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Exploring Solutions for Text-to-Speech Synthesis of Low-Resource Languages 4

Exploring Solutions for Text-to-Speech Synthesis of Low-Reso...

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4th international conference on Signal Processing and Communication, ICSPC 2023

作者： Gladston, Anushiya Rachel Pradeep, K.V. Vellore Institute of Technology Chennai School of Computer Science Engineering Chennai India

ISBN: (纸本)9798350300772

A text-to-speech synthesis system is expected to convert any given text to highly intelligible and natural speech that sounds as human-like as possible. It finds its place in a variety of applications, like virtual assistants, speech-assistive aids, and speech-to-speech translation systems. In recent years several deep learning-based methods have been proposed for text-to-speech synthesis, which result in synthetic speech that is intelligible and very natural. However, these methods require a large amount of speech data, in the order of tens of hours, preferably recorded from a single speaker and in a quiet/studio environment. While such a large amount of data might be easily available for certain languages, like English, which are widely spoken and used globally, this is not the case with several other languages. In this regard, the current work explores state-of-the-art text-to-speech synthesis methods and strategies for handling low-resource languages. the paper ultimately aims to provide insight into developing text-to-speech systems for Indian languages and hence reviews the work done so far on Indian languages as well. Since India is a multilingual country where several languages co-exist, a multilingual synthesizer would be preferable. Further, one recurring suggestion to handle data scarcity is to make use of data from multiple languages too. therefore, the current work focuses on exploring multilingual text-to-speech synthesis for low-resource languages. © 2023 IEEE.

关键词： Speech synthesis

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Research on users’ mental model in image retrieval 4

Research on users’ mental model in image retrieval

引用

4th international conference on Computer science and Communication Technology, ICCSCT 2023

作者： Hu, Danqi Qian, Wei Wei, Dong Institute for Cultural Heritage and History of Science & Technology University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781510671232

through the study of users’ mental model, this paper discusses the interactive process of image retrieval, and provides ideas for accurate image retrieval in line with user psychology. through the methods of concept list and user log, this paper builds a Users’ Mental Model in Image Retrieval (UMMIR), discusses the dimensions, characteristics and types of users of image retrieval, and applies UMMIR to the retrieval strategy, constructs the topological relationship of Tag-cloud, and verifies it with the visual prototype. the UMMIR consists of three levels: the Platform Technology Level (PTL), the Interactive Experience Level (IEL), and the Image Content Level (ICL). It includes 12 dimensions: Service Platform (SPD), Intelligent Drive (IDD), Intellectual Property (IPD), Retrieval Entrance (RED), Feedback and Sifting (FSD), Interactive Interface (IID), Topic (TD), Emotion (ED), Composition (CD), Style (SD), Purpose (PD), Quality (QD). UMMIR has the characteristics of Scenarios, Complexity, Imperfection, and Gradualness. Users in UMMIR can be divided into five types: Target-driven, art-driven, Structure-driven, Platform-dependent and AI-yearner. the visualization of UMMIR to the retrieval platform provides a direct basis for the optimization of the interactive process in image retrieval. © 2023 SPIE.

关键词： Visualization

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Mapping Techniques for an Automated Library Classification: the Case Study of Library Loans at Bibliotheca Hertziana 28th

Mapping Techniques for an Automated Library Classification: ...

引用

28th international conference on Linking theory and Practice of Digital Libraries (TPDL)

作者： Casey, Hannah Laureen Adamou, Alessandro Rodighiero, Dario Ecole Polytech Fed Lausanne Lausanne Switzerland Bibliotheca Hertziana Max Planck Inst Art Hist Rome Italy Univ Groningen Groningen Netherlands

ISBN: (纸本)9783031724367;9783031724374

this paper introduces an innovative analytical method for visualising research libraries, overcoming the limitations of the assumptions made by their classification systems. the approach combines user loan data with deep mapping techniques to graphically display usage patterns and thematic clusters. Dimensionality reduction is used to visualise the catalogue by book loans, and prompt engineering with large language models is used to describe loan clusters with detailed summaries and titles. this approach was applied to the library collection owned by Bibliotheca Hertziana, a renowned research institute for art history based in Rome. the final output was assessed by a group of experts through interviews supported by an atlas providing statistical information on clusters. this yielded promising results towards a more general framework for visually mapping textual collections and capturing their transformation and usage from an interdisciplinary perspective.

关键词： Data Visualisation Deep Mapping Digital Humanities Large Language Models Knowledge Organisation

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Using OpenNLP and GraalVM to Detect Sentences in Kubernetes While Comparing Helidon and Spring Boot’s Metrics 4th

Using OpenNLP and GraalVM to Detect Sentences in Kubernetes ...

引用

4th international conference on Data, engineering, and Applications, IDEA 2022

作者： Sharma, Aditya Dubey, Ghanshyam Prasad MP Bhopal India

ISBN: (纸本)9789819700363

We have resorted to using Java for our data science endeavours to handle data processing rather than relying on the computer language Python. However, when compared to the typical default package of JDK + JVM that is made accessible by Oracle or OpenJDK, GraalVM, an industry-leading high-performance JVM, provides several benefits that are not shared by the other two. So, we are using GraalVM as our default JDK and Apache OpenNLP for NLP-related tasks. Apache OpenNLP’s framework now incorporates widely used classifiers like maximum entropy, Naive Bayes, and perceptron, amongst others. this toolkit was created entirely in the Java programming language, and it helps with a broad number of activities that are generally connected with natural language processing (NLP). For example, OpenNLP is being used here so that we can recognise sentences. OpenNLP was incorporated into industry-leading frameworks such as Helidon and Spring Boot, and their performance and CPU and memory utilisation were tested with the help of the tool Apache JMeter on varying user thread loads in a containerised Kubernetes environment. We concluded that Spring Boot would be the preferable alternative if we desire a low CPU consumption and a quicker jar startup time. In addition, it has a large user base, making it simpler to start with and troubleshoot any problems that can arise with the code. Helidon is a new cloud-native framework in the industry and has made many promises. Helidon resulted in a very tiny jar and a modest size for the docker image. It also has a low error rate during user load and uses less memory, which are both significant features to be considered. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Java programming language

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TLN-NET: Triple Layer Norm for Aspect-based Sentiment Analysis 4

TLN-NET: Triple Layer Norm for Aspect-based Sentiment Analys...

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4th international conference on Digital Signal and Computer Communications, DSCC 2024

作者： Zhao, Jiyuan Gao, Bin Liu, Shutian Liu, Zhengjun College of Data Science and Technology Heilongjiang University Harbin150080 China School of Physics Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510681538

Aspect-based Sentiment Analysis (ABSA) is a specialized form of sentiment analysis that zeros in on pinpointing and harvesting sentiment details pertinent to distinct facets within textual content. In the field of sentiment analysis, traditional neural network models often face several challenges, including insufficient accuracy, low efficiency and poor generalization ability. therefore, we propose a model called TLN-NET, this approach utilizes an adapted Sentic Net graph convolutional network and is designed to carry out sentiment analysis at the aspect ***, we design a triple GLN (Graph Layer Normalization) architecture, which adds layer normalization technique to each GCN layer to maintain the stability of the gradient, thus improving the training efficiency of the model. We use the residual structure to combine the encoding output with the pooling output to enhance the expression ability of the model. Finally, we used Multi-layer Perceptron (MLP) and Softmax function to classify the final output, which improved the generalization ability of the model. We use the improved TLN-NET model to test on multiple datasets. the experimental results show that our proposed model has higher accuracy in judging aspect words, which is better than some state-of-the-art methods. © 2024 SPIE.

关键词： Efficiency

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Visual Tracking with Temporal Contextual Attention 4

Visual Tracking with Temporal Contextual Attention

引用

4th international conference on Computer Vision, Image and Deep Learning, CVIDL 2023

作者： Li, Qin Zou, Wenjie The School of Electrical Engineering and Automation Anhui University Hefei230601 China

ISBN: (纸本)9798350326444

Due to changes in the appearance, scale, and other aspects of tracking objects, accurate tracking has great difficulties in the current existing visual trackers. Recently, there has been significant progress in time-domain trackers, which has inspired us to solve this problem. therefore, in this paper, we effectively address the issue of object scale and appearance changes during the tracking process from the perspective of mining temporal context information. this paper proposes Visual Tracking with Temporal Contextual Attention (TCFormer). the modeling scheme not only extracts the target-specific distinguishing features and the association between the target and the search area, but also effectively integrate temporal and spatial context information. Specifically, the proposed method includes a temporal context feature extraction module (TCE) and a extraction and integration feature module (EIF). TCE uses attention mechanism to effectively fuse temporal and spatial context information of keyframes. EIF is utilized to extract features from templates and search regions in multi-stage feature networks, as well as effectively combine the template and search regions features. By multi-level matching of templates and search features, non-target features can be suppressed to achieve discriminative feature extraction. Experimental results on visual tracking benchmarks including OTB, UAV123, GOT-10K, LASOT and TrackingNet demonstrate that TCFormer achieves the convincing performance outperforming most state-of-the-art trackers. © 2023 IEEE.

关键词： Computer vision

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DSET-Net: Deep self-embedded transformer network for hyperspectral unmixing 4

DSET-Net: Deep self-embedded transformer network for hypersp...

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4th international conference on Geology, Mapping, and Remote Sensing, ICGMRS 2023

作者： You, Xueer Su, Yuanchao Gao, Lianru Sun, Xu Li, Pengfei Liu, Dongsheng College of Geomatics Xi'An University of Science and Technology Xi'an710054 China Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Piesat Information Technology Co. Ltd Beijing100195 China

ISBN: (纸本)9781510672741

Recently, transformer self-attention mechanisms have had significant advantages in the deep learning field, and it has been extensively used for natural language processing and video tracking. Furthermore, self-attention mechanisms have also been applied in hyperspectral unmixing. Although self-attention mechanisms are usually efficient and flexible tools, the original transformer might break the inner structure of data during learning, causing negative effects to unmixing. In this work, we employ transformer self-attention mechanisms to achieve a deep self-embedded transformer network(DSET-Net) for hyperspectral unmixing. the proposed DSET-Net adopts an autoencoder framework and achieves local and overall feature parameter sharing in the encoder through a 'Transformer in Transformer (TNT)' strategy. the DSET-Net preserves the spatial details of hyperspectral images and involves only one convolution operation in the encoder, substantially improving the learning performance. the effectiveness of the proposed method is evaluated by using real hyperspectral datasets. Our experimental results indicate that the newly proposed DSET-Net is very competitive compared with other state-of-the-art approaches. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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Transfer Learning via Laplacian Feature Embedding 4

Transfer Learning via Laplacian Feature Embedding

引用

4th international conference on Information science, Electrical, and Automation engineering, ISEAE 2022

作者： Qin, Jiangwei Sun, Xiaocun Zhao, Jie Jiang, Shizhong College of medical information engineering Guangdong Pharmaceutical University Guangdong Guangzhou China Department of Computer Science Guangdong Pharmaceutical University Guangdong Guangzhou China

ISBN: (纸本)9781510655157

In the real applications, labeled data is often in short supply, which make is difficult to train a reliable classifier. Transfer learning theory enables model to be trained by exploiting the auxiliary data from out domains. For transfer learning, it’s crucial to find a new feature representation where traditional classifier can be applied. In this paper, we propose a novel transfer algorithm by constructing a Laplacian feature embedding where data property is preserved and domain discrepancy is reduced by MMD principle. Furthermore, we exploit the label constraint during the embedding procedure which minimizes the empirical error on the labeled data. Extensive experiments are conducted to evaluate the effectiveness on two textual datasets, and the study shows the proposed method can significantly improve the classification accuracy over several state-of-the-art algorithms. © 2022 SPIE.

关键词： Embeddings

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Efficient Fusion of Multi-Focus Images Using DeepLabv3 4

Efficient Fusion of Multi-Focus Images Using DeepLabv3

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Kaur, Ravpreet Singh, Sarbjeet UIET Panjab University Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350353266

Multi-focus image fusion (MFIF) combines multiple images with varying depth-of-field (DOF) to create a full focused image. Accurately identifying and segmenting focus region is among the most difficult task in MFIF. Moreover the existing methods lacks in capturing image's global context resulting in inadequate fusion quality. To solve these issues, a DeepLabv3 architecture is used which is a semantic segmentation architecture that includes ResNet101 backbone having strong feature extraction capability and an Atrous Spatial Pyramid Pooling (ASPP) layer that derives the information at multiple scales. this design enables direct mapping of source images to binary masks, which efficiently integrates information at different depths and improves focus detection accuracy. Furthermore, a pair learning approach is used, which involves providing two complementary source images as input and producing two associated binary masks as output. Experimental findings demonstrates that the proposed technique excels over existing state-of-the-art techniques in both subjective and objective evaluation, showcasing Deeplabv3's usefulness in improving the image fusion quality. © 2024 IEEE.

关键词： Semantic Segmentation

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Implementation of Single Shot Multibox Detector (SSD) Algorithm for Object Detection 9

Implementation of Single Shot Multibox Detector (SSD) Algori...

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9th international conference on Business and Industrial Research, ICBIR 2024

作者： Sengaphone, Phongsavanh De Leon, Juan Miguel Augusto, Gerardo L. Gonzaga, Jeremias A. Chua, Joseph Aldrin Lim, Laurence A. Gan Concepcion, Ronnie Bandala, Argel A. Naguib, Raouf Mechanical Engineering Dept Manila Philippines Manufacturing Engineering and Management Dept Manila Philippines Electronics and Communications Engineering Dept Manila Philippines Mathematics Computer Science and Engineering Dept Liverpool United Kingdom

ISBN: (纸本)9798350383010

this study presents an innovative implementation of the Single Shot Multibox Detector (SSD) algorithm for real-time object detection on the Raspberry Pi 4. It harnesses the synergies of Python programming, the OpenCV computer vision library, and the SSD algorithm's adaptability. Python, chosen for its versatility, is the programming language, while OpenCV forms the backbone for implementing the finely tuned SSD algorithm. the resulting modular script, designed for real-time object detection, incorporates optimization techniques such as frame skipping and region of interest cropping to enhance Raspberry Pi 4 performance. the system demonstrates promising results, showcasing accurate and responsive object detection in resource-constrained environments. the Raspberry Pi 4's versatility makes it the solution for diverse edge computing applications, including surveillance, robotics, and intelligent environments. Computational constraints prompted the adoption of optimizations, while identified challenges illuminate future research directions exploring advanced algorithms and considering more potent hardware variants. this study contributes significantly to the intersection of computer vision and edge computing, underscoring the successful implementation of the SSD algorithm on the Raspberry Pi 4 and encapsulating technical achievements, practical implications, and a roadmap for future advancements in real-time object detection. © 2024 IEEE.

关键词： Python

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