OpenRaiser/Intern-Atlas huggingface dataset:
Information, Computation, Cognition. Agency-based Hierarchies of Levels
Venue: arXiv cs.AI 2013
Year: 2013

Nature can be seen as informational structure with computational dynamics (info-computationalism), where an (info-computational) agent is needed for the potential information of the world to actualize. Starting from the definition of information as the difference in one physical system that makes a difference in another physical system, which combines Bateson and Hewitt definitions, the argument is advanced for natural computation as a computational model of the dynamics of the physical world where information processing is constantly going on, on a variety of levels of organization. This setting helps elucidating the relationships between computation, information, agency and cognition, within the common conceptual framework, which has special relevance for biology and robotics.
An Iterative Geometric Mean Decomposition Algorithm for MIMO Communications Systems
Venue: arXiv cs.IT 2013
Year: 2013

This paper presents an iterative geometric mean decomposition (IGMD) algorithm for multiple-input-multiple-output (MIMO) wireless communications. In contrast to the existing GMD algorithms, the proposed IGMD does not require the explicit computation of the geometric mean of positive singular values of the channel matrix and hence is more suitable for hardware implementation. The proposed IGMD has a regular structure and can be easily adapted to solve problems with different dimensions. We show that the proposed IGMD is guaranteed to converge to the perfect GMD under certain sufficient condition. Three different constructions of the proposed algorithm are proposed and compared through computer simulations. Numerical results show that the proposed algorithm quickly attains comparable performance to that of the true GMD within only a few iterations.
Input-Output Logic based Fault-Tolerant Design Technique for SRAM-based FPGAs
Venue: arXiv cs.AR 2013
Year: 2013

Effects of radiation on electronic circuits used in extra-terrestrial applications and radiation prone environments need to be corrected. Since FPGAs offer flexibility, the effects of radiation on them need to be studied and robust methods of fault tolerance need to be devised. In this paper a new fault-tolerant design strategy has been presented. This strategy exploits the relation between changes in inputs and the expected change in output. Essentially, it predicts whether or not a change in the output is expected and thereby calculates the error. As a result this strategy reduces hardware and time redundancy required by existing strategies like Duplication with Comparison (DWC) and Triple Modular Redundancy (TMR). The design arising from this strategy has been simulated and its robustness to fault-injection has been verified. Simulations for a 16 bit multiplier show that the new design strategy performs better than the state-of-the-art on critical factors such as hardware redundancy, time redundancy and power consumption.
A Parallel Compressive Imaging Architecture for One-Shot Acquisition
Venue: arXiv cs.CV 2013
Year: 2013

A limitation of many compressive imaging architectures lies in the sequential nature of the sensing process, which leads to long sensing times. In this paper we present a novel architecture that uses fewer detectors than the number of reconstructed pixels and is able to acquire the image in a single acquisition. This paves the way for the development of video architectures that acquire several frames per second. We specifically address the diffraction problem, showing that deconvolution normally used to recover diffraction blur can be replaced by convolution of the sensing matrix, and how measurements of a 0/1 physical sensing matrix can be converted to -1/1 compressive sensing matrix without any extra acquisitions. Simulations of our architecture show that the image quality is comparable to that of a classic Compressive Imaging camera, whereas the proposed architecture avoids long acquisition times due to sequential sensing. This one-shot procedure also allows to employ a fixed sensing matrix instead of a complex device such as a Digital Micro Mirror array or Spatial Light Modulator. It also enables imaging at bandwidths where these are not efficient.
A novel soft keyboard for touchscreen phones: QWERT
Venue: arXiv cs.HC 2013
Year: 2013

The popularity of touchscreen phones has been growing around the world since the iPhones and Android phones were released. More and more mobile phones with large touchscreen have been produced, however, the phones with small size displays are still in the majority of touch phones. The foremost interface on touch smartphones is the information input module using soft keyboards. Traditional input methods on touch phones have either too small key buttons (such as QWERTY) or too many functions (such as 3$\times$4 keyboard), which are inconvenient to use. Moreover, the conventional soft keyboards only use tapping to input texts while current touch smartphones allow various gestures on the touchscreen, such as sliding. In this paper, a novel soft keyboard called QWERT is proposed for touchscreen-based smartphones. The users can interact with phones via finger gestures of tapping or sliding when input text by using the QWERT. In doing so, the interactions between users and smartphones will be faster and easier. An experiment carried out on inexperienced human subjects shows that they can learn very fast due to their familiarities with QWERTY. A simulation experiment based on a cognitive architecture, ACT-R, was also conducted to predict the movement time (MT) of experienced human subjects. The simulation results show that the MT using QWERT outperforms other default keyboards. These outcomes imply that the novel QWERT is a viable option for touch smartphone users. Based on the novel design, an application is released on Android systems. This application is expected to give better user experience for customers who use touch smartphones.
To parallelize or not to parallelize, bugs issue
Venue: arXiv cs.PL 2013
Year: 2013

Program correctness is one of the most difficult challenges in parallel programming. Message Passing Interface MPI is widely used in writing parallel applications. Since MPI is not a compiled language, the programmer will be enfaced with several programming bugs.This paper presents the most common programming bugs arise in MPI programs to help the programmer to compromise between the advantage of parallelism and the extra effort needed to detect and fix such bugs. An algebraic specification of an MPI-like programming language, called Simple MPI (SMPI), to be used in writing MPI programs specification has also been proposed. In addition, both nondeterminacy and deadlocks arise in SMPI programs have been verified using Maud system.
To parallelize or not to parallelize, control and data flow issue
Venue: arXiv cs.PL 2013
Year: 2013

New trends towards multiple core processors imply using standard programming models to develop efficient, reliable and portable programs for distributed memory multiprocessors and workstation PC clusters. Message passing using MPI is widely used to write efficient, reliable and portable applications. Control and data flow analysis concepts, techniques and tools are needed to understand and analyze MPI programs. If our point of interest is the program control and data flow analysis, to decide to parallelize or not to parallelize our applications, there is a question to be answered, " Can the existing concepts, techniques and tools used to analyze sequential programs also be used to analyze parallel ones written in MPI?". In this paper we'll try to answer this question.
Fast Self-Stabilizing Minimum Spanning Tree Construction Using Compact Nearest Common Ancestor Labeling Scheme
Venue: arXiv cs.DC 2013
Year: 2013

We present a novel self-stabilizing algorithm for minimum spanning tree (MST) construction. The space complexity of our solution is $O(\log^2n)$ bits and it converges in $O(n^2)$ rounds. Thus, this algorithm improves the convergence time of previously known self-stabilizing asynchronous MST algorithms by a multiplicative factor $Θ(n)$, to the price of increasing the best known space complexity by a factor $O(\log n)$. The main ingredient used in our algorithm is the design, for the first time in self-stabilizing settings, of a labeling scheme for computing the nearest common ancestor with only $O(\log^2n)$ bits.
Phoneme discrimination using neurons with symmetric nonlinear response over a spectral range
Venue: arXiv cs.SD 2013
Year: 2013

We consider the ability of a very simple feed-forward neural network to discriminate phonemes based on just relative power spectrum. The network consists of two neurons with symmetric nonlinear response over a spectral range. The output of the neurons is subsequently fed to a comparator. We show that often this is enough to achieve complete separation of data. We compare the performance of found discriminants with that of more general neurons. Our conclusion is that not much is gained in passing to real-valued weights. More likely higher number of neurons and preprocessing of input will yield better discrimination results. The networks considered are directly amenable to hardware (neuromorphic) designs. Other advantages include interpretability, guarantees of performance on unseen data and low Kolmogorov complexity.
Capacity and Delay Analysis of Next-Generation Passive Optical Networks (NG-PONs) - Extended Version
Venue: arXiv cs.IT 2013
Year: 2013

Building on the Ethernet Passive Optical Network (EPON) and Gigabit PON (GPON) standards, Next-Generation (NG) PONs (i) provide increased data rates, split ratios, wavelengths counts, and fiber lengths, as well as (ii) allow for all-optical integration of access and metro networks. In this paper we provide a comprehensive probabilistic analysis of the capacity (maximum mean packet throughput) and packet delay of subnetworks that can be used to form NG-PONs. Our analysis can cover a wide range of NG-PONs through taking the minimum capacity of the subnetworks making up the NG-PON and weighing the packet delays of the subnetworks. Our numerical and simulation results indicate that our analysis quite accurately characterizes the throughput-delay performance of EPON/GPON tree networks, including networks upgraded with higher data rates and wavelength counts. Our analysis also characterizes the trade-offs and bottlenecks when integrating EPON/GPON tree networks across a metro area with a ring, a Passive Star Coupler (PSC), or an Arrayed Waveguide Grating (AWG) for uniform and non-uniform traffic. To the best of our knowledge, the presented analysis is the first to consider multiple PONs interconnected via a metro network.
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems
Venue: arXiv cs.NE 2013
Year: 2013

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.
Guessing a password over a wireless channel (on the effect of noise non-uniformity)
Venue: arXiv cs.IT 2013
Year: 2013

A string is sent over a noisy channel that erases some of its characters. Knowing the statistical properties of the string's source and which characters were erased, a listener that is equipped with an ability to test the veracity of a string, one string at a time, wishes to fill in the missing pieces. Here we characterize the influence of the stochastic properties of both the string's source and the noise on the channel on the distribution of the number of attempts required to identify the string, its guesswork. In particular, we establish that the average noise on the channel is not a determining factor for the average guesswork and illustrate simple settings where one recipient with, on average, a better channel than another recipient, has higher average guesswork. These results stand in contrast to those for the capacity of wiretap channels and suggest the use of techniques such as friendly jamming with pseudo-random sequences to exploit this guesswork behavior.
An Efficient Method for Image and Audio Steganography using Least Significant Bit (LSB) Substitution
Venue: arXiv cs.MM 2013
Year: 2013

In order to improve the data hiding in all types of multimedia data formats such as image and audio and to make hidden message imperceptible, a novel method for steganography is introduced in this paper. It is based on Least Significant Bit (LSB) manipulation and inclusion of redundant noise as secret key in the message. This method is applied to data hiding in images. For data hiding in audio, Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) both are used. All the results displayed prove to be time-efficient and effective. Also the algorithm is tested for various numbers of bits. For those values of bits, Mean Square Error (MSE) and Peak-Signal-to-Noise-Ratio (PSNR) are calculated and plotted. Experimental results show that the stego-image is visually indistinguishable from the original cover-image when n<=4, because of better PSNR which is achieved by this technique. The final results obtained after steganography process does not reveal presence of any hidden message, thus qualifying the criteria of imperceptible message.
Motion and audio analysis in mobile devices for remote monitoring of physical activities and user authentication
Venue: arXiv cs.HC 2013
Year: 2013

In this article we propose the use of accelerometer embedded by default in smartphone as a cost-effective, reliable and efficient way to provide remote physical activity monitoring for the elderly and people requiring healthcare service. Mobile phones are regularly carried by users during their day-to-day work routine, physical movement information can be captured by the mobile phone accelerometer, processed and sent to a remote server for monitoring. The acceleration pattern can deliver information related to the pattern of physical activities the user is engaged in. We further show how this technique can be extended to provide implicit real-time security by analysing unexpected movements captured by the phone accelerometer, and automatically locking the phone in such situation to prevent unauthorised access. This technique is also shown to provide implicit continuous user authentication, by capturing regular user movements such as walking, and requesting for re-authentication whenever it detects a non-regular movement.
A Big Data Approach to Computational Creativity
Venue: arXiv cs.CY 2013
Year: 2013

Computational creativity is an emerging branch of artificial intelligence that places computers in the center of the creative process. Broadly, creativity involves a generative step to produce many ideas and a selective step to determine the ones that are the best. Many previous attempts at computational creativity, however, have not been able to achieve a valid selective step. This work shows how bringing data sources from the creative domain and from hedonic psychophysics together with big data analytics techniques can overcome this shortcoming to yield a system that can produce novel and high-quality creative artifacts. Our data-driven approach is demonstrated through a computational creativity system for culinary recipes and menus we developed and deployed, which can operate either autonomously or semi-autonomously with human interaction. We also comment on the volume, velocity, variety, and veracity of data in computational creativity.
Scalar Compandor Design Based on Optimal Compressor Function Approximating by Spline Functions
Venue: arXiv cs.IT 2013
Year: 2013

In this paper the approximation of the optimal compressor function using the first-degree spline functions and quadratic spline functions is done. Coefficients on which we form approximative spline functions are determined by solving equation systems that are formed from treshold conditions. For Gaussian source at the input of the quantizer, using the obtained approximate spline functions a companding quantizer designing is done. On the basis of the comparison with the SQNR of the optimal compandor it can be noticed that the proposed companding quantizer based on approximate spline functions achieved SQNR arbitrary close to that of the optimal compandor.
Analyzing wireless communication network vulnerability with homological invariants
Venue: arXiv cs.NI 2013
Year: 2013

This article explains how sheaves and homology theory can be applied to simplicial complex models of wireless communication networks to study their vulnerability to jamming. It develops two classes of invariants (one local and one global) for studying which nodes and links present more of a liability to the network's performance when under attack.
Polsat: A Portfolio LTL Satisfiability Solver
Venue: arXiv cs.LO 2013
Year: 2013

In this paper we present a portfolio LTL-satisfiability solver, called Polsat. To achieve fast satisfiability checking for LTL formulas, the tool integrates four representative LTL solvers: pltl, TRP++, NuSMV, and Aalta. The idea of Polsat is to run the component solvers in parallel to get best overall performance; once one of the solvers terminates, it stops all other solvers. Remarkably, the Polsat solver utilizes the power of modern multi-core compute clusters. The empirical experiments show that Polsat takes advantages of it. Further, Polsat is also a testing plat- form for all LTL solvers.
Persistence, Change, and the Integration of Objects and Processes in the Framework of the General Formal Ontology
Venue: arXiv cs.AI 2013
Year: 2013

In this paper we discuss various problems, associated to temporal phenomena. These problems include persistence and change, the integration of objects and processes, and truth-makers for temporal propositions. We propose an approach which interprets persistence as a phenomenon emanating from the activity of the mind, and which, additionally, postulates that persistence, finally, rests on personal identity. The General Formal Ontology (GFO) is a top level ontology being developed at the University of Leipzig. Top level ontologies can be roughly divided into 3D-ontologies, and 4D-ontologies. GFO is the only top level ontology, used in applications, which is a 4D-ontology admitting additionally 3D objects. Objects and processes are integrated in a natural way.
GooFit: A library for massively parallelising maximum-likelihood fits
Venue: arXiv cs.DC 2013
Year: 2013

Fitting complicated models to large datasets is a bottleneck of many analyses. We present GooFit, a library and tool for constructing arbitrarily-complex probability density functions (PDFs) to be evaluated on nVidia GPUs or on multicore CPUs using OpenMP. The massive parallelisation of dividing up event calculations between hundreds of processors can achieve speedups of factors 200-300 in real-world problems.
Exploring Deep and Recurrent Architectures for Optimal Control
Venue: arXiv cs.LG 2013
Year: 2013

Sophisticated multilayer neural networks have achieved state of the art results on multiple supervised tasks. However, successful applications of such multilayer networks to control have so far been limited largely to the perception portion of the control pipeline. In this paper, we explore the application of deep and recurrent neural networks to a continuous, high-dimensional locomotion task, where the network is used to represent a control policy that maps the state of the system (represented by joint angles) directly to the torques at each joint. By using a recent reinforcement learning algorithm called guided policy search, we can successfully train neural network controllers with thousands of parameters, allowing us to compare a variety of architectures. We discuss the differences between the locomotion control task and previous supervised perception tasks, present experimental results comparing various architectures, and discuss future directions in the application of techniques from deep learning to the problem of optimal control.
Verifiable Control System Development for Gas Turbine Engines
Venue: arXiv cs.SY 2013
Year: 2013

A control software verification framework for gas turbine engines is developed. A stability proof is presented for gain scheduled closed-loop engine system based on global linearization and linear matrix inequality (LMI) techniques. Using convex optimization tools, a single quadratic Lyapunov function is computed for multiple linearizations near equilibrium points of the closed-loop system. With the computed stability matrices, ellipsoid invariant sets are constructed, which are used efficiently for DGEN turbofan engine control code stability analysis. Then a verifiable linear gain scheduled controller for DGEN engine is developed based on formal methods, and tested on the engine virtual test bench. Simulation results show that the developed verifiable gain scheduled controller is capable of regulating the engine in a stable fashion with proper tracking performance.
Proactive bottleneck performance analysis in parallel computing using openMP
Venue: arXiv cs.DC 2013
Year: 2013

The aim of parallel computing is to increase an application performance by executing the application on multiple processors. OpenMP is an API that supports multi platform shared memory programming model and shared-memory programs are typically executed by multiple threads. The use of multi threading can enhance the performance of application but its excessive use can degrade the performance. This paper describes a novel approach to avoid bottlenecks in application and provide some techniques to improve performance in OpenMP application. This paper analyzes bottleneck performance as bottleneck inhibits performance. Performance of multi threaded applications is limited by a variety of bottlenecks, e.g. critical sections, barriers and so on. This paper provides some tips how to avoid performance bottleneck problems. This paper focuses on how to reduce overheads and overall execution time to get better performance of application.
The structure and functions of an automated project management system for the centers of scientific and technical creativity of students
Venue: arXiv cs.OH 2013
Year: 2013

This article discusses the possibility of automating of the student's projecting through the use of automated project management system. There are described the purpose, structure and formalism of automated workplace of student-designer (AWSD), and shown its structural-functional diagram.