Reviewerly/exHarmony huggingface dataset:
CKKP: Chinese-Korean Text Classification via Knowledge Base and Prompt Learning

Korean is the native and official language of Chinese-Korean people. Although the Chinese-Korean and the Korean in South Korea are the same language, there are differences in vocabulary, grammar, and other aspects due to cultural influences. Reasonable analysis and utilization of texts are of great significance for promoting the development of minority languages and enhancing ethnic unity. In this paper, we propose a few-shot Chinese-Korean text classification method (CKKP) based on pre-trained language models (PLMS) combined with knowledge base (KB) and prompt learning. In this work, Chinese-Korean news data are scraped from Yanbian Daily, and we clean and label the data to obtain a Chinese-Korean datasets. We focus on collecting external knowledge and integrating it into the utterance to form a prompt of knowledge to improve and stabilize the prompt. Specifically, we use the clustering algorithm to construct the label wordspace of the external knowledge base extended language. Then, before using the extended label word space for prediction, we use PLM itself to refine the extended label word space. Finally, a projection is constructed between the label space for prompt learning and the label word space. Extensive experiments on a Chinese-Korean news datasets collected from Yanbian Daily, Naver Korean, and Twitter Korean datasets which demonstrate the effectiveness of the knowledge prompt learning.
Cross-Lingual Entity Alignment via Two-Hop Neighbour Sampling and Distinguishable Relation Embedding

The task of aligning Knowledge Graphs involves learning and identifying entity nodes within the source and target Knowledge Graphs that refer to the same real-world entities. This endeavour aims to construct a comprehensive Knowledge Graph with abundant and cohesive content. In previous work, there is still potential for improvement in the utilization of knowledge graph structure, attributes, and relations information. These methods partly overlooked the role of certain components of them, and partly did not apply them in a detailed and precise manner. Our work more accurately utilizes the important information contained in a large number of two-hop neighbours by sampling them based on CSLS similarity. It is a more precise utilization of structural information contained in a large number of two-hop neighbours. At the same time, we use a simplified hyper-plane generation method in learning the embedding of relations of the Knowledge Graph. Reduced computational costs without compromising accuracy, a Two-Hop neighbour sampling and Distinguishable Relation embedding (TH-DR) framework were proposed. Thorough experiments conducted on DBP15K datasets demonstrate that our method outperforms existing systems by a significant margin.
A Multi-Label Feature Selection Method Based on Label Granulation

Multi-label feature selection is an essential component of data mining that can improve classification accuracy. By reducing redundant label information, the feature selection method can select a better feature subset. However, current methods remove too much redundant information and fail to preserve the original label information effectively. To address this problem, this paper proposes a new multi-label feature selection method called granular label dynamic feature selection (GLDFS). First, a label granularity algorithm is proposed to remove redundant label information by granulating highly correlated labels into the same information granule and representing the information granule with the label closest to the center. Second, an automatic granulation quantity algorithm is used to control the granularity size and reduce the loss of useful information. Third, dynamic relevance is introduced into multi-label feature selection from single-label feature selection to select a better feature subset. Finally, compared with six other methods, GLDFS outperforms other methods on three evaluation indicators of thirteen data sets in most cases.
Part-of-Speech (POS) Tagging

Part-of-Speech (POS) Tagging is the foundation of text processing in NLP. This chapter describes how it relates to NLP and NLU (Natural Language Understanding). Various types and algorithms for POS Tagging include Rule-based POS Tagging, Stochastic POS Tagging, and Hybrid POS Tagging with Brill Tagger and evaluation schemes. This chapter also serves as the conceptual basis for Workshop#3—Part-of-Speech using Natural Language Toolkit in Chap. 12 .
Satisfiability Verification of Integrity Constraints for Spatial Linked Data

In recent years, linked data has been introduced into the field of Geographic information science, and has become an important way to publish and share geographic information on the Web. In order to unify and share spatial information, the Open Geospatial Consortium has proposed the GeoSPARQL standard to describe geospatial linked data. However, currently, not all geospatial linked data published for spatial objects and spatial relationships is complete. Although now some standards of integrity constraints have been proposed such as SPIN, SHACL, GeoSHACL and etc., the verification mechanism for the satisfiability of integrity constraints is not considered. In this paper, we first introduced how to use GeoSHACL language to express spatial integrity constraints. Next, we provide a method to verify the satisfiability of spatial integrity constraints in GeoSHACL. In this method, we define a set of transformation syntax that maps GeoSHACL constraints to description logic axioms with specific domains. And then, we combine Tableau algorithm and topological composite table to check satisfiability. After that, we validate the effectiveness of our proposed method using SLIPO open-source data. The experimental results indicate that our method is more flexible and efficient. Satisfiability verification is the prerequisite of spatial integrity constraints. Both integrity constraints and its satisfiability will ensure the quality of spatial linked data.
GRU-Attention Interpretable Knowledge Tracking Model with Forgetting Law for Intelligent Education System

The advent of intelligent education systems and widespread distance learning have revolutionized the educational landscape. Extracting meaningful insights from this wealth of information is crucial for improving student learning outcomes. Knowledge tracking plays a pivotal role in monitoring and optimizing students’ comprehension by simulating their understanding of knowledge concepts. Previous studies on knowledge tracing models have largely overlooked the influence of forgetting law and lack effective representation methods for forgetting. To address this problem, we proposes a novel knowledge tracking model based on GRU-attention with integrated a forgetting law, abbreviated as FGAKT. FGAKT is composed of four essential modules: an embedding module that incorporates forgetting laws to optimize memory retention, an attention extraction module that employs an encoder-decoder structure for effective feature selection, a GRU module to capture long-term timing information, and a prediction module. To enhance the interpretability of the model, we introduce the weight of the attention extraction module’s output into the loss function, which encourages the model to prioritize features. Additionally, we use regulation parameters to balance the predictive performance and interpretability of the model. Experimental results demonstrate that FGAKT outperforms the baselines in terms of ACC and AUC. Furthermore, ablation experiments show the feasibility of each module and the impact of layers within the encoder-decoder structure. The student answering process is instantiated to provide an intuitive depiction of how the model simulates a student’s response.
Ontology-based Natural Language Processing for Sentimental Knowledge Analysis Using Deep Learning Architectures

When tested with popular datasets, sentiment categorization using deep learning (DL) algorithms produced positive results. Building a corpus on novel themes to train ML methods in sentiment classification with high assurance, on the other hand, will be difficult. This study proposes a way for representing efficient features of a dataset into a word embedding layer of DL methods in sentiment classification known as KPRO (knowledge processing and representation based on ontology), which procedures to embed knowledge in the ontology of opinion datasets. This research proposes novel methods in ontology-based Natural language processing (ONLP) utilizing feature extraction as well as classification by DL technique. Here input text has been taken as web ontology-based text and processed for word embedding. Then the feature mapping was carried out for this processed text using least square mapping in which the sentiment-based text has been mapped for feature extraction. The feature extraction is carried out using Markov model-based Auto-feature encoder (MarMod_AuFeaEnCod) . Extracted features are classified by utilizing hierarchical convolutional attention networks (HCAN). Based on this classified output, the sentiment of the text obtained from web data has been analysed. Results are carried out for Twitter and Facebook ontology-based sentimental analysis datasets in terms of accuracy, precision, Recall, F-1 score, RMSE and Loss curve. For the Twitter dataset, the proposed MarMod_AuFeaEnCod_HCAN attained an accuracy of 98%, precision of 95%, recall of 93%, f-1 score of 91%, RMSE of 88%, and loss curve analysis attained 70.2%. For Facebook ontology web dataset analysis is also carried out with the same parameters in which the proposed MarMod_AuFeaEnCod_HCAN acquired accuracy of 96%, precision attained is 92%, Recall is 94%, F-1 score is 91%, RMSE of 77%, loss curve analysis of 68.2%.
Workshop#1 Basics of Natural Language Toolkit (Hour 1–2)

With the basic NLP concept being learnt in Chap. 1 , this introductory workshop gives a NLTK overview and system installation procedures are the foundations of Python NLP development tool used for text processing which include simple text analysis, text analysis with lexical dispersion plot, text tokenization, and basic statistical tools in NLP. This workshop explores NLP basics including: (1) Concepts and installation procedures; (2) Text processing function with examples using NLTK; (3) Text analysis lexical dispersion plot in Python; (4) Tokenization in text analysis; (5) Statistical tools for text analysis.
Major NLP Applications

This chapter will study three major NLP applications: (1) Information Retrieval Systems (IR), (2) Text Summarization Systems (TS), and (3) Question-&-Answering Chatbot System (QA Chatbot). Information retrieval is the process of obtaining the required information from large-scale unstructured data relative to traditional structured database records from texts, images, audios, and videos. Information retrieval systems are not only common search engines but also recommendation systems like e-commerce sites, question and answer, or interactive systems. Text Summarization is the process of diminishing a set of data computationally, creating a subset or summary to represent relevant information for NLP tasks such as text classification, question answering, legal texts, news summarization, and headlines generation. Question-Answer (QA) system represents human–machine interaction system with human natural language is the communication medium. It is a task-oriented system to deal with objectives or answer specific questions through dialogues with sentiment analysis. This chapter also serves as the conceptual basis for Workshop#7: Building Chatbot with TensorFlow and Transformer Technology in Chap. 16 .
Transfer Learning and Transformer Technology

Transfer learning is a commonly used deep learning model to minimize computational resources. This chapter explores: (1) Transfer Learning (TL) against traditional Machine Learning (ML); (2) Recurrent Neural Networks (RNN), a significant component of transfer learning with core technologies such as Long Short-Term Memory (LSTM) Network and Bidirectional Recurrent Neural Networks (BRNNs) in NLP applications, and (3) Transformer technology architecture, Bidirectional Encoder Representation from Transformers (BERT) Model, and related technologies including Transformer-XL and ALBERT technologies. This chapter also serves as the conceptual basis for Workshop#6: Transformers with spaCy and Tensorflow in Chap. 15 .
Energy efficiency optimisation in massive multiple‐input, multiple‐output network for 5G applications using new quantum genetic algorithm

Abstract Devising efficient optimisation methods has been a subject of great research attention since current evolving trends in communication networks, machine learning, and other cutting‐edge systems that need a fast and accurate optimised computational model. Classical computers became incapable of handling new optimisation problems posed by newly emerging trends. Quantum optimisation algorithms appear as alternative solutions. The existing bottleneck that restricts the use of the newly developed quantum strategies is the limited qubit size of the available quantum computers (the size of the most recent universal quantum computer is 433 qubits). A new quantum genetic algorithm (QGA) is proposed that handles the presented problem. A quantum extreme value searching algorithm and quantum blind computing framework are utilised to extend the search capabilities of the GA. The quantum genetic strategy is exploited to maximise energy efficiency at full spectral efficiency of massive multiple‐input, multiple‐output (M‐MIMO) technology as a toy example for pointing out the efficiency of the presented quantum strategy. The authors run extensive simulations and prove how the presented quantum method outperforms the existing classical genetic algorithm.
Maize Farming System in The Rainfed Area Base on The Groundwater Management in Timor Indonesia

Farmer production in the rainfed area faces various challenges as long as the production process is conducted. The main challenge and crucial factor in this area for managing the agriculture sector is water limitation, as we know that rainfed in semi-arid regions significantly depends on the annual rainfall. Successful farming production is determined by the adequacy of rainfall in the rainy season of the year. The objectives of this research are (1) to know the economic aspect of management groundwater for maize farming system and (2) to evaluate the productivity of maize farming system in the rainfed area. This research will be conducted in Naiboat–Kupang during the dry season 2019. They were using demonstration farming methods and applying recommendation technology for maize farming. The result of the research is that farmers use groundwater for irrigating maize farming systems to avoid drought, and maize grows normally. The productivity of maize that apply recommendation technology can reach 8 ton/ha.
Cluster Identification of Agrotourism in Banten Province Indonesia

The post-pandemic era affects the tourism sector. Before the pandemic, agrotourism was growing in Banten Province in line with the development of special economic zones. To regain the competitiveness of agrotourism in Banten Province, characterization among regions is crucial. This study aims to cluster the regions in Banten Province into groups to identify the agrotourism potency. This study employed a descriptive quantitative study by applying k-medoid clustering in the secondary data of Banten in Figures and eliciting information from the Tourism Department of Banten Province. The number of agrotourism sites, the population number, and the number of restaurants were included in the clustering process. The clustering analysis that results in three clusters was validated and visualized via relative and internal criteria. Cluster 1 was the least potential cluster of agrotourism with members of two municipals Tangerang and Tangerang Selatan. Cluster 2 was the most potential agrotourism region which has members of three regions of Pandeglang, Lebak, and Serang and two municipals of Serang and Cilegon. It is characterized by the highest number of agrotourism sites. Cluster 3 was the second potential agrotourism cluster where the regency of Tangerang was the only member possessing the highest population as a potential market.
Laviosing: Virtual Reality (VR) 360 0 Videos as A Marketing Strategy Promotion of Village Tourism

360-degree virtual reality (VR) video is used in tourism marketing but is still uncommon in Indonesia. This technology is viewed as an efficient tourism marketing strategy since it provides users with an immersive, enjoyable, and unforgettable experience that cannot be attained through other conventional media. Osing Tourist Village in Banyuwangi Regency is one of the tourist villages that has utilized 360-degree video as a marketing tool. Hence, it is essential to test the marketing effect of deploying VR 360 based on virtual reality and then move to consumers’ tourism-related buy intentions. This study investigates the experiential marketing variables influencing a person’s decision to visit Osing Tourist Village extensively. Sensing, emotion, thought, and action were the factors used in this study. A total of 178 participants aged 20 to 55 years were surveyed. This study used the Partial Least Square (PLS) data analysis technique. The results indicated that the determinant variables of experiential marketing (sense, feel, think, and act) had a considerable and beneficial impact on visitors’ purchase intentions. The ramifications of this study can be utilized as a point of reference for future research so that the use of virtual reality (VR) 360-degree videos can be implemented in many additional Indonesian tourist communities.
A Generalization of Beall’s Off-Topic Interpretation

Abstract In one of his papers, JC Beall advanced a new and interesting interpretation of Weak Kleene logic, in terms of on-topic/off-topic. In brief, Beall suggests to read the third value as off-topic , whereas the two classical values are read as true and on-topic and false and on-topic . Building on Beall’s new interpretation, the aim of this paper is threefold. First, we discuss two motivations to enrich Beall’s interpretation, and offer an alternative semantic framework that reflects our motivations. Second, by making use of our new framework, we will offer a new interpretation of the logic of Catuskoti which combines Beall’s proposal of having FDE as the correct logic with the on-topic/off-topic divide. Finally, we will offer a general result that will allow us to make sense of a family of infectious logics in terms of Beall’s on-topic/off-topic reading.
Agriculture and International Organization in Indonesia: The Twitter Analysis of FAO Indonesia

World agriculture confronts diverse challenges, including climate change, food security, soil degradation, water scarcity, biodiversity loss, and rural poverty. Addressing these interlinked issues necessitates a collaborative, multidisciplinary approach involving governments, farmers, scientists, civil society, and international organizations. Understanding the role of the Food and Agriculture Organization (FAO) in Indonesia is pivotal. Analyzing the Twitter activities of FAO Indonesia (@FAOIndonesia) provides insights into their efforts related to Indonesian agriculture. This study employs qualitative content analysis, utilizing NVivo software to assess sentiment, dominant themes, and specific topics within @FAOIndonesia’s Twitter account. The findings reveal a prevalence of negative sentiment over positive sentiment in FAO Indonesia’s Twitter discourse. Furthermore, the dominance of “https” in word frequency indicates that FAO Indonesia primarily shares links to access detailed information on agriculture issues, rather than providing comprehensive information directly on Twitter. This research yields significant insights into agriculture issues in Indonesia, valuable for policymakers and international organizations operating in the country, highlighting the need for more direct communication and information dissemination to address the multifaceted challenges facing agriculture.
Utilization of Rainwater Puddles at Campus I State University of Malang

Heavy rain that occurs on the State University of Malang campus often results in puddles. With the large water catchment area in the campus area and the many locations where rainwater pools, it is necessary to research how much rainwater can be collected. The methodology used includes collecting rain data, observing inundation locations, calculating rain using the ranking method, rational method debit calculations, water balance calculations, and storage size calculations. From the survey results, the locations that can be used as reservoirs consist of 14 locations. The area of the catchment area that has the potential to drain water into the reservoir is 69,845.10 m 2 . The storage capacity calculation results obtained: G1 = 178.50 m 3 , G1 = 80 m 3 , G3 = 75 m 3 , G4 = 108 m 3 , G5 = 97.5 m 3 , G6 = 93.6 m 3 , G7 = 84 m 3 , G8 = 22.5 m 3 , G9 = 67.2 m 3 , G10 = 83.2 m 3 , G11 = 41.6 m 3 , G12 = 36.4 m 3 , G13 = 65 m 3 , G14 = 65 m 3 . The amount of standing water that can be accommodated in the storage pond is 852.72 m 3 . The amount of standing water that can potentially be stored for 1 year is 19,553.98 m 3 .
A Study of Digital Museum Collection Recommendation Algorithm Based on Improved Fuzzy Clustering Algorithm

With the rapid advancement of internet technology, various industries have accumulated vast amounts of data, including on user behavior and personal preferences. Traditional museums can leverage this user data to uncover individual preferences and offer personalized services to their visitors. However, the exponential growth of information has also led to the problem of information overload, making it challenging for users to find relevant information within the vast data landscape. Consequently, the utilization rate of available information decreases. By harnessing the power of cloud computing, big data analytics, and recommendation systems, museums can enhance visitors’ touring experiences by helping them discover collections aligned with their interests and connecting with like-minded individuals. To address this objective, the research focuses on optimizing the initial clustering centers of the fuzzy clustering algorithm and parallelizing the optimized algorithm using MapReduce, resulting in the development of a novel MapReduce-based k-prototype fuzzy c-means (MRKPFCM) algorithm. Subsequently, the MRKPFCM algorithm is combined with the classical collaborative filtering algorithm to create a hybrid and parallelized collaborative filtering recommendation algorithm, incorporating elements such as MRKPFCM, audience, and collection. This hybrid algorithm is further supplemented by a content-based recommendation approach to generate comprehensive and refined recommendation results. Experimental findings demonstrate that the predictive scoring errors, as measured by RMSE and MAE, exhibited a downward trend when the number of nearest neighbors for target users fell within the range of 10–20. For instance, the studied algorithm’s MAE value decreased from 0.7512 to 0.7179, surpassing the corresponding figures for the two comparison algorithms. Moreover, with an increase in the number of nearest neighbors within the same range, all three algorithms experienced improved accuracy in prediction results. In particular, the accuracy rate rose from 17.84% to 18.82%, outperforming the two comparison algorithms. In summary, the enhanced hybrid recommendation algorithm achieved through this study displays superior recommendation accuracy and holds significant practical value.