OpenRaiser/Intern-Atlas huggingface dataset:
Loo.py: From Fortran to performance via transformation and substitution rules
Venue: arXiv cs.PL 2015
Year: 2015

A large amount of numerically-oriented code is written and is being written in legacy languages. Much of this code could, in principle, make good use of data-parallel throughput-oriented computer architectures. Loo.py, a transformation-based programming system targeted at GPUs and general data-parallel architectures, provides a mechanism for user-controlled transformation of array programs. This transformation capability is designed to not just apply to programs written specifically for Loo.py, but also those imported from other languages such as Fortran. It eases the trade-off between achieving high performance, portability, and programmability by allowing the user to apply a large and growing family of transformations to an input program. These transformations are expressed in and used from Python and may be applied from a variety of settings, including a pragma-like manner from other languages.
Pain Intensity Estimation by a Self--Taught Selection of Histograms of Topographical Features
Venue: arXiv cs.CV 2015
Year: 2015

Pain assessment through observational pain scales is necessary for special categories of patients such as neonates, patients with dementia, critically ill patients, etc. The recently introduced Prkachin-Solomon score allows pain assessment directly from facial images opening the path for multiple assistive applications. In this paper, we introduce the Histograms of Topographical (HoT) features, which are a generalization of the topographical primal sketch, for the description of the face parts contributing to the mentioned score. We propose a semi-supervised, clustering oriented self--taught learning procedure developed on the emotion oriented Cohn-Kanade database. We use this procedure to improve the discrimination between different pain intensity levels and the generalization with respect to the monitored persons, while testing on the UNBC McMaster Shoulder Pain database.
A method for business process reengineering based on enterprise ontology
Venue: arXiv cs.SE 2015
Year: 2015

Business Process Reengineering increases enterprise's chance to survive in competition among organizations , but failure rate among reengineering efforts is high, so new methods that decrease failure, are needed, in this paper a business process reengineering method is presented that uses Enterprise Ontology for modelling the current system and its goal is to improve analysing current system and decreasing failure rate of BPR, and cost and time of performing processes, In this method instead of just modelling processes, processes with their : interactions and relations, environment, staffs and customers will be modelled in enterprise ontology. Also in choosing processes for reengineering step, after choosing them, processes which, according to the enterprise ontology, has the most connection with the chosen ones, will also be chosen to reengineer, finally this method is implemented on a company and As-Is and To-Be processes are simulated and compared by ARIS tools, Report and Simulation Experiment
Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients
Venue: arXiv cs.LG 2015
Year: 2015

Automated learning of patients demographics can be seen as multi-label problem where a patient model is based on different race and gender groups. The resulting model can be further integrated into Privacy-Preserving Data Mining, where it can be used to assess risk of identification of different patient groups. Our project considers relations between diabetes and demographics of patients as a multi-labelled problem. Most research in this area has been done as binary classification, where the target class is finding if a person has diabetes or not. But very few, and maybe no work has been done in multi-labeled analysis of the demographics of patients who are likely to be diagnosed with diabetes. To identify such groups, we applied ensembles of several multi-label learning algorithms.
ICONA: Inter Cluster ONOS Network Application
Venue: arXiv cs.NI 2015
Year: 2015

Several Network Operating Systems (NOS) have been proposed in the last few years for Software Defined Networks; however, a few of them are currently offering the resiliency, scalability and high availability required for production environments. Open Networking Operating System (ONOS) is an open source NOS, designed to be reliable and to scale up to thousands of managed devices. It supports multiple concurrent instances (a cluster of controllers) with distributed data stores. A tight requirement of ONOS is that all instances must be close enough to have negligible communication delays, which means they are typically installed within a single datacenter or a LAN network. However in certain wide area network scenarios, this constraint may limit the speed of responsiveness of the controller toward network events like failures or congested links, an important requirement from the point of view of a Service Provider. This paper presents ICONA, a tool developed on top of ONOS and designed in order to extend ONOS capability in network scenarios where there are stringent requirements in term of control plane responsiveness. In particular the paper describes the architecture behind ICONA and provides some initial evaluation obtained on a preliminary version of the tool.
Content-Based Bird Retrieval using Shape context, Color moments and Bag of Features
Venue: arXiv cs.CV 2015
Year: 2015

In this paper we propose a new descriptor for birds search. First, our work was carried on the choice of a descriptor. This choice is usually driven by the application requirements such as robustness to noise, stability with respect to bias, the invariance to geometrical transformations or tolerance to occlusions. In this context, we introduce a descriptor which combines the shape and color descriptors to have an effectiveness description of birds. The proposed descriptor is an adaptation of a descriptor based on the contours defined in article Belongie et al. [5] combined with color moments [19]. Specifically, points of interest are extracted from each image and information's in the region in the vicinity of these points are represented by descriptors of shape context concatenated with color moments. Thus, the approach bag of visual words is applied to the latter. The experimental results show the effectiveness of our descriptor for the bird search by content.
Robust State Feedback Control Design with Probabilistic System Parameters
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, a new polynomial chaos based framework for analyzing linear systems with probabilistic parameters is presented. Stability analysis and synthesis of optimal quadratically stabilizing controllers for such systems are presented as convex optimization problems, with exponential mean square stability guarantees. A Monte-Carlo approach for analysis and synthesis is also presented, which is used to benchmark the polynomial chaos based approach. The computational advantage of the polynomial chaos approach is shown with an example based on the design of an optimal EMS-stabilizing controller, for an F-16 aircraft model.
Proficiency of Power Values for Load Disaggregation
Venue: arXiv cs.IT 2015
Year: 2015

Load disaggregation techniques infer the operation of different power consuming devices from a single measurement point that records the total power draw over time. Thus, a device consuming power at the moment can be understood as information encoded in the power draw. However, similar power draws or similar combinations of power draws limit the ability to detect the currently active device set. We present an information coding perspective of load disaggregation to enable a better understanding of this process and to support its future improvement. In typical cases of quantity and type of devices and their respective power consumption, not all possible device configurations can be mapped to distinguishable power values. We introduce the term of proficiency to describe the suitability of a device set for load disaggregation. We provide the notion and calculation of entropy of initial device states, mutual information of power values and the resulting uncertainty coefficient or proficiency. We show that the proficiency is highly dependent from the device running probability especially for devices with multiple states of power consumption. The application of the concept is demonstrated by exemplary artificial data as well as with actual power consumption data from real-world power draw datasets.
Evaluating Asymmetric Multicore Systems-on-Chip using Iso-Metrics
Venue: arXiv cs.DC 2015
Year: 2015

The end of Dennard scaling has pushed power consumption into a first order concern for current systems, on par with performance. As a result, near-threshold voltage computing (NTVC) has been proposed as a potential means to tackle the limited cooling capacity of CMOS technology. Hardware operating in NTV consumes significantly less power, at the cost of lower frequency, and thus reduced performance, as well as increased error rates. In this paper, we investigate if a low-power systems-on-chip, consisting of ARM's asymmetric big.LITTLE technology, can be an alternative to conventional high performance multicore processors in terms of power/energy in an unreliable scenario. For our study, we use the Conjugate Gradient solver, an algorithm representative of the computations performed by a large range of scientific and engineering codes.
Development of an Electronic Medical Image Archiving System for Health Care in Nigeria
Venue: arXiv cs.CY 2015
Year: 2015

Medical images require immediate access by several physicians in different places within a medical facility and access to a critically injured persons medical image, such as the x-ray, on time can be a key factor in the diagnosis and treatment of the patient. The electronic medical image archive system can help to solve the problem faced in previous physical medium archiving, thus increasing productivity and time which patients are attended to. In this paper, simple but functional electronic medical image archive architecture was proposed and implemented. The system was further evaluated in a hospital setting by medical experts using sample patient image data. Results of the system evaluation shows that electronic medical image archiving systems can actually promote efficiency, quality improvement, provide timely availability of radiologic images, image consultation, and image interpretation in emergent and no emergent clinical care areas among other benefits
Distributed Evaluation and Convergence of Self-Appraisals in Social Networks
Venue: arXiv cs.SY 2015
Year: 2015

We consider in this paper a networked system of opinion dynamics in continuous time, where the agents are able to evaluate their self-appraisals in a distributed way. In the model we formulate, the underlying network topology is described by a rooted digraph. For each ordered pair of agents $(i,j)$, we assign a function of self-appraisal to agent $i$, which measures the level of importance of agent $i$ to agent $j$. Thus, by communicating only with her neighbors, each agent is able to calculate the difference between her level of importance to others and others' level of importance to her. The dynamical system of self-appraisals is then designed to drive these differences to zero. We show that for almost all initial conditions, the trajectory generated by this dynamical system asymptotically converges to an equilibrium point which is exponentially stable.
Distributed Estimation of Graph Spectrum
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we develop a two-stage distributed algorithm that enables nodes in a graph to cooperatively estimate the spectrum of a matrix $W$ associated with the graph, which includes the adjacency and Laplacian matrices as special cases. In the first stage, the algorithm uses a discrete-time linear iteration and the Cayley-Hamilton theorem to convert the problem into one of solving a set of linear equations, where each equation is known to a node. In the second stage, if the nodes happen to know that $W$ is cyclic, the algorithm uses a Lyapunov approach to asymptotically solve the equations with an exponential rate of convergence. If they do not know whether $W$ is cyclic, the algorithm uses a random perturbation approach and a structural controllability result to approximately solve the equations with an error that can be made small. Finally, we provide simulation results that illustrate the algorithm.
A System View of the Recognition and Interpretation of Observed Human Shape, Pose and Action
Venue: arXiv cs.CV 2015
Year: 2015

There is physiological evidence that our ability to interpret human pose and action from 2D visual imagery (binocular or monocular) engages the circuitry of the motor cortices as well as the visual areas of the brain. This implies that the capability of the motor cortices to solve inverse kinematics is flexible enough to apply to both motion planning as well as serving as a generative model for the visual processing of human figures, despite the differing functional requirements of the two tasks. This paper provides a computational model of the cooperation between visual and motor areas: in other words, a system view of an important class of brain computations. The model unifies the solution of the separate inverse problems involved in the task, visual transformation discovery, inverse kinematics, and adaptation to morphology variations, using several instances of the Map-seeking Circuit algorithm. While the paper is weighted toward the exposition of a neurobiological hypothesis, from mathematical formalization of the problem to neuronal circuitry, the algorithmic expression of the solution is also a functional machine vision system for human figure recognition, and 3D pose and body morphology reconstruction from monocular, perspective-less input imagery. With an inverse kinematic generative model capable of imposing a variety of endogenous and exogenous constraints the machine vision implementation acquires characteristics currently unique among such systems.
A Population Model for the Academic Ecosystem
Venue: arXiv cs.SI 2015
Year: 2015

In recent times, the academic ecosystem has seen a tremendous growth in number of authors and publications. While most temporal studies in this area focus on evolution of co-author and citation network structure, this systemic inflation has received very little attention. In this paper, we address this issue by proposing a population model for academia, derived from publication records in the Computer Science domain. We use a generalized branching process as an overarching framework, which enables us to describe the evolution and composition of the research community in a systematic manner. Further, the observed patterns allow us to shed light on researchers' lifecycle encompassing arrival, academic life expectancy, activity, productivity and offspring distribution in the ecosystem. We believe such a study will help develop better bibliometric indices which account for the inflation, and also provide insights into sustainable and efficient resource management for academia.
Counting independent sets via Divide Measure and Conquer method
Venue: arXiv cs.DM 2015
Year: 2015

In this paper we give an algorithm for counting the number of all independent sets in a given graph which works in time $O^*(1.1394^n)$ for subcubic graphs and in time $O^*(1.2369^n)$ for general graphs, where $n$ is the number of vertices in the instance graph, and polynomial space. The result comes from combining two well known methods "Divide and Conquer" and "Measure and Conquer". We introduce this new concept of Divide, Measure and Conquer method and expect it will find applications in other problems. The algorithm of Björklund, Husfeldt and Koivisto for graph colouring with our algorithm used as a subroutine has complexity $O^*(2.2369^n)$ and is currently the fastest graph colouring algorithm in polynomial space.
Assessing Excel VBA Suitability for Monte Carlo Simulation
Venue: arXiv cs.MS 2015
Year: 2015

Monte Carlo (MC) simulation includes a wide range of stochastic techniques used to quantitatively evaluate the behavior of complex systems or processes. Microsoft Excel spreadsheets with Visual Basic for Applications (VBA) software is, arguably, the most commonly employed general purpose tool for MC simulation. Despite the popularity of the Excel in many industries and educational institutions, it has been repeatedly criticized for its flaws and often described as questionable, if not completely unsuitable, for statistical problems. The purpose of this study is to assess suitability of the Excel (specifically its 2010 and 2013 versions) with VBA programming as a tool for MC simulation. The results of the study indicate that Microsoft Excel (versions 2010 and 2013) is a strong Monte Carlo simulation application offering a solid framework of core simulation components including spreadsheets for data input and output, VBA development environment and summary statistics functions. This framework should be complemented with an external high-quality pseudo-random number generator added as a VBA module. A large and diverse category of Excel incidental simulation components that includes statistical distributions, linear and non-linear regression and other statistical, engineering and business functions require execution of due diligence to determine their suitability for a specific MC project.
Online Query Scheduling on Source Permutation for Big Data Integration
Venue: arXiv cs.DB 2015
Year: 2015

Big data integration could involve a large number of sources with unpredictable redundancy information between them. The approach of building a central warehousing to integrate big data from all sources then becomes infeasible because of so large number of sources and continuous updates happening. A practical approach is to apply online query scheduling that inquires data from sources at runtime upon receiving a query. In this paper, we address the Time-Cost Minimization Problem for online query scheduling, and tackle the challenges of source permutation and statistics estimation to minimize the time cost of retrieving answers for the real-time receiving query. We propose the online scheduling strategy that enables the improvement of statistics, the construction of source permutation and the execution of query working in parallel. Experimental results show high efficiency and scalability of our scheduling strategy.
On Resilient Behaviors in Computational Systems and Environments
Venue: arXiv cs.SY 2015
Year: 2015

The present article introduces a reference framework for discussing resilience of computational systems. Rather than a property that may or may not be exhibited by a system, resilience is interpreted here as the emerging result of a dynamic process. Said process represents the dynamic interplay between the behaviors exercised by a system and those of the environment it is set to operate in. As a result of this interpretation, coherent definitions of several aspects of resilience can be derived and proposed, including elasticity, change tolerance, and antifragility. Definitions are also provided for measures of the risk of unresilience as well as for the optimal match of a given resilient design with respect to the current environmental conditions. Finally, a resilience strategy based on our model is exemplified through a simple scenario.
Uniform Information Exchange in Multi-channel Wireless Ad Hoc Networks
Venue: arXiv cs.DC 2015
Year: 2015

In the information exchange problem, k packets that are initially maintained by k nodes need to be disseminated to the whole network as quickly as possible. We consider this problem in single-hop multi- channel networks of n nodes, and propose a uniform protocol that with high probability accomplishes the dissemination in O(k/F + F \cdot log n) rounds, assuming F available channels and collision detection. This result is asymptotically optimal when k is large (k \geq F^2 \cdot log n). To our knowledge, this is the first uniform protocol for information exchange in multi-channel networks.
LSHTC: A Benchmark for Large-Scale Text Classification
Venue: arXiv cs.IR 2015
Year: 2015

LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thousands). This paper describes the dataset that have been released along the LSHTC series. The paper details the construction of the datsets and the design of the tracks as well as the evaluation measures that we implemented and a quick overview of the results. All of these datasets are available online and runs may still be submitted on the online server of the challenges.
On the Capacity of the Wiener Phase-Noise Channel: Bounds and Capacity Achieving Distributions
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, the capacity of the additive white Gaussian noise (AWGN) channel, affected by time-varying Wiener phase noise is investigated. Tight upper and lower bounds on the capacity of this channel are developed. The upper bound is obtained by using the duality approach, and considering a specific distribution over the output of the channel. In order to lower-bound the capacity, first a family of capacity-achieving input distributions is found by solving a functional optimization of the channel mutual information. Then, lower bounds on the capacity are obtained by drawing samples from the proposed distributions through Monte-Carlo simulations. The proposed capacity-achieving input distributions are circularly symmetric, non-Gaussian, and the input amplitudes are correlated over time. The evaluated capacity bounds are tight for a wide range of signal-to-noise-ratio (SNR) values, and thus they can be used to quantify the capacity. Specifically, the bounds follow the well-known AWGN capacity curve at low SNR, while at high SNR, they coincide with the high-SNR capacity result available in the literature for the phase-noise channel.
A New Alternative for Traffic Hotspot Localization in Wireless Networks Using O&M Metrics
Venue: arXiv cs.NI 2015
Year: 2015

In recent years, there has been an increasing awareness to traffic localization techniques driven by the problematic of hotspot offloading solutions, the emergence of heterogeneous networks (HetNet) with small cells' deployment and the green networks. The localization of traffic hotspots with a high accuracy is indeed of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they can be managed for sleep mode concept. We propose, in this paper, a new hotspot localization technique based on the direct exploitation of five Key Performance Indicators (KPIs) extracted from the Operation and Maintenance (O&M) database of the network. These KPIs are the Timing Advance (TA), the angle of arrival (AoA), the neighboring cell level, the load time and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these KPIs, projected over a coverage map, yields a promising localization precision and can be further optimized by exploiting commercial data on potential hotspots. This solution can be implemented in the network at an appreciable low cost when compared with widely used probing methods.