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Neural Machine Translation Approach for Low-resource Languages using Long Short-term Memory Model

Neural Machine Translation Approach for Low-resource Languag...

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

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

ISBN: (纸本)9798350320954

Communication is a form of translation that human learns naturally since early childhood. Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. One example of high-impact language translation is translating health protocols from the World Health Organization to many national languages to prevent the spread of the Covid-19 virus. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of the Long Short-term Memory model as a machine translation involving Bahasa Indonesia and the Sundanese languages. performance of the Sundanese language to Indonesian language translation is 0.93 average training and validation accuracy and 0.91 average testings BLEU score. Whilst, Indonesian language to Sundanese language translation is 0.92 average training and validation accuracy and 0.88 average testings BLEU score. Both models achieve 0.39 average training loss and 0.38 average validation loss. © 2023 IEEE.

关键词： Viruses

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Helmet Detection Method of Improved YOLOv7-tiny 2

Helmet Detection Method of Improved YOLOv7-tiny

引用

2nd International conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Lei, Mingwei Wang, Xingfen Beijing Information Science and Technology University School of Computer Beijing China Beijing Information Science and Technology University Institute of Business Intelligence Beijing China

ISBN: (纸本)9798350331417

Aiming at the problems of low recognition accuracy and high miss-detection and misdetection rates encountered in the task of helmet wear detection for site operations, this study proposes an improved YOLOv7-tiny target detection algorithm for enhancing the performance of helmet wear detection. The algorithm employs the Mosaic method to randomly splice the input images, and subsequently realizes efficient enhancement of the data by performing random rotation, scaling and cropping operations on the spliced images. To enhance the stability of the model, we replace the coordinate loss in the YOLOv7-tiny network with WIoU (Wise-IoU) loss. To enhance the fusion effect of the feature layer and to solve the problem of gradient vanishing in the deep network, we introduce the RepBlock layer in the original network. Experiments on the publicly available helmet detection dataset show that the improved YOLOv7-tiny target detection algorithm not only makes the convergence of the loss function more stable compared to the original network, but also improves the detection accuracy to 94.2%, which is an improvement of 18.4 percentage points compared to the original network;at the same time, the mAP is also significantly improved to 92.8%. In addition, the leakage false detection is also effectively reduced, thus proving the significant advantage of the improved algorithm over the original network in terms of detection performance. © 2023 IEEE.

关键词： component helmet detection Mosaic object detection RepBlock WIoU YOLOv7

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AVHash: Joint Audio-Visual Hashing for Video Retrieval 24

AVHash: Joint Audio-Visual Hashing for Video Retrieval

引用

32nd ACM International conference on Multimedia, MM 2024

作者： Zhou, Yuxiang Sun, Zhe Liu, Rui Chen, Yong Zhang, Dell School of Computer Science BUPT Beijing China School of Computer Science BUAA Beijing China TeleAI China Telecom Shanghai China

ISBN: (纸本)9798400706868

Video hashing is a technique of encoding videos into binary vectors, facilitating efficient video storage and high-speed computation. Current approaches to video hashing predominantly utilize sequential frame images to produce semantic binary codes. However, videos encompass not only visual but also audio signals. Therefore, we propose a tri-level Transformer-based audio-visual hashing technique for video retrieval, named AVHash. It first processes audio and visual signals separately using pre-trained AST and ViT large models, and then projects temporal audio and keyframes into a shared latent semantic space using a Transformer encoder. Subsequently, a gated attention mechanism is designed to fuse the paired audio-visual signals in the video, followed by another Transformer encoder leading to the final video representation. The training of this AVHash model is directed by a video-based contrastive loss as well as a semantic alignment regularization term for audio-visual signals. Experimental results show that AVHash significantly outperforms existing video hashing methods in video retrieval tasks. Furthermore, ablation studies reveal that while video hashing based solely on visual signals achieves commendable mAP scores, the incorporation of audio signals can further boost its performance for video retrieval. © 2024 ACM.

关键词： Binary codes

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Optimal Energy Management based on Dynamic programming

Optimal Energy Management based on Dynamic Programming

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2024 International conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Haripriya, D. Dahiwade, Mandakini Kotiyal, Arnav Al-Farouni, Mohammed Yadav, Kanchan Francis Raj, J. Relin Department of CSE Veltech Ranagarajan Dr.Saguntala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India Department of Basic Engineering Indira College of Engineering and Management Pune India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350355338

The strategy based on reinforced learning will be able to present a solution that is close to the optimal solution achieved by using Dynamic programming (DP), and the power distribution for hybrid vehicles also seems promising with DP. It addresses nonlinear dynamical systems' optimal control issues. However, DP cannot be applied to the majority of problems of practical interest due to its high complexity in terms of computation and dimensionality. In various energy policies, variability has not been addressed, and it is based on fixed driving profiles, which affect vehicle operation. The proposed study's goal is to create an optimal hybrid energy management system that generates more power at the lowest possible cost. The control strategy has been formed based on DP through the rule extraction process, and the fuel consumption must be minimized for the drive cycles. It has simple mathematical foundations, ensures the optimal solution for a given tolerance, and can be easily implemented in a short computer program. The strategy is evaluated in terms of real and predicted values, which makes the performance indices, and this comparison is done on different levels of time. © 2024 IEEE.

关键词： Hybrid power

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MISA-MD: A New Design of Molecular Dynamics Software for GPU Architecture 24

MISA-MD: A New Design of Molecular Dynamics Software for GPU...

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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： Chu, Genshen Chen, Tianyi Chen, Dandan Bai, He Li, Guannan Ren, Shuai Li, Yang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Research Center Zhejiang Lab Beijing China

ISBN: (纸本)9798350319934

Molecular dynamics is a significant method in computational material and many other fields, and is computationally intensive. In this paper, we present a new molecular dynamics software, MISA-MD, which is mainly designed for high computational performance on GPU architecture. The main contributions include a redesign of the atom data structure, communication optimizations, parallel strategies and a series of GPU optimizations on memory accessing. Compared to the CPU version, the presented version of MISA-MD can achieve more than 130 speedup when running on GPU hardware. Moreover, our MISAMD code also demonstrates 51 % performance improvement, when compared to the well-known software LAMMPS on GPU. © 2022 IEEE.

关键词： Graphics processing unit

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Testing the Unknown: A Framework for OpenMP Testing via Random program Generation

Testing the Unknown: A Framework for OpenMP Testing via Rand...

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high performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International conference for

作者： Ignacio Laguna Patrick Chapman Konstantinos Parasyris Giorgis Georgakoudis Cindy Rubio-González Center for Applied Scientific Computing Lawrence Livermore National Laboratory Department of Computer Science University of California Davis

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly generate thousands of tests, exposing OpenMP implementations to a wide range of program behaviors. We represent the space of possible random OpenMP tests using a grammar and implement our method as an extension of the Varity program generator. By generating 1,800 OpenMP tests, we find various performance anomalies and correctness issues when we apply them to three OpenMP implementations: GCC, Clang, and Intel. We also present several case studies that analyze the anomalies and give more details about the classes of tests that our approach creates.

关键词： Automatic programming high performance computing conferences computer bugs Production Encoding Grammar Testing

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U2USim - A UAV Telepresence Simulation Platform with Multi-agent Sensing and Dynamic Environment 24

U2USim - A UAV Telepresence Simulation Platform with Multi-a...

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32nd ACM International conference on Multimedia, MM 2024

作者： Han, Feilin Zhang, Leping Wang, Xin Zhao, Ke-Ao Zhong, Ying Su, Ziyi Feng, Tongtong Zhu, Wenwu Department of Film and Television Technology Beijing Film Academy Beijing China Department of Computer Science and Technology BNRist Tsinghua University Beijing China China Film High-Tech Research Institute Beijing Film Academy Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798400706868

Unmanned Aerial Vehicles (UAVs) are necessary across diverse domains, including disaster surveillance and wildlife conservation. However, the development and evaluation of UAV-related algorithms often encounter a significant hurdle: the scarcity of authentic training data. In this paper, we introduce U2USim, a telepresence simulation platform with a dynamic environment, serving as a realistic synthetic data generation, performance evaluation, and visualization tool for UAV-to-UAV (U2U) cooperative learning. This paper presents the architecture, features, and capabilities of U2USim. Leveraging Unreal Engine (UE), AirSim APIs, and ROS (Robot Operating System), our platform enables realistic simulations, mirroring real-world conditions and facilitating research in UAV technology. © 2024 Owner/Author.

关键词： Robot Operating System

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Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series 40

Sequential Multi-Dimensional Self-Supervised Learning for Cl...

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40th International conference on Machine Learning

作者： Raghu, Aniruddh Chandak, Payal Alam, Ridwan Guttag, John Stultz, Collin M. MIT Cambridge MA 02139 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA USA

Self-supervised learning (SSL) for clinical time series data has received significant attention in recent literature, since these data are highly rich and provide important information about a patient's physiological state. However, most existing SSL methods for clinical time series are limited in that they are designed for unimodal time series, such as a sequence of structured features (e.g., lab values and vitals signs) or an individual high-dimensional physiological signal (e.g., an electrocardiogram). These existing methods cannot be readily extended to model time series that exhibit multimodality, with structured features and high-dimensional data being recorded at each timestep in the sequence. In this work, we address this gap and propose a new SSL method - Sequential Multi-Dimensional SSL - where a SSL loss is applied both at the level of the entire sequence and at the level of the individual high-dimensional data points in the sequence in order to better capture information at both scales. Our strategy is agnostic to the specific form of loss function used at each level - it can be contrastive, as in SimCLR, or non-contrastive, as in VICReg. We evaluate our method on two real-world clinical datasets, where the time series contains sequences of (1) high-frequency electrocardiograms and (2) structured data from lab values and vitals signs. Our experimental results indicate that pre-training with our method and then fine-tuning on downstream tasks improves performance over baselines on both datasets, and in several settings, can lead to improvements across different self-supervised loss functions.

关键词： Supervised learning

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Proposing a Low-Rank Approximation Method with Mathematical Guarantees for high-Dimensional Tensor Data

Proposing a Low-Rank Approximation Method with Mathematical ...

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2024 IEEE International conference on Big Data, BigData 2024

作者： Hasegawa, Hiroki Yata, Kazuyoshi Okada, Yukihiko Kunimatsu, Jun University of Tsukuba Program in Service Engineering Tsukuba Japan Sciences University of Tsukuba Institute of Pure and Applied Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Tsukuba Institute for Advanced Research Center for Artificial Intelligence Research Tsukuba Japan University of Tsukuba Institute of Medicine Tsukuba Japan

ISBN: (纸本)9798350362480

The analysis of high-dimensional tensor data presents significant challenges for traditional methods such as CANDECOMP/PARAFAC (CP) decomposition and higher-Order Singular Value Decomposition (HOSVD) due to the size, complexity, and noise inherent in the data. Recent studies have emphasized the critical need for noise-robust approaches capable of reducing dimensionality while preserving data integrity, particularly for matrix-form data, underscoring the necessity of theoretical advancements in noise reduction. In this study, we propose a novel method, Noise-Reduced higher-Order Singular Value Decomposition (NR-HOSVD), which integrates noise reduction techniques into the HOSVD framework to enhance dimensionality reduction for matrix data. In comparative simulations with CP decomposition and HOSVD, NR-HOSVD consistently demonstrated lower relative errors as the rank increased, particularly for high-dimensional tensors, surpassing the performance of conventional methods beyond a certain rank threshold. Furthermore, NR-HOSVD exhibited superior stability and faster convergence on large-scale datasets compared to existing approaches. These findings suggest that NR-HOSVD provides a more accurate and efficient approach to high-dimensional tensor data analysis, making it particularly well-suited for applications in big data and machine learning. In the future, NR-HOSVD is expected to serve as a pivotal method for enhancing the accuracy and efficiency of high-dimensional tensor data analysis, with broad applicability anticipated in fields such as large-scale data processing and precision medicine. © 2024 IEEE.

关键词： Data integrity

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A Negative Sample Enhancement Strategy to Improve Contrastive Learning for Unsupervised Sentence Representation

A Negative Sample Enhancement Strategy to Improve Contrastiv...

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2024 IEEE International conference on Big Data, BigData 2024

作者： Li, Hao Xie, Chunzhi Cao, Zhoulin Lee, Yan-Li Liu, Jia Gao, Zhisheng Xihua University School of Computer and Software Engineering Chengdu China

ISBN: (纸本)9798350362480

Contrastive learning has achieved remarkable success in sentence representation research within the field of natural language processing. Nevertheless, most existing studies focus primarily on the construction of negative samples while paying insufficient attention to the mechanisms for handling these samples. Such methods tend to treat all negative samples within a batch as equally important, neglecting the crucial role that negative samples play in semantic learning. This oversight can result in suboptimal model performance in semantic understanding. To address these issues, this study proposes a negative sample enhancement strategy that applies fine-grained processing to different types of constructed negative samples based on their importance. In the high-dimensional semantic space, hard negative samples and false negative samples are treated respectively - by increasing the distance between hard negative samples and positive samples, while treating false negative samples as pseudo-positive samples to enhance the attraction between them and the anchor sample. This strategy enables the model to perform more effective semantic differentiation and representation. Experimental results on the Semantic Textual Similarity (STS) task demonstrate that the proposed method outperforms existing baseline methods in unsupervised sentence representation learning. © 2024 IEEE.

关键词： Contrastive Learning

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