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Convergent and Orthogonality Preserving Schemes for Approximating the Kohn-Sham Orbitals

Numerical Mathematics(Theory,Methods and Applications) 2023年第1期16卷 1-25页

作者： Xiaoying Dai Liwei Zhang Aihui Zhou LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China

To obtain convergent numerical approximations without using any orthogonalization operations is of great importance in electronic structure *** this paper,we propose and analyze a class of iteration schemes for the discretized Kohn-Sham Density Functional Theory model,with which the iterative approximations are guaranteed to converge to the Kohn-Sham orbitals without any orthogonalization as long as the initial orbitals are orthogonal and the time step sizes are given *** addition,we present a feasible and efficient approach to get suitable time step sizes and report some numerical experiments to validate our theory.

关键词： Gradient flow based model density functional theory orthogonality preserving scheme convergence temporal discretization

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Classifying Textual Sentiment Using Bidirectional Encoder Representations from Transformers 26

Classifying Textual Sentiment Using Bidirectional Encoder Re...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Ahsan, Shawly Tasnia, Fairooz Tabassum, Nafisa Das, Avishek Hoque, Mohammed Moshiul Siddique, Nazmul Chittagong University of Engineering and Technology Department of Computer Science & Engineering Chattogram4349 Bangladesh Ulster University School of Computing Engineering and Intelligent Systems United Kingdom

ISBN: (纸本)9798350359015

Textual sentiment analysis (TSA) has gained significant attention recently for its wide-ranging applications across various research domains and industries. However, most existing research and sentiment analysis tools are primarily tailored for English texts. The unique linguistic complexities of the Bengali language, coupled with a paucity of comprehensive resources and tools, pose distinctive challenges for TSA in Bengali. This paper introduces an intelligent approach, leveraging transformer-based learning techniques by harnessing the potent capabilities of self-attention mechanisms for dealing with Bengali sentences containing ungrammatical structures or local dialects. To tackle the downstream TSA task in Bengali, this work explores a range of machine learning (ML), deep learning (DL), and transformer-based baselines. Experimental results reveal that the Bangla BERT model outperforms the other baselines, achieving the highest weighted f1-score of 0.69. © 2023 IEEE.

关键词： Natural language processing Sentiment analysis Sentiment corpus Text processing Transformer learning

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Bidirectional Long-Short Term Memory with Byte Pair Encoding and Back Translation for Bangla-English Machine Translation 1st

Bidirectional Long-Short Term Memory with Byte Pair Encoding...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Tanvir, Md. Tasnin Oishy, Asfia Moon Akhand, M.A.H. Siddique, Nazmul Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (纸本)9783031346187

Machine Translation (MT) has emerged as an important research area in the computational intelligence domain to translate huge online resources cost-effectively. Among different MT approaches, Neural based Machine Translation (NMT) is in widespread use nowadays. In NMT, Bidirectional Long-Short Term Memory (BiLSTM) is popular due to its higher accuracy and improved performance. This study investigates different performance improvements with standard BiLSTM for Bangla-English MT (BEMT). Attention (ATT) mechanism, Byte Pair Encoding (BPE) and Back Translation (BT) are integrated independently and combinedly through a systematic approach. Four different models such as BiLSTM+ATT, BiLSTM+ATT+BPE, BiLSTM+ATT+BT and BiLSTM+ATT+BPE+BT have been tested rigorously on a benchmark corpus. The effectiveness of ATT, BPE and BT on a BiLSTM model are identified by comparing the performance of the individual models. Finally, the integrated model of BPE and BT with BiLSTM showed the best generalization ability and outperformed other models. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Long short-term memory

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Learning Discriminative Features with Attention Based Dual-Stream Decoder for Weakly Supervised Solar Panel Mapping 13

Learning Discriminative Features with Attention Based Dual-S...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Zhang, Jue Hu, Jiankun Jia, Xiuping University of New South Wales School of Engineering and Technology Canberra Australia University of New South Wales School of Systems and Computing Canberra Australia

ISBN: (纸本)9798350395570

With the availability of high-resolution aerial imagery, we propose an attention-based dual-stream decoder architecture for weakly supervised solar panel mapping with the advantage of both reduced annotation costs and enhanced mapping performance. The proposed method unifies a target mapping branch (TMB) and a proximity detection branch (PDB). The TMB is developed based on cross-scale attention accumulation to discover desired objects and generate fine predictions, while the PDB is designed to estimate the proximity of the desired objects. Coupled with the dual-stream decoder, we also propose a subspace contrast loss to maximize the distance between the features of the desired objects and their proximity regions, so that precise object boundaries can be reserved. To reveal the effectiveness of the proposed method, we take the Australian Capital Territory, Australia as the study area and presented comprehensive experiments. Experimental results show that the proposed method outperforms the state-of-the-art approaches by a significant margin in mapping performance and presents a superior ability to identify object boundaries. © 2023 IEEE.

关键词： Mapping

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Multilabel Aggressive Text Classification from Social Media using Transformer-based Approaches 26

Multilabel Aggressive Text Classification from Social Media ...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Hossain, Jawad Das, Avishek Hoque, Mohammed Moshiul Siddique, Nazmul Chittagong University of Engineering and Technology Department of Computer Science & Engineering Chattogram4349 Bangladesh Ulster University School of Computing Engineering and Intelligent Systems United Kingdom

ISBN: (纸本)9798350359015

The prevalence of multilabel aggressive text content on social media has a detrimental societal impact attracting the attention of government agencies and tech corporations to undertake measures against the spread of it. Hitherto research has focused on high-resource languages like English, leaving low-resource languages like Bengali out of the spotlight. This work presents a transformer-based technique to classify multilabel aggressive texts in Bengali into their targets to aid research in this area. A dataset (EM-BAD) containing 13728 texts is developed into five target classes: Religious Aggression (ReAG), Political Aggression (PoAG), Verbal Aggression (VeAG), Gender Aggression (GeAG), and Racial Aggression (RaAG) to perform the aggressive texts classification. Experimental results demonstrate that the Bangla-BERT with adjusted pooling layer and fine-tuning outdoes all ML, DL, and transformer-base baselines and existing techniques. The Bangla-BERT shows the highest weighted f1-score of 0.89 in the multilabel aggressive text classification task. © 2023 IEEE.

关键词： Aggressive text classification Deep learning Natural language processing Text corpora Text processing

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A Literature Review of Evaluation Approaches for Cyber-Physical Production systems 25

A Literature Review of Evaluation Approaches for Cyber-Physi...

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25th International Conference on Enterprise Information systems, ICEIS 2023

作者： Hostos, Hector Avila, Oscar Goepp, Virginie Department of Systems and Computing Engineering School of Engineering Universidad de los Andes Bogota Colombia ICube Laboratory INSA Strasbourg Strasbourg France

ISBN: (纸本)9789897586484

The role of Cyber-physical production systems (CPPSs) as Industry 4.0 enablers has raised the interest to upgrade legacy production systems. However, manufacturers face uncertainty when assessing if the transformation process is worth it. In this context, the aim of this study is to review the works in the existing literature that approach the evaluation of CPPSs in a context of production systems’ transformation. To do so, we adopted a systematic literature review process that comprises the development of a framework of six review questions that help us to analyze and characterize the literature found. From the literature review, this paper presents a conceptual model that aims at establishing the basis for a complete approach for CPPS evaluation. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Industry 4.0

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Calligraphy Alphabet Perception Using Artificial Intelligence 9

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning for Coverage-Guided Fuzzing: How Far are We?

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IEEE Transactions on Dependable and Secure computing 2022年 1-13页

作者： Li, Siqi Xie, Xiaofei Lin, Yun Li, Yuekang Feng, Ruitao Li, Xiaohong Ge, Weimin Dong, Jin Song College of Intelligence and Computing Tianjin University Tianjin China School of Computing and Information Systems Singapore Management University Singapore Singapore Shanghai Jiao Tong University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Computing National University of Singapore Singapore

Fuzzing is a widely-used software vulnerability discovery technology, many of which are optimized using coverage-feedback. Recently, some techniques propose to train deep learning (DL) models to predict the branch coverage of an arbitrary input owing to its always-available gradients etc. as a guide. Those techniques have proved their success in improving coverage and discovering bugs under different experimental settings. However, DL models, usually as a magic black-box, are notoriously lack of explanation. Moreover, their performance can be sensitive to the collected runtime coverage information for training, indicating potentially unstable performance. In this work, we conduct a systematic empirical study on 4 types of DL models across 6 projects to (1) revisit the performance of DL models on predicting branch coverage (2) demystify what specific knowledge do the models exactly learn, (3) study the scenarios where the DL models can outperform and underperform the traditional fuzzers, and (4) gain insight into the challenges of applying DL models on fuzzing. Our empirical results reveal that existing DL-based fuzzers do not perform well as expected, which is largely affected by the dependencies between branches, unbalanced sample distribution, and the limited model expressiveness. In addition, the estimated gradient information tends to be less helpful in our experiments. Finally, we further pinpoint the research directions based on our summarized challenges. IEEE

关键词： Deep learning

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Analysis of Bitcoin Fork by Colored Petri Nets

Analysis of Bitcoin Fork by Colored Petri Nets

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhou, Zeyu Liu, Ding Shmeleva, Tatiana R. Zaitsev, Dmitry A. School of Mechano-Electronic Engineering Xidian University Xi'an710071 China Max Planck Institute for Software Systems Saarbruecken Kaiserslautern Germany School of Computing University of Derby United Kingdom

ISBN: (纸本)9781665410205

Bitcoin is under the threat of fork since it operates with a distributed ledger. Predicting the fork probability in advance is beneficial for taking early action to avoid malicious attacks. In this study, we compose a colored Petri net model of Bitcoin. Our model consists of a given number of nodes, and each node has five subpages representing the node structure: proof of work, broadcast blocks, verify blocks, and the process of adding blocks to blockchain, respectively. Simulation results of fork probability can be easily obtained and analyzed by observing the data in the measuring components of subpages. The results show that our model correctly simulates the fork probability: on recent Bitcoin data, compared with the results of the wide-known SimBlock simulator, a difference of some 4.3% has been obtained. Thus, taking into account vivid graphical representation, our model has certain advantages for the developing techniques of attack avoidance. © 2024 IEEE.

关键词： Petri nets

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Feature Extraction Methods for Neural Networks in the Classification of Structural Health Anomalies 15

Feature Extraction Methods for Neural Networks in the Classi...

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15th International Joint Conference on Computational Intelligence, IJCCI 2023

作者： Hamilton, Natasha Harkin, Jim McDaid, Liam Liu, Junxiu Furey, Eoghan School of Computing Engineering and Intelligent Systems Ulster University Northland Road Derry United Kingdom Department of Computing Atlantic Technological University Port Road Donegal Ireland

ISBN: (纸本)9789897586743

Failure of large complex structures such as buildings and bridges can have monumental repercussions such as human mortality, environmental destruction and economic consequences. It is therefore paramount that detection of structural damage or anomalies are identified and managed early. This highlights the need to develop automated Structural Health Monitoring (SHM) systems that can continuously allow the safety status of structures to be determined, even in the worst and most isolated conditions, to ultimately help prevent destruction and save lives. Signal processing is a crucial step to detecting structural anomalies and recent work demonstrates the opportunities for neural networks, however the encoding of data for SHM requires the extraction of features due to often, noisy data. This paper focuses on feature extraction methods for artificial neural networks (ANNs) and spiking neural networks (SNNs) and aims to identify bespoke features which enable SNNs to encode data and perform the classification of anomalies. Results show that extraction of particular features in large real-world applications improve the classification accuracy of SNNs. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Feature extraction

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