pwc-archive/papers-with-abstracts huggingface dataset:
Self-supervised Graph Representation Learning for Black Market Account Detection
Authors: Zequan Xu, Lianyun Li, Hui Li, Qihang Sun, Shaofeng Hu, Rongrong Ji
Date: 2022-12-06 00:00:00
Tasks: Attribute, Graph Neural Network, Graph Representation Learning, Representation Learning, Self-Supervised Learning

Nowadays, Multi-purpose Messaging Mobile App (MMMA) has become increasingly prevalent. MMMAs attract fraudsters and some cybercriminals provide support for frauds via black market accounts (BMAs). Compared to fraudsters, BMAs are not directly involved in frauds and are more difficult to detect. This paper illustrates our BMA detection system SGRL (Self-supervised Graph Representation Learning) used in WeChat, a representative MMMA with over a billion users. We tailor Graph Neural Network and Graph Self-supervised Learning in SGRL for BMA detection. The workflow of SGRL contains a pretraining phase that utilizes structural information, node attribute information and available human knowledge, and a lightweight detection phase. In offline experiments, SGRL outperforms state-of-the-art methods by 16.06%-58.17% on offline evaluation measures. We deploy SGRL in the online environment to detect BMAs on the billion-scale WeChat graph, and it exceeds the alternative by 7.27% on the online evaluation measure. In conclusion, SGRL can alleviate label reliance, generalize well to unseen data, and effectively detect BMAs in WeChat.
Transformers for End-to-End InfoSec Tasks: A Feasibility Study
Authors: Ethan M. Rudd, Mohammad Saidur Rahman, Philip Tully
Date: 2022-12-05 00:00:00
Tasks: Binary Classification, Malware Detection

In this paper, we assess the viability of transformer models in end-to-end InfoSec settings, in which no intermediate feature representations or processing steps occur outside the model. We implement transformer models for two distinct InfoSec data formats - specifically URLs and PE files - in a novel end-to-end approach, and explore a variety of architectural designs, training regimes, and experimental settings to determine the ingredients necessary for performant detection models. We show that in contrast to conventional transformers trained on more standard NLP-related tasks, our URL transformer model requires a different training approach to reach high performance levels. Specifically, we show that 1) pre-training on a massive corpus of unlabeled URL data for an auto-regressive task does not readily transfer to binary classification of malicious or benign URLs, but 2) that using an auxiliary auto-regressive loss improves performance when training from scratch. We introduce a method for mixed objective optimization, which dynamically balances contributions from both loss terms so that neither one of them dominates. We show that this method yields quantitative evaluation metrics comparable to that of several top-performing benchmark classifiers. Unlike URLs, binary executables contain longer and more distributed sequences of information-rich bytes. To accommodate such lengthy byte sequences, we introduce additional context length into the transformer by providing its self-attention layers with an adaptive span similar to Sukhbaatar et al. We demonstrate that this approach performs comparably to well-established malware detection models on benchmark PE file datasets, but also point out the need for further exploration into model improvements in scalability and compute efficiency.
Efficient Malware Analysis Using Metric Embeddings
Authors: Ethan M. Rudd, David Krisiloff, Scott Coull, Daniel Olszewski, Edward Raff, James Holt
Date: 2022-12-05 00:00:00
Tasks: Attribute, Malware Analysis, Malware Detection, Metric Learning

In this paper, we explore the use of metric learning to embed Windows PE files in a low-dimensional vector space for downstream use in a variety of applications, including malware detection, family classification, and malware attribute tagging. Specifically, we enrich labeling on malicious and benign PE files using computationally expensive, disassembly-based malicious capabilities. Using these capabilities, we derive several different types of metric embeddings utilizing an embedding neural network trained via contrastive loss, Spearman rank correlation, and combinations thereof. We then examine performance on a variety of transfer tasks performed on the EMBER and SOREL datasets, demonstrating that for several tasks, low-dimensional, computationally efficient metric embeddings maintain performance with little decay, which offers the potential to quickly retrain for a variety of transfer tasks at significantly reduced storage overhead. We conclude with an examination of practical considerations for the use of our proposed embedding approach, such as robustness to adversarial evasion and introduction of task-specific auxiliary objectives to improve performance on mission critical tasks.
Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases
Date: 2022-12-05 00:00:00
Proceeding: NeurIPS 2023 11

We present a simple but effective method to measure and mitigate model biases caused by reliance on spurious cues. Instead of requiring costly changes to one's data or model training, our method better utilizes the data one already has by sorting them. Specifically, we rank images within their classes based on spuriosity (the degree to which common spurious cues are present), proxied via deep neural features of an interpretable network. With spuriosity rankings, it is easy to identify minority subpopulations (i.e. low spuriosity images) and assess model bias as the gap in accuracy between high and low spuriosity images. One can even efficiently remove a model's bias at little cost to accuracy by finetuning its classification head on low spuriosity images, resulting in fairer treatment of samples regardless of spuriosity. We demonstrate our method on ImageNet, annotating $5000$ class-feature dependencies ($630$ of which we find to be spurious) and generating a dataset of $325k$ soft segmentations for these features along the way. Having computed spuriosity rankings via the identified spurious neural features, we assess biases for $89$ diverse models and find that class-wise biases are highly correlated across models. Our results suggest that model bias due to spurious feature reliance is influenced far more by what the model is trained on than how it is trained.
Unifying Vision, Text, and Layout for Universal Document Processing
Authors: Zineng Tang, ZiYi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, Mohit Bansal
Date: 2022-12-05 00:00:00
Proceeding: CVPR 2023 1
Tasks: Document AI, document understanding, Image Reconstruction, Visual Question Answering (VQA)

We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation. With a novel Vision-Text-Layout Transformer, UDOP unifies pretraining and multi-domain downstream tasks into a prompt-based sequence generation scheme. UDOP is pretrained on both large-scale unlabeled document corpora using innovative self-supervised objectives and diverse labeled data. UDOP also learns to generate document images from text and layout modalities via masked image reconstruction. To the best of our knowledge, this is the first time in the field of document AI that one model simultaneously achieves high-quality neural document editing and content customization. Our method sets the state-of-the-art on 8 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites. UDOP ranks first on the leaderboard of the Document Understanding Benchmark.
Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?
Authors: Khaled Badran, Pierre-Olivier Côté, Amanda Kolopanis, Rached Bouchoucha, Antonio Collante, Diego Elias Costa, Emad Shihab, Foutse khomh
Date: 2022-12-05 00:00:00
Tasks: Fairness

As machine learning (ML) systems get adopted in more critical areas, it has become increasingly crucial to address the bias that could occur in these systems. Several fairness pre-processing algorithms are available to alleviate implicit biases during model training. These algorithms employ different concepts of fairness, often leading to conflicting strategies with consequential trade-offs between fairness and accuracy. In this work, we evaluate three popular fairness pre-processing algorithms and investigate the potential for combining all algorithms into a more robust pre-processing ensemble. We report on lessons learned that can help practitioners better select fairness algorithms for their models.
Learning to Optimize in Model Predictive Control
Authors: Jacob Sacks, Byron Boots
Date: 2022-12-05 00:00:00
Tasks: Computational Efficiency, Imitation Learning, model, Model Predictive Control

Sampling-based Model Predictive Control (MPC) is a flexible control framework that can reason about non-smooth dynamics and cost functions. Recently, significant work has focused on the use of machine learning to improve the performance of MPC, often through learning or fine-tuning the dynamics or cost function. In contrast, we focus on learning to optimize more effectively. In other words, to improve the update rule within MPC. We show that this can be particularly useful in sampling-based MPC, where we often wish to minimize the number of samples for computational reasons. Unfortunately, the cost of computational efficiency is a reduction in performance; fewer samples results in noisier updates. We show that we can contend with this noise by learning how to update the control distribution more effectively and make better use of the few samples that we have. Our learned controllers are trained via imitation learning to mimic an expert which has access to substantially more samples. We test the efficacy of our approach on multiple simulated robotics tasks in sample-constrained regimes and demonstrate that our approach can outperform a MPC controller with the same number of samples.
Learning Sampling Distributions for Model Predictive Control
Authors: Jacob Sacks, Byron Boots
Date: 2022-12-05 00:00:00
Tasks: model, Model Predictive Control

Sampling-based methods have become a cornerstone of contemporary approaches to Model Predictive Control (MPC), as they make no restrictions on the differentiability of the dynamics or cost function and are straightforward to parallelize. However, their efficacy is highly dependent on the quality of the sampling distribution itself, which is often assumed to be simple, like a Gaussian. This restriction can result in samples which are far from optimal, leading to poor performance. Recent work has explored improving the performance of MPC by sampling in a learned latent space of controls. However, these methods ultimately perform all MPC parameter updates and warm-starting between time steps in the control space. This requires us to rely on a number of heuristics for generating samples and updating the distribution and may lead to sub-optimal performance. Instead, we propose to carry out all operations in the latent space, allowing us to take full advantage of the learned distribution. Specifically, we frame the learning problem as bi-level optimization and show how to train the controller with backpropagation-through-time. By using a normalizing flow parameterization of the distribution, we can leverage its tractable density to avoid requiring differentiability of the dynamics and cost function. Finally, we evaluate the proposed approach on simulated robotics tasks and demonstrate its ability to surpass the performance of prior methods and scale better with a reduced number of samples.
A Mobility-Aware Deep Learning Model for Long-Term COVID-19 Pandemic Prediction and Policy Impact Analysis
Authors: Danfeng Guo, Zijie Huang, Junheng Hao, Yizhou Sun, Wei Wang, Demetri Terzopoulos
Date: 2022-12-05 00:00:00

Pandemic(epidemic) modeling, aiming at disease spreading analysis, has always been a popular research topic especially following the outbreak of COVID-19 in 2019. Some representative models including SIR-based deep learning prediction models have shown satisfactory performance. However, one major drawback for them is that they fall short in their long-term predictive ability. Although graph convolutional networks (GCN) also perform well, their edge representations do not contain complete information and it can lead to biases. Another drawback is that they usually use input features which they are unable to predict. Hence, those models are unable to predict further future. We propose a model that can propagate predictions further into the future and it has better edge representations. In particular, we model the pandemic as a spatial-temporal graph whose edges represent the transition of infections and are learned by our model. We use a two-stream framework that contains GCN and recursive structures (GRU) with an attention mechanism. Our model enables mobility analysis that provides an effective toolbox for public health researchers and policy makers to predict how different lock-down strategies that actively control mobility can influence the spread of pandemics. Experiments show that our model outperforms others in its long-term predictive power. Moreover, we simulate the effects of certain policies and predict their impacts on infection control.
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation
Authors: Zhongju Yuan, Zhenkun Wang, Genghui Li
Date: 2022-12-05 00:00:00
Tasks: Contrastive Learning, Cross-Domain Few-Shot, Domain Adaptation, Few-Shot Learning, Relation, Relation Extraction, Representation Learning, Sentence, Sentence Embedding, Sentence-Embedding, Transfer Learning

Few-shot relation extraction aims to recognize novel relations with few labeled sentences in each relation. Previous metric-based few-shot relation extraction algorithms identify relationships by comparing the prototypes generated by the few labeled sentences embedding with the embeddings of the query sentences using a trained metric function. However, as these domains always have considerable differences from those in the training dataset, the generalization ability of these approaches on unseen relations in many domains is limited. Since the prototype is necessary for obtaining relationships between entities in the latent space, we suggest learning more interpretable and efficient prototypes from prior knowledge and the intrinsic semantics of relations to extract new relations in various domains more effectively. By exploring the relationships between relations using prior information, we effectively improve the prototype representation of relations. By using contrastive learning to make the classification margins between sentence embedding more distinct, the prototype's geometric interpretability is enhanced. Additionally, utilizing a transfer learning approach for the cross-domain problem allows the generation process of the prototype to account for the gap between other domains, making the prototype more robust and enabling the better extraction of associations across multiple domains. The experiment results on the benchmark FewRel dataset demonstrate the advantages of the suggested method over some state-of-the-art approaches.
Enhancing Quantum Adversarial Robustness by Randomized Encodings
Authors: Weiyuan Gong, Dong Yuan, Weikang Li, Dong-Ling Deng
Date: 2022-12-05 00:00:00
Tasks: Adversarial Robustness, Quantum Machine Learning

The interplay between quantum physics and machine learning gives rise to the emergent frontier of quantum machine learning, where advanced quantum learning models may outperform their classical counterparts in solving certain challenging problems. However, quantum learning systems are vulnerable to adversarial attacks: adding tiny carefully-crafted perturbations on legitimate input samples can cause misclassifications. To address this issue, we propose a general scheme to protect quantum learning systems from adversarial attacks by randomly encoding the legitimate data samples through unitary or quantum error correction encoders. In particular, we rigorously prove that both global and local random unitary encoders lead to exponentially vanishing gradients (i.e. barren plateaus) for any variational quantum circuits that aim to add adversarial perturbations, independent of the input data and the inner structures of adversarial circuits and quantum classifiers. In addition, we prove a rigorous bound on the vulnerability of quantum classifiers under local unitary adversarial attacks. We show that random black-box quantum error correction encoders can protect quantum classifiers against local adversarial noises and their robustness increases as we concatenate error correction codes. To quantify the robustness enhancement, we adapt quantum differential privacy as a measure of the prediction stability for quantum classifiers. Our results establish versatile defense strategies for quantum classifiers against adversarial perturbations, which provide valuable guidance to enhance the reliability and security for both near-term and future quantum learning technologies.
Learning Representations that Enable Generalization in Assistive Tasks
Authors: Jerry Zhi-Yang He, aditi raghunathan, Daniel S. Brown, Zackory Erickson, Anca D. Dragan
Date: 2022-12-05 00:00:00

Recent work in sim2real has successfully enabled robots to act in physical environments by training in simulation with a diverse ''population'' of environments (i.e. domain randomization). In this work, we focus on enabling generalization in assistive tasks: tasks in which the robot is acting to assist a user (e.g. helping someone with motor impairments with bathing or with scratching an itch). Such tasks are particularly interesting relative to prior sim2real successes because the environment now contains a human who is also acting. This complicates the problem because the diversity of human users (instead of merely physical environment parameters) is more difficult to capture in a population, thus increasing the likelihood of encountering out-of-distribution (OOD) human policies at test time. We advocate that generalization to such OOD policies benefits from (1) learning a good latent representation for human policies that test-time humans can accurately be mapped to, and (2) making that representation adaptable with test-time interaction data, instead of relying on it to perfectly capture the space of human policies based on the simulated population only. We study how to best learn such a representation by evaluating on purposefully constructed OOD test policies. We find that sim2real methods that encode environment (or population) parameters and work well in tasks that robots do in isolation, do not work well in assistance. In assistance, it seems crucial to train the representation based on the history of interaction directly, because that is what the robot will have access to at test time. Further, training these representations to then predict human actions not only gives them better structure, but also enables them to be fine-tuned at test-time, when the robot observes the partner act. https://adaptive-caregiver.github.io.
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics
Authors: Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura Cruz, Kerrie Douglas, Andrew Lan, Christopher Brinton
Date: 2022-12-05 00:00:00
Tasks: Federated Learning, Knowledge Tracing, Personalized Federated Learning

Conventional methods for student modeling, which involve predicting grades based on measured activities, struggle to provide accurate results for minority/underrepresented student groups due to data availability biases. In this paper, we propose a Multi-Layer Personalized Federated Learning (MLPFL) methodology that optimizes inference accuracy over different layers of student grouping criteria, such as by course and by demographic subgroups within each course. In our approach, personalized models for individual student subgroups are derived from a global model, which is trained in a distributed fashion via meta-gradient updates that account for subgroup heterogeneity while preserving modeling commonalities that exist across the full dataset. The evaluation of the proposed methodology considers case studies of two popular downstream student modeling tasks, knowledge tracing and outcome prediction, which leverage multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums) in model training. Experiments on three real-world online course datasets show significant improvements achieved by our approach over existing student modeling benchmarks, as evidenced by an increased average prediction quality and decreased variance across different student subgroups. Visual analysis of the resulting students' knowledge state embeddings confirm that our personalization methodology extracts activity patterns clustered into different student subgroups, consistent with the performance enhancements we obtain over the baselines.
MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning
Authors: Yizhi Li, Ruibin Yuan, Ge Zhang, Yinghao Ma, Chenghua Lin, Xingran Chen, Anton Ragni, Hanzhi Yin, Zhijie Hu, Haoyu He, Emmanouil Benetos, Norbert Gyenge, Ruibo Liu, Jie Fu
Date: 2022-12-05 00:00:00
Tasks: Representation Learning, Self-Supervised Learning

The deep learning community has witnessed an exponentially growing interest in self-supervised learning (SSL). However, it still remains unexplored how to build a framework for learning useful representations of raw music waveforms in a self-supervised manner. In this work, we design Music2Vec, a framework exploring different SSL algorithmic components and tricks for music audio recordings. Our model achieves comparable results to the state-of-the-art (SOTA) music SSL model Jukebox, despite being significantly smaller with less than 2% of parameters of the latter. The model will be released on Huggingface(Please refer to: https://huggingface.co/m-a-p/music2vec-v1)
FEMa-FS: Finite Element Machines for Feature Selection
Authors: Lucas Biaggi, João P. Papa, Kelton A. P Costa, Danillo R. Pereira, Leandro A. Passos
Date: 2022-12-05 00:00:00
Tasks: Anomaly Detection, feature selection

Identifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emerge as an elegant solution to identify such scenarios and learn irrelevant information so that a reduction in the identification time and possible gain in accuracy can be obtained. This paper proposes a novel feature selection approach called Finite Element Machines for Feature Selection (FEMa-FS), which uses the framework of finite elements to identify the most relevant information from a given dataset. Although FEMa-FS can be applied to any application domain, it has been evaluated in the context of anomaly detection in computer networks. The outcomes over two datasets showed promising results.