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A Preprocessor for Creation of Large-Scale Computational Applications 25

A Preprocessor for Creation of Large-Scale Computational App...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Simonov, Victor S. Khairetdinov, Marat S. Dept. Computer Engineering Novosibirsk State Technical University Novosibirsk Russia Dept. Geophysical Informatics Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russia

ISBN: (纸本)9798350389234

The emergence of large-scale computing paradigms required the evolution of software development methods, moreover, the need to effectively transform traditionally sequential applications into parallelizable and scalable architectures. This transformation is key in harnessing the computing power of modern multicore and distributed systems. Python, with its vast ecosystem and simplicity, is becoming a powerful language that facilitates this transition. This article discusses the methodologies and development of a Python preprocessor tool aimed at converting sequential applications into forms suitable for large-scale computing environments. Through detailed analysis and implementation, we demonstrate the use of Spark functions and third-party libraries to automate this transition, making large-scale computing more accessible to a wider range of applications and developers. The tool includes a set of rules that identify different patterns of code input (for example, list-sequential looping) and transform consistent code. This article introduces a Python preprocessor designed to automate the transformation of sequential Python applications into ones that harness the power of large-scale computing environments. © 2024 IEEE.

关键词： Python

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Gradient-free Decoder Inversion in Latent Diffusion Models 38

Gradient-free Decoder Inversion in Latent Diffusion Models

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hong, Seongmin Jeon, Suh Yoon Lee, Kyeonghyun Ryu, Ernest K. Chun, Se Young Dept. of Electrical and Computer Engineering Korea Republic of INMC IPAI Seoul National University Korea Republic of Dept. of Mathematics University of California Los Angeles United States

In latent diffusion models (LDMs), denoising diffusion process efficiently takes place on latent space whose dimension is lower than that of pixel space. Decoder is typically used to transform the representation in latent space to that in pixel space. While a decoder is assumed to have an encoder as an accurate inverse, exact encoder-decoder pair rarely exists in practice even though applications often require precise inversion of decoder. In other words, encoder is not the left-inverse but the right-inverse of the decoder;decoder inversion seeks the left-inverse. Prior works for decoder inversion in LDMs employed gradient descent inspired by inversions of generative adversarial networks. However, gradient-based methods require larger GPU memory and longer computation time for larger latent space. For example, recent video LDMs can generate more than 16 frames, but GPUs with 24 GB memory can only perform gradient-based decoder inversion for 4 frames. Here, we propose an efficient gradient-free decoder inversion for LDMs, which can be applied to diverse latent models. Theoretical convergence property of our proposed inversion has been investigated not only for the forward step method, but also for the inertial Krasnoselskii-Mann (KM) iterations under mild assumption on cocoercivity that is satisfied by recent LDMs. Our proposed gradient-free method with Adam optimizer and learning rate scheduling significantly reduced computation time and memory usage over prior gradient-based methods and enabled efficient computation in applications such as noise-space watermarking and background-preserving image editing while achieving comparable error levels. © 2024 Neural information processing systems foundation. All rights reserved.

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Secure Automated Inventory Management system using Abstracted System Design Microservice Architecture 14

Secure Automated Inventory Management system using Abstracte...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Darshan, M. Raswanth, S.R. Chaitanya, V.S.S.K. Kalavalapalli, Venkata Sridhar Sai Kumar, Priyanka Srivastava, Gautam Amrita School of Computing Dept. of Computer Science and Engineering Coimbatore India University of Texas Dept. of Computer Science San Antonio United States Brandon University Dept. of Mathematics and Computer Science Brandon Canada

ISBN: (纸本)9798350335095

Managing inventory has forever been a difficult problem for e-commerce sellers. Even today manual labor is used for the major part of the job. Without much automation in this industry growth has been stagnant because of the same *** proposed system works on a microservice-based architecture that enables automated inventory management with the help of a social-media messaging bot in an abstract manner. The bot comprises an integrated hybrid recommender system at the customer's end for better recommendations based on image and textual data. The entire system is deployed on the cloud following a serverless architecture for resilient service. The proposed solution plays a key role in taking research forward in the direction of micro-service-based system design involving real-world data. The results of the experiment prove to be beneficial for customers and retailers since they are now connected using the micro-service. © 2023 IEEE.

关键词： Systems analysis

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Software Defect Predictor and Classifier Tool Using Machine Learning Techniques

Software Defect Predictor and Classifier Tool Using Machine ...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Shankar, Sahana P. Pushp, Divyansh Makadia, Nemi Dubey, Raghav Nayak, Anusha Shashikant Chaudhari, Shilpa Ramaiah University of Applied Sciences Dept. of Computer Science and Engineering Karnataka Bangalore India M S Ramaiah Institute of Technology Dept. of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

The Software testing is critical for ensuring the quality of software, but it can consume more than half of a software project's total expenses. This research can help software development teams identify potential bugs early on, leading to efficient and effective software creation. To improve the efficiency of software testing, this report presents research on software defect prediction using a hybrid machine learning algorithm that combines supervised and unsupervised learning techniques. The study used publicly available datasets to train and evaluate various machine learning models, including an ensemble learning model. A comparative analysis was conducted among KNN, Random Forest, Adaboost, Multilayer Perceptron, CNN, and Gaussian Naïve Bayes on different datasets. Feature selection was performed using various methods, including Recurrent Feature Elimination (RFE), SelectKBest, and Correlation, implemented by a tool created to select any feature selection algorithm and compare the results for various combinations with the above mentioned classifiers. The results achieved by the model are showed in detail for each of the algorithms on the selected datasets. The main objectives of the research are to create a tool to apply various machine learning techniques to the datasets and determine the most effective ones, as well as to explore different feature selection methods for improving the accuracy of defect prediction. © 2023 IEEE.

关键词： Software testing

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Position Information Emerges in Causal Transformers Without Positional Encodings via Similarity of Nearby Embeddings 31

Position Information Emerges in Causal Transformers Without ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zuo, Chunsheng Guerzhoy, Pavel Guerzhoy, Michael Dept. of Computer Science Johns Hopkins University United States Dept. of Mathematics University of Hawai'i at Mānoa United States Division of Engineering Science University of Toronto Canada

ISBN: (纸本)9798891761964

Transformers with causal attention can solve tasks that require positional information without using positional encodings. In this work, we propose and investigate a new hypothesis about how positional information can be stored without using explicit positional encoding. We observe that nearby embeddings are more similar to each other than faraway embeddings, allowing the transformer to potentially reconstruct the positions of tokens. We show that this pattern can occur in both the trained and the randomly initialized Transformer models with causal attention and no positional encodings over a common range of hyperparameters. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Adaptive Prescribed Time Path Following Controller for Underactuated Surface Vessels 13

Adaptive Prescribed Time Path Following Controller for Under...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Ghommam, Jawhar Saad, Maarouf Zhu, Quanmin Rahman, Mohammed H. Dept. of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Dept. of Electrical Engineering Ecole de Technologie Superieure Montreal Canada Dept. of Engineering Design and Mathematics University of the West of England Bristol United Kingdom Mechanical Engineering Department University of Wisconsin-Milwaukee Wisconsin United States

ISBN: (纸本)9798331516147

Heading control of marine vehicles is an important issue in navigation and control engineering. This paper investigates the design of a prescribed-time robust adaptive path following control problem of an underactuated surface vessel with parametric uncertainties, unknown disturbances and control input constraints. The establishment of prescribed-time property consists of a prescribed-time regulation of the steering control systems and novel prescribed-time Line of Sight (LOS) guidance control, both of which satisfy the prescribed-time requirement. The feasibility of separate independent control design for the steering and the guidance control systems is guaranteed through the development of new results on nonlinear prescribed-time cascade systems with non-vanishing uncertainties. The Lyapunov method is employed to show that the equilibrium point of the whole system is prescribed-time input to state stable. Numerical simulations are carried out to validate the performance of the proposed control strategy. © 2025 IEEE.

关键词： Steering

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Collaborative Training of Acoustic Encoder for Recognizing the Impaired Children Speech 5

Collaborative Training of Acoustic Encoder for Recognizing t...

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5th College of Science International Conference on Recent Trends in Information Technology, CSCTIT 2022

作者： Shareef, Sura Ramzi Mohammed, Yusra Faisal University of Mosul Dept. Computer Engineering of Engineering Mosul Iraq Dept. Computer Science of Computer Science and Mathematics Mosul Iraq

ISBN: (纸本)9798350334920

Encoder-decoder models have become an effective approach and are increasingly popular for sequence learning tasks like automatic speech recognition (ASR) due to their simplified processing stages. The traditional decoder-based approach usually learns a sequence-to-sequence mapping function from the source speech to target units (character-level, word-level, or phoneme-level). However, without sufficient and undefective training data it still has degraded performance. In a situation like impaired children's speech recognition, the low and disordered information within the audio signal due to impaired pronunciation requires processing with different levels to improve the model's ability. Therefore, this work adopts a particular encoder's structure that can improve the encoder's ability for encodes the input sequence into a sequence of hidden representations. This paper proposes a collaborative training of Sequence predictive models as an acoustic encoder for ASR systems for Arabic impaired children's speech. Each sequence model maps the input sequence to only one phoneme from the output sequence. Then, the encoder alignment the output phonemes within one output sequence. Experimental results on the Impaired Children's Speech of Arabic dataset show the collaboratively trained acoustic encoder to align phonemes sequence can provide up to a 10% relative improvement in accuracy compared to traditional methods. Especially, since this paper is the first in the area of recognizing impaired children's speech at the level of Arabic texts. © 2022 IEEE.

关键词： Speech recognition

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment 20

IoT-Enabled Remote-Controlled Raft for Enhanced Water Qualit...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Surasak, Thattapon Kitchat, Kotcharat Jiteurtragool, Nattagit King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Dept. Of Computer And Information Science Bangkok Thailand National Central University Dept. Of Computer Science And Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350382310

Water is vital for all life, serving as the foundation for plants and aquatic creatures and promoting abundance among all living beings. This study introduces an innovative approach by combining the Internet of Things (IoT) with advanced sensing techniques to assess water quality. The IoT-powered water quality testing raft has been developed to significantly enhance the monitoring capabilities. Equipped with sensors measuring key parameters such as turbidity, pH, and temperature, the raft provides a comprehensive understanding of water quality. Its GPS module enables real-time tracking and maintains location history, ensuring precise sampling. The collected data undergoes robust processing by a Raspberry Pi, ensuring integrity before being showcased on the Thingsboard. The system's uniqueness lies in its capacity to promptly issue alerts via LINE Notify upon detecting water quality abnormalities, ensuring timely intervention. This IoT-powered raft represents a paradigm shift in water quality assessment, characterised by its precision, remote capabilities, and user-focused approach. Practical applications have been demonstrated through real-world testing in a private lake in Cha-choengsao province, Thailand. The seamless integration of IoT into environmental monitoring marks a significant advancement in ensuring water quality and ecosystem health. © 2024 IEEE.

关键词： Internet of things

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An app-based Spaced Repetition Learning Environment with User Generated Video Content

An app-based Spaced Repetition Learning Environment with Use...

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2023 IEEE International Symposium on Multimedia, ISM 2023

作者： Schimanke, Florian Mertens, Robert Kolker, Lars Mertens, Robert Hsw University of Applied Sciences Dept. of Computer Science and Engineering Hameln Germany Bhh University of Applied Sciencees Hamburg Germany

ISBN: (纸本)9798350395761

Spaced repetition is a learning task that helps users repeat learning items at optimally computed intervals. It is best suited for small learning units such as learning cards. In a typical learning scenario these learning cards do not stand by themselves, rather they are the last step in the students' work process, summarizing the students' interaction with the learning material. In this paper a framework is developed that allows learning material curators and students to create spaced repetition learning units with back-references to multimedia learning content, i.e., learning videos. With the advent of video learning nuggets, a wealth of content was created that can now be used in spaced repetition learning. The approach also allows for linking to specific parts of those learning videos to enable students to quickly review learning content specific to the learning unit currently worked on. © 2023 IEEE.

关键词： Students

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Mechanics-informed data-driven prediction model of steel column strength

Mechanics-informed data-driven prediction model of steel col...

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2025 Annual Stability Conference Structural Stability Research Council, SSRC 2025

作者： Zhang, Zhidong Chen, Kai Cevik, Cem Bartu Moen, Cristopher D. Dept. of Civil and Environmental Engineering University of Virginia United States Dept. of Applied Mathematics and Statistics Johns Hopkins University United States Datatoolpack LLC United States RunToSolve LLC United States

The main objective of this study is to investigate the ability and application of mechanics-informed neural network models to predict steel column strength. Steel column strength prediction involves challenges associated with stability issues, buckling deformation, and initial geometric imperfections, and data-driven models informed by mechanics demonstrate significant potential for better accuracy and efficiency. A multilayer perceptron neural network is trained and tested. The training data is generated by sampling from the classical ordinary differential equation for a simply-supported steel column with an initial sweep imperfection combined with a first yield stress interaction failure criterion consistent with the Perry-Robertson equation. Prediction accuracy is evaluated for different network configurations and hyperparameters with the goal of establishing a model protocol that can be generally followed for mechanics-informed data models. Finally, governing equations are introduced to the loss function to further inform the neural network of more mechanics-based information. The performance of the mechanics-informed artificial neural network is discussed. This research develops high-quality data-driven models with mechanics-informed protocol enabled, which can further advance state-of-the-art steel column design and research. © 2025 SSRC. All Rights Reserved.

关键词： Multilayer neural networks

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