bluuebunny/crossref_metadata_2025 huggingface dataset:
CESAR CUI IN THE MIRROR OF THE RUSSIAN MEDIA OF THE LATE 19TH – EARLY 20TH CENTURIES (CONCERNING THE ISSUE OF CREATIVE REPUTATION)
Authors: Калимова М.В. , Купец Л.А.
Published: August 2024

В основе статьи лежит обзор петербургских печатных изданий за период с 1868 по 1918 год, так или иначе связанных с именем Цезаря Антоновича Кюи. В этот перечень входят как аналитические, так и новостные заметки, а также анонсы, рецензии, выдержки из развлекательных рубрик и даже сплетни, анекдоты и рекламные объявления. Анализ имеющегося материала позволяет частично реконструировать картину того, что было известно современникам о Кюи, и каким было отношение коллег, критиков и публики к композитору при его жизни.

The article is based on the overview of the Petersburg print media for the period from 1868 to 1918 related to the name of Cesar Cui. The list of sources includes analytical and news reports, announcements, reviews, excerpts from the entertainment sections and even gossip, jokes and advertisements. The analysis of this data partially allows to reconstruct at least particularly the picture of what did the contemporaries knew about Cui and how did the colleagues, the critics and the public treated him during his life.
SOPRANO CHARACTERS IN THE OPERA “LOVE FOR THREE ORANGES” BY S. PROKOFIEV: EXPRESSIVE AND PERFORMING MEANS
Authors: Нгуен Б.И.
Published: August 2024

Статья посвящена интерпретации вокальных партий Фата Морганы и Нинетты в опере С. С. Прокофьева «Любовь к трём апельсинам». Рассматриваются подробности, касающиеся истории создания произведения, особенностей оперного творчества композитора, его поисков в области драматургии на фоне состояния современного театра, влияния альтернативного музыкального театра и кинематографа. В опусе проявляются черты комедийности и пародии. Фигуры персонажей осмыслены композитором в контексте исторических портретов. Созданные им «интонационные» маски (О. Л. Девятова) обладают самостоятельной стилистикой. Персонажи сопрано относятся к волшебной образной сфере и, одновременно, к разным мирам – добра и зла. Установлено, что композитор прибегает к противоположным средствам музыкальной выразительности, что порождает контрастные исполнительские средства в интерпретации.

The article is devoted to the interpretation of the vocal parts of Fata Morgana and Ninetta in S. S. Prokofiev's opera “Love for Three Oranges”. The details concerning the history of the creation of the work, the peculiarities of the composer's opera work, his searches in the field of drama against the background of the state of modern theater, the influence of alternative musical theater and cinema are considered. The opus shows features of comedy and parody. The characters are meaningful to the author in the context of their historical portraits; musical “intonation” masks created by Prokofiev (O. L. Devyatova's term) have an independent style. The Soprano characters belong, on the one hand, to the same magical sphere, and, on the other hand, to different worlds of good and evil. It was found that the composer uses opposite means of musical expression, which generates contrasting performing means in interpretation.
FROM THE HISTORY OF WESTERN EUROPEAN CLASSIFICATIONS OF MUSICAL INSTRUMENTS OF XVI–XIX
Authors: Устюгова А.В.
Published: August 2024

В настоящей статье впервые в российской музыкальной науке основные концепции классификаций музыкальных инструментов, принадлежащие западноевропейским авторам разных эпох, рассматриваются в историческом аспекте. Материалом исследования послужили оригинальные трактаты теоретиков и органологов XVI–XIX веков. Античные традиции сохраняли своё значение на протяжении всей истории западноевропейских систематик музыкального инструментария вплоть до второй половины XIX века. Автор статьи показывает процесс перехода классификаций от двух- до четырехкатегорийных, к постепенному утверждению научного подхода к систематизации, в основе которого принцип единства классификационного критерия. Исследование избранных концепций позволяет не только выявить этапы становления мысли западноевропейских органологов о музыкальных инструментах, но и проследить истоки современной классификации, бытующей повсеместно в исполнительской практике и в музыкальном образовании.

In this article, for the first time in Russian musical science, the basic concepts of classifications of musical instruments belonging to Western European authors of different eras are considered from a historical perspective. The research material was the original treatises of theorists and organologists of the XVI–XIX centuries. Ancient traditions retained their importance throughout the history of Western European taxonomies of musical instruments until the second half of the XIX century. The author of the article shows the process of transition of classifications from two to four categorical ones, to the gradual approval of a scientific approach to systematization, based on the principle of unity of the classification criterion. The study of the selected concepts makes it possible not only to identify the stages of formation of the thought of Western European organologists on musical instruments, but also to trace the origins of the modern classification that exists everywhere in performing practice and in music education.
TRADITIONAL AEROPHONES OF THE UDMURTS (BASED ON THE MATERIALS OF THE XVIII – XX CENTURIES)
Authors: Пчеловодова И.В.
Published: August 2024

В статье освещается бытование удмуртских традиционных музыкальных инструментов на основе этнографических источников XVIII – первой половины XXвеков. Именно они представляют наибольшую ценность с точки зрения информации об использовании музыкальных инструментов в обрядовой ситуации прошлых веков, описания как самого инструмента, так и исполняемой на нём музыки. Исследование ограничивается группой аэрофонов как наиболее архаичного пласта инструментальной культуры удмуртов. Как показал обзор источников, в зависимости от статуса автора и его задач, сведения либо ограничиваются упоминанием инструмента в рамках обряда, либо содержат полноценное описание инструмента, способов игры на нём, а также его роли в жизни этноса.

The article examines the existence of Udmurt traditional musical instruments based on ethnographic sources of the XVIII – first half of the XX centuries. They are the most valuable in terms of information about the use of musical instruments in the rituals of the past centuries, descriptions of both the instrument itself and the music played on it. The study is limited to the group of aerophones as the most archaic layer of the Udmurt instrumental culture. As the review of sources shows, depending on the status of the author and his tasks, the information is either limited to mentioning the instrument within the framework of any ceremony, or contains a full description of the instrument, ways of playing it, as well as its role in the life of an ethnic group.
THE NATIONAL MUSICAL INSTRUMENT ERHU IN THE PRACTICE OF CHINESE PERFORMERS OF THE 20TH CENTURY
Authors: Ли М.
Published: August 2024

В статье рассмотрены происхождение и эволюция древнего китайского инструмента эрху, определены особенности его бытования и роль в современном музыкально-исполнительском искусстве Китая. Недостаточная освещённость этих вопросов в русском музыковедении, разрозненность сведений об эрху обусловили актуальность исследования. Определены выразительные возможности инструмента, в том числе тембровые. Проанализирован характер исторических изменений в содержании музыки для эрху, конструкции инструмента, в области приёмов игры и подходов современных исполнителей к репертуару. В XX в. наблюдается значительная эволюция технических и стилевых параметров игры, расширение репертуара, ориентированных, в том числе, и на западную специфику струнного смычкового исполнительства. Итогом изменений явился выход эрху из разряда бытового, прикладного инструмента в ранг концертного. Сделан вывод о вкладе современных китайских исполнителей в сохранение традиционного наследия, об успешной интеграции эрху в современную музыкальную культуру КНР.

The article examines the origin and evolution of the ancient Chinese instrument erhu, and determines its role in the modern musical art. In the study, an analysis of the performing approaches of modern performers was carried out, and the expressive capabilities of the national instrument were determined. There has been a significant evolution in the technical characteristics, performance styles, and repertoire of erhu over the last century. Contemporary Chinese performers have played a very important role in preserving the cultural heritage, developing the performing capabilities of the instrument and enriching its repertoire. All this contributes to the successful integration of the instrument into modern musical Chinese culture.
Katedra Metod Matematycznych Fizyki (KMMF) Wydziału Fizyki UW: tematyka, ludzie, historia
Authors:   Katedra Metod Matematycznych Fizyki,Wydział Fizyki UW, Jacek Wojtkiewicz
Published: August 2024

The aim of this article is continuation of memoirs devoted to the emergence and development of the Department for Mathematical Methods in Physics [1].The main subject of the present article is presentation of directions of research performed in the Department. I will also present some historic aspects of the Department, complementing the story told in [1]. I will also put few remarks concerning connections and interrelations between mathematics and physics.
A Study of Self-help Intervention System for Social Anxiety in Vitiligo Based on Cognitive Behavioral Therapy
Authors: Yin Tong
Published: August 2024

Vitiligo is a common depigmented skin disease. Changes in appearance and unstable treatment outcomes have a negative impact on the physical and mental health of patients. Not only can it cause psychological problems such as social anxiety and depression, but it can also affect the effectiveness of disease treatment. Aim of the study: In order to help vitiligo patients reduce social anxiety and improve the applicability and practicality of psychotherapy, this article proposes a self-help intervention system based on cognitive-behavioral therapy (CBT) to help vitiligo patients establish correct cognitive and social behavior patterns. Method: Firstly, the psychological characteristics and existing treatment methods of vitiligo patients were analyzed. Secondly, the feasibility of CBT in human-computer interaction applications was explored. Negative cognition, social avoidance, and constructing psychological models for vitiligo patients have been comprehensively designed. Finally, summarize the information framework and core functions of the self-help intervention system. Results: An app as a carrier establishes a psychological intervention system that combines the patient's own characteristics, including three main modules: cognitive training, social simulation, and psychological evaluation. Conclusion: The self-help intervention form of Internet CBT has innovated the method of psychotherapy for vitiligo, providing effective and cost-effective psychological treatment options.
An Optimized 4-bits Absolute Value Detector
Authors: Tang Yuyao
Published: August 2024

The binary absolute value detector is crucial in today's computer domain, especially in computer storage and analysis systems. It ensures the integrity and accuracy of data. Therefore, this paper proposes an optimized design of a 4-bit absolute value detector aimed at finding the circuit with the lowest energy consumption. Firstly, this paper introduces a design different from the traditional absolute value calculator, resulting in a reduced total number of stages in the circuit. Secondly, this paper calculates the delay of the main path can be using the logic effort formula and determine the specific gate sizing value for each logic component. Finally, this paper adjusts sizing and power supply ( ) ratio to achieve the lowest energy consumption at 1.5 times the minimum delay. The detector exhibits fewer critical path gates, resulting in lower average delay compared to traditional designs and features an even number of gates which means it has lower delay fluctuations across different inputs.
Innovative Applications of BP Neural Networks in Financial Fraud Recognition
Authors: Geng Zibin , Liu Junchen , Wang Jiuzhou
Published: August 2024

In this study, through the use of machine learning models and BP neural network, a large amount of financial data is deeply mined and analysed. The research results show that the constructed model improves the financial fraud recognition accuracy to 0.94, which can achieve the discrimination of financial fraudulent account behaviours at a high accuracy rate, provide strong support for financial institutions, and effectively prevent financial fraud risks. This study aims to solve the problem of high variability and concealment of financial fraud, different from the direction of departure of the more significant results of fraud identification for financial transactions, but focuses on the portrayal and analysis of the characteristics of the financial fraud account, so as to achieve the efficient identification of the source of financial fraud decision-making.
Research of Spinal Cord Stimulation and its Applications
Authors: Ren He
Published: August 2024

Spinal cord stimulation (SCS) is a neurostimulation technique that has emerged as a promising therapeutic option for the management of chronic pain and other neurological disorders. An SCS consists of a implantable pulse generator (IPG) connected to a lead with a cathode (negative electrode) and an anode (positive electrode). The cathode and anode create an electrical field within the biological tissue that can depolarize the target nerves. In order to achieve the best results, the stimulation parameters in spinal cord stimulation (SCS) are carefully adjusted. The standard variables used in SCS include frequency, pulse width, and amplitude. In traditional SCS therapies, the primary goal has been to alleviate pain by replacing it with a tingling or paresthesia sensation. This approach involves mapping the stimulation to the specific region of pain to effectively mask the sensation of discomfort. In recent years, there have been significant advancements in the field of stimulation frequencies, particularly in the use of high-frequency SCS (at 10,000 Hz) and burst SCS. High-frequency 10 SCS (HF10) is a groundbreaking advancement in the field of spinal cord stimulation (SCS) technologies. This innovative approach involves delivering a unique waveform at 10,000 Hz, which operates below the threshold for sensory perception, thereby providing effective pain relief without causing any paresthesia or abnormal sensations. Another exciting development is burst SCS, which involves delivering bursts of closely spaced electrical pulses instead of continuous stimulation.
Stock Market Prediction Based on BiLSTM
Authors: Wang Siyuan
Published: August 2024

In recent years, the application of deep learning techniques in financial forecasting has garnered increasing attention due to their potential to capture complex patterns in market data. This study employed a Bidirectional Long Short-Term Memory (BiLSTM) neural network to predict the stock price trends of Apple Inc. By analyzing data sourced from Yahoo Finance, a predictive model capable of capturing stock price trends and patterns was developed. The study demonstrated satisfactory performance on the test dataset, indicating the model's effectiveness in forecasting stock prices. The findings underscore the significance of utilizing deep learning techniques for stock price prediction, with implications for financial decision-making and risk management. Utilizing a Bidirectional Long Short-Term Memory (BiLSTM) neural network, this study successfully predicted Apple Inc.'s stock price trends with favorable accuracy, capturing complex temporal dependencies. Further research avenues include enhancing model robustness across market conditions and integrating sentiment analysis for improved predictive capabilities. Overall, this work contributes to advancing stock price prediction methods, facilitating informed financial decision-making.
Comparing ANN and Other Machine Learning Algorithms in the Study of Feature Classification Based on Multi-spectral Remote Sensing
Authors: Zhang Shangming
Published: August 2024

Due to the complexity and diversity of features, accurately recognizing their classification accuracy is significant for remote sensing data processing. Machine learning algorithms have high accuracy and can be applied to a variety of classification problems, providing excellent solutions to the problem of feature classification. In order to achieve fast and accurate classification of features and screen out the best model, this paper takes forests and airplanes as an example and compares and analyzes the accuracy effects of Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and AdaBoost (Adaptive Boosting) on feature classification based on multi-spectral remote sensing images collected by aerial. The results show that among the four models, SVM has the highest average accuracy, the best stability, and the fastest solution time, and can be used as the optimal model. ANN and AdaBoost are the next best, and RF is the worst. This paper can provide reference and guidance for feature classification model selection.
Research Progress of Sentiment Analysis Based on Deep Learning
Authors: Li Jiameng
Published: August 2024

Sentiment analysis technology is widely used in film and television media, social media, entertainment industry, medical and other fields, and has great commercial use value. In the early stages of research, sentiment analysis methods based on traditional machine learning are not effective, which greatly limits its application in the commercial field. In recent years, with the development of artificial intelligence, deep learning-based sentiment analysis have made great advances. However, the large variation in performance results and characteristics for different algorithms heightened the difficulty in practical application. According to the technical progress of sentiment analysis, this paper summarizes from two aspects: data set and algorithm, introducing the commonly used data sets, evaluation indicators and sentiment analysis methods based on deep learning. In addition, this paper also provided a comparison among different algorithms widely used for sentiment analysis. Finally, this paper analyzes the existing technical adjustment, and discusses the future research direction.
Exploring the application of cloud computing technology in computer big data analysis
Authors: Zhao Xiangjun , Qiao Zirui , Li Xiaoyang , Zhu Lingfei
Published: August 2024

In the future, cloud computing technology will continue to evolve in the direction of more openness, intelligence and integration; big data analytics will develop in the direction of generation prediction, personalized recommendation and autonomous driving. The integration of cloud computing technology and big data analytics will breed more opportunities and industrial changes. The two are closely related and the development trend of mutual promotion is unstoppable, worthy of in-depth attention and follow-up by enterprises and related institutions.
Wireless Spinal Cord Stimulator with Bipolar Multi-Channel Electrode Array
Authors: Dong Jiawen
Published: August 2024

Spinal cord stimulators (SCS) are a method used to treat chronic pain and are widely used in the field of modern neuroregulation. Based on the existing literature, this paper compares and discusses the principle and structure of a new wireless spinal cord stimulator with a small range of precise stimulation and the choice of working plan. In this spinal cord stimulator, a bipolar structure combined with multi-channel electrodes is considered, and a passive resonator is used to improve the efficiency of wireless energy transmission. The literature search and research show that the combination of bipolar structure and multi-channel electrode array can produce more concentrated stimulation areas, reduce the stimulation of non-target areas, and thus reduce side effects. In the selection of electrode materials and external equipment, this paper mainly considers the biocompatibility, considering the use of carbon nanotube (CNT) composite materials for the internal electrode materials, at the same time, this paper also considers the impact of voltage overshoot on the electronic equipment, and considers the work plan and material selection to reduce the impact.
The Application of Artificial Intelligence in The Field Of Gaming
Authors: Fu Yuhao
Published: August 2024

In recent years, the landscape of the electronic gaming industry has been profoundly reshaped by advancements in computer hardware and software capabilities. Notably, the proliferation of network and mobile gaming platforms has catalyzed unprecedented growth within the sector. This surge in popularity underscores the increasing demand for immersive and engaging gaming experiences across diverse demographics. This paper endeavors to delve into the intricacies of AI’s role in gaming, providing an in-depth exploration of its core processes and application methodologies. Specifically, it aims to elucidate how AI techniques are leveraged to create intelligent characters and generate dynamic content, thereby enriching the gaming experience. By examining real-world examples of AI integration in gaming and forecasting future trends, this paper seeks to offer valuable insights into the evolving intersection of technology and entertainment.As technologies such as deep learning and neural networks continue to evolve, the application of AI in games will also become more intelligent and adaptive, able to adjust the gaming experience in real time based on player behavior and preferences.
Analysis of Delay Limitation and Circuit Power Balance Optimisation for CMOS Based Circuits
Authors: Li Ruotong
Published: August 2024

As integrated circuits and technology have advanced, people's requirements for integrated circuits are getting higher and higher, and integrated circuits with high performance, low latency and low power consumption characteristics are needed to satisfy all kinds of human needs. However, meeting these needs requires a balanced optimization of circuit delay and energy consumption. The gate size and voltage need to be varied simultaneously, and the most suitable voltage and gate are found by combining the gate size and voltage ratio using a linear programming solver. This method can find the optimal gate size and voltage for minimum power circuits with delay requirements, and it also provides new possibilities for comprehensive optimization of Complementary Metal Oxide Semiconductor (CMOS) integrated circuits from both performance and energy perspectives and provides new ideas for IC optimization. In the future, it is necessary to continue to explore the automatic optimization techniques and use deep learning techniques to obtain more efficient circuit optimization methods.
Employing Cryptographic Techniques for Data Security and Privacy Preservation
Authors: Liu Honghao
Published: August 2024

This scholarly discourse delineates the foundational principles and prevalent algorithms underpinning information encryption technology, illuminating its critical role in fortifying information security through the lenses of data protection and privacy safeguarding. Encryption technology serves as a stalwart guardian of data confidentiality, integrity, and availability, undergirding the secure and robust functioning of digital networks and systems. As the landscape of cyber threats undergoes relentless transformation, the mandate for the advancement of encryption methodologies becomes ever more pressing. The perpetual refinement and evolution of cryptographic practices are imperative to surmount the increasingly intricate challenges posed by sophisticated cyber adversaries. In an era where data breaches and privacy incursions are rampant, encryption acts as the sine qua non of digital security strategies. It not only obfuscates sensitive information from unauthorized viewers but also ensures that even if data is intercepted, it remains an indecipherable enigma to the interloper. Furthermore, encryption technologies underpin trust in the burgeoning realms of e-commerce and online communication, where they authenticate transactional exchanges and preserve the sanctity of digital interactions.
Systematic Research and Optimization Design of Intelligent Electric Furnace Control System
Authors: Sun Yuheng
Published: August 2024

With the widespread application of smart homes, the study and design of control systems for smart electric stoves, an indispensable part of kitchen life, have garnered significant attention. This research is dedicated to exploring the theoretical foundations and practical applications of smart electric stove control systems, aiming to enhance the user experience of smart stoves while focusing on energy efficiency and environmental impact, thus contributing to the sustainable development of smart kitchens. Various algorithms have been employed for temperature control and energy management to achieve precise control and energy saving. In the design and implementation phases, this research has taken into account multiple factors such as hardware selection, software programming, and system integration, ensuring the reliability and efficiency of the smart electric stove control system. Future research will continue to optimize the control system and explore additional smart features, with the goal of providing users with a more comfortable and convenient kitchen experience while also contributing to environmental protection and energy conservation. This research not only enriches the theoretical study of smart electric stove control systems but also provides valuable references for the design and application of smart electric stoves, holding significant practical significance and long-term research value.
Functional data regression model based on basis function expansion and Group Lasso
Authors: Shi Xuyi , Guang Jiachen
Published: August 2024

In the analysis of functional data, functional data regression is a crucial statistical model. The preceding step is to smooth the discrete function, usually using the basis function expansion method. However, due to the diversity of basic functions, it is a difficult problem to choose the basis function suitable for specific data. In view of this, an adaptive basis function expansion function regression model based on Group LASSO is proposed in this paper, which is suitable for the scenario where the predictor is a random function and the response variable is a continuous scalar. The proposed method uses a variety of basis function expansion methods, significantly expands the function space, and can smooth complex random functions better than a single basis function expansion method. On the other hand, to avoid the overfitting problem caused by the over-complexity of the prediction function, this paper adopts the Group LASSO method to automatically select the best type of basis function to alleviate the overfitting problem. The effectiveness of the proposed method is verified by an empirical study on the NIR reflectance spectral datasets of meat and corn samples. The results show that the proposed method has smaller mean square error and absolute error and is robust compared with the functional regression model with single basis function expansion.
Survival Prediction and Comparison of the Titanic based on Machine Learning Classifiers
Authors: Wang Tony Wayne
Published: August 2024

This study conducts a comparison of machine learning algorithms, including Logistic Regression (LR), Decision Tree Classifier (DT), and Random Forest Classifier (RF), to predict the survival outcomes of passengers on the Titanic. The dataset used in the study includes variables such as socio-economic status, age, gender, and family relationships; this paper meticulously prepares and analyzes the data to train and evaluate these models. The study's objective is to determine the impact of various passenger features on survival outcomes, employing machine learning algorithms to generate survival predictions. The findings demonstrate that the RF model, particularly with 45 or 75 trees, significantly outperforms LR and DT in terms of precision and recall, establishing it as a more robust classifier for this dataset. The research underscores the importance of the utility of different machine learning models for binary classification tasks and the role of parameter tuning in enhancing model performance. This comparative analysis not only contributes to the ongoing exploration of the Titanic disaster through data science but also highlights key considerations in the application of machine learning algorithms for predictive modeling.
Different Modalities and Parameters in Spinal Cord Stimulation Treatment
Authors: Cai Jiacheng , Zheng Zhide
Published: August 2024

Electrical stimulation to treat diseases can be tranced back to the pharaonic era. Since then, people have found using the electrical pulse generated by electric cat-fish can mange the patient’s pain effectively. Foreign studies started early, and the technology was relatively mature. Spinal cord stimulation was produced in the 1960s by inserting stimulating electrodes into the spinal canal's epidural area. At the same time, the pulse generator was implanted subcutaneously to achieve the purpose of pain treatment with electrical pulses. This article will introduce three main types of stimulation mechanism: constant voltage stimulation, constant current stimulation and activation of the charge mode. Compare the impact of the given stimulus with the design's complexity. However, because the electrode impedance varies with position and time, it is difficult to precisely control the charge of the stimulus, resulting in difficulty in controlling the net charge balance. Secondly, introduce the different parameters and their role in spinal cord stimulation, including the threshold of the cell membrane decide if it can generate the action potential. And the relationship between stimulation amplitude and pulse width, the current intensity is gradually reduced with the grow of stimulation pulse width. And the frequency and waveform, these parameters influence each other and are considered as the most important factors for the patients’ pain management in SCS.
Obstacle Avoidance Algorithm for Mobile Robots
Authors: Xiao Wenkai , Ye Xinhao
Published: August 2024

Automatic car obstacle avoidance technology mainly utilizes advanced sensor technology to enhance the car's perception ability of the driving environment, feedback real-time information such as vehicle speed and position obtained by the perception system to the system, and judge and analyze potential safety hazards based on comprehensive information of road conditions and traffic flow. Mobile robots are increasingly used in applications, including in industrial production, agriculture, healthcare, rescue. However, mobile robots often face the challenge of avoiding obstacles while performing their tasks. Therefore, the development of intelligent obstacle avoidance algorithms is crucial to improve the autonomy, safety, and efficiency of mobile robots. In emergency situations, it automatically takes measures such as alarm prompts, braking or turning to assist and control the car to actively avoid obstacles, ensuring vehicle safety. At present, there are also many mature algorithms for local obstacle avoidance, and each algorithm has its own advantages and disadvantages. The current methods mainly include artificial potential field method (APF) and virtual force field method (VFF). This article aims to comprehensively review the latest research progress of obstacle avoidance algorithms for mobile robots. Chapter 2 introduces these methods in four different ways. The remaining part of the paper serves as the conclusion of the research and discusses future obstacle avoidance algorithms.
Path Planning Algorithm for Mobile Robots
Authors: Jian Hanwen
Published: August 2024

Path planning algorithm is a critical technology enabling mobile robots to realize autonomous navigation. In this paper, the path-planning technology of mobile robots is deeply discussed, and the operation mechanism and principle of different algorithms are analyzed in detail. Based on the understanding of the characteristics of mobile robot path planning algorithms, these algorithms are divided into three categories: traditional planning algorithm, intelligent planning algorithm, and sampling-based planning algorithm. This paper reviews and discusses the key research results in recent years, especially the advantages and limitations of various algorithms, which are analyzed in depth. Considering the current research status of mobile robot path planning technology, this paper also forecasts future research trends to provide new thinking direction for further development, such as the mixed-use of different types of path planning algorithms, the adoption of new technologies such as deep learning for path planning, and the application of multi-robot collaborative path planning.
Advancements in Deep Learning-Based Image Captioning
Authors: Wang Siqi , Zhuang Jihong
Published: August 2024

In the confluence of natural language processing and machine vision, the field of image captioning has experienced exponential growth since the introduction of the BLEU evaluation algorithm by IBM in 2002. This discipline serves to bridge the "semantic gap" between human and machine perception, translating visual information into semantic narratives. Such technology is extensively applied in areas like human-computer interaction, video subtitling, quiz generation, and image-based search functionalities. The paper presents an analysis of two primary methodologies in image captioning: template-based and encoder-decoder-based structures. Template-based approaches, defined by the use of pre-set templates, ensure syntactic accuracy yet offer limited flexibility in caption generation. Innovations within this methodology, including paraphrase back-translation and the integration of psycholinguistics, have enhanced caption diversity and descriptiveness. On the other hand, the encoder-decoder framework, particularly the CNN-RNN model, utilizes deep neural networks to learn directly from image-caption pairs. This method represents a more dynamic and adaptable approach to caption generation. The amalgamation of Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks within this framework has notably advanced the descriptive quality of captions, effectively addressing complex image contexts.
Exploiting Machine Learning Model Ensemble for AI-Generated Texts Detection
Authors: Zhou Chucheng
Published: August 2024

In recent years, the development of Large Language Models (LLMs) has reached a state of maturity, resulting in texts that are becoming increasingly indistinguishable from those written by humans. This progress has sparked a growing need for precise and efficient methods to detect Artificial Intelligence (AI)-generated texts, as the blend of human and machine authorship becomes more seamless. This study utilizes data from student-written papers and articles generated by various LLMs to develop a machine learning model capable of accurately distinguishing whether an article was written by a student or an LLM. Four different classification models (MultinomialNB, SGDClassifier, LGBMClassifier, and CatBoostClassifier) and their ensemble models with weighted combinations were chosen to detect AI-generated texts. To evaluate the effectiveness of these models, the Area Under Curve (AUC) score metric was employed. The results indicate that the CatBoostClassifier model performs the best in mitigating overfitting, while the ensemble model demonstrates the optimal predictive performance. This discovery holds significant importance for enhancing the accuracy of detecting AI-generated articles.
Analysis of Digital Media Image Tampering and Forgery Control
Authors: Tang Bin , Sun Mengyao , Zheng Haowen , Han Haiyun , Jin Yu
Published: August 2024

In recent years, the continuous innovation of computer vision processing technology has greatly improved the quality of people's production and life, while the accompanying tampering risk has also gradually increased. There are frequent incidents of people with ulterior motives using computer image processing technology to tamper with digital media and falsifying and cheating others. This paper mainly analyzes the technology and method of digital media image tampering and forgery, the social harm caused by the resulting tampering risk, and puts forward the corresponding management countermeasures.
Predicting the Effectiveness of Wildlife Trade Policies Using Machine Learning Techniques
Authors: Pu Dingtian , Sun Mingran , Yuan Jingyi
Published: August 2024

Deepening globalization has made the illegal wildlife trade a growing problem, and this paper uses modern information technologies, such as big data analysis and machine learning, for monitoring and evaluation, which are essential for understanding trade dynamics, predicting trends, and assigning responsibilities. This study uses Klein's comprehensive national power equation to establish a scoring system to select the countries or organizations with the most rights, resources, and interest in the management of illegal wildlife trade as the subject of behavioral implementation. Data-driven methods such as nonlinear regression, ARIMA time series forecasting, and the Random Forest algorithm were then used to demonstrate the relevance of the policies and actions of the study subjects to illegal wildlife trade management. The United States, with the highest score 93.0, 1.8 points higher than the second placed IUCN , was identified as the actor with existing policies and actions that are highly relevant to illegal wildlife trade management.
Real-world Applications of Bandit Algorithms: Insights and Innovations
Authors: Zhang Qianqian
Published: August 2024

In the rapidly evolving landscape of decision-making systems, the significance of Multi-Armed Bandit (MAB) algorithms has surged, showcasing a remarkable ability to address the exploration-exploitation dilemma across diverse domains. Originating from the probabilistic and statistical decision-making framework, MAB algorithms have established a critical role by offering a systematic approach to making choices in uncertain environments with limited information. These algorithms ingeniously balance the trade-off between exploiting known resources for immediate gains and exploring new possibilities for future benefits. The spectrum of MAB algorithms ranges from Stochastic Stationary Bandits, dealing with static reward distributions, to more complex forms like Restless and Contextual Bandits, each tailored to the dynamism and specificity of real-world challenges. Further, Structured Bandits explore the underlying patterns in reward distributions, providing strategic insights into decision-making processes. The practical applications of these algorithms span several fields, including healthcare, content recommendation, and education, demonstrating their versatility and efficacy in addressing specific contextual challenges. This paper aims to provide a comprehensive overview of the development, nuances, and practical applications of MAB algorithms, highlighting their pivotal role in advancing decision-making processes amidst uncertainty.
Exploiting Improved Cyclic Generative Adversarial Networks to Generate Images from Different Angles of Views
Authors: Ding Yiquan
Published: August 2024

Virtual Reality (VR), Augmented Reality (AR), and other similar technologies are becoming increasingly popular nowadays. However, despite the increasing number of related devices being launched, the feedback tends to be disappointing. A significant contributor to this dissatisfaction is the blurred viewpoints and unclear levels, which often lead to a suboptimal user experience. To address this issue, this research focuses on training a modified version of the Cycle-Consistent Generative Adversarial Networks (CycleGAN) model to generate an image from another angle of view based on a given image. This work has fine-tuned specific layers of the CycleGAN to better suit the requirements of this work. These improvements give the model enhanced adaptivity in handling visual image transformations. Consequently, the proposed model achieves superior results compared to other models, with minimal image distortion. This is likely due to the minimal angular difference between the generated images. In the future, it is expected to expand the number of viewpoints generated and enhance the model's efficiency in processing images with varying resolutions.
Exploring the Effectiveness of Hyperparameters in Deep Convolution Generative Adversarial Networks
Authors: Kang Zhiqiao
Published: August 2024

Traditional machine learning models are often only able to classify or predict data, and cannot generate new simulated data, while Generative Adversarial Network (GAN) provides the possibility for machines to generate high-quality and diverse data. GAN can be used for image data generation, image-to-image transformation, translation of image information and text information into each other, and so on. There are many different types of GAN can realize different types of functions. GAN has a strong generating ability, can generate some high-quality simulation data for human reference. The framework idea of GAN is also very innovative and has been widely developed for applications in unsupervised or semi-supervised learning. In addition, GAN can be integrated with other machine learning models to form a more powerful model, so as to solve the problems that are difficult to deal with traditional models. Therefore, GAN is a very promising model in the field of artificial intelligence. In this thesis, the author completed the basic reproduction of Deep Convolution Generative Adversarial Network (DCGAN), and analyzed the quality of DCGAN generated images, using the control variable method. Some parameters are modified, such as learning rate, batch size, latent size, and explored the influence of these parameters on the training effect of DCGAN. The characteristics of basic parameters on DCGAN image generation results are summarized.
Research and Analysis on Artificial Intelligence in Integrated Circuits
Authors: Yu Fangyi
Published: August 2024

In the contemporary digital era, artificial intelligence (AI) emerges as a pivotal industry, playing a critical role across various sectors. Concurrently, the integrated circuit (IC) industry represents a highly specialized field characterized by swift technological advancements and innovation. Anchored on this premise, this paper embarks on a comprehensive exploration of the IC domain, delineating its evolutionary trajectory and current dynamics. Subsequently, this paper extends to a detailed examination of the practical deployment of AI within the IC industry, highlighting specific application scenarios where AI's transformative potential is harnessed to enhance efficiency, design, and operational processes. This paper culminates in a forward-looking analysis, contemplating the prospective trends, opportunities, and challenges that define the intersection of AI and IC technologies. This paper inquiry not only sheds light on the symbiotic relationship between AI and the IC industry but also underscores the imperative for adaptive strategies in navigating the complexities of their integration.
Study on Pricing and Replenishment Decision of Vegetable Products Based on SARIMA Model
Authors: Yu Feifan , You Lei , Han Xinyue , Ma Haoze , Sun Hijun , Zhang Liyin , Zhao Yuesheng
Published: August 2024

In fresh food superstores, vegetable commodities are characterized by a short shelf life and deterioration in quality over time. To meet market demand and profit, merchants usually sell vegetables with shipping losses and deteriorating quality at a discount. Therefore, the pricing and replenishment strategies of vegetables have an important impact on the profit maximization of fresh produce superstores. The aim of this paper is to reveal the patterns by processing and analyzing the past sales data. The distribution pattern between different categories of vegetables is obtained through Pearson's coefficient. Then the correlation coefficients between different vegetable categories were calculated by ridge regression model. In the order of aquatic root vegetables, cauliflower vegetables, leafy vegetables, pepper vegetables, eggplant vegetables and edible mushroom vegetables, the coefficients of sales unit price were -0.535, -0.936, -2.498, -0.658, -0.279 and -1.157 respectively. Then the optimal model and the daily replenishment and pricing strategy for maximizing the revenue were determined by using the SARIMA model. The above model helps the superstore to adjust the replenishment plan and pricing strategy, and to better meet the market demand and increase sales revenue and profit. Also, it can be extended from a specific problem to other products with similar characteristics.
Student Evaluation Model Based on Emotion Recognition through Classroom Monitoring
Authors: Gao Yaxin
Published: August 2024

The improvement and perfection of higher education work relies on correct feedback from student evaluation of teaching, however, problems such as strong subjectivity and generalization of the indicator system are common in college student evaluation. Therefore, this article designs a student evaluation model based on emotion recognition through classroom monitoring. First, use classroom surveillance to collect class videos of teachers and students. After that the emotional information of teachers and students in class is captured through emotion recognition technology by calling Baidu AI Open Platform API and gesture recognition technology through improved you only watch once (YOWO) algorithm model, then entering into the database for statistics. Finally, statistical data is used to analyze teaching methods and teaching attitudes such as content mastery and enthusiasm, as well as learning effects such as student initiative and knowledge mastery, and multiple indicators of student evaluation are presented on a web page in the form of bar charts, pie charts, etc through plotting library. As a result, this model improves the objectivity and reliability of the evaluation results and provides data support for the quantification of the indicator system, which makes the evaluation results more intuitive and easy to understand.
Advancements and Applications in the Detection Technology of Small Unmanned Aerial Vehicles
Authors: Yin Junhong
Published: August 2024

This manuscript delves into the intricate realm of small target detection technology in Unmanned Aerial Vehicles (UAVs), a pivotal element in contemporary aviation dynamics, with widespread implications across military, civilian, and emergency response domains. Central to this is the detection of diminutive targets, a process entailing sophisticated sensors and image processing algorithms, contending with the intricacies of identifying small, non-distinct targets against multifaceted backgrounds. The paper conducts a comprehensive review of the primary challenges, technological breakthroughs, and a spectrum of algorithms including RNN, SSD, and YOLO, in addition to examining prominent techniques such as Faster R-CNN in UAV target discernment. Moreover, it accentuates the application of this technology in areas like military reconnaissance, agriculture, and disaster management, highlighting its promising potential in urban settings and within the Internet of Things framework. The discourse extends to prospective developments, emphasizing the surmounting of technical limitations through enhancements in algorithmic efficiency, integration of multi-sensor systems, and advancements in real-time processing capabilities. Empirical evaluations of the technology’s practical efficacy underscore its profound prospective impact. In summation, the technology of small target detection in UAVs, propelled by ceaseless innovation and pragmatic deployment, stands at the cusp of engendering substantial contributions across various sectors and in the evolution of smart urban concepts.