Reviewerly/exHarmony huggingface dataset:
RABANI ( Reaktor Baru Wudhu Masa Kini) : Sistem Daur Ulang Air Wudhu yang Syar’i Berbasis Neo

Indonesia is the country with the largest adherents of Islam in the world. The number of adherents of Islam is around 212.5 million people or about 80% of the total population. With the number of mosques in Indonesia around 731,096 buildings in 2013. Every Muslim (adherent of Islam) before praying always cleanses certain body parts commonly called ablution so that it is estimated that the use of water for ablution in a day is around 1.8 million m3. The dry season that occurred in the last few months has caused many areas to experience a clean water crisis. The use of water must be carried out optimally. Islam teaches how to be frugal and fair as taught in the apostles' Sunnah. Therefore, there is a need for a system designed to recycle used ablution so that it can be reused into ablution water. RABANI uses the NEO method (membrane based zeolite, electrocoagulation and ozonation) nanofiltration. The combination of several methods of water purification will increase the effectiveness of water purification so that better quality water is produced. This system produces output products in the form of clean water that is suitable for reuse for the purification process.Keywords: Water ablution, ozonizasi, referring
A Digital Services Centre for Improving Home Assistance

Telemedicine services based on ICT enable the provision of health care together with social care services at home. The developments in mobile communication technologies, together with the state of the art in research areas like Internet of Things (IoT) and wearables, have a positive impact in expanding the field of home assistance support services, both in complexity and in covering a larger number of requests, in densely populated areas and also in less populated areas like rural and remote areas. The paper presents a complex solution for the management of home teleassistance services through a digital platform.
Laying Chicken Algorithm (LCA) Based For Clustering

Numerous research and related applications of fuzzy clustering are still interesting and important. In this paper, Fuzzy C-Means (FCM) and Laying Chicken Algorithm (LCA) were modified to improve local optimum of Fuzzy Clustering presented by using UCI dataset. In this study, the proposed FCMLCA performance was also compared to baseline technique based on CSO methods. The simulation results indicate that the FCMLCA method have better performance than the compared methods.
Research on the Construction of College English Reading Teaching System Based on the “Internet+” Environment

College English teaching is of great significance to the improvement of students' English proficiency and the cultivation of English application ability, especially reading teaching can effectively improve college students' oral, comprehension and writing skills. However, many college students have the problem of weak enthusiasm when learning English reading. At this time, efficient English reading teaching should effectively use Internet technology to reconstruct the reading teaching system, optimize the quality of English reading teaching, and provide reading teaching attractive to students.
Integrated Embedding Approach for Knowledge Base Completion with CNN

To tackle specific problems in knowledge base completion such as computational complexity and complex relations or nodes with high indegree or outdegree, an algorithm called IEAKBC(short for Integrated Embedding Approach for Knowledge Base Completion) is proposed, in which entities and relations from triplets are first mapped into low-dimensional vector spaces, each original triplet represented in the form of 3-column, k dimensional matrix; then features from different relations are integrated into head and tail entities thus forming fused triplet matrices used as another input channel for convolution. In CNN feature maps are extracted by filters, concatenated and weighted for output scores to discern whether the original triplet holds or not. Experiments show that IEAKBC holds certain advantages over other models; when scaling up to relatively larger datasets, signs of superiority of IEAKBC stand out especially on relations with high cardinalities. At last we apply IEAKBC to a personalized search application, comparing its performance with strong baselines to verify its practicality in real environments.
Teacher Training: Pros And Cons Of Distance Education Through Students’ Eyes

In accordance with the Federal State Educational Standard of Higher Education adopted by the Ministry of Education and Science of the Russian Federation order in February 2018, the implementation of various educational programs calls for the use of educational technologies such as e-learning and distance technology. As for the distance technology, its application in the academic process, undoubtedly, contributes to the development and mastery of the students’ independent work skills. The article discusses distance learning as one of the options for undergraduate students’ independent work structuring. It presents the course and results of the experiment on distance learning technology implementation in Transbaikal State University. During the experiment, prospective teachers of foreign languages examined the effectiveness of distance learning at university. As a result, several observations were made regarding the quality and effectiveness of distance learning implementation. They can prove serviceable in the present revision of the distance learning possibilities and influence the development of enhanced methods for the organization of such an activity. The present article focuses on the students’ insights into the possibility of using distance technology in the learning process and their cause-result analysis. The university students consider distance learning technology as modern and progressive; moreover, they recognize its potential for the instructional materials enhancement. However, distance learning implementation should be thoroughly thought out and integrated into the traditional academic system and not considered as an alternative to it.
Language politeness in students’ text messages sent to the lecturers through WhatsApp application: A sociopragmatic study

Students and lecturers often communicate, both in formal and semi-formal situations. Good communication between students and lecturers remains within the limits of politeness even though it is not in a formal situation. The existence of social media certainly facilitates communication between students and lecturers. This research aims to describe and analyze the politeness of language in short messages from students to lecturers through the WhatsApp application at Universitas 'Aisyiyah Yogyakarta (UNISA). This research employed a qualitative research. The data from this study were 105 conversations between lecturers and students through the WhatsApp messaging application. The analysis in this research was carried out to determine 1) obedience and violation to the tact maxim, generosity maxim, approbation maxim, modesty maxim, agreement maxim and sympathy maxim, 2) forms of language politeness, that are positive face and negative face, and 3) function of language politeness that are representative, directives, expressive, commisive and declaratives. The results showed that most of students used polite language in their text messages and used formal language which contained complete politeness expressions such as greetings, self-introductions, apologies at the beginning and/or at the end of text messages, intention in sending messages, thank you –note and closing. Most of the text employs positive face. Students’ text messages mostly function as representative and directives. DOI: 10.26905/enjourme.v4i2.4926
ARCH-COMP20 Repeatability Evaluation Report

This report presents the results of the repeatability evaluation for the 4th International Competition on Verifying Continuous and Hybrid Systems (ARCH-COMP’20). The competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2020, affiliated with the IFAC World Congress. In its fourth edition, twenty-eight tools submitted artifacts through a Git repository for the repeatability evaluation, applied to solve benchmark problems for seven competition categories. The majority of participants adhered to the requirements for this year’s repeatability evaluation, namely to submit scripts to automatically install and execute tools in containerized virtual environments (specifically Dockerfiles to execute within Docker), and several categories used performance evaluation information from a common execution platform. The repeatability results represent a snapshot of the current landscape of tools and the types of benchmarks for which they are particularly suited and for which others may repeat their analyses. Due to the diversity of problems in verification of continuous and hybrid systems, as well as basing on standard practice in repeatability evaluations, we evaluate the tools with pass and/or failing being repeatable.
Improving Multiple Time Series Forecasting with Data Stream Mining Algorithms

This paper proposes a hybrid ensemble learning approach that combines statistical and data stream mining algorithms to obtain better forecasting performance in multiple time series prediction problems. Although some multiple time series algorithms perform surprisingly well in a variety of domains, it is well-known that no one is dominant for every existent domain. Therefore, we developed a meta-technique based on data stream mining and static ensemble selection strategy and evaluated its forecasting goodness-of-fit in time series datasets from M3 and M4 competitions. After training different regression models, we show how the combination of auto.arima and AdaGrad leads to improved forecasting rates, thus surpassing the results of state-of-art algorithms.
The Lexical Approach In Teaching English For Economics Online

The article is devoted to the topical problem of teaching English for Special Purposes (ESP) to students of Economics. The authors consider ESP teaching in the context of transferable skills. It is absolutely necessary for students to foster these skills to become qualified and successful specialists in any field. The authors’ focus is teaching English for Economics with the help of modern information technology. The presence of a significant number of terms and idiomatic expressions in English for Economics makes the Lexical Approach especially effective while fostering the foreign-language professional competence of future economists. To determine the level of students' awareness of various Internet services that can be used for teaching ESP, including those aimed at training and drilling certain language skills, an online Google Forms survey was conducted. Due to the threat of coronavirus proliferation, St Petersburg State University switched to distance learning in the blink of an eye. Students have learnt a large number of Internet services. Foreign language teachers have been using them daily, not only for homework, but also for online activities in class. The survey was completed by 1,892 students of St Petersburg State University. The results of the survey strongly indicate a high level of IT competence of the students. The most popular platforms and services are identified, including those that form foreign language competence using the Lexical Approach.
Secure eHealth: A Secure eHealth System to Detect COVID using Image Steganography

COVID-19 is a pandemic which has spread to all parts of the world. Detection of COVID infection is crucial to prevent the spread further. Contactless healthcare systems are essential which can be implemented with Cloud computing. Privacy and security of the medical image data transferred through untrusted channels cannot be ensured. The main aim is to secure the medical details when transferring them from the end device to the cloud and vice versa using image steganography. The medical lung images are masked under a normal and natural cover images.
Unsupervised Learning of Discourse Structures using a Tree Autoencoder

Discourse information, as postulated by popular discourse theories, such as RST and PDTB, has been shown to improve an increasing number of downstream NLP tasks, showing positive effects and synergies of discourse with important real-world applications. While methods for incorporating discourse become more and more sophisticated, the growing need for robust and general discourse structures has not been sufficiently met by current discourse parsers, usually trained on small scale datasets in a strictly limited number of domains. This makes the prediction for arbitrary tasks noisy and unreliable. The overall resulting lack of high-quality, high-quantity discourse trees poses a severe limitation to further progress. In order the alleviate this shortcoming, we propose a new strategy to generate tree structures in a task-agnostic, unsupervised fashion by extending a latent tree induction framework with an auto-encoding objective. The proposed approach can be applied to any tree-structured objective, such as syntactic parsing, discourse parsing and others. However, due to the especially difficult annotation process to generate discourse trees, we initially develop a method to generate larger and more diverse discourse treebanks. In this paper we are inferring general tree structures of natural text in multiple domains, showing promising results on a diverse set of tasks.
Interdisciplinarity and Crowdsourcing in Ecology as Reply to the Challenges of the Technogenic Civilization

The main characteristic of the modern environment is the negative change by its people – destruction and pollution. Man is part of the biosphere and the technogenic transformations of the biosphere inevitably affect him. Under the influence of technogenic civilization, all spheres of human activity undergo changes, and science above all. Ecology is especially keenly aware of the challenges of technogenic civilization. It focuses on anthropogenic factors, works with the human environment. At the same time, its problem field is expanding due to interdisciplinary research and the allocation of knowledge of new environmental disciplines into an independent industry. The interdisciplinarity of modern ecology is most clearly visible on the example of such a direction as informational ecology. The presence of the digital environment in human life has grown so much that it requires the separation of the digital information environment into a separate subsystem. Information ecology studies the laws of the influence of information on the formation and functioning of man. In turn, interdisciplinarity, assuming the use of knowledge from various branches of science, brings us to the problems of the collective agent of cognition and distributed knowledge. In ecology, the problems of the collective agent of cognition are implemented through crowd-sourcing technology. Ecology is a science that requires massive collection of observation data (samples of water, air, soil pollution in various, sometimes hard-to-reach corners of the planet, observation of fluctuations in the number of species of animals and plants). The popularity of crowdsourcing projects in the field of ecology is explained by the fact that the challenges and threats of anthropogenic civilization have generated such a trend of our time as environmental orientation or environmentalism. Ecological crowdsourcing projects, inspired by the philosophy of environmentalism, can serve as an answer to the challenges of technogenic civilization.
Use of proximal sensor for soil classes separation applying Principal Component Analysis (PCA)

The spectrally active components of the soil allow the realization of integrative analyzes of soil aspects such as their classification. The purpose of this study was to evaluate the separation of soil classes from spectral reflectance data using principal components analysis (PCA). The study was carried out in the Aiuaba Experimental Basin located in the municipality of Aiuaba, Ceará, Brazil. Soil samples were collected in Ustalfs, Ustults and Ustorthents profiles. The samples were submitted to spectral analysis by a spectroradiometer and, subsequently, to PCA. Principal components were used to identify which of them contribute more significantly to the separation of the soil classes analyzed, based on their relationship with the soil attributes using a two-dimensional graphical analysis. From the examination of spectral behavior data of the different soil classes, the use of PCA allowed the separation of the classes Ustorthents, Ustalfs and Ustults from each other.
Chatbot-Based Training for Project Management: Another Way of Corporate Training or a Must-Have Tool for Sustainable Education?

Chatbots or virtual conversational agents are computer programs that use natural language processing techniques to understand and communicate with people via a chat interface, which is usually textual or auditory. There are a lot of successful use cases of chatbots nowadays, having a wide area of applications, from e-commerce, customer relationship management, media, healthcare, project management and education. We claim that chatbots are very useful in corporate training, as they support professional development and knowledge sharing in day-to-day business practices, they reinforce learning and help professionals to find quickly the required information, thus contributing to a sustainable education. In this paper, we propose a pattern-based chatbot, useful for corporate training for competence enhancement. The solution was successfully deployed in a professional association and integrated in its digital platform.
PBI55 Patient Preferences for GENE Therapy in Hemophilia: Results from the Paving Threshold Technique Survey

The aim of the Patient preferences to Assess Value IN Gene therapies (PAVING) study was to investigate trade-offs that adult Belgian hemophilia A and B patients are willing to make when choosing between prophylactic factor replacement therapy (PFRT) and gene therapy. The threshold technique was used to quantify the minimum acceptable benefit (MAB) of a switch from PFRT to gene therapy for ‘Annual bleeding rate’ (ABR), ‘Chance to stop prophylaxis’ (STOP), and ‘Quality of life’ (QOL). ‘Time that side effects have been studied’ was also included as an attribute. An educational tool provided patients with information on gene therapy. The design was supported by stakeholder (including HTA, payers and patients) involvement. Threshold intervals were analyzed using interval regression models in Stata 16. A total of 117 patients completed the survey. Mean thresholds were identified for all benefits (ABR, STOP and QOL), but substantial preference heterogeneity was observed. Heterogeneity in STOP thresholds was found to be most extreme and the majority of patients could be classified in two opposite groups. Time spent on the educational tool and residence were found to impact MAB thresholds. Most patients (88%) would accept gene therapy if it would result in 0 bleeds per year (vs. 6 for PFRT), 90% chance to stop prophylaxis, had no impact on QOL, and if there was 10 years of follow-up on side effects (vs. 30 for PFRT). Results from this study confirmed the value of educating patients on novel treatments (e.g. through educational tools) prior to measuring their preferences. Moreover, preference heterogeneity for novel treatments can be expected and was confirmed in this study. In making decisions on gene therapy, regulators, HTA and payers should consider preference heterogeneity and the impact of patient education on acceptance.
An Improved Multi-State Particle Swarm Optimization for Discrete Combinatorial Optimization Problems

The binary-based algorithms including the binary gravitational search algorithm (BGSA) were designed to solve discrete optimization problems. Many improvements of the binary-based algorithms have been reported. In this paper, a variant of GSA called multi-state gravitational search algorithm (MSGSA) for discrete optimization problems is proposed. The MSGSA concept is based on a simplified mechanism of transition between two states. The performance of the MSGSA is empirically compared to the original BGSA based on six sets of selected benchmarks instances of traveling salesman problem (TSP). The results are statistically analyzed and show that the MSGSA has performed consistently in solving the discrete optimization problems.
On pseudorandom number generators

&lt;p class="Abstract"&gt;Statistical sampling and simulations produced by algorithms require fast random number generators; however, true random number generators are often too slow for the purpose, so pseudorandom number generators are usually more suitable. But choosing and using a pseudorandom number generator is no simple task; most pseudorandom number generators fail statistical tests. Default pseudorandom number generators offered by programming languages usually do not offer sufficient statistical properties. Testing random number generators so as to choose one for a project is essential to know its limitations and decide whether the choice fits the project’s objectives. However, this study presents a reproducible experiment that demonstrates that, despite all the contributions it made when it was first published, the popular NIST SP 800-22 statistical test suite as implemented in the software package is inadequate for testing generators.&lt;/p&gt;
Deep Learning-Based Deblur Using Gyroscope Data

Global scene blur of an image is caused by the movement of a camera during the exposure time, thus camera motion is one of the key factors in identifying blur artifacts. Among various sensors that can be utilized while taking a picture in a mobile environment, a gyroscope sensor is useful for deblurring tasks, because rotational velocities measured from the gyroscope can be processed to estimate the camera motion. For enhancement of deblurring performance in a mobile environment with an additional gyroscope sensor, this paper presents a method of using gyroscope data in a deep learning-based deblur network. The proposed method calculates homography matrices from the gyroscope data and warps the input image to imitate the camera motion. The stack of warped images aligns blur artifacts caused by camera movement channel-wise, and the network is trained to use image features related to the direction and magnitude of the blur. The proposed method shows superior performance in cases of both subtle and severe blur conditions compared to conventional gyroscope-aided deblurring network.
Klasifikasi Penelitian Dosen Menggunakan Naïve Bayes Classifier dan Algoritma Genetika

Research is an obligation for lecturers to develop their knowledge besides teaching. Until now all lecturer's research is managed by the Institute for Research and Community Service, often called LPPM. College research institute is a place where all lecturer research information can be obtained, problems encountered at tertiary research institutions are the grouping process in the field of lecturer research carried out by the LPPM data and information system section, this is also one of the problems related to text classification . Classification is the process of finding a model that describes and distinguishes classes or concepts that aim to be used to predict classes from objects whose class labels are unknown. Naive Bayes is a simple probabilistic based prediction technique based on the application of the Bayes theorem with strong independence assumptions. In this method there are deficiencies that can affect the accuracy caused by the Naive Bayes feature which is not always applicable. To deal with these problems, researchers conducted a feature selection process using Genetic Algorithms. The dataset used is 275 lecturer research data from all scientific fields. The results of experiments in this study indicate that the accuracy value increased by 26.06% with the use of Genetic Algorithms in the feature selection process.