OpenRaiser/Intern-Atlas huggingface dataset:
A Dataset of Naturally Occurring, Whole-Body Background Activity to Reduce Gesture Conflicts
Venue: arXiv cs.HC 2015
Year: 2015

In real settings, natural body movements can be erroneously recognized by whole-body input systems as explicit input actions. We call body activity not intended as input actions "background activity." We argue that understanding background activity is crucial to the success of always-available whole-body input in the real world. To operationalize this argument, we contribute a reusable study methodology and software tools to generate standardized background activity datasets composed of data from multiple Kinect cameras, a Vicon tracker, and two high-definition video cameras. Using our methodology, we create an example background activity dataset for a television-oriented living room setting. We use this dataset to demonstrate how it can be used to redesign a gestural interaction vocabulary to minimize conflicts with the real world. The software tools and initial living room dataset are publicly available (http://www.dgp.toronto.edu/~dustin/backgroundactivity/).
More Precise Methods for National Research Citation Impact Comparisons
Venue: arXiv cs.DL 2015
Year: 2015

Governments sometimes need to analyse sets of research papers within a field in order to monitor progress, assess the effect of recent policy changes, or identify areas of excellence. They may compare the average citation impacts of the papers by dividing them by the world average for the field and year. Since citation data is highly skewed, however, simple averages may be too imprecise to robustly identify differences within, rather than across, fields. In response, this article introduces two new methods to identify national differences in average citation impact, one based on linear modelling for normalised data and the other using the geometric mean. Results from a sample of 26 Scopus fields between 2009-2015 show that geometric means are the most precise and so are recommended for smaller sample sizes, such as for individual fields. The regression method has the advantage of distinguishing between national contributions to internationally collaborative articles, but has substantially wider confidence intervals than the geometric mean, undermining its value for any except the largest sample sizes.
Convergence of the Generalized Alternating Projection Algorithm for Compressive Sensing
Venue: arXiv cs.IT 2015
Year: 2015

The convergence of the generalized alternating projection (GAP) algorithm is studied in this paper to solve the compressive sensing problem $\yv = \Amat \xv + \epsilonv$. By assuming that $\Amat\Amat\ts$ is invertible, we prove that GAP converges linearly within a certain range of step-size when the sensing matrix $\Amat$ satisfies restricted isometry property (RIP) condition of $δ_{2K}$, where $K$ is the sparsity of $\xv$. The theoretical analysis is extended to the adaptively iterative thresholding (AIT) algorithms, for which the convergence rate is also derived based on $δ_{2K}$ of the sensing matrix. We further prove that, under the same conditions, the convergence rate of GAP is faster than that of AIT. Extensive simulation results confirm the theoretical assertions.
Exchange-Repairs: Managing Inconsistency in Data Exchange
Venue: arXiv cs.DB 2015
Year: 2015

In a data exchange setting with target constraints, it is often the case that a given source instance has no solutions. In such cases, the semantics of target queries trivialize. The aim of this paper is to introduce and explore a new framework that gives meaningful semantics in such cases by using the notion of exchange-repairs. Informally, an exchange-repair of a source instance is another source instance that differs minimally from the first, but has a solution. Exchange-repairs give rise to a natural notion of exchange-repair certain answers (XR-certain answers) for target queries. We show that for schema mappings specified by source-to-target GAV dependencies and target equality-generating dependencies (egds), the XR-certain answers of a target conjunctive query can be rewritten as the consistent answers (in the sense of standard database repairs) of a union of conjunctive queries over the source schema with respect to a set of egds over the source schema, making it possible to use a consistent query-answering system to compute XR-certain answers in data exchange. We then examine the general case of schema mappings specified by source-to-target GLAV constraints, a weakly acyclic set of target tgds and a set of target egds. The main result asserts that, for such settings, the XR-certain answers of conjunctive queries can be rewritten as the certain answers of a union of conjunctive queries with respect to the stable models of a disjunctive logic program over a suitable expansion of the source schema.
Dynamic graph connectivity with improved worst case update time and sublinear space
Venue: arXiv cs.DS 2015
Year: 2015

This paper considers fully dynamic graph algorithms with both faster worst case update time and sublinear space. The fully dynamic graph connectivity problem is the following: given a graph on a fixed set of n nodes, process an online sequence of edge insertions, edge deletions, and queries of the form "Is there a path between nodes a and b?" In 2013, the first data structure was presented with worst case time per operation which was polylogarithmic in n. In this paper, we shave off a factor of log n from that time, to O(log^4 n) per update. For sequences which are polynomial in length, our algorithm answers queries in O(log n/\log\log n) time correctly with high probability and using O(n \log^2 n) words (of size log n). This matches the amount of space used by the most space-efficient graph connectivity streaming algorithm. We also show that 2-edge connectivity can be maintained using O(n log^2 n) words with an amortized update time of O(log^6 n).
Detectability of the spectral method for sparse graph partitioning
Venue: arXiv cs.SI 2015
Year: 2015

We show that modularity maximization with the resolution parameter offers a unifying framework of graph partitioning. In this framework, we demonstrate that the spectral method exhibits universal detectability, irrespective of the value of the resolution parameter, as long as the graph is partitioned. Furthermore, we show that when the resolution parameter is sufficiently small, a first-order phase transition occurs, resulting in the graph being unpartitioned.
On the Complexity of Flanked Finite State Automata
Venue: arXiv cs.FL 2015
Year: 2015

We define a new subclass of nondeterministic finite automata for prefix-closed languages called Flanked Finite Automata (FFA). We show that this class enjoys good complexity properties while preserving the succinctness of nondeterministic automata. In particular, we show that the universality problem for FFA is in linear time and that language inclusion can be checked in polynomial time. A useful application of FFA is to provide an efficient way to compute the quotient and inclusion of regular languages without the need to use the powerset construction. These operations are the building blocks of several verification algorithms.
Automating the Verification of Realtime Observers using Probes and the Modal mu-calculus
Venue: arXiv cs.LO 2015
Year: 2015

A classical method for model-checking timed properties-such as those expressed using timed extensions of temporal logic-is to rely on the use of observers. In this context, a major problem is to prove the correctness of observers. Essentially, this boils down to proving that: (1) every trace that contradicts a property can be detected by the observer; but also that (2) the observer is innocuous, meaning that it cannot interfere with the system under observation. In this paper, we describe a method for automatically testing the correctness of realtime observers. This method is obtained by automating an approach often referred to as visual verification, in which the correctness of a system is performed by inspecting a graphical representation of its state space. Our approach has been implemented on the tool Tina, a model-checking toolbox for Time Petri Net.
Factors influencing risk acceptance of Cloud Computing services in the UK Government
Venue: arXiv cs.CY 2015
Year: 2015

Cloud Computing services are increasingly being made available by the UK Government through the Government digital marketplace to reduce costs and improve IT efficiency; however, little is known about factors influencing the decision-making process to adopt cloud services within the UK Government. This research aims to develop a theoretical framework to understand risk perception and risk acceptance of cloud computing services. Study subjects (N=24) were recruited from three UK Government organizations to attend a semi- structured interview. Transcribed texts were analyzed using the approach termed interpretive phenomenological analysis. Results showed that the most important factors influencing risk acceptance of cloud services are: perceived benefits and opportunities, organization risk culture and perceived risks. We focused on perceived risks and perceived security concerns. Based on these results, we suggest a number of implications for risk managers, policy makers and cloud service providers.
Magnetic Beamforming for Wireless Power Transfer
Venue: arXiv cs.IT 2015
Year: 2015

Magnetic resonant coupling (MRC) is an efficient method for realizing the near-field wireless power transfer (WPT). The use of multiple transmitters (TXs) each with one coil can be applied to enhance the WPT performance by focusing the magnetic fields from all TX coils in a beam toward the receiver (RX) coil, termed as "magnetic beamforming". In this paper, we study the optimal magnetic beamforming for an MRC-WPT system with multiple TXs and a single RX. We formulate an optimization problem to jointly design the currents flowing through different TXs so as to minimize the total power drawn from their voltage sources, subject to the minimum power required by the RX load as well as the TXs' constraints on the peak voltage and current. For the special case of identical TX resistances and neglecting all TXs' constraints on the peak voltage and current, we show that the optimal current magnitude of each TX is proportional to the mutual inductance between its TX coil and the RX coil. In general, the problem is a non-convex quadratically constrained quadratic programming (QCQP) problem, which is reformulated as a semidefinite programming (SDP) problem. We show that its semidefinite relaxation (SDR) is tight. Numerical results show that magnetic beamforming significantly enhances the deliverable power as well as the WPT efficiency over the uncoordinated benchmark scheme of equal current allocation.
Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games
Venue: arXiv cs.AI 2015
Year: 2015

Poker is a family of card games that includes many variations. We hypothesize that most poker games can be solved as a pattern matching problem, and propose creating a strong poker playing system based on a unified poker representation. Our poker player learns through iterative self-play, and improves its understanding of the game by training on the results of its previous actions without sophisticated domain knowledge. We evaluate our system on three poker games: single player video poker, two-player Limit Texas Hold'em, and finally two-player 2-7 triple draw poker. We show that our model can quickly learn patterns in these very different poker games while it improves from zero knowledge to a competitive player against human experts. The contributions of this paper include: (1) a novel representation for poker games, extendable to different poker variations, (2) a CNN based learning model that can effectively learn the patterns in three different games, and (3) a self-trained system that significantly beats the heuristic-based program on which it is trained, and our system is competitive against human expert players.
New Fuzzy LBP Features for Face Recognition
Venue: arXiv cs.CV 2015
Year: 2015

There are many Local texture features each very in way they implement and each of the Algorithm trying improve the performance. An attempt is made in this paper to represent a theoretically very simple and computationally effective approach for face recognition. In our implementation the face image is divided into 3x3 sub-regions from which the features are extracted using the Local Binary Pattern (LBP) over a window, fuzzy membership function and at the central pixel. The LBP features possess the texture discriminative property and their computational cost is very low. By utilising the information from LBP, membership function, and central pixel, the limitations of traditional LBP is eliminated. The bench mark database like ORL and Sheffield Databases are used for the evaluation of proposed features with SVM classifier. For the proposed approach K-fold and ROC curves are obtained and results are compared.
Unsupervised Online Bayesian Autonomic Happy Internet-of-Things Management
Venue: arXiv cs.NI 2015
Year: 2015

In Happy IoT, the revenue of service providers synchronizes to the unobservable and dynamic usage-contexts (e.g. emotion, environmental information, etc.) of Smart-device users. Hence, the usage-context-estimation from the unreliable Smart-device sensed data is justified as an unsupervised and non-linear optimization problem. Accordingly, Autonomic Happy IoT Management is aimed at attracting initial user-groups based on the common interests (i.e. recruitment ), then uncovering their latent usage-contexts from unreliable sensed data (i.e. revenue-renewal ) and synchronizing to usage-context dynamics (i.e. stochastic monetization). In this context, we have proposed an unsupervised online Bayesian mechanism, namely Whiz (Greek word, meaning Smart), in which, (a) once latent user-groups are initialized (i.e measurement model ), (b) usage-context is iteratively estimated from the unreliable sensed data (i.e. learning model ), (c) followed by online filtering of Bayesian knowledge about usage-context (i.e. filtering model ). Finally, we have proposed an Expectation Maximization (EM)-based iterative algorithm Whiz, which facilitates Happy IoT by solving (a) recruitment, (b) revenue-renewal and (c) stochastic- monetization problems with (a) measurement, (b) learning, and (c) filtering models, respectively.
Efficient Scheduling and Power Allocation for D2D-assisted Wireless Caching Networks
Venue: arXiv cs.IT 2015
Year: 2015

We study an one-hop device-to-device (D2D) assisted wireless caching network, where popular files are randomly and independently cached in the memory of end-users. Each user may obtain the requested files from its own memory without any transmission, or from a helper through an one-hop D2D transmission, or from the base station (BS). We formulate a joint D2D link scheduling and power allocation problem to maximize the system throughput. However, the problem is non-convex and obtaining an optimal solution is computationally hard. Alternatively, we decompose the problem into a D2D link scheduling problem and an optimal power allocation problem. To solve the two subproblems, we first develop a D2D link scheduling algorithm to select the largest number of D2D links satisfying both the signal to interference plus noise ratio (SINR) and the transmit power constraints. Then, we develop an optimal power allocation algorithm to maximize the minimum transmission rate of the scheduled D2D links. Numerical results indicate that both the number of the scheduled D2D links and the system throughput can be improved simultaneously with the Zipf-distribution caching scheme, the proposed D2D link scheduling algorithm, and the proposed optimal power allocation algorithm compared with the state of arts.
Fully automatic multi-language translation with a catalogue of phrases - successful employment for the Swiss avalanche bulletin
Venue: arXiv cs.CL 2015
Year: 2015

The Swiss avalanche bulletin is produced twice a day in four languages. Due to the lack of time available for manual translation, a fully automated translation system is employed, based on a catalogue of predefined phrases and predetermined rules of how these phrases can be combined to produce sentences. Because this catalogue of phrases is limited to a small sublanguage, the system is able to automatically translate such sentences from German into the target languages French, Italian and English without subsequent proofreading or correction. Having been operational for two winter seasons, we assess here the quality of the produced texts based on two different surveys where participants rated texts from real avalanche bulletins from both origins, the catalogue of phrases versus manually written and translated texts. With a mean recognition rate of 55%, users can hardly distinguish between thetwo types of texts, and give very similar ratings with respect to their language quality. Overall, the output from the catalogue system can be considered virtually equivalent to a text written by avalanche forecasters and then manually translated by professional translators. Furthermore, forecasters declared that all relevant situations were captured by the system with sufficient accuracy. Forecaster's working load did not change with the introduction of the catalogue: the extra time to find matching sentences is compensated by the fact that they no longer need to double-check manually translated texts. The reduction of daily translation costs is expected to offset the initial development costs within a few years.
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives
Venue: arXiv cs.RO 2015
Year: 2015

The importance of depth perception in the interactions that humans have within their nearby space is a well established fact. Consequently, it is also well known that the possibility of exploiting good stereo information would ease and, in many cases, enable, a large variety of attentional and interactive behaviors on humanoid robotic platforms. However, the difficulty of computing real-time and robust binocular disparity maps from moving stereo cameras often prevents from relying on this kind of cue to visually guide robots' attention and actions in real-world scenarios. The contribution of this paper is two-fold: first, we show that the Efficient Large-scale Stereo Matching algorithm (ELAS) by A. Geiger et al. 2010 for computation of the disparity map is well suited to be used on a humanoid robotic platform as the iCub robot; second, we show how, provided with a fast and reliable stereo system, implementing relatively challenging visual behaviors in natural settings can require much less effort. As a case of study we consider the common situation where the robot is asked to focus the attention on one object close in the scene, showing how a simple but effective disparity-based segmentation solves the problem in this case. Indeed this example paves the way to a variety of other similar applications.
Towards a Direct, By-Need Evaluator for Dependently Typed Languages
Venue: arXiv cs.PL 2015
Year: 2015

We present a C-language implementation of the lambda-pi calculus by extending the (call-by-need) stack machine of Ariola, Chang and Felleisen to hold types, using a typeless- tagless- final interpreter strategy. It has the advantage of expressing all operations as folds over terms, including by-need evaluation, recovery of the initial syntax-tree encoding for any term, and eliminating most garbage-collection tasks. These are made possible by a disciplined approach to handling the spine of each term, along with a robust stack-based API. Type inference is not covered in this work, but also derives several advantages from the present stack transformation. Timing and maximum stack space usage results for executing benchmark problems are presented. We discuss how the design choices for this interpreter allow the language to be used as a high-level scripting language for automatic distributed parallel execution of common scientific computing workflows.
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
Venue: arXiv cs.CE 2015
Year: 2015

In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. The documented results in this study demonstrate acceptable resemblance between target and predicted variables, and hence, encourage the application of integrated machine learning approaches such as Neuro-Fuzzy in reservoir characterization domain. Furthermore, visualization of the variation of sand probability in the study area would assist in identifying placement of potential wells for future drilling operations.
Analysis of the impact of studies published by Internext - Revista Eletrônica de Negócios Internacionais
Venue: arXiv cs.DL 2015
Year: 2015

This paper presents a citation analysis of Internext-Review of International Business to detect the impact caused by papers published for the period 2006-2013. The Publish or Perish (PoP) software is used, which retrieves articles and citations from Google Scholar database. As part of the applied indicators are: the distribution of authors by articles, citations per year, citation vs. self-citation, journal's citable vs. non citable documents, journal's cited vs. uncited documents, co-word analysis, and H Index. A total of 131 articles were obtained for 153 citations made until June, 2014. Most articles present multiple authorship. It is also detected an ascending line in the citation. The Journal has very low levels of self-citation, showing that most citing sources are Brazilian journals. The most cited articles have been published in the early years (2006-2008); whose main topics are related with the internationalization theory and strategy, the transaction analysis, and the corporate governance. The Internext' H Index is 6 and the G Index is 9.