Reviewerly/exHarmony huggingface dataset:
A Content-Based Image Retrieval Scheme for Encrypted Domain Using Feature Fusion Deep Supervised Hash

To improve the semantic representation of image features and to achieve secure and efficient image retrieval in cloud computing, we proposed a content-based image retrieval scheme for encrypted domains using supervised deep hash function (FFDSH) feature fusion. First, CNNs and VGG16 are used to extract deep features from the image, respectively, and the feature fusion technique is used to merge the two features. The final step is the construction of the deep hash sequence of the fusion features, which is used as the FFDSH sequence. During retrieval, retrieval of the bilayer similarity match is performed according to the obtained FFDSH sequence. The experimental results show that the proposed scheme can achieve efficient image recovery and has apparent benefits in recovery performance compared to existing image recovery schemes.
Research on Chinese Public Opinion on Releasing COVID-19 Prevention and Control Based on Improved LSTM

In December 2022, China gradually liberalized the control of COVID-19, which has caused a wide discussion in society. In this paper, Natural Language Processing (NLP) algorithm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> has been used to analyze online comment data on relevant topics on the SINA Weibo platform from November 2022 to March 2023. These data are related to the public's liberal views on COVID-19. This research is divided into three steps: (1) Use the Octopus Data Collector software to crawl the public opinion comment data in the SINA Weibo platform. Then, the crawled data has been cleaned, segmented, and manually annotated. At the same time, a marked data set has been obtained from the Paddle platform <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> . (2) A total of 45,000 processed training data has been input into the Attention-Bi-LSTM model for training, and the optimal model has been selected based on the three evaluation indicators of Accuracy, F1-score, and Recall for subsequent sentimental prediction. (3) Finally, the model has been utilized to predict the remaining 24145 comment data. The analysis results showed that after the announcement of the release of COVID-19 Epidemic prevention and Control (COVID-19 EPC), the number of daily relevant comments on the SINA Weibo platform has declined, indicating that the life of the Chinese public has begun to shift toward a better situation. It is proven that it’s feasible to use an NLP algorithm to predict the sentiment of the online topic at a certain time, and it provides a potential method for the mining of the Chinese public's online public opinion.
An Improved Q-Learning Algorithm for Path Planning

To address the issues of slow convergence speed and poor path planning performance in dynamic obstacle environments. This paper proposes an improved Q-Learning path planning algorithm for mobile robots. The algorithm introduces an exploration factor based on the mutation of probability to balance exploration and utilization and accelerate learning efficiency. Additionally, deep learning factors are designed in the update function to ensure the algorithm explores with probability. The integration of the Blending Inheritance algorithm prevents local path optimization and explores the optimal iteration step number by stages, reducing the repetition rate of dynamic map exploration.We conducted an experiment using the grid method to construct a map. Comparative experimental results showed significant optimization in terms of iteration times and path compared to traditional algorithms. The improved algorithm can better achieve path planning under dynamic maps, further confirming the practicality and effectiveness of the proposed method.
Blur Retail Product Images for Fine-grained Image Classification Combining Text Area Features

There are small differences between different retail product images belong to the same category, such as product models, sizes, and tastes. Retail product images do not have target local regions which can be used for detection and classification. Besides, the differences between subcategories are more subtle. These make fine-grained image classification for retail product images more challenging. It is hard to locate discriminative local areas in the product images, which are widely used in training for specific area detection such as head and trunk in fine-grained classification of birds images. However, the existing fine-grained image classification for product images only use image features to target local regions, but ignore text features in the images. Product images contain rich text information. Text feature is a very important component in product images. Since text recognition methods do not achieve satisfactory results on retail product images of low quality, we consider introducing text area features into fuzzy product image classification. We propose a fine-grained classification method for blur retail product images combining the features of text regions. First, we use the density-based clustering method to find category key areas in the text regions detected in the product images. Secondly, combine the key text areas features in the image when doing fine-grained classification. Experimental results show that our method achieves a higher classification accuracy in blur retail product image classifications, getting 4% improvement in the original classification results.
Research on Scrabble Prediction Based on Neural Network Model

Recently, Wordle is a daily Scrabble game provided by the New York Times, which has been loved by many players. Based on this game, this paper focuses on predicting the Scrabble game based on neural network models, using methods such as One-Hot Encoding, correlation test, and so on to solve the game, and the specific task is divided into four major problems. For problem one, firstly, the given data are preprocessed, some outliers are eliminated, while the original data are replaced by the mean value of the number of people, and the filtered data are normalized to the range [0, 1]. Then, a Nested Two-Layer LSTM model is constructed to predict the number of reported results, and the One-Hot Encoding property of words is analyzed to create a mapping of these discrete feature data using the OHE Finally, the correlation between the attributes and the percentage of difficulties was tested. The results showed that the number of results reported on March 1 was approximately 20520 and that the One-Hot Encoding attribute of words did not affect the percentage of scores played in the difficult mode. For problem two, a neural network model was constructed to implement multiple inputs and outputs and predict the distribution of reported results based on the feature data generated using unique heat coding in problem one. The model built uses a hidden layer with 10 neurons. Meanwhile, the model was used to predict the word EERIE, and the results showed that the distribution of the results for the word EERIE was [0.5,7.1,23.9,30.5,23.6,11.8,2.0], and the average absolute percentage error of the model on the test set reached 9%, which proved that the model had a good fitting ability. For problem three, the difficulty was first set to three levels of difficulty, medium and easy, and the classification criteria were judged based on the percentage of three or fewer passes, a convolutional neural network model was constructed using deep learning techniques. The results showed that the model constructed using convolutional neural networks predicted the best results and yielded a moderate difficulty level for the word EERIE.
Research on Information Intelligent Service Terminal Based on Big Data Intelligent ChatGPT Technology

ChatGPT is a new artificial intelligence technology driven natural language processing tool launched by Open AI, an American artificial intelligence research laboratory. It uses Transformer neural network architecture. A model used by the system to process sequential data. It trains the model by linking large corpora containing real-world conversations that allow ChatGPT to interact in the context of the chat. This paper analyzes ChatGPT's Transformer neural network architecture. A new parameter saving residual module is proposed, and a simplified network architecture is designed. This structure design can effectively avoid the obvious reduction of parameters by over-fitting merging. Compared with existing deep learning methods, the proposed method has certain advantages in time efficiency and accuracy. The accuracy can be improved by more than 11%, and the average processing time of a single sample is less than 1 Ms.
A Reversible Data Hiding Method Based on Super-resolution Reconstruction Network

With the emergence of deep learning, new possibilities have arisen for reversible data hiding. In this paper, we propose a reversible data hiding method that leverages an image super-resolution reconstruction network. The method employs a specific neural network as the "key" for both data embedding and extraction. Firstly, the original image is input into the neural network model to obtain a higher-resolution reconstructed image. Then, a portion of the pixels is used to store the original image while the rest of the pixels are used to embed the secret data, resulting in a carrier image. Finally, the original image can be extracted from the carrier image by feeding it back into the neural network model for reconstruction. The hidden data can then be extracted by comparing the difference between the reconstructed image and the cover image. The proposed method takes into consideration both the embedding capacity and image quality, providing a good embedding capacity while ensuring a certain level of image quality.
Research on Correcting the Accuracy of English Translation Based on Improved Particle Swarm Optimization Algorithm

With the development of cognitive technology, replacing manual English translation(ET) with machines has gradually come into people's sight, and the accuracy of translation is also constantly improving. When computers and other machines are used to translate English content, it often happens that a word is translated incorrectly or the context is not smooth due to the improper recognition of the word content and context, which is one of the shortcomings of traditional machine ET. In order to improve the intelligence level of machine ET, this paper proposes a method of correcting the accuracy of machine ET based on particle swarm optimization (PSO), constructs an information extraction model of machine ET, selects the optimal solution of fuzzy semantics, and draws a valid conclusion. The results show that the accuracy and correction ability of the model can significantly eliminate errors in ET, and can effectively improve the accuracy and rationality of ET.
Research on the Autonomous Parking Strategy Based on the Ultrasonic Echo Simulator

In order to solve the time and economic cost issues of automatic parking algorithm testing for intelligent vehicles in real scenarios, this paper develops a hardware in the loop virtual simulation system for automatic parking based on ultrasonic echo simulator. Based on the vehicle kinematics model, this paper establishes the optimal control problem of parking path planning, converts the optimal control problem into a linear programming problem, and uses the adaptive pseudo spectral method to obtain the optimal trajectory. Combining virtual environment and hardware in the loop system for verification, the experimental results show that accurate obstacle information in the virtual scene can be obtained based on ultrasonic echo signals. In the parallel parking scene of multiple target vehicles, intelligent vehicles can perform trajectory planning based on obstacle information and safely complete parking tasks.
Meta Learning Enabled Adversarial Defense

Machine learning (ML) models have been found to be vulnerable against adversarial attacks recently. Such examples are carefully crafted instances with a small magnitude of perturbation added to mislead the target ML models. This has raised great concerns and different approaches have been put forward to defend against these malicious adversarial attacks, among which adversarial training so far has been shown to be very effective empirically. However, one major limitation of adversarial training is that it requires exploring diverse factors such as different attacks and datasets to achieve good performance, which significantly limits its practical usage. We propose a novel and effective meta-learning based adversarial training framework named Meta-Adv in this work. We leverage the knowledge obtained from past tasks that are trained on a subset of attacks and datasets and show that such knowledge is sufficient to improve the learning robustness against different unforeseen adversarial attacks. We conducted extensive experiments on different datasets including CIFAR-10 and ImageNet and show that the proposed method outperforms the state-of-the-art adversarial training methods significantly, including the winners in the CAAD adversarial defense competition. Our method provides a simple and effective baseline to train robust ML models against adversarial attacks.
Semantic Contribution-based Binary Code Scoring

Binary code scoring analyzes a specified binary code region and provides an assessment of its weakness. Code weakness is not the same as software vulnerability; it represents the probability of future software vulnerabilities in the target code. Current vulnerability prediction research based on neural network models mostly focuses on directly locating vulnerable code positions. However, related studies have shown limitations in the neural network models' ability to learn binary code features, making it difficult to effectively distinguish subtle differences in code. Direct vulnerability detection using neural network models fails to identify vulnerabilities caused by processes such as code porting, functional adjustments, and patches. Additionally, due to the model's inherent limitations, it results in significant missed detections and false positives. To address these issues, this paper proposes a code scoring method based on semantic contribution. By utilizing the semantic function call graph (SemFCG) to represent code features and function call relationships, a matrix representation is achieved. The semantic attention model SemFCGAT is designed to learn semantic relationships between functions within SemFCG. Finally, code scoring is implemented based on the semantic contribution of neighboring functions to the central function. Experimental results demonstrate that our method can effectively score specified code regions, providing support for various software analysis tasks.
Detection and Defense of XSS Attack Script Based on Machine Learning

With the rapid development of the Internet, many things of people can be done online, and the Internet has been integrated into people's daily life. Web applications widely used in all walks of life are convenient for people to use, but also bring some security problems. Cross-Site Scripting(XSS) vulnerability detection method based on dynamic analysis is an effective XSS vulnerability detection method, which simulates real attack behavior to conduct penetration test on Web applications. However, there are still some problems such as low detection coverage, low detection efficiency and low detection accuracy. Based on this, this paper expounds the research status and background of XSS attack detection, puts forward the necessity of using machine learning algorithm for XSS attack detection, applies SVM algorithm to XSS detection, analyzes and extracts the features of XSS samples, selects radial basis kernel function for nonlinear SVM training, and verifies the generated detection model. It is found that the SVM model in this paper is also superior to the comparison algorithm in accuracy and false alarm rate.
Research on Network Digital Information Security Defense System under Computer Big Data Technology

This paper introduces a Web information security protection and Web data mining based on web content data collection, classification, type determination and other functions. The software mainly includes: web text acquisition, text classification, classification decision. Hardware: MFRC500 RF chip, TIMSP430 microprocessor, hardware interface, software interface, etc. In software design, the combination of cryptographic technology and display technology completes the design of network data security password. The scheme uses network-based authentication and secure login, file running based on dual monitoring and document encryption based on Internet. It can effectively prevent the enterprise's internal information leakage and technology theft.
Design of Electronic Guide Dog Based on User Experience

Guide dogs are excellent life assistants for the visually impaired. This article is based on the user experience perspective and is designed from the fundamental needs of the visually impaired. An electronic guide dog that can identify environmental information, automatically avoid obstacles, communicate through voice, make emergency calls, and has positioning and communication functions is necessary. This design uses an STM32 MCU, an HCSR04 ultrasonic module, an RFID radio frequency identification module, a voice module, a GPS positioning module, a GSM communication module, and a motor drive module. It realizes compliance with voice commands for the visually impaired, automatic obstacle avoidance, guidance for walking, positioning, and communication functions. This greatly facilitates the daily lives of the visually impaired and has significant practical value.
An Improved K-Nearest Neighbor Classifier Based on Z-Distance

KNN is a simple and effective algorithm for classification and regression analysis. Its non-parametric nature and ability to handle noisy data make it a popular choice for machine learning tasks. In KNN, the majority voting strategy is used to assign the class or regression value to a given data point. However, it is important to choose an appropriate value for K, as the value of K is too large to be underfitted and too small to be overfitted. This paper proposes a new classification strategy based on the Z-distance function calls improved Z-KNN (IZ-KNN). The Z-distance function takes the distances between the center points of different classes into consideration. This new feature makes the model less sensitive to the choice of K value than barely using the majority voting strategy. We conducted comparative experiments on four data sets. The result shows that the performance of IZ-KNN is better than that of Z-KNN.
Mechanical Mouse with Artificial Intelligence Vision

Pests not only harm agricultural production but also pose a threat to the lives of ordinary people. To maximize the efficiency of eliminating pests in the fields, after reading extensively about bio-mimicking, we set out to create a mechanical mouse. Learning from one of the traits of mice, flexibility, the design of the model is made up of three big servos and four mini servos. The mobile phone is used as a remote Bluetooth control, which successfully realizes the mouse’s various gestures. A camera is installed at the front of the mouse, and through multi-joint movements, the camera is capable of filming in various degrees and directions. Combining with the deep learning methods for insect recognition that we studied, including YOLOv5 object detection, semi-supervised pseudo-label learning, and fine-tuning, pests identification could eventually be realized. Through our experiments, the mouse has an average speed of 0.117m/s. The camera at the front could film in a wide range of upward 84 degrees, downward 47 degrees, leftward 85 degrees and rightward 85 degrees.When the mouse is working in the fields while filming in wide view, once a pest is detected, the person behind the mouse could efficiently and precisely eliminate the pest rather than spraying pesticides on a large scale. In this case, people could effectively reduce the issues such as environmental damage and human health impacts.
Efficient Disentangled Attention Network for Semantic Segmentation

Attention mechanisms for dense prediction tasks require a relatively long time, and many methods study efficient attention mechanisms. However, these methods often overlook the recovery of important details in segmentation tasks. Recently, Disentangled Non-Local Neural Networks (DNL) has proven that attention computation can be decoupled to learn different visual clues better. Therefore, we propose Efficient Disentangled Attention Network (EDAN). Based on the efficient dual-resolution transformer model, the attention computation is split into two terms and decoupled, which achieves both high efficiency and accuracy in segmenting edges and small objects. Extensive experiments on two datasets, Cityscapes and AED20K, demonstrate the effectiveness of our proposed EDAN.
Towards English-centric Zero-shot Neural Machine Translation: The Analysis and Solution

The proposal of multilingual Neural Machine Translation (NMT) models enables zero-shot translation without parallel data of the desired language pairs. However, in the real world, the data used to train multi- lingual NMT is usually English-centric (with en as the source or target). This sparsity of language pairs could affect the training of multilingual NMT for zero-shot translation. In this paper, we first give a quantitative analysis of the zero-shot NMT, and find the bias caused by the English-centric data leads to a dramatic drop of zero-shot translation performance. The reason is the model sometimes generates words in the wrong languages, often consecutively. To reduce the word language errors, we propose three new architectures to incorporate language-specific information into the model. For the error continuity problem, we propose a task-selection based curriculum learning method to efficiently enrich the valid word patterns of diversified language pairs during training. Experiments show that our methods effectively alleviate the English-centric problem, and significantly improve the performance of zero-shot NMT.
Attention-based U-Net Denoising Network

Due of its exceptional denoising performance, the denoising technique based on deep convolutional neural networks has attracted a lot of interest. This paper proposes a new Attention-based U-Net to increase the efficacy and practicability of denoising methods. We combine channel attention and spatial attention to better focus on important information in the input image, and apply residual learning and batch normalization to improve network performance. In addition, the quantitative indicators and visual quality evaluations of multiple algorithms on synthetic noise datasets confirm the model's superiority.
Design of a Student Information Management System Based on Homomorphic Encryption and Blockchain

The student information management system in universities faces challenges such as information silos and difficulties in data sharing. In this paper, we propose a secure and highly trusted student information management system based on the decentralized and tamper-proof nature of blockchain. The system incorporates ring signature technology to achieve fine-grained access control for student information, allowing controlled openness. Homomorphic encryption is employed to protect sensitive information, such as students' personal privacy, while enabling operations on encrypted data. Experimental results demonstrate that the system is robust and secure, meeting the design requirements.