Reviewerly/exHarmony huggingface dataset:
Educational Data Mining: The Application in The University's Feedback Survey Analysis using Classification and Clustering Techniques

XYZ University is one of the private universities that has a commitment to improve its education and institution quality is through the feedback from students. The current method to extract information from the results of student feedback is not sufficient to provide significant knowledge for the University. Currently, the feedbacks are obtained through student surveys are in the form of Open-finished Question (OEQ) and Closed-finished Question (CEQ). The survey itself is divided into two: Student Feedback Questionnaire (SFQ) and Facility Feedback Questionnaire (FFQ). The main goal of this research is therefore to process all of the data from the SFQ and FFQ using classification and clustering techniques for Educational Data Mining (EDM) in order to produce critical information for the decision makers in the University. In addition, this research utilizes order methods to foresee the viewpoint mark of the overview in the open-finished poll. The result of the research shows that the best multi class grouping model for SFQ is utilizing the SVM calculation with the assessment normal score of 69.82&#x0025;. Concerning the FSQ, the best multi name order model is determined utilizing the SVM calculation and the classifier chain as the best library multi mark with the assessment normal score of 73.325&#x0025;. This exploration is likewise utilizing bunching strategies for gathering the CEQ review respondent. Concerning SFQ, the best grouping model is obtained from the K-Means calculation with the assessment normal score of 0.582. In the interim, concerning the FSQ, the best bunching model is obtained from the Fuzzy C Means calculation with an assessment normal score of 0.586. (Abstract) Keywords&#x2500
Classifying Different Types of Talk During Collaboration

Pair programmers utilizing more Exploratory (critical, constructive) talk has been shown to help students achieve a better mutual understanding of problems they are solving. In this paper, we investigate the promise of fine tuning a pretrained transformer-based machine learning model to classify utterances into Exploratory, Cumulative, and Disputational talk. The task of classifying utterances into different types of collaborative talk was approached as a multi-label text classification problem. This is the first successful automatic classification of utterances into the different types of collaborative talk.
BGNN-XML: Bilateral Graph Neural Networks for Extreme Multi-label Text Classification

Extreme multi-label text classification (XMTC) aims to tag a text instance with the most relevant subset of labels from an extremely large label set. XMTC has attracted much recent attention due to massive label sets yielded by modern applications, such as news annotation and product recommendation. The main challenges of XMTC are the data <i>scalability</i> and <i>sparsity</i>, thereby leading to two issues: i) the intractability to scale to the extreme label setting, ii) the presence of long-tailed label distribution, implying that a large fraction of labels have few positive training instances. To overcome these problems, we propose BGNN-XML, a scalable graph neural network framework tailored for XMTC problems. Specifically, we exploit label correlations via excavating their co-occurrence patterns and build a label graph based on the correlation matrix. We then conduct the attributed graph clustering by performing graph convolution with a low-pass graph filter to jointly model label dependencies and label features, which induces semantic label clusters. We further propose a bilateral-branch graph isomorphism network to decouple representation learning and classifier learning for better modeling <i>tail labels</i>. Experimental results on multiple benchmark datasets demonstrate that BGNN-XML significantly outperforms state-of-the-art baselines while maintaining comparable prediction efficiency and model size.
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels

Large-scale multilingual pre-trained language models have achieved remarkable performance in zero-shot cross-lingual tasks.A recent study has demonstrated the effectiveness of self-learning-based approach on cross-lingual transfer, where only unlabeled data of target languages are required, without any efforts to annotate gold labels for target languages.However, it suffers from noisy training due to the incorrectly pseudo-labeled samples.In this work, we propose an uncertaintyaware Cross-Lingual Transfer framework with Pseudo-Partial-Label (CLTP) 1 to maximize the utilization of unlabeled data by reducing the noise introduced in the training phase.To estimate pseudo-partial-label for each unlabeled data, we propose a novel estimation method, considering both prediction confidence and the limitation to the number of similar labels.Extensive experiments are conducted on two cross-lingual tasks, including Named Entity Recognition (NER) and Natural Language Inference (NLI) across 40 languages, which shows our method can outperform the baselines on both high-resource and low-resource languages, such as 6.9 on Kazakh (kk) and 5.2 Marathi (mr) for NER.
Enriching Abusive Language Detection with Community Context

Uses of pejorative expressions can be benign or actively empowering.When models for abuse detection misclassify these expressions as derogatory, they inadvertently censor productive conversations held by marginalized groups.One way to engage with non-dominant perspectives is to add context around conversations.Previous research has leveraged userand thread-level features, but it often neglects the spaces within which productive conversations take place.Our paper highlights how community context can improve classification outcomes in abusive language detection.We make two main contributions to this end.First, we demonstrate that online communities cluster by the nature of their support towards victims of abuse.Second, we establish how community context improves accuracy and reduces the false positive rates of state-of-the-art abusive language classifiers.These findings suggest a promising direction for context-aware models in abusive language research.
CIAug: Equipping Interpolative Augmentation with Curriculum Learning

Interpolative data augmentation has proven to be effective for NLP tasks.Despite its merits, the sample selection process in mixup is random, which might make it difficult for the model to generalize better and converge faster.We propose CIAug, a novel curriculum-based learning method that builds upon mixup.It leverages the relative position of samples in hyperbolic embedding space as a complexity measure to gradually mix up increasingly difficult and diverse samples along training.CIAug achieves state-of-the-art results over existing interpolative augmentation methods on 10 benchmark datasets across 4 languages in text classification and named-entity recognition tasks.It also converges and achieves benchmark F1 scores 3 times faster.We empirically analyze the various components of CIAug, and evaluate its robustness against adversarial attacks.
A Fast NDFA-Based Approach to Approximate Pattern-Matching for Plagiarism Detection in Blockchain-Driven NFTs

We are presenting a fast and innovative approach to performing approximate pattern-matching for plagiarism detection, using an NDFA-based approach that significantly enhances performance compared to other existing similarity measures. We outline the advantages of our approach in the context of blockchain-based non-fungible tokens (NFTs). After testing in real-world scenarios, we conclude that our approach is suitable and adequate to perform approximate pattern-matching for plagiarism detection, yet significantly faster and therefore more suitable for big data analysis.
Context-Aware Contribution Estimation for Feature Aggregation in Video Face Recognition

The difficulties in video-based face recognition, such as dramatic pose variations and low quality, can be alleviated by leveraging the rich complementary information between the frames. However, limited by the mini-batch training strategy, the current deep learning methods only utilizes the frames in each batch during training, which ignore the context of the entire video. In this paper, we propose a context-aware feature aggregation scheme to aggregate complementary information between different frames. Firstly, a two-branch structure is designed as the Context-aware feature Aggregation Network (CAN). Secondly, a context-aware training strategy using context bank is proposed, which alleviates the limitation of mini-batch samples by using the context of the entire video or several images belonging to the same ID and thus achieves global contribution estimation result. Comparative studies on benchmark datasets, such as IJB-C, YouTube Face (YTF), PaSC and COX, confirm that the proposed approach can achieve state-of-the-art level. Meanwhile, qualitative analysis on Multi-PIE dataset indicates that the contribution learned by the CAN is reasonable and beneficial to video face recognition.
Considering Disengaged Responses in Bayesian and Deep Knowledge Tracing

In this study, we analyzed the influence of student disengagement on prediction accuracy in knowledge tracing models. During the data pre-processing stage, we prepared two training data: The disengaged responses were ignored in the baseline data whereas the disengaged responses were removed in the disengagement-adjusted data. Using visual analysis, we identified disengaged responses (i.e., hint abusers and rapid guessers) and removed them from the disengagement-adjusted data during the pre-processing phase since those responses do not reflect the true latent ability of the students. After fitting the knowledge tracing models to the baseline and disengagement-adjusted data, we found that the prediction accuracy of both models on test data has substantially increased when disengaged responses were removed during the pre-processing stage. Our results emphasized the importance of considering student disengagement in knowledge tracing models to produce more accurate prediction models.
L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition

This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2022 Task 11, Multilingual Complex Named Entity Recognition (MultiCoNER).The task focuses on detecting semantically ambiguous and complex entities in short and low-context monolingual and multilingual settings.We argue that using a language-specific and a multilingual language model could improve the performance of multilingual and mixed NER.Also, we consider that using additional contexts from the training set could improve the performance of a NER on short texts.Thus, we propose a straightforward technique for generating additional contexts with and without the presence of entities.Our findings suggest that, in our internal experimental setup, this approach is promising.However, we ranked above average for the highresource languages and lower than average for low-resource and multilingual models.
Research and Exploration on the Training of New Engineering Personnel from the Perspective of Artificial Intelligence

There is an increasing trend of cross integration among natural science, technological science and Humanities and social sciences. The breakthrough of cutting-edge technologies and subversive technologies such as information, life, manufacturing, energy, space and ocean puts forward higher requirements and challenges for cultivating &#x201C;new generation&#x201D; of engineering and scientific personnel. The construction of new engineering is not only the new strategic demand of national development, but also the new situation of international competition. Cloud computing, big data, Internet of things, etc. will serve the technical research and product application in artificial intelligence related fields. Firstly, this paper analyzed the new challenges faced by China&#x0027;s engineering education reform and the main position of artificial intelligence in the construction of new engineering subjects. Secondly, four training objectives of new engineering personnel from the perspective of artificial intelligence were designed. On this basis, this paper continued to illustrate the development design of new engineering disciplines, and systematically expounded the basic concept, brand-new mode and implementation path of new engineering education reform in the field of artificial intelligence in three aspects: optimizing the ecological layout of disciplines at the top level, promoting composite emerging disciplines through discipline intersection, and accelerating the upgrading of traditional disciplines. Finally, this paper put forward some suggestions on the basic literacy of new engineering personnel in the field of artificial intelligence.
Contrastive Deep Knowledge Tracing

Knowledge tracing (KT) aims to predict student performance on the next question according to historical records. Recently deep learning-based models for KT task successfully modeling student responses receive good prediction results of student performance. The student responses encoded as input of KT models use a one-hot encoding. We find that one-hot encoding represents student responses on different items related to the same concepts in completely different vectors. However, items related to the same concept have certain relationships in the real world so the student has a similar representation in these items. In this paper, we propose a new method named Contrastive Deep Knowledge Tracing (CDKT) for providing a reasonable representation of students. We evaluate our model using three public benchmark datasets and the experimental results demonstrate improvements over state-of-the-art methods.
Understanding Self-Directed Learning with Sequential Pattern Mining

We describe a study on the use of an online laboratory for self-directed learning through the construction and simulation of conceptual models of ecological systems. We analyzed the modeling behaviors of 315 learners and 822 instances of learner-generated models using a sequential pattern mining technique. We found three types of learner behaviors: observation, construction, and exploration. We found that while the observation behavior was most common, exploration led to models of higher quality.
Weakly Supervised Text Classification using Supervision Signals from a Language Model

Solving text classification in a weakly supervised manner is important for real-world applications where human annotations are scarce.In this paper, we propose to query a masked language model with cloze style prompts to obtain supervision signals.We design a prompt which combines the document itself and "this article is talking about [MASK]."A masked language model can generate words for the [MASK] token.The generated words which summarize the content of a document can be utilized as supervision signals.We propose a latent variable model to learn a word distribution learner which associates generated words to pre-defined categories and a document classifier simultaneously without using any annotated data.Evaluation on three datasets, AG-News, 20Newsgroups, and UCINews, shows that our method can outperform baselines by 2%, 4%, and 3%.
On Applicability of Neural Language Models for Readability Assessment in Filipino

In the field of automatic readability assessment (ARA), the current trend in the research community focuses on the use of large neural language models such as BERT as evidenced from its high performance in other downstream NLP tasks. In this study, we dissect the BERT model and applied it to readability assessment in a low-resource setting using a dataset in the Filipino language. Results show that extracting embeddings separately from various layers of BERT obtain relatively similar performance with models trained using a diverse set of handcrafted features and substantially better than using conventional transfer learning approach.
Simulation Game in Quasi-Professional Training of Pre-Service Teachers

The relevance of the study lies in the need to highlight the problem of using simulation games in the quasi-professional training of pre-service teachers, bringing the educational process closer to the teachers’ real working conditions. The purpose of this paper is to cover the method of using simulation games and its various techniques in the quasi-professional training of pre-service teachers while studying psychological and pedagogical disciplines and to investigate the attitude and level of motivation of pre-service teachers towards the use of simulation games in training for professional activities. Theoretical methods (analysis, synthesis, comparison) were used to examine the problem in the psychological and pedagogical literature, clarify the nature and features of the process of training pre-service teachers for professional activities using simulation games, as well as empirical methods (questionnaires, observations, expert assessments, self-assessment) which were used to investigate the attitude and level of motivation of pre-service teachers towards the use of simulation games in training for professional activities. This paper article presents the structure of the simulation game and methods of working with the simulation game. The authors describe the advantages and disadvantages of using the simulation game in training of preservice teachers in higher education. The results of students’ survey on the specific features of organising simulation games and increasing their efficiency are presented: students’ attitude towards the use of simulation games in the educational process and the degree of activity of pre-service teachers in organising simulation games. The study confirmed that the use of simulation games in the quasi-professional training of pre-service teachers contributes to the effective formation of professional competence of pre-service teachers and forms a positive attitude towards the teaching profession, enhances learning interests and students’ interest in future professional activities. The paper shows that most students are interested in simulation games and believe that participation in simulation games has a positive effect not only on learning, mood, and relationships of students, but also plays a considerable role in their professional development. The practical significance of the study is to give examples and recommendations for the use of simulation games while studying psychological and pedagogical disciplines, as well as to outline the subject and social contexts of educational activities of future teachers
Space games: evaluating game-based virtual reality in higher education

With increasing global dependence on satellite technology, space traffic has grown exponentially over the last decade. Enhanced education and training of future mission operators will be necessary to meet this growing demand. The complexity of satellite mission operations poses a challenge in education and training. Remote spacecraft are elusive and difficult for a trainee to visualize and involve a steep learning curve. However, the integration of game-based virtual reality into spacecraft simulation and training may assist in overcoming these challenges. This research study explored the integration of game-based virtual reality into a university course involving spacecraft operations. Virtual spacewalks allowed student participants to conduct visual inspections and interact directly with spacecraft components. The immersive virtual reality environment prolonged cognitive engagement and game mechanics influenced motivation, both cornerstones in learning. After completing the training scenarios, user experience was assessed with several validated scales measuring system usability, user satisfaction, cognitive loading, and any potential simulator sickness. Results revealed satisfactory scores in all categories with minimal simulator sickness. The integrated use of game-based virtual reality in the classroom provided an enhanced learning experience in a safe and repeatable environment that might be difficult with traditional teaching methods. This paper will evaluate game-based virtual reality when integrated into higher education or other training environments
Automated generation of concentric circles metro maps using mixed-integer optimization

The concentric circles (CC) map design is an alternative approach for schematically representing metro systems. Compared with traditional octo-linear maps, CC maps can effectively simplify the perception of a network by visually accenting circular line patterns. This design offers new insights into the schematic drawing of metro systems that can improve map readability and engagement. Automated mapping studies in the literature have mostly applied the traditional octo-linear design using optimization methods, where design criteria are modeled as constraints and/or objective functions in a constrained mixed-integer optimization program, whereas the automated CC map drawing approach has received less attention. In this article, we develop an automatic CC map drawing method by adopting map design criteria as a mixed-integer programming problem. Numerical experiments are conducted using (a) a simple network to illustrate the model procedure in detail, (b) two real-world metro networks in Vienna and Montréal to analyze the effects of the selected map center and parameter settings and (c) the Beijing subway to analyze the applicability of the proposed approach to large-scale metro networks.
Remote Sensing Image Encryption and Error Detection using Hamming Code

Abstract Encryption of remote sensing images is always required as this type of images carry sensitive data. It is the primary criteria to hide it in case of a country’s border image or video. Nowadays development in networking improves communication systems. Unfortunately, intruders are misusing this advantage and try to intrude into the network whose security is weak. During secure communication of remote sensing images, encryption is done before sending to a long distance. Here, we have discussed a new method of remote sensing image encoding with the help of hamming code. During analyzation, we have showed that it is not easy to decode the main image from encrypted data in the most popular mode of attacking- known plaintext attack. Moreover, we have also calculated that this system can detect the error in an effective way. The presented method is also applicable for effective encryption of more than one remote sensing images.
Performance Improvement of Face Recognition Method and its Application in COVID-19

Abstract In this paper an efficient face recognition technique is presented by integrating Discrete Wavelet Transform and Compressive Sensing based classifier. At first discrete wavelet transform has been applied on each face images. Then an image fusion technique has been applied on the decomposed image to provide better detail information of face images. Principal component Analysis is applied on fused face images to extract the feature vector. Finally, the feature vector of test images is extracted and classified by Compressive Sensing based Classifier. This proposed technique is also tested on two publicly available databases on AR and ORL. This technique is also tested on masked face images and experimental result shows improved performance compared to conventional PCA.