Reviewerly/exHarmony huggingface dataset:
Detection of fraudulent marketing of consumer product in social networking services

In recent days, malicious users try to captivate the consumers using their fraudulent marketing URL post in social networking sites. Such malicious URL posted by fake users in Social Networking Services (SNS) is hard to identify. Therefore, there occurs a need to detect such fraudulent URLs in SNS. In order to detect such URLS, this paper proposes a SNS Fraudulent Detection (SFD) scheme. The proposed SFD scheme includes a Deterministic Finite Automata Tokenization (DFA-T) and Web Crawler (WC) based Neuro Fuzzy System (WC-NFS). DFA-T extracts the URL features and calculates a Penalty Score (PS) based on the malicious words in the extracted URL. The DFA extracted URL features with PS are fed into WC-NFS. Subsequently, the WC fetches the numeric WC-Index (WCI) value from the URLs which are added to the WC-NFS. The existing URL data set is used to identify the malicious web links and suitable machine learning techniques are used to identify the malicious URLs. From the experimental results, it is found that the proposed SFD provides 92.6 % accuracy in classifying the benign from malicious URLs when compared with the existing methods.
Efficiency of Learning Using Augmented Reality Technology

The intensive development of information technologies has led to the introduction of many innovations in the educational process. Inno­vations often rely on the use of new technologies in the educational process and aim to increase the efficiency and effectiveness of learning. This paper presents the use of augmented reality in training students to process graph­ic images. The method of using augmented reality in the training has been presented in the paper, and afterward, the results of the research, which compared student efficiency using written text instructions and student ef­ficiency using augmented reality technology in the training process. The re­sults of the research did not show a significant difference in efficiency. But, it should be taken into account that the students, who were part of the re­search, encountered this type of training for the first time, so the opportuni­ties that augmented reality provides in the training process needs to be fur­ther investigated.
Usage of E-platforms Google Meet, Microsoft Teams and Zoom in Education

The crisis caused by COVID-19 has forced educational institu­tions to use electronics for educational purposes and thus to digitize the entire process of lectures as well as exams and other school activities. Dis­tance learning is, of course, welcome at a time when it is necessary to lim­it oneself socially as much as possible to help curb the spread of infections. But everything has both good and bad sides and e-learning is no exception. Rapid changes in this area required even faster adaptation by teachers, pro­fessors, or lecturers, as well as pupils and students. The latter, already born in the age of electronics and accustomed to drastic innovations in this field, probably had fewer problems with this. The purpose of the research is to identify the advantages and disadvantages of e-learning observed by users. The research will be limited to three of the most widespread e-meeting pro­grams. These are Google Meet, Microsoft Teams and Zoom. Assuming that these are the three most used programs, the satisfaction of the users of these programs is also an important issue in the research.
Framing a Model for Mobile Learning Using Augmented Reality

This paper considers the possibility of creating a framework for mobile learning with elements of augmented reality, intending to better adapt teaching content to students who are in the same or different loca­tions. The framework, in which the use of mobile devices in learning is ob­served, depends on the purpose for which the mobile device is used (for re­search purposes, simulation, etc.), on technical capabilities, and the level of user capabilities. The goal is to achieve a quick and efficient understanding of the learning content, as well as good cooperation from the participants. The sustainable link between AR and HE goals must be established in ac­cordance with the cognitive approach, according to the axioms of the theo­ries related to learning, with the aim of unifying the association of this tech­nology with the teaching–learning process.
A Text Mining Approach for Identifying Agroforestry Research Narratives

Agroforestry is most important for the sustainable usage of land, and we can observe that a large amount of digital data is emerging day by day. The amount of research work done in the field of agroforestry and identification of the changes year by year may help to perform any further research on agroforestry. In this approach, we are going to work on digital data that is available in IEEE publications, Wikipedia articles, and YouTube by taking them as data sources. Performing web scraping on the data sources because we cannot access the data directly from any resource and consider few keywords which relate to agroforestry like forest, trees, land, watershed and agroforestry etc. Then importing all the scraped data into a CSV file as of the form of a general dataset then perform all the preprocessing steps to make the data confined and grouping them based on the date of publication and also performing LDA to derive new topics from the corpora. Agroforestry is one of the prominent research areas where it needs to be analysed on the digitally available data such that we can draw various insights from it. The number of publications related to agroforestry may help us to analyse the work done on agroforestry and observe the changes on the number of publications made that are related to agroforestry.
Colossal: SAP Fiori Elements

In this chapter, we will unveil the immense power of SAP Fiori elements and explore the fundamental concepts behind the OData protocol and its role in enabling seamless integration between frontend and backend services.
Scalable Learning of Probabilistic Circuits

Probabilistic circuits (PCs) are a family of tractable probabilistic models capable of answering a wide range of queries exactly and in polytime. While inference is usually straightforward, learning PCs that both obey the needed restrictions for inference tractability and exploit their expressive power has proven to be a challenge. This dissertation aims to propose fast and scalable structure learning algorithms for PCs from two different standpoints: from a logical point of view, we efficiently construct a PC that takes certain knowledge in the form of logical constraints and scalably translate them into a probabilistic circuit; from the viewpoint of data guided structure search, we propose hierarchically building PCs from random hyperplanes. We empirically show that either approach is competitive against state-of-the-art methods of the same class in both performance and scalability.
THE CORRELATION BETWEEN STUDENTS’ HABIT IN LISTENING ENGLISH SONGS AND LISTENING COMPREHENSION

Students in Indonesia need the motivation to be more interested in English lessons, one of which is by giving songs in class. The English song can attract the students in the teaching and learning process for English class. The students at SMAN 1 Palu have a low ability in listening comprehension. This research aims to find out the relationship between the students’ habits in listening to English songs and their listening comprehension achievement. The result of this research consists of the interpretation of data obtained in this research, the relation of data obtained with the theories from both variables, and the relation comparison of this research with the previous students. The result data was obtained from 20 students of eleventh grade at MAN 1 Palu. Based on the result, the scores of sig between the effect Like Song and Don’t Like Song were 0,231 or more than 0,05. Then the researcher concluded that Listening to English song habits has a correlation with listening comprehension of the eleventh-grade students of MAN 1 Palu revealed by the mean score of the students’ questionnaire. The mean score level was 48.40%, which is a high category level.
weighted CapsuleNet networks for Persian multi-domain sentiment analysis

Sentiment classification is a fundamental task in natural language processing, assigning one of the three classes, positive, negative, or neutral, to free texts. However, sentiment classification models are highly domain dependent; the classifier may perform classification with reasonable accuracy in one domain but not in another due to the Semantic multiplicity of words getting poor accuracy. This article presents a new Persian/Arabic multi-domain sentiment analysis method using the cumulative weighted capsule networks approach. Weighted capsule ensemble consists of training separate capsule networks for each domain and a weighting measure called domain belonging degree (DBD). This criterion consists of TF and IDF, which calculates the dependency of each document for each domain separately; this value is multiplied by the possible output that each capsule creates. In the end, the sum of these multiplications is the title of the final output, and is used to determine the polarity. And the most dependent domain is considered the final output for each domain. The proposed method was evaluated using the Digikala dataset and obtained acceptable accuracy compared to the existing approaches. It achieved an accuracy of 0.89 on detecting the domain of belonging and 0.99 on detecting the polarity. Also, for the problem of dealing with unbalanced classes, a cost-sensitive function was used. This function was able to achieve 0.0162 improvements in accuracy for sentiment classification. This approach on Amazon Arabic data can achieve 0.9695 accuracies in domain classification.
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation

In multimodal-aware recommendation, the extraction of meaningful multimodal features is at the basis of high-quality recommendations. Generally, each recommendation framework implements its multimodal extraction procedures with specific strategies and tools. This is limiting for two reasons: (i) different extraction strategies do not ease the interdependence among multimodal recommendation frameworks; thus, they cannot be efficiently and fairly compared; (ii) given the large plethora of pre-trained deep learning models made available by different open source tools, model designers do not have access to shared interfaces to extract features. Motivated by the outlined aspects, we propose \framework, a unified framework for the extraction of multimodal features in recommendation. By integrating three widely-adopted deep learning libraries as backends, namely, TensorFlow, PyTorch, and Transformers, we provide a shared interface to extract and process features where each backend's specific methods are abstracted to the end user. Noteworthy, the extraction pipeline is easily configurable with a YAML-based file where the user can specify, for each modality, the list of models (and their specific backends/parameters) to perform the extraction. Finally, to make \framework accessible to the community, we build a public Docker image equipped with a ready-to-use CUDA environment and propose three demos to test its functionalities for different scenarios and tasks. The GitHub repository and the documentation are accessible at this link: https://github.com/sisinflab/Ducho.
Interpreting Deep Graph Convolutional Networks with Spectrum Perspective

Graph convolutional network (GCN) architecture is the basis of many neural networks and has been widely used in processing graph-structured data. When dealing with large and sparse data, deeper GCN models are often required. However, the models suffer from performance degradation as the number of layers increases. The mainstream attribution of the current research is over-smoothing, and there are also gradient vanishing, training difficulties, etc., so a consensus cannot be reached. In this paper, we theoretically analyze the degradation problem by adopting spectral graph theory to globally consider the propagation and transformation components of the GCN architecture, and conclude that the over-smoothing problem caused by the propagation matrices is not the key factor for performance degradation. Afterwards, in addition to using conventional experimental methods, we proposed an experimental analysis strategy under the guidance of random matrix theory to analyze the singular value distribution of the model weight matrix. We concluded that the key factor leading to the degradation of model performance is the transformation component. In the context of a lack of consensus on the problem of model performance degradation, the paper proposes a systematic analysis strategy, as well as theoretical and empirical evidence.
Characterization Method of Damage Information Based on Heterogeneous Network

Damage is the main form of conflict, and the characterization of damage information is an important component of conflict evaluation. In the existing research, damage mainly refers to the damage effect of a damage load on the target structure. However, in the actual conflict environment, damage is a complex process that includes the entire process from the initial introduction of the damage load to the target function. Therefore, in this paper, the transfer logic of the damage process is analyzed, and the damage process is sequentially divided into being discovered, being attacked, being hit, and being destroyed in succession. Specifically, first considering the multiple types of each process, the transmission of damage is likened to the flow of damage, a network model to characterize damage information based on heterogeneous network meta-path and network flow theory (HF-MCDI) is established. Then, the characteristics of damage information are analyzed based on the capacity of the damage network, the correlation of the damage path, and the importance of the damage node. In addition, HF-MCDI can not only represent the complete damage information and the transmission characteristics of the damage load but also the structural characteristics of the target. Finally, the feasibility and effectiveness of the established HF-MCDI method are fully demonstrated by the example analysis of the launch platform.
The application of logical decision trees to the decision-making process in ill-structured problems

Decision-making is a process which has accompanied the human being since time immemorial and in various areas of their activity. It should result in making a choice that meets the expectations (conditions) of the decision maker to the greatest extent. Decisions can be made on the basis of information which is not always complete and reliable. Sometimes it may happen that it is incomplete and reliable or complete but unreliable. Then, it concerns ill-structured problems. The purpose of this article is to verify whether ill-structured problems can be solved/supported in a way based on an operating algorithm. In the article, a case study with complete but unreliable information as well as with complete and reliable information was used. Logical decision trees were employed in the study. The result of the study allowed for, among others, establishing that the analysis of an ill-structured problem using logical decision trees consists in performing steps which follow one another in a logical sequence creating thus a sequence of operations, and therefore an operating algorithm, which confirms that solving ill-structured problems can be supported/implemented using an algorithm.
PENINGKATAN MOTIVASI BELAJAR SISWA PADA MUATAN MATEMATIKA MENGGUNAKAN MEDIA PEMBELAJARAN “RODA BANGUN DATAR” DI KELAS III UPT SD NEGERI KALIBOTO KABUPATEN BLITAR

Grade III students of UPT SD Negeri Kaliboto Blitar experienced problems with learning motivation which then led to low learning outcomes. The low learning motivation of students is caused by teachers who do not use a variety of learning models and methods. In addition, the teacher also never uses learning media at all, so that students do not experience meaningful learning, which results in them getting bored easily, often playing alone in learning, and not being interested in the learning carried out by the teacher. This research was conducted with the aim of increasing students' learning motivation so that their learning outcomes also increased. This type of research uses Collaborative Classroom Action Research (PTKK) and is carried out for 2 cycles. The research approach used is qualitative and quantitative. Data collection techniques using observation and documentation with research instruments in the form of learning motivation questionnaires. The results showed an increase in learning motivation from cycle I to cycle II. The percentage of learning motivation in cycle I is in the low category with a percentage of 44%. Then there was an increase in cycle II so that almost all students had high learning motivation with a percentage of 81%.
A Study on the RPA Application of Major Defense Project Management System for the Promotion of Defense Innovation 4.0 in the Era of the 4th Industrial Revolution

In this study, a development plan was proposed through a pilot project to prepare a plan to apply RPA to the major defense project management system to promote defense innovation 4.0 in the era of the 4th industrial revolution. The research procedure was conducted in the Defense Acquisition Program Administration’s major defense project management system using RPA applicable work judgment (step 1), pilot project target selection (step 2), pilot project execution (step 3), and performance analysis (step 4). For the purpose of the study, four projects were selected, including “private ○○ work,” “○○ review work,” “○○ list ○○ management work,” and “○○ organization work,” focusing on relatively small tasks rather than large-scale tasks. Three items were selected as evaluation items: work overview, applicability evaluation, and risk, and RPA software applied “CheckMate,” which has the strength of shortening work processing time due to virtual bot and parallel processing functions. As a result of the analysis, it was recognized that the definition and analysis of the requirements of the actual user were important to apply RPA to the actual field. In addition, when applying RPA, it is judged that there is a positive effect such as improving work efficiency, improve work efficiency, improve work hours. In the future, it is expected that it will be used as a reference for defining requirements such as processing time per unit work and the number of monthly savings personnel and setting analysis items when reviewing the feasibility of applying RPA.
UTILIZING CARTOON SERIES AS PEDAGOGICAL MEDIA TO ENRICH YOUNG LEARNERS’ VOCABULARY

Cartoon series is one media that can be used in teaching vocabulary for young learners, it consists of the interesting pictures and guessing vocabulary. This present study was conducted to see the utilization of cartoon series in the classroom and find out the effectiveness of cartoon series to enrich young learners’ vocabulary. The method that is used in this research is qualitative. The data collection technique used in this research is observation. The samples of this study are 10 young learners. The researcher uses World word’s cartoon as a media to teach vocabulary to young learners. Then, the researcher analyzes descriptively and summarizes the findings, and provides new ideas by collaborating on all findings at the sources. The finding of the research is using cartoon series in the learning process helps the students to learn new vocabulary and also interested and gain new experiences and knowledge that they can take from these lessons.
Creation of Bell-like state by a nonlinear quantum scissors interactive with two external coherent fields

The study described in this paper focuses on a model featuring two nonlinear oscillators, which are stimulated by two external coherent fields, and are referred to as a nonlinear quantum scissors (NQS). To analyze the system's evolution, the NQS method for quantum state engineering was applied, leading to the discovery of a wave function that comprises a composite of n-photon states. Through the implementation of NQS, the system's optical states undergo a truncation process, resulting in the production of two-qubit states that can be achieved due to the oscillators' nonlinear characteristics and their interaction. As the system evolves, maximally entangled states referred to as Belllike states are created.
A Study on Infringement of Educational Activities by Students and Teacher’s Right to Guide Students

학생의 교육활동 침해로 인한 교원의 교권 위축은 우리나라에서 최근 여러 가지 사건을 통하여 사회적 문제로 대두되고 있다. 더구나 학생의 교육활동 침해행위는 교원의 교권을 침해할 뿐만 아니라 다른 학생의 학습권을 방해하는 결과를 가져온다. 이러한 양상은 외국에서도 나타나고 있는바, 영국이나 미국의 일부 주에서는 법제를 통하여 학생의 교육활동 침해행위에 대하여 교원이 직접 해당 학생을 제재할 수 있는 다양한 방안을 마련하고 있다. 영국의 경우 교육법에서 교원에게 학생에 대한 통제권 및 교육활동 침해학생에 대한 지도 권한으로 금지품목에 대한 수색 및 압수 권한이나 방과 후 구류를 할 수 있는 권한을 부여하고 있다. 미국에서도 텍사스 주법은 교원의 훈육권 내지 지도권으로 학생을 교실에서 퇴출시킬 수 있는 권한을 보장하고 있고, 노스캐롤라이나 주법은 학생의 교육활동 침해행위를 학교폭력의 하나로 다루어 강하게 대응하고 있다. 그런데 학생의 교육활동 침해행위에 대해 교원이 학생을 직접 즉각적으로 제재하게 될 경우, 그 과정에서 학생의 인권과 교권은 불가피하게 서로 충돌할 가능성이 높다. 따라서 학생의 교육활동 침해에 대한 제재 관련 법제 개선에 있어서 정부와 국회의 대응 방향은 기본적으로 양자를 균형 있게 조율하는 것에 초점을 두어야 한다. 이를 위하여 첫째, 현행 법제에서는 여러 법률에 교육활동 침해행위 및 학교의 장과 교원이 침해학생에 대하여 행할 수 있는 조치가 산재되어 있어 체계적으로 정비하는 것이 필요하다. 둘째, 교원의 학생에 대한 직접적인 지도권한을 부여할 때 신체에 대한 적극적 유형력을 행사하는 것을 지양하고, 교원의 지도권한을 상황에 따라 세분화하여 규정하는 방법을 택하는 것이 바람직하다. 셋째, 학교폭력의 대상을 교원으로 확대하고, 학교폭력행위의 유형에 교육활동 침해행위까지 포함하는 것도 고려될 필요가 있다. The shrinking of teacher’s teaching authority due to students’ infringement of educational activities has emerged as a social problem through several recent incidents in South Korea. Infringement of educational activities by students results in the infringement of the teacher’s right to school and the interference of other students’ right to learn. For this reason, in the UK, Education Act grants teachers the right to control students and the right to guide students in case of violation of the teaching rights. Also in the USA, Behaviors that infringe on the school are also treated as a form of school violence. However, if the teacher directly and immediately sanctions the student for student’s infringement of educational activity, in the process, the student’s human rights and teacher’s rights inevitably collide with each other. Therefore, the direction of improvement of the legal system related to sanctions against students’ infringement of educational activities should basically focus on balancing the two rights. To this end, first, in the current legislation, measures that school principals and teachers can take against students are scattered in various laws, so it is necessary to systematically organize them. Second, it is desirable to adopt a method of subdividing and regulating the teacher’s guidance authority according to the situation, rather than exerting physical force on the teacher’s guidance for the student. Third, expanding the target of school violence to teachers and including acts of infringing on educational activities in the types of school violence will be a countermeasure that can be fully considered.
FAIR is not the end goal

I’ve been watching and participating in FAIR work for a while, first in terms of data, and more recently in research software, workflows, and machine learning models. Some of this has been based on my general interest in how research works and how we improve it, and some has been based on more specific work, such as raising the profile of research software and its developers and maintainers, and even some collaborative work in specific domains, such as high energy physics. Nowhere in...