OpenRaiser/Intern-Atlas huggingface dataset:
Feel the Static and Kinetic Friction
Venue: arXiv cs.HC 2019
Year: 2019

Multimodal simulations augment the presentation of abstract concepts facilitating theoretical models understanding and learning. Most simulations only engage two of our five senses: sight and hearing. If we employ additional sensory communication channels in simulations, we may gain a deeper understanding of illustrated concepts by increasing the communication bandwidth and providing alternative perspectives. We implemented the sense of touch in 3D simulations to teach important concepts in introductory physics. Specifically, we developed a visual/haptic simulation for friction. We prove that interactive 3D haptic simulations, if carefully developed and deployed, are useful in engaging students and allowing them to understand concepts faster. We hypothesize that large scale deployment of such haptic-based simulators in science laboratories is now possible due to the advancements in haptic software and hardware technology.
Mobile-App Analysis and Instrumentation Techniques Reimagined with DECREE
Venue: arXiv cs.SE 2019
Year: 2019

A large number of mobile-app analysis and instrumentation techniques have emerged in the past decade. However, those techniques' components are difficult to extract and reuse outside their original tools, their evaluation results are hard to reproduce, and the tools themselves are hard to compare. This paper introduces DECREE, an infrastructure intended to guide such techniques to be reproducible, practical, reusable, and easy to adopt in practice. DECREE allows researchers and developers to easily discover existing solutions to their needs, enables unbiased and reproducible evaluation, and supports easy construction and execution of replication studies. The paper describes DECREE's three modules and its potential to fundamentally alter how research is conducted in this area.
Developer Reading Behavior While Summarizing Java Methods: Size and Context Matters
Venue: arXiv cs.SE 2019
Year: 2019

An eye-tracking study of 18 developers reading and summarizing Java methods is presented. The developers provide a written summary for methods assigned to them. In total, 63 methods are used from five different systems. Previous studies on this topic use only short methods presented in isolation usually as images. In contrast, this work presents the study in the Eclipse IDE allowing access to all the source code in the system. The developer can navigate via scrolling and switching files while writing the summary. New eye-tracking infrastructure allows for this improvement in the study environment. Data collected includes eye gazes on source code, written summaries, and time to complete each summary. Unlike prior work that concluded developers focus on the signature the most, these results indicate that they tend to focus on the method body more than the signature. Moreover, both experts and novices tend to revisit control flow terms rather than reading them for a long period. They also spend a significant amount of gaze time and have higher gaze visits when they read call terms. Experts tend to revisit the body of the method significantly more frequently than its signature as the size of the method increases. Moreover, experts tend to write their summaries from source code lines that they read the most.
Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning
Venue: arXiv cs.LG 2019
Year: 2019

Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space. Focusing on discriminative tasks, MKL has been used successfully for feature selection and finding the significant modalities of the data. In such applications, each base kernel represents one dimension of the data or is derived from one specific descriptor. Therefore, MKL finds an optimal weighting scheme for the given kernels to increase the classification accuracy. Nevertheless, the majority of the works in this area focus on only binary classification problems or aim for linear separation of the classes in the kernel space, which are not realistic assumptions for many real-world problems. In this paper, we propose a novel multi-class MKL framework which improves the state-of-the-art by enhancing the local separation of the classes in the feature space. Besides, by using a sparsity term, our large-margin multiple kernel algorithm (LMMK) performs discriminative feature selection by aiming to employ a small subset of the base kernels. Based on our empirical evaluations on different real-world datasets, LMMK provides a competitive classification accuracy compared with the state-of-the-art algorithms in MKL. Additionally, it learns a sparse set of non-zero kernel weights which leads to a more interpretable feature selection and representation learning.
Analyzing Input and Output Representations for Speech-Driven Gesture Generation
Venue: arXiv cs.HC 2019
Year: 2019

This paper presents a novel framework for automatic speech-driven gesture generation, applicable to human-agent interaction including both virtual agents and robots. Specifically, we extend recent deep-learning-based, data-driven methods for speech-driven gesture generation by incorporating representation learning. Our model takes speech as input and produces gestures as output, in the form of a sequence of 3D coordinates. Our approach consists of two steps. First, we learn a lower-dimensional representation of human motion using a denoising autoencoder neural network, consisting of a motion encoder MotionE and a motion decoder MotionD. The learned representation preserves the most important aspects of the human pose variation while removing less relevant variation. Second, we train a novel encoder network SpeechE to map from speech to a corresponding motion representation with reduced dimensionality. At test time, the speech encoder and the motion decoder networks are combined: SpeechE predicts motion representations based on a given speech signal and MotionD then decodes these representations to produce motion sequences. We evaluate different representation sizes in order to find the most effective dimensionality for the representation. We also evaluate the effects of using different speech features as input to the model. We find that mel-frequency cepstral coefficients (MFCCs), alone or combined with prosodic features, perform the best. The results of a subsequent user study confirm the benefits of the representation learning.
Unsupervised Medical Image Translation Using Cycle-MedGAN
Venue: arXiv cs.CV 2019
Year: 2019

Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. However, for supervised image translation frameworks, co-registered datasets, paired in a pixel-wise sense, are required. This is often difficult to acquire in realistic medical scenarios. On the other hand, unsupervised translation frameworks often result in blurred translated images with unrealistic details. In this work, we propose a new unsupervised translation framework which is titled Cycle-MedGAN. The proposed framework utilizes new non-adversarial cycle losses which direct the framework to minimize the textural and perceptual discrepancies in the translated images. Qualitative and quantitative comparisons against other unsupervised translation approaches demonstrate the performance of the proposed framework for PET-CT translation and MR motion correction.
Talking about interaction*
Venue: arXiv cs.HC 2019
Year: 2019

Recent research has exposed disagreements over the nature and usefulness of what may (or may not) be Human-Computer Interaction's fundamental phenomenon: 'interaction'. For some, HCI's theorising about interaction has been deficient, impacting its capacity to inform decisions in design, suggesting the need either to perform first-principles definition work or broader administrative clarification and formalisation of the multitude of formulations of the concepts of interaction and their particular uses. For others, there remain open questions over the continued relevance of certain 'versions' of interaction as a useful concept in HCI at all. We pursue a different perspective in this paper, reviewing how HCI treats interaction through examining its 'conceptual pragmatics' within HCI's discourse. We argue that articulations of the concepts of interaction can be a site of productive conflict for HCI that for many reasons may resist attempts of formalisation as well as attempts to dispense with them. The main contribution of this paper is in specifying how we might go about talking of interaction and the value of interaction language as promiscuous concepts.
Set CRDT com Múltiplas Políticas de Resolução de Conflitos
Venue: arXiv cs.DS 2019
Year: 2019

Um CRDT é um tipo de dados que pode ser replicado e modificado concorrentemente sem coordenação, garantindo-se a convergência das réplicas através da resolução automática de conflitos. Cada CRDT implementa uma política específica para resolver conflitos. Por exemplo, um conjunto CRDT add-wins dá prioridade ao "add" aquando da execução concorrente de um "add" e "rem" do mesmo elemento. Em algumas aplicações pode ser necessário usar diferentes políticas para diferentes execuções de uma operação -- por exemplo, uma aplicação que utilize um conjunto CRDT add-wins pode querer que alguns "removes" ganhem sobre "adds" concorrentes. Neste artigo é apresentado e avaliado o desenho dum conjunto CRDT que implementa as semânticas referidas. --- Conflict-Free Replicated Data Types (CRDTs) allow objects to be replicated and concurrently modified without coordination. CRDTs solve conflicts automatically and provide eventual consistency. Typically each CRDT uses a specific policy for solving conflicts. For example, in an add-wins set CRDT, when an element is concurrently add and removed in different replicas, priority is given to add, i.e., the element stays in the set. Unfortunately, this may be inadequate for some applications - it may be desired to overrule the default policy for some operation executions. For example, an application using an add-wins set may want some removes to win over concurrent adds. This paper present the design of a set CRDT that implements such semantics.
A Three-Player GAN: Generating Hard Samples To Improve Classification Networks
Venue: arXiv cs.CV 2019
Year: 2019

We propose a Three-Player Generative Adversarial Network to improve classification networks. In addition to the game played between the discriminator and generator, a competition is introduced between the generator and the classifier. The generator's objective is to synthesize samples that are both realistic and hard to label for the classifier. Even though we make no assumptions on the type of augmentations to learn, we find that the model is able to synthesize realistically looking examples that are hard for the classification model. Furthermore, the classifier becomes more robust when trained on these difficult samples. The method is evaluated on a public dataset for traffic sign recognition.
On counting functions and slenderness of languages
Venue: arXiv cs.FL 2019
Year: 2019

We study counting-regular languages -- these are languages $L$ for which there is a regular language $L'$ such that the number of strings of length $n$ in $L$ and $L'$ are the same for all $n$. We show that the languages accepted by unambiguous nondeterministic Turing machines with a one-way read-only input tape and a reversal-bounded worktape are counting-regular. Many one-way acceptors are a special case of this model, such as reversal-bounded deterministic pushdown automata, reversal-bounded deterministic queue automata, and many others, and therefore all languages accepted by these models are counting-regular. This result is the best possible in the sense that the claim does not hold for either $2$-ambiguous PDA's, unambiguous PDA's with no reversal-bound, and other models. We also study closure properties of counting-regular languages, and we study decidability problems in regards to counting-regularity. For example, it is shown that the counting-regularity of even some restricted subclasses of PDA's is undecidable. Lastly, $k$-slender languages -- where there are at most $k$ words of any length -- are also studied. Amongst other results, it is shown that it is decidable whether a language in any semilinear full trio is $k$-slender.
Insertion Operations on Deterministic Reversal-Bounded Counter Machines
Venue: arXiv cs.FL 2019
Year: 2019

Several insertion operations are studied applied to languages accepted by one-way and two-way deterministic reversal-bounded multicounter machines. These operations are defined by the ideals obtained from relations such as the prefix, infix, suffix, and outfix relations, as well as operations defined from inverses of a type of deterministic transducer with reversal-bounded counters attached. The question of whether the resulting languages can always be accepted by deterministic machines with the same number (or larger number) of input-turns (resp., counters, counter-reversals, etc.) is investigated.
Application of Genetic Algorithms to the Multiple Team Formation Problem
Venue: arXiv cs.NE 2019
Year: 2019

Allocating of people in multiple projects is an important issue considering the efficiency of groups from the point of view of social interaction. In this paper, based on previous works, the Multiple Team Formation Problem (MTFP) based on sociometric techniques is formulated as an optimization problem taking into account the social interaction among team members. To solve the resulting optimization problem we propose a Genetic Algorithm due to the NP-hard nature of the problem. The social cohesion is an important issue that directly impacts the productivity of the work environment. So, maintaining an appropriate level of cohesion keeps a group together, which will bring positive impacts on the results of a project. The aim of the proposal is to ensure the best possible effectiveness from the point of view of social interaction. In this way, the presented algorithm serves as a decision-making tool for managers to build teams of people in multiple projects. In order to analyze the performance of the proposed method, computational experiments with benchmarks were performed and compared with the exhaustive method. The results are promising and show that the algorithm generally obtains near-optimal results within a short computational time.
Inductive Transfer for Neural Architecture Optimization
Venue: arXiv cs.LG 2019
Year: 2019

The recent advent of automated neural network architecture search led to several methods that outperform state-of-the-art human-designed architectures. However, these approaches are computationally expensive, in extreme cases consuming GPU years. We propose two novel methods which aim to expedite this optimization problem by transferring knowledge acquired from previous tasks to new ones. First, we propose a novel neural architecture selection method which employs this knowledge to identify strong and weak characteristics of neural architectures across datasets. Thus, these characteristics do not need to be rediscovered in every search, a strong weakness of current state-of-the-art searches. Second, we propose a method for learning curve extrapolation to determine if a training process can be terminated early. In contrast to existing work, we propose to learn from learning curves of architectures trained on other datasets to improve the prediction accuracy for novel datasets. On five different image classification benchmarks, we empirically demonstrate that both of our orthogonal contributions independently lead to an acceleration, without any significant loss in accuracy.
How fast can we reach a target vertex in stochastic temporal graphs?
Venue: arXiv cs.CC 2019
Year: 2019

Temporal graphs are used to abstractly model real-life networks that are inherently dynamic in nature. Given a static underlying graph $G=(V,E)$, a temporal graph on $G$ is a sequence of snapshots $G_t$, one for each time step $t\geq 1$. In this paper we study stochastic temporal graphs, i.e. stochastic processes $\mathcal{G}$ whose random variables are the snapshots of a temporal graph on $G$. A natural feature observed in various real-life scenarios is a memory effect in the appearance probabilities of particular edges; i.e. the probability an edge $e\in E$ appears at time step $t$ depends on its appearance (or absence) at the previous $k$ steps. In this paper we study the hierarchy of models memory-$k$, addressing this memory effect in an edge-centric network evolution: every edge of $G$ has its own independent probability distribution for its appearance over time. Clearly, for every $k\geq 1$, memory-$(k-1)$ is a special case of memory-$k$. We make a clear distinction between the values $k=0$ ("no memory") and $k\geq 1$ ("some memory"), as in some cases these models exhibit a fundamentally different computational behavior, as our results indicate. For every $k\geq 0$ we investigate the complexity of two naturally related, but fundamentally different, temporal path (journey) problems: MINIMUM ARRIVAL and BEST POLICY. In the first problem we are looking for the expected arrival time of a foremost journey between two designated vertices $s,y$. In the second one we are looking for the arrival time of the best policy for actually choosing a particular $s$-$y$ journey. We present a detailed investigation of the computational landscape of both problems for the different values of memory $k$. Among other results we prove that, surprisingly, MINIMUM ARRIVAL is strictly harder than BEST POLICY; in fact, for $k=0$, MINIMUM ARRIVAL is #P-hard while BEST POLICY is solvable in $O(n^2)$ time.
Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system and disturbances. This paper examines two different algorithms to solve the game, Robust Adversarial Reinforcement Learning and Neural Fictitious Self Play, and compares performance on an autonomous driving scenario. We extend the game formulation to a semi-competitive setting and demonstrate that the resulting adversary better captures meaningful disturbances that lead to better overall performance. The resulting robust policy exhibits improved driving efficiency while effectively reducing collision rates compared to baseline control policies produced by traditional reinforcement learning methods.
SAT-based Compressive Sensing
Venue: arXiv cs.IT 2019
Year: 2019

We propose to reduce the original well-posed problem of compressive sensing to weighted-MAX-SAT. Compressive sensing is a novel randomized data acquisition approach that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling rate. The original problem of compressive sensing in sparse recovery is NP-hard; therefore, in addition to restrictions for the uniqueness of the sparse solution, the coding matrix has also to satisfy additional stringent constraints -usually the restricted isometry property (RIP)- so we can handle it by its convex or nonconvex relaxations. In practice, such constraints are not only intractable to be verified but also invalid in broad applications. We first divide the well-posed problem of compressive sensing into relaxed sub-problems and represent them as separate SAT instances in conjunctive normal form (CNF). After merging the resulting sub-problems, we assign weights to all clauses in such a way that the aggregated weighted-MAX-SAT can guarantee successful recovery of the original signal. The only requirement in our approach is the solution uniqueness of the associated problems, which is notably looser. As a proof of concept, we demonstrate the applicability of our approach in tackling the original problem of binary compressive sensing with binary design matrices. Experimental results demonstrate the supremacy of the proposed SAT-based compressive sensing over the $\ell_1$-minimization in the robust recovery of sparse binary signals. SAT-based compressive sensing on average requires 8.3% fewer measurements for exact recovery of highly sparse binary signals ($s/N\approx 0.1$). When $s/N \approx 0.5$, the $\ell_1$-minimization on average requires 22.2% more measurements for exact reconstruction of the binary signals. Thus, the proposed SAT-based compressive sensing is less sensitive to the sparsity of the original signals.
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived "worst" case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, for the first time, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named RLGC has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Extensive case studies performed in both two-area four-machine system and IEEE 39-Bus system have demonstrated the excellent performance and robustness of the proposed schemes.
Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes
Venue: arXiv cs.CV 2019
Year: 2019

Dynamic scenes that contain both object motion and egomotion are a challenge for monocular visual odometry (VO). Another issue with monocular VO is the scale ambiguity, i.e. these methods cannot estimate scene depth and camera motion in real scale. Here, we propose a learning based approach to predict camera motion parameters directly from optic flow, by marginalizing depthmap variations and outliers. This is achieved by learning a sparse overcomplete basis set of egomotion in an autoencoder network, which is able to eliminate irrelevant components of optic flow for the task of camera parameter or motionfield estimation. The model is trained using a sparsity regularizer and a supervised egomotion loss, and achieves the state-of-the-art performances on trajectory prediction and camera rotation prediction tasks on KITTI and Virtual KITTI datasets, respectively. The sparse latent space egomotion representation learned by the model is robust and requires only 5% of the hidden layer neurons to maintain the best trajectory prediction accuracy on KITTI dataset. Additionally, in presence of depth information, the proposed method demonstrates faithful object velocity prediction for wide range of object sizes and speeds by global compensation of predicted egomotion and a divisive normalization procedure.