OpenRaiser/Intern-Atlas huggingface dataset:
FluidFFT: common API (C++ and Python) for Fast Fourier Transform HPC libraries
Venue: arXiv cs.MS 2018
Year: 2018

The Python package fluidfft provides a common Python API for performing Fast Fourier Transforms (FFT) in sequential, in parallel and on GPU with different FFT libraries (FFTW, P3DFFT, PFFT, cuFFT). fluidfft is a comprehensive FFT framework which allows Python users to easily and efficiently perform FFT and the associated tasks, such as as computing linear operators and energy spectra. We describe the architecture of the package composed of C++ and Cython FFT classes, Python "operator" classes and Pythran functions. The package supplies utilities to easily test itself and benchmark the different FFT solutions for a particular case and on a particular machine. We present a performance scaling analysis on three different computing clusters and a microbenchmark showing that fluidfft is an interesting solution to write efficient Python applications using FFT.
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning
Venue: arXiv cs.LG 2018
Year: 2018

Autoencoders are popular among neural-network-based matrix completion models due to their ability to retrieve potential latent factors from the partially observed matrices. Nevertheless, when training data is scarce their performance is significantly degraded due to overfitting. In this paper, we mit- igate overfitting with a data-dependent regularization technique that relies on the principles of multi-task learning. Specifically, we propose an autoencoder-based matrix completion model that performs prediction of the unknown matrix values as a main task, and manifold learning as an auxiliary task. The latter acts as an inductive bias, leading to solutions that generalize better. The proposed model outperforms the existing autoencoder-based models designed for matrix completion, achieving high reconstruction accuracy in well-known datasets.
Discrete Sampling using Semigradient-based Product Mixtures
Venue: arXiv cs.LG 2018
Year: 2018

We consider the problem of inference in discrete probabilistic models, that is, distributions over subsets of a finite ground set. These encompass a range of well-known models in machine learning, such as determinantal point processes and Ising models. Locally-moving Markov chain Monte Carlo algorithms, such as the Gibbs sampler, are commonly used for inference in such models, but their convergence is, at times, prohibitively slow. This is often caused by state-space bottlenecks that greatly hinder the movement of such samplers. We propose a novel sampling strategy that uses a specific mixture of product distributions to propose global moves and, thus, accelerate convergence. Furthermore, we show how to construct such a mixture using semigradient information. We illustrate the effectiveness of combining our sampler with existing ones, both theoretically on an example model, as well as practically on three models learned from real-world data sets.
Pontogammarus Maeoticus Swarm Optimization: A Metaheuristic Optimization Algorithm
Venue: arXiv cs.NE 2018
Year: 2018

Nowadays, metaheuristic optimization algorithms are used to find the global optima in difficult search spaces. Pontogammarus Maeoticus Swarm Optimization (PMSO) is a metaheuristic algorithm imitating aquatic nature and foraging behavior. Pontogammarus Maeoticus, also called Gammarus in short, is a tiny creature found mostly in coast of Caspian Sea in Iran. In this algorithm, global optima is modeled as sea edge (coast) to which Gammarus creatures are willing to move in order to rest from sea waves and forage in sand. Sea waves satisfy exploration and foraging models exploitation. The strength of sea wave is determined according to distance of Gammarus from sea edge. The angles of waves applied on several particles are set randomly helping algorithm not be stuck in local bests. Meanwhile, the neighborhood of particles change adaptively resulting in more efficient progress in searching. The proposed algorithm, although is applicable on any optimization problem, is experimented for partially shaded solar PV array. Experiments on CEC05 benchmarks, as well as solar PV array, show the effectiveness of this optimization algorithm.
Abstract Representation of Binders in OCaml using the Bindlib Library
Venue: arXiv cs.PL 2018
Year: 2018

The Bindlib library for OCaml provides a set of tools for the manipulation of data structures with variable binding. It is very well suited for the representation of abstract syntax trees, and has already been used for the implementation of half a dozen languages and proof assistants (including a new version of the logical framework Dedukti). Bindlib is optimised for fast substitution, and it supports variable renaming. Since the representation of binders is based on higher-order abstract syntax, variable capture cannot arise during substitution. As a consequence, variable names are not updated at substitution time. They can however be explicitly recomputed to avoid "visual capture" (i.e., distinct variables with the same apparent name) when a data structure is displayed.
Sharing a Library between Proof Assistants: Reaching out to the HOL Family
Venue: arXiv cs.LO 2018
Year: 2018

We observe today a large diversity of proof systems. This diversity has the negative consequence that a lot of theorems are proved many times. Unlike programming languages, it is difficult for these systems to co-operate because they do not implement the same logic. Logical frameworks are a class of theorem provers that overcome this issue by their capacity of implementing various logics. In this work, we study the STTforall logic, an extension of Simple Type Theory that has been encoded in the logical framework Dedukti. We present a translation from this logic to OpenTheory, a proof system and interoperability tool between provers of the HOL family. We have used this translation to export an arithmetic library containing Fermat's little theorem to OpenTheory and to two other proof systems that are Coq and Matita.
Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds
Venue: arXiv cs.LG 2018
Year: 2018

In this paper we propose two novel bounds for the log-likelihood based on Kullback-Leibler and the Rényi divergences, which can be used for variational inference and in particular for the training of Variational AutoEncoders. Our proposal is motivated by the difficulties encountered in training VAEs on continuous datasets with high contrast images, such as those with handwritten digits and characters, where numerical issues often appear unless noise is added, either to the dataset during training or to the generative model given by the decoder. The new bounds we propose, which are obtained from the maximization of the likelihood of an interval for the observations, allow numerically stable training procedures without the necessity of adding any extra source of noise to the data.
Counting Induced Subgraphs: A Topological Approach to #W[1]-hardness
Venue: arXiv cs.CC 2018
Year: 2018

We investigate the problem $\#\mathsf{IndSub}(Φ)$ of counting all induced subgraphs of size $k$ in a graph $G$ that satisfy a given property $Φ$. This continues the work of Jerrum and Meeks who proved the problem to be $\#\mathrm{W[1]}$-hard for some families of properties which include, among others, (dis)connectedness [JCSS 15] and even- or oddness of the number of edges [Combinatorica 17]. Using the recent framework of graph motif parameters due to Curticapean, Dell and Marx [STOC 17], we discover that for monotone properties $Φ$, the problem $\#\mathsf{IndSub}(Φ)$ is hard for $\#\mathrm{W[1]}$ if the reduced Euler characteristic of the associated simplicial (graph) complex of $Φ$ is non-zero. This observation links $\#\mathsf{IndSub}(Φ)$ to Karp's famous Evasiveness Conjecture, as every graph complex with non-vanishing reduced Euler characteristic is known to be evasive. Applying tools from the "topological approach to evasiveness" which was introduced in the seminal paper of Khan, Saks and Sturtevant [FOCS 83], we prove that $\#\mathsf{IndSub}(Φ)$ is $\#\mathrm{W[1]}$-hard for every monotone property $Φ$ that does not hold on the Hamilton cycle as well as for some monotone properties that hold on the Hamilton cycle such as being triangle-free or not $k$-edge-connected for $k > 2$. Moreover, we show that for those properties $\#\mathsf{IndSub}(Φ)$ can not be solved in time $f(k)\cdot n^{o(k)}$ for any computable function $f$ unless the Exponential Time Hypothesis (ETH) fails. In the final part of the paper, we investigate non-monotone properties and prove that $\#\mathsf{IndSub}(Φ)$ is $\#\mathrm{W[1]}$-hard if $Φ$ is any non-trivial modularity constraint on the number of edges with respect to some prime $q$ or if $Φ$ enforces the presence of a fixed isolated subgraph.
Lattice based Conceptual Spaces to Explore Cognitive Functionalities for Prosthetic Arm
Venue: arXiv cs.AI 2018
Year: 2018

Upper limb Prosthetic can be viewed as an independent cognitive system in order to develop a conceptual space. In this paper, we provide a detailed analogical reasoning of prosthetic arm to build the conceptual spaces with the help of the theory called geometric framework of conceptual spaces proposed by Gardenfors. Terminologies of conceptual spaces such as concepts, similarities, properties, quality dimensions and prototype are applied for a specific prosthetic system and conceptual space is built for prosthetic arm. Concept lattice traversals are used on the lattice represented conceptual spaces. Cognitive functionalities such as generalization (Similarities) and specialization (Differences) are achieved in the lattice represented conceptual space. This might well prove to design intelligent prosthetics to assist challenged humans. Geometric framework of conceptual spaces holds similar concepts closer in geometric structures in a way similar to concept lattices. Hence, we also propose to use concept lattice to represent concepts of geometric framework of conceptual spaces. Also, we extend our discussion with our insights on conceptual spaces of bidirectional hand prosthetics.
Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks
Venue: arXiv cs.LG 2018
Year: 2018

Recent developments in deep reinforcement learning have enabled the creation of agents for solving a large variety of games given a visual input. These methods have been proven successful for 2D games, like the Atari games, or for simple tasks, like navigating in mazes. It is still an open question, how to address more complex environments, in which the reward is sparse and the state space is huge. In this paper we propose a divide and conquer deep reinforcement learning solution and we test our agent in the first person shooter (FPS) game of Doom. Our work is based on previous works in deep reinforcement learning and in Doom agents. We also present how our agent is able to perform better in unknown environments compared to a state of the art reinforcement learning algorithm.
Open Logo Detection Challenge
Venue: arXiv cs.CV 2018
Year: 2018

Existing logo detection benchmarks consider artificial deployment scenarios by assuming that large training data with fine-grained bounding box annotations for each class are available for model training. Such assumptions are often invalid in realistic logo detection scenarios where new logo classes come progressively and require to be detected with little or none budget for exhaustively labelling fine-grained training data for every new class. Existing benchmarks are thus unable to evaluate the true performance of a logo detection method in realistic and open deployments. In this work, we introduce a more realistic and challenging logo detection setting, called Open Logo Detection. Specifically, this new setting assumes fine-grained labelling only on a small proportion of logo classes whilst the remaining classes have no labelled training data to simulate the open deployment. We further create an open logo detection benchmark, called OpenLogo,to promote the investigation of this new challenge. OpenLogo contains 27,083 images from 352 logo classes, built by aggregating/refining 7 existing datasets and establishing an open logo detection evaluation protocol. To address this challenge, we propose a Context Adversarial Learning (CAL) approach to synthesising training data with coherent logo instance appearance against diverse background context for enabling more effective optimisation of contemporary deep learning detection models. Experiments show the performance advantage of CAL over existing state-of-the-art alternative methods on the more realistic and challenging OpenLogo benchmark.
A Formal Ontology-Based Classification of Lexemes and its Applications
Venue: arXiv cs.CL 2018
Year: 2018

The paper describes the enrichment of OntoSenseNet - a verb-centric lexical resource for Indian Languages. A major contribution of this work is preservation of an authentic Telugu dictionary by developing a computational version of the same. It is important because native speakers can better annotate the sense-types when both the word and its meaning are in Telugu. Hence efforts are made to develop the aforementioned Telugu dictionary and annotations are done manually. The manually annotated gold standard corpus consists 8483 verbs, 253 adverbs and 1673 adjectives. Annotations are done by native speakers according to defined annotation guidelines. In this paper, we provide an overview of the annotation procedure and present the validation of the developed resource through inter-annotator agreement. Additional words from Telugu WordNet are added to our resource and are crowd-sourced for annotation. The statistics are compared with the sense-annotated lexicon, our resource for more insights.
Expressivity in Natural and Artificial Systems
Venue: arXiv cs.RO 2018
Year: 2018

Roboticists are trying to replicate animal behavior in artificial systems. Yet, quantitative bounds on capacity of a moving platform (natural or artificial) to express information in the environment are not known. This paper presents a measure for the capacity of motion complexity -- the expressivity -- of articulated platforms (both natural and artificial) and shows that this measure is stagnant and unexpectedly limited in extant robotic systems. This analysis indicates trends in increasing capacity in both internal and external complexity for natural systems while artificial, robotic systems have increased significantly in the capacity of computational (internal) states but remained more or less constant in mechanical (external) state capacity. This work presents a way to analyze trends in animal behavior and shows that robots are not capable of the same multi-faceted behavior in rich, dynamic environments as natural systems.
Reflection Analysis for Face Morphing Attack Detection
Venue: arXiv cs.CV 2018
Year: 2018

A facial morph is a synthetically created image of a face that looks similar to two different individuals and can even trick biometric facial recognition systems into recognizing both individuals. This attack is known as face morphing attack. The process of creating such a facial morph is well documented and a lot of tutorials and software to create them are freely available. Therefore, it is mandatory to be able to detect this kind of fraud to ensure the integrity of the face as reliable biometric feature. In this work, we study the effects of face morphing on the physically correctness of the illumination. We estimate the direction to the light sources based on specular highlights in the eyes and use them to generate a synthetic map for highlights on the skin. This map is compared with the highlights in the image that is suspected to be a fraud. Morphing faces with different geometries, a bad alignment of the source images or using images with different illuminations, can lead to inconsistencies in reflections that indicate the existence of a morphing attack.
Real-Time Subpixel Fast Bilateral Stereo
Venue: arXiv cs.CV 2018
Year: 2018

Stereo vision technique has been widely used in robotic systems to acquire 3-D information. In recent years, many researchers have applied bilateral filtering in stereo vision to adaptively aggregate the matching costs. This has greatly improved the accuracy of the estimated disparity maps. However, the process of filtering the whole cost volume is very time consuming and therefore the researchers have to resort to some powerful hardware for the real-time purpose. This paper presents the implementation of fast bilateral stereo on a state-of-the-art GPU. By highly exploiting the parallel computing architecture of the GPU, the fast bilateral stereo performs in real time when processing the Middlebury stereo datasets.
Representing scenarios for process evolution management
Venue: arXiv cs.AI 2018
Year: 2018

In the following writing we discuss a conceptual framework for representing events and scenarios from the perspective of a novel form of causal analysis. This causal analysis is applied to the events and scenarios so as to determine measures that could be used to manage the development of the processes that they are a part of in real time. An overall terminological framework and entity-relationship model are suggested along with a specification of the functional sets involved in both reasoning and analytics. The model is considered to be a specific case of the generic problem of finding sequential series in disparate data. The specific inference and reasoning processes are identified for future implementation.
Face Recognition Using Map Discriminant on YCbCr Color Space
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents face recognition using maximum a posteriori (MAP) discriminant on YCbCr color space. The YCbCr color space is considered in order to cover the skin information of face image on the recognition process. The proposed method is employed to improve the recognition rate and equal error rate (EER) of the gray scale based face recognition. In this case, the face features vector consisting of small part of dominant frequency elements which is extracted by non-blocking DCT is implemented as dimensional reduction of the raw face images. The matching process between the query face features and the trained face features is performed using maximum a posteriori (MAP) discriminant. From the experimental results on data from four face databases containing 2268 images with 196 classes show that the face recognition YCbCr color space provide better recognition rate and lesser EER than those of gray scale based face recognition which improve the first rank of grayscale based method result by about 4%. However, it requires three times more computation time than that of grayscale based method.
Digital Geometry, a Survey
Venue: arXiv cs.DM 2018
Year: 2018

This paper provides an overview of modern digital geometry and topology through mathematical principles, algorithms, and measurements. It also covers recent developments in the applications of digital geometry and topology including image processing, computer vision, and data science. Recent research strongly showed that digital geometry has made considerable contributions to modelings and algorithms in image segmentation, algorithmic analysis, and BigData analytics.
Recommending Insightful Comments for Source Code using Crowdsourced Knowledge
Venue: arXiv cs.SE 2018
Year: 2018

Recently, automatic code comment generation is proposed to facilitate program comprehension. Existing code comment generation techniques focus on describing the functionality of the source code. However, there are other aspects such as insights about quality or issues of the code, which are overlooked by earlier approaches. In this paper, we describe a mining approach that recommends insightful comments about the quality, deficiencies or scopes for further improvement of the source code. First, we conduct an exploratory study that motivates crowdsourced knowledge from Stack Overflow discussions as a potential resource for source code comment recommendation. Second, based on the findings from the exploratory study, we propose a heuristic-based technique for mining insightful comments from Stack Overflow Q & A site for source code comment recommendation. Experiments with 292 Stack Overflow code segments and 5,039 discussion comments show that our approach has a promising recall of 85.42%. We also conducted a complementary user study which confirms the accuracy and usefulness of the recommended comments.
Continuous-Scale Kinetic Fluid Simulation
Venue: arXiv cs.GR 2018
Year: 2018

Kinetic approaches, i.e., methods based on the lattice Boltzmann equations, have long been recognized as an appealing alternative for solving incompressible Navier-Stokes equations in computational fluid dynamics. However, such approaches have not been widely adopted in graphics mainly due to the underlying inaccuracy, instability and inflexibility. In this paper, we try to tackle these problems in order to make kinetic approaches practical for graphical applications. To achieve more accurate and stable simulations, we propose to employ the non-orthogonal central-moment-relaxation model, where we develop a novel adaptive relaxation method to retain both stability and accuracy in turbulent flows. To achieve flexibility, we propose a novel continuous-scale formulation that enables samples at arbitrary resolutions to easily communicate with each other in a more continuous sense and with loose geometrical constraints, which allows efficient and adaptive sample construction to better match the physical scale. Such a capability directly leads to an automatic sample construction which generates static and dynamic scales at initialization and during simulation, respectively. This effectively makes our method suitable for simulating turbulent flows with arbitrary geometrical boundaries. Our simulation results with applications to smoke animations show the benefits of our method, with comparisons for justification and verification.
Sum-Product Networks for Sequence Labeling
Venue: arXiv cs.LG 2018
Year: 2018

We consider higher-order linear-chain conditional random fields (HO-LC-CRFs) for sequence modelling, and use sum-product networks (SPNs) for representing higher-order input- and output-dependent factors. SPNs are a recently introduced class of deep models for which exact and efficient inference can be performed. By combining HO-LC-CRFs with SPNs, expressive models over both the output labels and the hidden variables are instantiated while still enabling efficient exact inference. Furthermore, the use of higher-order factors allows us to capture relations of multiple input segments and multiple output labels as often present in real-world data. These relations can not be modelled by the commonly used first-order models and higher-order models with local factors including only a single output label. We demonstrate the effectiveness of our proposed models for sequence labeling. In extensive experiments, we outperform other state-of-the-art methods in optical character recognition and achieve competitive results in phone classification.
The Influence of Differential Privacy on Short Term Electric Load Forecasting
Venue: arXiv cs.CR 2018
Year: 2018

There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to billing at the energy provider. However, exploitation is mostly limited to application of cryptographic security means between smart meters and energy providers. We illustrate along the use case of privacy preserving load forecasting that Differential Privacy is indeed a valuable addition that unlocks novel information flows for optimization. We show that (i) there are large differences in utility along three selected forecasting methods, (ii) energy providers can enjoy good utility especially under the linear regression benchmark model, and (iii) households can participate in privacy preserving load forecasting with an individual re-identification risk < 60%, only 10% over random guessing.