Miracle-dz/UniFAR-FTUs huggingface dataset:
Speeding Up Large-Scale Next Generation Sequencing Data Analysis with pBWA

Newly available DNA sequencing technologies can generate billions of DNA sequences in a single machine run, making it feasible to obtain an individual’s entire genome sequences very quickly and at a very affordable cost. These personal genome sequences can be used to identify genetic variations associated with variable traits, but they must first be aligned to a reference genome sequence using computer algorithms that make use of approximate string matching methods. The process of aligning a very large number of short sequence reads is computationally expensive and has led to the development of many sequence mapping programs. While new software, such as BWA, SOAP2, and Bowtie, efficiently align large numbers of short DNA sequences, we proposed that parallel computing would provide additional speedup in data processing to accommodate the rapidly increasing sequencing throughput. For this purpose, we developed pBWA, an efficient parallel version of BWA, based on the Open MPI library. pBWA retains the multi-threading capability provided by BWA while adding efficient parallelization for its core alignment functions. We have shown that pBWA’s wall-time speedup is bounded only by the size of the parallel system. pBWA can run in both parallel and multi threaded environments simultaneously, allowing it to be tailored to run on parallel systems of all configurations and sizes. By taking the advantage of high performance computing cluster, the use of pBWA can cut down the computing wall-time for the reference alignment step from weeks to hours for extremely large DNA sequence data. We expect that the availability of pBWA should facilitate the analysis of large-scale genome sequencing data generated by the new generations of sequencing technologies. The source code and detailed user manual of pBWA are freely available at http://sourceforge.net/projects/pbwa.
New cost accounting models in measuring of library employees' performance

Purpose – The current article aims to give an overview of how the new cost accounting models, such as activity‐based costing (ABC) and time‐driven activity‐based costing (TDABC) are researched and adapted by university libraries, focusing on the methods used for measuring work time allocation as well as on the strengths and weaknesses of both models.Design/methodology/approach – The data used in this paper are based on reviewing and summarizing of relevant studies which were conducted in libraries inspired by the ideas of modern theoretical considerations and treatments relating to cost accounting and costing, originally developed for industry and private sector organizations.Findings – The implementation of cost accounting systems in libraries has historically been treated as a technical innovation rather than an organizational or management innovation. The most important consideration is that librarians are not machines, which can be set at a given speed and expected to produce a uniform product. It tur.
EFFICIENT WAY OF USER SEARCH LOCATION IN QUERY PROCESSING

Rapid growth of the mobile search location information is an important concern. Usually mobile users search the query in service providers. In this paper, apply click through for finding the users’ interest in search engine mainly from their own search database. User preferences are classified as content concept and location concept. Ontology concepts are used to store the user preferences in the client side and Content extraction, Re-ranking are used in the server side to get the closest point search results. In this, use client– server architecture to get the user relevant information. Server provides the result based on user search. Classify the query based on the user click through. Instead of producing global search result, it performs ranking to get closest point result based on the server database. Frequently accessed user queries are stored in the client side for fast access. If the result is not available, then the user queries can be posted to get the updated result when the server is updated.
Simulation of the Performance of MapReduce Jobs

The MapReduce distributed computation paradigm introduced by Google made a huge frenzy and with the arrival of an open source solution, Apache Hadoop framework, major companies rapidly joined this solution. Today companies such as LinkedIn and Facebook use Hadoop has the main data processing tool. In the State of the Art was found that there is a massive research in MapReduce, and that there is a lot o research to be done, and innumerous challenges ahead. This dissertation presents a solution to simplify the research and testing of algorithms for the MapReduce framework. The solution proposed is a tool for the simulation of MapReduce jobs in a cluster. The solution is based on the Apache Hadoop framework, and is highly customizable and allows the user to specify his how algorithms. The simulation environment is built on top of the grid simulator SimGrid. The SimGrid simulator is a large scale distributed systems simulator for peer-to-peer and cloud systems, that incorporates a network representation scheme. This network representation allows for a network analysis in the MapReduce jobs.
Applying the relationship between potential vorticity fields and water vapour imagery to adjust initial conditions in NWP

The feasibility of using the proposed relationship between model‐generated upper‐tropospheric potential vorticity (PV) fields and Meteosat water vapour images in forecast initialisation has been investigated by a number of case studies involving model forecasts of North Atlantic cyclones. The objective of each case study was to reduce the errors in the forecast track and depth of a cyclone by re‐initialising the forecast via upper‐level PV adjustment and PV inversion. The methodology has employed the PV–water vapour relationship to detect mismatches between PV fields and water vapour images, and has attempted to correct these mismatches by manual adjustment of the PV fields. These PV adjustments have been converted to wind and temperature increments by means of PV inversion and used to initialise the model fields in a way that preserves dynamical balance. Five case studies with small initialisation errors were carried out. The results of the case studies show some partial positive impacts on forecasts of cyclonic development, but overall the results do not demonstrate that the use of the PV–water vapour relation for forecast initialisation is a practical proposition for operational use. Copyright © 2001 Royal Meteorological Society
Rule extraction from spatial data using local learning techniques

We are now in the fourth decade where techniques such as fuzzy systems, statistics, neural networks and machine learning have all been developed and more recently applied for the purpose of spatial data mining. However these methods act as global learning models and subsequently may not be able to learn the subtle nature of these types of data sets. Local learning models such as the Support Vector Machine (SVM) and a more recent method such as that proposed by (Gilardi 2002) address the problem of global versus local learning but fail to offer many solutions as to what underlying patterns may exist within the data set in order to better understand the data set. In this paper we propose the Evolving Fuzzy Neural Network (EFuNN) as a model for a local learning mechanism for the purpose of predicting rainfall within a region of Switzerland and also use this model to generate rules and then to visualise these rules that may help to describe any patterns that may exist within the data set.
Performance of uncoordinated and packetized code time division multiple access, and spread ALOHA

Uncoordinated and packetized code time division multiple access (CTDMA) is presented. It is shown that the only difference to spread ALOHA is the demodulation technique applied in the receiver. The capacity of uncoordinated and packetized CTDMA is then determined and evaluated. In particular, it is shown that CTDMA outperforms spread ALOHA, i.e., that a linear inverse filter outperforms a matched filter in terms of single-user capacity.
Protocols for mission-critical wireless sensor networks

Wireless Sensor Networks can be used in Mission-Critical applications where the success of the mission depends on stringent QoS requirements. In this dissertation, two innovative cross-layer delivery protocols targeted for mission-critical networks are proposed. Multi-path Multi-SPEED (MMSPEED) protocol provides service differentiation and probabilistic QoS guarantees in two QoS domains. By allowing higher layers to select the most appropriate level of service, MMSPEED allocates network resources to packets and provide probabilistic guarantee to deliver packets within the applications’s QoS requirements. The second protocol is called Sectored-Antenna MAC (SAMAC) protocol which is an integrated set of cross-layer communication protocols that exploits the potential benefits of sectored-antennas. In addition, we propose two analytical models for IEEE 802.11 DCF mechanism that accurately predict the performance of the DCF mechanism with a wide range of system parameters. Simplified versions of these models are used to optimize the performance of MMSPEED by selecting the optimal protocol parameters and to predict the E2E delay performance of SAMAC protocol.
Frequently Asked Questions about Rcpp

This document attempts to answer the most Frequently Asked Questions (FAQ) regarding the Rcpp (Eddelbuettel, Francois, Allaire, Ushey, Kou, Chambers, and Bates, 2016a; Eddelbuettel and Francois, 2011; Eddelbuettel, 2013) package.
Quick Pad Tagger : An Efficient Graphical User Interface for Building Annotated Corpora with Multiple Annotation Layers

More and more domain specific applications in the internet make use of Natural Language Processing (NLP) tools (e. g. Information Extraction systems). The output quality of these applications relies on the output quality of the used NLP tools. Often, the quality can be increased by annotating a domain specific corpus. However, annotating a corpus is a time consuming and exhaustive task. To reduce the annotation time we present a custom Graphical User Interface for different annotation layers.
A Modified Invasive Weed Optimization Algorithm for MultiObjective Flexible Jobe Shop Scheduling Problems

In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.
Spotlight TerraSAR-X Data Modeling using Spectral Space-Variant Measures, for scene Targets and Structure Indexing

The main objective of this paper is to define an analysis model for high resolution spotlight SAR imagery, which is able to integrate the radiometric, as well as geometric and texture properties of the SAR data, in order to facilitate large data-base queries by informational content indexing of the images. The model proposed in this paper uses the information contained in the spectra of the SAR signal. A scene is described by a number of parameters computed based on Short-Time Fourier Transform. Considering the properties of the spotlight imaging mode, a phase correction algorithm is applied to the images prior to feature extraction. The classification is done using a Bayesian Support Vector Machine classifier. The method allowed for the recognition of more than thirty targets and structures in the scenes. The method is extended on quadpol TerraSAR-X data, where a Clifford-Fourier Transform is employed. This gives a representation of the spectra in a multidimensional space.
Noise reduction and identification of subsurface radar images using recursive wavelet decomposition

Shape estimation and noise reduction are among most important issues in the signal processing of subsurface radars. The authors developed a noise reduction scheme based on a two-dimensional parabolic wavelet transform, which is designed to detect hyperbolic features associated to subsurface radar images. The major limitation of the proposed algorithm was that the parabolic wavelets do not give an orthogonal basis, which limits the reconstruction capability of the algorithm. The authors propose a scheme based on similar 2D wavelet bases, but employing the recursive non-orthogonal decomposition algorithm known as the matching pursuit. The idea is to repeat the procedure of fitting waveforms given in a redundant dictionary to the given waveform, and subtracting the best matched one recursively. The advantage is that the desired signal can be retrieved from a very noisy data if the waveform is included in the dictionary. The inherent problem of this procedure is a heavy computational load because a large number of iteration is needed. The authors develop schemes to substantially reduce the computation by customizing the algorithm to the signal processing of subsurface radars, and by taking into account the characteristics of the desired signals. The capability of the proposed algorithm in detecting various targets buried in the noise is evaluated based on simulated data for an attenuating and dispersive medium.
A Dynamic 3D, Object-Generating Algorithm from Multiple Viewpoints Using the Volume Intersection and Stereo Matching Methods

We are researching a modeling algorithm for the moving human body to generate a fine, dynamic, 3D object. In this paper, we propose a modeling method from multiple viewpoints. This modeling method uses an approximate shape, obtained by the volume intersection method with restrictive conditions set by the stereo matching method.In addition, in order to map the image texture on the surface of the dynamic 3D objects, we propose a vertex texture mapping method based on ray-casting. Our modeling experiments were performed using 19 Firewire cameras. We were able to confirm that the proposed methods are effective for generating fine, dynamic, 3D objects. The dynamic 3D object generated can be displayed as an “animation” by using a VRML browser.
Direct-sequence spread-spectrum code-division-multiple-access cellular systems in Rayleigh fading and log-normal shadowing channel

Direct-sequence spread-spectrum code-division-multiple-access (DS-SS-CDMA) techniques for personal and mobile communications are considered. A service area is divided into cells. The radio link is modeled as an additive white Gaussian noise channel with Rayleigh fading and log-normal shadowing, and is also corrupted by mutual interferences due to other users. L-branch microscopic diversity combining, K-site macroscopic diversity selection, and uplink power control are assumed. Under these assumptions, for uplink and downlink transmissions between users and cell-sites, analytical expressions of the probability distribution of the ratio of bit-energy to noise-plus-interference-density are derived. By numerically evaluating the derived expressions, the number of users per cell as a function of outage probability are obtained. Numerical results indicate that DS-SS-CDMA provides high capacity. <<ETX>>
VTDM-a dynamic multicast routing algorithm

In this paper, the dynamic multicast routing problem is studied. The multicast routing problem has been shown to be NP-complete. Many heuristics have been proposed to find the multicast trees for multicast connections. In computer networks, application services may allow nodes to join or leave the multicast connection dynamically. The multicast routing problem in which nodes are allowed to join or leave the multicast connection is called the dynamic multicast routing problem. A new dynamic multicast routing algorithm called virtual trunk dynamic multicast (VTDM) routing algorithm is proposed for this problem. A virtual trunk (VT) is a tree of the underlying graph. It is used as a template for constructing multicast trees. The VTDM routing algorithm constructs multicast trees based on the virtual trunk. Simulations are performed to study the performance of the VTDM routing algorithm. Simulation results show that the performance of the VTDM algorithm is close to that of the KMB algorithm (a near optimal heuristic for the static multicast routing problem).
Modeling Strategic Relationships for Process Reengineering

Existing models for describing a process (such as a business process or a software development process) tend to focus on the "what" or the "how" of the process. For example, a health insurance claim process would typically be described in terms of a number of steps for assessing and approving a claim. In trying to improve or redesign a process, however, one also needs to have an understanding of the "why"--for example, why do physicians submit treatment plans to insurance companies before giving treatment? and why do claims managers seek medical opinions when assessing treatment plans? An understanding of the motivations and interests of process participants is often crucial to the successful redesign of processes. This thesis proposes a modelling framework i* (pronounced i-star) consisting of two modelling components. The Strategic Dependency (SD) model describes a process in terms of intentional dependency relationships among agents. Agents depended on each other for goals to be achieved, tasks to be performed, and resources to be furnished. Agents are intentional in that they have desires and wants, and strategic in that they are concerned about opportunities and vulnerabilities. The Strategic Rationale (SR) model describes the issues and concerns that agents have about existing processes and proposed alternatives, and how they might be addressed, in terms of a network of means-ends relationships. An agent's routines for carrying out a process can be analyzed for their ability, workability, viability and believability. Means-ends rules are used to suggest methods for addressing issues, related issues to be raised, and assumptions to be challenged. The models are represented in the conceptual modelling language Telos. The modelling concepts are axiomatically characterized. The utility of the framework is illustrated each of four application areas: requirements engineering, business process reengineering, organizational impacts analysis, and software process modelling. Advantage of i* over existing modelling techniques in each of these areas are described.
A Study on Paraphrase Recognition Using Radial Basis Function Neural Network

Abstract Paraphrasing is the restatement of a given text using alternate words. Recognition of paraphrases is vital in applications such as question answering, information extraction, and multi-document summarization. Lexical, syntactic, and semantic features of text can be used either individually or in combinations for recognizing paraphrases. Several machine-learning classifiers such as support vector machines (SVM), nearest neighbour method, and decision trees have been used for paraphrase recognition with SVM recognizers being the most popular ones. This paper explores the applicability of neural networks for paraphrase recognition. A radial basis function neural network (RBFNN) has been designed and implemented for recognizing paraphrases. Experiments have been carried out on the Microsoft research paraphrase corpus. From the results of the experiments, it has been observed that the RBFNN recognizer consistently outperforms the SVM recognizer with respect to accuracy and that the best performance was achieved when a combination of lexical, syntactic, and semantic features were used.
A conversational framework for individual learning applied to the ‘Learning Organisation’ and the ‘Learning Society’

The paper sets out to test whether our understanding of the individual learning can be used to help us understand better the concepts of ‘the learning organisation’ and ‘the learning society’. It begins with a systems-oriented description of one model for the learning individual, the ‘conversational framework’, and extends Laurillard's earlier framework to one that makes explicit the learner's internal conversation. This extended framework is then reinterpreted for the university as a learning organisation. If the distinctive features of the conversational framework describe the minimal components and relations necessary for learning, to what extent can we find this structure mirrored in the way a university works? The application of the framework exposes the potential or missing links. The same argument is then extended to the higher education (HE) sector as a whole, and finally to ‘the learning society’. In the context of the HE sector, the analysis demonstrates the need for an ‘institute for learning and teaching in HE’ such as that proposed by the Dearing Committee in the UK. In the context of the wider society, it shows how universities must play their role in enabling it to be a ‘learning society’. Copyright © 1999 John Wiley & Sons, Ltd.
Throughput analysis of CDMA access policies in radio determination satellite service

Policies for accessing a packet radio network, under the assumptions of the L-channel CDMA (code division multiple access) model and nonunity receiver idle probability, are proposed, and analytical tools to evaluate their performances are developed. These policies and tools are applicable to random access packet radio networks, such as the radio determination satellite service (RDSS) systems, which require wideband or spread-spectrum techniques, but carrier sense, collision detection, and busy-tones techniques are inapplicable. These results are extended to general packet radio networks. <<ETX>>
Contrast enhancement using brightness preserving bi-histogram equalization

Histogram equalization is widely used for contrast enhancement in a variety of applications due to its simple function and effectiveness. Examples include medical image processing and radar signal processing. One drawback of the histogram equalization can be found on the fact that the brightness of an image can be changed after the histogram equalization, which is mainly due to the flattening property of the histogram equalization. Thus, it is rarely utilized in consumer electronic products such as TV where preserving the original input brightness may be necessary in order not to introduce unnecessary visual deterioration. This paper proposes a novel extension of histogram equalization to overcome such a drawback of histogram equalization. The essence of the proposed algorithm is to utilize independent histogram equalizations separately over two subimages obtained by decomposing the input image based on its mean with a constraint that the resulting equalized subimages are bounded by each other around the input mean. It is shown mathematically that the proposed algorithm preserves the mean brightness of a given image significantly well compared to typical histogram equalization while enhancing the contrast and, thus, provides a natural enhancement that can be utilized in consumer electronic products.