Reviewerly/exHarmony huggingface dataset:
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction

Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with massive features in online e-commerce platforms. Most existing methods manually design a maximal order and further filter out the useless interactions from them. Although they reduce the high computational costs caused by the exponential growth of high-order feature combinations, they still suffer from the degradation of model capability due to the suboptimal learning of the restricted feature orders. The solution to maintain the model capability and meanwhile keep it efficient is a technical challenge, which has not been adequately addressed. To address this issue, we propose an adaptive feature interaction learning model, named as EulerNet, in which the feature interactions are learned in a complex vector space by conducting space mapping according to Euler's formula. EulerNet converts the exponential powers of feature interactions into simple linear combinations of the modulus and phase of the complex features, making it possible to adaptively learn the high-order feature interactions in an efficient way. Furthermore, EulerNet incorporates the implicit and explicit feature interactions into a unified architecture, which achieves the mutual enhancement and largely boosts the model capabilities. Such a network can be fully learned from data, with no need of pre-designed form or order for feature interactions. Extensive experiments conducted on three public datasets have demonstrated the effectiveness and efficiency of our approach. Our code is available at: https://github.com/RUCAIBox/EulerNet.
MetroScope: An Advanced System for Real-Time Detection and Analysis of Metro-Related Threats and Events via Twitter

Metro systems are vital to our daily lives, but they face safety or reliability challenges, such as criminal activities or infrastructure disruptions, respectively. Real-time threat detection and analysis are crucial to ensure their safety and reliability. Although many existing systems use Twitter to detect metro-related threats or events in real-time, they have limitations in event analysis and system maintenance. Specifically, they cannot analyze event development, or prioritize events from numerous tweets. Besides, their users are required to continuously monitor system notifications, use inefficient content retrieval methods, and perform detailed system maintenance. We addressed those issues by developing the MetroScope system, a real-time threat/event detection system applied to Washington D.C. metro system. MetroScope can automatically analyze event development, prioritize events based on urgency, send emergency notifications via emails, provide efficient content retrieval, and self-maintain the system. Our MetroScope system is now available at http://orion.nvc.cs.vt.edu:5000/, with a video (https://www.youtube.com/watch?v=vKIK9M60-J8) introducing its features and instructions. MetroScope is a significant advancement in enhancing the safety and reliability of metro systems.
Exploring the current status of classes by area in the music education field after the post-pendemic

사회의 빠른 변화 속에서 교육의 패러다임이 변화하고 있으며, 음악교과 역시 예외는 아니다. 또한 코로나-19로 인해 음악의 다양한 활동 영역을 중심으로 하는 수업이 다양한 방향으로 변화하고 있다. 이에 본 연구에서는 학교 현장에서 다양하게 이뤄지고 있는 수업을 음악의 활동 영역별로 살펴보고 효율적인 음악수업을 위한 시사점을 도출하고자 하였다. 이와같이 음악의 다양한 활동 영역에서의 실제 수업들을 살펴본 결과 효율적인 음악수업을 위한 시사점을 다음과 같이 도출하였다. 첫째, 코로나-19 시대에 필연적으로 발전할 수 밖에 없었던 다양한 온라인 도구를 음악수업에 활용할 수 있는 방법에 대한 연구가 필요하다. 둘째, 음악교육 현장의 변화를 주도하는 음악교사들이 시대의 흐름에 맞춰 갈 수 있도록 연수 혹은 재교육을 통한 다양한 기회가 제공되어야 한다.The paradigm of education is changing amid rapid changes in society, and music subjects are no exception. In addition, due to COVID-19, classes centered on various areas of activity of music are changing in various directions. Therefore, this study attempted to examine various classes conducted in the school field by music activity area and derive implications for efficient music classes. As a result of examining actual classes in various areas of music, implications for efficient music classes were derived as follows. First, it is necessary to study how to use various online tools that inevitably developed in the COVID-19 era in music classes. Second, various opportunities through training or re-education should be provided so that music teachers who lead the change in the music education field can go along with the trend of the times.
The Effect of Online Education on Student’s Achievements of Piano Proficiency Course in Music Education Program

Piano proficiency course introduced the basics skills of piano which incorporated posture, hand position, finger strengthening, note-reading and melodic harmonization.It has switched model from face-to-face to online under the environment of epidemic.The goal of this study is to investigate the student's learning outcomes of the online class in piano proficiency course.This research was come out of at Universiti Pendidikan Sultan Idris (UPSI) in Perak, Malaysia and collect data through the qualitative research method.The semi-structured interviews with five students were applied as informants and then the thematic analysis method was applied to analyze the results.The participants of five students in UPSI were selected to interview and the researcher used non-random sampling method.Data was illustrated by Microsoft Word 2010 in tables and figures.The findings of this research were shown that the students should be separated into multiple classes to execute stratification through the Mini-class teaching.The significance of this study hopes to ameliorate teaching and learning of piano proficiency course.
Exploring User and Item Representation, Justification Generation, and Data Augmentation for Conversational Recommender Systems

Conversational Recommender Systems (CRS) aim to provide personalized and contextualized recommendations through natural language conversations with users. The objective of my proposed dissertation is to capitalize on the recent developments in conversational interfaces to advance the field of Recommender Systems in several directions. I aim to address several problems in recommender systems: user and item representation, justification generation, and data sparsity.
The Role of Parents’ Book Reading Style and Quality of Emotional in Improving Children’s Cognitive

This study aims to study the role of book reading style and the emotional quality of parents on children's cognitive abilities. Participants are families who participate in family literacy programs in Indonesia. The research sample is 150 parents and children who are involved in the family literacy program in the Bandung area, Indonesia. The correlational design was used in this study to see the role of parental style in reading books and the emotional quality of parents when interacting with children on children's cognitive abilities. The families involved are bilingual families who use more than one language in their family. The results showed that parents' book reading style (direct text reading/extratextual talk), emotional quality, and language use were factors that greatly influenced children's cognitive abilities. Extratextual reading style is a reading style that is considered superior in improving children's cognitive abilities compared to direct text reading style. In addition, reading will be more optimal in increasing children's grasping power if it is accompanied by quality parental emotions. This study provides implications that can be used as guidelines for stakeholders in providing signs for parents in an effort to improve children's literacy at preschool age.
Classification of Traffic Congestion in Indonesia Using the Naive Bayes Classification Method

The purpose of this research is to analyze the accuracy of congestion data using Google Colab in detecting congestion by the province in Indonesia the author tries to test strategies for dealing with congestion in the Indonesian region by utilizing the Naïve Bayes method. In this journal, apply with Google Collab . This research uses data that comes from crawling data on Twitter. Using the Naive Bayes method to find the shortest route is efficient and not congested. Implementation of online school transportation using the naive Bayes method in minimizing travel costs to pick up students can reduce traffic jams, reduce accidents, reduce student tardiness, and minimize travel costs. The Naive Bayes method can be used to identify relevant information about traffic jams in Indonesia through Twitter data with a good degree of accuracy. These results can assist decision-making and strategic planning in overcoming the problem of traffic congestion in Indonesia. Therefore, this research implies that it can help improve the accuracy of traffic congestion data in Indonesia. By using Google Colab, more advanced analysis methods and machine learning algorithms can be applied to process the existing traffic data. Additionally, utilizing Google Colab allows for fast and efficient data processing.
Idiom Translation Quality Assessment on “Landline” Novel by Rainbow Rowell

This study aimed at assessing the quality of idiom translation in a novel entitled “Landline” by Rainbow Rowell. By referring to Nababan’s Translation Quality Assessment model (2012), this study assessed the idioms’ translation according to the degree of accuracy, acceptability, and readability. The data analyzed in this study include the idioms and Indonesian translations, as well as the assessment result made by three expert assessors. Those idioms were identified and grouped based on the classification adapted from Palmer (1981). There are 37 idioms found, which consist of 2 idioms that can undergo syntactic changes, 10 idioms with syntactic limitation, 10 frozen idioms, 5 partial idioms, and 10 phrasal verbs. The findings show that the Indonesian translation of those idioms generally has a high-quality level, especially those which were translated into equivalent idioms in the target language. Paraphrasing also plays a major role as a translation strategy used by the translator. However, this strategy may sometimes lead to distortion of the original message in the target language.
Identification of breast lesion through integrated study of gorilla troops optimization and rotation-based learning from MRI images

Abstract Breast cancer has emerged as the most life-threatening disease among women around the world. Early detection and treatment of breast cancer are thought to reduce the need for surgery and boost the survival rate. The Magnetic Resonance Imaging (MRI) segmentation techniques for breast cancer diagnosis are investigated in this article. Kapur’s entropy-based multilevel thresholding is used in this study to determine optimal values for breast DCE-MRI lesion segmentation using Gorilla Troops Optimization (GTO). An improved GTO, is developed by incorporating Rotational opposition based-learning (RBL) into GTO called (GTORBL) and applied it to the same problem. The proposed approaches are tested on 20 patients’ T2 Weighted Sagittal (T2 WS) DCE-MRI 100 slices. The proposed approaches are compared with Tunicate Swarm Algorithm (TSA), Particle Swarm Optimization (PSO), Arithmetic Optimization Algorithm (AOA), Slime Mould Algorithm (SMA), Multi-verse Optimization (MVO), Hidden Markov Random Field (HMRF), Improved Markov Random Field (IMRF), and Conventional Markov Random Field (CMRF). The Dice Similarity Coefficient (DSC), sensitivity, and accuracy of the proposed GTO-based approach is achieved $$87.04\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>87.04</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , $$90.96\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>90.96</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , and $$98.13\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>98.13</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> respectively. Another proposed GTORBL-based segmentation method achieves accuracy values of $$99.31\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>99.31</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , sensitivity of $$95.45\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>95.45</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , and DSC of $$91.54\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>91.54</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> . The one-way ANOVA test followed by Tukey HSD and Wilcoxon Signed Rank Test are used to examine the results. Furthermore, Multi-Criteria Decision Making is used to evaluate overall performance focused on sensitivity, accuracy, false-positive rate, precision, specificity, $$F_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -score, Geometric-Mean, and DSC. According to both quantitative and qualitative findings, the proposed strategies outperform other compared methodologies.
Universal optimality and robust utility bounds for metric differential privacy1

We study the privacy-utility trade-off in the context of metric differential privacy. Ghosh et al. introduced the idea of universal optimality to characterise the “best” mechanism for a certain query that simultaneously satisfies (a fixed) ε-differential privacy constraint whilst at the same time providing better utility compared to any other ε-differentially private mechanism for the same query. They showed that the Geometric mechanism is universally optimal for the class of counting queries. On the other hand, Brenner and Nissim showed that outside the space of counting queries, and for the Bayes risk loss function, no such universally optimal mechanisms exist. Except for the universal optimality of the Laplace mechanism, there have been no generalisations of these universally optimal results to other classes of differentially-private mechanisms. In this paper, we use metric differential privacy and quantitative information flow as the fundamental principle for studying universal optimality. Metric differential privacy is a generalisation of both standard (i.e., central) differential privacy and local differential privacy, and it is increasingly being used in various application domains, for instance in location privacy and in privacy-preserving machine learning. Similar to the approaches adopted by Ghosh et al. and Brenner and Nissim, we measure utility in terms of loss functions, and we interpret the notion of a privacy mechanism as an information-theoretic channel satisfying constraints defined by ε-differential privacy and a metric meaningful to the underlying state space. Using this framework we are able to clarify Nissim and Brenner’s negative results by (a) that in fact all privacy types contain optimal mechanisms relative to certain kinds of non-trivial loss functions, and (b) extending and generalising their negative results beyond Bayes risk specifically to a wide class of non-trivial loss functions. Our exploration suggests that universally optimal mechanisms are indeed rare within privacy types. We therefore propose weaker universal benchmarks of utility called privacy type capacities. We show that such capacities always exist and can be computed using a convex optimisation algorithm. Further, we illustrate these ideas on a selection of examples with several different underlying metrics.
Empirical Evaluation of a Differentiated Assessment of Data Structures: The Role of Prerequisite Skills

There can be many reasons why students fail to answer correctly to summative tests in advanced computer science courses: often the cause is a lack of prerequisites or misconceptions about topics presented in previous courses. One of the ITiCSE 2020 working groups investigated the possibility of designing assessments suitable for differentiating between fragilities in prerequisites (in particular, knowledge and skills related to introductory programming courses) and advanced topics. This paper reports on an empirical evaluation of an instrument focusing on data structures, among those proposed by the ITiCSE working group. The evaluation aimed at understanding what fragile knowledge and skills the instrument is actually able to detect and to what extent it is able to differentiate them. Our results support that the instrument is able to distinguish between some specific fragilities (e.g., value vs. reference semantics), but not all of those claimed in the original report. In addition, our findings highlight the role of relevant skills at a level between prerequisite and advanced skills, such as program comprehension and reasoning about constraints. We also suggest ways to improve the questions in the instrument, both by improving the distractors of the multiple choice questions, and by slightly changing the content or phrasing of the questions. We argue that these improvements will increase the effectiveness of the instrument in assessing prerequisites as a whole, but also to pinpoint specific fragilities.
KOGNISI SOSIAL PADA NOVEL SUTI KARYA SAPARDI DJOKO DAMONO ANALISIS WACANA MODEL VAN DIJK

&#x0D; This study aims to analyze social cognition in the novel Suti by Sapardi Djoko Damono, discourse analysis of the van Dijk model, and utilize the results of this analysis as an alternative teaching material for class XII high school students. This research is a descriptive qualitative research, in which the data analysis results will be described in a narrative manner. The data source in this analysis is the novel Suti by Sapardi Djoko Damono. Novel Suti by Sapardi Djoko Damono and several books or other reading materials that can support research. Suti's novel consists of 58 data on social cognition, 27 data for social cognition knowledge and 31 data for social cognition opinions and attitudes. The findings from the results of the analysis of this novel show that van Dijk's type of social cognition used is social cognition of knowledge and attitude opinion, because this novel shows some of the author's knowledge from personal experience, traditions and culture that they adhere to. Furthermore, regarding opinion, attitudes are described by the author expressing his opinion and attitude regarding a problem in the novel. Suti has various views, opinions, and attitudes towards issues in the story.
Characterizing an Uncertainty Diagram and Kirkwood–Dirac Nonclassicality Based on Discrete Fourier Transform

In this paper, we investigate an uncertainty diagram and Kirkwood–Dirac (KD) nonclassicality based on discrete Fourier transform (DFT) in a d-dimensional system. We first consider the uncertainty diagram of the DFT matrix, which is a transition matrix from basis A to basis B. Here, the bases A, B are not necessarily completely incompatible. We show that for the uncertainty diagram of the DFT matrix, there is no “hole” in the region of the (nA,nB) plane above and on the line nA+nB=d+1. Then, we present where the holes are in the region strictly below the line and above the hyperbola nAnB=d. Finally, we provide an alternative proof of the conjecture about KD nonclassicality based on DFT.
DEMAND: LEARN TO TEACH IN SCHOOL CONFINEMENTNORMAL SUPERIOR OF MEXICO

The closure of schools due to the Covid-19 pandemic affected 1.4 million children, adolescents, young people and adults in Mexico (UN 2020); schoolchildren are in a situation of compulsory confinement.Uncertainty and its possible answers in a framework of imaginary, where it was assured that young people for being young mastered new technologies and vice versa, that adults are far from digital skills, in the course of 2020 we realized that these are not absolute truths.Given the urgency of planning a flexible program due to the circumstances, we proposed the methodology of problem solving from its immediate context, so we use new and old technologies (messages in emails), keeping in mind the human, considering the sensitivity of the circumstances, which resulted in a closer academic experience.
Development and Implementation of Blockchain Solution to Enable Data Security at University of Port Harcourt

Institutions globally, seek for a solution that intelligently manages the security of data without tampering or mutilations. To compete globally, It has become necessary for the educational sector in Nigeria to build a software solution to end the challenges of fake results, transcripts and certificates and the time it takes to verify their authenticity. This research aims to develop and implement a blockchain solution that would enforce the required security for the Centre for Information and Telecommunication Engineering, University of Port Harcourt (CITE, Uniport), Nigeria. In this study the various services that comprise the building of a decentralized blockchain network, the network consensus algorithm and block explorer application as a unit was put together using JavaScript and its associate libraries. The blockchain data structure was developed with the ability of proof of work, the mining of new blocks, the creation of transactions, the validation of the chain, retrieving the data and other functionalities when demanded. The decentralized blockchain network was developed and tested for ten API (application programming interface) servers and the result shows that the blocks and transactions created were broadcasted throughout the entire network. The results of the consensus algorithms proved that it was able to verify and ensure that the network nodes have valid data and are synchronized. The blockchain explorer application was implemented with a lot of features. It allow user to query the blockchain for specific blocks, results, transcript or certificate.
The Construction and Use of a Higher Education Singing Art Teaching System Combining Digital Multimedia Art and Information Processing Technology

At present, party organizations in vocational colleges have certain drawbacks in participation in decision-making.In order to improve the accuracy and scientific nature of participation in decision-making of party organization in vocational colleges, based on image text feature recognition technology, this paper constructs a support system for participation in decision-making of party organizations in party universities based on image text feature recognition.Moreover, this article uses image text feature extraction technology to perform data analysis and obtain reliable results through system analysis.In addition, this paper combines the DCT and pixel flip strategy methods to propose a dual-domain combination of multi-contour pixel flip text image feature recognition algorithm, improves the text feature recognition effect of party organization documents in vocational colleges, and enhances the robustness of text features in the case of visibility.Finally, this paper designs experiments to verify the performance of the model constructed in this paper.The research results show that the model constructed in this paper has a certain practical effect and can be applied to the decision-making of party organizations in vocational colleges.
Surfing toward development

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This paper identified the various ways that the metaverse would be useful for training and development purposes. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.