Reviewerly/exHarmony huggingface dataset:
ID-Agnostic User Behavior Pre-training for Sequential Recommendation

Recently, sequential recommendation has emerged as a widely studied topic. Existing researches mainly design effective neural architectures to model user behavior sequences based on item IDs. However, this kind of approach highly relies on user-item interaction data and neglects the attribute- or characteristic-level correlations among similar items preferred by a user. In light of these issues, we propose IDA-SR, which stands for ID-Agnostic User Behavior Pre-training approach for Sequential Recommendation. Instead of explicitly learning representations for item IDs, IDA-SR directly learns item representations from rich text information. To bridge the gap between text semantics and sequential user behaviors, we utilize the pre-trained language model as text encoder, and conduct a pre-training architecture on the sequential user behaviors. In this way, item text can be directly utilized for sequential recommendation without relying on item IDs. Extensive experiments show that the proposed approach can achieve comparable results when only using ID-agnostic item representations, and performs better than baselines by a large margin when fine-tuned with ID information.
Does Crypto Kill? Relationship between Electricity Consumption Carbon Footprints and Bitcoin Transactions

Cryptocurrencies are gaining more popularity due to their security, making counterfeits impossible. However, these digital currencies have been criticized for creating a large carbon footprint due to their algorithmic complexity and decentralized system design for proof of work and mining. We hypothesize that the carbon footprint of cryptocurrency transactions has a higher dependency on carbon-rich fuel sources than green or renewable fuel sources. We provide a machine learning framework to model such transactions and correlate them with the electricity generation patterns to estimate and analyze their carbon cost.
From Page to Screen: Exploring Wordsworth’s “I Wandered Lonely as a Cloud” Through the Computational Lens of “Transpoemation”

Literary critics value the process of “close reading” which involves focused word-by-word and line-by-line reading of a literary work to derive meaning from the entire corpus and establish the role of different aspects of text in this process. As this strategy may yield previously unnoticed connotations, it is rarely performed using computer software. Although this is a widely established view, in this paper, we posit that “distant reading” using an appropriate combination of automatic/computer-assisted analytical methods can still achieve this purpose. While we do not undermine the value of the traditional process, we demonstrate that a detailed visualization of the literary work in focus (in our case a poem by William Wordsworth) through digital tools like “transpoemation” could augment the literary analysis process.
PENGENALAN KARAKTER SIBI MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK

Communication between humans is an important thing for the activities of daily life. However, Humans were created with the advantages and disadvantages of each person and one of them is the difficulty of establishing or communicating and interacting for people who are deaf and speech impaired. Meanwhile, what solutions can be given about these shortcomings. So that there is no gap in society.This has led to the development of sign language recognition systems, which can automatically translate sign language into text and speech with effective pre-processing and accurate sign classification. According to recent developments in the field of deep learning, neural networks may have broad implications and implementations for sign language analysis. In the proposed system, Convolutional Neural Network (CNN) is used to classify sign language images because convolutional networks are faster in feature extraction and image classification than other classifiers.CNN architecture is carried out in 3 stages, 25 epochs, 50 epochs and 100 epochs. Based on the experiments conducted, the accuracy value obtained continues to increase in each stage, starting from 91.03%, 92.69% to the highest accuracy value in the training process of 94.25%. Likewise, the data prediction process also increases in each stage, starting from 90.62%, 93.75% until the highest accuracy value in data prediction is same 95.83%.
Penerapan Student Facilitator And Explaining pada Peningkatan Keaktifan, Motivasi, dan Hasil Belajar IPS

This research aimed to increase activeness, motivation, and social studies learning outcomes through the application of the Student Facilitator and Explaining model to fifth-grade students at SD Negeri Tunggorono Purworejo for the 2021/2022 academic year. This type of research is classroom action research. The research subjects were students of class V SD Negeri Tunggorono, totaling 12 students. The research stages include 4 stages, namely planning, implementation, observation, and reflection. Data collection techniques using documentation, questionnaires, observations, and tests. The data analysis technique used was descriptive quantitative with percentages. The results showed that 1) the use of the Student Facilitator and Explaining learning model could increase the activeness of students in the high category, from 33.33% in the pre-cycle, to 66.67% in the first cycle and 91.67% in the second cycle; 2) the use of the Student Facilitator and Explaining learning model can increase the motivation of students in the high category from 41.67% in the pre-cycle, to 75% in the first cycle and 91.67% in the second cycle; 3) the use of the Student Facilitator and Explaining learning model can improve the social studies learning outcomes of students who complete the pre-cycle by 33.33%, increase to 75% in the first cycle, and become 100% in the second cycle.
Exact Phase Transitions in Deep Learning

This work reports deep-learning-unique first-order and second-order phase transitions, whose phenomenology closely follows that in statistical physics. In particular, we prove that the competition between prediction error and model complexity in the training loss leads to the second-order phase transition for nets with one hidden layer and the first-order phase transition for nets with more than one hidden layer. The proposed theory is directly relevant to the optimization of neural networks and points to an origin of the posterior collapse problem in Bayesian deep learning.
Computer Forensic in Image Steganography

Abstract: The advancement of strong imaging tools, modifying photographs to change their data content is becoming a common task. In the computer forensic world, adding, erasing, or copying/moving image data without leaving a trace or unable to be discovered by the inquiry is a problem. The security of information exchanged over the Internet, such as photos and other confidential data, is critical. The goal of today's forensic Image investigation tools and methodologies is to uncover the tempering strategies and restore trust in digital media's trustworthiness. The difficulties of detecting steganography in computer forensics are investigated in this paper. These issues were investigated using open source software. The experiment focuses on steganography applications that employ the same methods to obsure and secure. Keywords: Image steganography, LSB steganography, LSB Spatial Algorithm, steganoanalysis
Directions for the Development of Agro-Engineering Education in Russia After the Pandemic

There is a decrease in general intellectual development level and mastery of a certain range of logical operations when learning. The decrease is compensated with the help of increased motivation and efficiency. When managing students’ cognitive processes and learning motives, it is important to consider the formation stages of logical and theoretical thinking. The authors found that insufficient professional orientation and low level of academic motivation hold a second position among the student dropout reasons, after the lack of desire to study and work in the chosen direction. If we consider the state of a graduate student at an agricultural university from the point of preparedness for the production process, then the professional education and competencies that the future specialist has mastered largely determine their competitiveness on the labor market. Since the current education system has never experienced the problems that the pandemic caused, it is necessary to reconsider the further development of the education system on an innovative base using not only distance (remote) education but also digital technologies.
Distance education psychosocial learning environment and graduate students' academic satisfaction during the pandemic

The closure of universities in the year caused by the pandemic that originated the COVID- determined the Peruvian government to impose distance The closure of the universities in the year caused by the pandemic that originated the COVID determined the Peruvian government to impose distance education. This required the enactment of regulations to ensure continuity of services and access to quality education adaptable to the distance learning modality. However, the psychosocial learning environment of this study modality has a social structure that differs from that of a face-to-face class, which could have a considerable impact on learning outcomes and therefore on students' academic satisfaction. For this reason, the objective of the study was to determine the relationship between the psychosocial learning environment of distance education and the academic satisfaction of graduate students during the pandemic. The methodology used was a quantitative approach with correlational design. The sample consisted of The sample consisted of graduate students of a public university, who were administered two questionnaires; the first to measure their perception of the psychosocial factors affecting their psychosocial learning environment of distance education during the pandemic, using the Sp-DELES scale; and the second to measure academic satisfaction using the SA scale.
Pengaruh Penggunaan Model Pembelajaran Word Square terhadap Hasil Belajar IPS di Sekolah Dasar

The selection of learning models used by teachers in learning activities is one of the determining factors for the success of student learning. This study aims to determine the effect of the Word Square learning model on social studies learning outcomes. This research was conducted at SD Negeri Bintaos, Tepus, Gunungkidul Regency. The research subjects were students of class VI SD Negeri Bintaos with a total of 20 students consisting of 9 male students and 11 female students. This research is a Quasi Experiment, using the Control Group Pretest and Posttest design. Data was collected by using test results in the form of pretest and posttest to determine students' social studies learning outcomes. The results showed that the difference in the average value in the experimental group and the control group. The average value of the experimental group was 82.70 while the average value of the control group was 74.70. The results of the calculation of the t test, sig. (2-tailed) 0.031 &lt;0.05 which indicates that Ho is rejected, meaning that there is a significant difference between the experimental group and the control group. Based on the results of the analysis, it can be said that the Word Square learning model has an effect on social studies learning outcomes.
DEVELOPMENT OF A VISUAL SEMANTIC WEB ONTOLOGY BASED LEARNING MANAGEMENT SYSTEM

This paper specifically aim at developing a visual semantic web ontology based e-Learning management system. The existing e-learning platforms have failed to address some challenges including managing the huge e-Learning content that is continuously grows on the web, meeting the learner's requirements when searching for an electronic learning content, representing the knowledge in a format that is easy to read and making it capable of thinking and thus allowing the re-use of e-Learning objects. In addition, most search results contain some irrelevant documents that cannot serve the need of the learner. Therefore, this thesis designed a common interface for learners, instructors, and administrators of academic institutions to upload and access learning materials. The new system created an ontology repository to maintain learners’ personalization details and taxonomy of the learning resources and integrate the knowledge embedded in many remote ontologies for improved e -Learning management system. In addition, an intelligent search engine through which learners can semantically search their learning materials and filter the search results returned by the search engine based on some semantics and the user’s preferences created. These designs simulated using a web-system developed with PHP, MySQL and JavaScript. The System Design followed the Object Oriented Design and Analysis Methodology for componentization of the system modules giving room for coupling, decoupling, modification, encapsulation and reuse, as well as easy maintainability. Unified Modeling Language extensively used to simplify the explanation of the system modules. The software performance wastested; the result shows that visual semantic web ontology-based e-Learning management system achieved 95% accuracy in returning the desired web search result.
Smart Vending Machine

Abstract: Now-a-days vending machines are becoming very popular in western countries because of their ease, less wastage of time and effort, availability near doorstep and variety of products. But in India, vending machines are still not so popular according to the customer's requirement. In this paper, a model of vending machine is proposed which dispenses liquid products by accepting the coin. The model of vending machine is customer friendly with a feature of fake coin detection. This machine is implemented using AVR ATmega16 microcontroller. Various modules of the machine are interfaced with the microcontroller. Coin recognition through CS-616 coin acceptor and IR sensor makes this model simple and inexpensive as compared to the previous work where either currency or tokens were used or difficult techniques like image recognition for detection of coin. Keywords: Atmega16,IR sensor, CS-616 Coin acceptor
Evaluating the Predictive Performance of Positive-Unlabelled Classifiers: a brief critical review and practical recommendations for improvement

Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances. Whilst much work has been done proposing methods for PU learning, little has been written on the subject of evaluating these methods. Many popular standard classification metrics cannot be precisely calculated due to the absence of fully labelled data, so alternative approaches must be taken. This short commentary paper critically reviews the main PU learning evaluation approaches and the choice of predictive accuracy measures in 51 articles proposing PU classifiers and provides practical recommendations for improvements in this area.
Implementasi Layanan Drive Thru dan Delivery Order Buku di Masa Pandemi Covid-19 pada Minat Baca Peserta Didik SDN Gajasari

The Covid-19 pandemic condition has forced the world of education to prepare appropriate and good steps to maintain the quality of literacy, especially reading interest in Gajasari Elementary School students. The drive thru service and book delivery orders during the pandemic are innovations in the management of the book-borrowing process in accordance with the COVID-19 pandemic conditions. Therefore, this study aims to: (1) explain the process of borrowing and returning books during the covid-19 pandemic; (2) explain the implementation of drive thru services and book delivery orders during the pandemic on the reading interest of students at SDN Gajasari; (3) identify the supporting and inhibiting factors for implementing drive thru services and book delivery orders during the pandemic on the reading interest of students at SDN Gajasari. This research uses a qualitative design with a case study approach. Sources of data from all students, teachers, and parents of students at SDN Gajasari, Nglipar, Gunungkidul. Data collection techniques are in the form of participatory observation and observation through the library slims application, observation through communication on WhatsApp, interviews, and documentation from February to April 2020. Then, the data is displayed to get conclusions. The results of this study, are: (1) The process of borrowing books back during the covid-19 pandemic there was a decrease in visitors to library members by 74.19%. (2) The implementation of drive thru services and book delivery orders during the pandemic on the reading interest of students at SDN Gajasari increased by 238,4%. (3) The process of innovating drive thru services and delivery of book orders during the pandemic on students' reading interest experienced obstacles in the socialization of the program. Support for the implementation of service management is provided by all teachers, employees, parents of students, committees, and DISPUSIP Gunungkidul Regency.
An innovative approach to classify hierarchical remarks with multi-class using BERT and customized naïve bayes classifier

Text classification is the process of grouping text into distinct categories. Text classifiers may automatically assess text input and allocate a set of pre-defined tags or categories depending on its content or a pre-trained model using Natural Language Processing (NLP), which actually is a subset of Machine Learning (ML). The notion of text categorization is becoming increasingly essential in enterprises since it helps firms to get ideas from facts and automate company operations, lowering manual labor and expenses. Linguistic Detectors (the technique of determining the language of a given document), Sentiment Analysis (the process of identifying whether a text is favorable or unfavorable about a given subject), Topic Detection (determining the theme or topic of a group of texts), and so on are common applications of text classification in industry. The nature of the dataset is Multi-class and multi-hierarchical, which means that the hierarchies are in multiple levels, each level of hierarchy is multiple class in nature. One of ML’s most successful paradigms is supervised learning from which one can build a generalization model. Hence, a custom model is built, so that the model fits with the problem. Deep learning (DL), part of Artificial Intelligence (AI) , does functions that replicate the human brain's data processing capabilities in order to identify text or artifacts, translate languages, detect voice, draw conclusions and so on. Bidirectional Encoder Representations from Transformers (BERT), a Deep Learning Algorithm performs an extra-ordinary task in NLP text classification and results in high accuracy. Therefore, BERT is combined with the Custom Model developed and compared with the native algorithm to ensure the increase in accuracy rates.
Pengembangan Model Fast Incremental Gaussian Mixture Network (IGMN) pada Interpolasi Spasial

Gathering geospatial information in an organization is one of the most critical processes to support decision-making and business sustainability. However, many obstacles can hinder this process, like uncertain natural conditions and a large geographical area. This problem causes the organization only to obtain a few sample points of observation, resulting in incomplete information. The data incompleteness problem can be solved by applying spatial interpolation to estimate or determine the value of unavailable data. Spatial interpolation generally uses geostatistical methods. These geostatistical methods require a variogram as a model built based on the knowledge and input of geostatistic experts. The existence of this variogram becomes a necessity to implement these methods. However, it becomes less suitable to be applied to organizations that do not have geostatistics experts. This research will develop a Fast IGMN model in solving spatial interpolation. In this study, results of the modified Fast IGMN model in spatial interpolation increase the interpolation accuracy. Fast IGMN without modification produces MSE = 1.234429691, while using Modified Fast IGMN produces MSE = 0.687391. The MSE value of the Fast IGMN-Modification model is smaller, which means that the smaller the MSE value, the higher the accuracy of the interpolation results. This modified Fast IGMN model can solve problems in gathering information for an organization that does not have geostatistics experts in the spatial data modeling process. However, it needs to be developed again with more varied input data.
From Bonehead to @realDonaldTrump

In many online services, we are identified by self-chosen usernames, also known as nicknames or pseudonyms. Usernames have been studied quite extensively within several academic disciplines, yet few existing literature reviews or meta-analyses provide a comprehensive picture of the name category. This article addresses this gap by thoroughly analyzing 103 research articles with usernames as their primary focus. Despite the great variety of approaches taken to investigate usernames, three main types of studies can be identified: (1) qualitative analyses examining username semantics, the motivations for name choices, and how the names are linked to the identities of the users; (2) experiments testing the communicative functions of usernames; and (3) computational studies analyzing large corpora of usernames to acquire information about the users and their behavior. The current review investigates the terminology, objectives, methods, data, results, and impact of these three study types in detail. Finally, research gaps and potential directions for future works are discussed. As this investigation will demonstrate, more research is needed to examine naming practices in social media, username-related online discrimination and harassment, and username usage in conversations.
Analysis of Applied Mathematics

Mathematics applied to applications involves using mathematics for issues that arise in various fields, e.g., science, engineering, engineering, or other areas, and developing new or better techniques to address the demands of the unique challenges. We consider it applied math to apply maths to problems in the real world with the double purpose of describing observed phenomena and forecasting new yet unknown phenomena. Thus, the focus is on math, e.g., creating new techniques to tackle the issues of the unique challenges and the actual world. The issues arise from a variety of applications, including biological and physical sciences as well as engineering and social sciences. They require knowledge of different branches of mathematics including the analysis of differential equations and stochastics. They are based on mathematical and numerical techniques. Most of our faculty and students work directly with the experimentalists to watch their research findings come to life. This research team investigates mathematical issues arising out of geophysical, chemical, physical, and biophysical sciences. The majority of these problems are explained by time-dependent partial integral or ordinary differential equations. They are also accompanied by complex boundary conditions, interface conditions, and external forces. Nonlinear dynamical systems provide an underlying geometrical and topological model for understanding, identifying, and quantifying the complex phenomena in these equations. The theory of partial differential equations lets us correctly formulate well-posed problems and study the behavior of solutions, which sets the stage for effective numerical simulations. Nonlocal equations result from the macroscopically modeling stochastic dynamical systems characterized by Levy noise and the modeling of long-range interactions. They also provide a better understanding of anomalous diffusions.