pwc-archive/papers-with-abstracts huggingface dataset:
Medial Spectral Coordinates for 3D Shape Analysis
Authors: Morteza Rezanejad, Mohammad Khodadad, Hamidreza Mahyar, Herve Lombaert, Michael Gruninger, Dirk B. Walther, Kaleem Siddiqi
Date: 2021-11-26 00:00:00
Proceeding: CVPR 2022 1
Tasks: Autonomous Driving, Object

In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects represented by surface meshes, their voxelized interiors, or surface point clouds. In part, this interest has been stimulated by the increased availability of RGBD cameras, and by applications of computer vision to autonomous driving, medical imaging, and robotics. In these settings, spectral coordinates have shown promise for shape representation due to their ability to incorporate both local and global shape properties in a manner that is qualitatively invariant to isometric transformations. Yet, surprisingly, such coordinates have thus far typically considered only local surface positional or derivative information. In the present article, we propose to equip spectral coordinates with medial (object width) information, so as to enrich them. The key idea is to couple surface points that share a medial ball, via the weights of the adjacency matrix. We develop a spectral feature using this idea, and the algorithms to compute it. The incorporation of object width and medial coupling has direct benefits, as illustrated by our experiments on object classification, object part segmentation, and surface point correspondence.
3D Pose Estimation and Future Motion Prediction from 2D Images
Authors: Ji Yang, Youdong Ma, Xinxin Zuo, Sen Wang, Minglun Gong, Li Cheng
Date: 2021-11-26 00:00:00
Tasks: 3D Pose Estimation, Decoder, motion prediction, Pose Estimation

This paper considers to jointly tackle the highly correlated tasks of estimating 3D human body poses and predicting future 3D motions from RGB image sequences. Based on Lie algebra pose representation, a novel self-projection mechanism is proposed that naturally preserves human motion kinematics. This is further facilitated by a sequence-to-sequence multi-task architecture based on an encoder-decoder topology, which enables us to tap into the common ground shared by both tasks. Finally, a global refinement module is proposed to boost the performance of our framework. The effectiveness of our approach, called PoseMoNet, is demonstrated by ablation tests and empirical evaluations on Human3.6M and HumanEva-I benchmark, where competitive performance is obtained comparing to the state-of-the-arts.
Designing a Trusted Data Brokerage Framework in the Aviation Domain
Authors: Evmorfia Biliri, Minas Pertselakis, Marios Phinikettos, Marios Zacharias, Fenareti Lampathaki, Dimitrios Alexandrou
Date: 2021-11-25 00:00:00

In recent years, there is growing interest in the ways the European aviation industry can leverage the multi-source data fusion towards augmented domain intelligence. However, privacy, legal and organisational policies together with technical limitations, hinder data sharing and, thus, its benefits. The current paper presents the ICARUS data policy and assets brokerage framework, which aims to (a) formalise the data attributes and qualities that affect how aviation data assets can be shared and handled subsequently to their acquisition, including licenses, IPR, characterisation of sensitivity and privacy risks, and (b) enable the creation of machine-processable data contracts for the aviation industry. This involves expressing contractual terms pertaining to data trading agreements into a machine-processable language and supporting the diverse interactions among stakeholders in aviation data sharing scenarios through a trusted and robust system based on the Ethereum platform.
Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models
Authors: Pranav Narayanan Venkit, Shomir Wilson
Date: 2021-11-25 00:00:00
Tasks: Fairness, Sentiment Analysis, Toxic Comment Classification

Sociodemographic biases are a common problem for natural language processing, affecting the fairness and integrity of its applications. Within sentiment analysis, these biases may undermine sentiment predictions for texts that mention personal attributes that unbiased human readers would consider neutral. Such discrimination can have great consequences in the applications of sentiment analysis both in the public and private sectors. For example, incorrect inferences in applications like online abuse and opinion analysis in social media platforms can lead to unwanted ramifications, such as wrongful censoring, towards certain populations. In this paper, we address the discrimination against people with disabilities, PWD, done by sentiment analysis and toxicity classification models. We provide an examination of sentiment and toxicity analysis models to understand in detail how they discriminate PWD. We present the Bias Identification Test in Sentiments (BITS), a corpus of 1,126 sentences designed to probe sentiment analysis models for biases in disability. We use this corpus to demonstrate statistically significant biases in four widely used sentiment analysis tools (TextBlob, VADER, Google Cloud Natural Language API and DistilBERT) and two toxicity analysis models trained to predict toxic comments on Jigsaw challenges (Toxic comment classification and Unintended Bias in Toxic comments). The results show that all exhibit strong negative biases on sentences that mention disability. We publicly release BITS Corpus for others to identify potential biases against disability in any sentiment analysis tools and also to update the corpus to be used as a test for other sociodemographic variables as well.
Unscented Kalman Filter for Long-Distance Vessel Tracking in Geodetic Coordinates
Authors: Blake Cole, Gabriel Schamberg
Date: 2021-11-25 00:00:00
Tasks: Collision Avoidance

This paper describes a novel tracking filter, designed primarily for use in collision avoidance systems on autonomous surface vehicles (ASVs). The proposed methodology leverages real-time kinematic information broadcast via the Automatic Information System (AIS) messaging protocol, in order to estimate the position, speed, and heading of nearby cooperative targets. The state of each target is recursively estimated in geodetic coordinates using an unscented Kalman filter (UKF) with kinematic equations derived from the spherical law of cosines. This improves upon previous approaches, many of which employ the extended Kalman filter (EKF), and thus require the specification of a local planar coordinate frame, in order to describe the state kinematics in an easily differentiable form. The proposed geodetic UKF obviates the need for this local plane. This feature is particularly advantageous for long-range ASVs, which must otherwise periodically redefine a new local plane to curtail linearization error. In real-world operations, this recurring redefinition can introduce error and complicate mission planning. It is shown through both simulation and field testing that the proposed geodetic UKF performs as well as, or better than, the traditional plane-Cartesian EKF, both in terms of estimation error and stability.
Look at here : Utilizing supervision to attend subtle key regions
Authors: ChangHwan Lee, Yeesuk Kim, Bong Gun Lee, Doosup Kim, Jongseong Jang
Date: 2021-11-25 00:00:00

Despite the success of deep learning in computer vision, algorithms to recognize subtle and small objects (or regions) is still challenging. For example, recognizing a baseball or a frisbee on a ground scene or a bone fracture in an X-ray image can easily result in overfitting, unless a huge amount of training data is available. To mitigate this problem, we need a way to force a model should identify subtle regions in limited training data. In this paper, we propose a simple but efficient supervised augmentation method called Cut\&Remain. It achieved better performance on various medical image domain (internally sourced- and public dataset) and a natural image domain (MS-COCO$_s$) than other supervised augmentation and the explicit guidance methods. In addition, using the class activation map, we identified that the Cut\&Remain methods drive a model to focus on relevant subtle and small regions efficiently. We also show that the performance monotonically increased along the Cut\&Remain ratio, indicating that a model can be improved even though only limited amount of Cut\&Remain is applied for, so that it allows low supervising (annotation) cost for improvement.
FedDropoutAvg: Generalizable federated learning for histopathology image classification
Authors: Gozde N. Gunesli, Mohsin Bilal, Shan E Ahmed Raza, Nasir M. Rajpoot
Date: 2021-11-25 00:00:00
Tasks: Classification, Federated Learning, image-classification, Image Classification, Medical Image Analysis

Federated learning (FL) enables collaborative learning of a deep learning model without sharing the data of participating sites. FL in medical image analysis tasks is relatively new and open for enhancements. In this study, we propose FedDropoutAvg, a new federated learning approach for training a generalizable model. The proposed method takes advantage of randomness, both in client selection and also in federated averaging process. We compare FedDropoutAvg to several algorithms in an FL scenario for real-world multi-site histopathology image classification task. We show that with FedDropoutAvg, the final model can achieve performance better than other FL approaches and closer to a classical deep learning model that requires all data to be shared for centralized training. We test the trained models on a large dataset consisting of 1.2 million image tiles from 21 different centers. To evaluate the generalization ability of the proposed approach, we use held-out test sets from centers whose data was used in the FL and for unseen data from other independent centers whose data was not used in the federated training. We show that the proposed approach is more generalizable than other state-of-the-art federated training approaches. To the best of our knowledge, ours is the first study to use a randomized client and local model parameter selection procedure in a federated setting for a medical image analysis task.
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
Authors: Tolga Birdal, Aaron Lou, Leonidas Guibas, Umut Şimşekli
Date: 2021-11-25 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Learning Theory, Topological Data Analysis

Disobeying the classical wisdom of statistical learning theory, modern deep neural networks generalize well even though they typically contain millions of parameters. Recently, it has been shown that the trajectories of iterative optimization algorithms can possess fractal structures, and their generalization error can be formally linked to the complexity of such fractals. This complexity is measured by the fractal's intrinsic dimension, a quantity usually much smaller than the number of parameters in the network. Even though this perspective provides an explanation for why overparametrized networks would not overfit, computing the intrinsic dimension (e.g., for monitoring generalization during training) is a notoriously difficult task, where existing methods typically fail even in moderate ambient dimensions. In this study, we consider this problem from the lens of topological data analysis (TDA) and develop a generic computational tool that is built on rigorous mathematical foundations. By making a novel connection between learning theory and TDA, we first illustrate that the generalization error can be equivalently bounded in terms of a notion called the 'persistent homology dimension' (PHD), where, compared with prior work, our approach does not require any additional geometrical or statistical assumptions on the training dynamics. Then, by utilizing recently established theoretical results and TDA tools, we develop an efficient algorithm to estimate PHD in the scale of modern deep neural networks and further provide visualization tools to help understand generalization in deep learning. Our experiments show that the proposed approach can efficiently compute a network's intrinsic dimension in a variety of settings, which is predictive of the generalization error.
DA$^{\textbf{2}}$-Net : Diverse & Adaptive Attention Convolutional Neural Network
Authors: Abenezer Girma, Abdollah Homaifar, M Nabil Mahmoud, Xuyang Yan, Mrinmoy Sarkar
Date: 2021-11-25 00:00:00

Standard Convolutional Neural Network (CNN) designs rarely focus on the importance of explicitly capturing diverse features to enhance the network's performance. Instead, most existing methods follow an indirect approach of increasing or tuning the networks' depth and width, which in many cases significantly increases the computational cost. Inspired by a biological visual system, we propose a Diverse and Adaptive Attention Convolutional Network (DA$^{2}$-Net), which enables any feed-forward CNNs to explicitly capture diverse features and adaptively select and emphasize the most informative features to efficiently boost the network's performance. DA$^{2}$-Net incurs negligible computational overhead and it is designed to be easily integrated with any CNN architecture. We extensively evaluated DA$^{2}$-Net on benchmark datasets, including CIFAR100, SVHN, and ImageNet, with various CNN architectures. The experimental results show DA$^{2}$-Net provides a significant performance improvement with very minimal computational overhead.
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
Authors: Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, Jakob Uszkoreit, Thomas Funkhouser, Andrea Tagliasacchi
Date: 2021-11-25 00:00:00
Proceeding: CVPR 2022 1
Tasks: Decoder, Novel View Synthesis, Semantic Segmentation

A classical problem in computer vision is to infer a 3D scene representation from few images that can be used to render novel views at interactive rates. Previous work focuses on reconstructing pre-defined 3D representations, e.g. textured meshes, or implicit representations, e.g. radiance fields, and often requires input images with precise camera poses and long processing times for each novel scene. In this work, we propose the Scene Representation Transformer (SRT), a method which processes posed or unposed RGB images of a new area, infers a "set-latent scene representation", and synthesises novel views, all in a single feed-forward pass. To calculate the scene representation, we propose a generalization of the Vision Transformer to sets of images, enabling global information integration, and hence 3D reasoning. An efficient decoder transformer parameterizes the light field by attending into the scene representation to render novel views. Learning is supervised end-to-end by minimizing a novel-view reconstruction error. We show that this method outperforms recent baselines in terms of PSNR and speed on synthetic datasets, including a new dataset created for the paper. Further, we demonstrate that SRT scales to support interactive visualization and semantic segmentation of real-world outdoor environments using Street View imagery.
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection
Authors: João Vitorino, Rui Andrade, Isabel Praça, Orlando Sousa, Eva Maia
Date: 2021-11-25 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Deep Reinforcement Learning, Intrusion Detection, Multi-class Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The digital transformation faces tremendous security challenges. In particular, the growing number of cyber-attacks targeting Internet of Things (IoT) systems restates the need for a reliable detection of malicious network activity. This paper presents a comparative analysis of supervised, unsupervised and reinforcement learning techniques on nine malware captures of the IoT-23 dataset, considering both binary and multi-class classification scenarios. The developed models consisted of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Isolation Forest (iForest), Local Outlier Factor (LOF) and a Deep Reinforcement Learning (DRL) model based on a Double Deep Q-Network (DDQN), adapted to the intrusion detection context. The most reliable performance was achieved by LightGBM. Nonetheless, iForest displayed good anomaly detection results and the DRL model demonstrated the possible benefits of employing this methodology to continuously improve the detection. Overall, the obtained results indicate that the analyzed techniques are well suited for IoT intrusion detection.
Unravelling multi-agent ranked delegations
Authors: Rachael Colley, Umberto Grandi, Arianna Novaro
Date: 2021-11-25 00:00:00

We introduce a voting model with multi-agent ranked delegations. This model generalises liquid democracy in two aspects: first, an agent's delegation can use the votes of multiple other agents to determine their own -- for instance, an agent's vote may correspond to the majority outcome of the votes of a trusted group of agents; second, agents can submit a ranking over multiple delegations, so that a backup delegation can be used when their preferred delegations are involved in cycles. The main focus of this paper is the study of unravelling procedures that transform the delegation ballots received from the agents into a profile of direct votes, from which a winning alternative can then be determined by using a standard voting rule. We propose and study six such unravelling procedures, two based on optimisation and four using a greedy approach. We study both algorithmic and axiomatic properties, as well as related computational complexity problems of our unravelling procedures for different restrictions on the types of ballots that the agents can submit.
Learning to Search in Task and Motion Planning with Streams
Authors: Mohamed Khodeir, Ben Agro, Florian Shkurti
Date: 2021-11-25 00:00:00
Tasks: Graph Neural Network, Motion Planning, Task and Motion Planning

Task and motion planning problems in robotics combine symbolic planning over discrete task variables with motion optimization over continuous state and action variables. Recent works such as PDDLStream have focused on optimistic planning with an incrementally growing set of objects until a feasible trajectory is found. However, this set is exhaustively expanded in a breadth-first manner, regardless of the logical and geometric structure of the problem at hand, which makes long-horizon reasoning with large numbers of objects prohibitively time-consuming. To address this issue, we propose a geometrically informed symbolic planner that expands the set of objects and facts in a best-first manner, prioritized by a Graph Neural Network that is learned from prior search computations. We evaluate our approach on a diverse set of problems and demonstrate an improved ability to plan in difficult scenarios. We also apply our algorithm on a 7DOF robotic arm in block-stacking manipulation tasks.
Ontology-Based Skill Description Learning for Flexible Production Systems
Authors: Anna Himmelhuber, Stephan Grimm, Thomas Runkler, Sonja Zillner
Date: 2021-11-25 00:00:00
Tasks: Inductive logic programming

The increasing importance of resource-efficient production entails that manufacturing companies have to create a more dynamic production environment, with flexible manufacturing machines and processes. To fully utilize this potential of dynamic manufacturing through automatic production planning, formal skill descriptions of the machines are essential. However, generating those skill descriptions in a manual fashion is labor-intensive and requires extensive domain-knowledge. In this contribution an ontology-based semi-automatic skill description system that utilizes production logs and industrial ontologies through inductive logic programming is introduced and benefits and drawbacks of the proposed solution are evaluated.
Monitoring Hybrid Process Specifications with Conflict Management: The Automata-theoretic Approach
Authors: Anti Alman, Fabrizio Maria Maggi, Marco Montali, Fabio Patrizi, Andrey Rivkin
Date: 2021-11-25 00:00:00
Tasks: Management

Business process monitoring approaches have thus far mainly focused on monitoring the execution of a process with respect to a single process model. However, in some cases it is necessary to consider multiple process specifications simultaneously. In addition, these specifications can be procedural, declarative, or a combination of both. For example, in the medical domain, a clinical guideline describing the treatment of a specific disease cannot account for all possible co-factors that can coexist for a specific patient and therefore additional constraints may need to be considered. In some cases, these constraints may be incompatible with clinical guidelines, therefore requiring the violation of either the guidelines or the constraints. In this paper, we propose a solution for monitoring the interplay of hybrid process specifications expressed as a combination of (data-aware) Petri nets and temporal logic rules. During the process execution, if these specifications are in conflict with each other, it is possible to violate some of them. The monitoring system is equipped with a violation cost model according to which the system can recommend the next course of actions in a way that would either avoid possible violations or minimize the total cost of violations.
Scene Graph Generation with Geometric Context
Authors: Vishal Kumar, Albert Mundu, Satish Kumar Singh
Date: 2021-11-25 00:00:00
Tasks: Activity Recognition, Graph Generation, Image Captioning, Question Answering, Scene Graph Generation, Self-Driving Cars, Visual Question Answering, Visual Question Answering (VQA)

Scene Graph Generation has gained much attention in computer vision research with the growing demand in image understanding projects like visual question answering, image captioning, self-driving cars, crowd behavior analysis, activity recognition, and more. Scene graph, a visually grounded graphical structure of an image, immensely helps to simplify the image understanding tasks. In this work, we introduced a post-processing algorithm called Geometric Context to understand the visual scenes better geometrically. We use this post-processing algorithm to add and refine the geometric relationships between object pairs to a prior model. We exploit this context by calculating the direction and distance between object pairs. We use Knowledge Embedded Routing Network (KERN) as our baseline model, extend the work with our algorithm, and show comparable results on the recent state-of-the-art algorithms.
GPR1200: A Benchmark for General-Purpose Content-Based Image Retrieval
Authors: Konstantin Schall, Kai Uwe Barthel, Nico Hezel, Klaus Jung
Date: 2021-11-25 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Even though it has extensively been shown that retrieval specific training of deep neural networks is beneficial for nearest neighbor image search quality, most of these models are trained and tested in the domain of landmarks images. However, some applications use images from various other domains and therefore need a network with good generalization properties - a general-purpose CBIR model. To the best of our knowledge, no testing protocol has so far been introduced to benchmark models with respect to general image retrieval quality. After analyzing popular image retrieval test sets we decided to manually curate GPR1200, an easy to use and accessible but challenging benchmark dataset with a broad range of image categories. This benchmark is subsequently used to evaluate various pretrained models of different architectures on their generalization qualities. We show that large-scale pretraining significantly improves retrieval performance and present experiments on how to further increase these properties by appropriate fine-tuning. With these promising results, we hope to increase interest in the research topic of general-purpose CBIR.