nils-herrmann/clean_scirepeval huggingface dataset:
Exploring the Prokaryotic Community Associated With the Rumen Ciliate Protozoa Population
Venue: Front. Microbiol.
Year: 2018

Ciliate protozoa are an integral part of the rumen microbiome and were found to exert a large effect on the rumen ecosystem itself as well as their host animal physiology. Part of these effects have been attributed to their ability to harbor a diverse ecto and endo symbiotic community of prokaryotic cells. Studies on the relationship between the protozoa population and their associated prokaryotic community in the rumen mainly focused on the methanogens, revealing that protozoa play a major role in enhancing methanogenesis potential. In contrast, little is known about the composition and function of the bacteria associated with rumen protozoa and the extent of this association. In this study, we characterize the prokaryotic communities associated with different protozoa populations and compare their structure to the free living prokaryotic population residing in the cow rumen. We show that the overall protozoa associated prokaryotic community structure differs significantly compared to the free living community in terms of richness and composition. The methanogens proportion was significantly higher in all protozoa populations compared to the free living fraction, while the Lachnospiraceae was the most prevalent bacterial family in the protozoa associated bacterial communities. Several taxa not detected or detected in extremely low abundance in the free living community were enriched in the protozoa associated bacterial community. These include members of the Endomicrobia class, previously identified as protozoa symbionts in the termite gut. Our results show that rumen protozoa harbor prokaryotic communities that are compositionally different from their surroundings, which may be the result of specific tropism between the prokaryotic community and protozoa.
The E health Literacy Demands of Australia's My Health Record: A Heuristic Evaluation of Usability.
Venue: Perspectives in health information management
Year: 2017

My Health Record is Australia's electronic personal health record system, which was introduced in July 2012. As of August 2017, approximately 21 percent of Australia's total population was registered to use My Health Record. Internationally, usability issues have been shown to negatively influence the uptake and use of electronic health record systems, and this scenario may particularly affect people who have low e health literacy. It is likely that usability issues are negatively affecting the uptake and use of My Health Record in Australia.  To identify potential e health literacy related usability issues within My Health Record through a heuristic evaluation method.  Between September 14 and October 12, 2016, three of the authors conducted a heuristic evaluation of the two consumer facing components of My Health Record the information website and the electronic health record itself. These two components were evaluated against two sets of heuristics the Health Literacy Online checklist and the Monkman Heuristics. The Health Literacy Online checklist and Monkman Heuristics are evidence based checklists of web design elements with a focus on design for audiences with low health literacy. During this heuristic evaluation, the investigators individually navigated through the consumer facing components of My Health Record, recording instances where the My Health Record did not conform to the checklist criteria. After the individual evaluations were completed, the investigators conferred and aggregated their results. From this process, a list of usability violations was constructed.  When evaluated against the Health Literacy Online Checklist, the information website demonstrated violations in 12 of 35 criteria, and the electronic health record demonstrated violations in 16 of 35 criteria. When evaluated against the Monkman Heuristics, the information website demonstrated violations in 7 of 11 criteria, and the electronic health record demonstrated violations in 9 of 11 criteria. The identified violations included usability issues with the reading levels used within My Health Record, the graphic design elements, the layout of web pages, and a lack of images and audiovisual tools to support learning. Other important usability issues included a lack of translated resources, difficulty using accessibility tools, and complexity of the registration processes.  My Health Record is an important piece of technology that has the potential to facilitate better communication between consumers and their health providers. However, this heuristic evaluation demonstrated that many usability related elements of My Health Record cater poorly to users at risk of having low e health literacy. Usability issues have been identified as an important barrier to use of personal health records internationally, and the findings of this heuristic evaluation demonstrate that usability issues may be substantial barriers to the uptake and use of My Health Record.
Short arc orbit determination and imminent impactors in the Gaia era
Year: 2018

Short arc orbit determination is crucial when an asteroid is first discovered. In these cases usually the observations are so few that the differential correction procedure may not converge. We have developed an initial orbit computation method, based on the systematic ranging, an orbit determination techniques which systematically explores a raster in the topocentric range and range rate space region inside the admissible region. We obtain a fully rigorous computation of the probability for the asteroid that could impact the Earth within few days from the discovery, without any a priori assumption. We test our method on the two past impactors 2008 TC3 and 2014 AA, on some very well known cases, and on two particular objects observed by the ESA Gaia mission.
Online human activity recognition employing hierarchical hidden Markov models
Venue: J. Ambient Intell. Humaniz. Comput.
Year: 2020

In the last few years there has been a growing interest in Human activity recognition (HAR) topic. Sensor based HAR approaches, in particular, has been gaining more popularity owing to their privacy preserving nature. Furthermore, due to the widespread accessibility of the internet, a broad range of streaming based applications such as online HAR, has emerged over the past decades. However, proposing sufficiently robust online activity recognition approach in smart environment setting is still considered as a remarkable challenge. This paper presents a novel online application of Hierarchical Hidden Markov Model in order to detect the current activity on the live streaming of sensor events. Our method consists of two phases. In the first phase, data stream is segmented based on the beginning and ending of the activity patterns. Also, on going activity is reported with every receiving observation. This phase is implemented using Hierarchical Hidden Markov models. The second phase is devoted to the correction of the provided label for the segmented data stream based on statistical features. The proposed model can also discover the activities that happen during another activity so called interrupted activities. After detecting the activity pane, the predicted label will be corrected utilizing statistical features such as time of day at which the activity happened and the duration of the activity. We validated our proposed method by testing it against two different smart home datasets and demonstrated its effectiveness, which is competing with the state of the art methods.
Feature Selection and Classification Based on Swarm Intelligence Approach to Detect Malware in Android Platform
Year: 2020

In these years, deep learning methods are used to detect Android malware due to low efficient manual checking and increase in Android malware. Data set quality defines the performance of these models. Low quality of training data set will result in reduced performance. Manual checking guarantees the data set quality in real world. Trained model may cause failure by malicious applications in Google Play. Artificial bee colony (ABC) algorithm based on selective swarm intelligence technique is proposed to rectify this issue, and it is robust detection technique of Android malware. The data set quality will not affect the effectiveness of solution. Better component learner's combination can be computed by genetic algorithm in proposed model, which will enhance the robustness of model. In same area, better robust performance is exhibited by the proposed technique than other methods.
Chromatic dispersion and nonlinear phase noise compensation based on KLMS method
Year: 2015

In this study, kernel least mean square (KLMS) algorithm with fractionally spaced equalizing structure is proposed for electrical compensation of chromatic dispersion (CD) and nonlinear phase noise (NLPN) in a dual polarization optical communications system with coherent detection. We consider single mode fiber channel. At the receiver, the additive optical noise is represented as additive white Gaussian noise. Phase modification is utilized at high signal powers to maintain the validity of Gaussian model of noise. We consider QAM and PSK modulations and evaluate the performance of the proposed method in terms of error rate, phase error, and error vector magnitude (EVM) The results are obtained in both linear and nonlinear regimes. In the linear region, the KLMS algorithm can compensate CD and NLPN effectively and outperforms the existing compensation methods such as LMS, minimum mean square error (MMSE) and time domain FIR filter. In nonlinear regime, where the input power is higher, NLPN is stronger which results in compensation performance degradation. However, KLMS still achieves better results than the above algorithms.
Estimating Potential Operational Cost Savings by Migrating On Premises to Cloud: A Study using Amazon TCO
Year: 2020

In order to use Amazon TCO(Total cost of ownership) calculator to compute the company operational cost saving by migrating on premises, Colocation workloads to the AWS cloud, we started investigating the case studies showing on the AWS web page. Cases are categories by either company type or by solution. Data are gathered from Amazon webpage. The following reports are from TCO calculator to show the IT cost savings based on both qualitative and quantitative methods. By solution, the parameters like Big Data, Data Center Migration, Enterprise Solutions, Financial Services, Healthcare Life Sciences, Internet of Things, Machine Learning Artificial Intelligence and Web Mobile Apps are normally considered. By company the parameters like type and size, Enterprises, Startup and Public Sector are considered.
Evidence for a Supergalactic Structure of Magnetic Deflection Multiplets of Ultra high energy Cosmic Rays
Venue: The Astrophysical Journal
Year: 2020

Evidence for a large scale supergalactic cosmic ray multiplet (arrival directions correlated with energy) structure is reported for ultra high energy cosmic ray (UHECR) energies above 10${19} eV using seven years of data from the Telescope Array (TA) surface detector and updated to 10 years. Previous energy position correlation studies have made assumptions regarding magnetic field shapes and strength, and UHECR composition. Here the assumption tested is that, since the supergalactic plane is a fit to the average matter density of the local Large Scale Structure (LSS) UHECR sources and intervening extragalactic magnetic fields are correlated with this plane. This supergalactic deflection hypothesis is tested by the entire field of view (FOV) behavior of the strength of intermediate scale energy angle correlations. These multiplets are measured in spherical cap section bins (wedges) of the FOV to account for coherent and random magnetic fields. The structure found is consistent with supergalactic deflection, the previously published energy spectrum anisotropy results of TA (the hotspot and coldspot) and toy model simulations of a supergalactic magnetic sheet. The seven year data post trial significance of this supergalactic structure of multiplets appearing by chance, on an isotropic sky, is found by Monte Carlo simulation to be 4.2$\sigma$ The ten years of data post trial significance is 4.1$\sigma$ Furthermore, the starburst galaxy M82 is shown to be a possible source of the TA Hotspot, and an estimate of the supergalactic magnetic field using UHECR measurements is presented.
Obstacle aware Adaptive Informative Path Planning for UAV based Target Search
Venue: 2019 International Conference on Robotics and Automation (ICRA)
Year: 2019

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g. search and rescue (SaR) However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle aware Adaptive Informative Path Planning (OA IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA IPP method performs better than state of the art planners, and we demonstrate its application in a realistic urban SaR scenario.
Reducing poverty in the least developed countries: The role of small and medium enterprises
Year: 2017

Alleviating poverty in the least developed countries (LDCs) requires raising the income level of local workers. We look at the poor as producers and explore drivers of higher employee wages. We focus on small and medium enterprises (SMEs) because they are a major driver of employment growth and poverty alleviation in these countries. To pay higher wages, SMEs need to increase organizational efficiency. We argue that in LDCs, characterized by substantial formal institutional constraints, SMEs can improve efficiency either by internalizing inefficient formal institutions or by taking advantage of institutional enablers, established not only by local governments, but also by international, non governmental, non profit, or social enterprises set up around the world. In a sample of 1273 SMEs from seven LDCs across Africa, Asia, and the Middle East, we find support for a mediated model, where SMEs with government contracts, higher exports, or female ownership achieve higher organizational efficiency, and in turn pay higher employee wages. The strongest mediation occurs for female owned firms and the weakest for exporting firms, while the direct relationships to wages are strongest for exporting SMEs. The data support the idea that SMEs with a government contract are more efficient in transacting with other constituents in the business environment, those with higher exports have a broad base of efficiency sources, and those with female ownership are particularly adept at exploiting local business and societal relationships.
Economic study of a large scale renewable hydrogen application utilizing surplus renewable energy and natural gas pipeline transportation in China
Year: 2020

A novel project solution for large scale hydrogen application is proposed utilizing surplus wind and solar generated electricity for hydrogen generation and NG pipeline transportation for hydrogen natural gas mixtures (called HCNG) This application can practically solve urgent issues of large scale surplus wind and solar generated electricity and increasing NG demand in China. Economic evaluation is performed in terms of electricity and equipment capacity estimation, cost estimation, sensitivity analysis, profitability analysis and parametric study. Equipment expenses are dominant in the construction period, especially those of the electrolysers. Electricity cost and transportation cost are the main annual operating costs and greatly influence the HCNG and pure hydrogen costs. The project proves to be feasible through the profitability analysis. The main influence items are tested individually to guarantee project profitability within 22 years. The project can reduce 388.40 M Nm3 CO2 emissions and increase 2998.52 M$ incomes for solar and wind power stations.
Electrochromic two dimensional covalent organic framework with a reversible dark to transparent switch
Venue: Nature communications
Year: 2020

Electrochromic (EC) materials with a dark to transmissive switch have great applications in optical communications, infrared wavelength detectors for spacecraft, and infrared camouflage coatings. However, such electroactive materials with high stability and cyclability are rare. Considering the advantages of the donor acceptor approach (wide range tuneable band position) and porous two dimensional (2D) covalent organic framework (COF, well ordered crystalline framework with stable structure and high surface area) in this work we constructed an extended delocalised p electron layered dark purple EC COF 1 by reacting the donor N,N,N',N' tetrakis(p aminophenyl) p benzenediamine (TPBD) with the acceptor 2,1,3 benzothiadiazole 4,7 dicarboxaldehyde (BTDD) A sandwiched device made of EC COF 1 exhibits the two band bleaching (370 nm and 574 nm) in the visible region and becomes transparent under the applied potential with an induced absorption centring at 1400 nm. This discovery of a stable dark to transmissive switch in COF might open another door for their application in many EC devices for various purposes.
Multiobjective Learning in the Model Space for Time Series Classification
Venue: IEEE Transactions on Cybernetics
Year: 2019

A well defined distance is critical for the performance of time series classification. Existing distance measurements can be categorized into two branches. One is to utilize handmade features for calculating distance, e.g. dynamic time warping, which is limited to exploiting the dynamic information of time series. The other methods make use of the dynamic information by approximating the time series with a generative model, e.g. Fisher kernel. However, previous distance measurements for time series seldom exploit the label information, which is helpful for classification by distance metric learning. In order to attain the benefits of the dynamic information of time series and the label information simultaneously, this paper proposes a multiobjective learning algorithm for both time series approximation and classification, termed multiobjective model metric (MOMM) learning. In MOMM, a recurrent network is exploited as the temporal filter, based on which, a generative model is learned for each time series as a representation of that series. The models span a non Euclidean space, where the label information is utilized to learn the distance metric. The distance between time series is then calculated as the model distance weighted by the learned metric. The network size is also optimized to learn parsimonious representations. MOMM simultaneously optimizes the data representation, the time series model separation, and the network size. The experiments show that MOMM achieves not only superior overall performance on uni/multivariate time series classification but also promising time series prediction performance.
Electronic, magnetic, and thermodynamic properties of the kagome layer compound FeSn
Year: 2019

Single crystals of the single Kagome layer compound FeSn are investigated using x ray and neutron scattering, magnetic susceptibility and magnetization, heat capacity, resistivity, Hall, Seebeck, thermal expansion, thermal conductivity measurements and density functional theory (DFT) FeSn is a planar antiferromagnet below TN 365 K and exhibits ferromagnetic magnetic order within each Kagome layer. The in plane magnetic susceptibility is sensitive to synthesis conditions. Resistivity, Hall and Seebeck results indicate multiple bands near the Fermi energy. The resistivity of FeSn is about 3 times lower for current along the stacking direction than in the plane, suggesting that transport and the bulk electronic structure of FeSn is not quasi 2D. FeSn is an excellent metal with Rho(300K)/Rho(2K) values about 100 in both directions. While the ordered state is antiferromagnetic, high temperature susceptibility measurements indicate a ferromagnetic Curie Weiss temperature of 173 K, reflecting the strong in plane ferromagnetic interactions. DFT calculations show a 3D electronic structure with the Dirac nodal lines along the K H directions in the magnetic Brillouin zone about 0.3 eV below the Fermi energy, with the Dirac dispersions at the K points gapped by spin orbit coupling except at the H point. The magnetism, however, is highly 2D with Jin plane/Jout of plane 10. The predicted spin wave spectrum is presented.
Entrepreneurial collaboration for R&D alliance performance: a role of social capital configuration
Year: 2020

Despite increasing research on the social nature of entrepreneurial collaboration in the context of alliances, its performance implication has been under debate. The present study tests a theoretical framework to elucidate the mediated relationship between social capital (SC) and research and development (R&D) alliance performance through the entrepreneurial orientation (EO) of alliance firms.,Based on the authors' literature review, SC is conceptualized as the sum of actual and potential assets, including structural, relational and cognitive capital, embedded within the networks of alliance partners. Alliance performance is regarded as a combination of technological performance and business performance, corresponding to the mutual and private benefits of R&D alliances, respectively. This study hypothesizes the potential impact of SC on alliance performance and the mediating role of EO in the relationship.,The results from an analysis of 218 Korean ventures that participated in R&D alliance projects as focal alliance partners show that each SC dimension drives alliance firms to enact EO, which eventually leads to increased performance in technology and business. Specifically, EO contributes to translating the implications of SC for technological performance partially and for business performance completely.,This study links fragmented theoretical perspectives in research, shedding new light on the importance of social nature in the context of R&D alliances, which conditions entrepreneurial collaboration for better alliance performance. The findings suggest that practitioners should adopt an ambidextrous approach to the SC EO interface at the alliance level, which opens a gateway to achieve greater performance by alliance.
"Predictive ads are not doctors" Mental health tracking and technology companies
Year: 2020

Although using mobile phone apps to track and monitor mental health is increasingly popular, we know little about how and why people with mental health diagnoses use such technologies, nor do we know of concerns they may express about these apps. Further, automated assessments of mental health status, or digital phenotyping, are also on the rise; how people with mental health conditions feel about such technologies is also an underexplored area of research. This paper presents an exploratory interview study with 12 people that begins to address these gaps. We focus specifically on how participants, who all have been diagnosed mental health conditions, perceive tech companies' involvement with existing mental health apps and mental health digital phenotyping. We find that participants satisfice in order to interact with existing mood tracking apps, and that they are wary of digital phenotyping for mental health diagnostics. Participants raise concerns related to profit motives, distrust, and fatalism in both cases, and they recommend regulations that may be put in place to keep tech companies in check. Though participants describe regulatory policies that may mitigate their concerns, we question whether regulations can truly foster sustainable interactions with mental health apps operated by technology companies.