pwc-archive/papers-with-abstracts huggingface dataset:
Statistical Limits for Testing Correlation of Hypergraphs
Authors: Mingao Yuan, Zuofeng Shang
Date: 2022-02-11 00:00:00

In this paper, we consider the hypothesis testing of correlation between two $m$-uniform hypergraphs on $n$ unlabelled nodes. Under the null hypothesis, the hypergraphs are independent, while under the alternative hypothesis, the hyperdges have the same marginal distributions as in the null hypothesis but are correlated after some unknown node permutation. We focus on two scenarios: the hypergraphs are generated from the Gaussian-Wigner model and the dense Erd\"{o}s-R\'{e}nyi model. We derive the sharp information-theoretic testing threshold. Above the threshold, there exists a powerful test to distinguish the alternative hypothesis from the null hypothesis. Below the threshold, the alternative hypothesis and the null hypothesis are not distinguishable. The threshold involves $m$ and decreases as $m$ gets larger. This indicates testing correlation of hypergraphs ($m\geq3$) becomes easier than testing correlation of graphs ($m=2$)
DS4DH at TREC Health Misinformation 2021: Multi-Dimensional Ranking Models with Transfer Learning and Rank Fusion
Authors: Boya Zhang, Nona Naderi, Fernando Jaume-Santero, Douglas Teodoro
Date: 2022-02-14 00:00:00
Tasks: Information Retrieval, Misinformation, Re-Ranking, Retrieval, Transfer Learning

This paper describes the work of the Data Science for Digital Health (DS4DH) group at the TREC Health Misinformation Track 2021. The TREC Health Misinformation track focused on the development of retrieval methods that provide relevant, correct and credible information for health related searches on the Web. In our methodology, we used a two-step ranking approach that includes i) a standard retrieval phase, based on BM25 model, and ii) a re-ranking phase, with a pipeline of models focused on the usefulness, supportiveness and credibility dimensions of the retrieved documents. To estimate the usefulness, we classified the initial rank list using pre-trained language models based on the transformers architecture fine-tuned on the MS MARCO corpus. To assess the supportiveness, we utilized BERT-based models fine-tuned on scientific and Wikipedia corpora. Finally, to evaluate the credibility of the documents, we employed a random forest model trained on the Microsoft Credibility dataset combined with a list of credible sites. The resulting ranked lists were then combined using the Reciprocal Rank Fusion algorithm to obtain the final list of useful, supporting and credible documents. Our approach achieved competitive results, being top-2 in the compatibility measurement for the automatic runs. Our findings suggest that integrating automatic ranking models created for each information quality dimension with transfer learning can increase the effectiveness of health-related information retrieval.
UA-FedRec: Untargeted Attack on Federated News Recommendation
Authors: Jingwei Yi, Fangzhao Wu, Bin Zhu, Jing Yao, Zhulin Tao, Guangzhong Sun, Xing Xie
Date: 2022-02-14 00:00:00
Tasks: Federated Learning, News Recommendation, Privacy Preserving, Recommendation Systems

News recommendation is critical for personalized news distribution. Federated news recommendation enables collaborative model learning from many clients without sharing their raw data. It is promising for privacy-preserving news recommendation. However, the security of federated news recommendation is still unclear. In this paper, we study this problem by proposing an untargeted attack called UA-FedRec. By exploiting the prior knowledge of news recommendation and federated learning, UA-FedRec can effectively degrade the model performance with a small percentage of malicious clients. First, the effectiveness of news recommendation highly depends on user modeling and news modeling. We design a news similarity perturbation method to make representations of similar news farther and those of dissimilar news closer to interrupt news modeling, and propose a user model perturbation method to make malicious user updates in opposite directions of benign updates to interrupt user modeling. Second, updates from different clients are typically aggregated by weighted-averaging based on their sample sizes. We propose a quantity perturbation method to enlarge sample sizes of malicious clients in a reasonable range to amplify the impact of malicious updates. Extensive experiments on two real-world datasets show that UA-FedRec can effectively degrade the accuracy of existing federated news recommendation methods, even when defense is applied. Our study reveals a critical security issue in existing federated news recommendation systems and calls for research efforts to address the issue.
Neural Re-ranking in Multi-stage Recommender Systems: A Review
Authors: Weiwen Liu, Yunjia Xi, Jiarui Qin, Fei Sun, Bo Chen, Weinan Zhang, Rui Zhang, Ruiming Tang
Date: 2022-02-14 00:00:00
Tasks: Recommendation Systems, Re-Ranking

As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely applied in industrial applications. This review aims at integrating re-ranking algorithms into a broader picture, and paving ways for more comprehensive solutions for future research. For this purpose, we first present a taxonomy of current methods on neural re-ranking. Then we give a description of these methods along with the historic development according to their objectives. The network structure, personalization, and complexity are also discussed and compared. Next, we provide benchmarks of the major neural re-ranking models and quantitatively analyze their re-ranking performance. Finally, the review concludes with a discussion on future prospects of this field. A list of papers discussed in this review, the benchmark datasets, our re-ranking library LibRerank, and detailed parameter settings are publicly available at https://github.com/LibRerank-Community/LibRerank.
Gain-loss ratio of storing intermediate data from workflows
Authors: Debasish Chakroborti
Date: 2022-02-14 00:00:00
Tasks: Management

Sequentially, the systematic processing of a significant amount of data can be necessary for input datasets to get desired outputs. In a workflow management system(WMS), usually, users build workflows by manually selecting and interconnecting various modules concerning some particular tasks. Thus, a system of automatically suggesting the appropriate intermediate datasets for modules and a suggestion for the decision of saving intermediate states will be helpful in a pipeline building process. This work investigates a technique for both automatically suggesting the intermediate datasets to use and store through mining and analyzing association rules from the previously developed workflows. Investigation on workflows shows that the association rule mining technique can help us to suggest subsequent modules for retrieving and storing data and explain them with gain-loss ratios.
Learning to Rank from Relevance Judgments Distributions
Authors: Alberto Purpura, Gianmaria Silvello, Gian Antonio Susto
Date: 2022-02-13 00:00:00
Tasks: Learning-To-Rank

Learning to Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available document-topic pair. Within the Cranfield framework, relevance labels result from merging either multiple expertly curated or crowdsourced human assessments. In this paper, we explore how to train LETOR models with relevance judgments distributions (either real or synthetically generated) assigned to document-topic pairs instead of single-valued relevance labels. We propose five new probabilistic loss functions to deal with the higher expressive power provided by relevance judgments distributions and show how they can be applied both to neural and GBM architectures. Moreover, we show how training a LETOR model on a sampled version of the relevance judgments from certain probability distributions can improve its performance when relying either on traditional or probabilistic loss functions. Finally, we validate our hypothesis on real-world crowdsourced relevance judgments distributions. Overall, we observe that relying on relevance judgments distributions to train different LETOR models can boost their performance and even outperform strong baselines such as LambdaMART on several test collections.
An Analysis of Variations in the Effectiveness of Query Performance Prediction
Authors: Debasis Ganguly, Suchana Datta, Mandar Mitra, Derek Greene
Date: 2022-02-13 00:00:00
Tasks: Retrieval

A query performance predictor estimates the retrieval effectiveness of an IR system for a given query. An important characteristic of QPP evaluation is that, since the ground truth retrieval effectiveness for QPP evaluation can be measured with different metrics, the ground truth itself is not absolute, which is in contrast to other retrieval tasks, such as that of ad-hoc retrieval. Motivated by this argument, the objective of this paper is to investigate how such variances in the ground truth for QPP evaluation can affect the outcomes of QPP experiments. We consider this not only in terms of the absolute values of the evaluation metrics being reported (e.g. Pearson's $r$, Kendall's $\tau$), but also with respect to the changes in the ranks of different QPP systems when ordered by the QPP metric scores. Our experiments reveal that the observed QPP outcomes can vary considerably, both in terms of the absolute evaluation metric values and also in terms of the relative system ranks. Through our analysis, we report the optimal combinations of QPP evaluation metric and experimental settings that are likely to lead to smaller variations in the observed results.
Web-Based File Clustering and Indexing for Mindoro State University
Authors: Christie A. Luzon, Luisito Lolong Lacatan, Harold Y. Bangalisan, Jayvee M. Osapdin
Date: 2022-02-13 00:00:00
Tasks: Clustering, Information Retrieval, Retrieval

The Web Based File Clustering and Indexing for Mindoro State University aim to organize data circulated over the Web into groups or collections to facilitate data availability and access and at the same time meet user preferences. The main benefits include increasing Web information accessibility, understanding users navigation behavior, improving information retrieval and content delivery on the Web. Web based file clustering could help in reaching the required documents that the user is searching for. In this paper a novel approach has been introduced for search results clustering that is based on the semantics of the retrieved documents rather than the syntax of the terms in those documents. Data clustering was used to improve the information retrieval from the collection of documents. Data were processed and analyzed using SPSS where the instrument was evaluated to test the reliability and validity of the measures used. Evaluation was based on a Likert scale of Excellent, Good, Fair, and Poor as described for the selected quality characteristics.
Modeling User Behavior with Graph Convolution for Personalized Product Search
Authors: Fan Lu, Qimai Li, Bo Liu, Xiao-Ming Wu, Xiaotong Zhang, Fuyu Lv, Guli Lin, Sen Li, Taiwei Jin, Keping Yang
Date: 2022-02-12 00:00:00
Tasks: Learning Semantic Representations, Retrieval

User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance by jointly learning semantic representations of products, users, and text tokens. However, existing methods are limited in their ability to model user preferences. They typically represent users by the products they visited in a short span of time using attentive models and lack the ability to exploit relational information such as user-product interactions or item co-occurrence relations. In this work, we propose to address the limitations of prior arts by exploring local and global user behavior patterns on a user successive behavior graph, which is constructed by utilizing short-term actions of all users. To capture implicit user preference signals and collaborative patterns, we use an efficient jumping graph convolution to explore high-order relations to enrich product representations for user preference modeling. Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences. Extensive experiments on eight Amazon benchmarks demonstrate the effectiveness and potential of our approach. The source code is available at \url{https://github.com/floatSDSDS/SBG}.
High-Dimensional Dynamic Stochastic Model Representation
Authors: Aryan Eftekhari, Simon Scheidegger
Date: 2022-02-14 00:00:00
Tasks: model, Vocal Bursts Intensity Prediction

We propose a scalable method for computing global solutions of nonlinear, high-dimensional dynamic stochastic economic models. First, within a time iteration framework, we approximate economic policy functions using an adaptive, high-dimensional model representation scheme, combined with adaptive sparse grids to address the ubiquitous challenge of the curse of dimensionality. Moreover, the adaptivity within the individual component functions increases sparsity since grid points are added only where they are most needed, that is, in regions with steep gradients or at nondifferentiabilities. Second, we introduce a performant vectorization scheme for the interpolation compute kernel. Third, the algorithm is hybrid parallelized, leveraging both distributed- and shared-memory architectures. We observe significant speedups over the state-of-the-art techniques, and almost ideal strong scaling up to at least $1,000$ compute nodes of a Cray XC$50$ system at the Swiss National Supercomputing Center. Finally, to demonstrate our method's broad applicability, we compute global solutions to two variates of a high-dimensional international real business cycle model up to $300$ continuous state variables. In addition, we highlight a complementary advantage of the framework, which allows for a priori analysis of the model complexity.
Fabricated Flips: Poisoning Federated Learning without Data
Authors: Jiyue Huang, Zilong Zhao, Lydia Y. Chen, Stefanie Roos
Date: 2022-02-07 00:00:00
Tasks: Federated Learning

Attacks on Federated Learning (FL) can severely reduce the quality of the generated models and limit the usefulness of this emerging learning paradigm that enables on-premise decentralized learning. However, existing untargeted attacks are not practical for many scenarios as they assume that i) the attacker knows every update of benign clients, or ii) the attacker has a large dataset to locally train updates imitating benign parties. In this paper, we propose a data-free untargeted attack (DFA) that synthesizes malicious data to craft adversarial models without eavesdropping on the transmission of benign clients at all or requiring a large quantity of task-specific training data. We design two variants of DFA, namely DFA-R and DFA-G, which differ in how they trade off stealthiness and effectiveness. Specifically, DFA-R iteratively optimizes a malicious data layer to minimize the prediction confidence of all outputs of the global model, whereas DFA-G interactively trains a malicious data generator network by steering the output of the global model toward a particular class. Experimental results on Fashion-MNIST, Cifar-10, and SVHN show that DFA, despite requiring fewer assumptions than existing attacks, achieves similar or even higher attack success rate than state-of-the-art untargeted attacks against various state-of-the-art defense mechanisms. Concretely, they can evade all considered defense mechanisms in at least 50% of the cases for CIFAR-10 and often reduce the accuracy by more than a factor of 2. Consequently, we design REFD, a defense specifically crafted to protect against data-free attacks. REFD leverages a reference dataset to detect updates that are biased or have a low confidence. It greatly improves upon existing defenses by filtering out the malicious updates and achieves high global model accuracy
Memory-Efficient Sequential Pattern Mining with Hybrid Tries
Authors: Amin Hosseininasab, Willem-Jan van Hoeve, Andre A. Cire
Date: 2022-02-06 00:00:00
Tasks: Sequential Pattern Mining

This paper develops a memory-efficient approach for Sequential Pattern Mining (SPM), a fundamental topic in knowledge discovery that faces a well-known memory bottleneck for large data sets. Our methodology involves a novel hybrid trie data structure that exploits recurring patterns to compactly store the data set in memory; and a corresponding mining algorithm designed to effectively extract patterns from this compact representation. Numerical results on small to medium-sized real-life test instances show an average improvement of 85% in memory consumption and 49% in computation time compared to the state of the art. For large data sets, our algorithm stands out as the only capable SPM approach within 256GB of system memory, potentially saving 1.7TB in memory consumption.
Reduced-Form Allocations with Complementarity: A 2-Person Case
Authors: Xu Lang
Date: 2022-02-13 00:00:00
Tasks: Form

We investigate the implementation of reduced-form allocation probabilities in a two-person bargaining problem without side payments, where the agents have to select one alternative from a finite set of social alternatives. We provide a necessary and sufficient condition for the implementability. We find that the implementability condition in bargaining has some new feature compared to Border's theorem. Our results have applications in compromise problems and package exchange problems where the agents barter indivisible objects and the agents value the objects as complements.
Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets
Authors: Bhuvesh Kumar, Jamie Morgenstern, Okke Schrijvers
Date: 2022-02-04 00:00:00
Tasks: Management

Online ad platforms offer budget management tools for advertisers that aim to maximize the number of conversions given a budget constraint. As the volume of impressions, conversion rates and prices vary over time, these budget management systems learn a spend plan (to find the optimal distribution of budget over time) and run a pacing algorithm which follows the spend plan. This paper considers two models for impressions and competition that varies with time: a) an episodic model which exhibits stationarity in each episode, but each episode can be arbitrarily different from the next, and b) a model where the distributions of prices and values change slowly over time. We present the first learning theoretic guarantees on both the accuracy of spend plans and the resulting end-to-end budget management system. We present four main results: 1) for the episodic setting we give sample complexity bounds for the spend rate prediction problem: given $n$ samples from each episode, with high probability we have $|\widehat{\rho}_e - \rho_e| \leq \tilde{O}(\frac{1}{n^{1/3}})$ where $\rho_e$ is the optimal spend rate for the episode, $\widehat{\rho}_e$ is the estimate from our algorithm, 2) we extend the algorithm of Balseiro and Gur (2017) to operate on varying, approximate spend rates and show that the resulting combined system of optimal spend rate estimation and online pacing algorithm for episodic settings has regret that vanishes in number of historic samples $n$ and the number of rounds $T$, 3) for non-episodic but slowly-changing distributions we show that the same approach approximates the optimal bidding strategy up to a factor dependent on the rate-of-change of the distributions and 4) we provide experiments showing that our algorithm outperforms both static spend plans and non-pacing across a wide variety of settings.
Resource allocation for reconfigurable intelligent surface aided broadcast channels
Authors: Cong Sun, Xian Liu, Bile Peng, Eduard Jorswieck
Date: 2022-02-14 00:00:00

A two-user downlink network aided by a reconfigurable intelligent surface is considered. The weighted sum signal to interference plus noise ratio maximization and the sum rate maximization models are presented, where the precoding vectors and the RIS matrix are jointly optimized. Since the optimization problem is non-convex and difficult, new approximation models are proposed. The upper bounds of the corresponding objective functions are derived and maximized. Two new algorithms based on the alternating direction method of multiplier are proposed. It is proved that the proposed algorithms converge to the KKT points of the approximation models as long as the iteration points converge. Simulation results show the good performances of the proposed models compared to state of the art algorithms.
Performance Analysis of Multi-Reconfigurable Intelligent Surface-Empowered THz Wireless Systems
Authors: Alexandros-Apostolos A. Boulogeorgos, Nestor Chatzidiamantis, Harilaos G. Sandalidis, Angeliki Alexiou, Marco Di Renzo
Date: 2022-02-13 00:00:00

In this paper, we introduce a theoretical framework for analyzing the performance of multi-reconfigurable intelligence surface (RIS) empowered terahertz (THz) wireless systems subject to turbulence and stochastic beam misalignment. In more detail, we extract a closed-form expression for the outage probability that quantifies the joint impact of turbulence and misalignment as well as the effect of transceivers' hardware imperfections. Our results highlight the importance of accurately modeling both turbulence and misalignment when assessing the performance of multi-RIS-empowered THz wireless systems.
DEEPCHORUS: A Hybrid Model of Multi-scale Convolution and Self-attention for Chorus Detection
Authors: Qiqi He, Xiaoheng Sun, Yi Yu, Wei Li
Date: 2022-02-13 00:00:00
Tasks: Rhythm

Chorus detection is a challenging problem in musical signal processing as the chorus often repeats more than once in popular songs, usually with rich instruments and complex rhythm forms. Most of the existing works focus on the receptiveness of chorus sections based on some explicit features such as loudness and occurrence frequency. These pre-assumptions for chorus limit the generalization capacity of these methods, causing misdetection on other repeated sections such as verse. To solve the problem, in this paper we propose an end-to-end chorus detection model DeepChorus, reducing the engineering effort and the need for prior knowledge. The proposed model includes two main structures: i) a Multi-Scale Network to derive preliminary representations of chorus segments, and ii) a Self-Attention Convolution Network to further process the features into probability curves representing chorus presence. To obtain the final results, we apply an adaptive threshold to binarize the original curve. The experimental results show that DeepChorus outperforms existing state-of-the-art methods in most cases.