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Harnessing Abstractive Summarization for Fact-Checked Claim Detection 29

Harnessing Abstractive Summarization for Fact-Checked Claim ...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Bhatnagar, Varad Kanojia, Diptesh Chebrolu, Kameswari Department of Computer Science and Engineering IIT Bombay India Surrey Institute for People-Centred AI United Kingdom Department of Computer Science University of Surrey United Kingdom

Social media platforms have become new battlegrounds for anti-social elements, with misinformation being the weapon of choice. Fact-checking organizations try to debunk as many claims as possible while staying true to their journalistic processes but cannot cope with its rapid dissemination. We believe that the solution lies in partial automation of the fact-checking life cycle, saving human time for tasks which require high cognition. We propose a new workflow for efficiently detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries. These queries can then be executed on a general-purpose retrieval system associated with a collection of previously fact-checked claims. We curate an abstractive text summarization dataset comprising noisy claims from Twitter and their gold summaries. It is shown that retrieval performance improves 2x by using popular out-of-the-box summarization models and 3x by fine-tuning them on the accompanying dataset compared to verbatim querying. Our approach achieves Recall@5 and MRR of 35% and 0.3, compared to baseline values of 10% and 0.1, respectively. Our dataset, code, and models are available publicly here. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Social networking (online)

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Diverse and Vivid Sound Generation from Text Descriptions 48

Diverse and Vivid Sound Generation from Text Descriptions

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Guangwei Xu, Xuenan Dai, Lingfeng Wu, Mengyue Yu, Kai Shanghai Jiao Tong University X-Lance Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai China

ISBN: (纸本)9781728163277

Previous audio generation mainly focuses on specified sound classes such as speech or music, whose form and content are greatly restricted. In this paper, we go beyond specific audio generation by using natural language description as a clue to generate broad sounds. Unlike visual information, a text description is concise by its nature but has rich hidden meanings beneath, which poses a higher possibility and complexity on the audio to be generated. A Variation-Quantized GAN is used to train a codebook learning discrete representations of spectrograms. For a given text description, its pre-trained embedding is fed to a Transformer to sample codebook indices to decode a spectrogram to be further transformed into waveform by a melgan vocoder. The generated waveform has high quality and fidelity while excellently corresponding to the given text. Experiments show that our proposed method is capable of generating natural, vivid audios, achieving superb quantitative and qualitative results. © 2023 IEEE.

关键词： Music

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Machine Learning Approach for Detecting Liver Tumours in CT images using the Gray Level Co-Occurrence Metrix 1

Machine Learning Approach for Detecting Liver Tumours in CT ...

引用

1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICaiSC 2023

作者： Aruna, S. Saranya, A. Guru Pandi, D. Kavya, S.P. Pareek, Piyush Kumar Computational Intelligence SRMIST Chennai India SRMIST Computational Intelligence Department Chennai India Panimalar Engineering College Department of ECE Chennai India KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Arasur India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350323795

Over 2.4 % of deaths in India each year are caused by liver diseases. Due to its mild early signs, liver disease is also challenging to diagnose. Frequently, the signs only become obvious when it is too late. Even more challenging than segmenting the liver is segmenting the tumour from the liver. Imaging procedures like computed tomography, magnetic resonance imaging, and ultrasound are utilised to separate the liver and liver tumour once a sample of liver tissue has been removed. This research suggests a machine learning method from CT images-based automatic assistance system for stage categorization. Then, the features are extracted from the CT images using the Gray-Level Co-Occurrence Matrix (GLCM) method. Finally, it is suggested that the computed tomography (CT) pictures of livers containing tumours be categorised using a Random Forest technique. Using the described method, liver tumour images are classified as benign or malignant. The real value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16% based on the experimental investigation. Using the described method, liver tumour images are classified as benign or malignant. These modifications improve the system's ability to recognise the tumour from the CT pictures. © 2023 IEEE.

关键词： Machine learning

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LongFNT: Long-Form Speech Recognition with Factorized Neural Transducer 48

LongFNT: Long-Form Speech Recognition with Factorized Neural...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gong, Xun Wu, Yu Li, Jinyu Liu, Shujie Zhao, Rui Chen, Xie Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering China Microsoft

ISBN: (纸本)9781728163277

Traditional automatic speech recognition (ASR) systems usually focus on individual utterances, without considering long-form speech with useful historical information, which is more practical in real scenarios. Simply attending longer transcription history for a vanilla neural transducer model shows no much gain in our preliminary experiments, since the prediction network is not a pure language model. This motivates us to leverage the factorized neural transducer structure, containing a real language model, the vocabulary predictor. We propose the LongFNT-Text architecture, which fuses the sentence-level long-form features directly with the output of the vocabulary predictor and then embeds token-level long-form features inside the vocabulary predictor, with a pre-trained contextual encoder RoBERTa to further boost the performance. Moreover, we propose the LongFNT architecture by extending the long-form speech to the original speech input and achieve the best performance. The effectiveness of our LongFNT approach is validated on LibriSpeech and GigaSpeech corpora with 19% and 12% relative word error rate (WER) reduction, respectively. © 2023 IEEE.

关键词： Speech recognition

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Iot-Enabled Cloud Storage Data Access Control Algorithm Based on Blockchain Technology

Iot-Enabled Cloud Storage Data Access Control Algorithm Base...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Salman, Hayder Mahmood Faris, Najlaa Nsrulaah Ahmed, Ibrahem Mustafa, Arkan RaoofEsmail Ali, Aqeel Al-Farouni, Mohammed AI-Turath University College Computer Technologies Engineering Baghdad Iraq NationalUniversity of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

Cloud-based data access control security systems (CSDA) are the most versatile and elegant choice for SMEs and mid-sized enterprises. Storage, The idea of 'the cloud' refers to the use of remote servers and data centers maintained by a third party, in this example, an internet service provider. There is no longer a need to invest in specialized data storage facilities due to the availability of on-demand data storage infrastructure with elastic storage capacity and variable storage prices. Authentication of Internet of Things devices is crucial for establishing that linked gadgets are what they claim to be. To reduce the likelihood of malicious behavior, access control regulates the access to the resources under the conditions. For cloud storage to function, at least one data server must be connected to the internet. Data supplied over the internet to a data server might be stored in 'the cloud,' where a copy of the data is kept. When necessary, the user may access this information through a web-based interface. The major issues with cloud computing are related to people's expectations of privacy and data security. Cryptography and other security methods may help keep personal information safe and secure. The data is separated into smaller bits by using a cloud storage service that relies on blockchain technology (BT). Then, it disperses this additional security over the network. Blockchain features such as hashing, private key encryption, and transaction ledgers make this possible. CSDA-BT cloud storage is a new approach to archiving data and other digital artifacts in the cloud. Files may be accessed and viewed from any location with an internet connection. The cloud has several benefits, including easy accessibility, backups, and high availability. To stay competitive while becoming closer to their customers, cloud storage businesses must work on lowering pricing and making storage faster. Cheap data storage enables novel forms of expression. © 2024 IEEE.

关键词： Cloud storage

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Emodiff: Intensity Controllable Emotional Text-to-Speech with Soft-Label Guidance 48

Emodiff: Intensity Controllable Emotional Text-to-Speech wit...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Guo, Yiwei Du, Chenpeng Chen, Xie Yu, Kai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

Although current neural text-to-speech (TTS) models are able to generate high-quality speech, intensity controllable emotional TTS is still a challenging task. Most existing methods need external optimizations for intensity calculation, leading to suboptimal results or degraded quality. In this paper, we propose EmoDiff, a diffusion-based TTS model where emotion intensity can be manipulated by a proposed soft-label guidance technique derived from classifier guidance. Specifically, instead of being guided with a one-hot vector for the specified emotion, EmoDiff is guided with a soft label where the value of the specified emotion and Neutral is set to α and 1 - α respectively. The α here represents the emotion intensity and can be chosen from 0 to 1. Our experiments show that EmoDiff can precisely control the emotion intensity while maintaining high voice quality. Moreover, diverse speech with specified emotion intensity can be generated by sampling in the reverse denoising process. © 2023 IEEE.

关键词： classifier guidance de-noising diffusion models emotion intensity control Emotional TTS

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Exploring Binary Classification Loss for Speaker Verification 48

Exploring Binary Classification Loss for Speaker Verificatio...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Han, Bing Chen, Zhengyang Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

The mismatch between close-set training and open-set testing usually leads to significant performance degradation for speaker verification task. For existing loss functions, metric learning-based objectives depend strongly on searching effective pairs which might hinder further improvements. And popular multi-classification methods are usually observed with degradation when evaluated on unseen speakers. In this work, we introduce SphereFace2 framework which uses several binary classifiers to train the speaker model in a pair-wise manner instead of performing multi-classification. Benefiting from this learning paradigm, it can efficiently alleviate the gap between training and evaluation. Experiments conducted on Voxceleb show that the SphereFace2 outperforms other existing loss functions, especially on hard trials. Besides, large margin fine-tuning strategy is proven to be compatible with it for further improvements. Finally, SphereFace2 also shows its strong robustness to class-wise noisy labels which has the potential to be applied in the semi-supervised training scenario with inaccurate estimated pseudo labels. © 2023 IEEE.

关键词： binary classification large margin fine-tuning speaker verification sphereface2

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A Novel Approach to Skin Cancer Detection using CNN, ResNet, and Hybrid Feature Extraction

A Novel Approach to Skin Cancer Detection using CNN, ResNet,...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： T.Srinivasa Ravi Kiran Y.Sudha Madhuri P Venkata Bala Annapurna A. Lakshmanarao B.S.N. Murthy Department of Computer Science Parvathaneni Brahmayya Siddhartha College of Arts & Science Vijayawada AP India Department of CSE(AI&ML|DS) Anil Neerukonda Institute of Technology and Sciences Department of AI&ML Aditya University Surampalem India Department of IT Aditya University Surampalem India Bonam Venkata Chalamayya Engineering College Odalarevu Andhra Pradesh

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Skin cancer is a prevalent and potentially life-threatening condition, making early and accurate detection crucial for effective treatment. This work proposes a novel hybrid approach to skin cancer detection, integrating both deep learning and traditional ml techniques. Utilizing a dataset from Kaggle, the process began with initial classification using a Convolutional Neural Network (CNN) and achieved an accuracy of 90%. Features were then extracted using Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and ResNet, adding multiple layers of feature richness. These features were combined into a comprehensive hybrid feature vector and analyzed using ml models, including Random Forest, XGBoost, and AdaBoost. The Random Forest model achieved an accuracy of 96%, while XGBoost obtained an accuracy of 95. 60%. AdaBoost reached an accuracy of 95%. The proposed method demonstrated significant improvements in detection accuracy and robustness, highlighting its effectiveness in enhancing skin cancer detection.

关键词： Accuracy Soft sensors Feature extraction Vectors Skin Robustness Real-time systems Convolutional neural networks Random forests Skin cancer

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The New Frontier in Media: ai-Driven Content Creation for Ad-Supported TV using Generative Adversarial Network 7

The New Frontier in Media: AI-Driven Content Creation for Ad...

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7th International Conference of computer and Informatics engineering, IC2IE 2024

作者： Ramagundam, Shashishekhar Karne, Niharika Comcast Software Development and Engineering in Content Discovery and Ai United States Troy University Masters in Computer Science AL. Department of Computer Science Montgomery Campus United States Dt Amtrak Sr. Principal Systems Engineer in C and Rs Platform Services United States University of South Alabama Masters in Electrical Engineering Department of Electrical Engineering United States

ISBN: (纸本)9798331505530

The use of Artificial intelligence (ai) technology in the content creation to produce creative aspects like editing, audience analysis, creating ideas, writing copy, etc. The major aim is to streamline and automate the process of content creation and convert it into a more efficient and effective way. There is a lack of transparency in the ai content production process and hence it does not exactly mimic human activities, which includes fantasizing and picking up new abilities. ai content creation requires more creativity while investigating with a specific goal in mind. Although, ai faces many challenges since it constantly expands its base knowledge. One of the biggest ethical problems is the probability that ai content is used to deceive or influence people. Hence, to overcome these difficulties a machine learning-based Artificial Intelligent content creation framework is generated for content creation in Ad-supported TV. Here, an effective model Generative Adversarial Network (GAN) introduced to deliver enterprise data, customize descriptions, and adjust content based on consumer behaviors. This developed model produces material that is closely related to the preferences of the user. ai content creation increases efficiency and productivity by saving time and money. It also leads to the development of better content. Finally, the experimental analysis is performed to find the effectiveness of the developed deep learning-based ai content creation framework via various metrics. © 2024 IEEE.

关键词： Generative adversarial networks

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Factorized AED: Factorized Attention-Based Encoder-Decoder for Text-Only Domain Adaptive ASR 48

Factorized AED: Factorized Attention-Based Encoder-Decoder f...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gong, Xun Wang, Wei Shao, Hang Chen, Xie Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

End-to-end automatic speech recognition (ASR) systems have gained popularity given their simplified architecture and promising results. However, text-only domain adaptation remains a big challenge for E2E systems. Text-to-speech (TTS) based approaches fine-tune ASR models by synthesized speech with an auxiliary TTS model, thus increase deployment costs. Language model (LM) fusion based approaches can achieve good performance but are sensitive to interpolation parameters. In order to factorize out the language component in the AED model, we propose the factorized attention-based encoder-decoder (Factorized AED) model whose decoder takes as input the posterior probabilities of a jointly trained LM. Moreover, in the context of domain adaptation, the domain specific LM serves as a plug-and-play component for a well-trained factorized AED model. In-domain experiments on LibriSpeech and out-of-domain experiments adapting from LibriSpeech to a variety of domains in GigaSpeech are conducted to validate the effectiveness of our proposed methods. Results show 20% / 24% relative word error rate (WER) reduction for LibriSpeech test sets and 8 ∼34% relative WER reduction for 8 GigaSpeech target domains test sets compared to the AED baseline. © 2023 IEEE.

关键词： domain adaptation end-to-end speech recognition factorized AED text-only

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