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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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Tensor-based Kernel Machines with Structured Inducing Points for Large and high-Dimensional Data 26

Tensor-based Kernel Machines with Structured Inducing Points...

引用

26th International conference on Artificial Intelligence and Statistics (AISTATS)

作者： Wesel, Frederiek Batselier, Kim Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands

Kernel machines are one of the most studied family of methods in machine learning. In the exact setting, training requires to instantiate the kernel matrix, thereby prohibiting their application to large-sampled data. One popular kernel approximation strategy which allows to tackle large-sampled data consists in interpolating product kernels on a set of grid-structured inducing points. However, since the number of model parameters increases exponentially with the dimensionality of the data, these methods are limited to small-dimensional datasets. In this work we lift this limitation entirely by placing inducing points on a grid and constraining the primal weights to be a low-rank Canonical Polyadic Decomposition. We derive a block coordinate descent algorithm that efficiently exploits grid-structured inducing points. The computational complexity of the algorithm scales linearly both in the number of samples and in the dimensionality of the data for any product kernel. We demonstrate the performance of our algorithm on large-scale and high-dimensional data, achieving state-of-the art results on a laptop computer. Our results show that grid-structured approaches can work in higher-dimensional problems.

关键词： Computational complexity

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XSS Attack Detection Using Machine Learning 2

XSS Attack Detection Using Machine Learning

引用

2nd International conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Elsersy, Wael Farouk Samy, Moataz Elshamy, Ahmed Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350362633

Cross-site scripting (XSS) is one of the severe problems in Web Applications. With more connected devices which use different Web Applications, the risk of XSS attacks is increasing. In a cross-site scripting attack, the attacker injects malicious client-side scripts into the source code of a reliable program or website. If the website does implement sane input validation and filtration methods, the script will execute later on the client browser who visits such websites. Cross-site scripting attacks have been happening since the 1990s. Even today, XSS is considered a significant threat to many web applications as E-commerce websites with user reviews and Social Networking Services (SNSs) such as Facebook and Twitter. Such platforms rely on Javascript and Ajax for seamless user experience with high communication frequency among its users, making them very prone to XSS attacks. In this paper, we focus on detecting XSS attacks using six different machine learning classifiers that classify web pages into two categories: XSS or non-XSS based on several extracted features. With the highest classifier achieving accuracy of 0.993, precision and recall of 0.99, the evaluation results show that our approach achieves high performance in detecting XSS attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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Cross Domain Transfer Network for State of Charge Estimation of Li-On Batteries 20

Cross Domain Transfer Network for State of Charge Estimation...

引用

20th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chongming, Yang Schoo of Computer Science and Technology University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350318982

Deep learning methods have garnered considerable inter-est in State of Charge (SOC) estimation due to their capabilities to capture complex temporal patterns. However, the success of these methods is based on the labelled target data and the pre-sumption that both the training and testing data originate from identical distributions. In the real word, the operating conditions of batteries are diverse, which makes existing methods in-valid. To address this challenge, a cross domain transfer network (CDTN) with multiscale distribution adaptation (MDA) is proposed to reduce domain distances and align high-dimensional features. In this method, a feature extractor incor-porating convolutional neural network (CNN) and bidirectional long-short term memory (Bi-LSTM) is utilized. In the transfer learning stage, adversarial adaptation and MDA are combined to minimize the distribution discrepancy. Through extensive experiments, the results show the adapted CDTN achieves the best performance under practical discharge applications, which can prevent the batteries from over-discharging and prolong the lifespan effectively. © 2023 IEEE.

关键词： Long short-term memory

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Exploring the Effect of Activation Function on Transformer Model performance for Official Announcement Translator from Indonesian to Sundanese Languages

Exploring the Effect of Activation Function on Transformer M...

引用

2023 International conference on computer Science, Information Technology and Engineering, ICCoSITE 2023

作者： Wijanarko, Bambang Dwi Fitria Murad, Dina Heryadi, Yaya Tho, Cuk Hashimoto, Kiyota BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket Campus Phuket83120 Thailand

ISBN: (纸本)9798350320954

Automated language translation involving low-resource language has gained wide interest from many research communities in the past decade. One lesson learned from the past COVID-19 pandemic, particularly in Indonesia, is that many local Governments have to release regular public announcements to keep people following health protocol especially when they are in public areas. Many studies showed some evidence that rural people in Indonesia which covers a large proportion of Indonesia's population, feel more convenience receiving official announcements in their local language. However, translating official announcement from the national language to many local languages in Indonesia require many experienced bilingual translators and time. This paper presents exploration results in developing an automated language translator model to translate texts in Bahasa Indonesia to the Sundanese language. In particular, this study aims to explore the effect of ReLU, Sigmoid, and Tanh activation functions of the Vanilla Transformer Model on its translation performance. The experiment results showed that the activation function under study gives similar training accuracy (0.98). However, ReLU achieves better performance than Tanh in terms of validation accuracy, training loss, and validation loss. © 2023 IEEE.

关键词： Chemical activation

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Multi-level Relational Reasoning and Attention Network for Medical Visual Question Answering 25

Multi-level Relational Reasoning and Attention Network for M...

引用

25th IEEE International conferences on high performance Computing and Communications, 9th International conference on Data Science and Systems, 21st IEEE International conference on Smart City and 9th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Jia, Xiteng Pan, Haiwei Zhang, Kejia Feng, Yuechun He, Shuning Gong, Cheng Harbin Engineering University Department of Computer Science Harbin China College of Computer Science and Engineering Ningxia Institute of Science and Technology Ningxia China

ISBN: (纸本)9798350330014

Medical Visual Question Answering (Med-VQA) aims to address clinical questions using medical radiological images. However, existing studies have mainly focused on in-putting visual and textual features into attention-based network structures (such as Transformer), neglecting the more advanced relational features present in radiological images. Therefore, we propose a Med- Vqamodel based on attention and visual relational reasoning. Firstly, we introduce a Bidirectional-guided Attention Module, enabling the model not only to use questions to guide attention to important regions in the image, but also to utilize the image to guide attention to key vocabulary in the question. Secondly, we design a Multi-level Visual Relational Module that models both global and local visual features. We employ a graph convolutional neural network to extract latent visual relational features, enriching the semantic information con-tained in visual features. Experimental results on the VQA-RAD dataset and SLAKE dataset show that our model outperforms other state-of-the-art Med- Vqamodels with an overall accuracy of 77.2 % and 80.3 %, respectively, while guaranteeing the model performance. © 2023 IEEE.

关键词： Convolutional neural networks

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Dual-Band high-Gain Waveguide Slot Antenna Based on MEMS Electrostatic Comb Actuator 3

Dual-Band High-Gain Waveguide Slot Antenna Based on MEMS Ele...

引用

3rd International conference on Electronic Information Engineering and computer Communication, EIECC 2023

作者： Zhai, Leiying Ye, Peiqian Nan, Jingchang Zhu, Hong Liaoning Technical University School of Electronics and Information Engineering Huludao China

ISBN: (纸本)9798350359961

The application of millimeter wave technology has emerged to address the existing requirements of RF communication systems, the waveguide slot antenna stands out due to its compact, reliable, and achievable. This study presents a dual-band waveguide cavity-backed slot antenna based on the radiation waveguide trapezoid slot and coupled waveguide rectangular slot. Additionally, the antenna incorporates a MEMS electrostatic comb driven the radiation waveguide with frequency tuning performance. The simulation results indicate that the antenna's return loss is below-40dB. The antenna exhibits a frequency tuning range of approximately 0.9GHz, achieves VSWR(Voltage Standing Wave Ratio)of less than 2 in the frequency ranges of 273-274GHz and 280-283GHz respectively. Additionally, the main lobe gain is greater than 11.3dB, while the side lobe electrical level is lower than-13dB. The antenna realizes low side electrical level performance and the radiation efficiency is high. © 2023 IEEE.

关键词： component coupled waveguide dual-band radiation waveguide Waveguide slot antenna

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Evolving Knowledge Distillation with Large Language Models and Active Learning 30

Evolving Knowledge Distillation with Large Language Models a...

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Joint 30th International conference on Computational Linguistics and 14th International conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Chengyuan Kang, Yangyang Zhao, Fubang Kuang, Kun Jiang, Zhuoren Sun, Changlong Wu, Fei College of Computer Science and Technology Zhejiang University China Institute for Intelligent Computing Alibaba Group China School of Public Affairs Zhejiang University China Shanghai Institute for Advanced Study of Zhejiang University China

ISBN: (纸本)9782493814104

Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. However, their computational costs are prohibitively high. To address this issue, previous research has attempted to distill the knowledge of LLMs into smaller models by generating annotated data. Nonetheless, these works have mainly focused on the direct use of LLMs for text generation and labeling, without fully exploring their potential to comprehend the target task and acquire valuable knowledge. In this paper, we propose EvoKD: Evolving Knowledge Distillation, which leverages the concept of active learning to interactively enhance the process of data generation using large language models, simultaneously improving the task capabilities of small domain model (student model). Different from previous work, we actively analyze the student model's weaknesses, and then synthesize labeled samples based on the analysis. In addition, we provide iterative feedback to the LLMs regarding the student model's performance to continuously construct diversified and challenging samples. Experiments and analysis on different NLP tasks, namely, text classification and named entity recognition show the effectiveness of EvoKD. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Distillation

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LiTformer: Efficient Modeling and Analysis of high-Speed Link Transmitters Using Non-Autoregressive Transformer 24

LiTformer: Efficient Modeling and Analysis of High-Speed Lin...

引用

43rd International conference on computer-Aided Design, ICCAD 2024

作者： Sun, Songyu Dong, Xiao Sha, Yanliang Chen, Quan Zhuo, Cheng Zhejiang University Hangzhou China Southern University of Science and Technology Shenzhen China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

ISBN: (纸本)9798400710773

high-speed serial links are fundamental to energy-efficient and high-performance computing systems such as artificial intelligence, 5G mobile and automotive, enabling low-latency and high-bandwidth communication. Transmitters (TXs) within these links are key to signal quality, while their modeling presents challenges due to nonlinear behavior and dynamic interactions with links. In this paper, we propose LiTformer: a Transformer-based model for high-speed link TXs, with a non-sequential encoder and a Transformer decoder to incorporate link parameters and capture long-range dependencies of output signals. We employ a non-autoregressive mechanism in model training and inference for parallel prediction of the signal sequence. LiTformer achieves precise TX modeling considering link impacts including crosstalk from multiple links, and provides fast prediction for various long-sequence signals with high data rates. Experimental results show that LiTformer achieves 148-456× speedup for 2-link TXs and 404-944× speedup for 16-link with mean relative errors of 0.68-1.25%, supporting 4-bit signals at Gbps data rates of single-ended and differential TXs, as well as PAM4 TXs. © 2024 Copyright is held by the owner/author(s).

关键词： Bandwidth

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Harnessing the Power of a Bidirectional Long Short-Term Memory-Based Prediction Model: A Case of Student Academic performance 9

Harnessing the Power of a Bidirectional Long Short-Term Memo...

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9th International conference on computer and Communication Engineering, ICCCE 2023

作者： Yunus, Fatima Abdulsalam Olanrewaju, Rashidah Funke Ajayi, Binyamin Adeniyi Salihu, Abdullahi Audu Nasarawa State University Department of Computer Science Keffi Nigeria

ISBN: (纸本)9798350325218

This paper aimed to develop a Bidirectional Long Short-Term Memory (BiLSTM) based model for predicting student academic performance using Principal Component Analysis (PCA) as a feature selection technique. The study was motivated by the concern about the high level and rates of failure of undergraduate students in Nasarawa State University Keffi, Nigeria, despite the University's efforts to provide an enabling learning environment. Previous studies in this area focused on the program-based approach, which may not fit appropriately into the local educational system. Additionally, the measures of academic performance used in those reviews fluctuated, bringing about a lacking assessment of indicator factors in understudy information. As a result, a novel strategy was proposed in this study by combining improved methods for selecting features with a BiLSTM model that has been successfully used in a variety of contexts. Data collection, feature selection, and the use of a deep learning model to predict student grades were all part of the method used. The most relevant characteristics that can be used to predict student academic performance were identified using PCA methods. The utilization of this component determination method assisted with lessening the dimensionality of the information while protecting the main data, bringing about a more exact prescient model. The model exploratory outcomes showed that it yielded the best outcomes when compared with other studies in the same area, achieving an accuracy of 90.16%. The study's findings could be useful for educators and administrators in Nigerian universities, contributing to improving the quality of education and reducing undergraduate student failure rates. © 2023 IEEE.

关键词： Principal component analysis

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Research on Elastic Extension of Multi Type Resources for OpenMP program 24

Research on Elastic Extension of Multi Type Resources for Op...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Junfeng Gao, Xuesong Li, Yuancong College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9798350319934

OpenMP is one of the most popular programming models in the field of parallel computing, which is widely used in multi-threaded programming with shared memory. But with the continuous change of program computing scale, the problem of resource supply has become increasingly prominent. Making use of the cloud computing to dynamically provide resources for OpenMP program is a meaningful work. Based on the internal structure characteristics of OpenMP program, a flexible elastic resource allocation strategy is designed, and a load prediction method combining active and reactive prediction based on machine learning is proposed to realize the load prediction of OpenMP program. Then, the fuzzy controller is used to coordinate the resources and infer the CPU and memory resources needed by the program in the next cycle. On this basis, the middleware OPEC is designed and implemented to realize the dynamic allocation of cloud resources during the operation of OpenMP program. Parsec, a benchmark test suite based on shared memory, is selected for experimental verification. The experimental results show that the solution proposed in this paper can dynamically provide a variety of resources for OpenMP program to meet program running requirements, effectively improve resource utilization efficiency and reduce program running costs. © 2022 IEEE.

关键词： Resource allocation

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