Reviewerly/exHarmony huggingface dataset:
Synthesizer: Rethinking Self-Attention in Transformer Models

The dot product self-attention is known to be central and indispensable to state-of-the-art Transformer models. But is it really required? This paper investigates the true importance and contribution of the dot product-based self-attention mechanism on the performance of Transformer models. Via extensive experiments, we find that (1) random alignment matrices surprisingly perform quite competitively and (2) learning attention weights from token-token (query-key) interactions is useful but not that important after all. To this end, we propose \textsc{Synthesizer}, a model that learns synthetic attention weights without token-token interactions. In our experiments, we first show that simple Synthesizers achieve highly competitive performance when compared against vanilla Transformer models across a range of tasks, including machine translation, language modeling, text generation and GLUE/SuperGLUE benchmarks. When composed with dot product attention, we find that Synthesizers consistently outperform Transformers. Moreover, we conduct additional comparisons of Synthesizers against Dynamic Convolutions, showing that simple Random Synthesizer is not only $60\%$ faster but also improves perplexity by a relative $3.5\%$. Finally, we show that simple factorized Synthesizers can outperform Linformers on encoding only tasks.
‘If I had dementia, the things that might help me to eat include...’: Exploring findings from a massive open online course.

Maximising opportunities to enhance nutrition are a crucial part of providing care to people with dementia, since their risk of malnutrition is increased due to progressive symptoms. A person-centred approach to eating coupled with skilled caregivers is essential to minimise symptom burden, reduce dependence and maintain quality of life. The aim of this study was to explore perceptions of encouraging eating for those with dementia from responses to a discussion board within a free and openly accessible course: the Understanding Dementia Massive Open Online Course (UD-MOOC), which asks 'If I had dementia, the things that might help me to eat include...'.A total of 3,651 participant discussion responses were collected from 2018 and 2019 course enrolments. Responses were analysed using structural topic modelling followed by secondary thematic analysis to develop meaningful themes to depict participant perceptions.The majority of participants were female, tertiary educated Australians over 50 years old. One third were paid caregivers. Thirteen topics were isolated from structural topic modelling, from which six major themes were identified: personalised food modifications (such as changes to texture, meal size and frequency of food offered), meal choice (including eating implements and providing food options), meal presentation (including sensory stimulation), eating environment (such as dining and social surroundings), eating assistance (with a focus on independence and encouragement) and end of life nutrition (such as eating safety and perceptions of tube feeding). Meal timing preference was seldom raised.By exploring perceptions from a discussion board provoking a first-person stance, participants demonstrated diverse awareness of important aspects to encourage eating in dementia. Furthermore, the opportunity to explore perceived experience provided an understanding of what eating means to caregivers and how they may approach person-centred eating for people with dementia. In the context of previous research, the findings of this study suggest a gap between perceived best care strategies and practical application of these strategies. This supports the need to nurture opportunities that allow awareness to be implemented in practice and education programs that support current and future dementia caregivers.
Concept level designs: high-level architecture

Chapter Contents: 4.1 Introduction 4.2 Shared mobility components and a general architecture 4.3 Shared mobility systems and their architectures 4.3.1 Incumbent shared mobility systems 4.3.2 Innovative shared mobility systems 4.4 Data architecture and management 4.4.1 Generalized data architecture 4.4.2 Data management 4.5 Summary Author contributions Declaration of conflicting interests References
Material Recommendation System for Distance Learning Based on Fuzzy Logic

The course material is the most important support material for the student throughout the learning process. Especially in distance learning, course materials gain even more importance. Materials to be presented in distance learning systems should be determined according to the student's readiness level and learning style. The main goal is how to best realize learning. In this study, fuzzy logic-based material recommendation system is modeled for distance learning systems. In the proposed model, in addition to readiness and learning style, material difficulty level and material type were also used. After the fuzzy rule base created, it was determined whether each material was suitable for the student
Design of a Personalized Recommendation System for Ideological and Political Courses in Colleges and Universities Based on a Collaborative Filtering Algorithm

In view of the problems existing in the curriculum of ideological and political education in colleges and universities, such as a single form, weak pertinence, lack of synergy and inability to form a personalized collaborative education mechanism, a college ideological and political curriculum recommendation system based on improved collaborative filtering technology is developed. Based on the system business process model, this research mainly includes two aspects: one is to design the system framework, design the architecture composed of the user operation layer, system function layer, business logic layer and data persistence layer, and briefly explain each layer. The second is to build a personalized accurate recommendation model and complete the recommendation algorithm design by calculating the score deviation, predicting the score of users’ nonrated resources, finding the nearest neighbor set and generating recommendation resources. The results show that the MAE value of the designed system has always maintained a relatively stable low Mae value level, indicating that the system can always maintain a more accurate recommendation under any number of nearest neighbors, which proves the effectiveness of the system.
Hybrid Recommendation Using Temporal Data for Accuracy Improvement in Item Recommendation

Recommender systems have become a vital entity to the business world in form of software tools to make decisions. It estimates the overloaded information and provides the suitable decisions in any kind of business work through online. Especially in the area of e-commerce, recommender systems provide suggestions to users on the items that are likely based upon user’s true interest. Collaborative Filtering and Content Based Filtering are the main techniques of recommender systems. Collaborative Filtering is considered to be the best in all domains and always outperforms Content Based filtering. But, both the techniques have some limitations like data sparsity, cold start, gray sheep and scalability issues. To overcome these limitations, Hybrid Recommender Systems are used by combining Collaborative Filtering and Content Based Filtering. This paper proposes such kind of hybrid system by combining Collaborative Filtering and Content Based Filtering using time variance and machine learning algorithm.
von MISES TAPERING: A NEW CIRCULAR WINDOWING

Discrete and continuous standard windowing are revisited and a a new taper is introduced, which is derived from the normal circular distribution by von Mises. Both the continuous-time and the discrete-time windows are considered, and their spectra obtained. A brief comparison with further classical window families is performed in terms of their properties in the spectral domain. These windows can be used in spectral analysis, and in particular, in the design of FIR (finite impulse response) filters as an alternative to the Kaiser window
Ideal Test Time for Coronavirus Disease 2019 Contact Tracing

Epidemiological contact tracing is a powerful tool to rapidly detect SARS-CoV-2 infection in persons with a close contact history with COVID-19-affected patients. However, it remains unclear whom and when should be PCR tested among the close contact subjects.We retrospectively analyzed 817 close contact subjects, including 144 potentially SARS-CoV-2-infected persons. The patient characteristics and contact type, duration between the date of the close contact and specimen sampling, and PCR test results in PCR positive and negative persons were compared.We found that male gender {adjusted odds ratio 1.747 [95% confidence interval (CI) 1.180-2.608]}, age ≥ 60 [1.749 (95% CI 1.07-2.812)], and household contact [2.14 (95% CI 1.388-3.371)] are independent risk factors for close contact SARS-CoV-2 infection. Symptomatic subjects were predicted 6.179 (95% CI 3.985-9.61) times more likely to be infected compared to asymptomatic ones. We could observe PCR test positivity between days 1 and 17 after close contact. However, no subject could be found with a Ct-value <30, considered less infective, after day 14 of close contact.Based on our results, we suggest that contact tracing should be performed on the high-risk subjects between days 3 and 13 after close contacts.
DEVELOPMENT OF TEACHING MATERIALS OF TEMBUNG (UPDATE CONTINUOUS) IN CLASS I ELEMENTARY SCHOOL

Curriculum writing is one of the language skills that must be mastered by lower grade elementary school students in accordance with the competency standards demanded by the curriculum. In fact, there are still many students who have not mastered the skill of writing tembung (upright cursive) because some students write with tembung which is very complicated, so that only a few skilled students are able to achieve the KKM. The development of tebung teaching materials is an alternative to improve the writing skills of elementary school students. The purpose of this study is "Describe the analysis of student writing tebung in the development of teaching materials in elementary school". This study uses the Design and Development (D&amp;D) method which was carried out at SDN 01 Gayamsari Semarang and SDN 02 Rejosari Semarang, the research subjects were 88 first grade elementary school students, 2 content or material expert lecturers, 1 design expert lecturer and 2 lower grade elementary school teachers. Data collection techniques in the form of interviews with homeroom I, distribution of teaching material validation questionnaires developed by researchers to expert lecturers and elementary school teachers. The data collected through the distribution of questionnaires were then analyzed using a Likert Scale. The results of the research on writing student tembung are still below the KKM. The teaching materials developed resulted in a product in the form of a writing guide book with the results of product trials, namely Very Good so that it was feasible to use.
Siamese-Based Attention Learning Networks for Robust Visual Object Tracking

Tracking with the siamese network has recently gained enormous popularity in visual object tracking by using the template-matching mechanism. However, using only the template-matching process is susceptible to robust target tracking because of its inability to learn better discrimination between target and background. Several attention-learning are introduced to the underlying siamese network to enhance the target feature representation, which helps to improve the discrimination ability of the tracking framework. The attention mechanism is beneficial for focusing on the particular target feature by utilizing relevant weight gain. This chapter presents an in-depth overview and analysis of attention learning-based siamese trackers. We also perform extensive experiments to compare state-of-the-art methods. Furthermore, we also summarize our study by highlighting the key findings to provide insights into future visual object tracking developments.
Chatbot for College Management System

Nowadays, Artificial Intelligence is being used extensively in a wide range of sectors, from product production to customer service in public relations. Artificial Intelligence (AI) chat bots play a vital role in helping solve their problems in any aspects. So, we implemented a virtual assistant based on AI that can deal with any query related to College Management System. A chatbot uses information stored in its database to recognize phrases and make decisions on its own in response to a query. The college inquiry chat-bot is built using the Rasa NLU framework that analyzes user's queries by understanding user’s text message. The response principle is matching the input sentence from a user. The college management system involves public user portal and student/staff portal. It keeps track records of all the information regarding students and the college. In the public portal, the user may use the chat-bot to ask any college-related questions without having to physically visit the campus. The Bot analyses the query and responds with a graphical user interface that makes it appear as though a real person is conversing with the user. The system's accuracy is estimated to be 95% and the time it takes to create responses corresponds to the number of lines of response.
Final Test Belajar dan Pembelajaran

The following are the results of the analysis related to the concept of learning and learning. Learning itself is a synchronization between planning, media, so that the implementation of learning runs smoothly and finally an evaluation will be carried out. These components will relate to the learning media needed in learning interactions. there is also a meaningful learning context and the use of information technology for learning. Information technology in the present era has undergone many developments and has led to creative innovations in society. A so that continues to develop rapidly causes educational technology to develop. There are many innovations that can be used as a reference for implementing technology-based learning, especially during the COVID-19 pandemic.
A Tertiary Review on Blockchain and Sustainability with Focus on Sustainable Development Goals

Blockchain as a disruptive technology and a driver for social changes has been applied to a wide range of applications, exhibiting a great potential to promote sustainable practices and help organizations and governments to achieve United Nations' Sustainable Development Goals (SDG). Literature reviews on blockchain and sustainability exist, but there is a need to consolidate existing results in terms of SDGs. This paper presents a tertiary review based on 28 existing literature reviews to investigate the relationship between blockchain and sustainability, focusing on SDGs. The findings include a novel mapping of impact-based interlinkage of blockchain and SDGs and a systematic overview of drivers and barriers to adopting blockchain for sustainability. Research gaps to overcome the barriers and enhance blockchain’s positive impacts on SDGs are identified. We found that blockchain applications are still in their early stages. Future empirical studies are needed regarding their large-scale deployment and long-term impacts on sustainability as well as standard tools and methods for implementing blockchain-based sustainable solutions from the software engineering perspective.
Online Assessment of Electric Circuit based on Machine Learning During Covid-19 Pandemic Situation

Due to the Covid-19 pandemic crisis, educational institutions have to change their teaching styles because students cannot go to the school (on-site). Therefore, online learning management is required, but the problem of online learning is that the assessment is difficult and not easy to realize based on standard assessment. To achieve the online assessment, machine learning has been applied as a powerful algorithm to realize the novel online assessment for electric circuit course of bachelor students at the department of electrical technology education, King Mongkut’s University of Technology Thonburi, Thailand. To achieve the data collection process, speech to text algorithm has been applied. Next, feature extraction would be adopted as the main key to extracting the knowledge from the data from speech to text algorithm. The output of feature extraction is the dataset of the proposed system. Finally, the clustering algorithm would be applied to set up the learning process of the proposed method. The accuracy of the proposed method can reach 100% when the word feature is appropriate.
The reinforcement for a precipice of “Nikko Kegon fall” by anchoring from the inside shaft without surface structure

In recent it is pointed out that a precipice of “Nikko Kegon fall” became dangerous due to the toppling failure of overhanging part for both the prefectural waterfall viewing platform and chargeable water fall viewing platform. As a countermeasure all works were performed inside the precipice for the sake of safety for tourists. That is why rock anchoring method was chosen without surface structures on the precipice. This construction, finished on April 2001, consists of a horizontal shaft and two vertical shafts in the jointed rock masses. Under these circumstances the severe observational method was needed to keep the safe construction considering long-term stability of the precipice. In this paper, the design concept and construction method of rock anchoring, the observational method and the behavior of precipice of “Nikko Kegon fall” during construction are discussed.
Plant Disease Diagnosis Expert System Cardamom (Ammomum Cardamomum l.) Using The Naive Bayes Method Web-Based

Diseases and pests on cardamom plants is one of the diseases and pests that can seriously attack cardamom plants. Cardamom plant diseases and pests can be diagnosed through the symptoms that are currently being experienced by the cardamom or through its clinical picture, through these symptoms an expert system can be made to make a diagnosis. An expert system is a system that seeks to adopt human knowledge to a computer that is built to solve problems like an expert. The expert system made in carrying out the diagnosis uses the Naïve Bayes method. This method is a simple probabilistic-based prediction technique based on the application of Bayes' rules with the assumption of strong (naive) independence. In other words, in Naïve Bayes the model used is an “independent feature model”. This expert system was built using PHP and MySQL programming as a database. In this expert system the types of cardamom disease diagnosed consisted of aphids, leaf-eating caterpillars, stem borers, fruit and roots, leaf beetles, mosaics, late blight, root rot, and fungus, which consisted of 33 symptoms. While the results of the diagnosis will inform about the results of the diagnosis containing a list of symptoms entered, information on the results of regulations regarding diseases and pests that are attacking cardamom plants and information about possible treatments that can be carried out and treatment solutions.
PENGEMBANGAN MODUL DIGITAL BERBASIS TEAM LEARNING PADA VIRTUAL CLASSROOM KONSEP DASAR IPS SD

This study aims to develop a digital module based on team learning for elementary social studies basic concepts. The research method used is a research and development method. The population in the study were PGSD students at the University of Sarjanawiyata Tamansiswa Yogyakarta in class 2A and 2D in the Even Semester 2020/2021. The sample used in this study used saturated sampling. Data collection techniques were carried out by distributing questionnaires to respondents. Data analysis was carried out descriptively with a frequency distribution table. The results of this research are 1) defining stage, 2) planning, 3) development, and 4) dissemination. In the definition stage, it is done by analyzing students, namely students, curriculum analysis, and material analysis. The planning stage is the selection of formats, the design of teaching materials. This development stage is carried out through validation and testing. While at the dissemination stage by conducting a seminar on results at PGSD.
Developing Digital Skills Among Students of the Sverdlovsk Region

Research on the development of digital skills and competencies has primarily been concerned with the current socio-economic situation. Based on statistical data compiled by the Institute for Statistical Studies and Economics of Knowledge of the National Research University Higher School of Economics, there is a gap in the level of digital skills of both the entire population of Russia and young people in comparison with the populations of most European countries. This research explored some of the issues of the structure of digital skills, identifying their levels and the areas necessary for assessing the development and formation among students. This study was based on the data of a sociological survey conducted among students of higher education institutions of the Sverdlovsk Region in their first year of a bachelor’s degree, as well as for various training profiles (humanitarian, science and mathematics, engineering and socio-economic). The role of the university as a filter was revealed, and also its role as an agent in the process of professional socialization of young people. However, the institutions did not meet the requirements of the modern digital economy. Keywords: students, economy digitalization, digital skills, socialization
Secure Integrity Auditing System for Electronic Patient Health Records using Advanced Encryption Techniques

Abstract: Cloud computing is one of evolving technology nowadays, giving versatile services. However, secure information sharing is vulnerable to cloud computing. With cloud storage services, users can remotely keep their information to the cloud and recognize the data sharing with others. Electronic wellbeing record (EHR) is a framework that gathers patients' computerized wellbeing data and offers it with other medical care suppliers in the cloud. Since EHR contains a lot of critical and delicate data about patients, it is necessitated that the framework guarantees reaction accuracy and capacity respectability. The verifiable database (VDB), where a user’s redistributes his huge data set to a cloud worker and makes questions once he needs certain information, is proposed as an effective updatable distributed storage model for asset compelled users. To improve productivity, most existing VDB plans use confirmation reuse and evidence refreshing strategy to demonstrate accuracy of the question results. Notwithstanding, it overlooks the "continuous" of confirmation age, which brings about an overhead that the user needs to perform additional cycle (for example evaluating plans) to check stockpiling trustworthiness. In this paper, we propose a publicly verifiable shared updatable EHR database scheme that supports privacy-preserving using secure encryption and batch integrity checking. Keywords: cloud storage, data integrity auditing, functional commitment, privacy-preserving auditing, sensitive information hiding, third-Party Auditor (TPA), user revocation Verifiable database.