Reviewerly/exHarmony huggingface dataset:
SAT-Net: SincNet-Based Attentive Temporal Convolutional Network for Motor Imagery Classification

Brain-computer interfaces (BCIs) are a promising method for users to interact with machines using brain signals, primarily electroencephalography (EEG). Motor imagery (MI)-BCI, which decodes EEG signals induced by the user's imagination of moving body parts, has gained great attention due to its applicability in various fields such as robotics and rehabilitation. MI - EEG signals exhibit class-discriminative patterns such as event-related de/synchronization across spectral-spatio-temporal (SST) domains. Numerous studies adopted deep learning framework, especially convolutional neural network (CNN), for learning SST feature representations automatically in a data-driven manner. In particular, most of the CNN-based methods adopted temporal convolution to learn the spectral information, as it can act as a band-pass filter. In this paper, we propose SAT-Net, a SincNet-based attentive temporal convolutional network for motor imagery classification. The proposed method utilizes Sinc convolution from SincNet for explicit extraction of spectral information from the input EEG signals with high interpretability. Moreover, we adopt an attentive temporal convolutional network to effectively learn SST feature representations while making full use of temporal information. We evaluate our proposed SAT-Net on the public BCI Competition IV-2a dataset, comparing it not only to conventional CNN-based approaches but also to the state-of-the-art method. The experimental results, supported by statistical analysis, demonstrate that our approach outperforms the competing methods.
ECdo: An Edge Computing Distributed Data-Driven Evolutionary Optimization Platform

Surrogate-assisted evolutionary algorithms (SAEAs) have become a popular method to solve data-driven optimization problems (DOPs), which are common in industry. However, with the development of the Internet of Things, data are collected, processed, and stored in a distributed manner, leading a new optimization paradigm for SAEAs. To make SAEAs adapt to these distributed DOPs, this paper employs the edge computing paradigm to develop a platform that provides technical support for SAEAs with distributed structures, named ECdo. Specifically, the platform utilizes KubeEdge, an open-source edge computing framework, to mount the cluster and combines microservice interface design with the containerization strategy to offer a flexible deployment approach for distributed SAEAs. In addition, an efficient and stable internal communication mechanism is designed for the interaction between distributed components within the platform. To demonstrate the application of ECdo, we take the examples of a class of distributed DOPs, in which the objective and constraints are expensive and need to be approximated by accumulated data. These problems are known as distributed and expensive constrained optimization problems (DECOPs). We implement a distributed SAEA on ECdo to address DECOPs in real-world scenarios. Experiments show that the ECdo can provide the expected implementation for distributed SAEAs with good network tolerance under tough network conditions.
Overfitting NN loop-filters in video coding

Overfitting is usually regarded as a negative condition since it impairs the generalisation power of a model. Nevertheless, overfitting a Neural Network (NN) on test data may be advantageous to improve the compression efficiency of image/video coding tools and systems. Previous research has demonstrated the benefits of NN overfitting for post-processing operations, i.e. post-filters, but not yet for actual decoding tools. Generally, the NN is overfitted on test data at the encoder end, and the weight update is coded and sent to the decoder end along the image/video bitstream. The proposed approach follows this strategy. In particular, the overfitting of the Low Operation Point (LOP) loop-filter in NN-based Video Coding (NNVC) software is studied. The overall approach yields Bjøntegaard Delta rate (BD-rate) of -7.74%, -13.73% and -12.49%, for the Y, U and V components, respectively. Out of these coding gains, 1.21%, 6.43% and 5.52%, for the Y, U and V components, are attributed to the overfitting. The boost in the coding gains comes with only 1.5% more complexity, due to the multiplier parameters introduced during the overfitting.
Building an AI-based Model to Extract and Classify Contents from Analog Medical History Forms

Medical history forms, often lacking standardization, pose challenges for healthcare professionals. This study employs an AI-based model to digitize and categorize these forms, enhancing accessibility. Results show the model achieved 79.65% accuracy with nine documents, rising to 88.74% with 99 documents, demonstrating the potential for improved medical history documentation.Clinical Relevance: This model serves as a tool to enhance universal medical text classification for quick understanding, reduce costs, save time, and minimize errors in patient history-taking. It can save doctors time and enhance clinical efficiency.
Multi-Feature Enhanced Multimodal Action Recognition

Action recognition applications achieve impressive success in various fields, yet existing approaches cannot make good use of different information flows. To tackle such an issue, multimodal action recognition exhibit remarkable potential in strengthening the video representation. However, the direct fusion may not enable the model to learn appropriate knowledge. To leverage extra feature information flow to help improve model performance, this paper proposes MFE, a multi-feature enhanced multimodal action recognition model. MFE introduces a guiding tag to explicit guide different extra feature information flow and designs a feature fusion module to fuse different information flows, to enhance hidden representation through more semantic supervision. The experimental results show that MFE achieves better or comparable accuracies with some advanced video action recognition models on several action recognition datasets.
Temporal-SNE-based Underwater Target Recognition Approach for Class-imbalanced Datasets

Aiming at the problem of low target recognition accuracy due to the small number of underwater target samples and the imbalance of dataset classes, a feature selection method with the name of t-distributed stochastic neighbor embedding based on temporal information (Temporal-SNE) is proposed, which describes the potential relationship between features and classes to enhance the essential representation of underwater target features. The method uses underwater target radiation noise to construct the feature space, and fuses the time-series information into the feature space in order to optimize the features with strong consistency of similarity and high differentiation of dissimilarity. The experiments use class-imbalanced underwater target datasets to compare the feature discrimination and recognition accuracy between Temporal-SNE method and the traditional methods. The results show that the introduction of temporal sequence improves the distinguishability of underwater target features and significantly increases the classification accuracy compared with the traditional methods.
Optimal Peaks Detected-Based Differential Evolution for Multimodal Optimization Problems

Multimodal optimization problems (MMOPs) have multiple global optima, hence the algorithm must preserve population diversity to locate multiple global optima and ensure the precision of the obtained solutions simultaneously. To achieve these, the niching technique is widely applied. Although the niching technique shows encouraging performance, some niches may continuously evolve even though accurate enough global optima in their regions have been found. This may cause the waste of computational resources and the inefficiency of search behavior. To maintain population diversity and accuracy, and to break through the mentioned deficiency, an optimal peaks detected-based differential evolution (OPPDE) algorithm is proposed, which has three novel components. Firstly, to maintain population diversity, OPDDE designs a parameter-insensitive OPTICS-based niching strategy to automatically partition niches. Secondly, to avoid wasting computation resources on founded global optima and enhance search efficiency, OPDDE designs an optimal peaks detection strategy that uses historical information to identify the founded global optima. Thirdly, a dynamic step local search strategy is used to refine solutions. The proposed OPDDE algorithm generally superiors some state-of-the-art algorithms regarding both the accuracy and completeness of solutions, according to experiments on widely used MMOP benchmarks.
Research on the Substation Alarm Event Model Based on Natural Language ParsingTechnology

In view of the current low efficiency of manual processing of massive monitoring alarm information and the need for deepening the application of power grid intelligence technology, an autonomous identification method of power grid equipment operation and maintenance alarm events based on natural language processing technology is proposed, which integrates neural network and unsupervised learning. The text of substation equipment alarm signal is vectorized based on word2vec algorithm, the time-density correlation between multiple alarm signals is established based on DBSCAN algorithm, and the "eventalization" model of alarm signal sequence is constructed based on TF-IDF algorithm. This paper proposes an application method based on natural language processing technology combining neural network and unsupervised learning algorithm to screen key "eventalization" alarms from a large number of discrete alarms, so as to realize the response efficiency and reliable identification of power grid monitoring alarm events.
HEROCA: Multimodal Sentiment Analysis Based on HEterogeneous Representation Optimization and Cross-Modal Attention

Multimodal sentiment analysis aims to determine human sentiment by extracting and integrating valuable information from multiple modalities, which has a wide range of application scenarios and can provide reliable references for opinion analysis, disease analysis, financial prediction and other fields. Many researchers in this field have proposed effective strategies from different perspectives. However, existing studies often fuse textual and non-textual modalities at the same layer while neglect the difference in information quality between them. The huge representation quality gap between modalities hinders the model from thoroughly learning the interactions between modalities and affects the model's performance. Based on the above reasons, we propose a multimodal sentiment analysis model based on heterogeneous representation optimization and cross-modal attention. The model designs a bimodal gated filtering mechanism which enhances the quality of semantic representations of non-text modalities by extracting and optimizing non-text modalities information. In addition, to fully exploit the useful inter-modal information and improve the quality of modality fusion, the model uses a cross-modal attention mechanism to model the complex dependencies between modalities and introduces a holistic view for semantic complementation in the prediction stage. To verify the effectiveness of our proposed method, we conduct experiments on two public datasets, CMU-MOSI and CMU-MOSEI. The experimental results show that our method can efficiently decrease the semantic gap between text and non-text modalities, significantly outperforming existing methods with superior generalization ability and strong competitiveness.
Block Chain based P2P energy trading strategy in power retail market for MVPPs

With the development of novel power system, the virtual power plant (VPP) which can aggregate and manage the distributed energy resources effectively has developed fast. Under the background of power market, the VPP acts an individual stakeholder in energy trading and network operation. This paper proposes a Block Chain based P2P energy trading strategy in power retail market for MVPPs. The operation framework for MVPPs is established with employment of blockchain, which is in a distributed structure to reduce information communication burden, low credibility of participants and privacy protection. The energy trading model is established to maximize the commercial profits with consideration of physical constraints. Numerical simulations are conducted on a four VPPs test system to verify the validity and reliability of the proposed strategy.
An Improved DRL Algorithm for Reliable Path Planning

In recent years, deep reinforcement learning (DRL) is an innovative way applied to target tracking of unmanned aerial vehicle (UAV). However, the instability of neural networks and the difficulty of designing reward functions have hindered its application in practice. To end these, this article proposes a novel method called LVFSAC, which combines the Lyapunov-vector-field (LVF) and soft actor-critic (SAC) of DRL, improving the stability of DRL and the efficiency of designing sophisticated reward functions. First of all, a UAV autonomous motion control (AMC) system is designed based on the framework of offline training- online decision-making, and the maximum entropy DRL soft actor-critic (SAC) is used for global path planning to guide real-time motion decisions. Secondly, to ensure the closed-loop stability of the AMC system, the reference paths are derived by using LVF theory based on the UAV situational information, which is fed back to adjust system training. Finally, the simulation platform with generalized scenarios is designed to validate the proposed method based on the UAV autonomous motion control application. The results show that our method not only achieves better stability during training but also obtains a higher success rate while ensuring path safety and reliability.
Augmented Transport Data-Free Class Incremental Learning

Although deep neural networks exhibit high performance on several specific tasks, they suffer from a severe issue of catastrophic forgetting when learning new tasks incrementally. Several incremental learning methods have been recently proposed to address this problem, some of which rely on storing data or constructing generative models to enhance the performance of incremental models. However, storing old data from previous tasks may be limited by memory or privacy issues, and generative models may often be unstable and inefficient during model training. In this study, we present a non-exemplar-based class-incremental learning method to mitigate catastrophic forgetting in incremental learning when saving old class samples is not possible, while distinguishing between old and new classes. We tackle the problem of representation bias and classifier bias that are inherent in class-incremental learning. Specifically, we introduce class enhancement, which enables the model to encounter more classes during training to acquire transferable and diverse representation features, mitigating representation bias. Furthermore, we use backtracking transfer, which employs semantic mapping to limit inter-class relationships by learning class-level semantic relationships across two tasks, to preserve discriminative old knowledge and prevent catastrophic forgetting due to classifier bias. We perform comprehensive experiments on three benchmark datasets that demonstrate the superiority of our proposed method over state-of-the-art methods.
Traffic Sign Detection in Complex Scenarios based on YOLOV7

In recent years, thanks to the development of the automobile industry and the advancement of industrial intelligence, driving assistance systems have been developing rapidly under the requirements of national policies and the demands of the consumer market. Traffic sign detection module, as an indispensable part of the driving assistance field, is in urgent need of new target detection algorithms in this field to solve the problems of poor detection accuracy, low computing speed and poor adaptability of the existing algorithms to the vehicle hardware. To solve the application problem, in this paper, we propose a traffic target detection algorithm based on YOLOV7. The model is trained using the CCTSDB dataset and tested in complex scenarios such as different lighting conditions and weather conditions. The mAP of YOLOV7 is stable at over 82.46% and the mAP value of YOLOV7-tiny is stable at over 78.57%, demonstrating the effectiveness of our method that it has the ability to detect and recognize our traffic signs in practical scenarios. In addition, this paper conducts a comparative study on the commonalities and differences of the above two models and draws the corresponding conclusions. In summary, YOLOV7 series can be effectively used in traffic sign detection work.
The Development History and Future Trend of Computer-Assisted Teaching

As the world population continues to grow, computer-assisted instruction can help alleviate the educational problems of a large population. Since the first example of computer-assisted instruction, Programmed Logic for Automated Teaching Operations, appeared in the 1960s, computer-assisted instruction's development has constantly been changing. Because of the attention and research of relevant workers, computer-assisted instruction covers more and more fields. Moreover, with the development of artificial intelligence technology, the functions of computer-assisted instruction are increasing, and the depth of teaching is increasing, which has a significant and far-reaching impact on the education sector and has a good development prospect in the future. This paper summarizes the systems, software, and algorithms of computer-assisted instruction since the 1960s compares them and combs the formation and development of computer-assisted instruction. After entering the 21st century, the development of Artificial Intelligence has provided a new direction for computer-assisted instruction. In the future, computer-assisted instruction will become more personalized and interactive, providing good learning guidance for self-learners.
MYELODYSPLASTIC SYNDROME NOT OTHERWISE CLASSIFIED. DO AUTOMATIC METHODS CORRELATE WITH THE MICROSCOPIC METHOD OF BLOOD SMEAR ASSESSMENT?

AMA Małecka-Giełdowska M, Poreda K, Skwarek A, Lulek M. MYELODYSPLASTIC SYNDROME NOT OTHERWISE CLASSIFIED. DO AUTOMATIC METHODS CORRELATE WITH THE MICROSCOPIC METHOD OF BLOOD SMEAR ASSESSMENT?. Health Problems of Civilization. 2024. doi:10.5114/hpc.2023.133623. APA Małecka-Giełdowska, M., Poreda, K., Skwarek, A., & Lulek, M. (2024). MYELODYSPLASTIC SYNDROME NOT OTHERWISE CLASSIFIED. DO AUTOMATIC METHODS CORRELATE WITH THE MICROSCOPIC METHOD OF BLOOD SMEAR ASSESSMENT?. Health Problems of Civilization. https://doi.org/10.5114/hpc.2023.133623 Chicago Małecka-Giełdowska, Milena, Katarzyna Poreda, Agata Skwarek, and Michalina Lulek. 2024. "MYELODYSPLASTIC SYNDROME NOT OTHERWISE CLASSIFIED. DO AUTOMATIC METHODS CORRELATE WITH THE MICROSCOPIC METHOD OF BLOOD SMEAR ASSESSMENT?". Health Problems of Civilization. doi:10.5114/hpc.2023.133623. Harvard Małecka-Giełdowska, M., Poreda, K., Skwarek, A., and Lulek, M. (2024). MYELODYSPLASTIC SYNDROME NOT OTHERWISE CLASSIFIED. DO AUTOMATIC METHODS CORRELATE WITH THE MICROSCOPIC METHOD OF BLOOD SMEAR ASSESSMENT?. Health Problems of Civilization. https://doi.org/10.5114/hpc.2023.133623 MLA Małecka-Giełdowska, Milena et al. "MYELODYSPLASTIC SYNDROME NOT OTHERWISE CLASSIFIED. DO AUTOMATIC METHODS CORRELATE WITH THE MICROSCOPIC METHOD OF BLOOD SMEAR ASSESSMENT?." Health Problems of Civilization, 2024. doi:10.5114/hpc.2023.133623. Vancouver Małecka-Giełdowska M, Poreda K, Skwarek A, Lulek M. MYELODYSPLASTIC SYNDROME NOT OTHERWISE CLASSIFIED. DO AUTOMATIC METHODS CORRELATE WITH THE MICROSCOPIC METHOD OF BLOOD SMEAR ASSESSMENT?. Health Problems of Civilization. 2024. doi:10.5114/hpc.2023.133623.
A study on interpretation of Korean AI competitions and leaderboards

To effectively bring together individuals aiming to advance artificial intelligence (AI) technology and language models and expedite the development of state-of-the-art solutions, the most appropriate approach is to organize competitions and maintain leaderboards. Over the past several decades, overseas efforts have promptly identified the performance limits of language models through various types of competitions and leaderboards and have implemented a comprehensive and systematic evaluation process to overcome them creatively. On the other hand, in Korea, there has been a scattered effort to hold both large and small-scale competitions and leaderboards in response to the surging demand for the development of Korean language AI in recent years, but a clear direction has yet to be determined for external organizational methods and internal evaluation criteria. Hence, this study collected a wide range of domestic and international cases of artificial intelligence-related competitions and leaderboards, and closely examined their organizational procedures and the utilization patterns of evaluation metrics. It summarized the general development trajectory of language model evaluation systems demonstrated in overseas cases. In light of the comparative analysis, we propose a four-stage Korean AI competition league that takes into account the unique Korean social background and linguistic attributes of the Korean language. We also propose a novel four-stage operational process for Korean language competitions led by the government in collaboration with industry and academia.
A Study on the Effectiveness of Deep Learning Architectures in Style Transfer: A Comparative Analysis of CNN, VGG16, and VGG19

This study explores the performance differences among different deep learning models, including VGG19, VGG16, and a basic CNN, in the context of image style transfer. Style transfer is an image processing technique aimed at transferring the artistic style of one image onto the content of another. Our research motivation stems from the potential applications of style transfer across various domains and the opportunities for enhancing model performance. Through a series of experiments, we assess the performance of these models in terms of the visual quality of generated images, style preservation, feature extraction, and training efficiency. The results demonstrate significant variations in performance across diverse and complex conditions, especially in tasks involving advanced features. VGG19 and VGG16 exhibit exceptional performance, accurately capturing and conveying high-level features, resulting in the generation of synthesized images with artistic value. However, for tasks emphasizing speed and resource efficiency, the basic CNN also exhibits notable advantages. Lastly, we discuss the limitations of the study and provide suggestions for future work to further optimize model performance. In conclusion, the choice of an appropriate deep learning model should be determined by the nature and requirements of the task. This research provides valuable insights into the applications of different models in the field of image style transfer.
A Video Security Verification Method Based on Blockchain

This paper proposes a blockchain-based video security verification method. In order to ensure the security of the video, we use data hiding technology to embed message that can ensure the security of the video into the video, at the same time, in order to improve the non-tamperability and non-forgery of data hiding. We construct the hash value of the message embedded in the original video, the hash value of the carrier video (video with embedded message) and the attribute message of the video as video metadata, and upload the video metadata to the blockchain network. Comparing the message extracted from the carrier video with the video metadata stored on the blockchain can achieve double verification of the video. At the same time, distributed storage of carrier video through IPFS can effectively store and manage large-scale video data, ensuring the security and high availability of videos.