Reviewerly/exHarmony huggingface dataset:
Research Trends in Korean Vocabulary Education – Focusing on domestic journal papers released in the last 10 years -

This study aims to examine the research trends over the last 10 years, in Korean vocabulary education using text mining and seek the direction for future research in Korean vocabulary education. The research list was selected based on the keyword “Korean vocabulary education” using Research Information Sharing Service and thereafter, the research trends were examined by research topic, research method, and research subjects. Consequently, the most common research topic was about vocabulary educating method and the most used research method was to suggest the educational measures. The language group of the learners was mostly Chinese and the most common learning purpose was for academic purposes. Learner proficiency was evenly distributed among beginner, intermediate, and advanced levels. A significant number of studies on Sino-Korean word education for Chinese learners were noticed owing to the increase in the number of Chinese international students in Korea. These results are consistent with the results of word cloud and network analysis using text mining. However, it has been identified that studies on sociolinguistic vocabulary and those that verify the effect of educational measures or reflect the learners’ requirements are highly insufficient. Thus, this study suggests that research should be conducted on the aforementioned lacking areas and learners in more diverse language groups.
Lévy flight and disrupt <scp>operator‐based</scp> elephant herding optimization for global optimization problems and feature selection to classify medical data

Summary Metaheuristic techniques have gained the attention of many researchers in the last few years. These techniques are used to solve real‐world optimization problems as well as for feature selection. This work proposes a lévy flight and disrupt operator‐based elephant herding optimization algorithm (LDEHO). In the proposed algorithm, opposition based‐learning is utilized to commence with better initial positions. Lévy flight and matriarch mean are introduced to update the positions of clan individuals. In addition, the disruption phenomenon is introduced to generate new individuals for replacing the worst clan individuals. Further, an elitism scheme is introduced to preserve the best search agents in consecutive iterations. The performance of the proposed LDEHO algorithm is validated on 97 benchmark functions. A comparative analysis of the proposed LDEHO algorithm with fourteen state‐of‐the‐art algorithms has been made. Results show the high potential of the LDEHO algorithm in solving the benchmark functions. Further, Friedman's mean rank test and multiple comparison tests are applied to demonstrate the statistically significant difference between the algorithms. Moreover, a binary version of the proposed LDEHO algorithm is introduced for feature selection to classify the medical datasets. The performance of the binary LDEHO is validated on 15 medical datasets and compared with six state‐of‐the‐art algorithms. Results show the supremacy of the binary LDEHO for feature selection to classify the medical data. Friedman's test proves that the proposed binary LDEHO algorithm is statistically different and better than other algorithms.
CoDEvo: Column family database evolution using model transformations

In recent years, software applications have been working with NoSQL databases as they have emerged to handle big data more efficiently than traditional databases. The data models of these databases are designed to satisfy the requirements of the software application, which means that the models must evolve when the requirements of the software application change. To avoid mistakes during the design and evolution of these NoSQL models, there are several methodologies that recommend using a conceptual model. This implies that consistency between the conceptual model and the schema must be maintained when either evolving the database or the software application. In this work, we propose CoDEvo, a model-driven engineering approach that uses model transformations to address the evolution of a NoSQL column family DBMS schema when the underlying conceptual model evolves due to software requirement changes, aiming to maintain consistency between the schema and conceptual model. We have addressed this problem by defining transformation rules that determine how to evolve the schema for a specific conceptual model change. To validate these transformations, we applied them to conceptual model changes from 9 open-source software applications, comparing the output schemas from CoDEvo with the schemas that were defined in these applications.
Task-driven approach to artificial intelligence

The paper considers the task-driven approach to artificial intelligence. It is shown that, on the one hand, it generalizes such approaches as the agent-based approach and general artificial intelligence, and, on the other hand, accurately reflects the cognitive processes and purposeful behavior described in the physiological Theory of functional brain systems.
Prediction of ICU Patients’ Deterioration Using Machine Learning Techniques

Assessing vital sign measurements within hospital settings presents a valuable opportunity for data analysis and knowledge extraction. By generating adaptable, personalized prediction models of patient vital signs, these models can yield clinically relevant insights not achievable through population-based models. This study aims to compare several statistical forecasting models to determine their real-life applicability.The primary objectives of this paper are to evaluate whether the following measurements: blood pressure, oxygen saturation, temperature and heart rate can predict deterioration in Intensive Care Unit (ICU) patients. Additionally, we aim to identify which of these measurements contributes most significantly to our prediction. Lastly, we seek to determine the most accurate data mining technique for real-life data applications.This retrospective chart review study utilized data from patients admitted to the ICU at a tertiary hospital between January and December, 2019. Data mining techniques for prediction included logistic regression, support vector machine classifier, k-nearest neighbors (KNN), gradient boosting classifier, and Naive Bayes classifier. A comprehensive comparison of these techniques was performed, focusing on accuracy, precision, recall, and F-measure.To achieve the research objectives, the SelectKBest class was applied to extract the most contributory features for prediction. Blood pressure ranked first with a score of 9.98, followed by respiratory rate, temperature, and heart rate. Analysis of 653 patient records indicated that 129 patients expired, while 542 patients were discharged either to their homes or other facilities. Among the five training models, two demonstrated the highest accuracy in predicting patient deterioration or survival at 88.83% and 84.72%, respectively. The gradient boosting classifier accurately predicted 115 out of 129 expired patients, while the KNN correctly predicted 109 out of 129 expired patients.Machine learning has the potential to enhance clinical deterioration prediction compared to traditional methods. This allows healthcare professionals to implement preventative measures and improve patients' quality of life, ultimately increasing average life expectancy. Although our research focused exclusively on ICU patients, data mining techniques can be applied in various contexts both within and outside the hospital setting.
A Method to Improve the Quality of Point-Light-Style Images Using Peripheral Difference Filters with Different Window Sizes

We propose a non-photorealistic rendering method for automatically generating point-light-style (PLS) images from photographic images using peripheral difference filters with different window sizes. The proposed method can express PLS patterns near the edges of photographic images as dots. To verify the effectiveness of the proposed method, experiments were conducted to visually confirm PLS images generated from various photographic images.
A Two-Step Rule for Backpropagation

We present a simplified computational rule for the back-propagation formulas for artificial neural networks. In this work, we provide a generic two-step rule for the back-propagation algorithm in matrix notation. Moreover, this rule incorporates both the forward and backward phases of the computations involved in the learning process. Specifically, this recursive computing rule permits the propagation of the changes to all synaptic weights in the network, layer by layer, efficiently. In particular, we use this rule to compute both the up and down partial derivatives of the cost function of all the connections feeding into the output layer.
PCGC: a performance compact graph compiler based on multilevel fusion-splitting rules

The existing deep learning compilers are unable to perform efficient hardware performance-related graph fusion when both time and power consumption are considered. In addition, the compilers optimize the computational graph of deep neural networks (DNNs) by performing static graph transformation based on the greedy algorithm, only considering the runtime performance, and ignoring the cost of the tuning process. To solve these problems, this paper proposes a DNN computational graph optimization compiler (PCGC). Through the performance feedback at runtime, PCGC designs a computational graph fusion and splitting optimization strategy based on multilevel operator layer fusion-splitting rules. First, PCGC uses a rule-guided graph segmentation algorithm to recursively segment the computational graph into smaller subgraph to achieve an efficient and detailed search. Then, PCGC uses the cost model to receive feedback from hardware performance information, proposes cost model and operator fusion rules to synthetically guide the partial fusion and partitioning of nodes and edges of the computational graph, and generates optimal subgraphs flexibly according to different hardware to optimize the search space for partial fusion. Finally, we make the cost model converge quickly to the loss value we set by manually adjusting the parameters. Compared with other advanced compilers, PCGC optimizes the overall power consumption on an embedded GPU by an average of 130.5% when the time consumption on each hardware is not lower than the average time consumption. On domain-specific architecture, PCGC optimizes power consumption by an average of 66.5%. On FPGA, PCGC optimizes power consumption by 66.1%. In a sense, PCGC can achieve high-speed inference in specific power supply scenarios, reducing the carbon emissions of edge computing.
Logics and collaboration

Abstract Since the early days of artificial intelligence (AI), many logics have been explored as tools for knowledge representation and reasoning. In the spirit of the Crossley Festscrift and recognizing John Crossley’s diverse interests and his legacy in both mathematical logic and computer science, I discuss examples from my own research that sit in the overlap of logic and AI, with a focus on supporting human–AI interactions.
Resource-Efficient Transfer Learning from Speech Foundation Model Using Hierarchical Feature Fusion

Self-supervised pre-training of a speech foundation model, followed by supervised fine-tuning, has shown impressive quality improvements on automatic speech recognition (ASR) tasks. Fine-tuning separate foundation models for many downstream tasks are expensive since the foundation model is usually very big. Parameter-efficient fine-tuning methods (e.g. adapter, sparse update methods) offer an alternative paradigm where a small set of parameters are updated to adapt the foundation model to new tasks. However, these methods still suffer from a high computational memory cost and slow training speed because they require backpropagation through the entire neural network at each step. In the paper, we analyze the performance of features at different layers of a foundation model on the speech recognition task and propose a novel hierarchical feature fusion method for resource-efficient transfer learning from speech foundation models. Experimental results show that the proposed method can achieve better performance on speech recognition task than existing algorithms with fewer number of trainable parameters, less computational memory cost and faster training speed. After combining with Adapters at all layers, the proposed method can achieve the same performance as fine-tuning the whole model with 97% fewer trainable encoder parameters and 53% faster training speed.
NC-WAMKD: Neighborhood Correction Weight-Adaptive Multi-Teacher Knowledge Distillation for Graph-Based Semi-Supervised Node Classification

Multi-teacher knowledge distillation can improve the performance of student networks in semi-supervised node classification tasks, but existing works ignore the importance of different teachers, using average of multiple teachers as final prediction. In addition, they rely on a large amount of labeled data, which is inconsistent with semi-supervised learning requirements. To solve these limitations, we propose a Neighborhood Correction Weight-Adaptive Multi-teacher Knowledge Distillation (NC-WAMKD) framework, which involves the knowledge distillation strategy WAMKD and the student model with Neighborhood Correction Label Propagation and Feature Transformation (NCLF). Specifically, WAMKD is designed to adaptively assign weights for multiple teachers to avoid misleading student by low-quality teachers. NCLF relies on neighborhood correction label propagation and feature transformation to validly predict unlabeled nodes. Experiments on semi-supervised node classification tasks demonstrate the effectiveness of the proposed framework. Code is available: https://github.com/para-99/NC-WAMKD.
Unsupervised Out-of-Distribution Detection Using Few in-Distribution Samples

This paper tackles the out-of-distribution (OOD) detection problem for natural language classifiers. While the previous OOD detection methods require large-scale in-distribution (ID) training data, we attack this problem from the few-shot perspective in an unsupervised manner where the training relies on only a few samples from ID data. First, we develop various baselines for Few-shot OOD (FSOOD) detection in text classification based on the three well-known few-shot learning approaches (well-explored in the vision domain), i.e., meta-learning, metric learning, and data augmentation (DA). Then, we introduce the concept of demonstration-based data augmentation with meta and metric-learning approaches to reap the combined benefit of both approaches. A pre-trained transformer is fine-tuned on a few available ID samples in all developed methods. In tandem with this fine-tuning, an OOD detector is fitted over the ID training samples to reject the data from the unknown classes using two kinds of distance metrics, namely Mahalanobis distance and Cosine similarity. At last, we present an extensive evaluation of three ID datasets and three OOD datasets. We also perform an ablation study to analyze the impact of various components of our method.
Flowreg: Latent Space Regularization Using Normalizing Flow For Limited Samples Learning

Modern deep neural network models have made remarkable success in many areas, supported by large sets of training samples. Yet the hunger for huge data has also become fatal in further expanding the use of deep models. Limited sample learning aims at learning generalized and transferable representations, without requiring large training data. In this paper, we propose FlowReg, a new learnable latent space regularization for limited sample problems. FlowReg modulates the latent space using a Normalizing Flow with a simple prior (such as Gaussian) while maintaining the complexity of the posterior distribution. We conduct thorough experiments on diverse tasks in limited label learning, as well as detailed in-depth analysis to comprehensively demonstrate the effectiveness of FlowReg.
Semantics-Aware Gamma Correction for Unsupervised Low-Light Image Enhancement

Low-light image enhancement aims to improve the visual quality of images captured under poor lighting conditions. While recent works have successfully developed deep learning-based solutions, a large number of existing works require ground-truth normal-light images during training, and most methods are not designed to exploit and preserve semantic information in the low-light inputs. In this paper, we propose a semantics-aware yet unsupervised low-light enhancement model based on gamma correction. Without observing ground-truth images or semantic annotations of the low-light inputs, our model learns via the introduced semantics-aware adversarial learning scheme with the associated objectives given a set of unpaired reference images of interest. Guided by such high-quality reference images and the inherent semantic practicality, our proposed method performs favorably against recent unsupervised low-light enhancement approaches.
No Reference Quality Assessment for Screen Content Images Based on Entire and High-Influence Regions

Screen content images (SCIs) are composed of text, graphics, natural scene images and other contents, for which the human eye will have different visual perception for different regions. Considering the impact of local distortions on the visual quality of the entire SCIs, this paper proposes a no-reference quality assessment method based on entire and high-influence regions. Firstly, we select the region with higher influence on the overall quality of the image by information entropy. Then the structural features based on phase congruency and color features based on opponent color space are extracted from the entire SCIs and high-influence region. The AdaBoosting neural network (ABNN) is adopted to predict scores of the entire image and the high-influence region. Finally, the quality score of the high-influence is used to slightly adjust the score of the entire SCIs to obtain the final score through the weighting strategy. Extensive experiments demonstrate the efficiency and robustness of the proposed method.
SDRNet: Shape Decoupled Regression Network for 3d face Reconstruction

In the field of computer vision, 3D face reconstruction from single-view images is a long-standing and challenging problem. Following the popular 3DMM-based reconstruction framework, recent works show great concerns about exploring discriminative information of identity and expression for shape regression commonly in coupling ways. Actually, identity and expression information may contribute differently in explaining the intrinsic shape of faces, and the former is inferior to the latter in explaining the great facial shape variations caused by extreme expression. In this paper, we propose a Shape Decoupled Regression Network (SDRNet) consisting of identity-focused branch and expression-focused branch with focused criteria for representation learning, which interact with the union branch to achieve the final 3DMM parameters regression for improved shape reconstruction. In SDRNet, the focused criteria estimate the 3D vertex prediction loss, while the predicted 3D shape is reconstructed only using the predicted parameter of identity or expression and introducing the ground-truth parameters of the left two. Extensive experiments on the challenging AFLW2000-3D and AFLW datasets demonstrate advanced performance in 3D face reconstruction and face alignment.
Disease Cluster Specific learning for Patient Treatment Prediction on High Dimensional EHR Data

Disease treatment Prediction is considered as significant research in the data analytics in medical domains. The autoencoder model is found be the highly productive model for clustering the disease treatment trajectory data. The processed dataset contains the disease related features which helps in transforming the data in high dimensional to the data in low dimensional way. Preprocessing of the trajectory data is carried out using missing value imputation and normalization process. The primary features are computed by the autoencoder learning process in a subsequent step while retaining the least reconstruction error. In order to avoid over fitting problems, the model also includes multiple layers to represent the different feature types and clustering. Additionally, the Loss function and encoder function are tuned along with all of the model's hyper parameters. In map structure, this encoder function technique is considered for mapping feature model with the latent feature model. To end, computation of the hyperparameter to be introduced for prediction of the disease treatment. Detailed experiments analyses were performed on EHR datasets to compare current architecture with conventional approaches. The experiment's results show that disease cluster specific clustering processes high dimensional data with great effectiveness and increased scalability
Leveraging multimodal features for knowledge graph entity alignment based on dynamic self-attention networks

Entity alignment establishes correspondence between entities across different knowledge graphs, which plays an important role in knowledge fusion. The key of entity alignment is to represent and fuse the features of entities across graphs. To improve efficiency, recent methods introduce graph embedding techniques that map different knowledge graphs into common latent spaces to encode structure and attribute features of entities. However, the structure embedding is generated by aggregating coarse connection information between entities without capturing semantics of the relations, which is incapable of dealing with entities of similar structures. Besides, multiple features are integrated by static arithmetic weighting method, which has difficulty in balancing the relative importance of structure and attribute features. To address these problems, we propose a dynamic self-attention based entity alignment model (DSEA), which leverages multimodal features for entity alignment through a dynamic self-attention network. To learn informative representations, we build a multimodal feature embedding component (MFE) to leverage structure, name, and position importance of entities. In MFE, semantics of adjacent entities and relations are exploited to enhance the structure embedding through a fine-granularity semantic augmentation network. Then a dynamic self-attention network is designed to adaptively evaluate the relative importance of structure and attribute features by learning the weights according to their contribution in entity alignment. Based on the feature weights, a similarity function of entities is formulated and the entity correspondence is established according to the similarities of entities. Extensive experiments conducted on five real-world datasets show that DSEA significantly outperforms state-of-the-art methods.
Self-learning interval type-2 hierarchical fuzzy system based on rule relevance with online regression prediction application

In this paper, a novel self-learning design of an interval type-2 hierarchical fuzzy system (IT2 HFS) based on rule relevance is proposed. Different from the existing methods, this paper uses data stream instead of batch data to construct the IT2 HFS. As time goes on, HFS gradually fills its rule base through fuzzy partition algorithm, where the rule base of HFS is empty at the beginning. A new self-updating method for consequent parameters is proposed based on rule relevance. When HFS is used for online learning, the proposed method can ensure that the system has better performance. The designed self-learning IT2 HFS is applied to online regression prediction issue, and is tested on two different online regression datasets. Self-learning type-1 HFS is also compared with self-learning IT2 HFS in prediction accuracy and system complexity. The results illustrate that the self-learning IT2 HFS performs better in prediction accuracy with lower rules.