Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterns for Product Line Engineering

Introduction Software Product Line Engineering (PLE) has become an important way of building and reusing software. In PLE, the focus is shifted from building isolated products to building families of related products, while reuse is discussed not at an individual object level , e.g. libraries or components, but as a whole: organizational, process wise, and also life cycle wise end to end from requirements to actually deployed variability at the customer. We consider only those platforms PLE efforts that are planned and prescribed to be reused in a dedicated product portfolio or to build solutions in a specific market segment, hence typical general purpose technology platforms, such as Eclipse, JBoss, or . NET, do not fall into this category.
Growing Up in Social Housing in the New Millennium: Housing, Neighbourhoods, and Early Outcomes for Children Born in 2000

This study draws on the Millennium Cohort Study to explore the housing and neighbourhood circumstances of children born in England in 2000 at the age of 5 in 2006. The majority of children experienced good housing conditions. Those in social rented homes, and to a lesser extent in private rented homes too, were markedly disadvantaged in terms of family circumstances and neighbourhood deprivation, while housing conditions and other neighbourhood characteristics also varied somewhat between tenures. Links were found between children’s housing tenure and test scores. These were largely explained by a combination of family characteristics and neighbourhood deprivation.
Psychological Model of Military Aggressive Behavior: Findings From Population-Based Surveys

The U.S. military has expressed concern about the influence of deployment and combat exposure on the criminal behavior of personnel returning from Iraq and Afghanistan. This study examines the role of behavioral health risk and protective factors associated with combat exposure and criminal and aggressive behavior in active duty Army and Naval personnel. Data from this cross-sectional study are based on two large anonymous, population-based health-related behavior surveys. Findings show that the proportion of all active duty Army and Naval service personnel who reported encounters with law enforcement or committed one or more aggressive acts in the past year increased from 19.41% in 2005 to 22.58% in 2008. Substance use, especially illicit drug use, and mental health problems were significant correlates of criminal and aggressive behavior, as were younger age, male sex, high impulsivity, and work/family stress. Path modeling suggested that several variables, notably combat exposure and history of childhood abuse, appeared to manifest their influence on criminal or aggressive behavior through increases in substance use and mental health problems. The potential mediating influence of substance use and mental health on combat and other trauma experiences has significant implications for preventing criminal and aggressive behavior among U.S. active duty military personnel.
Security Strength Measurement for Dongle-Protected Software

This article's objective is to develop a model for measuring the security strength of dongle-protected software. We believe such a measure is important because it can attach a clear, simple, and understandable monetization number to security. Dongles are USB keys or small boxes attached to the host parallel port. The copy-protected application interacts with the dongle and progresses its execution only if the dongle answers appropriately. The interaction between the software and the dongle takes place through calls to the dongle API.
Applying dynamic methods in off-line signature recognition

In this paper we present the work developed on off-line signature verification using Hidden Markov Models (HMM). HMM is a well-known technique used by other biometric features, for instance, in speaker recognition and dynamic or on-line signature verification. Our goal here is to extend Left-to-Right (LR)-HMM to the field of static or off-line signature processing using results provided by image connectivity analysis. The chain encoding of perimeter points for each blob obtained by this analysis is an ordered set of points in the space, clockwise around the perimeter of the blob. We discuss two different ways of generating the models depending on the way the blobs obtained from the connectivity analysis are ordered. In the first proposed method, blobs are ordered according to their perimeter length. In the second proposal, blobs are ordered in their natural reading order, i.e. from the top to the bottom and left to right. Finally, two LR-HMM models are trained using the parameters obtained by the mentioned techniques. Verification results of the two techniques are compared and some improvements are proposed.
JHK′ Imaging Photometry of Seyfert 1 Active Galactic Nuclei and Quasars. I. Multiaperture Photometry

Near-infrared JHK′ imaging photometry was obtained of 331 AGNs consisting mainly of Seyfert 1 active galactic nuclei (AGNs) and quasars (QSOs). This sample was selected to cover a range of radio emission strength, redshift from z = 0 to 1, and absolute B magnitude from MB = -29 mag to -18 mag. Among low-z AGNs with z < 0.3, Seyfert 1-1.5 AGNs are distributed over a region from a location typical of "galaxies" to a location typical of "QSOs" in the two-color J-H to H-K′ diagram, but Seyfert 1.8-2 AGNs are distributed around the location of "galaxies. " Moreover, bright AGNs with respect to absolute B magnitude are distributed near the location of "QSOs," while faint AGNs are near the location of "galaxies. " The distribution of such low-z AGNs in this diagram was found to have little dependence on their 6 cm radio flux. The near-infrared colors of the AGNs observed with an aperture of 7 pixels (7.″49) are more QSO-like than those observed with larger apertures up to 15 pixels (16.″1). This aperture effect may be explained by contamination from the light of host galaxies within larger apertures. This effect is more prominent for less luminous AGNs.
Optimal coordination of ramp metering and variable speed control-an MPC approach

We present a model predictive control (MPC) approach to optimally coordinate variable speed limits and ramp metering for highway traffic. The basic idea is that speed limits can increase the range in which ramp metering is useful. The control objective is to minimize the total time that vehicles spend in the network. For the prediction of the evolution of the traffic flows in the network we use an adapted version of the METANET model that takes the variable speed limits into account. The coordinated control results in a network with less congestion, a higher outflow, and a lower total time spent. In addition, the receding horizon approach of MPC results in an adaptive, online control strategy that automatically takes changes in the system parameters into account.
Chapter 10 POLYNOMIAL TIME MECHANISMS FOR COLLECTIVE DECISION MAKING

We investigate the problem of designing mechanisms to control collective decisions made by self-interested autonomous agents. In particular, we examine how results in the economics literature on mechanism design apply to collective decisions involving NP-hard optimization problems. We formalize the idea of polynomial time mechanism design and investigate mechanism design for a multiagent version of MAXSAT. We prove that there exists a polynomial time mechanism for multiagent MAXSAT that guarantees the outcome to be within a factor of 1/2 of the optimal outcome. We also show that, in general, a 1/2 approximation is the best approximation possible for dominant strategy, Nash, undominated Nash and subgame perfect implementation. Our analysis highlights some of the difficulties that arise in applying results from mechanism design to computational problems. Our results suggest that we may be much less successful using approximation to overcome hardness results in multiagent settings than in traditional computational settings especially when we require game theoretic guarantees on the outcomes. ∗Supported in part by National Science Foundation Grant CCR-97-34936 This is a preprint of an article that appears in Game theory and decision theory in agentbased systems, Parsons, S. , Gmytrasiewicz, P. and Wooldridge, M. J. (eds.), Kluwer Academic Publishers (2002).
A Bayesian Approach for Online Classifier Ensemble

We propose a Bayesian approach for recursively estimating the classifier weights in online learning of a classifier ensemble. In contrast with past methods, such as stochastic gradient descent or online boosting, our approach estimates the weights by recursively updating its posterior distribution. For a specified class of loss functions, we show that it is possible to formulate a suitably defined likelihood function and hence use the posterior distribution as an approximation to the global empirical loss minimizer. If the stream of training data is sampled from a stationary process, we can also show that our approach admits a superior rate of convergence to the expected loss minimizer than is possible with standard stochastic gradient descent. In experiments with real-world datasets, our formulation often performs better than state-of-the-art stochastic gradient descent and online boosting algorithms.
The Dynamic Relationship between Growth and Profitability under Long-Term Recession: The Case of Korean Construction Companies

We conducted an empirical analysis of the dynamic relationship between growth and profitability for small- and medium-sized construction companies that faced long-term economic stagnation in Korea. The period of the analysis spanned 2000 to 2014, and the full period was divided into two halves: before the 2008 global financial crisis and after it. Our empirical model was based on the system generalized method of moments model, and 264 construction companies were used as the study sample. The results of the empirical analysis are as follows. (1) A profitability-driven management strategy limits company growth, thus prolonging the economic downturn; (2) When the macroeconomic environment is relatively stable, high growth in the previous period fosters profitability in the current period. This implies that the phenomenon of dynamic increasing returns is present in the Korean construction industry, and learning through growth enhances productivity and profitability. Consequentially, a strategy oriented towards short-term profitability (popular with small- and medium-sized Korean construction companies) makes the corporate management less resilient, causing them to select “de-growth” during the long-term stagnation by decreasing their scale of operations. Accordingly, it is important for companies to maintain the balance between growth and profitability.
Acoustic emission beamforming for enhanced damage detection

As civil infrastructure ages, the early detection of damage in a structure becomes increasingly important for both life safety and economic reasons. This paper describes the analysis procedures used for beamforming acoustic emission techniques as well as the promising results of preliminary experimental tests on a concrete bridge deck. The method of acoustic emission offers a tool for detecting damage, such as cracking, as it occurs on or in a structure. In order to gain meaningful information from acoustic emission analyses, the damage must be localized. Current acoustic emission systems with localization capabilities are very costly and difficult to install. Sensors must be placed throughout the structure to ensure that the damage is encompassed by the array. Beamforming offers a promising solution to these problems and permits the use of wireless sensor networks for acoustic emission analyses. Using the beamforming technique, the azmuthal direction of the location of the damage may be estimated by the stress waves impinging upon a small diameter array (e.g. 30mm) of acoustic emission sensors. Additional signal discrimination may be gained via array processing techniques such as the VESPA process. The beamforming approach requires no arrival time information and is based on very simple delay and sum beamforming algorithms which can be easily implemented on a wireless sensor or mote.
Manifold elastic net for sparse learning

In this paper, we present the manifold elastic net (MEN) for sparse variable selection. MEN combines merits of the manifold regularization and the elastic net regularization, so it considers both the nonlinear manifold structure of a dataset and the sparse property of the redundant data representation. Face based gender recognition has received much attention in the psychophysical and video surveillance literatures. Most of existing works apply the appearance based information for data representation. A face image with size 40 by 40 could be seen as a point in a linear space with 1600 dimensions. For gender recognition, we have two classes (male and female) in total, so it is essential to find a small number of variables for representation to generalize duly. MEN can duly find the intrinsic structure of a dataset for separating males from the females. Sufficient experimental results on FERET and UMIST datasets suggest that MEN is more effective in selecting discriminative variables for face based gender recognition compared to principal component analysis, sparse principal component analysis, and discriminative locality alignment.
"Mental Rotation" by Optimizing Transforming Distance

The human visual system is able to recognize objects despite transformations that can drastically alter their appearance. To this end, much effort has been devoted to the invariance properties of recognition systems. Invariance can be engineered (e.g. convolutional nets), or learned from data explicitly (e.g. temporal coherence) or implicitly (e.g. by data augmentation). One idea that has not, to date, been explored is the integration of latent variables which permit a search over a learned space of transformations. Motivated by evidence that people mentally simulate transformations in space while comparing examples, so-called "mental rotation", we propose a transforming distance. Here, a trained relational model actively transforms pairs of examples so that they are maximally similar in some feature space yet respect the learned transformational constraints. We apply our method to nearest-neighbour problems on the Toronto Face Database and NORB.
Design and control of a powered knee orthosis for gait assistance

A powered knee orthosis named Assistive Knee Brace (AKB) is designed for the elderly and patients with knee dysfunctions to gain external support at their knee joints during gait. Safety, light-weight and user-friendly interface are the emphasis of system design. In the assistive controller design, gait analysis method based on Finite State Machine is developed for gait pattern estimation, with state estimated through fuzzy inference. Based on the gait pattern estimated online, reference gait pattern is generated as assistive control input of the powered knee orthosis to adjust the wearer's abnormal motion with torque support to track the normal pattern. To verify the assistive function of AKB, gait experiments under control are conducted on healthy subjects simulating certain limited mobility. Results show that with proper assistive torque from the AKB, wearers can perform motions in good patterns.
Ego depletion negatively affects knowledge retrieval in secondary school students

Abstract In the present study, we tested the assumption that performance in a knowledge retrieval test would be lower in secondary school students with temporarily depleted self-control strength (n = 53) compared to secondary school students with temporarily available self-control strength (n = 56). After manipulating self-control strength, students were instructed to memorise the names and the functions of the human eye and to recall as many names and functions as possible following a one-min distraction task. The results supported our assumptions, as students with depleted self-control strength performed significantly worse in the knowledge retrieval task compared to students whose self-control strength had not been temporarily depleted. The results suggest that depleted self-control strength may negatively affect academic performance and hinder a successful academic career. The theoretical and practical implications of these findings are discussed.
Multi-source noisy network coding

Noisy network coding unifies network coding by Ahlswede, Cai, Li, and Yeung for noiseless networks and compress-forward by Cover and El Gamal for noisy relay channels. In particular, it achieves the best known capacity inner bounds for multi-source multicast networks including deterministic networks by Avestimehr, Diggavi, and Tse and erasure networks by Dana, Gowaikar, Palanki, Hassibi, and Effros. This paper extends noisy network coding for multicast networks to networks with general message demand by combining the underlying noisy network coding scheme with decoding techniques for interference channels. At one extreme, noisy network coding is combined with simultaneous decoding, while at the other extreme interference is treated as noise. The potential of noisy network coding as a canonical building block for wireless networks is demonstrated via three examples of Gaussian networks that have drawn recent attentions.
Interactive function-based artistic shape modeling

This paper addresses interactive function-based shape modeling where relatively small formulas are used rather than thousands of polygons. Interactive modification of the function model with concurrent visualization of the respective polygonal mesh provides both interactivity and any required level of detail leading to a photorealistic appearance of the resulting shapes. We have proposed a rendering method capable of handling local shape modifications with any desired precision. We have also proposed methods which let us accelerate the final function evaluation - a common bottleneck for function-based shape modeling. Finally, we describe applications of interactive function-based shape modeling to photorealistic virtual embossing and carving.
Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction

Total generalized variation (TGV)-based computed tomography (CT) image reconstruction, which utilizes high-order image derivatives, is superior to total variation-based methods in terms of the preservation of edge information and the suppression of unfavorable staircase effects. However, conventional TGV regularization employs l1-based form, which is not the most direct method for maximizing sparsity prior. In this study, we propose a total generalized p-variation (TGpV) regularization model to improve the sparsity exploitation of TGV and offer efficient solutions to few-view CT image reconstruction problems. To solve the nonconvex optimization problem of the TGpV minimization model, we then present an efficient iterative algorithm based on the alternating minimization of augmented Lagrangian function. All of the resulting subproblems decoupled by variable splitting admit explicit solutions by applying alternating minimization method and generalized p-shrinkage mapping. In addition, approximate solutions that can be easily performed and quickly calculated through fast Fourier transform are derived using the proximal point method to reduce the cost of inner subproblems. The accuracy and efficiency of the simulated and real data are qualitatively and quantitatively evaluated to validate the efficiency and feasibility of the proposed method. Overall, the proposed method exhibits reasonable performance and outperforms the original TGV-based method when applied to few-view problems.
Image enhancement based on multiple parametric sigmoid functions

In a digital still camera system, a dynamic range is usually compressed linearly in logarithmic space. Therefore, the images taken by the digital still camera tend to be low contrast, and give us a flat impression without the liveliness in comparison with the conventional silver halide photographs. In this paper, we propose a new digital image enhancement method by using multiple parametric sigmoid functions. In the proposed method, intensity conversions by using parametric sigmoid functions are carried out based on the information of local regions with various sizes. And, by synthesizing each conversion result, the fine image enhancement is achieved. The effectiveness of the proposed method is verified by applying it to some images.
Recognition of leaf based on its tip and base using centroid contour gradient

This paper suggests normalization of the tip and base of leaf as both of them incline to one direction which is able to influence data extraction process. The extraction method we used is Centroid Contour Gradient (CCG) which calculates the gradient between pairs of boundary points corresponding to interval angle. CCG had outperformed its competitor which is Centroid Contours Distance (CCD), as it successfully captures the curvature of the tip and base of leaf. The accuracy in classifying the tip of leaf using CCG is 99.47%, but CCD is only 80.30%. For accuracy of leaf base classification, CCG (98%) also outperforms CCD (88%). The average accuracy for recognizing the 5 classes of plant is 96.6% for CCG and 74.4% for CCD. In this research, we utilized the Feed-forward Back-propagation as our classifier.
A 3-D Spectral Integral Method (SIM) for Surface Integral Equations

An efficient 3-D spectral integral method (SIM) has been proposed to speed up the method of moments (MOM) calculation of induced currents on a cuboid. This method utilizes the Toeplitz structure in the impedance matrix and the fast Fourier transform to accelerate the MOM solution. It reduces the memory and CPU time per iteration from <i>O</i>(<i>N</i> <sup>2</sup>) in the MOM to <i>O</i>(<i>N</i> <sup>1.5</sup>) and <i>O</i>(<i>N</i> <sup>1.5</sup>log<i>N</i>), respectively. Thus, the SIM can be also used as an efficient radiation boundary condition for the finite element method. Numerical results confirm the effectiveness of this method.
Efficient Labelling of Pedestrian Supervisions

Object detection is a fundamental goal to achieve intelligent visual perception by computers due to the fact that objects are the basic building blocks to achieve higher level image understanding. Among the numerous categories of objects in the real-world, pedestrians are among the most important due to several potential benefits brought about by successful pedestrian detection. Often, pedestrian detectors are trained in state-of-the-art systems using supervised machine learning algorithms which necessitates costly and often tedious manual annotation of pedestrians in the form of precise bounding boxes. In this paper, a novel weakly supervised learning algorithm is proposed to train a pedestrian detector that requires, instead of bounding boxes, only annotations of estimated centres of pedestrians. The algorithm makes use of a pedestrian prior learnt in an unsupervised way from the video and this prior is fused with the given weak supervision information in a systematic manner. By evaluating on publicly available datasets, we demonstrate that our weakly supervised algorithm reduces the cost of manual annotation of pedestrians by more than four times while achieving similar performance to a pedestrian detector trained with standard bounding box annotations.