong Kong Polytechnic University Hong Kong

It has been found that temporal action proposal generation, which aims to discover the temporal action instances within the range of the start and end frames in the untrimmed videos, can largely benefit from proper temporal and semantic context exploitation. The latest efforts were dedicated to considering the temporal context and similarity-based semantic context through self-attention modules. However, they still suffer from cluttered background information and limited contextual feature learning. In this paper, we propose a novel Pyramid Region-based Slot Attention (PRSlot) modules to address these issues. Instead of using the similarity computation, our PRSlot module directly learns the local relations in an encoder-decoder manner and generates the representation of a local region enhanced based on the attention over input features called slot. Specifically, upon the input snippet-level features, PRSlot module takes the target snippet as query, its surrounding region as key and then generates slot representations for each query-key slot by aggregating the local snippet context with a parallel pyramid strategy. based on PRSlot modules, we present a novel Pyramid Region-based Slot Attention network termed PRSA-Net to learn a unified visual representation with rich temporal and semantic context for better proposal generation. Extensive experiments are conducted on two widely adopted THUMOS14 and ActivityNet-1.3 benchmarks. In particular, we improve the AR@100 from the previous best 50.67% to 56.12% for proposal generation and raise the mAP under 0.5 tIoU from 51.9% to 58.7% for action detection on THUMOS14. Code is available at https://***/handhand123/PRSA-Net. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Semantics

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Simulation of intelligent Operation and Maintenance Optimization Model of Digital Distribution network based on Decision Tree Algorithm

Simulation of intelligent Operation and Maintenance Optimiza...

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Internet of Things, Robotics and distributed Computing (ICIRDC), International conference on

作者： Yundong Hu Meng Zhou Mengyuan Li Gang Wang State Grid Shanghai Electric Power Research Institute Shanghai China State Grid Shanghai Energy Interconnection Institute Co. LTD. Shanghai China State Grid Corporation of China Beijing China

With the continuous development of digital distribution network (DDN) technology, intelligent operation and maintenance (OM) of power system has become an important means to improve the efficiency and reliability of system operation. based on Decision Tree (DT) algorithm, this paper constructs an intelligent OM optimization model of DDN, and verifies its performance through simulation experiments. In the model construction, we make full use of the interpretability and adaptability of DT, and realize the intelligent support for the monitoring, fault diagnosis and maintenance decision of power equipment through reasonable selection of features. In the simulation experiment, the model is trained and tested by using the actual distribution network data. The experimental results show that the proposed intelligent OM model based on DT has achieved remarkable advantages in real-time alarm, fault diagnosis and maintenance decision. In order to meet the demand of large-scale data processing in DDN, the optimization strategy of parallel computing is also discussed. By means of data parallelism and feature parallelism, the training efficiency of the model is significantly improved. This provides practical technical support for deploying intelligent OM system in large-scale DDN. This model is not only practical, but also has a wide application prospect in improving the reliability of power system and reducing the OM cost.

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distributed Machine Learning based Mitigating Straggler in Big Data Environment

Distributed Machine Learning based Mitigating Straggler in B...

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IEEE International conference on Communications (ICC)

作者： Lu, Haodong Wang, Kun Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing Peoples R China Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA USA

ISBN: (纸本)9781728171227

In big data era, utilizing the parameter server paradigm has been regarded as an efficient and practical way to improve performance in processing deep learning (DL) applications. One of the main problems is that straggler greatly hinders DL training progress, but the previous methods cannot fully consider the resource utilization of the cluster when dealing with straggler. To mitigate straggler problem in parameter server, we propose a Deep Reinforcement Learning (DRL)-based framework called distributed Actor-critic Reinforcement Learning (DARL) that can automatically adapt each worker's training load to the dynamic cluster without parameter settings. DARL employs state-of-the-art techniques to stabilize training and improve convergence, including distributed framework, multiple actors and prioritized experience replay. Meanwhile, we also apply our customized experience sampling method to fully exploit potentially good samples. Experiments using real DL workloads show that DARL outperforms the representative Bulk Synchronous parallel (BSP) scheme by 57.8% and Stale Synchronous parallel (SSP) by 503% in terms of per-iteration time in heterogeneous environment.

关键词： Parameter Server Straggler Deep Reinforcement Learning

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17th IFIP WG 10.3 International conference on network and parallel Computing, NPC 2020

17th IFIP WG 10.3 International Conference on Network and Pa...

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17th IFIP WG 10.3 International conference on network and parallel Computing, NPC 2020

ISBN: (纸本)9783030794774

The proceedings contain 39 papers. The special focus in this conference is on network and parallel Computing. The topics include: LCache: Machine Learning-Enabled Cache Management in Near-Data processing-based Solid-State Disks;security Situation Prediction of network based on Lstm Neural network;Dynamic GMMU Bypass for Address Translation in Multi-GPU Systems;parallel Fast DOA Estimation Algorithm based on SML and Membrane Computing;segmented Merge: A New Primitive for parallel Sparse Matrix Computations;a Hierarchical Model of Control Logic for Simplifying Complex networks Protocol Design;FPGA-based Multi-precision Architecture for Accelerating Large-Scale Floating-Point Matrix Computing;a Configurable Hardware Architecture for Runtime Application of network Calculus;FEB3D : An Efficient FPGA-Accelerated Compression Framework for Microscopy Images;A Dynamic Mapping Model for General CNN Accelerator based on FPGA;NUMA-Aware Optimization of Sparse Matrix-Vector Multiplication on ARMv8-based Many-Core Architectures;CARAM: A Content-Aware Hybrid PCM/DRAM Main Memory System Framework;Optimization of RDMA-based HDFS Data Distribution Mechanism;reducing the Time of Live Container Migration in a Workflow;RDMA-based Apache Storm for High-Performance Stream Data processing;payment Behavior Prediction and Statistical Analysis for Shared Parking Lots;location-based Service Recommendation for Cold-Start in Mobile Edge Computing;an Adaptive Delay-Limited Offloading Scheme based on Multi-round Auction Model for Mobile Edge Computing;an Efficient Data Transmission Strategy for Edge-Computing-based Opportunistic Social networks;shadow Data: A Method to Optimize Incremental Synchronization in Data Center;A Dynamic Protection Mechanism for GPU Memory Overflow;DROAllocator: A Dynamic Resource-Aware Operator Allocation Framework in distributed Streaming processing;a Medical Support System for Prostate Cancer based on Ensemble Method in Developing Countries.

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