Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Unemployment in a Matching Model with Endogenous Skill-acquisition and Technological Di ¤ usion

In this paper, we develop a matching model where technological di¤usion and skills formation are the outcomes of an endogenous investment decision problem faced by workers and rms populating the economy. We focus on the interplay between skill-acquisition technological change and labour market outcomes, nding that, in the presence of frictions in the labour market, agents strategic interaction a¤ects technological di¤usion and unemployment. In particular, we consider unemployment outcomes and characterise how they are a¤ected by agents investment decisions. We show that conditions of skill-shortage or skill-redundancy emerge as endogenous features of the labour market and we argue that the matching mechanism is also endogenous in this framework. We characterise how unemployment responds to a change in the fundamental parameters of the economy through a revision of agents investment decisions and a modi cation of the matching outcome. In the nal part of the paper we discuss the implications for the policy-maker. Key words: matching models, technological di¤usion, skill-technology complementarities, heterogeneity. JEL classi cation: C78, J24, J64, O33. I would like to thank Robin Naylor, Myrna Wooders and Lei Zhang for their valuable help with the development of this paper. Research support from the Economic and Social Research Council and The University of Warwick is gratefully acknowledged. yContact details: Department of Economics, University of Warwick, Coventry, CV4 7AL, United Kingdom, E-mail: F.R. Arico@warwick.ac. uk, Phone: +44(0)24 7652 3479, Fax: +44(0)24 7652 3032.
The Impact of Multiword Expression Compositionality on Machine Translation Evaluation

In this paper, we present the first attempt to integrate predicted compositionality scores of multiword expressions into automatic machine translation evaluation, in integrating compositionality scores for English noun compounds into the TESLA machine translation evaluation metric. The attempt is marginally successful, and we speculate on whether a larger-scale attempt is likely to have greater impact.
Electronic Government Strategies and Research in the U.S

Since the mid-1990s, adoption of wide-area computer networks, such as the Internet and the World Wide Web (WWW), by the public, educational institutions and private sector organizations has helped spur an interest in using these new Information and Communication Technologies (ICT) as a means to increase the efficiency and effectiveness of organizational processes. Private sector firms have focused on using Internet-based technologies, especially the browser-based technologies of the WWW, as a means to conduct business transactions. The use of such electronic transmission technologies in carrying out business activities has generally been dubbed electronic commerce, or e-commerce (Schneider, 2003). Attempts to reinvent public organizations in the United States during the 1990s were heavily grounded in the belief that the adoption of new forms of ICT will streamline both service generation and delivery (Osborne & Gaebler, 1993; Gore, 1993). Some government actors and observers, such as the National Science Foundation, have more recently referred to the overall use of ICT to carry out the activities of government institutions as digital government. The term digital government has in many respects grown to refer to the development, adoption or use of ICT as a key component of a public organization’s internal information and control systems, as well as any use of ICT to facilitate interaction with external stakeholders. Some scholars have attempted to examine how governments have used ICT systems, such as the Internet and WWW, as a means to facilitate interactions with citizens and other stakeholders in an attempt to foster democratic processes via electronic media. These activities have been called electronic democracy, or e-democracy. This broad concept is then usually subdivided into two subsets of activities, electronic politics and electronic government. Electronic politics, or e-politics, centers on activities that facilitate civic awareness of political processes, as well as the ability of citizens to participate in those processes. Electronic government, or e-government, includes the use of ICT by government agencies to provide programmatic information and services to citizens and other stakeholders (Watson & Mundy, 2001).
Truth, lies, and data: Credibility representation in data analysis

The web has evolved in a scale free manner, with available information about different entities developing in different forms, different locations, and at massive scales. This paper addresses the cognitive limitations that information analysts typically experience as they approach the boundaries where automated analysis algorithms are sorely needed. An experiment is conducted to explore information analysts' interactions with recommendations from an automated fact-finder algorithm during the task of answering questions in a fictional humanitarian aid delivery scenario. An experiment (N=285) is performed using three increasingly complex user interfaces, with and without the presence of the automated recommendations. Results show that in the best performing group, interaction with the fact-finder recommendations was 47 percent greater than the worst performing group.
A statistical switched broadband channel model for HAPS links

Increase on the demand of the telecommunications services is related with systems based on different technological alternatives. One of them, that is being developed in the last years is the use of a aircraft or airships technologies. The high altitude platforms stations (HAPS) represent one attractive option today. In this paper, a statistical switched broadband channel model is postulated and analyzed by simulation and several conditions related with the coverage and environmental conditions are analyzed. A system model is postulated and evaluated considering different coded signals transmitted on the channel model proposed.
Parameter Sensitivity Analysis of Flux Observers for Induction Motors

This paper analyzes the effect of the variation of machine parameters on flux observers in the steady-state condition by the frequency-domain method. Both the flux estimation and the torque estimation are evaluated. The frequency response function (FRF) expressed by the ratio of the estimated flux/torque to the corresponding physical one is derived to evaluate the parameter sensitivity. The flux observers investigated in this paper include the open-loop voltage model, the open-loop current model, and three types of closed-loop adaptive flux observers with PI regulator. Computer simulations are conducted to validate the theoretical analysis.
Homography estimation using Analytical Fourier-Mellin Transform

The paper presents a registration algorithm based on the Analytical Fourier-Mellin Transform (AFMT). In the Fourier Mellin registration algorithm, the estimation of the geometrical transformation is only done in the Fourier Transform phase domain which is known by containing the shape information. The proposed algorithm will be used to estimate homography since in the vicinity of key points, we can assume that geometrical transformation is simply a similarity. So, we compute AFMT at each key point location and we use the phase correlation to match features. Then, we estimate homography parameters with RANSAC algorithm. An archeological application will illustrate the efficiency of AFMT registration in image mosaicing context by the construction of Punic Panorama.
Neural Network Ensembles for Intrusion Detection

The major problem of existing models is recognition of new attacks, low accuracy, detection time and system adaptability. In this paper the method of recognition of attack class on the basis of the analysis of the network traffic is described. Our first approach is based on combination principal component analysis (PCA) neural networks and multilayer perceptrons (MLP). The second approach performs recognition of a class of attack by means of the cumulative classifier with nonlinear recirculation neural networks (RNN) as private detectors. The proposed approaches are tested using KDD-99 dataset. The experimental results demonstrate that the designed models are promising in terms of accuracy and computational time for real world intrusion detection.
Intrusion Detection Scheme against Sinkhole Attacks in Directed Diffusion Based Sensor Networks

Summary Wireless Sensor Networks (WSNs) detect and report interesting events when they occur in the target region. These networks are vulnerable to security breach due to wireless communication and lack of infrastructure. In sinkhole attacks, an attacker attracts network traffic by forging or replaying routing messages through compromised nodes. Thus attracted traffic is used for selective forwarding, denial of service (DoS) and false report attacks. In this paper, we show the vulnerability of the directed diffusion routing protocol to sinkhole attacks. And we also propose the intrusion detection scheme using fuzzy logic for detecting and defending sinkhole attacks in directed diffusion based sensor networks.
Finding the causes of unrealizability of reactive system formal specifications

Reactive systems are the systems that maintain some interaction with their environment. Temporal logic is one of the methods for formal specification descriptions of reactive systems. The formal specifications of reactive systems enables to check the consistency of the specifications and whether they contain defects. By using a synthesis algorithm we also obtain reactive system programs from the formal specifications and prevent programming bugs. Thus, it is important to describe reactive system formal specifications. However, it is difficult to describe realizable reactive system specifications and it is necessary to find the causes of unrealizable reactive system specifications. In previous research, three properties have been introduced into unrealizable reactive system specifications and we suppose that this classification gives the hists of finding the causes of unrealizability. In this paper we propose several heuristics of finding the causes of unrealizability of reactive system formal specifications. To find the causes, we use tableau methods and the classification of the reactive system specifications.
A low-dispersive dynamic finite difference scheme for large-eddy simulation

In the past decade, the necessity for numerical quality in Direct Numerical Simulations (DNS) and Large Eddy Simulations (LES) of turbulent flows, has been recognized b y many authors among which Ghosal [1] and Chow et al. [2]. In a fully resolved DNS, the smallest resolved scales are located far into the dissipation range. The energy-content of those small scales is thus ve ry small compared to those of the largest resolved scales. However, in LES, the smallest resolved scales are part of the inertial subrange and thus contain still a significant amount of energy compared to the la rgest scales. Therefore, it might be reasonable to assume that numerical accuracy on the small scales is relatively more important for LES then for DNS. Moreover, some advanced subgrid modelling techniques such as the dynamic procedure or multiscale modelling strongly rely on the smallest resolved scales in LES, which make them even more important in terms of accuracy. Good numerical quality for an affordable LES is thus vital for accurate flow prediction as it directly influences resolved physic s as well as subgrid physics. Aside from aliasing errors, which should be prevented by eliminating scales beyond κc = 2 κmax, finite difference errors are mainly responsible for the loss of numerical accu racy. Since it is highly desirable
Development of a Novel Reference Plasmid for Accurate Quantification of Genetically Modified Kefeng6 Rice DNA in Food and Feed Samples

Reference plasmids are an essential tool for the quantification of genetically modified (GM) events. Quantitative real-time PCR (qPCR) is the most commonly used method to characterize and quantify reference plasmids. However, the precision of this method is often limited by calibration curves, and qPCR data can be affected by matrix differences between the standards and samples. Here, we describe a digital PCR (dPCR) approach that can be used to accurately measure the novel reference plasmid pKefeng6 and quantify the unauthorized variety of GM rice Kefeng6, eliminating the issues associated with matrix effects in calibration curves. The pKefeng6 plasmid was used as a calibrant for the quantification of Kefeng6 rice by determining the copy numbers of event- (77 bp) and taxon-specific (68 bp) fragments, their ratios, and their concentrations. The plasmid was diluted to five different concentrations. The third sample (S3) was optimized for the quantification range of dPCR according to previous reports. The ratio between the two fragments was 1.005, which closely approximated the value certified by sequencing, and the concentration was found to be 792 copies/μL. This method was precise, with an RSD of ~3%. These findings demonstrate the advantages of using the dPCR method to characterize reference materials.
Investigating the Development of Possible Selves in Teacher Education: Candidate Perceptions of Hopes, Fears, and Strategies

The theory of possible selves, as proposed by Marcus and Nurius (1986), framed a two-staged instrumental case study designed to give voice to an often-neglected source of insight: teacher candidates. The collection and analysis of hopes, fears, and process strategies gathered from a crosssection of thirteen candidates and alumni from a private Midwestern institution informed teacher educator practice and increased understanding in regards to influences that shaped teacher identity development. The applied theoretical framework allowed for the assessment of participants’ knowledge, skills, and dispositions, aided in the identification of perceived preparation needs, and enabled an appraisal of program effectiveness. Findings affirmed the utilitarian, investigative, and evaluative qualities of the theory of possible selves. Embedded use of the reflective framework throughout coursework may motivate and regulate candidate actions and yield adaptive experts capable of nurturing identity development beyond the preparation duration.
Asymmetric Multi-level Diversity Coding

Symmetric multilevel diversity coding was introduced by Roche et al, where a set of K information sources is encoded by K encoders and the decoders reconstruct sources 1,. ,k, where k is the number of encoders to which they have access. In this paper, we formulate an asymmetric multilevel diversity coding problem, where a set of 2K - 1 information sources is encoded by K encoders into K streams/descriptions. There are 2K - 1 decoders, each of which has access to a non-empty subset of the encoded messages. The decoders are assigned with ordered levels, and each of them has to decode a subset of the information sources, according to its level, which depends on the set of encoders to which it has access, not just the cardinality. We obtain a single letter characterization of the complete achievable rate region for the 3- description problem. In doing so, we show that it is necessary to jointly encode independent sources (i.e., similar to network coding), and that linear codes are optimal for this problem.
Voice modeling methods for automatic speaker recognition

Building a voice model means to capture the characteristics of a speaker’s voice in a data structure. This data structure is then used by a computer for further processing, such as comparison with other voices. Voice modeling is a vital step in the process of automatic speaker recognition that itself is the foundation of several applied technologies: (a) biometric authentication, (b) speech recognition and (c) multimedia indexing. Several challenges arise in the context of automatic speaker recognition. First, there is the problem of data shortage, i.e., the unavailability of sufficiently long utterances for speaker recognition. It stems from the fact that the speech signal conveys different aspects of the sound in a single, one-dimensional time series: linguistic (what is said? ), prosodic (how is it said? ), individual (who said it? ), locational (where is the speaker? ) and emotional features of the speech sound itself (to name a few) are contained in the speech signal, as well as acoustic background information. To analyze a specific aspect of the sound regardless of the other aspects, analysis methods have to be applied to a specific time scale (length) of the signal in which this aspect stands out of the rest. For example, linguistic information (i.e., which phone or syllable has been uttered? ) is found in very short time spans of only milliseconds of length. On the contrary, speakerspecific information emerges the better the longer the analyzed sound is. Long utterances, however, are not always available for analysis. Second, the speech signal is easily corrupted by background sound sources (noise, such as music or sound effects). Their characteristics tend to dominate a voice model, if present, such that model comparison might then be mainly due to background features instead of speaker characteristics. Current automatic speaker recognition works well under relatively constrained circumstances, such as studio recordings, or when prior knowledge on the number and identity of occurring speakers is available. Under more adverse conditions, such as in feature films or amateur material on the web, the achieved speaker recognition scores drop below a rate that is acceptable for an end user or for further processing. For example, the typical speaker turn duration of only one second and the sound effect background in cinematic movies render most current automatic analysis techniques useless. In this thesis, methods for voice modeling that are robust with respect to short utterances and background noise are presented. The aim is to facilitate movie
On the equality of kernel AdaTron and sequential minimal optimization in classification and regression tasks and alike algorithms for kernel machines

The paper presents the equality of a kernel AdaTron (KA) method (originating from a gradient ascent learning approach) and sequential minimal optimization (SMO) learning algorithm (based on an analytic quadratic pr o- gramming step) in designing the support vector machines (SVMs) having pos i- tive definite kernels. The conditions of the equality of two methods are esta b- lished. The equality is valid for both the nonlinear classification and the nonli n- ear regression tasks, and it sheds a new light to these seemingly different lear n- ing approaches. The paper also introduces other learning techniques related to the two mentioned approaches, such as the nonnegative conjugate gradient, cla s- sic Gauss-Seidel (GS) coordinate ascent procedure and its derivative known as the successive over-relaxation (SOR) algorithm as a viable and usual ly faster training algorithms for performing nonlinear classification and regression tasks. The convergence theorem for these related iterative algorithms is proven.
Integrated Population Modeling of Black Bears in Minnesota: Implications for Monitoring and Management

Background Wildlife populations are difficult to monitor directly because of costs and logistical challenges associated with collecting informative abundance data from live animals. By contrast, data on harvested individuals (e.g., age and sex) are often readily available. Increasingly, integrated population models are used for natural resource management because they synthesize various relevant data into a single analysis. Methodology/Principal Findings We investigated the performance of integrated population models applied to black bears (Ursus americanus) in Minnesota, USA. Models were constructed using sex-specific age-at-harvest matrices (1980–2008), data on hunting effort and natural food supplies (which affects hunting success), and statewide mark–recapture estimates of abundance (1991, 1997, 2002). We compared this approach to Downing reconstruction, a commonly used population monitoring method that utilizes only age-at-harvest data. We first conducted a large-scale simulation study, in which our integrated models provided more accurate estimates of population trends than did Downing reconstruction. Estimates of trends were robust to various forms of model misspecification, including incorrectly specified cub and yearling survival parameters, age-related reporting biases in harvest data, and unmodeled temporal variability in survival and harvest rates. When applied to actual data on Minnesota black bears, the model predicted that harvest rates were negatively correlated with food availability and positively correlated with hunting effort, consistent with independent telemetry data. With no direct data on fertility, the model also correctly predicted 2-point cycles in cub production. Model-derived estimates of abundance for the most recent years provided a reasonable match to an empirical population estimate obtained after modeling efforts were completed. Conclusions/Significance Integrated population modeling provided a reasonable framework for synthesizing age-at-harvest data, periodic large-scale abundance estimates, and measured covariates thought to affect harvest rates of black bears in Minnesota. Collection and analysis of these data appear to form the basis of a robust and viable population monitoring program.
On the downlink SINR and outage probability of stochastic geometry based LTE cellular networks with multi-class services

Due to the increasing number of irregularly spaced base stations (BSs) as well as the intrinsic random channel environment, modeling and analysis of cellular networks using classical hexagonal cell shapes is becoming ever more impractical. Therefore, stochastic geometry models having the ability to picture near realistic situations is gaining a wide acceptability for evaluating cellular network performance. In light of this, this paper presents a simulation-based investigation on the downlink signal-to-interference-noise-ratio (SINR) and the outage probability of orthogonal frequency division multiple access (OFDMA)-based long term evolution (LTE) cellular systems using stochastic geometry with multi-class services. Locations of BSs are modeled using both Poisson point process (PPP) as well as hard-core Poisson process (HCPP). Moreover, a computationally efficient method is proposed for capturing the effect of inter-cell interference in such stochastic geometry based cellular networks. Network performance including the outage probability of various multi-class services under varying shadow fading scenario and BS density is evaluated using Monte Carlo simulations, and compared with that of the traditional hexagonal models. Simulation results clearly demonstrates the over optimistic network performance of hexagonal model, while the most realistic HCPP model provides a compromise between the hexagonal and the PPP models.