nils-herrmann/clean_scirepeval huggingface dataset:
Outbreak of vancomycin resistant Enterococcus faecium clone ST796, Switzerland, December 2017 to April 2018
Venue: Euro surveillance bulletin Europeen sur les maladies transmissibles European communicable disease bulletin
Year: 2018

A large outbreak of vancomycin resistant enterococci (VRE) is affecting four hospitals in the Canton of Bern, Switzerland, since December 2017. Of 89 cases identified as carriers, 77 (86.5% VRE isolates were virtually indistinguishable using whole genome sequencing, and identified as multilocus sequence type (MLST) ST796. This clone, previously only described in Australia and New Zealand, is characterised by rapid spread and the ability to cause bloodstream infections. It requires a multifaceted infection prevention effort.
A probabilistic capacity planning methodology for plug in electric vehicle charging lots with on site energy storage systems
Year: 2020

Plug in electric vehicles (PEV) have gained popularity to support environmental sustainability and reach net zero emission goals. However, accommodating large numbers of PEVs is a complex problem as concurrent PEV demand significantly increases peak demand and stresses supporting network elements. In this paper, we present a large scale PEV charging lot equipped with an on site storage. Power drawn from the grid is utilized to meet customer demand and charge the storage unit which, in return, is employed to lower peak load and demand charges. By considering the probabilistic nature of the customer demand, the proposed architecture is modelled by a Markov modulated Poisson Process and a matrix geometric based algorithm is developed to solve the associated capacity planning problem. Station outage probability (defined as the probability of not serving PEV demand) is used as the main metric to size station resources. Case studies show that by accounting for the statistical variations in customer demand, the power required for the station is significantly less than the sum of chargers' rated power. In addition, on site storage can considerably reduce the stress on the supporting grid components and lower stations' running cost.
Resilience in the United States Air Force: Psychometric Properties of the Connor Davidson Resilience Scale (CD RISC)
Venue: Psychological assessment
Year: 2017

Results are presented for the largest study (N 53,692) ever conducted on the 25 item Connor Davidson Resilience Scale (CD RISC) a popular measure of resilience. We examined the internal consistency of the CD RISC items and associated mean resilience levels within a sample of enlisted basic trainees in the United States Air Force. In addition, the predictive validity of the CD RISC Total Score was examined for real life military outcomes, including attrition from service and mental health diagnosis. The CD RISC items demonstrated strong internal consistency. Item level examinations of scores revealed that most trainees reported relatively high resilience. Results indicated that resilience measured at the beginning of military service is a significant predictor of (a) attrition from service and (b) obtaining a mental health diagnosis within 6 months of entry. Implications and future directions are discussed.
How kelp produce blade shapes suited to different flow regimes: A new wrinkle.
Venue: Integrative and comparative biology
Year: 2008

Many species of macroalgae have flat, strap like blades in habitats exposed to rapidly flowing water, but have wide, ruffled "undulate" blades at protected sites. We used the giant bull kelp, Nereocystis luetkeana, to investigate how these ecomorphological differences are produced. The undulate blades of N. luetkeana from sites with low flow remain spread out and flutter erratically in moving water, thereby not only enhancing interception of light, but also increasing drag. In contrast, strap like blades of kelp from habitats with rapid flow collapse into streamlined bundles and flutter at low amplitude in flowing water, thus reducing both drag and interception of light. Transplant experiments in the field revealed that shape of the blade in N. luetkeana is a plastic trait. Laboratory experiments in which growing blades from different sites were subjected to tensile forces that mimicked the hydrodynamic drag experienced by blades in different flow regimes showed that change in shape is induced by mechanical stress. During growth experiments in the field and laboratory, we mapped the spatial distribution of growth in both undulate and strap like blades to determine how these different morphologies were produced. The highest growth rates occur near the proximal ends of N. luetkeana blades of both morphologies, but the rates of transverse growth of narrow, strap like blades are lower than those of wide, undulate blades. If rates of longitudinal growth at the edges of a blade exceed the rate of longitudinal growth along the midline of the blade, ruffles along the edges of the blade are produced by elastic buckling. In contrast, flat blades are produced when rates of longitudinal growth are similar across the width of a blade. Because ruffles are the result of elastic buckling, a compliant undulate N. luetkeana blade can easily be pushed into different configurations (e.g. the wavelengths of the ruffles along the edges of the blade can change, and the whole blade can twist into left and right handed helicoidal shapes) which may enhance movements of the blade in flowing water that reduce self shading and increase mass exchange along blade surfaces.
Graphology and Personality: An Empirical Study on Validity of Handwriting Analysis
Venue: Psychological reports
Year: 2009

The aim of the present work was to examine validity of the graphological method to assess personality traits. The results of two studies are described. In Study 1, the Big Five Questionnaire was administered to a sample of 101 university students who provided a sample of a handwritten text. Two graphologists were asked to detect the same dimensions and facets measured by the Big Five Questionnaire using a 9 point scale. Correlations between the Big Five Questionnaire and graphological evaluations did not confirm the capability of handwriting analysis to measure Big Five personality traits. Also, interrater reliability was very low. Study 2 (N 102) was carried out using handwritten texts with autobiographical content for the graphological analysis. Two different graphologists and two laypersons were involved. No evidence was found to validate the graphological method as a measure of personality.
Performance Improvement of Data Fusion Based Real Time Hand Gesture Recognition by Using 3 D Convolution Neural Networks With Kinect V2
Year: 2019

Hand gesture recognition is one of the most active areas of research in computer vision. It provides an easy way to interact with a machine without using any extra devices. Hand gestures are natural and intuitive communication way for the human being to interact with his environment. In this paper, we propose Data Fusion Based Real Time Hand Gesture Recognition using 3 D Convolutional Neural Networks and Kinect V2. To achieve the accurate segmentation and tracking with Kinect V2. Convolution neural network to improve the validity and robustness of the system. Based on the experimental results, the proposed model is accurate, robust and performance with very low processor utilization. The performance of our proposed system in real life application, which is controlling various devices using Kinect V2.  Hand gesture recognition, Kinect V2, data fusion, Convolutional Neural Networks DOI 10.7176/IKM/9 1 02
Design of the disturbance invariant speed controlled drive with induction motor
Venue: 2009 9th International Conference on Telecommunication in Modern Satellite, Cable, and Broadcasting Services
Year: 2009

This paper presents the design of digitally controlled speed drive invariant to arbitrary class of torque disturbances. For the rejection of torque disturbance effects on the steady state value of motor speed, the IMPACT (internal model principle and control together) regulation structure is applied, while the output drive torque is produced by using the indirect field oriented control (IFOC) of induction motor. The results of analytical design are verified by simulation and by experimental measurements on induction motor drive test setup.
Data security in mobile cloud computing paradigm: a survey, taxonomy and open research issues
Venue: The Journal of Supercomputing
Year: 2016

The incessant spurt of research activities to augment capabilities of resource constrained mobile devices by leveraging heterogeneous cloud resources has created a new research impetus called mobile cloud computing. However, this rapid relocation to the cloud has fueled security and privacy concerns as users' data leave owner's protection sphere and enter the cloud. Significant efforts have been devoted by academia and research community to study and build secure frameworks in cloud environment, but there exists a research gap for comprehensive study of security frameworks in mobile cloud computing environment. Therefore, we aim to conduct a comprehensive survey to analyze various cryptographic, biometric and multifactor lightweight solutions for data security in mobile cloud. This survey highlights the current security issues in mobile cloud environment and infrastructure, investigates various data security frameworks and provides a taxonomy of the state of the art data security frameworks and deep insight into open research issues for ensuring security and privacy of data in mobile cloud computing platform.
Poseidon An Efficient Communication Interface for Distributed Deep Learning on GPU Clusters
Year: 2017

Deep learning models can take weeks to train on a single GPU equipped machine, necessitating scaling out DL training to a GPU cluster. However, current distributed DL implementations can scale poorly due to substantial parameter synchronization over the network, because the high throughput of GPUs allows more data batches to be processed per unit time than CPUs, leading to more frequent network synchronization. We present Poseidon, an efficient communication interface for distributed DL on GPUs. Poseidon exploits the layered model structures in DL programs to overlap communication and computation, reducing bursty network communication. Moreover, Poseidon uses a hybrid communication scheme that optimizes the number of bytes required to synchronize each layer, according to layer properties and the number of machines. We show that Poseidon is applicable to different DL frameworks, by implementing Poseidon into Caffe and TensorFlow, and showing that, with Poseidon, both Caffe and TensorFlow can achieve 15.5x speed up on VGG19 22K network for image classification using 16 single GPU machines, even under limited bandwidth (10GbE) In particular, Poseidon enabled TensorFlow achieves 31.5x speed up with 32 single GPU machines on Inception V3, a 50% improvement over the original TensorFlow (20x speed up) The software is available at http:/poseidon release.readthedocs.io.
Community Awareness on Rabies Prevention and Control in Bicol, Philippines: Pre and Post Project Implementation
Venue: Tropical medicine and infectious disease
Year: 2018

Rabies is endemic in the Philippines. To support the rabies campaign in the Bicol region at the southeastern part of Luzon, the BAI OIE Stop Transboundary Animal Diseases and Zoonoses (STANDZ) Rabies project was implemented in the pilot provinces of Camarines Norte, Camarines Sur, Albay, and Masbate. A community awareness survey was conducted with the residents of these provinces to determine their knowledge, attitude, and practices (KAP) on rabies during the start and end of the project. Qualitative, descriptive research was done with a structured KAP questionnaire. Pet owners in the pilot provinces were chosen as respondents. Results showed that respondents know that they can acquire rabies in animals through the bite of a rabid dog (pre project implementation (PRI) 19.6% post project implementation (POI) 38.0% Vaccination was the top rabies preventive measure (PRI: 61.8% POI: 92.8% Biting incidents were noted in some respondents, and observing the dog and killing it immediately were some of the actions taken by bite victims. If a supposed rabid dog was seen, respondents would either: immediately kill the dog (PRI: 20.3% POI: 13.7% report it to authorities (PRI: 26.3% POI: 63.1% and capture and observe the dog concerned (PRI: 13.5% POI: 6.0% Pet owners increased their KAP about rabies prevention and control as compared to the pre implementation study. However, certain gaps in their KAP need to be given attention; thus continuous education of pet owners must be done.
Representacion de las primitivas conceptuales de UML 2.2 en logica de predicados de primer orden.
Year: 2010

Software Engineering uses software development process models and a set of techniques and methodologies for identifying, analyzing and designing information systems. The Unified Modeling Language (UML) is one of such techniques. UML, as a semi formal language, exhibits problems of ambiguity, clarity, and consistency. Some researchers are attempting to formalize UML by using first order predicate logic (FOPL) set theory, controlled and/or restricted languages, and meta modeling. However, these approaches have been not enough because they focus on a single chart and merely in syntax, leaving out semantics. In this paper we present a set of FOPL based rules for representing UML constructs, and a case study, based on UN Lencep requirement description of a system.
Towards Efficient Model Compression via Learned Global Ranking
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020

Pruning convolutional filters has demonstrated its effectiveness in compressing ConvNets. Prior art in filter pruning requires users to specify a target model complexity (e.g. model size or FLOP count) for the resulting architecture. However, determining a target model complexity can be difficult for optimizing various embodied AI applications such as autonomous robots, drones, and user facing applications. First, both the accuracy and the speed of ConvNets can affect the performance of the application. Second, the performance of the application can be hard to assess without evaluating ConvNets during inference. As a consequence, finding a sweet spot between the accuracy and speed via filter pruning, which needs to be done in a trial and error fashion, can be time consuming. This work takes a first step toward making this process more efficient by altering the goal of model compression to producing a set of ConvNets with various accuracy and latency trade offs instead of producing one ConvNet targeting some pre defined latency constraint. To this end, we propose to learn a global ranking of the filters across different layers of the ConvNet, which is used to obtain a set of ConvNet architectures that have different accuracy/latency trade offs by pruning the bottom ranked filters. Our proposed algorithm, LeGR, is shown to be 2x to 3x faster than prior work while having comparable or better performance when targeting seven pruned ResNet 56 with different accuracy/FLOPs profiles on the CIFAR 100 dataset. Additionally, we have evaluated LeGR on ImageNet and Bird 200 with ResNet 50 and Mo bileNetV2 to demonstrate its effectiveness. Code available at https:/github.com/cmu enyac/LeGR.
A Joint Long Short Term Memory and AdaBoost regression approach with application to remaining useful life estimation
Year: 2020

Along with wide application of sensors, multi dimensional time series data are commonly available for remaining useful life (RUL) estimation. This paper proposes a joint data driven approach that adapts two models, AdaBoost regression and Long Short Term Memory (LSTM) to estimate the RUL based on data trajectory extension. In RUL prediction, the data trajectories in the training set contain the data up to the units' failure while the data trajectories in the testing set do not. Although this fact has a significant negative effect on the accuracy of RUL estimation, it is considered by few literatures. The proposed approach adapts the LSTM to learn the time series dependencies of training data and then extend the trajectories of testing data, aiming at reducing the variance of the lengths of data trajectory between the training and testing sets. Then, the proposed approach adapts the AdaBoost regression to estimate the RUL using the extended time series data. The proposed approach is competitive with state of the art methods by demonstrating on two degradation datasets.
Aula invertida con enfoque multidisciplinar Flipped classroom with a multidisciplinary approach
Year: 2020

En la Escuela Tecnica Superior de Edificacion de la Universidad Politecnica de Madrid, el profesorado ha detectado varios problemas que estan relacionados con el aprendizaje del alumnado, entre otros nos encontramos con, el abandono de los estudios, el alto absentismo y la elevada compartimentacion de los conocimientos. En muchas ocasiones los conocimientos adquiridos por los estudiantes en una asignatura concreta, no se aplican mas alla de dicha asignatura o semestre. Con el fin de dar solucion a los problemas anteriormente mencionados, varios docentes de distintas disciplinas han preparado cooperativamente varias asignaturas mediante el modelo de ensenanza de Aula Invertida. Tras aplicar la metodologia de aula invertida propuesta en este trabajo y realizar encuestas al alumnado, se ha observado que en general los estudiantes estan contentos con la tecnica empleada. En concreto, del total de los encuestados se obtuvieron los siguientes porcentajes: el 3,5% muy en desacuerdo; el 8% en desacuerdo; el 21,5% no sabe; el 48% de acuerdo y el 19% muy de acuerdo. Por otro lado, a pesar de que el alumnado encuestado acepta la metodologia empleada existe un 31% de estudiantes que no se muestra favorable a que sea ampliada a otras asignaturas. El trabajo realizado se enclava dentro del proyecto de innovacion educativa "Conocimiento Transversal a traves de Aula Invertida" realizado por varios integrantes del Grupo de Innovacion Educativa UPM "Actitud Constructiva"  In the School of Building Engineering of the Universidad Politecnica de Madrid (UPM) lectures have identified several problems that are related to student learning. Among others we can highlight the abandonment of studies, the high absenteeism, and the high compartmentalization of knowledge. There are many situations in which the knowledge acquired by students in a specific subject is only applied to pass the subject and it is forgotten at the end of the semester. In order to solve the issues, a group of lectures from different disciplines have cooperatively prepared some subjects through the Flipped Classroom model. After applying the flipped classroom methodology proposed in this work and conducting student surveys, it has been observed that in general the students are happy with the method used. Specifically, the following percentages were obtained from the total of the respondents: 3.5% strongly disagree; 8% disagree; 21.5% do not know; 48% agree and 19% strongly agree. On the other hand, even though the surveyed students accept the methodology used, there are 31% of students who are not in favor of it being extended to other subjects. The work included in this paper were developed by several members of the "Actitud Constructiva" UPM Innovative Education Group. The work has been supported by the "Transversal Knowledge through flipped classroom" educational innovation.
Facial Emotion Recognition Abilities in Women Experiencing Eating Disorders
Venue: Psychosomatic medicine
Year: 2019

Impairments in facial emotion recognition are an underlying factor of deficits in emotion regulation and interpersonal difficulties in mental disorders and are evident in eating disorders (EDs)  We used a computerized psychophysical paradigm to manipulate parametrically the quantity of signal in facial expressions of emotion (QUEST threshold seeking algorithm) This was used to measure emotion recognition in 308 adult women (anorexia nervosa [n 61] bulimia nervosa [n 58] healthy controls [n 130] and mixed mental disorders [mixed, n 59] The M (SD) age was 22.84 (3.90) years. The aims were to establish recognition thresholds defining how much information a person needs to recognize a facial emotion expression and to identify deficits in EDs compared with healthy and clinical controls. The stimuli included six basic emotion expressions (fear, anger, disgust, happiness, sadness, surprise) plus a neutral expression.  Happiness was discriminated at the lowest, fear at the highest threshold by all groups. There were no differences regarding thresholds between groups, except for the mixed and the bulimia nervosa group with respect to the expression of disgust (F(3,302) 5.97, p .001, &eegr;2 .056) Emotional clarity, ED pathology, and depressive symptoms did not predict performance (R2Change .010, F(1,305) 5.74, p .079) The confusion matrix did not reveal specific biases in either group.  Overall, within subject effects were as expected, whereas between subject effects were marginal and psychopathology did not influence emotion recognition. Facial emotion recognition abilities in women experiencing EDs compared with women experiencing mixed mental disorders and healthy controls were similar. Although basic facial emotion recognition processes seems to be intact, dysfunctional aspects such as misinterpretation might be important in emotion regulation problems.
Dampak Karantina Wilayah Terhadap Perekonomian Indonesia
Year: 2020

Tujuan penelitian yaitu untuk mengetahui dan menganalisis menangani permasalahan perekonomian di Indonesia karena adanya virus pandemi covid 19 ini. Dengan menggunakan metode penelitian normatif dengan menganalisis kepustakaan penulis akan menganalisis bagaimana dampak penyebaran covid 19 pada perekonomian Indonesia dalam hal perdagangan dan pariwisata, serta Bagaimana solusi terhadap dampak penyebaran covid 19 dalam hal perdagangan dan pariwisata? Melalui tulisan ini kami mengajukan beberapa upaya untuk menangani permasalahan ekonomi yang di hadapi oleh Indonesia, dengan cara meningkatkan produksi dan konsumsi dalam negeri; dapat pula untuk mengantisipasi dengan cara menjaga stabilitas domestik; atau kemungkinan terburuk yaitu meminjam uang kepada IMF karena disadari bahwa hampir seluruh perusahaan menutup usahanya dan ini berlaku pula pada perekonomian seluruh dunia, karena sektor perdagangan yang tidak berjalan sebagaimana semestinya. Hal ini juga dialami oleh China. Dimana China memegang perekonomian kedua di dunia, dengan begitu otomatis seluruh negara yang bekerja sama dengan China akan terkena dampak.Dengan adanya pandemi ini yang di mulai dari China, pada Januari 2020 tercatat terjadi penurunan ekspor impor dan penurunan jumlah wisatawan yang datang ke Indonesia. Pada sektor pariwisata per Januari Indonesia telah mengalami penurunan jumlah wisatawan asing yang akan melakukan destinasi ke Indonesia. Dengan situasi seperti ini maka diperlukan adanya peran serta seluruh lapisan masyarakat untuk menghentikan permasalahan covid 19 ini, agar perekonomian di Indonesia dan dunia kembali berjalan sesuai sebagaimana mestinya