nils-herrmann/clean_scirepeval huggingface dataset:
Socially connecting and socially distancing consumer choices
Year: 2014

Can people use consumption to manage their social relationships? Across three essays, this dissertation explores why and how people make consumer choices that socially connect or distance themselves from others. Essay 1 examines how motives to signal social identity and uniqueness can lead people to make choices that both connect and distance them from other members of their social group. People are often conflicted between wanting to fit in and be different. This research demonstrates how consumers simultaneously satisfy competing motives for group identification and individual uniqueness along different dimensions of choice, thus allowing them to be similar and different at the same time. Essay 2 studies how consumers' gift choices can change how socially connected their recipients feel to them. This research examines actual and hypothetical gift exchanges in real life relationships and reveals that experiential gifts (events recipients live through) make recipients feel more connected to their gift giver than material gifts (objects for the recipient to keep) regardless of whether the gift is consumed together. Experiential gifts have this connecting effect because of the greater emotion they evoke when consumed. Essay 3 investigates how the emotion that motivates gift giving can affect how connected or disconnected gift givers and recipients feel to each other. This research shows that the same situation of social inequity can elicit feelings of gratitude or guilt, and explores the downstream social consequences of gifts that say "thanks" versus "sorry." Gifts can help restore relationships, but with differential effects for gift givers and recipients. Gift givers report greater improvements in social connection when giving out of guilt, whereas recipients report greater improvements when receiving a gift given out of gratitude. By studying relationships between people, this dissertation provides a richer understanding of the role of consumption in people's social lives and offers guidance to help people foster closer relationships with others. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Marketing First Advisor Cassie Mogilner Second Advisor Leaf Van Boven
Artistic Frames: An Arts Based Study of Teachers' Experiences with Arts Integrated English Language Arts for Students with Dis/abilities
Year: 2012

ARTISTIC FRAMES: AN ARTS BASED STUDY OF TEACHERS' EXPERIENCES WITH ARTS INTEGRATED ENGLISH LANGUAGE ARTS FOR STUDENTS WITH DIS/ABILITIES by Alisha M. White This arts based, qualitative investigation focused on high school English teachers of students with learning dis/abilities (Baglieri Knopf, 2004) who used visual arts integration (Eisner, 2002) to find out how teachers experience using visual arts in English and what their experiences mean (Zoss White, 2011) in order to understand why certain experiences stood out for the teachers as being important. I framed the study theoretically with complexity theories of teaching and learning (Davis, Sumara, LuceKapler, 2008) while combining aspects of sociocultural theory (Smagorinsky, 2001; Vygotsky, 1978; Wertsch, 1991) cognitive pluralism (John Steiner, 1997) and Dewey's notion of experience (1934/1980) The teacher participants were three high school English teachers employed at an independent school for students with learning dis/abilities. A/r/tography (Irwin Springgay, 2008; Springgay, Irwin, Kind, 2005, 2008) influenced my methodology in that I created visual art to theorize the data and my experiences conducting the study. I collected data during spring and summer 2011. Data sources included participant observation and field notes (Dewalt Dewalt, 2002) photography (Coover, 2004; Harper 2000, 2002) teachers' visual texts (La Jevic Springgay, 2008) artifacts (Prior, 2003) and interviews (Smagorinsky, 2008; Smagorinsky Coppock, 1994) I used qualitative methods of coding analysis (Charmaz, 2006; Ezzy, 2002; Saldana, 2009) and visual analysis (Riessman, 2008; Rose, 2001) as well as arts based methods for educational research (Cahnmann Taylor Siegesmund, 2008) This study fills a gap in empirical research in both English education and special education by examining English teachers integrating art in classes for students with dis/abilities. Furthermore, understanding how teachers experience visual arts integration can inform methods courses for teaching secondary English educators.  Visual arts integration, arts based learning strategies, secondary English language arts, teaching students with dis/abilities, teacher experience, meaning construction, a/r/tography, visual research methods, arts based educational research, qualitative research ARTISTIC FRAMES: AN ARTS BASED STUDY OF TEACHERS' EXPERIENCES WITH ARTS INTEGRATED ENGLISH LANGUAGE ARTS FOR STUDENTS WITH DIS/ABILITIES by Alisha M. White
HCAHF: A New Family of CA based Hash Functions
Year: 2019

Cryptographic hash functions (CHF) represent a core cryptographic primitive. They have application in digital signature and message authentication protocols. Their main building block are Boolean functions. Those functions provide pseudo randomness and sensitivity to the input. They also help prevent and lower the risk of attacks targeted at CHF. Cellular automata (CA) are a class of Boolean functions that exhibit good cryptographic properties and display a chaotic behavior. In this article, a new hash function based on CA is proposed. A description of the algorithm and the security measures to increase the robustness of the construction are presented. A security analysis against generic and dedicated attacks is included. It shows that the hashing algorithm has good security features and meet the security requirements of a good hashing scheme. The results of the tests and the properties of the CA used demonstrate the good statistical and cryptographic properties of the hash function.
Analisis de instrumentos de medicion del pensamiento critico
Year: 2017

Critical thinking is a relevant ability nowadays in higher education, but there is a lack of consensus on its definitions and assessment instruments. This article offers a conceptual and methodological review about the instruments used to measure critical thinking, in order to generate a discussion that allows a better understanding and appreciation of the aspects that are considered in this skill. The methodology consisted of a systematic review of studies in databases, selecting 31 studies out of 97 founded, and 8 of them for deep analysis; atopic content analysis in definitions and test characteristics is also used. Results show the existence of many differences in definitions and assessments of critical thinking, with a variety of tests and little consensus on the measured components. The need of an integrative model of Critical Thinking, considering cognitive, metacognitive and dispositional skills is discussed.
Learning 2 opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
Venue: ACML
Year: 2020

Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to improve solutions and achieve state of the art performance. However, few studies have focused on improvement heuristics, where a given solution is improved until reaching a near optimal one. In this work, we propose to learn a local search heuristic based on 2 opt operators via deep reinforcement learning. We propose a policy gradient algorithm to learn a stochastic policy that selects 2 opt operations given a current solution. Moreover, we introduce a policy neural network that leverages a pointing attention mechanism, which unlike previous works, can be easily extended to more general k opt moves. Our results show that the learned policies can improve even over random initial solutions and approach near optimal solutions at a faster rate than previous state of the art deep learning methods.
A novel battery thermal management system coupling with PCM and optimized controllable liquid cooling for different ambient temperatures
Year: 2020

In order to improve the working performance of the lithium ion battery pack in continuous operation under different ambient temperatures, a coupled composite phase change material and liquid cooling thermal management system is proposed. The simulation for this system under a cycle that a 3C rate discharging and then a 0.5C charging was conducted, as well as comparison tests concerning factors such as cell to cell spacing, cell to tube distance, channel number and coolant velocity. Simulation results showed that the coupled system with suitable design exhibited good thermal performance at an ambient temperature of 30 degC, which kept the maximum surface temperature and the temperature difference of the battery pack at 41.1 degC and 4 degC at the end of 3C discharge. Then, the latent heat of phase change material was also recovered by the liquid cooling during the 0.5C charge. Specially designed experiments have also been conducted to verify the effectiveness and practicability of the proposed coupled system. Based on this system, a liquid cooling strategy was proposed for controlling the velocity and inlet temperature of coolant by monitoring the temperature of the phase change material and environment. This further improved the thermal performance of the battery pack during cycling at different ambient temperatures and significantly reduced the unnecessary power consumption of liquid cooling during this process.
Pre Service Science Teachers' Perceptions of Self Regulated Learning in Physics
Year: 2013

The purpose of this study was to examine the effects of gender and academic achievement scores on pre service science teachers' self reported use of motivational strategies and learning strategies. The present study also investigated the relationship between motivational strategies and learning strategies. Data were collected by using Turkish version of Motivated Strategies for Learning Questionnaire (MSLQ) and personal information sheet during 2010 2011 spring semester. MSLQ which was originally developed as a self report questionnaire, comprised of two main dimensions as motivation and learning strategies including 81 items with a 7 point Likert scale format. The present study was designed as cross sectional. A total 104 pre service science teachers voluntarily participated in the study. Results revealed that pre service science teachers who had higher academic achievement scores in physics, also had higher self efficacy for learning beliefs. It was also found that there were no statistically significant mean differences among motivational beliefs, self regulated learning variables with respect to gender. Lastly, the subdimensions in the motivational and learning strategies sections were found to be positively correlated as specified by the theory.
Study on solar absorptance and thermal stability of solid particles materials used as TES at high temperature on different aging stages for CSP applications
Venue: Solar Energy Materials and Solar Cells
Year: 2019

The use of solid particles as heat transfer fluid (HTF) presents a great potential to overcome drawbacks addressed in commercial Concentrated Solar Power (CSP) plants. The solid particles thermal energy storage (TES) system allows achieving both high thermal performance at high temperature and low cost from the material perspective. The conversion efficiency of CSP solid particles based systems at high temperatures strongly depends on the optical properties and thermophysical properties of materials used both as HTF and as storage medium. The present study is aimed to provide more experimental data and evidences of the potential in using particulate solids for CSP application. The solar absorptance and the specific heat capacity of silicon carbide (SiC) silica sand (SiO2) and hematite (Fe2O3) are studied after different aging times at 750 degC and 900 degC. The solar absorptance slightly increases over the aging process except for the silica sand, which decreases its absorptance in the first 100 h, reaching a plateau. After the aging treatment, the specific heat capacity is increased for both SiC and silica sand. However, for the iron oxide the specific heat capacity is lower after aging. The black silicon carbide SiC is proven to be the best option to be used up to 900 degC as it shows the highest solar absorptance (96% and the highest heat storage capacity.
The Economics of Teaching in India Versus Teaching Aboard: Teacher Salary Differentials Using Purchasing Power Parity (PPP)
Venue: Journal of International Migration and Integration
Year: 2019

This study investigates the salary structure of teachers in India, compared to equivalent salary structures in four prominent migrating countries. Our study makes comparisons using a purchasing power parity (PPP) index and allows the identification of real differences in salaries for our selected countries (India, USA, UK, Canada, and the United Arab Emirates) for selected teaching categories. Results reveal that the incentive for teachers working in India to ply their trade abroad remains strong regardless of career experience. This potentially threatens India's ability to retain teachers, thereby compromising the ability to meet ambitious education targets.
Diseno de estrategia educativa para modificar estilos de vida en adultos jovenes con hipertension arterial. Sector Guayaquil, Centro de Salud Pedro Carbo. Ano 2018.
Year: 2018

La hipertension arterial esta identificada como una de las enfermedades mas frecuentes del mundo y a la vez es una de las primeras causas basicas de morbilidad y mortalidad en la poblacion, ademas es el principal factor de riesgo para el accidente cerebro vascular y la enfermedad coronaria.  Disenar una estrategia educativa para modificar estilos de vida en pacientes adultos jovenes con hipertension arterial del sector Guayaquil, Centro de salud Pedro Carbo en el ano 2018.  Se realizo un estudio observacional, descriptivo, transversal, el universo estuvo constituido por 81 adultos jovenes hipertensos del sector Guayaquil. Centro de salud Pedro Carbo ano 2018. Se aplico una encuesta confeccionada por la autora de la investigacion para evaluar el nivel de conocimiento sobre factores de riesgo de hipertension arterial, alimentacion saludable, actividad fisica, habitos toxicos y disenar una estrategia educativa.  Se evidencio que el grupo de edad que predomino fue el de 35 39 anos con un 51,9% El 59,3% estuvo representado por el sexo masculino. El 29,6% de los participantes tienen nivel de escolaridad primaria. El 66,7% de estado union libre. El 82,7% se autoidentificaron como mestizos y el 22,2% fueron amas de casa. El nivel de conocimiento de hipertension arterial fue alto en un 63,0% de los encuestados, el nivel de conocimiento fue bajo en 55,6% para los factores de riesgo de hipertension. La estrategia quedo conformada mediante charlas quincenales, de 30 minutos de duracion.  La mayoria de los adultos jovenes mostraron un bajo nivel de conocimiento sobre factores de riesgo para hipertension arterial, a pesar de ello, un alto porcentaje estan conscientes que deben mejorar su estilo de vida para tener una mejor calidad de vida.
Normal distributions transform Monte Carlo localization (NDT MCL)
Venue: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
Year: 2013

Industrial applications often impose hard requirements on the precision of autonomous vehicle systems. As a consequence industrial Automatically Guided Vehicle (AGV) systems still use high cost infrastructure based positioning solutions. In this paper we propose a map based localization method that fulfills the requirements on precision and repeatability, typical for industrial application scenarios. The proposed method Normal Distributions Transform Monte Carlo Localization (NDT MCL) is based on a well established probabilistic framework. In a novel contribution, we formulate the MCL localization approach using the Normal Distributions Transform (NDT) as an underlying representation for both map and sensor data. By relaxing the hard discretization assumption imposed by grid map models and utilizing the piece wise continuous NDT representation the proposed algorithm achieves substantially improved accuracy and repeatability. The proposed NDT MCL algorithm is evaluated using offline data sets from both a laboratory and a real world industrial environments. Additionally, we report a comparison of the proposed algorithm to grid based MCL and to a commercial localization system when used in a closed loop with the control system of an AGV platform. In all tests the proposed algorithm is demonstrated to provide performance superior to that of standard grid based MCL and comparable to the performance of the commercial infrastructure based positioning system.
The L2 Motivational Self System
Year: 2019

Despite the fact that learners' identities are thought to be shaped by foreign/second (L2) language learning, the emergence of self related issues in L2 motivation research is relatively recent. The aim of the present chapter is to summarize our knowledge about one particular self theory in relation to L2 learning motivation, that is, the L2 Motivational Self System (L2MSS) (Dornyei, The psychology of the language learner: Individual differences in second language acquisition. Mahwah,  2005) and its components: the ideal L2 self, the ought to L2 self and L2 learning experience. I start by describing the emergence of the model and introducing empirical investigations about the verification of constructs pertaining to the model. Next, I present research that has been conducted on the model, paying special attention to the interdisciplinary nature of the relevant studies, to the concept of motivation as vision, and to contextual issues, multilingualism, and the model's potential links to dynamic systems theory. Finally, future research directions are suggested by taking into account the strength of the model and the shortcomings of previous research.
Constrained waveform design for dual functional MIMO radar Communication system
Venue: Signal Process.
Year: 2020

This paper designs waveform for dual functional multiple input multiple output (MIMO) radar communication system. Reducing multi user interference (MUI) energy can improve the sum rate of communication, while a proper transmit beampattern can improve the target detection performance of radar. Considering the above facts, a weighted objective function is developed to design dual functional waveform, which suppresses the MUI energy and makes the synthesized transmit beampattern approach to our desire. Besides, similarity and constant modulus constraints are enforced to ensure the pulse compression performance and unimodular property of designed waveform. The resulted optimization problem is quartic and non convex. To tackle it, we develop an iterative algorithm based on the framework of block successive upper bound minimization (BSUM) The proposed algorithm, monotonic and convergent, requires for low computational complexity, which benefits from the careful derivation to the upper bound of objective function. Numerical simulations first analyze the convergence performance of the proposed algorithm and then comprehensively evaluate the performance of the designed waveform in terms of communication sum rate, transmit beampattern and pulse compression performance.
Collecting Speech Data using Amazon's Mechanical Turk for Evaluating Voice Search System
Year: 2011

This paper describes a crowd sourcing method to collect speech data using Amazon's Mechanical Turk (MTurk) We designed a task (HIT) to collect speech data as an evaluation set for voice search and another task to verify the quality of the collected speech data. More than a thousand utterances are collected very efficiently. It turned out that more than 90% of them are valid with correct transcript, and reasonable recognition accuracy is achieved. Using the data, we conducted evaluation of the voice book search system, and confirmed that the combination of slot based vector space models provides higher search accuracy than the conventional single vector space model.
AutoAssign: Differentiable Label Assignment for Dense Object Detection
Venue: ArXiv
Year: 2020

In this paper, we propose an anchor free object detector with a fully differentiable label assignment strategy, named AutoAssign. It automatically determines positive/negative samples by generating positive and negative weight maps to modify each location's prediction dynamically. Specifically, we present a center weighting module to adjust the category specific prior distributions and a confidence weighting module to adapt the specific assign strategy of each instance. The entire label assignment process is differentiable and requires no additional modification to transfer to different datasets and tasks. Extensive experiments on MS COCO show that our method steadily surpasses other best sampling strategies by \sim 1\ AP with various backbones. Moreover, our best model achieves 52.1\ AP, outperforming all existing one stage detectors. Besides, experiments on other datasets, \emph{e.g. PASCAL VOC, Objects365, and WiderFace, demonstrate the broad applicability of AutoAssign.
Time Series Transductive Classification on Imbalanced Data Sets: An Experimental Study
Venue: 2014 22nd International Conference on Pattern Recognition
Year: 2014

Graph based semi supervised learning (SSL) algorithms perform well on a variety of domains, such as digit recognition and text classification, when the data lie on a low dimensional manifold. However, it is surprising that these methods have not been effectively applied on time series classification tasks. In this paper, we provide a comprehensive empirical comparison of state of the art graph based SSL algorithms with respect to graph construction and parameter selection. Specifically, we focus in this paper on the problem of time series transductive classification on imbalanced data sets. Through a comprehensive analysis using recently proposed empirical evaluation models, we confirm some of the hypotheses raised on previous work and show that some of them may not hold in the time series domain. From our results, we suggest the use of the Gaussian Fields and Harmonic Functions algorithm with the mutual k nearest neighbors graph weighted by the RBF kernel, setting k 20 on general tasks of time series transductive classification on imbalanced data sets.
A novel poly (glycine) biosensor towards the detection of indigo carmine: A voltammetric study.
Venue: Journal of food and drug analysis
Year: 2018

The electrochemical behavior of indigo carmine (IC) at poly (glycine) modified carbon paste electrode (PGMCPE) was investigated by cyclic and differential pulse voltammetry. The oxidation peak of IC was observed in phosphate buffer of pH 6.5. The influence of different pH, scan rate, and concentration were analyzed. The probable reaction mechanism involved in the oxidation of IC was also proposed. Results showed that PGMCPE a remarkable electrocatalytic activity for the oxidation of IC under optimal conditions. The electrocatalytic response of the sensor was proportional to the IC concentration in the range of (2 x 10 6 1 x 10 5 M) and (1.5 x 10 5 6 x 10 5 M) with a limit of detection 11 x 10 8 M and limit of quantification 3.6 x 10 7 M. The modified electrode demonstrated many advantages such as simple preparation, high sensitivity, low detection of limit, excellent catalytic activity, short response time, and remarkable antifouling property toward IC and its oxidation product.