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Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models 29

Automatic Label Sequence Generation for Prompting Sequence-t...

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29th international conference on Computational Linguistics, COLING 2022

作者： Yu, Zichun Gao, Tianyu Zhang, Zhengyan Lin, Yankai Liu, Zhiyuan Sun, Maosong Zhou, Jie Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University China Princeton University United States Beijing National Research Center for Information Science and Technology China Gaoling School of Artificial Intelligence Renmin University of China China International Innovation Center of Tsinghua University Shanghai China Beijing Academy of Artificial Intelligence China Pattern Recognition Center WeChat AI Tencent Inc China

Prompting, which casts downstream applications as language modeling tasks, has shown to be sample efficient compared to standard fine-tuning with pre-trained models. However, one pitfall of prompting is the need of manually-designed patterns, whose outcome can be unintuitive and requires large validation sets to tune. To tackle the challenge, we propose AutoSeq, a fully automatic prompting method: (1) We adopt natural language prompts on sequence-to-sequence models, enabling free-form generation and larger label search space;(2) We propose label sequences – phrases with indefinite lengths to verbalize the labels – which eliminate the need of manual templates and are more expressive than single label words;(3) We use beam search to automatically generate a large amount of label sequence candidates and propose contrastive re-ranking to get the best combinations. AutoSeq significantly outperforms other no-manual-design methods, such as soft prompt tuning, adapter tuning, and automatic search on single label words;the generated label sequences are even better than curated manual ones on a variety of tasks. Our method reveals the potential of sequence-to-sequence models in few-shot learning and sheds light on a path to generic and automatic prompting. The source code of this paper can be obtained from https://***/thunlp/Seq2Seq-Prompt. © 2022 Proceedings - international conference on Computational Linguistics, COLING. All rights reserved.

关键词： Modeling languages

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A Proposed Gene Selection Approach for Disease Detection 1

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2nd international conference on Computational Intelligence in pattern recognition (CIPR)

作者： Sah, Anushree Choudhury, Tanupriya Rawat, Saurabh Tripathi, Amrendra Univ Petr & Energy Studies Dehra Dun Uttarakhand India Graph Era Univ Dehra Dun Uttarakhand India

ISBN: (数字)9789811524493

ISBN: (纸本)9789811524493;9789811524486

Development and advancement of medicinal field are the major motivations of research. The need of having an accurate early prediction, diagnosis, and detection of disease for humankind is the other motivation. Traditional approaches of disease classification use very limited information, and experience of diagnosis is lacking. Few doctors in a small hospital would be able to diagnose critical disease based on their experience, and when it comes to disease like cancer, where time and experience is very critical, it becomes important to get the right diagnosis done at the right time by experienced and trained system or doctors. If the diagnosis/treatment is provided on time (i.e., within the golden hour), then saving a patient would be an easier and more promising task.

关键词： Cancer prediction Gene expression Microarray soft computing

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Automatic Sport Video Mining using a Novel Fusion of Handcrafted Descriptors

Automatic Sport Video Mining using a Novel Fusion of Handcra...

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2021 international Mobile, Intelligent, and Ubiquitous computing conference, MIUCC 2021

作者： Neggaz, Nabil Abdelminaam, Diaa Salama Département D'informatique Faculté des Mathématiques et Informatique BP 1505 EL M'naouer Oran31000 Algeria Misr International University Faculty of Computer Science Egypt Faculty of Computers and Artificial Intelligent Benha University Egypt

ISBN: (纸本)9781665412438

Recently, video mining in sports has become an attractive topic for computer vision research. The task of recognizing sports activities comprises two important phases: one focused on feature extraction and another focused on the process of classification. This paper presents a novel version of the discrete co-sine transform (DCT) known as the mean multi-block DCT (MmDCT), which introduces powerful denoising capabilities. Furthermore, we propose a local shape feature descriptor for human activity recognition that combines moment invariants (MIs), the MmDCT, and uniform local binary patterns (ULBPs). The different descriptors utilized have different advantages. For example, MIs are invariant in the presence of linear transformations (rotation, translation, and scaling) and ULBPs are robust against illumination changes. Therefore, these two descriptors provide complementary information. Experimental results on three challenging datasets (KTH, UCFII, and HMDB51) demonstrate that our method can achieve better performance than numerous state-of-the-art methods. © 2021 IEEE.

关键词： Local binary pattern

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Impact of Activation Functions of Neural Network in Mammographic Masses 6

Impact of Activation Functions of Neural Network in Mammogra...

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6th international conference on Trends in Electronics and Informatics (ICOEI)

作者： Agrawal, Rashmi Gupta, Neha Manav Rachna Int Inst Res & Studies Dept Comp Applicat Chennai Tamil Nadu India

ISBN: (纸本)9781665483285;9781665483292

Computational intelligence is a well-established paradigm that has been creating new hypotheses based on good biological understanding. Defining computational intelligence is not an easy task because this area belongs to multiple disciplines like Artificial Intelligence, soft computing, Machine Intelligence, Machine Learning and many others. Research in this area include the computational techniques like support vector machines, fuzzy logic, neural networks along with a wide range of applications as modelling and pattern recognition. These methods have been applied to solve many complex and diverse problems. To solve the above mentioned complex problems, neural networks are used. The present paper will focus on relevance of neural network in mammographic masses dataset to determine its performance analysis using various activation function while performing the classification task.

关键词： Computational Intelligence Classification soft computing Neural Network Activation functions

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Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge 25th

Reference Algorithms for the Mitosis Domain Generalization...

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25th international conference on Medical Image computing and Computer-Assisted Intervention , MICCAI 2022

作者： Ammeling, Jonas Wilm, Frauke Ganz, Jonathan Breininger, Katharina Aubreville, Marc Technische Hochschule Ingolstadt Ingolstadt Germany Pattern Recognition Lab Computer Sciences Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (纸本)9783031336577

Robust mitosis detection on images from different tumor types, pathology labs, and species is a challenging task that was addressed in the MICCAI Mitosis Domain Generalization (MIDOG) 2022 challenge. In this work, we describe three reference algorithms that were provided as a baseline for the challenge: A Mask-RCNN-based instance segmentation model trained on the MIDOG 2022 dataset, and two different versions of the domain-adversarial RetinaNet which already served as the baseline for MIDOG 2021 challenge, one trained on the MIDOG 2022 dataset and the other trained only on human breast carcinoma from MIDOG 2021. The domain-adversarial RetinaNet trained on the MIDOG 2022 dataset had the highest F 1 score of 0.7135 on the final test set. When trained on breast carcinoma only, the same network had a much lower F 1 score of 0.4719, indicating a significant domain shift between mitotic figure and tissue representation in different tumor types. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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Two-Step Estimation for Modeling the Earthquake Occurrences in Sumatra by Neyman-Scott Cox Point Processes 6th

Two-Step Estimation for Modeling the Earthquake Occurrences ...

引用

6th international conference on soft computing in Data Science (SCDS)

作者： Choiruddin, Achmad Susanto, Tabita Yuni Metrikasari, Rahma Inst Teknol Sepuluh Nopember ITS Dept Stat Surabaya Indonesia

ISBN: (纸本)9789811673344;9789811673337

The Cox point process is highly considered for earthquake modeling. However, the complex earthquake data which involve a large number of occurrences and geological variables often require expensive computation. This study aims to propose an efficient algorithm based on the two-step procedure by constructing the first and second order composite likelihoods. We consider four Neyman-Scott Cox process models and apply them to fit the earthquake distribution in Sumatra. We conclude that the Cauchy cluster process performs best.

关键词： Big data problem Cluster point process Disaster risk reduction Earthquake modeling Spatial point pattern

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Low Probability of Intercept Radar Signal Classification using Multi-Channel 1D-CNN

Low Probability of Intercept Radar Signal Classification usi...

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international conference on Communications and Signal Processing

作者： Rakesh Reddy Yakkati Anudeep Bhaskar Boddu Bethi Pardhasaradhi Sreenivasa Reddy Yeduri Linga Reddy Cenkeramaddi Department of ICT University of Agder Grimstad Norway

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Modern radar systems are designed to emit low probability of intercept (LPI) waveforms to avoid interception and detection by enemies. In this process, automatic radar LPI waveform recognition becomes a helpful tool for electronic counter-measures. In this paper, we propose LPI-Network (LPI-Net) that uses the complex radar returns to the multi-channel 1-dimensional (1D)-CNN. The direct features of the complex radar signals such as real, imaginary, and absolute values are used as inputs to the multi-channel 1D-CNN network. This LPI radar waveform recognition considers thirteen different waveforms: Barker, Frank, P1, P2, P3, P4, linear frequency modulation, rectangular waveform, T1, T2, T3, T4, and Costas. As a preliminary investigation, we verified various combinations of multi-channel CNNs and observed that a three-channel (real, imaginary, and absolute) and two channels (real and imaginary/real and absolute) are the three suitable candidates for accurate recognition activity. The proposed three-channel LPI-Net is verified with 10 -fold testing validation and achieves $93.94 \pm 0.59 \%$ overall accuracy at $0 \mathrm{~dB}$ SNR. The model is deployed on different edge computing devices such as Raspberry and NVIDIA AGX to test the feasibility of real-time deployment. The proposed model achieves $78.7 \%$ accuracy at $-8 \mathrm{~dB}$ SNR with a model size of 1.9MB and an inference time of 0.41 milliseconds in NVIDIA A100 GPU.

关键词： Time-frequency analysis Computational modeling pattern classification Graphics processing units Radar Transforms Radar imaging

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Robust Knowledge Distillation from RNN-T Models with Noisy Training Labels Using Full-Sum Loss

Robust Knowledge Distillation from RNN-T Models with Noisy T...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mohammad Zeineldeen Kartik Audhkhasi Murali Karthick Baskar Bhuvana Ramabhadran Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Google LLC New York

This work studies knowledge distillation (KD) and addresses its constraints for recurrent neural network transducer (RNN-T) models. In hard distillation, a teacher model transcribes large amounts of unlabelled speech to train a student model. soft distillation is another popular KD method that distills the output logits of the teacher model. Due to the nature of RNN-T alignments, applying soft distillation between RNNT architectures having different posterior distributions is challenging. In addition, bad teachers having high word-error-rate (WER) reduce the efficacy of KD. We investigate how to effectively distill knowledge from variable quality ASR teachers, which has not been studied before to the best of our knowledge. We show that a sequence-level KD, full-sum distillation, outperforms other distillation methods for RNN-T models, especially for bad teachers. We also propose a variant of full-sum distillation that distills the sequence discriminative knowledge of the teacher leading to further improvement in WER. We conduct experiments on public datasets namely SpeechStew and LibriSpeech, and on in-house production data.

关键词： Knowledge engineering Training Recurrent neural networks Transducers Production Signal processing Acoustics

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A Brute-Force Attack Detection Method Based on the Combination of Artificial Neural Network and Auto Encoder

A Brute-Force Attack Detection Method Based on the Combinati...

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Cloud computing, Big Data Applications and software Engineering (CBASE), international conference on

作者： Xiaojun Ma Qingyuan Li Xiaofei Wang Quan Wen Yingming Zeng Beijing Institute of Computer Technology and Application Beijing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

The ongoing advancement of information technology has led to the emergence of a multitude of network attack methodologies and techniques. As a prevalent form of network attack, it is imperative to undertake targeted research on the deliberate disruption of network traffic. Two issues currently impede the efficacy of machine learning and deep learning models in detecting brute force attacks. First, the presence of noise in the traffic data reduces the accuracy and robustness of detection. Second, when an attacker modifies the frequency, intensity, time, and other parameters of their attack to evade detection, the characteristics of the attack traffic become more complex and diverse. This, in turn, results in a notable decline in detection accuracy. In order to address the aforementioned issues, this paper presents a novel detection model that integrates the capabilities of an artificial neural network (ANN) and an auto-encoder (AE). The proposed model leverages the ANN's proficiency in recognizing intricate data patterns and the AE's effectiveness in removing noise, thereby facilitating precise detection of brute-force attacks. The experimental results demonstrate that the proposed detection model exhibits an enhanced accuracy rate of 99.93% in CICIDS-2017, exhibiting superior overall detection performance compared to existing algorithmic models such as AE and DAE. Additionally, it demonstrates a reduced false alarm rate and effective mitigation of the impact of noise and interference on attack data.

关键词： Time-frequency analysis Accuracy Prevention and mitigation Noise Telecommunication traffic Feature extraction Brute force attacks Robustness pattern recognition software engineering

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A Study on pattern Analysis of Extracted Features from Bearing Fault Vibration Signals 3

A Study on Pattern Analysis of Extracted Features from Beari...

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3rd IEEE international Virtual conference on Innovations in Power and Advanced computing Technologies, i-PACT 2021

作者： Nayana, B.R. Geethanjali, P. Sir M Visvesvaraya Institute of Technology Dept. of Electrical and Electronics Engineering Bangalore India Vellore Institute of Technology School of Electrical Engineering Vellore India

ISBN: (纸本)9781665426916

In any pattern recognition challenge, extracted features play a vital role. It is important to extract useful features and then proceed with classification. Especially in applications like mechanical fault diagnosis of machinery, where the characteristics features of the fault are to be captured. This can avoid the need for feature reduction/ selection techniques that would be needed once the number of features is greater and does not help advance the act of the classifier. This work presents an attempt to investigate the efficacy of eighteen features for classification. Among 18 features, 12 are statistical time domain features in which 4 are 1st 4 moments of the signal, 5 are simple statistical features and 3 are counters. Furthermore, 6 frequency domain features are computed directly in time domain, which are log transformed higher order moments of frequency domain. Subsequently, the features are studied for their pattern and their correlation with each other by visualization. Visualization observations are validated using classification performance parameters. The features are extracted from a benchmark dataset and a 13-class classification is implemented using well-established classifiers. Finally, the paper concludes that 4 effective features will suffice to achieve 99.7% accuracy of classification. © 2021 IEEE.

关键词： Visualization

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