pwc-archive/papers-with-abstracts huggingface dataset:
More than Words: Twitter Chatter and Financial Market Sentiment
Authors: Travis Adams, Andrea Ajello, Diego Silva, Francisco Vazquez-Grande
Date: 2023-05-25 00:00:00

We build a new measure of credit and financial market sentiment using Natural Language Processing on Twitter data. We find that the Twitter Financial Sentiment Index (TFSI) correlates highly with corporate bond spreads and other price- and survey-based measures of financial conditions. We document that overnight Twitter financial sentiment helps predict next day stock market returns. Most notably, we show that the index contains information that helps forecast changes in the U.S. monetary policy stance: a deterioration in Twitter financial sentiment the day ahead of an FOMC statement release predicts the size of restrictive monetary policy shocks. Finally, we document that sentiment worsens in response to an unexpected tightening of monetary policy.
Modeling the Impact of Mentoring on Women's Work-LifeBalance: A Grounded Theory Approach
Authors: Parvaneh Bahrami, Saeed Nosratabadi, Khodayar Palouzian, Szilard Hegedus
Date: 2023-05-25 00:00:00
Tasks: Management

The purpose of this study was to model the impact of mentoring on women's work-life balance. Indeed, this study considered mentoring as a solution to create a work-life balance of women. For this purpose, semi-structured interviews with both mentors and mentees of Tehran Municipality were conducted and the collected data were analyzed using constructivist grounded theory. Findings provided a model of how mentoring affects women's work-life balance. According to this model, role management is the key criterion for work-life balancing among women. In this model, antecedents of role management and the contextual factors affecting role management, the constraints of mentoring in the organization, as well as the consequences of effective mentoring in the organization are described. The findings of this research contribute to the mentoring literature as well as to the role management literature and provide recommendations for organizations and for future research.
Social Sustainability of Digital Transformation: Empirical Evidence from EU-27 Countries
Authors: Saeed Nosratabadi, Thabit Atobishi, Szilard HegedHus
Date: 2023-05-25 00:00:00

In the EU-27 countries, the importance of social sustainability of digital transformation (SOSDIT) is heightened by the need to balance economic growth with social cohesion. By prioritizing SOSDIT, the EU can ensure that its citizens are not left behind in the digital transformation process and that technology serves the needs of all Europeans. Therefore, the current study aimed firstly to evaluate the SOSDIT of EU-27 countries and then to model its importance in reaching sustainable development goals (SDGs). The current study, using structural equation modeling, provided quantitative empirical evidence that digital transformation in Finland, the Netherlands, and Denmark are respectively most socially sustainable. It is also found that SOSDIT leads the countries to have a higher performance in reaching SDGs. Finally, the study provided evidence implying the inverse relationship between the Gini coefficient and reaching SDGs. In other words, the higher the Gini coefficient of a country, the lower its performance in reaching SDGs. The findings of this study contribute to the literature of sustainability and digitalization. It also provides empirical evidence regarding the SOSDIT level of EU-27 countries that can be a foundation for the development of policies to improve the sustainability of digital transformation. According to the findings, this study provides practical recommendations for countries to ensure that their digital transformation is sustainable and has a positive impact on society.
Classifying Rhoticity of /r/ in Speech Sound Disorder using Age-and-Sex Normalized Formants
Authors: Nina R Benway, Jonathan L Preston, Asif Salekin, Yi Xiao, Harshit Sharma, Tara McAllister
Date: 2023-05-25 00:00:00

Mispronunciation detection tools could increase treatment access for speech sound disorders impacting, e.g., /r/. We show age-and-sex normalized formant estimation outperforms cepstral representation for detection of fully rhotic vs. derhotic /r/ in the PERCEPT-R Corpus. Gated recurrent neural networks trained on this feature set achieve a mean test participant-specific F1-score =.81 ({\sigma}x=.10, med = .83, n = 48), with post hoc modeling showing no significant effect of child age or sex.
Acoustic-to-Articulatory Speech Inversion Features for Mispronunciation Detection of /r/ in Child Speech Sound Disorders
Authors: Nina R Benway, Yashish M Siriwardena, Jonathan L Preston, Elaine Hitchcock, Tara McAllister, Carol Espy-Wilson
Date: 2023-05-25 00:00:00

Acoustic-to-articulatory speech inversion could enhance automated clinical mispronunciation detection to provide detailed articulatory feedback unattainable by formant-based mispronunciation detection algorithms; however, it is unclear the extent to which a speech inversion system trained on adult speech performs in the context of (1) child and (2) clinical speech. In the absence of an articulatory dataset in children with rhotic speech sound disorders, we show that classifiers trained on tract variables from acoustic-to-articulatory speech inversion meet or exceed the performance of state-of-the-art features when predicting clinician judgment of rhoticity. Index Terms: rhotic, speech sound disorder, mispronunciation detection
The Power of Prosody and Prosody of Power: An Acoustic Analysis of Finnish Parliamentary Speech
Authors: Martti Vainio, Antti Suni, Juraj Šimko, Sofoklis Kakouros
Date: 2023-05-25 00:00:00

Parliamentary recordings provide a rich source of data for studying how politicians use speech to convey their messages and influence their audience. This provides a unique context for studying how politicians use speech, especially prosody, to achieve their goals. Here we analyzed a corpus of parliamentary speeches in the Finnish parliament between the years 2008-2020 and highlight methodological considerations related to the robustness of signal based features with respect to varying recording conditions and corpus design. We also present results of long term changes pertaining to speakers' status with respect to their party being in government or in opposition. Looking at large scale averages of fundamental frequency - a robust prosodic feature - we found systematic changes in speech prosody with respect opposition status and the election term. Reflecting a different level of urgency, members of the parliament have higher f0 at the beginning of the term or when they are in opposition.
Knowledge Distillation for Neural Transducer-based Target-Speaker ASR: Exploiting Parallel Mixture/Single-Talker Speech Data
Authors: Takafumi Moriya, Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix, Takanori Ashihara, Kohei Matsuura, Tomohiro Tanaka, Ryo Masumura, Atsunori Ogawa, Taichi Asami
Date: 2023-05-25 00:00:00
Tasks: Knowledge Distillation, Speech Extraction, speech-recognition, Speech Recognition

Neural transducer (RNNT)-based target-speaker speech recognition (TS-RNNT) directly transcribes a target speaker's voice from a multi-talker mixture. It is a promising approach for streaming applications because it does not incur the extra computation costs of a target speech extraction frontend, which is a critical barrier to quick response. TS-RNNT is trained end-to-end given the input speech (i.e., mixtures and enrollment speech) and reference transcriptions. The training mixtures are generally simulated by mixing single-talker signals, but conventional TS-RNNT training does not utilize single-speaker signals. This paper proposes using knowledge distillation (KD) to exploit the parallel mixture/single-talker speech data. Our proposed KD scheme uses an RNNT system pretrained with the target single-talker speech input to generate pseudo labels for the TS-RNNT training. Experimental results show that TS-RNNT systems trained with the proposed KD scheme outperform a baseline TS-RNNT.
Improving Scheduled Sampling for Neural Transducer-based ASR
Authors: Takafumi Moriya, Takanori Ashihara, Hiroshi Sato, Kohei Matsuura, Tomohiro Tanaka, Ryo Masumura
Date: 2023-05-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modelling, speech-recognition, Speech Recognition

The recurrent neural network-transducer (RNNT) is a promising approach for automatic speech recognition (ASR) with the introduction of a prediction network that autoregressively considers linguistic aspects. To train the autoregressive part, the ground-truth tokens are used as substitutions for the previous output token, which leads to insufficient robustness to incorrect past tokens; a recognition error in the decoding leads to further errors. Scheduled sampling (SS) is a technique to train autoregressive model robustly to past errors by randomly replacing some ground-truth tokens with actual outputs generated from a model. SS mitigates the gaps between training and decoding steps, known as exposure bias, and it is often used for attentional encoder-decoder training. However SS has not been fully examined for RNNT because of the difficulty in applying SS to RNNT due to the complicated RNNT output form. In this paper we propose SS approaches suited for RNNT. Our SS approaches sample the tokens generated from the distiribution of RNNT itself, i.e. internal language model or RNNT outputs. Experiments in three datasets confirm that RNNT trained with our SS approach achieves the best ASR performance. In particular, on a Japanese ASR task, our best system outperforms the previous state-of-the-art alternative.
Generalized domain adaptation framework for parametric back-end in speaker recognition
Authors: Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Takafumi Koshinaka
Date: 2023-05-24 00:00:00
Tasks: Domain Adaptation, Speaker Recognition, Unsupervised Domain Adaptation

State-of-the-art speaker recognition systems comprise a speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) back-end. The effectiveness of these components relies on the availability of a large amount of labeled training data. In practice, it is common for domains (e.g., language, channel, demographic) in which a system is deployed to differ from that in which a system has been trained. To close the resulting gap, domain adaptation is often essential for PLDA models. Among two of its variants are Heavy-tailed PLDA (HT-PLDA) and Gaussian PLDA (G-PLDA). Though the former better fits real feature spaces than does the latter, its popularity has been severely limited by its computational complexity and, especially, by the difficulty, it presents in domain adaptation, which results from its non-Gaussian property. Various domain adaptation methods have been proposed for G-PLDA. This paper proposes a generalized framework for domain adaptation that can be applied to both of the above variants of PLDA for speaker recognition. It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possible more flexible usage of available data in different domains. In particular, we introduce here two new techniques: (1) correlation-alignment in the model level, and (2) covariance regularization. To the best of our knowledge, this is the first proposed application of such techniques for domain adaptation w.r.t. HT-PLDA. The efficacy of the proposed techniques has been experimentally validated on NIST 2016, 2018, and 2019 Speaker Recognition Evaluation (SRE'16, SRE'18, and SRE'19) datasets.
Learned Wavelet Video Coding using Motion Compensated Temporal Filtering
Authors: Anna Meyer, Fabian Brand, André Kaup
Date: 2023-05-25 00:00:00
Tasks: Video Compression

We present an end-to-end trainable wavelet video coder based on motion-compensated temporal filtering (MCTF). Thereby, we introduce a different coding scheme for learned video compression, which is currently dominated by residual and conditional coding approaches. By performing discrete wavelet transforms in temporal, horizontal, and vertical dimension, we obtain an explainable framework with spatial and temporal scalability. We focus on investigating a novel trainable MCTF module that is implemented using the lifting scheme. We show how multiple temporal decomposition levels in MCTF can be considered during training and how larger temporal displacements due to the MCTF coding order can be handled. Further, we present a content adaptive extension to MCTF which adapts to different motion strengths during inference. In our experiments, we compare our MCTF-based approach to learning-based conditional coders and traditional hybrid video coding. Especially at high rates, our approach has promising rate-distortion performance. Our method achieves average Bj{\o}ntegaard Delta savings of up to 21% over HEVC on the UVG data set and thereby outperforms state-of-the-art learned video coders.
VEDA: Uneven light image enhancement via a vision-based exploratory data analysis model
Authors: Tian Pu, Shuhang Wang, Zhenming Peng, Qingsong Zhu
Date: 2023-05-25 00:00:00
Tasks: Image Enhancement

Uneven light image enhancement is a highly demanded task in many industrial image processing applications. Many existing enhancement methods using physical lighting models or deep-learning techniques often lead to unnatural results. This is mainly because: 1) the assumptions and priors made by the physical lighting model (PLM) based approaches are often violated in most natural scenes, and 2) the training datasets or loss functions used by deep-learning technique based methods cannot handle the various lighting scenarios in the real world well. In this paper, we propose a novel vision-based exploratory data analysis model (VEDA) for uneven light image enhancement. Our method is conceptually simple yet effective. A given image is first decomposed into a contrast image that preserves most of the perceptually important scene details, and a residual image that preserves the lighting variations. After achieving this decomposition at multiple scales using a retinal model that simulates the neuron response to light, the enhanced result at each scale can be obtained by manipulating the two images and recombining them. Then, a weighted averaging strategy based on the residual image is designed to obtain the output image by combining enhanced results at multiple scales. A similar weighting strategy can also be leveraged to reconcile noise suppression and detail preservation. Extensive experiments on different image datasets demonstrate that the proposed method can achieve competitive results in its simplicity and effectiveness compared with state-of-the-art methods. It does not require any explicit assumptions and priors about the scene imaging process, nor iteratively solving any optimization functions or any learning procedures.
Deep Representation Learning of Tissue Metabolome and Computed Tomography Images Annotates Non-invasive Classification and Prognosis Prediction of NSCLC
Authors: Marc Boubnovski Martell, Kristofer Linton-Reid, Sumeet Hindocha, Mitchell Chen, OCTAPUS-AI, Paula Moreno, Marina Álvarez-Benito, Ángel Salvatierra, Richard Lee, Joram M. Posma, Marco A Calzado, Eric O Aboagye
Date: 2023-05-24 00:00:00
Tasks: Computed Tomography (CT), Management, Prognosis, Representation Learning

The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT achieves state-of-the-art results in solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/ squamous cell carcinoma with an F1-score=0.78 and further asserts patients' prognosis with a c-index=0.72, surpassing the performance of radiomics models and clinical features. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.
Generative Adversarial Networks for Brain Images Synthesis: A Review
Authors: Firoozeh Shomal Zadeh, Sevda Molani, Maysam Orouskhani, Marziyeh Rezaei, Mehrzad Shafiei, Hossein Abbasi
Date: 2023-05-16 00:00:00
Tasks: Deep Learning, Generative Adversarial Network, Image Generation

In medical imaging, image synthesis is the estimation process of one image (sequence, modality) from another image (sequence, modality). Since images with different modalities provide diverse biomarkers and capture various features, multi-modality imaging is crucial in medicine. While multi-screening is expensive, costly, and time-consuming to report by radiologists, image synthesis methods are capable of artificially generating missing modalities. Deep learning models can automatically capture and extract the high dimensional features. Especially, generative adversarial network (GAN) as one of the most popular generative-based deep learning methods, uses convolutional networks as generators, and estimated images are discriminated as true or false based on a discriminator network. This review provides brain image synthesis via GANs. We summarized the recent developments of GANs for cross-modality brain image synthesis including CT to PET, CT to MRI, MRI to PET, and vice versa.
Entropy-Aware Similarity for Balanced Clustering: A Case Study with Melanoma Detection
Authors: Seok Bin Son, Soohyun Park, Joongheon Kim
Date: 2023-05-11 00:00:00
Tasks: Clustering

Clustering data is an unsupervised learning approach that aims to divide a set of data points into multiple groups. It is a crucial yet demanding subject in machine learning and data mining. Its successful applications span various fields. However, conventional clustering techniques necessitate the consideration of balance significance in specific applications. Therefore, this paper addresses the challenge of imbalanced clustering problems and presents a new method for balanced clustering by utilizing entropy-aware similarity, which can be defined as the degree of balances. We have coined the term, entropy-aware similarity for balanced clustering (EASB), which maximizes balance during clustering by complementary clustering of unbalanced data and incorporating entropy in a novel similarity formula that accounts for both angular differences and distances. The effectiveness of the proposed approach is evaluated on actual melanoma medial data, specifically the International Skin Imaging Collaboration (ISIC) 2019 and 2020 challenge datasets, to demonstrate how it can successfully cluster the data while preserving balance. Lastly, we can confirm that the proposed method exhibited outstanding performance in detecting melanoma, comparing to classical methods.
Machine Learning-Based Automatic Cardiovascular Disease Diagnosis Using Two ECG Leads
Authors: Cheng Guo, Sajid Ahmed, Mohamed-Slim Alouini
Date: 2023-05-25 00:00:00

The state-of-the-art cardiovascular disease diagnosis techniques use machine-learning algorithms based on feature extraction and classification. In this work, in contrast to a conventional single Electrocardiogram (ECG) lead, two leads are used, and autoregressive (AR) coefficients and statistical parameters are extracted to be used as features. Four machine-learning classifiers support-vector-machine (SVM), K-nearest neighbors (KNN), multi-layer perceptron (MLP), and Naive Bayes are applied on these features to test the accuracy of each classifier. For simulation, data is collected from the MIT-BIH and Shaoxing Peoples Hospital China (SPHC) database. To test the generalization ability of our proposed methodology machine-learning model is built on the SPHC database and tested on the MIT-BIH database and self-collected datasets. In the single-database simulation, the MLP performs better than the other three classifiers. While in the cross-database simulation, the SVM-based model trained by the SPHC database shows superiority. For normal and LBBB heartbeats, the predicted recall respectively reaches 100% and 98.4%. Simulation results show that the performance of our proposed methodology is better than the state-of-the-art techniques for the same database. While for cross-database simulation, the results are promising too. Finally, in the demonstration of our realized system, all heartbeats collected from healthy people are classified as normal beats.
Data-Aided CSI Estimation Using Affine-Precoded Superimposed Pilots in Orthogonal Time Frequency Space Modulated MIMO Systems
Authors: Anand Mehrotra, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo
Date: 2023-05-25 00:00:00

An orthogonal affine-precoded superimposed pilot-based architecture is developed for the cyclic prefix (CP)-aided SISO and MIMO orthogonal time frequency space systems relying on arbitrary transmitter-receiver pulse shaping. The data and pilot symbol matrices are affine-precoded and superimposed in the delay Doppler-domain followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver, the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided Bayesian learning (PA-BL) technique is conceived for the channel state information (CSI) estimation of SISO OTFS systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided Bayesian learning (DA-BL)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. In this scenario our sophisticated data detection rule also integrates the CSI uncertainty of channel estimation into our the linear minimum mean square error (LMMSE) detectors. The AP-SIP framework is also extended to MIMO OTFS systems, wherein the DD-domain input matrix is affine-precoded for each transmit antenna (TA). Then an EM algorithm-based PA-BL scheme is derived for simultaneous row-group sparse CSI estimation for this system, followed also by our data-aided DA-BL scheme that performs joint CSI estimation and data detection. Moreover, the Bayesian Cramer-Rao bounds (BCRBs) are also derived for both SISO as well as MIMO OTFS systems. Finally, simulation results are presented for characterizing the performance of the proposed CSI estimation techniques in a range of typical settings along with their bit error rate (BER) performance in comparison to an ideal system having perfect CSI.