OpenRaiser/Intern-Atlas huggingface dataset:
Roof fall hazard detection with convolutional neural networks using transfer learning
Venue: arXiv cs.CV 2020
Year: 2020

Roof falls due to geological conditions are major safety hazards in mining and tunneling industries, causing lost work times, injuries, and fatalities. Several large-opening limestone mines in the Eastern and Midwestern United States have roof fall problems caused by high horizontal stresses. The typical hazard management approach for this type of roof fall hazard relies heavily on visual inspections and expert knowledge. In this study, we propose an artificial intelligence (AI) based system for the detection roof fall hazards caused by high horizontal stresses. We use images depicting hazardous and non-hazardous roof conditions to develop a convolutional neural network for autonomous detection of hazardous roof conditions. To compensate for limited input data, we utilize a transfer learning approach. In transfer learning, an already-trained network is used as a starting point for classification in a similar domain. Results confirm that this approach works well for classifying roof conditions as hazardous or safe, achieving a statistical accuracy of 86%. However, accuracy alone is not enough to ensure a reliable hazard management system. System constraints and reliability are improved when the features being used by the network are understood. Therefore, we used a deep learning interpretation technique called integrated gradients to identify the important geologic features in each image for prediction. The analysis of integrated gradients shows that the system mimics expert judgment on roof fall hazard detection. The system developed in this paper demonstrates the potential of deep learning in geological hazard management to complement human experts, and likely to become an essential part of autonomous tunneling operations in those cases where hazard identification heavily depends on expert knowledge.
Mixed-SCORE+ for mixed membership community detection
Venue: arXiv cs.SI 2020
Year: 2020

Mixed-SCORE is a recent approach for mixed membership community detection proposed by Jin et al. (2017) which is an extension of SCORE (Jin, 2015). In the note Jin et al. (2018), the authors propose SCORE+ as an improvement of SCORE to handle with weak signal networks. In this paper, we propose a method called Mixed-SCORE+ designed based on the Mixed-SCORE and SCORE+, therefore Mixed-SCORE+ inherits nice properties of both Mixed-SCORE and SCORE+. In the proposed method, we consider K+1 eigenvectors when there are K communities to detect weak signal networks. And we also construct vertices hunting and membership reconstruction steps to solve the problem of mixed membership community detection. Compared with several benchmark methods, numerical results show that Mixed-SCORE+ provides a significant improvement on the Polblogs network and two weak signal networks Simmons and Caltech, with error rates 54/1222, 125/1137 and 94/590, respectively. Furthermore, Mixed-SCORE+ enjoys excellent performances on the SNAP ego-networks.
TRACE: Early Detection of Chronic Kidney Disease Onset with Transformer-Enhanced Feature Embedding
Venue: arXiv cs.LG 2020
Year: 2020

Chronic kidney disease (CKD) has a poor prognosis due to excessive risk factors and comorbidities associated with it. The early detection of CKD faces challenges of insufficient medical histories of positive patients and complicated risk factors. In this paper, we propose the TRACE (Transformer-RNN Autoencoder-enhanced CKD Detector) framework, an end-to-end prediction model using patients' medical history data, to deal with these challenges. TRACE presents a comprehensive medical history representation with a novel key component: a Transformer-RNN autoencoder. The autoencoder jointly learns a medical concept embedding via Transformer for each hospital visit, and a latent representation which summarizes a patient's medical history across all the visits. We compared TRACE with multiple state-of-the-art methods on a dataset derived from real-world medical records. Our model has achieved 0.5708 AUPRC with a 2.31% relative improvement over the best-performing method. We also validated the clinical meaning of the learned embeddings through visualizations and a case study, showing the potential of TRACE to serve as a general disease prediction model.
Bringing Cognitive Augmentation to Web Browsing Accessibility
Venue: arXiv cs.CY 2020
Year: 2020

In this paper we explore the opportunities brought by cognitive augmentation to provide a more natural and accessible web browsing experience. We explore these opportunities through \textit{conversational web browsing}, an emerging interaction paradigm for the Web that enables blind and visually impaired users (BVIP), as well as regular users, to access the contents and features of websites through conversational agents. Informed by the literature, our previous work and prototyping exercises, we derive a conceptual framework for supporting BVIP conversational web browsing needs, to then focus on the challenges of automatically providing this support, describing our early work and prototype that leverage heuristics that consider structural and content features only.
From Requirements to Autonomous Flight: An Overview of the Monitoring ICAROUS Project
Venue: arXiv cs.SE 2020
Year: 2020

The Independent Configurable Architecture for Reliable Operations of Unmanned Systems (ICAROUS) is a software architecture incorporating a set of algorithms to enable autonomous operations of unmanned aircraft applications. This paper provides an overview of Monitoring ICAROUS, a project whose objective is to provide a formal approach to generating runtime monitors for autonomous systems from requirements written in a structured natural language. This approach integrates FRET, a formal requirement elicitation and authoring tool, and Copilot, a runtime verification framework. FRET is used to specify formal requirements in structured natural language. These requirements are translated into temporal logic formulae. Copilot is then used to generate executable runtime monitors from these temporal logic specifications. The generated monitors are directly integrated into ICAROUS to perform runtime verification during flight.
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion
Venue: arXiv cs.CV 2020
Year: 2020

LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous driving. However, due to the severe sparsity and noise interference in the single sweep LiDAR point cloud, the accurate semantic segmentation is non-trivial to achieve. In this paper, we propose a novel sparse LiDAR point cloud semantic segmentation framework assisted by learned contextual shape priors. In practice, an initial semantic segmentation (SS) of a single sweep point cloud can be achieved by any appealing network and then flows into the semantic scene completion (SSC) module as the input. By merging multiple frames in the LiDAR sequence as supervision, the optimized SSC module has learned the contextual shape priors from sequential LiDAR data, completing the sparse single sweep point cloud to the dense one. Thus, it inherently improves SS optimization through fully end-to-end training. Besides, a Point-Voxel Interaction (PVI) module is proposed to further enhance the knowledge fusion between SS and SSC tasks, i.e., promoting the interaction of incomplete local geometry of point cloud and complete voxel-wise global structure. Furthermore, the auxiliary SSC and PVI modules can be discarded during inference without extra burden for SS. Extensive experiments confirm that our JS3C-Net achieves superior performance on both SemanticKITTI and SemanticPOSS benchmarks, i.e., 4% and 3% improvement correspondingly.
Using previous acoustic context to improve Text-to-Speech synthesis
Venue: arXiv cs.CL 2020
Year: 2020

Many speech synthesis datasets, especially those derived from audiobooks, naturally comprise sequences of utterances. Nevertheless, such data are commonly treated as individual, unordered utterances both when training a model and at inference time. This discards important prosodic phenomena above the utterance level. In this paper, we leverage the sequential nature of the data using an acoustic context encoder that produces an embedding of the previous utterance audio. This is input to the decoder in a Tacotron 2 model. The embedding is also used for a secondary task, providing additional supervision. We compare two secondary tasks: predicting the ordering of utterance pairs, and predicting the embedding of the current utterance audio. Results show that the relation between consecutive utterances is informative: our proposed model significantly improves naturalness over a Tacotron 2 baseline.
Budget-feasible Maximum Nash Social Welfare Allocation is Almost Envy-free
Venue: arXiv cs.GT 2020
Year: 2020

The Nash social welfare (NSW) is a well-known social welfare measurement that balances individual utilities and the overall efficiency. In the context of fair allocation of indivisible goods, it has been shown by Caragiannis et al. (EC 2016 and TEAC 2019) that an allocation maximizing the NSW is envy-free up to one good (EF1). In this paper, we are interested in the fairness of the NSW in a budget-feasible allocation problem, in which each item has a cost that will be incurred to the agent it is allocated to, and each agent has a budget constraint on the total cost of items she receives. We show that a budget-feasible allocation that maximizes the NSW achieves a 1/4-approximation of EF1 and the approximation ratio is tight. The approximation ratio improves gracefully when the items have small costs compared with the agents' budgets; it converges to 1/2 when the budget-cost ratio approaches infinity.
Stylometry for Noisy Medieval Data: Evaluating Paul Meyer's Hagiographic Hypothesis
Venue: arXiv cs.CL 2020
Year: 2020

Stylometric analysis of medieval vernacular texts is still a significant challenge: the importance of scribal variation, be it spelling or more substantial, as well as the variants and errors introduced in the tradition, complicate the task of the would-be stylometrist. Basing the analysis on the study of the copy from a single hand of several texts can partially mitigate these issues (Camps and Cafiero, 2013), but the limited availability of complete diplomatic transcriptions might make this difficult. In this paper, we use a workflow combining handwritten text recognition and stylometric analysis, applied to the case of the hagiographic works contained in MS BnF, fr. 412. We seek to evaluate Paul Meyer's hypothesis about the constitution of groups of hagiographic works, as well as to examine potential authorial groupings in a vastly anonymous corpus.
Traffic flow prediction using Deep Sedenion Networks
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we present our solution to the Traffic4cast2020 traffic prediction challenge. In this competition, participants are to predict future traffic parameters (speed and volume) in three different cities: Berlin, Istanbul and Moscow. The information provided includes nine channels where the first eight represent the speed and volume for four different direction of traffic (NE, NW, SE and SW), while the last channel is used to indicate presence of traffic incidents. The expected output should have the first 8 channels of the input at six future timing intervals (5, 10, 15, 30, 45, and 60min), while a one hour duration of past traffic data, in 5mins intervals, are provided as input. We solve the problem using a novel sedenion U-Net neural network. Sedenion networks provide the means for efficient encoding of correlated multimodal datasets. We use 12 of the 15 sedenion imaginary parts for the dynamic inputs and the real sedenion component is used for the static input. The sedenion output of the network is used to represent the multimodal traffic predictions. Proposed system achieved a validation MSE of 1.33e-3 and a test MSE of 1.31e-3.
Sample-efficient proper PAC learning with approximate differential privacy
Venue: arXiv cs.LG 2020
Year: 2020

In this paper we prove that the sample complexity of properly learning a class of Littlestone dimension $d$ with approximate differential privacy is $\tilde O(d^6)$, ignoring privacy and accuracy parameters. This result answers a question of Bun et al. (FOCS 2020) by improving upon their upper bound of $2^{O(d)}$ on the sample complexity. Prior to our work, finiteness of the sample complexity for privately learning a class of finite Littlestone dimension was only known for improper private learners, and the fact that our learner is proper answers another question of Bun et al., which was also asked by Bousquet et al. (NeurIPS 2020). Using machinery developed by Bousquet et al., we then show that the sample complexity of sanitizing a binary hypothesis class is at most polynomial in its Littlestone dimension and dual Littlestone dimension. This implies that a class is sanitizable if and only if it has finite Littlestone dimension. An important ingredient of our proofs is a new property of binary hypothesis classes that we call irreducibility, which may be of independent interest.
Learning Video Instance Segmentation with Recurrent Graph Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Most existing approaches to video instance segmentation comprise multiple modules that are heuristically combined to produce the final output. Formulating a purely learning-based method instead, which models both the temporal aspect as well as a generic track management required to solve the video instance segmentation task, is a highly challenging problem. In this work, we propose a novel learning formulation, where the entire video instance segmentation problem is modelled jointly. We fit a flexible model to our formulation that, with the help of a graph neural network, processes all available new information in each frame. Past information is considered and processed via a recurrent connection. We demonstrate the effectiveness of the proposed approach in comprehensive experiments. Our approach, operating at over 25 FPS, outperforms previous video real-time methods. We further conduct detailed ablative experiments that validate the different aspects of our approach.
Supporting User Autonomy with Multimodal Fusion to Detect when a User Needs Assistance from a Social Robot
Venue: arXiv cs.HC 2020
Year: 2020

It is crucial for any assistive robot to prioritize the autonomy of the user. For a robot working in a task setting to effectively maintain a user's autonomy it must provide timely assistance and make accurate decisions. We use four independent high-precision, low-recall models, a mutual gaze model, task model, confirmatory gaze model, and a lexical model, that predict a user's need for assistance. Improving upon our four independent models, we used a sliding window method and a random forest classification algorithm to capture temporal dependencies and fuse the independent models with a late fusion approach. The late fusion approach strongly outperforms all four of the independent models providing a more wholesome approach with greater accuracy to better assist the user while maintaining their autonomy. These results can provide insight into the potential of including additional modalities and utilizing assistive robots in more task settings.
Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately
Venue: arXiv cs.LG 2020
Year: 2020

The presence of spurious features interferes with the goal of obtaining robust models that perform well across many groups within the population. A natural remedy is to remove spurious features from the model. However, in this work we show that removal of spurious features can decrease accuracy due to the inductive biases of overparameterized models. We completely characterize how the removal of spurious features affects accuracy across different groups (more generally, test distributions) in noiseless overparameterized linear regression. In addition, we show that removal of spurious feature can decrease the accuracy even in balanced datasets -- each target co-occurs equally with each spurious feature; and it can inadvertently make the model more susceptible to other spurious features. Finally, we show that robust self-training can remove spurious features without affecting the overall accuracy. Experiments on the Toxic-Comment-Detectoin and CelebA datasets show that our results hold in non-linear models.
Performance Analysis of Keypoint Detectors and Binary Descriptors Under Varying Degrees of Photometric and Geometric Transformations
Venue: arXiv cs.CV 2020
Year: 2020

Detecting image correspondences by feature matching forms the basis of numerous computer vision applications. Several detectors and descriptors have been presented in the past, addressing the efficient generation of features from interest points (keypoints) in an image. In this paper, we investigate eight binary descriptors (AKAZE, BoostDesc, BRIEF, BRISK, FREAK, LATCH, LUCID, and ORB) and eight interest point detector (AGAST, AKAZE, BRISK, FAST, HarrisLapalce, KAZE, ORB, and StarDetector). We have decoupled the detection and description phase to analyze the interest point detectors and then evaluate the performance of the pairwise combination of different detectors and descriptors. We conducted experiments on a standard dataset and analyzed the comparative performance of each method under different image transformations. We observed that: (1) the FAST, AGAST, ORB detectors were faster and detected more keypoints, (2) the AKAZE and KAZE detectors performed better under photometric changes while ORB was more robust against geometric changes, (3) in general, descriptors performed better when paired with the KAZE and AKAZE detectors, (4) the BRIEF, LUCID, ORB descriptors were relatively faster, and (5) none of the descriptors did particularly well under geometric transformations, only BRISK, FREAK, and AKAZE showed reasonable resiliency.
Privacy-Preserving Spam Filtering using Functional Encryption
Venue: arXiv cs.CR 2020
Year: 2020

Traditional spam classification requires the end-user to reveal the content of its received email to the spam classifier which violates the privacy. Spam classification over encrypted emails enables the classifier to classify spam email without accessing the email, hence protects the privacy of email content. In this paper, we construct a spam classification framework that enables the classification of encrypted emails. Our classification model is based on a neural network with a quadratic network part and a multi-layer perception network part. The quadratic network architecture is compatible with the operation of an existing quadratic functional encryption scheme that enables our classification to predict the label of encrypted emails without revealing the associated plain-text email. The evaluation results on real-world spam datasets indicate that our proposed spam classification model achieves an accuracy of over 96%.
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
Venue: arXiv cs.LG 2020
Year: 2020

For multi-class classification under class-conditional label noise, we prove that the accuracy metric itself can be robust. We concretize this finding's inspiration in two essential aspects: training and validation, with which we address critical issues in learning with noisy labels. For training, we show that maximizing training accuracy on sufficiently many noisy samples yields an approximately optimal classifier. For validation, we prove that a noisy validation set is reliable, addressing the critical demand of model selection in scenarios like hyperparameter-tuning and early stopping. Previously, model selection using noisy validation samples has not been theoretically justified. We verify our theoretical results and additional claims with extensive experiments. We show characterizations of models trained with noisy labels, motivated by our theoretical results, and verify the utility of a noisy validation set by showing the impressive performance of a framework termed noisy best teacher and student (NTS). Our code is released.
Cost Sensitive Optimization of Deepfake Detector
Venue: arXiv cs.CV 2020
Year: 2020

Since the invention of cinema, the manipulated videos have existed. But generating manipulated videos that can fool the viewer has been a time-consuming endeavor. With the dramatic improvements in the deep generative modeling, generating believable looking fake videos has become a reality. In the present work, we concentrate on the so-called deepfake videos, where the source face is swapped with the targets. We argue that deepfake detection task should be viewed as a screening task, where the user, such as the video streaming platform, will screen a large number of videos daily. It is clear then that only a small fraction of the uploaded videos are deepfakes, so the detection performance needs to be measured in a cost-sensitive way. Preferably, the model parameters also need to be estimated in the same way. This is precisely what we propose here.
A Topological Method for Comparing Document Semantics
Venue: arXiv cs.CL 2020
Year: 2020

Comparing document semantics is one of the toughest tasks in both Natural Language Processing and Information Retrieval. To date, on one hand, the tools for this task are still rare. On the other hand, most relevant methods are devised from the statistic or the vector space model perspectives but nearly none from a topological perspective. In this paper, we hope to make a different sound. A novel algorithm based on topological persistence for comparing semantics similarity between two documents is proposed. Our experiments are conducted on a document dataset with human judges' results. A collection of state-of-the-art methods are selected for comparison. The experimental results show that our algorithm can produce highly human-consistent results, and also beats most state-of-the-art methods though ties with NLTK.
Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach
Venue: arXiv cs.AI 2020
Year: 2020

Predictive process analytics focuses on predicting the future states of running instances of a business process. While advanced machine learning techniques have been used to increase accuracy of predictions, the resulting predictive models lack transparency. Current explainable machine learning methods, such as LIME and SHAP, can be used to interpret black box models. However, it is unclear how fit for purpose these methods are in explaining process predictive models. In this paper, we draw on evaluation measures used in the field of explainable AI and propose functionally-grounded evaluation metrics for assessing explainable methods in predictive process analytics. We apply the proposed metrics to evaluate the performance of LIME and SHAP in interpreting process predictive models built on XGBoost, which has been shown to be relatively accurate in process predictions. We conduct the evaluation using three open source, real-world event logs and analyse the evaluation results to derive insights. The research contributes to understanding the trustworthiness of explainable methods for predictive process analytics as a fundamental and key step towards human user-oriented evaluation.