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Argument Mining using BERT and Self-Attention based Embeddings 4

Argument Mining using BERT and Self-Attention based Embeddin...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Srivastava, Pranjal Bhatnagar, Pranav Goel, Anurag Delhi Technological University Department of Computer Science and Engineering New Delhi110042 India

ISBN: (纸本)9781665474368

Argument mining automatically identifies and extracts the structure of inference and reasoning conveyed in natural language arguments. To the best of our knowledge, most of the state-of-the-art works in this field have focused on using tree-like structures and linguistic modeling. But, these approaches are not able to model more complex structures which are often found in online forums and real world argumentation structures. In this paper, a novel methodology for argument mining is proposed which employs attention-based embeddings for link prediction to model the causational hierarchies in typical argument structures prevalent in online discourse. © 2022 IEEE.

关键词： Embeddings

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Enhancing the Resolution of Ancient artworks using Generative Adversarial Networks 4

Enhancing the Resolution of Ancient Artworks using Generativ...

引用

4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Ashwini, R. Vani, R. Suvitha, S. Srm Institute of Science and Technology Department of Ece Chennai Ramapuram India S.A. Engineering College Department of Ece Chennai India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Inp ainting is a form of conservation that involves filling in damaged, deformed, or missing areas of an artwork in order to present a whole image. this method may be imp lemented in both traditional and digital forms of visual art, including chemistry-based photo prints, paintings done in oil or acrylic, sculptures, and digital video and still photos. the process of increasing the quality of a picture by filling in missing pixels using a digital image editor is known as 'digital image inpainting.' through the utilization of the Generative adversarial network, the focus of this investigation is to improve both the originality and the overall quality of the input picture. to provide a process for the long-term digital preservation and repair of historical works of art for future generations. © 2022 IEEE.

关键词： Generative adversarial networks

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Multi - Frame Detection and Angle Estimation Algorithm in 4D Millimeter Wave Radar 13

Multi - Frame Detection and Angle Estimation Algorithm in 4D...

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13th international conference on Control, Automation and Information sciences, ICCAIS 2024

作者： Li, Miao Chang, Qinyao Cui, Zhen Li, Wujun Yi, Wei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331542047

In this paper, the dynamic-programming track-before-detect (DP-TBD) algorithm is used to achieve weak target detection and high-precision angle estimation in four dimensions (4D) millimeter wave radar systems. Typically, the direction of arrival (DOA) estimation algorithm requires accurate snapshot data containing the target. However, the conventional single-frame detection methods inevitably have information loss after low threshold detection, which makes it difficult to realize accurate estimation of weak target azimuth and elevation angle by using the snapshot data. To solve the above problems, a DOA estimation method of 4D millimeter wave radar based on multi-frame joint detection and tracking is proposed in this paper. First, the target model and measurement model are introduced. the DP-TBD algorithm is introduced to utilize the correlation characteristics of target motion in multi-frame measurements for energy accumulation and initial trajectory extraction. then, the accurate snapshot data is extracted for angle estimation based on the initial trajectory to improve the angle estimation accuracy in the azimuth-elevation dimension. Finally, we can obtain an accurate target trajectory. the simulation experiments demonstrate the superiority of the proposed algorithm in terms of detection performance and estimation accuracy. © 2024 IEEE.

关键词： Dynamic programming

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Industrial artificial Intelligence Approach for Shape Reconstruction in Quality Assessment of Digital Data from Manufactured Workpieces 4

Industrial Artificial Intelligence Approach for Shape Recons...

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4th international conference on Industrial engineering and artificial Intelligence, IEAI 2023

作者： Gálvez, Akemi Iglesias, Andrés Fister, Iztok Fister, Iztok Toho University Faculty of Pharmaceutical Sciences Narashino Campus Funabashi Japan University of Cantabria Dept. of Applied Mathematics & Computational Sciences Santander Spain University of Maribor Faculty of Electrical Eng. & Comp. Sci. Maribor Slovenia

ISBN: (纸本)9798350324280

A classical process for quality assessment in man-ufacturing is to perform shape reconstruction of manufactured workpieces for generation of a digital counterpart of the already constructed physical good and further evaluation of the quality of shape. Typically, this process is carried out from a cloud of data points obtained from the workpiece by reverse engineering methods by applying surface reconstructions methods to the point cloud. In this work we assume that the normality of data is not satisfied, and that the point cloud follows other bivariate distribution. In particular, we consider that the approximation function is a combination of univariate exponential and polynomi-als functions, thus leading to a non-convex nonlinear constrained continuous minimization problem. To solve it, our method applies a simplified version of a popular metaheuristic technique called bat algorithm. the method is applied to three illustrative examples of point clouds. the graphical and numerical results show that the method performs quite well and can recover the underlying shape of data with good accuracy. © 2023 IEEE.

关键词： Nonlinear programming

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A Preliminary Work on Tenyidie Stemming Corpus Creation and Deep Learning Applications 4th

A Preliminary Work on Tenyidie Stemming Corpus Creation and...

引用

4th international conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Angami, Teisovi Tuithung, themrichon Department of Computer Science and Engineering National Institute of Technology Nagaland Dimapur India Department of Information Technology School of Engineering and Technology Nagaland University Lumami India

ISBN: (纸本)9789819757022

Tenyidie language belonging to the Tibeto-Burman family is one of the major languages of Nagaland in northeastern India. It is a low-resource, tonal, SOV, and a high agglutinative language. Stemming is an important Natural Language Processing (NLP) task wherein the stem or the root word is identified and extracted. To the best of the authors knowledge, there has been no work reported on corpus creation and stemming using deep learning for the Tenyidie language. In this paper, we build an annotated corpus of 5400 manually stemmed words in Tenyidie to apply deep learning to the stemming task. We applied state-of-the-art deep learning models, such as the Encoder-decoder models, and compared our results with the rule-based approach, which uses both affix stripping and root word matching methods. Our experimental result shows that the deep learning models outperform the rule-based approach. We achieved the highest accuracy of 85.55% with Encoder-decoder (BLSTM) with attention. We also provide an analysis of the overstemmed and understemmed words in our test dataset. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Improving Quantum Circuit Synthesis with Machine Learning 4

Improving Quantum Circuit Synthesis with Machine Learning

引用

4th IEEE international conference on Quantum Computing and engineering, QCE 2023

作者： Weiden, Mathias Younis, Ed Kalloor, Justin Kubiatowicz, John Iancu, Costin University of California Department of Electrical Engineering and Computer Science Berkeley United States Computational Research Division Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9798350343236

In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that mini-mize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs. Unitary synthesis, the process of finding a quantum circuit that implements some target unitary matrix, is able to solve this problem optimally in many cases. However, current bottom-up unitary synthesis algorithms are limited by their exponentially growing run times. We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. this paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries. QSeed maintains low gate counts and offers a speedup of 3.7 x in synthesis time over the state of the art for a 64 qubit modular exponentiation circuit, a core component in Shor's factoring algorithm. QSeed's performance improvements also generalize to families of circuits not seen during the training process. © 2023 IEEE.

关键词： Machine learning

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Deep Learning-Based Intrusion Detection Technique for IoT Security 4

Deep Learning-Based Intrusion Detection Technique for IoT Se...

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4th international conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Ahanger, Tariq Ahamed Aljumah, Abdullah Ullah, Imdad Prince Sattam Bin Abdulariz University Department of Management Information Systems CoBA Al-Kharj11942 Saudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia School of Computer Science The University of Sydney Faculty of Engineering SydneyNSW2006 Australia

ISBN: (纸本)9798350391183

Numerous connected machines compose the Internet of things (IoT) like sensors and actuators, via wired or wireless networks. the number of IoT apps, including Vehicular Ad-hoc Networks (VANETs), Healthcare, Smart communities, and Wearable have recently risen significantly. Data Protection and Security is becoming more critical as the number of IoT-linked devices grows. In this paper, we suggest a new deep learning-based intrusion detection system (DL-IDS) to identify security breaches in IoT environments to address the difficulties of protecting IoT applications. there are several IDSs developed by researchers, optimal learning and management of data set functions are neglected, which are essential problems that impact the accuracy of tracking threats. To achieve optimum detection recognition, the presented framework incorporates the SMO technique and the Deep stacked polynomial network. SMO chooses the optimal features in the data, and SDPN categorizes it as regular or vulnerable. the presented model is analyzed for performance assessment in comparison to the state-of-the-art decision-making models. In terms of accuracy, recall, precision, and F-score, the detailed review suggests that presented technique acquires enhanced performance and is immensely efficient. © 2024 IEEE.

关键词： Network intrusion

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An Efficient CL-Signature scheme over NTRU Lattices 4

An Efficient CL-Signature scheme over NTRU Lattices

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Prajapat, Sunil Kumar, Pankaj Sharma, Vishnu Central University of Himachal Pradesh Srinivasa Ramanujan Department of Mathematics HP Dharamshala176215 India Galgoitas College of Engineering and Technology Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9781665474368

the advantages of key escrow and certificate management features over a public key, and identity-based infrastructure has led to extensive use of the CL-Signature in ebusiness, e-government, and programming security to address these underlying issues. Numerous CL-Signature techniques have been proposed based on the elliptic curve, RSA, and chaos. However, the majority of them are vulnerable to attacks using quantum computing. But in quantum time, the lattice-based CL-Signature technique is safe. therefore, the CL-Signature Scheme, suggested in this study and shown to be secure in post-quantum environments, is implemented on the NTRU lattice. Additionally, the novel approach is more efficient than earlier lattice-based CL-Signature schemes. © 2022 IEEE.

关键词： Authentication

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VRLVMix: Combating Noisy Labels with Sample Selection based on Loss Variation 4

VRLVMix: Combating Noisy Labels with Sample Selection based ...

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4th international conference on Big Data, artificial Intelligence and Internet of things engineering, ICBAIE 2023

作者： Wang, Hailun Tu, Zhengzheng Jiang, Bo Ding, Yuhe Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center AHU-IAI Ai Joint Laboratory Hefei China Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350343618

Since deep neural networks can fully fit all data, including noisy labels, that is, mislabeled data, this will be detrimental to the robustness and generalization ability of the network. To address this problem, existing methods usually use small loss tricks to select clean samples for training. However, sample selection using only small loss techniques cannot distinguish between large loss 'hard' samples and noise samples. In this work, we analyze the reasons why clean samples are misidentified as noise samples and propose a balanced selection mechanism based on loss variation. this method sorts the amount of loss change, uses variance to count the rankings of multiple epochs to describe the stability of loss variation, and selects samples with stable loss as clean samples to reduce the misclassification of 'hard' samples. At the same time, semantic clustering is used to assist resampling and reweighting, thereby alleviating the negative impact 1 of class imbalance on sample selection. We conduct comparative experiments and ablation experiments on synthetic noise datasets and real-world datasets such as CIFAR-10/100 and Clothing1M. Our VRLVMix has been shown to outperform numerous state-of-the-art methods, as evidenced by the experimental results. Moreover, it demonstrates the ability to extract clean samples even from large loss sample. © 2023 IEEE.

关键词： Semantics

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Stock market time series forecasting using comparative machine learning algorithms 4

Stock market time series forecasting using comparative machi...

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4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Gupta, Samraj Nachappa, Sanchal Paramanandham, Nirmala School of Electronics Engineering Vellore Institute of Technology Chennai600127 India Department of Chemical Engineering Manipal Institute of Technology Manipal576104 India

Stock market trends prediction and their assessment have always been leading topics due to the market's extreme volatility and chaotic nature. Determining the stock market possesses an abstract representation of possession over enterprises and organizations, more commonly known as "stock."this kind of assessment is generally accepted as the foundation of market conduct is not provided with as many shares as needed admitted to financial failure. Foreseeing the performance of a single business on stock markets is a precarious one, as values of stocks are subjected to constant change. Nonetheless, viewing the stock market's behavior in retrospect and distributing investor expectations regarding future stock market values is a beneficial approach. Different models have been proposed in the last few years, and there is a saturation of work to compare efficiency, accuracy or robustness for identifying which model works best on different applications. In this paper, a detailed comparative study of different machine learning algorithms for stock market time series prediction has been shown. It provides baselines with widely used algorithms such as Linear Regression and Support Vector Machines, to state-of-the-art methods including Long Short-Term Memory networks, Convolutional Neural Networks and Transformer-based architectures. © 2025 the Authors. Published by Elsevier B.V.

关键词： Marketplaces

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