Reviewerly/exHarmony huggingface dataset:
Education and the Children's Book Market at the Crossroads: the Foundations for Creating a Romanian House of Children's Literature

Abstract Society has always relied on education for the building of progress ever since the dawn of history, and books have always been strong agents of change no matter the shape they came in (papyrus, codex, handwritten manuscripts, printed books, digital books, etc.). For this reason, analyzing the interplay between education and books is valuable and necessary if we wish to get a better sense of society at any moment. This paper explores how education and the book industry intersect in today's Romanian society. However, it also proposes a possible solution for strengthening their relationship and opening new horizons for culture and research. More specifically, my analysis focuses on children's literature since children's education is considered an investment into the future of society in modern democratic states. In the case of Romania, it is unfortunate that this type of literature is somewhat neglected compared to other countries, but this should never be a good enough reason for not trying to create more creative contexts to improve the situation. Drawing inspiration from what other countries are doing to develop bridges between education and the children's book market, but at the same time highlighting essential initiatives in Romania that can become reliable engines of change, I strongly advocate for the necessity and possibility of creating a Romanian House of Children's Literature, open to supporting researchers, professionals, and families in their shared mission to ensure quality development of children.
Hybrid of DCT And PCA For Image Size Compression

One of a person’s most crucial biometric traits is their face. Face recognition is a crucial first step for applications in many fields, such as biometrics, user interfaces for computers, and surveillance. Most of these techniques fail due to high memory usage. Therefore, image compression is very important today as it is used in most applications that are concerned with the amount of memory usage and speed of transferring information. In this research, the speed of data transmission was increased and the size of the images was reduced while maintaining accuracy by using discrete cosine transform (DCT) hybrid with principal component analysis (PCA). Face recognition techniques compare the desired face image with a set of face images stored in a database. An equalizer followed by the median filter has been used for suitable intensity and removing the noise from the faces images as a first step before the face recognition process. Different databases are implemented to evaluate the performance of the system’s algorithms such as ‘face94’. Concerned with memory amount, experimental results showed that the use of compression and dimensionality reduction with face recognition process produced better results compared with the applying the compression alone. DCT compressed the image memory size by 60% (from 6Kbyte to 2.4Kbyte), while based on Eigen vectors of 90, DCT+PCA compressed the image size by 83.5%. This last statement is the contribution of this research.
Activation Function Conundrums in the Modern Machine Learning Paradigm

The decision-making components of main neural networks are activation functions. Furthermore, they evaluate the output of the network’s neural node; consequently, they are critical to the overall network’s performance. As a result, selecting the most appropriate activation function in neural network calculation is crucial. These functions have various features that are thought to be necessary for successful learning. These features include their methods to ensure individual derivatives and finite range. The generally used cumulative functions, such as Sigmoid, Leaky ReLU, softmax, TanH and ReLU, and so on, will be evaluated in this study article. This will be followed by their qualities, individual disadvantages and advantages, and specific formula application advice.
Cross-Language Code Clone Detection Using Abstract Syntax Tree and Graph Neural Network

Code clones refer to code fragments that have similar functionality but may differ in syntax. When code duplication occurs, it can pose challenges during system maintenance and necessitate fixing errors in multiple locations. Existing methods for detecting code clones typically focus on clones within the same programming language. However, as the use of multiple programming languages becomes more prevalent, clones across different languages are becoming increasingly common. Recent research studies have explored the detection of cross-language code clones using Recurrent Neural Networks (RNN), specifically variants like LSTM and GRU. This paper presents an approach that combines the strengths of Abstract Syntax Trees (AST) and Graph Neural Networks (GNN) to identify cross-language code clones. The AST represents the code as a graph structure, while GNNs are capable of learning the state embeddings of each graph node, capturing information about its surroundings and the overall graph structure. Utilizing GNNs in the context of cross-language clone detection helps capture additional semantic information about the code fragments. Notably, GNNs have not been previously applied to the detection of cross-language code clones. Experimental results demonstrate that the proposed approach outperforms LSTM, GRU, and other state-of-the-art methods in terms of F1 score, precision, and recall.
Quantum Multi-Agent Reinforcement Learning as an Emerging AI Technology: A Survey and Future Directions

This paper surveys Quantum Multi-Agent Reinforcement Learning (QMARL), an emerging fusion of quantum computing and multi-agent systems. It begins by introducing the transformative potential of quantum computing in computational capabilities. Examining the principles of multi-agent reinforcement learning (MARL), the paper explores how quantum computing enhances learning efficiency and decision-making. Focusing on the current state of QMARL, it reviews literature, methodologies, and case studies showcasing the integration of quantum algorithms with MARL frameworks. The survey addresses challenges and opportunities arising from quantum technologies in multi-agent systems, such as entanglement and superposition, exploring their implications for agent coordination and learning dynamics. Practical applications in domains like cybersecurity and finance underscore QMARL’s transformative potential. Concluding, the paper identifies research gaps and proposes future directions, emphasizing the need for scalable quantum algorithms, exploring quantum-resistant strategies in adversarial settings, and integrating quantum principles in agent communication and collaboration. This concise survey serves as a foundational guide for researchers and practitioners, offering insights into the current state and future possibilities of QMARL.
Retrieve, Generate and Rerank: Simple and Effective Framework for Guided Human-Like Questions Generation

Most existing methods for question generation create questions purely depend on the source text. The problem is that these methods tend to directly copy the expressions from given passages and lack the sufficient capability to ask diverse and human-like questions. To alleviate this problem, we present a simple and effective Retrieve-Generate-Rerank (RGR) framework that guides the generation with references in training set. Specifically, for every inputted passage and answer, a variety of references are retrieved, with irrelevant information filtered to produce clues. Each clue guides the generation of a question with different patterns and expressions. These questions are then re-ranked to find the most human-like outcome. Experimental results show that our approach consistently improve the performance of existing models on two datasets, and achieves state-of-the-art result on NewsQA. In addition, further investigation and human evaluation demonstrate that our method can generate more diverse and consistent questions.
Algorithm of Pedestrian Pose Recognition Based on Keypoint Detection

With the rapid advancement in computer technology, the applications of pose recognition techniques are increasing. Accurately recognizing human poses is a challenge. This paper focuses on keypoint detection and pose recognition of pedestrians in images. To achieve keypoint detection, OpenPose algorithm is employed. Additionally, two optimizations are proposed for pose recognition. Firstly, the keypoint coordinates are utilized to calculate angles, limb distances, and ratios of limb distances, which are used as inputs to the models. Secondly, Long Short-Term Memory Network models are trained to achieve pose recognition. The results demonstrate that multiple pose recognition models achieve an accuracy rate of 95%, and best-performing model achieving an accuracy rate of 98%.
A Machine Learning Based Method to Establish Normative Action Model for Table Tennis

In recent years, how to apply machine learning methods to analyze and evaluate the movement of athletes or amateurs in sports videos becomes a hot research topic. In this paper, we design a machine learning based method to establish a normative action model of table tennis by analyzing collected training videos. In addition, we build a normative action data set from training videos of professional athletes and a comparative experimental data set for from training videos of amateurs. We define 10 action judgment rules according to suggestions from professional coaches and athletes, and verify the effectiveness of the proposed normative action model by experimental evaluations. We hope that the proposed method and built data sets can inspire more excellent work for action analysis of sports.
Prompt-Based Learning in Natural Language Processing from 2020 to 2022, a Survey

Prompt-based learning is a new paradigm of natural language processing by adding “prompt information” to input and adjusting downstream tasks to text generation tasks without significantly changing the structure and parameters of the pre-training language model. In this paper, the improvement of prompt-based learning in recent years is systematically sorted out along several dimensions, to display a clear vein for the development and improvement of prompt-based learning that has been done for beginners of prompt-based learning, help and enlighten prompt-based learning work in the future.
Empowering Biomedical Health with Federated Learning: Addressing Privacy and Data Sharing for Enhanced Disease Detection and Diagnosis

The advent of big data and AI has resulted in a data revolution across many sectors, healthcare among them. However, the private nature of patient information frequently prevents its sharing, resulting in data storage that slow the development of AI in healthcare. Federated learning (FL) emerges as a possible answer to these problems. As a result of FL, reli-able illness detection models can be developed on distributed data held by diverse individuals without compromising data privacy. In this study, we investigate how FL may help solve problems associated with data sharing and privacy in biomed-ical health. This research seeks to contribute to the incorporation of artificial intelligence in biological health by reviewing relevant literature and case studies to provide a full know l-edge of FL's benefits and limitations in disease identification and diagnosis.
Dynamic Adaptive Parametric Multi-Target Tracking Algorithm Based on Appearance and Pose Feature

In recent years, multi-target tracking missions have evolved considerably. Nevertheless, the fusion of pedestrian appearance and pose feature into association models is an area that has received insufficient attention. Furthermore, despite efforts to address ID switches through techniques like SORT, their effectiveness has been somewhat constrained. In this paper, a distinctive method is introduced wherein pedestrian appearance and pose features are combined in the association algorithm, complemented by the utilization of dynamic adaptive parameters to further optimize its performance. Through extensive experimentation on widely acknowledged datasets, including MOT17, MOT20, and DanceTrack, our approach demonstrates remarkable achievements. Our approach effectively decreases the occurrence of ID switches by approximately 50%. We are confident that our study sets a new standard and offers a novel viewpoint for the domain of multi-target tracking. The GitHub repository provides access to and allows for the downloading of the source code: https://github.com/WeiXiao-Hyy/shuangmu_renyuan_model.
Analysis and Guidance for Standing Broad Jump Based on Artificial Intelligence

In this paper we propose and implements a standing long jump movements analysis algorithm based on artificial intelligence to help students improve their performance. We select the BlazePose, a lightweight neural network under the Mediapipe framework for human pose estimation. We extracte the coordinate information of body joints with Mediapipe, and use the extracted coordinate information to design the algorithm of standing long jump action segmentation and key frame selection for quantitative analysis movements. To evaluate the effectiveness of the project, we recruit 24 students to attend the Experiments with two rounds of tests, Experiment results show that the proposed algorithm can automatically detect and identify problems and deficiencies in key movements, and give suggestions for improvement. By analyzing the data of jumpers' take-off skills, posture control and landing methods, we can provide suggestions to help them improve their skills and performance.
Research and Implementation of Intelligent Academic Question and Answer System Based on Knowledge Graph

The development of the education system has put forward a high demand for academic Q&A. In this paper, we use technologies such as Flask, WeChat public platform, knowledge graph, and Q&A pair matching to build a knowledge graph containing 9 categories with 20 knowledge entities and more than 200 categories with about 1000 entity attributes in size, using the important notification documents issued by colleges and schools as data sources, and realize an end-to-end intelligent academic Q&A system. The system has the advantages and features of real-time talkability, low latency, high fault tolerance, and human-computer dialogue through natural language. The system has a high value in the Q&A scenario in the field of academic teaching and learning in universities.
Chinese Name Entity Recognition with Label Semantics

Named Entity Recognition (NER) requires effective information capture from annotated instances and the transfer of useful knowledge from external resources. In this paper, we utilize the semantic knowledge embedded in the label names to provide the model with additional signals and enriched priors. In our experiment, we employ two distinct BERT encoders to generate semantic representations for both the label names and the text. This approach allows us to leverage the semantic information embedded in the label names, providing the model with additional signals and enriched priors. To optimize the model, we employ a specialized loss function called Focal Loss, which can address the challenges of our particular task and align with our objectives. The label semantics approach has demonstrated improved performance across multiple Chinese NER datasets, providing evidence for the effectiveness of our work.
Detection of Insulator Self-Blast with Improved YOLOV4 and Dilated Convolution

Due to prolonged high-load operation and the impact of external environmental factors, insulators often undergo selfblasting, leading to significant disruptions in the functioning of the power system. This research introduces a novel approach for detecting insulator self-blasting incidents based on the YOLOv4 model. This method combines several advanced techniques, including feature fusion, attention mechanisms, and dilated convolution, to achieve highly accurate detection results. In the proposed method, shallow-level information is utilized to compensate for the loss of intricate details in deep-level features. Additionally, an attention mechanism is employed to enhance the precision of target positioning. Furthermore, dilated convolution is integrated to capture vital target context information and expand the network's receptive field. The experimental results demonstrate a remarkable improvement in the average detection precision of the enhanced model, showcasing an impressive increase of 6.86% compared to the baseline YOLOv4 model. This advancement promises to contribute significantly to the early identification and mitigation of insulator self-blasting issues, thereby enhancing the overall reliability and performance of the power system.
Non-IID Federated Learning Based on Global Knowledge Sharing Without Out-of-Domain Data

Federated learning (FL) supports collaborative training of deep learning models on distributed data. The non-independent and identically distribution (non-IID) of user data brings great challenges to FL, which may cause the global model to converge slowly or even deviate from the optimal solution. As an effective method to solve the data non-IID problem, knowledge distillation often relies on public proxy datasets for model training, which violates the original intention of FL to protect privacy. In this paper, we propose a global knowledge sharing algorithm without out-of-domain data to solve FL with non-IID data. Firstly, to ensure that the data remains within the domain and to constrain the deviation of update directions between the local and global models, we propose a novel teacher-student network structure, which utilizes a personalized global knowledge transfer method to supervise local training. Secondly, we also design different knowledge transfer loss functions to maximize the use of global knowledge and prevent overfitting of local data. Finally, in order to continuously correct the update direction of the local model and improve the accuracy of the global model, the shared knowledge model on the client is updated using the exponential moving average (EMA) method. In order to evaluate, we manually constructed the non-IID dataset in the real scene. Experiments show that compared with the existing methods, the global model accuracy of the SKH-FL algorithm proposed in this paper is improved by 5.07%.
Real-World Wireless Channel Data Augmentation Using Adversarial Autoencoder for DL-Based Massive Mimo CSI Feedback

In massive multiple-input multiple-output (MIMO) systems, deep learning (DL)-based channel state information (CSI) feedback can provide high downlink throughput with limited feedback overhead. However, insufficient wireless channel data in the actual situation impairs the reconstruction accuracy of CSI feedback network. In this paper, we propose an adversarial autoencoder (AAE)-based wireless channel data augmentation network named CsiAAE to improve the CSI reconstruction accuracy of DL-based CSI feedback network trained with limited dataset. Experimental results under real-world wireless channel data show that compared with traditional data augmentation methods, CsiAAE can provide higher reconstruction accuracy for the DL-based CSI feedback network trained with insufficient dataset.
Dense Local Consistency Loss for Video Semantic Segmentation

Existing image semantic segmentation models often suffer from temporal inconsistency between consecutive frames when processing continuous video inputs. While using optical flow or incorporating historical frame information can alleviate this issue, the resulting increase in parameters and computational complexity is detrimental to real-time tasks. In contrast, we propose a dense local consistency loss dubbed DLCL, which introduces spatial local semantic consistency constraints between consecutive frames in the task of video semantic segmentation. During training, DLCL is calculated based on the cosine similarity of feature embeddings for the same object in consecutive frames. Our DLCL is simple yet effective, easily integrated into both single-frame and video semantic segmentation models, and improves the temporal consistency and segmentation accuracy of predicted frames without adding any parameters or computational overhead during inference. We conduct experiments on the large-scale multi-scene video semantic segmentation dataset: VSPW, to demonstrate the effectiveness of our approach. The results consistently show performance improvements in both singleframe and video semantic segmentation models, validating the efficacy of our method.
Bilinear Adversarial Network for Fine-Grained Domain Adaptation

Due to larger intra-class variation and inter-class similarity, fine-grained visual categorization (FGVC) is a challenging task. However, obtaining labelled samples for fine-grained datasets is more difficult than traditional datasets because of the need for expert-level domain knowledge. Consequently, the progress of FGVC has been limited by the availability of well-labelled datasets. In this paper, we propose a domain adaptation approach that leverages domain knowledge learned from existing large-scale fine-grained datasets to unlabeled real-life data, enabling FGVC applications in various daily life domains. Our approach utilizes high-dimensional features extracted by bilinear convolutional networks to bridge the source and target domains gap. We explore and compare different variants of bilinear convolutional networks, ultimately identifying the optimal method. Experimental results on two benchmarks verified the effectiveness of our proposed method. Additionally, detailed ablation experiments verify the contributions of each component in our method. Code is provided at https://github.com/PRIS-CV/Bilinear-Adversarial-Network.
Efficient Semi-Supervised Segmentation of Medical Images with RESA-Enhanced U-Net

Medical image analysis relies on accurately identifying anatomical structures. With the emergence of deep learning frameworks, it is now possible to create systems that can automate the segmentation of such data. However, challenges related to polycentric and spatiotemporal inefficiencies persist. Furthermore, the high costs associated with manual labeling and the scarcity of labeled data are significant obstacles in this domain. In this work, we provide a brand-new semi-supervised approach and an enhanced segmentation network to address these issues. By integrating the Recurrent Feature-Shift Aggregator(RESA) structure into the network, we leverage multiple step sizes for information transmission, mitigating potential information loss. In the FLARE2022 examples, our suggested quantitative assessment technique was able to get a DSC of 0.80 and an NSD of 0.75, indicating the effectiveness and resilience of our strategy.
Active learning-based optimal strategy for Wordle

This article describes a simulation model that utilizes a word bank to guess a puzzle answer and adjust the model in real-time based on results analysis. The model successfully predicted the percentage of the word "ERIRE" for each guess attempt. The presented algorithm classified words into three difficulty levels and analyzed the differences in each indicator. The article established a discriminant function to determine that guessing the word "EEIRE" was of medium difficulty.
Modeling and Analysis of Aspect-Oriented High-Speed Railway Database Connection Pool

The potential value in big data offers significant help in enhancing intelligent transportation systems, improving user travel experiences, and other aspects of vehicular networking. However, the explosive growth of data poses challenges in processing and storage. The Aspect-Oriented Programming (AOP) approach addresses the limitations of Object-Oriented Software Development in dealing with non-functional requirements. This paper focuses on the data access characteristics and proposes the application of Aspect-Oriented development principles in modeling and analyzing the high-speed railway database connection pool.