nils-herrmann/clean_scirepeval huggingface dataset:
Effects of local conditions on the multi variable and multi objective energy optimization of residential buildings using genetic algorithms
Year: 2020

The energy requalification of existing buildings entails the fulfillment of different, often conflicting, criteria, such as the reduction of the specific annual energy demand, the containment of the construction costs, the decrease in the annual energy operating cost and the reduction of climate change gas emissions. Therefore, optimization methods based on the application of computational algorithms are essential to determine solutions that meet multi objective criteria and so highly optimized to be on the Pareto frontier. In this work, a procedure for the optimization of existing buildings using genetic algorithms is presented. Building energy simulations conducted in the dynamic regime using EnergyPlus are coupled with an Active Archive Non dominated Sorting Genetic Algorithm (aNSGA II type) Using a residential building as a benchmark, this procedure is employed to evaluate the best retrofitting interventions for 19 European cities with different climates. The criteria taken into account in the optimization procedure are: the reduction in the annual specific energy demand, the decrease in the construction and installation costs, the reduction in the annual energy operating costs and the reduction in the greenhouse gas emissions. The results show the most advantageous energy retrofitting interventions fulfilling the criteria for the different geographical sites.
Six novel 6R metamorphic mechanisms induced from three series connected Bennett linkages that vary among classical linkages
Year: 2021

Metamorphic mechanisms are capable of changing their mobility by changing the number of effective joints and links under geometrical constraints. In this paper, a new approach in constructing 6R metamorphic mechanisms is proposed and demonstrated by a class of new mechanisms with bifurcated motion. By connecting three Bennett linkages in series and modifying geometrical parameters of the constructed mechanism, six novel metamorphic mechanisms are obtained. Those new mechanisms exhibit bifurcated motion among classical linkages such as the Bennett linkage, the Goldberg 5R linkage, and variant serial Goldberg 6R linkages. Fundamental geometrical constraints are derived with a salient kink angle being defined in the mechanism constructing and modeling. Further, the paper reveals in details the branch transformation and the graph representation of these metamorphic mechanisms.
The impact of e service quality and customer satisfaction on customer behavior in online shopping
Venue: Heliyon
Year: 2019

The purpose of this study is to develop new knowledge to better understand the most important dimensions of e service quality that have impact on customer satisfaction, customer trust, and customer behavior, building on existing literature on e service quality in online shopping. This study focuses on the four dimensions of e service quality model that better predict customer behavior. It not only tests the impact of customer satisfaction on customer behavior such as repurchase intention, word of mouth, and site revisit, but also the impact of customer trust. The result is expected to extend the knowledge about different country culture vis a vis different relevance of e service quality attributes. Data from an online survey of 355 Indonesian online consumers was used to test the research model using structural equation modelling. The analytical results showed that three dimensions of e service quality, namely website design, security/privacy and fulfilment affect overall e service quality. Meanwhile, customer service is not significantly related to overall e service quality. Overall e service quality is statistically significantly related to customer behavior. Future research should consider a variety of product segments and/or other industries to make sure that the measurement works equally well. In other industry setting, the measurement may need to be adjusted. Future research could also use different methodologies such as focus group and interviews.
Effective Cross lingual Transfer of Neural Machine Translation Models without Shared Vocabularies
Venue: ACL
Year: 2019

Transfer learning or multilingual model is essential for low resource neural machine translation (NMT) but the applicability is limited to cognate languages by sharing their vocabularies. This paper shows effective techniques to transfer a pretrained NMT model to a new, unrelated language without shared vocabularies. We relieve the vocabulary mismatch by using cross lingual word embedding, train a more language agnostic encoder by injecting artificial noises, and generate synthetic data easily from the pretraining data without back translation. Our methods do not require restructuring the vocabulary or retraining the model. We improve plain NMT transfer by up to +5.1% BLEU in five low resource translation tasks, outperforming multilingual joint training by a large margin. We also provide extensive ablation studies on pretrained embedding, synthetic data, vocabulary size, and parameter freezing for a better understanding of NMT transfer.
State Estimation in Visual Inertial Autonomous Helicopter Landing Using Optimisation on Manifold
Venue: ArXiv
Year: 2019

Autonomous helicopter landing is a challenging task that requires precise information about the aircraft states regarding the helicopters position, attitude, as well as position of the helipad. To this end, we propose a solution that fuses data from an Inertial Measurement Unit (IMU) and a monocular camera which is capable of detecting helipads position in the image plane. The algorithm utilises manifold based nonlinear optimisation over preintegrated IMU measurements and reprojection error in temporally uniformly distributed keyframes, exhibiting good performance in terms of accuracy and being computationally feasible. Our contributions of this paper are the formal address of the landmarks Jacobian expressions and the adaptation of equality constrained Gauss Newton method to this specific problem. Numerical simulations on MATLAB/Simulink confirm the validity of given claims.
Beyond counting: point supervised attention guided neural network for crowded object locating
Venue: Electronics Letters
Year: 2019

Recent advances in convolutional neural networks have achieved prominent results for object counting or locating in images. In these networks, some adopt detection based methods which require providing bounding box annotations for training and these methods usually cannot deal with the crowded cases well. Other networks adopt density regression based methods which require only point annotations for training but these methods can only predict the total count without providing any location information. In this Letter, the authors propose an attention guided convolutional neural network (AGNet) for both crowded object locating and counting using point annotation as supervision without annotated bounding boxes which are far more time consuming to label. Accordingly, they define a multi task objective loss function to lead the training of AGNet to achieve the goal of crowded object locating. The experimental results on four challenging benchmark datasets for crowded objects counting demonstrate the superiority of the proposed AGNet over recent state of the art approaches.
miR 125a and miR 34a expression predicts Richter syndrome in chronic lymphocytic leukemia patients.
Venue: Blood
Year: 2018

Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is characterized by the accumulation of CD19+/CD5+ lymphocytes and can have variable outcomes. Richter syndrome (RS) is a lethal complication in CLL patients that results in aggressive B cell lymphomas, and there are no tests to predict its occurrence. Because alterations in microRNA expression can predict the development and progression of several cancers, we investigated whether dysregulation of specific microRNAs can predict RS in CLL patients. Thus, we compared microRNA expression levels in samples from 49 CLL patients who later developed RS with samples from 59 CLL patients who did not. We found that high expression of miR 125a 5p or low expression of miR 34a 5p can predict ~50% of RS with a false positive rate of ~9% We found that CLL patients predicted to develop RS show either an increase of miR 125a 5p expression ~20 fold) or a decrease of miR 34a 5p expression ~21 fold) compared with CLL patients that are not predicted to develop RS. Thus, miR 125a 5p and miR 34a 5p can be valuable predictor markers of RS and have the potential to provide physicians with information that can indicate the best therapeutic strategy for CLL patients.
Influencia del tipo de feedback utilizado en el aprendizaje de una tarea motriz de equilibrio
Year: 2019

espanolEl feedback que aportan los docentes de Educacion Fisica puede favorecer el aprendizaje de los estudiantes en tareas motrices. La importancia de conocer la influencia del tipo de feedback a utilizar parece clave para programar con exito las tareas a realizar. Sin embargo, actualmente la literatura existente no es capaz de advertir que tipo de feedback es el mas efectivo para el aprendizaje de una tarea motriz en adolescentes. El objetivo del presente estudio fue analizar la influencia de tres tipos de feedback terminal verbal: i) sobre el resultado, ii) sobre el procedimiento o iii) sobre ambos (procedimiento resultado) en el aprendizaje de una tarea motriz. La muestra estuvo compuesta por 30 alumnos/as de primer ciclo de la Educacion Secundaria Obligatoria con edades comprendidas entre los 13 y 14 anos (divididos aleatoriamente en los 3 grupos) Las mediciones de los datos se realizaron durante el pretest, postest y prueba de retencion. Los resultados mostraron que los adolescentes que recibieron feedback sobre el procedimiento, obtuvieron unas puntuaciones mas bajas en Raiz Media Cuadratica (p EnglishDepending on the type of feedback provided by physical education teachers, students' motor learning might be enhanced or not. The importance of understanding the influence of types of feedback seems to be essential to organize any motor learning process appropriately. Currently, it is not clear which type of feedback is the most effective for adolescents' motor learning. The purpose of this study was to examine students' balance motor learning according to the type of terminal verbal feedback provided (i.e. feedback on result, feedback on process, or both) Thirty secondary school students aged between 13 and 14 years old (randomly distributed into 3 groups) participated in the study. Data collection was carried out using a balance motor task involving postural control on an unstable Wii Balance platform. Measurements were conducted at pretest, posttest, and follow up. The results showed that students who received the terminal verbal feedback on the process presented lower Root Mean Squared (p
Perspectives on influencing aspects for students' acceptance of multimedia materials in training programs
Year: 2015

This study was carried out in order to improve the understanding and learning of participants enrolled in face to face training programs, and to enhance their learning and retention of content through multimedia materials. A qualitative study was conducted to inquire about the perception of the participants and instructors of an Administrative training program. Through observation, interviews and a survey, the data was collected to analyze and interpret; it provided the knowledge to identify five perceived influencing aspects on the acceptance of multimedia in training programs, relating to comprehension, perspective of educational technology, beliefs and requirements of multimedia materials and academic performance. Such diagnosis provides a way to improve the instructional design of training courses; also designing multimedia materials that meet the expectations of future users and create competitive advantage by implementing multimedia materials, these would make a big difference from other courses.
Latitudinal variation in behaviors linked to risk tolerance is driven by nest site competition and spatial distribution in the ant Temnothorax rugatulus
Venue: Behavioral Ecology and Sociobiology
Year: 2015

Geographic range has long been noted to be associated with many organismic and ecological traits such as body size and species richness. However, much less is known about whether and how ecological variation across latitudinal gradients reflects behavioral variation. Ant colonies may also show behavioral variation, and Temnothorax rugatulus show a colony level behavioral syndrome that seems to reflect risk tolerance across their North American range. While it is presumed that this pattern is the result of adaptation to local environmental conditions, which ecological factors are driving this variation are unknown. Here, we test if colony risk tolerance is affected by competition, predation, resource availability, or environmental stress at each site. Our results show that increased competition, specifically for nest sites, as well as increased spatial clustering of colonies predicts higher risk tolerance. Additionally, the spatial clustering of colonies influences the structure of the risk taking syndrome, i.e. which colony level behaviors are correlated and how strongly. This emphasizes the need for understanding large scale geographic variation in behavior, as it may explain how ecological factors drive the evolution and maintenance of intraspecific behavioral variation across populations.
Sample Efficient Interactive End to End Deep Learning for Self Driving Cars with Selective Multi Class Safe Dataset Aggregation
Venue: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2019

The objective of this paper is to develop a sample efficient end to end deep learning method for self driving cars, where we attempt to increase the value of the information extracted from samples, through careful analysis obtained from each call to expert driver's policy. End to end imitation learning is a popular method for computing self driving car policies. The standard approach relies on collecting pairs of inputs (camera images) and outputs (steering angle, etc. from an expert policy and fitting a deep neural network to this data to learn the driving policy. Although this approach had some successful demonstrations in the past, learning a good policy might require a lot of samples from the expert driver, which might be resource consuming. In this work, we develop a novel framework based on the Safe Dataset Aggregation (safe DAgger) approach, where the current learned policy is automatically segmented into different trajectory classes, and the algorithm identifies trajectory segments/classes with the weak performance at each step. Once the trajectory segments with weak performance identified, the sampling algorithm focuses on calling the expert policy only on these segments, which improves the convergence rate. The presented simulation results show that the proposed approach can yield significantly better performance compared to the standard Safe DAgger algorithm while using the same amount of samples from the expert.
A Framework for Quantifying Qualitative Responses in Pairwise Experiments
Venue: J. Classif.
Year: 2019

Suppose an experiment is conducted on pairs of objects with outcome response a continuous variable measuring the interactions among the pairs. Furthermore, assume the response variable is hard to measure numerically but we may code its values into low and high levels of interaction (and possibly a third category in between if neither label applies) In this paper, we estimate the interaction values from the information contained in the coded data and the design structure of the experiment. A novel estimation method is introduced and shown to enjoy several optimal properties including maximum explained variance in the responses with minimum number of parameters and for any probability distribution underlying the responses. Furthermore, the interactions have the simple interpretation of correlation (in absolute value) size of error is estimable from the experiment, and only a single run of each pair is needed for the experiment. We also explore possible applications of the technique. Three applications are presented, one on protein interaction, a second on drug combination, and the third on machine learning. The first two applications are illustrated using real life data while for the third application, the data are generated via binary coding of an image.
HEMODIYALIZ HASTALARINDA SOSYAL DESTEK VE UMUTSUZLUK ARASINDAKI ILISKININ DEGERLENDIRILMESI
Year: 2010

Bu calisma hemodiyaliz hastalarinin algiladiklari sosyal destek ile umutsuzluklari arasindaki iliskiyi belirlemek amaciyla yurutulmustur. Veriler sosyodemografik ozellikleri belirleyen bir anket, 'Cok Boyutlu Algilanan Sosyal Destek Olcegi' (MSPSS) 'Beck Umutsuzluk Olcegi' (BUO) ile 136 hastadan elde edilmistir. Verilerin degerlendirilmesinde yuzdelik, Kruskall Wallis (KW) Mann Whitney U (MWU) t testi ve korelasyon analizi kullanilmistir. Hastalarin aileden algiladiklari sosyal destek puan ortalamalari 23.3+ 4.8, arkadaslardan 15.5+ 6.9, hemsirelerden 22+ 8.1, toplam destek puan ortalamasi 59.3+ 8, umutsuzluk puan ortalamasi ise 8.8+ 5.1 olarak bulunmustur. Aileden algilanan sosyal destek ile umutsuzluk arasinda negatif yonde iliski saptanmistir (r. 269, p 0.01) Arastirma sonucunda hemsirelerden ve arkadastan algilanan sosyal destek puani, aileden algilanan sosyal destek puanindan dusuk olarak bulunmustur. Sonuclar ailelerin hastalar uzerinde onemli role sahip olduklarini ve ailelerin hastalara uygun yaklasimda bulunabilmeleri icin profesyonel saglik ekibi tarafindan desteklenmeleri ve egitilmeleri gerektigini gostermektedir.
Smart farming and short food supply chains: Are they compatible?
Year: 2020

The challenge of sustainability and the need to secure the production of high quality, affordable and healthy food, have led to the emergence of alternative food production/distribution schemes that, based on technological or organizational innovation, can increase food production without burdening the environment. Both smart farming and short food supply chains (SFSCs) are considered as promising solutions towards this target. From a theoretical standpoint, the introduction of smart farming technologies into SFSCs could increase the value generating capacity of short food supply schemes. However, a pivotal question is whether such technologies are compatible with SFSCs. In this study, following a mixed research design, we analyze Greek farmers' and consumers' perceptions of the compatibility between smart technologies and SFSCs, and we examine the extent to which compatibility perception affects willingness to engage in "smart SFSCs." Quantitative results revealed that perceived (in)compatibility is central in predicting this willingness for both farmers and consumers. The qualitative strand of the study uncovered the existence of two different types of compatibility. Actual compatibility refers to the consistency of smart technologies with the technological advancement of farms and the real everyday needs of farmers. Symbolic compatibility relates to the meanings attributed to both SFSCs and smart technologies by farmers and consumers. In sum, the results indicated that smart technologies are viewed as tools that can lead to a conventionalization of SFSCs, thus altering their optimally distinct nature. Policies targeted at the promotion of smart farming should go beyond traditional views of smart technologies as tools that increase farm efficiency, by paying more attention to their compatibility with different "agricultures" and to the ways they can transform farming systems.
Industrial Policy in the 21st Century
Year: 2020

Industrial policy is back at the centre stage of policy debate, while the world is undergoing dramatic transformations. This article contributes to the debate by developing a new theory of industrial policy, incorporating some issues that have been neglected so far and taking into account the recent changes in economic reality. The authors explore how the incorporation of some of the neglected issues commitments under uncertainty, learning in production, macroeconomic management (especially demand management) and conflict management changes the theory. They then examine how the theory of industrial policy should be modified in light of recent changes in economic reality: the rise of the global value chain, financialization and new imperialism. This contribution aims at promoting a pragmatic approach to industrial policy and pointing to new areas for policy intervention in a changing world.
The Corporate Social Responsibility (CSR) in the Travel Supply Chain: A Literature Review
Year: 2020

Traditional travel agencies and tour operators are recognized as relevant stakeholders in the tourism distribution chain, even though their role as transforming agents in the achievement of more sustainable tourism is beginning to be accepted. This study aims at reviewing the main topics and the most recent approaches from the academic literature in its analysis of corporate social responsibility (CSR) practices developed by these intermediaries. The work has been structured around six recurring themes in most of the studies carried out: the new intermediaries that operate on the Internet; the role of travel agencies and tour operators in tourism sustainability; the influence of the size of the company; CSR as a disruptive innovation in this sector; the potential of the sustainable tourism supply chain; and the motivations and attitudes of the managers of these businesses when they engage with responsibility. This last topic is developed in greater detail since it is closely linked to the previous ones and dictates, in most cases, the meaning and function of the sustainable performance. The conclusions show how tourism intermediaries have only just begun to develop CSR policies, and those that are committed to their implementation mostly opt for actions aimed at the environment.
Coz: finding code that counts with causal profiling
Venue: SOSP
Year: 2015

Improving performance is a central concern for software developers. To locate optimization opportunities, developers rely on software profilers. However, these profilers only report where programs spent their time: optimizing that code may have no impact on performance. Past profilers thus both waste developer time and make it difficult for them to uncover significant optimization opportunities. This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts, and quantifies their potential impact. Causal profiling works by running performance experiments during program execution. Each experiment calculates the impact of any potential optimization by virtually speeding up code: inserting pauses that slow down all other code running concurrently. The key insight is that this slowdown has the same relative effect as running that line faster, thus "virtually" speeding it up. We present Coz, a causal profiler, which we evaluate on a range of highly tuned applications: Memcached, SQLite, and the PARSEC benchmark suite. Coz identifies previously unknown optimization opportunities that are both significant and targeted. Guided by Coz, we improve the performance of Memcached by 9% SQLite by 25% and accelerate six PARSEC applications by as much as 68% in most cases, these optimizations involve modifying under 10 lines of code.