Miracle-dz/UniFAR-FTUs huggingface dataset:
A Combined Analytical Modeling Machine Learning Approach for Performance Prediction of MapReduce Jobs in Cloud Environment

Nowadays MapReduce and its open source implementation, Apache Hadoop, are the most widespread solutions for handling massive dataset on clusters of commodity hardware. At the expense of a somewhat reduced performance in comparison to HPC technologies, the MapReduce framework provides fault tolerance and automatic parallelization without any efforts by developers. Since in many cases Hadoop is adopted to support business critical activities, it is often important to predict with fair confidence the execution time of submitted jobs, for instance when SLAs are established with end-users. In this work, we propose and validate a hybrid approach exploiting both queuing networks and support vector regression, in order to achieve a good accuracy without too many costly experiments on a real setup. The experimental results show how the proposed approach attains a 21% improvement in accuracy over applying machine learning techniques without any support from analytical models.
An integrated pan‐tropical biomass map using multiple reference datasets

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan‐tropical AGB map at 1‐km resolution using an independent reference dataset of field observations and locally calibrated high‐resolution biomass maps, harmonized and upscaled to 14 477 1‐km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N–23.4 S) of 375 Pg dry mass, 9–18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South‐East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15–21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha−1 vs. 21 and 28 Mg ha−1 for the input maps). The fusion method can be applied at any scale including the policy‐relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country‐specific reference datasets.
ChoreoGraphics: An Authoring Tool for Dance Shows

This work describes a computer system for authoring dance shows. The system can be used both as a mechanism to synthesize virtual group performances and as a tool for interactive design and visualization of real choreographies. To this end, we use motion capture of dance sequences that are synchronized with an underlying musical composition as input. We adapt motion synthesis methods for use in combination with a musical track. The authoring system provides methods for controlling group movement of dancers on stage, such as creating formations and following trajectories. Our integrated platform supports collaboration among artists, allowing the show to naturally evolve from iterative contributions from dancers, musicians, and choreographers.
Usability and acceptance of e-learning in statistics education, based on the compendium platform

E-learning systems, also known as a virtual learning environments (VLE’s), are systems that use modern information and communication technology to support education and training efforts. In this paper we present the evaluation of a newly developed Compendium Platform (CP) that can be used to create educational applications that support effective learning of statistics and related analytical skills. Using the web-enabled CP, students are empowered to easily archive, exchange and reproduce statistical computations. The CP was applied in three statistics courses. Based on behavioral concepts from the Technology Acceptance Model (usefulness, ease of use and future usage intention) and object-based beliefs about the e-learning environment (information and system qualities), we tested an integrated and predictive e-learning acceptance model. Using a sample of 200 students, we report that the CP was well accepted and that the majority of our hypotheses are confirmed. System quality has a considerable impact on ease of use and does directly influence the students’ intention to use the CP in the future. The effect of information quality on intention is fully mediated by relative advantage. Relative advantage was used as an alternative to usefulness and is clearly a better predictor of usage intention. A detailed analysis indicates that the attractiveness of the user interface, the presence of appropriate search options and the availability of sufficient relevant information are critical success factors of the CP’s acceptance.
Assessing Drivers’ Vigilance State During Monotonous Driving

The differential effects of three hours of monotonous daytime driving on subjective (sleepiness, inattention, monotony), performance (choice reaction time), and physiological (EEG alpha power, P300-amplitude, heart rate) vigilance measures were examined. A linear degradation of drivers’ subjective state, mean long reaction times (as opposed to short ones), P300-amplitude and parietal alpha power with time spent on the highway was identified. An improvement of the subjective measures towards the end of the driving task was not accompanied by any improvement in performance or physiological measures. This dissociation of self-assessment and objective vigilance measures has important implications for the design of modern driver assistant systems that aim to adapt to the driver’s state.
An Unsupervised System for the Synthesis of Variations from Audio Percussion Patterns

A system is introduced that learns the structure of an audio recording of a rhythmical percussion fragment in an unsupervised manner and synthesizes musical variations from it. The procedure consists of 1) segmentation, 2) symbolization (feature extraction, clustering, sequence structure analysis, temporal alignment), and 3) synthesis. The symbolization step yields a sequence of event classes. Simultaneously, representations are maintained that cluster the events into few or many classes. Moreover, a tempo estimation procedure is used to preserve the metrical structure in the generated sequence. Employing variable length Markov chains, the final synthesis is performed recombining the audio material derived from the sample itself. In particular, the level of refinement of the clustering procedure is selected, choosing a representation that displays maximal regularity. Examples synthesized from percussion patterns such as the amen break and beat boxing are available on the web. For a broad variety of musical styles the musical characteristics of the original are preserved. At the same time, considerable variability is introduced.
Scale-Invariant Divergences for Density Functions

Divergence is a discrepancy measure between two objects, such as functions, vectors, matrices, and so forth. In particular, divergences defined on probability distributions are widely employed in probabilistic forecasting. As the dissimilarity measure, the divergence should satisfy some conditions. In this paper, we consider two conditions: The first one is the scale-invariance property and the second is that the divergence is approximated by the sample mean of a loss function. The first requirement is an important feature for dissimilarity measures. The divergence will depend on which system of measurements we used to measure the objects. Scale-invariant divergence is transformed in a consistent way when the system of measurements is changed to the other one. The second requirement is formalized such that the divergence is expressed by using the so-called composite score. We study the relation between composite scores and scale-invariant divergences, and we propose a new class of divergences called H¨older divergence that satisfies two conditions above. We present some theoretical properties of H¨older divergence. We show that H¨older divergence unifies existing divergences from the viewpoint of scale-invariance.
A study of scalable search algorithm on unstructured P2P system

We proposed a search algorithm to unstructured P2P network, which consists of ranked neighbor caching, queryhit caching, and file replication to free riders. And the simulation results show that the algorithm can extend the search region but reduce the search traffic, and also balance the network load, so that acquires the whole network scalable.
A model of the cumulative effect of pitch and interaural delay differences for double vowel segregation

We evaluate the ability to separate and recognise two superi mposed spectra according to f0 difference and Interaural Time Differences (ITD) by both experimentation and simulation o f the classical double vowel recognition task. We have reprod uce this experiment with our own set of stimuli. Our results are g lobally compatible with those of previous psycho-acoustical e xperiments done by Shackleton and Meddis (1992). These show a clear improvement of concurrent vowels identification when a ITD is introduced between two vowels having different f0s: there is acumulativeeffect of f0 and ITD. We propose an extensive study of this effect by means of the coupling of three modules: the two primitive ones, f0-map and ITD-map, and a recognition stage. To evaluate robustness of models, w also vary the relative level of paired stationary/syntheti c vowels. First, we show recognition scores obtained with the two prim itive representations used in isolation, each able to segreg ate two spectra which are recognised independently. Second, we pro pose aswitchmodel in order to retrieve the psycho-acoustical cumulative effect. The principle is to well choose case by ca se the primitive representation where the two vowel spectra ar e better separated. A simple switch (S-switch) chooses systemat ically the ITD modality. A switch criterion is proposed to guide thi s choice. This leads to respectively a primitive (P-switch) o r a recognition switch (R-switch). To complete this study, we s ystematically compare two different segregation principles . The first one is based on the principle of channel selection follo wed by a partial recognition technique (PR method), and the second one is ashared-channels technique (SC method) allowing at each stage an estimation of energetic contributions within c annel and an effective spectral segregation.
Adaptive Genetic Algorithm Enhancements for Path Planning of Mobile Robots

an adaptive Genetic Algorithm (GA) is proposed, which focuses on the automatic adjustments of crossover probability and mutation probability with the changeable environmental parameters. The improved algorithm can overcome some disadvantages of traditional GA, such as, early falling into local optimum, lower convergence speed and large calculation etc. In sequence, the complementary characteristic between crossover probability and mutation probability is obtained through carrying out the numerical simulation. The results demonstrate that, compared with the traditional GA, the adaptive one leads to better performance in path curves and fitness, when 30 generations operations is implemented. This solution mentioned above, is proved to a better choice for practical application in path planning for mobile robots.
Tucupi: a flexible workflow system based on overridable constraints

This work presents the idea and a prototype of workflow systems whose definition is based on constraints. The flexibility is reached through the less rigid definition of workflow definitions - the workflow is defined as a set of pre and post conditions of activities, which are selected dynamically as the process instance unfolds. The workflow system besides dispatching activities that have all their preconditions fulfilled to be executed, also helps users to decide which activity to chose through what if scenarios. The system also includes an access control model which not only represents which users have the authority to chose and execute the activities but also the authority to override the constraints. In particular, overriding constraints is itself an activity and thus may have pre and post conditions defined in other constraints. The paper present Tucupi, a prototype of such constraint based WFMS.
The Semantics of history : model , methods and application

1 General introduction Semantic modeling of information, knowledge and language mostly assumes a static definition of the world. Concepts and instances of these concepts are defined as a static model; language and expressions are related to this specification. Even dynamic systems consider the most recent version of some reality as the preferred definition and only require updating the model to the actual facts. From a historic perspective this position is not adequate. History is typically a record of different realities in time and specifically focus on the changes in reality. Even stronger, the perception of history can be different for different participants and for different cultural and linguistic groups. Finally, the reflection on the past can be different based on our different views: history has been and will be re-written many times.
Active micromachines: Microfluidics powered by mesoscale turbulence

An ordered array of symmetric rotors immersed in active turbulence can turn persistently. Dense active matter, from bacterial suspensions and microtubule bundles driven by motor proteins to cellular monolayers and synthetic Janus particles, is characterized by mesoscale turbulence, which is the emergence of chaotic flow structures. By immersing an ordered array of symmetric rotors in an active fluid, we introduce a microfluidic system that exploits spontaneous symmetry breaking in mesoscale turbulence to generate work. The lattice of rotors self-organizes into a spin state where neighboring discs continuously rotate in permanent alternating directions due to combined hydrodynamic and elastic effects. Our virtual prototype demonstrates a new research direction for the design of micromachines powered by the nematohydrodynamic properties of active turbulence.
The Fc receptor on thymus-derived lymphocytes. I. Detection of a subpopulation of murine T lymphocytes bearing the Fc receptor

Utilizing the fluorescence-activated cell sorter (FACS) and washed murine antibody-antigen complexes formed in antibody excess, we have demonstrated the presence of the Fc receptor on the surface of a distinct subpopulation of murine T lymphocytes. No differences in intensity of labeling with the complexes was observed when the Fc+ T lymphocytes were compared with Fc+ B lymphocytes. The majority of Fc+ T lymphocytes are small lymphocytes determined by light-scattering characteristics on the FACS. Separating Fc+ from Fc- T lymphocytes from spleens of mice primed 1 wk or 1 mo previously with keyhole limpet hemocyanin (KLH) revealed that the T cells capable of cooperating with DNP-KLH primed B cells to give an adoptive anti-DNP PFC response do not bear the Fc receptor.
Gallager codes for CDMA applications. II. Implementations, complexity, and system capacity

We focus our attention on Gallager codes with parameters compatible with the IS-95 cellular radio standard. We discuss low complexity software and hardware implementations of an iterative decoder for {N,-,3} Gallager codes. We estimate that by using Gallager codes, a factor of five improvement in the code-division multiple-access system capacity relative to an uncoded system can be achieved, equivalent to a factor of two improvement relative to state-of-the art orthogonal convolutional codes. Simulation results demonstrate the good performance of short-frame Gallager codes in the additive white Gaussian noise and certain fading channels.
Reactive Whole-Body Control for Dynamic Mobile Manipulation

In this article, we present a control framework for reactive mobile manipulation of robotic systems with a large number of actuated degrees of freedom (DOF). We apply the concept to the humanoid robot Rollin’ Justin of the German Aerospace Center (DLR). As service robotics is expected to be established in households and human environments in the near future, we consider relevant aspects like safety, compliance and robust task execution. The multi-DOF manipulator achieves an interactive redundancy resolution while planning algorithms only have to be applied to the low-dimensional operational space concerning task execution. Various experiments have been conducted, e. g. , on reaching of a remote object, human-robot interaction, and self-collision avoidance of the manipulator. The results can serve as an interface to (re-)planning methods. Thanks to its interactivity, the approach can be applied in dynamic environments.
Coherent monochromatic terahertz-wave pulse detection using nonlinear parametric conversion at room temperature

We report on a coherent detection of monochromatic terahertz-wave pulses by using a frequency up-conversion in a nonlinear MgO:LiNbO3 crystal at room temperature. The generation of the detection signal is based on optical parametric mixing between a generated terahertz-wave radiation and a seeded idler beam, and an intense near-IR pumping beam. We measured the intensity and the phase of the input terahertz-wave.
A Simplified-Traditional Chinese Character Conversion Model Based on Log-Linear Models

With the growth of exchange activities between four regions of cross strait, the problem to correctly convert between Traditional Chinese (TC) and Simplified Chinese (SC) become more and more important. Numerous one-to-many mappings and term usage differences make it more difficult to convert from SC to TC. This paper proposed a novel simplified-traditional Chinese character conversion model based on log-linear models, in which features such as language models and lexical semantic consistency weighs are integrated. When estimating lexical semantic consistency weighs, cross-language word-based semantic spaces were used. Experiments were conducted and the results show that the proposed model achieve better performance.
Changes in Depressive Symptoms and Incidence of First Stroke Among Middle-Aged and Older US Adults

Background Although research has demonstrated that depressive symptoms predict stroke incidence, depressive symptoms are dynamic. It is unclear whether stroke risk persists if depressive symptoms remit. Methods and Results Health and Retirement Study participants (n=16 178, stroke free and noninstitutionalized at baseline) were interviewed biennially from 1998 to 2010. Stroke and depressive symptoms were assessed through self-report of doctors’ diagnoses and a modified Center for Epidemiologic Studies - Depression scale (high was ≥3 symptoms), respectively. We examined whether depressive symptom patterns, characterized across 2 successive interviews (stable low/no, onset, remitted, or stable high depressive symptoms) predicted incident stroke (1192 events) during the subsequent 2 years. We used marginal structural Cox proportional hazards models adjusted for demographics, health behaviors, chronic conditions, and attrition. We also estimated effects stratified by age (≥65 years), race or ethnicity (non-Hispanic white, non-Hispanic black, Hispanic), and sex. Stroke hazard was elevated among participants with stable high (adjusted hazard ratio 2.14, 95% CI 1.69 to 2.71) or remitted (adjusted hazard ratio 1.66, 95% CI 1.22 to 2.26) depressive symptoms compared with participants with stable low/no depressive symptoms. Stable high depressive symptom predicted stroke among all subgroups. Remitted depressive symptoms predicted increased stroke hazard among women (adjusted hazard ratio 1.86, 95% CI 1.30 to 2.66) and non-Hispanic white participants (adjusted hazard ratio 1.66, 95% CI 1.18 to 2.33) and was marginally associated among Hispanics (adjusted hazard ratio 2.36, 95% CI 0.98 to 5.67). Conclusions In this cohort, persistently high depressive symptoms were associated with increased stroke risk. Risk remained elevated even if depressive symptoms remitted over a 2-year period, suggesting cumulative etiologic mechanisms linking depression and stroke.
Learning from Web: Review of Approaches

Knowledge discovery is defined as non-trivial extraction of implicit, previously unknown and potentially useful information from given data. Knowledge extraction from web documents deals with unstructured, free-format documents whose number is enormous and rapidly growing. The artificial neural networks are well suitable to solve a problem of knowledge discovery from web documents because trained networks are able more accurately and easily to classify the learning and testing examples those represent the text mining domain. However, the neural networks that consist of large number of weighted connections and activation units often generate the incomprehensible and hard-to-understand models of text classification. This problem may be also addressed to most powerful recurrent neural networks that employ the feedback links from hidden or output units to their input units. Due to feedback links, recurrent neural networks are able take into account of a context in document. To be useful for data mining, self-organizing neural network techniques of knowledge extraction have been explored and developed. Self-organization principles were used to create an adequate neural-network structure and reduce a dimensionality of features used to describe text documents. The use of these principles seems interesting because ones are able to reduce a neural-network redundancy and considerably facilitate the knowledge representation.
Graphical Models Concepts in Compressed Sensing

This paper surveys recent work in applying ideas from graphical models and message passing algorithms to solve large scale regularized regression problems. In particular, the focus is on compressed sensing reconstruction via ell_1 penalized least-squares (known as LASSO or BPDN). We discuss how to derive fast approximate message passing algorithms to solve this problem. Surprisingly, the analysis of such algorithms allows to prove exact high-dimensional limit results for the LASSO risk. This paper will appear as a chapter in a book on `Compressed Sensing' edited by Yonina Eldar and Gitta Kutyniok.
Parallel Algorithm for Cograph Recognition with Applications

We present a parallel algorithm for recognizing cographs and constructing their cotrees. The algorithm takes O(log2n) time with O(n+m) processors on a CRCW PRAM, where n and m are the number of vertices and edges of the graph. Using cotree representation, we obtain a parallel algorithm for the permutation representation problem for cographs using O(log n) time with O(n) processors. We also present a parallel algorithm for the depthfirst spanning tree problem for permutation graphs (a class properly contains cographs) which takes O(log2n) time with O(n) processors.