nils-herrmann/clean_scirepeval huggingface dataset:
On the Asymptotic Distribution of a Weighted Least Absolute Deviation Estimate for a Bifurcating Autoregressive Process
Year: 2016

This paper introduces a new class of estimates for estimating the parameter vector of a first order bifurcating autoregressive model. The estimates minimize a sum of weighted absolute deviations where the weights are of the Schweppe variety. Asymptotic linearity properties are derived for the so called WL1 estimate. Based on these properties, the WL1 estimate is shown to be asymptotically normal at rate n1/2. The results hinge on two new law of large numbers theorems for bifurcating processes. As an application of the theory, some asymptotic relative efficiency comparisons are made.
Without stronger academic governance, Covid 19 will concentrate the corporate control of academic publishing
Year: 2020

Whilst the Covid 19 pandemic has led to a short term uptick in open research practices, both in response to the virus and the need for remote access to research and teaching materials. Samuel Moore argues that the long term impact of Covid 19 and its related economic impact will likely increase the corporate control of academic publishing. Citing the need for increased scholar led forms publishing operating outside of market interests, he suggests now is the time to rethink how scholars and research organisations can constructively engage with the governance of scholarly communication.
Using theoretical calculations to predict the redox potential of mononuclear manganese complexes
Year: 2018

The rational design of biomimetic complexes of metalloenzymes involved in redox processes is of great interest in bioinorganic chemistry. Its purpose is to utilize the redox properties of mimics and use them as redox active catalysts. To obtain efficient artificial catalysts, it is necessary to model structurally and electronically the active site of the enzymes. In particular, for redox catalysis, it is necessary that the biomimetic complexes achieve a certain redox potential so their prediction is a valuable tool to improve the rational design of catalysts with specific properties. In this work we set out to predict the one electron redox potential for a series of mononuclear Mn models of Mn superoxide dismutase enzymes using density functional calculations of low computational cost. We obtained an excellent linear correlation between calculated and experimental redox potentials referenced against the ferrocene/ferrocenium (Fc/Fc+ couple, when solvation effects were included. Additionally, we validated the strategy predicting the redox potentials of two complexes synthesized in our laboratory.
Time series forecasting and data mining of tornadoes in the United States
Year: 2016

The objectives of this thesis are the time series forecasting of tornadoes in Oklahoma, estimating property damage due to tornadoes in the United States using multivariate data mining models, and generating inferences from past tornado events in the United States through data mining. Firstly, univariate time series modeling is applied to generate monthly forecasts for the estimated property damage due to the tornado event, the length of the tornado, the width of the tornado, and the tornado strike location, which is determined by the beginning latitude and beginning longitude of the tornado event. Naive forecasts, seasonal naive forecasts, trailing moving average, the Holt Winters model, exponential smoothing, linear regression, ARIMA, and artificial neural networks are the time series models investigated in this research. In each case, after training and validating different time series models, the best performing model is selected to generate monthly forecasts with prediction intervals from January 2015 to December 2015. After comparing the monthly forecasts with the true values for the year 2015, it is observed that the true values lie within the forecasted 95% prediction intervals for each time series on 59 out of 60 occasions. Secondly, data mining models, namely multiple linear regression, ridge regression, the lasso, principal components regression, partial least squares regression, generalized additive models (GAMs) decision trees, bagging, random forests and boosting are applied to the dataset of 64519 tornado events over a time period of 66 years within the United States, with the property damage as the response variable, and other features as independent variables. The primary goal is estimation and the secondary goal is inference. After the training and 10 fold cross validation of different data mining models, boosting is found to generate the most accurate estimates for property damage. The results of inference from the lasso indicate that the property damage increases by $53819 for every 0.1 mile increase in the tornado length, and by $9780 for every 1 foot increase in tornado width.
Understanding the Importance of Heart Sound Segmentation for Heart Anomaly Detection
Venue: ArXiv
Year: 2020

Traditionally, abnormal heart sound classification is framed as a three stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed. Some researchers in the field argue the segmentation step is an unwanted computational burden, whereas others embrace it as a prior step to feature extraction. When comparing accuracies achieved by studies that have segmented heart sounds before analysis with those who have overlooked that step, the question of whether to segment heart sounds before feature extraction is still open. In this study, we explicitly examine the importance of heart sound segmentation as a prior step for heart sound classification, and then seek to apply the obtained insights to propose a robust classifier for abnormal heart sound detection. Furthermore, recognizing the pressing need for explainable Artificial Intelligence (AI) models in the medical domain, we also unveil hidden representations learned by the classifier using model interpretation techniques. Experimental results demonstrate that the segmentation plays an essential role in abnormal heart sound classification. Our new classifier is also shown to be robust, stable and most importantly, explainable, with an accuracy of almost 100% on the widely used PhysioNet dataset.
A Literature Review of the Effect and Mechanism of Gestalt Feature on Consumer Behavior
Year: 2020

Gestalt feature is an important concept of Gestalt psychology, which refers to the organizational feature that can make the originally separated elements easy to be perceived as a good "whole" The core idea of Gestalt theory is that "the whole is greater than the sum of its parts" It is believed that when individuals perceive external objects, they do not passively accept external stimulus input, but rather perceive the color, texture, spatial distribution and shape of the objects to complete the preliminary visual recognition, and then proactively perceive objects with good Gestalt feature such as proximity, similarity and symmetry as a meaningful "whole" At the same time, individuals can process those objects more quickly and efficiently, and express a more positive attitude.The study of Gestalt feature originates from the visual field, but it is not limited to this domain. In recent years, a number of studies have begun to focus on the effect of Gestalt feature on consumer behavior. According to the review of the previous literature, the vectors of Gestalt feature in the consumer field can be divided into five aspects: commodity advertising, brand identity, shopping environment, commodity display, and currency use. At the same time, according to consumers' contact with these five aspects, it can be further summarized into three consumption stages. In the pre consumption stage, the Gestalt feature of commodity advertising and brand identity would have a positive effect on consumers' brand attitudes; during the mid consumption stage, consumer behavior would be affected by the Gestalt feature of shopping environment, commodity display and currency. Finally, Gestalt feature would have a continuous impact on the commodity value evaluation and quantity estimation in the post consumption stage.In addition to explaining the impact of Gestalt feature on consumer behavior, this paper also analyzes its mechanism from the perspective of attention research and processing fluency. We believe that on the one hand, those objects with Gestalt feature can give consumers more aesthetic experience which could attract more attention in the appraisal stage and maintain during the coping stage, so that consumers would have a preference for the "whole" On the other hand, the Gestalt feature of consumer objects can bring higher processing fluency, and the positive emotions caused by this fluency can further strengthen consumers' preference for the "whole".Finally, on the basis of reviewing the existing research, this paper also has an original discussion on the future research direction. Future research can further explore the antecedent and consequence variables of the effect of Gestalt feature, as well as other relevant moderators. More effective research tools could also be used in the future research.
The Influence of Product Color on Perceived Weight and Consumer Preference
Year: 2016

This research investigates the influence of color lightness on perceived product weight, perceived product attributes, and consumer preference. Three preliminary experiments provide nuance to prior findings regarding the influence of color lightness on the perceived weight of objects. Four main studies subsequently provide evidence that consumers perceive a product to be more durable if its color is dark rather than light, but more convenient if its color is light rather than dark. Both effects arise because darker products are perceived to be heavier. The findings may be especially useful for managers in turbulent business environments, to the extent that the documented phenomena and their influence on consumer behavior do not depend on specific, transient changes in these environments.
Why Use Bourdieusian Theory to Study Gender, Class and Work? The Case for 'Gender Capital'
Year: 2012

Pierre Bourdieu's primary intellectual concern was the role of culture in the production and reproduction of power and inequality. As Atkinson (2009) suggests, Bourdieu's analysis of cultural life has been widely influential and has inspired a 'cultural turn' in the theorisation of class. In particular, he moved beyond economic determinism to explore alternative forms of social and cultural wealth. In doing so he provided an understanding of class that progressed beyond stratification and focussed on practice; his approach allows an analysis of the individual and their unique, yet structured, class experiences. And while his primary intellectual concern was class, his conceptual toolkit has proven to be adaptable making it suitable for the analysis of gender, class and occupation.
Public authority commitment and action to eliminate targeted harassment and violence
Year: 2011

This report describes the first attempt at a systematic investigation of what theaforementioned public authorities in Britain are doing to eliminate targetedharassment directed at people on the grounds of age, disability, gender, race,religion or belief, sexual orientation or transgender status.An online survey was developed to enable public authorities in England, Scotlandand Wales to self report their actions and received a total of 213 completedresponses. The survey was supplemented by detailed qualitative interviews with asmall sample of authorities. It is not possible to generalise from the survey findings to give a statistically accurate picture of the situation across Britain, or make meaningful comparisons between England, Scotland and Wales. Rather, the research provides invaluable insight into public authority activity on targeted harassment for the first time.
Lifelong Mixture of Variational Autoencoders
Venue: IEEE transactions on neural networks and learning systems
Year: 2021

In this article, we propose an end to end lifelong learning mixture of experts. Each expert is implemented by a variational autoencoder (VAE) The experts in the mixture system are jointly trained by maximizing a mixture of individual component evidence lower bounds (MELBO) on the log likelihood of the given training samples. The mixing coefficients in the mixture model control the contributions of each expert in the global representation. These are sampled from a Dirichlet distribution whose parameters are determined through nonparametric estimation during lifelong learning. The model can learn new tasks fast when these are similar to those previously learned. The proposed lifelong mixture of VAE (L MVAE) expands its architecture with new components when learning a completely new task. After the training, our model can automatically determine the relevant expert to be used when fed with new data samples. This mechanism benefits both the memory efficiency and the required computational cost as only one expert is used during the inference. The L MVAE inference model is able to perform interpolations in the joint latent space across the data domains associated with different tasks and is shown to be efficient for disentangled learning representation.
Anti microbial activity of curcumin nanoformulations: New trends and future perspectives.
Venue: Phytotherapy research PTR
Year: 2020

Curcumin has been used in numerous anti microbial research because of its low side effects and extensive traditional applications. Despite having a wide range of effects, the intrinsic physicochemical characteristics such as low bioavailability, poor water solubility, photodegradation, chemical instability, short half life and fast metabolism of curcumin derivatives limit their pharmaceutical importance. To overcome these drawbacks and improve the therapeutic ability of curcuminoids, novel approaches have been attempted recently. Nanoparticulate drug delivery systems can increase the efficiency of curcumin in several diseases, especially infectious diseases. These innovative strategies include polymeric nanoparticles, hydrogels, nanoemulsion, nanocomposite, nanofibers, liposome, nanostructured lipid carriers (NLCs) polymeric micelles, quantum dots, polymeric blend films and nanomaterial based combination of curcumin with other anti bacterial agents. Integration of curcumin in these delivery systems has displayed to improve their solubility, bioavailability, transmembrane permeability, prolong plasma half life, long term stability, target specific delivery and upgraded the therapeutic effects. In this review paper, a range of in vitro and in vivo studies have been critically discussed to explore the therapeutic viability and pharmaceutical significance of the nano formulated delivery systems to elevate the anti bacterial activities of curcumin and its derivatives.
IOT based greenhouse environment monitoring and controlling system using Arduino platform
Venue: 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)
Year: 2017

Greenhouses are controlled area environment to grow plants. In order to achieve maximum plant growth, the continuous monitoring and controlling of environmental parameters such as temperature, humidity, soil moisture, light intensity, soil pH etc. are necessary for a greenhouse system. The main aim of this project is to design a simple, low cost, Arduino based system to monitor the values of environmental parameters and that are continuously updated and controlled in order to achieve optimum plant growth and yield. DHT11 sensor, Soil Moisture sensor, LDR sensor and pH sensor are the main sensors used in this project which give the exact value of temperature, humidity, water content, light intensity and soil pH respectively. All environmental parameters are sent to android mobile phone via offline and online. A GSM (Global System for Mobile communication) modem is used to send SMS (Short Message Service) which displays the present status of the environmental parameters. The SMS is sent to the user when the sensor value exceeds a defined level. All farmers can control their greenhouses from any place by knowing the status of their greenhouse parameters at any time and they can control actuators (cooling fan, exhaust fan, water pump, artificial light and motor pump) to adjust environmental parameters by sending SMS. Ethernet is also used to send the data parameters to mobile phone which eliminates the SMS charges. All environmental parameters are sent to server through Ethernet and stored in the database. So the user can monitor and control parameters through android mobile application.
Multimodal adaptive dialogue management in OwlSpeak
Year: 2019

Spoken dialogue systems are employed in human computer interaction to support the natural communication method of humans. Multimodal dialogue systems can provide an even more human like interaction for the user by utilising additional communication channels besides speech, such as gestures, gaze, or facial expression. The ability to adapt the dialogue to the task, the situation, or the user, can further improve the user experience. This work focuses on the challenges of multimodal, adaptive dialogue systems for dialogue management. A dialogue manager is the component of a dialogue system that chooses the next system action in dependence on the user action and the dialogue history. Additional input can be provided to the dialogue manager and influence its decision, thus enabling adaptation. The work at hand examines models of emotion as well as culture, and the potential of their employment in dialogue management. Following these considerations, it describes adjustments of the OwlSpeak dialogue manager that enable it to handle the contemplated adaptations. Moreover, this work illustrates the integration of the speech based OwlSpeak dialogue manager into a multimodal dialogue system. Thereafter, a user study is presented that determines the impact of the implemented adaptations discussed earlier in this work on the perceived naturalness of human computer interaction. In conclusion, the insights of this work are summarised and areas of future work proposed.
Cluster Based Control Plane Messages Management in Software Defined Flying Ad Hoc Network
Venue: Sensors
Year: 2020

Collaboration between multiple Unmanned Aerial Vehicles (UAVs) to establish a Flying Ad hoc Network (FANET) is a growing trend since future applications claim for more autonomous and rapidly deployable systems. In this context, Software Defined Networking FANET (SDN FANET separates the control and data plane and provides network programmability, which considers a centralized controller to perform all FANET control functions based on global UAV context information, such as UAV positions, movement trajectories, residual energy, and others. However, control message dissemination in an SDN FANET with low overhead and high performance is not a trivial task due to FANET particular characteristics, i.e. high mobility, failures in UAV to UAV communication, and short communication range. With this in mind, it is essential to predict UAV information for control message dissemination as well as consider hierarchical network architecture, reducing bandwidth consumption and signaling overhead. In this article, we present a Cluster bAsed control Plane messages management in sOftware defined flying ad hoc NEtwork, called CAPONE. Based on UAV contextual information, the controller can predict UAV information without control message transmission. In addition, CAPONE divides the FANET into groups by computing the number of clusters using the Gap statistics method, which is input for a Fuzzy C means method to determine the group leader and members. In this way, CAPONE reduces the bandwidth consumption and signaling overhead, while guaranteeing the control message delivering in FANET scenarios. Extensive simulations are used to show the gains of the CAPONE in terms of Packet Delivery Ratio, overhead, and energy compared to existing SDN FANET architectures.
Application of Digital Technologies, Multimedia, and Brain Based Strategies: Nurturing Adult Education and Lifelong Learning
Year: 2019

Teaching adult learners is challenging because the characteristics of adult learners and their expectations are different from children/early adolescence. Recent advances in digital technology offer various opportunities that are particularly useful in fostering adult learning by transforming traditional "live" classroom based into "virtual." This chapter aims to explore how the digital technologies affect the way the brain learns and memorizes, including cognitive, emotional, and behavioral dimensions to promote personal and professional development. First, this chapter presents the application of digital technologies that support and engage adult learners in enhancing knowledge acquisition and retention, discusses the specific engagement techniques for adult, along with the research on multimedia learning. This chapter also covered neuroscience studies related to brain based learning and strategies. The opportunities and challenges of the use of digital technology and multimedia platform to be effective learning tools for academic context and lifelong learning are also presented. Application of Digital Technologies, Multimedia, and Brain Based Strategies: Nurturing Adult Education and Lifelong Learning
Hydrogen production by partial oxidation of methane over Co based, Ni and Ru monolithic catalysts
Year: 2015

Fossil fuels which supply most of the world energy demand are depletable, and they cause greenhouse gas emissions which eventually lead to global warming and climate change. Hydrogen, a clean and versatile energy carrier, can be converted into useful forms of energy in several ways. Catalytic partial oxidation of methane is a very promising process for hydrogen and synthesis gas production, besides steam reforming of methane, the leading technology. In the present work, catalysts for partial oxidation of methane have been developed and studied in terms of structural properties and chemical performance. For this purpose Co, Co Ni, Co Ru, Co Ni Ru, and Ni catalysts loaded onto cordierite ceramic monolithic supports were prepared via modified sol gel impregnation method. The catalysts were characterized by, SEM EDS, XRD, BET, and ICP OES techniques. Activity tests of the catalysts were performed in a tubular reactor at 450 ml/min total flow rate from 600 degC to 850 degC. Co Ni Ru was the most successful catalyst, with selectivity values of 93.10% H 2 and 93.81% CO, and CH 4 conversion of 98.71% and hydrogen production efficiency of 95.89% at 850 degC. During the activity tests of this catalyst 2.13% CO 2 was present in the product stream.