Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking Replication in Software Engineering: Can We See the Forest for the Trees?

In this paper, we argue that the concept of replication of empirical studies in software engineering should be understood more broadly than it currently is. In particular, the replication of case studies and surveys as a way of validating and extending theories should be incorporated in the mainstream view of replication, which at present is mostly focused on controlled experiments. A small-sample study of papers in IEEE Transactions on Software Engineering shows that about 10% of studies published in 2009 can be considered replications. However, none of these was self-labeled as replication. We think that the authors believed that labeling their work as replication might decrease its value in the eyes of reviewers and editors. We conclude that there is no acute shortage of replication studies in software engineering if taking a broader viewpoint to replication, but the definition and valuation of ‘replication studies’ need to be re-evaluated in the software engineering community.
What kind of hardware is necessary for a fuzzy rule based system?

Special hardware for a fast evaluation of fuzzy rule based systems (FRBSs) are available, but in most applications only standard hardware or specific non-fuzzy ASICs are used. In this paper we discuss some of the reasons of this gap and give hints for a fast realization of FRBSs, especially on digital standard processors. For the realization, a fast algorithm with an optimized calculation of the antecedent of the fuzzy rules is introduced which minimises the number of operations. This is possible because the FRBS is a universal approximator. Furthermore, some implementation possibilities are discussed depending on their complexity. <<ETX>>
TinyOS – An Operating System for Tiny Embedded Networked Sensors

This paper discusses the background and application requirements that motivated the development of TinyOS. It enumerates the characteristics associated with any typical Networked Sensor application. The hardware platform that was used for deploying TinyOS is also described. The design aspects of the Event based TinyOS is discussed in detail. This paper also enlightens the Tiny Active Messaging Model used by the TinyOS for data communication and its implementation concepts. The paper goes ahead and describes some application level communication concepts like managing packet buffers, Network discovery and Ad Hoc Routing and Media Access and Transmission Control. Then there is a bunch of discussions that includes topics like Evaluation of TinyOS, Comparison of TinyOS with other Operating Systems and Future Research Work.
Inflation, Relative Prices and Nominal Rigidities

This paper examines the distribution of Belgian consumer prices and its interaction with aggregate inflation over the period June 1976-September 2000. Given the fat-tailed nature of this distribution, both classical and robust measures of location, scale and skewness are presented. We found a positive short-run impact of the skewness of relative prices on aggregate inflation, irrespective of the average inflation rate. The dispersion of relative prices has also a positive impact on aggregate inflation in the short run and this impact is significantly lower in the sub-sample starting in 1988 than in the pre-1988 sub-sample, suggesting that the prevailing monetary policy regime has a substantial effect on this coefficient. The chronic right skewness of the distribution, revealed by the robust measures, is positively cointegrated with aggregate inflation, suggesting that it is largely dependent on the inflationary process itself and would disappear at zero inflation. These results have three important implications for monetary policy. First, as to the transmission of monetary policy, our results are in line with the predictions of menu cost models and therefore suggest that this type of friction can be an important factor behind the short run non-neutrality of monetary policy. Second, as to the design of robust estimators of core inflation, economic arguments based on menu cost models tend to highlight the importance of the absence of bias. We have proposed an unbiased estimator by taking the time-varying degree of chronic right skewness explicitly into account. Third, as to the optimal rate of inflation, the chronic right skewness found in the data provides no argument against price stability, as it appears as an endogenous response of optimising price setters and would disappear when targeting a zero inflation rate. This conclusion contrasts sharply with the implications of the exogenously assumed downward rigidity of Tobin (1972), which would justify targeting a sufficiently positive inflation rate in order to facilitate the adjustment of relative prices. Our empirical findings contradict the latter type of downward rigidity which implies a negative correlation between skewness and inflation. Therefore, the cross-sectional properties of Belgian inflation data do not provide strong arguments against a price stability-oriented monetary policy, such as the one pursued by the Eurosystem.
A Throughput Driven Task Scheduler for Improving MapReduce Performance in Job-Intensive Environments

MapReduce has been proven to be a highly desirable platform for scalable parallel data analysis. The task scheduling in MapReduce is very crucial for the job execution and has a marked impact on the system performance. To the best of our knowledge, the previous scheduling algorithms rarely consider the job-intensive environments and are not able to provide high system throughput. Hence this paper proposes a novel technique for job-intensive scheduling to improve the system throughput. Firstly, by making an in-depth analysis of job-intensive environments, we sum up 4 major factors which affect the system throughput. Secondly, based on the factors, an efficient technique, called throughput driven task scheduler is proposed, in which, we adopt a series of effective measures to improve the throughput of a MapReduce cluster system. Finally, plenty of simulation experiments are made and the experimental results show that the scheduler can provide higher throughput than the previous systems and is able to meet the requirements of practical job-intensive applications.
Predicting Debris-Slide Locations in Northwestern California

We tested four topographic models for predicting locations of debris-slide sources: 1) slope; 2) proximity to stream; 3) SHALSTAB with “standard” parameters; and 4) debris-slide-prone landforms, which delineates areas similar to “inner gorge” and “headwall swale” using experience-based rules. These approaches were compared in three diverse study areas of northwestern California having multiple inventories of historical non-road-related debris slides in a variety of topographic settings. We implemented the models in a GIS using USGS 10-m digital elevation models (DEMs). The topographic models show moderate predictive success. Slope performs comparatively well in all study areas. SHALSTAB is rarely superior. The proximity-to-stream model is competitive in one area but falls short in the others. The landforms model performs somewhat better than the others for nearly all the debris-slide data sets in all three areas, and appears especially effective for large debris slides that deliver sediment to streams. Large landslide deposits also influence the propensity for debris sliding in some areas. The areal density of historical debris-slide sources in steep ground within large, geomorphically fresh landslide deposits can be more than twice that in steep ground outside landslide deposits. Thus, prediction of debris-slide sources can be improved using maps of geomorphically fresh large landslide deposits.
Integrated Framework for Automating the Structural Design Iteration

Structural designers especially in India use STAAD (Structural Analysis And Design) software to execute the structural analysis and design. This analysis and design follow typical steps in sequence starting from model preparation by defining nodes and elements, material properties, support conditions, loads along their combinations, analyses, design options, etc. During this design process in STAAD, iterations generally occur either due to changes in structural loads and their combinations; or to obtain the optimal design section. In this entire design process, when any of the information exchange occurs along the cycles/loops it is termed as iteration. Automating these structural iterations can speed up the design evolution on STAAD. Till date, negligible steps have been taken towards automating these structural design iterations. DSM (Design Structure Matrix) is identified as a promising tool for representing and modelling iterative loops. It is a matrix-based tool with the same list of entities along the rows and columns encapsulated with relationships along the offdiagonal cells. Re-sequencing the DSM rows/columns can identify the iterations as blocks. OpenSTAAD is the API (Application Programming Interface) functionality in STAAD which is proposed to automate the iterative structural design in STAAD. The objective of the paper is to model and automate the structural design iterations using DSM and OpenSTAAD respectively. This proposed framework was applied to a case illustration of sixstorey building. Preliminary results of the model are
Pricing Convertible Bonds with Reset Clauses and Stochastic Interest Rates

By the martingale theory and transformation of probability measures, this paper obtains the analytical solution of the price of the convertible bond whose conversion price may reset at a predetermined time. The interest rate here follows an extended Vasicek model. In the numerical result, we find that the Monte Carlo method is efficient. Numerical result also shows that the correlated coefficient of the stock price and the interest rate is almost unacted on the price of the convertible bond.
Rumor source detection under probabilistic sampling

Consider a network where an unidentified source starts a rumor. The rumor spreads along the edges of the network to other nodes in the network. After a sufficiently long amount of time, we observe a subset of the nodes that have heard the rumor, and using this information wish to identify the source. Optimal estimators were recently proposed for regular (exponential growth) and irregular geometric (polynomial growth) trees when all nodes that heard the rumor reveal themselves. We provide the extension to the case in which nodes reveal whether they have heard the rumor with probability p, independent of each other. For geometric trees and p > 0, we achieve the same performance as the optimal estimator with p = 1. For regular trees, the estimator can achieve performance within ε of the optimal, provided that p is larger than a threshold.
Comprehensive approach for correction of motion and distortion in diffusion‐weighted MRI

Patient motion and image distortion induced by eddy currents cause artifacts in maps of diffusion parameters computed from diffusion‐weighted (DW) images. A novel and comprehensive approach to correct for spatial misalignment of DW imaging (DWI) volumes acquired with different strengths and orientations of the diffusion sensitizing gradients is presented. This approach uses a mutual information‐based registration technique and a spatial transformation model containing parameters that correct for eddy current‐induced image distortion and rigid body motion in three dimensions. All parameters are optimized simultaneously for an accurate and fast solution to the registration problem. The images can also be registered to a normalized template with a single interpolation step without additional computational cost. Following registration, the signal amplitude of each DWI volume is corrected to account for size variations of the object produced by the distortion correction, and the b‐matrices are properly recalculated to account for any rotation applied during registration. Both qualitative and quantitative results show that this approach produces a significant improvement of diffusion tensor imaging (DTI) data acquired in the human brain. Magn Reson Med 51:103–114, 2004. Published 2003 Wiley‐Liss, Inc.
Systematic raptor codes for atmospheric optical channels

In this paper, a rate-adaptive free-space optical (FSO) communication system using systematic Raptor codes is investigated based on experimental data measured over a 1.87 km terrestrial FSO link in different weather conditions. Using Raptor codes, the FSO communication system can adapt to the channel gain fluctuations caused by atmospheric turbulence and/or adverse weather conditions. To alleviate the burden of synchronizing between transmitter and receiver, messages are packetized and sent in sequence. For each packet, a cyclic-redundancy code (CRC) is added resulting in an effective packet erasure channel (PEC). Using experimental data and on-off-keying (OOK) modulation, the performance of the rate-adaptive FSO system is evaluated at different signaling rates. While an uncoded FSO system fails to efficiently operate during adverse weather conditions, the rate-adaptive technique provides reliable FSO communication under clear and rainy weather conditions.
Usefulness of spatially explicit population models in land management

Land managers need new tools, such as spatial models, to aid them in their decision-making processes because managing for biodiversity, water quality, or natural disturbance is challenging, and landscapes are complex and dynamic. Spatially explicit population models are helpful to managers because these models consider both species - habitat relationships and the arrangement of habitats in space and time. The visualizations that typically accompany spatially explicit models also permit managers to {open_quotes}see{close_quotes} the effects of alternative management strategies on populations of interest. However, the expense entailed in developing the data bases required for spatially explicit models may limit widespread implementation. In addition, many of the models are developed for one or a few species, and dealing with multiple species in a landscape remains a significant challenge. To be most useful to land managers, spatially explicit population models should be user friendly, easily portable, operate on spatial and temporal scales appropriate to management decisions, and use input and output variables that can be measured affordably. 20 refs.
Identification of the nucleic acid binding domain of the rotavirus VP2 protein.

The bovine rotavirus VP2 protein is the major component of the core and forms the most internal layer surrounding the dsRNA genome. We have constructed recombinant baculoviruses expressing truncated VP2 proteins. The nucleic acid binding activity of these truncated proteins was tested by North-Western blotting experiments with single-stranded and double-stranded probes. The nucleic acid binding domain in VP2 was localized between amino acids 1 to 132. Recombinant proteins bound single-stranded and double-stranded nucleic acids, but showed less affinity for double-stranded RNA and DNA. Interactions of VP2 with the genome were investigated in viral single-shelled particles by u.v. -cross-linking. In these experiments, only VP2 protein bound the genomic RNA in purified single-shelled particles.
A pervasive solution for risk awareness in the context of fall prevention

In the present work, we introduce Fallarm, a pervasive fall prevention solution suitable for hospitals and care facilities, as well as for home settings. We applied a multifaceted intervention strategy based on closed-loop information exchange between proactive and reactive methods: comprehensive assessment protocols determine the individuals' risk of falling; an innovative device continuously monitors subjects' activities, and it provides patients with constant feedback about their actual risk. Thus, it increases their awareness; simultaneously, it realizes measures to prevent adverse events, and it reports any incident and aims to reduce the level of injury. As a result, our solution offers a comprehensive strategy for the remote management of a person's risk of falling 24 hours a day, enabling many vulnerable people to remain living independently. In this paper, we detail the architecture of our system, and we discuss the results of an experimental study we conducted to demonstrate the applicability of Fallarm in both clinical and home settings.
A MAC protocol for delay-sensitive VANET applications with self-learning contention scheme

Packet delivery ratio and end-to-end delay are the two most important metrics for vehicular ad hoc network applications. In this paper, we propose a MAC layer protocol which can provide a high packet delivery ratio, low end-to-end delay, and high fairness for various scenarios. The proposed protocol uses a Q-Learning algorithm to adjust the contention window size in order to provide an efficient channel access scheme for various network situations. The simulation results demonstrate the advantage of the proposed protocol over other alternatives.
A QoS adaptive mobility prediction scheme for wireless networks

Mobility prediction based on an individual's movement history has been reported as an effective means to decrease the call-dropping probability and to shorten handover latency. Applying various basic prediction schemes to a realistic office environment, it is shown that mobility prediction using an individual's movement history has limitations, and the statistical randomness of the user motion can prevent accurate prediction performance. We propose a QoS adaptive mobility prediction scheme to resolve these problems. A stricter QoS compliance can be achieved through a pro-active probability update mechanism, a supplementary correlative movement history and the concept of prediction confidence ratio.
Free Energy, Precision and Learning: The Role of Cholinergic Neuromodulation

Acetylcholine (ACh) is a neuromodulatory transmitter implicated in perception and learning under uncertainty. This study combined computational simulations and pharmaco-electroencephalography in humans, to test a formulation of perceptual inference based upon the free energy principle. This formulation suggests that ACh enhances the precision of bottom-up synaptic transmission in cortical hierarchies by optimizing the gain of supragranular pyramidal cells. Simulations of a mismatch negativity paradigm predicted a rapid trial-by-trial suppression of evoked sensory prediction error (PE) responses that is attenuated by cholinergic neuromodulation. We confirmed this prediction empirically with a placebo-controlled study of cholinesterase inhibition. Furthermore, using dynamic causal modeling, we found that drug-induced differences in PE responses could be explained by gain modulation in supragranular pyramidal cells in primary sensory cortex. This suggests that ACh adaptively enhances sensory precision by boosting bottom-up signaling when stimuli are predictable, enabling the brain to respond optimally under different levels of environmental uncertainty.
The interest rate sensitivity of output in Finland

Monetary policy affects the economy through the exchange rate and interest rates.It is assumed that the relationship between interest rates and GDP is negative and that interest rates affect GDP more powerfully than the other way around.These conclusions are confirmed here.Estimated elasticities--which compare annual percentage changes in GDP to annual percentage changes in the interest rate-produce interest rate elasticity close to unity.It should also be stressed that a nominal interest rate effect is found instead of a real rate effect both in GDP and its components. For private consumption, the interest rate measures the relative future price of consumption, which includes substitution and income effects.According to estimations the interest rate elasticity for non-durable consumption is around -0.4, which indicates the dominance of the substitution effect. For durable purchases, the interest rate elasticity is much higher and is also volatile.Rough estimate showed that the interest rate elasticity for durables could be around -4.0. For private investment, the interest rate measures the opportunity cost of investment and a cost factor in debt financing.Financial deregulation has obscured the importance of interest rates in determining manufacturing investment.It seems that during the boom years domestic lending rates lost their significance in affecting investment plans.A rough estimate for a long-term interest rate elasticity of manufacturing investment could be -2.On the other hand financial deregulation has clearly increased the power of interest rates in determining the level of housing investment. During 1980s the real lending rate became a significant determinant of housing investment, and we cannot reject the hypothesis of unitary elasticity, ie. elasticity of -1.
Learning how to improve effort estimation in small software development companies

Small software development companies have special effort estimation requirements. In particular, small companies often find that they must tender for contracts for which they have no expertise in the project problem or solution domains. In addition, they have little spare capacity for project management and improvement. They have therefore no formal way of learning how to improve future effort estimates. A discussion of why small software company effort estimation needs are likely to be different to larger companies is provided; and a proposal for specific support for effort estimation in smaller companies is given. The support takes the form of a simple Bayesian statistical modelling approach. The approach can be used by estimators to learn how to improve the consistency of their estimation in existing and in novel problem and solution domains.
Optimal Scheduling in Sensor Networks Using Swarm Intelligence

This paper presents a swarm intelligence based approach for optimal scheduling in sensor networks. Sensors are characterized by their transaction times and interdependencies. In the presence of interdependencies the problem of optimal scheduling to minimize the overall transaction/response time is modeled as a graph partitioning problem. Graph partitioning problem is a well known NPcomplete problem. A methodology and cost function is developed to solve the problem. A swarm intelligence based algorithm, particle swarm optimization (PSO) is used to solve the problem. The PSO algorithm solves the problem and emerges with a optimal schedule.
Feature Selection for Reference Generation as Informed by Psycholinguistic Research

We present a method of feature selection for the production of appropriate types of referring expressions which attempts to bridge the gap between computational and empirical approaches to reference generation. Our task is to correctly identify the type of reference employed by a human author in a text where such references to the main subject have been tagged and removed. The goal is to provide a natural language generation system with guidelines to determine when the use of different types of reference (names, pronouns, etc.) is most appropriate. Our work combines experimental and computational approaches to develop a set of features capturing certain information below the surface level of text with which to train a classiﬁer system. Ultimately, this classiﬁer could be used to control the production of referring expressions in machine-generated communication. A decision tree trained on the features selected by our method produces results approaching those of the highest-performing purely empirical-based machine learning systems for the given task.