OpenRaiser/Intern-Atlas huggingface dataset:
Improving Heart Rate Variability Measurements from Consumer Smartwatches with Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV). Thus, continuous monitoring of HRV can contribute to determining and predicting issues in well-being and mental health. HRV can be measured in everyday life by consumer wearable devices such as smartwatches which are easily accessible and affordable. However, they are arguably accurate due to the stability of the sensor. We hypothesize a systematic error which is related to the wearer movement. Our evidence builds upon explanatory and predictive modeling: we find a statistically significant correlation between error in HRV measurements and the wearer movement. We show that this error can be minimized by bringing into context additional available sensor information, such as accelerometer data. This work demonstrates our research-in-progress on how neural learning can minimize the error of such smartwatch HRV measurements.
Subspace Inference for Bayesian Deep Learning
Venue: arXiv cs.LG 2019
Year: 2019

Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty. However, scaling Bayesian inference techniques to deep neural networks is challenging due to the high dimensionality of the parameter space. In this paper, we construct low-dimensional subspaces of parameter space, such as the first principal components of the stochastic gradient descent (SGD) trajectory, which contain diverse sets of high performing models. In these subspaces, we are able to apply elliptical slice sampling and variational inference, which struggle in the full parameter space. We show that Bayesian model averaging over the induced posterior in these subspaces produces accurate predictions and well calibrated predictive uncertainty for both regression and image classification.
Differentiable Disentanglement Filter: an Application Agnostic Core Concept Discovery Probe
Venue: arXiv cs.CL 2019
Year: 2019

It has long been speculated that deep neural networks function by discovering a hierarchical set of domain-specific core concepts or patterns, which are further combined to recognize even more elaborate concepts for the classification or other machine learning tasks. Meanwhile disentangling the actual core concepts engrained in the word embeddings (like word2vec or BERT) or deep convolutional image recognition neural networks (like PG-GAN) is difficult and some success there has been achieved only recently. In this paper we propose a novel neural network nonlinearity named Differentiable Disentanglement Filter (DDF) which can be transparently inserted into any existing neural network layer to automatically disentangle the core concepts used by that layer. The DDF probe is inspired by the obscure properties of the hyper-dimensional computing theory. The DDF proof-of-concept implementation is shown to disentangle concepts within the neural 3D scene representation - a task vital for visual grounding of natural language narratives.
Stereo Event Lifetime and Disparity Estimation for Dynamic Vision Sensors
Venue: arXiv cs.RO 2019
Year: 2019

Event-based cameras are biologically inspired sensors that output asynchronous pixel-wise brightness changes in the scene called events. They have a high dynamic range and temporal resolution of a microsecond, opposed to standard cameras that output frames at fixed frame rates and suffer from motion blur. Forming stereo pairs of such cameras can open novel application possibilities, since for each event depth can be readily estimated; however, to fully exploit asynchronous nature of the sensor and avoid fixed time interval event accumulation, stereo event lifetime estimation should be employed. In this paper, we propose a novel method for event lifetime estimation of stereo event-cameras, allowing generation of sharp gradient images of events that serve as input to disparity estimation methods. Since a single brightness change triggers events in both event-camera sensors, we propose a method for single shot event lifetime and disparity estimation, with association via stereo matching. The proposed method is approximately twice as fast and more accurate than if lifetimes were estimated separately for each sensor and then stereo matched. Results are validated on real-world data through multiple stereo event-camera experiments.
Conversational Help for Task Completion and Feature Discovery in Personal Assistants
Venue: arXiv cs.HC 2019
Year: 2019

Intelligent Personal Assistants (IPAs) have become widely popular in recent times. Most of the commercial IPAs today support a wide range of skills including Alarms, Reminders, Weather Updates, Music, News, Factual Questioning-Answering, etc. The list grows every day, making it difficult to remember the command structures needed to execute various tasks. An IPA must have the ability to communicate information about supported skills and direct users towards the right commands needed to execute them. Users interact with personal assistants in natural language. A query is defined to be a Help Query if it seeks information about a personal assistant's capabilities, or asks for instructions to execute a task. In this paper, we propose an interactive system which identifies help queries and retrieves appropriate responses. Our system comprises of a C-BiLSTM based classifier, which is a fusion of Convolutional Neural Networks (CNN) and Bidirectional LSTM (BiLSTM) architectures, to detect help queries and a semantic Approximate Nearest Neighbours (ANN) module to map the query to an appropriate predefined response. Evaluation of our system on real-world queries from a commercial IPA and a detailed comparison with popular traditional machine learning and deep learning based models reveal that our system outperforms other approaches and returns relevant responses for help queries.
The iWildCam 2019 Challenge Dataset
Venue: arXiv cs.CV 2019
Year: 2019

Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population density of animal species. The computer vision community has been making strides towards automating the species classification challenge in camera traps, but as we try to expand the scope of these models from specific regions where we have collected training data to different areas we are faced with an interesting problem: how do you classify a species in a new region that you may not have seen in previous training data? In order to tackle this problem, we have prepared a dataset and challenge where the training data and test data are from different regions, namely The American Southwest and the American Northwest. We use the Caltech Camera Traps dataset, collected from the American Southwest, as training data. We add a new dataset from the American Northwest, curated from data provided by the Idaho Department of Fish and Game (IDFG), as our test dataset. The test data has some class overlap with the training data, some species are found in both datasets, but there are both species seen during training that are not seen during test and vice versa. To help fill the gaps in the training species, we allow competitors to utilize transfer learning from two alternate domains: human-curated images from iNaturalist and synthetic images from Microsoft's TrapCam-AirSim simulation environment.
On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems
Venue: arXiv cs.IR 2019
Year: 2019

News recommender systems are designed to surface relevant information for online readers by personalizing their user experiences. A particular problem in that context is that online readers are often anonymous, which means that this personalization can only be based on the last few recorded interactions with the user, a setting named session-based recommendation. Another particularity of the news domain is that constantly fresh articles are published, which should be immediately considered for recommendation. To deal with this item cold-start problem, it is important to consider the actual content of items when recommending. Hybrid approaches are therefore often considered as the method of choice in such settings. In this work, we analyze the importance of considering content information in a hybrid neural news recommender system. We contrast content-aware and content-agnostic techniques and also explore the effects of using different content encodings. Experiments on two public datasets confirm the importance of adopting a hybrid approach. Furthermore, we show that the choice of the content encoding can have an impact on the resulting performance.
Fly Safe: Aerial Swarm Robotics using Force Field Particle Swarm Optimisation
Venue: arXiv cs.RO 2019
Year: 2019

Particle Swarm Optimisation (PSO) is a powerful optimisation algorithm that can be used to locate global maxima in a search space. Recent interest in swarms of Micro Aerial Vehicles (MAVs) begs the question as to whether PSO can be used as a method to enable real robotic swarms to locate a target goal point. However, the original PSO algorithm does not take into account collisions between particles during search. In this paper we propose a novel algorithm called Force Field Particle Swarm Optimisation (FFPSO) that designates repellent force fields to particles such that these fields provide an additional velocity component into the original PSO equations. We compare the performance of FFPSO with PSO and show that it has the ability to reduce the number of particle collisions during search to 0 whilst also being able to locate a target of interest in a similar amount of time. The scalability of the algorithm is also demonstrated via a set of experiments that considers how the number of crashes and the time taken to find the goal varies according to swarm size. Finally, we demonstrate the algorithms applicability on a swarm of real MAVs.
Envy, Regret, and Social Welfare Loss
Venue: arXiv cs.GT 2019
Year: 2019

Incentive compatibility (IC) is one of the most fundamental properties of an auction mechanism, including those used for online advertising. Recent methods by Feng et al. and Lahaie et al. show that counterfactual runs of the auction mechanism with different bids can be used to determine whether an auction is IC. In this paper we show that a similar result can be obtained by looking at the advertisers' envy, which can be computed with one single execution of the auction. We introduce two metrics to evaluate the incentive-compatibility of an auction: IC-Regret and IC-Envy. For position auction environments, we show that for a large class of pricing schemes (which includes e.g. VCG and GSP), IC-Envy $\ge$ IC-Regret (and IC-Envy = IC-Regret when bids are distinct). We consider non-separable discounts in the Ad Types environment of Colini-Baldeschi et al. where we show that for a generalization of GSP also IC-Envy $\ge$ IC-Regret. Our final theoretical result is that in all these settings IC-Envy be used to bound the loss in social welfare due advertiser misreports. Finally, we show that IC-Envy is useful as a feature to predict IC-Regret in auction environments beyond the ones for which we show theoretical results. In particular, using IC-Envy yields better results than training models using only price and value features.
Connecting Lyapunov Control Theory to Adversarial Attacks
Venue: arXiv cs.CR 2019
Year: 2019

Significant work is being done to develop the math and tools necessary to build provable defenses, or at least bounds, against adversarial attacks of neural networks. In this work, we argue that tools from control theory could be leveraged to aid in defending against such attacks. We do this by example, building a provable defense against a weaker adversary. This is done so we can focus on the mechanisms of control theory, and illuminate its intrinsic value.
Deep Invertible Networks for EEG-based brain-signal decoding
Venue: arXiv cs.LG 2019
Year: 2019

In this manuscript, we investigate deep invertible networks for EEG-based brain signal decoding and find them to generate realistic EEG signals as well as classify novel signals above chance. Further ideas for their regularization towards better decoding accuracies are discussed.
Thermomechanical modelling of ceramic pressing and subsequent sintering
Venue: arXiv cs.CE 2019
Year: 2019

An elastic-visco-plastic thermomechanical model for the simulation of cold forming and subsequent sintering of ceramic powders is introduced and based on micromechanical modelling of the compaction process of granulates. Micromechanics leads to an upper-bound estimate of the compaction curve of a granular material, which compares well with other models and finite element simulations. The parameters of the thermomechanical model are determined on the basis of available data and dilatometer experiments. Finally, after computer implementation, validation of the model is performed against experiments developed on specially designed ceramic pieces, characterized by zones of different density. The mechanical model is found to accurately describe forming and sintering of stoneware ceramics and can therefore be used to analyze and optimize industrial processes involving compaction of powders and subsequent firing of the greens.
Runtime Verification For Timed Event Streams With Partial Information
Venue: arXiv cs.LO 2019
Year: 2019

Runtime Verification (RV) studies how to analyze execution traces of a system under observation. Stream Runtime Verification (SRV) applies stream transformations to obtain information from observed traces. Incomplete traces with information missing in gaps pose a common challenge when applying RV and SRV techniques to real-world systems as RV approaches typically require the complete trace without missing parts. This paper presents a solution to perform SRV on incomplete traces based on abstraction. We use TeSSLa as specification language for non-synchronized timed event streams and define abstract event streams representing the set of all possible traces that could have occurred during gaps in the input trace. We show how to translate a TeSSLa specification to its abstract counterpart that can propagate gaps through the transformation of the input streams and thus generate sound outputs even if the input streams contain gaps and events with imprecise values. The solution has been implemented as a set of macros for the original TeSSLa and an empirical evaluation shows the feasibility of the approach.
Agro 4.0: A Green Information System for Sustainable Agroecosystem Management
Venue: arXiv cs.CY 2019
Year: 2019

Agriculture is one of the most critical activities developed today by humankind and is in constant technical evolution to supply food and other essential products to everlasting and increasing demand. New machines, seeds, and fertilizers were developed to increase the productivity of cultivated areas. It is estimated that by 2050 we will have a population of 9 billion people and the production of food to meet this demand must occur sustainably. To achieve this goal, it is paramount the adoption of sustainable management techniques for agroecosystems. However, this is a complex task due to a large number of variables involved. One of the solutions for the handling and treatment of such diverse data is the use of Green IS. In this work, we adopt a methodology called Indicators of Sustainability in Agroecosystems (Indicadores de Sustentabilidade em Agroecossistemas -- ISA), implement an information system based on it and apply Data Science techniques over the gathered data - from 100 real rural properties - to compute which are the most relevant ISA Indicators for the final ISA Sustainability Index Score. As a result, we have developed a set of tools for data collection, processing, visualization, and analysis of the sustainability of a rural property or region, following the ISA methodology. We also have that with only 7 of the 21 Indicators present in ISA we can identify the level of sustainability in more than 90% of cases, allowing for a new discussion about shrinking the amount of data needed for the computation of ISA, or remodelling the final computation of the Sustainability Index so other Indicators can be more expressive. Users of the solutions developed in this work can identify best practices for sustainability in participating agroecosystems.
Syntax and Stack Overflow: A methodology for extracting a corpus of syntax errors and fixes
Venue: arXiv cs.SE 2019
Year: 2019

One problem when studying how to find and fix syntax errors is how to get natural and representative examples of syntax errors. Most syntax error datasets are not free, open, and public, or they are extracted from novice programmers and do not represent syntax errors that the general population of developers would make. Programmers of all skill levels post questions and answers to Stack Overflow which may contain snippets of source code along with corresponding text and tags. Many snippets do not parse, thus they are ripe for forming a corpus of syntax errors and corrections. Our primary contribution is an approach for extracting natural syntax errors and their corresponding human made fixes to help syntax error research. A Python abstract syntax tree parser is used to determine preliminary errors and corrections on code blocks extracted from the SOTorrent data set. We further analyzed our code by executing the corrections in a Python interpreter. We applied our methodology to produce a public data set of 62,965 Python Stack Overflow code snippets with corresponding tags, errors, and stack traces. We found that errors made by Stack Overflow users do not match errors made by student developers or random mutations, implying there is a serious representativeness risk within the field. Finally we share our dataset openly so that future researchers can re-use and extend our syntax errors and fixes.
Growing a Brain: Fine-Tuning by Increasing Model Capacity
Venue: arXiv cs.CV 2019
Year: 2019

CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through fine-tuning a fixed-size network on new target data. Indeed, virtually every contemporary visual recognition system makes use of fine-tuning to transfer knowledge from ImageNet. In this work, we analyze what components and parameters change during fine-tuning, and discover that increasing model capacity allows for more natural model adaptation through fine-tuning. By making an analogy to developmental learning, we demonstrate that "growing" a CNN with additional units, either by widening existing layers or deepening the overall network, significantly outperforms classic fine-tuning approaches. But in order to properly grow a network, we show that newly-added units must be appropriately normalized to allow for a pace of learning that is consistent with existing units. We empirically validate our approach on several benchmark datasets, producing state-of-the-art results.
Understanding Video Content: Efficient Hero Detection and Recognition for the Game "Honor of Kings"
Venue: arXiv cs.CV 2019
Year: 2019

In order to understand content and automatically extract labels for videos of the game "Honor of Kings", it is necessary to detect and recognize characters (called "hero") together with their camps in the game video. In this paper, we propose an efficient two-stage algorithm to detect and recognize heros in game videos. First, we detect all heros in a video frame based on blood bar template-matching method, and classify them according to their camps (self/ friend/ enemy). Then we recognize the name of each hero using one or more deep convolution neural networks. Our method needs almost no work for labelling training and testing samples in the recognition stage. Experiments show its efficiency and accuracy in the task of hero detection and recognition in game videos.
A feasibility study of deep neural networks for the recognition of banknotes regarding central bank requirements
Venue: arXiv cs.CV 2019
Year: 2019

This paper contains a feasibility study of deep neural networks for the classification of Euro banknotes with respect to requirements of central banks on the ATM and high speed sorting industry. Instead of concentrating on the accuracy for a large number of classes as in the famous ImageNet Challenge we focus thus on conditions with few classes and the requirement of rejection of images belonging clearly to neither of the trained classes (i.e. classification in a so-called 0-class). These special requirements are part of frameworks defined by central banks as the European Central Bank and are met by current ATMs and high speed sorting machines. We also consider training and classification time on state of the art GPU hardware. The study concentrates on the banknote recognition whereas banknote class dependent authenticity and fitness checks are a topic of its own which is not considered in this work.
On the $\text{AC}^0[\oplus]$ complexity of Andreev's Problem
Venue: arXiv cs.CC 2019
Year: 2019

Andreev's Problem states the following: Given an integer $d$ and a subset of $S \subseteq \mathbb{F}_q \times \mathbb{F}_q$, is there a polynomial $y = p(x)$ of degree at most $d$ such that for every $a \in \mathbb{F}_q$, $(a,p(a)) \in S$? We show an $\text{AC}^0[\oplus]$ lower bound for this problem. This problem appears to be similar to the list recovery problem for degree $d$-Reed-Solomon codes over $\mathbb{F}_q$ which states the following: Given subsets $A_1,\ldots,A_q$ of $\mathbb{F}_q$, output all (if any) the Reed-Solomon codewords contained in $A_1\times \cdots \times A_q$. For our purpose, we study this problem when $A_1, \ldots, A_q$ are random subsets of a given size, which may be of independent interest.
Self-supervised Training of Proposal-based Segmentation via Background Prediction
Venue: arXiv cs.CV 2019
Year: 2019

While supervised object detection methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this in scenarios where annotating data is prohibitively expensive, we introduce a self-supervised approach to object detection and segmentation, able to work with monocular images captured with a moving camera. At the heart of our approach lies the observation that segmentation and background reconstruction are linked tasks, and the idea that, because we observe a structured scene, background regions can be re-synthesized from their surroundings, whereas regions depicting the object cannot. We therefore encode this intuition as a self-supervised loss function that we exploit to train a proposal-based segmentation network. To account for the discrete nature of object proposals, we develop a Monte Carlo-based training strategy that allows us to explore the large space of object proposals. Our experiments demonstrate that our approach yields accurate detections and segmentations in images that visually depart from those of standard benchmarks, outperforming existing self-supervised methods and approaching weakly supervised ones that exploit large annotated datasets.