Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapsembler, targeted assembly of larges genomes on a desktop computer

Background: The analysis of next-generation sequencing data from large genomes is a timely research topic. Sequencers are producing billions of short sequence fragments from newly sequenced organisms. Computational methods for reconstructing sequences (whole-genome assemblers) are typically employed to process such data. However, one of the main drawback of these methods is the high memory requirement. Results: We present Mapsembler, an iterative targeted assembler which processes large datasets of reads on commodity hardware. Mapsembler checks for the presence of given regions of interest in the reads and reconstructs their neighborhood, either as a plain sequence (consensus) or as a graph (full sequence structure). We introduce new algorithms to retrieve homologues of a sequence from reads and construct an extension graph. Conclusions: Mapsembler is the rst software that enables de novo discovery around a region of interest of gene homologues, SNPs, exon skipping as well as other structural events, directly from raw sequencing reads. Compared to traditional assembly software, memory requirement and execution time of Mapsembler are considerably lower, as data indexing is localized. Mapsembler can be used at http://mapsembler.genouest. org
FPGA Platform for Emulation of Composable and Predictable MPSoC Power Management

As the IC technology is advancing quickly, Multi-processor System-on-Chip (MPSoC) becomes the trend of embedded System-on-Chip (SoC) design. A typical modern MPSoC for embedded systems usually runs multiple applications, some of which have real-time requirements. To meet the real-time requirements, predictable system is needed. The interference between applications in the MPSoC usually result in exponential increase in design and verification complexity. To avoid such situations, we need composable system, on which the temporal behavior of different applications does not depend on each other. In modern IC design, especially for embedded systems, power consumption is becoming a major constraint, which means power management is essential for MPSoC. Hence we need composable and predictable MPSoC with power management capability. In this thesis, the design and implementation of an FPGA-based composable and predictable platform for emulating the power management of MPSoC are presented. The platform consists of multiple MicroBlaze processor cores and AEthereal Network on Chip (NoC) is used as the interconnection. The platform enables the sharing of different resources, including the processer, among different applications in a composable way. The support for data-flow application in the platform hardware and software enables the predictable execution of streaming applications. And with the power management hardware and software infrastructure, composable and predictable power management for each application is possible. The support for power management in data-flow application is implemented and demonstrated in the experiments, which saves up to 50% energy compared to the trivial power management policy.
Effects of clipping and filtering on the performance of OFDM

Orthogonal frequency division multiplexing (OFDM) is an attractive technique for wireless communication applications. However, an OFDM signal has a large peak-to-mean envelope power ratio (PMEPR), which can result in significant distortion when transmitted through a nonlinear device, such as a transmitter power amplifier. We investigate, through extensive computer simulations, the effects of clipping and filtering on the performance of OFDM, including the power spectral density, the bit error rate, and the PMEPR. Our results show that clipping and filtering is a promising technique for the transmission of OFDM signals using realistic linear amplifiers.
Intelligent Assistant for Controlling Iot Devices

We square measure in twenty first century, wherever everything is automized from home to industrial space. Devices automation reduces the human efforts and it additionally saves energy and time. it's additionally used for security purposed. With the event of latest technologies, that square measure able to connect with one another through network, the web of things (IoT), raised its quality. IoT (Internet of Things) may be a network of interconnected devices, designed to gather and exchange information which may then flip it into data, eventually into knowledge. IoT may be a region wherever digital world converges with physical world. This paper describes the implementation ANd configuration of an intelligent assistant for a domestic also as industrial controller. The project uses Blynk application that employs the combination of cloud networking and wireless communication by providing the user with remote numerous of varied of assorted electrical appliances and senses various information equivalent to humidness, temperature, moisture, gas, etc. and supply the specified information on blynk. during this paper we tend to focus our attention on the combination of Intelligent informal code Agents or Chatbots with IoT. Chatbots square measure being adopted in larger numbers thanks to major strides in development of platforms and frameworks. The novelty of this paper lies within the specific integration of Chatbots within the IoT state of affairs. we tend to analyzed the shortcomings of existing IoT systems and advance ways in which to tackle them by incorporating chatbots. -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 26-06-2018 Date of acceptance: 12-07-2018 -------------------------------------------------------------------------------------------------------------------------------------
Folia Linguistica

The present article is a systematic, large-scale corpus study of the varying position of the Old English adnominal genitive. Particular focus is put on intertextual variation and the potential influence of Latin, which is analysed alongside such intra-linguistic factors as syntactic weight and the animacy of the referent. The model of logistic regression adopted helps address a key issue in studies on genitive placement, namely, if and how multiple variables exert combined impact on the choice of the variant. The paper highlights the importance of a bottom-to-top approach in studies of older English syntax, as global tendencies turn out to be the corollary of significantly different contributions on the level of individual texts, whose translation status (original composition or translation) is also of importance to the variation studied.
Fluid Flow Between Two Glasses -

This paper proposes a novel mathematical model for studying the entropic elasticity and cooperative extensibility of double strand DNA (dsDNA) using LS-DYNA and equivalent theory. Through the proposed model, the dynamic structural transitions of the dsDNA under external force/torque can be accurately simulated within an affordable CPU time. Moreover, the proposed dsDNA model comprises the meso-mechanics equivalent theory of single molecule dsDNA, including the base-stacking interaction between DNA adjacent base pairs, the Hydrogen bond of complementary base-pairs and electrostatic interactions along double-helix sugar-phosphate backbones. Good agreement is achieved between the numerical simulation and the single molecular manipulation experimental result, and the mechanical behavior of stretching nicked dsDNA could be revealed.
Implementing Virtual Address Hardware Support on the ?-VEX Platform

The ? -VEX is a run-time reconfigurable Very Long Instruction Word (VLIW) processor. This unique processor allows separation of its issue lanes to form independently operating processing cores. Switching between these configuration during run-time allows optimizing the platform for the task(s) it is performing. Porting an Operating System (OS) to this platform is an important step towards a software layer that can control these reconfigurations. All contemporary operating systems implement a virtual memory abstraction layer. This technique makes the physical memory layout transparent to the processes hosted by the operating system. Implementing virtual memory is impossible without hardware support. This thesis presents the design and implementation of the addition of this hardware to the ? -VEX platform. The platforms unusual architecture puts specific requirement on the memory system. The implemented hardware fully supports the platforms abilities for static configuration and dynamic reconfiguration. To verify that the implemented solution is able to support an OS, software is designed that emulates the task switching and memory virtualization tasks of an OS. By running this software it is proven that the hardware support all features required by a real OS. Finally, the performance of the implemented hardware is measured running benchmarks in different static configurations and several dynamic reconfiguration scenarios. These measurements are compared and recommendations are made for performance optimization of real applications.
DETC 2009-87117 PMT : Modeling Enterprise Operations and Organizations

Enterprise environments are complex and multidisciplinary. As the market competition becomes more and more relentless, business companies must keep adapting their current processes to the new needs of the market. During the adaptation process, enterprises may not have any experience with the new business needs. Many enterprise models from different perspectives have been proposed to facilitate this adaption process and reduce risks. However, due to the complexity of the enterprise environment, none of them takes all needed enterprise elements into consideration. A uniform approach that deals with different elements of an enterprise is needed in order to model all elements and analyze their effects on enterprise operations. Inspired by Galbraith’s information processing view of organizations and the extant modeling work of project organizations, in this paper we propose a unique model of enterprise called PMT that can be used to analyze enterprise operations and their interactions with market demands. Market demand as an independent variable and enterprise capacity (organization, processes and resources) are modeled computationally and the relations between them are captured. In this paper, the background and the major concepts of the PMT model are introduced and an application example demonstrated to show the effectiveness of the PMT model.
Volunteer Computing Environment with Dynamic caching and Greedy Updates for Scaled Hadoop Clusters

The MapReduce adaptation has getting a appreciable parallel getting model for massive scale data-intensive applications like data mining as well as web categorization. Hadoop, open-source recommendations of MapReduce, is frequently applied to support cluster processing jobs requiring low response time. The current Hadoop application views that computing nodes in a lot up are homogeneous in personality. Data locality has not become chosen into thinking for launching curious map tasks, basically because it is believed that about map tasks can conveniently access their local data. Network setbacks due to data mobility during running time have become forgotten in the current Hadoop scientific studies. However, both the homogeneity and data locality presumptions in Hadoop are positive at best and complicated at worst, potentially showing performance issues in virtualized info centers. We demonstrate in this thesis that dismissing the data-locality difficulties in heterogeneous cluster handling environments can considerably reduce the performance of Hadoop. Without exploring the network hold-ups, the performance of Hadoop clusters will probably be significantly diminished. In consider to this below in this approximate we attempted out to scale the Hadoop cluster into virtualized Volunteer Computing circumstances with qualified mapping and storage cache updates, which may give extra computing power to the constellate. The system contains of a small secured set of specify nodes plus an flexible number of volatile volunteer nodes that can handle on demand with certified data mapping and cache enhancements.
A Survey on SQL Injection attacks, their Detection and Prevention Techniques

SQL injection is a technique that exploits a security vulnerability occurring in the database layer of an application. The vulnerability is present when user input is either incorrectly filtered for string literal escape characters embedded in SQL statements or user input is not strongly typed and thereby unexpectedly executed.SQL injection is a trick to SQL query or command as an input possibly via the web pages. They occur when data provided by user is not properly validates and is included directly in a SQL query. By leveraging these vulnerabilities, an attacker can submit SQL commands directly access to the database. In this paper we present all SQL injection attack types and also different technique and tools which can detect or prevent these attacks . Finally we assessed addressing all SQL injection attacks type among current technique and tools.
Indirect Comparisons and Network Meta-Analyses Estimation of Effects in the Absence of Head-to-Head Trials — Part 22 of a Series on Evaluation of Scientific Publications

Background: Systematic reviews provide a structured summary of the results of trials that have been carried out on any particular subject. If the data from multiple trials are sufficiently homogenous, a meta-analysis can be performed to calculate pooled effect estimates. Traditional meta-analysis involves groups of trials that compare the same two interventions directly (head to head). Lately, however, indirect comparisons and network metaanalyses have become increasingly common.
Parallel and Scalable Combinatorial String and Graph Algorithms on Distributed Memory Systems

Methods for processing and analyzing DNA and genomic data are built upon combinatorial graph and string algorithms. The advent of high-throughput DNA sequencing is enabling the generation of billions of reads per experiment. Classical and sequential algorithms can no longer deal with these growing data sizes which for the last 10 years have greatly out-paced advances in processor speeds. To process and analyze state-of-the-art genomic data sets require the design of scalable and efficient parallel algorithms and the use of large computing clusters. Here, we present our distributed-memory parallel algorithms for indexing large genomic datasets, including algorithms for construction of suffix arrays and LCP arrays, solving the All-Nearest-SmallerValues problem and its application to the construction of suffix trees. Our parallel algorithms exhibit superior runtime complexity as well as practical performance compared to the state-of-the-art. Furthermore, we present our work on distributed-memory algorithms for clustering de-bruijn graphs and its application to solving a grand challenge metagenomic dataset. 1 DISTRIBUTED STRING INDEXING
Analysing Requirements Issues in Global Software Development

The main reason companies use Global Software Development services is the higher development cost of the local service providers. Many organizations are participating in Global software development (GSD), expecting to gain benefits, which includes reduction in cost, better use of shortage resources, access to talent, tax benefit and many more. At the same time, GSD poses challenges and difficulties due to geographic dispersion, time-zone differences, as well as cultural and language differences. These generic and common problems have created considerable difficulties in conducting Requirement engineering activities. In distributed projects, physical distances between stakeholders and group members limit their face-to-face interaction and thus create special challenges in the communication, which is an essential part in software development process. Despite these challenges, GSD is expected to continue to be an important strategic option for the software industry. The motive of this research work is to analyse the types of requirement issues that arises in GSD. A tool based methodology is proposed to manage the resolution of those issues, using a repository of current requirement issues Issues can be of many types, i.e. communication issues, cultural issues, client-vendor conflict, technical issues, conflicts in RE approaches, low client involvement and many more. In this repository issues can be recorded in such a way that all stake holders can access them globally. The issues can also be prioritised by the help of issue tracker application. Analysis of empirical and qualitative studies are also been overviewed using case studies of different organisations.
The effects of health literacy on treatment adherence and health optimism in chronic kidney disease patients

Health literacy is a very critical aspect of a patient’s recovery, and low levels of it have been correlated to a large range of negative health outcomes. Health literacy’s effects on treatment adherence and health optimism was examined in a sample of 32 Chronic Kidney Disease (CKD) patients who were recruited from a transplant center in the southeastern section of the United States. Participants had a mean age of 47.55, with 45.2% (n=14) of the participants being female and 54.8% (n=17) being Caucasian. The participants completed a survey, which incorporated the tools of Health Literacy Questionnaire, Life Orientation Tool, and the PlAnk 4 scale. The study found that (1) Health literacy was positively correlated with Health Optimism and (2) Health Literacy had a positively correlated with Treatment Adherence, supporting both of the study’s hypotheses. This study’s findings highlight the importance of health literacy in CKD and its association with health outcomes.
Email Access by Face Recognition

In existing system, email account can be accessed b y providing username and password. Disadvantage of existing system is username is known to everyone and password can be guessed. It is a tedious job to remember password of each account as user has many accounts like on Gmail, yahoo, social networ king sites etc. Proposed system will overcome all these disadvantages. Aim of proposed system is to implement a 2D face recognition technique using image processing a nd design a SMTP/POP3. Email client application that will use the face recognition module for validation and authentication of user. A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a vid eo frame from a video source. This kind of email application ensures that a person is who they claim to be, eli minating any worry of someone using illicitly obtained keys or access cards.
The Risk Level Estimation Based on Deep Learning Method for Tianya Forum

Using the societal risk indicators from socio psychology, a deep learning method is applied to estimate the risk level of Tianya Forum. Due to the effectiveness in semantic and word order information extraction for documents, a deep learning method Post Vector is used to generate the distributed representations of BBS posts. Through the experimental comparison on societal risk classification of BBS posts, the performance of kNN based on Post Vector is superior to kNN based on Bag-of-Words, edit distance or Lu-cene-based search method. Therefore, with kNN based on Post Vector method and the annotated data of Tianya Zatan broad, the risk level of Baixing Shengyin broad in different months is estimated, and the reasonability of the estimated results is analyzed.
WAGE INEQUALITY BETWEEN AND WITHIN PUBLIC AND PRIVATE SECTOR IN SERBIA IN THE TIMES OF AUSTERITY

Responding to a high fiscal deficit in the country, Serbian government introduced a set of fiscal consolidation measures at the beginning of 2015, including a ten percent public sector wage cut. This paper analyses the changes in the public sector wages and public sector wage premium after the measures were introduced. We also compare the changes in two subsectors within the public sector: state sector and state-owned enterprises. Results show that the public sector wage premium dropped by 6 percentage points in 2015, indicating a decrease in the wage inequality between the public and the private sector. Within the public sector, before the fiscal consolidation, the wages were ceteris paribus higher in the state-owned enterprises than in the state sector, mainly due to higher premium at upper parts of the wage distribution. The drop of the premium between the years was lower for the state-owned enterprises, due to their lower compliance to the wage cut. This trend resulted in an increased wage inequality within the public sector as a consequence of the fiscal consolidation.
Space diversity antenna arrangements mounted on a metal pole for illuminating a street cell

Two-branch vertical space diversity (V-SD) and horizontal space diversity (H-SD) antenna arrangements mounted on a metal pole to illuminate a street cell are investigated. Measurement results taken in a Japanese suburban area are also presented. These results clarify the required arrangements for both V-SD and H-SD configurations. Measured results show that the optimum arrangement of the H-SD configuration depends on the diameter of the pole on which the antennas are mounted.
O C ] 2 0 Ju l 2 01 8 Improved Conic Reformulations for K-means Clustering

In this paper, we show that the popular K-means clustering problem can equivalently be reformulated as a conic program of polynomial size. The arising convex optimization problem is NP-hard, but amenable to a tractable semidefinite programming (SDP) relaxation that is tighter than the current SDP relaxation schemes in the literature. In contrast to the existing schemes, our proposed SDP formulation gives rise to solutions that can be leveraged to identify the clusters. We devise a new approximation algorithm for K-means clustering that utilizes the improved formulation and empirically illustrate its superiority over the state-of-the-art solution schemes.
Using Segment-based Genetic Algorithm with Local Search to Find Approximate Solution for Multi-Stage Supply Chain Network Design Problem

Designing an optimal supply chain network (SCN) is an NP-hard and highly nonlinear problem; therefore, this problem may not be solved efficiently using conventional optimization methods. In this article, we propose a genetic algorithm (GA) approach with segment-based operators combined with a local search technique (SHGA) to solve the multistage-based SCN design problems. To evaluate the performance of the proposed algorithm, we applied SHGA and other competing algorithms to SCNs with different features and different parameters. The results obtained show that the proposed algorithm outperforms the other competing algorithms.
Volume 36 , Issue 2 Financial Integration and Japanese Stock market Performance

Our paper tests the conditional version of the International Capital Asset Pricing Model (ICAPM) applying a parsimonious multivariate DCC-GARCH process. By permitting the prices of risk and the level of market integration to vary through time, our results show that Japan experienced increases in the degree of regional integration in last years. The increasing integration into regional financial markets alone is unlikely to provide a sound ground for a currency union in ASEAN+5 at this stage, but improvement in welfare gains in the ASEAN+5 economies by means of further risk sharing is possible. We made the corrections after receiving the comments. Citation: Sandrine Kablan and Khaled Guesmi, (2016) ''Financial Integration and Japanese Stock market Performance'', Economics Bulletin, Volume 36, Issue 2, pages 1064-1070 Contact: Sandrine Kablan sandrinekablan@hotmail.com, Khaled Guesmi kalloda_fr@yahoo.fr. Submitted: July 05, 2015. Published: June 11, 2016.