nils-herrmann/clean_scirepeval huggingface dataset:
Cooperative robotic networks for underwater surveillance: an overview
Year: 2017

Underwater surveillance has traditionally been carried out by means of surface and undersea manned vessels equipped with advanced sensor systems. This approach is often costly and manpower intensive. Marine robotics is an emerging technological area that enables the development of advanced networks for underwater surveillance applications. In contrast with the use of standard assets, these advanced networks are typically composed of small, low power, and possibly mobile robots, which have limited endurance, processing and wireless communication capabilities. When deployed in a region of interest, these robots can cooperatively form an intelligent network achieving high performance with significant features of scalability, adaptability, robustness, persistence and reliability. Such networks of robots can be the enabling technology for a wide range of applications in the maritime domain. However, they also introduce new challenges for underwater distributed sensing, data processing and analysis, autonomy and communications. The main thrust of this study is to review the underwater surveillance scenario within a framework of four research areas: (i) underwater robotics, (ii) acoustic signal processing, (iii) tracking and distributed information fusion, and (iv) underwater communications networks. Progress in each of these areas as well as future challenges is presented.
Optimization of Slotted Liner in Rubiales Field: Trade Off between Sand Control, Flow Performance, and Plugging Tendency
Year: 2020

Designing and selecting the proper sand control mechanism for horizontal wells in unconsolidated heavy oil reservoirs tend to be underlooked in some cases. Standalone completions pose some sand control challenges, which could jeopardize the oil production or even lead to critical problems. Massive sand production, screen/formation plugging, hot spots, and mechanical integrity failures are some of the well known issues. This study attempts to optimize the slotted liner design for horizontal wells in a heavy oil field in Colombia. A careful selection of representative core data was made to study the variation of sand particle size distribution (PSD) within the development area. Reservoir fluid properties were analyzed. Based on PSD variation and current design criteria in the industry, several seamed slotted liner configurations were proposed as an alternative completion for testing. Later, a series of large scale sand retention tests (SRTs) were performed to assess the selected alternatives under typical field production conditions. The effects of aperture size and open to flow area were investigated to evaluate flow and sand control performance. This investigation started with a detailed study of the PSD, particle shape variation, and composition of fines in the development area. The PSD then classified into four distinct minor and major sand facies, ranging from medium to very coarse sand with different fines content. Further investigations have shown that current design is only suitable for a limited number of the PSDs, while the overall PSD classes indicate the requirement for wider slot aperture sizes. The results of the SRTs indicated that the flow performance of the screen is mainly controlled by the slot aperture. Choosing the optimized aperture size avoids unacceptable sanding even for the multiphase flow scenarios with gas. Results also indicated that by increasing the aperture size and application of the seamed slots for the studied formation, plugging could be mitigated. A comprehensive sand control design workflow for cold primary heavy oil production in horizontal wells is presented in this work. The current study is one of the first that investigates and compares conventional straight slotted liners with seamed slotted liners at a larger scale for this field. Moreover, this study helps to better understand the effect of design parameters of seamed slotted liners on sand control, flow performance, and plugging tendency.
[Pandemics, Politics and Science: the Role of Science and Scientifics for the Solution of the COVID 19 Pandemic Conflicts]
Venue: Cuadernos de bioetica revista oficial de la Asociacion Espanola de Bioetica y Etica Medica
Year: 2020

The public health crisis caused by COVID 19 disease has bring again to the public arena the old debate of the role in decision making process of politics and science. In this paper we analyze, in the context of the current pandemics, how politics has left a stellar role for science, either because science has anticipated in its proposals to politics, or because politics has opted for it as a premeditated strategy. The references to the opinion of scientists as a decision making criterion or, furthermore, their presence as true spokesmen for authority have been so frequent. This means rediscovering science and health as instruments of power, in terms that remind us of what Foucault denounced almost fifty years ago with his Biopolitics.
Selfitis Behavior: Assessing the Italian Version of the Selfitis Behavior Scale and Its Mediating Role in the Relationship of Dark Traits with Social Media Addiction
Venue: International journal of environmental research and public health
Year: 2020

Research on selfie related behavior has recently flourished. The present study expands theoretical and empirical work on phenomenon by assessing the psychometric properties of the Selfitis Behavior Scale among an Italian sample and by examining its unexplored mediating role in the relationships between dark triad traits and social media addiction. A total of 490 participants (53.1% females) completed a self report survey including socio demographics, the Selfitis Behavior Scale (SBS) the Short Dark Triad Scale (SD3) and the Bergen Social Media Addiction Scale (BSMAS) Results showed the SBS had a five factor structure with good psychometrics properties in terms of reliability coefficients and measurement invariance across gender. In addition, findings from the path model supported the mediating role of selfitis behavior in the relationships of narcissism and psychopathy with social media addiction. Machiavellianism was found to be unrelated to selfitis behavior and social media addiction. The model shed light into the previous inconsistent findings on the associations between dark triad traits and social media addiction by taking into account the key role of selfitis behavior as an underlying mechanism. The findings may explain individual differences in personality traits associated with co dependence (i.e. the combination of the dependence on self and others and social media addiction)
Hierarchical Reinforcement Learning With Monte Carlo Tree Search in Computer Fighting Game
Venue: IEEE Transactions on Games
Year: 2019

Fighting games are complex environments where challenging action selection problems arise, mainly due to a diversity of opponents and possible actions. In this paper, we present the design and evaluation of a fighting player on top of the FightingICE platform that is used in the Fighting Game Artificial Intelligence (FTGAI) competition. Our proposal is based on hierarchical reinforcement learning (HRL) in combination with Monte Carlo tree search (MCTS) designed as options. By using the FightingICE framework, we evaluate our player against state of the art FTGAIs. We train our player against the current FTGAI champion (GigaThunder) The resulting learned policy is comparable with the champion in direct confront in regard to the number of victories, with the advantage of having less need for expert knowledge. We also evaluate the proposed player against the runners up and show that adaptation to the strategies of each opponent is necessary for building stronger fighting players.
Auxin and proline producing rhizobacteria mitigate salt induced growth inhibition of barley plants by enhancing water and nutrient status
Year: 2020

Although inoculation with plant growth promoting rhizobacteria (PGPR) can improve crop salt tolerance, it is not always clear how these beneficial microbes affect plant ionic and water status. To investigate this phenomenon, three salt tolerant bacteria were used to inoculate barley plants cultivated under salt stress. The bacterial strains Bacillus mojavensis S1, B. pumilus S2 and Pseudomonas fluorescens S3 showed an increase in IAA and proline production when cultivated on 100 and 200 mM NaCl supplemented medium. A salt sensitive barley cultivar Rihane was grown at 0 and 100 mM of NaCl, with or without bacterial inoculation. Salt treatment decreased shoot dry weight of uninoculated plants by about 25% In uninoculated plants, salinity decreased leaf water potential 0.8 MPa, increased roots and shoots Na+ concentrations by approximately 59% and 89% respectively and increased proline concentration. Bacterial inoculation with strains S1 and S3 prevented salt induced reduction of shoot dry weights respectively by 54% and 46% and root dry weights by almost 40% These two bacterial strains enhanced stomatal conductance and CO2 assimilation of plants subjected or not to salt. Under saline conditions, all bacterial strains decreased root and shoot Na+ concentrations, improved leaf water potential and the strain S1 maintained it close to control values. Salinity stress was mainly alleviated in barley plants after inoculation with S1 and S3 strains, having respectively high proline and IAA productions under 200 mM of NaCl, which contribute to the alleviation of salt stress and stimulate root development, enhancing water and nutrient uptake by plants. Future prospects of the PGP potential of halotolerant bacterial species will extend their application in agriculture.
A Hybrid Model Based on Multi Stage Principal Component Extraction, GRU Network and KELM for Multi Step Short Term Wind Speed Forecasting
Venue: IEEE Access
Year: 2020

Accurate wind speed forecasting exerts a critical role in energy conversion and management of wind power. In term of this purpose, a hybrid model based on multi stage principal component extraction, kernel extreme learning machine (KELM) and gated recurrent unit (GRU) network is developed in this paper, where the multi stage principal component extraction combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) singular spectrum analysis (SSA) and phase space reconstruction (PSR) Firstly, CEEMDAN is employed to decompose the raw wind speed data into a sequence of intrinsic mode functions (IMFs) and a residual component. Then the principal components and residual components of all IMFs are captured by SSA. Meanwhile, all residual components obtained by CEEMDAN decomposition and SSA processing are added to form a new component. Subsequently, PSR is utilized to construct each forecasting component obtained by CEEMDAN SSA into the input and output of training set and testing set for the prediction model. Later, KELM and GRU neural network are conducted to predict the high frequency and low frequency components, respectively. Eventually, the prediction values of each component are accumulated to acquire the final prediction result. To evaluate the performance of the proposed model, four datasets from Sotavento Galicia wind farm are adopted to conduct experimental research. The experimental results manifest that the proposed model achieves higher accuracy of multi step prediction than other comparative models.
NF2 Inhibits Proliferation and Cancer Stemness in Breast Cancer
Venue: Open medicine
Year: 2020

Previous studies have shown that NF2 plays a key role in tumorigenesis. NF2 has been illustrated to be downregulated in several types of human cancer. However, the role of NF2 in breast cancer remains unclear.  We used UALCAN and KM plotter database to study NF2 expression in human breast cancer and corresponding normal tissues and analyzed its relationship with clinicopathological parameters. We investigated the role of NF2 in breast cancer cells behavior by inhibiting its expression in MDA MB 231 and MCF 7 cells.  In this study, we found that NF2 was downregulated in breast cancer tissues compared to the adjacent normal tissues. We found that the low expression of NF2 was related with the tumor stage. NF2 overexpression inhibited the cell colon formation and stemness.  Our results indicate a role of NF2 in the progression of breast cancer.
Metacognitive Reading Strategies, Motivation, and Reading Comprehension Performance of Saudi EFL Students
Year: 2016

Metacognitive reading strategies and reading motivation play a significant role in enhancing reading comprehension. In an attempt to prove the foregoing claim in a context where there is no strong culture for reading, this study tries to find out if there is indeed a relationship between and among metacognitive reading strategies, reading motivation, and reading comprehension performance. Prior to finding out relationships, the study tried to ascertain the level of awareness and use of metacognitive reading strategies of the respondents when they read English academic texts, their level of motivation and reading interests, and their overall reading performance. Using descriptive survey and descriptive correlational methods with 60 randomly selected Saudi college level EFL students in an all male government owned industrial college in Saudi Arabia, the study found out that the respondents moderately use the different metacognitive reading strategies when reading academic texts. Of the three categories of metacognitive reading strategies, the Problem Solving Strategies (PROB) is the most frequently used. It was also revealed that the respondents have high motivation to read. They particularly prefer to read humor/comic books. On the level of reading comprehension performance, the respondents performed below average. Using t test, the study reveals that there is no correlation between metacognitive reading strategies and reading comprehension. There is also no correlation between reading interest/motivation and reading comprehension. However, there is positive correlation between reading strategies and reading motivation. The findings of this study interestingly contradict previous findings of most studies, thus invite more thorough investigation along the same line of inquiry.
Structured Pruning of Neural Networks With Budget Aware Regularization
Venue: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019

Pruning methods have shown to be effective at reducing the size of deep neural networks while keeping accuracy almost intact. Among the most effective methods are those that prune a network while training it with a sparsity prior loss and learnable dropout parameters. A shortcoming of these approaches however is that neither the size nor the inference speed of the pruned network can be controlled directly; yet this is a key feature for targeting deployment of CNNs on low power hardware. To overcome this, we introduce a budgeted regularized pruning framework for deep CNNs. Our approach naturally fits into traditional neural network training as it consists of a learnable masking layer, a novel budget aware objective function, and the use of knowledge distillation. We also provide insights on how to prune a residual network and how this can lead to new architectures. Experimental results reveal that CNNs pruned with our method are more accurate and less compute hungry than state of the art methods. Also, our approach is more effective at preventing accuracy collapse in case of severe pruning; this allows pruning factors of up to 16x without significant accuracy drop.
Reframing the mammoth steppe: Insights from analysis of isotopic niches
Venue: Quaternary Science Reviews
Year: 2019

Woolly mammoth (Mammuthus primigenius) horse (Equus spp. and bison (Bison spp. coexisted with a variety of mammalian megafauna across the Pleistocene mammoth steppe a megacontinental ecosystem that spanned northern Eurasia and northwestern North America. Previous research has suggested that highly conserved niches with minimal niche overlap allowed high levels of species diversity on the mammoth steppe. Here we evaluate previously published and some new collagen carbon and nitrogen isotope data (d13C, d15N) for mammoth steppe megaherbivores using Stable Isotope Bayesian Ellipses in R (SIBER) and linear regression models to determine isotopic niches for individual species during broad time intervals (pre during and post Last Glacial Maximum (LGM) at multiple geographic regions across the mammoth steppe. Individual species maintained relatively consistent isotopic niche positions at different geographic locations and during different times. Diet and habitat niches for any given species appear to have been similar across the mammoth steppe. Between some regions and times, however, species' isotopic niches changed, suggesting adaptation to local climatic conditions and/or changes in the nitrogen isotope patterns at the base of the food web. Isotopic niche overlap, including at the level of core niche overlap >60% overlap) was observed in at least one time and region for most species. This overlap suggests high levels of functional redundancy in the ecosystem, whereby one species could fulfil another's ecological role in the latter's absence. Despite spatial and temporal environmental variation, species' adaptability and functional redundancy within the ecosystem would have made the mammoth steppe a highly resilient ecosystem.
The Culture of Innovation Model
Year: 2018

The article examines the possibility of creating, implementing and managing a culture of innovation in modern business organizations. The author did a brief review of innovation landscape included theories on unique resources and core competencies, the creation and dissemination of organizational knowledge, learning organization and organizational learning, the types of innovation and the emphasis on the concept of open innovation. Researchers have not fully elucidated the search for a link between corporate culture and innovation. For this study, the relationship between some of the main components of the organization leadership, structure, strategy is outlined to feature the main characteristics of the innovation culture domain, according to the views of the author. By previous theoretical and empirical studies, the author draws the main dimensions of the culture of innovation and emphasizing the trinity strategy structure culture and has created a model of the culture of innovation with concrete indicators. The conclusions are related to the complexity and varied manifestations of the impact of corporate culture on innovation in the company as well as on the complexity of defining a certain and very specific typology of the culture of innovation. The essence of the model represents the interrelationship between strategy and corporate culture, but with the appropriate structure, as well as leaders, for understanding the overall positive importance of the culture of innovation.The guiding principle for managing the culture of innovation in the discovery of specific features to encourage members of the specific organization for framing, sharing and act by the values that support the realization of innovation and implementation of innovative processes.One of the most important features of the culture of innovation identifies it as associated with change and perception, and as a set of possibilities. The culture of innovation is in line with the realization of experiments, risk taking, the redefinition of parameters from the work activities. It is an interaction amongst all the stakeholder groups, the conditions of maximum transparency, ensuring distribution, sharing, generating knowledge, like trust, as a construct that brings together the members of the organization in the realization of its future.
Mobi IoST: Mobility Aware Cloud Fog Edge IoT Collaborative Framework for Time Critical Applications
Venue: IEEE Transactions on Network Science and Engineering
Year: 2020

The design of mobility aware framework for edge/fog computing for IoT systems with back end cloud is gaining research interest. In this paper, a mobility driven cloud fog edge collaborative real time framework, Mobi IoST, has been proposed, which has IoT, Edge, Fog and Cloud layers and exploits the mobility dynamics of the moving agent. The IoT and edge devices are considered to be the moving agents in a 2 D space, typically over the road network. The framework analyses the spatio temporal mobility data (GPS logs) along with the other contextual information and employs machine learning algorithm to predict the location of the moving agents (IoT and Edge devices) in real time. The accumulated spatio temporal traces from the moving agents are modelled using probabilistic graphical model. The major features of the proposed framework are: (i) hierarchical processing of the information using IoT Edge Fog Cloud architecture to provide better QoS in real time applications, (ii) uses mobility information for predicting next location of the agents to deliver processed information, and (iii) efficiently handles delay and power consumption. The performance evaluations yield that the proposed Mobi IoST framework has approximately 93% accuracy and reduced the delay and power by approximately 23 26% and 37 41% respectively than the existing mobility aware task delegation system.
Variational Bayesian data fusion of multi class discrete observations with applications to cooperative human robot estimation
Venue: 2010 IEEE International Conference on Robotics and Automation
Year: 2010

A new method is presented for fusing conventional continuous sensor observations with discrete multi categorical state dependent information, which can be furnished by humans in many cooperative human robot interaction problems. The hybrid likelihood function for mapping between continuous hidden states and categorical observations are specified via softmax models. Although softmax models avoid discretization of continuous states, they are challenging to implement for real time data fusion since they are not analytically integrable. An approximation based on variational Bayesian (VB) methods is presented here to obtain fast closed form Gaussian solutions to the desired posteriors in cases where the hidden continuous states have Gaussian pdfs. A joint human robot target localization example illustrates the properties and utility of the VB hybrid fusion strategy, which also applies more generally to inference in hybrid Bayesian networks and mixture models.
Factor Rankings in Influencing Spatial Pattern of Global Net Primary Production from 2005 to 2011
Year: 2017

Net primary production (NPP) is one of the fundamental sources of energy for human lives. Due to the tremendous speed of population growth and industrialization, the anthropogenic impact on natural resources is becoming more and more apparent. However, most methods in analyzing potential impact factors of NPP among biosphere parameters are based on process based models using experimental data mostly collected in the field, which are quite subjective and difficult to share. The process based models are limited by various premises due to various understandings in constructing the model. To objectively understand the impact factors on calculating global NPP, we build a data driven model to calculate NPP and analyze each feature variable importance. The features cover biosphere parameters, solar radiation parameters, anthropogenic activities, and atmospheric gas concentrations. The study shows our data driven model is capable to calculate NPP comparatively well than the process based model. It further suggests that anthropogenic activities, land cover type, and normalized vegetation index should be considered more in the future NPP relevant researches, compared with the commonly used biosphere features, such as aerosol index, soil moisture, and precipitation.