pwc-archive/papers-with-abstracts huggingface dataset:
Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper
Authors: Jasmin Bogatinovski, Sasho Nedelkoski, Alexander Acker, Florian Schmidt, Thorsten Wittkopp, Soeren Becker, Jorge Cardoso, Odej Kao
Date: 2021-01-15 00:00:00
Tasks: Decision Making, Management

Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between the research areas of machine learning, big data, streaming analytics, and the management of IT operations. AIOps, as a field, is a candidate to produce the future standard for IT operation management. To that end, AIOps has several challenges. First, it needs to combine separate research branches from other research fields like software reliability engineering. Second, novel modelling techniques are needed to understand the dynamics of different systems. Furthermore, it requires to lay out the basis for assessing: time horizons and uncertainty for imminent SLA violations, the early detection of emerging problems, autonomous remediation, decision making, support of various optimization objectives. Moreover, a good understanding and interpretability of these aiding models are important for building trust between the employed tools and the domain experts. Finally, all this will result in faster adoption of AIOps, further increase the interest in this research field and contribute to bridging the gap towards fully-autonomous operating IT systems. The main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field. The workshop aims to strengthen the community and unite it towards the goal of joining the efforts for solving the main challenges the field is currently facing. A consensus and adoption of the principles of openness and reproducibility will boost the research in this emerging area significantly.
The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements
Authors: Lukas Stappen, Alice Baird, Lea Schumann, Björn Schuller
Date: 2021-01-15 00:00:00
Tasks: Multimodal Sentiment Analysis, Sentiment Analysis

Truly real-life data presents a strong, but exciting challenge for sentiment and emotion research. The high variety of possible `in-the-wild' properties makes large datasets such as these indispensable with respect to building robust machine learning models. A sufficient quantity of data covering a deep variety in the challenges of each modality to force the exploratory analysis of the interplay of all modalities has not yet been made available in this context. In this contribution, we present MuSe-CaR, a first of its kind multimodal dataset. The data is publicly available as it recently served as the testing bed for the 1st Multimodal Sentiment Analysis Challenge, and focused on the tasks of emotion, emotion-target engagement, and trustworthiness recognition by means of comprehensively integrating the audio-visual and language modalities. Furthermore, we give a thorough overview of the dataset in terms of collection and annotation, including annotation tiers not used in this year's MuSe 2020. In addition, for one of the sub-challenges - predicting the level of trustworthiness - no participant outperformed the baseline model, and so we propose a simple, but highly efficient Multi-Head-Attention network that exceeds using multimodal fusion the baseline by around 0.2 CCC (almost 50 % improvement).
Counterfactual Generative Networks
Authors: Axel Sauer, Andreas Geiger
Date: 2021-01-15 00:00:00
Proceeding: ICLR 2021 1
Tasks: Classification, counterfactual, General Classification, GPU, image-classification, Image Classification, Image Generation, Object

Neural networks are prone to learning shortcuts -- they often model simple correlations, ignoring more complex ones that potentially generalize better. Prior works on image classification show that instead of learning a connection to object shape, deep classifiers tend to exploit spurious correlations with low-level texture or the background for solving the classification task. In this work, we take a step towards more robust and interpretable classifiers that explicitly expose the task's causal structure. Building on current advances in deep generative modeling, we propose to decompose the image generation process into independent causal mechanisms that we train without direct supervision. By exploiting appropriate inductive biases, these mechanisms disentangle object shape, object texture, and background; hence, they allow for generating counterfactual images. We demonstrate the ability of our model to generate such images on MNIST and ImageNet. Further, we show that the counterfactual images can improve out-of-distribution robustness with a marginal drop in performance on the original classification task, despite being synthetic. Lastly, our generative model can be trained efficiently on a single GPU, exploiting common pre-trained models as inductive biases.
A Particle Filtering Framework for Integrity Risk of GNSS-Camera Sensor Fusion
Authors: Adyasha Mohanty, Shubh Gupta, Grace Xingxin Gao
Date: 2021-01-15 00:00:00
Tasks: Position, Sensor Fusion, State Estimation

Adopting a joint approach towards state estimation and integrity monitoring results in unbiased integrity monitoring unlike traditional approaches. So far, a joint approach was used in Particle RAIM [l] for GNSS measurements only. In our work, we extend Particle RAIM to a GNSS-camera fused system for joint state estimation and integrity monitoring. To account for vision faults, we derive a probability distribution over position from camera images using map-matching. We formulate a Kullback-Leibler Divergence metric to assess the consistency of GNSS and camera measurements and mitigate faults during sensor fusion. The derived integrity risk upper bounds the probability of Hazardously Misleading Information (HMI). Experimental validation on a real-world dataset shows that our algorithm produces less than 11 m position error and the integrity risk over bounds the probability of HMI with 0.11 failure rate for an 8 m Alert Limit in an urban scenario.
Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks
Authors: Patrick Brandao, Odysseas Zisimopoulos, Evangelos Mazomenos, Gastone Ciuti, Jorge Bernal, Marco Visentini-Scarzanella, Arianna Menciassi, Paolo Dario, Anastasios Koulaouzidis, Alberto Arezzo, David J Hawkes, Danail Stoyanov
Date: 2021-01-15 00:00:00
Tasks: Diagnostic, Segmentation

Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), fine-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the first work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance
Motion-Based Handwriting Recognition and Word Reconstruction
Authors: Junshen Kevin Chen, Wanze Xie, Yutong He
Date: 2021-01-15 00:00:00
Tasks: Domain Adaptation, Handwriting Recognition

In this project, we leverage a trained single-letter classifier to predict the written word from a continuously written word sequence, by designing a word reconstruction pipeline consisting of a dynamic-programming algorithm and an auto-correction model. We conduct experiments to optimize models in this pipeline, then employ domain adaptation to explore using this pipeline on unseen data distributions.
Motion-Based Handwriting Recognition
Authors: Junshen Kevin Chen, Wanze Xie, Yutong He
Date: 2021-01-15 00:00:00
Tasks: Data Augmentation, Handwriting Recognition

We attempt to overcome the restriction of requiring a writing surface for handwriting recognition. In this study, we design a prototype of a stylus equipped with motion sensor, and utilizes gyroscopic and acceleration sensor reading to perform written letter classification using various deep learning techniques such as CNN and RNNs. We also explore various data augmentation techniques and their effects, reaching up to 86% accuracy.
Non-uniform Motion Deblurring with Blurry Component Divided Guidance
Authors: Pei Wang, Wei Sun, Qingsen Yan, Axi Niu, Rui Li, Yu Zhu, Jinqiu Sun, Yanning Zhang
Date: 2021-01-15 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Decoder, Image Deblurring, Image Generation

Blind image deblurring is a fundamental and challenging computer vision problem, which aims to recover both the blur kernel and the latent sharp image from only a blurry observation. Despite the superiority of deep learning methods in image deblurring have displayed, there still exists major challenge with various non-uniform motion blur. Previous methods simply take all the image features as the input to the decoder, which handles different degrees (e.g. large blur, small blur) simultaneously, leading to challenges for sharp image generation. To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree. Specifically, two component attentive blocks are employed to learn attention maps to exploit useful deblurring feature representations on both large and small blurry regions. Then, the blur-aware features are fed into two-branch reconstruction decoders respectively. In addition, a new feature fusion mechanism, orientation-based feature fusion, is proposed to merge sharp features of the two branches. Both qualitative and quantitative experimental results show that our method performs favorably against the state-of-the-art approaches.
Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge
Authors: Violetta Shevchenko, Damien Teney, Anthony Dick, Anton Van Den Hengel
Date: 2021-01-15 00:00:00
Proceeding: EACL (LANTERN) 2021 4
Tasks: Question Answering, Visual Question Answering (VQA), Visual Reasoning

The limits of applicability of vision-and-language models are defined by the coverage of their training data. Tasks like vision question answering (VQA) often require commonsense and factual information beyond what can be learned from task-specific datasets. This paper investigates the injection of knowledge from general-purpose knowledge bases (KBs) into vision-and-language transformers. We use an auxiliary training objective that encourages the learned representations to align with graph embeddings of matching entities in a KB. We empirically study the relevance of various KBs to multiple tasks and benchmarks. The technique brings clear benefits to knowledge-demanding question answering tasks (OK-VQA, FVQA) by capturing semantic and relational knowledge absent from existing models. More surprisingly, the technique also benefits visual reasoning tasks (NLVR2, SNLI-VE). We perform probing experiments and show that the injection of additional knowledge regularizes the space of embeddings, which improves the representation of lexical and semantic similarities. The technique is model-agnostic and can expand the applicability of any vision-and-language transformer with minimal computational overhead.
The Geometry of Deep Generative Image Models and its Applications
Authors: Binxu Wang, Carlos R. Ponce
Date: 2021-01-15 00:00:00
Tasks: Image-Variation

Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These networks are trained to map random inputs in their latent space to new samples representative of the learned data. However, the structure of the latent space is hard to intuit due to its high dimensionality and the non-linearity of the generator, which limits the usefulness of the models. Understanding the latent space requires a way to identify input codes for existing real-world images (inversion), and a way to identify directions with known image transformations (interpretability). Here, we use a geometric framework to address both issues simultaneously. We develop an architecture-agnostic method to compute the Riemannian metric of the image manifold created by GANs. The eigen-decomposition of the metric isolates axes that account for different levels of image variability. An empirical analysis of several pretrained GANs shows that image variation around each position is concentrated along surprisingly few major axes (the space is highly anisotropic) and the directions that create this large variation are similar at different positions in the space (the space is homogeneous). We show that many of the top eigenvectors correspond to interpretable transforms in the image space, with a substantial part of eigenspace corresponding to minor transforms which could be compressed out. This geometric understanding unifies key previous results related to GAN interpretability. We show that the use of this metric allows for more efficient optimization in the latent space (e.g. GAN inversion) and facilitates unsupervised discovery of interpretable axes. Our results illustrate that defining the geometry of the GAN image manifold can serve as a general framework for understanding GANs.
Walk in Wild: An Ensemble Approach for Hostility Detection in Hindi Posts
Authors: Chander Shekhar, Bhavya Bagla, Kaushal Kumar Maurya, Maunendra Sankar Desarkar
Date: 2021-01-15 00:00:00
Tasks: Binary Classification, Classification, General Classification, Multi-class Classification

As the reach of the internet increases, pejorative terms started flooding over social media platforms. This leads to the necessity of identifying hostile content on social media platforms. Identification of hostile contents on low-resource languages like Hindi poses different challenges due to its diverse syntactic structure compared to English. In this paper, we develop a simple ensemble based model on pre-trained mBERT and popular classification algorithms like Artificial Neural Network (ANN) and XGBoost for hostility detection in Hindi posts. We formulated this problem as binary classification (hostile and non-hostile class) and multi-label multi-class classification problem (for more fine-grained hostile classes). We received third overall rank in the competition and weighted F1-scores of ~0.969 and ~0.61 on the binary and multi-label multi-class classification tasks respectively.
Convolutional Neural Network with Pruning Method for Handwritten Digit Recognition
Authors: Mengyu Chen
Date: 2021-01-15 00:00:00
Tasks: Handwritten Digit Recognition

CNN model is a popular method for imagery analysis, so it could be utilized to recognize handwritten digits based on MNIST datasets. For higher recognition accuracy, various CNN models with different fully connected layer sizes are exploited to figure out the relationship between the CNN fully connected layer size and the recognition accuracy. Inspired by previous pruning work, we performed pruning methods of distinctiveness on CNN models and compared the pruning performance with NN models. For better pruning performances on CNN, the effect of angle threshold on the pruning performance was explored. The evaluation results show that: for the fully connected layer size, there is a threshold, so that when the layer size increases, the recognition accuracy grows if the layer size smaller than the threshold, and falls if the layer size larger than the threshold; the performance of pruning performed on CNN is worse than on NN; as pruning angle threshold increases, the fully connected layer size and the recognition accuracy decreases. This paper also shows that for CNN models trained by the MNIST dataset, they are capable of handwritten digit recognition and achieve the highest recognition accuracy with fully connected layer size 400. In addition, for same dataset MNIST, CNN models work better than big, deep, simple NN models in a published paper.
Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry
Authors: Rui Tian, Yunzhou Zhang, Delong Zhu, Shiwen Liang, Sonya Coleman, Dermot Kerr
Date: 2021-01-15 00:00:00
Tasks: Loop Closure Detection, Monocular Visual Odometry, Self-Driving Cars, Translation, Visual Odometry

Scale ambiguity is a fundamental problem in monocular visual odometry. Typical solutions include loop closure detection and environment information mining. For applications like self-driving cars, loop closure is not always available, hence mining prior knowledge from the environment becomes a more promising approach. In this paper, with the assumption of a constant height of the camera above the ground, we develop a light-weight scale recovery framework leveraging an accurate and robust estimation of the ground plane. The framework includes a ground point extraction algorithm for selecting high-quality points on the ground plane, and a ground point aggregation algorithm for joining the extracted ground points in a local sliding window. Based on the aggregated data, the scale is finally recovered by solving a least-squares problem using a RANSAC-based optimizer. Sufficient data and robust optimizer enable a highly accurate scale recovery. Experiments on the KITTI dataset show that the proposed framework can achieve state-of-the-art accuracy in terms of translation errors, while maintaining competitive performance on the rotation error. Due to the light-weight design, our framework also demonstrates a high frequency of 20Hz on the dataset.
Ensemble Learning Based Classification Algorithm Recommendation
Authors: Guangtao Wang, Qinbao Song, Xiaoyan Zhu
Date: 2021-01-15 00:00:00
Tasks: Classification, Ensemble Learning, General Classification

Recommending appropriate algorithms to a classification problem is one of the most challenging issues in the field of data mining. The existing algorithm recommendation models are generally constructed on only one kind of meta-features by single learners. Considering that i) ensemble learners usually show better performance and ii) different kinds of meta-features characterize the classification problems in different viewpoints independently, and further the models constructed with different sets of meta-features will be complementary with each other and applicable for ensemble. This paper proposes an ensemble learning-based algorithm recommendation method. To evaluate the proposed recommendation method, extensive experiments with 13 well-known candidate classification algorithms and five different kinds of meta-features are conducted on 1090 benchmark classification problems. The results show the effectiveness of the proposed ensemble learning based recommendation method.
Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke
Authors: Umberto A. Gava, Federico D'Agata, Enzo Tartaglione, Marco Grangetto, Francesca Bertolino, Ambra Santonocito, Edwin Bennink, Mauro Bergui
Date: 2021-01-15 00:00:00

Purpose: In this study we investigate whether a Convolutional Neural Network (CNN) can generate clinically relevant parametric maps from CT perfusion data in a clinical setting of patients with acute ischemic stroke. Methods: Training of the CNN was done on a subset of 100 perfusion data, while 15 samples were used as validation. All the data used for the training/validation of the network and to generate ground truth (GT) maps, using a state-of-the-art deconvolution-algorithm, were previously pre-processed using a standard pipeline. Validation was carried out through manual segmentation of infarct core and penumbra on both CNN-derived maps and GT maps. Concordance among segmented lesions was assessed using the Dice and the Pearson correlation coefficients across lesion volumes. Results: Mean Dice scores from two different raters and the GT maps were > 0.70 (good-matching). Inter-rater concordance was also high and strong correlation was found between lesion volumes of CNN maps and GT maps (0.99, 0.98). Conclusion: Our CNN-based approach generated clinically relevant perfusion maps that are comparable to state-of-the-art perfusion analysis methods based on deconvolution of the data. Moreover, the proposed technique requires less information to estimate the ischemic core and thus might allow the development of novel perfusion protocols with lower radiation dose.
Coarse-grained decomposition and fine-grained interaction for multi-hop question answering
Authors: Xing Cao, Yun Liu
Date: 2021-01-15 00:00:00
Tasks: Multi-hop Question Answering, Question Answering, Reading Comprehension

Recent advances regarding question answering and reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text, requiring only single-hop reasoning. However, in actual scenarios, lots of complex queries require multi-hop reasoning. The key to the Question Answering task is semantic feature interaction between documents and questions, which is widely processed by Bi-directional Attention Flow (Bi-DAF), but Bi-DAF generally captures only the surface semantics of words in complex questions and fails to capture implied semantic feature of intermediate answers. As a result, Bi-DAF partially ignores part of the contexts related to the question and cannot extract the most important parts of multiple documents. In this paper we propose a new model architecture for multi-hop question answering, by applying two completion strategies: (1) Coarse-Grain complex question Decomposition (CGDe) strategy are introduced to decompose complex question into simple ones under the condition of without any additional annotations (2) Fine-Grained Interaction (FGIn) strategy are introduced to better represent each word in the document and extract more comprehensive and accurate sentences related to the inference path. The above two strategies are combined and tested on the SQuAD and HotpotQA datasets, and the experimental results show that our method outperforms state-of-the-art baselines.