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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: Shayeb, Bayan;
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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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    Authors: Fernando, Dinithi;

    Chat-bots could be considered as one of widely used technologies since it increases the business efficiency and throughput. Proposed project is to develop a web based chat-bot that is capable of booking appointments via the website Telecare Plus. Machine learning techniques are used to provide an interactive and personalized learning experience for users. Users details are be retrieved by the bot to book an appointment, if the user gives permission. Various tools and resources such as python libraries are integrated with the chat-bot to help users to practice and provide answers. It is not easy for many users to book an online appointment because of the technical knowledge and skill levels they have. One of the main advantages of this chat-bot is the users are able to provide details to book an appointment in a similar way they do when a patient interacts with a live agent. User studies are used to evaluate the effectiveness of the chat-bot as well as to assess its usability, engagement, and outcomes. The medical field could have a significant impact because of this project by providing an accessible and efficient way to book appointments.

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    https://doi.org/10.7939/r3-93j...
    Other ORP type . 2023
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      https://doi.org/10.7939/r3-93j...
      Other ORP type . 2023
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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: Reid, Brady;

    The Canadian Indigenous group, the Beothuk, is known for their extinction sometime during the 19th century. Despite the Beothuk’s extinction becoming popular amongst researchers during the 20th century, the Beothuk narrative is still plagued with misconceptions. In my research paper entitled “Avoidance and Exhaustion,” I address misconceptions about the interactions between various Indigenous groups and Europeans that interacted with the Beothuk. The research paper was created as a final project for Tolly Bradford at Concordia University and will be the basis of my oral presentation. The Beothuk chose to avoid interaction with European fishermen or explorers unless necessary. As a result, it is a challenge to make any conclusive arguments for the causation of the Beothuk extinction or their cultural practices. Researchers agree that documented interactions between the Beothuk and Europeans are faulty. However, they implement the supposed interactions due to the lack of available sources. Publications about the Beothuk have slowed down since the turn of the century. However, recent excavations have provided new evidence. A more accurate portrayal of the Beothuk's extinction is achievable by relying on archaeological evidence over personal testimonials that resulted from folklore. By utilizing archaeological evidence, it is possible to determine the migration patterns of the Beothuk and their attempt to survive on the Newfoundland Islands’ resource-poor interior. I plan to incorporate the migration patterns, Beothuk’s pattern of avoidance, and harsh living conditions into a PowerPoint presentation. The presentation will comprise slides with bullet points, maps, and pictures. The maps will demonstrate the gradual withdrawal of the Beothuk to the Newfoundland island’s interior. The bullet point slides will take up most of my presentation and be used to defend my arguments. I intend to utilize my PowerPoint and talking points to support my thesis: “Their extinction is a combination of the Beothuk practice of avoidance, the environment of the Newfoundland Island interior, and Europeans exhausting the land of its resources and animal population.”

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    https://doi.org/10.7939/r3-2jm...
    Other ORP type . 2023
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      https://doi.org/10.7939/r3-2jm...
      Other ORP type . 2023
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    Authors: Sojitra, Ishita;

    The difficult and multidisciplinary process of automatically creating accurate and logical textual descriptions for photographs is known as automatic image captioning. Modern Neural Networks excel in tasks like Computer Vision and Natural Language Processing, but their memory and compute appetite hinder deployment on resource-limited edge devices. Researchers have developed pruning and quantization algorithms to compress networks without compromising efficacy. The process typically involves two main steps: Image understanding and Caption generation. This work presents an unconventional end-to-end compression pipeline for a CNN (Convolutional neural network)-LSTM (Long short-term memory)-based Image captioning model, achieving a 73.1 percentage reduction in model size, 71.3 percentage reduction in inference time, and 7.7 percentage increase in BLEU(bilingual evaluation understudy) score compared to uncompressed models. By comparing generated captions with reference captions created by humans, evaluation metrics like BLEU (bilingual evaluation understudy) and METEOR (metric for evaluation of translation with explicit ordering) are used to evaluate the quality of generated captions. The purpose of Automatic image processing is to extract useful information from photos, making analysis, interpretation, and manipulation faster, precise, and effective in various fields, using computational algorithms.

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    https://doi.org/10.7939/r3-8xq...
    Other ORP type . 2023
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      https://doi.org/10.7939/r3-8xq...
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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: Montgomery, Lindsay M.; Supernant, Kisha;

    Archaeology in 2021 was characterized by a continued call to use the tools of the discipline to document the violence of settler colonialism in the past and present, pushing anthropology to reckon with its own role in perpetuating historical trauma. The tension between disciplinary reflection and reform was most clearly articulated in the use of archaeological geophysics to detect the unmarked graves of incarcerated Indigenous children who died at residential and boarding schools in Canada and the United States. The highly publicized investigation of these schools has brought renewed attention to issues of repatriation and historical reclamation for many communities impacted by settler colonialism. These discussions have reverberated throughout the discipline, prompting revisions to the Society for American Archaeology's “Statement Concerning the Treatment of Human Remains,” reopening conversations around an African American Graves Protection and Repatriation Act, and informing debates around the ethics of DNA research. These conversations are part of a larger movement toward decolonizing the field by using archaeological methods to explore marginalized histories and support communities most impacted by the violences of settler colonialism.

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    Authors: Waldron, J.W.F.; McCausland, P.J.A.; Barr, S.M.; Schofield, D.I; +2 Authors

    Data associated with the paper: Waldron, J.W.F., McCausland, P.J.A., Barr, S.M., Schofield, D.I., Reusch, D., Wu, L., 2022. Terrane history of the Iapetus Ocean as preserved in the northern Appalachians and western Caledonides. Earth-Science Reviews 104163. doi:10.1016/j.earscirev.2022.104163

    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/ Education and Resear...arrow_drop_down
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    Education and Research Archive
    Dataset . 2022
    License: CC BY NC SA
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      Education and Research Archive
      Dataset . 2022
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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: Reyes Ayala, Brenda; Du, Qiufeng; Han, Juyi;

    Presented at the Linked Archives 2022: International Workshop on Archives and Linked Data at the 26th International Conference on Theory and Practice of Digital Libraries (TPDL2022), Padua, Italy.

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    https://doi.org/10.7939/r3-me0...
    Other ORP type . 2022
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      https://doi.org/10.7939/r3-me0...
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    Authors: Avalloni de Morais, Rene;

    Call Centers or Support Centers in different companies aggregate huge amount of audio data everyday. From all the conversations, few conversations demonstrate the disappointment of clients towards services, products or delivery. Finding the sentiment of the customer helps in determining whether the customer was satisfied with the service, product or not. However, manually analyzing the huge amount of audio data is time consuming, laborious, and burdensome. The aim of this research is to develop the deep learning based model which would enable to automatically evaluate the sentiment of a customer throughout a conversation with a call center agent. In this work, we developed Long Term Short Memory (LSTM) based deep learning models for customer audio call data analysis. The proposed pipeline consists of two sequential steps: a) Audio transcription: speech recognition of the conversations and document them in text; and b) Sentiment Analysis: conduct the sentiment analysis on the text data using Natural Language Processing (NLP). We compute spectrogram features from audio data and then fine-tune the LSTM based Deep Speech Model using customer call data. Deep Speech model can successfully transcribe the conversations between client and call center agent in a text form. Then we compute the 1-gram feature from text data which find the occurrence of the words responsible to identify the customer sentiments. We fed this feature into a LSTM based deep architecture which would enable to detect customer sentiments from text data. Recent advances in natural language understanding and generation facilitates to detect customer sentiments successfully as accurate as human experts. Both speech transcription and sentiment analysis part of the proposed tool are very generic in nature which could utilize for other audio data transcription and text sentiment analysis purposes. Tools were developed in python which can be easily transported and adapted in other programming environments.

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    Authors: Victor Cheruiyot;

    Software testing is a quality control activity which focuses on detecting defects which are then eventually removed. After completion of coding software products are subjected to system testing with the help of different test cases. These tests are essential and necessary to assess the effectiveness of the software. However, it is impossible to completely test any nontrivial module or system. Theoretically it suffers from halting problem: it is impossible to write a program that tests whether every program halts in a finite amount of time. Practically, executing all test cases involves enormous time and cost. Hence, it is crucial to test a subset of test cases which are important from user’s perspective based on the frequency of usage, criticality, and probability of failure. This research work is limited to test case selection for regression testing which retests software that has been changed or extended by new features during software development. In this research we carried out the following activities to develop a machine learning based test case selection strategy. First, we clean the data by dropping irrelevant features.Then we preprocess the data by encoding categorical features into its numerical values. After that we compute the bag of words for text data such as test case title, which identify the important functionality for test case selection. All these features are fed into machine learning based classifiers, such as Logistic regression, naãve Bayes, Gradient boosting, kNN, Decision tree, and Gaussian process. Experimental results demonstrate that decision tree classifier outperforms other classifiers for software test case selection. This research demonstrates that machine learning-based approach can reduce the bias and manual labour of domain expert for software regression testing. In the future, we would investigate the feature selection strategy for natural language processing to optimize the whole software test case selection procedure

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    https://doi.org/10.7939/r3-m9d...
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      https://doi.org/10.7939/r3-m9d...
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    Authors: Government of Alberta;
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    https://doi.org/10.7939/r3-ne4...
    Other ORP type . 2021
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      https://doi.org/10.7939/r3-ne4...
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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: Shayeb, Bayan;
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    Authors: Fernando, Dinithi;

    Chat-bots could be considered as one of widely used technologies since it increases the business efficiency and throughput. Proposed project is to develop a web based chat-bot that is capable of booking appointments via the website Telecare Plus. Machine learning techniques are used to provide an interactive and personalized learning experience for users. Users details are be retrieved by the bot to book an appointment, if the user gives permission. Various tools and resources such as python libraries are integrated with the chat-bot to help users to practice and provide answers. It is not easy for many users to book an online appointment because of the technical knowledge and skill levels they have. One of the main advantages of this chat-bot is the users are able to provide details to book an appointment in a similar way they do when a patient interacts with a live agent. User studies are used to evaluate the effectiveness of the chat-bot as well as to assess its usability, engagement, and outcomes. The medical field could have a significant impact because of this project by providing an accessible and efficient way to book appointments.

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    https://doi.org/10.7939/r3-93j...
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      https://doi.org/10.7939/r3-93j...
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    Authors: Reid, Brady;

    The Canadian Indigenous group, the Beothuk, is known for their extinction sometime during the 19th century. Despite the Beothuk’s extinction becoming popular amongst researchers during the 20th century, the Beothuk narrative is still plagued with misconceptions. In my research paper entitled “Avoidance and Exhaustion,” I address misconceptions about the interactions between various Indigenous groups and Europeans that interacted with the Beothuk. The research paper was created as a final project for Tolly Bradford at Concordia University and will be the basis of my oral presentation. The Beothuk chose to avoid interaction with European fishermen or explorers unless necessary. As a result, it is a challenge to make any conclusive arguments for the causation of the Beothuk extinction or their cultural practices. Researchers agree that documented interactions between the Beothuk and Europeans are faulty. However, they implement the supposed interactions due to the lack of available sources. Publications about the Beothuk have slowed down since the turn of the century. However, recent excavations have provided new evidence. A more accurate portrayal of the Beothuk's extinction is achievable by relying on archaeological evidence over personal testimonials that resulted from folklore. By utilizing archaeological evidence, it is possible to determine the migration patterns of the Beothuk and their attempt to survive on the Newfoundland Islands’ resource-poor interior. I plan to incorporate the migration patterns, Beothuk’s pattern of avoidance, and harsh living conditions into a PowerPoint presentation. The presentation will comprise slides with bullet points, maps, and pictures. The maps will demonstrate the gradual withdrawal of the Beothuk to the Newfoundland island’s interior. The bullet point slides will take up most of my presentation and be used to defend my arguments. I intend to utilize my PowerPoint and talking points to support my thesis: “Their extinction is a combination of the Beothuk practice of avoidance, the environment of the Newfoundland Island interior, and Europeans exhausting the land of its resources and animal population.”

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    https://doi.org/10.7939/r3-2jm...
    Other ORP type . 2023
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      https://doi.org/10.7939/r3-2jm...
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    Authors: Sojitra, Ishita;

    The difficult and multidisciplinary process of automatically creating accurate and logical textual descriptions for photographs is known as automatic image captioning. Modern Neural Networks excel in tasks like Computer Vision and Natural Language Processing, but their memory and compute appetite hinder deployment on resource-limited edge devices. Researchers have developed pruning and quantization algorithms to compress networks without compromising efficacy. The process typically involves two main steps: Image understanding and Caption generation. This work presents an unconventional end-to-end compression pipeline for a CNN (Convolutional neural network)-LSTM (Long short-term memory)-based Image captioning model, achieving a 73.1 percentage reduction in model size, 71.3 percentage reduction in inference time, and 7.7 percentage increase in BLEU(bilingual evaluation understudy) score compared to uncompressed models. By comparing generated captions with reference captions created by humans, evaluation metrics like BLEU (bilingual evaluation understudy) and METEOR (metric for evaluation of translation with explicit ordering) are used to evaluate the quality of generated captions. The purpose of Automatic image processing is to extract useful information from photos, making analysis, interpretation, and manipulation faster, precise, and effective in various fields, using computational algorithms.

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    https://doi.org/10.7939/r3-8xq...
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      https://doi.org/10.7939/r3-8xq...
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    Authors: Montgomery, Lindsay M.; Supernant, Kisha;

    Archaeology in 2021 was characterized by a continued call to use the tools of the discipline to document the violence of settler colonialism in the past and present, pushing anthropology to reckon with its own role in perpetuating historical trauma. The tension between disciplinary reflection and reform was most clearly articulated in the use of archaeological geophysics to detect the unmarked graves of incarcerated Indigenous children who died at residential and boarding schools in Canada and the United States. The highly publicized investigation of these schools has brought renewed attention to issues of repatriation and historical reclamation for many communities impacted by settler colonialism. These discussions have reverberated throughout the discipline, prompting revisions to the Society for American Archaeology's “Statement Concerning the Treatment of Human Remains,” reopening conversations around an African American Graves Protection and Repatriation Act, and informing debates around the ethics of DNA research. These conversations are part of a larger movement toward decolonizing the field by using archaeological methods to explore marginalized histories and support communities most impacted by the violences of settler colonialism.

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    Authors: Waldron, J.W.F.; McCausland, P.J.A.; Barr, S.M.; Schofield, D.I; +2 Authors

    Data associated with the paper: Waldron, J.W.F., McCausland, P.J.A., Barr, S.M., Schofield, D.I., Reusch, D., Wu, L., 2022. Terrane history of the Iapetus Ocean as preserved in the northern Appalachians and western Caledonides. Earth-Science Reviews 104163. doi:10.1016/j.earscirev.2022.104163

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    Education and Research Archive
    Dataset . 2022
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      Education and Research Archive
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    Authors: Reyes Ayala, Brenda; Du, Qiufeng; Han, Juyi;

    Presented at the Linked Archives 2022: International Workshop on Archives and Linked Data at the 26th International Conference on Theory and Practice of Digital Libraries (TPDL2022), Padua, Italy.

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    https://doi.org/10.7939/r3-me0...
    Other ORP type . 2022
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      https://doi.org/10.7939/r3-me0...
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    Authors: Avalloni de Morais, Rene;

    Call Centers or Support Centers in different companies aggregate huge amount of audio data everyday. From all the conversations, few conversations demonstrate the disappointment of clients towards services, products or delivery. Finding the sentiment of the customer helps in determining whether the customer was satisfied with the service, product or not. However, manually analyzing the huge amount of audio data is time consuming, laborious, and burdensome. The aim of this research is to develop the deep learning based model which would enable to automatically evaluate the sentiment of a customer throughout a conversation with a call center agent. In this work, we developed Long Term Short Memory (LSTM) based deep learning models for customer audio call data analysis. The proposed pipeline consists of two sequential steps: a) Audio transcription: speech recognition of the conversations and document them in text; and b) Sentiment Analysis: conduct the sentiment analysis on the text data using Natural Language Processing (NLP). We compute spectrogram features from audio data and then fine-tune the LSTM based Deep Speech Model using customer call data. Deep Speech model can successfully transcribe the conversations between client and call center agent in a text form. Then we compute the 1-gram feature from text data which find the occurrence of the words responsible to identify the customer sentiments. We fed this feature into a LSTM based deep architecture which would enable to detect customer sentiments from text data. Recent advances in natural language understanding and generation facilitates to detect customer sentiments successfully as accurate as human experts. Both speech transcription and sentiment analysis part of the proposed tool are very generic in nature which could utilize for other audio data transcription and text sentiment analysis purposes. Tools were developed in python which can be easily transported and adapted in other programming environments.

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    Authors: Victor Cheruiyot;

    Software testing is a quality control activity which focuses on detecting defects which are then eventually removed. After completion of coding software products are subjected to system testing with the help of different test cases. These tests are essential and necessary to assess the effectiveness of the software. However, it is impossible to completely test any nontrivial module or system. Theoretically it suffers from halting problem: it is impossible to write a program that tests whether every program halts in a finite amount of time. Practically, executing all test cases involves enormous time and cost. Hence, it is crucial to test a subset of test cases which are important from user’s perspective based on the frequency of usage, criticality, and probability of failure. This research work is limited to test case selection for regression testing which retests software that has been changed or extended by new features during software development. In this research we carried out the following activities to develop a machine learning based test case selection strategy. First, we clean the data by dropping irrelevant features.Then we preprocess the data by encoding categorical features into its numerical values. After that we compute the bag of words for text data such as test case title, which identify the important functionality for test case selection. All these features are fed into machine learning based classifiers, such as Logistic regression, naãve Bayes, Gradient boosting, kNN, Decision tree, and Gaussian process. Experimental results demonstrate that decision tree classifier outperforms other classifiers for software test case selection. This research demonstrates that machine learning-based approach can reduce the bias and manual labour of domain expert for software regression testing. In the future, we would investigate the feature selection strategy for natural language processing to optimize the whole software test case selection procedure

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    https://doi.org/10.7939/r3-m9d...
    Other ORP type . 2021
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      https://doi.org/10.7939/r3-m9d...
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    Authors: Government of Alberta;
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    https://doi.org/10.7939/r3-ne4...
    Other ORP type . 2021
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      https://doi.org/10.7939/r3-ne4...
      Other ORP type . 2021
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