Reviewerly/exHarmony huggingface dataset:
An Evaluation of Image Feature Detectors Based on Spatial Density and Temporal Robustness in Microsurgical Image Processing

Abstract Optical image processing is part of many applications used for brain surgeries. Microscope camera, or patient movement, like brain-movement through the pulse or a change in the liquor, can cause the image processing to fail. One option to compensate movement is feature detection and spatial allocation. This allocation is based on image features. The frame wise matched features are used to calculate the transformation matrix. The goal of this project was to evaluate different feature detectors based on spatial density and temporal robustness to reveal the most appropriate feature. The feature detectors included corner-, and blob-detectors and were applied on nine videos. These videos were taken during brain surgery with surgical microscopes and include the RGB channels. The evaluation showed that each detector detected up to 10 features for nine frames. The feature detector KAZE resulted in being the best feature detector in both density and robustness.
2D-Based Coarse-to-Fine Approaches for Small Target Segmentation in Abdominal CT Scans

Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CTAbdominal CT scans. However, the segmentation accuracy of small organsSmall organ (e.g., pancreas) or neoplasms (e.g., pancreatic cyst) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupy a large fraction of the input volume. In this chapter, we propose two coarse-to-fine mechanisms which use prediction from the first (coarse) stage to shrink the input region for the second (fine) stage. More specifically, the two stages in the first method are trained individually in a step-wise manner, so that the entire input region and the region cropped according to the bounding boxBounding box are treated separately. While the second method inserts a saliency transformation module between the two stages so that the segmentation probability mapProbability map from the previous iteration can be repeatedly converted as spatial weights to the current iteration. In training, it allows joint optimizationJoint optimization over the deep networks. In testing, it propagates multi-stage visual information throughout iterations to improve segmentation accuracy. Experiments are performed on several CT datasets, including NIH pancreas, JHMI multi-organ, and JHMI pancreatic cyst dataset. Our proposed approach gives strong results in terms of DSC.
An estimation algorithm of harmonic source location based on ELM

In order to accurately locate the harmonic source in the system, a method based on the extreme learning machine (ELM) is proposed. Firstly, the network topology method is adopted to optimize the configuration of measurement nodes to realize the observability of the system. Secondly, on the basis of the measurement data obtained from monitoring nodes, a harmonic source location model is established. Finally, the sample training time and positioning effect of the BP model and ELM model are compared. Simulation results show that ELM model not only requiring less training time, but also is more accurate than BP model.
ANALISIS CLUSTER MENGGUNAKAN ALGORITMA K-MEANS CLUSTER UNTUK CULSTERING JENIS PENYAKIT MENULAR PADA PUSKESMAS DI KECAMATAN KOTA TANGERANG

The research in this paper aims to cluster the types of diseases in the sub-district Puskesmas in the city of Tangerang, to facilitate the handling and improvement of the environment, and to find out which disease is the most dominant in each cluster, during clustering at the sub-district Puskesmas in Tangerang City. The subject of this research is the result of clustering of infectious diseases with the method used is Cluster Analysis using the K-means Cluster Algorithm using calculations using SPSS 22. And the results of this study can find out the type of disease with each Puskesmas according to the sub-district, for which clusters are determined there are four clusters where each cluster is a type of infectious disease itself, namely pulmonary Tuberculosis, pneumonia, dengue, and diarrhea, for each cluster each divided according to its sub-district or Puskesmas. With these results, it can be concluded that the clustering results know which sub-districts have contracted infectious diseases according to the cluster formed.
Neighborhood Optimization for Therapy Decision Support

Abstract This work targets the development of a neighborhood-based Collaborative Filtering therapy recommender system for clinical decision support. The proposed algorithm estimates outcome of pharmaceutical therapy options in order to derive recommendations. Two approaches, namely a Relief-based algorithm and a metric learning approach are investigated. Both adapt similarity functions to the underlying data in order to determine the neighborhood incorporated into the filtering process. The implemented approaches are evaluated regarding the accuracy of the outcome estimations. The metric learning approach can outperform the Relief-based algorithms. It is, however, inferior regarding explainability of the generated recommendations.
Traffic Flow Classification using a Video Descriptor and a Convolutional Neural Network

Trafﬁc congestion is a signiﬁcant problem in urban cities and affects economic, health, and social questions. Although many works have been published in the last years to trafﬁc applications based on video data, different techniques of computer vision can be explored in this area. In this work, we proposed a method for trafﬁc ﬂow classiﬁcation using StarRGB and Convolutional Neural Networks (CNN). The StarRGB describes a global representation of the trafﬁc video into a colored image based on motion elements in the scene. Then, the generated image passed as input to a pre-trained CNN to extract the features and classify the trafﬁc video activity in three classes: LIGHT, MEDIUM, and HEAVY. In our experiments using a trafﬁc video database, the proposed method reached an accuracy of 96.47%. Also, the results suggest that StarRGB is a good descriptor for trafﬁc video applications.
Penggunaan Alih Kode Instruktur dalam Proses Belajar Mengajar Bahasa Inggris di ELC Education Manado

This study focussing on the use of code switching produced by instructors in English teaching and learning process based on Sosiolinguistics and language teaching point of view. This research uses qualitative method and records the process of teaching English and interviews the instructors and students as a technique of this research to find the importance of code switching. The data were transcripted and analyzed use the concepts of Wardhaugh and Suwito about types of code switching and functions of code switching use the concept of Margana. The research shows that there are 4 types of code switching intern, extern, metaphorical dan situasional code switching. Extern code switching are code switching from English to Indonesian (i.e. do you know what day is tomorrow? Besok hari apa?) and English to Manado Malay (i.e. you know what I mean? Mangarti nda?). Intern code switching is code switching from Manado Malay to Indonesian (i.e. besok hari libur jadi nda ada les. Lesnya nanti minggu depan). Metaphorical code switching is a code switching without any changing topic. Situasional code switching is a code switching with changing topic. Also, there are 7 types of code switching, they are repetition, clarification, exploration, explanation, giving assignment, checking students’ understanding and giving warning/suggestion. It can be concluded that there are 4 types of code switching produced by instructors in English teaching and learning process. They are extern, intern, metaphorical and situasional code switching. Besides, there are 7 functions of code switching. They are repetition, clarification, exploration, explanation, giving assignment, checking students’ understanding and giving warning/suggestion.Keywords : Code switching, Instructors utterences, English teaching and learning process
Growing Professionals in Science and Technology of Korea’s New Marine Industry Sector

With consideration to research nature, scope of available materials and industrial feature, we investigate cultivation of professionals in the three new marine industries such as marine energy, marine bio-industry and submarine hydrothermal deposits development. More specific, we investigate trends of marine science and technology manpower at the national level in Korea nurturing policy to forecast mid and long term expert manpower demand and to respond to trends and forecasts of major fields. Cultivation of necessary professionals is an essential prerequisite for growth of new marine industry and its contribution to national economy. Because the new marine industry was recently introduced, the established system and policies for professionals in science and technology did not reflect demand for this new industry, which requires improvement. Since new marine industry is a new field, it is difficult for customized talent to be supplied directly from existing manpower training system. Therefore, it is necessary to cultivate talented people with fused thinking and ability. In the existing system, it is necessary to strengthen efforts to develop professional manpower with fusion competence, and it is also necessary to make policy efforts to build a new system such as cooperation process.
Automatic Assessment of Narrative Answers Using Information Retrieval Techniques

In this paper we propose a novel system for automatic assessment of narrative answers using information retrieval algorithms.It is designed to help professors to evaluate the answers that they receive from their students.It is a Java application that communicates through a REST API.This REST API has at its core the Lucene library and exposes all the great functionalities that Lucene has.The application has one UI for the students and one UI for the professor.The student will select the professor, select the question, upload the answer and send it.The professor will evaluate the student answer using the algorithms that will be discussed in this paper.Also in this paper a series of experiments will be presented, and their result will give us a better understanding of the algorithms and have a taste of how they work.
Pengembangan Sistem Presensi Menggunakan Quick Response Code Dinamis untuk Madrasah Aliyah Al Mukhlisin Bandung

QR Code is the development of barcode technology that is able to store various kinds of information such as URLs, telephone numbers, SMS messages or other texts.At first, this technology was used for tracking vehicle parts in automotive industry.This study aims to develop an automatic presence system by utilizing QR Code technology, so that the presence process is more effective when compared to the presence manually since it was using digital technology.The implementation of the presence system with a QR Code was carried out at the Madrasah Aliyah Al Mukhlisin Bandung, to help solving some problems in school administration.The system was built in the form of an application installed on each teacher's smartphone.Every student tapped their QR code image to confirm their attendance before the lesson begins.The advantage of this application was that it has an advantage on generated image of QR Code changed every user login.The results of development and testing showed that the developed system can be a solution for the presence process through the application of ICT technology with a QR Code.Testing of Quality of Service (QoS) on developed system was appropriate with ITU recommendation.Survey of users was applied to check the properness of application.
Review and Applications of Machine Learning and Artificial Intelligence in Engineering

This chapter presents a summary review of development of Artificial Intelligence (AI). Definitions of AI are given with basic features. The development process of AI and machine learning is presented. The developments of applications from the past to today are mentioned and use of AI in different categories is given. Prediction applications using artificial neural network are given for engineering applications. Usage of AI methods to predict optimum results is the current trend and it will be more important in the future.
Approach for social media content-based analysis for vacation resorts

The impact of social networks on our liveskeeps increasing because they provide content,generated and controlled by users, that is constantly evolving. They aid us in spreading news, statements, ideas and comments very quickly. Social platforms are currently one of the richest sources of customer feedback on a variety of topics. A topic that is frequently discussed is the resort and holiday villages and the tourist services offered there. Customer comments are valuable to both travel planners and tour operators. The accumulation of opinions in the web space is a prerequisite for using and applying appropriate tools for their computer processing and for extracting useful knowledge from them. While working with unstructured data, such as social media messages, there isn’t a universal text processing algorithm because each social network and its resources have their own characteristics. In this article, we propose a new approach for an automated analysis of a static set of historical data of user messages about holiday and vacation resorts, published on Twitter. The approach is based on natural language processing techniques and the application of machine learning methods. The experiments are conducted using softwareproduct RapidMiner.
Clustering Similarity Measures for Architecture Recovery of Evolving Software

Automated software architecture recovery of module views from source code is a challenging research issue. Different similarity measures are used to evaluate clustering algorithms in the software architecture recovery of module views. However, few studies seek to evaluate whether such measures accurately capture the similarities between two clusterings. This work presents an evaluation of six clustering similarity measures through the use of intrinsic quality and stability measures and the use of ground truth architectures proposed by developers. The results suggest that the MeCl metric is the most adequate to measure similarity in the context of comparison with ground truth models provided by developers. However, when the architectural models do not exist, the Purity metric shows the best results, as measured by the correlation with the intrinsic Silhouette coefficient.
Time Management as a Pedagogical Technology of PhD Students’ Effective Self-Management

Th e article reveals the importance of the time management problem and substantiates the expediency of its research.It describes peculiarities of time management as a pedagogical technology concentrating on its principles; analysis of technology genesis was carried out in the works by foreign and domestic scientists from diff erent fi elds of science.Five groups of problems solved with the help of this technology were identifi ed.It was found out that time management within our research meets the requirements of a pedagogical technology eff ectively facilitating PhD students' self-management.It was proved that it combines features of professional management technology as well as eff ective self-management.Th e research analyzed the infl uence of training sessions (36 hours) devoted to the development of time management skills (professional and personal) at the level of postgraduate students' time management competence formation.Th ere was ground to conclude that the conducted training sessions had a positive eff ect on the increase in all the indicators peculiar to the mentioned competence confi rming the effi ciency of time management technology in the process of PhD students' self-management.
Eğitim Fakültesinde Görevli Akademisyenlerin Alternatif Eğitim/Okul Hakkındaki Görüşleri

The main objective of this research is determining the views of academics working at education faculties in Turkey on alternative education / schools.In accordance with this basic aim, in the 2017-2018 academic year, academics working at education faculties in universities located in Turkey were reached via e-mail and data were collected through semistructured interview forms designed on the
Ranking on Very Large Knowledge Graphs

Ranking plays a central role in a large number of applications driven by RDF knowledge graphs. Over the last years, many popular RDF knowledge graphs have grown so large that rankings for the facts they contain cannot be computed directly using the currently common 64-bit platforms. In this paper, we tackle two problems: Computing ranks on such large knowledge bases efficiently and incrementally. First, we present ðare, a distributed approach for computing ranks on very large knowledge graphs. ðare assumes the random surfer model and relies on data partitioning to compute matrix multiplications and transpositions on disk for matrices of arbitrary size. Moreover, the data partitioning underlying ðare allows the execution of most of its steps in parallel. As very large knowledge graphs are often updated periodically, we tackle the incremental computation of ranks on large knowledge bases as a second problem. We address this problem by presenting \ihare, an approximation technique for calculating the overall ranking scores of a knowledge without the need to recalculate the ranking from scratch at each new revision. We evaluate our approaches by calculating ranks on the $3 \times 10^9$ and $2.4 \times 10^9$ triples from Wikidata resp. LinkedGeoData. Our evaluation demonstrates that ðare is the first holistic approach for computing ranks on very large RDF knowledge graphs. In addition, our incremental approach achieves a root mean squared error of less than $10^-7 $ in the best case. Both ðare and \ihare are open-source and are available at: \urlhttps://github.com/dice-group/incrementalHARE.
Evolution of Modern Recommender Systems with AI Techniques

Recommender systems (RSs) emerged several decades ago as software tools analyzing Web user preferences and making intelligent product and content suggestions based on their behavior and tastes.At first, they utilized simple comparison algorithms and analysis of explicitly stated user data in the form of ratings and likes.With the booming expansion of Web data and growing variety of products, services, and information sources online, RSs face pressing challenges to efficiency of recommendations such as data sparsity, cold start, and scalability requirements.The solutions to these issues come in the form of AI-based techniquesadvanced machine learning algorithms that adapt to the Web landscape and address the user requirements flexibly.With the trend of Web expansion certain to remain unchanged for many years to come, intelligent, AI-enriched RSs are expected to gain greater significance in users' decision-making and Web navigation.But to gain user trust and ensure a greater user buy-in, RSs have to offer transparency and perceived credibility.
DATA VISUALISASI SEBAGAI PENDUKUNG INVESTIGASI MEDIA SOSIAL

Social media is an application that can make everyone interact with each other and can consume information by sharing content quickly, efficiently and real time. Various kinds of information about someone's activities that we can find on social media, making social media can help to conduct investigations. Some research, using visualization with several graph methods to facilitate the process of analyzing data on social media that is so abundant. But the data used only comes from one social media, while there is still a lot of information on other social media that can be used as data sources for analysis purposes. In this study visualization using the directed graph method will be carried out, then calculate the value of network property and the value of centrality to find out which nodes have many activities which will be carried out in depth searches to find patterns of interaction or activity. Based on the results of the calculated centrality, it is found that on Twitter and Instagram accounts there are many interactions, this can be seen in the value of the indegree and outdegree node. Based on the results of the analysis in this study, information that is important for investigating social media is obtained, such as information about user profiles, posts, comments, preferred social media pages, location, and timestamp, all of which are connected by a line that shows the relationship between the node.
Parallel Processing Based on Fragmentation of English Text Pictures to Speed Up Recognition of Characters on a Multi-Core Computer

Script picture analyses use algorithms to turn images of text into readable computer data.The goal of script picture analyses is pattern recognition of script and graphics combinations from images for extraction of the data.This paper solves a long-existing problem in pattern recognition in script images regarding recognition of characters and words using a computer processor.The proposed system rely on main steps of execute, first step: is load English script of image file, second step: is partition image file to several image pages, third step: is divide the image into number of blocks, and final step: multi-block method can be used in each row of the script image with parallel processing using a multi-core computer (one processor P1, two processors P2, three processors P3, and four processors P4) with rapid processing for script images for pattern recognition .The outcome obtained with a multi-core computer is very fast for processing script images and is efficient and accurate.It can use the modified algorithm in multi-processor system that available now and not used all the capacity in processing in one processor as in old computers.
ANALISIS TINGKAT KEBERHASILAN CRYOTERAPY MENGGUNAKAN NEURAL NETWORK

Human health is very important to always pay attention especially after someone has been declared suffering from an illness that can inhibit positive activities. One of the most feared diseases of the 20th century is cancer. This disease requires treatment that is quite expensive. Alternative treatments are cryotherapy or ice therapy. But cryotherapy also has side effects, it is necessary to do research on its success by taking into account certain conditions of the parameters. So the purpose of this study is to analyze the success of cryotherapy so that the dataset can be used as one of the benchmarks for the success of the cryotherapy tratment method. The method used in this study is the machine learning method of Neural Network with 500 training cycles, learning rate of 0,003 and momentum 0,9 which results in a good classification of obtaining quite high accuracy of 87,78% and AUC value of 0,955.