nils-herrmann/clean_scirepeval huggingface dataset:
Network Topology Aware Moving Object Trajectory Clustering

This paper illustrates a three step clustering method for moving object trajectories to discover common sub paths. The proposed algorithm (TOPOSCAN) is inspired from the well known partition and group framework and density based algorithms. It takes advantage of topological relation of a predefined network, and reduces the dimension of trajectory databases and algorithm complexity. TOPOSCAN consists of three steps: network topology construction, trajectory mapping, and path clustering. For network topology construction, we define the network as a directed graph, associated with a network attribute table to describe link relationships. For the trajectory mapping, the authors adopt a map matching algorithm to assign trajectory points to network links. For the path clustering, the authors develop a framework with extraction, extension and exclusion steps to select the common sub paths. TOPOSCAN is implemented using simulated trajectory data. The experimental results demonstrate that TOPOSCAN correctly and efficiently discovers common sub paths from vehicle trajectory data. The recognized patterns are useful for transport management and operation design.
Transverse Compression of Tendons.
Venue: Journal of biomechanical engineering

A study was made of the deformation of tendons when compressed transverse to the fiber aligned axis. Bovine digital extensor tendons were compression tested between flat rigid plates. The methods included: in situ image based measurement of tendon cross sectional shapes, after preconditioning but immediately prior to testing; multiple constant load creep/recovery tests applied to each tendon at increasing loads; and measurements of the resulting tendon displacements in both transverse directions. In these tests, friction resisted axial stretch of the tendon during compression, giving approximately plane strain conditions. This, together with the assumption of a form of anisotropic hyperelastic constitutive model proposed previously for tendon, justified modeling the isochronal response of tendon as that of an isotropic, slightly compressible, neo Hookean solid. Inverse analysis, using finite element (FE) simulations of the experiments and 10 s isochronal creep displacement data, gave values for Young's modulus and Poisson's ratio of this solid of 0.31 MPa and 0.49, respectively, for an idealized tendon shape and averaged data for all the tendons and E 0.14 and 0.10 MPa for two specific tendons using their actual measured geometry. The compression load versus displacement curves, as measured and as simulated, showed varying degrees of stiffening with increasing load. This can be attributed mostly to geometrical changes in tendon cross section under load, varying according to the initial 3D shape of the tendon.
The effect of functional ions (Y3 F Ti4 on the structure, sintering and crystallization of diopside calcium pyrophosphate bioglasses

The effects of three functional ions (yttrium Y3 fluorine F titanium Ti4 on the glass forming ability, sintering, crystallization, and thermo physical properties of glasses and glass ceramics were studied in a diopside calcium pyrophosphate (90% CaMgSi2O6 10% Ca2P2O7) system. Three different percentages (1, 3 and 5 wt% for each additive were tested. The structural features of glasses were assessed through FT IR (Fourier Transform infra red spectroscopy) and 29Si and 31P NMR (nuclear magnetic resonance) showing that the silicate network in all the investigated glasses is predominantly coordinated in Q2 (Si) units, while phosphorus tends to remain in the orthophosphate (Q0) environment. All glasses exhibited fast rates of biomineralization, making them promising candidates for biomedical applications. The sintering and crystallization behaviours of the glass powders were studied by differential thermal analysis (DTA) while the coefficient of thermal expansion (CTE) was determined by dilatometry. Glass transition temperature (Tg) values of all doped glasses were lower than the parent glass, while CTE values decreased with initial addition (1 wt% of dopants before exhibiting an increase with further addition. In Y doped glasses, a gradual increase was seen in the values of maximum crystallization peak temperature, Tp, up to 3 wt% while an opposite trend was observed in Ti doped glasses, showing an enhancement of the stability of the Y doped glasses against devitrification. F doped glasses exhibited a similar trend. Crystalline phase evolution was analysed by X ray diffraction (XRD) and amorphous glass were obtained by sintering powder compacts from all the glasses at 800 degC for 1 h.
Synergy and Antagonism in Online Advertising
Venue: 2016 IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT)

An advertising campaign is usually composed of a series of coordinated advertisements, with various formats and delivered through different media channels. Several existing studies have attempted to measure the individual contribution of related advertisements in a campaign, resulting in rule based and data driven multi touch attribution models. However, most of these models ignored the interaction among different advertisements. We here specifically consider the synergy and antagonism interaction among advertisements and propose a data driven model. With the interaction, the influence of different advertisements is no longer linearly additive. We choose a vector representation for the influence of an individual advertisement, which can easily model the synergy and antagonism interactions. We validate the proposed method with a real world dataset and show that antagonism is prevalent among advertisements while synergy rarely appears. In addition, our method leads to more accurate purchase predictions.
I Know What You Did Last Cycle: Improving the Detection of State Campaign Finance Violations

This article investigates what state campaign finance regulatory agencies can do to increase perceptions among regulatees, specifically state legislators, that violations will be successfully detected. We test hypotheses that random audits and transparency enhancing policies will promote perceptions that campaign finance violations are identified. With the use of an original survey of state legislators, we found that both strategies are effective. Agencies increase perceptions that they detect violations when their regulatees are subject to random audit. Additionally, agencies can bolster perceptions of third party monitoring by increasing the information available to third party monitors through transparent disclosure practices.
Robust face recognition via sparse boosting representation
Venue: Neurocomputing

Recently linear representation provides an effective way for robust face recognition. However, the existing linear representation methods cannot make an adaptive adjustment in responding to the variations on facial image, so the generalization ability of these methods is limited. In this paper, we propose a sparse boosting representation classification (SBRC) for robust face recognition. To improve the effectiveness of representation coding, an error detection machine (EDM) with multiple error detectors (ED) in SBRC, is proposed to detect and remove destroyed features (i.e. pixels) on a testing image. SBRC has three advantages: First, it has good generalization ability, since the EDM can self adjust the number of ED according to different variations; Second, EDM would boost the sparsity of coding vector; Third, its implementation is simple and efficient as the EDM is based on l 2 n o r m In addition, five popular face image databases including AR database, Extended Yale B database, ORL database, FERET database and LFW database were applied to validate the performance of SBRC. The superiority of SBRC is confirmed by comparing it with the state of the art face recognition methods.
Meta analysis of lean case studies for the business process improvement
Venue: 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt)

Each company defines its goals to achieve an improvement approach for its business processes. However, identifying potentially achievable goals before the implementation of an improvement plan could prevent the company from wasting considerable time and budget on unreasonable goals that do not meet their expectations. Our meta analysis provides, in this sense, a preliminary study of expected objectives through the implementation of the Lean approach in different fields and by an analysis of several cases published in the literature.
Initial Exploration of a Construct Representing Native Language and Culture (NLC) in Elementary and Middle School Instruction.

Students from American Indian/Alaska Native (AI/AN) backgrounds have typically experienced poor academic and behavioral outcomes. In response, the educational community has recommended that teachers integrate Native Language and Culture (NLC) into instruction to create a welcoming and culturally relevant classroom environment. However, translating this recommendation into practice has been challenging. In this study, we take the first steps toward a formal exploration of the effects of NLC on AI/AN performance by attempting to define a scientifically defensible set of variables that can measure the degree to which teachers and schools make use of NLC in instruction. We used data collected by the National Indian Education Study (NIES) in 2009 and 2011, and conducted exploratory and confirmatory factor analyses with the Student, Teacher, and School (Administrator) Surveys. Contrary to expectations, we found that use of NLC in the classroom was a multidimensional construct: student perceptions differentiated between media based and live contact; teacher perceptions included both preparation and teaching activities; and, administrator reports included both instructional practices and access to local resources. Implications for further research are discussed.
Single Image Crowd Counting via Multi Column Convolutional Neural Network
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

This paper aims to develop a method than can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end, we have proposed a simple but effective Multi column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map. The proposed MCNN allows the input image to be of arbitrary size or resolution. By utilizing filters with receptive fields of different sizes, the features learned by each column CNN are adaptive to variations in people/head size due to perspective effect or image resolution. Furthermore, the true density map is computed accurately based on geometry adaptive kernels which do not need knowing the perspective map of the input image. Since exiting crowd counting datasets do not adequately cover all the challenging situations considered in our work, we have collected and labelled a large new dataset that includes 1198 images with about 330,000 heads annotated. On this challenging new dataset, as well as all existing datasets, we conduct extensive experiments to verify the effectiveness of the proposed model and method. In particular, with the proposed simple MCNN model, our method outperforms all existing methods. In addition, experiments show that our model, once trained on one dataset, can be readily transferred to a new dataset.
Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector

This Report summarizes the results of the activities of the LHC Higgs Cross Section Working Group in the period 2014 2016. The main goal of the working group was to present the state of the art of Higgs physics at the LHC, integrating all new results that have appeared in the last few years. The first part compiles the most up to date predictions of Higgs boson production cross sections and decay branching ratios, parton distribution functions, and off shell Higgs boson production and interference effects. The second part discusses the recent progress in Higgs effective field theory predictions, followed by the third part on pseudo observables, simplified template cross section and fiducial cross section measurements, which give the baseline framework for Higgs boson property measurements. The fourth part deals with the beyond the Standard Model predictions of various benchmark scenarios of Minimal Supersymmetric Standard Model, extended scalar sector, Next to Minimal Supersymmetric Standard Model and exotic Higgs boson decays. This report follows three previous working group reports: Handbook of LHC Higgs Cross Sections: 1. Inclusive Observables (CERN 2011 002) Handbook of LHC Higgs Cross Sections: 2. Differential Distributions (CERN 2012 002) and Handbook of LHC Higgs Cross Sections: 3. Higgs properties (CERN 2013 004) The current report serves as the baseline reference for Higgs physics in LHC Run 2 and beyond.
Rethinking GIS Towards The Vision Of Smart Cities Through CityGML

Smart cities present a substantial growth opportunity in the coming years. The role of GIS in the smart city ecosystem is to integrate different data acquired by sensors in real time and provide better decisions, more efficiency and improved collaboration. Semantically enriched vision of GIS will help evolve smart cities into tomorrow's much smarter cities since geospatial/location data and applications may be recognized as a key ingredient of smart city vision. However, it is need for the Geospatial Information communities to debate on "Is 3D Web and mobile GIS technology ready for smart cities?" This research places an emphasis on the challenges of virtual 3D city models on the road to smarter cities.
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
Venue: IEEE Transactions on Industrial Electronics

Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such processes take advantage of human ingenuity but are time consuming and labor intensive. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two stage learning method is proposed for intelligent diagnosis of machines. In the first learning stage of the method, sparse filtering, an unsupervised two layer neural network, is used to directly learn features from mechanical vibration signals. In the second stage, softmax regression is employed to classify the health conditions based on the learned features. The proposed method is validated by a motor bearing dataset and a locomotive bearing dataset, respectively. The results show that the proposed method obtains fairly high diagnosis accuracies and is superior to the existing methods for the motor bearing dataset. Because of learning features adaptively, the proposed method reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.
A Classification Schema for the Job Shop Scheduling Problem with Transportation Resources: State of the Art Review
Venue: CSOC

The Job Shop scheduling Problem (JSP) is one of the most known problems in the domain of the production task scheduling. The Job Shop scheduling Problem with Transportation resources (JSPT) is a generalization of the classical JSP consisting of two sub problems: the job scheduling problem and the generic vehicle scheduling problem. In this paper, we make a state of the art review of the different works proposed for the JSPT, where we present a new classification schema according to seven criteria such as the transportation resource number, the transportation resource type, the job complexity, the routing flexibility, the recirculation constraint, the optimization criteria and the implemented approaches.
Survey of anthracnose (Colletotrichum gloeosporioides) on mango (Mangifera indica) in North West Ethiopia

Field surveys were conducted in North West Ethiopia, Pawi district during 2011 and 2012 cropping seasons to determine the distribution of anthracnose and the association of disease parameters (incidence and severity) with climatic variables and crop management practices. Nineteen mango fields were surveyed and all were infected by anthracnose. The surveyed fields were at two growth stages, with flowering (2011) and fruiting (2012) There was no statistically significant difference for the incidence of anthracnose between the two seasons with mean disease incidence of 65.7% in 2011 and 66.5% in 2012. However, severity of anthracnose was statistically significantly different (p <0.05) in 2012 cropping season (81.2% than in the 2011 cropping season (59.8% Regression analysis for the relation between agronomical variables, as independent variables, and mango anthracnose severity, as dependent variable, showed contributions of environmental and agronomical variables such as planting stage, field size, plant density, altitude and villages for statistically significant anthracnose severity during fruiting stages than flowering stages. As altitude increases, disease severity decreases in the survey villages in Pawi district, Ethiopia.
A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification
Venue: EMNLP

Discourse parsing is considered as one of the most challenging natural language processing (NLP) tasks. Implicit discourse relation classification is the bottleneck for discourse parsing. Without the guide of explicit discourse connectives, the relation of sentence pairs are very hard to be inferred. This paper proposes a stacking neural network model to solve the classification problem in which a convolutional neural network (CNN) is utilized for sentence modeling and a collaborative gated neural network (CGNN) is proposed for feature transformation. Our evaluation and comparisons show that the proposed model outperforms previous state of the art systems.
Discourse Relation Sense Classification Using Cross argument Semantic Similarity Based on Word Embeddings
Venue: CoNLL

This paper describes our system for the CoNLL 2016 Shared Task's supplementary task on Discourse Relation Sense Classification. Our official submission employs a Logistic Regression classifier with several cross argument similarity features based on word embeddings and performs with overall F scores of 64.13 for the Dev set, 63.31 for the Test set and 54.69 for the Blind set, ranking first in the Overall ranking for the task. We compare the feature based Logistic Regression classifier to different Convolutional Neural Network architectures. After the official submission we enriched our model for Non Explicit relations by including similarities of explicit connectives with the relation arguments, and part of speech similarities based on modal verbs. This improved our Non Explicit result by 1.46 points on the Dev set and by 0.36 points on the Blind set.
Job Satisfaction and Gender

This study uses quantitative and qualitative survey data to examine the job satisfaction of male and female employees at Iowa newspapers. Responses to open ended questions indicated qualitative differences in the issues men and women considered salient and in the discourses they used to talk about their work. Male respondents were more likely to use language that evoked professional efficacy and emphasized presence. Female respondents more likely to use language that related to production and emphasized position. The findings also indicate a gender discrepancy related to income and financial status; men reported higher salaries and indicated greater financial security than women. Economic environment emerged as a salient issue for both male and female employees. Overall, the findings support the use of qualitative data to explore issues related to gender and job satisfaction and suggest organizational factors play an important role in newspaper employees' attitudes and perceptions about their work.
Freelancing in Flemish News Media and Entrepreneurial Skills as Pivotal Elements in Job Satisfaction

This article is concerned with a segment of journalists often neglected or marginalized in journalism research: freelance journalists. The most recent large scale survey among professional journalists in Flanders, the Dutch speaking part of Belgium, shows that one in five journalists is a freelancer. Only half of this group is satisfied with their job against 93 per cent of contractually employed journalists. Seen from this perspective, Flanders does not appear to be a very fertile ground for entrepreneurial journalism. Drawing on the database of the Flemish Union for Journalists (VVJ) we conducted qualitative in depth interviews with a group of freelancers built around three main research questions: (1) What are considered the advantages and disadvantages of working as a freelancer in Flanders? (2) How satisfied are freelancers in Flanders with their work and lives, and what are the reasons for their satisfaction/dissatisfaction? (3) What are the preconditions to make entrepreneurial journalism a succes.
Multiscale roughness analysis of engineering surfaces: A comparison of methods for the investigation of functional correlations

This study investigates the correlations between the topography of different damaged rough surfaces and process conditions. Several surfaces are measured and compared to determine if they can be discriminated. The analysis is performed by using Gaussian Filtering, Wavelet Transform and a more recent approach named Discrete Modal Decomposition. Standardized 3D roughness parameters are computed for each multiscale method, filter (e.g. high pass, low pass and band pass) and available scale. The relevance (i.e. the ability to discriminate surface topographies corresponding to different process conditions) is then investigated using a statistical analysis based on the MesRugTM expert system. The results indicate clear differences between the multiscale methods and show that the Wavelet approach is useful when characterizing localized surface defects while Gaussian Filtering is more appropriate for highly periodic morphological structures. For more complex topographies, this study also clearly shows that the Discrete Modal Decomposition exhibits compelling abilities that fall between those of the Gaussian and Wavelet approaches; this method is clearly more relevant than the Gaussian method in the case of localized defects and less relevant in the case of highly periodical structures and fractal surfaces 1 f a spectrum) This can be explained by the modulated frequency/amplitude descriptors generated via the modal basis.