Reviewerly/exHarmony huggingface dataset:
Galaxy recognition based on improved efficientNetV2S

In order to identify effective metrics that can accurately duplicate the probability distributions resulting from human classifications, this paper analyzes an improved approach for galaxy morphologies classification. At the present stage, this field still faces the problem of insufficient quality and quantity of image data, low accuracy of computer recognition and weak generalization ability of the model. From the previous research, Convolution Neural Network (CNN) can be a valid technique to complete this task but usually spends a large time and space complexity. For the purpose of increasing effectiveness, this paper improves EfficientNetV2S to construct recognition models and characterize their performance in galaxy recognition. The procedure includes data preparation and augmentation, model structure creation, attention mechanism addition, fine-tuning, and result visualization. A Fused mobile inverted bottleneck convolution (Fuse-MBConv) structure was used to accelerate the model's convergence speed. Besides, the Convolutional block attention module (CBAM) was used to improve performance and feature representation capabilities. The model in this study can minimize complexity with the number of parameters and utilize less memory while maintaining excellent accuracy. This research is conducted on the Galaxy10 DECals dataset. Experimental results show that it achieves an 87% high precision with 20.6m parameters which is more efficient than models currently used in previous research.
Application and analysis of text similarity in text clustering in the Chinese context

With the development of the Internet, information sharing is higher, and the amount of information that each user is exposed to is increasing. How to find the information peoples want from so much information is a very important question. The vast majority of these resources are related to textual information. The most intuitive manifestation of these problems is that when people usually use search engines, enter a piece of text, and search out the relevant website, if the algorithm is not good, the search results will be very unsatisfactory. Therefore, this paper studies the application of text similarity in text clustering in the Chinese context. First, the basic concept of text similarity is introduced. In addition, text clustering is explained/explained from three aspects: definition, application, and general processing process. Secondly, combined with the existing data, some mainstream clustering algorithms are comprehensively summarized. Then, combined with the above content, the similarity calculation method in text clustering is analyzed. Finally, the above methods are compared and analyzed according to the experimental results in the Python environment.
Hough transform and line rotation on analog gauge reading: A case study

While digital meters can automatically communicate with a database to store reading values, many industries still utilize analog meters which are not economically or physically viable to replace. Instead, computer vision modules may be attached to cameras to automatically read values and record them. This paper tests three implementations of gauge reading, two simple Hough line and circle transform methods and one lightweight naive line rotation method. A dataset was created for testing purposes, consisting of 46 images from various sources and variations including pointer rotations, binarizations, and text or logo removal. Results showed the line rotation technique substantially more robust and accurate than both Hough line implementations. Two major obstructions were detected: pointer tails and dense text/logos, and their removal via photoshop tools improved the average accuracy to roughly 1 degree from ground truth. This is accurate enough to replace human readers in most imprecise situations and is lightweight enough to function under nearly all circumstances. Future research seeks to validate these findings further by testing line rotation on more varied gauges.
Review on the face recognition based on deep learning

Face recognition has emerged as a new trend in the era of intelligence due to the rapid growth of artificial intelligence and other cutting-edge technologies. This paper investigates the research and application of face recognition technology based on deep learning through a comprehensive literature review and analysis. The discussion encompasses the classification and processes of facial recognition technology, the incorporation of deep learning into face recognition, and the application of face recognition across a variety of domains. Particular emphasis is placed on the use of deep belief networks (DBN) and convolutional neural networks (CNN) in face recognition. Face recognition technology, facilitated by deep learning techniques, has become pervasive in our daily lives, vastly enhancing our quality of life and productivity.
Parameter optimization in convolutional neural network for improving performance of facial expression recognition

The recognition of similar facial expressions presents a notable challenge, necessitating a focus on the parameters within the fundamental Convolutional Neural Network (CNN) architecture, which serves as a cornerstone in the field of image classification. This research endeavor aims to enhance the model's capacity for facial expression recognition by employing a controlled variable method to examine two specific parameters in a self-designed small CNN: the number of filters and convolutional layers. More specifically, while the filters were fixed at 3, the layers varied from 3 to 6 to 9. Similarly, as the number of the filters rose to 6, the number of the layers also incremented from 3 to 6 to 9. Furthermore, while the number of the filters reached 12, the number of the layers went from 3 to 6 to 9 too. Finally, with the filters increasing to 24, the layers rose from 3 to 6 to 9 as well. Experimental results indicate that both increasing the number of filters and convolutional layers can increment the performance of model in facial expression recognition. Furthermore, increasing the number of filters can exert a more prominent influence on improving the accuracy of facial expression recognition.
Facial expression recognition based on Feature Pyramid Network

Facial expression recognition with significant implications across fields such as psychology, computer science, and artificial intelligence. This paper proposes a combination of a Feature Pyramid Network (FPN) and a Residual Network (ResNet) to construct a recognition model. The main objective of the proposed model is to refine the multi-level feature representation of facial expressions. This approach aims to provide a more holistic understanding of the diverse and complex nature of facial expressions, recognizing the intricate interplay between macro and micro-expressions. Experimental results underscore the model's considerable superiority over traditional methods, particularly in terms of accuracy and adaptability to objects of varying sizes and complexities. This comprehensive approach to facial expression recognition showcases the potential of integrating different neural network architectures, furthering our understanding of the subtleties of facial expressions. The research, therefore, presents a significant contribution to the field of facial expression recognition, demonstrating the efficacy of integrating multi-scale feature extraction techniques to improve model performance. It sets the stage for future research directions in this domain, paving the way for more sophisticated emotion recognition systems that can be deployed in real-world applications.
MLOffense: Multilingual offensive language detection and target identification on social media using graph attention transformer

With the increasing use of social media in our daily lives, it is crucial to maintain safe and inclusive platforms for users of diverse backgrounds. Offensive content can inflict emotional distress, perpetuate discrimination towards targeted individuals and groups, and foster a toxic online environment. While natural language processing (NLP) has been employed for automatic offensive language detection, most studies focus on English only, leaving languages other than English understudied due to limited training data. This project fills this gap by developing a novel multilingual model for offensive language detection in 100 languages, leveraging existing English resources. The model employs graph attention mechanisms in transformers, improving its capacity to extend from English to other languages. Moreover, this work breaks new ground as the first study ever to identify the specific individuals or groups targeted by offensive posts. Statistical analysis using F1 scores shows high accuracy in offensive language classification and target recognition across multiple languages. This innovative model is expected to enable multilingual offensive language detection and prevention in social media settings. It represents a significant step forward in the field of offensive language detection, paving the way for a safer and more inclusive social media experience for users worldwide.
Detection of malicious websites across multiple classes using n-gram features and VGG based on URL analysis

Due to the ubiquity of the internet, cyber-attacks implemented through websites have become a severe issue with high frequency and appreciable overall financial damage. Detecting malicious URLs has become one of the most common solutions to tackle this threat, which is widely applied in the market and researched. Inspired by relevant work on URL classification using n-gram techniques and convolutional neural networks in other research areas, a method for detecting malicious websites using n-gram statistical features of URLs and a VGG-style neural network has been developed, which aims to provide classification for multiple website classes with arbitrary URL input lengths. Experimental results show that the method proposed in this paper provides an average accuracy of 96.60% on the 5-class ISCX-URL2016 dataset and 96.33% on the 4-class Malicious URLs dataset, which is 1.5 times larger. A further comparison reveals that the accuracies are competitive with similar methods for binary classifications that also use either n-gram features or a VGG-based network.
Target tracking and detection based on YOLOv5 algorithm

The YOLOv5 algorithm has gained popularity in recent years as an effective solution for real-time object detection in images and videos. This paper explores its potential for solving the problem of target tracking detection by proposing a modified YOLOv5 architecture that integrates object detection and tracking capabilities.The proposed YOLOv5-based tracking system includes three major components: object detection, object tracking, and object association. The object detection component uses a YOLOv5 model to detect and localize the target object in each frame of the video. The object tracking component then tracks the target object across frames using a Kalman filter and a Hungarian algorithm for data association. Finally, the object association component uses a motion model to handle occlusions and re-identifies the target object when it reappears in the field of view.The performance of the proposed YOLOv5-based tracking system is evaluated on several benchmark datasets, and its results are compared to state-of-the-art tracking algorithms. The experimental results show that the system achieves competitive tracking accuracy and real-time processing speed. Additionally, the effectiveness of the proposed motion model for handling occlusions and re-identification of the target object is demonstrated. In conclusion, the YOLOv5 algorithm has promising potential for target tracking detection in real-world scenarios, and it could have various applications in surveillance, robotics, and autonomous driving.
A research on PDF protection mechanisms

Portable Document Format, also named PDF, can integrate images, text, tables, or other elements into a single file, making it an easy-to-store and transfer document format. Due to the biggest feature of PDF being its portability, the modification of such files is very difficult and can ensure the security of the text to a certain extent. Therefore, it has been welcomed by a large number of researchers, office workers, and students. But as the demand for PDF files continues to rise, its security vulnerabilities are gradually being exposed. Consequently, a plethora of security measures for PDFs has come into view. This article primarily summarizes a portion of the overarching principles governing PDF security. Subsequently, this paper will focus on introducing the principles of steganography and cryptography in PDF security protocols while also selecting meaningful and representative research papers to summarize the advantages and disadvantages of the proposed methods.
Review of Adversarial Attacks in Object Detection

Object detection, a fundamental element of computer vision and artificial intelligence, has experienced considerable advancements through the incorporation of deep learning-based techniques. Yet, despite the impressive strides in both accuracy and efficiency, object detection algorithms harbor inherent vulnerabilities to adversarial attacks. These well-crafted disruptions pose significant risks, especially considering the broad application of object detection across an array of safety-critical sectors such as autonomous transportation, medical imaging, and security systems. This comprehensive paper offers a thorough review of adversarial attacks against object detection systems, dissecting the methods employed, and scrutinizing the implications of their exploits. It dives deep into the mechanics and consequences of both white-box and black-box attacks on prevalent object detection networks, including but not limited to Faster R-CNN, YOLO, and SSD. Furthermore, this paper underscores an assortment of defense strategies developed to mitigate the effects of adversarial attacks. These include adversarial training, gradient masking, input transformations, and randomized defenses. While these strategies hold promise, it is acknowledged that they have their limitations and do not offer a universal solution against all adversarial attacks. As such, this paper underscores the urgent necessity for robust defense mechanisms and stimulates further discourse and investigation into developing truly resilient object detection systems, capable of withstanding the growing threat of adversarial attacks.
Balance of rights in the protection of users' data interests in the era of big data

The protection of user data interests is of utmost significance in this day and age, when everyone's private life details and possessions are being digitised and stored in the cloud. Data now rules the world. This paper reviews the relevant literature in order to study the balance of rights in the protection of user data interests in the era of big data. It then suggests various countermeasures for the problem that it identifies. According to the findings of this study, the key factors contributing to the imbalance in the protection of user data rights are the progression of science and technology, the increase in the value of user data, and a lack of awareness regarding the protection of user personal data rights.
Analysis and application of key technologies in digital signal processing

With the uncreasing reform of times and the continuous advancement of my science and technology, in recent years, my overall national strength is strengthening gradually. I have entered an information age, one digital age, the use of digital signal processing is becoming more and more frequent and important. Therefore, this paper mainly analyzes the application of DSP, the digital signal processing technology show to make the exposition. Digitalization is a major trend in today's society and the main theme of the development of the times, while digital signal processor is a component born with the times. The so-called digital signal processing is to use a universal digital signal chip to extract useful information from the signal by digital calculation. In recent years, in the wake of the constantly improving of science and technology and the continuous reform and innovation of technology in China, DSP is more and more close to people's production and life, and plays an important role in practical applications. Therefore, it is necessary to discuss and analyze its development.
The fundamental theory of quantum computing, applications in practical fields, and future challenges

Building on the fundamental principles of quantum computing, this paper sequentially presents the development of quantum computing and its associated application domains. We also analyze and compare the differences between quantum and classical computing, highlighting the benefits of quantum computing for specific problems. To comprehend the advantages of quantum computing in particular fields, extensive research has been conducted in three areas: cryptography, chemistry, and artificial intelligence. We also discuss the wider applications of this emerging technology for future investigation, considering the current state of quantum computing development and anticipated trends.
Overcoming data silos in healthcare: The potential of blockchain and federated learning on the Hedera platform

The lack of access to extensive and varied datasets remains one of the major issues facing the field of machine learning, despite recent advancements. This is especially true in the healthcare sector, where it can be challenging to gather and use patient data for research because it is frequently compartmentalized across many healthcare providers. By enabling secure and privacy-preserving access to distributed data, blockchain technology, and federated learning have the potential to overcome these difficulties. In this article, we'll look at how federated learning and blockchain are used in the healthcare industry and talk about their benefits and drawbacks. We will also examine the Hedera platform, which makes use of blockchain technology and a new algorithm called Gossip Degree to provide a revolutionary method of federated learning. We will also go over the potential effects of federated learning on the healthcare sector and what it means for future research.
How AI evolved with game and implementation of modern AI in game

For a long time, game was a relatively unrecognized area by academic community, which lacks detailed and sufficient discussion. But with the growth of game industry, game AI has become a heated topic in recent years. As an important and evolving application of AI, there is a need to better discuss the application and future improvement of game AI technologies. This paper introduced history and breakthrough of AI made in game area. And made discussion centered on current implementation of some popular approaches for game AI, followed by the possible future of these technologies. Some new implementations like procedural content generation were then covered to further discuss future implementations of AI in game area. All in all, hot spots and development prospects of this research topic were prospected to enlighten future development of game AI.
Research on panoramic mosaic of multiple images

With the continuous improvement of the manufacturing process of mobile phone cameras, the demand for panoramic photos by the industry and the public is not limited to ordinary cylindrical panoramic images, so a series of accurate panoramic image stitching technologies have been derived. However, these techniques often fail to take into account the stitching effect and speed, and there is still a technical gap in current multi-image panoramic stitching techniques. This article mainly compares the accuracy and efficiency of various feature extraction algorithms to select the most suitable algorithm for obtaining feature point pairs and estimating the homography matrix. Finally, a dual band hybrid algorithm is used to distort and fuse multiple images into panoramic photos. The results show that the panoramic images generated with multiple images using the algorithm provided in this paper are very good, even though there is a little bit of light and dark interlacing and ghosting.
Research on digital currency based on encryption technology

Digital currencies have become an increasingly popular topic of discussion in recent years. Digital currencies are virtual forms of currency that operate outside the traditional banking system. They are based on cryptographic technologies and are often decentralized, meaning they are not controlled by a central authority. The most well-known digital currency is Bitcoin, but there are many other types of digital currencies in existence. Digital currencies can be used to purchase goods and services online or transferred between users directly without intermediaries like banks. They have gained popularity due to their potential for increased security, transparency, and efficiency in financial transactions. In today's digital currency, a variety of digital currencies emerge in an endless stream, and crypto technology is also constantly developing to improve the security of digital currency payments. In section 2, this paper briefly introduces several common digital currencies and encryption algorithms, and in section 3, this paper introduces these typical digital currencies in detail through the analysis of representative literature. Bitcoin is mainly encrypted based on blockchain technology, and its encryption principle is mainly divided into three parts: public key encryption, hash function, and proof of work. Ethereum is a distributed blockchain platform with encryption principles similar to Bitcoin, including public key encryption and hashing algorithms. Ripple is a distributed cryptocurrency. Its encryption principle mainly adopts the public-private key encryption system. In terms of encryption technology, blockchain technology, the Hash algorithm and symmetric and asymmetric encryption are also popular encryption algorithms in digital currencies.
The investigation of transferability related to the ImageNet-based transfer learning model in galaxy classification

The phenomenon of transfer learning, specifically the transferability of ImageNet, in the context of galaxy datasets has been relatively under-researched. This study seeks to address this gap by employing the Galaxy10 DECals dataset, which is a 10-classification dataset. Three classic models, namely MobileNet, VGG, and ResNet, were developed and customized to the current dataset by modifying the number of neurons in the last layer. The experimental phase is divided into three parts, including the comparison between the use of ImageNet and non-ImageNet, model performance comparison, and confusion matrix analysis. The results demonstrate that the utilization of ImageNet produces better outcomes, with the MobileNet model exhibiting the highest performance. Further analysis revealed that the inclusion of ImageNet weights can enhance the classification accuracy of some data. Although the present study was successful in achieving its objectives, future research should focus on exploring and elucidating the underlying mechanisms driving these findings.