pwc-archive/papers-with-abstracts huggingface dataset:
PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model
Authors: Abhinav Chakraborty, Anirban Chatterjee, Abhinandan Dalal
Date: 2024-01-29 00:00:00
Tasks: Privacy Preserving

The Ising model, originally developed as a spin-glass model for ferromagnetic elements, has gained popularity as a network-based model for capturing dependencies in agents' outputs. Its increasing adoption in healthcare and the social sciences has raised privacy concerns regarding the confidentiality of agents' responses. In this paper, we present a novel $(\varepsilon,\delta)$-differentially private algorithm specifically designed to protect the privacy of individual agents' outcomes. Our algorithm allows for precise estimation of the natural parameter using a single network through an objective perturbation technique. Furthermore, we establish regret bounds for this algorithm and assess its performance on synthetic datasets and two real-world networks: one involving HIV status in a social network and the other concerning the political leaning of online blogs.
Individualized Multi-Treatment Response Curves Estimation using RBF-net with Shared Neurons
Authors: Peter Chang, Arkaprava Roy
Date: 2024-01-29 00:00:00
Tasks: Heterogeneous Treatment Effect Estimation

Heterogeneous treatment effect estimation is an important problem in precision medicine. Specific interests lie in identifying the differential effect of different treatments based on some external covariates. We propose a novel non-parametric treatment effect estimation method in a multi-treatment setting. Our non-parametric modeling of the response curves relies on radial basis function (RBF)-nets with shared hidden neurons. Our model thus facilitates modeling commonality among the treatment outcomes. The estimation and inference schemes are developed under a Bayesian framework using thresholded best linear projections and implemented via an efficient Markov chain Monte Carlo algorithm, appropriately accommodating uncertainty in all aspects of the analysis. The numerical performance of the method is demonstrated through simulation experiments. Applying our proposed method to MIMIC data, we obtain several interesting findings related to the impact of different treatment strategies on the length of ICU stay and 12-hour SOFA score for sepsis patients who are home-discharged.
Parallel Affine Transformation Tuning of Markov Chain Monte Carlo
Authors: Philip Schär, Michael Habeck, Daniel Rudolf
Date: 2024-01-29 00:00:00

The performance of Markov chain Monte Carlo samplers strongly depends on the properties of the target distribution such as its covariance structure, the location of its probability mass and its tail behavior. We explore the use of bijective affine transformations of the sample space to improve the properties of the target distribution and thereby the performance of samplers running in the transformed space. In particular, we propose a flexible and user-friendly scheme for adaptively learning the affine transformation during sampling. Moreover, the combination of our scheme with Gibbsian polar slice sampling is shown to produce samples of high quality at comparatively low computational cost in several settings based on real-world data.
Topological Detection of Phenomenological Bifurcations with Unreliable Kernel Densities
Authors: Sunia Tanweer, Firas A. Khasawneh
Date: 2024-01-29 00:00:00
Tasks: Topological Data Analysis

Phenomenological (P-type) bifurcations are qualitative changes in stochastic dynamical systems whereby the stationary probability density function (PDF) changes its topology. The current state of the art for detecting these bifurcations requires reliable kernel density estimates computed from an ensemble of system realizations. However, in several real world signals such as Big Data, only a single system realization is available -- making it impossible to estimate a reliable kernel density. This study presents an approach for detecting P-type bifurcations using unreliable density estimates. The approach creates an ensemble of objects from Topological Data Analysis (TDA) called persistence diagrams from the system's sole realization and statistically analyzes the resulting set. We compare several methods for replicating the original persistence diagram including Gibbs point process modelling, Pairwise Interaction Point Modelling, and subsampling. We show that for the purpose of predicting a bifurcation, the simple method of subsampling exceeds the other two methods of point process modelling in performance.
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy
Authors: Ana Molina-Taborda, Pilar Cossio, Olga Lopez-Acevedo, Marylou Gabrié
Date: 2024-01-29 00:00:00
Tasks: Active Learning

Extracting consistent statistics between relevant free-energy minima of a molecular system is essential for physics, chemistry and biology. Molecular dynamics (MD) simulations can aid in this task but are computationally expensive, especially for systems that require quantum accuracy. To overcome this challenge, we develop an approach combining enhanced sampling with deep generative models and active learning of a machine learning potential (MLP). We introduce an adaptive Markov chain Monte Carlo framework that enables the training of one Normalizing Flow (NF) and one MLP per state, achieving rapid convergence towards the Boltzmann distribution. Leveraging the trained NF and MLP models, we compute thermodynamic observables such as free-energy differences or optical spectra. We apply this method to study the isomerization of an ultrasmall silver nanocluster, belonging to a set of systems with diverse applications in the fields of medicine and catalysis.
Improving conversion rate prediction via self-supervised pre-training in online advertising
Authors: Alex Shtoff, Yohay Kaplan, Ariel Raviv
Date: 2024-01-25 00:00:00
Tasks: Continual Learning

The task of predicting conversion rates (CVR) lies at the heart of online advertising systems aiming to optimize bids to meet advertiser performance requirements. Even with the recent rise of deep neural networks, these predictions are often made by factorization machines (FM), especially in commercial settings where inference latency is key. These models are trained using the logistic regression framework on labeled tabular data formed from past user activity that is relevant to the task at hand. Many advertisers only care about click-attributed conversions. A major challenge in training models that predict conversions-given-clicks comes from data sparsity - clicks are rare, conversions attributed to clicks are even rarer. However, mitigating sparsity by adding conversions that are not click-attributed to the training set impairs model calibration. Since calibration is critical to achieving advertiser goals, this is infeasible. In this work we use the well-known idea of self-supervised pre-training, and use an auxiliary auto-encoder model trained on all conversion events, both click-attributed and not, as a feature extractor to enrich the main CVR prediction model. Since the main model does not train on non click-attributed conversions, this does not impair calibration. We adapt the basic self-supervised pre-training idea to our online advertising setup by using a loss function designed for tabular data, facilitating continual learning by ensuring auto-encoder stability, and incorporating a neural network into a large-scale real-time ad auction that ranks tens of thousands of ads, under strict latency constraints, and without incurring a major engineering cost. We show improvements both offline, during training, and in an online A/B test. Following its success in A/B tests, our solution is now fully deployed to the Yahoo native advertising system.
Data-efficient Fine-tuning for LLM-based Recommendation
Authors: Xinyu Lin, Wenjie Wang, Yongqi Li, Shuo Yang, Fuli Feng, Yinwei Wei, Tat-Seng Chua
Date: 2024-01-30 00:00:00

Leveraging Large Language Models (LLMs) for recommendation has recently garnered considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the cost of fine-tuning LLMs on rapidly expanding recommendation data limits their practical application. To address this challenge, few-shot fine-tuning offers a promising approach to quickly adapt LLMs to new recommendation data. We propose the task of data pruning for efficient LLM-based recommendation, aimed at identifying representative samples tailored for LLMs' few-shot fine-tuning. While coreset selection is closely related to the proposed task, existing coreset selection methods often rely on suboptimal heuristic metrics or entail costly optimization on large-scale recommendation data. To tackle these issues, we introduce two objectives for the data pruning task in the context of LLM-based recommendation: 1) high accuracy aims to identify the influential samples that can lead to high overall performance; and 2) high efficiency underlines the low costs of the data pruning process. To pursue the two objectives, we propose a novel data pruning method based on two scores, i.e., influence score and effort score, to efficiently identify the influential samples. Particularly, the influence score is introduced to accurately estimate the influence of sample removal on the overall performance. To achieve low costs of the data pruning process, we use a small-sized surrogate model to replace LLMs to obtain the influence score. Considering the potential gap between the surrogate model and LLMs, we further propose an effort score to prioritize some hard samples specifically for LLMs. Empirical results on three real-world datasets validate the effectiveness of our proposed method. In particular, the proposed method uses only 2% samples to surpass the full data fine-tuning, reducing time costs by 97%.
The Influence of Presentation and Performance on User Satisfaction
Authors: Kanaad Pathak, Leif Azzopardi, Martin Halvey
Date: 2024-01-30 00:00:00

The effectiveness of an IR system is gauged not just by its ability to retrieve relevant results but also by how it presents these results to users; an engaging presentation often correlates with increased user satisfaction. While existing research has delved into the link between user satisfaction, IR performance metrics, and presentation, these aspects have typically been investigated in isolation. Our research aims to bridge this gap by examining the relationship between query performance, presentation and user satisfaction. For our analysis, we conducted a between-subjects experiment comparing the effectiveness of various result card layouts for an ad-hoc news search interface. Drawing data from the TREC WaPo 2018 collection, we centered our study on four specific topics. Within each of these topics, we assessed six distinct queries with varying nDCG values. Our study involved 164 participants who were exposed to one of five distinct layouts containing result cards, such as "title'', "title+image'', or "title+image+summary''. Our findings indicate that while nDCG is a strong predictor of user satisfaction at the query level, there exists no linear relationship between the performance of the query, presentation of results and user satisfaction. However, when considering the total gain on the initial result page, we observed that presentation does play a significant role in user satisfaction (at the query level) for certain layouts with result cards such as, title+image or title+image+summary. Our results also suggest that the layout differences have complex and multifaceted impacts on satisfaction. We demonstrate the capacity to equalize user satisfaction levels between queries of varying performance by changing how results are presented. This emphasizes the necessity to harmonize both performance and presentation in IR systems, considering users' diverse preferences.
Re3val: Reinforced and Reranked Generative Retrieval
Authors: EuiYul Song, Sangryul Kim, Haeju Lee, Joonkee Kim, James Thorne
Date: 2024-01-30 00:00:00
Tasks: Passage Retrieval, Reranking, Retrieval

Generative retrieval models encode pointers to information in a corpus as an index within the model's parameters. These models serve as part of a larger pipeline, where retrieved information conditions generation for knowledge-intensive NLP tasks. However, we identify two limitations: the generative retrieval does not account for contextual information. Secondly, the retrieval can't be tuned for the downstream readers as decoding the page title is a non-differentiable operation. This paper introduces Re3val, trained with generative reranking and reinforcement learning using limited data. Re3val leverages context acquired via Dense Passage Retrieval to rerank the retrieved page titles and utilizes REINFORCE to maximize rewards generated by constrained decoding. Additionally, we generate questions from our pre-training dataset to mitigate epistemic uncertainty and bridge the domain gap between the pre-training and fine-tuning datasets. Subsequently, we extract and rerank contexts from the KILT database using the rerank page titles. Upon grounding the top five reranked contexts, Re3val demonstrates the Top 1 KILT scores compared to all other generative retrieval models across five KILT datasets.
Taxonomy of Mathematical Plagiarism
Authors: Ankit Satpute, Andre Greiner-Petter, Noah Gießing, Isabel Beckenbach, Moritz Schubotz, Olaf Teschke, Akiko Aizawa, Bela Gipp
Date: 2024-01-30 00:00:00
Tasks: Math, Question Answering, Recommendation Systems

Plagiarism is a pressing concern, even more so with the availability of large language models. Existing plagiarism detection systems reliably find copied and moderately reworded text but fail for idea plagiarism, especially in mathematical science, which heavily uses formal mathematical notation. We make two contributions. First, we establish a taxonomy of mathematical content reuse by annotating potentially plagiarised 122 scientific document pairs. Second, we analyze the best-performing approaches to detect plagiarism and mathematical content similarity on the newly established taxonomy. We found that the best-performing methods for plagiarism and math content similarity achieve an overall detection score (PlagDet) of 0.06 and 0.16, respectively. The best-performing methods failed to detect most cases from all seven newly established math similarity types. Outlined contributions will benefit research in plagiarism detection systems, recommender systems, question-answering systems, and search engines. We make our experiment's code and annotated dataset available to the community: https://github.com/gipplab/Taxonomy-of-Mathematical-Plagiarism
FakeClaim: A Multiple Platform-driven Dataset for Identification of Fake News on 2023 Israel-Hamas War
Authors: Gautam Kishore Shahi, Amit Kumar Jaiswal, Thomas Mandl
Date: 2024-01-29 00:00:00
Tasks: Fact Checking, Language Modeling, Language Modelling, Sentence, Video Classification

We contribute the first publicly available dataset of factual claims from different platforms and fake YouTube videos on the 2023 Israel-Hamas war for automatic fake YouTube video classification. The FakeClaim data is collected from 60 fact-checking organizations in 30 languages and enriched with metadata from the fact-checking organizations curated by trained journalists specialized in fact-checking. Further, we classify fake videos within the subset of YouTube videos using textual information and user comments. We used a pre-trained model to classify each video with different feature combinations. Our best-performing fine-tuned language model, Universal Sentence Encoder (USE), achieves a Macro F1 of 87\%, which shows that the trained model can be helpful for debunking fake videos using the comments from the user discussion. The dataset is available on Github\footnote{https://github.com/Gautamshahi/FakeClaim}
Dissecting users' needs for search result explanations
Authors: Prerna Juneja, Wenjuan Zhang, Alison Marie Smith-Renner, Hemank Lamba, Joel Tetreault, Alex Jaimes
Date: 2024-01-29 00:00:00

There is a growing demand for transparency in search engines to understand how search results are curated and to enhance users' trust. Prior research has introduced search result explanations with a focus on how to explain, assuming explanations are beneficial. Our study takes a step back to examine if search explanations are needed and when they are likely to provide benefits. Additionally, we summarize key characteristics of helpful explanations and share users' perspectives on explanation features provided by Google and Bing. Interviews with non-technical individuals reveal that users do not always seek or understand search explanations and mostly desire them for complex and critical tasks. They find Google's search explanations too obvious but appreciate the ability to contest search results. Based on our findings, we offer design recommendations for search engines and explanations to help users better evaluate search results and enhance their search experience.
Within-basket Recommendation via Neural Pattern Associator
Authors: Kai Luo, Tianshu Shen, Lan Yao, Ga Wu, Aaron Liblong, Istvan Fehervari, Ruijian An, Jawad Ahmed, Harshit Mishra, Charu Pujari
Date: 2024-01-25 00:00:00
Tasks: Quantization

Within-basket recommendation (WBR) refers to the task of recommending items to the end of completing a non-empty shopping basket during a shopping session. While the latest innovations in this space demonstrate remarkable performance improvement on benchmark datasets, they often overlook the complexity of user behaviors in practice, such as 1) co-existence of multiple shopping intentions, 2) multi-granularity of such intentions, and 3) interleaving behavior (switching intentions) in a shopping session. This paper presents Neural Pattern Associator (NPA), a deep item-association-mining model that explicitly models the aforementioned factors. Specifically, inspired by vector quantization, the NPA model learns to encode common user intentions (or item-combination patterns) as quantized representations (a.k.a. codebook), which permits identification of users's shopping intentions via attention-driven lookup during the reasoning phase. This yields coherent and self-interpretable recommendations. We evaluated the proposed NPA model across multiple extensive datasets, encompassing the domains of grocery e-commerce (shopping basket completion) and music (playlist extension), where our quantitative evaluations show that the NPA model significantly outperforms a wide range of existing WBR solutions, reflecting the benefit of explicitly modeling complex user intentions.
An Information Retrieval and Extraction Tool for Covid-19 Related Papers
Authors: Marcos V. L. Pivetta
Date: 2024-01-20 00:00:00
Tasks: Information Retrieval, Retrieval

Background: The COVID-19 pandemic has caused severe impacts on health systems worldwide. Its critical nature and the increased interest of individuals and organizations to develop countermeasures to the problem has led to a surge of new studies in scientific journals. Objetive: We sought to develop a tool that incorporates, in a novel way, aspects of Information Retrieval (IR) and Extraction (IE) applied to the COVID-19 Open Research Dataset (CORD-19). The main focus of this paper is to provide researchers with a better search tool for COVID-19 related papers, helping them find reference papers and hightlight relevant entities in text. Method: We applied Latent Dirichlet Allocation (LDA) to model, based on research aspects, the topics of all English abstracts in CORD-19. Relevant named entities of each abstract were extracted and linked to the corresponding UMLS concept. Regular expressions and the K-Nearest Neighbors algorithm were used to rank relevant papers. Results: Our tool has shown the potential to assist researchers by automating a topic-based search of CORD-19 papers. Nonetheless, we identified that more fine-tuned topic modeling parameters and increased accuracy of the research aspect classifier model could lead to a more accurate and reliable tool. Conclusion: We emphasize the need of new automated tools to help researchers find relevant COVID-19 documents, in addition to automatically extracting useful information contained in them. Our work suggests that combining different algorithms and models could lead to new ways of browsing COVID-19 paper data.
Combining topic modelling and citation network analysis to study case law from the European Court on Human Rights on the right to respect for private and family life
Authors: M. Mohammadi, L. M. Bruijn, M. Wieling, M. Vols
Date: 2024-01-19 00:00:00

As legal case law databases such as HUDOC continue to grow rapidly, it has become essential for legal researchers to find efficient methods to handle such large-scale data sets. Such case law databases usually consist of the textual content of cases together with the citations between them. This paper focuses on case law from the European Court of Human Rights on Article 8 of the European Convention of Human Rights, the right to respect private and family life, home and correspondence. In this study, we demonstrate and compare the potential of topic modelling and citation network to find and organize case law on Article 8 based on their general themes and citation patterns, respectively. Additionally, we explore whether combining these two techniques leads to better results compared to the application of only one of the methods. We evaluate the effectiveness of the combined method on a unique manually collected and annotated dataset of Aricle 8 case law on evictions. The results of our experiments show that our combined (text and citation-based) approach provides the best results in finding and grouping case law, providing scholars with an effective way to extract and analyse relevant cases on a specific issue.
Mitigating Position Bias with Regularization for Recommender Systems
Authors: Hao Wang
Date: 2024-01-18 00:00:00
Tasks: Fairness, Position, Recommendation Systems

Fairness is a popular research topic in recent years. A research topic closely related to fairness is bias and debiasing. Among different types of bias problems, position bias is one of the most widely encountered symptoms. Position bias means that recommended items on top of the recommendation list has a higher likelihood to be clicked than items on bottom of the same list. To mitigate this problem, we propose to use regularization technique to reduce the bias effect. In the experiment section, we prove that our method is superior to other modern algorithms.
Drone-vs-Bird: Drone Detection Using YOLOv7 with CSRT Tracker
Authors: Sahaj K. Mistry, Shreyas Chatterjee, Ajeet K. Verma, Vinit Jakhetiya, Badri N. Subudhi, Sunil Jaiswal
Date: 2023-05-05 00:00:00
Proceeding: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023 5
Tasks: Object Tracking

Drone detection has become a critical component of surveillance, but detecting small drones remains a challenge. In this work, we introduce a new drone detection scheme that uses a customized YOLOv7 combined with a tracker. Using simple YOLOv7 has some limitations, including false positives and difficulty detecting drones in complex environments. To address these issues, we propose two solutions. First, we use a simple method based on the probability or confidence level of detected objects to reduce the number of false positives. Second, we integrate YOLOv7 with object tracking to detect drones in complex environments. Our proposed method was successful in achieving a top-5 ranking in the 6 th edition of the Drone-vs-Bird challenge, which was organized by the ICASSP Signal Processing Grand Challenge in 2023.