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An FFT-based CNN-Transformer Encoder for Semantic Segmentation of Radar Sounder Signal 28

An FFT-based CNN-Transformer Encoder for Semantic Segmentati...

引用

Conference on Image and Signal Processing for Remote Sensing XXVIII

作者： Ghosh, Raktim Bovolo, Francesca Univ Trento Trento Italy Fdn Bruno Kessler Trento Italy

ISBN: (纸本)9781510655386;9781510655379

Radar Sounders (RSs) are sensors operating in the nadir-looking geometry (with HF or VHF bands) by transmitting modulated electromagnetic (EM) pulses and receiving the backscattering response from different subsurface targets. Recently, convolutional neural network (CNN) architectures were established for characterizing RS signals under the semantic segmentation framework. In this paper, we design a Fast Fourier Transform (FFT) based CNN-Transformer encoder to effectively capture the long-range contexts in the radargram. In our hybrid architecture, CNN models the high-dimensional local spatial contexts, and the Transformer establishes the global spatial contexts between the local spatial ones. To overcome Transformer complex self-attention layers by reducing learnable parameters;- we replace the self-attention mechanism of the Transformer with unparameterized FFT modules as depicted in FNet architecture for Natural Language Processing (NLP). The experimental results on the MCoRDS dataset indicate the capability of the CNN-Transformer encoder along with the unparameterized FFT modules to characterize the radargram with limited accuracy cost and by reducing the time consumption. A comparative analysis is carried out with the state-of-the-art Transformer-based architecture.

关键词： radar sounder (RS) semantic segmentation transformers sequence-to-sequence model

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SeqAD: An Unsupervised and Sequential Autoencoder Ensembles based Anomaly Detection Framework for KPI 29

SeqAD: An Unsupervised and Sequential Autoencoder Ensembles ...

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29th IEEE/ACM International Symposium on Quality of Service (IWQOS)

作者： Zhao, Na Han, Biao Cai, Yang Su, Jinshu Natl Univ Def Technol Coll Comp Changsha Peoples R China Changsha Normal Univ Coll Informat Sci & Technol Changsha Peoples R China

ISBN: (纸本)9781665414944

Key Performance Indicator (KPI), a kind of time-series data, its anomalies are the most intuitive characteristics when failures occurred in IT systems. KPI anomaly detection is increasingly critical to provide reliable and stable services for IT systems. Unsupervised learning is a promising method because of lacking labels and the unbalance in KPI samples. However, existing unsupervised KPI anomaly detection methods suffer from high false alarm rates. They handle KPI sequence as non-sequential data and ignore the time information, which is an essential KPI character. To this end, in this paper, we propose an unsupervised and sequential autoencoder ensembles based anomaly detection framework called SeqAD. SeqAD inherits the advantages both from the sequence-to-sequence model and autoencoder ensembles. SeqAD reduces the KPI over-fitting problem effectively by introducing autoencoder ensembles. In order to better capture the time information of KPI, we propose a random step connection based recurrent neural network (RSC-RNN) to train the KPI sequence, which can provide random connections to construct autoencoders with different structures and retain time information to the most extent. Extensive experiments are conducted on two public KPI data-sets from real-world deployed systems to evaluate the efficiency and robustness of our proposed SeqAD framework. Results show that SeqAD is able to smoothly capture most of the characteristics in all KPI data-sets, as well as to achieve a high F1 score between 0.93 and 0.98, which is better than the state-of-art unsupervised KPI anomaly detection methods.

关键词： KPI anomaly detection autoencoder ensembles sequence-to-sequence model

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Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss 19

Improving Neural Response Diversity with Frequency-Aware Cro...

引用

World Wide Web Conference (WWW)

作者： Jiang, Shaojie Ren, Pengjie Monz, Christof de Rijke, Maarten Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781450366748

sequence-to-sequence (Seq2Seq) models have achieved encouraging performance on the dialogue response generation task. However, existing Seq2Seq-based response generation methods suffer from a low-diversity problem: they frequently generate generic responses, which make the conversation less interesting. In this paper, we address the low-diversity problem by investigating its connection with model over-confidence reflected in predicted distributions. Specifically, we first analyze the influence of the commonly used Cross-Entropy (CE) loss function, and find that the CE loss function prefers high-frequency tokens, which results in low-diversity responses. We then propose a Frequency-Aware CrossEntropy (FACE) loss function that improves over the CE loss function by incorporating a weighting mechanism conditioned on token frequency. Extensive experiments on benchmark datasets show that the FACE loss function is able to substantially improve the diversity of existing state-of-the-art Seq2Seq response generation methods, in terms of both automatic and human evaluations.

关键词： Chatbot Dialogue system sequence-to-sequence model

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A Context based Coverage model for Abstractive Document Summarization 10

A Context based Coverage Model for Abstractive Document Summ...

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10th International Conference on Information and Communication Technology Convergence (ICTC) - ICT Convergence Leading the Autonomous Future

作者： Kim, Heechan Lee, Soowon Soongsil Univ Dept Software Convergence Seoul South Korea Soongsil Univ Sch Software Seoul South Korea

ISBN: (纸本)9781728108933

Automatic abstractive summarization is one of natural language processing fields that generates a sequence of words representing important information of the input document. The sequence-to-sequence model, which is widely used in abstractive summarization, has a repetition problem in which the same sub-pattern is repeatedly generated in decode phases. To solve this problem, various coverage models have been proposed in machine translation. In automatic summarization, unlike machine translation, the lengths of input and summary documents are very different. Because the summary document is a compressed form of important meaning of the input document. Due to the nature of automatic summarization, it is difficult to apply a word position-based coverage model in machine translation directly. For automatic summarization, we propose a context based coverage model to consider the coverage based on the compressed meaning of the input document. The context based coverage is defined as the accumulated weighted average of the encoded word meaning by the attention scores. This considers the meaning of words rather than the position of words in the input document. In the experiment with CNN/DailyMail dataset, the proposed model shows better performance than the previous researches.

关键词： Context Coverage sequence-to-sequence model Abstractive Document Summarization Natural Language Processing

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Convolutional Auto-Encoder for Variable Length Respiratory Sound Compression and Reconstruction

Convolutional Auto-Encoder for Variable Length Respiratory S...

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2024 Biomedical Circuits and Systems Conference

作者： Tao, Shuailin Ho, Jinhai Goh, Wang Ling Gao, Yuan Nanyang Technol Univ Interdisciplinary Grad Programme AI X Singapore Singapore Nanyang Technol Univ NTU Sch Elect & Elect Engn Singapore Singapore ASTAR Inst Microelect IME Singapore Singapore

ISBN: (纸本)9798350354966;9798350354959

This paper presents a respiratory sound compression and reconstruction method based on convolutional Auto-Encoder. By utilizing convolutional and transpose convolutional layers, this model can process variable length sound waveform, which is an important feature for data transmission from edge-based medical devices to cloud server and reconstruct the signal with high fidelity. This work shows that utilizing a non-variational latent space in respiratory sounds compression generates smaller reconstruction error compared to other state-of-art solution. Additionally, this work proposes a new composite loss function to guide the network training. Tested with BioCAS 2024 Grand Challenge dataset, this method achieves a Percent Root Mean Square Difference (PRD) of 0.2230, Correlation Coefficient (CC) of 0.972, and Signal-to-Noise Ratio Loss (SNRL) -0.7129 dB with a compression rate of 222.

关键词： Audio Compression Respiratory sound processing sequence-to-sequence model Auto-Encoders

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Deep Learning Based LSTM and SeqToSeq models to Detect Monsoon Spells of India 19th

Deep Learning Based LSTM and SeqToSeq Models to Detect Monso...

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19th Annual International Conference on Computational Science (ICCS)

作者： Viswanath, Saicharan Saha, Moumita Mitra, Pabitra Nanjundiah, Ravi S. Natl Inst Technol Karnataka Surathkal India Indian Inst Sci Ctr Atmospher & Ocean Sci Bangalore Karnataka India Indian Inst Technol Kharagpur Kharagpur W Bengal India Indian Inst Sci Divecha Ctr Climate Change Bangalore Karnataka India Indian Inst Trop Meteorol Pune Maharashtra India

ISBN: (纸本)9783030227418;9783030227401

Monsoon spells are important climatic phenomenon modulating the quality and quantity of monsoon over a year. India being an agricultural country, identification of monsoon spells is extremely important to plan agricultural policies following the phases of monsoon to attain maximum productivity. Monsoon spells' detection involve analyzing and predicting monsoon at daily levels which make it more challenging as daily-variability is higher as compared to monsoon over a month or an year. In this article, deep-learning based long short-term memory and sequence-to-sequence models are utilized to classify monsoon days, which are finally assembled to detect the spells. Dry and wet days are classified with precision of 0.95 and 0.87, respectively. Break spells are observed to be forecast with higher accuracy than the active spells. Additionally, sequence-to-sequence model is noted to perform superior to that of long-short term memory model. The proposed models also outperform traditional classification models for monsoon spell detection.

关键词： Active spell Break spell Long short term memory sequence-to-sequence model Attention mechanism Classification Spells' detection

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TRANSFORMER-BASED DIRECT SPEECH-TO-SPEECH TRANSLATION WITH TRANSCODER

TRANSFORMER-BASED DIRECT SPEECH-TO-SPEECH TRANSLATION WITH T...

引用

IEEE Spoken Language Technology Workshop (SLT)

作者： Kano, Takatomo Sakti, Sakriani Nakamura, Satoshi Nara Inst Sci & Technol Ikoma Japan RIKEN Ctr Adv Intelligence Project AIP Wako Saitama Japan

ISBN: (纸本)9781728170664

Traditional speech translation systems use a cascade manner that concatenates speech recognition (ASR), machine translation (MT), and text-to-speech (TTS) synthesis to translate speech from one language to another language in a step-by-step manner. Unfortunately, since those components are trained separately, MT often struggles to handle ASR errors, resulting in unnatural translation results. Recently, one work attempted to construct direct speech translation in a single model. The model used a multi-task scheme that learns to predict not only the target speech spectrograms directly but also the source and target phoneme transcription as auxiliary tasks. However, that work was only evaluated Spanish-English language pairs with similar syntax and word order. With syntactically distant language pairs, speech translation requires distant word order, and thus direct speech frame-to-frame alignments become difficult. Another direction was to construct a single deep-learning framework while keeping the step-by-step translation process. However, such studies focused only on speech-to-text translation. Furthermore, all of these works were based on a recurrent neural network (RNN) model. In this work, we propose a step-by-step scheme to a complete end-to-end speech-to-speech translation and propose a Transformer-based speech translation using Transcoder. We compare our proposed and multi-task model using syntactically similar and distant language pairs.

关键词： speech-to-speech translation Transcoder Transformer sequence-to-sequence model multitask learning

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Abstractive Text Summarization Using Multimodal Information 10

Abstractive Text Summarization Using Multimodal Information

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10th International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Rafi, Shaik Das, Ranjita NIT MIZORAM Dept Comp Sci & Engn Aizawl 796012 Mizoram India NIT AGARTALA Dept Comp Sci & Engn Agartala 799046 Tripura India

ISBN: (纸本)9798350359374

Much text generates over the internet through news articles, story writing and blogs. Reading and understanding such an enormous amount of data to the user is problematic, including time and effort. Automatic abstractive text summarization has gained more importance to increase the user's understanding and reduce time. It shortens the given input by preserving the meaning and identifying the context of the whole document to generate meaningful sentences. The research community has proposed different methods for text reduction and generating abstractive summaries. However, problems like semantics and contextual relationship in the summary generation process must be still need to improve. The multimodal abstractive text summarization is a technique that combines text and image information which helps in addressing the semantics and contextual relationship by proposing Multimodality Image Text (MIT) layer that fuses the text-extracted global features by glove embedding and preserves the semantics of the vocabulary and text-related images are used to identify the contextual relationship features from inception v3, which cope in the MIT layer to generate efficient multimodal abstractive text summaries by training and testing with seq-to-seq model. Experiments with the MSMO dataset achieve superior performance on other state-of-art results.

关键词： Abstractive Text Summarization Multimodality Image Text (MIT) Attention Mechanism LSTM sequence-to-sequence model

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AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks 15th

AttentionHTR: Handwritten Text Recognition Based on Attentio...

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15th IAPR International Workshop on Document Analysis Systems (DAS)

作者： Kass, Dmitrijs Vats, Ekta Uppsala Univ Dept Informat Technol Uppsala Sweden Uppsala Univ Ctr Digital Humanities Uppsala Dept ALM Uppsala Sweden

ISBN: (纸本)9783031065552;9783031065545

This work proposes an attention-based sequence-to-sequence model for handwritten word recognition and explores transfer learning for data-efficient training of HTR systems. To overcome training data scarcity, this work leverages models pre-trained on scene text images as a starting point towards tailoring the handwriting recognition models. ResNet feature extraction and bidirectional LSTM-based sequence modeling stages together form an encoder. The prediction stage consists of a decoder and a content-based attention mechanism. The effectiveness of the proposed end-to-end HTR system has been empirically evaluated on a novel multi-writer dataset Imgur5K and the IAM dataset. The experimental results evaluate the performance of the HTR framework, further supported by an in-depth analysis of the error cases. Source code and pre-trained models are available at GitHub (https://***/dmitrijsk/AttentionHTR).

关键词： Handwritten text recognition Attention encoder-decoder networks sequence-to-sequence model Transfer learning Multi-writer

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SPEECH EMOTION RECOGNITION USING DEEP NEURAL NETWORK CONSIDERING VERBAL AND NONVERBAL SPEECH SOUNDS

SPEECH EMOTION RECOGNITION USING DEEP NEURAL NETWORK CONSIDE...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Huang, Kun-Yi Wu, Chung-Hsien Hong, Qian-Bei Su, Ming-Hsiang Chen, Yi-Hsuan Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781479981311

Speech emotion recognition is becoming increasingly important for many applications. In real-life communication, non-verbal sounds within an utterance also play an important role for people to recognize emotion. In current studies, only few emotion recognition systems considered nonverbal sounds, such as laughter, cries or other emotion interjection, which naturally exists in our daily conversation. In this work, both verbal and nonverbal sounds within an utterance were thus considered for emotion recognition of real-life conversations. Firstly, an SVM-based verbal/nonverbal sound detector was developed. A Prosodic Phrase ( PPh) auto-tagger was further employed to extract the verbal/ nonverbal segments. For each segment, the emotion and sound features were respectively extracted based on convolutional neural networks ( CNNs) and then concatenated to form a CNN-based generic feature vector. Finally, a sequence of CNN-based feature vectors for an entire dialog turn was fed to an attentive long short-term memory ( LSTM)-based sequence-to-sequence model to output an emotional sequence as recognition result. Experimental results on the recognition of seven emotional states in the NNIME ( The NTHU-NTUA Chinese interactive multimodal emotion corpus) showed that the proposed method achieved a detection accuracy of 52.00% outperforming the traditional methods.

关键词： Speech emotion recognition prosodic Phrase nonverbal segment convolutional neural network long-short term memory sequence-to-sequence model

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