OpenRaiser/Intern-Atlas huggingface dataset:
Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi
Venue: arXiv cs.CL 2020
Year: 2020

The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yorùbá and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yorùbá and Twi. As output of the work, we provide corpora, embeddings and the test suits for both languages.
ODRE Workshop: Probabilistic Dynamic Hard Real-Time Scheduling in HPC
Venue: arXiv cs.DC 2020
Year: 2020

Industry 4.0 is changing fundamentally the way data is collected, stored and analyzed in industrial processes. While this change enables novel application such as flexible manufacturing of highly customized products, the real-time control of these processes, however, has not yet realized its full potential. We believe that modern virtualization techniques, specifically application containers, present a unique opportunity to decouple control functionality from associated hardware. Through it, we can fully realize the potential for highly distributed and transferable industrial processes even with real-time constraints arising from time-critical sub-processes. In this paper, we present a specifically developed orchestration tool to manage the challenges and opportunities of shifting industrial control software from dedicated hardware to bare-metal servers or (edge) cloud computing platforms. Using off-the-shelf technology, the proposed tool can manage the execution of containerized applications on shared resources without compromising hard real-time execution determinism. Through first experimental results, we confirm the viability and analyzed the behavior of resource shared systems with strict real-time requirements. We then describe experiments set out to deliver expected results and gather performance, application scope and limits of the presented approach.
Region-Wise Attack: On Efficient Generation of Robust Physical Adversarial Examples
Venue: arXiv cs.LG 2020
Year: 2020

Deep neural networks (DNNs) are shown to be susceptible to adversarial example attacks. Most existing works achieve this malicious objective by crafting subtle pixel-wise perturbations, and they are difficult to launch in the physical world due to inevitable transformations (e.g., different photographic distances and angles). Recently, there are a few research works on generating physical adversarial examples, but they generally require the details of the model a priori, which is often impractical. In this work, we propose a novel physical adversarial attack for arbitrary black-box DNN models, namely Region-Wise Attack. To be specific, we present how to efficiently search for regionwise perturbations to the inputs and determine their shapes, locations and colors via both top-down and bottom-up techniques. In addition, we introduce two fine-tuning techniques to further improve the robustness of our attack. Experimental results demonstrate the efficacy and robustness of the proposed Region-Wise Attack in real world.
The Expressiveness of Looping Terms in the Semantic Programming
Venue: arXiv cs.LO 2020
Year: 2020

We consider the language of $Δ_0$-formulas with list terms interpreted over hereditarily finite list superstructures. We study the complexity of reasoning in extensions of the language of $Δ_0$-formulas with non-standard list terms, which represent bounded list search, bounded iteration, and bounded recursion. We prove a number of results on the complexity of model checking and satisfiability for these formulas. In particular, we show that the set of $Δ_0$-formulas with bounded recursive terms true in a given list superstructure $HW(\mathcal{M})$ is non-elementary (it contains the class kEXPTIME, for all $k\geqslant 1$). For $Δ_0$-formulas with restrictions on the usage of iterative and recursive terms, we show lower complexity.
ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking
Venue: arXiv cs.SE 2020
Year: 2020

Commit messages record code changes (e.g., feature modifications and bug repairs) in natural language, and are useful for program comprehension. Due to the frequent updates of software and time cost, developers are generally unmotivated to write commit messages for code changes. Therefore, automating the message writing process is necessitated. Previous studies on commit message generation have been benefited from generation models or retrieval models, but the code structure of changed code, i.e., AST, which can be important for capturing code semantics, has not been explicitly involved. Moreover, although generation models have the advantages of synthesizing commit messages for new code changes, they are not easy to bridge the semantic gap between code and natural languages which could be mitigated by retrieval models. In this paper, we propose a novel commit message generation model, named ATOM, which explicitly incorporates the abstract syntax tree for representing code changes and integrates both retrieved and generated messages through hybrid ranking. Specifically, the hybrid ranking module can prioritize the most accurate message from both retrieved and generated messages regarding one code change. We evaluate the proposed model ATOM on our dataset crawled from 56 popular Java repositories. Experimental results demonstrate that ATOM increases the state-of-the-art models by 30.72% in terms of BLEU-4 (an accuracy measure that is widely used to evaluate text generation systems). Qualitative analysis also demonstrates the effectiveness of ATOM in generating accurate code commit messages.
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception
Venue: arXiv cs.CL 2020
Year: 2020

Most research on emotion analysis from text focuses on the task of emotion classification or emotion intensity regression. Fewer works address emotions as a phenomenon to be tackled with structured learning, which can be explained by the lack of relevant datasets. We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, as well as the reader's perception of the emotion of the headline. This annotation task is comparably challenging, given the large number of classes and roles to be identified. We therefore propose a multiphase annotation procedure in which we first find relevant instances with emotional content and then annotate the more fine-grained aspects. Finally, we develop a baseline for the task of automatic prediction of semantic role structures and discuss the results. The corpus we release enables further research on emotion classification, emotion intensity prediction, emotion cause detection, and supports further qualitative studies.
Online Vector Balancing and Geometric Discrepancy
Venue: arXiv cs.DS 2020
Year: 2020

We consider an online vector balancing question where $T$ vectors, chosen from an arbitrary distribution over $[-1,1]^n$, arrive one-by-one and must be immediately given a $\pm$ sign. The goal is to keep the discrepancy small as possible. A concrete example is the online interval discrepancy problem where T points are sampled uniformly in [0,1], and the goal is to immediately color them $\pm$ such that every sub-interval remains nearly balanced. As random coloring incurs $Ω(T^{1/2})$ discrepancy, while the offline bounds are $Θ(\sqrt{n \log (T/n)})$ for vector balancing and $1$ for interval balancing, a natural question is whether one can (nearly) match the offline bounds in the online setting for these problems. One must utilize the stochasticity as in the worst-case scenario it is known that discrepancy is $Ω(T^{1/2})$ for any online algorithm. Bansal and Spencer recently show an $O(\sqrt{n}\log T)$ bound when each coordinate is independent. When there are dependencies among the coordinates, the problem becomes much more challenging, as evidenced by a recent work of Jiang, Kulkarni, and Singla that gives a non-trivial $O(T^{1/\log\log T})$ bound for online interval discrepancy. Although this beats random coloring, it is still far from the offline bound. In this work, we introduce a new framework for online vector balancing when the input distribution has dependencies across coordinates. This lets us obtain a $poly(n, \log T)$ bound for online vector balancing under arbitrary input distributions, and a $poly(\log T)$ bound for online interval discrepancy. Our framework is powerful enough to capture other well-studied geometric discrepancy problems; e.g., a $poly(\log^d (T))$ bound for the online $d$-dimensional Tusnády's problem. A key new technical ingredient is an {anti-concentration} inequality for sums of pairwise uncorrelated random variables.
Med2Meta: Learning Representations of Medical Concepts with Meta-Embeddings
Venue: arXiv cs.CL 2020
Year: 2020

Distributed representations of medical concepts have been used to support downstream clinical tasks recently. Electronic Health Records (EHR) capture different aspects of patients' hospital encounters and serve as a rich source for augmenting clinical decision making by learning robust medical concept embeddings. However, the same medical concept can be recorded in different modalities (e.g., clinical notes, lab results)-with each capturing salient information unique to that modality-and a holistic representation calls for relevant feature ensemble from all information sources. We hypothesize that representations learned from heterogeneous data types would lead to performance enhancement on various clinical informatics and predictive modeling tasks. To this end, our proposed approach makes use of meta-embeddings, embeddings aggregated from learned embeddings. Firstly, modality-specific embeddings for each medical concept is learned with graph autoencoders. The ensemble of all the embeddings is then modeled as a meta-embedding learning problem to incorporate their correlating and complementary information through a joint reconstruction. Empirical results of our model on both quantitative and qualitative clinical evaluations have shown improvements over state-of-the-art embedding models, thus validating our hypothesis.
Distinctiveness Centrality in Social Networks
Venue: arXiv cs.SI 2020
Year: 2020

The determination of node centrality is a fundamental topic in social network studies. As an addition to established metrics, which identify central nodes based on their brokerage power, the number and weight of their connections, and the ability to quickly reach all other nodes, we introduce five new measures of Distinctiveness Centrality. These new metrics attribute a higher score to nodes keeping a connection with the network periphery. They penalize links to highly-connected nodes and serve the identification of social actors with more distinctive network ties. We discuss some possible applications and properties of these newly introduced metrics, such as their upper and lower bounds. Distinctiveness centrality provides a viewpoint of centrality alternative to that of established metrics.
Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks
Venue: arXiv cs.LG 2020
Year: 2020

We investigate to what extent alternative variants of Artificial Neural Networks (ANNs) are susceptible to adversarial attacks. We analyse the adversarial robustness of conventional, stochastic ANNs and Spiking Neural Networks (SNNs) in the raw image space, across three different datasets. Our experiments reveal that stochastic ANN variants are almost equally as susceptible as conventional ANNs when faced with simple iterative gradient-based attacks in the white-box setting. However we observe, that in black-box settings, stochastic ANNs are more robust than conventional ANNs, when faced with boundary attacks, transferability and surrogate attacks. Consequently, we propose improved attacks and defence mechanisms for stochastic ANNs in black-box settings. When performing surrogate-based black-box attacks, one can employ stochastic models as surrogates to observe higher attack success on both stochastic and deterministic targets. This success can be further improved with our proposed Variance Mimicking (VM) surrogate training method, against stochastic targets. Finally, adopting a defender's perspective, we investigate the plausibility of employing stochastic switching of model mixtures as a viable hardening mechanism. We observe that such a scheme does provide a partial hardening.
DASZL: Dynamic Action Signatures for Zero-shot Learning
Venue: arXiv cs.CV 2020
Year: 2020

There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large. This makes end-to-end supervised training of a recognition system impractical as no training set is practically able to encompass the entire label set. In this paper, we present an approach to fine-grained recognition that models activities as compositions of dynamic action signatures. This compositional approach allows us to reframe fine-grained recognition as zero-shot activity recognition, where a detector is composed "on the fly" from simple first-principles state machines supported by deep-learned components. We evaluate our method on the Olympic Sports and UCF101 datasets, where our model establishes a new state of the art under multiple experimental paradigms. We also extend this method to form a unique framework for zero-shot joint segmentation and classification of activities in video and demonstrate the first results in zero-shot decoding of complex action sequences on a widely-used surgical dataset. Lastly, we show that we can use off-the-shelf object detectors to recognize activities in completely de-novo settings with no additional training.
Efficient Black-box Assessment of Autonomous Vehicle Safety
Venue: arXiv cs.LG 2020
Year: 2020

While autonomous vehicle (AV) technology has shown substantial progress, we still lack tools for rigorous and scalable testing. Real-world testing, the $\textit{de-facto}$ evaluation method, is dangerous to the public. Moreover, due to the rare nature of failures, billions of miles of driving are needed to statistically validate performance claims. Thus, the industry has largely turned to simulation to evaluate AV systems. However, having a simulation stack alone is not a solution. A simulation testing framework needs to prioritize which scenarios to run, learn how the chosen scenarios provide coverage of failure modes, and rank failure scenarios in order of importance. We implement a simulation testing framework that evaluates an entire modern AV system as a black box. This framework estimates the probability of accidents under a base distribution governing standard traffic behavior. In order to accelerate rare-event probability evaluation, we efficiently learn to identify and rank failure scenarios via adaptive importance-sampling methods. Using this framework, we conduct the first independent evaluation of a full-stack commercial AV system, Comma AI's OpenPilot.
$\mathtt{MedGraph:}$ Structural and Temporal Representation Learning of Electronic Medical Records
Venue: arXiv cs.LG 2020
Year: 2020

Electronic medical record (EMR) data contains historical sequences of visits of patients, and each visit contains rich information, such as patient demographics, hospital utilisation and medical codes, including diagnosis, procedure and medication codes. Most existing EMR embedding methods capture visit-code associations by constructing input visit representations as binary vectors with a static vocabulary of medical codes. With this limited representation, they fail in encapsulating rich attribute information of visits (demographics and utilisation information) and/or codes (e.g., medical code descriptions). Furthermore, current work considers visits of the same patient as discrete-time events and ignores time gaps between them. However, the time gaps between visits depict dynamics of the patient's medical history inducing varying influences on future visits. To address these limitations, we present $\mathtt{MedGraph}$, a supervised EMR embedding method that captures two types of information: (1) the visit-code associations in an attributed bipartite graph, and (2) the temporal sequencing of visits through a point process. $\mathtt{MedGraph}$ produces Gaussian embeddings for visits and codes to model the uncertainty. We evaluate the performance of $\mathtt{MedGraph}$ through an extensive experimental study and show that $\mathtt{MedGraph}$ outperforms state-of-the-art EMR embedding methods in several medical risk prediction tasks.
Maximal Information Propagation with Budgets
Venue: arXiv cs.GT 2020
Year: 2020

In this paper, we present an information propagation game on a network where the information is originated from a sponsor who is willing to pay a fixed total budget to the players who propagate the information. Our solution can be applied to real world situations such as advertising via social networks with limited budgets. The goal is to design a mechanism to distribute the budget such that all players in the social network are incentivized to propagate information to all their neighbours. We propose a family of mechanisms to achieve the goal, where propagating information to all neighbours is a dominant strategy for all players. Furthermore, we also consider the cases where the budget has to be completely shared.
Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Data
Venue: arXiv cs.LG 2020
Year: 2020

In this work, we propose a new learning approach for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). We develop a multimodal fusion of deep neural architectures for visual-inertial odometry. We train the model in an end-to-end fashion to estimate the current vehicle pose from streams of visual and inertial measurements. We first evaluate the accuracy of our estimation by comparing the prediction of the model to traditional algorithms on the publicly available EuRoC MAV dataset. The results illustrate a $25 \%$ improvement in estimation accuracy over the baseline. Finally, we integrate the architecture in the closed-loop flight control system of Airsim - a plugin simulator for Unreal Engine - and we provide simulation results for autonomous navigation and landing.
Probabilistic methods of bypassing the maze using stones and a random number sensor
Venue: arXiv cs.DS 2020
Year: 2020

In this paper, some open questions that are posed in Ajans' dissertation continue to be addressed: a robot bypass with a generator of random bits of integer spaces in the presence of a stone and a subspace of flags. This work is devoted to bypassing the maze with a finite state machine with a random bit generator. This task is part of the rapidly evolving theme of bypassing the maze by various finite state machines. or their teams, which is closely related to problems from the theory of computational complexity and probability theory. In this paper, it is shown at what dimensions a robot with a random bit generator and a stone can bypass integer space with a subspace of flags. In this paper, we will study the behavior of a finite state machine with a random bit generator on integer spaces. In particular, it was proved that the robot bypasses $ \ zs ^ 2 $ and cannot bypass $ \ zs ^ 3 $; a robot with a stone bypasses $ \ zs ^ 4 $ and cannot bypass $ \ zs ^ 5 $; a robot with a stone and a flag bypasses $ \ zs ^ 6 $ and cannot bypass $ \ zs ^ 7 $; a robot with a stone and a plane of flags bypasses $ \ zs ^ 8 $ and cannot bypass $ \ zs ^ 9 $.
End-to-end facial and physiological model for Affective Computing and applications
Venue: arXiv cs.CV 2020
Year: 2020

In recent years, Affective Computing and its applications have become a fast-growing research topic. Furthermore, the rise of Deep Learning has introduced significant improvements in the emotion recognition system compared to classical methods. In this work, we propose a multi-modal emotion recognition model based on deep learning techniques using the combination of peripheral physiological signals and facial expressions. Moreover, we present an improvement to proposed models by introducing latent features extracted from our internal Bio Auto-Encoder (BAE). Both models are trained and evaluated on AMIGOS datasets reporting valence, arousal, and emotion state classification. Finally, to demonstrate a possible medical application in affective computing using deep learning techniques, we applied the proposed method to the assessment of anxiety therapy. To this purpose, a reduced multi-modal database has been collected by recording facial expressions and peripheral signals such as Electrocardiogram (ECG) and Galvanic Skin Response (GSR) of each patient. Valence and arousal estimation was extracted using the proposed model from the beginning until the end of the therapy, with successful evaluation to the different emotional changes in the temporal domain.
Optimally Resilient Strategies in Pushdown Safety Games
Venue: arXiv cs.GT 2020
Year: 2020

Infinite-duration games with disturbances extend the classical framework of infinite-duration games, which captures the reactive synthesis problem, with a discrete measure of resilience against non-antagonistic external influence. This concerns events where the observed system behavior differs from the intended one prescribed by the controller. For games played on finite arenas it is known that computing optimally resilient strategies only incurs a polynomial overhead over solving classical games. This paper studies safety games with disturbances played on infinite arenas induced by pushdown systems. We show how to compute optimally resilient strategies in triply-exponential time. For the subclass of safety games played on one-counter configuration graphs, we show that determining the degree of resilience of the initial configuration is PSPACE-complete and that optimally resilient strategies can be computed in doubly-exponential time.