Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Heterogeneous Workload Performance in Server Virtualization Based on User Behaviors

Many organizations emphasize the reduction of power consumption in their IT enterprises. Virtualization techniques are gaining attention in recent years. Server virtualization can help to consolidate servers, improve hardware utilization and reduce the consumption of power and physical space in the data center. However, management of heterogeneous workloads in this system becomes a challenge. This research proposes a new concept for managing workloads based on user behavior. The experiment is categorized into two parts. First, data mining technique is used to explore the trend of user behaviors in each server service. The preliminary results showed that user behaviors are different in each type of service workload and time. Next, the prediction focuses on hardware resources, CPU and memory based on real behaviors are presented. Different prediction algorithms such as a simple exponential smoothing, a double exponential smoothing (Holt’s method) and a winters’ threeparameter trend and seasonal method are then applied.
Would you rather collect data in the rain or attend a virtual field trip? Findings from a series of virtual science field studies

Virtual field trips open new possibilities for instructional designers to create more interactive worlds for learners. Three virtual field trips are examined which have all been developed for the Open University undergraduate science courses and make clever and innovative use of QuickTime Virtual Reality to allow students to enter three contrasting environments. Learning gains have been ascertained from pre- and post-test cognitive change scores. Perceived learning was also measured with a post experience questionnaire. The findings are interesting in that students felt they learnt more from the virtual environment than standing in the cold identifying biological samples. However, when it came to dealing with rock samples, students wanted to handle the real thing. This paper describes the advantages and disadvantages of using virtual science field trips and suggests the fine-tuning of feedback to students requires careful consideration in these types of virtual learning environments.
Generalized Frequency-Domain Correlator for Software GPS Receiver: Preliminary Test Results and Analysis (PREPRINT)

Abstract : The generalized frequency-domain correlator (GFDC) is introduced in this paper within the framework of a software GPS receiver which implements a frequency-domain baseband signal processor. The use of a GFDC can offer several advantages. First, as a generalization of the FFT-implemented correlation with a block repetitive processing capability, it enables fast acquisition through simultaneous code delay and Doppler frequency search. Second, it produces the delay-Doppler map of (generalized) complex correlations from which the joint code delay and carrier phase and frequency error discriminator can be implemented for grid tracking with the joint code and carrier loops, thus blurring the line between acquisition and tracking. Third, the same operation is applicable, without any other changes except for the replica code, to both the BPSK codes such as GPS C/A-code and the BOC modulations such as M-code. Fourth, it can switch seamlessly among different types of correlation/matching operations with a wide peak base for acquisition and a narrow peak base for tracking. Fifth, the generalized correlation has a sharp peak with a reduced base width, thus making its timing estimation more accurate and less sensitive to multipath. In this paper, the generalized frequency-domain correlator (GFDC) is formulated and different implementation schemes for spectrum filtering are presented. The experimental data (a set GPS data recorded on a large aircraft) are used to demonstrate the ability of the GFDC to uncover closely-spaced multipath signals more easily than the conventional correlators. The practical issue of limited signal bandwidth and its effects on both the conventional correlators and the GFDC are analyzed via computer simulation.
Capital Structure Choice: Macroeconomic Conditions and Financial Constraints. (WorkingPaper # 279, Forthcoming Journal of Financial Economics, 2002)

The empirical literature provides conflicting assessments about how firms choose their capital structures. Distinguishing among the three main hypotheses (â€˜â€˜tradeoffâ€™â€™, pecking order, and market timing) requires that we know whether firms have long-run leverage targets and (if so) how quickly they adjust toward them. Yet many previous researchers have applied empirical specifications that fail to recognize the potential for incomplete adjustment. A more general, partial-adjustment model of firm leverage indicates that firms do have target capital structures. The typical firm closes about one-third of the gap between its actual and its target debt ratios each year.
Manufacturing firms' reliance on past or future sales information in inventory production: an international study / Haihong He

This study examines whether manufacturing firm’s preference for use of past or future period sales information in determining inventory production is associated with two country level variables – management control system score and shareholder protection level. This study uses archival data to empirically examine the association between inventory production and sales information across twenty countries with different management control system score measured by Bloom et al. (2012) and different shareholder protection level measured by La Porta et al. (1997, 1998) and Djankov et al. (2008). This study finds that the entire sample experience a positive association between current period inventory production and future period sales change and no association between current period inventory production and past period sales change, suggesting that firms generally use future or projected sales change information instead of past sales change information in making production decisions. The study further shows that, after partitioning the sample based on country characteristics, the reliance on the future sales change information appears greater when firms are in countries with low management control system score or with high shareholder protection level. This study uses a large sample to gauge systematic differences in the use of management accounting information in inventory production across countries. The results suggest that less emphasis on management control (which may indicate greater emphasis on decision making) and high shareholder protection level have a positive effect on the firm’s preference of future information.
SOFTWARE DEFINED NETWORK AND OPENFLOW: A CRITICAL REVIEW

Software Defined Networks (SDN) is an emerging new network paradigm which enables network programmability and breaks the network vertical integration by separating network intelligence from underlying network devices such as routers and switches. SDN promotes the logically centralized control to program the network. SDN decouples data plane and control plane of the network devices to simplify the network management and great innovation by network programmability, using OpenFlow as a communication protocol between SDN controller and network elements. This paper presents a comprehensive critical survey on SDN and OpenFlow. The main aim of this paper is to give a brief introduction of SDN, the basic architecture of SDN and to show the control plane and data plane separation. The building blocks of SDN as layers are provided with study of infrastructure, southbound, controllers, northbound and network applications. Later research challenges and distributed computing in SDN are discussed to provide future researcher's brief idea about the future scope in the field.
A new encoding scheme for solving job shop problems by genetic algorithm

A new encoding scheme in genetic algorithms is proposed by using an ordering string for the classic job shop scheduling problem. A genetic algorithm is designed for searching the semi-active schedule space, which is encoded by the string space. A new crossover, set-partition crossover is introduced in accompanying the genetic searching. An associated selection strategy and a production structure are properly established for this fashion of encoding. This encoding scheme naturally overcomes the infeasibility problem in genetic iterations. Experiments show that the proposed genetic algorithm is effective, and optimal solutions are attainable in some probability for Fisher and Thompson problems with definite hardness.
A hybrid NeoART/EBP architecture for hand-written digit recognition

The authors propose the architecture of a hybrid Neo-ART/EBP (adaptive resonance theory/error-back-propagation) neural network and describe the results that may be achieved for digit recognition applications. Joining together a simplified input ART layer and an output EBP network makes it possible to reduce the global number of hidden nodes/interconnections and to speed up the convergence time during the training phase. Different strategies are exploited during the learning step to achieve lower total error and faster convergence time. Moreover, in the pattern space, both circular and elliptical regions are investigated, and their influence is discussed. <<ETX>>
Performance of BPSK and GMSK with multiple cochannel interferers

The bit error rate performance of binary PSK (BPSK) and Gaussian minimum shift keying (GMSK) in the presence of multiple cochannel interferers and additive white Gaussian noise (AWGN) is analysed. In particular, a closed form expression is given to evaluate the bit error probability of BPSK with multiple cochannel interference, whereas GMSK is analyzed by simulation. It is shown that in order to determine the minimum signal-to-interference ratio required to obtain a fixed bit error probability, care must be given to the number of active interferers: in fact, strong differences are found when varying the number of interfering cochannel signals, using the same signal-to-total interference ratio.
Singular-vector-excited linear predictive speech coder

Excitation codebooks built around a set of basis vectors have spurred the development of high quality speech coders for mobile communications. The basic excitation search procedure of the code-excited linear predictive coder (CELP) is presented. The singular vector (SV) representation of the filter and the excitation is introduced. Thereby, a non-iterative excitation search procedure is defined. The proposed singular-vector-excited linear predictive (SVELP) coder is built around two adaptive codebooks, namely, the SV codebook and a standard lagged excitation adaptive codebook. Preliminary results evidence the potential of SVELP for mobile as well as multimedia communications. <<ETX>>
Determinants of International Remittance Inflows in Middle-Income Countries in Asia and the Pacific

The middle-income trap is a serious problem in developing Asia and Pacific economies. Middle-income trap is the situation in which a country’s growth slows after reaching middle-income levels and the transition to high-income levels becomes unattainable. International remittances of immigrants to their country of origin is one of the most important elements contributing to the development of middle-income countries. By using a data set consisting of 12 Asia and Pacific middle-income countries—most of which are well-known migrant-sending countries—and by employing a panel data analysis technique, we tried to find the determinants of international remittance. Results show that per capita gross domestic product growth in origin countries and wage growth rate in destination countries are positively correlated with remittance inflows in middle-income countries, respectively. On the other hand, net foreign direct investment (FDI) inflows are negatively correlated with remittance inflows. This can be interpreted as the paradigm shift of acquiring foreign capital in middle-income countries from remittances in earlier stages of development to more FDI when the country prepares the requirements for absorbing the foreign capital with an economic growth. Moreover, real effective exchange rate, the level of education, trade openness, and political stability are positively associated with remittance inflows.
HLTCOE Submission at TREC 2013: Temporal Summarization

Our team submitted runs for the first running of the TREC Temporal Summarization track. We focused on the Sequential Update Summarization task. This task involves simulating processing a temporally ordered stream of over 1 billion documents to identify sentences that are relevant to a specific breaking news stories which contain new content. In this paper, we describe our approach and evaluation results.
OPTIMIZATION OF PROCESS PLANNING PARAMETERS FOR ROTATIONAL COMPONENTS BY GENETIC ALGORITHMS

In CAPP systems process parameter optimization is one of the key areas for research and development. Traditional techniques have very limited scope because of the complexity of the optimization problem. Due to the rapid development of computer technology Genetic Algorithms (GAs), which are robust search algorithm, have been found to be suitable and efficient tools for optimization in such cases. In this work process planning parameters for machining rotational components are optimized by a Genetic Algorithm Optimization Toolbox developed in Matlab environment. Here machining time is considered as the objective function and constraints are machine capacity, limits of feed rate, depth of cut, cutting speed etc. Machining time is minimized through a series of generations while some genetic operators are applied at each generation. The result of the work shows how a complex optimization problem is handle by a genetic algorithm and converges very quickly.
Structural Analysis of Efficient Error Control Scheme in PLC Environment

Turbo codes are attractive compared with Low Density Parity Check (LDPC) codes for Forward Error Correction (FEC) applications mainly due to their better performance, specially at low Signal-to-Noise Ratio (SNR) as are common in Powerline channels. Turbo Code is patented hence it is costlier for HomePlug device so it cannot be used. For small block length other code like LDPC which gives better performance as Turbo Code but at low SNR. Complexity of LDPC is increased when block length increase and it works well with low code rate. Complexity of Polar code is not increase even though its block length increases. The objective of this paper is to implement Polar code on PLC Channel to reduce the complexity and improve BER using specific Block Length.
Organizational learning during policy implementation: lessons from U.S. forest planning

ABSTRACT Learning is critical for land management agencies implementing new policies in the face of rapid social and ecological change. We investigated learning in the U.S. Forest Service as it implemented new planning regulations. Our research objectives were to: (1) identify collective learning processes and outcomes during this time, and (2) understand factors within the organization supporting or impeding learning. Based on participant observation and 25 interviews with planning personnel, we found evidence of collective learning on individual national forests and across the organization. Several factors helped the agency act as a ‘learning organization,’ including internal networks and tools for information sharing, and meetings for staff to exchange lessons learned. Learning was compromised by limited time and capacity, and lack of internal clarity about balancing the desire for innovation with the need to ensure legal compliance and meet deadlines. This work contributes to the empirical foundations of collective learning theory, allowing us to identify learning processes and outcomes at multiple levels in a public organization, and identifying topics for future research. Based on our exploration of organizational learning, we offer suggestions for how to effectively support learning during times of new policy implementation.
A comparison of denoising methods for one dimensional time series

This report presents the results of a comparison of denoising methods for one dimensional time series. The comparison has been carried out within the DFG Priority Program 1114 ”Mathematical methods for time series analysis and digital image processing”. The aim of this report is to present an extensive comparison of some basic denoising methods and some more elaborated methods. We apply the different methods to a set of noisy test time series and measure the performance with different error measures. One surprising result is, that in some cases which are assumed to be difficult, the most easy methods (namely a simple moving average) yield the best results.
Neural classification with genetic algorithms

Classification of terrain cover using polarimetric radar is an area of considerable interest and research. This paper describes the application of neural network approach to the classification of a fully polarimetric SAR image. The structure and connection weights of the network are decided based on the cascade correlation algorithm. The procedures are performed by interleaving the minimization of the total output error and the maximization of the correlation of the new inserted hidden unit with the residual error. While the training is stuck at a local minimum, it is possible that a useless unit will be permanently installed to the network. This problem can be solved partially by applying other optimization methods such as genetic algorithms. Experimental results showed that the proposed genetic learning method has higher classification rate and can create more compact networks, in terms of number of hidden nodes, than that of the standard cascade correlation algorithm.
Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform .2. With context models

For pt. I see ibid., vol.46, p.755-88 (2000). The concept of context-free grammar (CFG)-based coding is extended to the case of countable-context models, yielding context-dependent grammar (CDG)-based coding. Given a countable-context model, a greedy CDG transform is proposed. Based on this greedy CDG transform, two universal lossless data compression algorithms, an improved sequential context-dependent algorithm and a hierarchical context-dependent algorithm, are then developed. It is shown that these algorithms are all universal in the sense that they can achieve asymptotically the entropy rate of any stationary, ergodic source with a finite alphabet. Moreover, it is proved that these algorithms' worst case redundancies among all individual sequences of length n from a finite alphabet are upper-bounded by d log log n/log n, as long as the number of distinct contexts grows with the sequence length n in the order of O(n/sup a/), where 0 < /spl alpha/ < 1 and d are positive constants. It is further shown that for some nonstationary sources, the proposed context-dependent algorithms can achieve better expected redundancies than any existing CFG-based codes, including the Lempel-Ziv (1978) algorithm, the multilevel pattern matching algorithm, and the context-free algorithms in Part I of this series of papers.
Performance Improvement of ICP-based Outdoor SLAM Using Terrain Classification

This research deals with outdoor SLAM based on local elevation map matching and proposes a new terrain classification method for efficient map matching. The main map used for outdoor SLAM is an elevation map which consists of 2-D grids with elevation information on each cell. 3-D environmental data acquired by tilting a 2-D laser scanner are used to build a local elevation map and classify the local terrain. The experimental results show that the proposed method can enhance performance of outdoor SLAM.
An Evaluation Method of Node Importance in Complex Network

The evaluation of node importance is a very meaningful research in complex networks. This paper analyze the characteristics of complex network and consider the effects of nodes for the evaluation of node importance, introduces the idea of data field in theoretical physics and establishes the evaluation method of node importance based on topological potential in complex network. Through the theoretical and experimental analysis, it is proved that this method can evaluate the importance of node in complex network in a fast and accurate way, which is significant both to theory and practice.
Reusability of Search Index over Encrypted Cloud Data on Dynamic update

Cloud computing is generating lot of interest to provide solution for data outsourcing and high quality data services. More and more institution, organizations and corporations are exploring the possibility of having their applications, data and their IT assets in cloud. As the data and there by the cloud's size increases searching of the relevant data is expected to be a challenge. To overcome this challenge, search index is created to aid in faster search. However, search Index creation and computation has been complex and time consuming, leading to cloud-down time there by hindering the swiftness in reacting to data request for mission critical requirements. Focus of this paper is to explain how in the proposed system, reusability of search index is helping to reduce the complexity of search index computation. Search index is proposed to be created using parameters like similarity relevance, user ranking and scheme robustness. User ranking helps to guarantee why a phrase or a sentence or a key word is used frequently in the uploaded data. The proposed system ensures that the reusability of search index concept, highly reduces cloud down time while maintaining the security using searchable symmetric encryption (SSE). The user requested file is retrieved from the cloud, using Two-round searchable encryption (TRSE) scheme that supports top-k multi-keyword retrieval.
Open-Domain Question-Answering

Open-Domain Question Answering is an introduction to the field of Question Answering (QA). It covers the basic principles of QA along with a selection of systems that have exhibited interesting and significant techniques, so it serves more as a tutorial than as an exhaustive survey of the field. Starting with a brief history of the field, it goes on to describe the architecture of a QA system before analysing in detail some of the specific approaches that have been successfully deployed by academia and industry designing and building such systems. Open-Domain Question Answering is both a guide for beginners who are embarking on research in this area, and a useful reference for established researchers and practitioners in this field.
A step-down boosted-wordline scheme for 1-V battery-operated fast SRAM's

Fast and low-power circuit techniques for battery-operated low-voltage SRAM's are described. To shorten the read access time with low power dissipation, the step-down boosted-wordline scheme, which is combined with current-sense amplifiers, is proposed. Boosting a selected-wordline voltage shortens the bitline delay before the stored data are sensed. The power dissipation while selecting a wordline is suppressed by stepping down the selected-wordline potential. Moreover, to reduce the standby power, a switched-capacitor-type boosted-pulse generator, which is controlled by an address transition detection (ATD) signal, is used. A 61 kword/spl times/16-bit organization SRAM test chip was fabricated using the 0.5-/spl mu/m multithreshold-voltage CMOS (MTCMOS) process. The power dissipation in the memory array is reduced to 57% (1 mW) at 10 kHz operation in comparison with the conventional boosted-wordline scheme.