Reviewerly/exHarmony huggingface dataset:
English Vocabulary Board Game for Upper Primary School Students in Small and Medium-Sized Schools

The objectives of this research were 1) to study the results of using board game to improve English vocabulary for upper primary school students in small and medium-sized schools, and 2) to study the students’ satisfaction after using board game. There were a total of 100 students: 50 students from small-sized schools, and 50 students from a medium-sized school. The samples were selected by using multi-stage sampling method. The research instruments were the English vocabulary board game, pre- and post-tests, a behavior observation form, an interview form, and a satisfaction evaluation form. Frequency, mean, percentage, standard deviation, and t-tests, were used to analyze the data. The research findings indicated that both small and medium-sized school students' scores were increased after using the board game at the statistically significant level of .001. Additionally, students’ satisfaction was at a high level supported by the observation data and interview results.
BF3D: Bi-directional fusion 3D detector with semantic sampling and geometric mapping

3D object detection is a key task in environmental awareness, which plays a vital role in autonomous driving safety. Lidars and cameras are widely used sensors that provide complementary information for accurate 3D detection. However, due to the domain difference between the two modalities, it is challenging to leverage their respective strengths and fuse them perfectly. In this paper, an end-to-end detector termed BF3D is proposed, which integrates with the semantic sampling module, geometric point-image mapping module, and bi-directional attention fusion module. Specifically, the semantic sampling module incorporates a novel downsampling strategy to preserve more foreground points and pixels. Additionally, the geometric point-image mapping module is developed to find geometric correlation pixels of the point and take advantage of the high density of image features. We also introduce a bi-directional attention fusion module to combine useful information from the two modalities by attention mechanism. Extensive experiments demonstrate that BF3D outperforms both single- and multi-modal 3D detectors. Codes are available at: https://github.com/hustzyj/BF3D.
Framework for learning and control in the classical and quantum domains

Control and learning are key to technological advancement, both in the classical and quantum domains, yet their interrelationship is insufficiently clear in the literature, especially between classical and quantum definitions of control and learning. We construct a framework that formally relates learning and control, both classical and quantum, to each other, with this formalism showing how learning can aid control. Furthermore, our framework helps to identify interesting unsolved problems in the nexus of classical and quantum control and learning and helps in choosing tools to solve problems. As a use case, we cast the well-studied problem of adaptive quantum-enhanced interferometric-phase estimation as a supervised learning problem for devising feasible control policies. Our unification of these fields relies on diagrammatically representing the state of knowledge, which elegantly summarizes existing knowledge and exposes knowledge gaps.
Introducing the open biomedical map of science

This article introduces work in progress to develop a new, open biomedical map of science (OBMS) using the PubMed citation database. The new science map represents bimodal network relationships between journals and medical subject heading (MeSH) descriptors, based on a journal's articles indexed in the MEDLINE component of PubMed. We review the current efforts to use PubMed data in science of science studies and science mapping. As part of the development process, we compare the journals indexed in PubMed with journals included in the 2011 UCSD map of science to establish a baseline of disciplinary coverage of PubMed for the period 2009-2019. Journal article frequency is analyzed to establish the minimum number of citations required by a journal for inclusion in a map of science. A prototype OBMS is presented, and we discuss the strengths and weaknesses of the OBMS, as well as the next steps for using and productizing this new open map for general and free usage.
Distribution of local curvature values as a structural feature for off-line handwritten signature verification

In the paper, a new feature for describing a digital image of a handwritten signature based on the frequency distribution of the values of the local curvature of the signature contours, is proposed. The calculation of this feature on the binary image of a signature is described in detail. A normalized histogram of distributions of local curvature values for 40 bins is formed. The frequency values recorded as a 40-dimensional vector are called the local curvature code of the signature. During verification, the proximity of signature pairs is determined by correlation between curvature codes and LBP codes described by the authors in [23]. To perform the signature verification procedure, a two-dimensional feature space is constructed containing images of the proximity of signature pairs. When verifying a signature with N authentic signatures of the same person, N(N-1)/2 patterns of the proximity of pairs of genuine signatures and N images of pairs of proximity of the analyzed signature with genuine signatures are presented in the feature space. The Support Vector Machine (SVM) is used as a classifier. Experimental studies were carried out on digitized images of genuine and fake signatures from two databases. The accuracy of automatic verification of signatures on the publicly available CEDAR database was 99,77 % and on TUIT was 88,62 %.
Deep learning-based autonomous real-time digital meter reading recognition method for natural scenes

Natural scenes with variable illumination, variable target scale and angular tilt pose significant challenges to the autonomous recognition of digital meter readings. Based on this, this paper proposes a deep learning-based autonomous real-time digital meter reading recognition method for natural scenes. First, the YOLO-style corner point detection method (YOLO-CPDM) for the reading area is proposed by reconstructing the detection heads and incorporating the corner detection loss function. Its localization accuracy is further refined by embedding attention mechanism module, implementing dynamic loss function and enhancing training data diversity through offline augmentation techniques like image rotation and flipping. Then, the detected corner points are used to geometrically correct the distorted reading area by perspective transformation to mitigate the interference caused by the shooting angle. Next, the YOLO-style end-to-end reading recognition method (YOLO-EERRM) is proposed to accurately extract the characters in the reading area. Finally, the validity of the YOLO-CPDM and YOLO-EERRM was verified on a produced dataset named SYSU-DM and 2 public datasets. Compared with the State of the arts (SOTA) keypoint detection model, the mean Average Precision @ 50:95 scores of the YOLO-CPDM improved by 2.8, 4.1, and 1.1 points, respectively, while the inference latency was only 5.3 ms, and YOLO-EERRM achieved 100 % accuracy and 3.1 ms inference latency on the SYSU-DM dataset. Statistically, the complete digital meter reading recognition method has 99.6 % accuracy and 8.6 ms inference latency, indicating that the system has high recognition accuracy and practicality.
SDE-RAE:CLIP-based realistic image reconstruction and editing network using stochastic differential diffusion

Generative Adversarial Networks (GANs) has long dominated the field of image reconstruction and editing. It is capable to train a generator in an adversarial way, which can fool the discriminator and enable the generated image to be of high quality. However, this approach is often difficult to train, and the final result is hard to converge. Each different style of image requires construction of different datasets and complex optimization functions, and the training process is uncertain. To solve this problem, we propose a realistic image reconstruction and editing method based on Stochastic Differential Equation (SDE-RAE), where the diffusion model converts Gaussian noise to real photos by iterative denoising. What we only need to do is to construct simple loss functions in the reconstruction process to achieve high-quality image reconstruction, and we propose a novel semantic enhancement CLIP (Contrastive Language-Image Pre-Training) to interfere with the SDE parameter optimization direction in the editing process. Simple text is needed to achieve unique image editing. Our method generates high-quality images that retain the texture and contour features of the original image. Specifically, we manipulate the initial image, perturb the image by adding random noise, and then iteratively denoise the image by reverse SDE, manipulating the image's RGB pixels to achieve image reconstruction and editing. Code and dataset https://github.com/haizhu12/SDE-RAE.
Use Of Power Point Media To Improve The Learning Outcomes Of Class 1 Students At SD Negeri 0702 Panyabungan

This research was conducted with the aim of increasing motivation and learning outcomes of online learning in grade 1 students of SD Negeri 0702 Panyabungan through powerpoint media. This type of research is classroom action research (CAR). The subjects were grade 1 students of SD Negeri 0702 Panyabungan, totaling 23 students. The CAR design uses the Kemmis and McTaggart models which include planning, action, observation, and reflection. Data collection techniques using observation and tests. Data analysis used quantitative and qualitative descriptive statistics. The results showed an increase in learning outcomes, this can be seen from the completeness of the pre-action KKM students and each cycle, namely in the pre-action by 65%. In cycle 1 83% and cycle 2 96%. This was accompanied by an increase in the average student learning outcomes from the actions of 65, cycle 1 was 72.22 and cycle 2 was 82.78. Likewise, the motivation of students from cycle 1 also increased in cycle 2. From the average cycle 1 of 75.5 the category was sufficient in cycle 2 of 89 good categories. Thus, it can be concluded that online learning using power point media can increase student motivation and learning outcomes
The Application of the Assignment Method to the Integrated Writing Ability of the Circ Method in View of the Use of Learning Media

This research is an action research of using different learning media. The CIRC method is applied by using image media and concept map chart media.The general objective of this research is to prove which students have better writing skills: (1) students who undergo learning using Task-Based Learning Methods, (2) students who undergo learning using pictorial media and concept map chart media, (3 ) students who undergo learning using the CIRC image media method and CIRC concept map media, (4) students who undergo learning using task-based concept map media, (5) students who undergo learning using illustrated media CIRC methods and media methods task-based pictures, and (6) students who take part in writing lessons using concept map media. Data was collected using a written test. Furthermore, data were collected in the form of tables and graphs, which were then analyzed. The data analysis method used in this study. conclusions can be drawn from this study. First, students who study using the CIRC method have better writing skills than students who study using the task-based method. Second, students who learn by using picture media have less developed writing skills than students who learn by using concept map media
Realistic Facial Deep Fakes Detection Through Self-Supervised Features Generated by a Self-Distilled Vision Transformer

Several large-scale datasets and models to detect deep fake content and aid in combatting its harms have emerged. The best models usually combine Vision Transformers with CNN-based architectures. However, the recent emergence of the so-called Foundation Models (FMs), in which deep learning models are fed with massive amounts of unlabeled data (usually by applying self-supervised techniques), has established a whole new perspective for many tasks previously addressed with specific-tailored models. This work investigates how good FMs can be in DeepFake detection, especially in the case of realistic facial production or adulteration. With this realm, we investigate a model using DINO, a foundation model based on Vision Transformers (ViT) that produces universal self-supervised features suitable for image-level visual tasks. Our experiments show that this model can improve deep fake facial detection in many scenarios with different baselines. In particular, the results showed that models trained with self-attention activation maps had higher AUC and F1-score than the baseline ones in all CNN architectures we used.
A microgrid power trading framework based on blockchain and deep reinforcement learning

With the development of renewable energy technologies and the emergence of distributed power generation devices, traditional centralized power trading markets no longer meet people's transactional needs. Peer-to-peer (P2P) electricity trading within microgrids has become the future direction. However, in distributed trading scenarios, both parties in P2P transactions lack a foundation of trust and incentive mechanisms. To address these issues, we have designed an electricity trading framework based on blockchain and deep reinforcement learning. Users utilize deep reinforcement learning for load prediction and power planning to maximize their own interests. We propose a proof-of-work (POW) consensus algorithm based on reputation values to further improve consensus efficiency and block creation time. By introducing the InterPlanetary File System (IPFS), offloaded transactions are uploaded to IPFS for storage, enabling the unloading of unnecessary information and improving the resource utilization efficiency of blocks. Our framework is beneficial for reducing the costs for blockchain users, implementing credit management for P2P e-commerce transactions, and thereby enhancing the stability and efficiency of transactions.
Learning High-Quality Bounding Box for Rotated Object Detection via Rotated Cascade Region Proposal Network

Existing two-stage detectors usually generate oriented proposals based on heuristically defined anchors with different scales, angles, and aspect ratios. This scheme usually suffers from severe memory-consuming and redundant computation. Additionally, misalignment between rotated proposals and horizontally aligned convolutional features exists when using a conventional Region Proposal Network (RPN), which leads to the inconsistency of classification confidence and positioning accuracy. To tackle these problems, we propose a Rotated Cascade Region Proposal Network (RCRPN), which effectively reduces memory usage and improves the quality of proposals through multi-stage refinement. Specifically, instead of using multiple anchors with predefined scales and aspect ratios, a single anchor per location is adopted in the first stage of RCRPN, and coarse proposals are generated in a horizontal convolution manner, this stage effectively takes the advantage of gliding vertex method to adapt the rotated bounding box. In the second stage, by taking the coarse proposals and image feature map as input, adaptive align-convolution is applied to learn the sampled rotated features guided by the coarse proposals, finally generating high-quality proposals for the downstream tasks. Extensive experiments demonstrate that our method can achieve better performance than baseline algorithm Oriented R-CNN on two commonly used datasets including DOTA and HRSC2016 for oriented object detection.
ANA: An Adaptive Non-outlier-detection-based Aggregation Algorithm Against Poisoning Attack for Federated Learning

Federated learning (FL) is an emerging learning paradigm in distributed machine learning (ML) that enables multiple data owners, referred to as clients, to collaboratively train a global model without sharing their local training data. The decentralized architecture of federated learning, which eliminates the need for central collection of client training data, has found widespread applications across various scenarios. However, the traditional federated learning approach, represented by FedAvg, faces unprecedented threats due to the emergence and development of poisoning attacks. In such attacks, malicious clients submit tampered gradients to the central server, leading to system failure even with a single malicious client. Although early federated aggregation algorithms, such as Krum and Median, can tolerate a certain degree of poisoning attacks, their evaluation of gradients is limited and heavily relies on outlier detection theory, making them less effective against recent non-outlier poisoning attacks. Moreover, these early methods often employ fixed threshold to filter gradients based on certain indicators, lacking flexibility. To address these challenges, this paper proposes ANA (Adaptive Non-outlier-detection-based Aggregation), a novel federated learning aggregation algorithm that overcomes the limitations of outlier detection methods. ANA evaluates gradients from multiple perspectives, including module length and direction, without relying on a fixed threshold. The proposed algorithm is tested on two real-world datasets, demonstrating its effectiveness in resisting poisoning attacks and outperforming early methods in defending against non-outlier poisoning attacks.Overall, the ANA algorithm presents a significant advancement in federated learning, providing improved resilience against poisoning attacks and offering a more comprehensive approach to gradient evaluation.
RSAA: Relation-Specific Attention and Global Alignment Based Joint Entity and Relation Extraction

Joint relational triple extraction is a crucial task in knowledge graph construction. However, most of these methods either perform a large number of redundant predictions on relations, resulting in slower inference speed or ignore the semantic information of relations in the task of extracting the subject and object. To overcome these deficiencies, we propose a model for joint extraction of entities and relations, named RSAA. Concretely, RSAA consists of a Relation-Based Attention module, a Relation-Specific Entity Extraction module, and a Subject-Object Alignment module. We first detect the relation in the text and add relational features to the sentence through the attention mechanism. Then in the Relation-Specific Entity Extraction module, extract the subject and object for the detected subset of relations instead of all relations. Finally, align the subject and object to a triple through a Subject-Object Alignment module. Experiments on NYT, NYT*, NYT10, WebNLG, and WebNLG* datasets demonstrate that the proposed model can efficiently extract overlapping triples and obtain strong generalization ability and faster inference speed, which outperforms other baselines.
A Comprehensive Survey on Text Filling Algorithms: A Research Review

Starting from the concept, application scenarios and research significance of text filling, this paper divides text filling algorithms into two categories: traditional methods and deep learning, summarizes the development process of text filling algorithms in detail, focuses on describing the algorithm principles of text filling algorithms in recent years, and summarizes and evaluates text filling algorithms from the aspects of experimental results, advantages and disadvantages. In addition, this paper details the most commonly used text datasets and the most popular evaluation indicators - BLEU and Perplexity. Finally, this paper summarizes the research on text filling algorithm and looks forward to the future development trend. This paper aims to provide researchers with a comprehensive review of text filling algorithms to guide and prospect future development.
PPL-net:Convolutional Neural Network-Based Polymorphic Points-Line verification framework

To address the problems that the line segments extracted by the existing line segment detectors have local ambiguity and insufficient fitting accuracy, we propose PPL-net, a line segment detector based on the Modified Convolutional Neural Network (CNN) and Polymorphic Point-Line (PPL) verification framework. This method uses the point and line extraction algorithm based on CNN to obtain the initial line segment and feature point respectively. Then, the Point-Line verification framework consisting of feature point distribution detection, parallel over-density detection, and intersection over-extraction line segment detection is used to improve the line segment extraction performance. Using the Wireframe line segment data set, the PPL-net is tested and compared with other line segment extraction algorithms. Experimental results show that PPL-net performs better on average precision (AP), average recall (AR), F-measure and processing time (FPS), and has better disambiguation effect in dealing with line-segment dense scenarios.
Multi-Stage Action Quality Assessment Method

In most of the existing mainstream action quality assessment methods, the score regression is performed on a complete action video to obtain the predicted score, which may prevent us from fully exploiting the multi-stage information in action video. In this paper, we attempt to divide a complete action video into clips according to the multiple phases it contains, and predict scores for each segment individually. In order to validate the effectiveness of the method, the FineDiving dataset is further divided into several action categories as the experimental dataset, and the improvement is applied to the mainstream USDL and CoRe methods. The proposed method achieves a significant performance enhancement in the metric of Spearman's correlation, which is commonly used in AQA tasks, thus validating the effectiveness of our proposed method.
Collaborative information and semantic information Fusion over Heterogeneous information Network for Top-N Recommendation System

In recent years, additional information in the side of both users and items are more and more common in the natural world. Traditional recommendation methods mainly utilize the the user-item rating information. These information, if properly used, can benefit to infer the preference of user. But due to the complexity of HIN(heterogeneous information network), HIN based recommendation system is faced with the following challenge: how to properly utilize the attribute of both users and items effectively.To solve such a problem, we propose the approach:coupling collaborative interaction information and semantic information for Top-N Recommendation System(CSRec). For collaborative information,we use graph neural networks to aggregate the neighborhood information. And for semantic information, we use intra-metapath to generate and inter-metapath semantic aggregation between user and item to generate semantic context information. Then we deeply combine two kinds of information and generate recommendation item. Finally, comprensive experiments on three real-world recommendation dataset prove the superiority of our model,in comparison with other baseline models.