nils-herrmann/clean_scirepeval huggingface dataset:
Estrategias multimedia para mejorar el manejo de suma y resta de numeros enteros en los estudiantes del grado septimo de la institucion educativa santa teresita de rosas Cauca
Year: 2016

La tecnologia ha incursionado de una manera vertiginosa en el desarrollo de la sociedad y la escuela no ha sido ajena a esta incursion. Por lo tanto los recursos tecnologicos de la informatica son el pilar fundamental del presente trabajo y son los estudiantes del grado septimo de la Institucion, el objeto de la presente investigacion. La propuesta pretende mejorar el manejo de la suma y resta de numeros enteros, pero dejando de lado los metodos tradicionales del aprendizaje e incluyendo las tecnologias de la informatica y comunicaciones (TIC) como herramientas pedagogicas. Por tal razon el conectivismo, teoria del aprendizaje que esta inmersa en la nueva era digital, es un modelo que adopta el proyecto, que busca construir una estrategia de aprendizaje colaborativo donde a las herramientas tecnologicas, sobre todo al internet se le debera sacar el mejor provecho.
Improving palliative care with deep learning
Venue: BMC Medical Informatics and Decision Making
Year: 2018

BackgroundAccess to palliative care is a key quality metric which most healthcare organizations strive to improve. The primary challenges to increasing palliative care access are a combination of physicians over estimating patient prognoses, and a shortage of palliative staff in general. This, in combination with treatment inertia can result in a mismatch between patient wishes, and their actual care towards the end of life.MethodsIn this work, we address this problem, with Institutional Review Board approval, using machine learning and Electronic Health Record (EHR) data of patients. We train a Deep Neural Network model on the EHR data of patients from previous years, to predict mortality of patients within the next 3 12 month period. This prediction is used as a proxy decision for identifying patients who could benefit from palliative care.ResultsThe EHR data of all admitted patients are evaluated every night by this algorithm, and the palliative care team is automatically notified of the list of patients with a positive prediction. In addition, we present a novel technique for decision interpretation, using which we provide explanations for the model's predictions.ConclusionThe automatic screening and notification saves the palliative care team the burden of time consuming chart reviews of all patients, and allows them to take a proactive approach in reaching out to such patients rather then relying on referrals from the treating physicians.
Finding Related Papers in Literature Digital Libraries
Venue: ECDL
Year: 2007

This paper is about searching literature digital libraries to find "related" publications of a given publication. Existing approaches do not take into account publication topics in the relatedness computation, allowing topic diffusion across query output publications. In this paper, we propose a new way to measure "relatedness" by incorporating "contexts" (representing topics) of publications. We utilize existing ontology terms as contexts for publications, i.e. publications are assigned to their relevant contexts, where a context characterizes one or more publication topics. We define three ways of context based relatedness, namely, (a) relatedness between two contexts (context to context relatedness) by using publications that are assigned to the contexts and the context structures in the context hierarchy, (b) relatedness between a context and a paper (paper to context relatedness) which is used to rank the relatedness of contexts with respect to a paper, and (c) relatedness between two papers (paper to paper relatedness) by using both paper to context and context to context relatedness measurements. Using existing biomedical ontology terms as contexts for genomics oriented publications, our experiments indicate that the context based approach is accurate, and solves the topic diffusion problem by effectively classifying and ranking related papers of a given paper based on the selected contexts of the paper.
Secure Massive MIMO With the Artificial Noise Aided Downlink Training
Venue: IEEE Journal on Selected Areas in Communications
Year: 2018

This paper considers a massive MIMO network that includes one multiple antenna base station, one multiple antenna eavesdropper, and $K$ single antenna users. The eavesdropper operates in passive mode and tries to overhear the confidential information from one of the users in the down link transmission. In order to secure the confidential information, two artificial noise (AN) aiding schemes are proposed. In the first scheme, AN is injected into the downlink training signals to prevent the eavesdropper from obtaining the correct channel state information of the eavesdropping link. In the second scheme, AN is deployed in both downlink training phase and payload data transmission phase to further degrade the eavesdropping channel. Analytical expressions and tight approximations of the achievable secrecy rate of the considered systems are derived with taking imperfect channel estimation and two types of precoding, i.e. maximum ratio transmission and zero forcing, into consideration. Optimization algorithms for power allocation are proposed to enhance the secrecy performance of the proposed AN aiding schemes. The results reveal that deploying AN in the downlink training phase of massive MIMO networks does not affect the downlink channel estimation process at users while enabling the system to suppress the downlink channel estimation process at eavesdropper. As a consequence, the proposed AN aided schemes improve the system performance significantly. Furthermore, implementing AN in both phases allows the considered system having a flexible solution to maximize its secrecy performance at the price of higher complexity.
Modified PID controller for automatic generation control of multi source interconnected power system using fitness dependent optimizer algorithm
Venue: PloS one
Year: 2020

In this paper, a modified form of the Proportional Integral Derivative (PID) controller known as the Integral Proportional Derivative (I PD) controller is developed for Automatic Generation Control (AGC) of the two area multi source Interconnected Power System (IPS) Fitness Dependent Optimizer (FDO) algorithm is employed for the optimization of proposed controller with various performance criteria including Integral of Absolute Error (IAE) Integral of Time multiplied Absolute Error (ITAE) Integral of Time multiplied Square Error (ITSE) and Integral Square Error (ISE) The effectiveness of the proposed approach has been assessed on a two area network with individual source including gas, hydro and reheat thermal unit and then collectively with all three sources. Further, to validate the efficacy of the proposed FDO based PID and I PD controllers, comprehensive comparative performance is carried and compared with other controllers including Differential Evolution based PID (DE PID) controller and Teaching Learning Based Optimization (TLBO) hybridized with Local Unimodal Sampling (LUS PID) controller. The comparison of outcomes reveal that the proposed FDO based I PD (FDO I PD) controller provides a significant improvement in respect of Overshoot (Osh) Settling time (Ts) and Undershoot (Ush) The robustness of an I PD controller is also verified by varying parameter of the system and load variation.
Effects of effective microorganisms biochar based fertilizer on photosynthetic characteristics and chlorophyll content of flue cured tobacco under water saving irrigation strategies
Year: 2020

Effective microorganism biochar based fertilizer (EMBF) can improve the physiological properties of tobacco (Nicotiana tabacum L. In this study, the irrigation and EMBF rates were applied as factors that influence the photosynthetic characteristics and chlorophyll content of tobacco. The experiment involved 12 treatments: CK1 CK3, T1 T3, T4 T6, and T7 T9; these four groups represented 0, 100, 300, and 600 g EMBF pot 1, respectively. Each group was irrigated at rates of 40, 80, and 120 L pot 1. When comparing with the control treatment CK, results showed that net photosynthetic rate (Pn) stomatal conductance (gs) intercellular CO2 concentration (Ci) transpiration rate (Tr) and soil plant analysis development (SPAD) increased by 8.21% 107.03% 18.78% 118.27% 18.78% 118.27% 7.24% 104.15% and 3.47%69.09% respectively, after EMBF application. The Pn, gs, Ci, and Tr at the growth and maturity stages were highly significant (P 0.01) and positively correlated with EMBF application. The Pn, gs, and Tr were significant (P 0.05) and positively correlated with irrigation, and Ci was less affected by irrigation (P 0.05) The SPAD value of flue cured tobacco at three growing stages was highly significant (P 0.01) and positively correlated with the irrigation and fertilization rates. The SPAD values at the growth and maturity stages were higher than those at the rooting stage by 46.73% 79.2% and 2.21%46.17% respectively. Furthermore, the 80 L pot 1 irrigation rate combined with 300 g EMBF pot 1 is the best water and fertilizer combination. The results of this study might provide theoretical and practical guidance for growing flue cured tobacco in production areas.
Interpret Neural Networks by Identifying Critical Data Routing Paths
Venue: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018

Interpretability of a deep neural network aims to explain the rationale behind its decisions and enable the users to understand the intelligent agents, which has become an important issue due to its importance in practical applications. To address this issue, we develop a Distillation Guided Routing method, which is a flexible framework to interpret a deep neural network by identifying critical data routing paths and analyzing the functional processing behavior of the corresponding layers. Specifically, we propose to discover the critical nodes on the data routing paths during network inferring prediction for individual input samples by learning associated control gates for each layer's output channel. The routing paths can, therefore, be represented based on the responses of concatenated control gates from all the layers, which reflect the network's semantic selectivity regarding to the input patterns and more detailed functional process across different layer levels. Based on the discoveries, we propose an adversarial sample detection algorithm by learning a classifier to discriminate whether the critical data routing paths are from real or adversarial samples. Experiments demonstrate that our algorithm can effectively achieve high defense rate with minor training overhead.
2 MR: a two phase mapreduce approach for incremental replication
Venue: ICBDT '18
Year: 2018

Database information evolves over time as new entries are added and existing entries are modified or deleted. To deliver database changes to downstream applications efficiently, log based data replication is widely used for data replication. Systems for change data capture (CDC) such as LinkedIn Databus, can achieve data consistency and real time transmission when mining database log. However, the existing approaches for processing the log suffer from the following problems: (1) There is no efficient way to process the log with high availability and low latency. (2) Traditional incremental computing methods are not compatible with database log processing, which exploits the specific mechanisms and semantics of databases. In this paper, we describe a two phase MapReduce approach for incremental replication. By employing an efficient, resilient result reuse mechanism with two phase MapReduce, the result can be restored rapidly. This approach is implemented on Spark Streaming, and by comparing with existing solutions, the result shows the higher effectiveness and efficiency than others.
Regional economic activity and absenteeism: a new approach to estimating the indirect costs of employee productivity loss.
Venue: Population health management
Year: 2015

This paper presents a new approach to estimating the indirect costs of health related absenteeism. Productivity losses related to employee absenteeism have negative business implications for employers and these losses effectively deprive the business of an expected level of employee labor. The approach herein quantifies absenteeism cost using an output per labor hour based method and extends employer level results to the region. This new approach was applied to the employed population of 3 health insurance carriers. The economic cost of absenteeism was estimated to be $6.8 million, $0.8 million, and $0.7 million on average for the 3 employers; regional losses were roughly twice the magnitude of employer specific losses. The new approach suggests that costs related to absenteeism for high output per labor hour industries exceed similar estimates derived from application of the human capital approach. The materially higher costs under the new approach emphasize the importance of accurately estimating productivity losses.
Reformulasi Sistem Pemilukada Tidak Langsung Berbasis Pancasila dan Kearifan Lokal
Venue: Kontekstualita
Year: 2019

Abstrak: Sejak dilaksanakan secara langsung, pemilukada telah membawa dampak, baik positif maupun negatif. Sisi positifnya adalah ia melibatkan rakyat secara langsung dalam menentukan pemimpinnya. Sementara sisi negatifnya, ia turut membawa problematika sosial politik, seperti terjadinya pergeseran nilai nilai kearifan lokal di daerah. Artikel ini melihat perlunya de regulasi sistem pemilukada di Indonesia dengan menggunakan pendekatan pancasila dan kearifan lokal. Dengan melakukan telaah pustaka yang relevan, artikel ini berkesimpulan bahwa pemilukada secara langsung telah membawa persoalan persoalan akut di tengah masyarakat seperti mahalnya biaya pemilukada, konflik kepentingan, meningkatnya KKN, politik uang serta kampanye hitam. Dengan demikian, diperlukan pengembangan demokrasi pancasila dengan menata regulasi undang undang pemilukada serta membangun sistem kepemimpinan daerah berbasis kearifan lokal.  pemilukada, demokrasi, pancasila, kearifan local  Since being implemented directly, the local election of Indonesia has brought the positive and negative impact. The positive impact is that it involves the people directly in determining their leaders. While the negative impact, it has brought the social and political problems, such as shifting values of local wisdom in the area. This article looks at the need for deregulation of local election system of Indonesia based on Pancasila and local wisdom approaches. By studying the relevant literatures, this article argues that the local election of Indonesia directly has brought the social political problems in the society, for instance, the high costs of the local election, conflicts of interest, increasing of corruption, collusion, and nepotism, money politics, and black campaigns. Thus, the development of Pancasila Democracy is needed by arranging the regulation of the local election as well as building the regional leadership systems based on local wisdom. Keyword: local election, democracy, pancasila, local wisdom
The Bleaching Carceral: Police, Native and Location in Nairobi, 1844 1906
Year: 2017

The Bleaching Carceral: Police, Native and Location in Nairobi, 1844 1906 Yannick Marshall This dissertation provides a history of the white supremacist police state in Nairobi beginning with the excursions of European led caravans and ending with the institutionalizing of the municipal entity known as the township of Nairobi. It argues that the town was not an entity in which white supremacist and colonial violence occurred but that it was itself an effect white supremacy. It presents the invasion of whiteness into the Nairobi region as an invasion of a new type of power: white supremacist police power. Police power is reflected in the flogging of indigenous peoples by explorers, settlers and administrators and the emergence of new institutions including the constabulary, the caravan, the "native location" and the punitive expedition. It traces the transformation of the figure of the indigenous other as "hostile native," "raw native," "native," "criminal African" and finally "African." The presence of whiteness, the things of whiteness, and bodies racialized as white in this settler colonial society were corrosive and destructive elements to indigenous life and were foundational to the construction of the first open air prison in the East African Hinterland.
Service allocation in the cloud environments using multi objective particle swarm optimization algorithm based on crowding distance
Venue: Swarm Evol. Comput.
Year: 2017

Cloud computing is an emerging Internet based computing paradigm, with its built in elasticity and scalability. In cloud computing field, a service provider offers a large number of resources like computing units, storage space, and software for customers with a relatively low cost. As the number of customer increases, fulfilling their requirements may become an important yet intractable matter. Therefore, service allocation is one of the most challenging issues in the cloud environments. The problem of service allocation in the cloud computing is thought to be a combinatorial optimization problem to a large company for numbers of their customers and owned resources could be huge enough. This paper considers three conflicting objectives, namely maximizing revenue for users and providers as well as finding the optimal solution at desired time. We use a Multi Objective Particle Swarm Optimization based on Crowding Distance (MOPSO CD) to solve the problem because MOPSO CD is highly competitive in converging towards the Pareto front and generates a well distributed set of non dominated solutions. In addition, fuzzy set theory is employed to specify the best compromise solution. We simulate the proposed method using Matlab and compare the performance of the method against the performance of two other multi objective algorithms, in order to prove that the proposed method is highly competitive with respect to them. Finally, the experiments results show that the method improves the speed of the execution of the resources allocation algorithm while generating high revenue for both the users and the providers and increasing the resource utilization.
k Space Decomposition Based Super resolution Three dimensional Imaging Method for Millimeter Wave Radar
Venue: 2020 IEEE Radar Conference (RadarConf20)
Year: 2020

Millimeter wave imaging radar is indispensible for collision avoidance of self driving system, especially in optically blurred visions. The range points migration (RPM) is one of the most promising imaging algorithms, which provides a number of advantages from synthetic aperture radar (SAR) in terms of accuracy, computational complexity, and potential for multifunctional imaging. The inherent problem in the RPM is that it suffers from lower angular resolution in narrower frequency band even if higher frequency e.g. millimeter wave, signal is exploited. To address this problem, the k space decomposition based RPM has been developed. This paper focuses on the experimental validation of this method using the X band or millimeter wave radar system, and demonstrated that our method significantly enhances the reconstruction accuracy in three dimensional images for the two simple spheres and realistic vehicle targets,
Hegel ja antiikin dialektiikka
Year: 2016

Tarkastelen artikkelissani Hegelin dialektisen ajattelun taustoja antiikin filosofiassa. Kiinnitan erityisesti huomiota ns. positiivisen dialektiikan (tai "spekulaation" juuriin antiikin perinteessa ja jatan Hegelin suhteen antiikin negatiiviseen dialektiikkaan, etenkin Elealaisen koulukunnan Zenoniin ja pyrrhonilaiseen skeptisismiin, tarkastelun ulkopuolelle. Korostan artikkelissani erityisesti antiikin dialektiikan ontologista merkitysta.
A Survey of Information Retrieval Techniques
Year: 2017

The explosive growth of resources stored in various forms and transmitted over the internet has necessitated researches into information retrieval technologies. The major information retrieval mechanisms commonly employed include vector space model, Boolean model, Fuzzy Set model, and probabilistic retrieval model. These models are used to find similarities between the query and the documents to retrieve documents that reflect the query. These approaches are based on key word, which uses lists of keywords to describe the information content. In this paper, a survey of these models is provided in order to understand their working mechanisms and shortcomings. This understanding is vital as it facilitates the choice of an information retrieval technique, based on the underlying requirements. The results of this survey revealed that the current information retrieval models fall short of the expectations in one way or the other. As such, they are not ideal for high precision information retrieval applications.
CFD modelling of combustion and associated emission of wet woody biomass in a 4 MW moving grate boiler
Year: 2018

Moving grate boilers are suitable for biomass combustion and used worldwide for domestic heating applications. However, these boilers cause emission of harmful pollutants and requires optimization technique for reducing emission and increasing efficiency. Numerical study can be helpful in this regard to predict different combustion parameters and emission characteristics precisely, reducing design optimization cost. But, there is no available CFD code which can solve the complex solid conversion process in a moving bed furnace. In this work, a comprehensive 3D computational fluid dynamics (CFD) model has been developed to numerically study the combustion and associated emission of highly moist woody biomass in a reciprocating grate boiler. Solid phase combustion has been modelled with a separate bed model and combined with the gas phase for numerical solution. Several sub models for thermal conversion of solid fuel, chemical reactions, heat and mass transfer, bed movement due to fuel consumption and moving grate bars are included in the model through user defined subroutines coupled to commercial CFD code AVL Fire for finite volume method based simulation. Bed combustion have been studied in detail with several solid phase scalars to describe in bed temperature, solid densities, solid fraction as well as gas temperature, gas species distribution etc. under different working conditions varying primary airflow rate and bed movement. Bed temperature profile and gas species concentrations are compared with the experimental result from literature. For high moisture biomass fuel, it is found that, ignition of fuel starts near the grate and then front propagates upward. Maximum bed temperature is found 1395 K at the combustion zone while a large section of the fuel bed is found at 370 K. Gas temperature and gas species concentrations have been reasonably predicted by the simulation. CO concentration is found to be increased while CO2 concentration has been decreased when an increase in primary airflow and bed movement is applied.