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opencc-by-4.0Dec 2020View details →

Fig. 3 in To The Knowledge Of Some Closely Related Species Of The Genus Pachyrhynchus Germar, 1824 (Coleoptera: Curculionidae: Pachyrhynchini) From Luzon Island (Philippines), With Usage Of Eversion Of Endophallus

Fig. 3. Dorsal habitus of 1, 2 – P. consobrinus Schultze, 1922; 3 – P. septentrionalis Yoshitake, 2017.

opencc-by-4.0Dec 2022View details →

Figure 2 in An ethno-botanical study of indigenous medicinal plants and their usage in rural valleys of Swabi and Hazara region of Pakistan

Figure 2. Gender and age character (age limit is <30>40 year) of peoples interviewed in the study area.

opencc-by-4.0Feb 2015View details →

Fig. 2 in Soluble proteins in Messor structor (Latreille, 1798) (Hymenoptera: Formicidae) populations from Bulgaria - genetic variability and possible usage as population-genetic markers

Fig. 2. Spectrum of soluble proteins of M. structor workers (7.5% PAGE): a. Elenovo population; b. Tsalapitsa population.

opencc-by-4.0Feb 2015View details →

Fig. 3. a in Soluble proteins in Messor structor (Latreille, 1798) (Hymenoptera: Formicidae) populations from Bulgaria - genetic variability and possible usage as population-genetic markers

Fig. 3. a. UPGMA dendrogram (Sneath et al. 1973); b. Neighbour-joining dendrogram (Saitou & Nei 1987).

opencc-by-4.0Jan 2018View details →

A Corpus of Online Drug Usage Guideline Documents Annotated with Type of Advice

Introduction: The goal of this dataset is to aid NLP research on recognizing safety critical information from drug usage guideline or patient handout data. This dataset contains annotated advice statements from 90 online DUG documents that corresponds to 90 drugs or medications that are used in the prescriptions of patients suffering from one or more chronic diseases. The advice statements are annotated in eight safety-critical categories: activity or lifestyle related, disease or symptom related, drug administration related, exercise related, food or beverage related, other drug related, pregnancy related, and temporal.  Data Collection: The data was collected from <a href="https://www.medscape.com">MedScape</a>. It is one of the most widely used reference for health care providers. At first, 34 real anonymized prescriptions of patients suffering from one or more chronic diseases are collected. These prescriptions contains 165 drugs that are used to treat chronic diseases. Then, MedScape was crawled to collect the drug user guideline (DUG) / patient handout for these 165 drugs. But, MedScape does not have DUG document for all drugs. We found DUG document for 90 drugs in MedScape.  Data Annotation tool: The data annotation tool is developed to ease the annotation process. It allows the user to select a DUG document and select a position from the document in terms of line number. It stores the user log from the annotator and loads the most recent position from the log when the application is launched. It supports annotating multiple files for the same drug, as often there are multiple overlapping sources of drug usage guidelines for a single drug. Often DUG documents contain formatted text. This tool aids annotation of the formatted text as well. The annotation tool is also available upon request.  Annotated Data Description: The annotated data contains the annotation tag(s) of each advice extracted from the 90 online DUG documents. It also contains the phrases or topics in the advice statement that triggers the annotation tag, such as, activity, exercise, medication name, food or beverage name, disease name, pregnancy condition (gestational, postpartum). Sometimes disease names are not directly mentioned rather mentioned as a condition (e.g., stomach bleeding, alcohol abuse) or state of a parameter (e.g., low blood sugar, low blood pressure). The annotated data is formatted as following: drug name, drug number, line number of the first sentence of the advice in the DUG document, advice Text, advice tag(s), medication, food, activity, exercise, and disease names mentioned in the advice.  Unannotated Data Description: The unannotated data contains the raw DUG document for 90 drugs. It also contains the drug interaction information for the 165 drugs. The drug interaction information is categorized in 4 classes, contraindicated, serious, monitor closely, and minor. This information can be utilized to automatically detect potential interaction and effect of interaction among multiple drugs.  Citation: If you use this dataset in your work, please cite the following reference in any publication: @inproceedings{preum2018DUG, title={A Corpus of Drug Usage Guidelines Annotated with Type of Advice}, author={Sarah Masud Preum, Md. Rizwan Parvez, Kai-Wei Chang, and John A. Stankovic}, booktitle={ Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, publisher = {European Language Resources Association (ELRA)}, year={2018} }

The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts-Figure 4. The artefact during the initial structuring stage

The IT developer elaborates the detailed structure of the artefact, while considering several aspects: — The resources that are necessary to the artefact in order to accomplish its mission; — The artefact’s resistance to the changes regarding the functional requirements; — The artefact’s resistance to the technological changes; — The reasonably priced integration of the artefact in the structure of the host system; — The assurance of a reasonable reusability coefficient of the artefact during the struc- turing process of other artefacts; — The flexibility of the relations that exist among the components of the artefact; — The flexibility of the artefact’s connections with the host system.

Figure 3. The artefact as it exists as a black box-The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts

Consequently, the artefact as it exists as a black box can be represented according to the representation in Figure 3. It can be noticed that the artefact as it exists as a black box begins to interact with the environment. Two main categories of interfaces may be utilized by any artefact in order to interact with the environment: — Human Computer Interfaces (HCI); — Shared Resource Interfaces (SRI).

Figure 1. Visual and synthetic representation of the modelling process in the software industry-The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts

The experience that is accumulated regarding the modelling paradigms in the software engineering is impressive. Thus, the software engineering recognizes modelling paradigms like object orientation, aspect orientation, component orientation, service orientation, agent orientation. In one form or another, these paradigms prove their ex- cellence in certain types of IT projects. At the same time, these paradigms reveal their objective limits when they are used to engineer the real world software systems. Every modelling paradigm represents, in fact, a modality to represent the real world using a specific formal framework. The specificity of the formal framework is defined from both a syntactic and semantic perspective. The  formal syntactic framework of a paradigm refers to the  concepts that are  used  by the  paradigm in  order to represent the  real  world,  but  also  to the recommended principles that allow  for these concepts to interact in a correct and  efficient manner. Both the concepts and the principles benefit from a formal representation that ultimately favours communication as a secondary modelling lever inside the IT projects. Every syntactic artefact of a paradigm can be associated with a certain real world semantics, which it abstracts. As a consequence, considering that the real world continuously enhances its semantic potential, the syntactic constructs that are favoured by the paradigm may become problematic.

The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts-Figure 2. The UML representation of the artefact as it exists as a metaphor

The accumulation of energy that exists in each ingenious metaphor is progressively released, thus contributing to the transformation of a theoretical promise into effective reality. The artefact successively goes through several maturation stages, as the creator is preoccupied with obtaining an as precise and as close as possible description of the artefact as it exists as a metaphor. The completion of these successive stages is achieved through a methodic abstraction process, while leaving open the possibility to innovate and  targeting three main objectives: — broadening the abstraction scope; — adding new details; — detecting and eliminating abstraction errors.

Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 4. Statistical Metrics distribution of model on RIO as response of Terasort application

Figure 4 shows the statistical metrics distribution of regression model on read rate as the response of Terasort application. The filled triangle point-up indicates the minimum stable sampling time for statistical metrics. The top-half of figure 4 shows the residual standard error (RSE) distribution as training data size increase. The remaining half is for the distribution of R2.

Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 3. Estimate coefficients distribution of model on read rate as response of Terasort application

Figure 3 shows estimate coefficients distribution of model on read rate as the response of Terasort application. The filled triangle point-up indicates the position of the minimum stable sampling time for the corresponding estimate coefficients as well as the number above it shows the exact position value. The dashed line represents the average estimated coefficient of the regression model.

Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 1. ACF and PACF plot of TeraSort application

In Figure 1, the autocorrelation plots of all resource usage parameters reveal non- randomness because of the corresponding autocorrelations, denoted by the circle, violate the dashed lines (95% confidence boundary) and are statistically significant for lags up to 100.  The filled triangle point-up in partial autocorrelation plots marks the largest partial autocorrelation of usage parameters as well as the corresponding lag number.

Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 6. RSE and R2 of regression models of MapReduce applications

Figure 6 shows the fit quality of regression models. It is following.The left panel and the right panel of figure 6 show the residual standard error (RSE) distribution and R2 distribution of each application. The good fit quality corresponds to a taller R2 bar and a shorter RSE bar. The R2 almost 1 and small RSE show the best fit quality of the regression models on memory usage as the response. The overall higher RSE and lower R2 of regression models on CPU as the response show the worse quality of fitting goodness. The regression models on read rate as the response also show a moderate fitting quality. For the regression models on write rate as the response, Terasort application exhibits the best quality and Teragen application as well. Others show the worse fitting quality. The results show that the regression models on intensive usage parameters as response exhibit the good fitting quality.

Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 2. ACF plot of Residuals of Regression Model of Terasort

, are uncorrelated. If the error term is uncorrelated, it proves that there exists strong randomness in residuals of the model and provides the evidence for the unbiased estimate for the true standard error. The autocorrelation plot is used to check this assumption. Figure 2 shows the autocorrelation plot of residuals of regression models of TeraSort application. In Figure 2, the horizontal axis represents lag time and the vertical axis indicates the autocorrelation between residual at time t and residual at other lag time. At lag 0, autocorrelation is always equal to 1 and represents time series itself. Most of the autocorrelation at other lag time fall into the 95% confidence interval, only few of them violate the dashed line. Such a shape of ACF plot proves that residuals are uncorrelated and respects to the independent assumption of the linear regression model residuals.

opencc-by-4.0May 2018View details →

Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 8. Minimum sample time of statistical metrics of MapReduce applications

Figure 8 presents the minimum sampling time distribution of statistic metrics which ensures the stable modeling. Overall, the minimum sampling time of statistic metrics is smaller than sampling time of estimated coefficients. For different applications, a time-consuming application like Terasort needs the largest sampling time to tend to be stable. The Pi application shows the smallest minimum sampling time to reach stability.

Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 7. Minimum sampling time of MapReduce applications on various response variable

Pi application needs the smallest sampling time. The remaining applications need the similar minimum sampling time. Overall, the stable regression models on memory usage as response show the least need for sampling time. The results show that various applications have different minimum sampling time to get stable. The application which performs more read/write operations shows larger sampling time need.

Stable Modeling on Resource Usage Parameters of MapReduce Application-Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary

In Figure 5, the positive dependency of different strength between each resource usage parameter and the corresponding previous usage parameter is exhibited for all MapReduce applications. It indicates that all current resource usage parameters are positively dependent on the previous values to some extent degree. Except for these common dependencies, there exist some special dependencies for different applications. On the top-left panel of Figure 5, CPU usage of Pi application shows the strongest positive dependency to lagged CPU usage, the Teragen application had the weakest positive dependency, and others exhibit the moderate positive dependency.

opencc-by-sa-4.0Oct 2018View details →

The usage of phrasal verbs (emu) ye den, (emu) ye duru and (emu) ye hare in Akan Twi Asante

A project done as part of the "Urbane Feldforschung"  seminar in Humboldt University of Berlin in 2018. This is a project on Akan Twi Asante, a language spoken in Ghana. The subject is the usage of phrasal verbs (emu) ye den, (emu) ye duru and (emu) ye hare in the Asante dialect of Akan Twi. Collected data includes 6 glossed sentences in Akan on the topic mentioned above and 56 words from the Swadesh List.

opencc-by-sa-4.0Oct 2018View details →

The usage of phrasal verbs (emu) ye den, (emu) ye duru and (emu) ye hare in Akan Twi Asante