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Analysis of Extractive Text Summarization Methods as a Binary Classification Problem 1

16th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2023 and 14th International Conference on EUropean Transnational Education, ICEUTE 2023

作者： Komorniczak, Joanna Wojciechowski, Szymon Klikowski, Jakub Kozik, Rafal Choraś, Michal Pattern Recognition Team of Department of Computer Systems and Networks Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland Bydgoszcz University of Science and Technology Al. prof. S. Kaliskiego 7 Bydgoszcz85-796 Poland

ISBN: (数字)9783031425196

ISBN: (纸本)9783031425189

One of the critical challenges for natural language processing methods is the issue of automatic content summarization. The enormous increase in the amount of data delivered to users by news services leads to an overload of information without meaningful content. There is a need to generate an automatic text summary that contains as much essential information as possible while keeping the resulting text smooth and concise. Methods of automatic content summarization fall into two categories: extractive and abstractive. This work converts the task of extractive summarization to a binary classification problem. The research focused on analyzing various techniques for extracting the abstract in a supervised learning manner. The results suggest that this different view of text summarization has excellent potential. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Text processing

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Data Fusion-Based Machine Learning Architecture for Intrusion Detection

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computers, Materials & Continua 2022年第2期70卷 3399-3413页

作者： Muhammad Adnan Khan Taher M.Ghazal Sang-Woong Lee Abdur Rehman Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)43600BangiSelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan

In recent years,the infrastructure of Wireless Internet of Sensor Networks(WIoSNs)has been more complicated owing to developments in the internet and devices’*** effectively prepare,control,hold and optimize wireless sensor networks,a better assessment needs to be *** field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data *** study investigates the methodology of Real Time Sequential Deep Extreme LearningMachine(RTS-DELM)implemented to wireless Internet of Things(IoT)enabled sensor networks for the detection of any intrusion *** fusion is awell-knownmethodology that can be beneficial for the improvement of data accuracy,as well as for the maximizing of wireless sensor networks *** also suggested an approach that not only makes the casting of parallel data fusion network but also render their computations more *** using the Real Time Sequential Deep Extreme Learning Machine(RTSDELM)methodology,an excessive degree of reliability with a minimal error rate of any intrusion activity in wireless sensor networks is *** results show that wireless sensor networks are optimized effectively to monitor and detect any malicious or intrusion activity through this proposed ***,threats and a more general outlook are explored.

关键词： Wireless internet of sensor networks machine learning deep extreme learning machine artificial intelligence data fusion

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Towards a Better Evaluation of Metrics for Machine Translation 5

Towards a Better Evaluation of Metrics for Machine Translati...

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5th Conference on Machine Translation, WMT 2020

作者： Stanchev, Peter Wang, Weiyue Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52056 Germany

ISBN: (纸本)9781948087810

An important aspect of machine translation is its evaluation, which can be achieved through the use of a variety of metrics. To compare these metrics, the workshop on statistical machine translation annually evaluates metrics based on their correlation with human judgement. Over the years, methods for measuring correlation with humans have changed, but little research has been performed on what the optimal methods for acquiring human scores are and how human correlation can be measured. In this work, the methods for evaluating metrics at both system- and segment-level are analyzed in detail and their shortcomings are pointed out. © 2020 Association for Computational Linguistics

关键词： Machine translation

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Same Semantics of the Signal - What Do We Cluster with what Representation

Same Semantics of the Signal - What Do We Cluster with what ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Alexander Barnhill Oliver Traub Andreas Maier Elmar Nöth Christian Bergler Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Electrical Engineering Media and Computer Science Technical University of Applied Sciences Amberg-Weiden Amberg Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic clustering of bioacoustic signals is crucial for a deeper understanding of intra-class differences. This is particularly important for understanding killer whale signals, as their vocalizations are learned behaviors and determination of matrilineal-specific dialects is reliant upon subtle differences within instances which may be characterized into a single larger category. Aspects of data collection may have an effect on how these calls are grouped, and it is therefore necessary to understand what the focus of the feature generation algorithm is. This study addresses the impact of factors such as recording conditions and environment, together with its respective relative noise levels, by first analyzing two different deep learning and data-driven feature representations, either derived by an undercomplete autoencoder or a supervised call type classifier. These are then compared with representations generated by two state-of-the-art transformer-based tools, namely HuBERT and Wav2Vec2.

关键词： Deep learning Semantics Whales Signal processing algorithms Data collection Transformers Recording Classification algorithms Speech processing Noise level

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Explainable Artificial Intelligence Solution for Online Retail

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computers, Materials & Continua 2022年第6期71卷 4425-4442页

作者： Kumail Javaid Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Allah Ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan Department of Computer Science University of South AsiaLahore54000Pakistan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Division of Science and Technology University of EducationLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for *** the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the *** this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one *** main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the *** permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the *** performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC *** model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree ***,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying *** research also reveals the importance of page value as a key feature for capturing online purchasing *** may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

关键词： Explainable artificial intelligence online retail neural network random forest regression

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Convolutional Neural Network Based Intelligent Handwritten Document recognition

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computers, Materials & Continua 2022年第3期70卷 4563-4581页

作者： Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Bangi 43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Department of Computer Science GC UniversityLahorePakistan Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

This paper presents a handwritten document recognition system based on the convolutional neural network *** today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired *** technology is also helpful for the automatic data entry *** the proposed systemprepared a dataset of English language handwritten character *** proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten *** this research,multiple experiments get very worthy recognition *** proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural *** this processing,the input document is segmented using line,word and character *** proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their *** recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given *** proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.

关键词： Convolutional neural network segmentation skew cursive characters recognition

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NLP-IISERB@Simpletext2022: To Explore the Performance of BM25 and Transformer Based Frameworks for Automatic Simplification of Scientific Texts

NLP-IISERB@Simpletext2022: To Explore the Performance of BM2...

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2022 Conference and labs of the Evaluation Forum, CLEF 2022

作者： Saha, Sourav Roy, Dwaipayan Goud, B. Yuvaraj Reddy, Chethan S. Basu, Tanmay Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Computational and Data Sciences Indian Institute of Science Education and Research Kolkata India Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India

CLEF SimpleText 2022 lab focuses on developing effective systems to identify relevant passages from a given set of scientific articles. The lab has organized three tasks this year. Task 1 is focused on passage retrieval from the given data for a query text. These passages can be complex and hence require further simplification to be carried out in tasks 2 and 3. The BioNLP research group at the Indian Institute of science Education and Research Bhopal (IISERB) in collaboration with two different information retrieval research groups at IISER Kolkata and ISI Kolkata participated only in Task 1 of this challenge and submitted three runs using three different retrieval models. The paper explores the performance of these retrieval models for the given task. We used a standard BM25 model as our first run to identify 1000 relevant passages for each query. Moreover, the passages for each query were ranked based on their similarity scores generated by the BM25 model. For our second run, we used a BERT (Bidirectional Encoder Representations from Transformers) based re-ranking method, called as Mono-BERT to further rank the 1000 passages retrieved by our first run for each query. A pre-trained sequence to sequence model based re-ranking method, called MonoT5 was used as our third run to reorder the 1000 passages retrieved by the Mono-BERT model for each query. As the official results of this task are not yet announced, we cannot explore the performance of our submissions. However, we have manually checked the retrieved results of many queries for each run, which indicate that the performance improved from run 1 to run 2 and further to run 3. © 2022 Copyright for this paper by its authors.

关键词： Information retrieval

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Simulation-informed learning for time-resolved angiographic contrast agent concentration reconstruction

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computers in Biology and Medicine 2024年 182卷 109178-109178页

作者： Maul, Noah Birkhold, Annette Wagner, Fabian Thies, Mareike Rohleder, Maximilian Berg, Philipp Kowarschik, Markus Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander Universität Erlangen-Nürnberg Germany Siemens Healthineers AG Forchheim Germany Research Campus STIMULATE University of Magdeburg Germany Department of Medical Engineering University of Magdeburg Germany

Three-dimensional Digital Subtraction Angiography (3D-DSA) is a well-established X-ray-based technique for visualizing vascular anatomy. Recently, four-dimensional DSA (4D-DSA) reconstruction algorithms have been developed to enable the visualization of volumetric contrast flow dynamics through time-series of volumes. This reconstruction problem is ill-posed mainly due to vessel overlap in the projection direction and geometric vessel foreshortening, which leads to information loss in the recorded projection images. However, knowledge about the underlying fluid dynamics can be leveraged to constrain the solution space. In our work, we implicitly include this information in a neural network-based model that is trained on a dataset of image-based blood flow simulations. The model predicts the spatially averaged contrast agent concentration for each centerline point of the vasculature over time, lowering the overall computational demand. The trained network enables the reconstruction of relative contrast agent concentrations with a mean absolute error of 0.02±0.02 and a mean absolute percentage error of 5.31±9.25 %. Moreover, the network is robust to varying degrees of vessel overlap and vessel foreshortening. Our approach demonstrates the potential of the integration of machine learning and blood flow simulations in time-resolved angiographic contrast agent concentration reconstruction. © 2024 The Authors

关键词： Digital elevation model

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Improving Language Model Integration for Neural Machine Translation

arXiv

引用

arXiv 2023年

作者： Herold, Christian Gao, Yingbo Zeineldeen, Mohammad Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

The integration of language models for neural machine translation has been extensively studied in the past. It has been shown that an external language model, trained on additional target-side monolingual data, can help improve translation quality. However, there has always been the assumption that the translation model also learns an implicit target-side language model during training, which interferes with the external language model at decoding time. Recently, some works on automatic speech recognition have demonstrated that, if the implicit language model is neutralized in decoding, further improvements can be gained when integrating an external language model. In this work, we transfer this concept to the task of machine translation and compare with the most prominent way of including additional monolingual data - namely back-translation. We find that accounting for the implicit language model significantly boosts the performance of language model fusion, although this approach is still outperformed by back-translation. Copyright © 2023, The Authors. All rights reserved.

关键词： Neural machine translation

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Tile selection method based on error minimization for photomosaic image creation

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Frontiers of computer science 2021年第3期15卷 165-172页

作者： Hongbo ZHANG Xin GAO Jixiang DU Qing LEI Lijie YANG Department of Computer Science and Technology Huaqiao UniversityXiamen 361021China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao UniversityXiamen 361021China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao UniversityXiamen 361021China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055China

Photomosaic images are composite images composed of many small images called *** its overall visual effect,a photomosaic image is similar to the target image,and photomosaics are also called“montage art”.Noisy blocks and the loss of local information are the major obstacles in most methods or programs that create photomosaic *** solve these problems and generate a photomosaic image in this study,we propose a tile selection method based on error minimization.A photomosaic image can be generated by partitioning the target image in a rectangular pattern,selecting appropriate tile images,and then adding them with a weight *** on the principles of montage art,the quality of the generated photomosaic image can be evaluated by both global and local *** the proposed framework,via an error function analysis,the results show that selecting a tile image using a global minimum distance minimizes both the global error and the local error ***,the weight coefficient of the image superposition can be used to adjust the ratio of the global and local ***,to verify the proposed method,we built a new photomosaic creation dataset during this *** experimental results show that the proposed method achieves a low mean absolute error and that the generated photomosaic images have a more artistic effect than do the existing approaches.

关键词： photomosaic image tile image target image error minimization mean absolute error

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