OpenRaiser/Intern-Atlas huggingface dataset:
An Integrated Development Environment for Planning Domain Modeling
Venue: arXiv cs.AI 2018
Year: 2018

In order to make the task, description of planning domains and problems, more comprehensive for non-experts in planning, the visual representation has been used in planning domain modeling in recent years. However, current knowledge engineering tools with visual modeling, like itSIMPLE (Vaquero et al. 2012) and VIZ (Vodrážka and Chrpa 2010), are less efficient than the traditional method of hand-coding by a PDDL expert using a text editor, and rarely involved in finetuning planning domains depending on the plan validation. Aim at this, we present an integrated development environment KAVI for planning domain modeling inspired by itSIMPLE and VIZ. KAVI using an abstract domain knowledge base to improve the efficiency of planning domain visual modeling. By integrating planners and a plan validator, KAVI proposes a method to fine-tune planning domains based on the plan validation.
Programming Parallel Dense Matrix Factorizations with Look-Ahead and OpenMP
Venue: arXiv cs.DC 2018
Year: 2018

We investigate a parallelization strategy for dense matrix factorization (DMF) algorithms, using OpenMP, that departs from the legacy (or conventional) solution, which simply extracts concurrency from a multithreaded version of BLAS. This approach is also different from the more sophisticated runtime-assisted implementations, which decompose the operation into tasks and identify dependencies via directives and runtime support. Instead, our strategy attains high performance by explicitly embedding a static look-ahead technique into the DMF code, in order to overcome the performance bottleneck of the panel factorization, and realizing the trailing update via a cache-aware multi-threaded implementation of the BLAS. Although the parallel algorithms are specified with a highlevel of abstraction, the actual implementation can be easily derived from them, paving the road to deriving a high performance implementation of a considerable fraction of LAPACK functionality on any multicore platform with an OpenMP-like runtime.
VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View
Venue: arXiv cs.CV 2018
Year: 2018

The automatic parking is being massively developed by car manufacturers and providers. Until now, there are two problems with the automatic parking. First, there is no openly-available segmentation labels of parking slot on panoramic surround view (PSV) dataset. Second, how to detect parking slot and road structure robustly. Therefore, in this paper, we build up a public PSV dataset. At the same time, we proposed a highly fused convolutional network (HFCN) based segmentation method for parking slot and lane markings based on the PSV dataset. A surround-view image is made of four calibrated images captured from four fisheye cameras. We collect and label more than 4,200 surround view images for this task, which contain various illuminated scenes of different types of parking slots. A VH-HFCN network is proposed, which adopts an HFCN as the base, with an extra efficient VH-stage for better segmenting various markings. The VH-stage consists of two independent linear convolution paths with vertical and horizontal convolution kernels respectively. This modification enables the network to robustly and precisely extract linear features. We evaluated our model on the PSV dataset and the results showed outstanding performance in ground markings segmentation. Based on the segmented markings, parking slots and lanes are acquired by skeletonization, hough line transform and line arrangement.
Automatic Vector-based Road Structure Mapping Using Multi-beam LiDAR
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we studied a SLAM method for vector-based road structure mapping using multi-beam LiDAR. We propose to use the polyline as the primary mapping element instead of grid cell or point cloud, because the vector-based representation is precise and lightweight, and it can directly generate vector-based High-Definition (HD) driving map as demanded by autonomous driving systems. We explored: 1) the extraction and vectorization of road structures based on local probabilistic fusion. 2) the efficient vector-based matching between frames of road structures. 3) the loop closure and optimization based on the pose-graph. In this study, we took a specific road structure, the road boundary, as an example. We applied the proposed matching method in three different scenes and achieved the average absolute matching error of 0.07. We further applied the mapping system to the urban road with the length of 860 meters and achieved an average global accuracy of 0.466 m without the help of high precision GPS.
Semantic Adversarial Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection. Of particular concern is the use of ML algorithms in cyber-physical systems (CPS), such as self-driving cars and aviation, where an adversary can cause serious consequences. However, existing approaches to generating adversarial examples and devising robust ML algorithms mostly ignore the semantics and context of the overall system containing the ML component. For example, in an autonomous vehicle using deep learning for perception, not every adversarial example for the neural network might lead to a harmful consequence. Moreover, one may want to prioritize the search for adversarial examples towards those that significantly modify the desired semantics of the overall system. Along the same lines, existing algorithms for constructing robust ML algorithms ignore the specification of the overall system. In this paper, we argue that the semantics and specification of the overall system has a crucial role to play in this line of research. We present preliminary research results that support this claim.
Attacking Convolutional Neural Network using Differential Evolution
Venue: arXiv cs.CV 2018
Year: 2018

The output of Convolutional Neural Networks (CNN) has been shown to be discontinuous which can make the CNN image classifier vulnerable to small well-tuned artificial perturbations. That is, images modified by adding such perturbations(i.e. adversarial perturbations) that make little difference to human eyes, can completely alter the CNN classification results. In this paper, we propose a practical attack using differential evolution(DE) for generating effective adversarial perturbations. We comprehensively evaluate the effectiveness of different types of DEs for conducting the attack on different network structures. The proposed method is a black-box attack which only requires the miracle feedback of the target CNN systems. The results show that under strict constraints which simultaneously control the number of pixels changed and overall perturbation strength, attacking can achieve 72.29%, 78.24% and 61.28% non-targeted attack success rates, with 88.68%, 99.85% and 73.07% confidence on average, on three common types of CNNs. The attack only requires modifying 5 pixels with 20.44, 14.76 and 22.98 pixel values distortion. Thus, the result shows that the current DNNs are also vulnerable to such simpler black-box attacks even under very limited attack conditions.
Backward Induction for Repeated Games
Venue: arXiv cs.GT 2018
Year: 2018

We present a method of backward induction for computing approximate subgame perfect Nash equilibria of infinitely repeated games with discounted payoffs. This uses the selection monad transformer, combined with the searchable set monad viewed as a notion of 'topologically compact' nondeterminism, and a simple model of computable real numbers. This is the first application of Escardó and Oliva's theory of higher-order sequential games to games of imperfect information, in which (as well as its mathematical elegance) lazy evaluation does nontrivial work for us compared with a traditional game-theoretic analysis. Since a full theoretical understanding of this method is lacking (and appears to be very hard), we consider this an 'experimental' paper heavily inspired by theoretical ideas. We use the famous Iterated Prisoner's Dilemma as a worked example.
Velocity-Porosity Supermodel: A Deep Neural Networks based concept
Venue: arXiv cs.CE 2018
Year: 2018

Rock physics models (RPMs) are used to estimate the elastic properties (e.g. velocity, moduli) from the rock properties (e.g. porosity, lithology, fluid saturation). However, the rock properties drastically vary for different geological conditions, and it is not easy to find a model that is applicable under all scenarios. There exist several empirical velocity-porosity transforms as well as first-principle-based models, however, each of these has its own limitations. It is not very straight-forward to choose the correct RPM, and templates exist, which are overlapped with the log data to decide on the correct model. In this work, we use deep machine learning and explore the concept of designing a supermodel that can be used for several different lithological conditions without any parameter tuning. In this paper, this test is restricted to only empirical velocity-porosity transforms, however, the future goal is to design a rock physics supermodel that can be used on a variety of rock properties. The goal of this paper to is to combine the advantages of several existing empirical velocity-porosity transforms under a single framework, and design a velocity-porosity supermodel (VPS) using artificial neural networks (ANN) based deep learning. Two test cases are used and based on the results presented in this paper, it is clear that deep neural networks can be a potential tool to develop a supermodel for lithological modeling and characterization.
Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification
Venue: arXiv cs.CV 2018
Year: 2018

A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. For two-class imbalanced problems, the classification success is typically measured by the geometric mean (GM) of the true positive and true negative rates. Here we prove that GM can be improved upon by instance selection, and give the theoretical conditions for such an improvement. We demonstrate that GM is non-monotonic with respect to the number of retained instances, which discourages systematic instance selection. We also show that balancing the distribution frequencies is inferior to a direct maximisation of GM. To verify our theoretical findings, we carried out an experimental study of 12 instance selection methods for imbalanced data, using 66 standard benchmark data sets. The results reveal possible room for new instance selection methods for imbalanced data.
Data-Driven Analysis of Pareto Set Topology
Venue: arXiv cs.NE 2018
Year: 2018

When and why can evolutionary multi-objective optimization (EMO) algorithms cover the entire Pareto set? That is a major concern for EMO researchers and practitioners. A recent theoretical study revealed that (roughly speaking) if the Pareto set forms a topological simplex (a curved line, a curved triangle, a curved tetrahedron, etc.), then decomposition-based EMO algorithms can cover the entire Pareto set. Usually, we cannot know the true Pareto set and have to estimate its topology by using the population of EMO algorithms during or after the runtime. This paper presents a data-driven approach to analyze the topology of the Pareto set. We give a theory of how to recognize the topology of the Pareto set from data and implement an algorithm to judge whether the true Pareto set may form a topological simplex or not. Numerical experiments show that the proposed method correctly recognizes the topology of high-dimensional Pareto sets within reasonable population size.
Joint MMSE Transceiver Design for Downlink Heterogeneous Network
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we propose a minimum mean square error (MMSE) based transceiver design scheme for a downlink multiple-input multiple-output (MIMO) two-tier heterogeneous network with general linear equality per-cell power constraints. Three practical channel models with both perfect and imperfect channel state information are used in simulations. In each channel model, we consider two system configurations, two data transmission schemes and two cellular cooperation scenarios. Our study shows that the proposed MMSE scheme is more flexible than interference alignment (IA) based scheme. For the cases where the IA-type scheme is applicable, the proposed scheme generally outperforms IA-type scheme in terms of average sum rate and bit error rate (BER), but is computationally more complex than the IA-type scheme.
Reliable IoT Storage: Minimizing Bandwidth Use in Storage Without Newcomer Nodes
Venue: arXiv cs.NI 2018
Year: 2018

This letter characterizes the optimal policies for bandwidth use and storage for the problem of distributed storage in Internet of Things (IoT) scenarios, where lost nodes cannot be replaced by new nodes as is typically assumed in Data Center and Cloud scenarios. We develop an information flow model that captures the overall process of data transmission between IoT devices, from the initial preparation stage (generating redundancy from the original data) to the different repair stages with fewer and fewer devices. Our numerical results show that in a system with 10 nodes, the proposed optimal scheme can save as much as 10.3% of bandwidth use, and as much as 44% storage use with respect to the closest suboptimal approach.
Investigating Negative Interactions in Multiplex Networks: A Mutual Information Approach
Venue: arXiv cs.SI 2018
Year: 2018

Many interesting real-world systems are represented as complex networks with multiple types of interactions and complicated dependency structures between layers. These interactions can be encoded as having a valence with positive links marking interactions such as trust and friendship and negative links denoting distrust or hostility. Extracting information from these negative interactions is challenging since standard topological metrics are often poor predictors of negative link formation, particularly across network layers. In this paper, we introduce a method based on mutual information which enables us to predict both negative and positive relationships. Our experiments show that SMLP (Signed Multiplex Link Prediction) can leverage negative relationship layers in multiplex networks to improve link prediction performance.
Randomized opinion dynamics over networks: influence estimation from partial observations
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, we propose a technique for the estimation of the influence matrix in a sparse social network, in which $n$ individual communicate in a gossip way. At each step, a random subset of the social actors is active and interacts with randomly chosen neighbors. The opinions evolve according to a Friedkin and Johnsen mechanism, in which the individuals updates their belief to a convex combination of their current belief, the belief of the agents they interact with, and their initial belief, or prejudice. Leveraging recent results of estimation of vector autoregressive processes, we reconstruct the social network topology and the strength of the interconnections starting from \textit{partial observations} of the interactions, thus removing one of the main drawbacks of finite horizon techniques. The effectiveness of the proposed method is shown on randomly generation networks.
The Algorithm of Accumulated Mutual Influence of The Vertices in Semantic Networks
Venue: arXiv cs.SI 2018
Year: 2018

In this article the algorithm for calculating a mutual influence of the vertices in cognitive maps is introduced. It has been shown, that in the proposed algorithm, there is no problem in comparing with a widely used method - the impulse method, as the proposed algorithm always gives a result regardless of whether impulse process, which corresponds to the weighted directed graph, is a stable or not. Also the result of calculation according to the proposed algorithm does not depend on the initial impulse, and vice versa the initial values of the weights of the vertices influence on the result of calculation. Unlike the impulse method, the proposed algorithm for calculating a mutual influence of the vertices does not violate the scale invariance after increasing the elements of the adjacent matrix, which corresponds to the cognitive map, in the same value. Also in this article the advantages of the proposed algorithm on numerous examples of analysis of cognitive maps are presented.
EBG: A Lazy Functional Programming Language Implemented on the Java Virtual Machine
Venue: arXiv cs.PL 2018
Year: 2018

This technical report describes the implementation of a lazy functional programming language on the Java VM.
A Time-Power Series Based Semi-Analytical Approach for Power System Simulation
Venue: arXiv cs.SY 2018
Year: 2018

Time domain simulation is the basis of dynamic security assessment for power systems. Traditionally, numerical integration methods are adopted by simulation software to solve nonlinear power system differential-algebraic equations about any given contingency under a specific operating condition. An alternative approach promising for online simulation is to offline derive a semi-analytical solution (SAS) and then online evaluate the SAS over consecutive time windows regarding the operating condition and contingency until obtaining the simulation result over a desired period. This paper proposes a general semi-analytical approach that derives and evaluates an SAS in the form of power series in time to approximate the solutions of power system differential equations. An error-rate upper bound of the SAS is also proposed to guarantee the reliable use of adaptive time windows for evaluation of the SAS. A dynamic bus method is proposed to extend the semi-analytical approach for solving general power system DAEs by efficiently linking the SASs for dynamic components through the numerical solution of the network algebraic equations. Case studies performed on the New England 39-bus system and the Polish 2383-bus system test the performance of the proposed semi-analytical approach and compare to existing methods. The results show that the SAS based approach has potentials for online simulations.
Weakly Supervised Representation Learning for Unsynchronized Audio-Visual Events
Venue: arXiv cs.CV 2018
Year: 2018

Audio-visual representation learning is an important task from the perspective of designing machines with the ability to understand complex events. To this end, we propose a novel multimodal framework that instantiates multiple instance learning. We show that the learnt representations are useful for classifying events and localizing their characteristic audio-visual elements. The system is trained using only video-level event labels without any timing information. An important feature of our method is its capacity to learn from unsynchronized audio-visual events. We achieve state-of-the-art results on a large-scale dataset of weakly-labeled audio event videos. Visualizations of localized visual regions and audio segments substantiate our system's efficacy, especially when dealing with noisy situations where modality-specific cues appear asynchronously.
Video based Contextual Question Answering
Venue: arXiv cs.CL 2018
Year: 2018

The primary aim of this project is to build a contextual Question-Answering model for videos. The current methodologies provide a robust model for image based Question-Answering, but we are aim to generalize this approach to be videos. We propose a graphical representation of video which is able to handle several types of queries across the whole video. For example, if a frame has an image of a man and a cat sitting, it should be able to handle queries like, where is the cat sitting with respect to the man? or ,what is the man holding in his hand?. It should be able to answer queries relating to temporal relationships also.
Calibration-free B0 correction of EPI data using structured low rank matrix recovery
Venue: arXiv cs.CV 2018
Year: 2018

We introduce a structured low rank algorithm for the calibration-free compensation of field inhomogeneity artifacts in Echo Planar Imaging (EPI) MRI data. We acquire the data using two EPI readouts that differ in echo-time (TE). Using time segmentation, we reformulate the field inhomogeneity compensation problem as the recovery of an image time series from highly undersampled Fourier measurements. The temporal profile at each pixel is modeled as a single exponential, which is exploited to fill in the missing entries. We show that the exponential behavior at each pixel, along with the spatial smoothness of the exponential parameters, can be exploited to derive a 3D annihilation relation in the Fourier domain. This relation translates to a low rank property on a structured multi-fold Toeplitz matrix, whose entries correspond to the measured k-space samples. We introduce a fast two-step algorithm for the completion of the Toeplitz matrix from the available samples. In the first step, we estimate the null space vectors of the Toeplitz matrix using only its fully sampled rows. The null space is then used to estimate the signal subspace, which facilitates the efficient recovery of the time series of images. We finally demonstrate the proposed approach on spherical MR phantom data and human data and show that the artifacts are significantly reduced. The proposed approach could potentially be used to compensate for time varying field map variations in dynamic applications such as functional MRI.
The Role-Relevance Model for Enhanced Semantic Targeting in Unstructured Text
Venue: arXiv cs.IR 2018
Year: 2018

Personalized search provides a potentially powerful tool, however, it is limited due to the large number of roles that a person has: parent, employee, consumer, etc. We present the role-relevance algorithm: a search technique that favors search results relevant to the user's current role. The role-relevance algorithm uses three factors to score documents: (1) the number of keywords each document contains; (2) each document's geographic relevance to the user's role (if applicable); and (3) each document's topical relevance to the user's role (if applicable). Topical relevance is assessed using a novel extension to Latent Dirichlet Allocation (LDA) that allows standard LDA to score document relevance to user-defined topics. Overall results on a pre-labeled corpus show an average improvement in search precision of approximately 20% compared to keyword search alone.
MIMO Channel Hardening: A Physical Model based Analysis
Venue: arXiv cs.NI 2018
Year: 2018

In a multiple-input-multiple-output (MIMO) communication system, the multipath fading is averaged over radio links. This well-known channel hardening phenomenon plays a central role in the design of massive MIMO systems. The aim of this paper is to study channel hardening using a physical channel model in which the influences of propagation rays and antenna array topologies are highlighted. A measure of channel hardening is derived through the coefficient of variation of the channel gain. Our analyses and closed form results based on the used physical model are consistent with those of the literature relying on more abstract Rayleigh fading models, but offer further insights on the relationship with channel characteristics.