pwc-archive/papers-with-abstracts huggingface dataset:
Metric Optimization and Mainstream Bias Mitigation in Recommender Systems
Authors: Roger Zhe Li
Date: 2023-11-11 00:00:00
Tasks: Recommendation Systems

The first part of this thesis focuses on maximizing the overall recommendation accuracy. This accuracy is usually evaluated with some user-oriented metric tailored to the recommendation scenario, but because recommendation is usually treated as a machine learning problem, recommendation models are trained to maximize some other generic criteria that does not necessarily align with the criteria ultimately captured by the user-oriented evaluation metric. Recent research aims at bridging this gap between training and evaluation via direct ranking optimization, but still assumes that the metric used for evaluation should also be the metric used for training. We challenge this assumption, mainly because some metrics are more informative than others. Indeed, we show that models trained via the optimization of a loss inspired by Rank-Biased Precision (RBP) tend to yield higher accuracy, even when accuracy is measured with metrics other than RBP. However, the superiority of this RBP-inspired loss stems from further benefiting users who are already well-served, rather than helping those who are not. This observation inspires the second part of this thesis, where our focus turns to helping non-mainstream users. These are users who are difficult to recommend to either because there is not enough data to model them, or because they have niche taste and thus few similar users to look at when recommending in a collaborative way. These differences in mainstreamness introduce a bias reflected in an accuracy gap between users or user groups, which we try to narrow.
An Empirical Study of Using ChatGPT for Fact Verification Task
Authors: Mohna Chakraborty, Adithya Kulkarni, Qi Li
Date: 2023-11-11 00:00:00
Tasks: Fact Verification

ChatGPT has recently emerged as a powerful tool for performing diverse NLP tasks. However, ChatGPT has been criticized for generating nonfactual responses, raising concerns about its usability for sensitive tasks like fact verification. This study investigates three key research questions: (1) Can ChatGPT be used for fact verification tasks? (2) What are different prompts performance using ChatGPT for fact verification tasks? (3) For the best-performing prompt, what common mistakes does ChatGPT make? Specifically, this study focuses on conducting a comprehensive and systematic analysis by designing and comparing the performance of three different prompts for fact verification tasks on the benchmark FEVER dataset using ChatGPT.
Mitigating Pooling Bias in E-commerce Search via False Negative Estimation
Authors: Xiaochen Wang, Xiao Xiao, Ruhan Zhang, Xuan Zhang, Taesik Na, Tejaswi Tenneti, Haixun Wang, Fenglong Ma
Date: 2023-11-11 00:00:00

Efficient and accurate product relevance assessment is critical for user experiences and business success. Training a proficient relevance assessment model requires high-quality query-product pairs, often obtained through negative sampling strategies. Unfortunately, current methods introduce pooling bias by mistakenly sampling false negatives, diminishing performance and business impact. To address this, we present Bias-mitigating Hard Negative Sampling (BHNS), a novel negative sampling strategy tailored to identify and adjust for false negatives, building upon our original False Negative Estimation algorithm. Our experiments in the Instacart search setting confirm BHNS as effective for practical e-commerce use. Furthermore, comparative analyses on public dataset showcase its domain-agnostic potential for diverse applications.
Most Apparent Distortion: Full-Reference Image Quality Assessment and the Role of Strategy
Authors: Eric C. Larson, Damon M. Chandler
Date: 2010-01-01 00:00:00
Proceeding: Journal of Electronic Imaging 2010 1
Tasks: Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment

The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant
strategy employed by the human visual system (HVS) when judging image quality (e.g., detecting visible differences, and extracting image structure/information). In this work, we suggest that a single strategy may not be sufficient; rather, we advocate that the HVS
uses multiple strategies to determine image quality. For images containing near-threshold distortions, the image is most apparent, and thus the HVS attempts to look past the image and look for the distortions (a detection-based strategy). For images containing clearly visible distortions, the distortions are most apparent, and thus the HVS attempts to look past the distortion and look for the image’s subject matter (an appearance-based strategy). Here, we present a quality assessment method [most apparent distortion (MAD)], which attempts to explicitly model these two separate strategies. Local luminance and contrast masking are used to estimate detection-based perceived distortion in high-quality images, whereas changes in the local statistics of spatial-frequency components are used to estimate appearance-based perceived distortion in low-quality images. We show that a combination of these two measures can perform well in predicting subjective ratings of image quality.
Theory and implementation of inelastic Constitutive Artificial Neural Networks
Authors: Hagen Holthusen, Lukas Lamm, Tim Brepols, Stefanie Reese, Ellen Kuhl
Date: 2023-11-10 00:00:00

Nature has always been our inspiration in the research, design and development of materials and has driven us to gain a deep understanding of the mechanisms that characterize anisotropy and inelastic behavior. All this knowledge has been accumulated in the principles of thermodynamics. Deduced from these principles, the multiplicative decomposition combined with pseudo potentials are powerful and universal concepts. Simultaneously, the tremendous increase in computational performance enabled us to investigate and rethink our history-dependent material models to make the most of our predictions. Today, we have reached a point where materials and their models are becoming increasingly sophisticated. This raises the question: How do we find the best model that includes all inelastic effects to explain our complex data? Constitutive Artificial Neural Networks (CANN) may answer this question. Here, we extend the CANNs to inelastic materials (iCANN). Rigorous considerations of objectivity, rigid motion of the reference configuration, multiplicative decomposition and its inherent non-uniqueness, restrictions of energy and pseudo potential, and consistent evolution guide us towards the architecture of the iCANN satisfying thermodynamics per design. We combine feed-forward networks of the free energy and pseudo potential with a recurrent neural network approach to take time dependencies into account. We demonstrate that the iCANN is capable of autonomously discovering models for artificially generated data, the response of polymers for cyclic loading and the relaxation behavior of muscle data. As the design of the network is not limited to visco-elasticity, our vision is that the iCANN will reveal to us new ways to find the various inelastic phenomena hidden in the data and to understand their interaction. Our source code, data, and examples are available at doi.org/10.5281/zenodo.10066805
Convolve and Conquer: Data Comparison with Wiener Filters
Authors: Deborah Pelacani Cruz, George Strong, Oscar Bates, Carlos Cueto, Jiashun Yao, Lluis Guasch
Date: 2023-11-11 00:00:00
Tasks: Data Compression, Imputation

Quantitative evaluations of differences and/or similarities between data samples define and shape optimisation problems associated with learning data distributions. Current methods to compare data often suffer from limitations in capturing such distributions or lack desirable mathematical properties for optimisation (e.g. smoothness, differentiability, or convexity). In this paper, we introduce a new method to measure (dis)similarities between paired samples inspired by Wiener-filter theory. The convolutional nature of Wiener filters allows us to comprehensively compare data samples in a globally correlated way. We validate our approach in four machine learning applications: data compression, medical imaging imputation, translated classification, and non-parametric generative modelling. Our results demonstrate increased resolution in reconstructed images with better perceptual quality and higher data fidelity, as well as robustness against translations, compared to conventional mean-squared-error analogue implementations.
STPGANsFusion: Structure and Texture Preserving Generative Adversarial Networks for Multi-modal Medical Image Fusion
Authors: Dhruvi Shah, Hareshwar Wani, Manisha Das, Deep Gupta, Petia Radeva, Ashwini Bakde
Date: 2022-07-04 00:00:00
Proceeding: National Conference on Communications (NCC) 2022 7
Tasks: Diagnostic

Medical images from various modalities carry diverse information. The features from these source images are combined into a single image, constituting more information content, beneficial for subsequent medical applications. Recently,
deep learning (DL) based networks have demonstrated the ability to produce promising fusion results by integrating the feature extraction and preservation task with less manual interventions. However, using a single network for extracting features from
multi-modal source images characterizing distinct information results in the loss of crucial diagnostic information. Addressing this problem, we present structure and texture preserving generative adversarial networks based medical image fusion method (STPGANsFusion). Initially, the textural and structural components of the source images are separated using structure gradient and texture decorrelating regularizer (SGTDR) based image decomposition for more complementary information preservation and higher robustness for the model. Next, the fusion of the structure and the texture components is carried out using two generative adversarial networks (GANs) consisting of a generator and two discriminators to get fused structure and texture components. The loss function for each GAN is framed as per the characteristic of the component being fused to minimize the loss of complementary information. The fused image is reconstructed and undergoes adaptive mask-based structure enhancement to further boost its contrast and visualization. Substantial experimentation is carried out on a wide variety of neurological images. Visual and qualitative results exhibit notable improvement in the fusion performance of the proposed method in comparison to the state-of-the-art fusion methods.
TURBO: The Swiss Knife of Auto-Encoders
Authors: Guillaume Quétant, Yury Belousov, Vitaliy Kinakh, Slava Voloshynovskiy
Date: 2023-11-11 00:00:00

We present a novel information-theoretic framework, termed as TURBO, designed to systematically analyse and generalise auto-encoding methods. We start by examining the principles of information bottleneck and bottleneck-based networks in the auto-encoding setting and identifying their inherent limitations, which become more prominent for data with multiple relevant, physics-related representations. The TURBO framework is then introduced, providing a comprehensive derivation of its core concept consisting of the maximisation of mutual information between various data representations expressed in two directions reflecting the information flows. We illustrate that numerous prevalent neural network models are encompassed within this framework. The paper underscores the insufficiency of the information bottleneck concept in elucidating all such models, thereby establishing TURBO as a preferable theoretical reference. The introduction of TURBO contributes to a richer understanding of data representation and the structure of neural network models, enabling more efficient and versatile applications.
CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization
Authors: Danish Gufran, Sudeep Pasricha
Date: 2023-11-10 00:00:00
Tasks: Indoor Localization

Indoor localization has become increasingly vital for many applications from tracking assets to delivering personalized services. Yet, achieving pinpoint accuracy remains a challenge due to variations across indoor environments and devices used to assist with localization. Another emerging challenge is adversarial attacks on indoor localization systems that not only threaten service integrity but also reduce localization accuracy. To combat these challenges, we introduce CALLOC, a novel framework designed to resist adversarial attacks and variations across indoor environments and devices that reduce system accuracy and reliability. CALLOC employs a novel adaptive curriculum learning approach with a domain specific lightweight scaled-dot product attention neural network, tailored for adversarial and variation resilience in practical use cases with resource constrained mobile devices. Experimental evaluations demonstrate that CALLOC can achieve improvements of up to 6.03x in mean error and 4.6x in worst-case error against state-of-the-art indoor localization frameworks, across diverse building floorplans, mobile devices, and adversarial attacks scenarios.
Personalized Federated Learning via ADMM with Moreau Envelope
Authors: Shengkun Zhu, Jinshan Zeng, Sheng Wang, Yuan Sun, Zhiyong Peng
Date: 2023-11-12 00:00:00
Tasks: Federated Learning, Personalized Federated Learning

Personalized federated learning (PFL) is an approach proposed to address the issue of poor convergence on heterogeneous data. However, most existing PFL frameworks require strong assumptions for convergence. In this paper, we propose an alternating direction method of multipliers (ADMM) for training PFL models with Moreau envelope (FLAME), which achieves a sublinear convergence rate, relying on the relatively weak assumption of gradient Lipschitz continuity. Moreover, due to the gradient-free nature of ADMM, FLAME alleviates the need for hyperparameter tuning, particularly in avoiding the adjustment of the learning rate when training the global model. In addition, we propose a biased client selection strategy to expedite the convergence of training of PFL models. Our theoretical analysis establishes the global convergence under both unbiased and biased client selection strategies. Our experiments validate that FLAME, when trained on heterogeneous data, outperforms state-of-the-art methods in terms of model performance. Regarding communication efficiency, it exhibits an average speedup of 3.75x compared to the baselines. Furthermore, experimental results validate that the biased client selection strategy speeds up the convergence of both personalized and global models.
Best Complete Approximations of Preference Relations
Authors: Hiroki Nishimura, Efe A. Ok
Date: 2023-11-11 00:00:00
Tasks: Relation

We investigate the problem of approximating an incomplete preference relation $\succsim$ on a finite set by a complete preference relation. We aim to obtain this approximation in such a way that the choices on the basis of two preferences, one incomplete, the other complete, have the smallest possible discrepancy in the aggregate. To this end, we use the top-difference metric on preferences, and define a best complete approximation of $\succsim$ as a complete preference relation nearest to $\succsim$ relative to this metric. We prove that such an approximation must be a maximal completion of $\succsim$, and that it is, in fact, any one completion of $\succsim$ with the largest index. Finally, we use these results to provide a sufficient condition for the best complete approximation of a preference to be its canonical completion. This leads to closed-form solutions to the best approximation problem in the case of several incomplete preference relations of interest.
Efficient Parallelization of a Ubiquitous Sequential Computation
Authors: Franz A. Heinsen
Date: 2023-10-27 00:00:00

We find a succinct expression for computing the sequence $x_t = a_t x_{t-1} + b_t$ in parallel with two prefix sums, given $t = (1, 2, \dots, n)$, $a_t \in \mathbb{R}^n$, $b_t \in \mathbb{R}^n$, and initial value $x_0 \in \mathbb{R}$. On $n$ parallel processors, the computation of $n$ elements incurs $\mathcal{O}(\log n)$ time and $\mathcal{O}(n)$ space. Sequences of this form are ubiquitous in science and engineering, making efficient parallelization useful for a vast number of applications. We implement our expression in software, test it on parallel hardware, and verify that it executes faster than sequential computation by a factor of $\frac{n}{\log n}$.
The Multi-BMBY Mechanism: Proportionality-Preserving and Strategyproof Ownership Restructuring in Private Companies
Authors: Gal Danino, Moran Koren, Omer Madmon
Date: 2023-11-12 00:00:00
Tasks: Decision Making

In privately held startups, restructuring ownership is challenging due to diverse and uncertain valuations among owners. Traditional approaches, including the BMBY mechanism for equal partnerships, fail to address the complexities of multi-owner settings and don't elicit true valuations. We propose a novel mechanism that extends the BMBY rationale to accommodate these complex scenarios. Our mechanism ensures truthful valuation elicitation while offering several advantages: it is easy to implement, budget balanced, resistant to collusion, individually rational, and allocates shares to those who value them most. Crucially, it preserves proportionality among remaining owners, maintaining existing power dynamics. The mechanism allows for adaptive control of the eventual number of owners, addressing unique startup needs such as incentivizing employee ownership. This paper contributes to the field of ownership restructuring by providing a practical, theoretically-grounded solution for the complex dynamics of startup recapitalization, potentially improving decision-making processes and stakeholder relationships in these pivotal business transitions.
Synthetic Speaking Children -- Why We Need Them and How to Make Them
Authors: Muhammad Ali Farooq, Dan Bigioi, Rishabh Jain, Wang Yao, Mariam Yiwere, Peter Corcoran
Date: 2023-11-08 00:00:00
Tasks: text-to-speech, Text to Speech

Contemporary Human Computer Interaction (HCI) research relies primarily on neural network models for machine vision and speech understanding of a system user. Such models require extensively annotated training datasets for optimal performance and when building interfaces for users from a vulnerable population such as young children, GDPR introduces significant complexities in data collection, management, and processing. Motivated by the training needs of an Edge AI smart toy platform this research explores the latest advances in generative neural technologies and provides a working proof of concept of a controllable data generation pipeline for speech driven facial training data at scale. In this context, we demonstrate how StyleGAN2 can be finetuned to create a gender balanced dataset of children's faces. This dataset includes a variety of controllable factors such as facial expressions, age variations, facial poses, and even speech-driven animations with realistic lip synchronization. By combining generative text to speech models for child voice synthesis and a 3D landmark based talking heads pipeline, we can generate highly realistic, entirely synthetic, talking child video clips. These video clips can provide valuable, and controllable, synthetic training data for neural network models, bridging the gap when real data is scarce or restricted due to privacy regulations.
AI Chatbot for Generating Episodic Future Thinking (EFT) Cue Texts for Health
Authors: Sareh Ahmadi, Edward A. Fox
Date: 2023-11-06 00:00:00
Tasks: Chatbot

We describe an AI-powered chatbot to aid with health improvement by generating Episodic Future Thinking (EFT) cue texts that should reduce delay discounting. In prior studies, EFT has been shown to address maladaptive health behaviors. Those studies involved participants, working with researchers, vividly imagining future events, and writing a description that they subsequently will frequently review, to ensure a shift from an inclination towards immediate rewards. That should promote behavior change, aiding in health tasks such as treatment adherence and lifestyle modifications. The AI chatbot is designed to guide users in generating personalized EFTs, automating the current labor-intensive interview-based process. This can enhance the efficiency of EFT interventions and make them more accessible, targeting specifically those with limited educational backgrounds or communication challenges. By leveraging AI for EFT intervention, we anticipate broadened access and improved health outcomes across diverse populations
Goal-oriented Estimation of Multiple Markov Sources in Resource-constrained Systems
Authors: Jiping Luo, Nikolaos Pappas
Date: 2023-11-13 00:00:00
Tasks: Deep Reinforcement Learning

This paper investigates goal-oriented communication for remote estimation of multiple Markov sources in resource-constrained networks. An agent decides the updating times of the sources and transmits the packet to a remote destination over an unreliable channel with delay. The destination is tasked with source reconstruction for actuation. We utilize the metric \textit{cost of actuation error} (CAE) to capture the state-dependent actuation costs. We aim for a sampling policy that minimizes the long-term average CAE subject to an average resource constraint. We formulate this problem as an average-cost constrained Markov Decision Process (CMDP) and relax it into an unconstrained problem by utilizing \textit{Lyapunov drift} techniques. Then, we propose a low-complexity \textit{drift-plus-penalty} (DPP) policy for systems with known source/channel statistics and a Lyapunov optimization-based deep reinforcement learning (LO-DRL) policy for unknown environments. Our policies significantly reduce the number of uninformative transmissions by exploiting the timing of the important information.
Stability analysis for large-scale multi-agent molecular communication systems
Authors: Taishi Kotsuka, Yutaka Hori
Date: 2023-11-12 00:00:00

Molecular communication (MC) is recently featured as a novel communication tool to connect individual biological nanorobots. It is expected that a large number of nanorobots can form large multi-agent MC systems through MC to accomplish complex and large-scale tasks that cannot be achieved by a single nanorobot. However, most previous models for MC systems assume a unidirectional diffusion communication channel and cannot capture the feedback between each nanorobot, which is important for multi-agent MC systems. In this paper, we introduce a system theoretic model for large-scale multi-agent MC systems using transfer functions, and then propose a method to analyze the stability for multi-agent MC systems. The proposed method decomposes the multi-agent MC system into multiple single-input and single-output (SISO) systems, which facilitates the application of simple analysis technique for SISO systems to the large-scale multi-agent MC system. Finally, we demonstrate the proposed method by analyzing the stability of a specific large-scale multi-agent MC system and clarify a parameter region to synchronize the states of nanorobots, which is important to make cooperative behaviors at a population level.