Reviewerly/exHarmony huggingface dataset:
Efekti kompjuterskih igrica u nastavi matematike

Games have long been proposed as a promising learning tool. Many computer games or software for learning mathematics primarily focus on repetition and practice, offering a motivational environment for performing mathematical activities that are important for achieving automaticity in knowledge. The aim is to analyze the advantages and disadvantages of using computer games in teaching mathematics. The paper is based on a theoretical analysis of relevant findings and research conducted to examine the effectiveness of computer games in the field of mathematics. Accordingly, it is concluded that the effects of computer games in mathematics teaching have not been sufficiently studied from an experimental point of view, while existing research shows that adequate use can improve students' motivation and creative abilities, which is important from the aspect of modern teaching.
Possibilities of improving the teaching process by applying ambient teaching

Intensive and dynamic development of information technologies distances students from the natural environment, and they also question the development of creativity in children, the development of imagination, and the lack of communication. Therefore, schools, as educational institutions, need to find mechanisms and develop strategies that will direct students towards nature, towards acquiring knowledge in certain natural or social environments. The paper discusses the possibilities of ambient learning as a factor that contributes to the improvement and modernization of teaching nature and society in primary school students. The aim of the paper is to point out, through the theoretical analysis of ambient teaching, the possibilities of improving the teaching process realized through the application of ambient learning and the positivity that this teaching provides.
Scientific Activity of Russian Libraries in the Field of Bibliography (Bibliographic Researches)

The historical process of separating bibliographic science from a single bibliography as a science is briefly examined. The role of domestic libraries and their employees in the development of bibliographic thought in the Soviet period and at present is revealed. It is noted that in the context of a reduction in the teaching of bibliographic disciplines in universities and the corresponding liquidation of special bibliographic departments, the center for conducting bibliographic research and specialized scientific events (conferences, seminars, methodological meetings etc.) is moving to libraries.
Reference and Bibliographic Services in Foreign National Libraries (Organizational Aspect)

Digitalization is changing the environment in which libraries operate. The processes of production, storage, dissemination and use of information are undergoing significant changes. Reference and bibliographic service in the operational mode “request — response” is the most sensitive to the ongoing transformations. The problem of developing an optimal functional and organisational model of reference and bibliographic service is of particular relevance. The article is devoted to the review and analysis of the organisation of reference and bibliographic services in ten national libraries of the world on the basis of monitoring of official sites. The author defines the main forms of reference and bibliographic services, communication channels and ways of interaction of reference and bibliographic services with users. Special attention is paid to the currently dominant multilevel service model and its modifications. The problem of the crisis of reference bureau — stationary point of reference and bibliographic service is considered as a private one. The main tendencies of modernization of reference and bibliographic service in the digital age are revealed. The key drivers of change, in the author’s opinion, are the desire to link the library more closely to institutional strategy and to provide enhanced research support to those who need it.
Automated Filipino Language Treebank Generator

Filipino is a changing language that poses several challenges. The study was done to overcome the time needed to construct a corpus with it syntactic structure, treebank, as a part of the project where the researchers' part is for Filipino Language Treebank. This project is, Open Collaboration for Developing and Using Asian Language Treebank (ALT). The objective of this study is to parse Tagalog text and determine grammar rules and part-of-speech tags using a Probabilistic Context-free Grammar. The model was trained and tested using the Asian Language Treebank Filipino dataset which includes Filipino sentences and their grammar rules. The system achieved 86.43% f-measure score in parsing the grammar rules and part-of-speech tags of Filipino sentences. The model can reliably be used to automatically create treebanks for Filipino sentence grammar.
Balanced Data Augmentation for Dialogue State Tracking

Data augmentation methods for dialogue state tracking (DST) have made progress on producing more data based on internal or external datasets. However, they fall short in the balancing the generated dataset. We propose a balanced data augmentation method for DST to improve the quality of generated dataset. Our approach has two steps: (1) balance method in selecting slots and values: modify the possibility of selecting each slot and value when adding or replacing slots; (2) balance method for under-generation and over-generation: generate or delete cases based on their overall frequency of slots. We apply our method to MultiWOZ and the dataset generated by our method is more balance than the original and COCO augmented data. Then we evaluate a strong DST model trained on our generated training set. The performance of this DST model improves 5.93% (from 55.04% to 60.97%) joint goal accuracy than original model, and also surpasses the performance of the model trained on the data generated by COCO. This demonstrates the advantages and potential of our approach to be incorporated into data augmentation for DST.
Self-supervised Learning on Graphs at Node and Graph Level

Self-supervised learning has recently shown great potential in graph representation learning. However, there are still challenges in using pre-trained models for downstream tasks such as molecule properties prediction. To address this, we propose node-level and graph-level self-supervised strategies for pre-training graph neural networks(GNNs) to capture molecular graphs’ local and global structural patterns. We propose a node-level method called local contrastive learning, which aims to embed nodes that appear in similar local structures across different graphs in close proximity to each other. At the graph level, we propose a predictive learning method to predict the occurrence of functional groups in a molecule. Our empirical results show that our proposed strategies achieves competitive or better performance compared to state-of-the-art strategies.
Classifying Sentiments on Social Media Texts: A GPT-4 Preliminary Study

In today's digital age, social media has become a hub for people to express their thoughts and feelings. Sentiment classification discerns public opinions and trends to understand their sentiments towards a certain topic. Often, achieving accurate sentiment classifications in large datasets necessitate the use of human-annotated training data which can be costly and time-consuming. Large Language Models (LLMs) like the Generative Pre-trained models by OpenAI have surged in popularity due to its capabilities in understanding the given tasks. In this preliminary study, we report the performance of the latest OpenAI GPT-4 using zero- and one-shot learning approaches on classifying sentiments when fed with social media dataset. Notably, the latter approach written in English which mimics the instructions designed for human annotators, achieved a substantial agreement (k = 0.77) with human annotations, displaying high accuracy, precision, and recall accordingly even without explicit training data. Meanwhile, the fine-tuned mBERT resulted to lower evaluation scores than the GPT-4. Our findings provide foundational insights into the strengths and limitations of GPT-4 for sentiment classification in a social media dataset, setting the groundwork for broad future research in this field.
Assessing the Quality of a Knowledge Graph via Link Prediction Tasks

Knowledge Graph (KG) Construction is the prerequisite for all other KG research and applications. Researchers and engineers have proposed various approaches to build KGs for their use cases. However, how can we know whether our constructed KG is good or bad? Is it correct and complete? Is it consistent and robust? In this paper, we propose a method called LP-Measure to assess the quality of a KG via a link prediction tasks, without using a gold standard or other human labour. Though theoretically, the LP-Measure can only assess consistency and redundancy, instead of the more desirable correctness and completeness, empirical evidence shows that this measurement method can quantitatively distinguish the good KGs from the bad ones, even in terms of incorrectness and incompleteness. Compared with the most commonly used manual assessment, our LP-Measure is an automated evaluation, which saves time and human labour.
Intelligent Deposit Product Recommendation for Flexible Employees of Housing Provident Fund Based on Random Forest

With the rapid development of big data and cloud computing, digital technology has been widely used to develop provident funds. We innovatively apply data mining and machine learning methods to analyze the impact factors of provident fund deposits and the recommendation model of the deposit products for flexible employees. After experiments, our model performs well in the performance evaluation index (such as Accuracy, Recall, and F1-score). Through our model and research analysis, the housing provident fund center can comprehensively analyze and mine the real data, promote more popular customer policies, and provide better customer service.
Design of Intelligent Search System for Online Education based on Knowledge Graph

Online education, known as MOOC, has caused great reflection on traditional education. With the rapid development of online education and the establishment of various online education platforms, in the face of massive curriculum knowledge, how to quickly obtain the knowledge for learners has become an urgent problem. Artificial intelligence technologies such as deep learning and knowledge graph provide an effective way for resource retrieval. The paper explores the use of knowledge graph, deep learning, natural language processing and other technologies to build an online education intelligent construction system, which consists of intention understanding, target finding, and result presentation. The intelligent retrieval system helps learners of online education platform begin to learn effective knowledge and solve learners’ questions in time, which helps to improve the stickiness of online learning platform and maintain learners’ desire for continuous learning.
Impact of Emojis in Emotion Analysis on Code-Mixed Text

With the maturity of sentiment analysis technology, researchers are gradually not satisfied with only excavating positive and negative attitudes from the text but hope to obtain more delicate emotions. In addition, the development of social media makes people from different backgrounds like to express their emotions and feelings on social platforms. However, textual communication from social media is too colloquial, leading to the code-mixed text phenomenon, where sentences contain different languages, which poses difficulties for text analysis research. We observed that emojis in text contain emotion content and are universal across various language texts. This paper proposes a deep learning method for multi-class code-mixed emotion analysis using emojis. The proposed method has achieved good results on media text datasets and attempts to solve the difficulty of mixed sentiment classification with different codes. In this work, experiments were carried out on Hindi-English and Chinese-English code-mixed datasets, and the results have shown that integrating emoji representation with multilingual language model yielded 4% improvement on emotion analysis. Emojis should be preserved in emotion analysis regardless of which language the text is in.
Depression Detection from Network Behavior using Anomaly Detection and Feature Extraction Techniques

Depression is a common mood disorder that affects a significant portion of the population. Traditional depression diagnosis methods have limitations, which always depend on the experience of the clinician and are subjective. To overcome the above limitations, a new depression detection method based on users' network behaviors is expected to provide a fast, real-time, and accurate diagnostic tool to help people deal with depression problems in a timely manner. In this paper, we propose a novel approach for detecting depression by network time-series data, which adapts an improved anomaly detection method, i.e., DBSC-LOF in the feature engineering stage. By combining statistical and frequency-domain features extracted, we select the best classifier AdaBoost which has the best overall performance. To evaluate the ability of our improved anomaly detection method, detection accuracy and computation time are tested. Also, both the significance test and contribution assessment demonstrate that our extracted features have great distinguishing abilities between depressed and normal users.
Dialogue Segmentation based on Dynamic Context Coherence

Dialogue segmentation plays a crucial role in various dialogue modeling tasks. Semantic coherence-based methods are a major trend, but previous methods have tended to take fixed-size units in assessing semantic coherence, which may suffer from semantic loss or noise introduction, thus obtaining inaccurate coherence scores. In this paper, we propose a simple but effective approach for dynamically assessing semantic coherence, which can improve the accuracy of coherence scoring by dynamically expanding the assessment unit from a single utterance to a semantically related context. Experimental results on three benchmark datasets in English and Chinese demonstrate that our proposal can achieve competitive performance over strong baselines.
Deploying a Speech Recognition Model for Under-Resourced Languages: A Case Study on Dioula Wake Words 1, 2, 3, and 4

Abstract: Speech recognition technology has the potential to provide valuable information and services to the 12.5 million Dioula speakers, especially the illiterates. However, these people, who could benefit the most, often do not have access to this technology because there are few data sets for resource-poor languages. This paper investigates the effectiveness of data augmentation in training wake words such as 1, 2, 3 and 4 in Dioula. The study contains two major contributions: the release of a Dioula language corpus for wake words 1, 2, 3 and 4, comprising 1.4 hours of audio with a labeled dataset, and a training of speech recognition model for 1, 2, 3, and 4 applying the data augmentation technique, which resulted in a significant improvement in accuracy from 51% to 96%. Additionally, the confusion matrices illustrate the model's enhanced predictive capacity, with an average of 1762 out of 1817 instances of the number "1" being correctly recognized after data augmentation. The study also uncovered an impressive reduction in loss from 205% to 14% after implementing data augmentation. These results underscore the pivotal role of data augmentation in improving the model's performance and mitigating overfitting issues, underscoring the promise of this technique in addressing data scarcity in underrepresented speech contexts. Training a speech recognition model to detect specific wake words, such as "1," "2," "3," and "4" in Dioula, can be highly valuable in constructing interactive voice response systems, thereby fostering greater inclusivity and accessibility for underserved communities.
Named Entity Recognition for Setswana Language: A conditional Random Fields (CRF) Approach

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) focused on identifying entities like individuals, organizations, and locations within text. Locating these entities can present initial challenges, and subsequent classification can be equally daunting. This complexity is exemplified in Setswana, where shared naming between locations and personal names adds an extra layer of intricacy. This study introduces a Setswana NER approach, featuring a Setswana Regex Annotator (SERxA) for preliminary entity classification, followed by BRAT tool annotation. Employing the Conditional Random Fields (CRF) algorithm, we establish a supervised statistical machine learning NER model for Setswana. Evaluation using standard metrics on a held-out test set attains impressive F1-scores of 0.94 for person entities and 0.79 for location entities. Our findings underscore the viability of NER in Setswana and emphasize the necessity of nurturing NLP resources for less-resourced languages.
TF-NERD: Tagalog Fine-grained Named Entity Recognition Dataset

Named entity recognition (NER) is a crucial foundational step in information extraction. This research aims to improve existing natural language processing (NLP) resources for the low-resource language Tagalog by constructing a human-annotated NER dataset. In this paper, we present TF-NERD, a fine-grained NER dataset that consists of 2,337 paragraphs written in Tagalog and scraped from various internet sources. These entities are classified into 12 categories - art, event, facility, geo-political, language, law, location, nationalities, organization, person, product, and other entities. Benchmark NER transformer models were constructed to assess the quality and usability of the dataset for future NER research. Based on the results, the NER model finetuned with the pretrained roBERTa language model performed best with 82.31% recall and 80.67% precision for entity type classification, and 88.02% recall and 86.27% precision for entity phrase identification.
CoPrompt: A Contrast-prompt Tuning Method for Multiparty Dialogue Character Relationship Extraction

Information extraction is a fundamental task in natural language processing, and relation extraction is one of its primary subfields. However, most previous research focuses on extracting relations from standard texts, such as news articles and Wikipedia entries, while overlooking the challenges present in dialogues - sparse details, cross-sentence links, and complex character relationships. To address these challenges, we propose a contrastive-prompt tuning method (CoPrompt) to better capture the relationships between characters in dialogues. Our method improves the performance of the relationship extraction model by constructing positive and negative sample pairs to obtain better embedding representations of relationship features through prompt learning. To evaluate our method, we separately built manual and continuous templates and conducted experiments on the DialogRE and CRECIL datasets. Our method consistently outperformed other methods, notably achieving state-of-the-art results on Chinese datasets(DialogRE_cn and CRECIL), underscoring its robust efficiency in Chinese language relation extraction tasks. We also demonstrate the effectiveness of our method in low-resource scenarios. Our code is available at https://github.com/LIyu810/CoPrompt_main.
Book Recommendation System based on Course Descriptions using Cosine Similarity

Ensuring the retrieval of books that match users' preferences is of paramount importance. A significant challenge users encounter is uncertainty regarding their choice of search terms, often stemming from a limited understanding of the content or exposure to new concepts. Offering users results that closely resemble their query represents one potential solution. This research aims to suggest books relevant to students' course topics, utilizing cosine similarity to compute similarity values within each document in the collection.
Sarcasm detection in product reviews using textual entailment approach

Sarcasm is a form of sentiment characterized by the use of words that express the opposite of what is meant. Sarcasm detection has applications in multiple domains ranging from sentiment analysis in product reviews to user feedback, and online forums. Sarcasm detection is important to understand user opinions and intentions in areas such as sentiment-based classification and opinion mining. This can result in better product development and customer service. Sarcasm detection can be a challenging task because sarcastic sentences may use positive expressions to convey negative meanings or may use negative sentences to convey positive meanings. Also, sarcastic sentences form a very small component of the entire communication. The increasing use of sarcasm in various social media such as Twitter, Reddit, Amazon product reviews, etc. has highlighted the importance of detecting and understanding sarcasm in various contexts. Sarcasm detection is a challenging problem for NLP systems that often rely on statistical models for performing sentiment analysis. In this research, the focus is on the use of a textual entailment approach for detecting sarcasm. Textual entailment is a natural language inference task that involves determining whether one text (hypothesis) can be derived from another text (premise). The underlying assumption behind this approach is that - if there is a contradiction between the premise and hypothesis, we can say that the hypothesis is sarcastic. To test our approach, an annotated corpus of 3000 product reviews was developed methodically from the Amazon Reviews dataset and tested using the textual entailment approach. The proposed approach achieved an F1 score of 0.76 on this dataset. The result is better than the baseline considered which is the BERT binary classifier which gives an F1 score of 0.48 on the same dataset.
A Study Of Dialog Summarization Across Datasets And Domains

This study of dialog summarization covers multi-domain, multimodal and multilingual datasets, and the potential challenges in the different domains. The scope and progress of this rapidly evolving topic rely on the availability of datasets and emerging domains. Such a study can facilitate the cross-application of datasets to different domains to refine models and also aid scenarios where there is a lack of data in privacy-sensitive settings. Further, our work can enable the cross-fertilization of ideas across domains and in different contexts. Our study encompasses current and emerging domains, a comprehensive compilation of datasets, and avenues for further research.
A Novel Image Retrieval Framework based on Generative Adversarial Network

Image retrieval has been studied for decades, and it is one of the core challenges in the computer vision community. Many different works have been proposed to improve image retrieval performance. We, in this work, proposed a generative adversarial network-based image retrieval framework to enhance the image retrieval pipeline’s efficiency and performance. In our proposed work, the images with similar instance objects are the output for the generator by synchronizing the images continuously, and it will lessen the dissimilarities between the query image and retrieved image. This approach analyzes the dissimilarity between the retrieved image and the query image. In the end, a well-trained generator is authorized for image retrieval. We perform our experiments on Oxford5k and Paris6k datasets. The performance evaluation is measured on query expansion and mean average precision benchmarks. The state-of-the-art performance of our proposed method illustrates that we outperformed all evaluations.