nils-herrmann/clean_scirepeval huggingface dataset:
Discrimination and characterization of management systems in semi arid rangelands of South Africa using RapidEye time series
Year: 2014

In South African grasslands, rangeland management is strongly related to land tenure. Communal farms are reported to exhibit less desirable vegetation conditions for livestock than commercial farms. Time series of high spatial and temporal resolution imagery may be useful for improved evaluation of these rangelands as they provide information at a spatial scale similar to the typical scale of field assessments and may thus overcome the limited spatio temporal representativeness of field measurements. A time series of 13 RapidEye images over one growing season (2010 2011) was used to explore spectral differences between and within two management systems (commercial vs. communal) Isomap ordination was applied to map continuous spectral dissimilarities of sample plots. Using regression with simultaneous autoregressive models (SAR) dissimilarities were subsequently related to ecological variables of plant and soil, including indicators for grazing effects. The largest differences were found between sample plots of communal and commercial farms. Vegetation attributes were significantly related to dissimilarities in reflectance, both from the growing season and the dormant period. However, these relationships did not suggest vegetation degradation on communal farms. They further suggest that a management related pattern of grazing disturbance in the summer months led to spectral differences between farms but could have impaired the detailed characterization of spectral dissimilarities related to differences in vegetation composition.
Proximal Policy Optimization Through a Deep Reinforcement Learning Framework for Multiple Autonomous Vehicles at a Non Signalized Intersection
Year: 2020

Advanced deep reinforcement learning shows promise as an approach to addressing continuous control tasks, especially in mixed autonomy traffic. In this study, we present a deep reinforcement learning based model that considers the effectiveness of leading autonomous vehicles in mixed autonomy traffic at a non signalized intersection. This model integrates the Flow framework, the simulation of urban mobility simulator, and a reinforcement learning library. We also propose a set of proximal policy optimization hyperparameters to obtain reliable simulation performance. First, the leading autonomous vehicles at the non signalized intersection are considered with varying autonomous vehicle penetration rates that range from 10% to 100% in 10% increments. Second, the proximal policy optimization hyperparameters are input into the multiple perceptron algorithm for the leading autonomous vehicle experiment. Finally, the superiority of the proposed model is evaluated using all human driven vehicle and leading human driven vehicle experiments. We demonstrate that full autonomy traffic can improve the average speed and delay time by 1.38 times and 2.55 times, respectively, compared with all human driven vehicle experiments. Our proposed method generates more positive effects when the autonomous vehicle penetration rate increases. Additionally, the leading autonomous vehicle experiment can be used to dissipate the stop and go waves at a non signalized intersection.
Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing
Venue: Mobicom '12
Year: 2012

Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform centric model where the platform provides a reward shared by participating users, and the user centric model where users have more control over the payment they will receive. For the platform centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user centric model, we design an auction based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.
An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter
Venue: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Year: 2020

High accuracy in pattern recognition based on electromyography(EMG) contributes to the effectiveness of prosthetics hand development. This study aimed to improve performance and simplify the deep learning pre processing based on the convolution neural network (CNN) algorithm for classifying ten hand motion from two raw EMG signals. The main contribution of this study is the simplicity of pre processing stage in classifier machine. For instance, the feature extraction process is not required. Furthermore, the performance of the classifier was improved by evaluating the best hyperparameter in deep learning architecture. To validate the performance of deep learning, the public dataset from ten subjects was evaluated. The performance of the proposed method was compared to other conventional machine learning, specifically LDA, SVM, and KNN. The CNN can discriminate the ten hand motion based on raw EMG signal without handcrafts feature extraction. The results of the evaluation showed that CNN outperformed other classifiers. The average accuracy for all motion ranges between 0.77 and 0.93. The statistical t test between using two channel(CH1 and CH2) and single channel(CH2) shows that there is no significant difference in accuracy with p value >0.05. The proposed method was useful in the study of prosthetic hand, which required the simple architecture of machine learning and high performance in the classification.
THE TEACHING TECHNIQUE AND MATERIALS USED BY THE STUDENT TEACHERS FOR FIFTH GRADE OF ENGLISH FOR YOUNG LEARNER PROGRAM AT UNIVERSITY OF MUHAMMADIYAH MALANG
Year: 2020

In teaching, a teacher uses teaching techniques and materials to achieve the teaching goals. Teaching techniques can also be called a way for teachers to deliver the material to the students. Then, teaching materials are facilities, tools or materials that are prepared and used by teachers in the learning process to achieve a learning goal. This research aims to investigate what teaching techniques used by the student teachers for the fifth grade of the EYL program academic year 2018/2019 and what materials used by the student teachers for the fifth grade of the EYL program. To obtain information about teaching techniques and materials, researchers used descriptive qualitative research. First, the researcher makes observations into the classroom to find out about the techniques used by student teachers. As well as analyzing teaching technical documents (lesson plans) to find out what materials are used by student teachers. The result revealed that the teacher who teaches in the fifth grade uses several teaching techniques. These techniques are brainstorming, games, interviews, roleplay, and project. Furthermore, the materials used by the students' teachers are song, text, date and month, video, picture, vocabulary, ordinal number, food, games, traditional costume. Both are important things for the teacher as a guideline about what will be learned and run or manage the class.
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
Venue: 2020 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS)
Year: 2020

An autotuning is an approach that explores a search space of possible implementations/configurations of a kernel or an application by selecting and evaluating a subset of implementations/configurations on a target platform and/or use models to identify a high performance implementation/configuration. In this paper, we develop an autotuning framework that leverages Bayesian optimization to explore the parameter space search. We select six of the most complex benchmarks from the application domains of the PolyBench benchmarks (syr2k, 3mm, heat 3d, lu, covariance, and Floyd Warshall) and apply the newly developed LLVM Clang/Polly loop optimization pragmas to the benchmarks to optimize them. We then use the autotuning framework to optimize the pragma parameters to improve their performance. The experimental results show that our autotuning approach outperforms the other compiling methods to provide the smallest execution time for the benchmarks syr2k, 3mm, heat 3d, lu, and covariance with two large datasets in 200 code evaluations for effectively searching the parameter spaces with up to 170,368 different configurations. We compare four different supervised learning methods within Bayesian optimization and evaluate their effectiveness. We find that the Floyd Warshall benchmark did not benefit from autotuning because Polly uses heuristics to optimize the benchmark to make it run much slower. To cope with this issue, we provide some compiler option solutions to improve the performance.
Analysis of Impact Implementation of Corporate Governance and Corporate Social Responsibility on Corporate Value in Banking Sector with Net Profit Margin and Management Quality as Moderating Variables
Year: 2020

This study aims to determine the impact of the disclosure of the implementation of corporate governance and corporate social responsibility to the value of the company with net profit and management quality variables as moderating variables in the banking sector on the Indonesia Stock Exchange. The value of the company in this study using Tobin's Q. Samples in this study amounted to 29 companies in the banking sector on the Indonesia Stock Exchange using annual financial reports and annual reports from 2009 to 2018. Based on the samples in this study, data processing is done by analysis Multiple regression to measure the impact of independent variables consisting of disclosure of the implementation of corporate governance and corporate social responsibility on the dependent variable, Tobin's Q, with moderating variables, namely net income and management quality. The results of the study note that the impact of corporate governance disclosure has a positive and significant effect on company value, while the disclosure of corporate social responsibility implementation has no significant effect on firm value. However, the existence of moderating variables net income and management quality can strengthen the effect of corporate social responsibility disclosure on firm value. In disclosing the implementation of Corporate Governance, the moderating variable of net income experiences the opposite, namely, net income weakens the influence of corporate governance on firm value, whereas management quality as measured by Return on Assets cannot moderate the effect of Corporate Governance on firm value.
Rain Water Energy Harvesting System
Year: 2019

Malaysia being a developing country is continually in exploring an alternative source of energy towards producing electricity. The demands for utilisation of natural renewable sources such as solar, water, biomass, wind and geothermal are increasing to aid in electricity grid stability. Hydropower technology is targeted from one of the renewable energy technologies to accomplish the current power demand. In the context of hydropower technology, rainwater and river are also viewed as renewable energy. The turbulent water resulted from rainwater or river can generate energy which is stored and convert into electricity. Thus, it is called hydropower or hydroelectric power. This paper studies the performance of Rainwater Harvesting (RWH) system which harvesting potential energy inherent in high rise buildings by using turbine which installed on the ground. Moreover, it is capable of generating power up to 20 kW with practically no environmental impact. RWH system using the collected rainwater from a storage tank which located at the top of the high rise building to produce electricity. Electric appliances such as a light bulb, television, radios, refrigerators, and food processors can harness this generated electricity. In Malaysia, every high rise building is equipped with a different high of the rooftop as well as the pressure of the rainwater flow through the pipeline. Therefore, the power produced relies on these factors. Several basic parameters allow to design and compute a model of the RWH system. This research presents a model description of the RWH system using the Simulink library from MATLAB to perform modelling, simulating and analyzing, due to well developed libraries.
A Brief Overview of Rule Learning
Venue: RuleML
Year: 2015

In this paper, we provide a brief summary of elementary research in rule learning. The two main research directions are descriptive rule learning, with the goal of discovering regularities that hold in parts of the given dataset, and predictive rule learning, which aims at generalizing the given dataset so that predictions on new data can be made. We briefly review key learning tasks such as association rule learning, subgroup discovery, and the covering learning algorithm, along with their most important prototypes. The paper also highlights recent work in rule learning on the Semantic Web and Linked Data as an important application area.
Improving Documentation of Cardio Vascular Disease Risk in Medical Records of Diabetic Patients attending Non Communicable Disease Clinics at West Bay Health Center in Qatar
Year: 2016

Patients with type 2 diabetes have an increased prevalence of lipid abnormalities, contributing to their high risk of CVD. Multiple clinical trials have demonstrated significant effects of pharmacological (primarily statin) therapy on CVD outcomes in individual subjects with CHD and for primary CVD prevention. Our aim is to improve documentation of cardiovascular disease risk in medical records of diabetic patients attending non communicable disease clinics at west bay health center from 13% to 70% using world health organization cardiovascular disease risk Prediction Charts from September 2014 to January 2015.  Baseline audit to determine the percentage of cardiovascular disease risk documentation, Brain storming session was done to identify the causes of such problem including fish bone analysis, Survey questionnaire was designated to assess the main causes of the problem and distributed to all nurses working in non communicable disease clinics, The results demonstrated in paretto chart, We discover the main causes of the problem lies on two main causes (nurses were not oriented to new non communicable disease format and some of them were not trained to calculate risk).Intervention in form of: Education session for nurses working in these clinics were done particularly on: Orientation about new format and cardiovascular disease riskdocumentation, Orientation sessions for clinic running physicians, Putting reminder posters in clinics and staff nurse stations. Several audits done retrospectively for two weeks (3 days each week) to show average percentage of documentation in pre intervention stage from 15th September to 13th November 2014, Several audits done for three weeks (3 days each week) and then weekly for 5 weeks to show average percentage in post intervention stage ending at 15th January 2015, All data was demonstrated using run chart.  The intervention began in November 2014 to January 2015 reveals that: Percentage of complete cardiovascular risk documentation for diabetic patients average was 7% in pre intervention stage and increased to average of 59% after the intervention  Intervention in the form of nurse education and physician orientation improved Documentation of cardiovascular disease risk by 52% Raising the average of documentation from 7% to 59% Sustainability is very important for success in such improvement processes.
A Sentinel 1 Based Processing Chain for Detection of Cyclonic Flood Impacts
Venue: Remote. Sens.
Year: 2020

In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane related floods. This paper presents an automated processing chain for Sentinel 1 synthetic aperture radar (SAR) data. This processing chain is based on the S1 Tiling algorithm and the normalized difference ratio (NDR) It is able to download and clip S1 images on Sentinel 2 tiles footprints, perform multi temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.
Hotel industriel urbain a Montbrillant: un belvedere sur les rails
Year: 2015

L'industrie n'occupe plus une place centrale dans nos villes, bien qu'elle ait depuis toujours joue un role structurant pour l'urbanisme et le developpement socio economique du pays. Le projet se caracterise par la volonte de favoriser la mixite entre le secteur secondaire et tertiaire et de repondre aux besoins de l'infrastructure ferroviaire presente sur le site, tout en creant deux espaces publics distincts: un belvedere et un parc. Veritable porte d'entree de Geneve, la zone situee entre Secheron et la gare Cornavin attire les projets d'architecture d'envergure representatifs du dynamisme de la ville toute entiere. Ainsi, immeubles de bureaux et de logements se regroupent le long du rail, tandis que la presence au sol des infrastructures ferroviaires (depot de train, batiment de maintenance) devient de plus en plus problematique, bien que necessaire. C'est dans ce contexte que s'inscrit le projet. Celui ci vise a repondre a deux besoins: conserver dans un premier temps un large faisceau de rails, dedie au depot plein air de wagons, destine a devenir un parc une fois son demantelement accompli; et batir a court terme un nouvel atelier d'entretien qui remplacerait les batiments existants obsoletes. Au dessus de ce premier niveau d'infrastructure formant un belvedere, un hotel industriel et divers programmes publics et administratifs sont projetes. Le batiment a le potentiel de devenir l'embleme du renouveau de l'industrie genevoise de par sa visibilite accrue.
A Stackelberg Game for Spectrum Leasing in Cooperative Cognitive Radio Networks
Venue: Int. J. Autom. Comput.
Year: 2013

According to the property rights model of cognitive radio, primary users (PUs) who own the spectrum resource have the right to lease part of spectrum to secondary users (SUs) in exchange for appropriate profit. In this paper, we propose a pricing based spectrum leasing framework between one PU and multiple SUs. In this scenario, the PU attempts to maximize its utility by setting the price of spectrum. Then, the selected SUs have the right to decide their power levels to help PU's transmission, aiming to obtain corresponding access time. The spectrum leasing problem can be cast into a stackelberg game, where the PU plays the seller level game and the selected SUs play the buyer level game. Through analysis based on the backward induction, we prove that there exists a unique equilibrium in the stackelberg game with certain constraints. Numerical results show that the proposed pricing based spectrum leasing framework is effective, and the performance of both PU and SUs is improved, compared to the traditional mechanism without cooperation.
Drone Assisted Multi Purpose Roadside Units for Intelligent Transportation Systems
Venue: 2018 IEEE 88th Vehicular Technology Conference (VTC Fall)
Year: 2018

As drones are becoming prevalent to be deployed in various civic applications, there is a need to integrate them into efficient and secure communications with the existing infrastructure. In this paper, considering emergency scenarios for intelligent transportation applications, we design a secure hybrid communication infrastructure for mobile road side units (RSUs) that are based on drones. The architecture tackles interoperability issues when Dedicated Short Range Communications (DSRC) wireless mesh, and LTE need to coexist for coordination. Specifically, we propose a novel tunneling protocol to integrate LTE with IEEE 802.11s mesh network. In addition, we ensure that only legitimate users can connect and control the mobile RSUs by integrating an authentication framework built on top of the recent OAuth 2.0 standard. A detailed communication protocol is proposed within the elements of the architecture from vehicles to control center for emergency operations. The proposed secure architecture is implemented in ns 3 and tested for its performance under heavy multimedia traffic. The results indicate that the proposed hybrid architecture can enable smooth multimedia traffic delivery via the mobile RSU.
Spatio temporal deep learning methods for motion estimation using 4D OCT image data
Venue: International Journal of Computer Assisted Radiology and Surgery
Year: 2020

Localizing structures and estimating the motion of a specific target region are common problems for navigation during surgical interventions. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resolution that has been used for intraoperative imaging and also for motion estimation, for example, in the context of ophthalmic surgery or cochleostomy. Recently, motion estimation between a template and a moving OCT image has been studied with deep learning methods to overcome the shortcomings of conventional, feature based methods.  We investigate whether using a temporal stream of OCT image volumes can improve deep learning based motion estimation performance. For this purpose, we design and evaluate several 3D and 4D deep learning methods and we propose a new deep learning approach. Also, we propose a temporal regularization strategy at the model output.  Using a tissue dataset without additional markers, our deep learning methods using 4D data outperform previous approaches. The best performing 4D architecture achieves an correlation coefficient (aCC) of 98.58% compared to 85.0% of a previous 3D deep learning method. Also, our temporal regularization strategy at the output further improves 4D model performance to an aCC of 99.06% In particular, our 4D method works well for larger motion and is robust toward image rotations and motion distortions.  We propose 4D spatio temporal deep learning for OCT based motion estimation. On a tissue dataset, we find that using 4D information for the model input improves performance while maintaining reasonable inference times. Our regularization strategy demonstrates that additional temporal information is also beneficial at the model output.