nils-herrmann/clean_scirepeval huggingface dataset:
Analisis Perkembangan Wilayah, Pemusatan Perubahan Penggunaan Lahan Hutan Dan Kaitannya Dengan Kehidupan Sosial Budaya Masyarakat Merapi (the Analysis of Area Development, Change Decentralization, Forest Use, and Their Relationship with Social)

ABSTRAK Dinamika wilayah desa tercermin dari perubahan terus menerus yang berkaitan dengan berbagai faktor lingkungan hidup.Tujuan penelitian ini untuk mengidentifikasi perubahan dan dinamika perubahan penggunaan lahan, pemusatan dan pertumbuhan penggunaan lahan, serta dampak sosial budaya masyarakat petani hutan rakyat di Kecamatan Cangkringan. Metode yang digunakan analisis loqation quotient (LQ) skalogram, korelasi, observasi, dan kuisioner. Hubungan LQ dengan indeks perkembangan desa (IPD) menggunakan analisis korelasi, dengan parameter nilai LQ, jumlah sarana prasarana, jumlah penduduk, serta nilai IPD. Berdasar nilai IPD, kelurahan yang mempunyai nilai IPD tertinggi adalah Argomulyo (72,952) nilai IPD sedang adalah Umbulharjo (50,971) Kepuharjo (48,781) Wukirsari (40,695) dan Glagaharjo (36,599) sedangkan kelurahan dengan nilai IPD terendah tidak ada. Berdasar hirarki wilayah dan nilai IPD tahun 2011 kelurahan di dataran bagian atas umumnya mempunyai hirarki yang lebih rendah. Hanya v ariabel IPD yang signifikan berpengaruh terhadap LQ pemukiman. Budaya, regulasi, dan kesepakatan masyarakat menjadi modal sosial untuk menangani perubahan.  The dynamics of rural areas is reflected in the continuous changes associated with various environmental factors. The purpose of this research is to identify the changes and the dynamics of land use changes, the centralization and the growth in land use, as well as the social and cultural impacts of community forest farmers in Cangkringan. The method used in the analysis stage was Loqation Quotient (LQ) schallogram, correlation, observation, and questionnaires. The relationship LQ with rural development index (IPD) used correlation analysis, with parameter values LQ, the amount of infrastructure, population, and the value of IPD. Based on the IPD value, villages that had the highest IPD value was Argomulyo (72.952) the average IPD value was Umbulharjo (50.971) Kepuharjo (48.781) Wukirsari (40.695) and Glagaharjo (36.599) and there was no village with the lowest IPD value. Based on the hierarchy of the region and the IPD value in 2011, the villages in high lands generally had a lower hierarchy. Only IPD variables significantly affected the LQ settlement. Culture, regulations, and agreements society became social capital to deal with changes
A Multi Modal Driver Fatigue and Distraction Assessment System
Venue: Int. J. Intell. Transp. Syst. Res.

In this paper, we present a multi modal approach for driver fatigue and distraction detection. Based on a driving simulator platform equipped with several sensors, we have designed a framework to acquire sensor data, process and extract features related to fatigue and distraction. Ultimately the features from the different sources are fused to infer the driver's state of inattention. In our work, we extract audio, color video, depth map, heart rate, and steering wheel and pedals positions. We then process the signals according to three modules, namely the vision module, audio module, and other signals module. The modules are independent from each other and can be enabled or disabled at any time. Each module extracts relevant features and, based on hidden Markov models, produces its own estimation of driver fatigue and distraction. Lastly, fusion is done using the output of each module, contextual information, and a Bayesian network. A dedicated Bayesian network was designed for both fatigue and distraction. The complementary information extracted from all the mod ules allows a reliable estimation of driver inattention. Our experimental results show that we are able to detect fatigue with 98.4 accuracy and distraction with 90.5
Data Programming: Creating Large Training Sets, Quickly
Venue: NIPS

Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions, which are programs that label subsets of the data, but that are noisy and may conflict. We show that by explicitly representing this training set labeling process as a generative model, we can "denoise" the generated training set, and establish theoretically that we can recover the parameters of these generative models in a handful of settings. We then show how to modify a discriminative loss function to make it noise aware, and demonstrate our method over a range of discriminative models including logistic regression and LSTMs. Experimentally, on the 2014 TAC KBP Slot Filling challenge, we show that data programming would have led to a new winning score, and also show that applying data programming to an LSTM model leads to a TAC KBP score almost 6 F1 points over a state of the art LSTM baseline (and into second place in the competition) Additionally, in initial user studies we observed that data programming may be an easier way for non experts to create machine learning models when training data is limited or unavailable.
Data programming with DDLite: putting humans in a different part of the loop
Venue: HILDA '16

Populating large scale structured databases from unstructured sources is a critical and challenging task in data analytics. As automated feature engineering methods grow increasingly prevalent, constructing sufficiently large labeled training sets has become the primary hurdle in building machine learning information extraction systems. In light of this, we have taken a new approach called data programming [7] Rather than hand labeling data, in the data programming paradigm, users generate large amounts of noisy training labels by programmatically encoding domain heuristics as simple rules. Using this approach over more traditional distant supervision methods and fully supervised approaches using labeled data, we have been able to construct knowledge base systems more rapidly and with higher quality. Since the ability to quickly prototype, evaluate, and debug these rules is a key component of this paradigm, we introduce DDLite, an interactive development framework for data programming. This paper reports feedback collected from DDLite users across a diverse set of entity extraction tasks. We share observations from several DDLite hackathons in which 10 biomedical researchers prototyped information extraction pipelines for chemicals, diseases, and anatomical named entities. Initial results were promising, with the disease tagging team obtaining an F1 score within 10 points of the state of the art in only a single day long hackathon's work. Our key insights concern the challenges of writing diverse rule sets for generating labels, and exploring training data. These findings motivate several areas of active data programming research.
Progress in American Superconductor's HTS wire and optimization for fault current limiting systems

American Superconductor has developed composite coated conductor tape shaped wires using high temperature superconductor (HTS) on a flexible substrate with laminated metal stabilizer. Such wires enable many applications, each requiring specific optimization. For example, coils for HTS rotating machinery require increased current density J at 25 50 K. A collaboration with Argonne, Brookhaven and Los Alamos National Laboratories and several universities has increased J using an optimized combination of precipitates and ion irradiation defects in the HTS. Major commercial opportunities also exist to enhance electric power grid resiliency by linking substations with distribution voltage HTS power cables  Such links provide alternative power sources if one substation's transmission voltage power is compromised. But they must also limit fault currents which would otherwise be increased by such distribution level links. This can be done in an HTS cable, exploiting the superconductor to resistive transition when current exceeds the wires' critical J. A key insight is that such transitions are usually nonuniform; so the wire must be designed to prevent localized hot spots from damaging the wire or even generating gas bubbles in the cable causing dielectric breakdown. Analysis shows that local heating can be minimized by increasing the composite tape's total thickness, decreasing its total resistance in the normal state and decreasing its critical J. This conflicts with other desirable wire characteristics. Optimization of these conflicting requirements is discussed.
Exploring plausible causes of differential item functioning in the PISA science assessment: language, curriculum or culture

In recent years, large scale international assessments have been increasingly used to evaluate and compare the quality of education across regions and countries. However, measurement variance between different versions of these assessments often posts threats to the validity of such cross cultural comparisons. In this study, we investigated the cross language, cross cultural validity of the Programme for International Student Assessment 2006 Science assessment via three differential item functioning (DIF) analyses between the USA and Canada, Chinese Hong Kong and mainland China, and between the USA and mainland China. Furthermore, we explored three plausible causes of DIF via content analysis, namely language, curriculum and cultural differences. Our results revealed that differential curriculum coverage was the most serious cause of DIF among the three factors we investigated in this study, and differential content familiarity also contributed to DIF here. We discussed the implications of the findings for future international assessment development, and for how to best define 'scientific literacy' for students around the world.
XBT Science: Assessment of Instrumental Biases and Errors

AbstractExpendable bathythermograph (XBT) data were the major component of the ocean temperature profile observations from the late 1960s through the early 2000s, and XBTs still continue to provide critical data to monitor surface and subsurface currents, meridional heat transport, and ocean heat content. Systematic errors have been identified in the XBT data, some of which originate from computing the depth in the profile using a theoretically and experimentally derived fall rate equation (FRE) After in depth studies of these biases and discussions held in several workshops dedicated to discussing XBT biases, the XBT science community met at the Fourth XBT Science Workshop and concluded that XBT biases consist of 1) errors in depth values due to the inadequacy of the probe motion description done by standard FRE and 2) independent pure temperature biases. The depth error and temperature bias are temperature dependent and may depend on the data acquisition and recording system. In addition, the depth bia.
Mathematics Anxiety and Beliefs of Turkish Pre Service Elementary Teachers.

The purpose of this study was to investigate the relationship between pre service elementary teachers' mathematics teaching beliefs and mathematics anxiety. The participants of the study consist of 96 pre service elementary teachers from a department of primary education in a state university in Turkey during the 2010 2011 spring term.  Scale were used as the measuring instruments. The results reveal that pre service elementary teachers' scores on beliefs about teaching mathematics were high whereas mathematics anxiety levels were low in general. The correlations between the sub scales of the MARS SV and child centeredness beliefs are not significant at the 0.05 level. As a result, some recommendations concerning the situation and future research were made.
Mathematics Anxiety and Pre Service Teachers

Mathematics anxiety has been a topic of interest since the 1950s. It has been shown that apprehensive elementary mathematics teachers unintentionally transfer anxiety to students in their classroom. In this study we assessed the change in 34 pre service teacher's anxiety, self efficacy and perception of ability during and after a content specific mathematics course. This study used a mixed method approach for analyze data. The results suggests that anxiety decreased over the semester and perception of ability and self efficacy increased. The levels of confidence with the material were recorded before and after three exams in the course. The pre service teacher's change in levels of confidence following the exams rose from an average of 2.98 to a 4.30 level out of 5. We chose three students from the participants who gave different pictures of their feelings towards mathematics to investigate in detail.
Monitoring spray drift in aerial spray application based on infrared thermal imaging technology
Venue: Comput. Electron. Agric.

A first study to measure pesticide drift distribution using thermal imaging.Tiny thermal differences during spray application had been measured.The drift distribution and spray range could be detected.The testing results had well agreement with the water sensitive paper method. The pesticide drift in aerial spray applications is harmful both to environment and human beings but hard to be measured. We demonstrated a new method for real time evaluation of pesticide drift which used infrared thermal imaging technology to detect the thermal differences before and after spraying process and then measured the range and concentration distribution of droplets. The drift distribution and spray range are detected using infrared thermal imaging system combined with image processing algorithms. The experiments were carried out in both ground spray and aerial spray applications. The results showed that this method had the ability to detect the tiny thermal differences during spray application and thus to monitor the aerial drifts. Furthermore, the testing results using this method had well agreement with the water sensitive paper method. Therefore, it is verified that infrared thermal imaging method combined with infrared image processing algorithms can be used to monitor the pesticide drift in aerial spray application with advantages of fast, non contact and able to realize real time measurement.
A new paradigm of oral cancer detection using digital infrared thermal imaging
Venue: SPIE Medical Imaging

Histopathology is considered the gold standard for oral cancer detection. But a major fraction of patient pop ulation is incapable of accessing such healthcare facilities due to poverty. Moreover, such analysis may report false negatives when test tissue is not collected from exact cancerous location. The proposed work introduces a pioneering computer aided paradigm of fast, non invasive and non ionizing modality for oral cancer detection us ing Digital Infrared Thermal Imaging (DITI) Due to aberrant metabolic activities in carcinogenic facial regions, heat signatures of patients are different from that of normal subjects. The proposed work utilizes asymmetry of temperature distribution of facial regions as principle cue for cancer detection. Three views of a subject, viz. front, left and right are acquired using long infrared (7:5 13mm) camera for analysing distribution of temperature. We study asymmetry of facial temperature distribution between: a) left and right profile faces and b) left and right half of frontal face. Comparison of temperature distribution suggests that patients manifest greater asymmetry compared to normal subjects. For classification, we initially use k means and fuzzy k means for unsupervised clustering followed by cluster class prototype assignment based on majority voting. Average classification accuracy of 91:5% and 92:8% are achieved by k mean and fuzzy k mean framework for frontal face. The corresponding metrics for profile face are 93:4% and 95% Combining features of frontal and profile faces, average accuracies are increased to 96:2% and 97:6% respectively for k means and fuzzy k means framework.
Developpement de nouvelles methodes de mesure du frottement transversal entre fibres

Le comportement mecanique des structures fibreuses, particulierement le comportement transverse de files de fibres et de meches multi filamentaires est tributaire entre autre du contact et glissement inter fibre. La connaissance de loi de comportement en compression transverse est un verrou scientifique de taille lors de la modelisation numerique du comportement mecanique de structures fibreuses. La mesure des proprietes frictionnelles des fibres, dont les dimensions transversales sont en general tres faibles, est une entreprise tres delicate. Le contact et le mouvement relatif entre deux fibres, malgre leurs complexites peuvent etre resumes par trois cas simples, schematises ci dessous. Il existe dans la litterature scientifique un certain nombre d'etudes et de dispositifs sur le contact et le frottement interfibre, mais ils se concentrent tous sur les cas A et B, rien ne permet pour l'instant de modeliser et de mesurer le frottement interfibre dans la configuration C. Or, c'est bien dans cette derniere configuration que se produisent les mouvements interfibre lors de la compression transversale des files ou meches multi filamentaire. De nouveaux dispositifs experimentaux ont ete concus et developpes afin de caracteriser le frottement transversal entre fibres.
Chapter 4 CyGraph: Graph Based Analytics and Visualization for Cybersecurity

This chapter describes CyGraph, a system for improving network security posture, maintaining situational awareness in the face of cyberattacks, and focusing on protection of mission critical assets. CyGraph adopts a unified graph based cybersecurity model relevant to potential and actual cyberattacks, defenses, and mission impacts. It captures incremental attack vulnerability, security events, and mission dependencies within a network environment, builds a predictive model of possible attack paths and critical vulnerabilities, and correlates events to known vulnerability paths. It also includes dependencies among mission requirements and network assets, for analysis in the context of mission assurance. The resulting knowledge graph captures the complex relationships among entities in the cybersecurity domain. CyGraph brings together isolated data and events into an overall picture for decision support and situational awareness. It prioritizes exposed vulnerabilities, mapped to potential threats, in the context of mission critical assets. In the face of actual attacks, it correlates intrusion alerts to known vulnerability paths and suggests best courses of action for responding to attacks. For postattack forensics, it shows vulnerable paths that may warrant deeper inspection. CyGraph also supports CyQL (CyGraph Query Language) a domain specific query language for expressing graph patterns of interest, with interactive visualization of query results. To help manage visual complexity, CyGraph supports the separation of graph models into interdependent layers. For time dependent graph models, it provides dynamic visualization of evolving graph state. CyGraph also integrates with third party tools for visualizing graph state changes (e.g. driven by simulations) Furthermore, it has capabilities for synthesizing graph models with particular statistical properties.
Physics reach of the XENON1T dark matter experiment

The XENON1T experiment is currently in the commissioning phase at the Laboratori Nazionali del Gran Sasso, Italy. In this article we study the experiment's expected sensitivity to the spin independent WIMP nucleon interaction cross section, based on Monte Carlo predictions of the electronic and nuclear recoil backgrounds. The total electronic recoil background in 1 tonne fiducial volume and (1, 12) keV electronic recoil equivalent energy region, before applying any selection to discriminate between electronic and nuclear recoils, is (1.80 0.15) 10( (4) (kg*day*keV) (1) mainly due to the decay of (222)Rn daughters inside the xenon target. The nuclear recoil background in the corresponding nuclear recoil equivalent energy region (4, 50) keV, is composed of (0.6 0.1) (t*y) (1) from radiogenic neutrons, (1.8 0.3) 10( (2) (t*y) (1) from coherent scattering of neutrinos, and less than 0.01 (t*y) (1) from muon induced neutrons. The sensitivity of XENON1T is calculated with the Profile Likelihood Ratio method, after converting the deposited energy of electronic and nuclear recoils into the scintillation and ionization signals seen in the detector. We take into account the systematic uncertainties on the photon and electron emission model, and on the estimation of the backgrounds, treated as nuisance parameters. The main contribution comes from the relative scintillation efficiency Script L(eff) which affects both the signal from WIMPs and the nuclear recoil backgrounds. After a 2 y measurement in 1 t fiducial volume, the sensitivity reaches a minimum cross section of 1.6 10( (47) cm(2) at m(kh) 50 GeV/c(2)
Towards an ontology driven approach for systems interoperability and energy management in the smart city
Venue: 2016 International Multidisciplinary Conference on Computer and Energy Science (SpliTech)

Modern Information and Communication Technologies are definitely a key factor to develop the green and sustainable applications that the so called "smart city" needs. Effective management of resources, gathering and interpreting data as well as ecological considerations are prerequisites to turn such a vision into reality. The European FP7 project DIMMER address these issues by providing a flexible Internet of Thing platform for application development and data integration, exploiting information about buildings, energy distribution grids and user behaviors. Among those applications, the possibility to real time access and aggregate information about building environmental characteristics and energy consumption enables the optimization of energy management and control, as well as the user's awareness about, which is the scope of the DIMMER project. The paper will describe the ontology driven approach, as well as the actual design, exploited to model the physical world within the context of this project, adding a special emphasis on the state of art research in the field of energy profiling.