Miracle-dz/UniFAR-FTUs huggingface dataset:
THE D R A C U L A DYNAMIC NETWORK MICROSIMULATION MODEL

Recent years have seen a tremendous interest world wide in the use of microsimulation techniques to model traffic in congested road networks. This interest is particularly associated with the development of real-time, high-tech based traffic management and control strategies that react to the highly dynamic and variable nature of traffic conditions and driver behaviour. This paper introduces the DRACULA dynamic traffic network model, with the main focus on its traffic microsimulation component. The paper describes the model's theoretical and behavioural foundations, and presents illustrative examples of applying the model in the evaluation of real-time management strategies. Our experience shows that this is a particularly suitable framework for the realistic modelling of real-time technological strategies.
UNIVERSITY OF VAASA FACULTY OF BUSINESS STUDIES DEPARTMENT OF MANAGEMENT TURKKA NUPPONEN HR OUTSOURCING TRENDS AND ITS EFFECTS ON THE ROLES OF HR PROFESSIONALS

9 1. INTRODUCTION 11 1.1 Background of the study 11 1.2 Research problem 12 1.3 Structure of the study 13 2. LITERATURE REVIEW 15 2.1 HR outsourcing 15 2.1.1 Definitions and HR outsourcing trends 15 2.2 Reasons for HRO 18 2.3 Risks concerning HRO 19 2.3. 1 Arguments for and against HR outsourcing 21 2.4 Theoretical perspectives on HRO 24 2.4. 1 Transaction Cost Theory 24 2.4. 2 Limitations of TCT 27 2.4. 3 Transaction Cost Theory in HRO 28 2.4. 4 Resource-­‐based view of the firm 32 2.4. 5 Limitations of RBV 34 2.4. 6 Resource-­‐based view in HRO 35 2.5 HR transformation 37 2.6 The implications of HRO in MNCs 39 2.6. 1 HR Business Partners 40 2.6. 2 In-­‐house HR professionals 42 2.6. 3 Line Managers 43 2.6. 4 Employees receiving HR services 43 2.6. 5 Outsourced HR staff 44 2.7 Summary 45
Humanoid Full-Body Manipulation Motion Planning with Multiple Initial-Guess and Key Postures

Whole body manipulation plays a very important role in the usage of humanoid robot, which meet the requirement of service and household in domestic environment, as well as disaster recover in extreme environment. Because of the high redundancy system, optimization methods are widely used to solve Inverse Kinematics (IK) of humanoid robot. However, most of current methods suffer a problem of getting stacked in local minimum or singularities, and lack of the ability to generate diverse motions. In this paper, we present an optimization method to solve high redundancy IK problems and generate a good optimal trajectory for humanoid robot full-body manipulation statics motion. Our algorithm could effectively avoid unexpected local minimum solutions caused by bad initial guess and develop diverse motion variations. The basic idea of our algorithm is to generate multiple diverse initial guesses for each manually set key postures, use traditional optimization method to generate optimal solutions based on these initial guesses. Then we evaluate those optimal solutions inside the whole motion process and find the best optimal solution series. Finally, we interpolate the discrete solution series into a smooth trajectory. The performance of our approach is implemented in Gazebo simulation using Atlas by Boston Dynamics.
Diseases classification using support vector machine (SVM)

The paper proposed a new method: disease classification based on protein sequence. Support vector machine was used for this problem and a new encoding for the multicode protein sequence was suggested. Two extracted features were selected for classifying, the results showed the capability of SVM for such bioinformatics problems and the goodness of the system of protein sequence based disease classification. It gave error around 4-5%, which presented that although it is good for such problems, improvement of the algorithm also should be made.
Adaptive mesh generation using shrink wrapping approach

We consider the problem of mesh preparation for subsequent simulations. The Cartesian shrink-wrapping technique is used to construct a triangular mesh on dirty input geometry. Supported imperfections are gaps, overlaps, inconsistent normal orientations and self-intersections. Wrapping is stored using octree. An octree is refined adaptively to follow a form of the original mesh. Initial watertight mesh which is extracted from octree is improved using nodes projection to original triangulation and by local operations such as swap edge, collapse edge, and Laplacian smoothing.
Mapping Tropical Cyclone Disturbance of the Great Barrier Reef

Tropical cyclones periodically cross the Great Barrier Reef (GBR). The large waves they generate can damage coral reefs. This paper will describe the challenges of mapping cyclone disturbance across such a vast geographic region, and how cartographic visualisation can help us understand the implications of these challenges on the quality of the results.
KitAi-PI : Summarization System for NTCIR-14 QA Lab-PoliInfo

This paper describes a summarization system for NTCIR14 QA Lab-PoliInfo. For the summarization task, participants of the task need to generate a summary corresponding to an assemblyperson’s speech in assembly minutes within the limit length. Our method extracts important sentences to summarize an assemblyperson’s speech in the minutes. Our method applies a machine learning model to predict the important sentences. However, the given assembly minutes’ data do not contain information about the importance of the sentences. Therefore, we construct training data for the importance prediction model using a word similarity between sentences in a speech and those in the summary. On the formal run, some scores by our method were the best in all the submitted runs of all participants. The result shows the effectiveness of our method.
Radar signature of the sea surface perturbed by rain

The attenuation and reflectivity of drops falling through the atmosphere affects the radar signature of the sea surface. Another, much less documented, effect is the perturbation of the surface roughness due to the impacting drops. It is the subject of the present paper. As for the surface description, the present authors refer to the rain-only experiments performed by Bliven et al. (1997) at the NASA/WallopslRain-Sea Interaction Facility. These experiments consisted of wire-probe measurements of a water surface perturbed by rain, which was generated by a simulator with 2.8 mm drops placed at 13 m height; this height enabled the drops to fall practically at terminal velocity. The observed frequency spectrum S/sub f/ has been modelled by a log-Gaussian shape and then transformed to an isotropic surface spectrum with the help of the dispersion relationship of surface waves w(K). The present authors decided to introduce two minor improvements in this model. First, for very small rain rates, the surface roughness tends toward the superposition of independent ring-waves. Hence, they decided to linearize the peak value of the spectrum S/sub f,peak/ between O and 5 mm hr/sup -1/. Secondly, they introduced an exponential law to represent the high frequency tail (f>12 Hz) of the rain spectrum. As for the wind-only spectrum the used model is the fully-developed limit of the model proposed by Lemaire et al. In a first approach, they assume that the rain spectrum simply adds to the wind-only spectrum. The related nonlinearities are described.
Recurrent Gradient Temporal Difference Networks

Temporal-difference (TD) networks (Sutton and Tanner, 2004) are a predictive representation of state in which each node is an answer to a question about future observations or questions. Unfortunately, existing algorithms for learning TD networks are known to diverge, even in very simple problems. In this paper we present the first sound learning rule for TD networks. Our approach is to develop a true gradient descent algorithm that takes account of all three roles performed by each node in the network: as state, as an answer, and as a target for other questions. Our algorithm combines gradient temporal-difference learning (Maei et al., 2009) with real-time recurrent learning (Williams and Zipser, 1994). We provide a generalisation of the Bellman equation that corresponds to the semantics of the TD network, and prove that our algorithm converges to a fixed point of this equation. We also demonstrate empirically that our learning algorithm converges to the correct solution in a benchmark problem for which prior learning rules diverge.
Northwest Madagascar

Prior herpetological surveys in 1996 and 2000 identified 14 species of amphibians and 32 species of reptiles from the Sahamalaza Peninsula. This work increases the total number of amphibian and reptile species known from this area to 20 and 43 respectively. To maximise our chances of species detection, survey effort covered the entire wet season and part of the dry season, and utilised a combination of opportunistic searching, transect searching, pitfall trapping, and acoustic recording. We identified species through an integrative taxonomic approach, combining morphological, bioacoustic and molecular taxonomy. Together, this enabled the detection of cryptic and seasonally inactive species that were missed in the shorter prior surveys that relied on morphological identification alone. The taxonomic identification of amphibians utilised a fragment of the mitochondrial 16S rRNA gene; taxonomic identification of reptiles utilised a fragment of the mitochondrial COI gene, and when necessary, also mitochondrial fragments of the 16S rRNA ND1, ND2, ND4 genes. All sequences were deposited in Genbank and COI sequences were also deposited in the BOLD database to foster taxonomic identification of malagasy reptiles. We report two new taxa: a species of Boophis, since described as B. ankarafensis, and a candidate new species of microhylid (genus: Stumpffia). We document range expansions of Boophis tsilomaro, Cophyla berara, Blaesodactylus ambonihazo beyond their type localities. Along with significant range expansions across a range of taxa, including Blommersia sp. Ca05, Boophys brachychir, Brookesia minima, Ebenavia inunguis, Geckolepis humbloti, Madascincus stumpffi, Pelomedus subrufa and Phelsuma kochi. Forest in the peninsula is under extreme pressure from human exploitation. Unless unsustainable agricultural and pastoral practices encroaching on these habitats halt immediately, both forest and the species that occur there, several of which appear to be local endemics, may be irreversibly lost.
Deaf learners : developments in curriculum and instruction

"Deaf Learners: Developments in Curriculum and Instruction", edited by Donald F. Moores and David S. Martin, presents an in-depth collection by 17 renowned international scholars that details a developmental framework to maximize academic success for deaf students from kindergarten through grade 12. Part One: The Context commences with an overview of the state of general education and that of deaf learners, followed by a state-of-the art philosophical position on the selection of curriculum. Part Two: The Content considers critical subjects for deaf learners and how to deliver them, including mathematics, print literacy, science, social studies, and physical education. This section also addresses the role of itinerant services, as well as how to teach Deaf culture, provide for students with multiple disabilities, and facilitate school-to-work transitions. Chapters in Part Three: Instructional Considerations across the Curriculum provides suggestions and guidelines for assessing and planning programs for deaf students using meaningful contexts; optimizing the academic performance of deaf students with emphasis on access and opportunities; implementing a cognitive strategy that encourages teaching for and about thinking as an overriding principle; establishing instructional and practical communication in the classroom, especially in relation to ASL and English-based signing; and solving old problems with new strategies, including web-based technologies, resources, and applications. The lessons of these assembled scholars coalesce in the Part Four: Summary as a general recommendation for ongoing adaptability, a fitting capstone to this extraordinary volume of work.
An examination of some heart rate variability analysis indicators in the case of children

The paper presents an elaboration of some dependencies concerning time and spectral Heart Rate Variability (HRV) analysis indicators in the case of schoolboys and schoolgirls aged 9 12 years. W e have taken into account the following HRV analysis indicators: average HR, modal value of RR intervals in every 5 minutes RR intervals recorded file, RR intervals variability range and load index. With the reference to spectral indicators we have examined the maximum amplitude value of power spectral density in the following frequency intervals: T(O.O1-0.09 Hz), V(O.10-0.15 Hz), R(0.16-0.40 Hz) as a function of age. Results which were obtained seem to be interesting from the elaboration of clinical and diagnostic conclusions point of view.
Conference Report : Eighth International Conference on Computational Creativity

Introduction From 19th to 23rd June 2017, around 150 researchers met for the Eighth International Conference on Computational Creativity (ICCC’17) at Georgia Tech in Atlanta, USA. Computational Creativity is the “philosophy, science and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviours that unbiased observers would deem to be creative” [3]. The field has a long history [2], and is thriving: since the first dedicated workshop in 2004 and the first conference in 2010, more people come together every year to present their research to an open-minded and interdisciplinary audience. The conference stream is permanently available on YouTube. I am grateful for a generous travel grant from the AISB to participate in this thought-provoking event.
Why Machine Learning May Lead to Unfairness: Evidence from Risk Assessment for Juvenile Justice in Catalonia

In this paper we study the limitations of Machine Learning (ML) algorithms for predicting juvenile recidivism. Particularly, we are interested in analyzing the trade-off between predictive performance and fairness. To that extent, we evaluate fairness of ML models in conjunction with SAVRY, a structured professional risk assessment framework, on a novel dataset originated in Catalonia. In terms of accuracy on the prediction of recidivism, the ML models slightly outperform SAVRY; the results improve with more data or more features available for training (AUCROC of 0.64 with SAVRY vs. AUCROC of 0.71 with ML models). However, across three fairness metrics used in other studies, we find that SAVRY is in general fair, while the ML models tend to discriminate against male defendants, foreigners, or people of specific national groups. For instance, foreigners who did not recidivate are almost twice as likely to be wrongly classified as high risk by ML models than Spanish nationals. Finally, we discuss potential sources of this unfairness and provide explanations for them, by combining ML interpretability techniques with a thorough data analysis. Our findings provide an explanation for why ML techniques lead to unfairness in data-driven risk assessment, even when protected attributes are not used in training.
EFFECT OF TWO CHEMOTYPES OF OREGANO ESSENTIAL OIL ON BROILER PERFORMANCE , NUTRIENT BALANCE , AND LIPID PEROXIDATION OF BREAST MEAT

This study evaluated the functional effects of two chemotypes of oregano essential oil (OEO): Lippia Origanoides (LO) and a comercial product Synergy Essence (SE). Broilers were randomly assigned to one of eight dietary treatments: 1) Basal diet (BD); 2) BD+ antibiotic growth promoters (AGP) (bacitracin 50g/ton); 3) BD+LO125 (125g/ton); 4) BD+LO250 (250g/ton); 5) BD+LO500 (500g/ton); 6) BD+SE125 (125g/ton); 7) BD+SE250 (250g/ton); 8) BD+SE500 (500g/ton). At the time of slaughter (42 d), five birds per treatment were slaughtered. A portion of the breast was minced and stored at -4 °C for 3, 6, and 9 days. Lipid peroxidation was determined by measuring of malondialdehyde (MDA), data were analyzed as a completely randomized design with a factorial arrangement of treatments (8x4). Broilers fed SE250 had greater average daily gain (ADG) compared to broilers fed BD (P<0.05). Broilers fed LO500 had greater (P<0.05) organic matter digestibility than the ones fed BD. As days of storage increased, MDA concentration increased (P<0.05). Broilers fed SE500 had lower (P<0.05) MDA concentration than those fed BD and AGP. These data indicate that birds fed OEO grow faster than those fed a BD, similar to birds fed AGP. Some of these responses are possibly explained by increased digestibility and energy utilization. In addition, providing OEO from SE at 500g/ton resulted in lower lipid peroxidation in ground breast meat throughout the tested storage time.
Damage characterization in a bar using guided waves

The waveguide geometry of numerous structures used in civil and mechanical engineering can be exploited for the use of guided waves in damage detection. The present study examines the response of a bar to an impulsive force along its axis and points out the differences emerging when the bar has a damage of notch-type. Relationships between the damage parameters and the amplitude and time-delay of the reflected and transmitted waves are exploited to formulate a damage characterization procedure. The presented procedure is tested analytically and experimentally in different damage configurations.
Assessing the Contribution of GARCH-type Models with Realized Measures to B 3 Stocks Allocation

In this paper we perform an extensive backtesting study targeting as a main goal to assess the performance of global minimum variance (GMV) portfolios built on volatility forecasting models that make use of high frequency (compared to daily) data. The study is based on a broad intradaily financial dataset comprising 41 assets listed on the B3 from 2009 to 2017. We evaluate volatility forecasting models that are inspired by the ARCH literature, but also include realized measures. They are the GARCH-X, the High-Frequency Based Volatility (HEAVY) and the Realized GARCH models. Their performances are benchmarked against portfolios built on the sample covariance matrix, covariance matrix shrinkage methods, DCC-GARCH as well as the naive (equally weighted) portfolio and the Ibovespa index. Since the nature of this paper is multivariate and in order to make possible the estimation of large covariance matrices, we resort to the Dynamic Conditional Correlation (DCC) specification. We use three different rebalancing schemes (daily, weekly and monthly) and four different sets of constraints on portfolio weights. The performance assessment relies on economic measures such as annualized portfolio returns, annualized volatility, Sharpe ratio, maximum drawdown, Value at Risk, Expected Shortfall and turnover. We also account for transaction costs. As a conclusion, for our dataset the use of intradaily returns (sampled every 1 minute) does not enhance the performance of GMV portfolios.
TESTING THE SENSITIVITY OF Staphylococcus aureus ANTIBIOTICS

This study has in view to establish and test the sensitivity of Staphylococcus aureus antibiotics. There are different injuries caused by superficial skin infections: from simple pimples to infections that endanger our lives, like an abscess, furuncle septicemia, meningitis, toxic food, urinary tract infection at sexually active young women. Samples have been taken from 30 people with staphylococcus infections. They were nineteen women and eleven men, between the age of 2 and 79. During this study some antibiograms have been made, based on pharyngeal exudates, acne secretion and urine culture. It has been established that the most efficient recommended antibiotics are: oxacilin, erythromycin, rifampicin and ciprofloxacin. The penicillin turned out to be less efficient to remove and destroy the Staphylococcus aureus species.
VRP with Time Windows

This paper presents a survey of the research on the Vehicle Routing Problem with Time Windows (VRPTW), an extension of the Capacitated Vehicle Routing Problem. In the VRPTW, the service at each customer must start within an associated time window and the vehicle must remain at the customer location during service. Soft time windows can be violated at a cost while hard time windows do not allow for a vehicle to arrive at a customer after the latest time to begin service. We first present a multi-commodity network flow formulation with time and capacity constraints for the VRPTW. Approximation methods proposed in the literature to derive upper bounds are then reviewed. Then we explain how lower bounds can be obtained using optimal approaches, namely, Lagrangean relaxation and column generation. Next, we provide branching and cutting strategies that can be embedded within these optimal approaches to produce integer solutions. Special cases and extensions to the VRPTW follow as well as our conclusions.
Economic Profitability and Adoption of Bt Cotton and non-Bt Cotton in North India

Cotton is one of the major cash crop in India accounting an area of 9.4 Million hectares and 4.39 Million tonnes to production. In India , Bt cotton is the first genetically modified crop introduced in 2002 and currently it covers more than 75 % of the area under cotton. The adoption of Bt cotton is high in India in comparison of other countries. There is a phenomenal change in area, cotton yields and input use for Bt cotton compared to non-Bt cotton over years. There exist strong favour and strong protest in the adoption of Bt cotton. The current paper clarifies the debate by analysing Bt cotton and non-Bt cotton farmers profitability and adoption in Haryana and Punjab states of India considering primary data from 160 Bt cotton farmers and 40 non-Bt farmers using simple random sampling. Results are analysed using partial budgeting and logistic regression. Results indicate that Bt farmers are getting three times more returns than non-Bt farmers reflecting economic profitability of Bt cotton. In econometric analysis for adoption, it was found that farmers can benefit significantly from technology. Farmers are adopting Bt cotton due to higher yields and reduction of insecticide use even though seed cost is high in comparison of nonBt cotton. Education and farmer’s network played an important role in adoption. But the adoption factors varied from state to state. Punjab state farmers are earlier adopter than Haryana Farmers. In Haryana state, cotton experienced farmers are adopting more this technology. The results will give crucial information for the policy makers for promoting production of Bt cotton aiming at improving farmers socio-economic status.
Impromptu Collaborative Projection for Storytelling

Pico projectors are steadily becoming more common, but research to date has mainly investigated their usage as output devices. In this paper we consider how pico projectors might be used for input. We describe a concept for using projected content to create animated stories, allowing groups of people projecting photos, sketches, or even videos, to record and share the stories they tell. The concept is demonstrated by an early prototype using sensor data and a simple calibration method for device location and orientation tracking.
Migration among simulation paradigms and tools

The Simulation is used as an active research method when analytical solutions are inconvenient or even impossible. One problem is the incompatibility of created simulations on a tool level, but other is model incompatibility because of different simulation methods. The article dealt with determination of basic principles for effective model migration and accentuates the use of appropriate simulation methods for the development of specific model groups.