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Speech Enhancement Using U-Net-Based Progressive Learning with Squeeze-TCN 5th

Speech Enhancement Using U-Net-Based Progressive Learning wi...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Vanambathina, Sunny Dayal Nandyala, Sivaprasad Jannu, Chaitanya Sirisha Devi, J. Yechuri, Sivaramakrishna Parisae, Veeraswamy School of Electronics Engineering VIT-AP University Amaravati India Secure Systems Research Center Technology Innovation Institute Masdar City United Arab Emirates NRI Institute of Technology Vijayawada Agiripalli India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9789819735228

Speech enhancement is crucial in many speech processing applications. Recently, researchers have been exploring ways to improve performance by effectively capturing the long-term contextual relationships within speech signals. Nevertheless, these investigations often neglect the significance of the time-frequency (T-F) distribution details of speech spectral elements, which are also important for improving speech quality. Moreover, accurately mapping spectral information in supervised methods requires both global and local information. This paper presents a novel speech enhancement model that utilizes long-term and time-frequency (T-F) information of speech spectral elements for enhancing target speakers. The system is composed of a feature extractor and reconstructor. The feature extractor introduces the U-Net block to enable feature recalibration from multiple scales. Furthermore, the time-frequency attention (TFA) within the U-net’s feature extractor delineates the prominent time-frequency distribution of speech through the simultaneous utilization of two branches: one for time-frame attention and another for frequency channel attention. TFA involves the assignment of distinct attention weights to individual time-frequency spectral components, empowering the models to concentrate on both the "when" (time frames) and the "what" (frequency-wise channels). In the reconstructor, several filtering modules (FMs) are stacked to facilitate the reconstruction of a spectrum in a progressive manner to obtain a coarse estimation by reducing noise in the magnitude domain. Furthermore, the intermediate outcome can be progressively improved across stages by unfolding the filtering modules repeatedly, ultimately resulting in the estimation of the spectrum. The proposed approach is tested on the Librispeech and Voicebank datasets. The findings indicate that this method surpasses earlier models and attains the state-of-the-art performance on these datasets. © The Author(s), under

关键词： Deep neural networks

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SP-Net: A Sparse Convolution and Point-Encoding Enhanced Network for 3D Object Detection in LiDAR Point Clouds

SP-Net: A Sparse Convolution and Point-Encoding Enhanced Net...

引用

2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Liu, Meng Niu, Jianwei Liu, Yu China North Vehicle Research Institute Beijing100072 China Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing100191 China Beihang University School of Computer Science and Engineering Beijing100191 China

ISBN: (纸本)9798350327182

LiDAR 3D object detection for autonomous driving is an important issue. To address this issue, this paper provides a two-stage anchor-based solution. Firstly, voxel feature encoding and sparse convolution networks were employed for proposal generation. Secondly, we performed voxel feature aggregation and hierarchical feature learning in combination with the proposals, multi-level and multiscale voxel features, point encoding and voxel-wise ROI pooling. The aggregated object-wise voxel features were utilized to refine the proposals. Extensive experiments were conducted on three challenging datasets: KITTI, nuScenes and Waymo. Experimental results demonstrated that our SP-Net can effectively achieve multi-category 3D object detection in diverse scenarios offering satisfactory accuracy and speed (28.6 FPS). © 2023 IEEE.

关键词： Object detection

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Enhancing Language Models Through Prompt engineering - A Survey

Enhancing Language Models Through Prompt Engineering - A Sur...

引用

Intelligent systems, Smart and Green Technologies (ICISSGT), IEEE International Conference on

作者： Chinimilli Venkata Rama Padmaja S Lakshminarayana Computer Science & Engineering Institute of Aeronautical Engineering Hyderabad India Computer Science & Systems Engineering Andhra University Visakhapatnam India

ISBN: (数字)9798350304329

ISBN: (纸本)9798350304336

In recent years, language models have undergone significant advancements with models like GPT-3, showcasing impressive abilities in natural language processing and generation. However, these models often experience from issues such as generating biased or unreliable content, producing inappropriate responses, or failing to fulfill specific user needs. To address these challenges, prompt engineering emerged as a promising approach by providing explicit guidance to language models. It refers to the process of designing and constructing effective prompts or instructions to guide language models like GPT-3 in generating desired outputs. In this article an attempt was made to address the role of prompt engineering, its techniques, and its potential impact on enhancing the performance and reliability of language models.

关键词： Surveys Ethics Technological innovation Privacy Refining Reliability engineering User experience Natural language processing Prompt engineering Standards

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Data Migration from Relational to NoSQL Database: Review and Comparative Study

Data Migration from Relational to NoSQL Database: Review a...

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Saadouni, Chaimae El Bouchti, Karim Reda, Oussama Mohamed Ziti, Soumia Department of Computer Science Intelligent Processing Systems and Security Team Mohammed V University Rabat Morocco Algorithms Networks Intelligent Systems and Software Engineering Research Team Faculty of Sciences Mohammed V University Rabat Morocco

ISBN: (纸本)9783031263835

New storage requirements, analysis, and visualization of Big Data, which includes structured, semi-structured, and unstructured data, have caused the developers in the past decade to begin preferring Big Data databases, such as NoSQL, that comes to overcome the boundaries of relational Databases such as unlimited scalability, high performance, data modeling, data distribution, and continuous availability. Due to those benefits ensured by this new technology, quite recently, considerable attention has been paid to data migration from relational Database to NoSQL Database. So, in this context, the purpose of this paper is to provide a comparative study of some recent approaches proposed by various researchers to migrate data from relational to NoSQL databases, as well as discussing and analyzing the results of those proposed techniques. This research allowed us to gain a basic understanding of the contributions and approaches that have already been made, as well as the benefits and limits of each proposal. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Performance Enhancement of IoT LoRa Network for Mobile Devices based on Improved AMILCC Technique 1

Performance Enhancement of IoT LoRa Network for Mobile Devic...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Venkatesan, C. Thamaraimanalan, T. Mohammed Ibrahim, M. Vinoly, G. Ramkumar, M. Saranya, K. HKBK College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Tamilnadu Thiruchengode India Knowledge Institute of Technology Department of Computer Science and Business Systems Tamilnadu Salem India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353648

In Internet of Things (IoT) networks, long-range (LoRa) technology is well-known for its wide communication range, but using it with mobile devices presents unique difficulties. Frequently occurring channel attenuation, which causes packet loss, increased energy consumption, and longer transmission times, is one of these difficulties. An improved Adaptive Mobility-based IoT LoRa Clustering Communication (AMILCC) strategy addressed to solve these problems. AMILCC partitions the network into optimal Spreading Factor (SF) areas by using a 2D random waypoint mobility model. The Hybrid Adaptive Data Rate (HADR) technique, which combines intra-and inter-SF region modifications to improve resource allocation for mobile IoT LoRa devices, is a key component of AMILCC. The simulation findings demonstrate the advantages of AMILCC over conventional stationary techniques, even under low mobility conditions (up to 5 m/s). In particular, AMILCC decreases energy usage by 56.5%, improves packet success ratio (PSR) by 72 %, and decreases end-to-end delay by 45.32%. These outcomes demonstrate how well AMILCC works to enhance efficiency and performance for mobile LoRa applications. © 2024 IEEE.

关键词： Resource allocation

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An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design 13

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Hong, Wenjing Chen, Cheng Zhu, Zexuan Tang, Ke Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design. © 2024 IEEE.

关键词： Vaccines

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Digital Assistant for Visually Impaired People 5

Digital Assistant for Visually Impaired People

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Thayaparan, Thuvaraka Jayakumar, Sanjay Fernando, B.A.I. Ananthanadarajan, Gobisan Abeywardana, Lakmini Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research paper presents a comprehensive exploration of various components to enhance the capabilities of a digital assistant tailored for visually impaired individuals. The first component explores how various image captioning architectures and Vision Encoder Decoder models can be used for better environmental interaction. The second component concerns with facial expression recognition for better social interaction and the third component is a currency note identification component that aims to aid visually impaired individuals with cash transaction whilst the final component that explores a voice bot aims to improve a voice bot and how to manage accents, speech accuracy and naturality using modern deep learning techniques. By investigating these components, this research advances assistive technology, empowering visually impaired individuals with a conversational digital assistant that enhances environmental interaction, supports social interactions, and aids in currency identification. © 2023 IEEE.

关键词： Deep learning

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Global-Local Consistency for Cost-Effective Anomaly Localization with Point Annotations

Global-Local Consistency for Cost-Effective Anomaly Localiza...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Keiichi Nakanishi Ryoya Katafuchi Terumasa Tokunaga Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

Creating pixel-level ground-truth (GT) masks is quite costly for deep learning-based image segmentation. Specialists in areas such as anomaly detection and medical diagnostics face difficulties in producing many GT masks due to limited resources. To reduce this burden, we propose a cost-effective image segmentation framework with point annotations (CoSPA) that performs image segmentation with only point annotations. Point annotations refer to the partial and sparse labeled coordinates in an image. The key idea is to ensure consistency between the predictions for the same coordinates from the two different networks of CoSPA. This new consistency enables the CoSPA to improve segmentation performance and extend to semi-supervised learning. For the MVTec AD dataset, we verified the cost-effectiveness of CoSPA through an anomaly detection task. We demonstrated that the point annotating costs were reduced by 80% compared to creating GT masks. Subsequently, the CoSPA realized by 87% of the mean Intersection over Union (mIoU) achieved using the fully supervised method, DeepLabV3+. Moreover, the mIoU of CoSPA using only 30% of all point annotations defeated that of the unsupervised method, PaDiM. This study offers a new direction for economic anomaly localization.

关键词： Location awareness Image segmentation Costs Annotations Supervised learning Semisupervised learning Medical diagnosis Informatics Anomaly detection Faces

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Predicting the Hardness of Low Alloy Metal using Machine Learning Model 6

Predicting the Hardness of Low Alloy Metal using Machine Lea...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Logeswaran, K. Savitha, S. Suresh, P. Sah, Swati Priyanka, B. Ragav, R. Vishal, R.S. Mohamed Arsath, H. Kongu Engineering College Department of Artificial Intelligence TamilNadu Erode638060 India Vellore Institute of Technology School of Computer Science and Engineering Department of Database Systems TamilNadu Vellore632014 India K.S.R College of Engineering Department of Computer Science and Engineering Tamilnadu Trichengode637215 India Sharda University School of Engineering and Technology Nodia India

ISBN: (纸本)9798331516796

In industries like materials engineering and manufacturing, predicting the hardness of low alloy metals is crucial for ensuring quality and performance. Traditional methods, which often rely on formulas or manual testing, can be slow and inaccurate due to variations in material properties. This paper presents a new approach for predicting metal hardness using machine learning techniques, including Multi-linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. These algorithms are used to improve the accuracy and reliability of hardness predictions compared to older methods. The paper covers how the data was collected, which features were chosen, and how the models were trained. It also compares the performance of these algorithms, looking at how accurate and effective they are in different situations. The results show that the machine learning models offer better predictions than traditional methods, leading to more consistent and reliable outcomes. Finally, the research discusses how these models can be used in real-world applications to make processes more efficient and reduce errors. © 2024 IEEE.

关键词： Prediction models

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Learnable Graph Convolutional Attention Networks 11

Learnable Graph Convolutional Attention Networks

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11th International Conference on Learning Representations, ICLR 2023

作者： Javaloy, Adrián Sánchez-Martín, Pablo Levi, Amit Valera, Isabel Department of Computer Science Saarland University Saarbrücken Germany Max Planck Institute for Intelligent Systems Tübingen Germany Huawei Noah's Ark Lab Montreal Canada Max Planck Institute for Software Systems Saarbrücken Germany

Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by applying a non-uniform score (attending) to the features. Recent works have shown the strengths and weaknesses of the resulting GNN architectures, respectively, GCNs and GATs. In this work, we aim at exploiting the strengths of both approaches to their full extent. To this end, we first introduce the graph convolutional attention layer (CAT), which relies on convolutions to compute the attention scores. Unfortunately, as in the case of GCNs and GATs, we show that there exists no clear winner between the three-neither theoretically nor in practice-as their performance directly depends on the nature of the data (i.e., of the graph and features). This result brings us to the main contribution of our work, the learnable graph convolutional attention network (L-CAT): a GNN architecture that automatically interpolates between GCN, GAT and CAT in each layer, by adding two scalar parameters. Our results demonstrate that L-CAT is able to efficiently combine different GNN layers along the network, outperforming competing methods in a wide range of datasets, and resulting in a more robust model that reduces the need of cross-validating. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Graph neural networks

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