pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Modal Transformers Utterance-Level Code-Switching Detection
Authors: Krishna D N
Date: 2020-11-04 00:00:00
Tasks: Sentence

An utterance that contains speech from multiple languages is known as a code-switched sentence. In this work, we propose a novel technique to predict whether given audio is mono-lingual or code-switched. We propose a multi-modal learning approach by utilising the phoneme information along with audio features for code-switch detection. Our model consists of a Phoneme Network that processes phoneme sequence and Audio Network(AN), which processes the mfcc features. We fuse representation learned from both the Networks to predict if the utterance is code-switched or not. The Audio Network and Phonetic Network consist of initial convolution, Bi-LSTM, and transformer encoder layers. The transformer encoder layer helps in selecting important and relevant features for better classification by using self-attention. We show that utilising the phoneme sequence of the utterance along with the mfcc features improves the performance of code-switch detection significantly. We train and evaluate our model on Microsoft code-switching challenge datasets for Telugu, Tamil, and Gujarati languages. Our experiments show that the multi-modal learning approach significantly improved accuracy over the uni-modal approaches for Telugu-English, Gujarati-English, and Tamil-English datasets. We also study the system performance using different neural layers and show that the transformers help obtain better performance.
The electronic board to replace the reference voltage on the earlobe for EEG measurement
Authors: Ildar Rakhmatulin
Date: 2020-11-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

An unstable reference point is a critical problem for electroencephalography (EEG) research since its instability introduces a significant distortion into the interpretation of the EEG signal. The main requirement for a reference point - electric potential should be unchanged. This is extremely difficult to implement in practice, due to biological processes occurring in the human body. Unlike other artifacts (network noise, electrical noise, blinking) that can be solved both by using analog circuitry (bandpass filters for noise, etc.) and by using various mathematical methods (method of head components for eye blink, etc.), a change in the potential of the reference point during the EEG measurement will be present in any case. Today, for a reference point an earlobe and overall average referential reference are used. The problems of using these methods are described in many works. In this manuscript, we propose a new method of using the reference point, electric potential which is generated by a 24-bit digital-to-analog converter (DAC). At the initial stage, a 24-bit analog-to-digital converter (ADC) reads the potential between the earlobes, and then on the STM 32 microcontroller through the DAC it generates such potential with an accuracy of 0.1 μV. From this moment, to calculate the voltage at the electrode, the voltage at the output of the DAC is used as the reference potential. Subsequently, the ADC makes comparisons of the potential between two points in the earlobes, which ideally should equal 0 V. In the case, if the difference between earlobes voltage is more than 0.1 μV, the DAC starts to compensate this value at its output, thereby averaging the value between the two earlobes. Algorithms in software to exclude instantaneous changes in potential on the earlobe were written in the STM32. Algorithms eliminate the uncontrolled effect of an increase of neuronal activity in the temporal region of the head. Thus, the developed prototype by DAC of the device replaces the potential on the earlobes and, based on mathematical calculations, provides a stable reference voltage for calculating the voltage on the electrodes.
Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition
Authors: Thanh-Hung Vo, Guee-Sang Lee, Hyung-Jeong Yang, Soo-Hyung Kim
Date: 2020-07-17 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Super-Resolution

Facial Expression Recognition (FER) is a challenging task that improves natural human-computer interaction. This paper focuses on automatic FER on a single in-the-wild (ITW) image. ITW images suffer real problems of pose, direction, and input resolution. In this study, we propose a pyramid with super-resolution (PSR) network architecture to solve the ITW FER task. We also introduce a prior distribution label smoothing (PDLS) loss function that applies the additional prior knowledge of the confusion about each expression in the FER task. Experiments on the three most popular ITW FER datasets showed that our approach outperforms all the state-of-the-art methods.
Scalable Recognition with a Vocabulary Tree
Authors: David Nist ́er and Henrik Stew ́enius
Date: 2006-06-22 00:00:00
Tasks: Quantization, Retrieval

A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD’s.The scheme builds upon popular techniques of indexing descriptors extracted from local regions, and is robust to background clutter and occlusion.  The local region descriptors are hierarchically quantized in a vocabulary tree.   The vocabulary tree allows a larger and more discriminatory vocabulary to be used efficiently, which we show experimentally leads to a dramatic improvement in retrieval quality.  The most significant property of the scheme is that the tree directly defines the quantization. The quantization and the indexing are therefore fully integrated,essentially being one and the same.The recognition quality is evaluated through retrieval on a database with ground truth, showing the power of the vocabulary tree approach, going as high as 1 million images.
Recent Advance in Content-based Image Retrieval: A Literature Survey
Authors: Wengang Zhou, Houqiang Li, Qi Tian
Date: 2017-06-19 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval, Survey

The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research.
Nonlinear GARCH model and 1/f noise
Authors: Aleksejus Kononovicius, Julius Ruseckas
Date: 2014-12-19 00:00:00
Tasks: model

Auto-regressive conditionally heteroskedastic (ARCH) family models are still used, by practitioners in business and economic policy making, as a conditional volatility forecasting models. Furthermore ARCH models still are attracting an interest of the researchers. In this contribution we consider the well known GARCH(1,1) process and its nonlinear modifications, reminiscent of NGARCH model. We investigate the possibility to reproduce power law statistics, probability density function and power spectral density, using ARCH family models. For this purpose we derive stochastic differential equations from the GARCH processes in consideration. We find the obtained equations to be similar to a general class of stochastic differential equations known to reproduce power law statistics. We show that linear GARCH(1,1) process has power law distribution, but its power spectral density is Brownian noise-like. However, the nonlinear modifications exhibit both power law distribution and power spectral density of the power law form, including 1/f noise.
Lightweight Model For The Prediction of COVID-19 Through The Detection And Segmentation of Lesions in Chest CT Scans
Authors: Aram Ter-Sarkisov
Date: 2020-11-04 00:00:00
Tasks: COVID-19 Diagnosis, COVID-19 Image Segmentation, Instance Segmentation, Lesion Segmentation, Semantic Segmentation, Sensitivity

We introduce a lightweight Mask R-CNN model that segments areas with the Ground Glass Opacity and Consolidation
in chest CT scans. The model uses truncated ResNet18 and ResNet34 nets with a single layer of Feature Pyramid Network
as a backbone net, thus substantially reducing the number of the parameters and the training time compared to similar
solutions using deeper networks. Without any data balancing and manipulations, and using only a small fraction of
the training data, COVID-CT-Mask-Net classification model with 6.12M total and 600K trainable parameters derived
from Mask R-CNN, achieves 91.35% COVID-19 sensitivity, 91.63% Common Pneumonia sensitivity, 96.98% true negative
rate and 93.95% overall accuracy on COVIDx-CT dataset (21191 images). We also present a thorough analysis of the
regional features critical to the correct classification of the image. The full source code, models and pretrained weights
are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net.
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis
Authors: Desh Raj, Pavel Denisov, Zhuo Chen, Hakan Erdogan, Zili Huang, Maokui He, Shinji Watanabe, Jun Du, Takuya Yoshioka, Yi Luo, Naoyuki Kanda, Jinyu Li, Scott Wisdom, John R. Hershey
Date: 2020-11-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition, Speech Separation

Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation. With technical advances in systems dealing with speech separation, speaker diarization, and automatic speech recognition (ASR) in the last decade, it has become possible to build pipelines that achieve reasonable error rates on this task. In this paper, we propose an end-to-end modular system for the LibriCSS meeting data, which combines independently trained separation, diarization, and recognition components, in that order. We study the effect of different state-of-the-art methods at each stage of the pipeline, and report results using task-specific metrics like SDR and DER, as well as downstream WER. Experiments indicate that the problem of overlapping speech for diarization and ASR can be effectively mitigated with the presence of a well-trained separation module. Our best system achieves a speaker-attributed WER of 12.7%, which is close to that of a non-overlapping ASR.
What is an intelligent system?
Authors: Martin Molina
Date: 2020-08-31 00:00:00

The concept of intelligent system has emerged in information technology as a type of system derived from successful applications of artificial intelligence. The goal of this paper is to give a general description of an intelligent system, which integrates previous approaches and takes into account recent advances in artificial intelligence. The paper describes an intelligent system in a generic way, identifying its main properties and functional components. The presented description follows a pragmatic approach to be used in an engineering context as a general framework to analyze and build intelligent systems. Its generality and its use is illustrated with real-world system examples and related with artificial intelligence methods.
Penalty-based Sequence Generative Adversarial Networks with Enhanced Transformer for Text Generation
Authors: Mingjun Duan, Yubai Li
Date: 2020-09-28 00:00:00
Tasks: Text Generation

In this paper, we propose a new model to solve the problem of text generation, which is based on the concept of seqGAN, combines self-attention with modeling localness, and introduces the penalty-based objective function. This model has much better performance than the original model. In the original model, the generator's ability of the text feature extraction is insufficient. We introduce self-attention with modeling localness, which greatly enhances its ability to capture long distance and shortrange dependencies. In addition, we use the penalty-based objective function instead of the loss function of the original model to solve the problem of mode collapse. Experimental results demonstrate that our model consistently outperforms several state-of-the-art text generation methods in the quality of generated texts.
CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
Authors: Jean Bégaint, Fabien Racapé, Simon Feltman, Akshay Pushparaja
Date: 2020-11-05 00:00:00
Tasks: Image Compression, MS-SSIM, SSIM, Video Compression

This paper presents CompressAI, a platform that provides custom operations, layers, models and tools to research, develop and evaluate end-to-end image and video compression codecs. In particular, CompressAI includes pre-trained models and evaluation tools to compare learned methods with traditional codecs. Multiple models from the state-of-the-art on learned end-to-end compression have thus been reimplemented in PyTorch and trained from scratch. We also report objective comparison results using PSNR and MS-SSIM metrics vs. bit-rate, using the Kodak image dataset as test set. Although this framework currently implements models for still-picture compression, it is intended to be soon extended to the video compression domain.
Distributed Weighted Least Squares Estimator Based on ADMM
Authors: Shun Liu, Zhifei Li, Weifang Zhang, Yan Liang
Date: 2020-11-05 00:00:00
Tasks: Relation, State Estimation

Wireless sensor network has recently received much attention due to its broad applicability and ease-of-installation. This paper is concerned with a distributed state estimation problem, where all sensor nodes are required to achieve a consensus estimation. The weighted least squares (WLS) estimator is an appealing way to handle this problem since it does not need any prior distribution information. To this end, we first exploit the equivalent relation between the information filter and WLS estimator. Then, we establish an optimization problem under the relation coupled with a consensus constraint. Finally, the consensus-based distributed WLS problem is tackled by the alternating direction method of multiplier (ADMM). Numerical simulation together with theoretical analysis testify the convergence and consensus estimations between nodes.
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach
Authors: SHENGLI PAN
Date: 2019-09-18 00:00:00
Tasks: Cloud Computing, Edge-computing, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Mobile edge computing (MobEC) builds an Information Technology (IT) service environment to enable cloud-computing capabilities at the edge of mobile networks. To tackle the restrictions in the battery power and computation capability of mobile devices, task offloading for using MobEC is developed and used to reduce the service latency and to ensure high service efficiency. However, most of the existing schemes only focus on one-shot offloading, while taking less into consideration the task dependency. It
is urgently needed a more comprehensive and adaptive way to take both the energy constraint and the
inherent dependency of tasks into account,since modern communication networks have increasingly become complicated and dynamic. To this end, in this paper, we are motivated to study the problem of dependency-aware task offloading decision in MobEC, aiming at minimizing the execution time for mobile applications with constraints on energy consumption. To solve this problem, we propose a model-free approach based on reinforcement learning (RL), i.e., a Q-learning approach that adaptively learns to optimize the offloading decision and energy consumption jointly by interacting with the network environment. Simulation results show that our RL-based approach is able to achieve significant reduction on the total execution time with comparably less energy consumption.
Training Transformers for Information Security Tasks: A Case Study on Malicious URL Prediction
Authors: Ethan M. Rudd, Ahmed Abdallah
Date: 2020-11-05 00:00:00

Machine Learning (ML) for information security (InfoSec) utilizes distinct data types and formats which require different treatments during optimization/training on raw data. In this paper, we implement a malicious/benign URL predictor based on a transformer architecture that is trained from scratch. We show that in contrast to conventional natural language processing (NLP) transformers, this model requires a different training approach to work well. Specifically, we show that 1) pre-training on a massive corpus of unlabeled URL data for an auto-regressive task does not readily transfer to malicious/benign prediction but 2) that using an auxiliary auto-regressive loss improves performance when training from scratch. We introduce a method for mixed objective optimization, which dynamically balances contributions from both loss terms so that neither one of them dominates. We show that this method yields performance comparable to that of several top-performing benchmark classifiers.
Teaching with Commentaries
Authors: Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey Hinton
Date: 2020-11-05 00:00:00
Proceeding: ICLR 2021 1
Tasks: Data Augmentation

Effective training of deep neural networks can be challenging, and there remain many open questions on how to best learn these models. Recently developed methods to improve neural network training examine teaching: providing learned information during the training process to improve downstream model performance. In this paper, we take steps towards extending the scope of teaching. We propose a flexible teaching framework using commentaries, learned meta-information helpful for training on a particular task. We present gradient-based methods to learn commentaries, leveraging recent work on implicit differentiation for scalability. We explore diverse applications of commentaries, from weighting training examples, to parameterising label-dependent data augmentation policies, to representing attention masks that highlight salient image regions. We find that commentaries can improve training speed and/or performance, and provide insights about the dataset and training process. We also observe that commentaries generalise: they can be reused when training new models to obtain performance benefits, suggesting a use-case where commentaries are stored with a dataset and leveraged in future for improved model training.
Fast Rates for Contextual Linear Optimization
Authors: Yichun Hu, Nathan Kallus, Xiaojie Mao
Date: 2020-11-05 00:00:00
Tasks: Decision Making

Incorporating side observations in decision making can reduce uncertainty and boost performance, but it also requires we tackle a potentially complex predictive relationship. While one may use off-the-shelf machine learning methods to separately learn a predictive model and plug it in, a variety of recent methods instead integrate estimation and optimization by fitting the model to directly optimize downstream decision performance. Surprisingly, in the case of contextual linear optimization, we show that the naive plug-in approach actually achieves regret convergence rates that are significantly faster than methods that directly optimize downstream decision performance. We show this by leveraging the fact that specific problem instances do not have arbitrarily bad near-dual-degeneracy. While there are other pros and cons to consider as we discuss and illustrate numerically, our results highlight a nuanced landscape for the enterprise to integrate estimation and optimization. Our results are overall positive for practice: predictive models are easy and fast to train using existing tools, simple to interpret, and, as we show, lead to decisions that perform very well.
From Note-Level to Chord-Level Neural Network Models for Voice Separation in Symbolic Music
Authors: Patrick Gray, Razvan Bunescu
Date: 2020-11-05 00:00:00

Music is often experienced as a progression of concurrent streams of notes, or voices. The degree to which this happens depends on the position along a voice-leading continuum, ranging from monophonic, to homophonic, to polyphonic, which complicates the design of automatic voice separation models. We address this continuum by defining voice separation as the task of decomposing music into streams that exhibit both a high degree of external perceptual separation from the other streams and a high degree of internal perceptual consistency. The proposed voice separation task allows for a voice to diverge to multiple voices and also for multiple voices to converge to the same voice. Equipped with this flexible task definition, we manually annotated a corpus of popular music and used it to train neural networks that assign notes to voices either separately for each note in a chord (note-level), or jointly to all notes in a chord (chord-level). The trained neural models greedily assign notes to voices in a left to right traversal of the input chord sequence, using a diverse set of perceptually informed input features. When evaluated on the extraction of consecutive within voice note pairs, both models surpass a strong baseline based on an iterative application of an envelope extraction function, with the chord-level model consistently edging out the note-level model. The two models are also shown to outperform previous approaches on separating the voices in Bach music.
Language Model is All You Need: Natural Language Understanding as Question Answering
Authors: Mahdi Namazifar, Alexandros Papangelis, Gokhan Tur, Dilek Hakkani-Tür
Date: 2020-11-05 00:00:00
Tasks: All, Language Modeling, Language Modelling, Natural Language Understanding, Question Answering, Transfer Learning

Different flavors of transfer learning have shown tremendous impact in advancing research and applications of machine learning. In this work we study the use of a specific family of transfer learning, where the target domain is mapped to the source domain. Specifically we map Natural Language Understanding (NLU) problems to QuestionAnswering (QA) problems and we show that in low data regimes this approach offers significant improvements compared to other approaches to NLU. Moreover we show that these gains could be increased through sequential transfer learning across NLU problems from different domains. We show that our approach could reduce the amount of required data for the same performance by up to a factor of 10.
From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation
Authors: Patrick Huber, Giuseppe Carenini
Date: 2020-11-05 00:00:00
Proceeding: COLING 2020 8
Tasks: Prediction, Sentiment Analysis

Sentiment analysis, especially for long documents, plausibly requires methods capturing complex linguistics structures. To accommodate this, we propose a novel framework to exploit task-related discourse for the task of sentiment analysis. More specifically, we are combining the large-scale, sentiment-dependent MEGA-DT treebank with a novel neural architecture for sentiment prediction, based on a hybrid TreeLSTM hierarchical attention model. Experiments show that our framework using sentiment-related discourse augmentations for sentiment prediction enhances the overall performance for long documents, even beyond previous approaches using well-established discourse parsers trained on human annotated data. We show that a simple ensemble approach can further enhance performance by selectively using discourse, depending on the document length.