pwc-archive/papers-with-abstracts huggingface dataset:
Latent class analysis with weighted responses
Authors: Huan Qing
Date: 2023-10-17 00:00:00

The latent class model has been proposed as a powerful tool for cluster analysis of categorical data in various fields such as social, psychological, behavioral, and biological sciences. However, one important limitation of the latent class model is that it is only suitable for data with binary responses, making it fail to model real-world data with continuous or negative responses. In many applications, ignoring the weights throws out a lot of potentially valuable information contained in the weights. To address this limitation, we propose a novel generative model, the weighted latent class model (WLCM). Our model allows data's response matrix to be generated from an arbitrary distribution with a latent class structure. In comparison to the latent class model, our WLCM is more realistic and more general. To our knowledge, our WLCM is the first model for latent class analysis with weighted responses. We investigate the identifiability of the model and propose an efficient algorithm for estimating the latent classes and other model parameters. We show that the proposed algorithm enjoys consistent estimation. The performance of the proposed algorithm is investigated using both computer-generated and real-world weighted response data.
On Coherence-based Predictors for Dense Query Performance Prediction
Authors: Maria Vlachou, Craig Macdonald
Date: 2023-10-17 00:00:00
Tasks: Prediction, Retrieval

Query Performance Prediction (QPP) estimates the effectiveness of a search engine's results in response to a query without relevance judgments. Traditionally, post-retrieval predictors have focused upon either the distribution of the retrieval scores, or the coherence of the top-ranked documents using traditional bag-of-words index representations. More recently, BERT-based models using dense embedded document representations have been used to create new predictors, but mostly applied to predict the performance of rankings created by BM25. Instead, we aim to predict the effectiveness of rankings created by single-representation dense retrieval models (ANCE & TCT-ColBERT). Therefore, we propose a number of variants of existing unsupervised coherence-based predictors that employ neural embedding representations. In our experiments on the TREC Deep Learning Track datasets, we demonstrate improved accuracy upon dense retrieval (up to 92% compared to sparse variants for TCT-ColBERT and 188% for ANCE). Going deeper, we select the most representative and best performing predictors to study the importance of differences among predictors and query types on query performance. Using existing distribution-based evaluation QPP measures and a particular type of linear mixed models, we find that query types further significantly influence query performance (and are up to 35% responsible for the unstable performance of QPP predictors), and that this sensitivity is unique to dense retrieval models. Our approach introduces a new setting for obtaining richer information on query differences in dense QPP that can explain potential unstable performance of existing predictors and outlines the unique characteristics of different query types on dense retrieval models.
Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation
Authors: Daniele Malitesta, Claudio Pomo, Tommaso Di Noia
Date: 2023-10-17 00:00:00
Tasks: Graph Learning, Recommendation Systems

Graph neural networks (GNNs) have gained prominence in recommendation systems in recent years. By representing the user-item matrix as a bipartite and undirected graph, GNNs have demonstrated their potential to capture short- and long-distance user-item interactions, thereby learning more accurate preference patterns than traditional recommendation approaches. In contrast to previous tutorials on the same topic, this tutorial aims to present and examine three key aspects that characterize GNNs for recommendation: (i) the reproducibility of state-of-the-art approaches, (ii) the potential impact of graph topological characteristics on the performance of these models, and (iii) strategies for learning node representations when training features from scratch or utilizing pre-trained embeddings as additional item information (e.g., multimodal features). The goal is to provide three novel theoretical and practical perspectives on the field, currently subject to debate in graph learning but long been overlooked in the context of recommendation systems.
AdsCVLR: Commercial Visual-Linguistic Representation Modeling in Sponsored Search
Authors: Yongjie Zhu, Chunhui Han, Yuefeng Zhan, Bochen Pang, Zhaoju Li, Hao Sun, Si Li, Boxin Shi, Nan Duan, Ruofei Zhang, Liangjie Zhang, Weiwei Deng, Qi Zhang
Date: 2022-10-10 00:00:00
Proceeding: Conference 2022 10
Tasks: Contrastive Learning, Image-text matching

Sponsored search advertisements (ads) appear next to search results when consumers look for products and services on search engines. As the fundamental basis of search ads, relevance modeling has attracted increasing attention due to the significant research challenges and tremendous practical value. In this paper, we address the problem of multi-modal modeling in sponsored search, which models the relevance between user query and commercial ads with multi-modal structured information. To solve this problem, we propose a transformer architecture with Ads data on Commercial Visual-Linguistic Representation (AdsCVLR) with contrastive learning that naturally extends the transformer encoder with the complementary multi-modal inputs, serving as a strong aggregator of image-text features. We also make a public advertising dataset, which includes 480K labeled query-ad pairwise data with structured information of image, title, seller, description, and so on. Empirically, we evaluate the AdsCVLR model over the large industry dataset, and the experimental results of online/offline tests show the superiority of our method.
The effect of retirement on healthcare utilization: Evidence from China
Authors: Yi Zhang, Martin Sam, Arthur Van Soest
Date: 2018-11-01 00:00:00
Proceeding: Journal of Health Economics 2018 11

We examine the effect of retirement on healthcare utilization in China using longitudinal data. We use a nonparametric fuzzy regression discontinuity design, exploiting the statutory retirement age in urban China as a source of exogenous variation in retirement. In contrast to previous results for developed countries, we find that in China retirement increases healthcare utilization. This increase can be attributed to deteriorating health and in particular to the reduced opportunity cost of time after retirement. For the sample as a whole, income is not a dominating mechanism. People with low education, however, are more likely to forego recommended inpatient care after retirement.
Large language models can replicate cross-cultural differences in personality
Authors: Paweł Niszczota, Mateusz Janczak, Michał Misiak
Date: 2023-10-12 00:00:00
Tasks: Language Modeling, Language Modelling

We use a large-scale experiment (N=8000) to determine whether GPT-4 can replicate cross-cultural differences in the Big Five, measured using the Ten-Item Personality Inventory. We used the US and South Korea as the cultural pair, given that prior research suggests substantial personality differences between people from these two countries. We manipulated the target of the simulation (US vs. Korean), the language of the inventory (English vs. Korean), and the language model (GPT-4 vs. GPT-3.5). Our results show that GPT-4 replicated the cross-cultural differences for each factor. However, mean ratings had an upward bias and exhibited lower variation than in the human samples, as well as lower structural validity. We provide preliminary evidence that LLMs can aid cross-cultural researchers and practitioners.
Understanding and managing blockchain protocol risks
Authors: Alex Nathan, Dimosthenis Kaponis, Saul Lustgarten
Date: 2023-10-16 00:00:00
Tasks: Management

This paper addresses the issue of blockchain protocol risks, a foundational category of risks affecting Distributed Ledger Technology (DLT) which underpins digital assets, smart contracts, and decentralised applications. It presents a comprehensive risk management framework developed in collaboration with financial institutions, blockchain development teams and regulators that applies a traditional risk management taxonomy to address certain overlooked blockchain protocol risks. The approach offers a structured way to identify, measure, monitor and report blockchain protocol risks. The paper provides real-world use cases to demonstrate the practicality and implementation of the proposed framework. The findings of this work contribute to the evolving understanding of blockchain protocol risks and provide valuable insights on how these risks affect the adoption of DLT by financial institutions.
Exploring Spectrum Sensing Techniques in Cognitive Radio Systems Using Time-Domain Symbol Cross-correlation
Authors: Ahmed Temtam, Dimitrie Popescu
Date: 2023-10-16 00:00:00

In order to enable spectrum sharing, spectrum sensing plays a crucial role in wireless communication. The challenges in wireless spectrum require collaboration among stakeholders to devise innovative solutions. This research explores the use of a Cognitive Radio (CR) system that employs a Time-Domain Symbol Cross-correlation (TDSC) based spectrum sensing algorithm. WiMAX and LTE standards are utilized as case studies to demonstrate the efficacy of the TDSC method. The study presents theoretical and simulation results and also suggests future research to investigate the performance of the TDSC method in WiMAX and LTE systems. Additionally, this study compares the spectrum sensing capabilities of WiMAX and LTE.
Generation or Replication: Auscultating Audio Latent Diffusion Models
Authors: Dimitrios Bralios, Gordon Wichern, François G. Germain, Zexu Pan, Sameer Khurana, Chiori Hori, Jonathan Le Roux
Date: 2023-10-16 00:00:00
Tasks: AudioCaps, Memorization, Retrieval

The introduction of audio latent diffusion models possessing the ability to generate realistic sound clips on demand from a text description has the potential to revolutionize how we work with audio. In this work, we make an initial attempt at understanding the inner workings of audio latent diffusion models by investigating how their audio outputs compare with the training data, similar to how a doctor auscultates a patient by listening to the sounds of their organs. Using text-to-audio latent diffusion models trained on the AudioCaps dataset, we systematically analyze memorization behavior as a function of training set size. We also evaluate different retrieval metrics for evidence of training data memorization, finding the similarity between mel spectrograms to be more robust in detecting matches than learned embedding vectors. In the process of analyzing memorization in audio latent diffusion models, we also discover a large amount of duplicated audio clips within the AudioCaps database.
Advancing Audio Emotion and Intent Recognition with Large Pre-Trained Models and Bayesian Inference
Authors: Dejan Porjazovski, Yaroslav Getman, Tamás Grósz, Mikko Kurimo
Date: 2023-10-16 00:00:00
Tasks: Bayesian Inference, Emotion Recognition, Intent Recognition

Large pre-trained models are essential in paralinguistic systems, demonstrating effectiveness in tasks like emotion recognition and stuttering detection. In this paper, we employ large pre-trained models for the ACM Multimedia Computational Paralinguistics Challenge, addressing the Requests and Emotion Share tasks. We explore audio-only and hybrid solutions leveraging audio and text modalities. Our empirical results consistently show the superiority of the hybrid approaches over the audio-only models. Moreover, we introduce a Bayesian layer as an alternative to the standard linear output layer. The multimodal fusion approach achieves an 85.4% UAR on HC-Requests and 60.2% on HC-Complaints. The ensemble model for the Emotion Share task yields the best rho value of .614. The Bayesian wav2vec2 approach, explored in this study, allows us to easily build ensembles, at the cost of fine-tuning only one model. Moreover, we can have usable confidence values instead of the usual overconfident posterior probabilities.
Real-time Speech Enhancement and Separation with a Unified Deep Neural Network for Single/Dual Talker Scenarios
Authors: Kashyap Patel, Anton Kovalyov, Issa Panahi
Date: 2023-10-16 00:00:00
Tasks: Speech Enhancement, Speech Separation

This paper introduces a practical approach for leveraging a real-time deep learning model to alternate between speech enhancement and joint speech enhancement and separation depending on whether the input mixture contains one or two active speakers. Scale-invariant signal-to-distortion ratio (SI-SDR) has shown to be a highly effective training measure in time-domain speech separation. However, the SI-SDR metric is ill-defined for zero-energy target signals, which is a problem when training a speech separation model using utterances with varying numbers of talkers. Unlike existing solutions that focus on modifying the loss function to accommodate zero-energy target signals, the proposed approach circumvents this problem by training the model to extract speech on both its output channels regardless if the input is a single or dual-talker mixture. A lightweight speaker overlap detection (SOD) module is also introduced to differentiate between single and dual-talker segments in real-time. The proposed module takes advantage of the new formulation by operating directly on the separated masks, given by the separation model, instead of the original mixture, thus effectively simplifying the detection task. Experimental results show that the proposed training approach outperforms existing solutions, and the SOD module exhibits high accuracy.
A cry for help: Early detection of brain injury in newborns
Authors: Charles C. Onu, Samantha Latremouille, Arsenii Gorin, Junhao Wang, Innocent Udeogu, Uchenna Ekwochi, Peter O. Ubuane, Omolara A. Kehinde, Muhammad A. Salisu, Datonye Briggs, Yoshua Bengio, Doina Precup
Date: 2023-10-12 00:00:00
Tasks: Specificity

Since the 1960s, neonatal clinicians have known that newborns suffering from certain neurological conditions exhibit altered crying patterns such as the high-pitched cry in birth asphyxia. Despite an annual burden of over 1.5 million infant deaths and disabilities, early detection of neonatal brain injuries due to asphyxia remains a challenge, particularly in developing countries where the majority of births are not attended by a trained physician. Here we report on the first inter-continental clinical study to demonstrate that neonatal brain injury can be reliably determined from recorded infant cries using an AI algorithm we call Roseline. Previous and recent work has been limited by the lack of a large, high-quality clinical database of cry recordings, constraining the application of state-of-the-art machine learning. We develop a new training methodology for audio-based pathology detection models and evaluate this system on a large database of newborn cry sounds acquired from geographically diverse settings -- 5 hospitals across 3 continents. Our system extracts interpretable acoustic biomarkers that support clinical decisions and is able to accurately detect neurological injury from newborns' cries with an AUC of 92.5% (88.7% sensitivity at 80% specificity). Cry-based neurological monitoring opens the door for low-cost, easy-to-use, non-invasive and contact-free screening of at-risk babies, especially when integrated into simple devices like smartphones or neonatal ICU monitors. This would provide a reliable tool where there are no alternatives, but also curtail the need to regularly exert newborns to physically-exhausting or radiation-exposing assessments such as brain CT scans. This work sets the stage for embracing the infant cry as a vital sign and indicates the potential of AI-driven sound monitoring for the future of affordable healthcare.
End-to-end Online Speaker Diarization with Target Speaker Tracking
Authors: Weiqing Wang, Ming Li
Date: 2023-10-12 00:00:00
Tasks: Action Detection, Activity Detection, Clustering, speaker-diarization, Speaker Diarization

This paper proposes an online target speaker voice activity detection system for speaker diarization tasks, which does not require a priori knowledge from the clustering-based diarization system to obtain the target speaker embeddings. By adapting the conventional target speaker voice activity detection for real-time operation, this framework can identify speaker activities using self-generated embeddings, resulting in consistent performance without permutation inconsistencies in the inference phase. During the inference process, we employ a front-end model to extract the frame-level speaker embeddings for each coming block of a signal. Next, we predict the detection state of each speaker based on these frame-level speaker embeddings and the previously estimated target speaker embedding. Then, the target speaker embeddings are updated by aggregating these frame-level speaker embeddings according to the predictions in the current block. Our model predicts the results for each block and updates the target speakers' embeddings until reaching the end of the signal. Experimental results show that the proposed method outperforms the offline clustering-based diarization system on the DIHARD III and AliMeeting datasets. The proposed method is further extended to multi-channel data, which achieves similar performance with the state-of-the-art offline diarization systems.
Crowdsourced and Automatic Speech Prominence Estimation
Authors: Max Morrison, Pranav Pawar, Nathan Pruyne, Jennifer Cole, Bryan Pardo
Date: 2023-10-12 00:00:00
Tasks: Emotion Recognition, text-to-speech, Text to Speech

The prominence of a spoken word is the degree to which an average native listener perceives the word as salient or emphasized relative to its context. Speech prominence estimation is the process of assigning a numeric value to the prominence of each word in an utterance. These prominence labels are useful for linguistic analysis, as well as training automated systems to perform emphasis-controlled text-to-speech or emotion recognition. Manually annotating prominence is time-consuming and expensive, which motivates the development of automated methods for speech prominence estimation. However, developing such an automated system using machine-learning methods requires human-annotated training data. Using our system for acquiring such human annotations, we collect and open-source crowdsourced annotations of a portion of the LibriTTS dataset. We use these annotations as ground truth to train a neural speech prominence estimator that generalizes to unseen speakers, datasets, and speaking styles. We investigate design decisions for neural prominence estimation as well as how neural prominence estimation improves as a function of two key factors of annotation cost: dataset size and the number of annotations per utterance.
A Single Speech Enhancement Model Unifying Dereverberation, Denoising, Speaker Counting, Separation, and Extraction
Authors: Kohei Saijo, Wangyou Zhang, Zhong-Qiu Wang, Shinji Watanabe, Tetsunori Kobayashi, Tetsuji Ogawa
Date: 2023-10-12 00:00:00
Tasks: Denoising, Speech Enhancement, Speech Separation, Target Speaker Extraction

We propose a multi-task universal speech enhancement (MUSE) model that can perform five speech enhancement (SE) tasks: dereverberation, denoising, speech separation (SS), target speaker extraction (TSE), and speaker counting. This is achieved by integrating two modules into an SE model: 1) an internal separation module that does both speaker counting and separation; and 2) a TSE module that extracts the target speech from the internal separation outputs using target speaker cues. The model is trained to perform TSE if the target speaker cue is given and SS otherwise. By training the model to remove noise and reverberation, we allow the model to tackle the five tasks mentioned above with a single model, which has not been accomplished yet. Evaluation results demonstrate that the proposed MUSE model can successfully handle multiple tasks with a single model.
Constrained FoV Radiated Power as a Figure of Merit of Phased Arrays
Authors: Alejandro Antón Ruiz, Samar Hosseinzadegan, John Kvarnstrand, Klas Arvidsson, Andrés Alayón Glazunov
Date: 2023-10-17 00:00:00

In this paper, we propose quantifying the radiated power of phased arrays or, in general, directive antennas, by the Constrained-View Radiated Power (CVRP). The constrained view shall be interpreted here as the Field-of-View (FoV) of an antenna that defines a region in space where focusing the radiated power is highly desired. In the limiting cases, we have that CVRP equals the Total Radiated Power (TRP) when the FoV covers the whole sphere, while, if the FoV reduces to a single point in space, the CVRP equals the Equivalent Isotropic Radiated Power (EIRP). We further present an analysis based on measured radiation patterns of a 16-element, linearly polarized, millimeter-Wave (mmWave), planar phased array antenna operating at 28 GHz. We compare the results to two ideal planar array antennas with the same number of Huygens and cosine elements. The evaluated figure of merit is computed for different scanning angles, as well as for different malfunctions of antenna elements, both for the real and simulated arrays. The results show that the introduced figure of merit could be potentially used for the detection of malfunctioning elements in antenna arrays as well as to characterize the impact of scan loss. Furthermore, CVRP is useful to straightforwardly and significantly characterize the performance of a directive antenna in terms of the power radiated towards a specific region in space.
Secure Motion-Copying via Homomorphic Encryption
Authors: Haruki Takanashi, Kaoru Teranishi, Kiminao Kogiso
Date: 2023-10-17 00:00:00

This study aims to develop an encrypted motion-copying system using homomorphic encryption for secure motion preservation and reproduction. A novel concept of encrypted motion-copying systems is introduced, realizing the preservation, edition, and reproduction of the motion over encrypted data. The developed motion-copying system uses the conventional encrypted four-channel bilateral control system with robotic arms to save the leader's motion by a human operator in the ciphertext in a memory. The follower's control system reproduces the motion using the encrypted data loaded from the secure memory. Additionally, the developed system enables us to directly edit the motion data preserved in the memory without decryption using homomorphic operation. Finally, this study demonstrates the effectiveness of the developed encrypted motion-copying system in free motion, object contact, and spatial scaling scenarios.