Reviewerly/exHarmony huggingface dataset:
Ship target radiated noise modulation spectrum processing: integration with deep learning methods

The modulation spectrum processing of ship target radiated noise is an important means of detecting, tracking, and recognizing targets in naval operations. Improving the processing performance of modulation spectrum is crucial for enhancing naval combat capabilities. This paper combines traditional modulation spectrum processing methods with deep learning methods and proposes a modulation spectrum feature enhancement method based on deep learning. The experimental results show that, the method can effectively improve the clarity and extraction quality of the line spectrum in the modulation spectrum time-frequency map.
QR code detection and recognition in industrial production environment based on SSD and image processing

QR code is widely used in production management. For example, we scan the QR code printed on the fertilizer bag during the production of fertilizer to record production information. We propose a method to intelligently obtain the information carried by QR code through machine vision, combining deep learning and image processing technology. We obtain the original image through the industrial camera, locate the QR code area in the image based on the SSD target detection model, use the image processing method to rectify the QR code area, and finally decode it through the Zxing library. Through the real production environment test, the recognition rate of the method is 98.6%, and the average detection and recognition time of a frame image is 94 ms.
MDET: a GAN-based target detection algorithm

Target detection is a key computer vision technology that is utilized extensively in crucial sectors including aerospace and aviation as well as fine-grained security detection. One of the most frequent problems in computer vision is the implementation of adversarial training in vision algorithms. The target detection technique we present in this research is an adversarial machine learning-based approach that uses GANs. The images are inputted and sent to the GAN network and the detection network, respectively, and then the images produced by the GAN learning are blended with the real images and sent to the target detection network. This algorithm uses GAN as an adversarial trainer for the baseline target detection algorithm. Finally, YOLOv5s is selected for experimental validation, and the data of VOC hybrid 2007 and 2012 are selected for experiment, and the data in the Based on YOLOv5s, its map0.5 and map0.95 have a good improvement in detection effect compared to the original YOLOv5s, and finally based on the comparison experiments and ablation experiments, it shows that the GAN-based target detection algorithm out of this paper is effective.
A decoupled graph neural network with dropping repeated nodes

Graph Neural Networks (GNNs) and their variants have significant success in graph representation learning tasks. However, existing GNNs still need to overcome the challenges of over-smoothing and over-fitting. To address these issues, we propose a new decoupled graph neural network with DropRE sparsification named DropRE-GCN. Specifically, we first design a graph sparsification method called Drop Repetition (DropRE), which avoids over-smoothing in GNNs by dropping repetitive edges and making node connections sparser in graph. Different from the traditional random drop method in that it deterministically drops the nodes aggregated in the previous layer in each round of training to remove the randomness in the conventional drop method. In addition, based on the decoupled graph neural network framework, we pre-compute the process of multi-hop neighborhood aggregation to alleviate the overall computation. Finally, DropRE-GCN balances each node's local and global information by a node-level adaptive method. Extensive experimentation on the dataset indicates that DropRE-GCN performs better than other competing baseline models, alleviating the over-smoothing and over-fitting problems.
Statistical processing of uncertainty in artificial intelligence

Dealing with uncertainty is an important issue in artificial intelligence, as real-world data and scenarios often come with uncertainty. The existence of uncertainty requires artificial intelligence systems to have the ability to handle incomplete information, randomness and unknown. Statistics provides multiple methods to deal with uncertainty. The methods can assist in reasoning and prediction in artificial intelligence systems. Artificial intelligence has the robustness and adaptability. It is crucial to consider how to better handle uncertainty. Because it can enhance the robustness and adaptability of the artificial intelligence system.
Research on the classification of online question text based on deep learning

In view of the current problems of untimely information updates, incomplete interactive responses and impractical classification systems often arising from the question-and-answer sections of government websites, this paper provides a research direction for the organic integration of online question-and-answer and text classification by taking natural language processing technology as the research perspective and combining the construction and application of deep learning algorithm models. This paper improves the algorithms in text representation and deep learning models from the perspective of text representation and feature extraction. In response to the shortcomings of Bert model which weakens the text location information, LSTM model can effectively learn the dependencies on the observation sequence and extract the global features of the preceding and following texts. Therefore, this paper combines the Bert and LSTM models for feature extraction and introduces the Attention mechanism to filter and fuse the extracted features in depth to build the Bert-LSTM model. The classification results of this model and the Bert model on the same dataset are compared, and it is proved that the new hybrid depth model can improve the classification accuracy of the questioned text.
Development and implementation of computer emotion recognition software based on deep learning

In order to reduce the development and promotion cost of developers and accelerate the development speed of developers, emotion recognition technology is widely used in Internet and other scenarios, and an emotion recognition open platform based on deep learning is proposed. This article provides a detailed introduction to the design and implementation of an open emotion recognition platform. Overall, the platform has the following characteristics: it uses the Struts2+Spring+JPA framework, adopts interface-oriented programming, and has good scalability; The Convolutional neural network in the deep learning technology is used to realize expression recognition, which has good recognition accuracy; OpenID+OAuth authentication and authorization scheme is adopted to make the platform have better security. The results show that the ck+ dataset, which is different from the training dataset, is used to test, and the contempt emotion in ck+ is removed. The last three faces in each directory are regarded as the same emotion, corresponding to the emotion file, and the first face in each directory is regarded as the natural emotion. There are seven emotional types in total: anger, jealousy, fear, happiness, sadness, surprise and nature. After that, the face of each picture is extracted, and then the platform interface is used to identify it. Compared with the real tag corresponding to the picture, the accuracy rate is 72.2%. The open platform of emotion recognition based on deep learning enables external programs to use emotion recognition services through open API, providing more convenient and faster development methods for developers, thus promoting the wide application and promotion of emotion recognition technology. The next step is to further strengthen the robustness of the platform, and constantly optimize the emotion recognition algorithm to improve the recognition accuracy.
Research on badminton path tracking algorithm based on machine vision

Badminton is a sport for young and old alike. To capture the dynamic path of badminton quickly and accurately, a badminton robot needs to be designed for real-time dynamic tracking of badminton moving at high speed. In this paper, we analyze the effects of aerodynamic forces and gravity on badminton flight in the air. The main factors affecting the flight trajectory of badminton are analyzed using experiments. The badminton flight trajectory captured by a high-speed camera is used to establish a steady-state flight model as well as a spin model for badminton. In the process of badminton trajectory prediction, because there are model errors as well as measurement errors, particle filtering is an inference model based on Monte Carlo sampling, which does not require a priori information of state transfer and is more conducive to badminton state estimation. Therefore, in this paper, particle filtering is chosen to track the path of badminton. After experimental verification, the algorithm can track the motion path of badminton very well.
Named entity recognition in medical field based on BERT model

Chinese medical named entity recognition is a crucial task in information extraction within the medical field. Its primary objective is to extract entities of medical significance, such as diseases, clinical manifestations, and drugs, from medical texts. Due to the specialized nature of the medical domain, the grammatical structure of medical texts is often more complex than that of other texts, frequently containing a plethora of abbreviations and terms. To enhance the effectiveness of Chinese medical entity recognition, this paper introduced a pre-trained BERT-BiLSTM-CRF model, the CHIP2020 medical text dataset was chosen for the experiment, including 9 types of entities, such as diseases, clinical manifestations, drugs, medical equipment, medical procedures, body parts, medical examination items, microorganisms, and departments. BERT effectively utilizes context information to comprehend the semantics and relevance of words in sentences, effectively resolving the challenge of word polysemy and exhibiting strong language representation capabilities. Additionally, BiLSTM is employed to extract features, efficiently capturing the sequence information and context between words. Finally, CRF is utilized to convert the probability sequence output from BiLSTM into the optimal annotation sequence. Experimental results demonstrate that, compared with three other models, the proposed method yields improved accuracy in named entity recognition on the CHIP2020 dataset, with an impressive F1 value of 64.93%. This approach effectively extracts medical entities.
Laplace transform for Mittag-Leffler function in cryptography

The security of information has been a significant part of human civilization since the ancient era. In the information society, the protection of information becomes increasingly crucial for humankind, and new technologies are progressing in an unending stream. Cryptography is one of the greatest methods intended for protecting the transmission of messages and the security of data. For instance, digital commerce, electronic telecommunications such as mobile phone networks, sending personal emails, commercial transactions, paying television, the security of bank cards, cryptocurrency, etc, affect many areas of our everyday lives. Cryptography enables secrecy and protection for secret information by concealing it. It is made through a mathematical method. In this study, we built a new mathematical technique for cryptography in which we applied the Laplace transform (L-Transform) for the Mittag-Leffler function (or E-function) to plain text for encryption and the correspondent inverse L-Transform related to the E-function for decryption. Eventually, we use the Monobit test and correlation test for measuring the security level of encryption and the Python programming language for easily attaining cipher text, the original message, and computations of statistical tests.
A surveillance cameras-based system for indoor localization

As surveillance cameras have become ubiquitous in daily life, these widely deployed cameras deliver an opportunity to localize and navigate people, especially in indoor environment where the ability of Global Position System (GPS) is limited. Thus, this paper aims to leverage monocular surveillance cameras to locate people in the physical real world. We propose a Surveillance Cameras-based System (SCS) for indoor localization. This system detects people in video images and use visual positioning techniques to extract features to locate and track people in the real world with 6-DoF pose. We design a real-time people detection algorithm, especially for a surveillance camera that is an edge device. Besides, we design a visual-based people localization algorithm. We implement SCS on an Ubuntu server and conduct extensive evaluation. The overall performance of SCS shows that the average absolute trajectory error and relative trajectory error of SCS is 0.67m and 0.29m, respectively. The average relative rotation error of SCS is less than 15º.
Public opinion evolution in social networks based on latent Dirichlet allocation and sentiment analysis

In recent years, social network public opinion analysis technology, represented by text mining and sentiment analysis, has become an important tool to quickly understand public opinion. In this paper, we focus on the public opinion evolution in Sina Weibo and take the illustration event of textbooks in China’s primary schools in 2022 as an example. We crawl 13, 833 microblog hot comments on the illustration event of the textbook. Firstly, we pre-process the data, divide the evolution process into the outbreak period, the response period, and the final period, and give the word cloud and econometric analysis to display the data. Then, in order to study the public opinion evolution, we adopt the Latent Dirichlet Allocation (i.e., LDA) model and the lexicon-based sentiment analysis method. In particular, for each period, we capture the topic distribution of comments through the LDA model and learn the sentiment polarity through the lexicon-based sentiment analysis method. Finally, we utilize a knowledge graph to visualize results. The results show that during the outbreak period and response period, public sentiment is negative, and hot discussion topics are problems existing in the textbook, and also show their impact on education, and national ideology. In the final period, the discussion focuses on supporting the Ministry of Education to intervene in the strict inspection of elementary textbooks, and the public sentiment is positive.
ViT-based classification of oracle bones for periodization of the Shang Dynasty

Oracle bone inscriptions are the oldest systematically written characters in China, and their periodization and chronology are essential tasks in historical research. However, current deep-learning models face significant challenges when they are used to recognize oracle bone images directly. These models can only achieve an accuracy of no more than 45%. As the research on the periodization of oracle bones mainly relies on the characters inscribed on the bones, a domain knowledge-based oracle bone classification model is proposed that improves the recognition accuracy of Vision Transformer (ViT) by 5.28%. The proposed approach incorporates results obtained from oracle bone character detection to provide a more effective recognition object. Moreover, a novel model with a style network and category embedding is proposed in this paper that focuses on characters, resulting in a notable improvement in recognition accuracy of 16.01%. Our study reveals the importance of domain knowledge in the classification of historical documents, and it presents new insights into using artificial intelligence for assisting in historical document organization tasks.
Chaotic multi-group optimization algorithm based on heterogeneous computing

Traditional particle swarm optimization has attracted attention in various fields because of its relatively simple form and flexible parameter setting, but it also has the disadvantages of slow convergence speed and easy to fall into local optimization in the face of large-scale multivariate data. To solve this kind of problem, a chaotic multi-group optimization algorithm (CM-PSO) based on Graphics Processing Unit (GPU) is proposed. In the algorithm initialization stage, chaotic mapping is introduced to enhance population diversity, and then the population is divided into multiple small subgroups according to the idea of island model, and the Feng's topology is adopted within each subgroup to improve the search efficiency and reduce the computational complexity. Finally, the CUDA stream (streams) technology is used to realize grid-level parallelism, further improve the degree of algorithm parallelism, and improve the algorithm performance while ensuring the accuracy of the algorithm.
A hybrid approach to maze generation algorithms

Maze generation algorithms play a significant role in video games with mazes. Most of the existing methods focus on improving the performance of nonuniform maze creation algorithms. However, generating different mazes with equal probability through uniform algorithms is crucial to increase the user’s gaming experience. In this paper, we develop a hybrid of two uniform maze generation algorithms, Aldous-Broder and Wilson, by alternating between the two at some critical running phase to reduce the execution time. We tested several turning points and analyzed the results. The experimental data shows that the performance is better compared to either algorithm by itself when the percentage of visited cells reaches a specific value.
Teleamplification on the Borealis boson-sampling device

A recent theoretical proposal for teleamplification requires preparation of Fock states, programmable interferometers, and photon-number resolving detectors to herald the teleamplification of an input state. These enable teleportation and heralded noiseless linear amplification of a photonic state up to an arbitrarily large energy cutoff. We report on adapting this proposal for the Borealis boson-sampling device and demonstrating teleamplification of squeezed-vacuum states with variable amplification factors. The results match the theoretical predictions and exhibit features of amplification in the teleported mode, with fidelities from 50 to 93%. This demonstration motivates the continued development of photonic quantum computing hardware for noiseless linear amplification's applications across quantum communication, sensing, and error correction.
Perceptions of Self-Learners of English Toward Mobile Language Learning

This mixed-method research was conducted to examine the perceptions of self-learners of English as a foreign language (EFL) toward mobile learning (m-learning) apps. It integrated several constructs from the technology acceptance model (TAM) and constructivist learning theory (CLT) to provide a detailed picture of the learners' perceptions. The study also examined variations in learner perceptions based on their gender and age. A total of 292 participants took part in this research, and data were triangulated from quantitative and qualitative instruments. The findings revealed positive perceptions among the learners toward English m-learning apps as well as a strong and positive relationship between the learners' TAM-based and CLT-based perceptions. Although gender did not significantly affect the learners' perceptions, age did exert some influence on both behavioral intention and actual use of the apps. The study concludes with several pedagogical implications and recommendations for future research.
A new method for discovering domain specific words in Chinese texts based on machine learning

Although there are many differences in writing and vocabulary between the late Qing Dynasty to the early Republic of China (QDRC) of Chinese and modern Chinese, there is not much difference in grammar, semantics, etc. It is a transitional stage between ancient Chinese and modern Chinese. We seem more willing to refer to this stage of Chinese as the pre modern Chinese period. This paper refers to the method of discovering new words and phrases in the natural language processing (NLP) field, aiming to explore a new method of discovering domain specific words (DSW) in Chinese texts from the QDRC period. Through the comprehensive application of corpus linguistics and computational linguistics, we have successfully constructed a multi-level analytical framework to reveal the unique lexical characteristics of Chinese texts in this period. First, this paper establishes a small Chinese corpus at the QDRC period, and uses bigram and trigram of N-gram to preprocess the texts, including word segmentation, parts of speech tagging, etc. The word frequency, pointwise mutual information, left and right entropy of bigram and trigram fragments were calculated, and 909 key words with special status in social, political, cultural and other fields in the QDRC were finally identified through manual proofreading. This article validates the effectiveness of the proposed method and discovers some distinctive Chinese words in the QDRC that have not been previously studied in the experiments.
Fusion of AI and Ethical Finance

Artificial intelligence (AI) and ethical finance constitute a dynamic and developing field with major consequences for both the financial sector and society at large. AI is in financial decision-making processes, including fraud detection, portfolio optimization, and investment analysis. AI's growing use in evaluating ESG standards and locating opportunities for ethical investment is changing the standards of ethical finance. The fusion of ethical finance and AI represents an exciting frontier with the potential to drive more responsible and sustainable financial practices. The current study aims to explore the practical AI application for ethical financing implementations. The study will systematically review publicly available research manuscripts and reports and challenges faced by companies while exercising. This study will emphasis on the need for proactive ethical involvement in the development and application of AI in the financial industry.
Control Actions

This chapter analyses the functional relations between a control system and the process in a SCPS by applying the dyadic and triadic transformation types, the roles types and the theory of action systems developed in Chaps. 11 , 13 , and 15 . The overall aim is to demonstrate applications of conceptual schemas for functional modelling of control systems and their interaction with the process under control.
Managing rogue quantum amplitudes: A possible control in quantum walks

We investigate the emergence of rogue quantum amplitudes in discrete-time quantum walks (DTQWs) influenced by phase disorder. Our study reveals the statistics of occupation probability amplitudes in space and time, uncovering optimal disorder regimes that favor rogue wave events. Through numerical simulations, we demonstrate that the probability of rogue waves increases with quantum coins close to the Pauli-$Z$ choice, regardless of the disorder degree. Conversely, for coins near the Pauli-$X$ choice, rogue events are scarce, except under weak disorder. A monotonic threshold is observed between rare and high-probability rogue wave regimes, depending on the quantum coin. We provide a comprehensive analysis of the coin-disorder interplay in regard to rogue wave events. Our findings shed light on the possible control of extreme quantum amplitudes through quantum coins in disordered DTQWs.