Reviewerly/exHarmony huggingface dataset:
How to Use a Script or Function

While scripts are just listings of code stored outside of R, functions are objects of mode function and are stored in the workspace. Most functions require specific kinds of arguments, which must be input into the function correctly. For example, if a function calls for a matrix and a data.frame is input, the function will return an error. Since external tables are often read into the R workspace as data.frames, using a data.frame for a matrix is quite a common error. This chapter covers calling a function, using arguments in a function, and accessing the output of a function, as well as an example of using a script to do a simple mining of Twitter.
Improving the Search for Architecture Knowledge in Online Developer Communities.

To make good architecture design decisions, developers need to know suitable architecture solution alternatives. However, with the rapid and continuous increase of solution alternatives (e.g. technologies, patterns, tactics), it is challenging to acquire architecture knowledge and to ensure that this knowledge is up to date. Our goal is to improve how architects search for architecturally relevant information in online developer communities. We developed a new search approach for architecturally relevant information using Stack Overflow as an example of an online developer community. Our search approach differs from a conventional keyword-based search in that it considers semantic information of architecturally relevant concepts in Stack Overflow. We also implemented the search approach as a web-based search engine. To show the effectiveness of the search approach compared to a conventional keyword-based search, we conducted an experiment with 16 practitioners. To ensure realism of the experiment, tasks given to practitioners are based on real scenarios identified in interviews with a different set of practitioners. The experiment showed that the new search approach outperforms a conventional keyword based search.
Optimal Gamma Correction based Gaussian Unsharp Masking Framework for Enhancementof Histopathological Images

In this paper, an optimal quality enhancement approach is proposed for Haematoxylin and Eosin stained histopathology images. This objective is achieved by introducing an optimally weighted framework. It is framed by harvesting the collective benefits of cumulative distribution based gamma- corrected interim-channel and adaptively sharpened interim intensity channel. The adaptive augmentation of Gaussian filter based sharpened image and cumulative distribution based gamma correction leads to the overall image enhancement. The main intelligence is achieved here by involving on-demand, behavioral Gaussian filter to impart the associated unsharp masking. Also, the image-dependent optimally included gamma corrected channel takes care of overall illumination. In this paper, cuckoo search optimizer is employed for attaining the desired level of enhancement, by exploring the 2-D search space in an efficient manner. Comparative performance evaluation is presented in this paper, so that the outperformance of proposed approach can be underlined.
ES-ESens: Detection of Event Sentences Based on Evaluation of the Explicitness and Significance of Information

Extracting event sentences with explicit and significant information is a basic work of semantic analysis based retrieval of text information. Current methods of event extraction normally lack evaluation of the quality of information embedded in a sentence, thus the extracted event sentences usually contain inexplicit or insignificant information that is unusable for real-world applications. In this paper, we introduce ES-ESens, a deep learning based methodology for detecting event sentences with high-quality information. To evaluate the explicitness and significance of the information embedded in a sentence, ES-ESens adopts Recurrent Neural Network with attention mechanism to perform deep semantic analysis on the context of the sentence and the article that explains the background of the event. Based on datasets of practical applications, experiments are presented to show the performance of our methodology.
Variants of Fuzzy Neural Gas

Neural Gas is a prototype based clustering technique taking the ranking of the prototypes regarding their distance to the data samples into account. Previously, we proposed a fuzzy version of this approach, yet restricted our method to probabilistic cluster assignments. In this paper we extend this method by combining possibilistic and probabilistic assignments. Further we provide modifications to handle non-vectorial data.
A Simple NLP-based Approach to Support Onboarding and Retention in Open Source Communities.

Successful open source communities are constantly looking for new members and helping them become active developers. A common approach for developer onboarding in open source projects is to let newcomers focus on relevant yet easy-to-solve issues to familiarize themselves with the code and the community. The goal of this research is twofold. First, we aim at automatically identifying issues that newcomers can resolve by analyzing the history of resolved issues by simply using the title and description of issues. Second, we aim at automatically identifying issues, that can be resolved by newcomers who later become active developers. We mined the issue trackers of three large open source projects and extracted natural language features from the title and description of resolved issues. In a series of experiments, we optimized and compared the accuracy of four supervised classifiers to address our research goals. Random Forest, achieved up to 91% precision (F1-score 72%) towards the first goal while for the second goal, Decision Tree achieved a precision of 92% (F1-score 91%). A qualitative evaluation gave insights on what information in the issue description is helpful for newcomers. Our approach can be used to automatically identify, label, and recommend issues for newcomers in open source software projects based only on the text of the issues.
Experimental nonlocality-based randomness generation with nonprojective measurements

We report on an optical setup generating more than one bit of randomness from one entangled bit. The amount of randomness is certified through the observation of Bell nonlocal correlations. To attain this result we implemented a high-purity entanglement source and a nonprojective three-outcome measurement. Our implementation achieves a gain of 27% of randomness as compared with the standard methods using projective measurements. Additionally, we estimate the amount of randomness certified in a one-sided device-independent scenario, through the observation of Einstein-Podolsky-Rosen steering. Our results prove that nonprojective quantum measurements allow extending the limits for nonlocality-based certified randomness generation using current technology.
IMPLEMENTASI METODE LBS (LOCATION BASED SERVICE ) PADA PENCARIAN RUTE PANTI ASUHAN DI KOTA MAKASSAR

Many people want to support the orphanage but still have difficulty getting accurate information about the orphanage and the location of the orphanage so they have to go to the social service office or by asking the community to get information about the local orphanage. This surely has an impact on the activities of donors who donates on a regular basis without knowing the conditions of other orphanages in the local area that are more likely to receive the donation. This application applies a Location Based Service (LBS) system that is able to detect the location of an orphanage, then can provide information services in accordance with the user's location. LBS is a location-based service, which is an internet-based service that is able to display position geographically from a smartphone or provide location information from the destined address. The aim of this research is to create an application that can provide information to donors through android about the location of orphanages in Makassar City with navigation features and display information related to orphanages to make it easier for donors to conclude which orphanages need more donations. This application can provide information for donors of android users to get convenience in finding and knowing the location of orphanages in the city of Makassar through an android mobile device to do donation activities.
Catalogação de documentos históricos no sistema Tainacan: análise temática, processamento e digitalização de documentos históricos sobre a imprensa e a circulação de livros no estado de Goiás, Goiânia, Brasil

The work of cataloging is presented through the Tainacan system of historical documents related to reading, books, press and libraries in Goias, Goiânia, Brazil contributing to the preservation and availability of information to researchers who study the subject. The purpose of this work was to analyze the materials of the collection in order to identify those that deal with the subject of reading, books, press and libraries of the state of Goias, which are part of the oldest period of the document, 1861, by the end of the year and then catalog the selected documents in the Tainacan system.The materials were donated to the course of Librarianship by Bento Alves Araujo Jaime Fleury Curado and deal in good part of documents related to the subject described above. The historical documents were rescued and stored by Benedict's relatives on the occasion of the transfer of the capital of Goias, Goias, Goias, Brazil to Goiânia in 1937, after the dismissal of the same by public servants vilaboenses. Bento later added to the collection newspapers and clippings related to culture, reading, literature, the press and libraries in Goias and Brazil. The collection is digitalized by trainees in previous projects and consists of 1071 materials, with dates ranging from the year 1861 to 2016.It is not the cataloging of all documents without distinction. The proposal was to catalog and make available in the system subjects related to reading in the State of Goias, covering the press, education, libraries (among which the Goiano Literary Cabinet stands out), and whose date of origin or publication are between the years of 1861 and 1989.
How to Build Emergent Software Systems (Tutorial)

Emergent software systems take a reward signal, an environment signal, and a collection of possible behavioural compositions implementing the system logic in a variety of ways, to learn in real-time how to best assemble a system to maximise reward. This reduces the burden of complexity in systems building by making human programmers responsible only for developing potential building blocks while the system determines how best to use them in its deployment conditions - with no architectural models or training regimes. Instead of adaptation being a special capability, emergent systems treat adaptation as continuous self-assembly, where a system is constantly reviewing its own behavioural composition to find alternative building blocks which better suit the currently perceived environment.
IMU Sensor Module for the Measurement of High-speed Motion in the Analysis of Human Skills

Sports experts develop skills by repeatedly practicing of ideal and appropriate motions. To improve athletic ability or reduce the risk of injury associated with incorrect motion in beginners, it is necessary to quantify the information pertaining to expert skills. While inertial motion capture is a suitable method for motion analysis, existing inertial motion capture systems cannot measure acceleration and angular velocity in case of high-speed motion. The objectives of this study are to develop an inertial measurement unit (IMU) sensor module that can measure high-speed motion and to corroborate its basic performance. The IMU sensor module consists of an IMU sensor, acceleration and gyro sensors with wide dynamic ranges of measurement, and it can change measurement ranges according to the motion. The experiment to evaluate basic performance showed that the output exhibited a linear relationship between the evaluation system whose value was calculated from the rotational speed of the motor as a true value and the sensor module even while switching between the two types of sensors with different measurement ranges. Finally, the acceleration and angular velocity in pitching motion was measured using the developed IMU sensor module, and we confirmed that it could perform measurements at high speeds. In conclusion, an IMU sensor module that can measure high-speed motion was developed, and its basic performance was confirmed in this study.
Study on implementing tor communication in connection to storage service provider

The problem of the Storage Service Provider (SSP) is there is no standarisation for the privacy and security. The user can not ensure the availability and reliability of the system. Currently many types of SSPs include Google Drive and Dropbox. To determine the toughness of the system against suspicious activities, a test is carried out by accessing the SSP page and performing a login process using Tor communication to determine the response of the SSP system. The results show that the Google Drive and Dropbox systems consider Tor communication to be a suspicious activity to their system. This shows that the Google Drive and Dropbox systems have actions against suspicious activities that can interupt the security of sytstem.
Tiny Data: Building a Community of Practice around Humanities Datasets

Quantitative data, the foundation of scientific research, have been in the foreground of discussions about data creation, curation, and publication pipelines. However, data for humanistic and social scientific inquiries take many forms, including physical and ephemeral primary resources (books, objects, performances, interactions); qualitative, free-form observations; as well as quantitative, structured data and metadata. At the Vanderbilt University Jean and Alexander Heard Library, we started the Tiny Data Working Group (TDWG) in 2016 to tackle some of the humanistic research data creation and curation issues in a constructive, collaborative, and interdisciplinary format. The present paper considers what it means to be FAIR with humanities data, as well as how to build a community of data-literate humanists, based on our experiences with the TDWG.
The Optimization Management of Combination Research Methods in Improving the Quality of Writing Research Reports

Management in writing research reports is a crucial activity for a researcher who wants to publish his research results. The research method is a method used to get results and discussion of research. In this study, the research method was used to explain the characteristics of quantitative, combination and qualitative methods with as many as 12 (twelve) points, and literature studies on the definition of 4 (four). The core of this study discusses management of the philosophical foundation of combination research, the characteristics of quantitative, qualitative and combination methods. Then discussed also the definition of combination methods, the nature of the combination method, variations of combination methods, combination research methods models: combination model or sequential explanatory design, combination model or sequential exploratory design, combination model or concurrent triangulation design and model combination method or concurrent embedded design. By doing this research, it is hoped that it can add insight to the authors of research reports regarding the management of combination research methods.
Bayesian Data Fusion: a Reliable Approach for Descriptive Modeling of Ore Deposits

Recognition of ore deposit genesis is still a controversial challenge for economic geologists. Here, this task was addressed by the virtue of Bayesian data fusion (BDF) implementing available proofs: semi-schematic examples with two (Cu and Pb + Zn) and three (Cu, Pb + Zn and Ag) evidences. The data, in current paper are just concentrations of indicated elements, were collected from Angouran’s deposit in Iran at prospecting and general exploration stages. BDF was used for discrimination between three geneses of Massive Sulfide, Mississippi and SEDEX types. Better genesis recognition with clear discrimination between the geneses was achieved by BDF as compared with earlier studies. The results showed that uncertainties were reduced from 50% to less than 30% and deposit recognition was improved greatly. Furthermore, we believe that using more properties can have a beneficial effect on the overall outcome. The comparison made between 2 and 3 properties showed that the amount of probable belonging values to any type of deposit was greater in 3 properties. It was also confirmed that using the completed information from the various stages of exploration progress can be amplified and be used for genesis recognition via BDF.
Extending Dynamic Range and Evaluating Method for Measuring Degree of Red Blood Cell Aggregation with Ultrasonic Peak Frequency

A method for monitoring the degree of red blood cell aggregation by using an ultrasonic peak frequency is proposed. To demonstrate the effectiveness of the method, a technique for controlling the degree with a wide dynamic range was necessary. However, efficiently large aggregations could not be generated only by using flocculants for the red blood cells. In this study, we tried to create large aggregations under alkaline conditions, which are known as an aggregation-accelerating factor. We aimed to extend the dynamic range and evaluate the monitoring method.
BLSTM harvesting of auxiliary NCHLT speech data

Since the release of the National Centre for Human Language Technology (NCHLT) Speech corpus, very few additional resources for automatic speech recognition (ASR) system development have been created for South Africa's eleven official languages. The NCHLT corpus contained a curated but limited subset of the collected data. In this study the auxiliary data that was not included in the released corpus was processed with the aim to improve the acoustic modelling of the NCHLT data. Recent advances in ASR modelling that incorporate deep learning approaches require even more data than previous techniques. Sophisticated neural models seem to accommodate the variability between related acoustic units better and are capable of exploiting speech resources containing more training examples. Our results show that time delay neural networks (TDNN) combined with bi-directional long short-term memory (BLSTM) models are effective, significantly reducing error rates across all languages with just 56 hours of training data. In addition, a cross-corpus evaluation of an Afrikaans system trained on the original NCHLT data plus harvested auxiliary data shows further improvements on this baseline.
Group messaging solution

In every organization, in order to work collaboratively, we need people who come up together and communicate so that they can express their ideas and share best thoughts and have good teamwork. In organizations, employees working on the same project may be residing in different places of the world to communicate, they need channels which can hold their confidential messages and also protect the messages from networking security threats. Currently, we have a number of messaging applications like Slack, Gmail, WhatsApp, etc. Prominently organizations use Gmail for emailing or their own mail sending services for communication. But for mail reception, we have a condition that mail receptor should know who has sent him mail. But there can be a condition in which someone might spoof, pretending to be someone. For organizations which to segregate authorities as senior and junior and communicate among each other, this kind of platform could be vulnerable. In current systems, hackers could even attach viruses and other malware to emails. If you want to send messages, you have to manually add the desired recipients. The project implements an application which would provide a way for communicating amongst teams and senior members of the team without hesitating and by implementing this project, members will also be able to put forward their views and points easily in front of the whole group members and senior authorities. Also, this project will make the communication effective which will, in turn, benefit the team and their work. Also, threat agents won’t be able to send malicious files as this will be the only message based and hence more secure. Also, it will have a news feed section where the latest news about the company will occur which will keep everyone informed about the current scenario and everyone will be on the same page. The project also aims to identify confidential message sent using this messenger so that the confidentiality remains intact and admin can manage and see which user is using the confidential words also by implementing this, spam and rumor detection can also be implemented.
Pressure sensitive paint image registration combined with gray level information

Pressure-sensitive paint technology is a wind tunnel pressure measurement frontier technology with high economical efficiency and high speed. In the wind tunnel test, due to the strong wind, the model will be distorted, making the wind image and the windless image difficult to register, which will seriously affect the pressure measurement accuracy. In response to this problem, this paper innovatively applies the two-dimensional non-rigid iterative closest point (ICP) algorithm to solve this problem. The point cloud method is used to make the image detail area to be effectively registered, and it is also conducive to the subsequent three-dimensional reconstruction work. However, due to the two-dimensional non-rigid ICP algorithm, only the two-dimensional coordinate positional relationship is considered. The correlation of the pixel grayscales of the pressure-sensitive paint image is neglected, so that the registration accuracy is not too high. However, if the three-dimensional non-rigid ICP algorithm is directly used, misregistration will occur. Therefore, in order to further improve the registration accuracy, this paper proposes a non-rigid ICP algorithm based on pixel-based search strategy. The algorithm designs a dual-target search strategy that takes 2D coordinates and pixel gray values into consideration and achieves accurate local matching, realizing point search and double goal optimization. The algorithm of this paper is compared with five registration algorithms on multiple sets of pressure sensitive paint images. The experimental results show that the proposed algorithm has the best registration accuracy. Compared to the suboptimal algorithm, the RMSE is improved by more than 15% and the NMI is increased by about 5%.
Facilitating social recommendation with collaborative topic regression and social trust

Social networks make users more dependent on online information regarding purchasing decision making. Networks which make users more dependent on online information regarding purchasing decision making. Therefore, social network information can be utilised to improve the performance of recommender systems that aim to mitigate information overload and provide users with the most attractive and relevant items. To improve recommender systems by incorporating social network information, this paper exploits multi-sourced information to predict ratings and make recommendations. An improved collaborative topic regression model that incorporates social trust, in which users' decisions regarding ratings are affected by their preferences and the favours of trusted friends, is proposed. In addition, an approach to calculating the maximum a posteriori estimates is proposed to learn model parameters. Empirical experiments using two real-world datasets are conducted to evaluate the performance of our model. The results indicate that the proposed model has better accuracy and robustness than other methods for making recommendations.