Reviewerly/exHarmony huggingface dataset:
Smart Education System to Enhance Public Awareness about Climate Change: A Literature Review

Climate Change has become the greatest challenge of the 21st century threatening all aspects of society because it's caused severe changes for people, natural ecosystems and economies around the world.Indonesia is one of the countries most at risk of climate change impact.Knowledge of climate change is very important so people can take mitigation actions.This study aims to ensure the effectiveness of smart education systems to enhance public awareness about climate change.This research method using a qualitative systematic literature review was conducted in several publication platforms in the last 5 years with the search keywords used are; climate change, public awareness and smart education system.A total of 10 research journal articles were included in this literature review.The results of this research are the use of Augmented Reality (AR), Internet of Things (IOT), Artificial Intelligence (AI) and Educational Mobile Learning Apps has been shown to be effective in enhancing students' learning experience and well-received in terms of adopting and using such technology for educational purposes.Here, Ministries of Education and schools can play a major role in ensuring that students have access to technology and support climate change syllabus enrichment.
Analysis of Internal Fraud in the Microloan Process with Confirmatory Factor Analysis (CFA) and the Extreme Gradient Boosting (XGBoost) Method

Internal fraud in the microcredit business process has caused significant losses to the banking industry and financial institutions.Internal fraud is one type of operational risk frequently faced by banks/financial institutions focusing on microcredit services.The typical fraud frequently found is Corruption and Misappropriation of Assets (ACFE).For instance, 'Tempilan' credit, Mask credit, and Fictitious Credit.Machine learning automatically predicts the possibility of internal fraud in the microloan business process.This research aimed to determine which the most dominant component from the theory of social identity and the fraud triangle that influence someone to commit fraud is.The research used the CFA (Confirmatory Factor Analysis) method and the Extreme Gradient Boosting (XGBoost) method to predict the possibility of fraud.The results revealed that the fraud incident conducted by Relationship Managers (RM) is caused by rationalization factors.The Path diagram of the Second Order CFA fraud risk showed the marketer that the most significant factor loading value was in rationalization, followed by pressure and opportunity.Hence, the results of this research indicated that rationalization had the most significant influence on the fraud risk committed by RM.Therefore It was recommended that the bank or financial institution put its focus more on rationalization matters.
Language Acquisition for Children Age 5–6 in Learning Syllables and Word Meanings Through Media Worldwall at TK Aisyiyah 58 Surabaya

Language ability is obtained by humans in stages start from infancy, children, adolescents, and adults.Children's first language skills are obtained from spoken language like changing vocabulary, syllables and words to sentences.The second language skill is written language.This study aims to find 1) how the written and spoken language skills of children aged 5-6 years about syllables of nouns and verbs using worldwall learning media, 2) how the written and spoken language skills of children aged 5-6 years about the meaning of words in sentences.This research is a cross sectional qualitative research with descriptive analysis presentation.The subject of this research is the language acquisition of children aged 5-6 years in learning syllables and word meanings through worldwall learning media in TK Aisyiyah 58 Surabaya.The language learning stages are starting with learning the syllables of nouns that are often known by children of that age.Followed by the stage of recognizing the form of affixed verbs.The difference in language acquisition at the two stages is that children find it easier to solve the basic syllable puzzle of body parts compared to the syllable forms of verbs with affixes.This is caused by internal, namely the development of cognition and IQ.Second, the external factors are from the social environment.
TAGADA Media Development (Cultural Diversity Map) in Indonesian Language Learning Material Main Idea

This study aims to develop and produce learning media based on Macromedia Flash Professional 8 software which is used in Indonesian subjects with the main idea material.To develop learning media, researchers chose the 4-D model (Four-D Model) developed by Thiagarajan, et al. which includes four stages of development, namely defining, designing, developing and disseminating.The subjects of this study were fourth grade students of MI Muhammadiyah 13 Sendangagung, Paciran District, Lamongan Regency.Data collection techniques in this study are check lists and media validation from experts.The research results obtained include: 1) Indonesian language learning media with the main idea material named Tagada (Map of Cultural Diversity), has a file size of 10 MB in .swf,.exe,and windows projector file formats, 2) Expert validation results the material is in a very feasible criterion of 90%, and the results of validation by media experts are also in a very feasible criterion of 87.3%.Very feasible criteria means that the TAGADA media is very valid and feasible to use in learning Indonesian with the main idea material.
THE ROLE OF THE PRINCIPLE OF CURATION IN THE CONTEMPORARY CULTURE

The article analyzes phenomenon of the «principle of curation» in historical and philosophical development. In the modern world, overloaded with large volumes of information, it is not just access to information that becomes important, but the ability to navigate it. Problems of critical reflection, choice, interpretation in the information space are gaining in importance. That is exactly why the use of the principle of curation is proposed to implement these processes. The authors consider history of the emergence of the concepts of «curator» and «curatorship», delineation of the features of curatorial principle and differences in meanings and understanding of the principle of curation. Having considered the history of transformation of these concepts, having analyzed their use in various fields, the authors come to the conclusion that semantic component of the curator changes over time. If initial connotations were limited to the view of the curator as a guardian and caretaker of collections, then in the modern sense curator him/herself is a creative subject. Therefore, the interpretation of works of art essentially depends on the creative activity of the curator and the space that he creates for presentation. Modern practice of curation should be considered as a newly formed field of activity, which is fundamentally different from the forms that preceded it. In the 21st century, the principle of curation is becoming a universal and necessary method of presenting selected and orderly information, which aims to solve the problem. Curators are the ones who work with this flow of information in the era of information overload: they process it, systematize, organize, conceptualize and present it to the public, thus becoming liquidators of the problem of information overload. The article puts forward a hypothesis for further philosophical reflection: the curatorial principle is a necessary component for organizing the presentation space of a cultural archive.
Synthesizing Metareasoning Policies

After implementing a metareasoning approach, the next step is to determine the specifics of the metareasoning policy. The metareasoning policy controls the object level reasoning (by selecting an algorithm or adjusting a parameter value, for instance). This chapter describes a systematic, data-driven approach for determining the metareasoning policy and some case studies to illustrate the process. Although machine learning techniques have been employed to determine metareasoning policies, using engineering judgment, based on empirical results, is an appropriate technique as well.
Implementing Metareasoning

This chapter describes options for locating the software that will implement the metareasoning process on a robot. The details of the implementation depend upon the robot’s autonomy software architecture, so this chapter reviews some common architectures. This chapter also discusses three possible locations for metareasoning: (1) as a separate software that runs in parallel to the autonomy software, (2) as a separate module that is added to the autonomy software, and (3) as additional code that is added to one or more parts of the autonomy software. This chapter also discusses the details of a specific implementation in the autonomy stack of a mobile ground robot.
Popularity Bias Analysis of Recommendation Algorithm Based on ABM Simulation

With the rapid development of the Internet, recommendation algorithms are increasingly influencing consumers’ decision-making. The issue of fairness in recommendation algorithms, especially popularity bias, is also becoming more and more heated. For the research on popularity bias, most existing methods cannot balance long-term interactivity and low cost. Using the ABM simulation method, various variables in the recommender system can be controlled, and the long-term impact of the continuous interaction between the recommender system and the user can be studied, especially the influence of popularity bias. In this paper, we construct and implement a recommendation algorithm simulation framework based on ABM, and two algorithms, Item-Based CF and SVD, are respectively deployed on it to count item popularity distribution and Gini coefficient under multiple rounds of recommendation. The indicators, combined with the visualization results of user interest offset, are used to explore the popularity bias problem of two classic recommendation algorithms under multiple rounds of interaction. We also summarize the existing problems and make an outlook for future improvements.
A Unified Stream and Batch Graph Computing Model for Community Detection

An essential challenge in graph data analysis and mining is to simply and effectively deal with large-scale network data that is expanding dynamically. Although batch-based parallel graph computation frameworks have better accuracy, they cannot process incremental data on time and need to be recomputed. To process real-time data, stream processing applications need to be redeveloped, which increases the redundancy of work, and some existing dynamic graph computation schemes are not generalizable. This paper proposes a unified stream and batch graph computing model(USBGM). The model is compatible with both stream and batch graph computing. Graph operators and algorithms developed based on the model can handle stream and batch graph data in a unified manner. The experiments on real-world and artificial networks verified the effectiveness and efficiency of the model.
Privacy-Preserving Federated Learning Framework in Knowledge Concept Recommendation

Online education has become a new growth point, providing a massive worldwide learning opportunity for college students and workers. The recommendation system is provided to help users choose suitable courses wisely to satisfy their learning needs better. However, the traditional recommendation methods are more inclined to recommend a series of courses to users and ignore the user’s interest in knowledge concepts, and conventional centralized training algorithms risk data leakage. In this paper, we propose a federated framework FedAttn that can train model using decentralized data and aggregate gradient using an attention-based method. Additionally, we conduct experiments utilizing real-world datasets to validate that our approach can achieve a decent result and effectively protect user privacy.
A Feature Fusion-Based Service Classification Approach for Collaborative Development

Collaborative development, as one of the most widely used development models, is very effective at increasing development efficiency and software quality. In collaborative development, developers deploy services on the Internet and provide stable business functionality to the public. However, developers with different levels of development experience and code levels may classify the services into the wrong categories. As a result, reusing these services is impossible due to the confusing service categories, which has a significant impact on developers’ development efficiency. Most existing service classification approaches rely on textual information such as Web service descriptions and names, which may not provide enough information to determine their categories when the descriptions and names are too brief or vague. This paper proposes a Service Classification Approach based on Feature Fusion (SCAFF), which combines semantic and structural features. SCAFF first extracts service semantic feature vectors and service structural feature vectors by SimCSE, MixGCF, and LightGCN. Then it combines these two features and finally obtains the service category using two fully connected layers. Compared with TextCNN, LSTM, and ServeNet, SCAFF has higher accuracy on Top-1 accuracy and Top-5 accuracy.
CoSBERT: A Cosine-Based Siamese BERT-Networks Using for Semantic Textual Similarity

By mining rich semantic information from large-scale unlabeled texts and incorporating it into pre-trained models, BERT and RoBERTa have achieved impressive performance on many natural language processing tasks. However, these pre-trained models rely on fine-tuning for specific tasks, and it is very difficult to use native BERT or RoBERTa for the task of Semantic Textual Similarity (STS). In this paper, we present CoSBERT, a cosine-based siamese BERT-Networks modified from the pre-trained BERT or RoBERT models to derive meaningfully semantic embeddings. Its main feature is to optimize the cosine-similarity between the semantic embeddings of input texts in training stage. And it also improves the efficiency and accuracy for the computation of STS tasks in prediction stage. Experiments on multiple STS tasks prove that CoSBERT performs well and its effectiveness is verified. In addition, by deploying CoSBERT into the SCHOLAT user recommendation system, the efficiency and accuracy of the system has been improved.
Memory-Effective Parallel Mining of Incremental Frequent Itemsets Based on Multi-scale

Frequent Itemset Mining (FIM), as an effective means of discovering related information or knowledge, has high time and space complexity. However, in the era of big data, data shows distributed characteristics and dynamic growth, which brings greater challenges to frequent itemsets mining. Considering that the data in the practical application field usually involves different concept hierarchies and granularity, the multi-scale concept is introduced into the incremental mining process of frequent itemsets to avoid the huge overhead of rescanning the dataset and adjusting the tree structure in the maintenance process. Simultaneously, in order to effectively deal with large-scale and massive data, a memory-effective parallel incremental FIM algorithm is proposed based on Spark parallel computing platform, which can ensure the load balance of the node calculation as much as possible by estimating the load of each group. And in the RDD caching strategy of the parallel algorithm, factors such as RDD access frequency and cost are comprehensively considered to reduce the memory occupancy rate and the recalculation of RDDs with high computational cost. Extensive experimental results verify that the memory-effective parallel algorithm has good scalability and high efficiency.
Requirements Classification and Identification Approach for E-Collaboration Systems

In the background of global economic integration and the pandemic era, e-collaboration systems have become the main tools for people to learn and work. E-collaboration systems in the Internet era face rapid iteration requires, it’s important to research how to quickly access the requirements of e-collaboration systems to help developers better update and iterate on them and improve the user experience. As the number of user reviews in the application market is large, covering a wide range, a large number of requirements are contained in it, therefore, this paper starts from two dimensions of functional requirements and non-functional requirements, and proposes 15 requirements categories for the collaboration characteristics and timely characteristics of the e-collaboration system. First crawl the collaboration system user reviews as a dataset, use TextRank ranking algorithm and TextCNN model to classify them, and finally by building a network of co-occurrence relationships between words to feed the users most concerned in a certain classification to the developers. Our method has proven to be excellent in terms of Precision, Recall and F1-measure, and can help developers to obtain requirements from large amount of review data.
University Knowledge Graph Construction Based on Academic Social Network

Knowledge graph is an important knowledge representation method in the era of big data. It has become one of the key technologies of artificial intelligence and has been applied in different fields. However, there are relatively few studies on university knowledge graphs combined with academic social networks. Therefore, in this paper, we combine the academic social network SCHOLAT to complete the construction of the university knowledge graph. We first construct the ontology of the knowledge graph, then extract and fuse knowledge from data that come from different sources, and add the output knowledge to the knowledge graph. The university knowledge graph has 191,089 entities and 1,638,275 relationship pairs after the construction is completed, and we store it in the Neo4j database to provide knowledge reserve for subsequent applications. In addition to the construction, we also conduct an application analysis to study its application in university knowledge graph-based Q &A system.
A Joint Framework for Knowledge Extraction from Flight Training Comments

The flight training comments are textual evaluation from the instructor to the flight training students. Extracting the assessment experience and knowledge from the flight training comments can help improve the flight training level. To this end, a joint extraction framework is proposed in this paper. For the model input, we use an end-to-end grid tagging scheme as the decoding algorithm to extract aspect terms, opinion terms and opinion pairs simultaneously. Considering the continuity of terms, we propose WoBERT as encoder, which modifies the pre-trained word segmentation based on BERT, and realizes Chinese word segmentation at word-level granularity. We conduct extensive experiments on a real flight training comments dataset. The results show that the model with WoBERT as the encoder achieves the best performance with a F1-score of 0.647, which can be used for the task of knowledge extraction from the flight training comments.
Research on User Personality Characteristics Mining Based on Social Media

As an important medium for users to share experiences and feelings, social media is the carrier of users’ emotions, hobbies and interests, and it has become an important research content to mine user's personality information. Due to the unstructured and sparse characteristics of social texts, it is difficult to grasp the fine-grained word segmentation and contain a large number of noise words in mining user personality characteristics. However, the probability model of traditional language rules has weak generalization ability, and the deep learning model has poor anti-noise performance and poor feature extraction ability. This paper proposes a text analysis model based on attention mechanism, referred to as ATCNN-BiGRU. The model uses the attention mechanism to construct the input feature matrix of the text, solves the interference of noise in the social text, and integrates the double-layer gated unit network to overcome the problem that the fixed-step scanning mechanism in the convolutional neural network affects the extraction of global contextual semantic features. Through the experimental results on datasets in three different domains and different languages, it is found that compared with the existing mainstream text analysis models, the ATCNN-BiGRU proposed in this paper has a significant improvement in the prediction accuracy, especially in the Chinese data set by 2%.
Segmenting Bitcoin Transactions for Direction of Price Movement Prediction

Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin price movement. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy than existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants on asset price movement. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies.
Convolutional Self-attention Network for Sequential Recommendation

Sequential recommendation has important applications in many fields that need to predict users’ intentions from their historical behavior. Modeling for sequential recommendation is a challenging problem. In particular, Bert4Rec uses deep bidirectional self-attention to model, which solves the limitations of one-way modeling from left to right. However, it can still be optimized in terms of short-term feature extraction of users’ behavior sequences and enhancing the distinguishability between recommended items. To address these limitations, we propose a sequential recommendation model called TR-BERT4Rec-CNN, based on the Bert4Rec. To fully extract the features of users’ behavior sequences, we design a convolutional layer to extract the local dependencies for the hidden dimension of input data and extract the potential relationships for the sequence dimension of input data. To enhance the discriminability of items, we use a triplet loss to fine-tune our model. We conduct experiments on the Scholat-courses and the MovieLens datasets. Experiments show that our model outperforms the methods commonly used in recent years.
Towards Heterogeneous Federated Learning

Federated Learning (FL), a novel distributed machine learning framework, made it possible to model collaboratively without risking participants’ privacy. All components of FL, including devices, networks, data, and models, are heterogeneous because of the dispersed feature. These heterogeneity issues impeded FL’s performance. HFL (Heterogeneous Federated Learning) offers a viable solution to these issues. HFL has become an emerging research topic. We have conducted detailed research into the unique characteristics and challenges of HFL in the paper. And summaries methods of HFL at different levels. We reviewed the evaluation methods for HFL and provided an outlook on the future direction of HFL by analyzing the strengths and limits of the existing study.