pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks
Authors: Zachary Ravichandran, Lisa Peng, Nathan Hughes, J. Daniel Griffith, Luca Carlone
Date: 2021-08-02 00:00:00
Tasks: 2D Semantic Segmentation, Graph Neural Network, Semantic Segmentation

Representations are crucial for a robot to learn effective navigation policies. Recent work has shown that mid-level perceptual abstractions, such as depth estimates or 2D semantic segmentation, lead to more effective policies when provided as observations in place of raw sensor data (e.g., RGB images). However, such policies must still learn latent three-dimensional scene properties from mid-level abstractions. In contrast, high-level, hierarchical representations such as 3D scene graphs explicitly provide a scene's geometry, topology, and semantics, making them compelling representations for navigation. In this work, we present a reinforcement learning framework that leverages high-level hierarchical representations to learn navigation policies. Towards this goal, we propose a graph neural network architecture and show how to embed a 3D scene graph into an agent-centric feature space, which enables the robot to learn policies for low-level action in an end-to-end manner. For each node in the scene graph, our method uses features that capture occupancy and semantic content, while explicitly retaining memory of the robot trajectory. We demonstrate the effectiveness of our method against commonly used visuomotor policies in a challenging object search task. These experiments and supporting ablation studies show that our method leads to more effective object search behaviors, exhibits improved long-term memory, and successfully leverages hierarchical information to guide its navigation objectives.
Knowledge-intensive Language Understanding for Explainable AI
Authors: Amit Sheth, Manas Gaur, Kaushik Roy, Keyur Faldu
Date: 2021-08-02 00:00:00
Tasks: Decision Making, Explainable Artificial Intelligence (XAI), Fairness

AI systems have seen significant adoption in various domains. At the same time, further adoption in some domains is hindered by inability to fully trust an AI system that it will not harm a human. Besides the concerns for fairness, privacy, transparency, and explainability are key to developing trusts in AI systems. As stated in describing trustworthy AI "Trust comes through understanding. How AI-led decisions are made and what determining factors were included are crucial to understand." The subarea of explaining AI systems has come to be known as XAI. Multiple aspects of an AI system can be explained; these include biases that the data might have, lack of data points in a particular region of the example space, fairness of gathering the data, feature importances, etc. However, besides these, it is critical to have human-centered explanations that are directly related to decision-making similar to how a domain expert makes decisions based on "domain knowledge," that also include well-established, peer-validated explicit guidelines. To understand and validate an AI system's outcomes (such as classification, recommendations, predictions), that lead to developing trust in the AI system, it is necessary to involve explicit domain knowledge that humans understand and use.
PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors
Authors: Andrei-Marius Avram, Vasile Pais, Dan Tufis
Date: 2021-08-02 00:00:00
Proceeding: RANLP 2021 9
Tasks: Document Classification, Specificity

EuroVoc is a multilingual thesaurus that was built for organizing the legislative documentary of the European Union institutions. It contains thousands of categories at different levels of specificity and its descriptors are targeted by legal texts in almost thirty languages. In this work we propose a unified framework for EuroVoc classification on 22 languages by fine-tuning modern Transformer-based pretrained language models. We study extensively the performance of our trained models and show that they significantly improve the results obtained by a similar tool - JEX - on the same dataset. The code and the fine-tuned models were open sourced, together with a programmatic interface that eases the process of loading the weights of a trained model and of classifying a new document.
Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles
Authors: Sakib Mahmud Khan, Gurcan Comert, Mashrur Chowdhury
Date: 2021-08-02 00:00:00
Tasks: Anomaly Detection

Connected vehicles (CVs), because of the external connectivity with other CVs and connected infrastructure, are vulnerable to cyberattacks that can instantly compromise the safety of the vehicle itself and other connected vehicles and roadway infrastructure. One such cyberattack is the false information attack, where an external attacker injects inaccurate information into the connected vehicles and eventually can cause catastrophic consequences by compromising safety-critical applications like the forward collision warning. The occurrence and target of such attack events can be very dynamic, making real-time and near-real-time detection challenging. Change point models, can be used for real-time anomaly detection caused by the false information attack. In this paper, we have evaluated three change point-based statistical models; Expectation Maximization, Cumulative Summation, and Bayesian Online Change Point Algorithms for cyberattack detection in the CV data. Also, data-driven artificial intelligence (AI) models, which can be used to detect known and unknown underlying patterns in the dataset, have the potential of detecting a real-time anomaly in the CV data. We have used six AI models to detect false information attacks and compared the performance for detecting the attacks with our developed change point models. Our study shows that change points models performed better in real-time false information attack detection compared to the performance of the AI models. Change point models having the advantage of no training requirements can be a feasible and computationally efficient alternative to AI models for false information attack detection in connected vehicles.
Learning-based Preference Prediction for Constrained Multi-Criteria Path-Planning
Authors: Kevin Osanlou, Christophe Guettier, Andrei Bursuc, Tristan Cazenave, Eric Jacopin
Date: 2021-08-02 00:00:00

Learning-based methods are increasingly popular for search algorithms in single-criterion optimization problems. In contrast, for multiple-criteria optimization there are significantly fewer approaches despite the existence of numerous applications. Constrained path-planning for Autonomous Ground Vehicles (AGV) is one such application, where an AGV is typically deployed in disaster relief or search and rescue applications in off-road environments. The agent can be faced with the following dilemma : optimize a source-destination path according to a known criterion and an uncertain criterion under operational constraints. The known criterion is associated to the cost of the path, representing the distance. The uncertain criterion represents the feasibility of driving through the path without requiring human intervention. It depends on various external parameters such as the physics of the vehicle, the state of the explored terrains or weather conditions. In this work, we leverage knowledge acquired through offline simulations by training a neural network model to predict the uncertain criterion. We integrate this model inside a path-planner which can solve problems online. Finally, we conduct experiments on realistic AGV scenarios which illustrate that the proposed framework requires human intervention less frequently, trading for a limited increase in the path distance.
Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging
Authors: Md. Shirajum Munir, Ki Tae Kim, Kyi Thar, Dusit Niyato, Choong Seon Hong
Date: 2021-08-02 00:00:00
Tasks: Autonomous Vehicles, Scheduling

In this paper, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an EV charging facility for human-driven connected vehicles (CVs) and autonomous vehicles (AVs). The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its actual need. Therefore, the scheduling policy of each EVSE must be adaptively accumulated the irrational charging request to satisfy the charging demand of both CVs and AVs. To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO. Thus, we devise a rational reward maximization problem to adapt the irrational behavior by CVs in a data-informed manner. We propose a novel risk adversarial multi-agent learning system (RAMALS) for CAV-CI to solve the formulated RDSS problem. In RAMALS, the DSO acts as a centralized risk adversarial agent (RAA) for informing the laxity risk to each EVSE. Subsequently, each EVSE plays the role of a self-learner agent to adaptively schedule its own EV sessions by coping advice from RAA. Experiment results show that the proposed RAMALS affords around 46.6% improvement in charging rate, about 28.6% improvement in the EVSE's active charging time and at least 33.3% more energy utilization, as compared to a currently deployed ACN EVSE system, and other baselines.
Learning Nonlinear Waves in Plasmon-induced Transparency
Authors: Jiaxi Cheng, Zhenhao Cen, Siliu Xu
Date: 2021-07-31 00:00:00

Plasmon-induced transparency (PIT) displays complex nonlinear dynamics that find critical phenomena in areas such as nonlinear waves. However, such a nonlinear solution depends sensitively on the selection of parameters and different potentials in the Schr\"odinger equation. Despite this complexity, the machine learning community has developed remarkable efficiencies in predicting complicated datasets by regression. Here, we consider a recurrent neural network (RNN) approach to predict the complex propagation of nonlinear solitons in plasmon-induced transparency metamaterial systems with applied potentials bypassing the need for analytical and numerical approaches of a guiding model. We demonstrate the success of this scheme on the prediction of the propagation of the nonlinear solitons solely from a given initial condition and potential. We prove the prominent agreement of results in simulation and prediction by long short-term memory (LSTM) artificial neural networks. The framework presented in this work opens up a new perspective for the application of RNN in quantum systems and nonlinear waves using Schr\"odinger-type equations, for example, the nonlinear dynamics in cold-atom systems and nonlinear fiber optics.
Sequence Adaptation via Reinforcement Learning in Recommender Systems
Authors: Stefanos Antaris, Dimitrios Rafailidis
Date: 2021-07-31 00:00:00
Tasks: Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Accounting for the fact that users have different sequential patterns, the main drawback of state-of-the-art recommendation strategies is that a fixed sequence length of user-item interactions is required as input to train the models. This might limit the recommendation accuracy, as in practice users follow different trends on the sequential recommendations. Hence, baseline strategies might ignore important sequential interactions or add noise to the models with redundant interactions, depending on the variety of users' sequential behaviours. To overcome this problem, in this study we propose the SAR model, which not only learns the sequential patterns but also adjusts the sequence length of user-item interactions in a personalized manner. We first design an actor-critic framework, where the RL agent tries to compute the optimal sequence length as an action, given the user's state representation at a certain time step. In addition, we optimize a joint loss function to align the accuracy of the sequential recommendations with the expected cumulative rewards of the critic network, while at the same time we adapt the sequence length with the actor network in a personalized manner. Our experimental evaluation on four real-world datasets demonstrates the superiority of our proposed model over several baseline approaches. Finally, we make our implementation publicly available at https://github.com/stefanosantaris/sar.
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce
Authors: Ding Xiang, Becky West, Jiaqi Wang, Xiquan Cui, Jinzhou Huang
Date: 2021-07-30 00:00:00
Tasks: Thompson Sampling

E-commerce sites strive to provide users the most timely relevant information in order to reduce shopping frictions and increase customer satisfaction. Multi armed bandit models (MAB) as a type of adaptive optimization algorithms provide possible approaches for such purposes. In this paper, we analyze using three classic MAB algorithms, epsilon-greedy, Thompson sampling (TS), and upper confidence bound 1 (UCB1) for dynamic content recommendations, and walk through the process of developing these algorithms internally to solve a real world e-commerce use case. First, we analyze the three MAB algorithms using simulated purchasing datasets with non-stationary reward distributions to simulate the possible time-varying customer preferences, where the traffic allocation dynamics and the accumulative rewards of different algorithms are studied. Second, we compare the accumulative rewards of the three MAB algorithms with more than 1,000 trials using actual historical A/B test datasets. We find that the larger difference between the success rates of competing recommendations the more accumulative rewards the MAB algorithms can achieve. In addition, we find that TS shows the highest average accumulative rewards under different testing scenarios. Third, we develop a batch-updated MAB algorithm to overcome the delayed reward issue in e-commerce and enable an online content optimization on our App homepage. For a state-of-the-art comparison, a real A/B test among our batch-updated MAB algorithm, a third-party MAB solution, and the default business logic are conducted. The result shows that our batch-updated MAB algorithm outperforms the counterparts and achieves 6.13% relative click-through rate (CTR) increase and 16.1% relative conversion rate (CVR) increase compared to the default experience, and 2.9% relative CTR increase and 1.4% relative CVR increase compared to the external MAB service.
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRI
Authors: Ruizhe Li, Matteo Bastiani, Dorothee Auer, Christian Wagner, Xin Chen
Date: 2021-08-03 00:00:00
Tasks: Age Estimation, Generative Adversarial Network, Image Augmentation, Image Generation, Medical Image Analysis, regression

Brain age estimation based on magnetic resonance imaging (MRI) is an active research area in early diagnosis of some neurodegenerative diseases (e.g. Alzheimer, Parkinson, Huntington, etc.) for elderly people or brain underdevelopment for the young group. Deep learning methods have achieved the state-of-the-art performance in many medical image analysis tasks, including brain age estimation. However, the performance and generalisability of the deep learning model are highly dependent on the quantity and quality of the training data set. Both collecting and annotating brain MRI data are extremely time-consuming. In this paper, to overcome the data scarcity problem, we propose a generative adversarial network (GAN) based image synthesis method. Different from the existing GAN-based methods, we integrate a task-guided branch (a regression model for age estimation) to the end of the generator in GAN. By adding a task-guided loss to the conventional GAN loss, the learned low-dimensional latent space and the synthesised images are more task-specific. It helps to boost the performance of the down-stream task by combining the synthesised images and real images for model training. The proposed method was evaluated on a public brain MRI data set for age estimation. Our proposed method outperformed (statistically significant) a deep convolutional neural network based regression model and the GAN-based image synthesis method without the task-guided branch. More importantly, it enables the identification of age-related brain regions in the image space. The code is available on GitHub (https://github.com/ruizhe-l/tgb-gan).
Comparison of modern open-source visual SLAM approaches
Authors: Dinar Sharafutdinov, Mark Griguletskii, Pavel Kopanev, Mikhail Kurenkov, Gonzalo Ferrer, Aleksey Burkov, Aleksei Gonnochenko, Dzmitry Tsetserukou
Date: 2021-08-03 00:00:00

SLAM is one of the most fundamental areas of research in robotics and computer vision. State of the art solutions has advanced significantly in terms of accuracy and stability. Unfortunately, not all the approaches are available as open-source solutions and free to use. The results of some of them are difficult to reproduce, and there is a lack of comparison on common datasets. In our work, we make a comparative analysis of state of the art open-source methods. We assess the algorithms based on accuracy, computational performance, robustness, and fault tolerance. Moreover, we present a comparison of datasets as well as an analysis of algorithms from a practical point of view. The findings of the work raise several crucial questions for SLAM researchers.
Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation
Authors: Victor Besnier, Andrei Bursuc, David Picard, Alexandre Briot
Date: 2021-08-03 00:00:00
Proceeding: ICCV 2021 10
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection, Segmentation, Semantic Segmentation

In this paper, we tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. By analyzing the literature, we found that current methods are either accurate or fast but not both which limits their usability in real world applications. To get the best of both aspects, we propose to mitigate the common shortcomings by following four design principles: decoupling the OOD detection from the segmentation task, observing the entire segmentation network instead of just its output, generating training data for the OOD detector by leveraging blind spots in the segmentation network and focusing the generated data on localized regions in the image to simulate OOD objects. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA). We validate the soundness of our approach across numerous ablation studies. We also show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
From augmented microscopy to the topological transformer: a new approach in cell image analysis for Alzheimer's research
Authors: Wooseok Jung
Date: 2021-08-03 00:00:00
Tasks: image-classification, Image Classification, Transfer Learning

Cell image analysis is crucial in Alzheimer's research to detect the presence of A$\beta$ protein inhibiting cell function. Deep learning speeds up the process by making only low-level data sufficient for fruitful inspection. We first found Unet is most suitable in augmented microscopy by comparing performance in multi-class semantics segmentation. We develop the augmented microscopy method to capture nuclei in a brightfield image and the transformer using Unet model to convert an input image into a sequence of topological information. The performance regarding Intersection-over-Union is consistent concerning the choice of image preprocessing and ground-truth generation. Training model with data of a specific cell type demonstrates transfer learning applies to some extent. The topological transformer aims to extract persistence silhouettes or landscape signatures containing geometric information of a given image of cells. This feature extraction facilitates studying an image as a collection of one-dimensional data, substantially reducing computational costs. Using the transformer, we attempt grouping cell images by their cell type relying solely on topological features. Performances of the transformers followed by SVM, XGBoost, LGBM, and simple convolutional neural network classifiers are inferior to the conventional image classification. However, since this research initiates a new perspective in biomedical research by combining deep learning and topology for image analysis, we speculate follow-up investigation will reinforce our genuine regime.
Del-Net: A Single-Stage Network for Mobile Camera ISP
Authors: Saumya Gupta, Diplav Srivastava, Umang Chaturvedi, Anurag Jain, Gaurav Khandelwal
Date: 2021-08-03 00:00:00
Tasks: Deblurring, Deep Learning, Demosaicking, Denoising, Image Denoising, Super-Resolution

The quality of images captured by smartphones is an important specification since smartphones are becoming ubiquitous as primary capturing devices. The traditional image signal processing (ISP) pipeline in a smartphone camera consists of several image processing steps performed sequentially to reconstruct a high quality sRGB image from the raw sensor data. These steps consist of demosaicing, denoising, white balancing, gamma correction, colour enhancement, etc. Since each of them are performed sequentially using hand-crafted algorithms, the residual error from each processing module accumulates in the final reconstructed signal. Thus, the traditional ISP pipeline has limited reconstruction quality in terms of generalizability across different lighting conditions and associated noise levels while capturing the image. Deep learning methods using convolutional neural networks (CNN) have become popular in solving many image-related tasks such as image denoising, contrast enhancement, super resolution, deblurring, etc. Furthermore, recent approaches for the RAW to sRGB conversion using deep learning methods have also been published, however, their immense complexity in terms of their memory requirement and number of Mult-Adds make them unsuitable for mobile camera ISP. In this paper we propose DelNet - a single end-to-end deep learning model - to learn the entire ISP pipeline within reasonable complexity for smartphone deployment. Del-Net is a multi-scale architecture that uses spatial and channel attention to capture global features like colour, as well as a series of lightweight modified residual attention blocks to help with denoising. For validation, we provide results to show the proposed Del-Net achieves compelling reconstruction quality.