pwc-archive/papers-with-abstracts huggingface dataset:
AutoFR: Automated Filter Rule Generation for Adblocking
Authors: Hieu Le, Salma Elmalaki, Athina Markopoulou, Zubair Shafiq
Date: 2022-02-25 00:00:00
Tasks: Blocking

Adblocking relies on filter lists, which are manually curated and maintained by a community of filter list authors. Filter list curation is a laborious process that does not scale well to a large number of sites or over time. In this paper, we introduce AutoFR, a reinforcement learning framework to fully automate the process of filter rule creation and evaluation for sites of interest. We design an algorithm based on multi-arm bandits to generate filter rules that block ads while controlling the trade-off between blocking ads and avoiding visual breakage. We test AutoFR on thousands of sites and we show that it is efficient: it takes only a few minutes to generate filter rules for a site of interest. AutoFR is effective: it generates filter rules that can block 86% of the ads, as compared to 87% by EasyList, while achieving comparable visual breakage. Furthermore, AutoFR generates filter rules that generalize well to new sites. We envision that AutoFR can assist the adblocking community in filter rule generation at scale.
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications
Authors: Bokun Wang, Tianbao Yang
Date: 2022-02-24 00:00:00
Tasks: Meta-Learning, Stochastic Optimization, Survival Analysis

This paper studies stochastic optimization for a sum of compositional functions, where the inner-level function of each summand is coupled with the corresponding summation index. We refer to this family of problems as finite-sum coupled compositional optimization (FCCO). It has broad applications in machine learning for optimizing non-convex or convex compositional measures/objectives such as average precision (AP), p-norm push, listwise ranking losses, neighborhood component analysis (NCA), deep survival analysis, deep latent variable models, etc., which deserves finer analysis. Yet, existing algorithms and analyses are restricted in one or other aspects. The contribution of this paper is to provide a comprehensive convergence analysis of a simple stochastic algorithm for both non-convex and convex objectives. Our key result is the improved oracle complexity with the parallel speed-up by using the moving-average based estimator with mini-batching. Our theoretical analysis also exhibits new insights for improving the practical implementation by sampling the batches of equal size for the outer and inner levels. Numerical experiments on AP maximization, NCA, and p-norm push corroborate some aspects of the theory.
Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradation
Authors: Cunzhi Zhao, Xingpeng Li
Date: 2022-02-24 00:00:00
Tasks: Scheduling

Battery energy storage system (BESS) can effec-tively mitigate the uncertainty of variable renewable generation. Degradation is unpreventable and hard to model and predict for batteries such as the most popular Lithium-ion battery (LiB). In this paper, we propose a data driven method to predict the bat-tery degradation per a given scheduled battery operational pro-file. Particularly, a neural network based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. When incorpo-rating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can establish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely with the proposed cycle based battery usage processing (CBUP) method for the NNBD model. Since the proposed NNBD model is highly non-linear and non-convex, BDMDS would be very hard to solve. To address this issue, a neural network and optimization decoupled heuristic (NNODH) algorithm is proposed in this paper to effectively solve this neural network embedded optimization problem. Simulation results demonstrate that the proposed NNODH algorithm is able to ob-tain the optimal solution with lowest total cost including normal operation cost and battery degradation cost.
Ensemble Method for Estimating Individualized Treatment Effects
Authors: Kevin Wu Han, Han Wu
Date: 2022-02-25 00:00:00
Tasks: Model Selection

In many medical and business applications, researchers are interested in estimating individualized treatment effects using data from a randomized experiment. For example in medical applications, doctors learn the treatment effects from clinical trials and in technology companies, researchers learn them from A/B testing experiments. Although dozens of machine learning models have been proposed for this task, it is challenging to determine which model will be best for the problem at hand because ground-truth treatment effects are unobservable. In contrast to several recent papers proposing methods to select one of these competing models, we propose an algorithm for aggregating the estimates from a diverse library of models. We compare ensembling to model selection on 43 benchmark datasets, and find that ensembling wins almost every time. Theoretically, we prove that our ensemble model is (asymptotically) at least as accurate as the best model under consideration, even if the number of candidate models is allowed to grow with the sample size.
GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection
Authors: Mudit Dhawan, Shakshi Sharma, Aditya Kadam, Rajesh Sharma, Ponnurangam Kumaraguru
Date: 2022-02-25 00:00:00
Tasks: Fake News Detection, Graph Attention, Graph Neural Network

Social media in present times has a significant and growing influence. Fake news being spread on these platforms have a disruptive and damaging impact on our lives. Furthermore, as multimedia content improves the visibility of posts more than text data, it has been observed that often multimedia is being used for creating fake content. A plethora of previous multimodal-based work has tried to address the problem of modeling heterogeneous modalities in identifying fake content. However, these works have the following limitations: (1) inefficient encoding of inter-modal relations by utilizing a simple concatenation operator on the modalities at a later stage in a model, which might result in information loss; (2) training very deep neural networks with a disproportionate number of parameters on small but complex real-life multimodal datasets result in higher chances of overfitting. To address these limitations, we propose GAME-ON, a Graph Neural Network based end-to-end trainable framework that allows granular interactions within and across different modalities to learn more robust data representations for multimodal fake news detection. We use two publicly available fake news datasets, Twitter and Weibo, for evaluations. Our model outperforms on Twitter by an average of 11% and keeps competitive performance on Weibo, within a 2.6% margin, while using 65% fewer parameters than the best comparable state-of-the-art baseline.
On the Effectiveness of Dataset Watermarking in Adversarial Settings
Authors: Buse Gul Atli Tekgul, N. Asokan
Date: 2022-02-25 00:00:00
Tasks: Model extraction

In a data-driven world, datasets constitute a significant economic value. Dataset owners who spend time and money to collect and curate the data are incentivized to ensure that their datasets are not used in ways that they did not authorize. When such misuse occurs, dataset owners need technical mechanisms for demonstrating their ownership of the dataset in question. Dataset watermarking provides one approach for ownership demonstration which can, in turn, deter unauthorized use. In this paper, we investigate a recently proposed data provenance method, radioactive data, to assess if it can be used to demonstrate ownership of (image) datasets used to train machine learning (ML) models. The original paper reported that radioactive data is effective in white-box settings. We show that while this is true for large datasets with many classes, it is not as effective for datasets where the number of classes is low $(\leq 30)$ or the number of samples per class is low $(\leq 500)$. We also show that, counter-intuitively, the black-box verification technique is effective for all datasets used in this paper, even when white-box verification is not. Given this observation, we show that the confidence in white-box verification can be improved by using watermarked samples directly during the verification process. We also highlight the need to assess the robustness of radioactive data if it were to be used for ownership demonstration since it is an adversarial setting unlike provenance identification. Compared to dataset watermarking, ML model watermarking has been explored more extensively in recent literature. However, most of the model watermarking techniques can be defeated via model extraction. We show that radioactive data can effectively survive model extraction attacks, which raises the possibility that it can be used for ML model ownership verification robust against model extraction.
Machine Learning based refinement strategies for polyhedral grids with applications to Virtual Element and polyhedral Discontinuous Galerkin methods
Authors: P. F. Antonietti, F. Dassi, E. Manuzzi
Date: 2022-02-25 00:00:00

We propose two new strategies based on Machine Learning techniques to handle polyhedral grid refinement, to be possibly employed within an adaptive framework. The first one employs the k-means clustering algorithm to partition the points of the polyhedron to be refined. This strategy is a variation of the well known Centroidal Voronoi Tessellation. The second one employs Convolutional Neural Networks to classify the "shape" of an element so that "ad-hoc" refinement criteria can be defined. This strategy can be used to enhance existing refinement strategies, including the k-means strategy, at a low online computational cost. We test the proposed algorithms considering two families of finite element methods that support arbitrarily shaped polyhedral elements, namely the Virtual Element Method (VEM) and the Polygonal Discontinuous Galerkin (PolyDG) method. We demonstrate that these strategies do preserve the structure and the quality of the underlaying grids, reducing the overall computational cost and mesh complexity.
Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems
Authors: Pedro J. Freire, Bernhard Spinnler, Daniel Abode, Jaroslaw E. Prilepsky, Abdallah A. I. Ali, Nelson Costa, Wolfgang Schairer, Antonio Napoli, Andrew D. Ellis, Sergei K. Turitsyn
Date: 2022-02-25 00:00:00
Tasks: Domain Adaptation

We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up to 99\% training process reduction, which we demonstrate in three experimental setups.
State-of-the-art in speaker recognition
Authors: Marcos Faundez-Zanuy, Enric Monte-Moreno
Date: 2022-02-23 00:00:00
Tasks: Speaker Recognition

Recent advances in speech technologies have produced new tools that can be used to improve the performance and flexibility of speaker recognition While there are few degrees of freedom or alternative methods when using fingerprint or iris identification techniques, speech offers much more flexibility and different levels for performing recognition: the system can force the user to speak in a particular manner, different for each attempt to enter. Also with voice input the system has other degrees of freedom, such as the use of knowledge/codes that only the user knows, or dialectical/semantical traits that are difficult to forge. This paper offers and overview of the state of the art in speaker recognition, with special emphasis on the pros and contras, and the current research lines. The current research lines include improved classification systems, and the use of high level information by means of probabilistic grammars. In conclusion, speaker recognition is far away from being a technology where all the possibilities have already been explored.
Early Disease Stage Characterization in Parkinson's Disease from Resting-state fMRI Data Using a Long Short-term Memory Network
Authors: Xueqi Guo, Sule Tinaz, Nicha C. Dvornek
Date: 2022-02-11 00:00:00
Tasks: BIG-bench Machine Learning, Functional Connectivity

Parkinson's disease (PD) is a common and complex neurodegenerative disorder with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it is challenging to classify early stages 1 and 2 and detect brain function alterations. Functional magnetic resonance imaging (fMRI) is a promising tool in revealing functional connectivity (FC) differences and developing biomarkers in PD. Some machine learning approaches like support vector machine and logistic regression have been successfully applied in the early diagnosis of PD using fMRI data, which outperform classifiers based on manually selected morphological features. However, the early-stage characterization in FC changes has not been fully investigated. Given the complexity and non-linearity of fMRI data, we propose the use of a long short-term memory (LSTM) network to characterize the early stages of PD. The study included 84 subjects (56 in stage 2 and 28 in stage 1) from the Parkinson's Progression Markers Initiative (PPMI), the largest available public PD dataset. Under a repeated 10-fold stratified cross-validation, the LSTM model reached an accuracy of 71.63%, 13.52% higher than the best traditional machine learning method, indicating significantly better robustness and accuracy compared with other machine learning classifiers. We used the learned LSTM model weights to select the top brain regions that contributed to model prediction and performed FC analyses to characterize functional changes with disease stage and motor impairment to gain better insight into the brain mechanisms of PD.
Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health
Authors: Marcos Faundez-Zanuy, Julian Fierrez, Miguel A. Ferrer, Moises Diaz, Ruben Tolosana, Réjean Plamondon
Date: 2022-02-24 00:00:00

Background- This paper summarizes the state-of-the-art and applications based on online handwritting signals with special emphasis on e-security and e-health fields. Methods- In particular, we focus on the main achievements and challenges that should be addressed by the scientific community, providing a guide document for future research. Conclusions- Among all the points discussed in this article, we remark the importance of considering security, health, and metadata from a joint perspective. This is especially critical due to the double use possibilities of these behavioral signals.
Towards Optimal Lower Bounds for k-median and k-means Coresets
Authors: Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn
Date: 2022-02-25 00:00:00
Tasks: Clustering

Given a set of points in a metric space, the $(k,z)$-clustering problem consists of finding a set of $k$ points called centers, such that the sum of distances raised to the power of $z$ of every data point to its closest center is minimized. Special cases include the famous k-median problem ($z = 1$) and k-means problem ($z = 2$). The $k$-median and $k$-means problems are at the heart of modern data analysis and massive data applications have given raise to the notion of coreset: a small (weighted) subset of the input point set preserving the cost of any solution to the problem up to a multiplicative $(1 \pm \varepsilon)$ factor, hence reducing from large to small scale the input to the problem. In this paper, we present improved lower bounds for coresets in various metric spaces. In finite metrics consisting of $n$ points and doubling metrics with doubling constant $D$, we show that any coreset for $(k,z)$ clustering must consist of at least $\Omega(k \varepsilon^{-2} \log n)$ and $\Omega(k \varepsilon^{-2} D)$ points, respectively. Both bounds match previous upper bounds up to polylog factors. In Euclidean spaces, we show that any coreset for $(k,z)$ clustering must consists of at least $\Omega(k\varepsilon^{-2})$ points. We complement these lower bounds with a coreset construction consisting of at most $\tilde{O}(k\varepsilon^{-2}\cdot \min(\varepsilon^{-z},k))$ points.
Curriculum Meta-Learning for Next POI Recommendation
Authors: Yudong Chen, Xin Wang, Miao Fan, Jizhou Huang, Shengwen Yang, and Wenwu Zhu.
Date: 2021-08-14 00:00:00
Proceeding: KDD 2021 8
Tasks: Meta-Learning

Next point-of-interest (POI) recommendation is a hot research field where a recent emerging scenario, next POI to search recommendation, has been deployed in many online map services such as Baidu Maps. One of the key issues in this scenario is providing satisfactory recommendation services for cold-start cities with a limited number of user-POI interactions, which requires transferring the knowledge hidden in rich data from many other cities to these cold-start cities. Existing literature either does not consider the city-transfer issue or cannot simultaneously tackle the data sparsity and pattern diversity issues among various users in multiple cities. To address these issues, we explore city-transfer next POI to search recommendation that transfers the knowledge from multiple cities with rich data to cold-start cities with scarce data. We propose a novel Curriculum Hardness Aware Meta-Learning (CHAML) framework, which incorporates hard sample mining and curriculum learning into a meta-learning paradigm. Concretely, the CHAML framework considers both city-level and user-level hardness to enhance the conditional sampling during meta training, and uses an easy-to-hard curriculum for the city-sampling pool to help the meta-learner converge to a better state. Extensive experiments on two real-world map search datasets from Baidu Maps demonstrate the superiority of CHAML framework.
XCS as a reinforcement learning approach to automatic test case prioritization
Authors: Lukas Rosenbauer, Anthony Stein, Roland Maier, David Pätzel, Jörg Hähner
Date: 2020-07-12 00:00:00
Proceeding: GECCO 2020 7
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Testing is a crucial part in the development of new products. With the rise of test automation methods, companies start relying on an even higher number of tests. Sometimes it is not feasible to run all tests and the goal is to determine which tests are crucial and which are less important. This prioritization problem has just recently gotten into the focus of reinforcement learning. A neural network combined with prioritized experience replay (ER) was used to identify critical tests. We are the first to apply XCS classifier systems (XCS) for this use case and reveal that XCS is not only suitable for this problem, but can also be superior to the aforementioned neural network and leads to more stable results. In this work, we adapt XCS's learning mechanism to the task by introducing a batch update which is based on Monte Carlo control. Further, we investigate if prioritized ER has the same positive effects on XCS as on the neural network for this test prioritization problem. Our experiments show that in general this is not the case for XCS.
XCSF for Automatic Test Case Prioritization
Authors: Lukas Rosenbauer, Anthony Stein, David Pätzel, Jörg Hähner
Date: 2020-11-04 00:00:00
Proceeding: IJCCI 2020 11
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Testing is a crucial part in the development of a new product. Due to the change from manual testing to
automated testing, companies can rely on a higher number of tests. There are certain cases such as smoke
tests where the execution of all tests is not feasible and a smaller test suite of critical test cases is necessary.
This prioritization problem has just gotten into the focus of reinforcement learning. A neural network and an
XCS classifier system have been applied to this task. Another evolutionary machine learning approach is the
XCSF which produces, unlike XCS, continuous outputs. In this work we show that XCSF is superior to both
the neural network and XCS for this problem.
XCSF with Experience Replay for Automatic Test Case Prioritization
Authors: Lukas Rosenbauer, Anthony Stein, David Pätzel, Jörg Hähner
Date: 2020-12-04 00:00:00
Proceeding: IEEE Symposium Series on Computational Intelligence 2020 12

The verification of a new product is of major importance for companies. With the rise of test automation, companies start to rely on huge numbers of tests. Often, it is not feasible to run all available tests due to time constraints. Thus, a test suite of critical tests has to be determined. Recent research has shown that reinforcement learning is suitable for this prioritization problem: neural networks and XCS(F) learning classifier systems have been applied to this task. We extend the existing XCSF-based agent by incorporating experience replay (ER) in order to improve learning efficiency. In an experimental evaluation we show that this not only boosts performance but also enables our agent to exceed the aforementioned solutions. For XCSF without ER and neural networks, the most suitable reward function is strongly dependent on the underlying data set. This is in practice a downside as reward functions usually need to be chosen a priori in order to ensure the quality of the chosen test suite. However, for our improved agent this is not the case and we can give a clear recommendation for the reward function.
Transfer Learning for Automated Test Case Prioritization Using XCSF
Authors: Lukas Rosenbauer, Anthony Stein, David Pätzel, Jörg Hähner
Date: 2021-03-15 00:00:00
Proceeding: Springer EvoStar 2021 3
Tasks: reinforcement-learning, Reinforcement Learning (RL), Transfer Learning

With the rise of test automation, companies start to rely on large amounts of test cases. However, there are situations where it is unfeasible to perform every test case as only a limited amount of time is available. Under such circumstances a set of crucial tests has to be compiled. Recent research has shown that reinforcement learning methods such as XCSF classifier systems are well-suited for this task. This work investigates whether reusing knowledge of XCSF-based agents is beneficial for prioritizing test cases and subsequently selecting test suites in terms of performance. We developed a simplistic population transformation and evaluate it in a series of experiments. Our evaluation shows that XCSF may indeed benefit from transfer learning for this use case.
Generic approaches for parallel rule matching in learning classifier systems
Authors: Lukas Rosenbauer, Anthony Stein, Jörg Hähner
Date: 2020-07-12 00:00:00
Proceeding: GECCO 2020 7
Tasks: CPU

The XCS classifier system (XCS) constitutes the most deeply investigated evolutionary rule-based machine learning algorithm. Due to its online learning nature, it is not as computationally intense as deep learning approaches. However, in order to increase XCS's realtime capabilities, it is worth to improve its runtime for the inference step. A iteration through XCS's main loop involves various steps. Among them, matching usually constitutes the computationally most expensive part. Various ways for improvements have been proposed, e.g. using specialized hardware or vector commands. However, existing approaches require either a certain hardware coupled with a specific instruction set, or a dedicated multiprocessing programming language. In this context, parallel programs are often evaluated in terms of their speed up, but it turns out that qualitative criteria such as flexibility and independence of a specific hardware are also relevant. We therefore present a generic parallel approach for matching. We only rely on the minimum assumption of a multicore CPU with standard synchronization mechanisms, e.g., locks. Those assumptions are satisfied in almost all modern computing systems and high-level programming languages today. Thus we show that our approaches cannot only decrease the runtime, but are also reusable for most learning classifier systems and computing systems.