Reviewerly/exHarmony huggingface dataset:
ListMLE-based personalized teaching resources recommendation for online-to-offline hybrid course under smart education environment

Smart education is a product of the information-based education development, which enhances the intelligence of traditional education and realizes innovative education. According to the personalized learning characteristics of students, with a student-centered approach and relying on Online-to-offline hybrid courses, we have designed a new architecture for recommending educational resources to improve learning effectiveness and promote effective teaching by tapping into students' potential. The use of sequential learning technology for teaching resource recommendation is a popular research direction in intelligent education, and its core is the recommendation algorithm of personalized resources. In order to solve the problem of insufficient location information and low accuracy in the result table based on sorting learning, a recommendation algorithm of interest points based on ListMLE is proposed. Firstly, the ListMLE algorithm is applied to interest point recommendation based on the attention difference of interest point location in the recommendation list. Secondly, the influence of users' social relations is incorporated into the scoring function of ListMLE. Finally, a cost-sensitive method is introduced in the recommendation list calculation process. This paper proposes an online education resource recommendation method for personalized learning. Experimental results show that the algorithm outperforms the baseline ranking learning algorithm in terms of accuracy and recall. The method can be used to study students' learning behaviors and provide a theoretical basis for designing personalized learning programs based on students' learning status.
Resonant Anomaly Detection with Multiple Reference Datasets

An important class of techniques for resonant anomaly detection in high energy physics builds models that can distinguish between reference and target datasets, where only the latter has appreciable signal. Such techniques, including Classification Without Labels (CWoLa) and Simulation Assisted Likelihood-free Anomaly Detection (SALAD) rely on a single reference dataset. They cannot take advantage of commonly-available multiple datasets and thus cannot fully exploit available information. In this work, we propose generalizations of CWoLa and SALAD for settings where multiple reference datasets are available, building on weak supervision techniques. We demonstrate improved performance in a number of settings with realistic and synthetic data. As an added benefit, our generalizations enable us to provide finite-sample guarantees, improving on existing asymptotic analyses.
CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding

Story generation and understanding -- as with all NLG/NLU tasks -- has seen a surge in neurosymbolic work. Researchers have recognized that, while large language models (LLMs) have tremendous utility, they can be augmented with symbolic means to be even better and to make up for any flaws that the neural networks might have. However, symbolic methods are extremely costly in terms of the amount of time and expertise needed to create them. In this work, we capitalize on state-of-the-art Code-LLMs, such as Codex, to bootstrap the use of symbolic methods for tracking the state of stories and aiding in story understanding. We show that our CoRRPUS system and abstracted prompting procedures can beat current state-of-the-art structured LLM techniques on pre-existing story understanding tasks (bAbI Task 2 and Re^3) with minimal hand engineering. We hope that this work can help highlight the importance of symbolic representations and specialized prompting for LLMs as these models require some guidance for performing reasoning tasks properly.
Identifying Themes of Right-Wing Extremism in Hindutva Discourse on Twitter

Hindutva, the core political ideology of India's current ruling party, the Bharatiya Janata Party (BJP), seeks to transform constitutionally secular India into a Hindu Rashtra (`Hindu nation'). Although Hindutva has all of the features of right-wing extremism (RWE), it is nevertheless viewed as an insular sociopolitical phenomenon due to the Eurocentric nature of RWE discourse. Recent theoretical and analytical research has sought to showcase the similarity between RWE and Hindutva, whereas empirical research on their relationship has been largely missing. To fill that gap, in our study we collected 15 million tweets, and in network analysis, identified prominent themes of RWE, including exclusionary nationalism, conspiracy theories, and anti-minority violence and hate speech among the supporters of Hindutva and BJP. Furthermore, in our toxicity analysis performed to understand which themes produced higher levels of toxicity, we found that Hindi-language tweets related to conspiracy theories and anti-minority violence or hate speech were more toxic than English-language tweets. Given that the growing global presence and normalization of RWE-based ideas and movements are sources of concern for everyone invested in the idea of liberal democratic society, our research broadens the discussion on RWE in the Indian context and invites researchers to further investigate Hindutva from the perspective of RWE.
House Price Prediction Using Advanced Regression Techniques

Using econometric models or regression techniques, we can predict the price of a property based upon certain features. In this paper, we try to predict the sale price of the residential properties using advanced regression techniques and determine what features most affect the sale price. We have employed various feature engineering techniques to be applied to the raw data to make it productive for the machine learning models. An approach is discussed for understanding the black-box machine learning models, and interpretations are made using techniques like partial dependency plot. Such prediction models can be used in real-life scenarios to estimate the price of a property.
Secured Storage of Information Using Audio Steganography

The use of smartphones and cloud storage has expanded dramatically as technology progresses. The data that are kept and used are now more vulnerable to breaches. Using the Internet services, the end users’ data files can be accessed and manipulated from any other computer. If the secrecy of the information is of very high value, it should be secured. If we want to prevent unauthorized disclosure or change of information, it must be protected. The old cryptographic methods rely on keys and have a point of failure, and they attract the third party who is on lookout for crucial information. Steganography can be used to tackle this problem. It is a secret method of encrypting data in another form so that it can be securely stored and transferred over any medium. In the following paper, the idea of steganography, implementation methods, and storing the data in an audio file is discussed.
A Virtual Reality Whiteboard System for Remote Collaboration Using Natural Handwriting

The COVID-19 pandemic has increased the significance of remote collaboration. This study proposes a virtual reality (VR) whiteboard system that enables remote collaboration among multiple participants using natural handwriting. In total, three experiments were conducted to investigate, respectively, collaboration efficiency, user experience, and system delay. First, we compared the collaboration efficiency of the traditional whiteboard, the electronic whiteboard, and the proposed virtual reality whiteboard in a series of controlled experiments. It was discovered that the VR whiteboard significantly improves collaboration efficiency in comparison to the mouse-based electronic whiteboard and is comparable to the traditional whiteboard. Second, we assessed the user experience with a survey scale (questionnaires). The subsequent results demonstrate that the VR whiteboard provides a superior user experience in terms of efficiency, usability, etc., compared to the traditional whiteboard. We also measured an end-to-end latency of approximately 115 milliseconds, which is sufficient for remote collaboration.
Kesalahan Ejaan Bahasa Indonesia dalam Naskah Dinas di Kantor-Kantor Desa Wilayah Perbatasan

Reality reveals that official script writing in government institutions in various regions, including border areas, still deviates from the Indonesian spelling. If left unchecked, this will affect the pattern of language habits in the future. Hence, this study aims at describing the forms of misspellings and the factors that influence the occurrence of misspellings in official document. Sources of data were procured from official documents and staff at the village offices of East Miomafo District, North Central Timor District, East Nusa Tenggara Province as the border region between Indonesia and Timor Leste. The techniques applied in data collection were documentation and interviews, then the data were analyzed by employing a flow model. The outcome of the research figured out that the forms of spelling errors comprised errors in the use of letters (capitals and bold letters), errors in word writing (affixes, bound forms, combinations of words, prepositions, abbreviations, numbers and numbers, as well as errors in the use of punctuation marks (colons, commas, dashes, and slashes). Factors causing spelling errors in official document were the writer's lack of understanding of Indonesian spelling and following examples of existing fallacious manuscripts. Based on these findings, official script writers in every government agency should be able to minimize factors that cause errors spelling by studying and understanding Indonesian spelling so that it no longer follows the example of an erroneous official document.
ICT complement for supporting flute study at home

Many countries propose flute studies in primary school. Usually, students have only one class per week, which makes it inconvenient for teachers to pay attention to students individually. Another problem is that, after lessons, students have to practice alone at home. Thus, the responsibility for progress lies almost entirely with the learner. This paper proposes a digital tool as a complement to alleviate both problems. The app provides a listening tool, music scores view, records learner performances and offers feedback about students' mistakes during their performances. Finally, the student evolution is sent to the teacher in an adequate manner, to achieve more effective teaching in each class.
Tonne Kilometre Per Hour (TKPH) Prediction for Lifetime Tire Estimate Using Statistical Analysis: A Case Study at Coal Mine

To expedite mining production activities, the condition of the transportation equipment of fuel and spare parts must be ready because the loss of time due to refueling and waiting for spare parts can reduce the Physical Availability value of the dump truck. In this study, the type of tire used as the research object is a tire measuring 33.00-51. The initial identification of tire conditions showed that the Hours meter average was smaller than the Key Performance Indicator (KPI) Hours meter, which was 2719 HM. The type of tire damage that dominates is Sidewall Separation, with a damage percentage of 54%. This study aims to predict the value of TKPH with ten independent variables that exist in the activity of the transportation cycle. The method used to predict is the Multiple Regression and ARIMA statistical methods. From the results of Multiple Regression using the SPSS application, three models are produced, each with different independent variables. As for ARIMA seen from the choleogram of the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), the number of lags = 1, so ARIMA models that can be applied may be (1,0,1) and (1,1,0). The results of the study prove that the prediction model that is close to this is the Multiple Regression Model 3 where the Mean Absolute Deviation (MAD) value is 69.16, the Mean Absolute Prediction Error (MAPE) is 66.07, and the R-square value is 52.41%.
Сonceptualization of chaos in WEB 2.0 and the chaotic subject of the Web

Introduction. The article considers the theme of chaos, traditional for philosophy, in the perspective of materiality and spatiality, associated with manifestations of WEB 2.0: the stages of development of the global Web, where interactive content and content from the network users themselves prevail. Theoretical analysis. Here, an attempt is made to comprehend the question of chaos, using methods and models adequate for the digital sphere, as well as thematizing the concept of cyberchaos in order to answer the question of its relevance for modern Internet research. Understanding Internet subject, cyberchaos and the Internet as a chaotic system at the stage of WEB 2.0, where disorder and over-intensity of content predominate, shows it to be both a creative beginning and a method of catastrophic influence on the world, emerging from digital, but at the same time available for control and suppression. Conclusion. The current consideration makes it possible to take into account the scale of Deleuzian nomadology in the WEB 2.0 space. Its result is a series of proposals for the conceptualization of chaos and segments of modern life that relate to the Internet and WEB 2.0: social network subjectivity, information chaos and instant messengers as a driver of chaotic socio-political research.
Unsupervised Detection and Clustering of Malicious TLS Flows

Malware abuses TLS to encrypt its malicious traffic, preventing examination by content signatures and deep packet inspection. Network detection of malicious TLS flows is an important, but challenging, problem. Prior works have proposed supervised machine learning detectors using TLS features. However, by trying to represent all malicious traffic, supervised binary detectors produce models that are too loose, thus introducing errors. Furthermore, they do not distinguish flows generated by different malware. On the other hand, supervised multi-class detectors produce tighter models and can classify flows by malware family, but require family labels, which are not available for many samples. To address these limitations, this work proposes a novel unsupervised approach to detect and cluster malicious TLS flows. Our approach takes as input network traces from sandboxes. It clusters similar TLS flows using 90 features that capture properties of the TLS client, TLS server, certificate, and encrypted payload; and uses the clusters to build an unsupervised detector that can assign a malicious flow to the cluster it belongs to, or determine it is benign. We evaluate our approach using 972K traces from a commercial sandbox and 35M TLS flows from a research network. Our clustering shows very high precision and recall with an F1 score of 0.993. We compare our unsupervised detector with two state-of-the-art approaches, showing that it outperforms both. The false detection rate of our detector is 0.032% measured over four months of traffic.
Nonlinearity as a strategy for creating postmodern musical texts in the 1970-1990s

It is relevant in this research context to consider the ways of organizing the text, algorithms, and ideological principles of modeling the content components of a musical work. The purpose of the study is to establish strategies for the development of postmodernism in music, as well as non-linearity in music as a creative strategy of postmodernism. The study is devoted to fragmentation as perception and accumulation of information, a form of artistic experience reflected in musical creativity. Precedent phenomena that perform the function of meaning-making in the music of the last decades of the 20th century are determined by their symbolism, the possibility of mentonization and evaluation. Thus, in the musical text of postmodernism, the new work is incorporated into the figurative and expressive system in the space of fragmentary discourse, and the extratextual content of the musical text is formed. The research methodology is based on complex approaches. The main methods used in work are description, analysis, and synthesis, the method of intertextuality and the comparative-historical method were used to work with the material. The result of the work is the definition of new methods of constructing musical works of fragmented discourse, where an effective means is the selection of precedent phenomena, an individualistic vision of the audience, and intertext.
Introduction and Training of Database Systems for Vocational High School Students in Palembang City

In ICT needed to learn database management. One application in database management is MySQL. The service team conducted an analysis of the community's needs regarding the application of the ICT application, especially at SMKs in the city of Palembang. In practice, service participants at SMK are accompanied by tutorials where the presenters conduct demonstrations by working on the material that has been prepared, which then the participants follow and implement the material simultaneously with the presenters. In the end, the service team found that the participants had gained knowledge and skills from this training activity. This can be seen from the feedback given by the participants where 78% of the participants stated that they understood the material provided well and the material presented was very useful.
Modelling the Process of Performing Activity Formation of a Future Music Teacher in the System of Professional Education

The aim of the paper is to substantiate the necessity and possibility of implementing the process of pedagogical modelling of the musical and educational space in order to form the performing activity of a music teacher in piano classes. The paper provides a theoretical justification for a pedagogical model of performing activity formation of a music teacher in piano classes in the context of professional music education, which allows predicting the results of this process and improving its content in accordance with the goals and objectives of future teachers’ professional activities in modern sociocultural conditions. Scientific novelty of the study lies in developing a pedagogical model for performing activity formation of a music teacher in piano classes in the context of professional music education. As a result, the paper sheds light on the content of the following blocks of the author’s model: organisational-conceptual, content-technological, methodological, evaluative-criterial and analytical-resultative. It has been found that the content of each block of the presented model reveals the content of the main stages of performing activity formation of future music teachers within the system of professional music education institutions.
PENGEMBANGAN MEDIA PEMBELAJARAN SPARKOL VIDEOSCRIBE UNTUK MENULIS TEKS FABEL KELAS VII SMP NEGERI 42 PALEMBANG

This study aims to determine the needs of teachers and students for fable text learning media using Sparkol Videoscribe, designing learning media, and appropriate learning media. This study uses research and development methods using research and development steps Sugiyono. Data collection techniques in this study used interview techniques, questionnaires, and assessment sheets. The source of the data for this research is the seventh grade students of SMP Negeri 42 Palembang. The results of the study are that teachers and students need learning media based on Sparkol VideoScribe to foster students' motivation and interest in writing and help students learn independently. The developed media includes general structure, linguistic features, and steps to write a fable text. The learning media validity test was validated by media experts with a score of 3.50, material experts with a score of 3.67, and linguists with a score of 3.00 with a maximum score of 4.00. Based on the results of the research, the learning media for writing fables based on Sparkol Videoscribe is feasible and suitable to be tested in learning to write fables.
Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) Algorithm for Analysis as Supporting System

Abstract In the event of a coronavirus (covid-19) pandemic, those in need will be helped through a government aid program known as the Village Cash Direct Assistance beneficiary fund (abbreviated BLT). Because of the COVID-19 pandemic, which has spread to nearly every corner of the globe, citizens’ activities around the world have been hindered, especially those involving employment to support life’s basic needs. The local economy suffers as a result of people losing their jobs. They have no idea how they will pay for the necessities of life. So, in order to help the locals, the government set up a Village BLT fund support program. Limag Village is one of the settlements that plans to ask for help. When the final decision is made, the village head will choose from the list of households that fit the criteria. A decision support system is one option for achieving clear and exact findings based on the strategy utilized. For a ranking-based decision-support system, the VIKOR approach (Vise Kriterijumska Optimizacija I Kompromisno Resenje) is an excellent choice. In order to reduce the amount of time spent on guesswork when choosing which communities will benefit from BLT Desa money, this study aims to examine and quantify the results of decisions made using criteria that match those criteria. With the use of the following five criteria: profession (K1), number of dependents (K2), social safety net (K3), medical history (K4), and family card (K5): (K5). The results show that the VIKOR approach is able to obtain ranking values from the ten samples it was applied to..
Multi-scale and attention enhanced graph convolution network for skeleton-based violence action recognition

Graph convolution networks (GCNs) have been widely used in the field of skeleton-based human action recognition. However, it is still difficult to improve recognition performance and reduce parameter complexity. In this paper, a novel multi-scale attention spatiotemporal GCN (MSA-STGCN) is proposed for human violence action recognition by learning spatiotemporal features from four different skeleton modality variants. Firstly, the original joint data are preprocessed to obtain joint position, bone vector, joint motion and bone motion datas as inputs of recognition framework. Then, a spatial multi-scale graph convolution network based on the attention mechanism is constructed to obtain the spatial features from joint nodes, while a temporal graph convolution network in the form of hybrid dilation convolution is designed to enlarge the receptive field of the feature map and capture multi-scale context information. Finally, the specific relationship in the different skeleton data is explored by fusing the information of multi-stream related to human joints and bones. To evaluate the performance of the proposed MSA-STGCN, a skeleton violence action dataset: Filtered NTU RGB+D was constructed based on NTU RGB+D120. We conducted experiments on constructed Filtered NTU RGB+D and Kinetics Skeleton 400 datasets to verify the performance of the proposed recognition framework. The proposed method achieves an accuracy of 95.3% on the Filtered NTU RGB+D with the parameters 1.21M, and an accuracy of 36.2% (Top-1) and 58.5% (Top-5) on the Kinetics Skeleton 400, respectively. The experimental results on these two skeleton datasets show that the proposed recognition framework can effectively recognize violence actions without adding parameters.