Miracle-dz/UniFAR-FTUs huggingface dataset:
PRELIMINARY PAPER PRETHODNO PRIOP Ć ENJE DYNAMICS IN WOOD INDUSTRY IN SLOVENIA

This paper investigates the dynamics of large wood industry enterprises on the dynamics of micro and small wood industry enterprises using firm-level survey data and time-series data. In addition, entry and exit of micro and small wood industry enterprises is explained by factors of demand, value-added, and profitability of capital. The empirical results show relatively modest impact of the dynamics of large enterprises on the dynamics of micro and small enterprises in the wood industry. The rate of value-added per employee and the rate of capital profitability are significantly associated with investments, particularly investments into new technological equipment. Easier access to financial means for micro and small enterprises at acceptable guarantees and reduction in costs of financing of capital are among possible measures to improve wood industry enterprise performance and entry into foreign markets as well as to efficiently reduce imports of wood raw materials and low value-added wood semi-products and to increase exports of higher value-added products and services. JEL Classification: D22, L25, L69, C12, C81;
Analyzing the Effectiveness of Adversary Modeling in Security Games

Recent deployments of Stackelberg security games (SSG) have led to two competing approaches to handle boundedly rational human adversaries: (1) integrating models of human (adversary) decision-making into the game-theoretic algorithms, and (2) applying robust optimization techniques that avoid adversary modeling. A recent algorithm (MATCH) based on the second approach was shown to outperform the leading modeling-based algorithm even in the presence of significant amount of data. Is there then any value in using human behavior models in solving SSGs? Through extensive experiments with 547 human subjects playing 11102 games in total, we emphatically answer the question in the affirmative, while providing the following key contributions: (i) we show that our algorithm, SU-BRQR, based on a novel integration of human behavior model with the subjective utility function, significantly outperforms both MATCH and its improvements; (ii) we are the first to present experimental results with security intelligence experts, and find that even though the experts are more rational than the Amazon Turk workers, SU-BRQR still outperforms an approach assuming perfect rationality (and to a more limited extent MATCH); (iii) we show the advantage of SU-BRQR in a new, large game setting and demonstrate that sufficient data enables it to improve its performance over MATCH.
Politics of Presence? Congresswomen and Symbolic Representation

Gender politics literature stresses the symbolic importance of electing more women to high-level political office. Despite references to the heightened legitimacy that women in politics bring to the political process, and the manner in which they affect constituents’ political attitudes and behavior, little empirical evidence exists regarding the actual benefits of symbolic representation. Using pooled National Election Study data from 1980 to 1998, I attempt to fill a void in the literature, exploring whether the presence of women officeholders affects constituents’ evaluations of their members of Congress, levels of political efficacy and trust in government, and propensity to participate politically. After controlling for party congruence between the representative and his/her constituent, I uncover little evidence of the independent symbolic effects scholars typically ascribe to women’s presence in Congress. Women represented by women tend to offer more positive evaluations of their members of Congress, but this difference does not consistently translate into political attitudes or behavior. The findings represent an initial attempt to use available survey data to explore the extent to which symbolic representation independently affects citizens’ political attitudes and engagement.
Bullying - Action and Intervention

All those who work in schools recognise that bullying goes on. Generally, they feel powerless to deal adequately with the problem. Arora is an Educational Psychologist working in secondary schools, and she has led workshops on bullying for the last few years. Through her own observation in a secondary school where the issues were addressed, by reading the literature available, and from the ideas that have emerged from her workshops, Mrs Arora has concluded that some strategies appear to be effective in reducing bullying. Her article considers some of these and provides useful pointers for practice in schools when an incident of bullying comes to a teacher's attention.
Extracting stay regions with uncertain boundaries from GPS trajectories: a case study in animal ecology

In this paper we present a time-aware, density-based clustering technique for the identification of stay regions in trajectories of low-sampling-rate GPS points, and its application to the study of animal migrations. A stay region is defined as a portion of space which generally does not designate a precise geographical entity and where an object is significantly present for a period of time, in spite of relatively short periods of absence. Stay regions can delimit for example the residence of animals, i.e. the home-range. The proposed technique enables the extraction of stay regions represented by dense and temporally disjoint sub-trajectories, through the specification of a small set of parameters related to density and presence. While this work takes inspiration from the field of animal ecology, we argue that the approach can be of more general concern and used in perspective in different domains, e.g. the study of human mobility over large temporal scales. We experiment with the approach on a case study, regarding the seasonal migration of a group of roe deer.
Learning Mixtures of Linear Classifiers

We consider a discriminative learning (regression) problem, whereby the regression function is a convex combination of k linear classifiers. Existing approaches are based on the EM algorithm, or similar techniques, without provable guarantees. We develop a simple method based on spectral techniques and a 'mirroring' trick, that discovers the subspace spanned by the classifiers' parameter vectors. Under a probabilistic assumption on the feature vector distribution, we prove that this approach has nearly optimal statistical efficiency.
Osteotome technique with injectable tissue-engineered bone and simultaneous implant placement by cell therapy.

OBJECTIVES The purpose of this study was to evaluate the effects of tissue-engineered bone (TEB) on osteotome technique with simultaneous implant placement that is applied to the severe bone resorption in the maxilla for earlier bone regeneration and minimal invasive operation. MATERIAL AND METHODS We applied injectable TEB, which was composed of bone marrow-derived mesenchymal stem cells (BMMSCs) as stem cell source and platelet-rich plasma as an autologous scaffold and signal molecules, with osteotome technique for 23 cases. After dental implants were pre-prepared with pilot drills and/or using the Summers Osteotome Kits, TEB was injected and followed by dental implants insertion. The outcomes were evaluated by radiographs. Statistical evaluation was performed by Friedman test and Wilcoxon signed-rank test (P ≤ 0.05). RESULTS The grafted BMMSCs possessed a phenotype of mesenchymal stem cells. The lift-up bone height by TEB using BMMSCs showed the increase of 6.1 ± 1.5 mm. Moreover, the mean regenerated bone height was 8.2 ± 1.6 mm and 8.0 ± 1.4 mm, and the average alveolar bone height was 15.6 ± 1.2 mm and 15.1 ± 1.4 mm, at 3 and 6 months, respectively. There were significant differences between pre-operative values and post-operative ones (at 3, 6 months). No perforations of the Schneider membrane were found and the inserted implants were successful after 1 year. CONCLUSIONS This novel application using osteotome technique with injectable TEB would stably predict the success of bone formation and dental implants, and provide minimally invasive cell therapy.
The Exact Closest String Problem as a Constraint Satisfaction Problem

We report (to our knowledge) the first evaluation of Constraint Satisfaction as a computational framework for solving closest string problems. We show that careful consideration of symbol occurrences can provide search heuristics that provide several orders of magnitude speedup at and above the optimal distance. We also report (to our knowledge) the first analysis and evaluation -- using any technique -- of the computational difficulties involved in the identification of all closest strings for a given input set. We describe algorithms for web-scale distributed solution of closest string problems, both purely based on AI backtrack search and also hybrid numeric-AI methods.
Relationships between Yo-Yo Intermittent Recovery Tests and Development of Aerobic and Anaerobic Fitness in U-13 and U-17 Soccer Players

The study investigated the Yo-Yo intermittent recovery level 1 and level 2 tests (YYIR1 and YYIR2) performed by soccer players at puberty (U-13) and post-puberty (U-17) and related the YYIR1 and YYIR2 results to anaerobic and aerobic fitness. The subjects were sixteen male pubescent players aged 13.3(0.2) yrs (U-13) and fifteen male post-pubescent players aged 17.0(0.6) yrs (U-17). The subjects performed YYIR1 and YYIR2 on artificial turf and were also measured for fat-free mass (FFM), Wingate anaerobic test and VO2max during treadmill running in a laboratory. The results in YYIR1 and YYIR2 (2365 and 843 m) for the U-17 were significantly higher than the U-13 (1963 and 550 m). Results from YYIR1 and YYIR2 in the U-17 correlated significantly with VO2max (r=0.76 and 0.53) but not with Wingate anaerobic test, whereas YYIR1 and YYIR2 results in the U-13 correlated significantly with Wingate anaerobic test (r=0.61 and 0.64) but not with VO2max even though they showed high VO2max levels. The correlation coefficient of VO2max with YYIR1 and YYIR2 in the U-17 was equivalent to those reported in adult players and indicated that VO2max determined YYIR1 and YYIR2 performances moderately and highly in post-pubescent players. However, neither of the YYIR1 and YYIR2 performances in the U-13 reflected aerobic fitness but did reflect anaerobic fitness.
Implementing a large‐scale reform in secondary schools: the role of the consultant within England’s Secondary National Strategy

This article examines the role of the consultants in secondary schools and local authorities within a large‐scale consultancy‐based reform, the Secondary National Strategy (SNS), in London, UK. The SNS follows a cascade model of implementation in which nationally created initiatives are introduced and supported within local authorities (LA) and schools through LA‐based consultant teams. The article is drawn from a larger interpretive case study that follows the experiences of SNS consultants working within the reform from 2002 to 2006. The article uses semi‐structured interviews to reveal the various and diverse experiences that occur in schools as a result of the introduction of consultants within LAs. The article argues that introducing an SNS consultancy structure to work within LAs and secondary schools created dynamic, complex and shifting effects across secondary schools. While consultants often attempted to create developmental or learning experiences that resonated with teachers’ and departments’ experiences, these activities may also have served to further the institutional control of teachers’ workspaces. These effects are part of the complexities that exist within school change agendas. Exploring these contradictions helps in understanding school change within urban secondary schools.
Theories of Motivation in Addiction Treatment: Testing the Relationship of the Transtheoretical Model of Change and Self-Determination Theory

This study explored the relationship between 2 theories of motivation: self-determination theory (SDT) and the transtheoretical model of change (TTM), and sought to determine whether the source of motivation described by SDT would predict TTM's stage of change. SDT was operationalized as the level of internal or external motivation for treatment, and TTM was operationalized as 3 stages of change: precontemplation, contemplation, and action. Our data came from the Drug Abuse Treatment Outcome Study published in 2004. A multinomial logistic regression analysis indicated that there was a significant relationship between source of motivation and stage of change at intake. Controlling for severity, treatment history, legal status, and primary substance use, persons entering treatment with higher levels of internal motivation were more likely to be in the action stage than the precontemplation stage. Higher levels of internal motivation also predicted a greater likelihood of being in the contemplation rather than the precontemplation stage.
Financial Capacity Assessment Instrument (IACFin): Development and Qualitative Study Using Focus Groups

Changing from a diagnostic to a functional approach in making capacity determinations had several consequences. One of these was the demand for specific Forensic Assessment Instruments (FAI) to reach specific and specialised answers in forensic mental health evaluations. Under Portuguese law, there are two protective legal measures for incapacitated adults: interdiction and inhabilitation. In those legal proceedings, a forensic evaluation by mental health professionals is important and includes an examination of financial capacity. The main purpose of this study was to develop the Financial Capacity Assessment Instrument (IACFin), a FAI to assess financial abilities. The experimental version of the IACFin was developed by reviewing the literature and other assessment instruments and procedures, analysing interdiction and inhabilitation judicial processes, and having discussions with legal and clinical experts. This experimental version was tested using qualitative research methods with focus groups. The final version of the IACFin contains three modules: (1) screening, which assesses orientation, sensory function, motor function, attention and consciousness, and language; (2) general financial, which covers eight domains, including values and preferences, basic skills, shopping, bill payment, banking, decision-making, risk of crime against the estate and supply will instruments; and (3) estate disposition, which covers estate determination, donations and wills.
The devil is in the details: genetic variation in introduced populations and its contributions to invasion

The influence of genetic variation on invasion success has captivated researchers since the start of the field of invasion genetics 50 years ago. We review the history of work on this question and conclude that genetic variation—as surveyed with molecular markers—appears to shape invasion rarely. Instead, there is a significant disconnect between marker assays and ecologically relevant genetic variation in introductions. We argue that the potential for adaptation to facilitate invasion will be shaped by the details of genotypes affecting phenotypes, and we highlight three areas in which we see opportunities to make powerful new insights. (i) The genetic architecture of adaptive variation. Traits shaped by large‐effect alleles may be strongly impacted by founder events yet more likely to respond to selection when genetic drift is strong. Large‐effect loci may be especially relevant for traits involved in biotic interactions. (ii) Cryptic genetic variation exposed during invasion. Introductions have strong potential to uncover masked variation due to alterations in genetic and ecological environments. (iii) Genetic interactions during admixture of multiple source populations. As divergence among sources increases, positive followed by increasingly negative effects of admixture should be expected. Although generally hypothesized to be beneficial during invasion, admixture is most often reported among sources of intermediate divergence, supporting the possibility that incompatibilities among divergent source populations might be limiting their introgression. Finally, we note that these details of invasion genetics can be coupled with comparative demographic analyses to link genetic changes to the evolution of invasiveness itself.
Towards Better Program Obfuscation: Optimization via Language Models

As a common practice in software development, program obfuscation aims at deterring reverse engineering and malicious attacks on released source or binary code. Owning ample obfuscation techniques, we have relatively little knowledge on how to most effectively use them. The biggest challenge lies in identifying the most useful combination of these techniques. We propose a unified framework to automatically generate and optimize obfuscation based on an obscurity language model and a Monte Carlo Markov Chain (MCMC) based search algorithm. We further instantiate it for JavaScript programs and developed the Closure* tool. Compared to the well-known Google Closure Compiler, Closure* outperforms its default setting by 26%. For programs which have already been well obfuscated, Closure* can still outperform by 22%.
Aligning Real-Time Opinion Poll Responses with an Expectation-Maximization Algorithm

React Labs [8], a mobile polling application, makes it possible to analyze individuals’ realtime reactions and opinions on a large scale. While users tend to respond nearly instantaneously to events as they see them [4], in the setting of a televised debate users may actually be viewing the same event at different times. As a result, the timestamps attached to responses collected using React Labs may not align correctly with a reference such as a transcript of the debate. We propose a generative model for this problem in which each user responds to latent events at a fixed scalar offset, also latent, and we derive an Expectation Maximization (EM) algorithm to estimate both the times of events and the offsets of users. We evaluate the method using a corpus of reactions obtained during the first 2012 presidential debate [3]. Since the true offsets of the users in the real data are unknown, we create artificial data with known offsets, add error and omissions, and place these alongside real data. The EM algorithm recovers the offsets accurately, suggesting that this method may be used to estimate offsets, which can then be used to adjust response timestamps and create a modified, more accurately aligned dataset.
GOOD SAMPLE CONSENSUS ESTIMATION OF 2D-HOMOGRAPHIES FOR VEHICLE MOVEMENT DETECTION FROM THERMAL VIDEOS

In this contribution we describe a method to assess the activity of vehicles based on airborne image sequences taken by an infrared camera. Active vehicles often appear as a configuration of a dark and a bright spot close to each other. The sensor movement is inferred from image sequences. Due to the fast velocity of the platform estimations of vehicle movements require a precise measurement of the sensor movement. The camera may be tilted with the aircraft giving arbitrarily oblique views. Camera movements are treated as projective 2D-homographies. For the search of a subset of image point correspondences that is free of outliers and gives a precise estimate of the movement we use a production system implementing good sample consensus (GSAC). This new method is derived from the well known RANSAC-decisions and improves them by preferring good samples to random samples. As assessment criterion for minimal samples the area of the smallest triangle in the sample is used. We motivate the criterion for the quality of samples by error propagation through the estimated homography. A comparison is made with other robust estimation techniques namly RANSAC and iterative re-weighted least squares.
Independence is good: dependency-based histogram synopses for high-dimensional data

Approximating the joint data distribution of a multi-dimensional data set through a compact and accurate histogram synopsis is a fundamental problem arising in numerous practical scenarios, including query optimization and approximate query answering. Existing solutions either rely on simplistic independence assumptions or try to directly approximate the full joint data distribution over the complete set of attributes. Unfortunately, both approaches are doomed to fail for high-dimensional data sets with complex correlation patterns between attributes. In this paper, we propose a novel approach to histogram-based synopses that employs the solid foundation of statistical interaction models to explicitly identify and exploit the statistical characteristics of the data. Abstractly, our key idea is to break the synopsis into (1) a statistical interaction model that accurately captures significant correlation and independence patterns in data, and (2) a collection of histograms on low-dimensional marginals that, based on the model, can provide accurate approximations of the overall joint data distribution. Extensive experimental results with several real-life data sets verify the effectiveness of our approach. An important aspect of our general, model-based methodology is that it can be used to enhance the performance of other synopsis techniques that are based on data-space partitioning (e.g., wavelets) by providing an effective tool to deal with the “dimensionality curse”.
Spatial Parsing within the Topos 3 D Environment

Spatial parsers augment spatial hypermedia systems by letting the computer perceive the informal – but visually apparent – groupings formed by humans working with a spatial hypermedia tool. A number of research systems implementing 2D spatial parsing have been described in recent years. This position paper describes an implementation of a tailorable 3D spatial parser for the Topos system: a 3D information organization tool for use on desktops, interactive whiteboards and tables. A number of issues pertaining to 3D parsing as opposed to 2D parsing are discussed.
Current-fed full-bridge DC-DC converter with nonlinear control scheme

This paper presents a control algorithm for dealing with the constraints of energy power source and a current-fed full-bridge converter. The advantages of this control scheme include transient and steady-state responses and robustness. The prototype of 26 V/100 V 250 W and its controller were implemented. A dSPACE DS1104 was used to implement the outer control loop by using flatness system's properties. An analog control card was employed as an inner control loop by using hysteresis-based PWM. The validation of the proposed control was demonstrated through experiment results.
Anthropometric predictors and Artificial Neural Networks in the diagnosis of hypertension

Artificial Neural Networks (ANNs) play a vital role in the medical field in solving various health problems like estimating the risk of cardiovascular diseases. The article concerns the process of developing ANNs for estimating the risk of arterial hypertension. ANNs proposed in this article use anthropometrical predictors, easy to control for everybody at home without special equipment. In the article we analyze four different models of ANNs and try to find out which model and set of anthropometrical predictors estimates the risk the most accurately. We use dataset of 2485 real cases of patients from the city of Lodz. The experiment was done in the Matlab environment. The performance of the proposed method in terms of accuracy and facility of use shows that ANNs can be effective tools for preliminary tests of arterial hypertension.