Reviewerly/exHarmony huggingface dataset:
Передовые технологии, трансформирующие образование: обзор Международной конференции EdCrunch Томск 2020

The author reviews the international conference EdCrunch Tomsk 2020 on advanced learning technologies. The key idea of the conference is to identify and discuss technologies that are transforming the landscape of modern education, as well as the conditions that ensure their effectiveness when solving urgent problems of modern society at the stage of transition to an individualized, student-centered, distributed, accessible education. The review presents ideas and conclusions formulated at the conference which regard the change of the educational paradigm in the digital era. The sections of the conference focus on pedagogical design and digital pedagogy; continuing education and heutagogy; experimental educational design; futures literacy. There are considered development strategies of such advanced learning technologies as virtual and augmented reality, open educational resources, platform solutions, and adaptive technologies. Within the new opportunities for education management, based on the materials of the conference, the challenges and opportunities of higher education are discussed in the context of the pandemic and after it, as well as new technological solutions for education management and barriers to their implementation, new digital capabilities of libraries. Peculiar attention is paid to digital economy personnel training and to the sphere of artificial intelligence. The cross-cutting topics of the conference are as follows: the growth of demand for collaborations and networks in the field of education and science; lacking «soft» skills and opportunities for their formation among students within the framework of traditional education; insufficient legislation for advanced technologies application; the fundamental lack of common solutions and the need for institutions to be flexible and focused when «switching» to digital education.
Implementation of Expert System for Detection Periodontitis Disease Using Fuzzy Logic

The wellbeing area is one region that requires innovation in technology. Wellbeing is for sure a costly thing for people. In this manner it takes individual affectability to care for it. One of the solid organs that people frequently disregard is teeth and mouth. Periodontitis is a dental and oral illness that is frequently grumbled of by numerous individuals. In any case, as of recently, periodontitis has not been a critical worry of Indonesian individuals. It is important to give data and comprehension about periodontitis side effects to dynamic smokers by making an expert system. A system is expected to distinguish the underlying indications and straightforwardly beat these side effects. The expert system is a part of Computer Science that fills in as a spot to move information from specialists so computers can have the aptitude to tackle issues utilizing these specialists' information. The technique utilized in this examination is the Fuzzy Logic strategy. This present investigation's eventual outcomes are analytic outcomes from the fluffy rationale technique, where the exactness of the Fuzzy Logic strategy is 93.33%.
OPANAS: One-Shot Path Aggregation Network Architecture Search for Object

Recently, neural architecture search (NAS) has been exploited to designfeature pyramid networks (FPNs) and achieved promising results for visualobject detection. Encouraged by the success, we propose a novel One-Shot PathAggregation Network Architecture Search (OPANAS) algorithm, which significantlyimproves both searching efficiency and detection accuracy. Specifically, wefirst introduce six heterogeneous information paths to build our search space,namely top-down, bottom-up, fusing-splitting, scale-equalizing, skip-connectand none. Second, we propose a novel search space of FPNs, in which each FPNcandidate is represented by a densely-connected directed acyclic graph (eachnode is a feature pyramid and each edge is one of the six heterogeneousinformation paths). Third, we propose an efficient one-shot search method tofind the optimal path aggregation architecture, that is, we first train asuper-net and then find the optimal candidate with an evolutionary algorithm.Experimental results demonstrate the efficacy of the proposed OPANAS for objectdetection: (1) OPANAS is more efficient than state-of-the-art methods (e.g.,NAS-FPN and Auto-FPN), at significantly smaller searching cost (e.g., only 4GPU days on MS-COCO); (2) the optimal architecture found by OPANASsignificantly improves main-stream detectors including RetinaNet, Faster R-CNNand Cascade R-CNN, by 2.3-3.2 % mAP comparing to their FPN counterparts; and(3) a new state-of-the-art accuracy-speed trade-off (52.2 % mAP at 7.6 FPS) atsmaller training costs than comparable state-of-the-arts. Code will be releasedat https://github.com/VDIGPKU/OPANAS.
The linguistic construction of sentiment expressions in student opinionated content: A corpus-based study

Abstract Motivated by an increasing use of social media for the expression of personal stance towards a certain target, we analyse the language used to produce such opinionated content with expressions of sentiment, which represents the main data source for sentiment analysis. We use the first manually annotated corpus for sentiment analysis of the Serbian language developed for the service sector of higher education. Our study focuses on how various linguistic constructions, used in different context, influence the sentiment polarity of a text. Our findings indicate that sentiment expressions and negation have a most significant role in determining whether the text conveys positive, neutral, or negative sentiment, while intensifiers (words which either increase or decrease sentiment) have a considerable influence on sentiment intensity. We also present an analysis of the impact of conjunctions, conditional sentences, comparative and modal verbs, and pronouns on sentiment polarity and intensity. Based on the derived observations, we propose a set of rules that could be integrated with machine learning algorithms into an automated sentiment analysis system for the Serbian language. Our findings also make a much-needed contribution to the few currently available resources for natural language processing of Serbian.
MEDIA PEMBELAJARAN “SI SIDOL” UNTUK MENINGKATKAN KETERAMPILAN MEMBACA DAN MENULIS AKSARA JAWA KELAS VIIA DI SMP NEGERI 4 WATES

The objective of this research is to improve the activeness and success in learning to read and write Javanese letters in class VII SMP Negeri 4 Wates. This research is action research. The research is done in two cycles with the stages: planning, action, observation, and reflection. The subject of the research is class VIIA students of SMP Negeri 4 Wates, 32 students. The data collection technique uses observation, daily notes, and reading and writing Javanese letters skill test. The research instruments are observation sheets, daily notes, and the assessment of reading and writing Javanese letters skill. The data analysis for the learning process uses descriptive qualitative analysis, while the learning success is analyzed with descriptive-quantitative analysis. The result of the research shows that the learning media Si Didol can improve the activeness reading and writing Javanese letters skill of class VIIA  students in SMP Negeri 4 Wates. The result of the observation and field notes show that there is an improvement in students learning activeness. The result of the reading and writing Javanese letters skill assessment also shows improvement, 81% in cycle 1 becomes 94% in cycle 2.
EARLY WARNING SYSTEM UNTUK MONITORING KINERJA DOSEN

Abstract — Monitoring the performance of lecturers is very important for the division portion faculty workload according to Tridharma Pendidikan . Lecturer Workload should be in monitoring anytime and anywhere that can be conducted in accordance with the portion of the standards prescribed. This study aims to divide the portion of faculty workload in accordance with the standards prescribed and monitor the performance of lecturers in accordance Tridharma Pendidikan . With the methods to be used in analyzing and designing systems is Object Oriented Analysis and Design using the Unified Modeling Language. The results of the research in the form of application development is Early Warning System which can be accessed via the web to monitor the performance of lecturers, and will provide a reference for executing a lecturer who does not conform with the standards that have been granted by the Government of Indonesia . Keywords — Early Warning System, Monitoring, Lecturer Workload (BKD), Performance, Lecturer
Task-Oriented Dialogue as Dataflow Synthesis

We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends this graph. Programs include metacomputation operators for reference and revision that reuse dataflow fragments from previous turns. Our graph-based state enables the expression and manipulation of complex user intents, and explicit metacomputation makes these intents easier for learned models to predict. We introduce a new dataset, SMCalFlow, featuring complex dialogues about events, weather, places, and people. Experiments show that dataflow graphs and metacomputation substantially improve representability and predictability in these natural dialogues. Additional experiments on the MultiWOZ dataset show that our dataflow representation enables an otherwise off-the-shelf sequence-to-sequence model to match the best existing task-specific state tracking model. The SMCalFlow dataset and code for replicating experiments are available at https://www.microsoft.com/en-us/research/project/dataflow-based-dialogue-semantic-machines.
Prediksi Belanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM)

Abstract &#x0D; Estimates of government expenditure for the next period are very important in the government, for instance for the Ministry of Finance of the Republic of Indonesia, because this can be taken into consideration in making policies regarding how much money the government should bear and whether there is sufficient availability of funds to finance it. As is the case in the health, education and social fields, modeling technology in machine learning is expected to be applied in the financial sector in government, namely in making modeling for spending predictions. In this study, it is proposed the application of Long Short-Term Memory (LSTM) Model for expenditure predictions. Experiments show that LSTM model using three hidden layers and the appropriate hyperparameters can produce Mean Square Error (MSE) performance of 0.2325, Root Mean Square Error (RMSE) of 0.4820, Mean Average Error (MAE) of 0.3292 and Mean Everage Presentage Error (MAPE) of 0.4214. This is better than conventional modeling using the Auto Regressive Integrated Moving Average (ARIMA) as a comparison model.
Counterfactuals and dependencies on causal teams: expressive power and deduction systems

We analyze the causal-observational languages that were introduced in Barbero and Sandu (2018), which allow discussing interventionist counterfactuals and functional dependencies in a unified framework. In particular, we systematically investigate the expressive power of these languages in causal team semantics, and we provide complete natural deduction calculi for each language. As an intermediate step towards the completeness, we axiomatize the languages over a generalized version of causal team semantics, which turns out to be interesting also in its own right.
Practice Research on the Inheritance and Innovation of Traditional Culture from the Perspective of New Media

In order to carry forward the excellent traditional culture of the Chinese nation, new media technology has played an important role. The new media’s transmission ability and artistic display ability are outstanding, which can perfectly integrate the flash point of traditional culture and let it bloom with vitality. New media has a unique way of development, so that colleges and universities can cultivate Chinese excellent traditional culture, master the basic concepts of traditional culture, and make better contributions to the revival of the great “Chinese dream”. Therefore, this paper explores the inheritance and innovation of traditional culture from the perspective of new media, and puts forward guiding suggestions by investigating the status quo, opportunities and challenges of traditional culture inheritance and innovation in Colleges and universities. The effectiveness of the proposal is verified by questionnaire survey in Colleges and universities. Results the response is good, and it is worth promoting to the universities.
Pseudo-Random Key Stream Generation Algorithm for Encryption Purposes

For both chaos-based stream ciphers and chaosbased block ciphers, key streams have a crucial influence on their security.A well designed pseudochaotic number generator (PCNG) that exhibits both chaotic properties and pseudo-randomness is a good candidate for creating the cryptographic key stream for encryption purposes.PCNGs are based on multiple chaotic maps.Since the majority of the chaotic maps are defined using real numbers, most of the proposed PCNGs use floating-point notations.However, this data type, especially the double-precision notation, has disadvantages of high computation cost and inefficient resource utilization.Also, the quantification errors may undermine the reliability of the produced key stream.To overcome these drawbacks, a key stream generation algorithm using a PCNG scheme is proposed in this paper.The PCNG is based on reformulated skew tent maps over a 32-bit integer field.It not only reduces the resource utilization from the hardware perspective, but also ensures the key stream performance over various operation platforms.Furthermore, the proposed PCNG uses a parameter changeable strategy, which can help to expand the key space, and thus increases the immunity against the brute-force attack.The quality of the key stream produced by the PCNG has been tested in a stream cipher.The analysis and the obtained test results have demonstrated that the proposed PCNG is secure and reliable to generate cryptographic key streams for encryption purposes.
Analysis of Physical Activity Environment and Cooperative Development of 3-year-old Infants

The study selected 68 3-year-old infant men and 82 women who are volunteering at a daycare center in K-do I, Gyeonggi-do. The research was retrieved from October through November 2017 by delivering research questionnaires to infants and guardians. The contents of the questionnaire were made by parents and guardians to write items such as the time, place, time, method, and attitude of physical activities with their parents. The data were analyzed by using the statistical program of SPSS Windows version 22.0 to construct a cross tabulation by comparing the frequency and percentage of the combination frequency between the independent and dependent variables. First, parents believe that infants need physical activity, but parents lack participation in physical activities with infants. It was found that mothers spend more time with infants than their fathers. Second, parents were positively judging the attitude and appearance of infants participating in physical activities. Third, parents were allowed by the majority if infants asked for physical activity, and the main place was indoors (living room). It was also found that infants were allowed to play outdoors if they wanted to. However, the number of infants attending sports clubs outside of the country was small. Fourth, parents judged that it was possible for infants to receive the ball, kick the ball, dance, ride the tricycle, and stop the ball, but they judged that two-footed biking and jumping rope were impossible.
Pendampingan Pengembangan Sistem Informasi Antrian Disabilitas Rumah Sakit Panti Waluyo Purworejo

Every patient has the right to receive healthcare service. However, not everyone has equal access to these services. This problem is especially apparent in patients with disabilities. Duta Wacana Christian University’s Faculty of Information Technology is working alongside Panti Waluyo Hospital in Purworejo to build a priority queueing system for disabled patients. This system is expected to improve patient admission and queueing process. The proposed system consists of a website and a mobile application which has undergo 6 months of continuous development and online mentorship, starting from January 2020 up until June 2020.
Sistem Asosiasi Penyusunan Obat Pada Apotek Balai Rehabilitasi Badan Narkotika Nasional Baddoka Menggunakan Algoritma Apriori

Abstract. This research was conducted to implement a priori algorithm in drug association. The research method used is descriptive. The results of this study indicate that the drug association application can provide drug associations in accordance with calculations carried out manually based on the results of the a priori algorithm testing minimum support 3 and minimum confidence 3 showing that the results obtained are the same, with an accuracy level of 90 -98. %. So that it can be used in determining drug associations based on drugs that are often used simultaneously.
A SURVEY ON DATAMINING IN AGRICULTURE

Agriculture assumes an essential part in the life of an economy. It is the backbone for creating nations like India as over 70% of populace relies upon agriculture. To expand crop production numerous components are mindful like soil, climate, downpour, fertilizers and pesticides. The Author has utilized soil boundaries to expand crop production since it is a fundamental key factor of agriculture. To keep up supplement levels in the soil if there should arise an occurrence of inadequacy, fertilizers are added to soil. The basic issue existing among the Indian ranchers is that they pick surmised measure of fertilizers and include them physically. Abundance or lacking expansion of manure can hurt the vegetation and diminish the yield. This paper gives audit of different data mining procedures utilized on agriculture soil dataset for compost suggestion. Basically I zeroed in on different soil boundaries like Fe, S, Zn, Cu, N and Ph esteem and so forth. In this review, we additionally depict some Agriculture issues that can be illuminated by utilizing data mining strategies.
Pengembangan Media Buku Bergambar Berbasis Visual Thinking Strategies (VTS) dalam Pembelajaran Tematik di Sekolah Dasar

This study aims to: (1) identify the use of media, (2) produce picture book media, (3) test the feasibility, and (4) test the effectiveness of picture book media based on visual thinking strategies (VTS) in thematic learning at SD Muhammadiyah 16 Karangasem . This type of research is Research and Development. The data analysis technique used is the Mann-Withney test with a significance of 0.05%. The results of the research include: (1) the use of media that has been carried out in thematic learning at SD Muhammadiyah 16 is image media, (2) the form of media development carried out is picture book media based on visual thinking strategies (vts) in thematic learning in elementary schools, (3) media feasibility test is carried out by media validation and product trial results, expert judgment states that picture book media products are appropriate with category A very by material experts and B good by media experts. The teacher and student responses using picture book media show category A very good, (4) the effectiveness test is based on the N-gain calculation in the SPSS table, the control group shows an average value of 68.3033 and the experimental group shows an average value of 80.4325, Thus the development of picture book media based on visual thinking strategies (VTS) is feasible and effective to use in thematic learning in elementary schools.
Implementasi Jaringan Syaraf Tiruan (JST) Untuk Memprediksi Jumlah Penjualan Gas 3Kg Menggunakan Metode Backpropagation

In a service company, there are customers who become company consumers. Customer satisfaction is formed from the level of performance and service of the company. One level of customer satisfaction can be measured from the level of gas sales of 3 Kg. Therefore, the 3 kg LPG gas pankalan Pematangsiantar needs to overcome the problem of overcoming the amount of 3 kg gas sales at each LPG base in order to improve their performance and service. So research is needed to predict the amount of gas sales of 3 Kg through the Neural Network method with the backpropagation algorithm to find the best results that will be used to solve the problems faced.
Governance and ICT4D Initiative Success: A Longitudinal Field Study of Ten Villages in Rural India

Initiatives to leverage information and communication technologies for development (ICT4D) have attracted huge investments, especially in less developed countries. However, the success rate of such initiatives has been low. Prior research on this topic has argued for various individual and network characteristics as predictors of information and communication technology (ICT) use and consequent benefits. We argue that, in order to garner potential benefits of the local information and knowledge resources embedded in citizens’ advice networks, hybrid governance from a combination of the local government and the technology sponsor is required. We further theorize that leadership by the local government or the technology sponsor for different stages of the ICT4D initiative affects the effectiveness of the pathways through which benefits of citizens’ advice networks accrue. We found support, in a longitudinal field study in ten villages in India (2,980 heads of households), for our theory that hybrid governance outperforms homogeneous governance models. Leadership by the local government for the pre-launch stage and by the technology sponsor for the post-launch stage was the most effective in promoting the behavioral pathway for economic benefits—that is, leveraging advice networks for ICT use and consequent gains in income. In contrast, leadership by the technology sponsor for the pre-launch stage and by the local government for the post-launch stage was the most effective in promoting the informational pathway—that is, leveraging information and knowledge from advice networks to directly generate gains in income. Adjacent villages that did not have a similar ICT4D intervention did not experience a comparable growth in farmer income.
Deep Learning Assisted Differential Cryptanalysis for the Lightweight Cipher SIMON

SIMON and SPECK are two families of lightweight block ciphers that have excellent performance on hardware and software platforms.At CRYPTO 2019, Gohr first introduces the differential cryptanalysis based deep learning on round-reduced SPECK32/64, and finally reduces the remaining security of 11-round SPECK32/64 to roughly 38 bits.In this paper, we are committed to evaluating the safety of SIMON cipher under the neural differential cryptanalysis.We firstly prove theoretically that SIMON is a non-Markov cipher, which means that the results based on conventional differential cryptanalysis may be inaccurate.Then we train a residual neural network to get the 7-, 8-, 9-round neural distinguishers for SIMON32/64.To prove the effectiveness for our distinguishers, we perform the distinguishing attack and key-recovery attack against 15-round SIMON32/64.The results show that the real ciphertexts can be distinguished from random ciphertexts with a probability close to 1 only by 2 8.7 chosen-plaintext pairs.For the key-recovery attack, the correct key was recovered with a success rate of 23%, and the data complexity and computation complexity are as low as 2 8 and 2 20.1 respectively.All the results are better than the existing literature.Furthermore, we briefly discussed the effect of different residual network structures on the training results of neural distinguishers.It is hoped that our findings will provide some reference for future research.