OpenRaiser/Intern-Atlas huggingface dataset:
Fusion of Monocular Vision and Radio-based Ranging for Global Scale Estimation and Drift Mitigation
Venue: arXiv cs.RO 2018
Year: 2018

Monocular vision-based Simultaneous Localization and Mapping (SLAM) is used for various purposes due to its advantages in cost, simple setup, as well as availability in the environments where navigation with satellites is not effective. However, camera motion and map points can be estimated only up to a global scale factor with monocular vision. Moreover, estimation error accumulates over time without bound, if the camera cannot detect the previously observed map points for closing a loop. We propose an innovative approach to estimate a global scale factor and reduce drifts in monocular vision-based localization with an additional single ranging link. Our method can be easily integrated with the back-end of monocular visual SLAM methods. We demonstrate our algorithm with real datasets collected on a rover, and show the evaluation results.
Semi-dense Stereo Matching using Dual CNNs
Venue: arXiv cs.CV 2018
Year: 2018

A robust solution for semi-dense stereo matching is presented. It utilizes two CNN models for computing stereo matching cost and performing confidence-based filtering, respectively. Compared to existing CNNs-based matching cost generation approaches, our method feeds additional global information into the network so that the learned model can better handle challenging cases, such as lighting changes and lack of textures. Through utilizing non-parametric transforms, our method is also more self-reliant than most existing semi-dense stereo approaches, which rely highly on the adjustment of parameters. The experimental results based on Middlebury Stereo dataset demonstrate that the proposed approach outperforms the state-of-the-art semi-dense stereo approaches.
Sketching for Latent Dirichlet-Categorical Models
Venue: arXiv cs.LG 2018
Year: 2018

Recent work has explored transforming data sets into smaller, approximate summaries in order to scale Bayesian inference. We examine a related problem in which the parameters of a Bayesian model are very large and expensive to store in memory, and propose more compact representations of parameter values that can be used during inference. We focus on a class of graphical models that we refer to as latent Dirichlet-Categorical models, and show how a combination of two sketching algorithms known as count-min sketch and approximate counters provide an efficient representation for them. We show that this sketch combination -- which, despite having been used before in NLP applications, has not been previously analyzed -- enjoys desirable properties. We prove that for this class of models, when the sketches are used during Markov Chain Monte Carlo inference, the equilibrium of sketched MCMC converges to that of the exact chain as sketch parameters are tuned to reduce the error rate.
Efficient Estimation of Equilibria of Large Congestion Games with Heterogeneous Players
Venue: arXiv cs.GT 2018
Year: 2018

Computing an equilibrium in congestion games can be challenging when the number of players is large. Yet, it is a problem to be addressed in practice, for instance to forecast the state of the system and be able to control it. In this work, we analyze the case of generalized atomic congestion games, with coupling constraints, and with players that are heterogeneous through their action sets and their utility functions. We obtain an approximation of the variational Nash equilibria---a notion generalizing Nash equilibria in the presence of coupling constraints---of a large atomic congestion game by an equilibrium of an auxiliary population game, where each population corresponds to a group of atomic players of the initial game. Because the variational inequalities characterizing the equilibrium of the auxiliary game have smaller dimension than the original problem, this approach enables the fast computation of an estimation of equilibria in a large congestion game with thousands of heterogeneous players.
Unsupervised Machine Learning of Open Source Russian Twitter Data Reveals Global Scope and Operational Characteristics
Venue: arXiv cs.SI 2018
Year: 2018

We developed and used a collection of statistical methods (unsupervised machine learning) to extract relevant information from a Twitter supplied data set consisting of alleged Russian trolls who (allegedly) attempted to influence the 2016 US Presidential election. These unsupervised statistical methods allow fast identification of (i) emergent language communities within the troll population, (ii) the transnational scope of the operation and (iii) operational characteristics of trolls that can be used for future identification. Using natural language processing, manifold learning and Fourier analysis, we identify an operation that includes not only the 2016 US election, but also the French National and both local and national German elections. We show the resulting troll population is composed of users with common, but clearly customized, behavioral characteristics.
Cutting Diamonds: Temporal DLs with Probabilistic Distributions over Data
Venue: arXiv cs.LO 2018
Year: 2018

Recent work has studied a probabilistic extension of the temporal logic LTL that refines the eventuality (or diamond) constructor with a probability distribution on when will this eventuality be satisfied. In this paper, we adapt this notion to a well established temporal extension of DL-Lite, allowing the new probabilistic constructor only in the ABox assertions. We investigate the satisfiability problem of this new temporal DL over equiparametric geometric distributions.
The Effect of Data Marshalling on Computation Offloading Decisions
Venue: arXiv cs.NI 2018
Year: 2018

We conducted an extensive set of experiments with an offloading testbed to understand the impact that data marshalling techniques have on computation offloading decisions. We find that the popular JSON format to marshall data between client and server comes at a significant computational expense compared to a minimalistic raw data transfer. The computational time is significant in that it affects computation offloading decisions in a variety of conditions. We outline some of these conditions.
Rough set based lattice structure for knowledge representation in medical expert systems: low back pain management case study
Venue: arXiv cs.AI 2018
Year: 2018

The aim of medical knowledge representation is to capture the detailed domain knowledge in a clinically efficient manner and to offer a reliable resolution with the acquired knowledge. The knowledge base to be used by a medical expert system should allow incremental growth with inclusion of updated knowledge over the time. As knowledge are gathered from a variety of knowledge sources by different knowledge engineers, the problem of redundancy is an important concern here due to increased processing time of knowledge and occupancy of large computational storage to accommodate all the gathered knowledge. Also there may exist many inconsistent knowledge in the knowledge base. In this paper, we have proposed a rough set based lattice structure for knowledge representation in medical expert systems which overcomes the problem of redundancy and inconsistency in knowledge and offers computational efficiency with respect to both time and space. We have also generated an optimal set of decision rules that would be used directly by the inference engine. The reliability of each rule has been measured using a new metric called credibility factor, and the certainty and coverage factors of a decision rule have been re-defined. With a set of decisions rules arranged in descending order according to their reliability measures, the medical expert system will consider the highly reliable and certain rules at first, then it would search for the possible and uncertain rules at later stage, if recommended by physicians. The proposed knowledge representation technique has been illustrated using an example from the domain of low back pain. The proposed scheme ensures completeness, consistency, integrity, non-redundancy, and ease of access.
A Generic Framework for Task Offloading in mmWave MEC Backhaul Networks
Venue: arXiv cs.NI 2018
Year: 2018

With the emergence of millimeter-Wave (mmWave) communication technology, the capacity of mobile backhaul networks can be significantly increased. On the other hand, Mobile Edge Computing (MEC) provides an appropriate infrastructure to offload latency-sensitive tasks. However, the amount of resources in MEC servers is typically limited. Therefore, it is important to intelligently manage the MEC task offloading by optimizing the backhaul bandwidth and edge server resource allocation in order to decrease the overall latency of the offloaded tasks. This paper investigates the task allocation problem in MEC environment, where the mmWave technology is used in the backhaul network. We formulate a Mixed Integer NonLinear Programming (MINLP) problem with the goal to minimize the total task serving time. Its objective is to determine an optimized network topology, identify which server is used to process a given offloaded task, find the path of each user task, and determine the allocated bandwidth to each task on mmWave backhaul links. Because the problem is difficult to solve, we develop a two-step approach. First, a Mixed Integer Linear Program (MILP) determining the network topology and the routing paths is optimally solved. Then, the fractions of bandwidth allocated to each user task are optimized by solving a quasi-convex problem. Numerical results illustrate the obtained topology and routing paths for selected scenarios and show that optimizing the bandwidth allocation significantly improves the total serving time, particularly for bandwidth-intensive tasks.
A Comparative Study of Neural Network Models for Sentence Classification
Venue: arXiv cs.CL 2018
Year: 2018

This paper presents an extensive comparative study of four neural network models, including feed-forward networks, convolutional networks, recurrent networks and long short-term memory networks, on two sentence classification datasets of English and Vietnamese text. We show that on the English dataset, the convolutional network models without any feature engineering outperform some competitive sentence classifiers with rich hand-crafted linguistic features. We demonstrate that the GloVe word embeddings are consistently better than both Skip-gram word embeddings and word count vectors. We also show the superiority of convolutional neural network models on a Vietnamese newspaper sentence dataset over strong baseline models. Our experimental results suggest some good practices for applying neural network models in sentence classification.
Towards Low-level Cryptographic Primitives for JavaCards
Venue: arXiv cs.CR 2018
Year: 2018

JavaCard is a multi-application security platform deployed to over twenty billion smartcards, used in applications ranging from secure payments to telecommunications. While the platform is a popular choice for established commercial use cases (e.g., SIM cards in telecommunication networks), it has notably low adoption rates in: 1) application scenarios requiring recently-standardized cryptographic algorithms, 2) research projects, and 3) open source initiatives. We attribute this to the restricted access to low-level cryptographic primitives (e.g., elliptic curve operations) and the lack of essential data types (e.g., Integers). While the underlying hardware has those capabilities, the JavaCard API does not provide calls for the corresponding functionality. Until now, the only available workaround was manufacturer-specific proprietary APIs that come with very restrictive non-disclosure agreements. In this paper, we introduce a methodology to efficiently derive essential data types and low-level cryptographic primitives from high-level operations. Our techniques are ideal for resource-constrained platforms, and make optimal use of the underlying hardware, while having a small memory footprint. We also introduce JCMathLib, which, to the best of our knowledge, is the first generic library for low-level cryptographic operations in JavaCards that does not rely on a proprietary API. Without any disclosure limitations, JCMathLib enables open code sharing, release of research prototypes and public and third-party code audits.
A characterization of the Edge of Criticality in Binary Echo State Networks
Venue: arXiv cs.NE 2018
Year: 2018

Echo State Networks (ESNs) are simplified recurrent neural network models composed of a reservoir and a linear, trainable readout layer. The reservoir is tunable by some hyper-parameters that control the network behaviour. ESNs are known to be effective in solving tasks when configured on a region in (hyper-)parameter space called \emph{Edge of Criticality} (EoC), where the system is maximally sensitive to perturbations hence affecting its behaviour. In this paper, we propose binary ESNs, which are architecturally equivalent to standard ESNs but consider binary activation functions and binary recurrent weights. For these networks, we derive a closed-form expression for the EoC in the autonomous case and perform simulations in order to assess their behavior in the case of noisy neurons and in the presence of a signal. We propose a theoretical explanation for the fact that the variance of the input plays a major role in characterizing the EoC.
Disambiguating Music Artists at Scale with Audio Metric Learning
Venue: arXiv cs.IR 2018
Year: 2018

We address the problem of disambiguating large scale catalogs through the definition of an unknown artist clustering task. We explore the use of metric learning techniques to learn artist embeddings directly from audio, and using a dedicated homonym artists dataset, we compare our method with a recent approach that learn similar embeddings using artist classifiers. While both systems have the ability to disambiguate unknown artists relying exclusively on audio, we show that our system is more suitable in the case when enough audio data is available for each artist in the train dataset. We also propose a new negative sampling method for metric learning that takes advantage of side information such as music genre during the learning phase and shows promising results for the artist clustering task.
Two (Known) Results About Graphs with No Short Odd Cycles
Venue: arXiv cs.DM 2018
Year: 2018

Consider a graph with $n$ vertices where the shortest odd cycle is of length $>2k+1$. We revisit two known results about such graphs: (I) Such a graph is almost bipartite, in the sense that it can be made bipartite by removing from it $O\bigl( (n/k) \log (n/k) \bigr)$ vertices. While this result is known [GKL97] -- our new proof seems to yield slightly better constants, and is (arguably) conceptually simpler. To this end, we state (and prove) a version of CKR partitions [CKR01, FRT04] that has a small vertex separator, and it might be of independent interest. While this must be known in the literature, we were unable to find a reference to it, and it is included for the sake of completeness. (II) While such graphs can be quite dense (e.g., consider a the bipartite clique, which has no odd cycles), they have a large independent set. Specifically, we prove that such graphs have independent sets of size $\geq \bigl(1-o(1)\bigr)n^{k/(k+1)}$. Again, this result is known and is implied by the work of Shearer [She95], but our proof is simpler and (seems to) yield a better constant.
Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy
Venue: arXiv cs.AI 2018
Year: 2018

Building effective, enjoyable, and safe autonomous vehicles is a lot harder than has historically been considered. The reason is that, simply put, an autonomous vehicle must interact with human beings. This interaction is not a robotics problem nor a machine learning problem nor a psychology problem nor an economics problem nor a policy problem. It is all of these problems put into one. It challenges our assumptions about the limitations of human beings at their worst and the capabilities of artificial intelligence systems at their best. This work proposes a set of principles for designing and building autonomous vehicles in a human-centered way that does not run away from the complexity of human nature but instead embraces it. We describe our development of the Human-Centered Autonomous Vehicle (HCAV) as an illustrative case study of implementing these principles in practice.
A Non-linear Approach to Space Dimension Perception by a Naive Agent
Venue: arXiv cs.LG 2018
Year: 2018

Developmental Robotics offers a new approach to numerous AI features that are often taken as granted. Traditionally, perception is supposed to be an inherent capacity of the agent. Moreover, it largely relies on models built by the system's designer. A new approach is to consider perception as an experimentally acquired ability that is learned exclusively through the analysis of the agent's sensorimotor flow. Previous works, based on H.Poincaré's intuitions and the sensorimotor contingencies theory, allow a simulated agent to extract the dimension of geometrical space in which it is immersed without any a priori knowledge. Those results are limited to infinitesimal movement's amplitude of the system. In this paper, a non-linear dimension estimation method is proposed to push back this limitation.
Grounding the Experience of a Visual Field through Sensorimotor Contingencies
Venue: arXiv cs.LG 2018
Year: 2018

Artificial perception is traditionally handled by hand-designing task specific algorithms. However, a truly autonomous robot should develop perceptive abilities on its own, by interacting with its environment, and adapting to new situations. The sensorimotor contingencies theory proposes to ground the development of those perceptive abilities in the way the agent can actively transform its sensory inputs. We propose a sensorimotor approach, inspired by this theory, in which the agent explores the world and discovers its properties by capturing the sensorimotor regularities they induce. This work presents an application of this approach to the discovery of a so-called visual field as the set of regularities that a visual sensor imposes on a naive agent's experience. A formalism is proposed to describe how those regularities can be captured in a sensorimotor predictive model. Finally, the approach is evaluated on a simulated system coarsely inspired from the human retina.
Relaxed Quantization for Discretized Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks that can be effectively discretized without loss of performance, we introduce a differentiable quantization procedure. Differentiability can be achieved by transforming continuous distributions over the weights and activations of the network to categorical distributions over the quantization grid. These are subsequently relaxed to continuous surrogates that can allow for efficient gradient-based optimization. We further show that stochastic rounding can be seen as a special case of the proposed approach and that under this formulation the quantization grid itself can also be optimized with gradient descent. We experimentally validate the performance of our method on MNIST, CIFAR 10 and Imagenet classification.
RPSE: Reification as Paradigm of Software Engineering
Venue: arXiv cs.SE 2018
Year: 2018

The paper introduces RPSE, Reification as a Paradigm of Software Engineering, and enumerates the most important theoretical and practical problems of the development and application of this paradigm. Main thesis: Software engineering is the reification (materialization of ideas) via the transformation of mental models into code executed on computers . Within the proposed paradigm: 1.All basic processes of software engineering are concrete variants (implementations) of the process of constructing chains of mental and material models I1, I2,..In, M1, M2, ..Mm. The last most specific model in this chain is, as a rule, program code. 2.The essence of software engineering is the construction of such chains. 3.All main issues of optimizing the development, its cost, and quality can be reduced to the optimization of construction of the corresponding chain of models.
Comparison of Reinforcement Learning algorithms applied to the Cart Pole problem
Venue: arXiv cs.LG 2018
Year: 2018

Designing optimal controllers continues to be challenging as systems are becoming complex and are inherently nonlinear. The principal advantage of reinforcement learning (RL) is its ability to learn from the interaction with the environment and provide optimal control strategy. In this paper, RL is explored in the context of control of the benchmark cartpole dynamical system with no prior knowledge of the dynamics. RL algorithms such as temporal-difference, policy gradient actor-critic, and value function approximation are compared in this context with the standard LQR solution. Further, we propose a novel approach to integrate RL and swing-up controllers.
CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction
Venue: arXiv cs.CV 2018
Year: 2018

Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy computations of a matched-filtering step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications. We address this shortcoming by arranging dictionary atoms in the form of cover tree structures and adopt the corresponding fast approximate nearest neighbour searches to accelerate matched-filtering. For datasets belonging to smooth low-dimensional manifolds cover trees offer search complexities logarithmic in terms of data population. With this motivation we propose an iterative reconstruction algorithm, named CoverBLIP, to address large-size MRF problems where the fingerprint dictionary i.e. discrete manifold of Bloch responses, encodes several intrinsic NMR parameters. We study different forms of convergence for this algorithm and we show that provided with a notion of embedding, the inexact and non-convex iterations of CoverBLIP linearly convergence toward a near-global solution with the same order of accuracy as using exact brute-force searches. Our further examinations on both synthetic and real-world datasets and using different sampling strategies, indicates between 2 to 3 orders of magnitude reduction in total search computations. Cover trees are robust against the curse-of-dimensionality and therefore CoverBLIP provides a notion of scalability -- a consistent gain in time-accuracy performance-- for searching high-dimensional atoms which may not be easily preprocessed (i.e. for dimensionality reduction) due to the increasing degrees of non-linearities appearing in the emerging multi-parametric MRF dictionaries.
Verification for Machine Learning, Autonomy, and Neural Networks Survey
Venue: arXiv cs.AI 2018
Year: 2018

This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances in artificial intelligence (AI) and machine learning (ML) through approaches such as deep neural networks (DNNs), embedded in so-called learning enabled components (LECs) that accomplish tasks from classification to control. Recently, the formal methods and formal verification community has developed methods to characterize behaviors in these LECs with eventual goals of formally verifying specifications for LECs, and this article presents a survey of many of these recent approaches.