pwc-archive/papers-with-abstracts huggingface dataset:
Inkorrect: Online Handwriting Spelling Correction
Authors: Andrii Maksai, Henry Rowley, Jesse Berent, Claudiu Musat
Date: 2022-02-28 00:00:00
Tasks: Spelling Correction

We introduce Inkorrect, a data- and label-efficient approach for online handwriting (Digital Ink) spelling correction - DISC. Unlike previous work, the proposed method does not require multiple samples from the same writer, or access to character level segmentation. We show that existing automatic evaluation metrics do not fully capture and are not correlated with the human perception of the quality of the spelling correction, and propose new ones that correlate with human perception. We additionally surface an interesting phenomenon: a trade-off between the similarity and recognizability of the spell-corrected inks. We further create a family of models corresponding to different points on the Pareto frontier between those two axes. We show that Inkorrect's Pareto frontier dominates the points that correspond to prior work.
Logical Fallacy Detection
Authors: Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schölkopf
Date: 2022-02-28 00:00:00
Tasks: Language Modeling, Language Modelling, Logical Fallacies, Logical Fallacy Detection, Misinformation

Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate). Detecting logical fallacies is a hard problem as the model must understand the underlying logical structure of the argument. We find that existing pretrained large language models perform poorly on this task. In contrast, we show that a simple structure-aware classifier outperforms the best language model by 5.46% on Logic and 4.51% on LogicClimate. We encourage future work to explore this task as (a) it can serve as a new reasoning challenge for language models, and (b) it can have potential applications in tackling the spread of misinformation. Our dataset and code are available at https://github.com/causalNLP/logical-fallacy
Towards Robust Stacked Capsule Autoencoder with Hybrid Adversarial Training
Authors: Jiazhu Dai, Siwei Xiong
Date: 2022-02-28 00:00:00

Capsule networks (CapsNets) are new neural networks that classify images based on the spatial relationships of features. By analyzing the pose of features and their relative positions, it is more capable to recognize images after affine transformation. The stacked capsule autoencoder (SCAE) is a state-of-the-art CapsNet, and achieved unsupervised classification of CapsNets for the first time. However, the security vulnerabilities and the robustness of the SCAE has rarely been explored. In this paper, we propose an evasion attack against SCAE, where the attacker can generate adversarial perturbations based on reducing the contribution of the object capsules in SCAE related to the original category of the image. The adversarial perturbations are then applied to the original images, and the perturbed images will be misclassified. Furthermore, we propose a defense method called Hybrid Adversarial Training (HAT) against such evasion attacks. HAT makes use of adversarial training and adversarial distillation to achieve better robustness and stability. We evaluate the defense method and the experimental results show that the refined SCAE model can achieve 82.14% classification accuracy under evasion attack. The source code is available at https://github.com/FrostbiteXSW/SCAE_Defense.
Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding
Authors: Maxime Elkael, Massinissa Ait Aba, Andrea Araldo, Hind Castel, Badii Jouaber
Date: 2022-02-28 00:00:00
Tasks: Network Embedding, reinforcement-learning, Reinforcement Learning (RL)

In this article, we consider the Virtual Network Embedding (VNE) problem for 5G networks slicing. This problem requires to allocate multiple Virtual Networks (VN) on a substrate virtualized physical network while maximizing among others, resource utilization, maximum number of placed VNs and network operator's benefit. We solve the online version of the problem where slices arrive over time. Inspired by the Nested Rollout Policy Adaptation (NRPA) algorithm, a variant of the well known Monte Carlo Tree Search (MCTS) that learns how to perform good simulations over time, we propose a new algorithm that we call Neighborhood Enhanced Policy Adaptation (NEPA). The key feature of our algorithm is to observe NRPA cannot exploit knowledge acquired in one branch of the state tree for another one which starts differently. NEPA learns by combining NRPA with Neighbordhood Search in a frugal manner which improves only promising solutions while keeping the running time low. We call this technique a monkey business because it comes down to jumping from one interesting branch to the other, similar to how monkeys jump from tree to tree instead of going down everytime. NEPA achieves better results in terms of acceptance ratio and revenue-to-cost ratio compared to other state-of-the-art algorithms, both on real and synthetic topologies.
Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks
Authors: Yile Chen, Xiucheng Li, Gao Cong, Cheng Long, Zhifeng Bao, Shang Liu, Wanli Gu, Fuzheng Zhang
Date: 2022-02-28 00:00:00
Tasks: Graph Neural Network

As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers. Most of the existing methods for relationship inference are not targeted at POI, thus failing to capture unique spatial characteristics that have huge effects on POI relationships. In this work we propose PRIM to tackle POI relationship inference for multiple relation types. PRIM features four novel components, including a weighted relational graph neural network, category taxonomy integration, a self-attentive spatial context extractor, and a distance-specific scoring function. Extensive experiments on two real-world datasets show that PRIM achieves the best results compared to state-of-the-art baselines and it is robust against data sparsity and is applicable to unseen cases in practice.
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation
Authors: Chulun Zhou, Fandong Meng, Jie zhou, Min Zhang, Hongji Wang, Jinsong Su
Date: 2022-02-28 00:00:00
Proceeding: ACL 2022 5
Tasks: Decoder, Knowledge Distillation, Language Modelling, Machine Translation, NMT, Translation

Most dominant neural machine translation (NMT) models are restricted to make predictions only according to the local context of preceding words in a left-to-right manner. Although many previous studies try to incorporate global information into NMT models, there still exist limitations on how to effectively exploit bidirectional global context. In this paper, we propose a Confidence Based Bidirectional Global Context Aware (CBBGCA) training framework for NMT, where the NMT model is jointly trained with an auxiliary conditional masked language model (CMLM). The training consists of two stages: (1) multi-task joint training; (2) confidence based knowledge distillation. At the first stage, by sharing encoder parameters, the NMT model is additionally supervised by the signal from the CMLM decoder that contains bidirectional global contexts. Moreover, at the second stage, using the CMLM as teacher, we further pertinently incorporate bidirectional global context to the NMT model on its unconfidently-predicted target words via knowledge distillation. Experimental results show that our proposed CBBGCA training framework significantly improves the NMT model by +1.02, +1.30 and +0.57 BLEU scores on three large-scale translation datasets, namely WMT'14 English-to-German, WMT'19 Chinese-to-English and WMT'14 English-to-French, respectively.
Avalanche RL: a Continual Reinforcement Learning Library
Authors: Nicolò Lucchesi, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu
Date: 2022-02-28 00:00:00
Tasks: Continual Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences). In this paper, we describe Avalanche RL, a library for Continual Reinforcement Learning which allows to easily train agents on a continuous stream of tasks. Avalanche RL is based on PyTorch and supports any OpenAI Gym environment. Its design is based on Avalanche, one of the more popular continual learning libraries, which allow us to reuse a large number of continual learning strategies and improve the interaction between reinforcement learning and continual learning researchers. Additionally, we propose Continual Habitat-Lab, a novel benchmark and a high-level library which enables the usage of the photorealistic simulator Habitat-Sim for CRL research. Overall, Avalanche RL attempts to unify under a common framework continual reinforcement learning applications, which we hope will foster the growth of the field.
Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets
Authors: Abdulmalik Alwarafy, Bekir Sait Ciftler, Mohamed Abdallah, Mounir Hamdi, Naofal Al-Dhahir
Date: 2022-02-28 00:00:00
Tasks: Deep Reinforcement Learning

This paper considers the problem of cost-aware downlink sum-rate maximization via joint optimal radio access technologies (RATs) assignment and power allocation in next-generation heterogeneous wireless networks (HetNets). We consider a future HetNet comprised of multi-RATs and serving multi-connectivity edge devices (EDs), and we formulate the problem as mixed-integer non-linear programming (MINP) problem. Due to the high complexity and combinatorial nature of this problem and the difficulty to solve it using conventional methods, we propose a hierarchical multi-agent deep reinforcement learning (DRL)-based framework, called DeepRAT, to solve it efficiently and learn system dynamics. In particular, the DeepRAT framework decomposes the problem into two main stages; the RATs-EDs assignment stage, which implements a single-agent Deep Q Network (DQN) algorithm, and the power allocation stage, which utilizes a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. Using simulations, we demonstrate how the various DRL agents efficiently interact to learn system dynamics and derive the global optimal policy. Furthermore, our simulation results show that the proposed DeepRAT algorithm outperforms existing state-of-the-art heuristic approaches in terms of network utility. Finally, we quantitatively show the ability of the DeepRAT model to quickly and dynamically adapt to abrupt changes in network dynamics, such as EDs mobility.
Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps
Authors: Lixuan Yang, Dario Rossi
Date: 2022-02-28 00:00:00
Tasks: Management

Artificial Intelligence (AI) has recently attracted a lot of attention, transitioning from research labs to a wide range of successful deployments in many fields, which is particularly true for Deep Learning (DL) techniques. Ultimately, DL models being software artifacts, they need to be regularly maintained and updated: AIOps is the logical extension of the DevOps software development practices to AI-software applied to network operation and management. In the lifecycle of a DL model deployment, it is important to assess the quality of deployed models, to detect "stale" models and prioritize their update. In this article, we cover the issue in the context of network management, proposing simple yet effective techniques for (i) quality assessment of individual inference, and for (ii) overall model quality tracking over multiple inferences, that we apply to two use cases, representative of the network management and image recognition fields.
Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection
Authors: Tommaso Zoppi, Andrea Ceccarelli
Date: 2022-02-28 00:00:00
Tasks: Benchmarking, Intrusion Detection, Meta-Learning, Network Intrusion Detection

In the last decades, researchers, practitioners and companies struggled in devising mechanisms to detect malicious activities originating security threats. Amongst the many solutions, network intrusion detection emerged as one of the most popular to analyze network traffic and detect ongoing intrusions based on rules or by means of Machine Learners (MLs), which process such traffic and learn a model to suspect intrusions. Supervised MLs are very effective in detecting known threats, but struggle in identifying zero-day attacks (unknown during learning phase), which instead can be detected through unsupervised MLs. Unfortunately, there are no definitive answers on the combined use of both approaches for network intrusion detection. In this paper we first expand the problem of zero-day attacks and motivate the need to combine supervised and unsupervised algorithms. We propose the adoption of meta-learning, in the form of a two-layer Stacker, to create a mixed approach that detects both known and unknown threats. Then we implement and empirically evaluate our Stacker through an experimental campaign that allows i) debating on meta-features crafted through unsupervised base-level learners, ii) electing the most promising supervised meta-level classifiers, and iii) benchmarking classification scores of the Stacker with respect to supervised and unsupervised classifiers. Last, we compare our solution with existing works from the recent literature. Overall, our Stacker reduces misclassifications with respect to (un)supervised ML algorithms in all the 7 public datasets we considered, and outperforms existing studies in 6 out of those 7 datasets. In particular, it turns out to be more effective in detecting zero-day attacks than supervised algorithms, limiting their main weakness but still maintaining adequate capabilities in detecting known attacks.
Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations
Authors: Qi Liu, Bo Yang, Zhaojian Wang, Dafeng Zhu, Xinyi Wang, Kai Ma, Xinping Guan
Date: 2022-02-28 00:00:00
Tasks: Fault Diagnosis, Federated Learning

Due to the different losses caused by various photovoltaic (PV) array faults, accurate diagnosis of fault types is becoming increasingly important. Compared with a single one, multiple PV stations collect sufficient fault samples, but their data is not allowed to be shared directly due to potential conflicts of interest. Therefore, federated learning can be exploited to train a collaborative fault diagnosis model. However, the modeling efficiency is seriously affected by the model update mechanism since each PV station has a different computing capability and amount of data. Moreover, for the safe and stable operation of the PV system, the robustness of collaborative modeling must be guaranteed rather than simply being processed on a central server. To address these challenges, a novel asynchronous decentralized federated learning (ADFL) framework is proposed. Each PV station not only trains its local model but also participates in collaborative fault diagnosis by exchanging model parameters to improve the generalization without losing accuracy. The global model is aggregated distributedly to avoid central node failure. By designing the asynchronous update scheme, the communication overhead and training time are greatly reduced. Both the experiments and numerical simulations are carried out to verify the effectiveness of the proposed method.
Generalizable task representation learning from human demonstration videos: a geometric approach
Authors: Jun Jin, Martin Jagersand
Date: 2022-02-28 00:00:00
Tasks: Representation Learning

We study the problem of generalizable task learning from human demonstration videos without extra training on the robot or pre-recorded robot motions. Given a set of human demonstration videos showing a task with different objects/tools (categorical objects), we aim to learn a representation of visual observation that generalizes to categorical objects and enables efficient controller design. We propose to introduce a geometric task structure to the representation learning problem that geometrically encodes the task specification from human demonstration videos, and that enables generalization by building task specification correspondence between categorical objects. Specifically, we propose CoVGS-IL, which uses a graph-structured task function to learn task representations under structural constraints. Our method enables task generalization by selecting geometric features from different objects whose inner connection relationships define the same task in geometric constraints. The learned task representation is then transferred to a robot controller using uncalibrated visual servoing (UVS); thus, the need for extra robot training or pre-recorded robot motions is removed.
Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey
Authors: Aoran Xiao, Jiaxing Huang, Dayan Guan, Xiaoqin Zhang, Shijian Lu, Ling Shao
Date: 2022-02-28 00:00:00
Tasks: Autonomous Driving, Representation Learning

Point cloud data have been widely explored due to its superior accuracy and robustness under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved very impressive success in various applications such as surveillance and autonomous driving. The convergence of point cloud and DNNs has led to many deep point cloud models, largely trained under the supervision of large-scale and densely-labelled point cloud data. Unsupervised point cloud representation learning, which aims to learn general and useful point cloud representations from unlabelled point cloud data, has recently attracted increasing attention due to the constraint in large-scale point cloud labelling. This paper provides a comprehensive review of unsupervised point cloud representation learning using DNNs. It first describes the motivation, general pipelines as well as terminologies of the recent studies. Relevant background including widely adopted point cloud datasets and DNN architectures is then briefly presented. This is followed by an extensive discussion of existing unsupervised point cloud representation learning methods according to their technical approaches. We also quantitatively benchmark and discuss the reviewed methods over multiple widely adopted point cloud datasets. Finally, we share our humble opinion about several challenges and problems that could be pursued in future research in unsupervised point cloud representation learning. A project associated with this survey has been built at https://github.com/xiaoaoran/3d_url_survey.
Rethinking and Refining the Distinct Metric
Authors: Siyang Liu, Sahand Sabour, Yinhe Zheng, Pei Ke, Xiaoyan Zhu, Minlie Huang
Date: 2022-02-28 00:00:00
Proceeding: ACL 2022 5
Tasks: Diversity, Text Generation

Distinct-$n$ score\cite{Li2016} is a widely used automatic metric for evaluating diversity in language generation tasks. However, we observed that the original approach for calculating distinct scores has evident biases that tend to assign higher penalties to longer sequences. We refine the calculation of distinct scores by scaling the number of distinct tokens based on their expectations. We provide both empirical and theoretical evidence to show that our method effectively removes the biases existing in the original distinct score. Our experiments show that our proposed metric, \textit{Expectation-Adjusted Distinct (EAD)}, correlates better with human judgment in evaluating response diversity. To foster future research, we provide an example implementation at \url{https://github.com/lsy641/Expectation-Adjusted-Distinct}.
Learning Parameters for a Generalized Vidale-Wolfe Response Model with Flexible Ad Elasticity and Word-of-Mouth
Authors: Yanwu Yang, Baozhu Feng, Daniel Zeng
Date: 2022-02-28 00:00:00

In this research, we investigate a generalized form of Vidale-Wolfe (GVW) model. One key element of our modeling work is that the GVW model contains two useful indexes representing advertiser's elasticity and the word-of-mouth (WoM) effect, respectively. Moreover, we discuss some desirable properties of the GVW model, and present a deep neural network (DNN)-based estimation method to learn its parameters. Furthermore, based on three realworld datasets, we conduct computational experiments to validate the GVW model and identified properties. In addition, we also discuss potential advantages of the GVW model over econometric models. The research outcome shows that both the ad elasticity index and the WoM index have significant influences on advertising responses, and the GVW model has potential advantages over econometric models of advertising, in terms of several interesting phenomena drawn from practical advertising situations. The GVW model and its deep learning-based estimation method provide a basis to support big data-driven advertising analytics and decision makings; in the meanwhile, identified properties and experimental findings of this research illuminate critical managerial insights for advertisers in various advertising forms.
Filter-enhanced MLP is All You Need for Sequential Recommendation
Authors: Kun Zhou, Hui Yu, Wayne Xin Zhao, Ji-Rong Wen
Date: 2022-02-28 00:00:00
Tasks: All, Sequential Recommendation

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference characteristics from logged user behavior data for accurate recommendation. However, in online platforms, logged user behavior data is inevitable to contain noise, and deep recommendation models are easy to overfit on these logged data. To tackle this problem, we borrow the idea of filtering algorithms from signal processing that attenuates the noise in the frequency domain. In our empirical experiments, we find that filtering algorithms can substantially improve representative sequential recommendation models, and integrating simple filtering algorithms (eg Band-Stop Filter) with an all-MLP architecture can even outperform competitive Transformer-based models. Motivated by it, we propose \textbf{FMLP-Rec}, an all-MLP model with learnable filters for sequential recommendation task. The all-MLP architecture endows our model with lower time complexity, and the learnable filters can adaptively attenuate the noise information in the frequency domain. Extensive experiments conducted on eight real-world datasets demonstrate the superiority of our proposed method over competitive RNN, CNN, GNN and Transformer-based methods. Our code and data are publicly available at the link: \textcolor{blue}{\url{https://github.com/RUCAIBox/FMLP-Rec}}.