pwc-archive/papers-with-abstracts huggingface dataset:
Legislator Representation Learning with Social Context and Expert Knowledge
Authors: Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo
Date: 2021-08-09 00:00:00
Tasks: Representation Learning, Stance Detection

Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks. Existing approaches are generally limited to textual data and voting records, while they neglect the rich social context and valuable expert knowledge for holistic evaluation. In this paper, we propose a representation learning framework of political actors that jointly leverages social context and expert knowledge. Specifically, we retrieve and extract factual statements about legislators to leverage social context information. We then construct a heterogeneous information network to incorporate social context and use relational graph neural networks to learn legislator representations. Finally, we train our model with three objectives to align representation learning with expert knowledge, model ideological stance consistency, and simulate the echo chamber phenomenon. Extensive experiments demonstrate that our learned representations successfully advance the state-of-the-art in three downstream tasks. Further analysis proves the correlation between learned legislator representations and various socio-political factors, as well as bearing out the necessity of social context and expert knowledge in modeling political actors.
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach
Authors: Mao V. Ngo, Tie Luo, Tony Q. S. Quek
Date: 2021-08-09 00:00:00
Tasks: Anomaly Detection, Edge-computing, Model Selection

The advances in deep neural networks (DNN) have significantly enhanced real-time detection of anomalous data in IoT applications. However, the complexity-accuracy-delay dilemma persists: complex DNN models offer higher accuracy, but typical IoT devices can barely afford the computation load, and the remedy of offloading the load to the cloud incurs long delay. In this paper, we address this challenge by proposing an adaptive anomaly detection scheme with hierarchical edge computing (HEC). Specifically, we first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network. We also incorporate a parallelism policy training method to accelerate the training process by taking advantage of distributed models. We build an HEC testbed using real IoT devices, implement and evaluate our contextual-bandit approach with both univariate and multivariate IoT datasets. In comparison with both baseline and state-of-the-art schemes, our adaptive approach strikes the best accuracy-delay tradeoff on the univariate dataset, and achieves the best accuracy and F1-score on the multivariate dataset with only negligibly longer delay than the best (but inflexible) scheme.
KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media
Authors: Shangbin Feng, Zilong Chen, Wenqian Zhang, Qingyao Li, Qinghua Zheng, Xiaojun Chang, Minnan Luo
Date: 2021-08-09 00:00:00
Tasks: Argument Mining, Knowledge Graphs

Identifying political perspectives in news media has become an important task due to the rapid growth of political commentary and the increasingly polarized political ideologies. Previous approaches focus on textual content and leave out the rich social and political context that is essential in the perspective detection process. To address this limitation, we propose KGAP, a political perspective detection method that incorporates external domain knowledge. Specifically, we construct a political knowledge graph to serve as domain-specific external knowledge. We then construct heterogeneous information networks to represent news documents, which jointly model news text and external knowledge. Finally, we adopt relational graph neural networks and conduct political perspective detection as graph-level classification. Extensive experiments demonstrate that our method consistently achieves the best performance on two real-world perspective detection benchmarks. Ablation studies further bear out the necessity of external knowledge and the effectiveness of our graph-based approach.
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs
Authors: David Tolpin, Tomer Dobkin
Date: 2021-08-09 00:00:00
Tasks: Bayesian Inference, Probabilistic Programming, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

It is well known that reinforcement learning can be cast as inference in an appropriate probabilistic model. However, this commonly involves introducing a distribution over agent trajectories with probabilities proportional to exponentiated rewards. In this work, we formulate reinforcement learning as Bayesian inference without resorting to rewards, and show that rewards are derived from agent's preferences, rather than the other way around. We argue that agent preferences should be specified stochastically rather than deterministically. Reinforcement learning via inference with stochastic preferences naturally describes agent behaviors, does not require introducing rewards and exponential weighing of trajectories, and allows to reason about agents using the solid foundation of Bayesian statistics. Stochastic conditioning, a probabilistic programming paradigm for conditioning models on distributions rather than values, is the formalism behind agents with probabilistic preferences. We demonstrate realization of our approach on case studies using both a two-agent coordinate game and a single agent acting in a noisy environment, showing that despite superficial differences, both cases can be modeled and reasoned about based on the same principles.
Towards to Robust and Generalized Medical Image Segmentation Framework
Authors: Yurong Chen
Date: 2021-08-09 00:00:00
Tasks: Image Reconstruction, Image Segmentation, Medical Image Segmentation, Representation Learning, Segmentation, Semantic Segmentation, Transfer Learning

Deep learning-based computer-aided diagnosis is gradually deployed to review and analyze medical images. However, this paradigm is restricted in real-world clinical applications due to the poor robustness and generalization. The issue is more sinister with a lack of training data. In this paper, we address the challenge from the transfer learning point of view. Different from the common setting that transferring knowledge from the natural image domain to the medical image domain, we find the knowledge from the same domain further boosts the model robustness and generalization. Therefore, we propose a novel two-stage framework for robust generalized medical image segmentation. Firstly, an unsupervised tile-wise autoencoder pretraining architecture is proposed to learn local and global knowledge. Secondly, the downstream segmentation model coupled with an auxiliary reconstruction network is designed. The reconstruction branch encourages the model to capture more general semantic features. Experiments of lung segmentation on multi chest X-ray datasets are conducted. Comprehensive results demonstrate the superior robustness of the proposed framework to corruption and high generalization performance on unseen datasets, especially under the scenario of the limited training data.
Time-Frequency Localization Using Deep Convolutional Maxout Neural Network in Persian Speech Recognition
Authors: Arash Dehghani, Seyyed Ali Seyyedsalehi
Date: 2021-08-09 00:00:00
Tasks: speech-recognition, Speech Recognition

In this paper, a CNN-based structure for the time-frequency localization of information is proposed for Persian speech recognition. Research has shown that the receptive fields' spectrotemporal plasticity of some neurons in mammals' primary auditory cortex and midbrain makes localization facilities improve recognition performance. Over the past few years, much work has been done to localize time-frequency information in ASR systems, using the spatial or temporal immutability properties of methods such as HMMs, TDNNs, CNNs, and LSTM-RNNs. However, most of these models have large parameter volumes and are challenging to train. For this purpose, we have presented a structure called Time-Frequency Convolutional Maxout Neural Network (TFCMNN) in which parallel time-domain and frequency-domain 1D-CMNNs are applied simultaneously and independently to the spectrogram, and then their outputs are concatenated and applied jointly to a fully connected Maxout network for classification. To improve the performance of this structure, we have used newly developed methods and models such as Dropout, maxout, and weight normalization. Two sets of experiments were designed and implemented on the FARSDAT dataset to evaluate the performance of this model compared to conventional 1D-CMNN models. According to the experimental results, the average recognition score of TFCMNN models is about 1.6% higher than the average of conventional 1D-CMNN models. In addition, the average training time of the TFCMNN models is about 17 hours lower than the average training time of traditional models. Therefore, as proven in other sources, time-frequency localization in ASR systems increases system accuracy and speeds up the training process.
P-WAE: Generalized Patch-Wasserstein Autoencoder for Anomaly Screening
Authors: Yurong Chen
Date: 2021-08-09 00:00:00
Tasks: Anomaly Detection, Variational Inference

Anomaly detection plays a pivotal role in numerous real-world scenarios, such as industrial automation and manufacturing intelligence. Recently, variational inference-based anomaly analysis has attracted researchers' and developers' attention. It aims to model the defect-free distribution so that anomalies can be classified as out-of-distribution samples. Nevertheless, there are two disturbing factors that need us to prioritize: (i) the simplistic prior latent distribution inducing limited expressive capability; (ii) the strong probability distance notion results in collapsed features. In this paper, we propose a novel Patch-wise Wasserstein AutoEncoder (P-WAE) architecture to alleviate those challenges. In particular, a patch-wise variational inference model coupled with solving the jigsaw puzzle is designed, which is a simple yet effective way to increase the expressiveness of the latent manifold. This makes using the model on high-dimensional practical data possible. In addition, we leverage a weaker measure, sliced-Wasserstein distance, to achieve the equilibrium between the reconstruction fidelity and generalized representations. Comprehensive experiments, conducted on the MVTec AD dataset, demonstrate the superior performance of our proposed method.
Toward Human-Level Artificial Intelligence
Authors: Deokgun Park
Date: 2021-08-09 00:00:00
Tasks: Hippocampus

In this paper, we present our research on programming human-level artificial intelligence (HLAI), including 1) a definition of HLAI, 2) an environment to develop and test HLAI, and 3) a cognitive architecture for HLAI. The term AI is used in a broad meaning, and HLAI is not clearly defined. I claim that the essence of Human-Level Intelligence to be the capability to learn from others' experiences via language. The key is that the event described by language has the same effect as if the agent experiences it firsthand for the update of the behavior policy. To develop and test models with such a capability, we are developing a simulated environment called SEDRo. There is a 3D Home, and a mother character takes care of the baby (the learning agent) and teaches languages. The environment provides comparable experiences to that of a human baby from birth to one year. Finally, I propose a cognitive architecture of HLAI called Modulated Heterarchical Prediction Memory (mHPM). In mHPM, there are three components: a universal module that learns to predict the next vector given the sequence of vector signals, a heterarchical network of those modules, and a reward-based modulation of learning. mHPM models the workings of the neocortex but the innate auxiliary units such hippocampus, reward system, instincts, and amygdala play critical roles, too.
Symptom based Hierarchical Classification of Diabetes and Thyroid disorders using Fuzzy Cognitive Maps
Authors: Anand M. Shukla, Pooja D. Pandit, Vasudev M. Purandare, Anuradha Srinivasaraghavan
Date: 2021-08-08 00:00:00
Tasks: Decision Making

Fuzzy Cognitive Maps (FCMs) are soft computing technique that follows an approach similar to human reasoning and human decision-making process, making them a valuable modeling and simulation methodology. Medical Decision Systems are complex systems consisting of many factors that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall diagnosis with a different degree. Thus, FCMs are suitable to model Medical Decision Support Systems. The proposed work therefore uses FCMs arranged in hierarchical structure to classify between Diabetes, Thyroid disorders and their subtypes. Subtypes include type 1 and type 2 for diabetes and hyperthyroidism and hypothyroidism for thyroid.
Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims
Authors: Ipek Baris Schlicht, Erhan Sezerer, Selma Tekir, Oul Han, Zeyd Boukhers
Date: 2021-08-08 00:00:00
Tasks: Fact Checking, Language Modeling, Language Modelling, Multi-Task Learning, News Classification, Question Answering

Widespread and rapid dissemination of false news has made fact-checking an indispensable requirement. Given its time-consuming and labor-intensive nature, the task calls for an automated support to meet the demand. In this paper, we propose to leverage commonsense knowledge for the tasks of false news classification and check-worthy claim detection. Arguing that commonsense knowledge is a factor in human believability, we fine-tune the BERT language model with a commonsense question answering task and the aforementioned tasks in a multi-task learning environment. For predicting fine-grained false news types, we compare the proposed fine-tuned model's performance with the false news classification models on a public dataset as well as a newly collected dataset. We compare the model's performance with the single-task BERT model and a state-of-the-art check-worthy claim detection tool to evaluate the check-worthy claim detection. Our experimental analysis demonstrates that commonsense knowledge can improve performance in both tasks.
Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents
Authors: Cristian Millán-Arias, Bruno Fernandes, Francisco Cruz
Date: 2021-08-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Proxemics is a branch of non-verbal communication concerned with studying the spatial behavior of people and animals. This behavior is an essential part of the communication process due to delimit the acceptable distance to interact with another being. With increasing research on human-agent interaction, new alternatives are needed that allow optimal communication, avoiding agents feeling uncomfortable. Several works consider proxemic behavior with cognitive agents, where human-robot interaction techniques and machine learning are implemented. However, environments consider fixed personal space and that the agent previously knows it. In this work, we aim to study how agents behave in environments based on proxemic behavior, and propose a modified gridworld to that aim. This environment considers an issuer with proxemic behavior that provides a disagreement signal to the agent. Our results show that the learning agent can identify the proxemic space when the issuer gives feedback about agent performance.
Meta-Reinforcement Learning in Broad and Non-Parametric Environments
Authors: Zhenshan Bing, Lukas Knak, Fabrice Oliver Robin, Kai Huang, Alois Knoll
Date: 2021-08-08 00:00:00
Tasks: Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of interaction to learn new skills. Meta-reinforcement learning (meta-RL) addresses this challenge by leveraging knowledge learned from training tasks to perform well in previously unseen tasks. However, current meta-RL approaches limit themselves to narrow parametric task distributions, ignoring qualitative differences between tasks that occur in the real world. In this paper, we introduce TIGR, a Task-Inference-based meta-RL algorithm using Gaussian mixture models (GMM) and gated Recurrent units, designed for tasks in non-parametric environments. We employ a generative model involving a GMM to capture the multi-modality of the tasks. We decouple the policy training from the task-inference learning and efficiently train the inference mechanism on the basis of an unsupervised reconstruction objective. We provide a benchmark with qualitatively distinct tasks based on the half-cheetah environment and demonstrate the superior performance of TIGR compared to state-of-the-art meta-RL approaches in terms of sample efficiency (3-10 times faster), asymptotic performance, and applicability in non-parametric environments with zero-shot adaptation.
OVIS: Open-Vocabulary Visual Instance Search via Visual-Semantic Aligned Representation Learning
Authors: Sheng Liu, Kevin Lin, Lijuan Wang, Junsong Yuan, Zicheng Liu
Date: 2021-08-08 00:00:00
Tasks: Instance Search, Representation Learning

We introduce the task of open-vocabulary visual instance search (OVIS). Given an arbitrary textual search query, Open-vocabulary Visual Instance Search (OVIS) aims to return a ranked list of visual instances, i.e., image patches, that satisfies the search intent from an image database. The term "open vocabulary" means that there are neither restrictions to the visual instance to be searched nor restrictions to the word that can be used to compose the textual search query. We propose to address such a search challenge via visual-semantic aligned representation learning (ViSA). ViSA leverages massive image-caption pairs as weak image-level (not instance-level) supervision to learn a rich cross-modal semantic space where the representations of visual instances (not images) and those of textual queries are aligned, thus allowing us to measure the similarities between any visual instance and an arbitrary textual query. To evaluate the performance of ViSA, we build two datasets named OVIS40 and OVIS1600 and also introduce a pipeline for error analysis. Through extensive experiments on the two datasets, we demonstrate ViSA's ability to search for visual instances in images not available during training given a wide range of textual queries including those composed of uncommon words. Experimental results show that ViSA achieves an mAP@50 of 21.9% on OVIS40 under the most challenging setting and achieves an mAP@6 of 14.9% on OVIS1600 dataset.
Event-driven Vision and Control for UAVs on a Neuromorphic Chip
Authors: Antonio Vitale, Alpha Renner, Celine Nauer, Davide Scaramuzza, Yulia Sandamirskaya
Date: 2021-08-08 00:00:00
Tasks: Drone Controller, Event-based vision

Event-based vision sensors achieve up to three orders of magnitude better speed vs. power consumption trade off in high-speed control of UAVs compared to conventional image sensors. Event-based cameras produce a sparse stream of events that can be processed more efficiently and with a lower latency than images, enabling ultra-fast vision-driven control. Here, we explore how an event-based vision algorithm can be implemented as a spiking neuronal network on a neuromorphic chip and used in a drone controller. We show how seamless integration of event-based perception on chip leads to even faster control rates and lower latency. In addition, we demonstrate how online adaptation of the SNN controller can be realised using on-chip learning. Our spiking neuronal network on chip is the first example of a neuromorphic vision-based controller solving a high-speed UAV control task. The excellent scalability of processing in neuromorphic hardware opens the possibility to solve more challenging visual tasks in the future and integrate visual perception in fast control loops.
Online Evolutionary Batch Size Orchestration for Scheduling Deep Learning Workloads in GPU Clusters
Authors: Zhengda Bian, Shenggui Li, Wei Wang, Yang You
Date: 2021-08-08 00:00:00
Tasks: Deep Learning, GPU, Scheduling

Efficient GPU resource scheduling is essential to maximize resource utilization and save training costs for the increasing amount of deep learning workloads in shared GPU clusters. Existing GPU schedulers largely rely on static policies to leverage the performance characteristics of deep learning jobs. However, they can hardly reach optimal efficiency due to the lack of elasticity. To address the problem, we propose ONES, an ONline Evolutionary Scheduler for elastic batch size orchestration. ONES automatically manages the elasticity of each job based on the training batch size, so as to maximize GPU utilization and improve scheduling efficiency. It determines the batch size for each job through an online evolutionary search that can continuously optimize the scheduling decisions. We evaluate the effectiveness of ONES with 64 GPUs on TACC's Longhorn supercomputers. The results show that ONES can outperform the prior deep learning schedulers with a significantly shorter average job completion time.
Understanding the computational demands underlying visual reasoning
Authors: Mohit Vaishnav, Remi Cadene, Andrea Alamia, Drew Linsley, Rufin VanRullen, Thomas Serre
Date: 2021-08-08 00:00:00
Tasks: Visual Reasoning

Visual understanding requires comprehending complex visual relations between objects within a scene. Here, we seek to characterize the computational demands for abstract visual reasoning. We do this by systematically assessing the ability of modern deep convolutional neural networks (CNNs) to learn to solve the "Synthetic Visual Reasoning Test" (SVRT) challenge, a collection of twenty-three visual reasoning problems. Our analysis reveals a novel taxonomy of visual reasoning tasks, which can be primarily explained by both the type of relations (same-different vs. spatial-relation judgments) and the number of relations used to compose the underlying rules. Prior cognitive neuroscience work suggests that attention plays a key role in humans' visual reasoning ability. To test this hypothesis, we extended the CNNs with spatial and feature-based attention mechanisms. In a second series of experiments, we evaluated the ability of these attention networks to learn to solve the SVRT challenge and found the resulting architectures to be much more efficient at solving the hardest of these visual reasoning tasks. Most importantly, the corresponding improvements on individual tasks partially explained our novel taxonomy. Overall, this work provides an granular computational account of visual reasoning and yields testable neuroscience predictions regarding the differential need for feature-based vs. spatial attention depending on the type of visual reasoning problem.