Miracle-dz/UniFAR-FTUs huggingface dataset:
An Effective Face Detection Algorithm in Identify Authentication of Information System

In recent years, face recognition has become a new research in the identity authentication with the in-depth study of the research of face recognition. The face recognition has two parts, including the face detection and localization, and feature extraction and recognition. In this paper, a face detection algorithm based on the AdaBoost was presented, which can be enhance several dozens times of its training speed.
The BiCOR and CORS Iterative Algorithms for Solving Nonsymmetric Linear Systems

We present two iterative algorithms for solving real nonsymmetric and complex non-Hermitian linear systems of equations and that were developed from variants of the nonsymmetric Lanczos method. In this paper, we give the theoretical background of the two iterative methods and discuss their main computational aspects. Using a large number of numerical experiments, we analyze their convergence properties, and we also compare them with other popular nonsymmetric iterative solvers in use today.
“White People Like Hiking”Some Implications of NPS Narratives of Relevance and Diversity

For the past few decades there have been repeated calls from within and without the National Park Service for more inclusive interpretation in the park system. Concurrently there has been great concern about the lack of diversity among visitors to Park Service units. The 2009 Comprehensive Survey of the American Public, repeatedly cited in the popular press, reported that recent visitors to a Park Service unit were “disproportionately” “white, non-Hispanic. ” This paper looks to problematize this survey and its conclusions that concern for the environment is predominantly white, that attendance at NPS sites is tantamount to national belonging, and that future support for NPS is threatened as the United States becomes majority nonwhite. The paper also looks to question the link between the racial and ethnic identity of visitors and “relevant” interpretation.
Interferon-Gamma Release Assay Improves the Diagnosis of Tuberculosis in Children

Background: Interferon-&ggr; release assays (IGRAs) have been recently developed for the diagnosis of tuberculosis (TB) infection. The aim of the present study was to evaluate the performance of an enzyme-linked immunosorbent assay (ELISA)-based IGRA for detecting TB in children. Methods: A prospective study in 336 children at risk for TB infection was carried out. All children were tested with tuberculin skin test (TST) and a commercial ELISA-based IGRA [QuantiFERON-TB Gold In-Tube (Cellestis)]. Results: TST were positive in 58 of 336 (17.3%) and IGRA in 60 of 336 (17.9%) children. Two (0.6%) IGRA results were indeterminate. The overall agreement between the 2 tests was intermediate (86.2%, κ = 0.533). IGRA was positive in 15 of 16 (93.8%) children with active pulmonary TB. The discordant pattern IGRA−/TST+ was significantly associated with Bacille Calmette-Guérin (BCG) vaccination. Among IGRA+ children (excluding cases of TB disease), TST− were significantly younger than TST+ children. Conclusions: The good agreement between positive IGRA and active TB disease suggests a good sensitivity of IGRA. Discrepancies between IGRA and TST can be a result of higher specificity of IGRA that is not influenced by previous BCG vaccination. IGRA may be more sensitive in children younger than 48 months.
STUDENT ROLE ADJUSTMENT IN ONLINE COMMUNITIES OF INQUIRY: MODEL AND INSTRUMENT VALIDATION

The purpose of this study is to validate an instrument to study role adjustment of students new to an online community of inquiry. The community of inquiry conceptual model for online learning was used to shape this research and identify the core elements and conditions associated with role adjustment to online learning (Garrison, Anderson and Archer, 2000). Through a factor analytic process it is shown that the instrument did reflect the theoretical model. It was also useful in refining the items for the questionnaire. The instrument is for use in future research designed to measure and understand student role adjustment in online learning.
Earthquake cycles and neural reverberations: Collective oscillations in systems with pulse-coupled threshold elements.

Driven systems of interconnected blocks with stick-slip friction capture main features of earthquake processes. The microscopic dynamics closely resemble those of spiking nerve cells. We analyze the differences in the collective behavior and introduce a class of solvable models. We prove that the models exhibit rapid phase locking, a phenomenon of particular interest to both geophysics and neurobiology. We study the dependence upon initial conditions and system parameters, and discuss implications for earthquake modeling and neural computation. Many natural phenomena involve interacting threshold elements. Slowly driven by some external force, single elements discharge rapidly when they reach a trigger threshold, then become quiescent again. This activity may in turn stimulate neighboring elements. Prominent examples include earthquakes, avalanches, forest fires, colonies of flashing fireflies, and assemblies of spiking nerve cells. The corresponding models, although closely related on a formal level, exhibit distinct collective properties that range from systemwide synchronization [1] to self-organized criticality [2,3]. Phase locking without global synchronization has been identified as an important borderline phenomenon between both cases [4,5] and has been observed under various conditions [2 — 12]. In this Letter, a class of solvable models is introduced. The dynamics resemble those of previous earthquake models [2,3,7 — 10] and integrate-and-fire neurons [1,6, 11,12], and reduce to Abelian avalanches [8] in a limiting case. We prove that for nearest-neighbor couplings and periodic boundary conditions, the models rapidly relax to phase-locked solutions — the attractors are reached as soon as every element has discharged once. Next, we study the dependence upon initial conditions, coupling parameters , and boundary conditions. We then extend the class of interactions and discuss the connection with networks of spiking neurons. Finally, we outline implications for earthquake modeling and neural computation. Geophysical description of the model. — Seismic activity occurs predominantly on faults located at the boundaries between tectonic plates. Relative plate movement leads to a slow accumulation of stress that is quickly released during earthquakes. We follow Refs. [2,3,7 — 10] and concentrate on the dynamics of a single fault, represented by a rectangular two-dimensional lattice. The stress at site i is modeled by a scalar variable F;.
Evolutionary Consequences of Dusty Tori in Active Galactic Nuclei

Deep surveys by Chandra and the Hubble Space Telescope (HST) show that active galactic nucleus (AGN) populations are changing with hard X-ray luminosities. Thus, an interesting question arises as to whether or not the dusty torus is evolving with the central engines. We assemble a sample of 50 radio-quiet Palomar-Green quasars to tackle this problem. The covering factors of the dusty tori can be estimated from the multiwavelength continuum. We find that they are strongly correlated with the hard X-ray luminosity. Interestingly, this correlation agrees with the fraction of type II AGNs discovered by Chandra and HST, implying strong evidence of the changing results of the AGN population from the evolution of the tori. We also find that the frequencies of the dips at around 1 μm in the continuum correlate with the covering factors in the present sample, indicating that the dip frequencies are adjusted by the covering factors. In the scenario of the fueling of a black hole from the torus, the covering factor is good, and the dip frequency is a potential indicator of the torus evolution.
Analytical Derivation of the Excess Current in Fu lly Incoherent Josephson Junctions

In thi s work we present an analytical derivation of the excess current in Josephson microbridges, following the Octavio-Blonder-Tinkham-Klapwijk (OBTK) model. We can confirm previously found numerical results, including the prediction of negative excess current for low transparencies, which might possibly shed new light on questions concerning the validity of the OBTK approach.
Bachelor ’ s Thesis in Informatics Kinect-enabled activity recognition of multiple human actors for a service robot Kinect-basierte Aktivitätserkennung mehrerer menschlicher Akteure für einen Serviceroboter

The purpose of this bachelor’s thesis is to present a machine learning based approach to recognize several activities of persons interacting with a robot, based on the position of their joints. In order for service robots to understand human behavior and to interact with humans, this thesis investigates human-robot interaction within the JAMES project (Joint Action for Multimodal Embodied Social Systems) on the basis of a bar scenario, in which a robot bartender serves human actors. Motivating hidden Markov models (HMMs) to fulfill our activity recognition task, we first focus on the theory behind them. Whereas the joint data is obtained by the Kinect, a motion sensing device, this thesis further examines how to develop a software program to provide us with our final number of 2307 labeled activities we utilize to train and test our HMM. The results of the 695 labeled activities used for testing reveal that about 90% of these were correctly recognized. Considering activities which were recognized without occurrence in the test data, we obtain an accuracy of over 70%. As this performance is in the same order of magnitude as the training results, our approach for solving the activity recognition task in our bar scenario is fairly reliable and robust. The contribution of this thesis is to provide initial research in activity recognition within the JAMES project and hopefully to lay a foundation for further investigation and improvement in this area of research.
The Impact of Weight Matrices on Parameter Estimation and Inference: A Case Study of Binary Response Using Land Use Data

This paper develops two new models and evaluates the impact of using different weight matrices on parameter estimates and inference in three distinct spatial specifications for discrete response. These specifications rely on a conventional, sparse, inverse-distance weight matrix for a spatial auto-regressive probit (SARP), a spatial autoregressive approach where the weight matrix includes an endogenous distance-decay parameter (SARPα), and a matrix exponential spatial specification for probit (MESSP). These are applied in a binary choice setting using both simulated data and parcel-level land use data. Parameters of all models are estimated using Bayesian methods. In simulated tests, adding a distance-decay parameter term to the spatial weight matrix did not alter the quality of estimation and inference, but the added sampling loop required to estimate the distance-decay parameter substantially increased computing times. By contrast, the MESSP model's obvious advantage is its fast computing time, thanks to elimination of a log-determinant calculation for the weight matrix. In the model tests using actual land use data, the MESSP approach emerged as the clear winner, in terms of fit and computing times. Results from all three models offer consistent interpretation of parameter estimates, with locations farther away from the regional central business district (CBD) and closer to roadways being more prone to (mostly residential) development (as expected). Again, the MESSP model offered the greatest computing-time savings benefits, but all three specifications yielded similar marginal effects estimates, showing how a focus on the spatial interactions and net (direct plus indirect) effects across observational units is more important than a focus on slope-parameter estimates, when properly analyzing spatial data.
Frequency recognition based on the Inter-Battery Factor Analysis for SSVEP-based BCIs

The Brain-Computer Interface is a system mainly designed to provide people suffering from severe neuromuscular disorder with a new mean of communication and control. Increasing the system accuracy rate is the goal of several studies. In fact, ameliorating this criterion allows to minimize the correction phase and makes the use of the system more natural. This is very important to develop Brain-Computer interface systems for everyday use outside the laboratory. This paper introduces a new Brain-Computer Interface based on the Inter-Battery Factor Analysis method. The results show that the proposed BCI system has a higher accuracy than systems based on Canonical Correlation Analysis or Multivariate Synchronization Index. The accuracy rate has reached 93.2% for the five participants using only the two electrodes O1 and O2 with data acquired over a period of 2.25s.
Finger Vein Recognition Based on a Personalized Best Bit Map

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition.
Trainees' reactions to training: shaping groups and courses for happier trainees

The extensive use of training demands more extensive evaluations of its real effects. Human resource scholars attempt to develop multi-dimensional training evaluation models, often ignored by practitioners, whereas training managers tend to evaluate only reaction evaluations, the first dimension in several models. To provide more practically relevant research, this study includes five training parameters in a model that assesses trainees' overall satisfaction with training (OST), which results from perceived usefulness of training, perceived trainer performance and perceived training efficiency. Structured survey data from a sample of 13,753 trainees, grouped in 1230 courses, involving 3047 companies, and financed by an Italian inter-professional fund, confirm the role of all three antecedents of OST. Moreover, of the five course- and participant-related variables, participation of female workers (which is also a moderator variable), the length of the course and the type of target audience influence trainees' OST. These results provide useful implications for practitioners in shaping their courses and audiences to maximise the trainees' reactions.
Scheduling in mapreduce-like systems for fast completion time

Large-scale data processing needs of enterprises today are primarily met with distributed and parallel computing in data centers. MapReduce has emerged as an important programming model for these environments. Since today's data centers run many MapReduce jobs in parallel, it is important to find a good scheduling algorithm that can optimize the completion times of these jobs. While several recent papers focused on optimizing the scheduler, there exists very little theoretical understanding of the scheduling problem in the context of MapReduce. In this paper, we seek to address this problem by first presenting a simplified abstraction of the MapReduce scheduling problem, and then formulate the scheduling problem as an optimization problem.We devise various online and offline algorithms to arrive at a good ordering of jobs to minimize the overall job completion times. Since optimal solutions are hard to compute (NP-hard), we propose approximation algorithms that work within a factor of 3 of the optimal. Using simulations, we also compare our online algorithm with standard scheduling strategies such as FIFO, Shortest Job First and show that our algorithm consistently outperforms these across different job distributions.
The NIST 2014 Speaker Recognition i-vector Machine Learning Challenge

During late-2013 through mid-2014 NIST coordinated a special machine learning challenge based on the i-vector paradigm widely used by state-of-the-art speaker recognition systems. The i-vector challenge was run entirely online and used as source data fixed-length feature vectors projected into a low-dimensional space (i-vectors) rather than audio recordings. These changes made the challenge more readily accessible, enabled system comparison with consistency in the front-end and in the amount and type of training data, and facilitated exploration of many more approaches than would be possible in a single evaluation as traditionally run by NIST. Compared to the 2012 NIST Speaker Recognition Evaluation, the i-vector challenge saw approximately twice as many participants, and a nearly two orders of magnitude increase in the number of systems submitted for evaluation. Initial results indicate that the leading system achieved a relative improvement of approximately 38% over the baseline system.
The effect of decision delay on MMSE-TH precoding systems

Minimum mean square error Tomlinson-Harashima (MMSE-TH) precoding is an important nonlinear precoding method. In this paper, the effect of decision delay on the performance of MMSE-TH precoding systems is investigated over time invariant and Rayleigh fading channels, respectively. Based on the observation on the expression of the interference signal power, we propose a method of searching for the optimum decision delay to minimize the interference signal power. Moreover, the complexity of the proposed method has also been analyzed. Computer simulation shows that the system bit error ratio (BER) varies with different decision delay, and less interference signal power renders smaller BER. The results imply that we can find the optimal decision delay by the proposed method and configure it in MMSE-TH system in advance, so that the BER can achieve the minimum.
Unresolved questions in nudging research: Putting the psychology back in nudging

Nudging interventions are broadly defined as a rearrangement of a choice context that gently suggests a specific choice. Their increasing popularity has attracted attention and discussion from researchers, policy makers, and practitioners alike. After some applications to domains such as health, environmental issues, and retirement savings, the next step in nudging is to understand the psychological boundary conditions when applied to varied domains of daily life. It is yet unclear for example to what extent nudging interventions can be transparent without losing effectiveness, or how permanent the effects are. These unresolved questions may have contributed to heated political and scientific discussions, on for example the ethics of using nudges in the public health domain, due to the missing scientific evidence. Indeed, this popularity may have led to forget how nudging harnesses insights from decades of research in psychology to change people's behavior. The aim of this paper is to shift the focus back to the psychological premises nudges were built upon. It summarizes several outstanding questions that future research in the psychology of nudging should address. Advancing research in nudging will help to improve our understanding of applied nudging interventions and provide clarity to debates such as ethical appropriateness, effectiveness, and public approval.
A Cable-less Interaction Device for AR and VR Environments

This paper describes the Magic Pen, a modified pen that allows natural cable-less interaction with AR environments. The pen is a normal pen with bright Light Emitting Diodes (LEDs) mounted at each end. Using computer vision techniques, these LEDs are tracked in the image captured by a head-mounted camera, and the pen’s position and orientation are computed. This permits the use of image plane interaction techniques for interacting with virtual images. We describe the computer vision algorithms used, the tracking results and the interaction supported.
A new approach for the minimum power broadcast problem in wireless networks

In this paper we consider the problem of assigning transmission powers to the nodes of a wireless network in such a way that all the nodes of the network are connected and the total power consumption is minimized. A mixed integer programming formulation is presented together with some new valid reinforcing inequalities. Computational results show the effectiveness of the new valid inequalities and that the new approach we propose outperforms more complex methods recently appeared in the literature.
Restricted and Unrestricted Dominance for Welfare, Inequality and Poverty Orderings

This paper extends the previous literature on the normative links between the measurement of poverty, social welfare and inequality. We show how, when the range of possible poverty lines is unbounded above, a robust ranking of absolute poverty may be interpreted as a robust ranking of social welfare, and a robust ranking of relative poverty may be interpreted as a robust ranking of inequality for any order of stochastic dominance. This interpretation is also valid when the maximum poverty line is bounded and for certain orders of stochastic dominance, so long as social welfare and inequality judgements are "censored". We also develop new criteria of restricted inequality dominance, and find that they warn against the use of some popular indices of relative poverty and censored inequality. Finally, we illustrate geometrically how the new criteria of restricted inequality dominance extends the rankings power of previously proposed dominance criteria.
Collaborative Development of a Rule-Based Machine Translator between Croatian and Serbian

This paper describes the development and current state of a bidirectional Croatian-Serbian machine translation system based on the open-source Apertium platform. It has been created inside the Abu-MaTran project with the aims of creating free linguistic resources as well as having non-experts and experts work together. We describe the collaborative way of collecting the necessary data to build our system, which outperforms other available systems
Who uses fair value accounting for non-financial assets after IFRS adoption?

We examine whether and why companies prefer fair value to historical cost when they can choose between the two valuation methods. With the exception of investment property owned by real estate companies, historical cost by far dominates fair value in practice. Indeed, fair value accounting is not used for plant, equipment, and intangible assets. We find that companies using fair value accounting rely more on debt financing than companies that use historical cost. This evidence is consistent with companies using fair value to signal asset liquidation values to their creditors, and is not consistent with equity investors demanding fair value accounting for non-financial assets. Our evidence broadly speaks to the importance of accounting for contracting.
Applying MTBE Manually: a Method and an Example

The idea of model transformation by example (MTBE) is to use an example of the transformation to derive transformation rules. Although some works propose automatic and semi-automatic approaches, there are some limitations to use them in practice. As a result, depending on the model transformation it may be necessary to apply an MTBE approach manually. Therefore, this work presents a manual MTBE method. It also describes an example of its use in a stakeholder requirements to software requirements transformation, which was the motivation to create the method.