pwc-archive/papers-with-abstracts huggingface dataset:
Towards Reliable Item Sampling for Recommendation Evaluation
Authors: Dong Li, Ruoming Jin, Zhenming Liu, Bin Ren, Jing Gao, Zhi Liu
Date: 2022-11-28 00:00:00
Tasks: Recommendation Systems

Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are "inconsistent" with respect to the global metrics (even in expectation), there have been a few studies on the sampling-based recommender system evaluation. Existing methods try either mapping the sampling-based metrics to their global counterparts or more generally, learning the empirical rank distribution to estimate the top-$K$ metrics. However, despite existing efforts, there is still a lack of rigorous theoretical understanding of the proposed metric estimators, and the basic item sampling also suffers from the "blind spot" issue, i.e., estimation accuracy to recover the top-$K$ metrics when $K$ is small can still be rather substantial. In this paper, we provide an in-depth investigation into these problems and make two innovative contributions. First, we propose a new item-sampling estimator that explicitly optimizes the error with respect to the ground truth, and theoretically highlight its subtle difference against prior work. Second, we propose a new adaptive sampling method which aims to deal with the "blind spot" problem and also demonstrate the expectation-maximization (EM) algorithm can be generalized for such a setting. Our experimental results confirm our statistical analysis and the superiority of the proposed works. This study helps lay the theoretical foundation for adopting item sampling metrics for recommendation evaluation, and provides strong evidence towards making item sampling a powerful and reliable tool for recommendation evaluation.
A multi-task learning network using shared BERT models for aspect-based sentiment analysis
Authors: Quanzhen Liu, Mizuho Iwaihara
Date: 2020-12-27 00:00:00
Proceeding: DEIM Forum 2020 12
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Category Sentiment Analysis, Multi-Task Learning, Sentiment Analysis

Abstract Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of specific aspect words occurring
in a text. ABSA includes aspect-category sentiment analysis (ACSA) and aspect-target sentiment analysis (ATSA). There have
been many previous studies addressing both tasks through RNNs and other neural models. With BERT's remarkable
performance on NLP tasks, several studies have enhanced aspect word extraction to solve ATSA by building new BERT-based
models. But such an approach is not directly applicable to the ACSA task, because aspect words in ACSA are often not
explicitly present in the text, so aspect word extraction becomes more difficult. In this paper, we propose a multi-task learning
(MTL) approach to solve these problems. Our approach is based on a shared BERT model to construct a multi-task learning
network, which is trained by strongly and weakly related tasks. We also use a multi-head self-attention layer to replace a linear
layer in traditional multi-task learning networks, to enhance the ability to capture global semantics. We also propose a new
fine-tuning strategy that can better improve the performance of the model. Experiments were conducted on four datasets from
the ATSA task and four datasets from the ACSA task: laptop, restaurant, restaurant-2014, and restaurant-large from
SemEval-2014. Our experimental results show that: In the ACSA task, our model outperformed all the baseline models,
achieving the current state-of-the-art performance on the multiple datasets. For the ATSA task, our model performs close to the
state-of-the-art, with much simpler architecture.
A Joint Learning Framework With BERT for Spoken Language Understanding
Authors: Zhang, ZC (Zhang, Zhichang)  ; Zhang, ZW (Zhang, Zhenwen) ; Chen, HY (Chen, Haoyuan) ; Zhang, ZM (Zhang, Zhiman)
Date: 2019-11-20 00:00:00
Proceeding: journal 2019 11
Tasks: Decoder, intent-classification, Intent Classification, Intent Classification and Slot Filling, Multi-Task Learning, slot-filling, Slot Filling, Spoken Language Understanding

Intent classification and slot filling are two essential tasks for spoken language understanding. Recently, joint learning has been shown to be effective for the two tasks. However, most joint learning methods only consider joint learning using shared parameters on the surface level rather than the semantic level, and these methods suffer from small-scale human-labeled training data, resulting in poor generalization capabilities, especially for rare words. In this paper, we propose a novel encoder-decoder framework based multi-task learning model, which conducts joint training for intent classification and slot filling tasks. For the encoder of our model, we encode the input sequence as context representations using bidirectional encoder representation from transformers (BERT). For the decoder, we implement two-stage decoder process in our model. In the first stage, we use an intent classification decoder to detect the user's intent. In the second stage, we leverage the intent contextual information into the slot filling decoder to predict the semantic concept tags for each word. We conduct experiments on three popular benchmark datasets: ATIS, Snips and Facebook multilingual task-oriented datasets. The experimental results show that our proposed model outperforms the state-of-the-art approaches and achieves new state-of-the-art results on both three datasets.
Utilising physics-guided deep learning to overcome data scarcity
Authors: Jinshuai Bai, Laith Alzubaidi, Qingxia Wang, Ellen Kuhl, Mohammed Bennamoun, Yuantong Gu
Date: 2022-11-24 00:00:00
Tasks: Deep Learning, Medical Diagnosis

Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challenging or even impossible in many real-world applications, such as structural risk estimation and medical diagnosis. This presents a significant barrier to the practical implementation of DL in these fields. Physics-guided deep learning (PGDL) is a novel type of DL that can integrate physics laws to train neural networks. This can be applied to any systems that are controlled or governed by physics laws, such as mechanics, finance and medical applications. It has been demonstrated that, with the additional information provided by physics laws, PGDL achieves great accuracy and generalisation in the presence of data scarcity. This review provides a detailed examination of PGDL and offers a structured overview of its use in addressing data scarcity across various fields, including physics, engineering and medical applications. Moreover, the review identifies the current limitations and opportunities for PGDL in relation to data scarcity and offers a thorough discussion on the future prospects of PGDL.
Evaluation of Entropy and Fractal Dimension as Biomarkers for Tumor Growth and Treatment Response using Cellular Automata
Authors: Juan Uriel Legaria-Peña, Félix Sánchez-Morales, Yuriria Cortés-Poza
Date: 2022-11-27 00:00:00
Tasks: Time Series, Time Series Analysis

Cell-based models provide a helpful approach for simulating complex systems that exhibit adaptive, resilient qualities, such as cancer. Their focus on individual cell interactions makes them a particularly appropriate strategy to study the effects of cancer therapies, which often are designed to disrupt single-cell dynamics. In this work, we also propose them as viable methods for studying the time evolution of cancer imaging biomarkers (IBM). We propose a cellular automata model for tumor growth and three different therapies: chemotherapy, radiotherapy, and immunotherapy, following well-established modeling procedures documented in the literature. The model generates a sequence of tumor images, from which time series of two biomarkers: entropy and fractal dimension, is obtained. Our model shows that the fractal dimension increased faster at the onset of cancer cell dissemination, while entropy was more responsive to changes induced in the tumor by the different therapy modalities. These observations suggest that the predictive value of the proposed biomarkers could vary considerably with time. Thus, it is important to assess their use at different stages of cancer and for different imaging modalities. Another observation derived from the results was that both biomarkers varied slowly when the applied therapy attacked cancer cells in a scattered fashion along the automatons' area, leaving multiple independent clusters of cells at the end of the treatment. Thus, patterns of change of simulated biomarkers time series could reflect on essential qualities of the spatial action of a given cancer intervention.
An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation
Authors: Wentao Yu, Yifei Shen, Hengtao He, Xianghao Yu, Shenghui Song, Jun Zhang, Khaled B. Letaief
Date: 2022-11-29 00:00:00

Terahertz ultra-massive MIMO (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless networks, for which channel estimation is highly challenging. Traditional analytical estimation methods are no longer effective, as the enlarged array aperture and the small wavelength result in a mixture of far-field and near-field paths, constituting a hybrid-field channel. Deep learning (DL)-based methods, despite the competitive performance, generally lack theoretical guarantees and scale poorly with the size of the array. In this paper, we propose a general DL framework for THz UM-MIMO channel estimation, which leverages existing iterative channel estimators and is with provable guarantees. Each iteration is implemented by a fixed point network (FPN), consisting of a closed-form linear estimator and a DL-based non-linear estimator. The proposed method perfectly matches the THz UM-MIMO channel estimation due to several unique advantages. First, the complexity is low and adaptive. It enjoys provable linear convergence with a low per-iteration cost and monotonically increasing accuracy, which enables an adaptive accuracy-complexity tradeoff. Second, it is robust to practical distribution shifts and can directly generalize to a variety of heavily out-of-distribution scenarios with almost no performance loss, which is suitable for the complicated THz channel conditions. For practical usage, the proposed framework is further extended to wideband THz UM-MIMO systems with beam squint effect. Theoretical analysis and extensive simulation results are provided to illustrate the advantages over the state-of-the-art methods in estimation accuracy, convergence rate, complexity, and robustness.
CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback
Authors: Wei Chen, Weixiao Wan, Shiyue Wang, Peng Sun, Geoffrey Ye Li, Bo Ai
Date: 2022-11-29 00:00:00
Tasks: All, Denoising

To reduce multiuser interference and maximize the spectrum efficiency in orthogonal frequency division duplexing massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) estimated at the user equipment (UE) is required at the base station (BS). This paper presents a novel method for massive MIMO CSI feedback via a one-sided one-for-all deep learning framework. The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep learning (DL) with plug-and-play priors (PPP). Instead of using handcrafted regularizers for the wireless channel responses, the proposed approach, namely CSI-PPPNet, exploits a DL based denoisor in place of the proximal operator of the prior in an alternating optimization scheme. In this way, a DL model trained once for denoising can be repurposed for CSI recovery tasks with arbitrary compression ratio. The one-sided one-for-all framework reduces model storage space, relieves the burden of joint model training and model delivery, and could be applied at UEs with limited device memories and computation power. Extensive experiments over the open indoor and urban macro scenarios show the effectiveness and advantages of the proposed method.
Data-driven port-Hamiltonian structured identification for non-strictly passive systems
Authors: Charles Poussot-Vassal, Denis Matignon, Ghilslain Haine, Pierre Vuillemin
Date: 2022-11-29 00:00:00

In this work, we detail a procedure to construct a reduced order model on the basis of frequency-domain data, that preserves the non-strictly passive property and the port-Hamiltonian structure. The proposed scheme is based on Benner et al. (2020) contribution, which has been adapted (i) to handle non-strictly passive model, and (ii) to handle numerical issues observed when applying the Loewner framework on complex configurations. We validate the proposed scheme on a very complex two-dimensional wave equation, for which the discretized version preserves the port-Hamiltoninan form.
Analysis of constant-Q filterbank based representations for speech emotion recognition
Authors: Premjeet Singh, Shefali Waldekar, Md Sahidullah, Goutam Saha
Date: 2022-11-29 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

This work analyzes the constant-Q filterbank-based time-frequency representations for speech emotion recognition (SER). Constant-Q filterbank provides non-linear spectro-temporal representation with higher frequency resolution at low frequencies. Our investigation reveals how the increased low-frequency resolution benefits SER. The time-domain comparative analysis between short-term mel-frequency spectral coefficients (MFSCs) and constant-Q filterbank-based features, namely constant-Q transform (CQT) and continuous wavelet transform (CWT), reveals that constant-Q representations provide higher time-invariance at low-frequencies. This provides increased robustness against emotion irrelevant temporal variations in pitch, especially for low-arousal emotions. The corresponding frequency-domain analysis over different emotion classes shows better resolution of pitch harmonics in constant-Q-based time-frequency representations than MFSC. These advantages of constant-Q representations are further consolidated by SER performance in the extensive evaluation of features over four publicly available databases with six advanced deep neural network architectures as the back-end classifiers. Our inferences in this study hint toward the suitability and potentiality of constant-Q features for SER.
On Word Error Rate Definitions and their Efficient Computation for Multi-Speaker Speech Recognition Systems
Authors: Thilo von Neumann, Christoph Boeddeker, Keisuke Kinoshita, Marc Delcroix, Reinhold Haeb-Umbach
Date: 2022-11-29 00:00:00
Tasks: speech-recognition, Speech Recognition

We propose a general framework to compute the word error rate (WER) of ASR systems that process recordings containing multiple speakers at their input and that produce multiple output word sequences (MIMO). Such ASR systems are typically required, e.g., for meeting transcription. We provide an efficient implementation based on a dynamic programming search in a multi-dimensional Levenshtein distance tensor under the constraint that a reference utterance must be matched consistently with one hypothesis output. This also results in an efficient implementation of the ORC WER which previously suffered from exponential complexity. We give an overview of commonly used WER definitions for multi-speaker scenarios and show that they are specializations of the above MIMO WER tuned to particular application scenarios. We conclude with a discussion of the pros and cons of the various WER definitions and a recommendation when to use which.
Hiding speaker's sex in speech using zero-evidence speaker representation in an analysis/synthesis pipeline
Authors: Paul-Gauthier Noé, Xiaoxiao Miao, Xin Wang, Junichi Yamagishi, Jean-François Bonastre, Driss Matrouf
Date: 2022-11-29 00:00:00
Tasks: Voice Conversion

The use of modern vocoders in an analysis/synthesis pipeline allows us to investigate high-quality voice conversion that can be used for privacy purposes. Here, we propose to transform the speaker embedding and the pitch in order to hide the sex of the speaker. ECAPA-TDNN-based speaker representation fed into a HiFiGAN vocoder is protected using a neural-discriminant analysis approach, which is consistent with the zero-evidence concept of privacy. This approach significantly reduces the information in speech related to the speaker's sex while preserving speech content and some consistency in the resulting protected voices.
Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition
Authors: Injy Hamed, Amir Hussein, Oumnia Chellah, Shammur Chowdhury, Hamdy Mubarak, Sunayana Sitaram, Nizar Habash, Ahmed Ali
Date: 2022-11-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Benchmarking, speech-recognition, Speech Recognition, Text Normalization, Transliteration

Code-switching poses a number of challenges and opportunities for multilingual automatic speech recognition. In this paper, we focus on the question of robust and fair evaluation metrics. To that end, we develop a reference benchmark data set of code-switching speech recognition hypotheses with human judgments. We define clear guidelines for minimal editing of automatic hypotheses. We validate the guidelines using 4-way inter-annotator agreement. We evaluate a large number of metrics in terms of correlation with human judgments. The metrics we consider vary in terms of representation (orthographic, phonological, semantic), directness (intrinsic vs extrinsic), granularity (e.g. word, character), and similarity computation method. The highest correlation to human judgment is achieved using transliteration followed by text normalization. We release the first corpus for human acceptance of code-switching speech recognition results in dialectal Arabic/English conversation speech.
Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network: An Indian Case Study
Authors: Ayu Parmar, Spanddhana Sara, Ayush Kumar Dwivedi, C. Rajashekar Reddy, Ishan Patwardhan, Sai Dinesh Bijjam, Sachin Chaudhari, K. S. Rajan, Kavita Vemuri
Date: 2022-11-29 00:00:00
Tasks: Data Visualization, Spatial Interpolation

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad out-of this, 43 devices were developed as part of this work and 6 devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor. A thorough analysis of data collected for seven months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM. In addition, event-driven spatio-temporal analysis is done for PM values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.
On Robust Observer Design for System Motion on SE(3) Using Onboard Visual Sensors
Authors: Tong Zhang, Ying Tan, Xiang Chen, Zike Lei
Date: 2022-11-29 00:00:00

Onboard visual sensing has been widely used in the unmanned ground vehicle (UGV) and/or unmanned aerial vehicle (UAV), which can be modeled as dynamic systems on SE(3). The onboard sensing outputs of the dynamic system can usually be applied to derive the relative position between the feature marks and the system, but bearing with explicit geometrical constraint. Such a visual geometrical constraint makes the design of the visual observer on SE(3) very challenging, as it will cause a time-varying or switching visible set due to the varying number of feature marks in this set along different trajectories. Moreover, the possibility of having mis-identified feature marks and modeling uncertainties might result in a divergent estimation error. This paper proposes a new robust observer design method that can accommodate these uncertainties from onboard visual sensing. The key design idea for this observer is to estimate the visible set and identify the mis-identified features from the measurements. Based on the identified uncertainties, a switching strategy is proposed to ensure bounded estimation error for any given trajectory over a fixed time interval. Simulation results are provided to demonstrate the effectiveness of the proposed robust observer.
Distributed Computation of Exact Average Degree and Network Size in Finite Number of Steps under Quantized Communication
Authors: Apostolos I. Rikos, Themistoklis Charalambous, Christoforos N. Hadjicostis, Karl H. Johansson
Date: 2022-11-29 00:00:00
Tasks: Quantization

We consider the problems of computing the average degree and the size of a given network in a distributed fashion under quantized communication. We present two distributed algorithms which rely on quantized operation (i.e., nodes process and transmit quantized messages), and are able to calculate the exact solutions in a finite number of steps. Furthermore, during the operation of our algorithms, each node is able to determine in a distributed manner whether convergence has been achieved and thus terminate its operation. To the best of the authors' knowledge these algorithms are the first to find the exact solutions under quantized communication (i.e., there is no error in the final calculation). Additionally, note that our network size calculation algorithm is the first in the literature which calculates the exact size of a network in a finite number of steps without introducing a final error. This error in other algorithms can be either due to quantization or asymptotic convergence. In our case, no error is introduced since the desired result is calculated in the form of a fraction involving a quantized numerator and a quantized denominator. Finally, we demonstrate the operation of our algorithms and their potential advantages.
Sample Complexity for Evaluating the Robust Linear Observers Performance under Coprime Factors Uncertainty
Authors: Yifei Zhang, Sourav Kumar Ukil, Andrei Sperila, Serban Sabau
Date: 2022-11-29 00:00:00
Tasks: Time Series, Time Series Analysis

This paper addresses the end-to-end sample complexity bound for learning in closed loop the state estimator-based robust H2 controller for an unknown (possibly unstable) Linear Time Invariant (LTI) system, when given a fixed state-feedback gain. We build on the results from Ding et al. (1994) to bridge the gap between the parameterization of all state-estimators and the celebrated Youla parameterization. Refitting the expression of the relevant closed loop allows for the optimal linear observer problem given a fixed state feedback gain to be recast as a convex problem in the Youla parameter. The robust synthesis procedure is performed by considering bounded additive model uncertainty on the coprime factors of the plant, such that a min-max optimization problem is formulated for the robust H2 controller via an observer approach. The closed-loop identification scheme follows Zhang et al. (2021), where the nominal model of the true plant is identified by constructing a Hankel-like matrix from a single time-series of noisy, finite length input-output data by using the ordinary least squares algorithm from Sarkar et al. (2020). Finally, a H-infinity bound on the estimated model error is provided, as the robust synthesis procedure requires bounded additive uncertainty on the coprime factors of the model.