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Simultaneously Learning Syntactic Dependency and Semantics Reasonability for Relation Extraction

Simultaneously Learning Syntactic Dependency and Semantics R...

引用

international conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Wang, Xin Yin, Nan Zhang, Xiang Bai, Xinyi Luo, Zhigang College of Computer National University of Defense Technology Changsha China Institute for Quantum and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789811669620

Relation extraction as an important Natural Language Processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to capture syntactic features and achieved attractive performance. However, most existing dependency-based approaches ignore the positive influence of the words outside the dependency trees, sometimes conveying rich and useful information on relation extraction. In this paper, we propose a novel model, Entity-aware Self-attention Contextualized GCN (ESC-GCN), which efficiently incorporates syntactic structure of input sentences and semantic context of sequences. To be specific, relative position self-attention obtains the overall semantic pairwise correlation related to word position, and contextualized graph convolutional networks capture rich intra-sentence dependencies between words by adequately pruning operations. In this way, our proposed model not only reduces the noisy impact from dependency trees but also obtains easily-ignored entity-related semantic representation. Extensive experiments demonstrate that our model achieves encouraging performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Designing an Independent Study to Create HPC Learning Experiences for Undergraduates

Designing an Independent Study to Create HPC Learning Experi...

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IEEE international conference on High Performance computing Workshops (HiPCW)

作者： Sandino Vargas-Pérez Department of Computer Science Kalamazoo College Kalamazoo MI USA

This paper aims to present a multi-tiered approach to designing learning experiences in HPC for undergraduate students that significantly reinforce comprehension of CS topics while working with new concepts in parallel and distributed computing. The paper will detail the experience of students working in the design, construction, and testing of a computing cluster including budgeting, hardware purchase and setup, software installation and configuration, interconnection networks, communication, benchmarking, and running parallel code using MPI and OpenMP. The case study of building a relatively low-cost, small-scale computing cluster that can be used as a template for CS senior projects or independent studies, also yielded an opportunity to involve students in the creation of teaching tools for parallel computing at many levels of the CS curriculum.

关键词： Computer science parallel programming Education Taxonomy Hardware Supercomputers Software

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Job2Vec: A Self-Supervised Contrastive Learning Based HPC Job Power Consumption Prediction Framework

Job2Vec: A Self-Supervised Contrastive Learning Based HPC Jo...

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international conference on parallel and distributed Systems (ICPADS)

作者： Jie Zhang Jian Song Xiang Li Xuesen Tian Zhigang Zhao Lu Wu Chunxiao Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In the context of the rapid development of big data and artificial intelligence, the field of HPC is facing significant challenges in energy consumption. To address this challenge, this paper proposes a power consumption prediction framework for HPC jobs based on self-supervised contrastive learning, named Job2Vec. Firstly, HPC jobs are clustered into different clusters based on job logs and power consumption curve shapes. Next, high-quality features of power consumption data at different granularities within each cluster are captured through self-supervised contrastive learning. Lastly, an incremental update strategy is introduced to handle the dynamic changes in HPC data. The experimental results demonstrate that, on the Jinan Supercomputing dataset comprising 1170 HPC jobs, the Job2Vec framework outperforms the baseline models overall in terms of prediction performance. Additionally, we also investigated the model’s performance in single-step and multi-step forecasting, the impact of different indicators on prediction accuracy, and conducted ablation experiments to confirm the indispensability of each module. This is the first time that self-supervised contrastive learning has been introduced into the HPC field, aiming to provide support for energy scheduling in data centers.

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Lazy Read: Asynchronous Execution of Synchronous File I/O

Lazy Read: Asynchronous Execution of Synchronous File I/O

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IEEE international conference on Big Data

作者： Ansgar Lößer Florian Raskob Björn Scheuermann Communication Networks TU Darmstadt Darmstadt Germany

In the realm of High-Performance computing (HPC), the disparity between I/O and processing capabilities can hinder application performance. One source of this I/O gap is the latency of the devices, especially in case of distributed filesystems (DFS). Prior solutions to minimize such negative effects (e.g. using asynchronous operations or separate I/O-Threads), have to be considered when designing an application. This work aims to execute these operations asynchronously for existing applications without necessitating modifications or even recompilation. To attain this objective, we introduce the Lazy Read approach, which alters how the operating system handles read requests. This modification enables the application to start multiple reads or do CPU work in parallel instead of waiting for completion of the read operation. For evaluation, we implemented the Lazy Read approach in the Linux kernel. We then showed that the approach adds overhead, however, multiple read operations can be executed in parallel. In our specific evaluation the maximum possible speedup was achieved, reducing the execution time by up to 50%.

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BEW-YOLO: An Improved Method for PCB Defect Detection Based on YOLOv7

BEW-YOLO: An Improved Method for PCB Defect Detection Based ...

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international conference on parallel and distributed Systems (ICPADS)

作者： Zhiyao Li Aimin Li Yuechen Zhang Xiaotong Kong Wenqiang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The PCB defect object size is small and the detection process is susceptible to background interference, usually have the problem of missed and false detection. In order to solve the above problems, an improved method based on YOLOv7 is proposed in this paper. Firstly, the bi-level routing attention (BRA) has been added to the header of the original YOLOv7 model to capture global dependencies and ensure the accuracy of small object detection and localization. Secondly, the explicit visual center (EVC) block is introduced before the fusion of mid-level features and high-level features to capture the global remote dependencies of top-level features, extract the features in local corner regions, achieve a comprehensive feature representation. Finally, the loss function is improved by changing the loss function of the original model to Wise-IoU, which allows our model to focus more on ordinary-quality anchor boxes and improve the performance of the detector while decreasing the competitiveness of anchor boxes for high-quality samples and reducing the influence of low-quality samples on the detection results. The experimental results show that the improved model improves 5% in AP and 1.9% in both AR and mAP over the original YOLOv7 model. Meanwhile, comparison experiments are conducted on the data-enhanced PCB dataset, which proves the superiority of our model over other state-of-the-art models.

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BuildEnVR: An Immersive Analysis System for Environmental Field

BuildEnVR: An Immersive Analysis System for Environmental Fi...

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international conference on parallel and distributed Systems (ICPADS)

作者： Zhenghan Zhou Kebin Liu Yantong Xie Hangxu Jin Shi Liu Ruiqing Wang Haitian Zhao Borong Lin Xiaofang Mu Hui Qi Global Innovation Exchange Tsinghua University Fuzhou Fuyao Institute for Advanced Study School of Architecture Carnegie Mellon University School of Architecture Tsinghua University College of Computer Science and Technology Taiyuan Normal University Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Amidst global warming and escalating extreme weather events, indoor environmental quality’s impact on human health and public hygiene gains prominence. Environmental parameters exist essentially as fields, which are characterized by high dimensionality, density and complexity, and contain massive amounts of information in space. To facilitate visualization and analysis of indoor environmental field, we design and implement BuildEnVR, an immersive analysis system by virtual reality, enabling remote analysis of real-time and historical environmental field data. Grounded in user needs and cognitive psychology, three visualization modes emerge: the Virtual Sensor mode enables users to access perceptual data in real-time at any 3D coordinates in ambient space, the 4D Heatmap mode visualizes spatial variations and trends over time in environmental field data, and the Synaesthesia mode realizes the fusion display of multi-dimensional environmental field data, allowing users to quickly understand the overall condition of the indoor environment with a low cognitive load. Extensive user surveys validate BuildEnVR’s intuitiveness and precision, and it is suitable for both experts and general users.

关键词： Wireless communication Wireless sensor networks Three-dimensional displays Buildings Psychology Data visualization Cognitive load Real-time systems Space heating Meteorology Climate change Global warming Environmental factors Immersive learning

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Accelerating Federated Learning with Two-phase Gradient Adjustment

Accelerating Federated Learning with Two-phase Gradient Adju...

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international conference on parallel and distributed Systems (ICPADS)

作者： Jiajun Wang Yingchi Mao Xiaoming He Tong Zhou Jun Wu Jie Wu School of Computer and Information Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China Center of Networked Computing Temple University Philadelphia USA

With the advent of the Internet of Things (IoT) era and 5G, ubiquitous sensing devices (e.g., smartphones, surveillance sites, and security cameras) have been widely used in various fields, resulting in the generation of a huge amount of monitoring data. The rise of federated learning makes it possible to leverage monitoring data to train deep neural networks through cloud-edge collaboration without compromising privacy. However, the non identically and independently distributed (called Non-IID) data collected by IoT devieces creates a client drift phenomenon, resulting in a slow convergence of the global model. To this end, we propose a new Federated learning framework based on Gradient Variance Reduction with a correction weight control mechanism and Global gradient descent with Momentum, named FedGVRGM to conduct gradient correction and reduce the negative impacts of prediction parameters. Specifically, in the local training phase, FedGVRGM combines gradient variance reduction with a correction weight control mechanism to further correct the local model parameters, thus reducing the dispersion of model parameters among clients. In the global aggregation phase, FedGVRGM integrates the historical change states of the global model through the gradient descent with momentum to reduce the oscillations and improve the convergence speed of the global model. We refer to the above methods of gradient adjustment in the local and global training phases as FedGVR and FedGM, respectively. Numerous evaluations are conducted on CIFAR-100, CIFAR-10, and MNIST datasets to prove that FedGVRGM has a faster convergence rate than other stateof-the-art approaches such as Federated Averaging (FedAvg), FedProx, FedReg, FedGVR, and FedGM.

关键词： Training Weight control Federated learning Surveillance distributed databases Internet of Things Oscillators

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Non-Linear behavior of CNN model interpretation using Saliency Map

Non-Linear behavior of CNN model interpretation using Salien...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Sudhanshu Saurabh P.K. Gupta Department of Computer Science Engineering Jaypee University of Informationn Technology Solan H.P. India

ISBN: (纸本)9781665454025

The complex behavior of the network limits human perception of the network’s success, which is among the major challenges of the convolutional neural network. The saliency map is an useful approach for analyzing and visualizing the non -linear behavior of a feed forward network. Since the DNN model is nonlinear, the interpretation is not straightforward. We intro-duce an interpretation method that observes the saliency map and visual input, which is used to calculate the component-wise product to remove the noise. Using gradient-based visualization, multimodal convolutional networks perform for visual inputs. The component-wise product is used to visualize the input by analyzing the learned weights of the layers. The saliency map is generated by the guided back-propagation of the ReLU activation function. In this paper, we aim at a popular branch of description techniques, often referred to as saliency methods. Our main objective is to find input features such as image pixels.

关键词： Neuroimaging Visualization Gradient methods Image analysis Computer architecture Functional magnetic resonance imaging Grid computing

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Securing Cyber Infrastructure of IoT-Based networks using AI and ML

Securing Cyber Infrastructure of IoT-Based Networks using AI...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Kapil Mehta Vandana Mohindru Sood Jashanpreet Singh Divyam Sharma Pratham Chhabra Department of Computer Science and Engineering Chandigarh Group of Colleges Mohali Punjab India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9781665454025

The Internet of Things (IoT) has emerged as one of the most advanced technologies, a subject of interest or field of study to researchers, and a sector with promising economic prospects. Numerous gadgets must be connected to one another and people in order for them to work. To handle its data interchange and processing, IoT needs a cloud computing environment. To achieve the automation process in many applications, artificial intelligence (AI) is required to evaluate the data stored in cloud infrastructure and make quick, accurate judgments. IoT paired with Machine Learning (ML) and Deep Learning (DL) models are used to increase the functionality of complicated applications. Cloud vulnerability and IoT device networking remain significant dangers, although AI is presently playing a significant role in enhancing traditional cybersecurity. Huge volumes of data are generated by the rapidly evolving IoT devices and networks in many different ways, which calls for careful authentication and security. One of the most efficacious methods for confronting cybersecurity breaches and encrypting data is artificial intelligence (AI). In this paper, we have investigated the effectiveness of Artificial Intelligence (AI), deep learning (DL) and machine learning (ML) techniques for IoT security. Additionally, this investigation sheds light on the AI roadmap for identifying risks based on attack types.

关键词： Deep learning Economics Cloud computing Automation Computational modeling Biological system modeling Authentication

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An empirical analysis of Cyber Crimes, their prevention measures, and laws in India

An empirical analysis of Cyber Crimes, their prevention meas...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Nitisha Aggarwal Mitu Sehgal Ashima Arya I.T. Department Panipat Institute of Engg. & Tech. (K.U.K) Panipat India

ISBN: (纸本)9781665454025

Cybercrime is pervasive, widespread, and becoming more intertwined with other elements of the criminal ecosystem. The Theft of a person's identity to the destruction of a nation's Internet connectivity as a result of a large attack on that nation's networking and computer capabilities are just a few examples. The term "Cybercrime" refers to any criminal activity involving computers or networks. Moreover, Cybercrime includes criminal acts committed online. Cybercrime is a new area of crime, and with each day that goes by, more and more new types of Cybercrime are becoming visible. This study examines the various types of Cybercrime, including those committed against people, property, and the government, and the steps taken to prevent these crimes. The rate of crime has increased along with digitization. Such crimes must be prevented by law and *** to observations, there are numerous cyber laws and acts framed to prevent Cybercrimes, including The IT ACT of 2000 and cyber security regulations. This paper illustrates an analysis of Cybercrime and their emergence, prevention measures, and laws that have become a need and necessity in India.

关键词： Technological innovation Law enforcement Government Ecosystems Grid computing Regulation Internet

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