OpenRaiser/Intern-Atlas huggingface dataset:
Autoencoding Features for Aviation Machine Learning Problems
Venue: arXiv cs.LG 2020
Year: 2020

The current practice of manually processing features for high-dimensional and heterogeneous aviation data is labor-intensive, does not scale well to new problems, and is prone to information loss, affecting the effectiveness and maintainability of machine learning (ML) procedures. This research explored an unsupervised learning method, autoencoder, to extract effective features for aviation machine learning problems. The study explored variants of autoencoders with the aim of forcing the learned representations of the input to assume useful properties. A flight track anomaly detection autoencoder was developed to demonstrate the versatility of the technique. The research results show that the autoencoder can not only automatically extract effective features for the flight track data, but also efficiently deep clean data, thereby reducing the workload of data scientists. Moreover, the research leveraged transfer learning to efficiently train models for multiple airports. Transfer learning can reduce model training times from days to hours, as well as improving model performance. The developed applications and techniques are shared with the whole aviation community to improve effectiveness of ongoing and future machine learning studies.
Uncertainty-Oriented Ensemble Data Visualization and Exploration using Variable Spatial Spreading
Venue: arXiv cs.GR 2020
Year: 2020

As an important method of handling potential uncertainties in numerical simulations, ensemble simulation has been widely applied in many disciplines. Visualization is a promising and powerful ensemble simulation analysis method. However, conventional visualization methods mainly aim at data simplification and highlighting important information based on domain expertise instead of providing a flexible data exploration and intervention mechanism. Trial-and-error procedures have to be repeatedly conducted by such approaches. To resolve this issue, we propose a new perspective of ensemble data analysis using the attribute variable dimension as the primary analysis dimension. Particularly, we propose a variable uncertainty calculation method based on variable spatial spreading. Based on this method, we design an interactive ensemble analysis framework that provides a flexible interactive exploration of the ensemble data. Particularly, the proposed spreading curve view, the region stability heat map view, and the temporal analysis view, together with the commonly used 2D map view, jointly support uncertainty distribution perception, region selection, and temporal analysis, as well as other analysis requirements. We verify our approach by analyzing a real-world ensemble simulation dataset. Feedback collected from domain experts confirms the efficacy of our framework.
SelfPose: 3D Egocentric Pose Estimation from a Headset Mounted Camera
Venue: arXiv cs.CV 2020
Year: 2020

We present a solution to egocentric 3D body pose estimation from monocular images captured from downward looking fish-eye cameras installed on the rim of a head mounted VR device. This unusual viewpoint leads to images with unique visual appearance, with severe self-occlusions and perspective distortions that result in drastic differences in resolution between lower and upper body. We propose an encoder-decoder architecture with a novel multi-branch decoder designed to account for the varying uncertainty in 2D predictions. The quantitative evaluation, on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric approaches. To tackle the lack of labelled data we also introduced a large photo-realistic synthetic dataset. xR-EgoPose offers high quality renderings of people with diverse skintones, body shapes and clothing, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of theart results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint.
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?
Venue: arXiv cs.CL 2020
Year: 2020

Emotion recognition is predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory (e.g., fear, joy, anger, disgust, sadness, surprise, trust, anticipation). More recently, semantic role labeling approaches have been developed to extract structures from the text to answer questions like: "who is described to feel the emotion?" (experiencer), "what causes this emotion?" (stimulus), and at which entity is it directed?" (target). Though it has been shown that jointly modeling stimulus and emotion category prediction is beneficial for both subtasks, it remains unclear which of these semantic roles enables a classifier to infer the emotion. Is it the experiencer, because the identity of a person is biased towards a particular emotion (X is always happy)? Is it a particular target (everybody loves X) or a stimulus (doing X makes everybody sad)? We answer these questions by training emotion classification models on five available datasets annotated with at least one semantic role by masking the fillers of these roles in the text in a controlled manner and find that across multiple corpora, stimuli and targets carry emotion information, while the experiencer might be considered a confounder. Further, we analyze if informing the model about the position of the role improves the classification decision. Particularly on literature corpora we find that the role information improves the emotion classification.
Codes from the Incidence Matrices of a zero-divisor Graphs
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we examine the linear codes with respect to the Hamming metric from incidence matrices of the zero-divisor graphs with vertex set is the set of all non-zero zero-divisors of the ring $\mathbb{Z}_n$ and two distinct vertices being adjacent iff their product is zero over $\mathbb{Z}_n.$ The main parameters of the codes are obtained.
Towards a Universal Gating Network for Mixtures of Experts
Venue: arXiv cs.LG 2020
Year: 2020

The combination and aggregation of knowledge from multiple neural networks can be commonly seen in the form of mixtures of experts. However, such combinations are usually done using networks trained on the same tasks, with little mention of the combination of heterogeneous pre-trained networks, especially in the data-free regime. This paper proposes multiple data-free methods for the combination of heterogeneous neural networks, ranging from the utilization of simple output logit statistics, to training specialized gating networks. The gating networks decide whether specific inputs belong to specific networks based on the nature of the expert activations generated. The experiments revealed that the gating networks, including the universal gating approach, constituted the most accurate approach, and therefore represent a pragmatic step towards applications with heterogeneous mixtures of experts in a data-free regime. The code for this project is hosted on github at https://github.com/cwkang1998/network-merging.
Automated simulation and verification of process models discovered by process mining
Venue: arXiv cs.AI 2020
Year: 2020

This paper presents a novel approach for automated analysis of process models discovered using process mining techniques. Process mining explores underlying processes hidden in the event data generated by various devices. Our proposed Inductive machine learning method was used to build business process models based on actual event log data obtained from a hotel's Property Management System (PMS). The PMS can be considered as a Multi Agent System (MAS) because it is integrated with a variety of external systems and IoT devices. Collected event log combines data on guests stay recorded by hotel staff, as well as data streams captured from telephone exchange and other external IoT devices. Next, we performed automated analysis of the discovered process models using formal methods. Spin model checker was used to simulate process model executions and automatically verify the process model. We proposed an algorithm for the automatic transformation of the discovered process model into a verification model. Additionally, we developed a generator of positive and negative examples. In the verification stage, we have also used Linear temporal logic (LTL) to define requested system specifications. We find that the analysis results will be well suited for process model repair.
Joint Entity and Relation Extraction with Set Prediction Networks
Venue: arXiv cs.CL 2020
Year: 2020

The joint entity and relation extraction task aims to extract all relational triples from a sentence. In essence, the relational triples contained in a sentence are unordered. However, previous seq2seq based models require to convert the set of triples into a sequence in the training phase. To break this bottleneck, we treat joint entity and relation extraction as a direct set prediction problem, so that the extraction model can get rid of the burden of predicting the order of multiple triples. To solve this set prediction problem, we propose networks featured by transformers with non-autoregressive parallel decoding. Unlike autoregressive approaches that generate triples one by one in a certain order, the proposed networks directly output the final set of triples in one shot. Furthermore, we also design a set-based loss that forces unique predictions via bipartite matching. Compared with cross-entropy loss that highly penalizes small shifts in triple order, the proposed bipartite matching loss is invariant to any permutation of predictions; thus, it can provide the proposed networks with a more accurate training signal by ignoring triple order and focusing on relation types and entities. Experiments on two benchmark datasets show that our proposed model significantly outperforms current state-of-the-art methods. Training code and trained models will be available at http://github.com/DianboWork/SPN4RE.
A spatial hue similarity measure for assessment of colourisation
Venue: arXiv cs.CV 2020
Year: 2020

Automatic colourisation of grey-scale images is an ill-posed multi-modal problem. Where full-reference images exist, objective performance measures rely on pixel-difference techniques such as MSE and PSNR. These measures penalise any plausible modes other than the reference ground-truth; They often fail to adequately penalise implausible modes if they are close in pixel distance to the ground-truth; As these are pixel-difference methods they cannot assess spatial coherency. We use the polar form of the a*b* channels from the CIEL*a*b* colour space to separate the multi-modal problems, which we confine to the hue channel, and the common-mode which applies to the chroma channel. We apply SSIM to the chroma channel but reformulate SSIM for the hue channel to a measure we call the Spatial Hue Similarity Measure (SHSM). This reformulation allows spatially-coherent hue channels to achieve a high score while penalising spatially-incoherent modes. This method allows qualitative and quantitative performance comparison of SOTA colourisation methods and reduces reliance on subjective human visual inspection.
Amortized Variational Deep Q Network
Venue: arXiv cs.LG 2020
Year: 2020

Efficient exploration is one of the most important issues in deep reinforcement learning. To address this issue, recent methods consider the value function parameters as random variables, and resort variational inference to approximate the posterior of the parameters. In this paper, we propose an amortized variational inference framework to approximate the posterior distribution of the action value function in Deep Q Network. We establish the equivalence between the loss of the new model and the amortized variational inference loss. We realize the balance of exploration and exploitation by assuming the posterior as Cauchy and Gaussian, respectively in a two-stage training process. We show that the amortized framework can results in significant less learning parameters than existing state-of-the-art method. Experimental results on classical control tasks in OpenAI Gym and chain Markov Decision Process tasks show that the proposed method performs significantly better than state-of-art methods and requires much less training time.
Smart-Corpus: an Organized Repository of Ethereum Smart Contracts Source Code and Metrics
Venue: arXiv cs.SE 2020
Year: 2020

Many empirical software engineering studies show that there is a great need for repositories where source code is acquired, filtered and classified. During the last few years, Ethereum block explorer services have emerged as a popular project to explore and search Ethereum blockchain data such as transactions, addresses, tokens, smart-contracts' source code, prices and other activities taking place on the Ethereum blockchain. Despite the availability of this kind of services, retrieving specific information useful to empirical software engineering studies, such as the study of smart-contracts' software metrics might require many sub-tasks, such as searching specific transactions in a block, parsing files in HTML format and filtering the smart-contracts to remove duplicated code or unused smart-contracts. In this paper we afford this problem creating Smart Corpus', a Corpus of Smart Contracts in an organized reasoned and up to date repository where Solidity source code and other metadata about Ethereum smart contracts can easily and systematically be retrieved. We present the Smart Corpus' design and its initial implementation and we show how the data-set of smart contracts' source code in a variety of programming languages can be queried and processed, get useful information on smart contracts and their software metrics. The Smart Corpus aims to create a smart-contracts' repository where smart contracts data (source code, ABI and byte-code) are freely and immediately available and also classified based on the main software metrics identified in the scientific literature. Smart-contracts source code has been validated by EtherScan and each contract comes with its own associated software metrics as computed by the freely available software PASO. Moreover, Smart Corpus can be easily extended, as the number of new smart-contracts increases day by day.
Search Problems in Trees with Symmetries: near optimal traversal strategies for individualization-refinement algorithms
Venue: arXiv cs.DS 2020
Year: 2020

We define a search problem on trees that closely captures the backtracking behavior of all current practical graph isomorphism algorithms. Given two trees with colored leaves, the goal is to find two leaves of matching color, one in each of the trees. The trees are subject to an invariance property which promises that for every pair of leaves of equal color there must be a symmetry (or an isomorphism) that maps one leaf to the other. We describe a randomized algorithm with errors for which the number of visited leaves is quasilinear in the square root of the size of the smaller of the two trees. For inputs of bounded degree, we develop a Las Vegas algorithm with a similar running time. We prove that these results are optimal up to logarithmic factors. We show a lower bound for randomized algorithms on inputs of bounded degree that is the square root of the tree sizes. For inputs of unbounded degree, we show a linear lower bound for Las Vegas algorithms. For deterministic algorithms we can prove a linear bound even for inputs of bounded degree. This shows why randomized algorithms outperform deterministic ones. Our results explain why the randomized "breadth-first with intermixed experimental path" search strategy of the isomorphism tool Traces (Piperno 2008) is often superior to the depth-first search strategy of other tools such as nauty (McKay 1977) or bliss (Junttila, Kaski 2007). However, our algorithm also provides a new traversal strategy, which is theoretically near optimal with better worst case behavior than traversal strategies that have previously been used.
Goal recognition via model-based and model-free techniques
Venue: arXiv cs.AI 2020
Year: 2020

Goal recognition aims at predicting human intentions from a trace of observations. This ability allows people or organizations to anticipate future actions and intervene in a positive (collaborative) or negative (adversarial) way. Goal recognition has been successfully used in many domains, but it has been seldom been used by financial institutions. We claim the techniques are ripe for its wide use in finance-related tasks. The main two approaches to perform goal recognition are model-based (planning-based) and model-free (learning-based). In this paper, we adapt state-of-the-art learning techniques to goal recognition, and compare model-based and model-free approaches in different domains. We analyze the experimental data to understand the trade-offs of using both types of methods. The experiments show that planning-based approaches are ready for some goal-recognition finance tasks.
Analytical aspects of non-differentiable neural networks
Venue: arXiv cs.LG 2020
Year: 2020

Research in computational deep learning has directed considerable efforts towards hardware-oriented optimisations for deep neural networks, via the simplification of the activation functions, or the quantization of both activations and weights. The resulting non-differentiability (or even discontinuity) of the networks poses some challenging problems, especially in connection with the learning process. In this paper, we address several questions regarding both the expressivity of quantized neural networks and approximation techniques for non-differentiable networks. First, we answer in the affirmative the question of whether QNNs have the same expressivity as DNNs in terms of approximation of Lipschitz functions in the $L^{\infty}$ norm. Then, considering a continuous but not necessarily differentiable network, we describe a layer-wise stochastic regularisation technique to produce differentiable approximations, and we show how this approach to regularisation provides elegant quantitative estimates. Finally, we consider networks defined by means of Heaviside-type activation functions, and prove for them a pointwise approximation result by means of smooth networks under suitable assumptions on the regularised activations.
Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning
Venue: arXiv cs.CV 2020
Year: 2020

This paper tackles the challenging problem of estimating the intensity of Facial Action Units with few labeled images. Contrary to previous works, our method does not require to manually select key frames, and produces state-of-the-art results with as little as $2\%$ of annotated frames, which are \textit{randomly chosen}. To this end, we propose a semi-supervised learning approach where a spatio-temporal model combining a feature extractor and a temporal module are learned in two stages. The first stage uses datasets of unlabeled videos to learn a strong spatio-temporal representation of facial behavior dynamics based on contrastive learning. To our knowledge we are the first to build upon this framework for modeling facial behavior in an unsupervised manner. The second stage uses another dataset of randomly chosen labeled frames to train a regressor on top of our spatio-temporal model for estimating the AU intensity. We show that although backpropagation through time is applied only with respect to the output of the network for extremely sparse and randomly chosen labeled frames, our model can be effectively trained to estimate AU intensity accurately, thanks to the unsupervised pre-training of the first stage. We experimentally validate that our method outperforms existing methods when working with as little as $2\%$ of randomly chosen data for both DISFA and BP4D datasets, without a careful choice of labeled frames, a time-consuming task still required in previous approaches.
Growth and dynamics of Econophysics: A bibliometric and network analysis
Venue: arXiv cs.DL 2020
Year: 2020

Digitization of publications, advancement in communication technology, and the availability of bibliographic data have made it easier for the researchers to study the growth and dynamics of any discipline. We present a study on "Econophysics" metadata extracted from the Web of Science managed by the Clarivate Analytics from 2000-2019. The study highlights the growth and dynamics of the discipline by measures of a number of publications, citations on publications, other disciplines contribution, institutions participation, country-wise spread, etc. We investigate the impact of self-citations on citations with every five-year interval. Also, we find the contribution of other disciplines by analyzing the cited references. Results emerged from micro, meso and macro-level analysis of collaborations show that the distributions among authors collaboration and affiliations of authors follow a power law. Thus, very few authors keep producing most of the papers and are from a few institutions. We find that China is leading in the production of a number of authors and a number of papers; however, shares more of national collaboration rather than international, whereas the USA shares more international collaboration. Finally, we demonstrate the evolution of the author's collaborations and affiliations networks from 2000-2019. Overall the analysis reveals the "small-world" property of the network with average path length 5. As a consequence of our analysis, this study can serve as in-depth knowledge to understand the growth and dynamics of the Econophysics network both qualitatively and quantitatively.
Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model
Venue: arXiv cs.LG 2020
Year: 2020

The objective of this work was to assess the clinical performance of an unsupervised machine learning model aimed at identifying unusual medication orders and pharmacological profiles. We conducted a prospective study between April 2020 and August 2020 where 25 clinical pharmacists dichotomously (typical or atypical) rated 12,471 medication orders and 1,356 pharmacological profiles. Based on AUPR, performance was poor for orders, but satisfactory for profiles. Pharmacists considered the model a useful screening tool.
Palette diagram: A Python package for visualization of collective categorical data
Venue: arXiv cs.DB 2020
Year: 2020

Categorical data, wherein a numerical quantity is assigned to each category (nominal variable), are ubiquitous in data science. A palette diagram is a visualization tool for a large number of categorical datasets, each comprising several categories.
Face-work for Human-Agent Joint Decision-Making
Venue: arXiv cs.RO 2020
Year: 2020

We propose a method to integrate face-work, a common social ritual related to trust, into a decision-making agent that works collaboratively with a human. Face-work is a set of trust-building behaviors designed to "save face" or prevent others from "losing face." This paper describes the design of a decision-making process that explicitly considers face-work as part of its action selection. We also present a simulated robot arm deployed in an online environment that can be used to evaluate the proposed method.