pwc-archive/papers-with-abstracts huggingface dataset:
Learning with Geometry: Including Riemannian Geometric Features in Coefficient of Pressure Prediction on Aircraft Wings
Authors: Liwei Hu, Wenyong Wang, Yu Xiang, Stefan Sommer
Date: 2023-12-22 00:00:00
Tasks: 3D geometry, Deep Attention

We propose to incorporate Riemannian geometric features from the geometry of aircraft wing surfaces in the prediction of coefficient of pressure (CP) on the aircraft wing. Contrary to existing approaches that treat the wing surface as a flat object, we represent the wing as a piecewise smooth manifold and calculate a set of Riemannian geometric features (Riemannian metric, connection, and curvature) over points of the wing. Combining these features in neighborhoods of points on the wing with coordinates and flight conditions gives inputs to a deep learning model that predicts CP distributions. Experimental results show that the method with incorporation of Riemannian geometric features, compared to state-of-the-art Deep Attention Network (DAN), reduces the predicted mean square error (MSE) of CP by an average of 15.00% for the DLR-F11 aircraft test set.
Transduce: learning transduction grammars for string transformation
Authors: Francis Frydman, Philippe Mangion
Date: 2023-12-14 00:00:00
Tasks: Inductive Bias

The synthesis of string transformation programs from input-output examples utilizes various techniques, all based on an inductive bias that comprises a restricted set of basic operators to be combined. A new algorithm, Transduce, is proposed, which is founded on the construction of abstract transduction grammars and their generalization. We experimentally demonstrate that Transduce can learn positional transformations efficiently from one or two positive examples without inductive bias, achieving a success rate higher than the current state of the art.
A Smoothing Algorithm for l1 Support Vector Machines
Authors: Ibrahim Emirahmetoglu, Jeffrey Hajewski, Suely Oliveira, David E. Stewart
Date: 2023-12-17 00:00:00

A smoothing algorithm is presented for solving the soft-margin Support Vector Machine (SVM) optimization problem with an $\ell^{1}$ penalty. This algorithm is designed to require a modest number of passes over the data, which is an important measure of its cost for very large datasets. The algorithm uses smoothing for the hinge-loss function, and an active set approach for the $\ell^{1}$ penalty. The smoothing parameter $\alpha$ is initially large, but typically halved when the smoothed problem is solved to sufficient accuracy. Convergence theory is presented that shows $\mathcal{O}(1+\log(1+\log_+(1/\alpha)))$ guarded Newton steps for each value of $\alpha$ except for asymptotic bands $\alpha=\Theta(1)$ and $\alpha=\Theta(1/N)$, with only one Newton step provided $\eta\alpha\gg1/N$, where $N$ is the number of data points and the stopping criterion that the predicted reduction is less than $\eta\alpha$. The experimental results show that our algorithm is capable of strong test accuracy without sacrificing training speed.
Voxceleb-ESP: preliminary experiments detecting Spanish celebrities from their voices
Authors: Beltrán Labrador, Manuel Otero-Gonzalez, Alicia Lozano-Diez, Daniel Ramos, Doroteo T. Toledano, Joaquin Gonzalez-Rodriguez
Date: 2023-12-20 00:00:00
Tasks: Speaker Identification, Speaker Recognition

This paper presents VoxCeleb-ESP, a collection of pointers and timestamps to YouTube videos facilitating the creation of a novel speaker recognition dataset. VoxCeleb-ESP captures real-world scenarios, incorporating diverse speaking styles, noises, and channel distortions. It includes 160 Spanish celebrities spanning various categories, ensuring a representative distribution across age groups and geographic regions in Spain. We provide two speaker trial lists for speaker identification tasks, each of them with same-video or different-video target trials respectively, accompanied by a cross-lingual evaluation of ResNet pretrained models. Preliminary speaker identification results suggest that the complexity of the detection task in VoxCeleb-ESP is equivalent to that of the original and much larger VoxCeleb in English. VoxCeleb-ESP contributes to the expansion of speaker recognition benchmarks with a comprehensive and diverse dataset for the Spanish language.
Compression with Bayesian Implicit Neural Representations
Authors: Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato
Date: 2023-05-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Audio Compression, Quantization

Many common types of data can be represented as functions that map coordinates to signal values, such as pixel locations to RGB values in the case of an image. Based on this view, data can be compressed by overfitting a compact neural network to its functional representation and then encoding the network weights. However, most current solutions for this are inefficient, as quantization to low-bit precision substantially degrades the reconstruction quality. To address this issue, we propose overfitting variational Bayesian neural networks to the data and compressing an approximate posterior weight sample using relative entropy coding instead of quantizing and entropy coding it. This strategy enables direct optimization of the rate-distortion performance by minimizing the $\beta$-ELBO, and target different rate-distortion trade-offs for a given network architecture by adjusting $\beta$. Moreover, we introduce an iterative algorithm for learning prior weight distributions and employ a progressive refinement process for the variational posterior that significantly enhances performance. Experiments show that our method achieves strong performance on image and audio compression while retaining simplicity.
Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport
Authors: Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
Date: 2023-05-24 00:00:00
Proceeding: NeurIPS 2023 11

Optimal Transport (OT) problem investigates a transport map that bridges two distributions while minimizing a given cost function. In this regard, OT between tractable prior distribution and data has been utilized for generative modeling tasks. However, OT-based methods are susceptible to outliers and face optimization challenges during training. In this paper, we propose a novel generative model based on the semi-dual formulation of Unbalanced Optimal Transport (UOT). Unlike OT, UOT relaxes the hard constraint on distribution matching. This approach provides better robustness against outliers, stability during training, and faster convergence. We validate these properties empirically through experiments. Moreover, we study the theoretical upper-bound of divergence between distributions in UOT. Our model outperforms existing OT-based generative models, achieving FID scores of 2.97 on CIFAR-10 and 6.36 on CelebA-HQ-256. The code is available at \url{https://github.com/Jae-Moo/UOTM}.
A Model-Based Framework for Assessing the Physiologic Structure of Electrodermal Activity
Authors: Sandya Subramanian, Patrick L. Purdon, Riccardo Barbieri, Emery N. Brown
Date: 2021-04-06 00:00:00
Proceeding: IEEE Transactions on Biomedical Engineering 2021 4

Objective: We present a statistical model for extracting physiologic characteristics from electrodermal activity (EDA) data in observational settings. Methods: We based our model on the integrate-and-fire physiology of sweat gland bursts, which predicts inverse Gaussian (IG) inter-pulse interval structure. At the core of our model-based paradigm is a subject-specific amplitude threshold selection process for EDA pulses based on the statistical properties of four right-skewed models including the IG. By performing a sensitivity analysis across thresholds and fitting all four models, we selected for IG-like structure and verified the pulse selection with a goodness-of-fit analysis, maximizing capture of physiology at the time scale of EDA responses. Results: We tested the model-based paradigm on simulated EDA time series and data from two different experimental cohorts recorded during different experimental conditions, using different equipment. In both the simulated and experimental data, our model-based method robustly recovered pulses that captured the IG-like structure predicted by physiology, despite large differences in noise level. In contrast, established EDA analysis tools, which attempted to estimate neural activity from slower EDA responses, did not provide physiological validation and were susceptible to noise. Conclusion: We present a computationally efficient, statistically rigorous, and physiology-informed paradigm for pulse selection from EDA data that is robust across individuals and experimental conditions, yet adaptable to varying noise level. Significance: The robustness of the model-based paradigm and its physiological basis provide empirical support for the use of EDA as a clinical marker for sympathetic activity in conditions such as pain, anxiety, depression, and sleep states.
OCEAN-AI framework with EmoFormer cross-hemiface attention approach for personality traits assessment
Authors: Elena Ryumina, Maxim Markitantov, Dmitry Ryumin, Alexey Karpov
Date: 2023-11-06 00:00:00
Proceeding: Expert Systems with Applications 2023 11
Tasks: Personality Trait Recognition, Personality Trait Recognition by Face

Psychological and neurological studies earlier suggested that a personality type can be determined by the whole face as well as by its sides. This article discusses novel research using deep neural networks that address the features of both sides of the face (hemifaces) to assess the human’s Big Five personality traits (PT). For this, we have developed a real-time approach called EmoFormer with cross-hemiface attention. The novelty of the presented approach lies in the confirmation that each hemiface exhibits high predictive capabilities in terms of human’s PT distinction. Our approach is based on a novel mid-level emotional feature extractor for each hemiface and a cross-hemiface attention fusion strategy for hemiface feature aggregation. The consequent fusion of both hemifaces has outperformed the use of the whole face by the relative value of 3.6% in terms of Concordance Correlation Coefficient (0.634 vs. 0.612) on the ChaLearn First Impressions V2 corpus. The proposed approach has also outperformed all the existing state-of-the-art approaches for PT assessment based on the face modality. We have also analyzed the “best hemiface”, the one that predicts PT more accurately in terms of demographic characteristics (gender, ethnicity, and age). We have found that the best hemiface for two of the five PT (Openness to experience and Non-Neuroticism) is different depending on demographic characteristics. For the other three traits, the right hemiface is dominant for Extraversion, while the left one is more indicative of Conscientiousness and Agreeableness. These findings support previous psychological and neurological research. Besides, we provide an open-source framework referred to as OCEAN-AI that can be seamlessly integrated into expert systems with practical applications in various domains including healthcare, education, and human resources.
Personality Traits and Job Candidate Screening via Analyzing Facial Videos
Authors: S. E. Bekhouche, F. Dornaika, A. Ouafi and A. Taleb-Ahmed
Date: 2017-07-21 00:00:00
Proceeding: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2017 7
Tasks: Attribute, GPR, Personality Trait Recognition by Face

In this paper, we propose a novel approach for estimating the Big Five personality traits and the job candidate screening attribute through facial videos. At running time, the proposed system feeds the Pyramid Multi-Level (PML) texture features extracted from the whole video sequence to 5 Support Vector Regressors in order to estimate the personality traits. These estimated five scores are then considered as new input features to the interview score regressor. The latter is given by a Gaussian Process Regression (GPR). The experimental results on ChaLearn LAP APA2016 dataset achieve good performance. Furthermore, they demonstrate that the computational cost of both the training and the testing of the proposed framework are very competitive in terms of accuracy and computational cost.
Multimodal analysis of personality traits on videos of self-presentation and induced behavior
Authors: Dersu Giritlioğlu, Burak Mandira, Selim Fırat Yılmaz, Can Ufuk Ertenli, Berhan Faruk Akgür, Merve Kınıklıoğlu, Aslı Gül Kurt, Emre Mutlu, Şeref Can Gürel, Hamdi Dibeklioğlu
Date: 2020-11-02 00:00:00
Proceeding: Journal on Multimodal User Interfaces 2020 11
Tasks: Personality Trait Recognition by Face

Personality analysis is an important area of research in several fields, including psychology, psychiatry, and neuroscience. With the recent dramatic improvements in machine learning, it has also become a popular research area in computer science. While the current computational methods are able to interpret behavioral cues (e.g., facial expressions, gesture, and voice) to estimate the level of (apparent) personality traits, accessible assessment tools are still substandard for practical use, not to mention the need for fast and accurate methods for such analyses. In this study, we present multimodal deep architectures to estimate the Big Five personality traits from (temporal) audio-visual cues and transcribed speech. Furthermore, for a detailed analysis of personality traits, we have collected a new audio-visual dataset, namely: Self-presentation and Induced Behavior Archive for Personality Analysis (SIAP). In contrast to the available datasets, SIAP introduces recordings of induced behavior in addition to self-presentation (speech) videos. With thorough experiments on SIAP and ChaLearn LAP First Impressions datasets, we systematically assess the reliability of different behavioral modalities and their combined use. Furthermore, we investigate the characteristics and discriminative power of induced behavior for personality analysis, showing that the induced behavior indeed includes signs of personality traits.
Cr-net: A deep classification-regression network for multimodal apparent personality analysis
Authors: Yunan Li, Jun Wan, Qiguang Miao, Sergio Escalera, Huijuan Fang, Huizhou Chen, Xiangda Qi, Guodong Guo
Date: 2020-12-01 00:00:00
Proceeding: International Journal of Computer Vision 2020 12
Tasks: Personality Trait Recognition by Face, regression

First impressions strongly influence social interactions, having a high impact in the personal and professional life. In this paper, we present a deep Classification-Regression Network (CR-Net) for analyzing the Big Five personality problem and further assisting on job interview recommendation in a first impressions setup. The setup is based on the ChaLearn First Impressions dataset, including multimodal data with video, audio, and text converted from the corresponding audio data, where each person is talking in front of a camera. In order to give a comprehensive prediction, we analyze the videos from both the entire scene (including the person’s motions and background) and the face of the person. Our CR-Net first performs personality trait classification and applies a regression later, which can obtain accurate predictions for both personality traits and interview recommendation. Furthermore, we present a new loss function called Bell Loss to address inaccurate predictions caused by the regression-to-the-mean problem. Extensive experiments on the First Impressions dataset show the effectiveness of our proposed network, outperforming the state-of-the-art.
Multimodal assessment of apparent personality using feature attention and error consistency constraint
Authors: Süleyman Aslan, Uğur Güdükbay, Hamdi Dibeklioğlu
Date: 2021-03-24 00:00:00
Proceeding: Image and Vision Computing 2021 3
Tasks: Informativeness, Personality Trait Recognition by Face

Personality computing and affective computing, where the recognition of personality traits is essential, have gained increasing interest and attention in many research areas recently. We propose a novel approach to recognize the Big Five personality traits of people from videos. To this end, we use four different modalities, namely, ambient appearance (scene), facial appearance, voice, and transcribed speech. Through a specialized subnetwork for each of these modalities, our model learns reliable modality-specific representations and fuse them using an attention mechanism that re-weights each dimension of these representations to obtain an optimal combination of multimodal information. A novel loss function is employed to enforce the proposed model to give an equivalent importance for each of the personality traits to be estimated through a consistency constraint that keeps the trait-specific errors as close as possible. To further enhance the reliability of our model, we employ (pre-trained) state-of-the-art architectures (i.e., ResNet, VGGish, ELMo) as the backbones of the modality-specific subnetworks, which are complemented by multilayered Long Short-Term Memory networks to capture temporal dynamics. To minimize the computational complexity of multimodal optimization, we use two-stage modeling, where the modality-specific subnetworks are first trained individually, and the whole network is then fine-tuned to jointly model multimodal data. On the large scale ChaLearn First Impressions V2 challenge dataset, we evaluate the reliability of our model as well as investigating the informativeness of the considered modalities. Experimental results show the effectiveness of the proposed attention mechanism and the error consistency constraint. While the best performance is obtained using facial information among individual modalities, with the use of all four modalities, our model achieves a mean accuracy of 91.8%, improving the state of the art in automatic personality analysis.
CNN-FEBAC: A framework for attention measurement of autistic individuals
Authors: Akter, Tania Shahriare Satu, Md Khan, Md Imran Ali, Mohammad Hanif Uddin, Shahadat Lio, Pietro Quinn, Julian M.W. Moni, Mohammad Ali
Date: 2023-05-09 00:00:00
Proceeding: Biomedical Signal Processing and Control 2023 5
Tasks: feature selection

Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disabilities that are not curable but may be ameliorated by early interventions. We gathered early-detected ASD datasets relating to toddlers, children, adolescents and adults, and applied several feature transformation methods, including log, Z-score and sine functions to these datasets. Various classification techniques were then implemented with these transformed ASD datasets and assessed for their performance. We found SVM showed the best performance for the toddler dataset, while Adaboost gave the best results for the children dataset, Glmboost for the adolescent and Adaboost for the adult datasets. The feature transformations resulting in the best classifications was sine function for toddler and Z-score for children and adolescent datasets. After these analyses, several feature selection techniques were used with these Z-score-transformed datasets to identify the significant ASD risk factors for the toddler, child, adolescent and adult subjects. The results of these analytical approaches indicate that, when appropriately optimised, machine learning methods can provide good predictions of ASD status. This suggests that it may possible to apply these models for the detection of ASD in its early stages.
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning
Authors: Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang
Date: 2023-10-07 00:00:00
Proceeding: NeurIPS 2023 11

Real-world datasets are typically imbalanced in the sense that only a few classes have numerous samples, while many classes are associated with only a few samples. As a result, a na\"ive ERM learning process will be biased towards the majority classes, making it difficult to generalize to the minority classes. To address this issue, one simple but effective approach is to modify the loss function to emphasize the learning on minority classes, such as re-weighting the losses or adjusting the logits via class-dependent terms. However, existing generalization analysis of such losses is still coarse-grained and fragmented, failing to explain some empirical results. To bridge this gap, we propose a novel technique named data-dependent contraction to capture how these modified losses handle different classes. On top of this technique, a fine-grained generalization bound is established for imbalanced learning, which helps reveal the mystery of re-weighting and logit-adjustment in a unified manner. Furthermore, a principled learning algorithm is developed based on the theoretical insights. Finally, the empirical results on benchmark datasets not only validate the theoretical results but also demonstrate the effectiveness of the proposed method.
You Only Condense Once: Two Rules for Pruning Condensed Datasets
Authors: Yang He, Lingao Xiao, Joey Tianyi Zhou
Date: 2023-10-21 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Dataset Condensation

Dataset condensation is a crucial tool for enhancing training efficiency by reducing the size of the training dataset, particularly in on-device scenarios. However, these scenarios have two significant challenges: 1) the varying computational resources available on the devices require a dataset size different from the pre-defined condensed dataset, and 2) the limited computational resources often preclude the possibility of conducting additional condensation processes. We introduce You Only Condense Once (YOCO) to overcome these limitations. On top of one condensed dataset, YOCO produces smaller condensed datasets with two embarrassingly simple dataset pruning rules: Low LBPE Score and Balanced Construction. YOCO offers two key advantages: 1) it can flexibly resize the dataset to fit varying computational constraints, and 2) it eliminates the need for extra condensation processes, which can be computationally prohibitive. Experiments validate our findings on networks including ConvNet, ResNet and DenseNet, and datasets including CIFAR-10, CIFAR-100 and ImageNet. For example, our YOCO surpassed various dataset condensation and dataset pruning methods on CIFAR-10 with ten Images Per Class (IPC), achieving 6.98-8.89% and 6.31-23.92% accuracy gains, respectively. The code is available at: https://github.com/he-y/you-only-condense-once.
DP-TrajGAN_ A privacy-aware trajectory generation model with differential privacy
Authors: Jing Zhang, Qihan Huang, Yirui Huang, Qian Ding, Pei-Wei Tsai
Date: 2022-09-26 00:00:00
Proceeding: Future Generation Computer Systems 2022 9
Tasks: Generative Adversarial Network, Privacy Preserving

Open Data Processing Services (ODPS) offers vast storage capacity and excellent efficiency, which
collects and stores a lot of data. As an essential component of ODPS, location-based services (LBS)
are widely used in many aspects. However, LBS generates tens of thousands trajectories, which have
a significant likelihood of revealing personal information. In order to address this kind of privacy
concerns, a novel model, namely Privacy-Aware Trajectory Generation Model with Differential Privacy
(DP-TrajGAN), is proposed in this paper. Firstly, the long short-term memory network (LSTM) is
improved and introduced into in the generative adversarial network (GAN) to learn the original
distribution. Subsequently, the privacy-preserving of GAN is further enhanced using differential privacy
(DP) while retaining the original features of the trajectories, which is called DP-TrajGAN. Furthermore,
the privacy budget allocation in the DP is modeled using the Partially Observable Markov Decision
Process (POMDP), which takes into account the trade-off between privacy and utility. The experimental
findings demonstrate that, when compared to other models, DP-TrajGAN can provide higher quality
trajectories with retained statistical features and effective privacy preservation.