nils-herrmann/clean_scirepeval huggingface dataset:
Generative Adversarial Networks with Decoder-Encoder Output Noise

In recent years, research on image generation methods has been developing fast. The auto-encoding variational Bayes method (VAEs) was proposed in 2013, which uses variational inference to learn a latent space from the image database and then generates images using the decoder. The generative adversarial networks (GANs) came out as a promising framework, which uses adversarial training to improve the generative ability of the generator. However, the generated pictures by GANs are generally blurry. The deep convolutional generative adversarial networks (DCGANs) were then proposed to leverage the quality of generated images. Since the input noise vectors are randomly sampled from a Gaussian distribution, the generator has to map from a whole normal distribution to the images. This makes DCGANs unable to reflect the inherent structure of the training data. In this paper, we propose a novel deep model, called generative adversarial networks with decoder-encoder output noise (DE-GANs), which takes advantage of both the adversarial training and the variational Bayesain inference to improve the performance of image generation. DE-GANs use a pre-trained decoder-encoder architecture to map the random Gaussian noise vectors to informative ones and pass them to the generator of the adversarial networks. Since the decoder-encoder architecture is trained by the same images as the generators, the output vectors could carry the intrinsic distribution information of the original images. Moreover, the loss function of DE-GANs is different from GANs and DCGANs. A hidden-space loss function is added to the adversarial loss function to enhance the robustness of the model. Extensive empirical results show that DE-GANs can accelerate the convergence of the adversarial training process and improve the quality of the generated images.
Quarterly Journal of Economics a Model of Reference-dependent Preferences*

We develop a model of reference-dependent preferences and loss aversion where “gain–loss utility” is derived from standard “consumption utility” and the reference point is determined endogenously by the economic environment. We assume that a person’s reference point is her rational expectations held in the recent past about outcomes, which are determined in a personal equilibrium by the requirement that they must be consistent with optimal behavior given expectations. In deterministic environments, choices maximize consumption utility, but gain–loss utility influences behavior when there is uncertainty. Applying the model to consumer behavior, we show that willingness to pay for a good is increasing in the expected probability of purchase and in the expected prices conditional on purchase. In within-day labor-supply decisions, a worker is less likely to continue work if income earned thus far is unexpectedly high, but more likely to show up as well as continue work if expected income is high.
Does Foreign Direct Investment Accelerate Economic Growth ?

195 With commercial bank lending to developing economies drying up in the 1980s, most countries eased restrictions on foreign direct investment (FDI) and many aggressively offered tax incentives and subsidies to attract foreign capital (Aitken and Harrison 1999; World Bank 1997a, 1997b). Along with these policy changes, a surge of noncommercial bank private capital flows to developing economies in the 1990s occurred. Private capital flows to emerging-market economies exceeded $320 billion in 1996 and reached almost $200 billion in 2000. Even the 2000 figure is almost four times larger than the peak commercial bank lending years of the 1970s and early 1980s. Furthermore, FDI now accounts for over 60 percent of private capital flows. While the explosion of FDI flows is unmistakable, the growth effects remain unclear. Theory provides conflicting predictions concerning the growth effects of FDI. The economic rationale for offering special incentives to attract FDI frequently derives from the belief that foreign investment produces externalities in the form of technology transfers and spillovers. Romer (1993), for example, argues that important “idea gaps” between rich and poor countries exist. He notes that foreign investment can ease the transfer of technological 8
Research on human activity recognition based on image classification methods

Human activity recognition is a significant component of many innovative and human-behavior based systems. The ability to recognize various human activities enables the developing of intelligent control system. Usually the task of human activity recognition is mapped to the classification task of images representing person’s actions. This paper addresses the problem of human activities’ classification using various machine learning methods such as Convolutional Neural Networks, Bag of Features model, Support Vector Machine and K-Nearest Neighbors. This paper provides the comparison study on these methods applied for human activity recognition task using the set of images representing five different categories of daily life activities. The usage of wearable sensors that could improve classification results of human activity recognition is beyond the scope of this research. Keywords–activity recognition; machine learning; CNN; BoF; KNN; SVM
Edge Computing-Based Intelligent Manhole Cover Management System for Smart Cities

An intelligent manhole cover management system (IMCS) is one of the most important basic platforms in a smart city to prevent frequent manhole cover accidents. Manhole cover displacement, loss, and damage pose threats to personal safety, which is contrary to the aim of smart cities. This paper proposes an edge computing-based IMCS for smart cities. A unique radio frequency identification tag with tilt and vibration sensors is used for each manhole cover, and a Narrowband Internet of Things is adopted for communication. Meanwhile, edge computing servers interact with corresponding management personnel through mobile devices based on the collected information. A demonstration application of the proposed IMCS in the Xiasha District of Hangzhou, China, showed its high efficiency. It efficiently reduced the average repair time, which could improve the security for both people and manhole covers.
The Web as a Knowledge-base for Answering Complex Questions

Answering complex questions is a timeconsuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. Conversely, semantic parsers have been successful at handling compositionality, but only when the information resides in a target knowledge-base. In this paper, we present a novel framework for answering broad and complex questions, assuming answering simple questions is possible using a search engine and a reading comprehension model. We propose to decompose complex questions into a sequence of simple questions, and compute the final answer from the sequence of answers. To illustrate the viability of our approach, we create a new dataset of complex questions, COMPLEXWEBQUESTIONS, and present a model that decomposes questions and interacts with the web to compute an answer. We empirically demonstrate that question decomposition improves performance from 20.8 precision@1 to 27.5 precision@1 on this new dataset.71
School pupils and university students surveyed for drinking beverages containing caffeine.

Caffeine is a commonly found ingredient in many beverages. Its main dietary source is coffee, cola drinks and in recent years, energy drinks.



To compare the consumption of drinks containing caffeine (coffee, colas and energy drinks) and the reasons and circumstances under which they were drunk by middle school (junior high school) pupils and university students.



Surveyed subjects were 90 middle school pupils from Warsaw and Kutno together with 100 students attending the Warsaw University of Life Sciences (SGGW). A questionnaire, designed by the authors, was used to determine the amounts, frequency and the reasons or circumstances in which coffee, colas and energy drinks were consumed. Statistics used, consisted of the Mann-Whitney U and Chi-square (chi2) tests, with significance taken as alpha < or = 0.05.



Cola drinks were found to be the most popularly consumed beverages containing caffeine; 97% pupils and 93% students. Coffee was however drunk twice less by pupils compared to students, whilst similar amounts of energy drinks were consumed by both groups; respectively 48% and 53%. Gender differences were observed for the energy drinks with young men drinking the most. Coffee and energy drink consumption also rose with age by respectively 39% and 57%. The mean caffeine intake in pupils and students were respectively estimated to be 141 and 163 mg/day(d). The reasons why these beverages were drunk varied, from drinking coffee to keeping awake and drinking cola because of its good taste. Pupils also drank energy drinks due to its taste but students because of improved mental performance and in staying awake.



Drinking caffeine containing drinks by adolescents can be very variable and comes from many different sources. Thus, its intakes may be very high and so require monitoring, particularly for the youngest. Further observational studies are needed to assess the consumption of energy drinks in relation to physical activity.
Structural Developments in Tall Buildings : Current Trends and Future Prospects

Tall building developments have been rapidly increasing worldwide. This paper reviews the evolution of tall building’s structural systems and the technological driving force behind tall building developments. For the primary structural systems, a new classification – interior structures and exterior structures – is presented. While most representative structural systems for tall buildings are discussed, the emphasis in this review paper is on current trends such as outrigger systems and diagrid structures. Auxiliary damping systems controlling building motion are also discussed. Further, contemporary “out-of-the-box” architectural design trends, such as aerodynamic and twisted forms, which directly or indirectly affect the structural performance of tall buildings, are reviewed. Finally, the future of structural developments in tall buildings is envisioned briefly.
Factors affecting teachers' use of information and communications technology: a review of the literature

This article reports on the literature associated with practising teachers’ uptake of information and communications technology (ICT). Studies reveal a number of factors which influence teachers’ decisions to use ICT in the classroom: access to resources, quality of software and hardware, ease of use, incentives to change, support and collegiality in their school, school and national polices, commitment to professional learning and background in formal computer training. The review highlights the role of pedagogy and suggests that teachers’ beliefs about teaching and learning with ICT are central to integration. It is suggested that successful implementation of ICT needs to address three interlocking frameworks for change: the teacher, the school and policy makers.
Modeling and simulation of a grid connected PV system based on the evaluation of main PV module parameters

In this work we present a new method for the modeling and simulation study of a photovoltaic grid connected system and its experimental validation. This method has been applied in the simulation of a grid connected PV system with a rated power of 3.2 Kwp, composed by a photovoltaic generator and a single phase grid connected inverter. First, a PV module, forming part of the whole PV array is modeled by a single diode lumped circuit and main parameters of the PV module are evaluated. Results obtained for the PV module characteristics have been validated experimentally by carrying out outdoor I–V characteristic measurements. To take into account the power conversion efficiency, the measured AC output power against DC input power is fitted to a second order efficiency model to derive its specific parameters. The simulation results have been performed through Matlab/Simulink environment. Results has shown good agreement with experimental data, whether for the I–V characteristics or for the whole operating system. The significant error indicators are reported in order to show the effectiveness of the simulation model to predict energy generation for such PV system.
Inventory models with lateral transshipments: A review

Lateral transshipments within an inventory system are stock movements between locations of the same echelon. These transshipments can be conducted periodically at predetermined points in time to proactively redistribute stock, or they can be used reactively as a method of meeting demand which cannot be satisfied from stock on hand. The elements of an inventory system considered, e.g. size, cost structures and service level definition, all influence the best method of transshipping. Models of many different systems have been considered. This paper provides a literature review which categorizes the research to date on lateral transshipments, so that these differences can be understood and gaps within the literature can be identified.
Cohort profile: Study of Transition, Outcomes and Gender (STRONG) to assess health status of transgender people

The Study of Transition, Outcomes and Gender (STRONG) was initiated to assess the health status of transgender people in general and following gender-affirming treatments at Kaiser Permanente health plans in Georgia, Northern California and Southern California. The objectives of this communication are to describe methods of cohort ascertainment and data collection and to characterise the study population.



A stepwise methodology involving computerised searches of electronic medical records and free-text validation of eligibility and gender identity was used to identify a cohort of 6456 members with first evidence of transgender status (index date) between 2006 and 2014. The cohort included 3475 (54%) transfeminine (TF), 2892 (45%) transmasculine (TM) and 89 (1%) members whose natal sex and gender identity remained undetermined from the records. The cohort was matched to 127 608 enrollees with no transgender evidence (63 825 women and 63 783 men) on year of birth, race/ethnicity, study site and membership year of the index date. Cohort follow-up extends through the end of 2016.


 TO DATE
About 58% of TF and 52% of TM cohort members received hormonal therapy at Kaiser Permanente. Chest surgery was more common among TM participants (12% vs 0.3%). The proportions of transgender participants who underwent genital reconstruction surgeries were similar (4%-5%) in the two transgender groups. Results indicate that there are sufficient numbers of events in the TF and TM cohorts to further examine mental health status, cardiovascular events, diabetes, HIV and most common cancers.


FUTURE PLANS
STRONG is well positioned to fill existing knowledge gaps through comparisons of transgender and reference populations and through analyses of health status before and after gender affirmation treatment. Analyses will include incidence of cardiovascular disease, mental health, HIV and diabetes, as well as changes in laboratory-based endpoints (eg, polycythemia and bone density), overall and in relation to gender affirmation therapy.
Rule Extraction using Artificial Neural Networks

Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. This paper presents an efficient algorithm to extract rules from artificial neural networks. We use two-phase training algorithm for backpropagation learning. In the first phase, the number of hidden nodes of the network is determined automatically in a constructive fashion by adding nodes one after another based on the performance of the network on training data. In the second phase, the number of relevant input units of the network is determined using pruning algorithm. The pruning process attempts to eliminate as many connections as possible from the network. Relevant and irrelevant attributes of the data are distinguished during the training process. Those that are relevant will be kept and others will be automatically discarded. From the simplified networks having small number of connections and nodes we may easily able to extract symbolic rules using the proposed algorithm. Extensive experimental results on several benchmarks problems in neural networks demonstrate the effectiveness of the proposed approach with good generalization ability.
The color lexicon of American English.

This article describes color naming by 51 American English-speaking informants. A free-naming task produced 122 monolexemic color terms, with which informants named the 330 Munsell samples from the World Color Survey. Cluster analysis consolidated those terms into a glossary of 20 named color categories: the 11 Basic Color Term (BCT) categories of Berlin and Kay (1969, p. 2) plus nine nonbasic chromatic categories. The glossed data revealed two color-naming motifs: the green-blue motif of the World Color Survey and a novel green-teal-blue motif, which featured peach, teal, lavender, and maroon as high-consensus terms. Women used more terms than men, and more women expressed the novel motif. Under a constrained-naming protocol, informants supplied BCTs for the color samples previously given nonbasic terms. Most of the glossed nonbasic terms from the free-naming task named low-consensus colors located at the BCT boundaries revealed by the constrained-naming task. This study provides evidence for continuing evolution of the color lexicon of American English, and provides insight into the processes governing this evolution.
Dual-band slotted waveguide antenna array for adaptive mm-wave 5G networks

This paper describes the design, simulation and measurement of a dual-band slotted waveguide antenna array for adaptive 5G networks, operating in the millimeter wave frequency range. Its structure is composed by two groups of slots milled onto the opposite faces of a rectangular waveguide, enabling antenna operation over two different frequency bands, namely 28 and 38 GHz. Measured and numerical results, obtained using ANSYS HFSS, demonstrate two bandwidths of approximately 26.36% and 9.78% for 28 GHz and 38 GHz, respectively. The antenna gain varies from 12.6 dBi for the lower frequency band to 15.6dBi for the higher one.
From New Public Management to Public Value: Paradigmatic Change and Managerial Implications

Both practitioners and scholars are increasingly interested in the idea of public value as a way of understanding government activity, informing policy-making and constructing service delivery. In part this represents a response to the concerns about ‘new public management’, but it also provides an interesting way of viewing what public sector organisations and public managers actually do. The purpose of this article is to examine this emerging approach by reviewing new public management and contrasting this with a public value paradigm. This provides the basis for a conceptual discussion of differences in approach, but also for pointing to some practical implications for both public sector management and public sector managers.
Discourse and the study of organization : Toward a structurational perspective

A B S T R AC T Existing approaches to organizational discourse, which we label as ‘managerialist’, ‘interpretive’ and ‘critical’, either privilege agency at the expense of structure or the other way around. This tension reflects that between approaches to discourse in the social sciences more generally but is sharper in the organizational context, where discourse is typically temporally and contextually specific and imbued with attributions of instrumental intent. As the basis for a more sophisticated understanding of organizational discourse, we draw on the work of Giddens to develop a structurational conceptualization in which discourse is viewed as a duality of communicative actions and structural properties, recursively linked through the modality of actors’ interpretive schemes. We conclude by exploring some of the theoretical implications of this conceptualization and its consequences for the methodology of organizational discourse analysis.
Are tangibles more fun?: comparing children's enjoyment and engagement using physical, graphical and tangible user interfaces

This paper presents the results of an exploratory comparative study in which we investigated the relationship between interface style and school-aged children's enjoyment and engagement while doing puzzles. Pairs of participants played with a jigsaw puzzle that was implemented using three different interface styles: physical (traditional), graphical and tangible. In order to investigate interactional differences between the three interface styles, we recorded subjective ratings of enjoyment, three related subscales, measured times and counts of behavioral based indications of engagement. Qualitative analysis based on observational notes and audio responses to open interview questions helped contextualize the quantitative findings and provided key insights into interactional differences not apparent in the quantitative findings. We summarize our main findings and discuss the design implications for tangible user interfaces.
Customer relationship management : Finding value drivers

Despite significant interest from both academicians and practitioners, customer relationship management (CRM) remains a huge investment with little measured payback. Intuition suggests that increased management of customer relationships should improve business performance, but this intuition has only inconsistent empirical or real world support. To remedy this situation, this study identifies a core group of expected CRM benefits and examines their ability to increase a firm's value equity, brand equity and relationship equity which are components of customer equity. Ten propositions explore the anticipated effects of these drivers and form an agenda for future research. These propositions establish a framework for measuring CRM and supporting the link between CRM and performance.
An Empirical Examination of Users' Switch from Online Payment to Mobile Payment

As an emerging service, mobile payment has not been widely adopted by users. Integrating both perspectives of enablers and inhibitors, this research examined user switch from online p ayment to mobile payment. Enablers include trust in mobile payment, flow and satisfaction, whereas the inhibitor is switching costs. Flow represents an optimal experience associated with using mobile payment. The results indicated that user switch receives a dual influence from both enablers and inhibitors. Among them, flow has the largest effect on switch intention. The results imply that service providers need to consider both aspects of enablers and inhibitors in order to facilitate user switch from online payment to mobile payment. An Empirical Examination of Users’ Switch from Online Payment to Mobile Payment