OpenRaiser/Intern-Atlas huggingface dataset:
Local Frequency Interpretation and Non-Local Self-Similarity on Graph for Point Cloud Inpainting
Venue: arXiv cs.CV 2018
Year: 2018

As 3D scanning devices and depth sensors mature, point clouds have attracted increasing attention as a format for 3D object representation, with applications in various fields such as tele-presence, navigation and heritage reconstruction. However, point clouds usually exhibit holes of missing data, mainly due to the limitation of acquisition techniques and complicated structure. Further, point clouds are defined on irregular non-Euclidean domains, which is challenging to address especially with conventional signal processing tools. Hence, leveraging on recent advances in graph signal processing, we propose an efficient point cloud inpainting method, exploiting both the local smoothness and the non-local self-similarity in point clouds. Specifically, we first propose a frequency interpretation in graph nodal domain, based on which we introduce the local graph-signal smoothness prior in order to describe the local smoothness of point clouds. Secondly, we explore the characteristics of non-local self-similarity, by globally searching for the most similar area to the missing region. The similarity metric between two areas is defined based on the direct component and the anisotropic graph total variation of normals in each area. Finally, we formulate the hole-filling step as an optimization problem based on the selected most similar area and regularized by the graph-signal smoothness prior. Besides, we propose voxelization and automatic hole detection methods for the point cloud prior to inpainting. Experimental results show that the proposed approach outperforms four competing methods significantly, both in objective and subjective quality.
Redundant Robot Assignment on Graphs with Uncertain Edge Costs
Venue: arXiv cs.RO 2018
Year: 2018

We provide a framework for the assignment of multiple robots to goal locations, when robot travel times are uncertain. Our premise is that time is the most valuable asset in the system. Hence, we make use of redundant robots to counter the effect of uncertainty and minimize the average waiting time at destinations. We apply our framework to transport networks represented as graphs, and consider uncertainty in the edge costs (i.e., travel time). Since solving the redundant assignment problem is strongly NP-hard, we exploit structural properties of our problem to propose a polynomial-time solution with provable sub-optimality bounds. Our method uses distributive aggregate functions, which allow us to efficiently (i.e., incrementally) compute the effective cost of assigning redundant robots. Experimental results on random graphs show that the deployment of redundant robots through our method reduces waiting times at goal locations, when edge traversals are uncertain.
A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem
Venue: arXiv cs.AI 2018
Year: 2018

Rebalancing is a critical service bottleneck for many transportation services, such as Citi Bike. Citi Bike relies on manual orchestrations of rebalancing bikes between dispatchers and field agents. Motivated by such problem and the lack of smart autonomous solutions in this area, this project explored a new RL architecture called Distributed RL (DiRL) with Transfer Learning (TL) capability. The DiRL solution is adaptive to changing traffic dynamics when keeping bike stock under control at the minimum cost. DiRL achieved a 350% improvement in bike rebalancing autonomously and TL offered a 62.4% performance boost in managing an entire bike network. Lastly, a field trip to the dispatch office of Chariot, a ride-sharing service, provided insights to overcome challenges of deploying an RL solution in the real world.
A Family of Maximum Margin Criterion for Adaptive Learning
Venue: arXiv cs.LG 2018
Year: 2018

In recent years, pattern analysis plays an important role in data mining and recognition, and many variants have been proposed to handle complicated scenarios. In the literature, it has been quite familiar with high dimensionality of data samples, but either such characteristics or large data have become usual sense in real-world applications. In this work, an improved maximum margin criterion (MMC) method is introduced firstly. With the new definition of MMC, several variants of MMC, including random MMC, layered MMC, 2D^2 MMC, are designed to make adaptive learning applicable. Particularly, the MMC network is developed to learn deep features of images in light of simple deep networks. Experimental results on a diversity of data sets demonstrate the discriminant ability of proposed MMC methods are compenent to be adopted in complicated application scenarios.
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras
Venue: arXiv cs.CV 2018
Year: 2018

Aerators are essential and crucial auxiliary devices in intensive culture, especially in industrial culture in China. The traditional methods cannot accurately detect abnormal condition of aerators in time. Surveillance cameras are widely used as visual perception modules of the Internet of Things, and then using these widely existing surveillance cameras to realize real-time anomaly detection of aerators is a cost-free and easy-to-promote method. However, it is difficult to develop such an expert system due to some technical and applied challenges, e.g., illumination, occlusion, complex background, etc. To tackle these aforementioned challenges, we propose a real-time expert system based on computer vision technology and existing surveillance cameras for anomaly detection of aerators, which consists of two modules, i.e., object region detection and working state detection. First, it is difficult to detect the working state for some small object regions in whole images, and the time complexity of global feature comparison is also high, so we present an object region detection method based on the region proposal idea. Moreover, we propose a novel algorithm called reference frame Kanade-Lucas-Tomasi (RF-KLT) algorithm for motion feature extraction in fixed regions. Then, we present a dimension reduction method of time series for establishing a feature dataset with obvious boundaries between classes. Finally, we use machine learning algorithms to build the feature classifier. The experimental results in both the actual video dataset and the augmented video dataset show that the accuracy for detecting object region and working state of aerators is 100% and 99.9% respectively, and the detection speed is 77-333 frames per second (FPS) according to the different types of surveillance cameras.
Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services
Venue: arXiv cs.NI 2018
Year: 2018

Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, Deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users' feedback for training purposes. In this paper, we propose a semi-supervised deep reinforcement learning model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. The model utilizes Variational Autoencoders (VAE) as the inference engine for generalizing optimal policies. To the best of our knowledge, the proposed model is the first investigation that extends deep reinforcement learning to the semi-supervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on BLE signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model.
Matrix-free construction of HSS representation using adaptive randomized sampling
Venue: arXiv cs.NA 2018
Year: 2018

We present new algorithms for the randomized construction of hierarchically semi-separable matrices, addressing several practical issues. The HSS construction algorithms use a partially matrix-free, adaptive randomized projection scheme to determine the maximum off-diagonal block rank. We develop both relative and absolute stopping criteria to determine the minimum dimension of the random projection matrix that is sufficient for the desired accuracy. Two strategies are discussed to adaptively enlarge the random sample matrix: repeated doubling of the number of random vectors, and iteratively incrementing the number of random vectors by a fixed number. The relative and absolute stopping criteria are based on probabilistic bounds for the Frobenius norm of the random projection of the Hankel blocks of the input matrix. We discuss parallel implementation and computation and communication cost of both variants. Parallel numerical results for a range of applications, including boundary element method matrices and quantum chemistry Toeplitz matrices, show the effectiveness, scalability and numerical robustness of the proposed algorithms.
A Brief Survey on Autonomous Vehicle Possible Attacks, Exploits and Vulnerabilities
Venue: arXiv cs.HC 2018
Year: 2018

Advanced driver assistance systems are advancing at a rapid pace and all major companies started investing in developing the autonomous vehicles. But the security and reliability is still uncertain and debatable. Imagine that a vehicle is compromised by the attackers and then what they can do. An attacker can control brake, accelerate and even steering which can lead to catastrophic consequences. This paper gives a very short and brief overview of most of the possible attacks on autonomous vehicle software and hardware and their potential implications.
Autonomous Urban Localization and Navigation with Limited Information
Venue: arXiv cs.RO 2018
Year: 2018

Urban environments offer a challenging scenario for autonomous driving. Globally localizing information, such as a GPS signal, can be unreliable due to signal shadowing and multipath errors. Detailed a priori maps of the environment with sufficient information for autonomous navigation typically require driving the area multiple times to collect large amounts of data, substantial post-processing on that data to obtain the map, and then maintaining updates on the map as the environment changes. This paper addresses the issue of autonomous driving in an urban environment by investigating algorithms and an architecture to enable fully functional autonomous driving with limited information. An algorithm to autonomously navigate urban roadways with little to no reliance on an a priori map or GPS is developed. Localization is performed with an extended Kalman filter with odometry, compass, and sparse landmark measurement updates. Navigation is accomplished by a compass-based navigation control law. Key results from Monte Carlo studies show success rates of urban navigation under different environmental conditions. Experiments validate the simulated results and demonstrate that, for given test conditions, an expected range can be found for a given success rate.
Fair Division Minimizing Inequality
Venue: arXiv cs.GT 2018
Year: 2018

Behavioural economists have shown that people are often averse to inequality and will make choices to avoid unequal outcomes. In this paper, we consider how to allocate indivisible goods fairly so as to minimize inequality. We consider how this interacts with axiomatic properties such as envy-freeness, Pareto efficiency and strategy-proofness. We also consider the computational complexity of computing allocations minimizing inequality. Unfortunately, this is computationally intractable in general so we consider several tractable greedy online mechanisms that minimize inequality. Finally, we run experiments to explore the performance of these methods.
Adding 32-bit Mode to the ACL2 Model of the x86 ISA
Venue: arXiv cs.LO 2018
Year: 2018

The ACL2 model of the x86 Instruction Set Architecture was built for the 64-bit mode of operation of the processor. This paper reports on our work to extend the model with support for 32-bit mode, recounting the salient aspects of this activity and identifying the ones that required the most work.
The Fundamental Theorem of Algebra in ACL2
Venue: arXiv cs.LO 2018
Year: 2018

We report on a verification of the Fundamental Theorem of Algebra in ACL2(r). The proof consists of four parts. First, continuity for both complex-valued and real-valued functions of complex numbers is defined, and it is shown that continuous functions from the complex to the real numbers achieve a minimum value over a closed square region. An important case of continuous real-valued, complex functions results from taking the traditional complex norm of a continuous complex function. We think of these continuous functions as having only one (complex) argument, but in ACL2(r) they appear as functions of two arguments. The extra argument is a "context", which is uninterpreted. For example, it could be other arguments that are held fixed, as in an exponential function which has a base and an exponent, either of which could be held fixed. Second, it is shown that complex polynomials are continuous, so the norm of a complex polynomial is a continuous real-valued function and it achieves its minimum over an arbitrary square region centered at the origin. This part of the proof benefits from the introduction of the "context" argument, and it illustrates an innovation that simplifies the proofs of classical properties with unbound parameters. Third, we derive lower and upper bounds on the norm of non-constant polynomials for inputs that are sufficiently far away from the origin. This means that a sufficiently large square can be found to guarantee that it contains the global minimum of the norm of the polynomial. Fourth, it is shown that if a given number is not a root of a non-constant polynomial, then it cannot be the global minimum. Finally, these results are combined to show that the global minimum must be a root of the polynomial. This result is part of a larger effort in the formalization of complex polynomials in ACL2(r).
Convex Functions in ACL2(r)
Venue: arXiv cs.LO 2018
Year: 2018

This paper builds upon our prior formalisation of R^n in ACL2(r) by presenting a set of theorems for reasoning about convex functions. This is a demonstration of the higher-dimensional analytical reasoning possible in our metric space formalisation of R^n. Among the introduced theorems is a set of equivalent conditions for convex functions with Lipschitz continuous gradients from Yurii Nesterov's classic text on convex optimisation. To the best of our knowledge a full proof of the theorem has yet to be published in a single piece of literature. We also explore "proof engineering" issues, such as how to state Nesterov's theorem in a manner that is both clear and useful.
DeFind: A Protege Plugin for Computing Concept Definitions in EL Ontologies
Venue: arXiv cs.LO 2018
Year: 2018

We introduce an extension to the Protege ontology editor, which allows for discovering concept definitions, which are not explicitly present in axioms, but are logically implied by an ontology. The plugin supports ontologies formulated in the Description Logic EL, which underpins the OWL 2 EL profile of the Web Ontology Language and despite its limited expressiveness captures most of the biomedical ontologies published on the Web. The developed tool allows to verify whether a concept can be defined using a vocabulary of interest specified by a user. In particular, it allows to decide whether some vocabulary items can be omitted in a formulation of a complex concept. The corresponding definitions are presented to the user and are provided with explanations generated by an ontology reasoner.
Prediction of the Influence of Navigation Scan-path on Perceived Quality of Free-Viewpoint Videos
Venue: arXiv cs.CV 2018
Year: 2018

Free-Viewpoint Video (FVV) systems allow the viewers to freely change the viewpoints of the scene. In such systems, view synthesis and compression are the two main sources of artifacts influencing the perceived quality. To assess this influence, quality evaluation studies are often carried out using conventional displays and generating predefined navigation trajectories mimicking the possible movement of the viewers when exploring the content. Nevertheless, as different trajectories may lead to different conclusions in terms of visual quality when benchmarking the performance of the systems, methods to identify critical trajectories are needed. This paper aims at exploring the impact of exploration trajectories (defined as Hypothetical Rendering Trajectories: HRT) on perceived quality of FVV subjectively and objectively, providing two main contributions. Firstly, a subjective assessment test including different HRTs was carried out and analyzed. The results demonstrate and quantify the influence of HRT in the perceived quality. Secondly, we propose a new objective video quality assessment measure to objectively predict the impact of HRT. This measure, based on Sketch-Token representation, models how the categories of the contours change spatially and temporally from a higher semantic level. Performance in comparison with existing quality metrics for FVV, highlight promising results for automatic detection of most critical HRTs for the benchmark of immersive systems.
Lazy-CFR: fast and near optimal regret minimization for extensive games with imperfect information
Venue: arXiv cs.LG 2018
Year: 2018

Counterfactual regret minimization (CFR) is the most popular algorithm on solving two-player zero-sum extensive games with imperfect information and achieves state-of-the-art performance in practice. However, the performance of CFR is not fully understood, since empirical results on the regret are much better than the upper bound proved in \cite{zinkevich2008regret}. Another issue is that CFR has to traverse the whole game tree in each round, which is time-consuming in large scale games. In this paper, we present a novel technique, lazy update, which can avoid traversing the whole game tree in CFR, as well as a novel analysis on the regret of CFR with lazy update. Our analysis can also be applied to the vanilla CFR, resulting in a much tighter regret bound than that in \cite{zinkevich2008regret}. Inspired by lazy update, we further present a novel CFR variant, named Lazy-CFR. Compared to traversing $O(|\mathcal{I}|)$ information sets in vanilla CFR, Lazy-CFR needs only to traverse $O(\sqrt{|\mathcal{I}|})$ information sets per round while keeping the regret bound almost the same, where $\mathcal{I}$ is the class of all information sets. As a result, Lazy-CFR shows better convergence result compared with vanilla CFR. Experimental results consistently show that Lazy-CFR outperforms the vanilla CFR significantly.
The Laplacian in RL: Learning Representations with Efficient Approximations
Venue: arXiv cs.LG 2018
Year: 2018

The smallest eigenvectors of the graph Laplacian are well-known to provide a succinct representation of the geometry of a weighted graph. In reinforcement learning (RL), where the weighted graph may be interpreted as the state transition process induced by a behavior policy acting on the environment, approximating the eigenvectors of the Laplacian provides a promising approach to state representation learning. However, existing methods for performing this approximation are ill-suited in general RL settings for two main reasons: First, they are computationally expensive, often requiring operations on large matrices. Second, these methods lack adequate justification beyond simple, tabular, finite-state settings. In this paper, we present a fully general and scalable method for approximating the eigenvectors of the Laplacian in a model-free RL context. We systematically evaluate our approach and empirically show that it generalizes beyond the tabular, finite-state setting. Even in tabular, finite-state settings, its ability to approximate the eigenvectors outperforms previous proposals. Finally, we show the potential benefits of using a Laplacian representation learned using our method in goal-achieving RL tasks, providing evidence that our technique can be used to significantly improve the performance of an RL agent.
Multimodal Speech Emotion Recognition Using Audio and Text
Venue: arXiv cs.CL 2018
Year: 2018

Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers. In this paper, we propose a novel deep dual recurrent encoder model that utilizes text data and audio signals simultaneously to obtain a better understanding of speech data. As emotional dialogue is composed of sound and spoken content, our model encodes the information from audio and text sequences using dual recurrent neural networks (RNNs) and then combines the information from these sources to predict the emotion class. This architecture analyzes speech data from the signal level to the language level, and it thus utilizes the information within the data more comprehensively than models that focus on audio features. Extensive experiments are conducted to investigate the efficacy and properties of the proposed model. Our proposed model outperforms previous state-of-the-art methods in assigning data to one of four emotion categories (i.e., angry, happy, sad and neutral) when the model is applied to the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.
The Hidden Cost of Window Management
Venue: arXiv cs.HC 2018
Year: 2018

Most window management systems support multitasking by allowing users to open, resize, position, and switch between application windows. Although multitasking has become a way of life for most knowledge workers, our current understanding of how users use window management features to switch between multiple tasks---which may comprise multiple application windows---is limited. In this paper, we present a study providing an in-depth analysis of how task switching is supported in Windows 7. As part of analysis, we developed an interface-agnostic classification of common task switching operations supported by window managers which can be used to quantify the time spent on each constituting action. Our study shows that task switching is a time intensive activity and highlights the dominant actions that contribute to task switch time. Furthermore, our classification highlights the specific operations that are optimized by more recent and experimental window managers and allows identifying opportunities for design that could further reduce the overhead of switching between tasks.