Miracle-dz/UniFAR-FTUs huggingface dataset:
Abstracting and Interacting with Vehicles in the Web of Things

ing and Interacting with Vehicles in the Web of Things Benjamin Klotz1,2[0000−0002−9008−2120], Daniel Alvarez-Coello1,3[0000−0001−5663−7543], Daniel Wilms1[0000−0002−0451−0265], and Raphaël Troncy2[0000−0003−0457−1436] 1 BMW Research, New Technologies, Innovations, Garching, Germany daniel.dw. wilms@bmw.com 2 EURECOM, Sophia Antipolis, France klotz@eurecom.fr, raphael.troncy@eurecom.fr 3 University of Oldenburg, Germany daniel.alvarez@uol.de Abstract. The Web of Things offers a platform-independent solution The Web of Things offers a platform-independent solution for interacting with connected devices. An important vertical of the WoT is the transportation domain with, at its core, autonomous systems and among others, connected vehicles. They can be seen as complex artefacts, as they are composed of many sensors and actuators, legacy specifications and safety criticality, which leads to additional challenges for cross-domain interoperability, scalability and safety. In this paper, we argue that the specifications around the Web of Things used with domain specific ontologies such as VSSo and the driving context ontology are relevant for connected vehicles. Our position is that Thing Descriptions require adaptations for complex Things or networks of Things: fine-grained access control and safety impacted by security. We propose a demonstration of a connected vehicle and a discussion on the topics of complex connected things acting as gateways of Things.
Connected Education: Teachers’ Attitudes towards Student Learning in a 1:1 Technology Middle School Environment

Online and blended forms of learning has been increasingly common in K-12 settings, along with the technological advancement with always-on and connected devices. The study purports to understand teachers’ attitudes towards the middle school’s one-laptop-per-student (1:1) policy and students’ frequent use of always-on and connected technology, as well as their concerns about middle school students’ capabilities of using mobile devices and technologies in 1:1 environments. Using a transcendental phenomenological approach, data was obtained through semi-structured interviews, preand postteacher open-ended surveys, along with classroom and lab observations. The study concluded that teachers typically embraced student use of school issued connected technology, as well as personal, connected mobile devices in a 1:1 environment. Meanwhile, teachers are cognizant of the potential drawbacks, implementing differing strategies to balance the use of such device for productive classroom learning and student engagement of personal non-course related activities. The pivotal role of teacher guidance is reiterated by teachers’ perceptions of students’ inability to engage in self-directed and self-motivated learning. The challenges reveal what middle school teachers’ may face when planning a curriculum and instruction for connected digital age learners.
Connected Education : Teachers ' Attitudes Towards Student Learning in a 1 : 1 : Technology Middle School Environment

This Article is brought to you for free and open access by the STEM Education & Professional Studies at ODU Digital Commons. It has been accepted for inclusion in STEMPS Faculty Publications by an authorized administrator of ODU Digital Commons. For more information, please contact digitalcommons@odu.edu. Repository Citation Luo, Tian and Murray, Alexander, "Connected Education: Teachers' Attitudes Towards Student Learning in a 1:1: Technology Middle School Environment" (2018). STEMPS Faculty Publications. 73. https://digitalcommons.odu.edu/stemps_fac_pubs/73
Kinesin-12 motors cooperate to suppress microtubule catastrophes and drive the formation of parallel microtubule bundles

Significance During cell division, molecular motors from the kinesin superfamily, in particular Kinesin-12 (Kif15) and Kinesin-5 (Eg5), play a crucial role in formation of the spindle—a bipolar microtubule array that is essential for accurate chromosome segregation. While Eg5 is well studied, the mechanism by which Kif15 maintains spindle bipolarity in the absence of Eg5 is unknown. In this work we reconstitute Kif15 motors on dynamic microtubules in vitro. We reveal that Kif15 is a multi-function motor that cross-links microtubules and drives transport of one along the other, which results in the formation of parallel microtubule bundles. Additionally, motors track the microtubule tips, maintaining them in a growing state, which can synchronize microtubule dynamics within a bundle. Human Kinesin-12 (hKif15) plays a crucial role in assembly and maintenance of the mitotic spindle. These functions of hKif15 are partially redundant with Kinesin-5 (Eg5), which can cross-link and drive the extensile sliding of antiparallel microtubules. Although both motors are known to be tetramers, the functional properties of hKif15 are less well understood. Here we reveal how single or multiple Kif15 motors can cross-link, transport, and focus the plus-ends of intersecting microtubules. During transport, Kif15 motors step simultaneously along both microtubules with relative microtubule transport driven by a velocity differential between motor domain pairs. Remarkably, this differential is affected by the underlying intersection geometry: the differential is low on parallel and extreme on antiparallel microtubules where one motor domain pair becomes immobile. As a result, when intersecting microtubules are antiparallel, canonical transport of one microtubule along the other is allowed because one motor is firmly attached to one microtubule while it is stepping on the other. When intersecting microtubules are parallel, however, Kif15 motors can drive (biased) parallel sliding because the motor simultaneously steps on both microtubules that it cross-links. These microtubule rearrangements will focus microtubule plus-ends and finally lead to the formation of parallel bundles. At the same time, Kif15 motors cooperate to suppress catastrophe events at polymerizing microtubule plus-ends, raising the possibility that Kif15 motors may synchronize the dynamics of bundles that they have assembled. Thus, Kif15 is adapted to operate on parallel microtubule substrates, a property that clearly distinguishes it from the other tetrameric spindle motor, Eg5.
Computational identification of developmental enhancers: conservation and function of transcription factor binding-site clusters in drosophila melanogaster and drosophila psedoobscura

</p> Abstract Background: The identification of sequences that control transcription in metazoans is a major goal of genome analysis. In a previous study, we demonstrated that searching for clusters of predicted transcription factor binding sites could discover active regulatory sequences, and identified 37 regions of the Drosophila melanogaster genome with high densities of predicted binding sites for five transcription factors involved in anterior-posterior embryonic patterning. Nine of these clusters overlapped known enhancers. Here, we report the results of in vivo functional analysis of 27 remaining clusters. Results: We generated transgenic flies carrying each cluster attached to a basal promoter and reporter gene, and assayed embryos for reporter gene expression. Six clusters are enhancers of adjacent genes: giant , fushi tarazu , odd - skipped , nubbin , squeeze and pdm2 ; three drive expression in patterns unrelated to those of neighboring genes; the remaining 18 do not appear to have enhancer activity. We used the Drosophila pseudoobscura genome to compare patterns of evolution in and around the 15 positive and 18 false-positive predictions. Although conservation of primary sequence cannot distinguish true from false positives, conservation of binding-site clustering accurately discriminates functional binding-site clusters from those with no function. We incorporated conservation of binding-site clustering into a new genome-wide enhancer screen, and predict several hundred new regulatory sequences, including 85 adjacent to genes with embryonic patterns. Conclusions: Measuring conservation of sequence features closely linked to function - such as binding-site clustering - makes better use of comparative sequence data than commonly used methods that examine only sequence identity.
The Learnable Typewriter: A Generative Approach to Text Line Analysis

We present a generative document-speciﬁc approach to character analysis and recognition in text lines. Our main idea is to build on unsupervised multi-object segmentation methods and in particular those that reconstruct images based on a limited amount of visual elements, called sprites. Our approach can learn a large number of different characters and leverage line-level annotations when available. Our contribution is twofold. First, we provide the ﬁrst adaptation and evaluation of a deep unsupervised multi-object segmentation approach for text line analysis. Since these methods have mainly been evaluated on synthetic data in a completely unsupervised setting, demonstrating that they can be adapted and quantitatively evaluated on real text images and that they can be trained using weak supervision are signiﬁcant progresses. Second, we demonstrate the potential of our method for new applications, more speciﬁcally in the ﬁeld of paleography, which studies the history and variations of handwriting, and for cipher analysis. We evaluate our approach on three very different datasets: a printed volume of the Google1000 dataset [19,46], the Copiale cipher [2,27] and historical handwritten charters from the 12th and early 13th century [6,44].
THE ROLE OF COGNITIVE FIT IN THE RELATIONSHIP BETWEEN SOFTWARE COMPREHENSION AND MODIFICATION 1

Although there is a long tradition of empirical studies of software developers, few studies have focused on software maintenance. Prior work is predicated on the belief that higher levels of software comprehension are associated with higher levels of performance on modification tasks. This study provides a more complete understanding of the relationship between software comprehension and modification. We conceptualize software maintenance as inter-linking comprehension and modification, and argue that the relationship between the two is moderated by cognitive fit. Specifically, cognitive fit exists when the software maintainer's dominant mental representation of the software and their mental representation of the modification task emphasize the same type of knowledge. We hypothesize that when cognitive fit exists, greater improvements in comprehension are associated with higher levels of performance on a modification task. When cognitive fit does not exist, however, the software maintainer's mental representations of the software and of the modification task do not emphasize the same type of knowledge, which may mean that attention is devoted to comprehension at the expense of modification, resulting in lower performance on the modification task. In these circumstances, comprehension and modification tasks may interfere with each other, an effect known as dual-task interference. We therefore hypothesize that performance on a modification task is moderated by the fit between the mental representation of the software and that of the modification task. We tested our theory by varying cognitive fit to create matched and mismatched conditions in a single experiment that used IT professionals as subjects. Our findings support our theory: cogni-tive fit moderates the relationship between comprehension and modification. Specifically, changes in software comprehension and modification performance are positively related when cognitive fit exists and negatively related when cognitive fit does not exist. Our findings demonstrate the need to examine more complex relationships among the numerous types of tasks involved in software development rather than examining software comprehension alone.
Adversarial Policy Learning in Two-player Competitive Games

Proposition 1. In a two-player Markov game, if one agent follows a fixed policy, the state transition of the game system will depend only upon the policy of the other agent rather than their joint policies. Proof. Without the loss of generalizability, we denote the agent as α and υ, and assume the agent υ follows a fixed policy. At state st, the probability of taking the joint actions (a α t , a υ t ) and transiting to st+1 is P (st+1, a α t , a υ t |st) =P (st+1|at , at , st)P (at , at |st) = P (st+1|at , at , st)P (at |at , st)π(at |st) =c · P (st+1|at , at , st)P (at |at , st) = c · P (st+1|at , at , st)π(at |st) , (1)
Algorithms for the minimum non-separating path and the balanced connected bipartition problems on grid graphs ( With errata )

For given a pair of nodes in a graph, the minimum non-separating path problem looks for a minimum weight path between the two nodes such that the remaining graph after removing the path is still connected. The balanced connected bipartition (BCP2) problem looks for a way to bipartition a graph into two connected subgraphs with their weights as equal as possible. In this paper we present an algorithm in time O(N logN) for finding a minimum weight non-separating path between two given nodes in a grid graph of N nodes with positive weight. This result leads to a 5/4-approximation algorithm for the BCP2 problem on grid graphs, which is the currently best ratio achieved in polynomial time. We also developed an exact algorithm for the BCP2 problem on grid graphs. Based on the exact algorithm and a rounding technique, we show an approximation scheme, which is a fully polynomial time approximation scheme for fixed number of rows.
Probabilistic Matrix Addition

We introduce Probabilistic Matrix Addition (PMA) for modeling real-valued data matrices by simultaneously capturing covariance structure among rows and among columns. PMA additively combines two latent matrices drawn from two Gaussian Processes (GPs) respectively over rows and columns. The resulting joint distribution over the observed matrix does not factorize over entries, rows, or columns, and can thus capture intricate dependencies in the matrix. Exact inference in PMA is possible, but involves inversion of large matrices, and can be computationally prohibitive. Efficient approximate inference is possible due to the sparse dependency structure among latent variables. We propose two families of approximate inference algorithms for PMA based on Gibbs sampling and MAP inference. We demonstrate the effectiveness of PMA for matrix missing values problems and multi-label classification problems.
An Empirical Study of the Effect of Source-level Transformations on Compiler Stability

Loop-level compiler optimizations are applied in a complex process with no guarantee that the code produced is optimal. Compilers also struggle to maintain a stable performance on different loops with the same semantics. This paper presents an analysis of the stability of the compilation process and shows potential for state-of-the-art compilers to improve code performance. In the study, loop nests are first extracted from benchmarks; then, sequences of source-level loop transformations are applied to these loop nests to create numerous semantically equivalent mutations; finally, the impact of transformations on code quality in terms of locality, dynamic instruction count, and vectorization is analyzed for different compilers. Our results show that up to 47% of the loops can be improved with at least a 1.15x speedup by this process while the average speedup can reach 1.6x for the improved loops. In addition, we propose a novel stability score that demonstrates the difference in stability from the studied compilers. The study concludes that the effect of source-level transformations varies among compilers, and the evaluated compilers have long ways to go until reaching stable.
NUCLEAR MAGNETIC RESONANCE TO CHARACTERIZE THE PORE SYSTEM OF COQUINAS FROM MORRO DO CHAVES FORMATION, SERGIPE-ALAGOAS BASIN, BRAZIL

ABSTRACT. Nuclear magnetic resonance (NMR) is a recognized petrophysical tool in the oil and gas industry to characterize reservoir rocks and fluids. In this study, the pore system of coquinas from a single bed of a quarry in the Morro do Chaves Formation was evaluated. These sedimentary rocks have been considered as a potential analogous to some Brazilian pre-salt reservoir rocks. The objective of this work was to characterize the porous system of coquinas in terms of total porosity and pore size distribution using low-field NMR. Conversion of T2 relaxation times to pore size radii was performed and literature cut-offs were applied for porosity partitioning. Coquinas were classified and ranked according to their percentage of macro, meso and micro porosity. This work verified quantitatively the pore system heterogeneities for the coquina samples and the variation in the layer from which they were extracted. The study provides some clues on lateral porosity and pore size variation in any reservoir for which this unit is an analogue.Keywords: Petrophysics, NMR, Total Porosity, Pore Size Distribution, Porosity Partitioning.RESUMO. Ressonância magnética nuclear (RMN) é uma técnica petrofísica reconhecida na indústria de óleo e gás pela sua capacidade de caracterizar rochas reservatório e seus fluidos saturantes. Neste estudo, foi avaliado o sistema poroso de coquinas pertencentes à uma camada de uma pedreira na Formação Morro do Chaves. Essas rochas sedimentares foram consideradas possíveis análogos de algumas rochas carbonáticas do pré-sal brasileiro. O objetivo do trabalho foi caracterizar o sistema poroso dessas coquinas em termos de porosidade total e distribuição de tamanho de poros utilizando RMN de baixo campo. Realizou-se a conversão dos tempos de relaxação T2 para raios de poro e empregou-se cut-offs da literatura para o particionamento da porosidade. As coquinas foram classificadas e ranqueadas de acordo com a sua porcentagem de macro, meso e micro poros. Verificou-se quantitativamente a heterogeneidade do sistema poroso das coquinas estudadas e a variação da camada sedimentar em que os plugues foram retirados. O estudo fornece informações sobre a variação lateral de porosidade e tamanho de poros para reservatórios que tenham a unidade estudada como análogo. Palavras-chave: Petrofísica, RMN, Porosidade Total, Distribuição do Tamanho de Poros, Particionamento da Porosidade.
Data Fusion: Identification Problems, Validity, and Multiple Imputation

Data fusion techniques typically aim to achieve a complete data file from different sources which do not contain the same units. Traditionally, data fusion, in the US also addressed by the term statistical matching, is done on the basis of variables common to all files. It is well known that those approaches establish conditional independence of the (specific) variables not jointly observed given the common variables, although they may be conditionally dependent in reality. However, if the common variables are (carefully) chosen in a way that already establishes conditional independence, then inference about the actually unobserved association is valid. In terms of regression analysis, this implies that the explanatory power of the common variables is high concerning the specific variables. Unfortunately, this assumption is not testable yet. Hence, we structure and discuss the objectives of statistical matching in the light of their feasibility. Four levels of validitya matching technique may achieve are introduced. By means of suitable multiple imputation (MI) techniques, the identification problem which is inherent in data fusion is reflected. In a simulation study it is also shown that MI allows to efficiently and easily use auxiliary information.
Review of the State of the Art: Discovering and Associating Semantics to Tags in Folksonomies

Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. Abstract This paper describes and compares the most relevant approaches for associating tags with semantics in order to make explicit the meaning of those tags. We identify a common set of steps that are usually considered across all these approaches and frame our descriptions according to them, providing a unified view of how each approach tackles the different problems that appear during the semantic association process. Furthermore, we provide some recommendations on (a) how and when to use each of the approaches according to the characteristics of the data source, and (b) how to improve results by leveraging the strengths of the different approaches.
UvA-DARE (Digital Academic Health literacy and online health information processing: Unraveling the underlying mechanisms

The usefulness of the Internet as a health information source largely depends on the receiver ’ s health literacy. This study investigates the mechanisms through which health literacy affects information recall and website attitudes. Using 2 independent surveys addressing different Dutch health websites ( N = 423 and N = 395), we tested the mediating role of cognitive load, imagination ease, and website involvement. The results showed that the influence of health literacy on information recall and website attitudes was mediated by cognitive load and imagination ease but only marginally by website involvement. Thus, to improve recall and attitudes among people with lower health literacy, online health communication should consist of information that is not cognitively demanding and that is easy to imagine.
Notes on PCA, Regularization, Sparsity and Support Vector Machines

We derive a new representation for a function as a linear combination of local correlation kernels at optimal sparse locations and discuss its relation to PCA, regularization, sparsity principles and Support Vector Machines. We first review previous results for the approximation of a function from discrete data (Girosi, 1998) in the context of Vapnik''s feature space and dual representation (Vapnik, 1995). We apply them to show 1) that a standard regularization functional with a stabilizer defined in terms of the correlation function induces a regression function in the span of the feature space of classical Principal Components and 2) that there exist a dual representations of the regression function in terms of a regularization network with a kernel equal to a generalized correlation function. We then describe the main observation of the paper: the dual representation in terms of the correlation function can be sparsified using the Support Vector Machines (Vapnik, 1982) technique and this operation is equivalent to sparsify a large dictionary of basis functions adapted to the task, using a variation of Basis Pursuit De-Noising (Chen, Donoho and Saunders, 1995; see also related work by Donahue and Geiger, 1994; Olshausen and Field, 1995; Lewicki and Sejnowski, 1998). In addition to extending the close relations between regularization, Support Vector Machines and sparsity, our work also illuminates and formalizes the LFA concept of Penev and Atick (1996). We discuss the relation between our results, which are about regression, and the different problem of pattern classification.
GridSpice: A distributed simulation platform for the smart grid

GridSpice is a simulation framework for the smart grid that integrates existing electric power point tools. The framework provides computational scale and modeling capability to represent diverse scenarios in large interconnected grid systems. Currently, GridSpice integrates a transmission and economic dispatch package based on MatPOWER and the distribution system simulator Gridlab-D. GridSpice provides computational scale by parallelizing large simulation jobs across many virtual machines using Amazon Web Services. GridSpice provides three primary mechanisms for setting up simulations: a Python library, a browser-based user interface, and a Representational State Transfer (REST) API, which allows users to interface with existing data management systems or post-processing tools. GridSpice is available in open-source under the BSD license.