OpenRaiser/Intern-Atlas huggingface dataset:
Spectral Precoding for Out-of-band Power Reduction under Condition Number Constraint in OFDM-Based System
Venue: arXiv cs.IT 2015
Year: 2015

Due to the flexibility in spectrum shaping, orthogonal frequency division multiplexing (OFDM) is a promising technique for dynamic spectrum access. However, the out-of-band (OOB) power radiation of OFDM introduces significant interference to the adjacent users. This problem is serious in cognitive radio (CR) networks, which enables the secondary system to access the instantaneous spectrum hole. Existing methods either do not effectively reduce the OOB power leakage or introduce significant bit-error-rate (BER) performance deterioration in the receiver. In this paper, a joint spectral precoding (JSP) scheme is developed for OOB power reduction by the matrix operations of orthogonal projection and singular value decomposition (SVD). We also propose an algorithm to design the precoding matrix under receive performance constraint, which is converted to matrix condition number constraint in practice. This method well achieves the desirable spectrum envelope and receive performance by selecting zero-forcing frequencies. Simulation results show that the OOB power decreases significantly by the proposed scheme under condition number constraint.
DNA Cyclic Codes Over The Ring $ \F_2[u,v]/\langle u^2-1,v^3-v,uv-vu \rangle$
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we mainly study the some structure of cyclic DNA codes of odd length over the ring $R = \F_2[u,v]/\langle u^2-1,v^3-v,uv-vu \rangle$ which play an important role in DNA computing. We established a direct link between the element of ring $R$ and 64 codons by introducing a Gray map from $R$ to $R_1 = F_2 + uF_2, u^2 = 1$ where $R_1$ is the ring of four elements. The reverse constrain and the reverse-complement constraint codes over $R$ and $R_1$ are studied in this paper. Binary image of the cyclic codes over R also study. The paper concludes with some example on DNA codes obtained via gray map.
A visual analysis of the process of process modeling
Venue: arXiv cs.SE 2015
Year: 2015

The construction of business process models has become an important requisite in the analysis and optimization of processes. The success of the analysis and optimization efforts heavily depends on the quality of the models. Therefore, a research domain emerged that studies the process of process modeling. This paper contributes to this research by presenting a way of visualizing the different steps a modeler undertakes to construct a process model, in a so-called process of process modeling Chart. The graphical representation lowers the cognitive efforts to discover properties of the modeling process, which facilitates the research and the development of theory, training and tool support for improving model quality. The paper contains an extensive overview of applications of the tool that demonstrate its usefulness for research and practice and discusses the observations from the visualization in relation to other work. The visualization was evaluated through a qualitative study that confirmed its usefulness and added value compared to the Dotted Chart on which the visualization was inspired.
Properly Learning Poisson Binomial Distributions in Almost Polynomial Time
Venue: arXiv cs.DS 2015
Year: 2015

We give an algorithm for properly learning Poisson binomial distributions. A Poisson binomial distribution (PBD) of order $n$ is the discrete probability distribution of the sum of $n$ mutually independent Bernoulli random variables. Given $\widetilde{O}(1/ε^2)$ samples from an unknown PBD $\mathbf{p}$, our algorithm runs in time $(1/ε)^{O(\log \log (1/ε))}$, and outputs a hypothesis PBD that is $ε$-close to $\mathbf{p}$ in total variation distance. The previously best known running time for properly learning PBDs was $(1/ε)^{O(\log(1/ε))}$. As one of our main contributions, we provide a novel structural characterization of PBDs. We prove that, for all $ε>0,$ there exists an explicit collection $\cal{M}$ of $(1/ε)^{O(\log \log (1/ε))}$ vectors of multiplicities, such that for any PBD $\mathbf{p}$ there exists a PBD $\mathbf{q}$ with $O(\log(1/ε))$ distinct parameters whose multiplicities are given by some element of ${\cal M}$, such that $\mathbf{q}$ is $ε$-close to $\mathbf{p}$. Our proof combines tools from Fourier analysis and algebraic geometry. Our approach to the proper learning problem is as follows: Starting with an accurate non-proper hypothesis, we fit a PBD to this hypothesis. More specifically, we essentially start with the hypothesis computed by the computationally efficient non-proper learning algorithm in our recent work~\cite{DKS15}. Our aforementioned structural characterization allows us to reduce the corresponding fitting problem to a collection of $(1/ε)^{O(\log \log(1/ε))}$ systems of low-degree polynomial inequalities. We show that each such system can be solved in time $(1/ε)^{O(\log \log(1/ε))}$, which yields the overall running time of our algorithm.
Going Deeper in Facial Expression Recognition using Deep Neural Networks
Venue: arXiv cs.NE 2015
Year: 2015

Automated Facial Expression Recognition (FER) has remained a challenging and interesting problem. Despite efforts made in developing various methods for FER, existing approaches traditionally lack generalizability when applied to unseen images or those that are captured in wild setting. Most of the existing approaches are based on engineered features (e.g. HOG, LBPH, and Gabor) where the classifier's hyperparameters are tuned to give best recognition accuracies across a single database, or a small collection of similar databases. Nevertheless, the results are not significant when they are applied to novel data. This paper proposes a deep neural network architecture to address the FER problem across multiple well-known standard face datasets. Specifically, our network consists of two convolutional layers each followed by max pooling and then four Inception layers. The network is a single component architecture that takes registered facial images as the input and classifies them into either of the six basic or the neutral expressions. We conducted comprehensive experiments on seven publically available facial expression databases, viz. MultiPIE, MMI, CK+, DISFA, FERA, SFEW, and FER2013. The results of proposed architecture are comparable to or better than the state-of-the-art methods and better than traditional convolutional neural networks and in both accuracy and training time.
Investigating the Process of Process Modeling with Eye Movement Analysis
Venue: arXiv cs.SE 2015
Year: 2015

Research on quality issues of business process models has recently begun to explore the process of creating process models by analyzing the modeler's interactions with the modeling environment. In this paper we aim to complement previous insights on the modeler's modeling behavior with data gathered by tracking the modeler's eye movements when engaged in the act of modeling. We present preliminary results and outline directions for future research to triangulate toward a more comprehensive understanding of the process of process modeling. We believe that combining different views on the process of process modeling constitutes another building block in understanding this process that will ultimately enable us to support modelers in creating better process models.
Realisability semantics of abstract focussing, formalised
Venue: arXiv cs.LO 2015
Year: 2015

We present a sequent calculus for abstract focussing, equipped with proof-terms: in the tradition of Zeilberger's work, logical connectives and their introduction rules are left as a parameter of the system, which collapses the synchronous and asynchronous phases of focussing as macro rules. We go further by leaving as a parameter the operation that extends a context of hypotheses with new ones, which allows us to capture both classical and intuitionistic focussed sequent calculi. We then define the realisability semantics of (the proofs of) the system, on the basis of Munch-Maccagnoni's orthogonality models for the classical focussed sequent calculus, but now operating at the higher level of abstraction mentioned above. We prove, at that level, the Adequacy Lemma, namely that if a term is of type A, then in the model its denotation is in the (set-theoretic) interpretation of A. This exhibits the fact that the universal quantification involved when taking the orthogonal of a set, reflects in the semantics Zeilberger's universal quantification in the macro rule for the asynchronous phase. The system and its semantics are all formalised in Coq.
Multiplicative-Additive Focusing for Parsing as Deduction
Venue: arXiv cs.LO 2015
Year: 2015

Spurious ambiguity is the phenomenon whereby distinct derivations in grammar may assign the same structural reading, resulting in redundancy in the parse search space and inefficiency in parsing. Understanding the problem depends on identifying the essential mathematical structure of derivations. This is trivial in the case of context free grammar, where the parse structures are ordered trees; in the case of categorial grammar, the parse structures are proof nets. However, with respect to multiplicatives intrinsic proof nets have not yet been given for displacement calculus, and proof nets for additives, which have applications to polymorphism, are involved. Here we approach multiplicative-additive spurious ambiguity by means of the proof-theoretic technique of focalisation.
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Venue: arXiv cs.CV 2015
Year: 2015

Salient object detection increasingly receives attention as an important component or step in several pattern recognition and image processing tasks. Although a variety of powerful saliency models have been intensively proposed, they usually involve heavy feature (or model) engineering based on priors (or assumptions) about the properties of objects and backgrounds. Inspired by the effectiveness of recently developed feature learning, we provide a novel Deep Image Saliency Computing (DISC) framework for fine-grained image saliency computing. In particular, we model the image saliency from both the coarse- and fine-level observations, and utilize the deep convolutional neural network (CNN) to learn the saliency representation in a progressive manner. Specifically, our saliency model is built upon two stacked CNNs. The first CNN generates a coarse-level saliency map by taking the overall image as the input, roughly identifying saliency regions in the global context. Furthermore, we integrate superpixel-based local context information in the first CNN to refine the coarse-level saliency map. Guided by the coarse saliency map, the second CNN focuses on the local context to produce fine-grained and accurate saliency map while preserving object details. For a testing image, the two CNNs collaboratively conduct the saliency computing in one shot. Our DISC framework is capable of uniformly highlighting the objects-of-interest from complex background while preserving well object details. Extensive experiments on several standard benchmarks suggest that DISC outperforms other state-of-the-art methods and it also generalizes well across datasets without additional training. The executable version of DISC is available online: http://vision.sysu.edu.cn/projects/DISC.
Acyclic colourings of graphs with bounded degree
Venue: arXiv cs.DM 2015
Year: 2015

A $k$-colouring (not necessarily proper) of vertices of a graph is called {\it acyclic}, if for every pair of distinct colours $i$ and $j$ the subgraph induced by the edges whose endpoints have colours $i$ and $j$ is acyclic. In the paper we consider some generalised acyclic $k$-colourings, namely, we require that each colour class induces an acyclic or bounded degree graph. Mainly we focus on graphs with maximum degree 5. We prove that any such graph has an acyclic $5$-colouring such that each colour class induces an acyclic graph with maximum degree at most 4. We prove that the problem of deciding whether a graph $G$ has an acyclic 2-colouring in which each colour class induces a graph with maximum degree at most 3 is NP-complete, even for graphs with maximum degree 5. We also give a linear-time algorithm for an acyclic $t$-improper colouring of any graph with maximum degree $d$ assuming that the number of colors is large enough.
Procedural wood textures
Venue: arXiv cs.GR 2015
Year: 2015

Existing bidirectional reflectance distribution function (BRDF) models are capable of capturing the distinctive highlights produced by the fibrous nature of wood. However, capturing parameter textures for even a single specimen remains a laborious process requiring specialized equipment. In this paper we take a procedural approach to generating parameters for the wood BSDF. We characterize the elements of trees that are important for the appearance of wood, discuss techniques appropriate for representing those features, and present a complete procedural wood shader capable of reproducing the growth patterns responsible for the distinctive appearance of highly prized ``figured'' wood. Our procedural wood shader is random-access, 3D, modular, and is fast enough to generate a preview for design.
Volume-based Semantic Labeling with Signed Distance Functions
Venue: arXiv cs.CV 2015
Year: 2015

Research works on the two topics of Semantic Segmentation and SLAM (Simultaneous Localization and Mapping) have been following separate tracks. Here, we link them quite tightly by delineating a category label fusion technique that allows for embedding semantic information into the dense map created by a volume-based SLAM algorithm such as KinectFusion. Accordingly, our approach is the first to provide a semantically labeled dense reconstruction of the environment from a stream of RGB-D images. We validate our proposal using a publicly available semantically annotated RGB-D dataset and a) employing ground truth labels, b) corrupting such annotations with synthetic noise, c) deploying a state of the art semantic segmentation algorithm based on Convolutional Neural Networks.
Low-Cost Eye-Trackers: Useful for Information Systems Research?
Venue: arXiv cs.HC 2015
Year: 2015

Research investigating cognitive aspects of information systems is often dependent on detail-rich data. Eye-trackers promise to provide respective data, but the associated costs are often beyond the researchers' budget. Recently, eye-trackers have entered the market that promise eye-tracking support at a reasonable price. In this work, we explore whether such eye-trackers are of use for information systems research and explore the accuracy of a low-cost eye-tracker (Gazepoint GP3) in an empirical study. The results show that Gazepoint GP3 is well suited for respective research, given that experimental material acknowledges the limits of the eye-tracker. To foster replication and comparison of results, all data, experimental material as well as the source code developed for this study are made available online.
ICON Challenge on Algorithm Selection
Venue: arXiv cs.AI 2015
Year: 2015

We present the results of the ICON Challenge on Algorithm Selection.
Deep Reflectance Maps
Venue: arXiv cs.CV 2015
Year: 2015

Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constraint nature of this inverse problem. While significant progress has been made on inferring shape, materials and illumination from images only, progress in an unconstrained setting is still limited. We propose a convolutional neural architecture to estimate reflectance maps of specular materials in natural lighting conditions. We achieve this in an end-to-end learning formulation that directly predicts a reflectance map from the image itself. We show how to improve estimates by facilitating additional supervision in an indirect scheme that first predicts surface orientation and afterwards predicts the reflectance map by a learning-based sparse data interpolation. In order to analyze performance on this difficult task, we propose a new challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg) using both synthetic and real images. Furthermore, we show the application of our method to a range of image-based editing tasks on real images.
Personality Profiles of Software Engineers and Their Software Quality Preferences
Venue: arXiv cs.SE 2015
Year: 2015

Studies related to human aspects in software engineering (SE) have been performed from different perspectives. These perspectives include the study of human factors in different phases of software life cycle, effect of team performance in software development, how can a personality trait suit a particular task, and about some other miscellaneous issues. This research work aims to establish personality profiles of Pakistani software engineers using the Myers-Briggs Type Indicator (MBTI) instrument. In this survey, we have collected personality profiles of 110 software engineers. Moreover, their preferences of software quality attributes have also been collected. Analysis of the study shows that the most prominent personality type is a combination of introversion, sensing, thinking and judging. Investigative results indicate that most of the software engineers consider usability and functionality as the most important software quality attributes.
An Algorithmic Framework for Efficient Large-Scale Circuit Simulation Using Exponential Integrators
Venue: arXiv cs.CE 2015
Year: 2015

We propose an efficient algorithmic framework for time domain circuit simulation using exponential integrator. This work addresses several critical issues exposed by previous matrix exponential based circuit simulation research, and makes it capable of simulating stiff nonlinear circuit system at a large scale. In this framework, the system's nonlinearity is treated with exponential Rosenbrock-Euler formulation. The matrix exponential and vector product is computed using invert Krylov subspace method. Our proposed method has several distinguished advantages over conventional formulations (e.g., the well-known backward Euler with Newton-Raphson method). The matrix factorization is performed only for the conductance/resistance matrix G, without being performed for the combinations of the capacitance/inductance matrix C and matrix G, which are used in traditional implicit formulations. Furthermore, due to the explicit nature of our formulation, we do not need to repeat LU decompositions when adjusting the length of time steps for error controls. Our algorithm is better suited to solving tightly coupled post-layout circuits in the pursuit for full-chip simulation. Our experimental results validate the advantages of our framework.
Character-based Neural Machine Translation
Venue: arXiv cs.CL 2015
Year: 2015

We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words. Since word-level information provides a crucial source of bias, our input model composes representations of character sequences into representations of words (as determined by whitespace boundaries), and then these are translated using a joint attention/translation model. In the target language, the translation is modeled as a sequence of word vectors, but each word is generated one character at a time, conditional on the previous character generations in each word. As the representation and generation of words is performed at the character level, our model is capable of interpreting and generating unseen word forms. A secondary benefit of this approach is that it alleviates much of the challenges associated with preprocessing/tokenization of the source and target languages. We show that our model can achieve translation results that are on par with conventional word-based models.
Implementation and comparative quantitative assessment of different multispectral image pansharpening approches
Venue: arXiv cs.CV 2015
Year: 2015

In remote sensing, images acquired by various earth observation satellites tend to have either a high spatial and low spectral resolution or vice versa. Pansharpening is a technique which aims to improve spatial resolution of multispectral image. The challenges involve in the pansharpening are not only to improve the spatial resolution but also to preserve spectral quality of the multispectral image. In this paper, various pansharpening algorithms are discussed and classified based on approaches they have adopted. Using MATLAB image processing toolbox, several state-of-art pan-sharpening algorithms are implemented. Quality of pansharpened images are assessed visually and quantitatively. Correlation coefficient (CC), Root mean square error (RMSE), Relative average spectral error (RASE) and Universal quality index (Q) indices are used to easure spectral quality while to spatial-CC (SCC) quantitative parameter is used for spatial quality measurement. Finally, the paper is concluded with useful remarks.
Optimization of the Block-level Bit Allocation in Perceptual Video Coding based on MINMAX
Venue: arXiv cs.MM 2015
Year: 2015

In video coding, it is expected that the encoder could adaptively select the encoding parameters (e.g., quantization parameter) to optimize the bit allocation to different sources under the given constraint. However, in hybrid video coding, the dependency between sources brings high complexity for the bit allocation optimization, especially in the block-level, and existing optimization methods mostly focus on frame-level bit allocation. In this paper, we propose a macroblock (MB) level bit allocation method based on the minimum maximum (MINMAX) criterion, which has acceptable encoding complexity for offline applications. An iterative-based algorithm, namely maximum distortion descend (MDD), is developed to reduce quality fluctuation among MBs within a frame, where the Structure SIMilarity (SSIM) index is used to measure the perceptual distortion of MBs. Our extensive experimental results on benchmark video sequences show that the proposed method can greatly enhance the encoding performance in terms of both bits saving and perceptual quality improvement.
On the Origin of Polar Coding
Venue: arXiv cs.IT 2015
Year: 2015

Polar coding was conceived originally as a technique for boosting the cutoff rate of sequential decoding, along the lines of earlier schemes of Pinsker and Massey. The key idea in boosting the cutoff rate is to take a vector channel (either given or artificially built), split it into multiple correlated subchannels, and employ a separate sequential decoder on each subchannel. Polar coding was originally designed to be a low-complexity recursive channel combining and splitting operation of this type, to be used as the inner code in a concatenated scheme with outer convolutional coding and sequential decoding. However, the polar inner code turned out to be so effective that no outer code was actually needed to achieve the original aim of boosting the cutoff rate to channel capacity. This paper explains the cutoff rate considerations that motivated the development of polar coding.
Isogeometric Boundary Element Analysis with elasto-plastic inclusions. Part 1: Plane problems
Venue: arXiv cs.NA 2015
Year: 2015

In this work a novel approach is presented for the isogeometric Boundary Element analysis of domains that contain inclusions with different elastic properties than the ones used for computing the fundamental solutions. In addition the inclusion may exhibit inelastic material behavior. In this paper only plane stress/strain problems are considered. In our approach the geometry of the inclusion is described using NURBS basis functions. The advantage over currently used methods is that no discretization into cells is required in order to evaluate the arising volume integrals. The other difference to current approaches is that Kernels of lower singularity are used in the domain term. The implementation is verified on simple finite and infinite domain examples with various boundary conditions. Finally a practical application in geomechanics is presented.