pwc-archive/papers-with-abstracts huggingface dataset:
Modularity based linkage model for neuroevolution
Authors: Yukai Qiao, Marcus Gallagher
Date: 2023-06-02 00:00:00
Tasks: Community Detection, model

Crossover between neural networks is considered disruptive due to the strong functional dependency between connection weights. We propose a modularity-based linkage model at the weight level to preserve functionally dependent communities (building blocks) in neural networks during mixing. A proximity matrix is built by estimating the dependency between weights, then a community detection algorithm maximizing modularity is run on the graph described by such matrix. The resulting communities/groups of parameters are considered to be mutually independent and used as crossover masks in an optimal mixing EA. A variant is tested with an operator that neutralizes the permutation problem of neural networks to a degree. Experiments were performed on 8 and 10-bit parity problems as the intrinsic hierarchical nature of the dependencies in these problems are challenging to learn. The results show that our algorithm finds better, more functionally dependent linkage which leads to more successful crossover and better performance.
Robust Bayesian Inference for Berkson and Classical Measurement Error Models
Authors: Charita Dellaporta, Theodoros Damoulas
Date: 2023-06-02 00:00:00
Tasks: Bayesian Inference

Measurement error occurs when a covariate influencing a response variable is corrupted by noise. This can lead to misleading inference outcomes, particularly in problems where accurately estimating the relationship between covariates and response variables is crucial, such as causal effect estimation. Existing methods for dealing with measurement error often rely on strong assumptions such as knowledge of the error distribution or its variance and availability of replicated measurements of the covariates. We propose a Bayesian Nonparametric Learning framework that is robust to mismeasured covariates, does not require the preceding assumptions, and can incorporate prior beliefs about the error distribution. This approach gives rise to a general framework that is suitable for both Classical and Berkson error models via the appropriate specification of the prior centering measure of a Dirichlet Process (DP). Moreover, it offers flexibility in the choice of loss function depending on the type of regression model. We provide bounds on the generalization error based on the Maximum Mean Discrepancy (MMD) loss which allows for generalization to non-Gaussian distributed errors and nonlinear covariate-response relationships. We showcase the effectiveness of the proposed framework versus prior art in real-world problems containing either Berkson or Classical measurement errors.
Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning
Authors: Cathy Shyr, Boyu Ren, Prasad Patil, Giovanni Parmigiani
Date: 2023-06-01 00:00:00

Estimating heterogeneous treatment effects (HTEs) is crucial for precision medicine. While multiple studies can improve the generalizability of results, leveraging them for estimation is statistically challenging. Existing approaches often assume identical HTEs across studies, but this may be violated due to various sources of between-study heterogeneity, including differences in study design, study populations, and data collection protocols, among others. To this end, we propose a framework for multi-study HTE estimation that accounts for between-study heterogeneity in the nuisance functions and treatment effects. Our approach, the multi-study R-learner, extends the R-learner to obtain principled statistical estimation with machine learning (ML) in the multi-study setting. It involves a data-adaptive objective function that links study-specific treatment effects with nuisance functions through membership probabilities, which enable information to be borrowed across potentially heterogeneous studies. The multi-study R-learner framework can combine data from randomized controlled trials, observational studies, or a combination of both. It's easy to implement and flexible in its ability to incorporate ML for estimating HTEs, nuisance functions, and membership probabilities. In the series estimation framework, we show that the multi-study R-learner is asymptotically normal and more efficient than the R-learner when there is between-study heterogeneity in the propensity score model under homoscedasticity. We illustrate using cancer data that the proposed method performs favorably compared to existing approaches in the presence of between-study heterogeneity.
Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation
Authors: Jianling Wang, Haokai Lu, Sai Zhang, Bart Locanthi, HaoTing Wang, Dylan Greaves, Benjamin Lipshitz, Sriraj Badam, Ed H. Chi, Cristos Goodrow, Su-Lin Wu, Lexi Baugher, Minmin Chen
Date: 2023-06-02 00:00:00

Recommendation system serves as a conduit connecting users to an incredibly large, diverse and ever growing collection of contents. In practice, missing information on fresh (and tail) contents needs to be filled in order for them to be exposed and discovered by their audience. We here share our success stories in building a dedicated fresh content recommendation stack on a large commercial platform. To nominate fresh contents, we built a multi-funnel nomination system that combines (i) a two-tower model with strong generalization power for coverage, and (ii) a sequence model with near real-time update on user feedback for relevance. The multi-funnel setup effectively balances between coverage and relevance. An in-depth study uncovers the relationship between user activity level and their proximity toward fresh contents, which further motivates a contextual multi-funnel setup. Nominated fresh candidates are then scored and ranked by systems considering prediction uncertainty to further bootstrap content with less exposure. We evaluate the benefits of the dedicated fresh content recommendation stack, and the multi-funnel nomination system in particular, through user corpus co-diverted live experiments. We conduct multiple rounds of live experiments on a commercial platform serving billion of users demonstrating efficacy of our proposed methods.
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction
Authors: Canjia Li, Xiaoyang Wang, Dongdong Li, Yiding Liu, Yu Lu, Shuaiqiang Wang, Zhicong Cheng, Simiu Gu, Dawei Yin
Date: 2023-06-02 00:00:00
Tasks: Language Modeling, Language Modelling

Search engine plays a crucial role in satisfying users' diverse information needs. Recently, Pretrained Language Models (PLMs) based text ranking models have achieved huge success in web search. However, many state-of-the-art text ranking approaches only focus on core relevance while ignoring other dimensions that contribute to user satisfaction, e.g., document quality, recency, authority, etc. In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner. In particular, we leverage the capacities of PLMs on both textual and numerical inputs, and apply a multi-field input that modularizes each dimension of user satisfaction as an input field. Overall, SAT-Ranker is an effective, extensible, and data-centric framework that has huge potential for industrial applications. On rigorous offline and online experiments, SAT-Ranker obtains remarkable gains on various evaluation sets targeting different dimensions of user satisfaction. It is now fully deployed online to improve the usability of our search engine.
Influence Maximization with Fairness at Scale (Extended Version)
Authors: Yuting Feng, Ankitkumar Patel, Bogdan Cautis, Hossein Vahabi
Date: 2023-06-02 00:00:00
Tasks: Decision Making, Fairness

In this paper, we revisit the problem of influence maximization with fairness, which aims to select k influential nodes to maximise the spread of information in a network, while ensuring that selected sensitive user attributes are fairly affected, i.e., are proportionally similar between the original network and the affected users. Recent studies on this problem focused only on extremely small networks, hence the challenge remains on how to achieve a scalable solution, applicable to networks with millions or billions of nodes. We propose an approach that is based on learning node representations for fair spread from diffusion cascades, instead of the social connectivity s.t. we can deal with very large graphs. We propose two data-driven approaches: (a) fairness-based participant sampling (FPS), and (b) fairness as context (FAC). Spread related user features, such as the probability of diffusing information to others, are derived from the historical information cascades, using a deep neural network. The extracted features are then used in selecting influencers that maximize the influence spread, while being also fair with respect to the chosen sensitive attributes. In FPS, fairness and cascade length information are considered independently in the decision-making process, while FAC considers these information facets jointly and considers correlations between them. The proposed algorithms are generic and represent the first policy-driven solutions that can be applied to arbitrary sets of sensitive attributes at scale. We evaluate the performance of our solutions on a real-world public dataset (Sina Weibo) and on a hybrid real-synthethic dataset (Digg), which exhibit all the facets that we exploit, namely diffusion network, diffusion traces, and user profiles. These experiments show that our methods outperform the state-the-art solutions in terms of spread, fairness, and scalability.
Système de recommandations basé sur les contraintes pour les simulations de gestion de crise
Authors: Ngoc Luyen Le, Jinfeng Zhong, Elsa Negre, Marie-Hélène Abel
Date: 2023-06-02 00:00:00
Tasks: Management, Recommendation Systems

In the context of the evacuation of populations, some citizens/volunteers may want and be able to participate in the evacuation of populations in difficulty by coming to lend a hand to emergency/evacuation vehicles with their own vehicles. One way of framing these impulses of solidarity would be to be able to list in real-time the citizens/volunteers available with their vehicles (land, sea, air, etc.), to be able to geolocate them according to the risk areas to be evacuated, and adding them to the evacuation/rescue vehicles. Because it is difficult to propose an effective real-time operational system on the field in a real crisis situation, in this work, we propose to add a module for recommending driver/vehicle pairs (with their specificities) to a system of crisis management simulation. To do that, we chose to model and develop an ontology-supported constraint-based recommender system for crisis management simulations.
An OPC UA-based industrial Big Data architecture
Authors: Eduard Hirsch, Simon Hoher, Stefan Huber
Date: 2023-06-02 00:00:00
Tasks: ERP, Time Series, Time Series Analysis

Industry 4.0 factories are complex and data-driven. Data is yielded from many sources, including sensors, PLCs, and other devices, but also from IT, like ERP or CRM systems. We ask how to collect and process this data in a way, such that it includes metadata and can be used for industrial analytics or to derive intelligent support systems. This paper describes a new, query model based approach, which uses a big data architecture to capture data from various sources using OPC UA as a foundation. It buffers and preprocesses the information for the purpose of harmonizing and providing a holistic state space of a factory, as well as mappings to the current state of a production site. That information can be made available to multiple processing sinks, decoupled from the data sources, which enables them to work with the information without interfering with devices of the production, disturbing the network devices they are working in, or influencing the production process negatively. Metadata and connected semantic information is kept throughout the process, allowing to feed algorithms with meaningful data, so that it can be accessed in its entirety to perform time series analysis, machine learning or similar evaluations as well as replaying the data from the buffer for repeatable simulations.
Reducing Popularity Bias in Recommender Systems through AUC-Optimal Negative Sampling
Authors: Bin Liu, Erjia Chen, Bang Wang
Date: 2023-06-02 00:00:00
Tasks: Fairness, Recommendation Systems

Popularity bias is a persistent issue associated with recommendation systems, posing challenges to both fairness and efficiency. Existing literature widely acknowledges that reducing popularity bias often requires sacrificing recommendation accuracy. In this paper, we challenge this commonly held belief. Our analysis under general bias-variance decomposition framework shows that reducing bias can actually lead to improved model performance under certain conditions. To achieve this win-win situation, we propose to intervene in model training through negative sampling thereby modifying model predictions. Specifically, we provide an optimal negative sampling rule that maximizes partial AUC to preserve the accuracy of any given model, while correcting sample information and prior information to reduce popularity bias in a flexible and principled way. Our experimental results on real-world datasets demonstrate the superiority of our approach in improving recommendation performance and reducing popularity bias.
JEPOO: Highly Accurate Joint Estimation of Pitch, Onset and Offset for Music Information Retrieval
Authors: Haojie Wei, Jun Yuan, Rui Zhang, Yueguo Chen, Gang Wang
Date: 2023-06-02 00:00:00
Tasks: Information Retrieval, Melody Extraction, Music Information Retrieval, Retrieval

Melody extraction is a core task in music information retrieval, and the estimation of pitch, onset and offset are key sub-tasks in melody extraction. Existing methods have limited accuracy, and work for only one type of data, either single-pitch or multipitch. In this paper, we propose a highly accurate method for joint estimation of pitch, onset and offset, named JEPOO. We address the challenges of joint learning optimization and handling both single-pitch and multi-pitch data through novel model design and a new optimization technique named Pareto modulated loss with loss weight regularization. This is the first method that can accurately handle both single-pitch and multi-pitch music data, and even a mix of them. A comprehensive experimental study on a wide range of real datasets shows that JEPOO outperforms state-ofthe-art methods by up to 10.6%, 8.3% and 10.3% for the prediction of Pitch, Onset and Offset, respectively, and JEPOO is robust for various types of data and instruments. The ablation study shows the effectiveness of each component of JEPOO.
A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things
Authors: Amar Khelloufi, Huansheng Ning, Abdenacer Naouri, Abdelkarim Ben Sada, Attia Qammar, Abdelkader Khalil, Sahraoui Dhelim, Lingfeng Mao
Date: 2023-06-01 00:00:00
Tasks: Recommendation Systems

The Social Internet of Things (SIoT), is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation systems. As the SIoT grows in scope and complexity, it becomes increasingly important for businesses and individuals, and SIoT objects alike to have reliable sources for products, services, and information that are tailored to their specific needs. Few works have been proposed to provide service recommendations for SIoT environments. However, these efforts have been confined to only focusing on modeling user-item interactions using contextual information, devices' SIoT relationships, and correlation social groups but these schemes do not account for latent semantic item-item structures underlying the sparse multi-modal contents in SIoT environment. In this paper, we propose a latent-based SIoT recommendation system that learns item-item structures and aggregates multiple modalities to obtain latent item graphs which are then used in graph convolutions to inject high-order affinities into item representations. Experiments showed that the proposed recommendation system outperformed state-of-the-art SIoT recommendation methods and validated its efficacy at mining latent relationships from multi-modal features.
Cross-modal information fusion for voice spoofing detection
Authors: Junxiao Xue, Hao Zhou, Huawei Song, Bin Wu, Lei Shi
Date: 2023-02-01 00:00:00
Proceeding: journal 2023 2
Tasks: Automatic Speech Recognition, fake voice detection, Speaker Verification, Speech Synthesis, Voice Anti-spoofing

In recent years, speaker verification systems have been used in many production scenarios. Unfortunately, they are still very vulnerable to different kinds of spoofing attacks, such as speech synthesis attacks, replay attacks, etc. Researchers have proposed many methods to defend against these attacks, but in the existing methods, researchers just focus on speech features. In recent studies, researchers have found that speech contains a large amount of face information. In fact, we can determine the speaker's gender, age, mouth shape, and other information by voice. These information can help us distinguish spoofing attacks. Inspired by this phenomenon, we propose a generalized framework named GACMNet. To cope with different attack scenarios, we instantiated two different models. Our framework is mainly divided into data pre-processing phase, feature extraction phase, feature fusion phase, and classification phase. Specifically, our framework consists of two branches. On the one hand, we extract face features in speech by a convolutional neural network. On the other hand, we use a densely connected network to extract speech features. For the more, we designed a global attention-based information fusion mechanism to distinguish the importance of each part of the features. Our solution was proven to be effective in two large scenarios. Compared to the existing methods, our model improves the tandem decision cost function (t-DCF) and equal error rate (EER) scores by 9% and 11% in the logical access scenario, respectively,  our model improves the EER score by 10% in the physical access scenario.
Computer Vision System for Eye Gaze Tracking
Authors: Puja P. Sorate, Gyankamal J. Chhajed
Date: 2017-06-03 00:00:00
Proceeding: International Journal of Modern Computer Science (IJMCS) 2017 6
Tasks: Gaze Estimation

: Eye gaze tracking is a technique use for checking the usability problems in the Human Computer Interaction (HCI). Initially they are
present tracking technology and key elements. Eye gaze tracking technique is based on the behavior of the user when they are looking. It can use
for different kinds of methods i.e. “electro-oculography(EOG), Sceleral Search Coils, infrared oculography(IOG), video oculography(VOG),
different models, probable approaches i.e. “shape based approach, appearance based methods, 2D and 3D models based approach. The eye
tracking applications likes human computer interaction, brain computer interaction, assistive technology, e-learning, psychology investigation,
pilot training assistance, virtual and augmented reality and so on. The Proposed system uses Head pose detection, eye region detection, eye
feature detection, eye vector calibration, Gaze Estimation and eye gaze tracking modules. This system is to design and implement eye gaze
tracking system in desktop environment.
Human skeletons and change detection for efficient violence detection in surveillance videos
Authors: Guillermo Garcia-Cobo, Juan C. San Miguel
Date: 2023-05-20 00:00:00
Proceeding: Computer Vision and Image Understanding 2023 5
Tasks: Activity Recognition, Change Detection, Violence and Weaponized Violence Detection

In our constantly monitored world, surveillance cameras play a crucial role in curbing crime and violence in public spaces by serving as a deterrent. To enhance their effectiveness, there is a growing need for automated tools that can detect crimes in real time. In this paper, we propose a novel deep learning architecture that accurately and efficiently detects violent crimes in surveillance videos. We rely on what we believe are the most essential pieces of information to detect violence, namely: human bodies and their interaction. To this end, we employ human pose extractors and change detectors as the input of our proposal. Subsequently, we combine them using a novel method, which relies on additions instead of multiplications to guarantee the transmission of information even when one of the inputs provides a zero-valued signal; outperforming other combination alternatives of the literature. Finally, to account for both spatial and temporal information, we use a convolutional alternative of the standard LSTM, the ConvLSTM. The experiments performed on several benchmark datasets demonstrate the efficacy and efficiency of our proposal, achieving state-of-the-art results with much fewer trainable parameters. We release the code to replicate the proposed architecture at https://github.com/atmguille/Violence-Detection-With-Human-Skeletons
Speech to text and text to speech recognition systems-Areview
Authors: Ayushi Trivedi, Navya Pant, Pinal Shah, Simran Sonik and Supriya Agrawal
Date: 2018-03-17 00:00:00
Proceeding: 2018-03-02 2018 3
Tasks: speech-recognition, Speech Recognition, Speech-to-Text, text-to-speech, Text to Speech

In present industry, communication is the key element to progress. Passing on information, to the
right person, and in the right manner is very important, not just on a corporate level, but also on a personal
level. The world is moving towards digitization, so are the means of communication. Phone calls, emails, text
messages etc. have become an integral part of message conveyance in this tech-savvy world. In order to serve
the purpose of effective communication between two parties without hindrances, many applications have come to
picture, which acts as a mediator and help in effectively carrying messages in form of text, or speech signals over
miles of networks. Most of these applications find the use of functions such as articulatory and acoustic-based
speech recognition, conversion from speech signals to text, and from text to synthetic speech signals, language
translation amongst various others. In this review paper, we’ll be observing different techniques and algorithms
that are applied to achieve the mentioned functionalities.