nils-herrmann/clean_scirepeval huggingface dataset:
Develop Green Behaviour through Ecoliteracy for Early Children
Year: 2020

This study aims to provide a description of ecoliteracy as an effort to build green behavior in early childhood. In the era of the industrial revolution 4.0 and society 5.0, the paradigm of developing issues is related to global issues, one of which is the issue of the earth issue, in which there is a study on the importance of humans to protect and maintain ecosystem balance. The introduction of green behavior through ecoliteracy is very important for children through the role of adults. Children must have an awareness of loving the environment so that when the child has the awareness to love the environment, the child will happily show behavior to protect the universe.  This is a literature review of various sources related to ecoliteracy that are relevant to embedding green behavior in early childhood. There are several interesting ways to introduce green behavior through ecoliteracy in early childhood which are obtained from the results of the study of relevant studies, among others, by using storytelling methods, conducting field trips or field trips, and carrying out project activities to preserve the surrounding environment.
Video Segmentation by Non Local Consensus voting
Venue: BMVC
Year: 2014

We address the problem of Foreground/Background segmentation of "unconstrained" video. By "unconstrained" we mean that the moving objects and the background scene may be highly non rigid (e.g. waves in the sea) the camera may undergo a complex motion with 3D parallax; moving objects may suffer from motion blur, large scale and illumination changes, etc. Most existing segmentation methods fail on such unconstrained videos, especially in the presence of highly non rigid motion and low resolution. We propose a computationally efficient algorithm which is able to produce accurate results on a large variety of unconstrained videos. This is obtained by casting the video segmentation problem as a voting scheme on the graph of similar 're occurring' regions in the video sequence. We start from crude saliency votes at each pixel, and iteratively correct those votes by 'consensus voting' of re occurring regions across the video sequence. The power of our consensus voting comes from the non locality of the region re occurrence, both in space and in time enabling propagation of diverse and rich information across the entire video sequence. Qualitative and quantitative experiments indicate that our approach outperforms current state of the art methods.
Developing a Sensor based Homecare System The Role of Bluetooth Low Energy in Activity Monitoring
Venue: HEALTHINF
Year: 2018

Home healthcare systems have become a focus of research due to the shifting care requirements of the elderly. Malnourishment, independence and activity are becoming vital metrics when monitoring in patient's illness. Monitoring devices described in research however express issues in the consistent remote capture of these metrics. This work presents the role of Bluetooth Low Energy Beacons (BLE) in community based healthcare by examining how passive activity monitoring can assist patients coping with independence and disease management within their homes as an Indoor Proximity System (IPS) BLE sensors will be placed on the patient, in their home and on objects of interest (OOI) such as water bottles, kettles and microwaves. Research described in this paper will focus on accuracy of BLE beacons as an IPS for lifestyle monitoring and its application to intelligent healthcare. This is achieved by creating a model of patient care requirements structured using activities of daily living (ADL) which is evaluated using patient activity pattern recognition in captured sensor data. Pattern analysis uses the changing distance values between BLE sensors to determine movement, motion and location which contribute to the activity, sensor based care model. Results support efficacy when using BLE beacons as an IPS with patient activity patterns becoming observable through monitoring with a consistent ability to distinguish interactions in activity patterns captured. Future experiments will focus on analysing captured sensor metrics to determine care outcomes.
The Validity of Construct Analysis on Assessment Instrument of Basketball Skill for Senior High School in Yogyakarta City
Year: 2020

This research is quantitative research which aims to know the quality of instrument proved by the validity of construct. The empirical validity of research instrument is tested to senior high school students both public and private school in Special Region of Yogyakarta which hold the basketball competition among senior high school. The result of tested then is analyzed by using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) The EFA analysis uses software support of SPSS 23.0 and CFA analysis uses software LISREL 8.8. The result of analysis by EFA shows that the grouping data into two big groups such as skill execution and decision making. The result analysis of EFA also shows that the value of KMO is about 0.747 0.50. The value of Bartlett's test and Chi square is about 318.020 and the significance is on 0.000, therefore the analysis tested factor can be continued. The result of assessment calculation of basketball skill by using first order CFA shows that the value of p value=0.16220 >0.05) RMSEA 0.042 >0.08) and chi square =11.76 0.05 which means that the model is appropriate with the data, the analysis of model structural equation, and decision making effect the ability of playing basketball. The amount of contribution of decision making toward the ability of playing basketball is about 26 percent, while the contribution is about 74 percent influenced by another variable.
Object Recognition, Tracking and Trajectory Generation in Real Time Video Sequence
Year: 2013

The aim of tracking is to detect objects through images. This paper focuses on detection of moving objects in a scene and tracking of the objects as long as they remain in camera view. Initially noise removal and image enhancement is carried out. The objects are detected from the supervised Euclidean segmentation of the image frames. An object recognition algorithm then classifies the segmented objects by employing minimum distance classifiers approach, and by comparing various length and shape descriptors. Finally, object trajectory is generated based on centroid location of geometric object. For the case study, we have considered tracking of a tennis ball and shown its trajectory after tracking. This method assures accurate image segmentation via specifying color intensities of the object. Although median based approach gives much better results but it's rather much more expensive. Same case is with Census Transform method that is computationally expensive and is complex if multiple objects are there per frame.
A hat matrix for monotonicity constrained B spline and P spline regression
Year: 2015

Splines constitute an interesting way to flexibly estimate a nonlinear relationship between several covariates and a response variable using linear regression techniques. The popularity of splines is due to their easy application and hence the low computational costs since their basis functions can be added to the regression model like usual covariates. As long as no inequality constraints and penalties are imposed on the estimation, the degrees of freedom of the model estimation can be determined straightforwardly as the number of estimated parameters. This paper derives a formula for computing the hat matrix of a penalized and inequality constrained splines estimator. Its trace gives the degrees of freedom of the model estimation which are necessary for the calculation of several information criteria that can be used e.g. for specifying the parameters for the spline or for model selection.
Fast Facts for Stroke Care Nursing: An Expert Guide in a Nutshell
Year: 2014

This highly practical, pocket sized reference for stroke care nurses provides quick, bedside access to the most current guidelines for diagnostic tests, state of the art treatments, and best practice standards. The first of its kind, the resource distills lengthy and often complex national stroke care guidelines into clear, bite size pieces of information. Not only does it provide comprehensive stroke care information, it also explains the rationale behind stroke standards, making them easier to embrace and incorporate into practice. The book is organized consistently to foster rapid information retrieval. Each chapter includes objectives and such helpful features as "Fast Facts in a Nutshell" and "Clinical Pearls," bulleted segments that provide critical information at a glance. Written for stroke care nurses, as well as those who educate them, the guide also serves as an instructive resource for nurses not specializing in neurology but who are increasingly being called on to care for stroke patients. The book is also a helpful resource for stroke program coordinators and as a review for SCRN and CNRN certification exams.
Photosensitization Versus Photocyclization: Competitive Reactions of Phenylphenalenone in Its Role as Phytoanticipins in Plant Defense Strategies.
Venue: The journal of physical chemistry. A
Year: 2018

Phenalenone (PN) derivatives are involved in plant defense strategies, producing molecular singlet oxygen in a photosensitization process. Many experimental and theoretical studies determined that PN can performe this process with a quantum yield close to 1. However, it has been observed that the efficiency of some of its derivatives is much lower. This is the case of 9 phenylphenalenone (9 PhPN) To elucidate the factors that determine the different photochemistry of PN and its derivate 9 PhPN, we developed a complete active space self consistent field/multi configurational second order perturbation theory study where several deactivation paths through the lowest excited states were explored. We found that the characteristics of the low lying excited states are similar for both PN and 9 PhPN in the areas near the geometry of excitation. Consequently, the first processes that take place immediately after absorption are possible in both systems, including the population of the triplet state responsible for oxygen sensitization. However, 9 PhPN can also undergo cyclization by a bond formation between the carbonyl oxygen and a carbon atom of the phenyl substituent. This process competes favorably with population of triplet states and is responsible for the decrease of the quantum yield of oxygen sensitization in 9 PhPN relative to PN.
Directional Statistics based Deep Metric Learning for Image Classification and Retrieval
Venue: Pattern Recognit.
Year: 2019

Deep distance metric learning (DDML) which is proposed to learn image similarity metrics in an end to end manner based on the convolution neural network, has achieved encouraging results in many computer vision tasks.$L2$ normalization in the embedding space has been used to improve the performance of several DDML methods. However, the commonly used Euclidean distance is no longer an accurate metric for $L2$ normalized embedding space, i.e. a hyper sphere. Another challenge of current DDML methods is that their loss functions are usually based on rigid data formats, such as the triplet tuple. Thus, an extra process is needed to prepare data in specific formats. In addition, their losses are obtained from a limited number of samples, which leads to a lack of the global view of the embedding space. In this paper, we replace the Euclidean distance with the cosine similarity to better utilize the $L2$ normalization, which is able to attenuate the curse of dimensionality. More specifically, a novel loss function based on the von Mises Fisher distribution is proposed to learn a compact hyper spherical embedding space. Moreover, a new efficient learning algorithm is developed to better capture the global structure of the embedding space. Experiments for both classification and retrieval tasks on several standard datasets show that our method achieves state of the art performance with a simpler training procedure. Furthermore, we demonstrate that, even with a small number of convolutional layers, our model can still obtain significantly better classification performance than the widely used softmax loss.
A Secured Steganography Technique for Hiding Multiple Images in an Image Using Least Significant Bit Algorithm and Arnold Transformation
Venue: Intelligent Data Communication Technologies and Internet of Things
Year: 2019

Security systems are well liked in several areas wherever technologies are developing day by day. Using secret writing called encryption and data hiding called Steganography information security are often achieved. A traditionally used technique in Steganography is the LSB technique. Hide info is that the art and science of hidden writing, it's a technique of masking information in an exceedingly different style of data. Additionally, we will send text or a picture disguised by the cover image and change the components of the first to the recipient. A hidden text or image move into the cover image will defend the attacker's original information. In this we proposed a hybrid technique to secure the images in an image. Here Steganography LSB technique is used to hide the images in an image after that used the scrambling Arnold technique to scramble the cover image and improved the security of the hidden image.
Autofrettage analysis of a fibre reinforced composite tube structure incorporated with a SMA
Year: 2009

By adopting an advanced material design concept that combines a composite with a shape memory alloy (SMA) a structure was developed that not only has the characteristics of the composite, such as high specific strength and an adjustable design, but also the shape memory effect of the SMA. This design method replaces the traditional autofrettage method of pre stressing and improves the concentration of stress and the structural deficiency of a cylindrical pressure vessel. This research investigated the improvement obtained by adopting this advanced material concept in the design of a tube structure with a thick wall, comparing the application of the SMA laminations in various configurations with specific activation temperatures. A layer wise linear 3 D elastic analysis model was adopted to explore the displacement and stress conditions of the composite tube combined with the SMA. In the analysis process, the material's behaviours, such as the change in stiffness after the activation of the SMA and the activated recovery stress, were integrated into the mechanical model of the laminated tube. By further re computing the overall stress strain relationship, the reaction of the tube produced by activating the alloy with heat could be analysed. This analysis showed that 20 30% of the shear stress, t xth and 7.9% of the hoop stress could be reduced. As for the reduction in the radial and axial stresses, the improvement was relatively insignificant. With regard to the overall efficiency, the advanced material's structural design concept prevented the delamination condition in the laminates, achieved the objective of autofrettage, and enhanced the load bearing effectiveness of the tube structure.
Concentrations of imidacloprid and thiamethoxam in pollen, nectar and leaves from seed dressed cotton crops and their potential risk to honeybees (Apis mellifera L.
Venue: Chemosphere
Year: 2018

Neonicotinoid insecticides (NIs) have recently been recognized as co factors in the decline of honeybee colonies because most neonicotinoids are systemic and can transfer into the pollen and nectar of many pollinated crops. In this study, we collected pollen, nectar and leaves from a cotton crop treated with imidacloprid and thiamethoxam to measure the residue levels of these two NIs at different application doses during the flowering period. Then, the residual data were used to assess the risk posed by the systemic insecticides to honeybees following mandated methods published by the European Food Safety Authority (EFSA) and a highly toxic risk to honeybees was highlighted. Imidacloprid was found in both pollen and nectar samples, whereas thiamethoxam was found in 90% of pollen samples and over 60% of nectar samples. Analysis of the pollen and nectar revealed residual amounts of imidacloprid ranging from 1.61 to 64.58 ng g 1 in the pollen and from not detected (ND) to 1.769 ng g 1 in the nectar. By comparison, the thiamethoxam concentrations in pollen and nectar ranged from ND to 14.521 ng g 1 and from ND to 4.285 ng g 1, respectively. The results of this study provide information on the transfer of two NIs from seed treatment to areas of the plant and provides an understanding of the potential exposure of the bee and other pollinators to systemic insecticides.
Desenvolvimento de Solucao BI para Sistema de Producao Industrial
Year: 2017

A avaliacao do desempenho e dos custos associados a operacao tem vindo a tornar se uma crescente necessidade para qualquer organizacao que pretenda ocupar um lugar competitivo no mercado em que opera. Num mundo laboral, onde termos como lean e kaizen se tornaram o foco de muitas organizacoes, surge a necessidade da implementacao de aplicacoes de Business Intelligence capazes de providenciar informacao que suporte a tomada de decisao.Como tal, o objetivo desta dissertacao reside na especificacao e desenvolvimento de um sistema de apoio a decisao, na area da producao industrial, capaz de utilizar os dados nao tratados, existentes na base de dados, para apresentar a informacao de modo eficiente e intuitivo. Seguindo as nocoes de Business Intelligence, a aplicacao desenvolvida nao se foca na quantidade de dados apresentados, mas na sua importancia para a tarefa do utilizador, proporcionando uma interface simples e um conjunto de ferramentas orientados a tomada de decisoes. Este projeto foi motivado por uma declarada necessidade da organizacao TMG Automotive que, desta forma, potenciou a implementacao pratica desta aplicacao.De forma a obter uma aplicacao significativa para a organizacao, foi utilizada a metodologia dos processos dos sistemas de engenharia, partindo da analise de solucoes ja existentes nesta area. O passo seguinte consistiu na organizacao de um conjunto de necessidades, no desenho concetual de uma solucao e, finalmente, na sua implementacao. Para tal, foram utilizadas as ferramentas Microsoft Visual Studio 2012, Microsoft SQL Server 2012 e Microsoft PowerBI.
SYNTHESIS OF DIGITAL TRANSFORMATION BEYOND TECHNOLOGY PERSPECTIVE: DIGITAL STRATEGY, LEADERSHIP CULTURE
Year: 2020

Underlining evidence towards the strategic importance of Digital Transformation (DX) is highly conclusive. Across industries, Digital Transformation has become a focal point where organizations explore to enhance digital capabilities to sustain hyper competitive markets whilst inclined to extend personalise the experience to digitally savvy customers. Despite central attention and correct investments, many organizations fail DX efforts, as many failures derived where organization initiates transformation within standalone technology focus architectures in IS (Information Systems) IT (Information Technology) platforms. On contrary many scholars depicted that sustainability in successful DX efforts would derive by the synchronised synergy between Technology and Nontechnology antecedents. These focal antecedents such as Business Strategy, Leadership, and Culture bundle with key success to transformational agents such as Customer centricity, Digital Business process and Structural agility are being discussed in isolation in a different school of academia, However literature to Digital Transformation and connected Nontechnological antecedents are scares. This paper intends to address this by presenting connected literature in defining DX and exploring literature to validate how each Nontechnology antecedents enable successful DX whilst support to enhance digital maturity where benefits of disruptive technologies could be managed and convert as capabilities as against act as a threat of existence.
A Study on Improvement of Blockchain Application to Overcome Vulnerability of IoT Multiplatform Security
Year: 2019

IoT devices are widely used in the smart home, automobile, and aerospace areas. Note, however, that recent information on thefts and hacking have given rise to many problems. The aim of this study is to overcome the security weaknesses of existing Internet of Things (IoT) devices using Blockchain technology, which is a recent issue. This technology is used in Machine to Machine (M2M) access payment KYD (Know Your Device) based on the reliability of existing IoT devices. Thus, this paper proposes a BoT (Blockchain of Things) ecosystem to overcome problems related to the hacking risk of IoT devices to be introduced, such as logistics management and history management. There are also many security vulnerabilities in the sensor multi platform from the IoT point of view. In this paper, we propose a model that solves the security vulnerability in the sensor multi platform by using blockchain technology on an empirical model. The color spectrum chain mentioned in this paper suggests a blockchain technique completed by using the multiple agreement algorithm to enhance Thin Plate Spline (TPS) performance and measure various security strengths. In conclusion, we propose a radix of the blockchain's core algorithm to overcome the weaknesses of sensor devices such as automobile, airplane, and close circuit television (CCTV) using blockchain technology. Because all IoT devices use wireless technology, they have a fundamental weakness over wired networks. Sensors are exposed to hacking and sensor multi platforms are vulnerable to security by multiple channels. In addition, since IoT devices have a lot of security weaknesses we intend to show the authentication strength of security through the color spectrum chain and apply it to sensor and multi platform using Blockchain in the future.
The asymmetric role of corporate governance in CEO pay determination: evidence from South Africa
Venue: Applied Economics
Year: 2019

Previous studies suggest that CEOs may be incentivised to pursue actions aimed at restoring equity when pay deviates from the predicted CEO labour market compensation rate. This study investigates the effect of corporate governance (CG) quality on CEO pay deviation in an emerging economy characterised by weaker external corporate regulatory environment. Using a unique hand collected data of 185 South African listed firms over a six year period, We document that whereas CG quality impacts negatively on total CEO pay deviation, this is only conspicuous when the CEO is overpaid. CG quality has no effect on CEO compensation when the CEO is underpaid. We find that CEO underpayment reduces firm value in poorly governed firms. In contrast, CEO overpayment has no effect on firm value irrespective of the level of CG quality. The findings imply that recent CEO pay level agitations have resulted in the design of CG mechanisms that reduces the tendency of CEO pay to adjust upwards. The results are robust to alternative econometric techniques and endogeneity concerns.