Reviewerly/exHarmony huggingface dataset:
Establishment and Verification of the Thermodynamic Models for Hotpot Meatballs and Meat Slices

As a traditional Chinese cuisine, hotpot is popular all over the world. Hotpot meat is often presented in the form of meat-slices and meatballs. In daily life, people observe the discoloration and up-floating to judge whether the meatball is well cooked. Experiments have confirmed that such methods lack scientific basis and pose threat to food safety. Firstly, a thermodynamic model of meatballs heating in boiling water is constructed. The theoretical calculation is established and solved by MATLAB and Ansys Discovery Live. The effect of change of meatball diameter and hotpot heating power on the unsteady-state temperature field of meatballs is obtained, concluding that the heating power barely affects the heating process of meatballs. Secondly, the thermodynamic model of meatballs is verified through experiments. Two types of meatballs made of beef with diameters of 30mm and 35mm respectively are used as the experimental objects. A commonly-used household induction heater and a hotpot are used as the heating devices. Several K-type armored thermocouples and an electronic thermometer are used as temperature measuring and data collecting devices. Regarding the difference on meatball diameters and heating powers, multiple experiments are operated. The experimental data graphs are drawn by Excel and MATLAB. The experimental results and the theoretical calculations fit well. The heat transfer coefficient between meat and boiling water is estimated accordingly. A similar thermodynamic model of meat slices is constructed and solved by analytical method. MATLAB is used to draw the temperature curve of the heating process. However, the cooking time calculated by the theoretical model is apparently longer than the actual cooking time. The convective mass transfer phenomenon is further introduced to better describe the heating process of meat slices. Through theoretical derivation, it is found that the rate of mass transfer is related to the surface area to volume ratio of meat. Hence, the heating process of meat slices, compared to that of meatballs, is more significantly affected by mass transfer phenomenon. The conclusions of this paper can provide guidance on eating hotpot food safely.
ARTIFICIAL NEURAL NETWORK ALGORITHMS FOR REMOTE SENSING IMAGE CLASSIFICATION – AN OVERVIEW

Remote sensing described as any procedure by which information on an item, region or phenomena is collected without being connected; the word remote sensing is more precisely linked with assessing interactions of the earth's surface materials and electromagnetic radiation. In recent years, the remote sensing community has been focusing on the neural network method to data classification because this methodology requires no previous knowledge of statistical data distribution and quick categorization. This study focuses on the examination and utilization of several neural network algorithms, used in remote sensing image classification
Are the Optimum Angles of Photovoltaic Systems So Important?

Traditional point of view suggests photovoltaic systems to be installed at the optimum angles. However, various reasons require more flexible photovoltaic module installations. In this study, it is found that the optimum angles for photovoltaic systems are not so important. Because photovoltaic modules only suffer 5% of energy losses, compared to the maximum plane-of-array irradiation, in quite a wide range of installation angles which is defined as the 5% Loss Bound. The Annual Bound Equations were derived to quantitatively calculate the annual 5% Loss Bound. If local information on the optimum angles is known, the improved Annual Bound Equation can be used for higher accuracy; if not, its simplified version is suggested at latitudes lower than 40o. Simulations by the Annual Bound Equations are independent of local irradiance data, which is significantly faster and more convenient than conventional irradiance-based approaches.
Conversational dominance and politeness strategy on a political discussion among peers

This paper aims to explore the differences of female and male linguistic features analyzed using the conversational dominance analysis and the common politeness strategies when discussing Kabinet Indonesia Maju (Onward Indonesia Cabinet). The participants were a group of Master Degree students consisting of two male and two female students. The data was gathered by conducting a focus group discussion, then the verbal utterance was orthographically transcribed. The writer used quantitative and qualitative methods to analyse the data. The finding showed that other-oriented interruption was the most-occurred dominance, in which both male and female participants interrupted the conversation to exhibit the agreement, support the speakers, and reinforce the flow of the conversation. The female participants did self-oriented interruption more than male participants to ask questions. This finding conversed Lakoff (1973), Coates (1991), and West and Zimmerman (1987)’s female language features in which the female participants stood for their own opinion, shared their critic, gave suggestions, and asked questions. This study found that the participants showed mostly positive politeness strategy which they agreed to each other's statement, supported one another's idea, and contributed new topics to the discussion to maintain the discussion. This paper concluded that gender is not the only factor determining linguistics features and politeness strategy in this discussion among peers. The other possible influencing factors were the participants’ relationship closeness, different cultural backgrounds, topic mastery, and their equal education level.
Automatic Question Generation and Evaluation

Generation of questions from an extract is a very tedious task for humans and an even tougher one for machines. In Automatic Question Generation (AQG), it is extremely important to examine the ways in which this can be achieved with sufficient levels of accuracy and efficiency. The way in which this can be taken ahead is by using Natural Language Processing (NLP) to process the input and to work with it for AQG. Using NLP with question generation algorithms the system can generate the questions for a better understanding of the text document. The input is pre-processed before actually moving in for the question generation process. The questions formed are first checked for proper context satisfaction with the context of the input to avoid invalid or unanswerable question generation. It is then preprocessed using various NLP-based mechanisms like tokenization, named entity recognition(NER) tagging, parts of speech(POS) tagging, etc. The question generation system consists of a machine learning classification-based Fill in the blank(FIB) generator that also generates multiple choices and a rule-based approach to generate Wh-type questions. It also consists of a question evaluator where the user can evaluate the generated question. The results of these evaluations can help in improving our system further. Also, evaluation of Wh questions has been done using the BLEU score to determine whether the automatically generated questions resemble closely the human-generated ones. This system can be used in various places to help ease the question generation and also at self-evaluator systems where the students can assess themselves so as to determine their conceptual understanding. Apart from educational use, it would also be helpful in building chatbot-based applications. This work can help improve the overall understanding of the level to which the concept given is understood by the candidate and the ways in which it can be understood more properly. We have taken a simple yet effective approach to generate the questions. Our evaluation results show that our model works well on simpler sentences.
초등 과학수업에서 학생들이 구성한 비주얼 씽킹의 유형 및 수업 효과

Based on the importance of visual representation for scientific understanding, this study applied visual thinking in elementary science classes. This study analyzed elementary students’ visual thinking and investigated the instructional influences. Students’ perceptions on the class applying visual thinking were also investigated. The subject were 38 fourth grade students, 18 in experimental group and 20 in control group. For the unit of ‘Shadow and mirror’, on-line and off-line blended classes were applied in both group because of COVID-19. The experimental group student were asked to construct their own visual thinking, while the control group students used traditional workbook. The results were as follows. First, students’ visual thinking can be classified into three different types, which are ‘activity recall type’, ‘result summary type’, and ‘core concept representation type’ based on what they represent rather than how they represent. Second, applying visual thinking in science class showed significant effects on science academic achievement, science related attitude, and creative academic efficacy. Third, students’ perceptions on applying visual thinking in science classes were very positive. Students perceived visual thinking activities were interesting and helpful for understanding science. Educational implications of applying visual thinking in elementary science classes were discussed.
A Machine Vision Model for Solid Waste Classification using Smart Intelligence

The waste sorting is carried out using machines still require high precision is discriminating objects into appropriate categories. The waste objects are fed in a conveyor where the objects are separated manually and by tools. The cost of separating manually becomes high and time consuming with humans and the performance of separating tools depends on the precision of classifying the objects automatically. The classification of each object is presently aided through computer vision and machine learning technology. Data mining is a process of extracting useful information and patterns hidden inside large volume of data. Machine learning is a division of data mining through which algorithms gain the ability to improve their performance through self-learning. Data mining and machine learning algorithms are used in variety of data formats which includes numerical data, signals, multimedia formats, spatial data, sensor data. In this paper a novel idea for the solid waste classifications are given along with the future enhancements.
Smart Navigation Of Wheel Chair Using Human Machine Interface

This manuscript deals with smart navigation of wheelchair using human machine interface. Physically challenged people are those who suffer from different physical disabilities. In day-to-day life, physically challenged people are facing many problems and one of them being to move around without external support, being independent and the freedom to move around independently is an integral part of one’s life. This project is carefully designed by keeping in mind the problem's faced by physically challenged people. To implement the manuscript, Arduino UNO is used to control the motor coupled to the wheel of the wheelchair which is controlled by voice command from the android mobile phone through a Bluetooth module. Apart from moving the motor using voice command, the prototype also has an ultrasonic sensor which senses the obstacle on the way and controls the direction of the wheels accordingly. Apart from physically challenged people, aged people will also get benefited with this prototype.
Influence of its technologies on contemporary art

This paper examines the modern impact of IT technologies on art and its further development in the culture of postmodernism. In general, the author reveals the features of cultural phenomena influencing the formation and development of contemporary art. The art of the modern era of postmodernism was born on the basis of the classical culture of Western civilization; it is not always capable of evoking positive emotions and does not want to be a source of beauty and morality. It should be noted that, despite the history of the development of contemporary art available to a wide audience, contemporary art and its multidimensionality raises many questions related to the content and aesthetic perception of its art objects. The main questions that are posed before the comprehension of modern abstract art sometimes sound like an accusation of the absence of a semantic component. In addition, the existence of the very phenomenon of art itself is often questioned.
On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths

We give a simple proof for the global convergence of gradient descent in training deep ReLU networks with the standard square loss, and show some of its improvements over the state-of-the-art. In particular, while prior works require all the hidden layers to be wide with width at least $\Omega(N^8)$ ($N$ being the number of training samples), we require a single wide layer of linear, quadratic or cubic width depending on the type of initialization. Unlike many recent proofs based on the Neural Tangent Kernel (NTK), our proof need not track the evolution of the entire NTK matrix, or more generally, any quantities related to the changes of activation patterns during training. Instead, we only need to track the evolution of the output at the last hidden layer, which can be done much more easily thanks to the Lipschitz property of ReLU. Some highlights of our setting: (i) all the layers are trained with standard gradient descent, (ii) the network has standard parameterization as opposed to the NTK one, and (iii) the network has a single wide layer as opposed to having all wide hidden layers as in most of NTK-related results.
Using e-texbook in a digital learning environment

The rapid development of e-learning (electronic learning) in modern Russia is caused by the high rate of informatization, digitalization, upgrading of information and communication technologies (ICT), and the necessity for open and accessible life-long education, which underlies the post-industrial knowledge society. All these processes stimulate the active search for creative educational technologies that meet the requirements of the knowledge society. A modern specialist must constantly learn and improve skills and capabilities in order to keep up with the times. In the framework of the up-to-date educational paradigm, a student is considered as a subject of knowledge, and not an object of pedagogical influence. In the early 2010s, e-learning actively contributed to this process. E-learning allows students and teachers to integrate into the global scientific community and take an active part in the interaction of cultures, including the exchange of moral and spiritual values. Implementation of e-learning is one of the key tasks in the context of the modernization of Russian education. The term “electronic learning” is translated into Russian in different ways. The most popular options are: “distance learning/education”, ‘mobile learning”, “virtual training”. The European Commission interprets e-learning as the use of new multimedia and Internet technologies to improve the quality of training and education, the availability of support services and educational resources, as well as remote knowledge sharing and collaboration. This is a learning process based on interactive electronic means of storage, delivery, and presentation of information: the Internet, local networks, CDs, and other storage devices
KNOWLEDGE MANAGEMENT AND ISO/IEC 19796-1-BASED E-LEARNING BUSINESS ARCHITECTURE DESIGN FOR INDONESIAN HIGHER EDUCATION WITH TOGAF ADM APPROACH

e-Learning and distance learning implementation is part of the Indonesian Government's response to face the industrial revolution 4.0 and the disruption of higher education innovations through the Ministry of Research, Technology and Higher Education. -Learning and distance learning implementation in Indonesian higher education must be in line with Tridharma Perguruan Tinggi, and adopt a quality design approach that comes from a good framework. This research offers a design of e-learning business architecture in indonesian higher education that is in line with Tridharma Perguruan Tinggi, Indonesian Higher Education Accreditation, follows the quality framework and approach from ISO 19796 and PDCA cycle, and continuous improvement based on knowledge management. e-Learning business architecture design, carried out using TOGAF ADM approach. The e-learning business architecture design results for indonesian higher education are outlined in 5 parts, which is e-learning business architecture motivation, e-learning business functions and services, e-learning organizations, e-learning business processes, and e-learning business roles and actor.
GAN: Era of Image to Image translation

Image to image translation is a new sensation in the field of machine learning, artificial intelligence, and vision communities. The principle of an image-to-image translation is to convert an image of the source domain to a target domain image. Recent innovations and techniques have shown remarkable success in the field of an image to image translation for two domains. But the existing methods have limited functionality and methods when we perform translation between more than two domain images. Here, GAN (Generative Adversarial Network) plays a vital role, GAN provides us functional approach that can perform image-to-image translation between multiple domains using a single conversation model. This paper empirically discusses and analyzes image-to-image translation using GAN.
Effects of humidity on natural gut harp strings

Natural gut harp strings are notoriously sensitive to changes in humidity, but the nature of this sensitivity is poorly understood. The results of some recent measurements will be presented, in which a number of well-settled strings were held at constant temperature and subjected to changes in humidity. It will be shown that there appears to be some form of coupling between humidity-induced changes in the string linear density and its tension. Moreover, these changes in linear density and tension largely cancel out, with little net effect on the string frequency. It appears, instead, that the major effect of changes in humidity is to trigger episodes of additional string creep. The longevity of gut harp strings may therefore be determined as much by their propensity to creep as by their breaking strength.
Machine Learning analysis of seismic signals recorded at Stromboli Volcano

&amp;lt;p&amp;gt;At Stromboli, minor volcanic eruptions occur at time intervals of approximately five minutes on average, making it one of the most active volcanoes worldwide. In addition to these mostly harmless events, there are also stronger eruptions and paroxysms which pose a serious threat to residents and tourists. In light of recent developments in Machine Learning, this study attempts to apply these new tools for the analysis of the time-varying volcanic eruptions at Stromboli. As input for the Machine-Learning approach, we use continuous recordings of seismic signals from two seismometers on the island. The data is available from IRIS &amp;amp;#160;and includes records starting in 2012 up to the present.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;One primary challenge is to label and classify the data, i.e., to discriminate events of interest from noise. The variety of signal-appearance in the recorded data is wide, in some periods the events are clearly distinguishable from noise whereas, in other cases relevant events are obscured by the high noise level. To enable the event-detection in all cases, we developed the following algorithm: in the first step, the seismic data is pre-processed with an STA/LTA-Filter, which allows detection of events based on a prominence threshold. However, due to the diversity of signal patterns, a fixed set of hyperparameters (STA- and LTA-window length, prominence threshold, correlation coefficient) fails to reliably extract the relevant events in a consistent manner. Therefore, the (time-varying) noise level of the recordings is used as an additional key indicator. After this, the hyperparameters are optimized. The automatic adaptation is then used for labeling the continuous seismic data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;After extracting the events based on this approach, a machine learning model is trained to analyze the recordings for possible patterns in the interval times and the event amplitudes. This study is expected to provide constraints on the possibility to detect complex time-dependent patterns of the eruption history at Stromboli.&amp;lt;/p&amp;gt;
Procedural Level Generation with Difficulty Level Estimation for Puzzle Games

This paper presents a complete solution for procedural creation of new levels, implemented in an existing puzzle video game. It explains the development, going through an adaptation to the genre of game of the approach to puzzle generation and talking in detail about various difficulty metrics used to calculate the resulting grade. Final part of the research presents the results of grading a set of hand-crafted levels to demonstrate the viability of this method, and later presents the range of scores for grading generated puzzles using different settings. In conclusion, the paper manages to deliver an effective system for assisting a designer with prototyping new puzzles for the game, while leaving room for future performance improvements.
Semantic based Inter-Operable Cloud Resource Allocation

Cloud computing is the process of lending the required resources that includes computing power, network access, database storage, applications, and other IT resources in on demand basis through a cloud services platform with pay-as- you-go pricing. Here, users access services according to their requirements, without knowing where the services are hosted or how they’re delivered. So Resource Management is tedious process between different clouds. Since different clouds will have different types of cloud virtualization techniques with corresponding middleware's with unique mechanism and protocols, we face lot of interoperability issues. So there is a need to address these interoperability issues between different clouds to achieve the highest availability of cloud resources. This motivates us to conceive a proposal Semantic based Interoperable Cloud Resource Allocation (SICRA) that provides a common mechanism to access the resources of different clouds Proposed work also incorporates the semantic services for identifying best potential resource. Thus an ontology based indexing and retrieving algorithms are designed and provided with ranking which in turn increases the success rate.
Iranian EFL Learners' Attitudes Toward Language Learning Through Social Media During the Covid-19 Pandemic

The current research aimed at exploring Iranian English as a foreign language (EFL) learners’ attitudes and perceptions toward learning a language by means of social media during the outbreak of the Covid-19 pandemic. To do so, 850 participants were selected voluntarily based on inclusion criteria from 1000 language learners enrolling in the general English courses at some specific institutions and under some tutors. To collect the intended data, a researcher-made questionnaire, devised based on the available literature to scrutinize their attitude using 5-point Likert scale items, was administered by means of social media to the participants. Out of the collected questionnaires, 850 were included for the analysis in this research. The results revealed that EFL learners consider social media as a significant element of their learning a foreign language, especially throughout the Covid-19 pandemic. The findings of the present research can pave the way for the inclusion of social media as an indispensable instrument of nurturing collaboration and communication among learners even after the pandemic eases. Keywords: EFL learners, social media, the covid-19 pandemic, attitudes and perceptions, language learning DOI: 10.7176/NMMC/96-03 Publication date: May 31 st 2021