nils-herrmann/clean_scirepeval huggingface dataset:
What Lies Behind Teaching and Learning Green Chemistry to Promote Sustainability Education? A Literature Review
Venue: International journal of environmental research and public health
Year: 2020

In this qualitative study, we aim to identify suitable pedagogical approaches to teaching and learning green chemistry among college students and preservice teachers by examining the teaching methods that have been used to promote green chemistry education (GCE) and how these methods have supported green chemistry learning (GCL) We found 45 articles published in peer reviewed scientific journals since 2000 that specifically described teaching methods for GCE. The content of the articles was analyzed based on the categories of the teaching methods used and the revised version of Bloom's taxonomy. Among the selected articles, collaborative and interdisciplinary learning, and problem based learning were utilized in 38 and 35 articles, respectively. These were the most frequently used teaching methods, alongside a general combination of multiple teaching methods and teacher presentations. Developing collaborative and interdisciplinary learning skills, techniques for increasing environmental awareness, problem centered learning skills, and systems thinking skills featuring the teaching methods were seen to promote GCL in 44, 40, 34, and 29 articles, respectively. The results showed that the integration of green chemistry teaching (GCT) e.g. with sustainable education, promoted GCL by fostering environmental consciousness and behavioral change and cognitive processes in a sustainable direction.
The Deadweight Scale
Year: 2012

The deadweight scale provides a method for estimating the additional draft or for determining the extra load that can be taken onboard when a vessel is being loaded in water of density less than that of salt water. This deadweight scale displays readings for freeboard, deadweight in salt water and in fresh water, draft of the ship (mean) displacement in salt water and in fresh water, tonnes per cm (TPC) in salt water and in fresh water, and moment to change trim 1 cm (MCTC) The use of the deadweight scale is illustrated with a worked example.
Racial Differences in the Returns to Financial Literacy Education
Year: 2019

We examine financial literacy and the returns to financial literacy education, specifically focusing on the racial financial literacy gap. We confirm evidence that whites have higher financial literacy scores relative to minorities and that financial literacy increases with participation in financial literacy education. However, we find the benefit of participation in financial literacy education is higher for whites than that for minorities. Thus, the impact of being white alone persists, indicating a racial financial literacy and/or behavioral difference despite financial literacy education. Our findings have implications for policymakers interested in narrowing the racial wealth gap via financial literacy education.
CLIP Q: Deep Network Compression Learning by In parallel Pruning Quantization
Venue: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018

Deep neural networks enable state of the art accuracy on visual recognition tasks such as image classification and object detection. However, modern deep networks contain millions of learned weights; a more efficient utilization of computation resources would assist in a variety of deployment scenarios, from embedded platforms with resource constraints to computing clusters running ensembles of networks. In this paper, we combine network pruning and weight quantization in a single learning framework that performs pruning and quantization jointly, and in parallel with fine tuning. This allows us to take advantage of the complementary nature of pruning and quantization and to recover from premature pruning errors, which is not possible with current two stage approaches. Our proposed CLIP Q method (Compression Learning by In Parallel Pruning Quantization) compresses AlexNet by 51 fold, GoogLeNet by 10 fold, and ResNet 50 by 15 fold, while preserving the uncompressed network accuracies on ImageNet.
Think Pair Share with Formative Assessment for Junior High School Student
Year: 2017

Geometry is a science related to abstract thinking ability so that not many students are able to understand this material well. In this case, the learning model plays a crucial role in improving student achievement. This means that a less precise learning model will cause difficulties for students. Therefore, this study provides a quantitative explanation of the Think Pair Share learning model combined with the formative assessment. This study aims to test the Think Pair Share with the formative assessment on junior high school students. This research uses a quantitative approach of Pretest Posttest in control group and experiment group. ANOVA test and Scheffe test used to analyse the effectiveness this learning. Findings in this study are student achievement on the material geometry with Think Pair Share using formative assessment has increased significantly. This happens probably because this learning makes students become more active during learning. Hope in the future, Think Pair Share with formative assessment be a useful learning for teachers and this learning applied by the teacher around the world especially on the material geometry.
A use of the central limit theorem to obtain a classical limit for the center of mass tomogram
Year: 2009

We investigate the dependence of the center of mass tomogram of a system with many degrees of freedom $N$ on the Planck constant \hbar It is shown that to use the central limit theorem under taking the limit $N\to \infty one should keep the energy of the system to be constant. In the case, the resulting distribution is Gaussian if the initial distribution is a product of independent excited states of a quantum oscillator or even and odd coherent states either. Then, if one turns the Planck constant \hbar \to 0$ we get \delta function associated with the distribution concentrated in zero with the probability equal to one.
Fantasy Football (Soccer) Playing and Internet Addiction Among Online Fantasy Football Participants: A Descriptive Survey Study
Venue: International Journal of Mental Health and Addiction
Year: 2020

The aim of the present study was to ascertain the levels of possible internet addiction within fantasy football (FF) (soccer) participants and the characteristics of the participants within this group. An online survey of questions regarding characteristics of regular FF participants and consumption of FF related content was posted on FF internet forums (Reddit and Boards.ie) Self selecting participants (N 684) completed the survey containing questions on FF (time spent during weekdays/weekend on FF, gambling on FF, devices used to access FF) internet use (time spent on internet on weekdays/weekends) and an internet addiction screening questionnaire (Chen Internet Addiction Scale) Subgroup analysis was performed on each variable by nationality (Irish, UK and worldwide) Of the 684 participants, 17.5% (diagnostic) and 24.9% (screening) participants met criteria for internet addiction, above the expected level in the general population. The most frequent time spent on FF during weekdays was 30 60 min per day (32.2% and 1 2 h per day on weekends (29.1% Over half of participants (50.6% gambled on FF with the majority (61.3% gambling once per year and 74.3% of participants gambling less than EUR50 per year on FF. Avid FF participants demonstrated an increased likelihood of internet addiction compared prevalence rates of previous epidemiological studies among different cohorts. This may be due to FF itself and the increased consumption of FF related content. Further large scale nationally representative studies are required to compare regular and casual participants of FF in relation to possible internet addiction.
The Effect of Prosody on Distributional Learning in 12 to 13 Month Old Infants
Year: 2012

Distributional information is a potential cue for learning syntactic categories. Recent studies demonstrate a developmental trajectory in the level of abstraction of distributional learning in young infants. Here we investigate the effect of prosody on infants' learning of adjacent relations between words. Twelve to thirteen month old infants were exposed to an artificial language comprised of 3 word sentences of the form aXb and cYd, where X and Y words differed in the number of syllables. Training sentences contained a prosodic boundary between either the first and the second word or the second and the third word. Subsequently, infants were tested on novel test sentences that contained new X and Y words and also contained a flat prosody with no grouping cues. Infants successfully discriminated between novel grammatical and ungrammatical sentences, suggesting that the learned adjacent relations can be abstracted across words and prosodic conditions. Under the conditions tested, prosody may be only a weak constraint on syntactic categorization.
Chapter 2 Game Analytics The Basics
Year: 2012

Overview of important key terms in game analytics Introduction to game telemetry as a source of business intelligence. In depth description and discussion of user derived telemetry and metrics. Introduction to feature selection in game analytics Introduction to the knowledge discovery process in game analytics References to essential further reading. 1 Analytics a new industry paradigm Developing a profitable game in today's market is a challenging endeavor. Thousands of commercial titles are published yearly, across a number of hardware platforms and distribution channels, all competing for players' time and attention, and the game industry is decidedly competitive. In order to effectively develop games, a variety of tools and techniques from e.g. business practices, project management to user testing have been developed in the game industry, or adopted and adapted from other IT sectors. One of these methods is analytics, which in recent years has decidedly impacted the game industry and game research environment. Analytics is the process of discovering and communicating patterns in data, towards solving problems in business or conversely predictions for supporting enterprise decision management, driving action and/or improving performance. The meth odological foundations for analytics are statistics, data mining, mathematics, programming and operations research, as well as data visualization in order to communicate insights learned to the relevant stakeholders. Analytics is not just the querying and reporting of BI data, but rests on actual analysis, e.g. statistical analysis, predic tive modeling, optimization, forecasting, etc. (Davenport and Harris, 2007) Analytics typically relies on computational modeling. There are several branches or domains of analytics, e.g. marketing analytics, risk analytics, web analytics and game analytics. Importantly, analytics is not the same thing as data analysis. Analytics is an umbrella term, covering the entire methodology of finding and communicating patterns in data, whereas analysis is used for individual applied instances, e.g. running
Mechanistic investigation on tuning the conductivity type of cuprous oxide (Cu2O) thin films via deposition potential
Year: 2018

The conductivity type of cuprous oxide (Cu2O) thin films is tuned by controlling the deposition potential of an electrochemical process in an acid cupric acetate solution containing sodium dodecyl sulfate. The morphology and chemical composition of the deposited Cu2O films are studied by SEM, XRD and XPS. The change of the conductivity type of Cu2O films is further studied through zero bias photocurrent and Mott Schottky measurements. The results indicate that the Cu2O films behave as n type semiconductors when the overpotentials are low (potentials higher than 0.05 V) and p type semiconductors when the overpotentials are high (potentials lower than 0.10 V) The transformation of conductivity from n type to p type comes from the competition reactions between forming Cu2O and forming metallic Cu from Cu2+ When the potential is lower than 0.10 V, most of Cu2+ are consumed by the growth of metallic Cu at the film/solution interface, so that the Cu2+ provided to grow Cu2O film are insufficient and copper vacancies form in the film, leading to the p type conductivity.
LANGUAGE ONLINE: INVESTIGATING DIGITAL TEXTS AND PRACTICES. Barton David and Lee Carmen. New York, NY: Routledge, 2013. Pp. x 224.
Venue: Studies in Second Language Acquisition
Year: 2014

The online world has become a new territory for the exploration of language acquisition and development. In their book, Barton and Lee aim at two types of audiences: linguists and social scientists interested in language as situated practice. The authors examine language users' dynamic participation in online activity and computer mediated discourse and examine central concepts such as language choice, identity, stance, multimodality, multilingualism, and discourses of language learning. This book explores multilingual speakers who use instant messaging (IM) Flickr, and Facebook. Across spaces, users may take on multiple identities of who they are and how they wish to be seen online. The book highlights how users, in terms of metalinguistic awareness, can adopt either language learner or language teacher identities when they provide explanations, translations, or comments. The authors' work indicates that language improvement is achieved through active participation in online spaces and social networks, and users may develop vernacular practices online together with more socially shared practices offl ine. Barton and Lee propose mixed methods to research language learning online: multiple case studies through interviews, collection of chat logs, surveys, and techno biographies; the use of methods that draw on online technologies is particularly encouraged. They argue that researchers interested in online practices and learning should become inside users of the tools of the communities under scrutiny, and their research methods should embrace online instruments such as online interviewing and online surveys. Although the book does not offer sophisticated explanations in terms of the participating users' acquisition of language as a system, readers will fi nd careful representations of online language as social practice based on perceptions and observations. IM, Flickr, and Facebook as well as other online spaces are under constant evolution, and therefore they had already changed at the time of publication, but the study undertaken by Barton and Lee may still be seen as an invitation to explore online language through research methods from a variety of disciplines and carried out both onand offl ine.
Normative Influences on Adolescents' Self Reported Pro Environmental Behaviors: The Role of Parents and Friends
Year: 2019

Pro environmental behavioral patterns are influenced by relevant others' actions and expectations. Studies about the intergenerational transmission of environmentalism have demonstrated that parents play a major role in their children's pro environmental actions. However, little is known about how other social agents may shape youth's environmentalism. This cross sectional study concentrates on the role that parents and peers have in the regulation of 12 to 19 year olds' pro environmental behaviors. We also consider the common response bias effect by examining the associations between parents, peers, and adolescents' pro environmentalism in two independent data sets. Data Set 1 (N 330) includes adolescents' perceptions of relevant others' behaviors. Data Set 2 (N 152) includes relevant others' self reported pro environmental behavior. Our results show that parents' and peers' descriptive and injunctive norms have a direct effect on adolescents' pro environmental behavior and an indirect one, through personal norms. Adolescents seem to be accurate in the perception of their close ones' environmental actions.
Indigenous Entrepreneurship Research: Themes and Variations
Year: 2006

The purpose of this paper is to offer an overview of the current study of indigenous entrepreneurship. First, while there is broad agreement on the application of the term "indigenous," there are differences of emphasis and outright controversies about empirical description of indigenous people, especially concerning the role of ownership and private property in their culture and traditions. Second, the concept of entrepreneurship is as controversial in this field as elsewhere in management studies. There are fundamental disagreements as to how flexible the requirements of entrepreneurship are, and whether true indigenous entrepreneurship can transform entrepreneurship into an authentic and distinctive form. Third, the concept of indigenous entrepreneurship as a total concept is open to debate and discussion. Not only does it inherit the question of whether the notion of entrepreneurship can be culturally transformed, there is also a difference of approach concerning the location and ultimate goals of indigenous entrepreneurship. Fourth, there are a number of critical discussion points related to indigenous populations, and in turn, their relationship to entrepreneurial activities and enterprises. These include, but are not limited to, the pursuit of multiple goals, including social objectives; the notion of collective organization, ownership and outcomes; and a population's association with the land, characteristically leading to a high degree of environmental sensitivity, drawing on traditional knowledge and fostered by a sense of spiritual connection with the land and its resources. The theme of partnerships involving indigenous enterprises with other indigenous enterprises and non indigenous bodies, is recognized as a vital topic demanding further attention.
Continuous PPG Based Blood Pressure Monitoring Using Multi Linear Regression
Year: 2020

In this work, we present the Senbiosys blood pressure monitoring algorithm (SB BPM) that solely requires a photoplethysmography (PPG) signal. The technology is based on pulse wave analysis (PWA) of PPG signals retrieved from different body locations to continuously estimate the systolic blood pressure (SBP) and the diastolic blood pressure (DBP)
Common Mode EMI Noise Suppression for Bridgeless PFC Converters
Venue: IEEE Transactions on Power Electronics
Year: 2008

The goal of this paper is to study the possibility of minimizing common mode (CM) noise emission in bridgeless power factor correction (PFC) converters. Two approaches are proposed. In the first approach, the bridgeless PFC is modified to achieve symmetry. A CM noise model for symmetric topology is derived and the conditions for symmetry are summarized. Parasitics critical to the symmetrical condition are studied and carefully controlled. As a result, CM noise can be minimized with good cancellation. The second approach is to introduce a balance technique to bridgeless PFC converters. The topology is modified so that the balance technique can be applied so as to minimize CM noise. Experimental results validate that both approaches can greatly reduce CM noise up to 30 dBmuV. The two approaches are compared in terms of both its effects on CM noise and their implementations.
Neural Tangent Kernel Empowered Federated Learning
Year: 2021

Federated learning (FL) is a privacy preserving paradigm where multiple participants jointly solve a machine learning problem without sharing raw data. Unlike traditional distributed learning, a unique characteristic of FL is statistical heterogeneity, namely, data distributions across participants are different from each other. Meanwhile, recent advances in the interpretation of neural networks have seen a wide use of neural tangent kernel (NTK) for convergence and generalization analyses. In this paper, we propose a novel FL paradigm empowered by the NTK framework. The proposed paradigm addresses the challenge of statistical heterogeneity by transmitting update data that are more expressive than those of the traditional FL paradigms. Specifically, sample wise Jacobian matrices, rather than model weights/gradients, are uploaded by participants. The server then constructs an empirical kernel matrix to update a global model without explicitly performing gradient descent. We further develop a variant with improved communication efficiency and enhanced privacy. Numerical results show that the proposed paradigm can achieve the same accuracy while reducing the number of communication rounds by an order of magnitude compared to federated averaging.
A review of failure modes, condition monitoring and fault diagnosis methods for large scale wind turbine bearings
Year: 2020

Large scale wind turbine bearings including main bearings, gearbox bearings, generator bearings, blade bearings and yaw bearings, are critical components for wind turbines to convert kinetic wind energy into electrical energy. Unlike general purpose industrial bearings, the loads and rotation speeds of wind turbine bearings change considerably because of dynamic wind flows. In the case of some extreme operating conditions, large scale wind turbine bearings suffer excessive loads and they can be potentially damaged. Therefore, it is essential to develop reliable and cost effective condition monitoring and fault diagnosis methods to assess the damage level and failure modes so that a proper maintenance plan can be designed. This paper aims at systematically and comprehensively summarizing current large scale wind turbine bearing failure modes and condition monitoring and fault diagnosis achievements. Firstly, the representative failure modes of large scale wind turbine bearings are reviewed in detail which can help to understand the causes and effects of the tribological issues of these bearings. Then, condition monitoring and fault diagnosis methods of large scale wind turbine bearings are presented; within which failure modes, experimental scale and signal processing approaches are summarized. Finally, a number of popular condition monitoring and fault diagnosis approaches that can be potentially used for wind turbine bearings are reviewed, followed by a brief summary of future research directions for wind turbine bearing fault diagnosis.
Influence Function based Data Poisoning Attacks to Top N Recommender Systems
Venue: WWW
Year: 2020

Recommender system is an essential component of web services to engage users. Popular recommender systems model user preferences and item properties using a large amount of crowdsourced user item interaction data, e.g. rating scores; then top N items that match the best with a user's preference are recommended to the user. In this work, we show that an attacker can launch a data poisoning attack to a recommender system to make recommendations as the attacker desires via injecting fake users with carefully crafted user item interaction data. Specifically, an attacker can trick a recommender system to recommend a target item to as many normal users as possible. We focus on matrix factorization based recommender systems because they have been widely deployed in industry. Given the number of fake users the attacker can inject, we formulate the crafting of rating scores for the fake users as an optimization problem. However, this optimization problem is challenging to solve as it is a non convex integer programming problem. To address the challenge, we develop several techniques to approximately solve the optimization problem. For instance, we leverage influence function to select a subset of normal users who are influential to the recommendations and solve our formulated optimization problem based on these influential users. Our results show that our attacks are effective and outperform existing methods.