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embedding</title> <meta name="description" content="Search results for: text embedding"> <meta name="keywords" content="text embedding"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="text embedding"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1504</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: text embedding</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1504</span> Improved Processing Speed for Text Watermarking Algorithm in Color Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamza%20A.%20Al-Sewadi">Hamza A. Al-Sewadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Akram%20N.%20A.%20Aldakari"> Akram N. A. Aldakari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Copyright protection and ownership proof of digital multimedia are achieved nowadays by digital watermarking techniques. A text watermarking algorithm for protecting the property rights and ownership judgment of color images is proposed in this paper. Embedding is achieved by inserting texts elements randomly into the color image as noise. The YIQ image processing model is found to be faster than other image processing methods, and hence, it is adopted for the embedding process. An optional choice of encrypting the text watermark before embedding is also suggested (in case required by some applications), where, the text can is encrypted using any enciphering technique adding more difficulty to hackers. Experiments resulted in embedding speed improvement of more than double the speed of other considered systems (such as least significant bit method, and separate color code methods), and a fairly acceptable level of peak signal to noise ratio (PSNR) with low mean square error values for watermarking purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=steganography" title="steganography">steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=watermarking" title=" watermarking"> watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20complexity%20measurements" title=" time complexity measurements"> time complexity measurements</a>, <a href="https://publications.waset.org/abstracts/search?q=private%20keys" title=" private keys"> private keys</a> </p> <a href="https://publications.waset.org/abstracts/85280/improved-processing-speed-for-text-watermarking-algorithm-in-color-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85280.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">143</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1503</span> Text Similarity in Vector Space Models: A Comparative Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omid%20Shahmirzadi">Omid Shahmirzadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20Lugowski"> Adam Lugowski</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenneth%20Younge"> Kenneth Younge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to-patent similarity and compare TFIDF (and related extensions), topic models (e.g., latent semantic indexing), and neural models (e.g., paragraph vectors). Contrary to expectations, the added computational cost of text embedding methods is justified only when: 1) the target text is condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF performs surprisingly well in other cases: in particular for longer and more technical texts or for making finer-grained distinctions between nearest neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun phrases or calculating term weights incrementally, were not helpful in our context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=patent" title=" patent"> patent</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20embedding" title=" text embedding"> text embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20similarity" title=" text similarity"> text similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20space%20model" title=" vector space model"> vector space model</a> </p> <a href="https://publications.waset.org/abstracts/102930/text-similarity-in-vector-space-models-a-comparative-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102930.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">175</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1502</span> A Word-to-Vector Formulation for Word Representation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Rizkallah">Sandra Rizkallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20F.%20Atiya"> Amir F. Atiya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a novel word to vector representation that is based on embedding the words into a sphere, whereby the dot product of the corresponding vectors represents the similarity between any two words. Embedding the vectors into a sphere enabled us to take into consideration the antonymity between words, not only the synonymity, because of the suitability to handle the polarity nature of words. For example, a word and its antonym can be represented as a vector and its negative. Moreover, we have managed to extract an adequate vocabulary. The obtained results show that the proposed approach can capture the essence of the language, and can be generalized to estimate a correct similarity of any new pair of words. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20to%20vector" title=" word to vector"> word to vector</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20similarity" title=" text similarity"> text similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a> </p> <a href="https://publications.waset.org/abstracts/81808/a-word-to-vector-formulation-for-word-representation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81808.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">275</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1501</span> TransDrift: Modeling Word-Embedding Drift Using Transformer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nishtha%20Madaan">Nishtha Madaan</a>, <a href="https://publications.waset.org/abstracts/search?q=Prateek%20Chaudhury"> Prateek Chaudhury</a>, <a href="https://publications.waset.org/abstracts/search?q=Nishant%20Kumar"> Nishant Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Srikanta%20Bedathur"> Srikanta Bedathur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modern NLP applications, word embeddings are a crucial backbone that can be readily shared across a number of tasks. However, as the text distributions change and word semantics evolve over time, the downstream applications using the embeddings can suffer if the word representations do not conform to the data drift. Thus, maintaining word embeddings to be consistent with the underlying data distribution is a key problem. In this work, we tackle this problem and propose TransDrift, a transformer-based prediction model for word embeddings. Leveraging the flexibility of the transformer, our model accurately learns the dynamics of the embedding drift and predicts future embedding. In experiments, we compare with existing methods and show that our model makes significantly more accurate predictions of the word embedding than the baselines. Crucially, by applying the predicted embeddings as a backbone for downstream classification tasks, we show that our embeddings lead to superior performance compared to the previous methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NLP%20applications" title="NLP applications">NLP applications</a>, <a href="https://publications.waset.org/abstracts/search?q=transformers" title=" transformers"> transformers</a>, <a href="https://publications.waset.org/abstracts/search?q=Word2vec" title=" Word2vec"> Word2vec</a>, <a href="https://publications.waset.org/abstracts/search?q=drift" title=" drift"> drift</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20embeddings" title=" word embeddings"> word embeddings</a> </p> <a href="https://publications.waset.org/abstracts/165423/transdrift-modeling-word-embedding-drift-using-transformer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165423.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1500</span> High Secure Data Hiding Using Cropping Image and Least Significant Bit Steganography</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalid%20A.%20Al-Afandy">Khalid A. Al-Afandy</a>, <a href="https://publications.waset.org/abstracts/search?q=El-Sayyed%20El-Rabaie"> El-Sayyed El-Rabaie</a>, <a href="https://publications.waset.org/abstracts/search?q=Osama%20Salah"> Osama Salah</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20El-Mhalaway"> Ahmed El-Mhalaway</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a high secure data hiding technique using image cropping and Least Significant Bit (LSB) steganography. The predefined certain secret coordinate crops will be extracted from the cover image. The secret text message will be divided into sections. These sections quantity is equal the image crops quantity. Each section from the secret text message will embed into an image crop with a secret sequence using LSB technique. The embedding is done using the cover image color channels. Stego image is given by reassembling the image and the stego crops. The results of the technique will be compared to the other state of art techniques. Evaluation is based on visualization to detect any degradation of stego image, the difficulty of extracting the embedded data by any unauthorized viewer, Peak Signal-to-Noise Ratio of stego image (PSNR), and the embedding algorithm CPU time. Experimental results ensure that the proposed technique is more secure compared with the other traditional techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=steganography" title="steganography">steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=stego" title=" stego"> stego</a>, <a href="https://publications.waset.org/abstracts/search?q=LSB" title=" LSB"> LSB</a>, <a href="https://publications.waset.org/abstracts/search?q=crop" title=" crop"> crop</a> </p> <a href="https://publications.waset.org/abstracts/44747/high-secure-data-hiding-using-cropping-image-and-least-significant-bit-steganography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44747.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">269</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1499</span> Deep Learning Based-Object-classes Semantic Classification of Arabic Texts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imen%20Elleuch">Imen Elleuch</a>, <a href="https://publications.waset.org/abstracts/search?q=Wael%20Ouarda"> Wael Ouarda</a>, <a href="https://publications.waset.org/abstracts/search?q=Gargouri%20Bilel"> Gargouri Bilel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We proposes in this paper a Deep Learning based approach to classify text in order to enrich an Arabic ontology based on the objects classes of Gaston Gross. Those object classes are defined by taking into account the syntactic and semantic features of the treated language. Thus, our proposed approach is a hybrid one. In fact, it is based on the one hand on the object classes that represents a knowledge based-approach on classification of text and in the other hand it uses the deep learning approach that use the word embedding-based-approach to classify text. We have applied our proposed approach on a corpus constructed from an Arabic dictionary. The obtained semantic classification of text will enrich the Arabic objects classes ontology. In fact, new classes can be added to the ontology or an expansion of the features that characterizes each object class can be updated. The obtained results are compared to a similar work that treats the same object with a classical linguistic approach for the semantic classification of text. This comparison highlight our hybrid proposed approach that can be ameliorated by broaden the dataset used in the deep learning process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep-learning%20approach" title="deep-learning approach">deep-learning approach</a>, <a href="https://publications.waset.org/abstracts/search?q=object-classes" title=" object-classes"> object-classes</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20classification" title=" semantic classification"> semantic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic" title=" Arabic"> Arabic</a> </p> <a href="https://publications.waset.org/abstracts/176532/deep-learning-based-object-classes-semantic-classification-of-arabic-texts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176532.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1498</span> Extraction of Text Subtitles in Multimedia Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amarjit%20Singh">Amarjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a method for extraction of text subtitles in large video is proposed. The video data needs to be annotated for many multimedia applications. Text is incorporated in digital video for the motive of providing useful information about that video. So need arises to detect text present in video to understanding and video indexing. This is achieved in two steps. First step is text localization and the second step is text verification. The method of text detection can be extended to text recognition which finds applications in automatic video indexing; video annotation and content based video retrieval. The method has been tested on various types of videos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video" title="video">video</a>, <a href="https://publications.waset.org/abstracts/search?q=subtitles" title=" subtitles"> subtitles</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction" title=" extraction"> extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=annotation" title=" annotation"> annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=frames" title=" frames"> frames</a> </p> <a href="https://publications.waset.org/abstracts/24441/extraction-of-text-subtitles-in-multimedia-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24441.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">601</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1497</span> A Summary-Based Text Classification Model for Graph Attention Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuo%20Liu">Shuo Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20natural%20language%20processing" title="Chinese natural language processing">Chinese natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=abstract%20extraction" title=" abstract extraction"> abstract extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20attention%20network" title=" graph attention network"> graph attention network</a> </p> <a href="https://publications.waset.org/abstracts/158060/a-summary-based-text-classification-model-for-graph-attention-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158060.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">100</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1496</span> Urdu Text Extraction Method from Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samabia%20Tehsin">Samabia Tehsin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumaira%20Kausar"> Sumaira Kausar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the vast increase in the multimedia data in recent years, efficient and robust retrieval techniques are needed to retrieve and index images/ videos. Text embedded in the images can serve as the strong retrieval tool for images. This is the reason that text extraction is an area of research with increasing attention. English text extraction is the focus of many researchers but very less work has been done on other languages like Urdu. This paper is focusing on Urdu text extraction from video frames. This paper presents a text detection feature set, which has the ability to deal up with most of the problems connected with the text extraction process. To test the validity of the method, it is tested on Urdu news dataset, which gives promising results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=caption%20text" title="caption text">caption text</a>, <a href="https://publications.waset.org/abstracts/search?q=content-based%20image%20retrieval" title=" content-based image retrieval"> content-based image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20analysis" title=" document analysis"> document analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20extraction" title=" text extraction"> text extraction</a> </p> <a href="https://publications.waset.org/abstracts/9566/urdu-text-extraction-method-from-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9566.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">516</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1495</span> Image Steganography Using Least Significant Bit Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Preeti%20Kumari">Preeti Kumari</a>, <a href="https://publications.waset.org/abstracts/search?q=Ridhi%20Kapoor"> Ridhi Kapoor</a> </p> <p class="card-text"><strong>Abstract:</strong></p>  In any communication, security is the most important issue in today’s world. In this paper, steganography is the process of hiding the important data into other data, such as text, audio, video, and image. The interest in this topic is to provide availability, confidentiality, integrity, and authenticity of data. The steganographic technique that embeds hides content with unremarkable cover media so as not to provoke eavesdropper’s suspicion or third party and hackers. In which many applications of compression, encryption, decryption, and embedding methods are used for digital image steganography. Due to compression, the nose produces in the image. To sustain noise in the image, the LSB insertion technique is used. The performance of the proposed embedding system with respect to providing security to secret message and robustness is discussed. We also demonstrate the maximum steganography capacity and visual distortion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=steganography" title="steganography">steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=LSB" title=" LSB"> LSB</a>, <a href="https://publications.waset.org/abstracts/search?q=encoding" title=" encoding"> encoding</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20hiding" title=" information hiding"> information hiding</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20image" title=" color image"> color image</a> </p> <a href="https://publications.waset.org/abstracts/35755/image-steganography-using-least-significant-bit-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35755.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1494</span> Small Text Extraction from Documents and Chart Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rominkumar%20Busa">Rominkumar Busa</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahira%20K.%20C."> Shahira K. C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Lijiya%20A."> Lijiya A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=small%20text%20extraction" title="small text extraction">small text extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=scene%20text%20recognition" title=" scene text recognition"> scene text recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=CRNN" title=" CRNN"> CRNN</a> </p> <a href="https://publications.waset.org/abstracts/150310/small-text-extraction-from-documents-and-chart-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150310.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">126</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1493</span> Text Data Preprocessing Library: Bilingual Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kabil%20Boukhari">Kabil Boukhari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the context of information retrieval, the selection of the most relevant words is a very important step. In fact, the text cleaning allows keeping only the most representative words for a better use. In this paper, we propose a library for the purpose text preprocessing within an implemented application to facilitate this task. This study has two purposes. The first, is to present the related work of the various steps involved in text preprocessing, presenting the segmentation, stemming and lemmatization algorithms that could be efficient in the rest of study. The second, is to implement a developed tool for text preprocessing in French and English. This library accepts unstructured text as input and provides the preprocessed text as output, based on a set of rules and on a base of stop words for both languages. The proposed library has been made on diﬀerent corpora and gave an interesting result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20preprocessing" title="text preprocessing">text preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20extraction" title=" knowledge extraction"> knowledge extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=normalization" title=" normalization"> normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20generation" title=" text generation"> text generation</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/150846/text-data-preprocessing-library-bilingual-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150846.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1492</span> Robust Image Design Based Steganographic System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadiq%20J.%20Abou-Loukh">Sadiq J. Abou-Loukh</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20M.%20Habbi"> Hanan M. Habbi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a steganography to hide the transmitted information without excite suspicious and also illustrates the level of secrecy that can be increased by using cryptography techniques. The proposed system has been implemented firstly by encrypted image file one time pad key and secondly encrypted message that hidden to perform encryption followed by image embedding. Then the new image file will be created from the original image by using four triangles operation, the new image is processed by one of two image processing techniques. The proposed two processing techniques are thresholding and differential predictive coding (DPC). Afterwards, encryption or decryption keys are generated by functional key generator. The generator key is used one time only. Encrypted text will be hidden in the places that are not used for image processing and key generation system has high embedding rate (0.1875 character/pixel) for true color image (24 bit depth). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=encryption" title="encryption">encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%0D%0Apredictive%20coding" title=" differential predictive coding"> differential predictive coding</a>, <a href="https://publications.waset.org/abstracts/search?q=four%20triangles%20operation" title=" four triangles operation "> four triangles operation </a> </p> <a href="https://publications.waset.org/abstracts/16654/robust-image-design-based-steganographic-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16654.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">493</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1491</span> Secured Embedding of Patient’s Confidential Data in Electrocardiogram Using Chaotic Maps</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Butta%20Singh">Butta Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a chaotic map based approach for secured embedding of patient’s confidential data in electrocardiogram (ECG) signal. The chaotic map generates predefined locations through the use of selective control parameters. The sample value difference method effectually hides the confidential data in ECG sample pairs at these predefined locations. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through various statistical and clinical performance measures. Statistical metrics comprise of Percentage Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR). Further, a comparative analysis between proposed method and existing approaches was also performed. The results clearly demonstrated the superiority of proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chaotic%20maps" title="chaotic maps">chaotic maps</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20steganography" title=" ECG steganography"> ECG steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20embedding" title=" data embedding"> data embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a> </p> <a href="https://publications.waset.org/abstracts/78602/secured-embedding-of-patients-confidential-data-in-electrocardiogram-using-chaotic-maps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78602.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">196</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1490</span> OCR/ICR Text Recognition Using ABBYY FineReader as an Example Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Bagirzade">A. R. Bagirzade</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sh.%20Najafova"> A. Sh. Najafova</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Yessirkepova"> S. M. Yessirkepova</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20S.%20Albert"> E. S. Albert</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article describes a text recognition method based on Optical Character Recognition (OCR). The features of the OCR method were examined using the ABBYY FineReader program. It describes automatic text recognition in images. OCR is necessary because optical input devices can only transmit raster graphics as a result. Text recognition describes the task of recognizing letters shown as such, to identify and assign them an assigned numerical value in accordance with the usual text encoding (ASCII, Unicode). The peculiarity of this study conducted by the authors using the example of the ABBYY FineReader, was confirmed and shown in practice, the improvement of digital text recognition platforms developed by Electronic Publication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ABBYY%20FineReader%20system" title="ABBYY FineReader system">ABBYY FineReader system</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20symbol%20recognition" title=" algorithm symbol recognition"> algorithm symbol recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR%2FICR%20techniques" title=" OCR/ICR techniques"> OCR/ICR techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition%20technologies" title=" recognition technologies"> recognition technologies</a> </p> <a href="https://publications.waset.org/abstracts/130255/ocricr-text-recognition-using-abbyy-finereader-as-an-example-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130255.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1489</span> Graph Neural Networks and Rotary Position Embedding for Voice Activity Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=YingWei%20Tan">YingWei Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=XueFeng%20Ding"> XueFeng Ding</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Attention-based voice activity detection models have gained significant attention in recent years due to their fast training speed and ability to capture a wide contextual range. The inclusion of multi-head style and position embedding in the attention architecture are crucial. Having multiple attention heads allows for differential focus on different parts of the sequence, while position embedding provides guidance for modeling dependencies between elements at various positions in the input sequence. In this work, we propose an approach by considering each head as a node, enabling the application of graph neural networks (GNN) to identify correlations among the different nodes. In addition, we adopt an implementation named rotary position embedding (RoPE), which encodes absolute positional information into the input sequence by a rotation matrix, and naturally incorporates explicit relative position information into a self-attention module. We evaluate the effectiveness of our method on a synthetic dataset, and the results demonstrate its superiority over the baseline CRNN in scenarios with low signal-to-noise ratio and noise, while also exhibiting robustness across different noise types. In summary, our proposed framework effectively combines the strengths of CNN and RNN (LSTM), and further enhances detection performance through the integration of graph neural networks and rotary position embedding. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=voice%20activity%20detection" title="voice activity detection">voice activity detection</a>, <a href="https://publications.waset.org/abstracts/search?q=CRNN" title=" CRNN"> CRNN</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20neural%20networks" title=" graph neural networks"> graph neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=rotary%20position%20embedding" title=" rotary position embedding"> rotary position embedding</a> </p> <a href="https://publications.waset.org/abstracts/179624/graph-neural-networks-and-rotary-position-embedding-for-voice-activity-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179624.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">72</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1488</span> Resume Ranking Using Custom Word2vec and Rule-Based Natural Language Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subodh%20Chandra%20Shakya">Subodh Chandra Shakya</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajendra%20Sapkota"> Rajendra Sapkota</a>, <a href="https://publications.waset.org/abstracts/search?q=Aakash%20Tamang"> Aakash Tamang</a>, <a href="https://publications.waset.org/abstracts/search?q=Shushant%20Pudasaini"> Shushant Pudasaini</a>, <a href="https://publications.waset.org/abstracts/search?q=Sujan%20Adhikari"> Sujan Adhikari</a>, <a href="https://publications.waset.org/abstracts/search?q=Sajjan%20Adhikari"> Sajjan Adhikari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lots of efforts have been made in order to measure the semantic similarity between the text corpora in the documents. Techniques have been evolved to measure the similarity of two documents. One such state-of-art technique in the field of Natural Language Processing (NLP) is word to vector models, which converts the words into their word-embedding and measures the similarity between the vectors. We found this to be quite useful for the task of resume ranking. So, this research paper is the implementation of the word2vec model along with other Natural Language Processing techniques in order to rank the resumes for the particular job description so as to automate the process of hiring. The research paper proposes the system and the findings that were made during the process of building the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chunking" title="chunking">chunking</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20similarity" title=" document similarity"> document similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20extraction" title=" information extraction"> information extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=word2vec" title=" word2vec"> word2vec</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20embedding" title=" word embedding"> word embedding</a> </p> <a href="https://publications.waset.org/abstracts/129534/resume-ranking-using-custom-word2vec-and-rule-based-natural-language-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129534.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1487</span> Secure Watermarking not at the Cost of Low Robustness</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jian%20Cao">Jian Cao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a novel watermarking technique which we call the random direction embedding (RDE) watermarking. Unlike traditional watermarking techniques, the watermark energy after the RDE embedding does not focus on a fixed direction, leading to the security against the traditional unauthorized watermark removal attack. In addition, the experimental results show that when compared with the existing secure watermarking, namely natural watermarking (NW), the RDE watermarking gains significant improvement in terms of robustness. In fact, the security of the RDE watermarking is not at the cost of low robustness, and it can even achieve more robust than the traditional spread spectrum watermarking, which has been shown to be very insecure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robustness" title="robustness">robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=spread%20spectrum%20watermarking" title=" spread spectrum watermarking"> spread spectrum watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=watermarking%20security" title=" watermarking security"> watermarking security</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20direction%20embedding%20%28RDE%29" title=" random direction embedding (RDE)"> random direction embedding (RDE)</a> </p> <a href="https://publications.waset.org/abstracts/13722/secure-watermarking-not-at-the-cost-of-low-robustness" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13722.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">385</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1486</span> Programmed Speech to Text Summarization Using Graph-Based Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamsini%20Pulugurtha">Hamsini Pulugurtha</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20V.%20S.%20L.%20Jagadamba"> P. V. S. L. Jagadamba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Programmed Speech to Text and Text Summarization Using Graph-based Algorithms can be utilized in gatherings to get the short depiction of the gathering for future reference. This gives signature check utilizing Siamese neural organization to confirm the personality of the client and convert the client gave sound record which is in English into English text utilizing the discourse acknowledgment bundle given in python. At times just the outline of the gathering is required, the answer for this text rundown. Thus, the record is then summed up utilizing the regular language preparing approaches, for example, solo extractive text outline calculations <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siamese%20neural%20network" title="Siamese neural network">Siamese neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20speech" title=" English speech"> English speech</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20text" title=" English text"> English text</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20extractive%20text%20summarization" title=" unsupervised extractive text summarization"> unsupervised extractive text summarization</a> </p> <a href="https://publications.waset.org/abstracts/143079/programmed-speech-to-text-summarization-using-graph-based-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143079.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">219</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1485</span> On-Road Text Detection Platform for Driver Assistance Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guezouli%20Larbi">Guezouli Larbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Belkacem%20Soundes"> Belkacem Soundes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20detection" title="text detection">text detection</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=PZM" title=" PZM"> PZM</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/161507/on-road-text-detection-platform-for-driver-assistance-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161507.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">83</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1484</span> Reducing Accidents Using Text Stops</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benish%20Chaudhry">Benish Chaudhry</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of the accidents these days are occurring because of the ‘text-and-drive’ concept. If we look at the structure of cities in UAE, there are great distances, because of which it is impossible to drive without using or merely checking the cellphone. Moreover, if we look at the road structure, it is almost impossible to stop at a point and text. With the introduction of TEXT STOPs, drivers will be able to stop different stops for a maximum of 1 and a half-minute in order to reply or write a message. They can be introduced at a distance of 10 minutes of driving on the average speed of the road, so the drivers can look forward to a stop and can reply to a text when needed. A user survey indicates that drivers are willing to NOT text-and-drive if they have such a facility available. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transport" title="transport">transport</a>, <a href="https://publications.waset.org/abstracts/search?q=accidents" title=" accidents"> accidents</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20planning" title=" urban planning"> urban planning</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20planning" title=" road planning"> road planning</a> </p> <a href="https://publications.waset.org/abstracts/44563/reducing-accidents-using-text-stops" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44563.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">394</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1483</span> Index t-SNE: Tracking Dynamics of High-Dimensional Datasets with Coherent Embeddings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaelle%20Candel">Gaelle Candel</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Naccache"> David Naccache</a> </p> <p class="card-text"><strong>Abstract:</strong></p> t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concept%20drift" title="concept drift">concept drift</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20visualization" title=" data visualization"> data visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=dimension%20reduction" title=" dimension reduction"> dimension reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=embedding" title=" embedding"> embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=reusability" title=" reusability"> reusability</a>, <a href="https://publications.waset.org/abstracts/search?q=t-SNE" title=" t-SNE"> t-SNE</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a> </p> <a href="https://publications.waset.org/abstracts/135557/index-t-sne-tracking-dynamics-of-high-dimensional-datasets-with-coherent-embeddings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135557.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1482</span> Structure Analysis of Text-Image Connection in Jalayrid Period Illustrated Manuscripts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahsa%20Khani%20Oushani">Mahsa Khani Oushani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text and image are two important elements in the field of Iranian art, the text component and the image component have always been manifested together. The image narrates the text and the text is the factor in the formation of the image and they are closely related to each other. The connection between text and image is an interactive and two-way connection in the tradition of Iranian manuscript arrangement. The interaction between the narrative description and the image scene is the result of a direct and close connection between the text and the image, which in addition to the decorative aspect, also has a descriptive aspect. In this article the connection between the text element and the image element and its adaptation to the theory of Roland Barthes, the structuralism theorist, in this regard will be discussed. This study tends to investigate the question of how the connection between text and image in illustrated manuscripts of the Jalayrid period is defined according to Barthes’ theory. And what kind of proportion has the artist created in the composition between text and image. Based on the results of reviewing the data of this study, it can be inferred that in the Jalayrid period, the image has a reference connection and although it is of major importance on the page, it also maintains a close connection with the text and is placed in a special proportion. It is not necessarily balanced and symmetrical and sometimes uses imbalance for composition. This research has been done by descriptive-analytical method, which has been done by library collection method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=structure" title="structure">structure</a>, <a href="https://publications.waset.org/abstracts/search?q=text" title=" text"> text</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalayrid" title=" Jalayrid"> Jalayrid</a>, <a href="https://publications.waset.org/abstracts/search?q=painter" title=" painter"> painter</a> </p> <a href="https://publications.waset.org/abstracts/138869/structure-analysis-of-text-image-connection-in-jalayrid-period-illustrated-manuscripts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138869.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">234</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1481</span> Genomic Sequence Representation Learning: An Analysis of K-Mer Vector Embedding Dimensionality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=James%20Jr.%20Mashiyane">James Jr. Mashiyane</a>, <a href="https://publications.waset.org/abstracts/search?q=Risuna%20Nkolele"> Risuna Nkolele</a>, <a href="https://publications.waset.org/abstracts/search?q=Stephanie%20J.%20M%C3%BCller"> Stephanie J. Müller</a>, <a href="https://publications.waset.org/abstracts/search?q=Gciniwe%20S.%20Dlamini"> Gciniwe S. Dlamini</a>, <a href="https://publications.waset.org/abstracts/search?q=Rebone%20L.%20Meraba"> Rebone L. Meraba</a>, <a href="https://publications.waset.org/abstracts/search?q=Darlington%20S.%20Mapiye"> Darlington S. Mapiye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When performing language tasks in natural language processing (NLP), the dimensionality of word embeddings is chosen either ad-hoc or is calculated by optimizing the Pairwise Inner Product (PIP) loss. The PIP loss is a metric that measures the dissimilarity between word embeddings, and it is obtained through matrix perturbation theory by utilizing the unitary invariance of word embeddings. Unlike in natural language, in genomics, especially in genome sequence processing, unlike in natural language processing, there is no notion of a “word,” but rather, there are sequence substrings of length k called k-mers. K-mers sizes matter, and they vary depending on the goal of the task at hand. The dimensionality of word embeddings in NLP has been studied using the matrix perturbation theory and the PIP loss. In this paper, the sufficiency and reliability of applying word-embedding algorithms to various genomic sequence datasets are investigated to understand the relationship between the k-mer size and their embedding dimension. This is completed by studying the scaling capability of three embedding algorithms, namely Latent Semantic analysis (LSA), Word2Vec, and Global Vectors (GloVe), with respect to the k-mer size. Utilising the PIP loss as a metric to train embeddings on different datasets, we also show that Word2Vec outperforms LSA and GloVe in accurate computing embeddings as both the k-mer size and vocabulary increase. Finally, the shortcomings of natural language processing embedding algorithms in performing genomic tasks are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=word%20embeddings" title="word embeddings">word embeddings</a>, <a href="https://publications.waset.org/abstracts/search?q=k-mer%20embedding" title=" k-mer embedding"> k-mer embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensionality%0D%0Areduction" title=" dimensionality reduction"> dimensionality reduction</a> </p> <a href="https://publications.waset.org/abstracts/151370/genomic-sequence-representation-learning-an-analysis-of-k-mer-vector-embedding-dimensionality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151370.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1480</span> Robust Medical Image Watermarking Using Frequency Domain and Least Significant Bits Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Volkan%20Kaya">Volkan Kaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Ersin%20Elbasi"> Ersin Elbasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Watermarking and stenography are getting importance recently because of copyright protection and authentication. In watermarking we embed stamp, logo, noise or image to multimedia elements such as image, video, audio, animation and text. There are several works have been done in watermarking for different purposes. In this research work, we used watermarking techniques to embed patient information into the medical magnetic resonance (MR) images. There are two methods have been used; frequency domain (Digital Wavelet Transform-DWT, Digital Cosine Transform-DCT, and Digital Fourier Transform-DFT) and spatial domain (Least Significant Bits-LSB) domain. Experimental results show that embedding in frequency domains resist against one type of attacks, and embedding in spatial domain is resist against another group of attacks. Peak Signal Noise Ratio (PSNR) and Similarity Ratio (SR) values are two measurement values for testing. These two values give very promising result for information hiding in medical MR images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=watermarking" title="watermarking">watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20image" title=" medical image"> medical image</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20domain" title=" frequency domain"> frequency domain</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20significant%20bits" title=" least significant bits"> least significant bits</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a> </p> <a href="https://publications.waset.org/abstracts/75214/robust-medical-image-watermarking-using-frequency-domain-and-least-significant-bits-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75214.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">288</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1479</span> Optimal Classifying and Extracting Fuzzy Relationship from Query Using Text Mining Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faisal%20Alshuwaier">Faisal Alshuwaier</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Areshey"> Ali Areshey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text mining techniques are generally applied for classifying the text, finding fuzzy relations and structures in data sets. This research provides plenty text mining capabilities. One common application is text classification and event extraction, which encompass deducing specific knowledge concerning incidents referred to in texts. The main contribution of this paper is the clarification of a concept graph generation mechanism, which is based on a text classification and optimal fuzzy relationship extraction. Furthermore, the work presented in this paper explains the application of fuzzy relationship extraction and branch and bound method to simplify the texts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extraction" title="extraction">extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=max-prod" title=" max-prod"> max-prod</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20relations" title=" fuzzy relations"> fuzzy relations</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=memberships" title=" memberships"> memberships</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=memberships" title=" memberships"> memberships</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/23970/optimal-classifying-and-extracting-fuzzy-relationship-from-query-using-text-mining-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23970.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">582</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1478</span> Mask-Prompt-Rerank: An Unsupervised Method for Text Sentiment Transfer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yufen%20Qin">Yufen Qin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text sentiment transfer is an important branch of text style transfer. The goal is to generate text with another sentiment attribute based on a text with a specific sentiment attribute while maintaining the content and semantic information unrelated to sentiment unchanged in the process. There are currently two main challenges in this field: no parallel corpus and text attribute entanglement. In response to the above problems, this paper proposed a novel solution: Mask-Prompt-Rerank. Use the method of masking the sentiment words and then using prompt regeneration to transfer the sentence sentiment. Experiments on two sentiment benchmark datasets and one formality transfer benchmark dataset show that this approach makes the performance of small pre-trained language models comparable to that of the most advanced large models, while consuming two orders of magnitude less computing and memory. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=language%20model" title="language model">language model</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=prompt" title=" prompt"> prompt</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20sentiment%20transfer" title=" text sentiment transfer"> text sentiment transfer</a> </p> <a href="https://publications.waset.org/abstracts/173904/mask-prompt-rerank-an-unsupervised-method-for-text-sentiment-transfer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173904.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">81</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1477</span> Exploratory Analysis of A Review of Nonexistence Polarity in Native Speech</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deawan%20Rakin%20Ahamed%20Remal">Deawan Rakin Ahamed Remal</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinthia%20Chowdhury"> Sinthia Chowdhury</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharun%20Akter%20Khushbu"> Sharun Akter Khushbu</a>, <a href="https://publications.waset.org/abstracts/search?q=Sheak%20Rashed%20Haider%20Noori"> Sheak Rashed Haider Noori</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Native Speech to text synthesis has its own leverage for the purpose of mankind. The extensive nature of art to speaking different accents is common but the purpose of communication between two different accent types of people is quite difficult. This problem will be motivated by the extraction of the wrong perception of language meaning. Thus, many existing automatic speech recognition has been placed to detect text. Overall study of this paper mentions a review of NSTTR (Native Speech Text to Text Recognition) synthesis compared with Text to Text recognition. Review has exposed many text to text recognition systems that are at a very early stage to comply with the system by native speech recognition. Many discussions started about the progression of chatbots, linguistic theory another is rule based approach. In the Recent years Deep learning is an overwhelming chapter for text to text learning to detect language nature. To the best of our knowledge, In the sub continent a huge number of people speak in Bangla language but they have different accents in different regions therefore study has been elaborate contradictory discussion achievement of existing works and findings of future needs in Bangla language acoustic accent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=TTR" title="TTR">TTR</a>, <a href="https://publications.waset.org/abstracts/search?q=NSTTR" title=" NSTTR"> NSTTR</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20to%20text%20recognition" title=" text to text recognition"> text to text recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/149060/exploratory-analysis-of-a-review-of-nonexistence-polarity-in-native-speech" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149060.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">132</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1476</span> Reversible Information Hitting in Encrypted JPEG Bitstream by LSB Based on Inherent Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vaibhav%20Barve">Vaibhav Barve</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reversible information hiding has drawn a lot of interest as of late. Being reversible, we can restore unique computerized data totally. It is a plan where mystery data is put away in digital media like image, video, audio to maintain a strategic distance from unapproved access and security reason. By and large JPEG bit stream is utilized to store this key data, first JPEG bit stream is encrypted into all around sorted out structure and then this secret information or key data is implanted into this encrypted region by marginally changing the JPEG bit stream. Valuable pixels suitable for information implanting are computed and as indicated by this key subtle elements are implanted. In our proposed framework we are utilizing RC4 algorithm for encrypting JPEG bit stream. Encryption key is acknowledged by framework user which, likewise, will be used at the time of decryption. We are executing enhanced least significant bit supplanting steganography by utilizing genetic algorithm. At first, the quantity of bits that must be installed in a guaranteed coefficient is versatile. By utilizing proper parameters, we can get high capacity while ensuring high security. We are utilizing logistic map for shuffling of bits and utilization GA (Genetic Algorithm) to find right parameters for the logistic map. Information embedding key is utilized at the time of information embedding. By utilizing precise picture encryption and information embedding key, the beneficiary can, without much of a stretch, concentrate the incorporated secure data and totally recoup the first picture and also the original secret information. At the point when the embedding key is truant, the first picture can be recouped pretty nearly with sufficient quality without getting the embedding key of interest. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20embedding" title="data embedding">data embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=decryption" title=" decryption"> decryption</a>, <a href="https://publications.waset.org/abstracts/search?q=encryption" title=" encryption"> encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20data%20hiding" title=" reversible data hiding"> reversible data hiding</a>, <a href="https://publications.waset.org/abstracts/search?q=steganography" title=" steganography"> steganography</a> </p> <a href="https://publications.waset.org/abstracts/32863/reversible-information-hitting-in-encrypted-jpeg-bitstream-by-lsb-based-on-inherent-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32863.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">288</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1475</span> Anatomical Survey for Text Pattern Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Tehsin">S. Tehsin</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Kausar"> S. Kausar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ultimate aim of machine intelligence is to explore and materialize the human capabilities, one of which is the ability to detect various text objects within one or more images displayed on any canvas including prints, videos or electronic displays. Multimedia data has increased rapidly in past years. Textual information present in multimedia contains important information about the image/video content. However, it needs to technologically testify the commonly used human intelligence of detecting and differentiating the text within an image, for computers. Hence in this paper feature set based on anatomical study of human text detection system is proposed. Subsequent examination bears testimony to the fact that the features extracted proved instrumental to text detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biologically%20inspired%20vision" title="biologically inspired vision">biologically inspired vision</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20based%20retrieval" title=" content based retrieval"> content based retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20analysis" title=" document analysis"> document analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20extraction" title=" text extraction"> text extraction</a> </p> <a href="https://publications.waset.org/abstracts/9629/anatomical-survey-for-text-pattern-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9629.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">444</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=text%20embedding&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=text%20embedding&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=text%20embedding&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=text%20embedding&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=text%20embedding&page=6">6</a></li> <li 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