Reviewerly/exHarmony huggingface dataset:
Sistem Informasi Inventaris pada Panti Asuhan Aisyiyah II Kadipiro Surakarta Berbasis Web

Inventory data is something that needs to be managed properly. The data collection process includes periodic activities that result in irregular changes in the amount and condition of the data. Inventory data must be well structured and structured in order to produce reports or information that are useful for the smooth running of an agency's data collection. Data recording at the Kadipiro Aisyiyah II Orphanage still uses conventional recording methods which have resulted in various problems in the inventory data collection process. Problems that arise can be in the form of accumulation of data due to errors in entering data, inconsistencies in writing data that allow errors in reporting, data loss that often occurs due to the absence of a structured backup of data files. The purpose of this study was to develop an inventory information system at the Aisyiyah II Kadipiro Orphanage. The method used is the SDLC method by utilizing the Waterfall approach, including requirements analysis, system design, writing program code, program testing and program implementation. The coding in the development of this system uses the programming language PHP and MySQL as a database and uses the codeigneter framework. The system has gone through the black box testing stage which results in the conclusion that all the features contained in the system are running properly. The results of this inventory information system can process all the needs related to the process of data collection or inventory recording at the Kadipiro Aisyiyah II Orphanage.
Effect of Compensation on Employee Performance: In Case of Mizan Tepi University Teaching Hospital

Compensation is very important for the performance of the employees.Therefore they are very important for the organization too.This study aimed to examine the effect of compensation on employee performance in Mizan Tepi University Teaching Hospital.The study was conducted from November 2018 to June 2019 and included all employees of the hospital.A questionnaire was designed to collect the data on the elements related to compensation like basic salary, bonus, house rent allowance, overtime policy, leave policy and employee performance.The study was used explanatory research design and both primary and secondary data was used to conduct this study.Stratified random sampling technique was used to infer the target population of the study.The data was collected from the employees of Mizan Tepi university teaching hospital.The collected data was analyzed by SPSS 20 Version.Descriptive and explanatory statistics was used to analyze the data and a sample of 213 employees was used to collect the data.However, only 200 employees responded to the questionnaires and hence, the response rate was 94 percent.Besides, descriptive statistical tools like frequency, percentage, mean and standard deviation are used to illustrate the demographic characteristics of the respondents and to assess the perception of employees from data collected through questionnaire and the study revealed that majority of the respondents feel disagreed on compensation elements of the organization.Inferential statistical methods like correlation analysis was used to assess the relationship between compensation elements and employee performance, multiple linear regression analysis also was employed to examine the influence of compensation elements on employee performance by using primary data collected through five scale likert questionnaire.Based on the findings of the study, all factors of employee performance have a positive and statistically significant correlation with employee performance.The multiple linear regression analysis (R2) implies that about 54.4% shows that compensation elements determine the variation of employee performance of Mizan Tepi university teaching hospital.Also the regression result revealed that all explanatory variables except bonus have a significant effect on employee performance.This indicated that compensation elements determine employee performance of Mizan Tepi university teaching hospital.Therefore, Mizan Tepi university teaching hospital should implement compensation elements appropriately and continuously as an indispensable tool to improve and maintain sustainable employee performance.
Do you speak Generation Z? A qualitative study on how the youngest generation is contextualized in modern mediated society through business perspective.

This research examines the contextualization of the discourse of the Generation Z. The identity ofthe generation is delineated based on definitions such as “digital natives”, “the most diversegeneration so far” and “the overindulged and materialistic generation”. The contradictory elementsof Gen Zers’ identity drew marketers’ attention, as the fluidity of their characteristics made thegeneration rather unapproachable to the advertising industry. Using the research question “How isthe discourse of Generation Z established in business – oriented newspapers”, the study delves intohow the characteristics of the generation are represented in financial media outlets. This researchaims to reveal which are the subtle elements of the generational identity, and to re – contextualize itthrough a business perspective. It draws upon 177 newspaper articles from two business – orientednewspapers, The Wall Street Journal and the Financial Times. In order for the research to disclosethe underlying elements of the articles, corpus linguistics and critical discourse analysis wereemployed. The combined methodological strategy intends to address the contextualization ofGeneration Z by critically engaging to statistically significant textual elements. The use of those twomethods aided the utilization of a relatively big corpus of articles. Therefore, the results arereflective of actual societal phenomena, as those methods can stress the social interdependences ofthe Generation Z. The data analysis resulted in the generational contextualization based on thediscourses of materialism, technology and diversity, along with a discourse that defines socialelements in the identity of the generation. The interdiscursivity of the three main discoursesillustrated that the identity of Generation Z is re-contextualized under the weight of balancingbetween their hopes to adopt a diverse, caring and all – inclusive lifestyle in the empoweringtechnological environment and the marketers’ inquires to harness their attributes by transformingtheir identity elements into commodities. The commodification of the identity of Generation Z allowsits members to establish their consumerist identity, but this tactic leaves Gen Zers in a precariousstate concerning their individuality.
On the Readability of Kernel-based Deep Learning Models in Semantic Role Labeling Tasks over Multiple Languages

Sentence embeddings are effective input vectors for the neural learning of a number of inferences about content and meaning.Unfortunately, most of such decision processes are epistemologically opaque as for the limited interpretability of the acquired neural models based on the involved embeddings.In this paper, we concentrate on the readability of neural models, discussing an embedding technique (the Nyström methodology) that corresponds to the reconstruction of a sentence in a kernel space, capturing grammatical and lexical semantic information.From this method, we build a Kernel-based Deep Architecture that is characterized by inherently high interpretability properties, as the proposed embedding is derived from examples, i.e., landmarks, that are both human readable and labeled.Its integration with an explanation methodology, the Layer-wise Relevance Propagation, supports here the automatic compilation of argumentations for the Kernel-based Deep Architecture decisions, expressed in form of analogy with activated landmarks.Quantitative evaluation against the Semantic Role Labeling task, both in English and Italian, suggests that explanations based on semantic and syntagmatic structures are rich and characterize convincing arguments, as they effectively help the user in assessing whether or not to trust the machine decisions.
Fluent Python nuggets. A brief presentation.

Some nuggets of Python coding wisdom out of the book Fluent Python by Lucian Ramalho. A light-weight presentation.
Χωροχρονικές διακυμάνσεις στην εκτίμηση και πρόγνωση της νέφωσης και της ηλιακής ακτινοβολίας με τη χρήση δορυφορικών και επίγειων δεδομένων υψηλής ανάλυσης

The present thesis deals with the estimation and forecast of cloud cover as well as the solar irradiance by processing satellite-based and high-quality ground-based data.
&lt;i&gt;K&lt;/i&gt;-anonymity scheme for privacy preservation in location-based services on IoT environment

Location-based services have important impacts on many applications of internet of things. In these services location of users are revealed in front of third party server that makes the privacy of user vulnerable. It is required to collect real time data regarding ongoing events to response the query of user. Wireless sensor network is most popular to collect the data sensed by the sensor nodes and assist the third party server to response the query of user. The energy constraint sensor nodes motivate the researchers to design energy efficient routing. This paper introduces two schemes for two different aspects of internet of things environment. First one is a k-anonymity-based privacy preservation scheme for quality query response service and second one is an energy efficient secure routing scheme to collect real time data from sensor nodes in order to response the query of user. The performance analysis shows that the proposed scheme performs better with compared to existing schemes.
Comunicació Social Media a les biblioteques universitàries. El cas del CRAI UB

Presentation on social media communication by university libraries with references and examples of CRAI UB libraries (University of Barcelona). The different social channels and the different types of content are presented with examples, such as: operational and functional information, dissemination of the collection, services, activities, and the centers's research, sector content curation and campaigns.
I skuggan av en pandemi : En komparativ studie av svensk- och dansk medierapportering angående våld i nära relationer under Coronapandemin 2020

This comparative study examines how the issue of domestic violence is covered and framed in the media during the Covid-19 pandemic 2020. The study compares the potential differences and similaritie ...
IMPROVING STUDENTS’ ABILITY IN WRITING DESCRIPTIVE TEXT OF FIRST GRADE STUDENTS AT MTs. DDI KEL. BARU TOLITOLI THROUGH PICTURE

The objective of this research is to find out whether the application of picture effective to improve students’ ability in writing descriptive text. It is a pre-experimental research. The study is the first grade of MTs. DDI Kel. Baru Tolitoli. It is conducted class (VII A). The data are obtained through a pre-test and post-test. The writer use SPSS 16.0 program to analyzed the data collection. The result of the analysis show that the application of picture contribute greatly to the students in writing descriptive text. The mean score of  pre-test is 65.70 while in the post-test improve up to 79.46. Moreover, the Minimum Mastery Criterion of English lesson is 75 (seventy-five). Then the data of  both test are analyzed by using T-test, and the result show that the T-count value is 10.839 greater than T-table value is 2.045. It can be inferred that the application of picture is very effective to improve students’ ability in writing descriptive text. Key words: picture, writing, descriptive text.
Nitrogen Test Tool in Rice Leaves Based on Android Application

This study aims to make an application for measuring nitrogen requirements in rice leaves based on an android application. The method used is the On Touch Motion Event. The onTouch method will be used when a touch will be sent to be displayed, then MotionEvent contains information about the touch made. System testing is carried out to display images using the Open Camera button using a smartphone camera, after that look for the RGB value then convert it to the BWD scale. This research has succeeded in building an application that can determine nitrogen levels in rice leaves through leaf color and provide recommendations for the amount of fertilization according to the desired results. The results of tests that have been carried out on 6 samples of rice leaves with 60 samples of RGB value data produce an accuracy rate of using the application of 78.33% and the remaining 21.66% is an error value. And how to calculate the land area, the results obtained from the need for urea per plant in 1 ha are 120 kg, where the BWD table shows the value of leaf color on a scale of 2-3 and requires a urea measurement of 100 which is estimated to get a yield rate of 6 t / ha.
Hybrid Resource Scheduling Strategy on Cloud Infrastructure as a Service Environment

Cloud computing is an emerging and the maximum substantial era in every discipline of information technology and aid shared resources over the network. Moving to this promising technology has solid challenges on behalf of resource usage. The heterogeneity of resource offerings, the new appearance of the system, and the unlimited user interest inconsistency blurs the usability of cloud. To adjust and overcome these challenges, service quality management measures is unobtrusive issues to be progressed for the benefit of resource availability; useful resource utilization is a key to improve the continuity of this promising technology for the near future. Even though, cloud providers vision is launching a convenient, complete and faithful service for end user, the heterogeneity, uncertainty and dispersion of resources appearance encounter challenge of resources provision, which cannot address to a minimal management effort. Therefore, resource scheduling for appropriate services to cloud workloads is really a tough and challenging job. This research work proposes a hybrid resource scheduling strategy on one of cloud computing service model named as Infrastructure as a Service Environment. The proposed scheduling strategy is done by combining two well-known virtual machine scheduling policies. This scheduling approach performed through organizing the resources within the distinct cloud nature.
Language Awareness On English Online Learning ForSenior High School Students

This research is about students' awareness in using English when learning online. Thisstudy aims to determine how are students' Awareness in using English on online learning forSenior High School Students at MAN 1 Murung Raya.This research uses qualitative research with descriptive qualitative methods. This studyinvolved 1 (one) English teacher and students at MAN 1 Murung Raya. The instruments usedwere observation and interview. Observations were made for eight times and interviews wereconducted for their times.The results of this study during the observation on the online classes used the ZoomMeeting students had their mother languages are Bakumpai and Banjar language. Although forstudents taking online learning and learning English it is sometimes difficult to signal and quitedifficult. But students remain very enthusiastic and excited and also have a tolerance for learningnew theories from their second language. So students are said to be very aware of English ononline learning and included inthere are five sub-categories of language awareness formulated byJames & Garrett (1991) and students enter the five domains of language awareness, including: a)The Affective Domain, b) The Social Domain, c) The Power Domain, d) The Cognitive Domain,e) The Performance Domain
Comprehensive Study on Denoising of Medical Images Utilizing Neural Network-Based Autoencoder

Nawarathne, Thoshara Withanage, Thanushi Gunarathne, Samitha Delay, Upekha Somathilake, Eranda Senanayake, Janith Godaliyadda, Roshan Ekanayake, Parakrama Rathnayake, Chathura Wijayakulasooriya, JanakaFetal motion discernment utilizing spectral images extracted from accelerometric data incident on pregnant mothers abdomen has gained substantial attention in the state-of-the-art research. It is an essential practice to avoid adverse scenarios such as stillbirths and intrauterine growth restrictions. However, this endeavor of ensuring fetus safety has been arduous due to the existence of random noise in medical images. This novel research is an in-depth approach to analyze how the interference of different noise variations affects the retrieval of information in those images. For that, an algorithm employing autoencoder-based deep learning was modeled and the accuracy of reconstruction of the STFT images mitigating the noise has been measured examining the loss. From the results, it is manifested that even a substantial addition of the super-Gaussian noises which have a higher correlation of the frequencies possessed by the fetal movement images can be restored successfully with the slightest error.
Bandgap optimization in combinatorial graphs with tailored ground states: application in quantum annealing

A mixed-integer linear programming (MILP) formulation is presented for parameter estimation of the Potts model. Two algorithms are developed; the first method estimates the parameters such that the set of ground states replicate the user-prescribed data set; the second method allows the user to prescribe the ground states multiplicity. In both instances, the optimization process ensures that the bandgap is maximized. Consequently, the model parameter efficiently describes the user data for a broad range of temperatures. This is useful in the development of energy-based graph models to be simulated on Quantum annealing hardware where the exact simulation temperature is unknown. Computationally, the memory requirement in this method grows exponentially with the graph size. Therefore, this method can only be practically applied to small graphs. Such applications include learning of small generative classifiers and spin-lattice model with energy described by Ising hamiltonian. Learning large data sets poses no extra cost to this method; however, applications involving the learning of high dimensional data are out of scope.
The Design of Online Environments (Political Hashtags) and the Quality of Democratic Discourse At-Scale

Author(s): Rho, Eugenia Ha Rim | Advisor(s): Mazmanian, Melissa | Abstract: Facilitating democratic discourse, or people's ability to access factual information in service of thoughtful discussion of social issues, is critical for democracies to function properly. However, with the rise of online fake news, misinformation, and political extremism, it is becoming increasingly difficult to have civil conversations on the internet. As a first step to addressing this issue, scholars need to understand how the current design of online environments shapes people’s ability to respectfully engage across social and political differences. In this dissertation, I investigate how common social media design features, such as hashtags directly impact the quality of democratic discourse at-scale. Using natural language processing, statistics, and experimental design, I empirically demonstrate how linguistic behavior and the presence of political hashtags in online social media news articles impact the quality of discussions surrounding race, gender, and equality. Through my findings, I provide a theoretical examination of functionality and intertextuality as critical aspects of online design. Online design considerations that consider functionality alone tend to promote a digital public sphere that predominantly favors hashtag (or content) producers over non-users and passive content consumers. The sole emphasis on the functionality of design features drives frequency-driven research practices that prioritize discourse conditions for hashtag producers through volume-based definitions of discussion quality. Collectively, the research studies in this thesis are motivated by a desire to understand how online spaces can be better designed to foster interaction and discourse that can bridge rather than sharpen social differences. Results from this dissertation research strongly indicate that scholars, designers, and engineers need to rethink and evaluate how current methodological approaches that prioritize the functionality of online design choices are limiting the way we understand the quality of democratic discourse on the internet. As a step towards this direction, I evoke Kristeva’s notion of intertextuality to demonstrate how online design choices facilitate the power of language in which important social topics are discussed across networks.
Represent Human Digestive System By Using RAD Business Process Models

Through the above topic and studies, it appears that there is a novel approach to using business process models to represent the human digestive system. The proposed approach adopted, expanding the role activity diagram notation (RAD) to bridge the gap between business process models and human digestive systems, we show how to analyze, the human digestive system can be applied and evaluated on the process model, developed using the role activity diagram to activate the required quality, more clearly in the model . This gives a useful extension of modeling methods similar to RAD, in addition to providing a basis for improving the business of various commercial enterprises, according to RAD, it is clear that any business is not far from any biological system, as the first step in the project act is approval of the project, a library for example And the same is true for the digestive system and the related things between this and the progressive system and work have evolved within the commercial enterprises (Role-Activity Diagram (RAD), The objectives required in the research have been verified as to know the similarities between commercial and vital organs, and the possibility of representing A complete vital member by RAD, and easy understanding of the mechanism necessary for business for businessmen, and the digestive system has been more accurately identified, its mechanism of action, its apparatus and its usefulness, and this is what is required to be proven in the events of this study.
A Page-topic Relevance Algorithm Based on BM25 and Paragraph-Semantic Correlation

Abstract It is difficult for the traditional topic relevance algorithm based on word frequency and probability statistics model to deal with the ambiguous topic of search keywords, which leads to the retrieval results containing much information that users are not interested in. To solve this problem, a page-topic relevance algorithm based on BM25 and paragraph-semantic correlation is proposed in this paper. The semantic correlation of one page is calculated by the paragraph-semantic classification using a pre-trained deep neural network model, and is weighted with the BM25 retrieval score. Experimental results show that compared with the traditional BM25 algorithm, this algorithm can effectively improve the retrieval accuracy.