nils-herrmann/clean_scirepeval huggingface dataset:
What are the Benefits of Interacting with Nature?

There is mounting empirical evidence that interacting with nature delivers measurable benefits to people. Reviews of this topic have generally focused on a specific type of benefit, been limited to a single discipline, or covered the benefits delivered from a particular type of interaction. Here we construct novel typologies of the settings, interactions and potential benefits of people-nature experiences, and use these to organise an assessment of the benefits of interacting with nature. We discover that evidence for the benefits of interacting with nature is geographically biased towards high latitudes and Western societies, potentially contributing to a focus on certain types of settings and benefits. Social scientists have been the most active researchers in this field. Contributions from ecologists are few in number, perhaps hindering the identification of key ecological features of the natural environment that deliver human benefits. Although many types of benefits have been studied, benefits to physical health, cognitive performance and psychological well-being have received much more attention than the social or spiritual benefits of interacting with nature, despite the potential for important consequences arising from the latter. The evidence for most benefits is correlational, and although there are several experimental studies, little as yet is known about the mechanisms that are important for delivering these benefits. For example, we do not know which characteristics of natural settings (e.g., biodiversity, level of disturbance, proximity, accessibility) are most important for triggering a beneficial interaction, and how these characteristics vary in importance among cultures, geographic regions and socio-economic groups. These are key directions for future research if we are to design landscapes that promote high quality interactions between people and nature in a rapidly urbanising world.
The Impact of Business Intelligence Systems on Management Accounting Systems : The Consultant ’ s Perspective

The use of Business Intelligence (BI) and Business Analytics for supporting decision-making is widespread in the world of praxis and their relevance for Management Accounting (MA) has been outlined in non-academic literature. Nonetheless, current research on Business Intelligence systems’ implications for the Management Accounting System is still limited. The purpose of this study is to contribute to understanding how BI system implementation and use affect MA techniques and Management Accountants’ role. An explorative field study, which involved BI consultants from Italian consulting companies, was carried out. We used the qualitative field study method since it permits dealing with complex “how” questions and, at the same time, taking into consideration multiple sites thus offering a comprehensive picture of the phenomenon. We found that BI implementation can affect Management Accountants’ expertise and can bring about not only incremental changes in existing Management Accounting techniques but also more relevant ones, by supporting the introduction of new and advanced MA techniques. By identifying changes in the Management Accounting System as well as factors which can prevent or favor a virtuous relationship between BI and Management Accounting Systems this research can be useful both for consultants and for client-companies in effectively managing BI projects.
Pretense and Representation : The Origins of “ Theory of Mind ”

One of the major developments of the second year of human life is the emergence of the ability to pretend. A child's knowledge of a real situation is apparently contradicted and distorted by pretense. If, as generally assumed, the child is just beginning to construct a system for internally representing such knowledge, why is this system of representation not undermined by its use in both comprehend ing and producing pretense? In this article I present a theoretical analysis of the representational mechanism underlying this ability. This mechanism extends the power of the infant's existing capacity for (primary) representation, creating a capacity for metarepresentation. It is this, developing toward the end of infancy, that underlies the child's new abilities to pretend and to understand pretense in others. There is a striking isomorphism between the three fundamental forms of pretend play and three crucial logical properties of mental stale expressions in language. This isomorphism points to a common underlying form of internal representation that is here called metarepresentation. A performance model, the decoupler, is outlined embodying ideas about how an infant might compute the complex function postulated to underlie pretend play. This model also reveals pretense as an early manifestation of the ability to understand mental states. Aspects of later preschool development, both normal and abnormal, are discussed in the light of the new model. This theory begins the task of characterizing the specific innate basis of our commonsense “theory of mind.”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
Teacher-Student Relationships and Engagement : Conceptualizing , Measuring , and Improving the Capacity of Classroom Interactions *

Classrooms are complex social systems, and student-teacher relationships and interactions are also complex, multicomponent systems. We posit that the nature and quality of relationship interactions between teachers and students are fundamental to understanding student engagement, can be assessed through standardized observation methods, and can be changed by providing teachers knowledge about developmental processes relevant for classroom interactions and personalized feedback/support about their interactive behaviors and cues. When these supports are provided to teachers’ interactions, student engagement increases. In this chapter, we focus on the theoretical and empirical links between interactions and engagement and present an approach to intervention designed to increase the quality of such interactions and, in turn, increase student engagement and, ultimately, learning and development. Recognizing general principles of development in complex systems, a theory of the classroom as a setting for development, and a theory of change specifi c to this social setting are the ultimate goals of this work. Engagement, in this context, is both an outcome in its own R. C. Pianta , Ph.D. (*) Curry School of Education , University of Virginia , PO Box 400260 , Charlottesville , VA 22904-4260 , USA e-mail: rcp4p@virginia.edu B. K. Hamre , Ph.D. Center for Advanced Study of Teaching and Learning , University of Virginia , Charlottesville , VA , USA e-mail: bkh3d@virginia.edu  allen@virginia.edu Teacher-Student Relationships and Engagement: Conceptualizing, Measuring, and Improving the Capacity of Classroom Interactions* Robert C. Pianta , Bridget K. Hamre , and Joseph P. Allen *Preparation of this chapter was supported in part by the Wm. T. Grant Foundation, the Foundation for Child Development, and the Institute of Education Sciences. 366 R.C. Pianta et al.
Deep k-Means: Jointly Clustering with k-Means and Learning Representations

We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks are performed jointly. We propose here such an approach for k-Means clustering based on a continuous reparametrization of the objective function that leads to a truly joint solution. The behavior of our approach is illustrated on various datasets showing its efficacy in learning representations for objects while clustering them.
LEARNING NODE REPRESENTATIONS

Representation learning is one of the foundations of Deep Learning and allowed big improvements on several Machine Learning fields, such as Neural Machine Translation, Question Answering and Speech Recognition. Recent works have proposed new methods for learning representations for nodes and edges in graphs. In this work, we propose a new unsupervised and efficient method, called here Neighborhood Based Node Embeddings (NBNE), capable of generating node embeddings for very large graphs. This method is based on SkipGram and uses nodes’ neighborhoods as contexts to generate representations. NBNE achieves results comparable or better than state-of-the-art feature learning algorithms in three different datasets and, differently from our main baseline (Node2Vec), which needs to have its parameters tuned in a validation set, is completely unsupervised.
Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

We present a model to estimate motion from monocular visual and inertial measurements. We analyze the model and characterize the conditions under which its state is observable, and its parameters are identifiable. These include the unknown gravity vector, and the unknown transformation between the camera coordinate frame and the inertial unit. We show that it is possible to estimate both state and parameters as part of an on-line procedure, but only provided that the motion sequence is “rich enough,” a condition that we characterize explicitly. We then describe an efficient implementation of a filter to estimate the state and parameters of this model, including gravity and camera-to-inertial calibration. It runs in real-time on an embedded platform, and its performance has been tested extensively. We report experiments of continuous operation, without failures, re-initialization, or re-calibration, on paths of length up to 30Km. We also describe an integrated approach to “loop-closure,” that is the recognition of previously-seen locations and the topological re-adjustment of the traveled path. It represents visual features relative to the global orientation reference provided by the gravity vector estimated by the filter, and relative to the scale provided by their known position within the map; these features are organized into “locations” defined by visibility constraints, represented in a topological graph, where loop closure can be performed without the need to re-compute past trajectories or perform bundle adjustment. The software infrastructure as well as the embedded platform is described in detail in a technical report (Jones and Soatto (2009).
Audience effects: what can they tell us about social neuroscience, theory of mind and autism?

An audience effect arises when a person's behaviour changes because they believe someone else is watching them. Though these effects have been known about for over 110 years, the cognitive mechanisms of the audience effect and how it might vary across different populations and cultures remains unclear. In this review, we examine the hypothesis that the audience effect draws on implicit mentalising abilities. Behavioural and neuroimaging data from a number of tasks are consistent with this hypothesis. We further review data suggest that how people respond to audiences may vary over development, personality factors, cultural background and clinical diagnosis including autism and anxiety disorder. Overall, understanding and exploring the audience effect may contribute to our models of social interaction, including reputation management and mentalising.
Source VS Synthetic Target Source VS Source Synthetic Source VS Target Synthetic Source VS Synthetic Source

In this paper we make two contributions to unsupervised domain adaptation in the convolutional neural network. First, our approach transfers knowledge in the deep side of neural networks for all convolutional layers. Previous methods usually do so by directly aligning higherlevel representations, e.g., aligning the activations of fullyconnected layers. In this case, although the convolutional layers can be modified through gradient back-propagation, but not significantly. Our approach takes advantage of the natural image correspondence built by CycleGAN. Departing from previous methods, we use every convolutional layer of the target network to uncover the knowledge shared by the source domain through an attention alignment mechanism. The discriminative part of an image is relatively insensitive to the change of image style, ensuring our attention alignment particularly suitable for robust knowledge adaptation. Second, we estimate the posterior label distribution of the unlabeled data to train the target network. Previous methods, which iteratively update the pseudo labels by the target network and refine the target network by the updated pseudo labels, are straightforward but vulnerable to noisy labels. Instead, our approach uses category distribution to calculate the cross-entropy loss for training, thereby ameliorating deviation accumulation. The two contributions make our approach outperform the state-of-theart methods by +2.6% in all the six transfer tasks on Office31 on average. Notably, our approach yields +5.1% improvement for the challenging D→ A task.
Stigmergy as a universal coordination mechanism I: Definition and components

The concept of stigmergy has been used to analyze self-organizing activities in an ever-widening range of domains, including social insects, robotics, web communities and human society. Yet, it is still poorly understood and as such its full power remains underappreciated. The present paper clarifies the issue by defining stigmergy as a mechanism of indirect coordination in which the trace left by an action in a medium stimulates subsequent actions. It then analyses the fundamental concepts used in the definition: action, agent, medium, trace and coordination. It clarifies how stigmergy enables complex, coordinated activity without any need for planning, control, communication, simultaneous presence, or even mutual awareness. The resulting self-organization is driven by a combination of positive and negative feedbacks, amplifying beneficial developments while suppressing errors. Thus, stigmergy is applicable to a very broad variety of cases, from chemical reactions to bodily coordination and Internet-supported collaboration in Wikipedia.
Sentiment analysis using product review data

Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing). Sentiment analysis has gain much attention in recent years. In this paper, we aim to tackle the problem of sentiment polarity categorization, which is one of the fundamental problems of sentiment analysis. A general process for sentiment polarity categorization is proposed with detailed process descriptions. Data used in this study are online product reviews collected from Amazon.com. Experiments for both sentence-level categorization and review-level categorization are performed with promising outcomes. At last, we also give insight into our future work on sentiment analysis.
Image Denoising and Inpainting with Deep Neural Networks

We present a novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA). We propose an alternative training scheme that successfully adapts DA, originally designed for unsupervised feature learning, to the tasks of image denoising and blind inpainting. Our method achieves state-of-the-art performance in the image denoising task. More importantly, in blind image inpainting task, the proposed method provides solutions to some complex problems that have not been tackled before. Specifically, we can automatically remove complex patterns like superimposed text from an image, rather than simple patterns like pixels missing at random. Moreover, the proposed method does not need the information regarding the region that requires inpainting to be given a priori. Experimental results demonstrate the effectiveness of the proposed method in the tasks of image denoising and blind inpainting. We also show that our new training scheme for DA is more effective and can improve the performance of unsupervised feature learning.
Towards an integrated crowdsourcing definition

Crowdsourcing” is a relatively recent concept that encompasses many practices. This diversity leads to the blurring of the limits of crowdsourcing that may be identified virtually with any type of Internet-based collaborative activity, such as co-creation or user innovation. Varying definitions of crowdsourcing exist and therefore, some authors present certain specific examples of crowdsourcing as paradigmatic, while others present the same examples as the opposite. In this paper, existing definitions of crowdsourcing are analyzed to extract common elements and to establish the basic characteristics of any crowdsourcing initiative. Based on these existing definitions, an exhaustive and consistent definition for crowdsourcing is presented and contrasted in eleven cases.
The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances

Ant Colony Optimization (ACO)  is a recently propose d metaheuristic approach for solving hard combinatorial optimization proble ms. The inspiring source of ACO is the pheromone trail laying and following behavior o f eal ants which use pheromones as a communication medium. In analogy to the biol ogical example, ACO is based on the indirect communication of a colony of simp le agents, called (artificial) ants, mediated by (artificial) pheromone trail s. The pheromone trails in ACO serve as a distributed, numerical information which t he ants use to probabilistically construct solutions to the problem being sol ved and which the ants adapt during the algorithm’s execution to reflect their sear ch experience. The first example of such an algorithm is Ant System (AS) [29, 3 6, 7, 38], which was proposed using as example application the well kno wn Traveling Salesman Problem (TSP) [58, 74]. Despite encouraging initial res ults, AS could not compete with state-of-the-art algorithms for the TSP. Neve rtheless, it had the important role of stimulating further research on algorithmi c variants which obtain much better computational performance, as well as on applic ations to a large variety of different problems. In fact, there exists now a cons iderable amount of applications obtaining world class performance on problem s like the quadratic assignment, vehicle routing, sequential ordering, scheduli ng, routing in Internet-like networks, and so on [21, 25, 44, 45, 66, 83]. Motivated by this success, the ACO metaheuristic has been proposed [31, 32] as a common framewo rk for the existing
Power , effect and sample size using GPower : practical issues for researchers and members of research ethics committees

Since the introduction of a new UK Ethics Committee Authority (UKECA) in 2004 and the setting up of the Central Office for Research Ethics Committees (COREC), research proposals have come under greater scrutiny than ever before. The era of self-regulation in UK research ethics has ended (Kerrison and Pollock, 2005). The UKECA recognise various committees throughout the UK that can approve proposals for research in NHS facilities (National Patient Safety Agency, 2007), and the scope of research for which approval must be sought is defined by the National Research Ethics Service, which has superceded COREC. Guidance on sample size (Central Office for Research Ethics Committees, 2007: 23) requires that 'the number should be sufficient to achieve worthwhile results, but should not be so high as to involve unnecessary recruitment and burdens for participants'. It also suggests that formal sample estimation size should be based on the primary outcome, and that if there is more than one outcome then the largest sample size should be chosen. Sample size is a function of three factors – the alpha level, beta level and magnitude of the difference (effect size) hypothesised. Referring to the expected size of effect, COREC (2007: 23) guidance states that 'it is important that the difference is not unrealistically high, as this could lead to an underestimate of the required sample size'. In this paper, issues of alpha, beta and effect size will be considered from a practical perspective. A freely-available statistical software package called GPower (Buchner et al, 1997) will be used to illustrate concepts and provide practical assistance to novitiate researchers and members of research ethics committees. There are a wide range of freely available statistical software packages, such as PS (Dupont and Plummer, 1997) and STPLAN (Brown et al, 2000). Each has features worth exploring, but GPower was chosen because of its ease of use and the wide range of study designs for which it caters. Using GPower, sample size and power can be estimated or checked by those with relatively little technical knowledge of statistics. Alpha and beta errors and power Researchers begin with a research hypothesis – a 'hunch' about the way that the world might be. For example, that treatment A is better than treatment B. There are logical reasons why this can never be demonstrated as absolutely true, but evidence that it may or may not be true can be obtained by …
Behaviour of U-shaped RC walls under quasi-static cyclic diagonal loading

a Earthquake Engineering and Structural Dynamics Laboratory (EESD), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), EPFL ENAC IIC EESD, GC B2 515, Station 18, CH – 1015 Lausanne, Switzerland b Earthquake Engineering and Structural Dynamics Laboratory (EESD), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), EPFL ENAC IIC EESD, GC B2 504, Station 18, CH – 1015 Lausanne, Switzerland
Slotless PM Machines With Skewed Winding Shapes: 3-D Electromagnetic Semianalytical Model

The 3-D modeling technique presented in this paper, predicts, with high accuracy, electromagnetic fields and corresponding dynamic effects in conducting regions for rotating machines with slotless windings, e.g., self-supporting windings. The presented modeling approach can be applied to a wide variety of slotless winding configurations, including skewing and/or different winding shapes. It is capable to account for induced eddy currents in the conductive rotor parts, e.g., permanent-magnet (PM) eddy-current losses, albeit not iron, and winding ac losses. The specific focus of this paper is to provide the reader with the complete implementation and assumptions details of such a 3-D semianalytical approach, which allows model validations with relatively short calculation times. This model can be used to improve future design optimizations for machines with 3-D slotless windings. It has been applied, in this paper, to calculate fixed parameter Faulhaber, rhombic, and diamond slotless PM machines to illustrate accuracy and applicability.