Reviewerly/exHarmony huggingface dataset:
A Pragmatic Analysis on Vague Language Used in Disaster News Articles on Thejakartapost.com

This paper presents the findings analysis of categories and functions on vague language used in disaster news articles on Thejakartapost.com based on the theory of Channell (1994). In the journalism context, especially in disaster news article, the information often contains vague language that has imprecise statement since it is harmful. Therefore, to avoid wrong statements, the reporters often use vague language in presenting information accurately. The study employed a qualitative descriptive method. All data were 24 news articles. There were 12 news articles of natural disasters and 12 news articles of human-caused disasters. The period was from April 2018 until March 2019. The findings of this study showed that there were three categories of vague language, namely vague additives to numbers that were realized by approximators and adjectives. The others were vagueness of choice of vague words that were realized by nouns, and vagueness by scalar implicatures that were realized by quantifiers, numbers, and exaggerations. In addition, they also have its functions of vague language. Firstly, giving the right amount of information, it is used since the reporters just shared the right number of information although the exact number was not available. Secondly, filling in lexical gaps of uncertainty, it is used since the reporters wanted to cover the imprecise information with another word, and generalized word that was difficult to identify. Last but not least, self-protection. It is used since the reporters wanted to protect and hedge their statements from imprecise information. Keywords: Vague Language, Categories, News Articles, Disasters, implicature
PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA KE INDONESIA MENGGUNAKAN METODE SUPPORT VECTOR REGRESSION DAN BACKPROPAGATION

Tourism is one of the important sectors in the growth of Indonesia’s economy. Tobe able to achieve the target and increase foreign tourist visits to Indonesia it isnecessary to plan appropriate promotion and sustainable development that must bein line with the development of foreign touriststo be in target, effective and efficient.In this study forecasting to the level of foreign tourists visiting Indonesia in orderto obtain accurate data. Forecasting is done by comparing 3 forecast methods, viz.traditional method, Support Vector Regression (SVR), and BackpropagationNeural Network (BPNN) method. In this research, there are 36 monthly visit datafrom 2017 to 2020 that are used to forecast. The result of this research indicatesthat the best forecasting is done by the Support Vector Regression (SVR) methodwith a MAPE value is 2.5614 % whereas, Backpropagation Neural Network(BPNN) has a MAPE value 31.3777%.
Automatic Configuration of Fast Automated Multi‐Objective Treatment Planning in Radiotherapy

At Erasmus MC, automated plan generation is used to treat cancer patients. The system for automated planning is called Erasmus-iCycle which is used to consistently generate a high-quality and Pareto-optimal treatment plan with optimized beam directions for each patient. In this thesis, a new planning method, the lexicographic reference point method (LRPM), was developed and implemented in Erasmus-iCycle. The LRPM aims to improve the clinically used planning method in Erasmus-iCycle in two ways. Firstly, the LRPM should decrease the computation time of automatically generating a plan without compromising in its quality. Secondly, instead of the manual time-consuming and labour-intensive configuration procedure of the clinically used planning method, the LRPM should come with an automatic configuration procedure.
Midpoint Regularization: From High Uncertainty Training Labels to Conservative Classification Decisions

Label Smoothing (LS) improves model generalization through penalizing models from generating overconfident output distributions. For each training sample the LS strategy smooths the one-hot encoded training signal by distributing its distribution mass over the non-ground truth classes. We extend this technique by considering example pairs, coined PLS. PLS first creates midpoint samples by averaging random sample pairs and then learns a smoothing distribution during training for each of these midpoint samples, resulting in midpoints with high uncertainty labels for training. We empirically show that PLS significantly outperforms LS, achieving up to 30% of relative classification error reduction. We also visualize that PLS produces very low winning softmax scores for both in and out of distribution samples.
Parameter learning and fractional differential operators: Applications in regularized image denoising and decomposition problems

In this paper, we focus on learning optimal parameters for PDE-based image denoising and decomposition models. First, we learn the regularization parameter and the differential operator for gray-scale image denoising using the fractional Laplacian in combination with a bilevel optimization problem. In our setting the fractional Laplacian allows the use of Fourier transform, which enables the optimization of the denoising operator. We prove stable and explainable results as an advantage in comparison to machine learning approaches. The numerical experiments correlate with our theoretical model settings and show a reduction of computing time in contrast to the Rudin-Osher-Fatemi model. Second, we introduce a new regularized image decomposition model with the fractional Laplacian and the Riesz potential. We provide an explicit formula for the unique solution and the numerical experiments illustrate the efficiency.
NUIG at TIAD 2021: Cross-lingual word embeddings for translation inference

This work has received funding from the EU’s Horizon 2020 Research and Innovation programme through the ELEXIS project under grant agreement No.731015.
Detection of Malaria Using Machine Learning

According to the WHO 2018 report, there were approx. 228 million cases worldwide and the estimated number of deaths stood 405000. Malaria is a life-threatening, hazardous disease brought about by parasites that are transmitted to individuals through the bites of infected mosquitoes. Malaria should be considered a potential medical emergency. Delay in diagnosis and treatment is a leading cause of death in malaria patients. Malaria can be suspected on the basis of physical findings and symptoms in any patient. Laboratorians may lack experience with malaria and neglect to identify parasites while examining blood smears under the microscope. Automation of the diagnosis process will ensure accurate diagnosis of the disease and hence may deliver reliable results to resource-scarce areas.
Towards Optimization-Based Predictive Congestion Control for the Tor Network

Providing online anonymity to a broad range of Internet users, the Tor network today faces not only security concerns, but increasingly also performance issues. Due to its multi-hop nature, proper congestion control has been identified to be challenging in this situation. In this paper, we focus on PredicTor, a novel approach towards multi-hop congestion control based on distributed model predictive control (MPC), an advanced optimization-based control technique. We investigate PredicTor's significance for congestion control research. In particular, we carry out a simulation study to evaluate PredicTor's performance in non-trivial network scenarios. Our results indicate the great potential to push the status quo of congestion control, heavily improving achieved latency and fairness. By pointing out benefits and challenges of distributed MPC in this context, we open up a new promising research direction for congestion control
BeCoMe - the best compromise solutions in the ill structured decision making situations like COVID-19 pandemic any many others

ion. Statistical methods often fail, too, because of a lack of relevant data, such as probability distributions of variables in formal models. It is vital in such situations to have an averaging operator, which represents a compromise of all individual opinions.
Dr ID Ryabchikov from the Academy of Sciences USSR with Dr David Green and Mr Bill Hibberson

Research School of Earth Sciences - Dr. Ian MacDougall, Wang Sung Shan, Dr. Bill Compston, Chung Fu Tao, Dr. S. W. J. Clements, W. Sinclair, A. E. Ringwood, Prof. Lew Gustafson, Leigh Schmidt, Dr. John Cleary, Dr. Jack Ryan, Dr. I. D. Ryabchikov, Dr. David Green, Mr. Bill Hibberson, Dr. Mike McElhinny, Mr. Charles Barton, Mr. Kurt Kloska, Mr. Dieter Burmann, Prof. Hales & others
LibGuides: Library Intranet: Venue Booking

To share teaching, learning and research support information Booking of computer lab room at Building 3 - Library and Information Services, there are 2 computer labs rooms 022 and 008 each with 32 computers
Subject & Study Guides: Remote Support from the Library: Internet Searching and evaluating information

Here are the ways that Unitec Library can support you with your studies when you're unable to come onto campus
TEACHING READING NARRATIVE TEXT AT CERDAS ISTIMEWA CLASS OF SMPN 1 BANJARMASIN

English is a foreign language. Learning a foreign language needs a lot of practice and learning. A teacher plays an important role in learning English, especially for the Cerdas Istimewa program. The success of this class depends on the material, media, and techniques used by the English teacher. This research aims to describe the implementation of teaching reading in terms of material, media, and techniques at the fourth semester of Cerdas Istimewa class in SMPN 1 Banjarmasin academic year 2019/2020. This research used qualitative research. The subject of this research is the English teacher at Cerdas Istimewa class. The instruments of this research are observation, interview, and documentation. Based on the research finding, the teacher used material from textbooks and also from other sources such as the internet. The teacher used various media in his teachings such as LCD, pictures, music videos, and film. For the techniques in teaching reading, the teacher used SQ3R, the three-phase technique, and the question and answer relationship. The teacher implemented media based on curriculum 2013, selected the material based on students’ needs, and also from the syllabus of Cerdas Istimewa.
Breast Cancer Detection using Clustering and SVM

Breast cancer is one of the preeminent impetus of deaths in India. Current Indian data show that one in 22 women develop breast cancer. Out of the two who develop it, one of them would have a higher chance of not surviving it. Breast cancer in India is different from that in the West. Here, it is affecting younger women and more than half of them present themselves in advanced stages. By 2030, breast cancer will cause maximum deaths among women in India than any other cancer. Effective and efficient early detection and classification tools are required to reduce the mortality rates due to breast cancer. Machine learning provides ways to detect and classify these early by finding some recurring patterns in the mammograms. In this research work, fuzzy c-means (FCM) clustering is presented as an approach for performing segmentation and SVM as an approach for performing classification. Image aggrandizing approaches, segmentation, feature adumbration and classification approaches have been delved into and administered on mammographic images obtained from the mini-MIAS repository(Abstract)
ANALISIS LATAR BELAKANG PENDIDIKAN DAN PENGALAMAN MENGAJAR GURU TERHADAP PRESTASI BELAJAR SISWA DI MTs. AS’ADIYAH PUTERI I SENGKANG KABUPATEN WAJO

This study aims to determine and analyze the educational background of teachers and the effect on student achievement in teaching and learning teachers together influence the student achievement affect the student learning outcomes in MTs. As'adiyah Puteri I Sengkang Regency. The study was conducted on MTs teachers. As'adiyah Puteri I Sengkang, Wajo Regency for 3 months from August to October 2019, with a total of 25 teachers, all the population was taken as a sample (saturated sample). The method used is quantitative descriptive, with regression testing. The results of this study have a significant effect between the educational background of teachers on student achievement; teacher's teaching experience competence towards student learning outcomes; there is a significant influence between the educational background of teachers on student achievement in MTs. As'adiyah Puteri I Sengkang, Wajo Regency.
RANCANG BANGUN APLIKASI PELAYANAN PENGADUAN MASYARAKAT DENGAN METODE NAÏVE BAYES PADA DINAS PERHUBUNGAN KABUPATEN LAMPUNG UTARA

The application of classifying public complaint reports is made because in the process of complaints by the community, an admin must select the data of disturbances one by one to be able to follow up on the existing public complaint reports. Naive Bayes is a method that uses probability calculation where the algorithm utilizes probability and statistical methods that predict future probabilities based on previous experience. The application of the naive bayes classifier method with text mining as an initial data processor of the complaint report classification application can be concluded that this research yields an accuracy of 90% probability value and proves that the naive bayes method can be used to help classify complaints reports sent by the public.
White Matter Hyperintensities Segmentation with Prototype Learning

This paper proposes a new method -based on meta-learning- for the WMH Segmentation Challenge, organized by UMC Utrecht, VU Amsterdam, and NUHS Singapore hospitals. The purpose of this challenge is to compare methods for the semantic segmentation of white matter hyperintensities (WMH), which are brain white matter lesions, of presumably vascular origin in brain imaging obtained with magnetic resonance. White matter hyperintensities are found in patients with brain diseases like Parkinson, Alzheimer or stroke. Semantic segmentation refers to the process of linking each pixel in an image to a class label. The semantic segmentation of images has had a great advance with convolution neural networks, but they require a large number of images to be able to obtain good results. Convolutional neural networks are a type of neural networks specialized on images which architecture is similar to neurons’ pattern in human brain and they were inspired by the organization of the visual cortex. With the aim to reduce the number of images required in training, in this work, we propose the use of meta-learning algorithms, in particular prototype learning, to do this semantic segmentation. In addition, this approach also allows the network to be used in a different task for which it was not trained, which can improve its potential use. Results obtained suggest that it could be possible to use the network trained to a specific task (i.e., detect WMH in the brain), to another task (i.e. detect any kind of tumors in the brain).
Analisis dan Rancangan User Experience Website OAIL Menggunakan Metode Task Centered System Design (TCSD)

The increase in internet service users in obtaining information and knowledge has made many institutions or organizations begin to create and design websites. One of the institutions that started to build a website is UPT OAIL. In the process of forming the OAIL website, a good User experience (UX) design is needed because it affects user satisfaction in using the website. The analysis and design of user experience in this study uses the Task Centered System Design (TCSD) method. The TCSD method can identify user needs and task needs. The stages in the research are user identification and observation, user and organization requirements analysis, design as scenario and walkthrough evaluate. In this study, identification and observation were carried out by interviewing UPT OAIL staff as well as prospective users. The test is based on the usability method using USE Questionare on the ease of use dimension. The results of the study obtained that the interpretation score for testing the ease of use dimension was 94.2% with the conclusion that the average user or respondent chose the design of each page and the features made were very easy to use.
Design and Development of Information System for Administration and Monitoring of Authentic Deed Services for Notary and Conveyancing Using the Prototyping Method

Notary Asep Sudrajat, SH., M.Kn. is a company that provides legal services in making authentic deeds and other legally regulated authorities. Currently, Notary Asep Sudrajat, SH., M.Kn has many requests for authentic deed processing, which is currently the filing process and also for submitting information, clients must come to the office or call the notary to find out the stages of transactions that the client has submitted at this time. The notary must look for files to provide information on the progress of deed processing to clients. Sometimes the delivery of information still occurs problems because the deed completion process takes about 1 to 3 months, this can make it difficult for the notary to find the file. From these problems, an Authentic Deed Service Administration and Monitoring Information System will be built. This information system will be developed based on the website in the notary section with the PHP and MYSQL programming language as the database and the android application will be used by the client. As well as the development method using the prototyping method with 2 iterations. Based on the test results, the system built was in accordance with the requests and needs of the Notary Asep Sudrajat, SH., M.kn. The application also runs well with usability testing with the SUS method resulting in a total value of 84, these results indicate that the application has been accepted by the notary client.
Parental Education and the Need to Train Parents in 21st Century

The challenges of the 21st century impose on today's parent the need to take part in a new type of education, namely, parental education. This type of education takes into account the discipline of the parent, by providing relevant information on: the characteristics of children according to their age, parental typologies with the advantages and disadvantages of each, parenting strategies that ensure streamlining the parent-child relationship, as well as the obstacles encountered in the process of raising and educating the child. The multitude of information stated above, arouses the interest for training parents in the field of parenting, but also the need to implement training programs with a central theme, parental education. Following the application of a focus group interview, which was attended by eight parents whose children are part of primary school, it was found that they are aware of the shortcomings they have, but also of the mistakes they make as parents, concluding that a training program in the field of parental education would be a real guide for parenting. In agreement with the current society, a technological society, it is necessary to design and implement a training program that aims, on the one hand: issues related to parenting, on the other hand, issues related to technological resources, establishing the following objectives: to make some correspondences between the particularities of the children and the parental practices, in the technological era; streamlining the parent-child relationship in the digital age; openness to the use of digital tools; providing the necessary resources for an optimal adaptation of the parent to the digital age.
Curriculum Design in an Era of Ubiquitous Information and Technology

This chapter argues that Passow was absolutely correct about the vital relationship between goals and curriculum. Without clear goals, recommendations for curriculum and learning designs are meaningless. The gifted STEM program develops elite talent in mathematics, science, engineering, and technology in order to place students in prestigious colleges and universities to pursue degrees and careers in STEM fields. The high school in this comparison approaches gifted education as a process of elite talent development in the age of technology. Technology is used to leverage time and compact curriculum. Technology and widely available information should be seen as game-changers for developing elite talents and exceptional students. Embrace information and technology as tools that redefine our capacity to develop elite talents and place students on trajectories of excellence and potential eminence. Finding statements of the goal of gifted education in the field’s literature is surprisingly difficult.
Motivation for Learning Activities of Students of Technical Universities in the Classes of the Russian Language

The article is devoted to the analysis of the formation of educational motivation in the classroom of the Russian language in a technical university. An interpretation of the internal and external motivation of students in the classroom is given. The relevance of the proposed article lies in the fact that it presents an analysis of the survey conducted among four institutes of the university and presents the survey questions among students. The purpose of the article is to develop positive motivation for learning the Russian language using a competency-based approach to teaching. Materials and methods: integration, competence-based approaches, the method of comparative analysis were used. The results of the survey show that the self-sufficiency of the competence-based approach, its focus on results, while creating all the necessary conditions, contribute to the formation of long-term motivation and productive mastering of the Russian language.