Reviewerly/exHarmony huggingface dataset:
Fuzzy Set Theory-Based Approach for Mining Spatial Association Rules: Road Accident as a Case Study

Despite intensive research over the last decades in spatial data mining, a considerable amount of collected data brings new challenges to the extraction of spatial association rules. Notwithstanding, association rules are examining the numerical and categorical data accurately; however, it is not directly extensible to efficiently analyze real-world spatial data. Analyzing spatial data exposes significant difficulties, including particular representations of spatial information like geometrical measures, topological measures, and spatial object dependencies. Most current studies ignore the integration of geographical information in the association rules mining process, which is not beneficial for the decision-makers to extract relevant association rules. To deal with this issue, we proposed a fuzzy set theory-based approach for mining spatial association rules; This proposed approach would be especially valuable for decision-makers suffering from imprecision and non-quality of extracted association rules. An analysis of road accidents using spatial data shows that the use of fuzzy set theory improves the quality and the precision of extracted association rules. Overall, the proposed approach contributes to a better understanding of spatial association rules and provides meaningful information that can aid decision-makers to improve the performances and precision of spatial association rules.
Information System Design for Water Maintenance Activities at PT. Rezeki Surya Intimakmur

PT. Rezeki Surya Intimakmur is a company engaged in home and building maintenance. Currently PT. Rezeki Surya Intimakmur is currently under a work contract with PT. Chevron Pacific Indonesia (PT. CPI) in terms of clean water maintenance activities in the company environment and the housing for employees of PT. CPI. In managing clean water maintenance data every day, the company uses a manual method by recording, which is filling out a work assignment form. This method results in the management of clean water maintenance work being hampered because data on workers and work results are often inconsistent with the actual situation—applications designed using research and product development methods with the waterfall model. The application design process uses Visual Basic.Net programming language and MySQL database. The resulting can make it easier for admin staff to manage clean water maintenance activities to produce maintenance reports.
Apple single product category resource information platform construction: the example of Shandong Province

A solution for building a resource information platform of apple single product category in Shandong province is introduced to solve some problems of traditional platform with the purpose of ease of use and security.
Hierarchical Preference Hash Network for News Recommendation

Personalized news recommendation is becoming increasingly important for online news platforms to help users alleviate information overload and improve news reading experience. A key problem in news recommendation is learning accurate user representations to capture their interest. However, most existing news recommendation methods usually learn user representation only from their interacted historical news, while ignoring the clustering features among users. Here we proposed a hierarchical user preference hash network to enhance the representation of users' interest. In the hash part, a series of buckets are generated based on users' historical interactions. Users with similar preferences are assigned into the same buckets automatically. We also learn representations of users from their browsed news in history part. And then, a Route Attention is adopted to combine these two parts (history vector and hash vector) and get the more informative user preference vector. As for news representation, a modified transformer with category embedding is exploited to build news semantic representation. By comparing the hierarchical hash network with multiple news recommendation methods and conducting various experiments on the Microsoft News Dataset (MIND) validate the effectiveness of our approach on news recommendation.
EXTENDED EUCLIDEAN ALGORITHM OF AUNU BINARY POLYNOMIALS OF CARDINALITY ELEVEN

Binary polynomials representation of Aunu permutation patterns has been used to perform arithmetic operations on words and sub-words of the polynomials using addition, multiplication, and division modulo two. The polynomials were also found to form some mathematical structures such as group, ring, and field. This paper presents the extension of our earlier work as it reports the Aunu binary polynomials of cardinality eleven and how to find their greatest common divisor (gcd) using the extended Euclidean algorithm. The polynomials are pairly permuted and the results found showed that one polynomial is a factor of the other polynomial or one polynomial is relatively prime to the other and some gave different results. This important feature is of combinatorial significance and can be investigated further to formulate some theoretic axioms for this class of Aunu permutation pattern. Binary polynomials have important applications in coding theory, circuit design, and the construction of cryptographic primitives.
Pengaruh Penggunaan Bahan Ajar Leaflet Geografi Terhadap Hasil Belajar Siswa SMA NU Genteng Banyuwangi

Abstract The results of observations on students of class XI IPS 1 and XI IPS 2 at SMA NU Genteng Banyuwangi are still below the minimum completeness score of 75 as many as 35 students. researchers use leaflet teaching materials supplements to improve student learning outcomes. This research was conducted in January 2020 at SMA NU Genteng. The population and sample in this study were students of class XI IPS 1 and XI IPS 2 with the control class selected using cluster random sampling technique. The data used are quantitative to calculate the N-gain value from the difference between the pretest and posttest scores. The data were analyzed using t test and Mann Whitney-U. The increase in student learning outcomes is indicated by the difference in the average pretest score of 63.6 and posttest of 84.6. The average value is in accordance with the minimum completeness set by the school, which is 75. In addition, it is also supported by an N-Gain value of 83.2 which shows an increase in student learning outcomes before and after learning is carried out. The percentage of students who dare to express their opinions is 72.9%, students who are active in group work are 78.6%, who are actively asking questions are 76.4%, dare to present the results of discussions are 81.6% and exchange information are 75.7% . These indicators show the improvement experienced by students. Students who experienced an increase in aspects of knowledge (C1), aspects of understanding (C2) and aspects of analyzing had an average N-gain of 71.2.
Tomographic Reconstruction for Flow Parameters Based on Extreme Learning Machine

Tomographic laser absorption spectroscopy is a versatile imaging technique in flow and combustion diagnostics. While the linear tomographic methods need a large number of line-of-sight measurements, the nonlinear methods involve solving optimization algorithms and suffer from high computational cost. Taking advantages of easy implementation, fast learning speed and good generalization performance of the extreme learning machine, a simultaneous tomographic algorithm for temperature and species concentration is proposed. Simulation results show that this algorithm can locate the peaks of the multimodal Gaussian flames faithfully under both noiseless and noisy measurement conditions and can simultaneously image the temperature and species concentration distribution of the tomographic field within 1 millisecond.
Communicative Competencies Assessment of Teachers at Engineering University

Higher school is one of important resources for the society, region, country sustainability and their social stability; therefore, it is necessary to assess the competencies of all participants in the educational process. The actors directly involved in this process is teaching staff. The goal of this paper is to identify and scientifically substantiate a set of relevant indicators of teachers’ communicative competencies at engineering university in order to achieve high-quality pedagogical practice. Communicative competencies allow teachers both to implement educational process ahead of reality due to independence, freedom in their educational and research activities, and to open interest, motivation of students to study. In the course of the present paper an analytical study of psychological, pedagogical and sociological information were used, as well as survey methods and self-diagnostics. It makes possible to determine indicators for assessing the communicative competencies of teachers, such as mastery of the knowledge transmission technique, the implementation of professional communication methods in the real and information environment, the design of a communication mechanism, proficiency in intercultural, technological, professional, educational communication, and empathy. In the course of the experiment, a survey of students and teachers was carried out to determine the most significant indicators for assessing the teachers’ communicative competencies, as well as their ranking. A discrepancy was revealed within the determining of the significance of some indicators among students of different years and degree programs as well as teaching staff. Self-diagnostics of teachers’ communicative competencies indicators show the need for continuous professional development and reflection. The most sensitive points are indicators related to design of a communication mechanism in the field of neoclassical and classical pedagogy.
Ranking YouTube Videos Based on Comments Sentiment

For as long as we can remember, when we purchase any item, the first thing we tend to do is look for an opinion from the salesman, friends, or even previous consumers, and in the age of technology, it has become even easier for one to gauge out a review from the comments under a product. This is Sentiment Analysis. It is very widely used for trend analysis for companies to see if their products are selling or not. It is used in customer service and during elections to see which candidate is more favourable. That is the case for YouTube videos too. Usually, the most viewed videos show up at the top and the lesser viewed videos at the bottom. In this paper, we have set out to find the sentiment of the comments of top five videos (based on views) under the recipe for ‘Rasbora’, a Bengali delicacy, to see if the most viewed video is actually the one with the most positive sentiment and to discuss what factors play a part in a video gaining more views and the problems faced in analysing the sentiment of the comments under a YouTube video.
Validación de la Escala Uso y Función de las TIC en Contextos Educativos para estudiantes de Educación Superior

The importance of ICT in Higher Education is present in international and national policies; in contextual moments where distance and asynchronous education is prioritized, it is urgent to identify the competences in university students. This research sought to validate in the Mexican population a scale to evaluate the frequency of use and function of ICTs and the educational contexts in which they are used, that supports knowledge about their strengths and areas of opportunity. The methodology was quantitative and divided into two phases: 1) descriptive study to design and pilot the scale; and 2) validation of the final version of the instrument with 390 students from three states of the country. From the analysis of descriptive studies, normality, construct validity (factor analysis) and reliability, the instrument was make up of two components: a) 26 items distributed in 4 factors that measure frequency of use and function; and b) 8 multiple - choice items to identify educational contexts of use. This scale explained 42.7% of the variance, with a global Cronbach Alpha of .91. The use of reliable assessment tools in higher education, will facilitate the intervention and the development of psycho - educational strategies for the acquisition of ICT skills.
Pitfalls and Progress: Overcoming Dispensing Challenges of Palforzia in an Academic Institution

Like many academic institutions, Michigan Medicine Allergy Specialty clinic cannot accept the Palforzia Office Dose Kit (ODK) and we are unable to get an exclusion to the “no sample policy” from the Pharmacy and Therapeutics Committee (P&T). We developed an alternative process that allows patient access to Palforzia and developed in-clinic processes to assure administration success. The pharmacist at the Domino’s Farms Food Allergy and Specialty Clinic, in conjunction with P&T, nursing administrators and allergy faculty, developed a ‘white bagging’ procurement process to receive Palforzia directly from a specialty pharmacy, bypassing the need for the ODK. Patient specific 15-day up-dose packs (UDP) are shipped to the clinic. One dose from the UDP is administered as the in-office test dose. If tolerated, the remaining doses are sent home with the patient. Ordering and administration of Palforzia and bi-weekly up-dose appointments continue, coordinated by clinic personnel dedicated to specific tasks: verification of REMS, ordering medication, establishing contacts at specialty pharmacies, arranging patient appointments and in-clinic observation of patients during up-dosing. Patients at Michigan Medicine are successfully up-dosing using this procurement and administration process, successfully reaching, and continuing maintenance Palforzia therapy. A multidisciplinary team was able to overcome institutional barriers to in-clinic up-dosing. This buy-in from disparate groups was essential. In addition, dedicated staff that ensure dosing and ordering and administration continues without interruption are essential to success in a busy academic allergy clinic. The described methods should be successful at other institutions.
Gardens of Artificial Delights: Residual Memories of the Digital in Contemporary Art

Tamiko Thiel’s Unexpected Growth (2018) and Clement Valla’s Surface Proxy (2015) utilize new media technologies such as augmented reality (AR) and image mapping/print processing to broadcast an image. Both are examples of new media that hone in on formal proximity and immersion at a digital medium-specificity. Incidentally, both also deal in cyber-botanical terrain: Thiel’s Unexpected Growth is an AR installation that presents an uncanny wilderness not exactly of this earth’s, but composed of its discarded waste products. Similarly, Clement Valla’s Surface Proxy reconstructs a third-dimensionality based on flat and two-dimensional renderings of natural objects like plants and stone, effectively making previously organic forms of life synthetic. Both artists utilize digital tools (embedded interfaces, transcription and digital rendering) to present a vitalist materiality that is uncannily nonorganic; instead of the complete, 1080p reproduction of the image, Thiel and Valla’s artworks employ visual queues that effectively splinter reality, emphasizing the loss intrinsic to digital conversion. Yet this materiality is both less than and more than human: these artworks think the relation between digital memory and its withdrawal through nonhuman botanical life. One does not find immediate remedies for the all too human problem of memory loss at the sites of Surface Proxy and Unexpected Growth. Instead, one finds half-rendered relics, overgrown weeds of memory and splintered representations of a future - not of ours, but of sedimentary and botanical life.
Strategi Pembelajaran Guru Pada KB. Permata Hijau (Telaah Terhadap Sekolah Tertinggal di Kabupaten Polewali Mandar)

This journal discusses the application of learning strategies used by teachers in early childhood in 2020-2021. This research is motivated by the implementation of various strategies that have been and have not been optimally applied in teaching and learning activities so far. The purpose of this research is to find data, information and find out about the implementation of learning strategies in family planning. Green Gem for the year2020-2021. The type of research used is a type of qualitative research through a case study approach carried out at KB Permata Hijau for the 2020-2021 academic year. KB. Permata Hijau was used as a source of data to obtain teacher data and to find out the implementation of the teaching strategies used by the teacher. The data were obtained using the interview method and documentation method. After the data is collected, it is analyzed interactively and lasts until it is complete. Then it is described descriptively, which is manifested in the form of a detailed description. This research shows that the implementation of learning strategies carried out by teachers to early childhood in the academic year 2020-2021 has been implemented properly in accordance with the theory that has been put forward.
Automatic Mask Detection at Public Places

COVID-19's fast spread has resulted in tens of millions of individuals becoming infected around the world. Because there is no specific cure for COVID-19, wearing masks has proven an effective technique of preventing transmission and is mandatory in most public places, resulting in an increase in demand for automatic real-time mask detection systems to replace manual reminders. However, there are just a few investigations on the detection of face masks. The performance of mask detectors must be improved urgently. However, there have been few studies on the detection of face masks. Mask detector performance must be improved immediately. The Properly Wearing Masked Face Detection Dataset (PWMFD) was proposed in this paper, and it includes 9205 photos of mask-wearing samples divided into three groups. Squeeze and Excitation (SE)-YOLOv3, a mask detector with roughly balanced effectiveness and efficiency, was also proposed. by incorporating the SE block into Darknet53, I was able to incorporate the attention mechanism and obtain the relationships between channels, allowing the network to focus more on the relevant feature. To increase the stability of bounding box regression, I used GIoUloss, which can better express the spatial difference between predicted and ground truth boxes. The significant foreground-background class imbalance was solved via focal loss. In addition, I used image augmentation techniques to boost the model's robustness on the challenge. In comparison to YOLOv3, SE-YOLOv3 outperformed YOLOv3 and other state-of-the-art detectors on PWMFD, achieving a higher 8.6 percent mAP while maintaining a comparable detection speed.
Company Profit Prediction Based On Forecasting Of Port Throughput Using Time Series-Adaptive Neuro Fuzzy Inference System

As a maritime country, ports play an important role in economic development in Indonesia. Throughput is an important factor affecting Port Profits. This prediction is needed in an effort to find out the company's prospects, help estimate the long-term profitability of representatives, predict earnings, and estimate risk in investment. In this research, forecasting data throughput will be carried out, such as container traffic, number of ships, export traffics, goods traffic, animal flow and passenger traffic for the next year using Time Series-Adaptive Neuro Fuzzy Inference System (TS-ANFIS) as an input parameter in the decision support system. Before predicting the benefits of the port using the ANFIS method, principal component analysis (PCA) was applied to reduce parameters that did not sufficiently affect the profits of the port. The data used are time series data from 2009 to 2018. From the system built it is expected to be able to provide good results in predicting the value of port throughput using TS-ANFIS and to predict profit values using the ANFIS method. The best results from profit prediction using ANFIS obtained R2 of 0.947, RMSE of 28524582.39, MAPE of 14.74% and MAAPE of 0.145. From the prediction results, it can be used as a reference for company projections in investing, managing cash flow, managing assets and global bonds.
Развитие цифровой системы контроля над обществом в России

The article analyzes the reasons for the emergence of a digital system of public control and monitoring of citizens' behavior in large cities in Russia. The author considers the development of the technical component of digital control – the growth of the number of video surveillance cameras and the introduction of a facial recognition system. The author pays a special attention to the practices of the development of digital monitoring of public space in Moscow, as the leader of this direction in Russia.
ALGORITHM OF PROTECTING CONFIDENTIAL DATA IN THE CLOUD MEDICAL INFORMATION SYSTEM

The aim of the work is the development and implementation of the architecture of a cloudstorage system, systematization and processing of survey results (for example, EEG) and an algorithmfor ensuring the protection of confidential data based on a completely homomorphic cryptosystem.The object of the research is the technologies of storage, transmission, processing andprotection of confidential information in distributed medical information systems. The architectureof a cloud platform for distributed storage, processing, systematization and protection of confidentialdata (results of medical examinations) has been developed, which makes it possible to interactwith various medical information systems and diagnostic hardware in order to generate big data.An algorithm has been developed to ensure the safety of medical data stored in a cloud platform in electronic form, recorded during patient examinations in order to calculate the average value foreach of the brain activity rhythms (based on the results of a series of examinations over a longperiod of time) using a fully homomorphic encryption algorithm. Based on the test results (analysisof the execution time of such operations as: encryption, decryption, addition, multiplication,signal-to-noise ratio of ciphertext to plaintext), the optimal algorithm. According to the results ofthe work, it is shown that the fully homomorphic encryption scheme CKKS is the most effective,especially in the context of the criticality of the requirements for a high level of security of confidentialdata, which determines the choice of this scheme for the implementation of the algorithmproposed in this work.
Arcade: A Guide to the Operas in the Doctoral Project "An Operatic Game Changer"

This exposition is a guide to the four ludo-immersive chamber operas in the doctoral project An Operatic Game-Changer. The operas were created during the period 2016–2020 and are contextualized and discussed in the dissertation with the same title, in which the practice of facilitating operas as adventures rather than spectacles is fleshed out. Through these participatory and multi-disciplinary concepts, conceived with the purpose of generating and analyzing live encounters between professional opera artists and co-playing visitors, immersive features are realized and explored. Arcade is also a kaleidoscopic performance and an exhibition, designed as an operatic amusement park in miniature, with artefacts and excerpts from the operas, and presented live in connection with the public defense of the thesis in 2022 at the Academy of Music and Drama in Gothenburg. This online resource functions as an archive of information about the operatic works and their performances, both for those attending the performance/exhibition in Gothenburg, and for those wanting to learn about the project without experiencing this event.