Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Fiscal Policy in the DSGE-VAR Framework: The Case of the Czech Republic

In this paper we explore the potential of the DSGE-VAR modelling approach for examining the effects of fiscal policy. The combination of the VAR and DSGE frameworks leads theoretically to more accurate estimates of impulse responses and consequently of fiscal multipliers. Moreover, the framework allows for discussion about the differences of the effects of fiscal shocks in DSGE and VAR models and to some extent discussion about misspecification in fiscal DSGE models. The DSGE-VAR model is estimated on Czech data covering the period from 1996 to 2011 at quarterly frequency. The government consumption multiplier attains a value close to 0.4 at the horizon of four years. The public investment multiplier is about 0.4 higher, which confirms findings in the literature. On the other hand, the DSGE model alone implies a similar government consumption multiplier but a much lower public investment multiplier, suggesting misspecification of the fiscal DSGE model.
Predicting the relevance of search results for e-commerce systems

Search engines (e.g. Google.com, Yahoo.com, and Bing.com) have become the dominant model of online search. Large and small e-commerce provide built-in search capability to their visitors to examine the products they have. While most large business are able to hire the necessary skills to build advanced search engines, small online business still lack the ability to evaluate the results of their search engines, which means losing the opportunity to compete with larger business. The purpose of this paper is to build an open-source model that can measure the relevance of search results for online businesses as well as the accuracy of their underlined algorithms. We used data from a Kaggle.com competition in order to show our model running on real data.
The Flipped Approach: Past Research, Practical Applications, and Experiences in K-12 Science and Math Classrooms

Learning is an important part of the development of a child into a productive member of society. However, in today’s society much of the pressure of learning is not put on the student, but rather the teacher. In order to promote adults as lifelong learners who can go out and help solve problems in this world, the focus of learning in the classroom needs to be on the students. A flipped classroom rather than a traditional classroom approach may help with this endeavor. Students gain many benefits from the flipped classroom where they can watch lectures at home that pass important concepts along to the students. This gives students more time in class with the teacher to perform guided inquiries, labs, problem sets, or extra practice on a content area. The effectiveness of student achievement, growth, lifelong learning ability, and opinion of integrating a flipped classroom into a secondary education classroom are examined in this chapter.
Evaluation of Extracurricular Sports Activities as an Educational Element for Sustainable Development in Educational Institutions

Education that instills healthy habits has acquired much attention in recent years due to concerns related to obesity and the sedentary lifestyle of the scholastic population. Extracurricular sports activities can contribute to the creation of healthy habits and can promote active lifestyles. These positive habits provide social benefits and are a facilitator of sustainable development. Thisstudy had two objectives: To assess the quality and value of extracurricular sports activities offered by schools, as well as the satisfaction of the participants and their future intentions to participate; and to assess the relationship between these constructs, with the aim of identifying factors that encourage schoolchildren to be active. Information was gathered from 1080 children in secondary education in Spain (n = 1080) (65.90% boys, 13.76 ± 1.39 years). The instruments used were the Scale of Perception of Sports Organizations (EPOD2) and a future intentions scale. The evaluation of satisfaction, quality, and value, as well as the intentions expressed by the young athletes to continue participating in extracurricular sports activities were positive, with average values close to the maximum. The best-rated quality variables were human resources. A significant association was identified between communication and loyalty, and response capacity and sports spaces. Likewise, perceived satisfaction and value were related to loyalty and price. In short, student perceptions establish a clear relationship between evaluations of activities and the intention to continue practicing sports in schools.
Productivity as a Determinant of Labour Wage in New Zealand's Construction Sector

The empirical relationships between labour wages, unemployment rate and the labour productivity index in New Zealand’s construction sector (for the period of 1983–2017) were investigated. The Johansen cointegration test and vector error correction mechanism were used to determine the existence of long-run relationships between the variables and the adjustment process of the short-run disequilibrium into the long-run equilibrium. The results show that the labour productivity index positively affects the labour wage, while the effect of unemployment rate is negative in the long run. That is, the more productive the labour, the more the wages earned. Related statistical tests on the residuals proved that the model and its findings are reliable.
Impacts of FDI Inflows on Domestic Investment, Trade, Education, Labor Forces and Energy Consumption in Bangladesh

This study examines the impact of foreign direct investment on the domestic investment, trade, education, labor forces, and energy consumption in Bangladesh using annual time series data. It employs multivariate ordinary least square regression to find out the link between the variables. Multivariate OLS regression results show that FDI has a positive effect on the domestic investment, education, labor forces, and energy consumption in the country. It means that FDI assists the local investment, encourage education level, and energy consumption in promoting the economy. To explore the long-run relationship and cointegration between FDI and domestic investment, trade, and labor forces, the study uses Autoregressive Distributed Lag (ARDL) bound testing approach and Error Correction Model (ECM). Augmented Dickey-Fuller (ADF) test is used to check the stationarity of the variables. The ARDL model finds the long-run relation between FDI and domestic investment, trade, and labor forces in Bangladesh. So, the ARDL framework asserts that the variables move together in the long-run.
Cluster Splitting Based High Dimensional Metric Space Index B~+-Tree

In order to improve the query efficiency,K-means cluster approach is often used to estimate the data distribution in the context of high dimensional metric space index.But in previous work,the parameters of clustering are usually selected according to some heuristic manner.This paper presents a new high dimensional index approach—cluster splitting based high dimensional B~+-tree.Through cluster splitting,the data space is partitioned more finely to reduce the cost of data access.The relationship between cluster and the query cost is discussed,and based on the query cost model,this paper give formulas to compute the"optimal"parameters of the cluster which can minimize the query cost in theory.Experiment results show that the efficiency of the methods is better than iDistance,M-Tree and sequence scan,and the parameters computed by the formulas are very close to the real optimal one.
Cluster Assignment and Instruction Scheduling for Partitioned Register-Set Machines

For half a century, computer architects have been striving to improve uniprocessor computer performance. Many of their successful designs such as vliw and superscalar machines use multiple functional units trying to exploit instruction level parallelism in computer programs. As the number of functional units rises, another hardware constraint enters the picture — the number of register-file ports needed grows directly with the number of functional units. At some point, the multiplexing logic on register ports can come to dominate the processor’s cycle time. A reasonable solution is to partition the register file into independent sets and associate each functional unit with a specific register set. Such partitioned register sets have appeared in a number of commercial machines, such as Texas Instruments TMS320C6xxx dsp chips. Partitioned register-set architectures present a new set of challenges to compiler designers — the compiler must assign each operation to a specific clusters and coordinate data movement between clusters. In this thesis, we investigate five instruction scheduling methods with different scopes to find a suitable one for partitioned register-set architectures. Next, we examine previous algorithms for the combined cluster assignment and scheduling problem and propose two new algorithms that improve upon the prior art. Then we study the difficulties introduced by limited number of registers and provide an approach to handle them. Finally we take several other measurements of partitioned register-set architectures that may shed light on some of the architectural decisions.
Government As Customer of Last Resort: The Stabilizing Effects of Government Purchases on Firms

I document a beneficial effect of the government’s participation in product markets. Exploiting the 2008–2009 financial crisis as a natural experiment, I show that federal procurement contracts insulated government contractors’ performance from the crisis. By 2009, government contractors had 15% higher market capitalization, had 18% higher capital expenditures, and received 26% more bank credit than did similar firms. This stabilizing effect, in turn, spilled over into neighboring firms. An average amount of government purchases reduced local employment losses by 35% in retail industries and by 48% in industries supplying government contractors. Spillovers were particularly strong in high economic slack areas. Author has furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
Interacting implications of climate change, population dynamics, and urban heat mitigation for future exposure to heat extremes

One near-term expression of climate change is increased occurrence and intensity of extreme heat events. The evolution of extreme heat risk in cities depends on the interactions of large-scale climate change with regional dynamics and urban micro-climates as well as the distribution and demographic characteristics of people who live and work within these micro-climate areas. Here we use California as a testbed where we employ a suite of high-resolution (1.5 km) future regional climate simulations coupled with a satellite-driven urban canopy model and a spatially explicit population projection to investigate the interacting effects of climate change, population growth, and urban heat mitigation measures, such as cool roofs, on exposure to extreme heat events. We find that climate change and population growth reinforce with one another to drive substantial increases in future exposure to heat extremes, which are poised to become more frequent, longer, and more intense. Exposure to events analogous to historic high-mortality extreme heat waves increases by 3.5–6 folds. Widespread implementation of cool roofs can offset a substantial fraction (51%–100%) of the increased heat exposure and associated building energy demand owing to climate change in urbanized regions.
Adaptive Genetic Algorithm Based Artificial Neural Network for Software Defect Prediction

To meet the requirement of an efficient software defect prediction,in this paper an evolutionary computing based neural network learning scheme has been developed that alleviates the existing Artificial Neural Network (ANN) limitations such as local minima and convergence issues. To achieve optimal software defect prediction, in this paper, Adaptive-Genetic Algorithm (A-GA) based ANN learning and weightestimation scheme has been developed. Unlike conventional GA, in this paper we have used adaptive crossover and mutation probability parameter that alleviates the issue of disruption towards optimal solution. We have used object oriented software metrics, CK metrics for fault prediction and the proposed Evolutionary Computing Based Hybrid Neural Network (HENN)algorithm has been examined for performance in terms of accuracy, precision, recall, F-measure, completeness etc, where it has performed better as compared to major existing schemes. The proposed scheme exhibited 97.99% prediction accuracy while ensuring optimal precision, Fmeasure and recall.
Characterization of the macromolecular interactions of the key NHEJ components.

Non-homologous end-joining (NHEJ) is a major mechanism for repairing DNA double strand breaks in mammalian cells. Six ‘core’ NHEJ components have been identified to date: Ku70, Ku80, the DNA dependent protein kinase catalytic subunit DNA-PKcs, DNA ligase IV, XRCC4, and XLF-Cemunnos. In this thesis, the DNA binding properties of the Ku70/80 heterodimer are characterised by means of fluorescence anisotropy and electrophoretic mobility shift assays. Studies with duplexes holding 1, 2 or 3 heterodimers allowed to develop a binding model that can be applied to analyse the interactions of Ku with duplexes of any length. In addition, salt dependent studies indicate that electrostatic interactions play a major role in the binding of Ku to nucleic acids. The interaction of the Ku heterodimer with the DNA ligase IV/XRCC4 (LX) complex and the role of DNA-PKcs in regulating the NHEJ complex assembly are investigated. Protein-protein interaction experiments show that Ku interacts with DNA ligase IV via its tandem BRCT domain and this interaction is enhanced by the presence of XRCC4 and DNA. In particular, mutagenesis studies indicate that residues 643-748 encompassing the first BRCT domain of DNA ligase IV are necessary for binding. Moreover, Ku needs to be in its heterodimeric form to bind LX and the C-terminal of Ku80 is dispensable for this interaction. The presence of DNA-PKcs favours the interaction between Ku and LX, although its kinase activity induces the disassembly of the complex. Finally, the interaction of Ku with other factors potentially involved in NHEJ such as the replication protein A, PARP-1, and the WRN helicase is investigated. Collectively, these findings provide novel information on the macromolecular interactions that regulate the NHEJ pathway.
Le problème de l'influence sur les médias sociaux : étude d'une campagne stratégique de développement de notoriété sur Twitter

Les medias socionumeriques occupent une place grandissante dans le domaine de la communication et des campagnes de relations publiques. Ce travail de recherche pose un regard critique sur les objectifs et la conception d'une campagne strategique d'influence numerique par l'entremise d'influenceurs. Il s'agit ici de comprendre la perception de l'influence Web chez les professionnels en relations publiques ainsi que de distinguer les roles et les caracteristiques de l'influenceur, l'ambassadeur et le leader d'opinion. Les professionnels cherchent a mettre en place des campagnes d'influence, grâce a des notions d'engagement, de co-creation et la valorisation d'une image, mais il semblerait qu'ils organisent plutot des campagnes ameliorant la notoriete de l'organisation mandatrice. Dans le cadre de ce memoire, nous analysons les donnees tirees de la campagne de relations publiques de Tourisme Montreal de 2013, #MTLmoments. La demarche de recherche s'inscrit dans une methodologie essentiellement qualitative, bien qu'utilisant en partie des donnees de nature quantitative. La recherche s'articule d'abord autour de l'analyse de donnees socionumeriques collectees, puis eclaircit les notions d'influence et d'engagement telles que percues par les professionnels grâce a une entrevue individuelle avec un employe en relations publiques de Tourisme Montreal. Cette analyse nous amene a proposer une actualisation du concept de l'influence tel que vue par le sociologue Elihu Katz (1957), le Two-Step Flow of Communication. Plusieurs constats concernant les professionnels du milieu decoulent de cette recherche empirique : - la perception de l'influence s'appuierait sur le nombre d'abonnes plutot que sur le calcul de l'influence, - l'incapacite a etablir des objectifs quantifiables lors des campagnes numeriques, ce qui engendrerait une confusion entre la notion d'influence et la notion de portee, - la difficulte a cerner les differentes caracteristiques appartenant a l'influenceur, au leader d'opinion et a l'ambassadeur. Ce travail de recherche leve le voile sur la necessite d'etablir une valeur d'influence mesurable. ______________________________________________________________________________ MOTS-CLES DE L’AUTEUR : Influence, notoriete, relations publiques, medias socionumeriques, influenceur
Video-Based FLame Detection for the protection of Cultural Heritage a , O. Gunay b , K. Kose b , F. Erden b ,

The majority of cultural heritage and archaeological sites, especially in the Mediterranean region, are covered with vegetation, which increases the risk of fires. These fires may also break out and spread towards nearby forests and other wooded land, or conversely start in nearby forests and spread to archaeological sites. Beyond taking precautionary measures to avoid a forest fire, early warning and immediate response to a fire breakout are the only ways to avoid great losses and environmental and cultural heritage damages. The use of terrestrial systems, typically based on video cameras, is currently the most promising solution for advanced automatic wildfire surveillance and monitoring due to its low cost and short response time. Early and accurate detection and localization of flame is an essential requirement of these systems, however, it remains a challenging issue due to the fact that many natural objects have similar characteristics with fire. This paper presents and compares three video-based fl.
Research needs in systems engineering: Report from a University of Alabama in Huntsville workshop

The success of systems engineering (SE) efforts to solve development issues benefit from the mutually advantageous relationship between academia and practice. This relationship can be threatened by lag in adoption and differences in focus between the two worlds. In an effort to help maintain alignment between SE research and practice, a 2‐day workshop was held at the University of Alabama in Huntsville on research needs in SE. The workshop was attended by individuals from academia, the Department of Defense, NASA, and the aerospace industry. This paper presents summaries of the discussions that took place, as well as possible research questions informed by the results of the workshop. The workshop consisted of an introductory roundtable and three breakouts sessions focusing on requirements, verification, and validation; configuration and data management; and systems acquisition and design. Several topics emerged as important concepts for future research, and research questions were developed relating to model‐based systems engineering (MBSE), requirements, modeling, sociotechnical issues, and education.
Neural computation of visual motion in macaque area MT

How does the visual system determine the direction and speed of moving objects? In the primate brain, visual motion is processed at several stages. Neurons in primary visual cortex (V1) filter incoming signals to extract the motion of oriented edges at a fine spatial scale. V1 neurons send these measurements to the extrastriate visual area MT, where neurons are selective for direction and speed in a way that is invariant to simple or complex patterns. Previous theoretical work proposed that MT neurons achieve selectivity to pattern motion by combining V1 inputs consistent with a common velocity. Here, we performed two sets of experiments to test this hypothesis. In the first experiment, we recorded single-unit V1 and MT responses to drifting sinusoidal gratings and plaids (two gratings superimposed). These stimuli either had jointly varying direction and drift rate (consistent with a constant velocity) or independently varying direction and drift rate. In the second experiment, we presented arbitrary, randomly chosen combinations of gratings in rapid succession in order to sample, as widely as possible, the space of stimuli that could excite or suppress neural responses. Responses to single gratings alone were insufficient to uniquely identify the organization of MT selectivity. To account for MT responses to both simple and compound stimuli, we developed new versions of an existing cascaded linear-nonlinear model in which each MT neuron pools inputs from V1. We fit these models to our data. By comparing the performance of the different model variants and examining the parameters that best accounted for the data, we showed that MT responses are best described when selectivity is organized along a common ve-
Intrusion Detection: Modeling System State to Detect and Classify Aberrant Behavior

We present a dual-phase host-based intrusion detection process. We have demonstrated, through experimental validation, that our process improves the current state of intrusion detection capabilities. The first phase uses cluster analysis to compare samples of low-level operating system data to an established model of normalcy. The second phase takes instances of non-conforming data from phase-1, maps that data to instances of our target-centric ontology and reasons over it. The reasoning process serves two purposes: primarily it is intended to classify the anomalous data as a specific type, or class, of attack. Its secondary purpose is to provide an orthogonal test to differentiate between true and false positives. We developed a novel metric (self-distance) to quantify the streams of system calls generated by a process and we have constructed a feature set from the low-level operating system data, which is subsequently used as input to the clustering process. We experimented with different clustering algorithms (Fuzzy c-Medoid, k-Means, and Principal Direction Divisive Partitioning), distance measures (Euclidean and Mahalanobis), and the effects of znormalizing the data set. Our experiments indicated that the Fuzzy c-Mediod algorithm using the Mahalanobis metric as a distance measure was the optimal performer, yielding an F-Measure of . 9822. The F-Measure is a common method for describing accuracy and is combination of precision and recall. We experimentally demonstrated the case for migrating from taxonomic classification systems and their syntactical representation languages to ontologies and semantically rich ontology specification languages. We created a data model of the relationships that hold between the low-level data and instances of attacks and intrusions. We used the DARPA Agent Markup Language + Ontology Inference Layer to specify the data model as a ontology and the Java Theorem Prover, a sound and complete First Order Logic theorem prover, to reason over and classify instances data that were deemed to be anomalous in the first phase of our process. Our classification mechanism achieved an F-Measure of . 9776. The overall F-Measure of our dual-phase process was . 9718. Ignoring the characteristics of the data population is a classic mistake that is made when evaluating intrusion systems. This is also referred to as the base-rate fallacy. When evaluating the posterior probability (the probability of an alarm given an intrusion) of our process, we achieved a score of . 998. We also present two novel mechanisms to detect and mitigate aberrant behaviors encountered in Mobile Ad Hoc and Wireless Sensor networks. Both of these networks consist of resource constrained devices. Accordingly, we present our intrusion detection mechanisms as protocols that monitor network state rather than system state.