nils-herrmann/clean_scirepeval huggingface dataset:
Portable and Efficient Fingerprint Authentication System Based on a Microcontroller
Venue: International Journal of Electrical and Computer Engineering (IJECE)
Year: 2019

This paper presents the design of a fingerprint authentication system based on a simple microcontroller and the fingerprint sensor. The circuit diagram and details regarding the procedure are included. The system was programed in MPLAB and then embedded into the microcontroller. Communication between the PIC and sensor is by RS232 protocol. The results show that the system recognizes the fingerprint in less than 1 second. It is portable and there is no need for image processing. Furthermore, the system shows a high effectiveness when storing and verifying fingerprints.
UNCOVERING YOUNG CHILDREN'S EMERGING IDENTITIES RELATED TO THEIR LITERACY EXPERIENCES: SUGGESTIONS TO STRENGTHEN LANGUAGE EDUCATION
Year: 2015

The study explored how young children's identities emerged from their drawings and accounts of their favourite stories as we argue the importance of understanding children in the context of school and language education. Sixty six (n=66) children of two urban schools in Pretoria, South Africa were asked to write about and draw their favourite story. The participants were between the ages of six and seven years. Vygotsky's socio cultural theory and Chen's theory of the construction of identity in a social context were used as conceptual framework. This conceptual framework could be linked to the findings which suggested that the children related their drawings and versions of their favourite stories to their interpretations of their life worlds. The prominent themes from the data could be associated with the self, the family, familiar objects and known animals. Their literacy experiences and the socio cultural influences on the children's construction of their identities were apparent in their work. We argue that teachers need to better understand how children understand themselves in relation to the world around them when making decisions about effective language education.
GA based synthesis approach for machining scheme selection and operation sequencing optimization for prismatic parts
Year: 2007

To obtain global and near global optimal process plans based on the combinations of different machining schemes selected from each feature, a genetic algorithm based synthesis approach for machining scheme selection and operation sequencing optimization is proposed. The memberships derived from the fuzzy logic neural network (FL NN) which contains the membership function of each machining operation to batch size, are presented to determine the priorities of alternative machining operations for each feature. After all alternative machining schemes for each feature are generated, their memberships are obtained by calculation. The proposed approach contains the outer iteration and nested genetic algorithm (GA) In an outer iteration, one machining scheme for each feature is selected by using the roulette wheel approach or highest membership approach in terms of its membership first, and then the corresponding operation precedence constraints are generated automatically. These constraints, which can be modified freely in different outer iterations, are then used in a constraints adjustment algorithm to ensure the feasibility of process plan candidates generated in GA. After that, GA obtains an optimal process plan candidate. At last, the global and near global optimal process plans are obtained by comparing the optimal process plan candidates in the whole outer iteration. The proposed approach is experimentally validated through a case study.
Estrategias de lectura y la comprension lectora en textos digitales de los estudiantes de la Universidad Nacional Amazonica de Madre de Dios, Peru
Year: 2021

La investigacion busca conocer como inciden las estrategias docentes en la capacidad de estudiantes para comprender textos digitales. El estudio fue no experimental, cuantitativo y correlacional, con una muestra de 126 estudiantes. Se utilizo la encuesta y el cuestionario. El parametro de comparacion fue la R de Spearman. La estrategia de lectura fue excelente con el 61,11% y la comprension lectora de textos digitales fue alta con 81,75% La correlacion muy alta (R 0,818) y un R2 0,67.
Convergence within binary market scoring rules
Year: 2019

Prediction markets are run to extract information from its participants through financial incentive. The market scoring rule mechanism represents a way of organizing markets in order to foster agents to make sincere predictions. Market scoring rules are usually presented in a context of asset trading, but they also boil down to a sequential probability report process analyzed here. If the future state space is binary (i.e. composed of only two possible states) and only two agents participate alternatively in the market, it is proven that for strictly proper market scoring rules, the report sequences of each agent converge toward limit probability reports which are closer to each other than the subjective probabilities of the agents.
Low Certainty Need (LCN) supply chains: a new perspective in managing disruption risks and resilience
Venue: Int. J. Prod. Res.
Year: 2019

This study suggests a new approach to supply chain (SC) disruption risk management where SC behaviour is less dependent on the certainty of our knowledge about the environment and its changes. The unpredictability of the occurrence of disruption and its magnitude suggests that designing SCs with a low need for 'certainty' may be as important, if not more so, than predetermined disruption control strategies. In this setting, this study calls for the development of a new perspective in SC disruption management, i.e. low certainty need (LCN) SCs. A number of principles and concepts is derived in recent, relevant literature to structure the characteristics of the LCN framework and its management. Structural variety, process flexibility, and parametrical redundancy are identified as key LCN SC characteristics that ensure efficient disruption resistance as well as recovery resource allocation. Two efficiency capabilities of the LCN SC are shown, i.e. low need for uncertainty consideration in planning decisions and low need for recovery coordination efforts based on a combination of lean and resilient elements. The results allow the identification of an LCN SC framework, concepts and technologies for its implementation as well as missing themes and new research questions which contribute to a better understanding of SC disruption risks. Special focus is directed on the digital technology usage in the LCN framework implementation.
Spatially explicit network analysis reveals multi species annual cycle movement patterns of sea ducks
Venue: Ecological applications a publication of the Ecological Society of America
Year: 2019

Conservation of long distance migratory species poses unique challenges. Migratory connectivity, that is, the extent to which groupings of individuals at breeding sites are maintained in wintering areas, is frequently used to evaluate population structure and assess use of key habitat areas. However, for species with complex or variable annual cycle movements, this traditional bimodal framework of migratory connectivity may be overly simplistic. Like many other waterfowl, sea ducks often travel to specific pre and post breeding sites outside their nesting and wintering areas to prepare for migration by feeding extensively and, in some cases, molting their flight feathers. These additional migrations may play a key role in population structure, but are not included in traditional models of migratory connectivity. Network analysis, which applies graph theory to assess linkages between discrete locations or entities, offers a powerful tool for quantitatively assessing the contributions of different sites used throughout the annual cycle to complex spatial networks. We collected satellite telemetry data on annual cycle movements of 672 individual sea ducks of five species from throughout eastern North America and the Great Lakes. From these data, we constructed a multi species network model of migratory patterns and site use over the course of breeding, molting, wintering, and migratory staging. Our results highlight inter and intra specific differences in the patterns and complexity of annual cycle movement patterns, including the central importance of staging and molting sites in James Bay, the St. Lawrence River, and southern New England to multi species annual cycle habitat linkages, and highlight the value of Long tailed Ducks (Calengula haemalis) as an umbrella species to represent the movement patterns of multiple sea duck species. We also discuss potential applications of network migration models to conservation prioritization, identification of population units, and integrating different data streams.
OCR Free Table of Contents Detection in Urdu Books
Venue: 2012 10th IAPR International Workshop on Document Analysis Systems
Year: 2012

Table of Contents (ToC) is an integral part of multiple page documents like books, magazines, etc. Most of the existing techniques use textual similarity for automatically detecting ToC pages. However, such techniques may not be applied for detection of ToC pages in situations where OCR technology is not available, which is indeed true for historical documents and many modern Nabataean (Arabic) and Indic scripts. It is, therefore, necessary to develop tools to navigate through such documents without the use of OCR. This paper reports a preliminary effort to address this challenge. The proposed algorithm has been applied to find Table of Contents (ToC) pages in Urdu books and an overall initial accuracy of 88% has been achieved.
The Impact of Foreign Direct Investment on Economic Growth A Case Study of South Korea
Year: 2012

The purpose of this paper is to explore the impact of Foreign Direct Investment (FDI) on economic growth in South Korea. In this study I use the secondary data. This study covers the time period from 1980 2009. This study attempts to determine empirical impact of FDI on South Korean economy using macroeconomic annual time series data. FDI, domestic investment, employment, export and human capital are considered as the endogenous variables for economic growth. The multiple regression are employed in this study. This study finds that there is a strong and positive impact of FDI on South Korean economic growth. Furthermore, the study indicates that human capital, employment and export also have positive and significant impact, while domestic investment has no significant impact on South Korean economic growth. The interaction effects of FDIhuman capital and FDI export indicate that the transfer of high technology and knowledge has an adverse impact on South Korean economic growth.
Analysis of traditional knowledge in medicinal plants used by Yuan in Thailand.
Venue: Journal of ethnopharmacology
Year: 2013

ETHNOPHARMACOLOGICAL RELEVANCE We studied traditional knowledge of medicinal plants use of the Yuan in northern Thailand, documenting and analysing traditional medical practices and its trend in the younger generations.  To providing useful information for appropriate and sustainable management under the urbanization and other developments and use of natural resources in their communities. In addition, traditional medicinal plant used, and knowledge that leads to discovery of new medicines can be promoted.  Traditional medicinal plant knowledge of the Yuan in Lamphun province was studied from October 2009 through September 2011 in order to determine the important medicinal plant species and dominant use categories in 5 villages. In each village, questionnaire interviews about medicinal plants uses were applied to 30 informants (5 informants per each of six stratified age groups) The relative importance of plant species was captured by calculation of use value (UV) Likewise, the dominant use categories were determined by calculation of the informant agreement ratio (IAR) Correlations between informants' age and number of medicinal plants known by them were determined with the coefficient of determination (R(2) RESULTS A total of 93 medicinal plant species in 82 genera and 49 families were recorded in the five villages. The most important species of medicinal plants were Aloe vera (L. Burm.f. Andrographis paniculata Ness, Chromolaena odorata (L. R.M.King and H.Rob. Jatropha podagrica Hook. and Thunbergia laurifolia Lindl. which had UVs of 1.02, 1.01, 0.75, 0.71, and 0.65, respectively. Likewise, the most dominant use categories were injuries, which accounted for 0.91 of the IAR. The age of informants and medicinal plants reported by each of them were positively correlated (R(2)=0.96, p<0.01)  Most of the Yuan's traditional medicinal plant knowledge is used for treating basic ailments. However we should be concerned that there is an imminent danger that it will be lost in the near future because their lifestyle was changing.
Can Analysis of Historic Lagg Forms Be of Use in the Restoration of Highly Altered Raised Bogs? Examples from Burns Bog, British Columbia
Year: 2009

Natural bogs are generally surrounded by a zone of hydrologic, hydrochemical, and ecological gradients called a lagg. In laggs, large changes over short lateral distances result in distinctive ecological gradients and vegetation patterns. Part of the restoration planning challenge for Burns Bog involves recreating such water and chemistry gradients to establish and maintain conditions for appropriate plant and animal communities that reflect natural transitions from nutrient poor bog to adjacent mineral soil influenced wetlands. We present a conceptual model inferred from historic air photos and vegetation maps from the margins of Burns Bog and theorize how particular vegetation represents the hydrological and hydrochemical gradients of the past that existed in transition to surrounding landscapes. Understanding lagg ecosystems and how they function is important not only to restoring the ecological integrity of Burns Bog, but also to developing a conceptual model useful for predicting and interpreting these gradients in other peatlands.
Implementation and Numerical Techniques for One EFlop/s HPL AI Benchmark on Fugaku
Venue: 2020 IEEE/ACM 11th Workshop on Latest Advances in Scalable Algorithms for Large Scale Systems (ScalA)
Year: 2020

Our performance benchmark of HPL AI on the supercomputer Fugaku was awarded the 55th Top500. The effective performance was 1.42 EFlop/s, and the world's first achievement to exceed the wall of Exa scale in a floating point arithmetic benchmark. Because HPL AI is brand new and has no reference code for large systems, several challenges exists in the large scale benchmark from a low precision numerical viewpoint. It is not sufficient to replace FP64 operations solely with those of FP32 or FP16. At the least, we need thoughtful numerical analysis for lower precision arithmetic and the introduction of optimization techniques on extensive computing such as on Fugaku. This study presents some technical analysis and insights on the accuracy issues, implementation, performance improvement, and report on the Exa scale benchmark on Fugaku.
Multi Attention Object Detection Model in Remote Sensing Images Based on Multi Scale
Venue: IEEE Access
Year: 2019

Ground object detection, based on remote sensing satellite imagery, provides the groundwork of numerous applications, so the detection accuracy is of vital importance. The background of remote sensing images is complex, the object size is various, and there are many small objects. In view of the above problems, a multi attention object detection method (MA FPN) based on multi scale is proposed in this paper, which can effectively make the network pay attention to the location of the object and reduce the loss of small object information. According to feature pyramid network (FPN) we firstly put forward a global spatial attention module, which extracts spatial location related information from shallow features and fuses it with deep features to enhance the position expression ability of deep features. Besides, the paper provides a pixel feature attention module: the multi scale convolution kernel is employed to generate the feature map of the same size as the input, as well as channel attention is used to assign weights to each layer of feature maps to obtain pixel level attention maps with good details. Experiments on NWPU, RSOD, and DOTA datasets show that the proposed algorithm outperforms state of the art methods.
Comparative Effects of Salt and Alkali Stress on Antioxidant System in Cotton (Gossypium Hirsutum L. Leaves
Year: 2019

This pot experiment was to evaluate how salts (NaCl, Na2SO4) and alkali (Na2CO3+NaHCO3) affect the physiological and biochemical characteristics during the seedling stage of two cotton cultivars (salt tolerant, L24; salt sensitive, X45) Salt and alkali stress reduced seedling emergence rate, relative biomass, and chlorophyll content, however, the REC and MDA content increased. Salt and alkali stress increased markedly superoxide dismutase (SOD) activity. Peroxidase (POD) activity increased first and then decreased as the increase of salt and alkali stress. Catalase (CAT) activity initially increased and then decreased as NaCl stress increased. In addition, the SOD activity, REC, and MDA content was markedly higher in salt stress than that in alkali stress. The proline content of L24 was higher than that of X45 under salt and alkali stress. However, glycine betaine and soluble sugar content of L24 was lower than that of X45 under alkali stress. The REC and MDA content of L24 were lower than those of X45, however, the relative biomass, chlorophyll content, SOD, POD, CAT, and Pro were higher than those of X45. In conclusion, salt tolerant cotton cultivars may possess a superior protection effect by increasing antioxidant enzymes activity under salt and alkali stress.
A Review on Distributed Application Processing Frameworks in Smart Mobile Devices for Mobile Cloud Computing
Venue: IEEE Communications Surveys Tutorials
Year: 2013

The latest developments in mobile devices technology have made smartphones as the future computing and service access devices. Users expect to run computational intensive applications on Smart Mobile Devices (SMDs) in the same way as powerful stationary computers. However in spite of all the advancements in recent years, SMDs are still low potential computing devices, which are constrained by CPU potentials, memory capacity and battery life time. Mobile Cloud Computing (MCC) is the latest practical solution for alleviating this incapacitation by extending the services and resources of computational clouds to SMDs on demand basis. In MCC, application offloading is ascertained as a software level solution for augmenting application processing capabilities of SMDs. The current offloading algorithms offload computational intensive applications to remote servers by employing different cloud models. A challenging aspect of such algorithms is the establishment of distributed application processing platform at runtime which requires additional computing resources on SMDs. This paper reviews existing Distributed Application Processing Frameworks (DAPFs) for SMDs in MCC domain. The objective is to highlight issues and challenges to existing DAPFs in developing, implementing, and executing computational intensive mobile applications within MCC domain. It proposes thematic taxonomy of current DAPFs, reviews current offloading frameworks by using thematic taxonomy and analyzes the implications and critical aspects of current offloading frameworks. Further, it investigates commonalities and deviations in such frameworks on the basis significant parameters such as offloading scope, migration granularity, partitioning approach, and migration pattern. Finally, we put forward open research issues in distributed application processing for MCC that remain to be addressed.
Cyber Security Situational Awareness among Students: A Case Study in Malaysia
Year: 2017

This paper explores the need for a national baseline study on understanding the level of cyber security situational awareness among primary and secondary school students in Malaysia. The online survey method was deployed to administer the data collection exercise. The target groups were divided into three categories: Group 1 (primary school aged 7 9 years old) Group 2 (primary school aged 10 12 years old) and Group 3 (secondary school aged 13 17 years old) A different questionnaire set was designed for each group. The survey topics/areas included Internet and digital citizenship knowledge. Respondents were randomly selected from rural and urban areas throughout all 14 states in Malaysia. A total of 9,158 respondents participated in the survey, with most states meeting the minimum sample size requirement to represent the country's demographics. The findings and recommendations from this baseline study are fundamental to develop teaching modules required for children to understand the security risks and threats associated with the Internet throughout their years in school. Early exposure and education will help ensure healthy cyber habits among millennials in Malaysia.  Cyber security awareness, cyber security education, cyber security, students.
Evaluation of the robustness of critical infrastructures by Hierarchical Graph representation, clustering and Monte Carlo simulation
Venue: Reliab. Eng. Syst. Saf.
Year: 2016

In this paper, we present a methodological work that adopts a system of systems (SoS) viewpoint for the evaluation of the robustness of interdependent critical infrastructures (CIs) We propose a Hierarchical Graph representation, where the product flow is dispatched to the demand nodes in consideration of different priorities. We use a multi state model to describe different degrees of degradation of the individual components, where the transitions between the different states of degradation occur stochastically. The quantitative evaluation of the CIs robustness is performed by Monte Carlo simulation. The methodological approach proposed is illustrated by way of two case studies: the first one concerns small sized gas and electricity networks and a supervisory control and data acquisition (SCADA) system; the second one considers a moderately large power distribution network, adapted from the IEEE 123 node test feeders. The large size of the second case study requires hierarchical clustering for performing the robustness analysis.
!Que vienen los robots! La autora senala que al igual que el sector se ocupa de "Extender conceptos, ideas y estilos de vida" es tambien su responsabilidad rebajar el temor de los ciudadanos hacia la inteligencia artificial.
Year: 2019

Decir que los ciudadanos tienen recelo hacia la inteligencia artificial quizas es quedarse corto. Lo que vemos es temor a que los avances tecnologicos acaben con nuestro trabajo y con la esencia del ser humano.