nils-herrmann/clean_scirepeval huggingface dataset:
WATERMELON MOSAIC VIRUS OF PUMPKIN (Cucurbita maxima) FROM SULAWESI: IDENTIFICATION, TRANSMISSION, AND HOST RANGE

A mosaic disease of pumpkin (Cucurbita maxima) was spread widely in Sulawesi. Since the virus had not yet been identified, a study was conducted to identify the disease through mechanical inoculation, aphid vector transmission, host range, and electron microscopic test. Crude sap of infected pumpkin leaf samples was rubbed on the cotyledons of healthy pumpkin seedlings for mechanical inoculation. For insect transmission, five infective aphids were infected per seedling. Seedlings of eleven different species were inoculated mechanically for host range test. Clarified sap was examined under the electron microscope. Seeds of two pumpkin fruits from two different infected plants were planted and observed for disease transmission up to one month old seedlings. The mosaic disease was transmitted mechanically from crude sap of different leaf samples to healthy pumpkin seedlings showing mosaic symptoms. The virus also infected eight cucurbits, i.e. cucumber (Cucumis sativus) green melon (Cucumis melo) orange/rock melon (C. melo) zucchini (Cucurbita pepo) pumpkin (Cucurbita maxima) water melon (Citrulus vulgaris) Bennicosa hispida, and blewah (Cucurbita sp. Aphids transmitted the disease from one to other pumpkin seedlings. The virus was not transmitted by seed. The mosaic disease of pumpkin at Maros, South Sulawesi, was associated with flexious particles of approximately 750 nm length, possibly a potyvirus, such as water melon mosaic virus rather than papaya ringspot virus or zucchini yellow mosaic virus.
On implementation of DCTCP on three tier and fat tree data center network topologies
Venue: SpringerPlus

A data center is a facility for housing computational and storage systems interconnected through a communication network called data center network (DCN) Due to a tremendous growth in the computational power, storage capacity and the number of inter connected servers, the DCN faces challenges concerning efficiency, reliability and scalability. Although transmission control protocol (TCP) is a time tested transport protocol in the Internet, DCN challenges such as inadequate buffer space in switches and bandwidth limitations have prompted the researchers to propose techniques to improve TCP performance or design new transport protocols for DCN. Data center TCP (DCTCP) emerge as one of the most promising solutions in this domain which employs the explicit congestion notification feature of TCP to enhance the TCP congestion control algorithm. While DCTCP has been analyzed for two tier tree based DCN topology for traffic between servers in the same rack which is common in cloud applications, it remains oblivious to the traffic patterns common in university and private enterprise networks which traverse the complete network interconnect spanning upper tier layers. We also recognize that DCTCP performance cannot remain unaffected by the underlying DCN architecture hence there is a need to test and compare DCTCP performance when implemented over diverse DCN architectures. Some of the most notable DCN architectures are the legacy three tier, fat tree, BCube, DCell, VL2, and CamCube. In this research, we simulate the two switch centric DCN architectures; the widely deployed legacy three tier architecture and the promising fat tree architecture using network simulator and analyze the performance of DCTCP in terms of throughput and delay for realistic traffic patterns. We also examine how DCTCP prevents incast and outcast congestion when realistic DCN traffic patterns are employed in above mentioned topologies. Our results show that the underlying DCN architecture significantly impacts DCTCP performance. We find that DCTCP gives optimal performance in fat tree topology and is most suitable for large networks.
Modelling of the EPB TBM shield tunnelling advance as a tool for geological characterization

One of the main causes of problems during the construction of tunnels with tunnel boring machines (TBMs) is a lack of characterization of the soil. Both geological and hydrogeological characterizations are essential to avoid unexpected events. The advance of TBMs produces groundwater oscillations due to hydraulic and mechanical effects. The magnitude of these oscillations depends on the characteristics of the soil and on the "parameters" of the TBM (e.g. earth pressures, penetration) Given that the impact caused in the groundwater could be estimated numerically, this paper proposes to use hydrogeological models based on the parameters of the TBM to validate or improve the previous geological characterization. This procedure was tested by modelling the advance of a TBM type earth pressure balance (EPB) at a real site. This study arose during the construction of the tunnel for the high speed train in Barcelona. The previous geological characterization revealed a vertical fault whose exact position was unknown. The advance of the EPB was modelled to validate the previous characterization and to locate the fault. The numerical model included a detailed geology and hydrogeology of the study site and the parameters of the EPB. Note that the parameters of the EPB used in the model were more related to the groundwater response. These were determined statistically from all of the measures taken by the machine. Given the results obtained, hydrogeological modelling of EPBs was revealed to be a useful tool to validate previous characterizations, both the geological and the hydrogeological, and to determine the position of some geological structures, such as faults.
Optimizing Sparse Matrix Vector Multiplication for Large Scale Data Analytics
Venue: ICS

Sparse Matrix Vector multiplication (SpMV) is a fundamental kernel, used by a large class of numerical algorithms. Emerging big data and machine learning applications are propelling a renewed interest in SpMV algorithms that can tackle massive amount of unstructured data rapidly approaching the TeraByte range with predictable, high performance. In this paper we describe a new methodology to design SpMV algorithms for shared memory multiprocessors (SMPs) that organizes the original SpMV algorithm into two distinct phases. In the first phase we build a scaled matrix, that is reduced in the second phase, providing numerous opportunities to exploit memory locality. Using this methodology, we have designed two algorithms. Our experiments on irregular big data matrices (an order of magnitude larger than the current state of the art) show a quasi optimal scaling on a large scale POWER8 SMP system, with an average performance speedup of 3.8x, when compared to an equally optimized version of the CSR algorithm. In terms of absolute performance, with our implementation, the POWER8 SMP system is comparable to a 256 node cluster. In terms of size, it can process matrices with up to 68 billion edges, an order of magnitude larger than state of the art clusters.
Hardware accelerator for analytics of sparse data
Venue: 2016 Design, Automation Test in Europe Conference Exhibition (DATE)

Rapid growth of Internet led to web applications that produce large unstructured sparse datasets (e.g. texts, ratings) Machine learning (ML) algorithms are the basis for many important analytics workloads that extract knowledge from these datasets. This paper characterizes such workloads on a high end server for real world datasets and shows that a set of sparse matrix operations dominates runtime. Further, they run inefficiently due to low compute per byte and challenging thread scaling behavior. As such, we propose a hardware accelerator to perform these operations with extreme efficiency. Simulations and RTL synthesis to 14nm ASIC demonstrate significant performance and performance/Watt improvements over conventional processors, with only a small area overhead.
The Gamma Matrix to Summarize Dense and Sparse Data Sets for Big Data Analytics
Venue: IEEE Transactions on Knowledge and Data Engineering

Data summarization is an essential mechanism to accelerate analytic algorithms on large data sets. On the other hand, array DBMSs enable scalable computation with large matrices. With that motivation in mind, we propose a parallel array operator, based on a specific form of matrix multiplication, that computes a comprehensive data summarization matrix. By deriving equivalent equations based on the summarization matrix, statistical methods are adapted to work in two phases: (1) Parallel summarization of the data set in one pass; (2) Iteration exploiting the summarization matrix in many intermediate computations. We prove our summarization matrix captures essential statistical properties of the data set and it allows iterative algorithms to work faster in main memory, by decreasing the number of times the data set is scanned, and by reducing the number of CPU operations. Specifically, we show our summarization matrix benefits statistical models, including PCA, linear regression, and variable selection. From a systems perspective, we carefully study the efficient computation of the summarization matrix on the SciDB parallel array DBMS and how to exploit it in the R language statistical system. To achieve best performance, we introduce two specialized array operators for dense and sparse data sets, respectively. We present an experimental evaluation comparing SciDB, R, a columnar DBMS (a fast SQL engine) and Spark (a popular Hadoop system) Our experiments show R working together with SciDB eliminates main memory and performance limitations from R. More importantly, our R+SciDB prototype is significantly faster and more scalable than Spark and the columnar DBMS.
Sparse p adic data coding for computationally efficient and effective big data analytics
Venue: ArXiv

We develop the theory and practical implementation of p adic sparse coding of data. Rather than the standard, sparsifying criterion that uses the L0 pseudo norm, we use the p adic norm.We require that the hierarchy or tree be node ranked, as is standard practice in agglomerative and other hierarchical clustering, but not necessarily with decision trees. In order to structure the data, all computational processing operations are direct reading of the data, or are bounded by a constant number of direct readings of the data, implying linear computational time. Through p adic sparse data coding, efficient storage results, and for bounded p adic norm stored data, search and retrieval are constant time operations. Examples show the effectiveness of this new approach to content driven encoding and displaying of data.
Traffic Aware Cloud RAN: A Key for Green 5G Networks
Venue: IEEE Journal on Selected Areas in Communications

Next generation 5G wireless networks envision innovative radio technologies for ultra dense deployment with improved coverage and higher data rates. However, the deployment of ultra dense 5G networks, with relatively smaller cells, raises significant challenges in network energy consumption. Emerging green cloud radio access networks (C RANs) are providing assurance of energy efficient cellular operations for reduction of both greenhouse emissions and operators' energy bill. Cellular traffic dynamics play a significant role in efficient network energy management. In this paper, we first identify the complexity of the optimal traffic awareness in 5G C RAN and design a framework for traffic aware energy optimization. The virtual base station cluster (VBSC) of C RAN exploits an information theoretic approach to model and analyze the uncertainty of cellular traffic, captured by remote radio heads (RRH) Subsequently, using an online, stochastic game theoretic algorithm, the VBS instances optimize and learn the cellular traffic patterns. Efficient learning makes the C RAN aware of the near future traffic. Traffic awareness helps in selective switching of a subset of RRHs, thus reducing the overall energy consumption. Our VBS prototype implementation, testbed experiments, and simulation results, performed with actual cellular traffic traces, demonstrate that our framework results in almost 25% daily energy savings and 35% increased energy efficiency with a negligible overhead.
Factors affecting the quality of hospital hotel services from the patients and their companions' point of view: A national study in Iran
Venue: Journal of research in medical sciences the official journal of Isfahan University of Medical Sciences

The hospitality design of a hospital is a complex process that depends on careful planning, systematic thinking, and consideration of various factors. This study aimed to determine the viewpoints of patients and their relatives on factors affecting hospital hotel services in Iran in 2015. The results of this study can be used to design a suitable model for the assessment and improvement of hospitality service quality.  In this cross sectional descriptive study, 10 hospitals of Iran were included. The subjects of the study included 480 patients and their companions from different internal and surgical wards. Simple random sampling method was performed at the hospitals, where patients were selected through stratified sampling based on hospital wards, and in each ward, through systematic sampling based on the bed numbers. A researcher made questionnaire was used as the study tool which was developed through reviewing the literature and opinions of experts. Its internal reliability was determined based on Cronbach's alpha coefficient (a =0.85)  In reviewing the eleven aspects of hospital hotel services regarding the patients' and their companions' viewpoint, services related to all aspects, whether human, economic, operational, personnel identification, safety, health care services, physical, clinical welfare, cultural, patient guidance, or public welfare services, received mean scores of higher than three (out of five)  The present study showed that in the patients' and their companions' viewpoint, factors affecting hospital hotel services in the country are very important. The tool used in this study can be a criterion for assessing the status of the hotel services of the country's major hospitals, so accordingly, the assessment and improvement of the existing conditions can be possible.
Long Distance Dispersal Shaped Patterns of Human Genetic Diversity in Eurasia
Venue: Molecular biology and evolution

Most previous attempts at reconstructing the past history of human populations did not explicitly take geography into account or considered very simple scenarios of migration and ignored environmental information. However, it is likely that the last glacial maximum (LGM) affected the demography and the range of many species, including our own. Moreover, long distance dispersal (LDD) may have been an important component of human migrations, allowing fast colonization of new territories and preserving high levels of genetic diversity. Here, we use a high quality microsatellite data set genotyped in 22 populations to estimate the posterior probabilities of several scenarios for the settlement of the Old World by modern humans. We considered models ranging from a simple spatial expansion to others including LDD and a LGM induced range contraction, as well as Neolithic demographic expansions. We find that scenarios with LDD are much better supported by data than models without LDD. Nevertheless, we show evidence that LDD events to empty habitats were strongly prevented during the settlement of Eurasia. This unexpected absence of LDD ahead of the colonization wave front could have been caused by an Allee effect, either due to intrinsic causes such as an inbreeding depression built during the expansion or due to extrinsic causes such as direct competition with archaic humans. Overall, our results suggest only a relatively limited effect of the LGM contraction on current patterns of human diversity. This is in clear contrast with the major role of LDD migrations, which have potentially contributed to the intermingled genetic structure of Eurasian populations.
On the Asymptotic Superlinear Convergence of the Augmented Lagrangian Method for Semidefinite Programming with Multiple Solutions

Solving large scale convex semidefinite programming (SDP) problems has long been a challenging task numerically. Fortunately, several powerful solvers including SDPNAL, SDPNAL+ and QSDPNAL have recently been developed to solve linear and convex quadratic SDP problems to high accuracy successfully. These solvers are based on the augmented Lagrangian method (ALM) applied to the dual problems with the subproblems being solved by semismooth Newton CG methods. Noticeably, thanks to Rockafellar's general theory on the proximal point algorithms, the primal iteration sequence generated by the ALM enjoys an asymptotic Q superlinear convergence rate under a second order sufficient condition {for the primal problem} This second order sufficient condition implies that the primal problem has a unique solution, which can be restrictive in many applications. For gaining more insightful interpretations on the high efficiency of these solvers, in this paper we conduct an asymptotic superlinear convergence analysis of the ALM for convex SDP when the primal problem has multiple solutions (can be unbounded) Under a fairly mild second order growth condition, we prove that the primal iteration sequence generated by the ALM converges asymptotically Q superlinearly, while the dual feasibility and the dual objective function value converge asymptotically R superlinearly. Moreover, by studying the metric subregularity of the Karush Kuhn Tucker solution mapping, we also provide sufficient conditions to guarantee the asymptotic R superlinear convergence of the dual iterate.
Navigating Towards Shared Responsibility in Research and Innovation: Approach, Process and Results of the Res AGorA Project

The Res AGorA project Res AGorA was a three year, EU FP7 project (2013 2016) which has co constructed a good practice framework, the "Responsibility Navigator" with practitioners and strategic decision makers. This framework facilitates reflective processes involving multiple stakeholders and policy makers with the generic aim of making European research and innovation more responsible, responsive, and sustainable. This framework was developed based on three years of intensive empirical research comprising an extensive programme of in depth case studies, systematic "scientometric" literature analysis, country level monitoring (RRI Trends) and five broadbased co construction stakeholder workshops.  Responsibility Navigator was conceived as a means to provide orientation without normatively steering research and innovation in a specific direction. Furthermore, Res AGorA's "Co construction Method" is a collaborative methodology designed to systematically support and facilitate the practical use of the Responsibility Navigator with stakeholders. The Responsibility Navigator, the Co construction Method and accompanying materials areready to use by actors who wish to navigate Research and Innovation towards Responsible Research and Innovation. This book provides an overview of the project's journey, its conceptual underpinnings and main results.
EFFECT OF TALENT MANAGEMENT STRATEGIES ON EMPLOYEE RETENTION IN THE INSURANCE INDUSTY

Success of any organization depends on the strategies put in place to ensure they retain talented employees. Today's manager's challenge is to keep the staff engaged and ensure attraction and retention of talented employees. The manager has to know the current acquisition and retention strategies employed by other organisations. The goal of most companies is to benefit from talented employee behavior and contribution in the workplace by promoting a win win situation for both the company and workers. The purpose of this study was to evaluate effect of talent management strategies on employee retention in the insurance industry. This study was conducted within the insurance industry of which 5 Companies were sampled. The study was descriptive as the respondents only described the phenomena, but was not involved in any manipulation of the variables. The target population constituted 200 Assistant and Managers. Samples of 70 staff were selected based on simple random sampling method. Primary data was collected using a structured questionnaire. Data was analyzed using the SPSS package Version 21.0. Simple descriptive statistics was used to illustrate the various qualitative and quantitative factors that were conceptualized to influence the employee retention. The results were tabulated in form of frequency tables, pie charts and bar graphs to capture the number of responses to a particular variable in each determination. Further, the study adopted correlation and regression analysis at 5% level of significance to determine strength and direction of the relationship of the variables under study. The analysis showed that talent acquisition had the strongest positive (Pearson correlation coefficient .877) influence on employee retention. In addition, talent development, compensation strategies, succession planning are positively correlated to employee retention .588, .705, .0424 respectively).The study established that the management of the organization did not properly plan and manage career path. The succession plan strategy employed by the organisation has not improved their willingness to be retained in the organization. The study also recommends that on boarding programmes should help to ensure employees retention. The study can be conducted with emphasis to Kenyan owned companies with a more diverse group of employee. Also, different variables could be chosen that are different from the ones used in this study for the generalization of the findings of this study.  Talent Acquisition, Talent Development, Talent Compensation Strategies, Succession Planning, Employee Retention, Insurance Industry
Stock Market Prediction and Analysis Using Naive Bayes

The stock market is the most popular investing places for users. Because of its expected high profit. Recently forecasting stock market returns gaining more attention. The prediction of stock markets is regarded as a challenging task. Data analysis is the way of predicting future value. if future stocks prices will increase or decrease. The main objective of this paper is to predict future stock price using prediction concept. In that Parse Records then calculate predicted value and send to user. And automatically perform operations like purchase and sale shares using Automation concept. For that use Naive Bayes Algorithm. There is Real time Access by Download log forms yahoo finance website and Store in dataset. KeywordsShares, Artificial Neural Network; Naive Bayes Algorithm;Automation; Prediction; Stock Market __________________________________________________**_________________________________________________
Digital India: Unleashing Prosperity

Developments in the information and communication technology (ICT) have been taking place at a rapid pace. India in its E literate resources is fast emerging as a major initiator in adoption of Digitalization. In past we had just government and governance while the future will be characterized by Digital system means electronic government and electronic governance. The bigger challenge for government of India is to interact and communicate with the public because of large population size, geographical area and cultural diversity. Government was in the need of a system that must provide help in easy, quick and efficient communication with the public and can provide better services to them. With the help of ICT, Government of India has changed its processes and policies from manual system to computerized system. The Digital India initiative is a dream project of Government to transform India into a digitally empowered society and knowledge economy. Digital India is the dream project of the government of India and a blessing for the citizens, could help in connecting the dots of various projects, past and present to bring India to a global platform. Now, India becomes the powerhouse of IT in the world.  Digital India, ICT, Visions, Digital India initiatives, Pillars