Miracle-dz/UniFAR-FTUs huggingface dataset:
Drivers with limited perception: model and application to traffic simulation

We propose a model of the driver perception suited for microscopic, agent-based traffic simulations. The model includes both top-down and bottom-up perception, and takes into account the limited amount of perceptive resource which gain access to short-term memory. The driving task is split into sub-tasks, which can be activated in parallel (e.g. car following and crossroads passing). Perceived entities (percepts) as well as subtasks are ranked with respect to their subjective value, and due to the bounded perception, only the more “valuable” percepts are sent to the decision module of the cognitive model. The competition among percepts to gain access to the short-term memory simulates attentional processes. A computational implementation of the model is proposed for the driver, using agent-based modeling. It is implemented in a traffic simulation environment and allows the driver-agent to manage the conflicts and the longitudinal space in the middle of the crossroads. This way, we improve the realism of the simulation. Furthermore, this model can lead to a new way of identifying and explaining near accidents. We illustrate some benefits for a microscopic traffic simulation at crossroads in two situations. The first scenario explores the traffic parameters at a crossroads, simulating various distributions of the driver’s age. The second one demonstrates the adaptive behavior of simulated drivers facing a dangerous (e.g. hypo-vigilant) driver.
2 Testing for the intensity of an activation in SPMs 2 . 1 Theory

7 Discussion and conclusion 17 7.1 Which test should be used and when ? : : : : : : : : : : : : : : : : : : : 17 7.2 Sensitivity and Speci city : : : : : : : : : : : : : : : : : : : : : : : : : : 18 7.3 How do we choose the parameters ? : : : : : : : : : : : : : : : : : : : : : 18 7.4 Smoothness Variability : : : : : : : : : : : : : : : : : : : : : : : : : : : : 19 7.5 Smoothness estimation on the residuals : : : : : : : : : : : : : : : : : : : 20 7.6 A priori hypothesis : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 22 7.7 Testing for commonalities : : : : : : : : : : : : : : : : : : : : : : : : : : 22
Channel estimation for visible light communications using neural networks

Visible light communications (VLC) is an emerging field in technology and research. Estimating the channel taps is a major requirement for designing reliable communication systems. Due to the nonlinear characteristics of the VLC channel those parameters cannot be derived easily. They can be calculated by means of software simulation. In this work, a novel methodology is proposed for the prediction of channel parameters using neural networks. Measurements conducted in a controlled experimental setup are used to train neural networks for channel tap prediction. Our experiment results indicate that neural networks can be effectively trained to predict channel taps under different environmental conditions.
Brain-based teaching in programming courses

Brain-based teaching is neither a method nor a concept. It is rather a way of teaching that tries to support the whole learning and memory process by considering how the brain works. The concept of "Brain-based Programming" is one attempt of putting neurodidactical principles into practice in order to improve the learning out-comes in introductory programming courses. In the pilot project this aim could be achieved [1] and the results of the ongoing study in three of seven parallel groups confirm the success in part.
On reducing model transformation testing overhead

Model-Driven Engineering is essentially based in metamodel definition, model edition and the specification of model transformations (MT). In many cases the development and maintenance of these transformations are still carried out without the support of proper methods and tools to reduce the effort and cost related to them. As a result, several model transformation testing approaches have been defined over the past decade. However, they usually introduce some kind of overhead on transformation development or maintenance which has not been properly considered heretofore. In this work, we will present MoTe, a novel contract-based model transformation testing approach specifically designed to reduce testing overhead. First, we present main overhead sources in core stages of contract-based model transformation testing. Then, we give a detailed introduction of the main features and design decisions behind MoTe in order to reduce overhead in all those main stages. Furthermore, we will discuss some significant results from a comparative case study.
Digital Rights Protection outside Multimedia ∗

Information is probably the most valuable asset of humanity today. By enabling comparably cost-free, fast, and accurate access channels to information in digital form, computers radically changed the way we think and express ourselves. As increasingly more of it is produced, packaged and delivered in digital form in a fast, networked environment, one of its main features threatens to become its worst bug: zero-cost verbatim copies. The inherent ability to produce duplicates of digital Works for virtually no incurred cost can be now misused for illegal profit. This dramatically increases the requirement for an effective rights protection mechanism in the digital world. Different avenues are available, each with its own advantages and drawbacks. Enforcement by legal means is usually ineffective in this new framework, unless augmented by a digital counter-part such as Information Hiding. Digital Information Hiding as a method of Rights Protection (also known as Digital Watermarking), hides an indelible “rights witness” (watermark) within the digital Work to be protected, by slightly altering it. The soundness of such a method relies on the assumption that (i) the insertion of the mark does not destroy the value of the Work (i.e. it is still useful for its intended purpose); and that (ii) it is difficult for a malicious adversary (Mallory) to remove or alter the mark beyond detection without destroying the value of the Work. Mallory, and the ability to resist his attacks (mostly aiming at removing the embedded watermark) turn out to be one of the major concerns in the design of a watermarking solution. There exists a multitude of semantic frameworks for information processing and distribution. Each distinct data domain would benefit from the availability of a suitable watermarking solution. The overwhelming majority of research efforts in digital watermarking have been invested in the multimedia data domain (e.g. images, video and audio) [5] [6]. Very recently, other data domains have also been considered, such as natural language [1] and software [4]. In this work we analyze Digital Information Hiding as a method of Rights Protection from a higher level, domain-independent perspective. We propose a theoretical model for Watermarking. We ask: what are the limits of Watermarking? When can these be reached? We then propose, design and analyze watermarking solutions for (i) numeric and categorical relational data (ii) streams and (iii) arbitrary semi-structured content.
A Semidefinite Programming based approach for finding Bayesian optimal designs for nonlinear models

This paper uses Semidefinite Programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here ex- tends the formulation of the optimal designs problem as a SDP problem from linear to nonlinear models. Gaussian Quadrature Formulas (GQF) are used to compute the expectation in the Bayesian design criterion, such as D-, A- or E-optimality. As an illustrative example, we demonstrate the ap- proach using the power logistic model and compare results in the literature. Additionally, we investigate how the optimal design is impacted by different discretizing schemes for the design space, different amount of uncertainty in the parameter values, different choices of GQF and different prior distribu- tions for the vector of model parameters, including normal priors with and without correlated components. Further applications to find Bayesian D- optimal designs with two regressors for a logistic model and a two-variable generalized linear model with a gamma distributed response are discussed and some limitations of our approach are noted.
Civil Religion and Its Discontents

What does it mean to be an American? What (if any) "sacred ties" bind us together as a special people with a special destiny? And what is the proper place for quasi-religious icons, like the flag, and creedal affirmations, like the Pledge of Allegiance, in constituting ourselves as a special community? These timely questions have been sharply posed in recent months by the presidential campaign of George Bush, a proud, albeit adopted, son of the Lone Star state. But these questions are more than timely-they are timeless. Indeed, months before the general election took shape, these and related questions were posed with even more crispness-and with far more elegance, eloquence, and thoughtfuilnessby another adopted son of Texas, Professor Sanford Levinson.1 At the outset of his book Constitutional Faith, Professor Levinson promises "many more questions than answers in the pages to come" 2 and he is true to his word. This book is not for those who seek quick solutions to deep issues. But for those who savor seriousness and sincerity, for those left unsatisfied by the recent campaign's fast-food, thirtysecond-ad-bite approach to enduring questions of American identity, Constitutional Faith is appetizing and nourishing fare. The book, however, will leave some readers, especially lawyers, hungry for more-more determinacy, more answers, more traditional legal analysis, more attention to legal process concerns-than Professor Levinson has chosen to serve up. In Part I of this Review, I present a detailed account of Professor Levinson's arguments. In Part II, I offer a few of my reactions.
Imputation in Mixed Attribute Datasets using Higher Order Kernel Functions

The real world data today present have missing values that occur due to a variety of reasons. The methods used for data analyzing such as classification, clustering and dimension reduction procedures require complete data. The missing data present in these must be either removed or preferably estimated . Missing data imputation is a key issue in learning from incomplete data. Many techniques had been developed on dealing with missing values in data sets with homogeneous attributes (their independent attributes are all either continuous or discrete). This paper proposes a higher order spherical kernel based iterative estimator to impute mixed-attribute data sets. The estimators are evaluated with extensive experiments, and the result demonstrates that the proposed approach is better than these existing imputation methods in terms of co relation coefficient and root mean square error (RMSE) at different missing ratios.
Extraction and visualization of traceability relationships between documents and source code

Traceability links between artifacts in a software system aid developers in comprehension, development, and effective management of the system. Traceability systems to date have been confronting the difficulties in retrieving relationships between artifacts with high quality and accuracy, and in visualizing extracted relationships in a natural and intuitive way. This research aims to combine several traceability recovery techniques to make up for each other's weaknesses to extract relationships between artifacts at a high-level accuracy and quality. Moreover, the recovered relationships are visualized in a hierarchical rich graphical tree that can be expanded and contracted to help users easily interact with these links and move easily between artifacts and their related artifacts and vice versa. Our preliminary evaluation demonstrated that integration of several traceability recovery techniques can improve the quality and accuracy of retrieved links.
Three essays on Foreign Direct Investment (FDI)

This thesis examines the effects of joining currency unions and trade agreements as well as political risk on FDI. It also engages in the empirical examination of the Eclectic Paradigm. The aim of this research is to extend the current knowledge on the determinants of FDI, as various empirical studies have found mixed results. The first empirical chapter investigates the impact of membership of currency unions and trade agreements on FDI inflows, outflows, and net FDI (inflows-outflows) by using pooled OLS estimation method for a sample of 180 countries during the period of 1970 to 2007. The second empirical chapter analyses the impact of political risk on FDI inflows into OECD countries by using pooled OLS estimation and fixed effects panel data methods throughout the period of 1975 to 2009. The third empirical chapter examines the relationship between determinants of FDI from the perspective of Eclectic Paradigm for the sample of 196 countries for the period of 1970 to 2009. My study uses up-to-date large macro datasets for long periods. Insights are provided on the impact of regional trade agreements and currency unions on FDI, a topic on which the literature is relatively scarce. Similarly, another contribution is the analysis of FDI outflows and net FDI, which did not receive much attention in previous studies. This thesis further investigates the impact of political environment in the country on FDI inflows using a wide range of political indicators. Lastly, the investigation presented here confirms the predictions of the Eclectic
Evolutionary History of the PER3 Variable Number of Tandem Repeats (VNTR): Idiosyncratic Aspect of Primate Molecular Circadian Clock

The PER3 gene is one of the clock genes, which function in the core mammalian molecular circadian system. A variable number of tandem repeats (VNTR) locus in the 18th exon of this gene has been strongly associated to circadian rhythm phenotypes and sleep organization in humans, but it has not been identified in other mammals except primates. To better understand the evolution and the placement of the PER3 VNTR in a phylogenetical context, the present study enlarges the investigation about the presence and the structure of this variable region in a large sample of primate species and other mammals. The analysis of the results has revealed that the PER3 VNTR occurs exclusively in simiiforme primates and that the number of copies of the primitive unit ranges from 2 to 11 across different primate species. Two transposable elements surrounding the 18th exon of PER3 were found in primates with published genome sequences, including the tarsiiforme Tarsius syrichta, which lacks the VNTR. These results suggest that this VNTR may have evolved in a common ancestor of the simiiforme branch and that the evolutionary copy number differentiation of this VNTR may be associated with primate simiiformes sleep and circadian phenotype patterns.
Object-Based Multi-feature Competitive Model for Visual Saliency Detection

Saliency detection is an important topic for the researches on visual attention selective computation model. This paper aims at the conflict between the local saliency detection and the huge computational cost, and proposes an object-based multi-feature competitive model for visual saliency detection. The proposed model is based on the biologically motivated color-antagonistic model and is advanced by introducing the saturation channel. Gabor wavelet decomposition is used to obtain the feature maps. Object-based local inhibition is also proposed to accelerate the generation of feature conspicuity maps. The competition between multiple feature channels is simulated to implement the fusion of various visual features. The simulation results show that the proposed model has some advantages in local saliency detection and in computational speed.
We Fight To Win: Inequality and the Politics of Youth Activism

In an adult-dominated society, teenagers are often shut out of participation in politics. ""We Fight to Win"" offers a compelling account of young people's attempts to get involved in community politics, and documents the battles waged to form youth movements and create social change in schools and neighborhoods. Hava Rachel Gordon compares the struggles and successes of two very different youth movements: a mostly white, middle-class youth activist network in Portland, Oregon, and a working-class network of minority youth in Oakland, California. She examines how these young activists navigate schools, families, community organizations, and the mainstream media, and employ a variety of strategies to make their voices heard on some of today's most pressing issues - war, school funding, the environmental crisis, the prison industrial complex, standardized testing, corporate accountability, and educational reform. ""We Fight to Win"" is one of the first books to focus on adolescence and political action and deftly explore the ways that the politics of youth activism are structured by age inequality as well as race, class, and gender.
Protein Structure Alignment Using a Graph Matching Technique

This paper proposes new algorithms for protein structure alignment. Protein structure alignment is, given two three-dimensional protein structures, to nd spatially equivalent residue pairs. Each algorithm consists of the following two steps: rst an initial superposition is computed; then a structure alignment is computed and re ned using bipartite graph matching. The proposed algorithms are shown to be useful through an experimental comparison with a previous alignment algorithm.
Quality of life following third molar removal under conscious sedation

Aim: The aim of this study was to assess quality of life (QoL) and degree of satisfaction among outpatients subjected to surgical extraction of all four third molars under conscious sedation. A second objective was to describe the evolution of self-reported pain measured in a visual analogue scale (VAS) in the 7 days after extraction. Study design: Fifty patients received a questionnaire assessing social isolation, working isolation, eating and speaking ability, diet modifications, sleep impairment, changes in physical appearance, discomfort at suture removal and overall satisfaction at days 4 and 7 after surgery. Pain was recorded by patients on a 100-mm pain visual analogue scale (VAS) every day after extraction until day 7. Results: Thirty-nine patients fulfilled correctly the questionnaire. Postoperative pain values suffered small fluctuations until day 5 (range: 23 to 33 mm in a 100-mm VAS), when dicreased significantly. A positive association was observed between difficult ranked surgeries and higher postoperative pain levels. The average number of days for which the patient stopped working was 4.9. Conclusion: The removal of all third molars in a single appointment causes an important deterioration of the patient’s QoL during the first postoperative week, especially due to local pain and eating discomfort. Key words:Third molar removal, quality of life, sedation.
FAKTOR-FAKTOR YANG MEMPENGARUHI PEMILIHAN KANTOR AKUNTAN PUBLIK PADA INDUSTRI MANUFAKTUR DI BURSA EFEK INDONESIA

ABSTRAK Konflik keagenan muncul karena adanya perbedaan kepentingan antara pihak agent dan principal. Konflik keagenan dapat diminimalisasi dengan mekanisme pengawasan. Salah satu mekanisme pengawasan tersebut adalah dengan menggunakan jasa akuntan publik. Tujuan penelitian ini untuk mengetahui faktor-faktor yang mempengaruhi pemilihan kantor akuntan publik oleh perusahaan manufaktur yang terdaftar di BEI pada tahun 2012. Pemilihan kantor akuntan publik diukur dengan variabel dummy. Faktor-faktor tersebut antara lain kepemilikan manajerial, leverage dan ukuran perusahaan. Sampel sebanyak 96 perusahaan ditentukan berdasarkan metode purposive sampling. Teknik analisis yang digunakan adalah regresi logistik.Berdasarkan analisis diperoleh hasil bahwa kepemilikan manajerial dan ukuran perusahaan berpengaruh negatif pada pemilihan kantor akuntan publik, sedangkan leverage tidak berpengaruh pada pemilihan kantor akuntan publik. Kata Kunci: kepemilikan manajerial, leverage, ukuran perusahaan, pemilihan kantor akuntan publik
A Discrete Mixture Model for Chord Labelling

Chord labels for recorded audio are in high demand both as an end product used by musicologists and hobby musicians and as an input feature for music similarity applications. Many past algorithms for chord labelling are based on chromagrams, but distribution of energy in chroma frames is not well understood. Furthermore, non-chord notes complicate chord estimation. We present a new approach which uses as a basis a relatively simple chroma model to represent short-time sonorities derived from melody range and bass range chromagrams. A chord is then modelled as a mixture of these sonorities, or subchords. We prove the practicability of the model by implementing a hidden Markov model (HMM) for chord labelling, in which we use the discrete subchord features as observations. We model gammadistributed chord durations by duplicate states in the HMM, a technique that had not been applied to chord labelling. We test the algorithm by five-fold cross-validation on a set of 175 hand-labelled songs performed by the Beatles. Accuracy figures compare very well with other state of the art approaches. We include accuracy specified by chord type as well as a measure of temporal coherence.
The Improved Variable Length Counting Bloom Filter Based on Buffer

In this paper, a variable-length counting Bloomfilter is studied for a variable-length-count Bloom filter(VLCBF) when configuring updates to the median groupfrequently-shifting. Our proposed improved VLCBF deleteseach hash value stored in the first buffer insertion. Only theauxiliary Bloom filter bit is modified accordingly until thebuffer is full when the group of digits updates a shift. Themethod reduces the number of bits set of shift operations toimprove the efficiency of its configuration updates. Theoperations processes of inserting, querying, and deleting in theimproved VLCBF are described in details, followed bycomparative experiments to illustrate the method's efficiencyand applicability.
A NOVEL 2 STEP RANDOM COLORED GRID GRAPHICAL PASSWORD AUTHENTICATION SYSTEM

Security in the computer is largely supported by passwords for authentication process. Use of alphanumeric passwords is the most common Authentication method. This conventional authentication method has been shown to have significant drawbacks. To overcome the vulnerabilities of traditional methods, numerous graphical password authentication systems have been proposed. These graphical passwords are usually seen as complex and time consuming. Furthermore, the existing graphical passwords are susceptible to spyware and shoulder surfing attacks. In this paper we propose this novel graphical password scheme to abolish well known security threats like brute force attacks, dictionary attacks, phishing attacks and spyware attacks.
Text-dependent speaker verification using lyapunov exponents

The characterization of a speech signal using nonlinear dynamical features has been focus of intense research lately. In this work, the results obtained with time-dependent largest Lyapunov exponents (TDLEs) in a text-dependent speaker verification task are reported. The baseline system used 10 cepstral coefficients and 10 delta cepstral coefficients, and it is shown how the addition of TDLEs can improve the system’s accuracy. Cepstral mean subtraction (CMS) was applied to all features in the tests, as well as silence removal. The telephone speech corpus used, obtained from a subset of CSLU Speaker Recognition corpus, was composed by 91 different speakers, speaking the same sentence.
Self-Tuning Semantic Image Segmentation

In this paper we present a method for finding optimal parameters of graph Laplacian-based semantic segmentation. This method is fully unsupervised and provides parameters individually for each image. In the experiments on Graz dataset the accuracy of segmentation obtained with the parameters provided by our method is very close to the accuracy of segmentation obtained with the parameters chosen on the test set.