pwc-archive/papers-with-abstracts huggingface dataset:
Learning Efficient Representations for Keyword Spotting with Triplet Loss
Authors: Roman Vygon, Nikolay Mikhaylovskiy
Date: 2021-01-12 00:00:00
Proceeding: SPECOM 2021
Tasks: Classification, Keyword Spotting, Representation Learning, speech-recognition, Speech Recognition, Triplet

In the past few years, triplet loss-based metric embeddings have become a de-facto standard for several important computer vision problems, most no-tably, person reidentification. On the other hand, in the area of speech recognition the metric embeddings generated by the triplet loss are rarely used even for classification problems. We fill this gap showing that a combination of two representation learning techniques: a triplet loss-based embedding and a variant of kNN for classification instead of cross-entropy loss significantly (by 26% to 38%) improves the classification accuracy for convolutional networks on a LibriSpeech-derived LibriWords datasets. To do so, we propose a novel phonetic similarity based triplet mining approach. We also improve the current best published SOTA for Google Speech Commands dataset V1 10+2 -class classification by about 34%, achieving 98.55% accuracy, V2 10+2-class classification by about 20%, achieving 98.37% accuracy, and V2 35-class classification by over 50%, achieving 97.0% accuracy.
THE EFFECTIVE RANK: A MEASURE OF EFFECTIVE DIMENSIONALITY
Authors: Olivier Roy, Martin Vetterli
Date: 2007-01-01 00:00:00
Proceeding: EURASIP, EUPSICO 2007 1

Many signal processing algorithms include numerical problems where the solution is obtained by adjusting the value of parameters such that a specific matrix exhibits rank deficiency. Since rank minimization is generally not practicable owing to its integer nature, we propose a real-valued extension that we term effective rank. After proving some of its properties, the effective rank is provided with an operational meaning using a result on the coefficient rate of a stationary random process. Finally, the proposed measure is assessed in a practical scenario and other potential applications are suggested.
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution
Authors: Yuki Kondo, Norimichi Ukita
Date: 2021-07-25 00:00:00
Proceeding: International Conference on Machine Vision and Applications (MVA) 2021
Tasks: Crack Segmentation, Segmentation, Semantic Segmentation, Super-Resolution

This paper proposes a method for crack segmentation on low-resolution images. Detailed cracks on their high-resolution images are estimated by super resolution from the low-resolution images. Our proposed method optimizes super-resolution images for the crack segmentation. For this method, we propose the Boundary Combo loss to express the local details of the crack. Experimental results demonstrate that our method outperforms the combinations of other previous approaches.
A Complex Systems Approach To Feature Extraction for Chaotic Behavior Recognition
Authors: Alireza Pourafzal, Alireza Fereidunian
Date: 2021-02-15 00:00:00
Proceeding: 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) 2021 2
Tasks: Time Series, Time Series Analysis

Chaotic behavior may be observed in many natural and human-made time series, thus one of the first things to acquire is the knowledge on their chaotic behavior. Several  approaches including model-based and data-driven classifications are utilized to address this issue; however, the computational burden arisen with the higher performance seems  to be a challenge in real world problems. Chaos theory and the complexity theory share considerable similarities, which inspired us to exploit complex system characteristics, such as complexity, emergence, self-organization and predictability as the features of chaotic time series. The degree of complexity in Lorenz model is a well-known instance of chaotic behavior, demonstrated from periodic nonlinear to a chaotic state by analysis of Hopf and Pitchfork bifurcations and then the corresponding time series are gathered in a dataset. Subsequently, the complex systems characteristics are introduced as proper features for the generated dataset, and finally these features are extracted and validated via different machine learning methods with different evaluation metrics. The simulation results reveal the excellent performance of 92 percent precision on the test data, based on the extracted features. It is shown that the most important features to recognize the chaotic behavior are complexity, emergence, predictability, and self-organization.
A Joint-Entropy Approach To Time-series Classification
Authors: Kimia Safarihamid, Alireza Pourafzal, Alireza Fereidunian
Date: 2022-03-11 00:00:00
Proceeding: 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS) 2022 3
Tasks: Classification, Time Series, Time Series Analysis, Time Series Classification

In this paper, the problem of entropy-based classification of time-series into stochastic, chaotic, and periodic is addressed, followed by proposing an alternative joint-entropy approach to time series classification. These data-driven methods describe the behavior of a signal, using the association of the entropy of a time-series with emergence and self-organization, as complex systems characteristics. First, we deduce that certain groups of entropies, namely Fuzzy entropy, and Distribution entropy, share more similarities with emergence, while permutation and dispersion entropies could be associated with self-organization. Then, we utilize these resemblances to propose a joint-entropy alternative approach, in which one of the specific entropies is presented for each characteristic. Further, in simulations, we evaluated the performance of our proposed approach, comparing with single entropy methods, using different classifiers and decision boundaries. The results reveal an excellent performance of 98% accuracy for simultaneous utilization of the Distribution and Permutation entropies as the input features of Random Forest classifier, while this value is at best 89% for when only a single entropy is fed to the classifier.
POS-Tagging based Neural Machine Translation System for European Languages using Transformers
Authors: Preetham Ganesh, Bharat S. Rawal, Alexander Peter, Andi Giri
Date: 2021-05-31 00:00:00
Proceeding: WSEAS Transactions on Information Science and Applications 2021 5
Tasks: Machine Translation, NMT, Part-Of-Speech Tagging, POS, POS Tagging, Translation

The interaction between human beings has always faced different kinds of difficulties. One of those difficulties is the language barrier. It would be a tedious task for someone to learn all the syllables in a new language in a short period and converse with a native speaker without grammatical errors. Moreover, having a language translator at all times would be intrusive and expensive. We propose a novel approach to Neural Machine Translation (NMT) system using interlanguage word similaritybased model training and Part-Of-Speech (POS) Tagging based model testing. We compare these approaches using two classical architectures: Luong Attention-based Sequence-to-Sequence architecture and Transformer based model. The sentences for the Luong Attention-based Sequence-to-Sequence were tokenized using SentencePiece tokenizer. The sentences for the Transformer model were tokenized using Subword Text Encoder. Three European languages were selected for modeling, namely, Spanish, French, and German. The datasets were downloaded from multiple sources such as Europarl Corpus, Paracrawl Corpus, and Tatoeba Project Corpus. Sparse Categorical CrossEntropy was the evaluation metric during the training stage, and during the testing stage, the Bilingual Evaluation Understudy (BLEU) Score, Precision Score, and Metric for Evaluation of Translation with Explicit Ordering (METEOR) score were the evaluation metrics.
Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts
Authors: Zhaoming Xie, Sebastian Starke, Hung Yu Ling, Michiel Van de Panne
Date: 2022-07-24 00:00:00
Proceeding: SIGGRAPH 2022 7
Tasks: Deep Reinforcement Learning, Humanoid Control, Imitation Learning, Mixture-of-Experts, Reinforcement Learning (RL)

Learning physics-based character controllers that can successfully integrate diverse motor skills using a single policy remains a challenging problem. We present a system to learn control policies for multiple soccer juggling skills, based on deep reinforcement learning. We introduce a task-description framework for these skills which facilitates the specification of individual soccer juggling tasks and the transitions between them. Desired motions can be authored using interpolation of crude reference poses or based on motion capture data. We show that a layer-wise mixture-of-experts architecture offers significant benefits. During training, transitions are chosen with the help of an adaptive random walk, in support of efficient learning. We demonstrate foot, head, knee, and chest juggles, foot stalls, the challenging around-the-world trick, as well as robust transitions. Our work provides a significant step towards realizing physics-based characters capable of the precision-based motor skills of human athletes.
Relation-Guided Few-Shot Relational Triple Extraction
Authors: Xin Cong, Jiawei Sheng, Shiyao Cui, Bowen Yu, Tingwen Liu, Bin Wang
Date: 2022-07-07 00:00:00
Proceeding: SIGIR 2022 7
Tasks: Relation, RTE, Sentence

In few-shot relational triple extraction (FS-RTE), one seeks to extract relational triples from plain texts by utilizing only few annotated samples. Recent work first extracts all entities and then classifies their relations. Such an entity-then-relation paradigm ignores the entity discrepancy between relations. To address it, we propose a novel task decomposition strategy, Relation-then-Entity, for FS-RTE. It first detects relations occurred in a sentence and then extracts the corresponding head/tail entities of the detected relations. To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relationrelevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities. Experimental results show that our model outperforms previous work by an absolute gain (18.98%, 28.85% in F1 in two few-shot settings).
An effective spatial-temporal attention based neural network for traffic flow prediction
Authors: Loan N.N. Do, Hai L. Vu, Bao Q. Vo, Zhiyuan Liu, Dinh Phung
Date: 2019-09-10 00:00:00
Proceeding: Transportation Research Part C: Emerging Technologies 2019 9
Tasks: Prediction

Due to its importance in Intelligent Transport Systems (ITS), traffic flow prediction has been the
focus of many studies in the last few decades. Existing traffic flow prediction models mainly
extract static spatial-temporal correlations, although these correlations are known to be dynamic
in traffic networks. Attention-based models have emerged in recent years, mostly in the field of
natural language processing, and have resulted in major progresses in terms of both accuracy and
interpretability. This inspires us to introduce the application of attentions for traffic flow prediction. In this study, a deep learning based traffic flow predictor with spatial and temporal
attentions (STANN) is proposed. The spatial and temporal attentions are used to exploit the
spatial dependencies between road segments and temporal dependencies between time steps
respectively. Experiment results with a real-world traffic dataset demonstrate the superior performance of the proposed model. The results also show that the utilization of multiple data
resolutions could help improve prediction accuracy. Furthermore, the proposed model is demonstrated to have potential for improving the understanding of spatial-temporal correlations in
a traffic network.
Boosting Lightweight Single Image Super-resolution via Joint-distillation
Authors: Xiaotong Luo, Qiuyuan Liang, Ding Liu, Yanyun Qu
Date: 2021-10-17 00:00:00
Proceeding: ACM 2021 10
Tasks: Image Super-Resolution, Meta-Learning, Super-Resolution

The rising of deep learning has facilitated the development of single image super-resolution (SISR). However, the growing burdensome model complexity and memory occupation severely hinder its practical deployments on resource-limited devices. In this paper, we propose a novel joint-distillation (JDSR) framework to boost the representation of various off-the-shelf lightweight SR models. The framework includes two stages: the superior LR generation and the joint-distillation learning. The superior LR is obtained from the HR image itself. With less than $300$K parameters, the peer network using superior LR as input can achieve comparable SR performance with large models, e.g., RCAN, with 15M parameters, which enables it as the input of peer network to save the training expense. The joint-distillation learning consists of internal self-distillation and external mutual learning. The internal self-distillation aims to achieve model self-boosting by transferring the knowledge from the deeper SR output to the shallower one. Specifically, each intermediate SR output is supervised by the HR image and the soft label from subsequent deeper outputs. To shrink the capacity gap between shallow and deep layers, a soft label generator is designed in a progressive backward fusion way with meta-learning for adaptive weight fine-tuning. The external mutual learning focuses on obtaining interaction information from a peer network in the process. Moreover, a curriculum learning strategy and a performance gap threshold are introduced for balancing the convergence rate of the original SR model and its peer network. Comprehensive experiments on benchmark datasets demonstrate that our proposal improves the performance of recent lightweight SR models by a large margin, with the same model architecture and inference expense.
The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices
Authors: Wanlun Ma, Derui Wang, Ruoxi Sun, Minhui Xue, Sheng Wen, Yang Xiang
Date: 2022-09-23 00:00:00
Tasks: valid

Deep Neural Networks (DNNs) are susceptible to backdoor attacks during training. The model corrupted in this way functions normally, but when triggered by certain patterns in the input, produces a predefined target label. Existing defenses usually rely on the assumption of the universal backdoor setting in which poisoned samples share the same uniform trigger. However, recent advanced backdoor attacks show that this assumption is no longer valid in dynamic backdoors where the triggers vary from input to input, thereby defeating the existing defenses. In this work, we propose a novel technique, Beatrix (backdoor detection via Gram matrix). Beatrix utilizes Gram matrix to capture not only the feature correlations but also the appropriately high-order information of the representations. By learning class-conditional statistics from activation patterns of normal samples, Beatrix can identify poisoned samples by capturing the anomalies in activation patterns. To further improve the performance in identifying target labels, Beatrix leverages kernel-based testing without making any prior assumptions on representation distribution. We demonstrate the effectiveness of our method through extensive evaluation and comparison with state-of-the-art defensive techniques. The experimental results show that our approach achieves an F1 score of 91.1% in detecting dynamic backdoors, while the state of the art can only reach 36.9%.
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond
Authors: Fan Chen, Song Mei, Yu Bai
Date: 2022-09-23 00:00:00
Tasks: PAC learning, Reinforcement Learning (RL)

Modern Reinforcement Learning (RL) is more than just learning the optimal policy; Alternative learning goals such as exploring the environment, estimating the underlying model, and learning from preference feedback are all of practical importance. While provably sample-efficient algorithms for each specific goal have been proposed, these algorithms often depend strongly on the particular learning goal and thus admit different structures correspondingly. It is an urging open question whether these learning goals can rather be tackled by a single unified algorithm. We make progress on this question by developing a unified algorithm framework for a large class of learning goals, building on the Decision-Estimation Coefficient (DEC) framework. Our framework handles many learning goals such as no-regret RL, PAC RL, reward-free learning, model estimation, and preference-based learning, all by simply instantiating the same generic complexity measure called "Generalized DEC", and a corresponding generic algorithm. The generalized DEC also yields a sample complexity lower bound for each specific learning goal. As applications, we propose "decouplable representation" as a natural sufficient condition for bounding generalized DECs, and use it to obtain many new sample-efficient results (and recover existing results) for a wide range of learning goals and problem classes as direct corollaries. Finally, as a connection, we re-analyze two existing optimistic model-based algorithms based on Posterior Sampling and Maximum Likelihood Estimation, showing that they enjoy sample complexity bounds under similar structural conditions as the DEC.
The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022
Authors: Yu Zheng, Jinghan Peng, Yihao Chen, Yajun Zhang, Jialong Wang, Min Liu, Minqiang Xu
Date: 2022-09-23 00:00:00
Tasks: Speaker Verification, Task 2, Transfer Learning

This paper describes speaker verification (SV) systems submitted by the SpeakIn team to the Task 1 and Task 2 of the Far-Field Speaker Verification Challenge 2022 (FFSVC2022). SV tasks of the challenge focus on the problem of fully supervised far-field speaker verification (Task 1) and semi-supervised far-field speaker verification (Task 2). In Task 1, we used the VoxCeleb and FFSVC2020 datasets as train datasets. And for Task 2, we only used the VoxCeleb dataset as train set. The ResNet-based and RepVGG-based architectures were developed for this challenge. Global statistic pooling structure and MQMHA pooling structure were used to aggregate the frame-level features across time to obtain utterance-level representation. We adopted AM-Softmax and AAM-Softmax to classify the resulting embeddings. We innovatively propose a staged transfer learning method. In the pre-training stage we reserve the speaker weights, and there are no positive samples to train them in this stage. Then we fine-tune these weights with both positive and negative samples in the second stage. Compared with the traditional transfer learning strategy, this strategy can better improve the model performance. The Sub-Mean and AS-Norm backend methods were used to solve the problem of domain mismatch. In the fusion stage, three models were fused in Task1 and two models were fused in Task2. On the FFSVC2022 leaderboard, the EER of our submission is 3.0049% and the corresponding minDCF is 0.2938 in Task1. In Task2, EER and minDCF are 6.2060% and 0.5232 respectively. Our approach leads to excellent performance and ranks 1st in both challenge tasks.
involve-MI: Informative Planning with High-Dimensional Non-Parametric Beliefs
Authors: Gilad Rotman, Vadim Indelman
Date: 2022-09-23 00:00:00
Tasks: Decision Making, Dimensionality Reduction, Vocal Bursts Intensity Prediction

One of the most complex tasks of decision making and planning is to gather information. This task becomes even more complex when the state is high-dimensional and its belief cannot be expressed with a parametric distribution. Although the state is high-dimensional, in many problems only a small fraction of it might be involved in transitioning the state and generating observations. We exploit this fact to calculate an information-theoretic expected reward, mutual information (MI), over a much lower-dimensional subset of the state, to improve efficiency and without sacrificing accuracy. A similar approach was used in previous works, yet specifically for Gaussian distributions, and we here extend it for general distributions. Moreover, we apply the dimensionality reduction for cases in which the new states are augmented to the previous, yet again without sacrificing accuracy. We then continue by developing an estimator for the MI which works in a Sequential Monte Carlo (SMC) manner, and avoids the reconstruction of future belief's surfaces. Finally, we show how this work is applied to the informative planning optimization problem. This work is then evaluated in a simulation of an active SLAM problem, where the improvement in both accuracy and timing is demonstrated.
Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval
Authors: Xiang Fang, Daizong Liu, Pan Zhou, Yuchong Hu
Date: 2022-09-23 00:00:00
Tasks: cross-modal alignment, Information Retrieval, Moment Retrieval, Retrieval

As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given language query. Most previous methods depend heavily on numerous manual annotations (i.e., moment boundaries), which are extremely expensive to acquire in practice. In addition, due to the domain gap between different datasets, directly applying these pre-trained models to an unseen domain leads to a significant performance drop. In this paper, we focus on a novel task: cross-domain VMR, where fully-annotated datasets are available in one domain (``source domain''), but the domain of interest (``target domain'') only contains unannotated datasets. As far as we know, we present the first study on cross-domain VMR. To address this new task, we propose a novel Multi-Modal Cross-Domain Alignment (MMCDA) network to transfer the annotation knowledge from the source domain to the target domain. However, due to the domain discrepancy between the source and target domains and the semantic gap between videos and queries, directly applying trained models to the target domain generally leads to a performance drop. To solve this problem, we develop three novel modules: (i) a domain alignment module is designed to align the feature distributions between different domains of each modality; (ii) a cross-modal alignment module aims to map both video and query features into a joint embedding space and to align the feature distributions between different modalities in the target domain; (iii) a specific alignment module tries to obtain the fine-grained similarity between a specific frame and the given query for optimal localization. By jointly training these three modules, our MMCDA can learn domain-invariant and semantic-aligned cross-modal representations.