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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Moudhich, Ihab; Fennan, Abdelhadi;

    This paper presents a comprehensive exploration of graph embedding techniques for sentiment analysis. The objective of this study is to enhance the accuracy of sentiment analysis models by leveraging the rich contextual relationships between words in text data. We investigate the application of graph embedding in the context of sentiment analysis, focusing on it is effectiveness in capturing the semantic and syntactic information of text. By representing text as a graph and employing graph embedding techniques, we aim to extract meaningful insights and improve the performance of sentiment analysis models. To achieve our goal, we conduct a thorough comparison of graph embedding with traditional word embedding and simple embedding layers. Our experiments demonstrate that the graph embedding model outperforms these conventional models in terms of accuracy, highlighting it is potential for sentiment analysis tasks. Furthermore, we address two limitations of graph embedding techniques: handling out-of-vocabulary words and incorporating sentiment shift over time. The findings of this study emphasize the significance of graph embedding techniques in sentiment analysis, offering valuable insights into sentiment analysis within various domains. The results suggest that graph embedding can capture intricate relationships between words, enabling a more nuanced understanding of the sentiment expressed in text data.

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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Maradithaya, Sumana; Katti, Anantshesh;

    The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.

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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Zakia Labd; Said Bahassine; Khalid Housni; Fatima Zahrae Ait Hamou Aadi; +1 Authors

    Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.

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    Authors: Alaoui Soulimani, Younes; El Achaak, Lotfi; Bouhorma, Mohammed;

    Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research is available on automatic grading for Arabic. Datasets are important to ASAG, and limited datasets are available in Arabic. In this research, we have collected a set of questions, answers, and associated grades in Arabic. We have made this dataset publicly available. We have extended to Arabic the solutions used for English ASAG. We have tested how automatic grading works on answers in Arabic provided by schoolchildren in 6th grade in the context of serious games. We found out those schoolchildren providing answers that are 5.6 words long on average. On such answers, deep learning-based grading has achieved high accuracy even with limited training data. We have tested three different recurrent neural networks for grading. With a transformer, we have achieved an accuracy of 95.67%. ASAG for school children will help detect children with learning problems early. When detected early, teachers can solve learning problems easily. This is the main purpose of this research.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sylvester, Zoltan; Straub, Kyle; Covault, Jacob;

    Data files needed to reproduce some of the figures in the paper "Stratigraphy in space and time: A reproducible approach to analysis and visualization" by Zoltán Sylvester, Kyle M. Straub, and Jacob A. Covault, in review at Earth Science Reviews.

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    ZENODO
    Dataset . 2024
    License: CC BY
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      ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sen, Swadhin;

    জমির উপরে নির্দিষ্ট একটি খাত দিয়ে প্রবাহিত জলধারা বা জলপ্রবাহই কি নদী? বিভিন্ন জলধারা বা নদীকে দৈর্ঘ্য, প্রশস্ততা, জলের পবিমান, পলিপ্রবাহের ধরন, অনুভূমিকভাবে দৃশ্যমান রূপ, গতিপথের স্থান ও জমিন, ইত্যাদি বিভিন্ন মানদণ্ড অনুসারে ভিন্ন ভিন্ন শ্রেণিতে বা বর্গে বিভক্ত করা ও মানচিত্রায়ন করা হলেও, নদীরা কি এসব শ্রেণি ও বর্গ মেনে চলে? নদীদের কি মানবায়িত বিশ্ববীক্ষা অনুসারে কেবল জড় প্রপঞ্চ হিসেবে বিবেচনা করা সম্ভব? বৃহত্তর ও শাস্ত্রান্তরী দৃষ্টিভঙ্গিতে নদীকে অনুভব করার, বোঝার ও পর্যালোচনা করা কীভাবে সম্ভব? নদীকে প্রচলিত ও প্রতিষ্ঠিত ধারণাগুলোর বাইরে গিয়ে বোঝাবুঝির প্রয়োজন কী? এমন বিভিন্ন জিজ্ঞাসার পাটাতনে বাংলাদেশের পদ্মা নদীকে কেন্দ্রে রেখে এই প্রবন্ধে আলাপ করার চেষ্টা করা হয়েছে। অবিভক্ত বাংলার অথবা গঙ্গা-ব্রহ্মপুত্র-মেঘনা বদ্বীপে পদ্মা নদীকে বিভিন্ন মিথষ্ক্রিয়াময়, জল-স্থল-অন্তরীক্ষের সঙ্গে সতত সংলগ্ন, জীবনপ্রবাহের সঙ্গে লিপ্ত একটি সত্তা হিসেবে বিবেচনা করলে সংবেদে-অনুভবে-উপলব্ধিতে-বিশ্লেষণে-ইতিহাসে পদ্মাসমেত বিভিন্ন জলপলিপ্রাণপ্রবাহধারা হিসেবে নদ-নদীগুলোকে এই বদ্বীপীয় পরিপ্রেক্ষিতে বিবেচনা করার সম্ভাবনাও এই প্রবন্ধে পেশ করা হয়েছে।

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    Article . 2024
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    Context: Artificial Intelligence (AI) has gained significant prominence in various fields such as medicine, education, and finance, spurring advances through technologies like BERT, ChatGPT, and GANs. The ethical and security implications of AI have become growing concerns. Objective: The primary aim of this work is to create an automated classifier, named the Ethical Requirements Classifier for AI (ERC4AI),using Machine Learning to identify and validate ethical requirements in AI applications, aligning with the RE4AI Ethical Guide. This assists product owners and developers in the ethical development of AI applications. Method: We adopted the Cross Industry Standard Process for Data Mining (CRISP-DM). Our process initiated with analyzing the RE4AI Ethical Guide, progressed through data collection, manual analysis, data preprocessing, and the development of a text classifier using algorithms like BERT and RoBERTa. Results: BERT emerged as the superior model with a precision of 0.96, recall of 0.81, and an F1-score of 0.88. The study confirmed the RE4AI Ethical Guide's alignment with ethical AI requirements, particularly in transparency and accountability. Conclusion: The ERC4AI showed high precision in classifying AI's ethical requirements. The RE4AI Ethical Guide is effective for incorporating ethical considerations in AI development, leading to future research in ethical text classification models. Keywords: Ethics in Artificial Intelligence, Ethical Requirements, Natural Language Processing, Machine Learning

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    Other literature type . 2024
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Franco, Adrian;

    Manual transcription of approx. 38 min lengthy voiceovers in German from the digitized version of the documentary film "Terremoto - Erdbeben in Peru" from 1970 (producer: Ferdinand Khittl KG München, commissioner: Bayer AG Leverkusen) using the program f4transcript for time coding. The documentary shows the joint humanitarian relief operation of Bayer AG with the German Red Cross (DRK) in the 1970 earthquake disaster zone of Caraz in Peru, Callejón de Huaylas, Province of Ancash. The original and digitized film is preserved and licensed by Bayer Corporate Archives Leverkusen, Germany.

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    ZENODO
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      ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Zimmermann, Elias; Bähler, Ursula; Baumgartner, Reto; Stricker, Yann;

    The main goal of this ORD project funded by swissuniversities is to test and further develop prototypical workflows in the conception phase of digital edition projects. The conception phase is particularly crucial from an ORD perspective: it is here that the foundations are laid for ensuring that research data remain accessible and can be re-used later. Proto4DigEd answers to a growing need for standardisation: "The challenge here is to imagine what a Prêt-à-Porter edition might look like, that is, to model the digital editions of the future and their editors, or, better, the skills they need to acquire." (Pierazzo 2019, 214). For this aim, the project tests and documents the use of proven tools (Transkribus, TEI Publisher) using the planned digital letter edition PARES (see below) as an example. The involvement of various stakeholders and the national and international ORD community are of great importance. Proto4DigEd is not only a model edition, but creates possibilities for exchanges between said stakeholders in workshops (see below). Besides these official events, Proto4DigEd is interested in low threshold exchanges and invites anybody interested to contact its coordinator: elias.zimmermann@uzh.ch

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sylvester, Zoltán; Straub, Kyle; Covault, Jacob;

    These visualizations were created with the Python module 'stratigraph', using a variety of stratigraphic datasets. They accompany the paper "Stratigraphy in space and time: A reproducible approach to analysis and visualization", by Zoltán Sylvester, Kyle M. Straub, and Jacob A. Covault, to be published in the journal 'Earth Science Reviews'. A brief description of the files follows. 'SM_1_Barrell_plot_original.mp4': Animation of Barrell's (1917) synthetic time-elevation curve (Barrell, J., 1917, Rhythms and the measurements of geologic time: Geological Society of America Bulletin, v. 28, p. 745–904, doi:10.1130/GSAB-28-745). 'SM_2_Wheeler_original_diagram.mp4': Animation of the geologic cross section and corresponding chronostratigraphic diagram published by Wheeler (1964) - see Wheeler, H.E. (1964). Baselevel, lithosphere surface, and time stratigraphy. Geological Society of America Bulletin, v. 75, p. 599–610. 'SM_3_meanderpy_model_plane_widget.mp4': Slicing through a three-dimensional chronostratigraphic diagram of a meandering river model; red colors represent erosion, blue colors deposition. The meandering river model was generated using 'meanderpy'. 'SM_4_erosional_surfaces_meandering_river.mp4': Erosional surfaces generated by a meandering river, colored by age (purple = older, yellow = younger). The meandering river model was generated using 'meanderpy' and it is the same model as the one above. 'SM_5_XES02_3D_cut_sides_1.mp4': Visualization of the XES-02 experiment through time. The experiment was conducted at St. Anthony Falls Laboratory, University of Minnesota. Layers are colored by water depth (not grain size). See the following reference for more details on this experiment: Martin, J., Paola, C., Abreu, V., Neal, J., and Sheets, B., 2009, Sequence stratigraphy of experimental strata under known conditions of differential subsidence and variable base level: AAPG Bulletin, v. 93, p. 503–533. 'SM_6_XES_02_dip_and_strike_sections.mp4': Dip- and strike sections of deposits of the XES-02 experiment through time. Layers are colored by water depth. Chronostratigraphic diagrams are shown in the bottom panels. 'SM_7_TDWB_17_Barrell_plots.mp4': Proximal- to distal time-elevation plots, generated from the TDWB-17-1 experiment, conducted at the Tulane University Sediment Dynamics and Stratigraphy Laboratory. See the following paper for more details on the experiment: Straub, K.M., 2019, Morphodynamics and Stratigraphic Architecture of Shelf-Edge Deltas Subject to Constant vs. Dynamic Environmental Forcings: A Laboratory Study: Frontiers in Earth Science, v. 7, p. 121, doi:10.3389/feart.2019.00121. 'SM_8_TDWB_17_strike_sections_through_time.mp4': Strike sections through time, generated from the TDWB-17-1 experiment, conducted at the Tulane University Sediment Dynamics and Stratigraphy Laboratory. Layers are colored by water depth. 'SM_9_TDWB_17_exploding_view.mp4': Taking apart and putting back together the deposits of the TDWB-17-1 experiment, conducted at the Tulane University Sediment Dynamics and Stratigraphy Laboratory, along strike- and dip sections. Layers are colored by water depth.

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44,264 Research products
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Moudhich, Ihab; Fennan, Abdelhadi;

    This paper presents a comprehensive exploration of graph embedding techniques for sentiment analysis. The objective of this study is to enhance the accuracy of sentiment analysis models by leveraging the rich contextual relationships between words in text data. We investigate the application of graph embedding in the context of sentiment analysis, focusing on it is effectiveness in capturing the semantic and syntactic information of text. By representing text as a graph and employing graph embedding techniques, we aim to extract meaningful insights and improve the performance of sentiment analysis models. To achieve our goal, we conduct a thorough comparison of graph embedding with traditional word embedding and simple embedding layers. Our experiments demonstrate that the graph embedding model outperforms these conventional models in terms of accuracy, highlighting it is potential for sentiment analysis tasks. Furthermore, we address two limitations of graph embedding techniques: handling out-of-vocabulary words and incorporating sentiment shift over time. The findings of this study emphasize the significance of graph embedding techniques in sentiment analysis, offering valuable insights into sentiment analysis within various domains. The results suggest that graph embedding can capture intricate relationships between words, enabling a more nuanced understanding of the sentiment expressed in text data.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Maradithaya, Sumana; Katti, Anantshesh;

    The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Zakia Labd; Said Bahassine; Khalid Housni; Fatima Zahrae Ait Hamou Aadi; +1 Authors

    Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Alaoui Soulimani, Younes; El Achaak, Lotfi; Bouhorma, Mohammed;

    Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research is available on automatic grading for Arabic. Datasets are important to ASAG, and limited datasets are available in Arabic. In this research, we have collected a set of questions, answers, and associated grades in Arabic. We have made this dataset publicly available. We have extended to Arabic the solutions used for English ASAG. We have tested how automatic grading works on answers in Arabic provided by schoolchildren in 6th grade in the context of serious games. We found out those schoolchildren providing answers that are 5.6 words long on average. On such answers, deep learning-based grading has achieved high accuracy even with limited training data. We have tested three different recurrent neural networks for grading. With a transformer, we have achieved an accuracy of 95.67%. ASAG for school children will help detect children with learning problems early. When detected early, teachers can solve learning problems easily. This is the main purpose of this research.

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    Authors: Sylvester, Zoltan; Straub, Kyle; Covault, Jacob;

    Data files needed to reproduce some of the figures in the paper "Stratigraphy in space and time: A reproducible approach to analysis and visualization" by Zoltán Sylvester, Kyle M. Straub, and Jacob A. Covault, in review at Earth Science Reviews.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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