OpenRaiser/Intern-Atlas huggingface dataset:
User Interface (UI) Design Issues for the Multilingual Users: A Case Study
Venue: arXiv cs.HC 2017
Year: 2017

A multitude of web and desktop applications are now widely available in diverse human languages. This paper explores the design issues that are specifically relevant for multilingual users. It reports on the continued studies of Information System (IS) issues and users' behaviour across cross-cultural and transnational boundaries. Taking the BBC website as a model that is internationally recognised, usability tests were conducted to compare different versions of the website. The dependant variables derived from the questionnaire were analysed (via descriptive statistics) to elucidate the multilingual UI design issues. Using Principal Component Analysis (PCA), five de-correlated variables were identified which were then used for hypotheses tests. A modified version of Herzberg's Hygiene-motivational Theory about the Workplace was applied to assess the components used in the website. Overall, it was concluded that the English versions of the website gave superior usability results and this implies the need for deeper study of the problems in usability of the translated versions.
Global spectral graph wavelet signature for surface analysis of carpal bones
Venue: arXiv cs.GR 2017
Year: 2017

In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of human wrist. We apply a metric called global spectral graph wavelet signature for representation of cortical surface of the carpal bone based on eigensystem of Laplace-Beltrami operator. Furthermore, we propose a heuristic and efficient way of aggregating local descriptors of a carpal bone surface to global descriptor. The resultant global descriptor is not only isometric invariant, but also much more efficient and requires less memory storage. We perform experiments on shape of the carpal bones of ten women and ten men from a publicly-available database. Experimental results show the excellency of the proposed GSGW compared to recent proposed GPS embedding approach for comparing shapes of the carpal bones across populations.
Learning Robotic Manipulation of Granular Media
Venue: arXiv cs.RO 2017
Year: 2017

In this paper, we examine the problem of robotic manipulation of granular media. We evaluate multiple predictive models used to infer the dynamics of scooping and dumping actions. These models are evaluated on a task that involves manipulating the media in order to deform it into a desired shape. Our best performing model is based on a highly-tailored convolutional network architecture with domain-specific optimizations, which we show accurately models the physical interaction of the robotic scoop with the underlying media. We empirically demonstrate that explicitly predicting physical mechanics results in a policy that out-performs both a hand-crafted dynamics baseline, and a "value-network", which must otherwise implicitly predict the same mechanics in order to produce accurate value estimates.
Prosocial learning agents solve generalized Stag Hunts better than selfish ones
Venue: arXiv cs.AI 2017
Year: 2017

Deep reinforcement learning has become an important paradigm for constructing agents that can enter complex multi-agent situations and improve their policies through experience. One commonly used technique is reactive training - applying standard RL methods while treating other agents as a part of the learner's environment. It is known that in general-sum games reactive training can lead groups of agents to converge to inefficient outcomes. We focus on one such class of environments: Stag Hunt games. Here agents either choose a risky cooperative policy (which leads to high payoffs if both choose it but low payoffs to an agent who attempts it alone) or a safe one (which leads to a safe payoff no matter what). We ask how we can change the learning rule of a single agent to improve its outcomes in Stag Hunts that include other reactive learners. We extend existing work on reward-shaping in multi-agent reinforcement learning and show that that making a single agent prosocial, that is, making them care about the rewards of their partners can increase the probability that groups converge to good outcomes. Thus, even if we control a single agent in a group making that agent prosocial can increase our agent's long-run payoff. We show experimentally that this result carries over to a variety of more complex environments with Stag Hunt-like dynamics including ones where agents must learn from raw input pixels.
Joint Calibration of Panoramic Camera and Lidar Based on Supervised Learning
Venue: arXiv cs.CV 2017
Year: 2017

In view of contemporary panoramic camera-laser scanner system, the traditional calibration method is not suitable for panoramic cameras whose imaging model is extremely nonlinear. The method based on statistical optimization has the disadvantage that the requirement of the number of laser scanner's channels is relatively high. Calibration equipments with extreme accuracy for panoramic camera-laser scanner system are costly. Facing all these in the calibration of panoramic camera-laser scanner system, a method based on supervised learning is proposed. Firstly, corresponding feature points of panoramic images and point clouds are gained to generate the training dataset by designing a round calibration object. Furthermore, the traditional calibration problem is transformed into a multiple nonlinear regression optimization problem by designing a supervised learning network with preprocessing of the panoramic imaging model. Back propagation algorithm is utilized to regress the rotation and translation matrix with high accuracy. Experimental results show that this method can quickly regress the calibration parameters and the accuracy is better than the traditional calibration method and the method based on statistical optimization. The calibration accuracy of this method is really high, and it is more highly-automated.
Homotheties and incidences
Venue: arXiv cs.CG 2017
Year: 2017

We consider problems involving rich homotheties in a set S of n points in the plane (that is, homotheties that map many points of S to other points of S). By reducing these problems to incidence problems involving points and lines in R^3, we are able to obtain refined and new bounds for the number of rich homotheties, and for the number of distinct equivalence classes, under homotheties, of k-element subsets of S, for any k >= 3. We also discuss the extensions of these problems to three and higher dimensions.
Hilbert's Space-filling Curve for Regions with Holes
Venue: arXiv cs.RO 2017
Year: 2017

The paper presents a systematic strategy for implementing Hilbert's space filling curve for use in online exploration tasks and addresses its application in scenarios wherein the space to be searched obstacles (or holes) whose locations are not known a priori. Using the self-similarity and locality preserving properties of Hilbert's space filling curve, a set of evasive maneuvers are prescribed and characterized for online implementation. Application of these maneuvers in the case of non-uniform coverage of spaces and for obstacles of varying sizes is also presented. The results are validated with representative simulations demonstrating the deployment of the approach.
Tabloids in the Era of Social Media? Understanding the Production and Consumption of Clickbaits in Twitter
Venue: arXiv cs.SI 2017
Year: 2017

With the growing shift towards news consumption primarily through social media sites like Twitter, most of the traditional as well as new-age media houses are promoting their news stories by tweeting about them. The competition for user attention in such mediums has led many media houses to use catchy sensational form of tweets to attract more users - a process known as clickbaiting. In this work, using an extensive dataset collected from Twitter, we analyze the social sharing patterns of clickbait and non-clickbait tweets to determine the organic reach of such tweets. We also attempt to study the sections of Twitter users who actively engage themselves in following clickbait and non-clickbait tweets. Comparing the advent of clickbaits with the rise of tabloidization of news, we bring out several important insights regarding the news consumers as well as the media organizations promoting news stories on Twitter.
Weightwise perfectly balanced functions with high weightwise nonlinearity profile
Venue: arXiv cs.CR 2017
Year: 2017

Boolean functions with good cryptographic criteria when restricted to the set of vectors with constant Hamming weight play an important role in the recent FLIP stream cipher. In this paper, we propose a large class of weightwise perfectly balanced (WPB) functions, which is not extended affinely (EA) equivalent to the known constructions. We also discuss the weightwise nonlinearity profile of these functions, and present general lower bounds on $k$-weightwise nonlinearity, where $k$ is a power of $2$. Moreover, we exhibit a subclass of the family. By a recursive lower bound, we show that these subclass of WPB functions have very high weightwise nonlinearity profile.
Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
Venue: arXiv cs.LG 2017
Year: 2017

Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging countries. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electricity providers are reluctant to move to large-scale deployments of automated systems that learn NTL profiles from data due to the latter's propensity to suggest a large number of unnecessary inspections. In this paper, we propose a novel system that combines automated statistical decision making with expert knowledge. First, we propose a machine learning framework that classifies customers into NTL or non-NTL using a variety of features derived from the customers' consumption data. The methodology used is specifically tailored to the level of noise in the data. Second, in order to allow human experts to feed their knowledge in the decision loop, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram. Our approach allows domain experts to put the classification results into the context of the data and to incorporate their knowledge for making the final decisions of which customers to inspect. This work has resulted in appreciable results on a real-world data set of 3.6M customers. Our system is being deployed in a commercial NTL detection software.
AppTechMiner: Mining Applications and Techniques from Scientific Articles
Venue: arXiv cs.CL 2017
Year: 2017

This paper presents AppTechMiner, a rule-based information extraction framework that automatically constructs a knowledge base of all application areas and problem solving techniques. Techniques include tools, methods, datasets or evaluation metrics. We also categorize individual research articles based on their application areas and the techniques proposed/improved in the article. Our system achieves high average precision (~82%) and recall (~84%) in knowledge base creation. It also performs well in application and technique assignment to an individual article (average accuracy ~66%). In the end, we further present two use cases presenting a trivial information retrieval system and an extensive temporal analysis of the usage of techniques and application areas. At present, we demonstrate the framework for the domain of computational linguistics but this can be easily generalized to any other field of research.
Design Methods for Polymorphic Combinational Logic Circuits based on the Bi_Decomposition Approach
Venue: arXiv cs.ET 2017
Year: 2017

Polymorphic circuits are a special kind of digital logic components, which possess multiple build-in functions. In different environments, a polymorphic circuit would perform different functions. Evolutionary Algorithms, Binary Decision Diagrams (BDD) and the multiplex method have been adopted to design polymorphic circuits. However, the evolutionary methods face the scalable problem. The BDD method consumes too much gate resource. The polymorphic circuit built by the multiplex method rarely contains polymorphic gates. In this paper, based on the traditional Bi_Decomposition circuit design approach, two methods, i.e. the Poly_Bi_Decomposition method and the Transformation&Bi_Decomposition method, are proposed for designing polymorphic circuits. The Poly_Bi_Decomposition method can design relatively large and gate-efficient polymorphic circuits with a higher percentage of polymorphic gates. The Transformation&Bi_Decomposition method can use the traditional circuit design approaches and tools, e.g. Bi_Decomposition, to design polymorphic circuits directly. The experimental results show the good performance of the proposed methods.
Cognitive networks: brains, internet, and civilizations
Venue: arXiv cs.AI 2017
Year: 2017

In this short essay, we discuss some basic features of cognitive activity at several different space-time scales: from neural networks in the brain to civilizations. One motivation for such comparative study is its heuristic value. Attempts to better understand the functioning of "wetware" involved in cognitive activities of central nervous system by comparing it with a computing device have a long tradition. We suggest that comparison with Internet might be more adequate. We briefly touch upon such subjects as encoding, compression, and Saussurean trichotomy langue/langage/parole in various environments.
Computational Machines in a Coexistence with Concrete Universals and Data Streams
Venue: arXiv cs.AI 2017
Year: 2017

We discuss that how the majority of traditional modeling approaches are following the idealism point of view in scientific modeling, which follow the set theoretical notions of models based on abstract universals. We show that while successful in many classical modeling domains, there are fundamental limits to the application of set theoretical models in dealing with complex systems with many potential aspects or properties depending on the perspectives. As an alternative to abstract universals, we propose a conceptual modeling framework based on concrete universals that can be interpreted as a category theoretical approach to modeling. We call this modeling framework pre-specific modeling. We further, discuss how a certain group of mathematical and computational methods, along with ever-growing data streams are able to operationalize the concept of pre-specific modeling.
R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting
Venue: arXiv cs.LG 2017
Year: 2017

Multivariate time-series modeling and forecasting is an important problem with numerous applications. Traditional approaches such as VAR (vector auto-regressive) models and more recent approaches such as RNNs (recurrent neural networks) are indispensable tools in modeling time-series data. In many multivariate time series modeling problems, there is usually a significant linear dependency component, for which VARs are suitable, and a nonlinear component, for which RNNs are suitable. Modeling such times series with only VAR or only RNNs can lead to poor predictive performance or complex models with large training times. In this work, we propose a hybrid model called R2N2 (Residual RNN), which first models the time series with a simple linear model (like VAR) and then models its residual errors using RNNs. R2N2s can be trained using existing algorithms for VARs and RNNs. Through an extensive empirical evaluation on two real world datasets (aviation and climate domains), we show that R2N2 is competitive, usually better than VAR or RNN, used alone. We also show that R2N2 is faster to train as compared to an RNN, while requiring less number of hidden units.
Vector Field Guidance for Convoy Monitoring Using Elliptical Orbits
Venue: arXiv cs.RO 2017
Year: 2017

We propose a novel vector field based guidance scheme for tracking and surveillance of a convoy, moving along a possibly nonlinear trajectory on the ground, by an aerial agent. The scheme first computes a time varying ellipse that encompasses all the targets in the convoy using a simple regression based algorithm. It then ensures convergence of the agent to a trajectory that repeatedly traverses this moving ellipse. The scheme is analyzed using perturbation theory of nonlinear differential equations and supporting simulations are provided. Some related implementation issues are discussed and advantages of the scheme are highlighted.
Evaluation of Classical Features and Classifiers in Brain-Computer Interface Tasks
Venue: arXiv cs.HC 2017
Year: 2017

Brain-Computer Interface (BCI) uses brain signals in order to provide a new method for communication between human and outside world. Feature extraction, selection and classification are among the main matters of concerns in signal processing stage of BCI. In this article, we present our findings about the most effective features and classifiers in some brain tasks. Six different groups of classical features and twelve classifiers have been examined in nine datasets of brain signal. The results indicate that energy of brain signals in α and \b{eta} frequency bands, together with some statistical parameters are more effective, comparing to the other types of extracted features. In addition, Bayesian classifier with Gaussian distribution assumption and also Support Vector Machine (SVM) show to classify different BCI datasets more accurately than the other classifiers. We believe that the results can give an insight about a strategy for blind classification of brain signals in brain-computer interface.
SO-MRS: a multi-robot system architecture based on the SOA paradigm and ontology
Venue: arXiv cs.RO 2017
Year: 2017

A generic architecture for a class of distributed robotic systems is presented. The architecture supports openness and heterogeneity, i.e. heterogeneous components may be joined and removed from the systems without affecting its basic functionality. The architecture is based on the paradigm of Service Oriented Architecture (SOA), and a generic representation (ontology) of the environment. A device (e.g. robot) is seen as a collection of its capabilities exposed as services. Generic protocols for publishing, discovering, arranging services are proposed for creating composite services that can accomplish complex tasks in an automatic way. Also generic protocols for execution of composite services are proposed along with simple protocols for monitoring the executions, and for recovery from failures. A software platform built on a multi-robot system (according to the proposed architecture) is a multi-agent system.
Automated Identification of Trampoline Skills Using Computer Vision Extracted Pose Estimation
Venue: arXiv cs.CV 2017
Year: 2017

A novel method to identify trampoline skills using a single video camera is proposed herein. Conventional computer vision techniques are used for identification, estimation, and tracking of the gymnast's body in a video recording of the routine. For each frame, an open source convolutional neural network is used to estimate the pose of the athlete's body. Body orientation and joint angle estimates are extracted from these pose estimates. The trajectories of these angle estimates over time are compared with those of labelled reference skills. A nearest neighbour classifier utilising a mean squared error distance metric is used to identify the skill performed. A dataset containing 714 skill examples with 20 distinct skills performed by adult male and female gymnasts was recorded and used for evaluation of the system. The system was found to achieve a skill identification accuracy of 80.7% for the dataset.
Modeling and identification of uncertain-input systems
Venue: arXiv cs.SY 2017
Year: 2017

In this work, we present a new class of models, called uncertain-input models, that allows us to treat system-identification problems in which a linear system is subject to a partially unknown input signal. To encode prior information about the input or the linear system, we use Gaussian-process models. We estimate the model from data using the empirical Bayes approach: the input and the impulse responses of the linear system are estimated using the posterior means of the Gaussian-process models given the data, and the hyperparameters that characterize the Gaussian-process models are estimated from the marginal likelihood of the data. We propose an iterative algorithm to find the hyperparameters that relies on the EM method and results in simple update steps. In the most general formulation, neither the marginal likelihood nor the posterior distribution of the unknowns is tractable. Therefore, we propose two approximation approaches, one based on Markov-chain Monte Carlo techniques and one based on variational Bayes approximation. We also show special model structures for which the distributions are treatable exactly. Through numerical simulations, we study the application of the uncertain-input model to the identification of Hammerstein systems and cascaded linear systems. As part of the contribution of the paper, we show that this model structure encompasses many classical problems in system identification such as classical PEM, Hammerstein models, errors-in-variables problems, blind system identification, and cascaded linear systems. This allows us to build a systematic procedure to apply the algorithms proposed in this work to a wide class of classical problems.
Constant-Weight Array Codes
Venue: arXiv cs.IT 2017
Year: 2017

Binary constant-weight codes have been extensively studied, due to both their numerous applications and to their theoretical significance. In particular, constant-weight codes have been proposed for error correction in store and forward. In this paper, we introduce constant-weight array codes (CWACs), which offer a tradeoff between the rate gain of general constant-weight codes and the low decoding complexity of liftings. CWACs can either be used in the on-shot setting introduced earlier or in a multi-shot approach, where one codeword consists of several messages. The multi-shot approach generalizes the one-shot approach and hence allows for higher rate gains. We first give a construction of CWACs based on concatenation, which generalizes the traditional erasure codes, and also provide a decoding algorithm for these codes. Since CWACs can be viewed as a generalization of both binary constant-weight codes and nonrestricted Hamming metric codes, CWACs thus provide an additional degree of freedom to both problems of determining the maximum cardinality of constant-weight codes and nonrestricted Hamming metric codes. We then investigate their theoretical significance. We first generalize many classical bounds derived for Hamming metric codes or constant-weight codes in the CWAC framework. We finally relate the maximum cardinality of a CWAC to that of a constant-weight code, of a nonrestricted Hamming metric code, and of a spherical code.