Reviewerly/exHarmony huggingface dataset:
DummyMAE: automatic encoder based on edge-to-vertex transformation

Autoencoders, as a type of generative self-supervised learning, have received increasing attention in information processing and data mining in recent years. However, existing autoencoders usually generate graph data conforming to the feature distribution from only one aspect of reconstructing edge or node features, which allows the models to extract only a single level of information, limiting their application in real-world applications. In this paper, we propose DummyMAE, a generative self-supervised learning framework that synchronously generates edge and node features. In general, it losslessly converts vertex graphs into corresponding line graphs by introducing edge-to-vertex transformations. The vertex graph provides the model with information on node features, and the line graph provides the model with the ability to capture information on the graph structure, which complements each other. The task of simultaneously reconstructing edges and features is achieved in this way. The task of graph classification serves as a pivotal component within the realm of graph learning, we have conducted sufficient experiments on four widely used graph classification datasets, and the results show that DummyMAE outperforms the current state-of-the-art baselines for the graph classification task.
Clothing Image retrieval method based on ConvNeXt

This article proposes a clothing image retrieval method based on the ConvNeXt network. This research uses the ConvNeXt network, which is obtained by improving and adjusting the classic ResNet50 network based on some advanced ideas of the transformer network, as a feature extractor to conduct research on a subset (In-Shop Clothes Retrieval) of the DeepFashion dataset. In order to improve model training speed and retrieval performance, this article uses transfer learning, which utilizes the ConvNeXt network model pre-trained on the ImageNet_1k dataset. Considering the limitations of using only a global branch, a local branch is added, and the global branch and local branch are trained together during training. During retrieval, only global features are used, as local features help global features learn some knowledge of the local features, resulting in better results. Experimental results show that this method can extract more expressive clothing image features and effectively improve clothing image retrieval performance.
FPGA architecture for convolutional neural network training

Convolutional neural networks (CNNs) have been gradually applied to aerospace equipment in recent years. However, CNN requires a significant amount of hardware resources, while parameters need to be updated for different tasks in space. We propose an FPGA architecture for CNN training through a multi-module collaborative design strategy. On the basis of the intra-layer parallelism, we analyze the data storage and the operations to deduce forward and backward propagation on embedded devices. Based on the experimental results, the working frequency can reach 250 MHz and the performance is 15.9 GOPS. The resource usage and efficiency of this design are superior to those of other similar hardware acceleration platforms.
VPN encrypted traffic identification model based on improved residual network

Accurate classification of network traffic is the research basis for tasks such as network management, service quality optimization, abnormal traffic detection, etc. Aiming at the problems of incomplete feature extraction of existing methods, a VPN encrypted traffic identification model based on improved residual network is proposed. The model takes the conversation composed of bidirectional flows as the analysis object, first uses the multi-layer convolutional network to extract the spatial structure features of the conversation, then introduces the improved residual unit to increase the width of the backbone network, and uses 1x1 and 3x3 different sizes of convolution kernels on the same level to obtain different scale feature information, and improve the model’s feature representation ability. Finally, the extracted features are expanded into one-dimensional vectors and Softmax funciton is used to classify the encrypted traffic. The Experimental results on public VPN-non VPN dataset show that the proposed model performs better than traditional identification methods, and compared with the well-known methods, the accuracy, precision and recall metrics are improved by 3.32%, 2.65% and 3.79%, respectively.
Small sample prediction research based on optimized convolutional neural networks

The Wordle game was very popular after it was launched, attracting many players in the early stage. Under the rules of the game, the different characteristics of words may affect the distribution of scores in the different mode. The purpose of this report is to establish a model to study on the percentage distribution of attempts to submit reports. The innovation of this paper lies in the combination of convolutional neural network (CNN), bidirectional long and short term memory network (BiLSTM) and double attention mechanism (DA), and proposes a DCNN-BiLSTM network structure suitable for small sample prediction. Finally, a good prediction result is obtained.
Fully connected-based nonnegative matrix factorization neural network

Nonnegative matrix factorization (NMF) is a powerful method for feature extraction, offering explanatory and dimensionality reduction. Alternatively, combining NMF with a neural network requires iterative optimization of an objective function, followed by constructing a specialized neural network based on the derived formula. The interpretability and universality of this approach are limited. To address these issues, this paper introduces a novel model called FCNMFN, which leverages a fully connected neural network to implement NMF. In this model, each layer of the fully connected neural network corresponds to the transpose of the base matrix, the coefficient matrix, and the sample matrix of NMF. This design ensures strong interpretability while achieving nonnegative matrix factorization. To demonstrate the effectiveness of the proposed model, we apply it to emotion recognition using the DEAP dataset. Experimental results confirm its efficacy and showcase its potential in accurately identifying and analyzing emotions.
Generation of Multiple Choice Questions from Indian Educational Text

Multiple Choice Questions(MCQs) are becoming increasingly popular among testing organizations, educational institutes, and MOOC creators due to their ease of evaluation. With a rapid increase in the number of educational institutes in India, standardized tests in India such as the Joint Entrance Examination(JEE) are adopting MCQs as the format of questioning. Human evaluators find the difficulty of questions generated by existing automatic MCQ generators on the Indian context easy. To avoid this an automatic MCQ generator capable of capturing paragraph level dependencies, framing questions around them, and generating semantically close distractors is proposed, that is optimised on Indian high school geography and biology textbooks of the CBSE curriculum. The MCQ generator has two distinct modules, a question and answer generation module and a distractor generation module. The question and answer generation module contains a T-5 transformer trained on the Stanford Question Answering Dataset(SQuAD) dataset and fine-tuned on a custom handmade dataset to train the model to generate questions linking concepts in different sentences in different parts of the source text and perform well in the context of Central Board of Secondary Education(CBSE) textbooks. The distractor generator uses a combination of the outputs from a Part of Speech(POS) tagger, a T5 transformer, a sense2vec model, and a syntax checker to generate distractor options semantically close to the answer option. The model was judged by human evaluators for fluency, relevance, answerability, grammar, and difficulty and outscored the popular encoder-decoder based question generators. The model also promises a better F1 score than the encoder-decoder based question generators.
Dance motion recognition method based on feature fusion

The poor rate of dance recognition is caused by the motions' high degree of intricacy and issues with self-occlusion when people are dancing. We provide a dance motion recognition approach based on feature fusion to address this issue. This article extracts the characteristics of the direction gradient histogram, optical flow histogram, and auditory frequency and applies multi-feature fusion to dance action detection research. This work naturally combines three different types of features for dance action detection using multi-kernel learning while also taking the issue of heterogeneous feature fusion into consideration. The experimental findings demonstrate that the algorithm presented in this research can retain a certain recognition rate for complicated dance motions, and the approach presented in this paper can still guarantee
Four New Families of Quantum Stabilizer Codes from Hermitian Self-Orthogonal MDS codes

We construct codes from rational function fields and provide sufficient conditions for a rational algebraic geometry code to be Hermitian self-orthogonal. A method to embed such codes yields new families of Hermitian self-orthogonal MDS codes with large dimensions. Using the stabilizer formalism, we construct four new families of quantum MDS codes with parameters $[N, N-2 K, K+1]_{q}$. We give conditions on the length N and the values of K of the dimension N-2K. Parameter comparisons over small fields highlight the novelty of the quantum codes that we obtain.
An LSTM-Based Outlier Detection Approach for IoT Sensor Data in Hierarchical Edge Computing

Outlier detection in sensor data has recently gained significant recognition, particularly with the proliferation of wireless sensor networks (WSNs) and the Internet of Things (IoT). Several challenges face outlier detection in WSNs and IoTs, including sensor nodes’ limited energy and processing capabilities and high communication costs. This paper presents a novel deep learning-based outlier detection method for IoT sensor data in hierarchical edge computing. First, we proposed a hierarchical edge computing framework to save energy, provide load balance, and low latency data processing at sensor nodes. Then, we designed an outlier detection algorithm that resides on each edge server. The proposed algorithm consists of two modules: a predictor model and an outlier detector. The predictor module uses Long Short-Term Memory networks (LSTM) to forecast the subsequent data measurements of sensor nodes. The predicted values are then passed to an outlier detector module, which decides whether a data measurement is an outlier. The proposed method is evaluated on twelve different datasets. The simulation results provided by our proposal are promising in terms of a set of metrics.
Multi-objective path planning for mobile robot based on ACO -APF fusion algorithm

Path planning of mobile robot is an important research direction of mobile robot, there are some defects in the path planned by the traditional Ant Colony Optimization (ACO) algorithm, many turning points appear in the planned path, some points in the planned path are very closed to the obstacles, all of these increase the probability that the mobile robot will hit an obstacle. Although the path planned by the Artificial Potential Field (APF) algorithm is relatively smooth, it often has redundant segments, and at the same time, it is not necessarily the optimal path. In order to solve these problems, ACO -APF fusion algorithm is proposed in this paper. The path planned by the ACO -APF fusion algorithm can keep a safe distance from the obstacles and have fewer turning points. To verify the performance of the fusion algorithm, the path smoothing performance and the select performance are tested. The ACO -APF fusion algorithm can also be used for multi-objective point path planning. In the simulation test, the grid map of the hospital scene is created, by inputting the starting point and different target points in the algorithm, a path containing four target points is planned. Simulation results show that ACO -APF fusion algorithm can plan out multi-objective point path according to user requirements.
A classroom student behavior detection model based on coordinate attention and fused residual networks along with its talent evaluation system

With the continuous development of the Internet+ education concept and the rapid advancement of online, offline, and blended teaching technologies, the existing methods of evaluating students based on teachers' subjective opinions are insufficiently objective and lack a solid foundation. To improve this situation, we propose a classroom student behavior detection model based on coordinate attention and fused residual networks, along with its talent evaluation system. The classroom student behavior detection model, based on coordinate attention and fused residual networks, effectively combines the coordinate attention mechanism and residual networks. It highlights the relevant information in student image samples by effectively utilizing channel and spatial information. The talent evaluation system accumulates and analyzes classroom data through the first application terminal, forming a learning quality report that judges the quality of talent training and provides type information of talent training. Additionally, the system receives human resource information data through the second data terminal and performs employment classification based on talent information data, obtaining occupational tendency data. Therefore, this thesis will help enhance the reliability and accuracy of human resource judgment and increase the compatibility between human resources and careers.
Time optimal trajectory planning of robot based on honey badger algorithm

Aiming at a point-to-point trajectory planning problem for industrial robots, we propose a method for time optimal optimization of trajectories in the robot joint space by employing the honey badger algorithm. Interpolation points at each joint are first acquired using robotic kinematics; Then, to enable the robot to smoothly complete the work task, a 4-5-4 piecewise polynomial is employed to interpolate its trajectory; Finally, we use the honey badger algorithm to optimize the interpolation time of the segmented polynomials to achieve the goal of optimal trajectory planning for the robot's time. The simulation through MATLAB software to obtain the change information of angular displacement, angular velocity and angular acceleration of each joint of the robot, under the restriction conditions that meet the kinematic parameters of the robot, the robot's motion time is shortened by 43.17% compared to that before optimization, which verifies the effectiveness of the honey badger algorithm in the optimization of robotic tracks.
Anomaly detection method of power purchase material data based on BIRCH clustering algorithm and time series

At present, the conventional detection methods for abnormal data of power purchase materials mainly use correlation vector machines to extract and reduce the dimensions of data features. Due to less dimensions of data feature extraction, the detection effect is poor. In this regard, a method based on BIRCH clustering algorithm and time series for anomaly detection of power purchase material data is proposed. The data is preprocessed by removing outliers and supplementing missing values, and a time series autoregression model is constructed according to data dimensions to extract the flow characteristics of material data. The determination of abnormal data is realized by using local density threshold. In the experiment, the detection performance of the designed detection method is tested. The final results can prove that the proposed method has a low false detection rate and an ideal detection effect when it is used for abnormal detection of power purchase material data.
EZSN: efficient zero-shot network for blind super-resolution

Most existing super-resolution (SR) methods are usually achieved through fully supervised means with massive training samples, and assume the degradation of low-resolution images corresponding to high-resolution images is fixed. However, the degradation process of real-world images is often more complex. Therefore, these models often perform poorly when dealing with low-resolution (LR) images with unknown degradation. In addition, runtime is also an important factor in deploying image super-resolution models, especially on devices with limited resources. However, the runtime of existing zero-shot blind super-resolution models is not ideal. In this paper, we propose an efficient zero-shot network (EZSN) for blind super-resolution. Specifically, we propose a high-frequency feature extraction block (HFFEB), which speeds up network inference by stacking highly optimized convolution and activation layers and reducing the use of feature fusion. In addition, we also propose an enhanced residual block (ERB) for extracting more features to improve model performance. The experimental results indicate that our proposed EZSN method has a significant advantage in terms of runtime compared to previous ZSSR methods when dealing with benchmark datasets that have unknown degradation.
UAV image stabilization algorithm based on improved Shi-Tomasi

As the UAV is susceptible to its own engine vibration and irregular airflow in the inspection process, the image sequence collected has jitter, which affects the reliability of the inspection results. To solve this problem, this paper proposes an improved Shi-Tomasi algorithm combined with a selective mechanistic motion model for UAV inspection image stabilization algorithm to eliminate the jitter component of images and produce stable images. Firstly, the feature points in the image are extracted by using the improved Shi-Tomasi algorithm, and feature point matching is performed by using optical flow tracking. Then the motion model is determined to describe the inter-frame motion by a selection mechanism based on the matched feature point pairs. The obtained original motion trajectories are turned into smooth trajectories by sliding filtering, and finally stable images are generated by warping and cropping. The experimental data use the UAV inspection image sequences collected by ourselves. The experimental results demonstrate the good performance of the proposed algorithm, which can quickly and effectively eliminate the image dithering.
A novel blind medical image watermarking scheme In NSST domain based on schur decomposition and DWT

Nowadays, medical images face numerous information security challenges. On the one hand, medical images should be associated with patient information while ensuring data security and privacy protection. On the other hand, with the increasing use of technologies such as deep learning in medical images, there is a growing concern about copyright protection for medical image data used for sharing and training. Digital watermarking technology presents a potential solution to address these issues. This paper proposes a medical image watermarking scheme that utilizes Schur decomposition in combination with non-subsampled shearlet transform (NSST), discrete wavelet transform (DWT) and region of interest (ROI) identification for minimizing modifications to the original image. Furthermore, our proposed scheme does not require the original cover image for watermark extraction, reducing the reliance on additional information. The proposed method has been tested under various type of attacks, and the results have shown that it has high robustness and imperceptibility.
MFAD: a graph anomaly detection framework based on multi-frequency reconstruction

Graph anomaly detection in graph data has received significant attention due to its practical significance in various vital applications such as network security, finance, and social networks. The current mainstream approach for attribute graph anomaly detection is based on contrastive learning using graph neural networks, which only consider homogeneous low-frequency signals. However, in attribute networks, normal and anomalous nodes exhibit different frequency patterns. This motivates the proposal of a graph anomaly detection framework based on multi-frequency reconstruction to capture the signal patterns of anomaly. Specifically, our method constructs multiple filters based on target nodes and utilizes two modules, namely, low-frequency reconstruction and contrastive learning, for anomaly detection. The generative low-frequency reconstruction module enables us to capture anomalies in the high-frequency attribute space, while the contrastive learning module leverages richer structural information from multiple subgraphs to capture anomalies in the structural and mixed spaces. We conducted extensive experiments on five publicly available datasets, demonstrating that our method significantly outperforms state-of-the-art approaches.
Machine learning based wordle difficulty analysis

Wordle is a popular game, and it is necessary to analyze the massive amount of data to help improve the game. For the prediction problem of the number of reports, the three-times exponential smoothing prediction model with better prediction results was selected. This study collected the relationship between word attributes and the percentage of the number of reports in the difficult mode. After data preprocessing, the RMAE test and 𝑅<sup>2</sup> test were performed on the prediction effects of the two models; For the problem of predicting the percentage of attempts (1, 2, 3, 4, 5, 6, X), a BP neural network model optimized using a genetic algorithm was built and the results showed that the confidence level of the model was 0.742, which tested by prediction of the percentage distribution of words corresponding to the number of attempts per day; For the word difficulty prediction grading problem, this study built a hierarchical clustering model and solved the optimal number of clusters K=4 for word classification according to the elbow rule, and obtained the classification results using the improved BP neural network model to train the data. The results showed that the accuracy of the model was 0.831. This study can improve Wordle, enhance the user's sense of game experience, and attract more people to participate in the game.
Intelligent detection and identification method of radar moving target

According to the characteristics of Doppler frequency shift of radar moving target in the fast time dimension, this paper proposes an intelligent detection and identification method of radar moving target based on YOLO(you only look once)v5s. First, the dataset is constructed based on the received target echo signal, and then the echo data dimension is placed and converted into images. Finally, the data is input into YOLOv5s for training, and the detection and classification of radar moving targets are realized by learning the characteristics of moving target Doppler frequency shift. The simulation experiment shows that the detection method can realize the effective detection and identification of radar motion and stationary targets.
No-reference image quality assessment based on deep learning

Images form the basis of human vision and are an important source of information for both human perception and machine pattern recognition. Since the development of the image quality evaluation field, a large number of image quality evaluation algorithms have emerged. In recent years, deep learning has become a hot area and has also been applied to the image domain. This paper focuses on reference-free image quality evaluation and reviews the no-reference image quality assessment based on deep learning. Firstly, the classification of IQA, technical indexes for IQA algorithm evaluation, and several public IQA databases available online are introduced. Then, several deep learning models applied to NR-IQA are discussed, compared, and evaluated in detail. Finally, an outlook on future research is provided.