Reviewerly/exHarmony huggingface dataset:
Prototype Contrastive Learning for Personalized Federated Learning

Federated learning (FL) is a decentralized learning paradigm in which multiple clients collaborate to train the global model. However, the generalization of a global model is often affected by data heterogeneity. The goal of Personalized Federated Learning (PFL) is to develop models tailored to local tasks that overcomes data heterogeneity from the clients’ perspective. In this paper, we introduce Prototype Contrastive Learning into FL (FedPCL) to learn a global base encoder, which aggregates knowledge learned by local models not only in the parameter space but also in the embedding space. Furthermore, given that some client resources are limited, we employ two prototype settings: multiple prototypes and a single prototype. The federated process combines with the Expectation Maximization (EM) algorithm. During the iterative process, clients perform the E-step to compute prototypes and the M-step to update model parameters by minimizing the ProtoNCE-M (ProtoNCE-S) loss. This process leads to achieving convergence of the global model. Subsequently, the global base encoder that extracts more compact representations is customized according to the local task to ensure personalization. Experimental results demonstrate the consistent increase in performance as well as its effective personalization ability.
Lightweight Reference-Less Summary Quality Evaluation via Key Feature Extraction

One of the main problems with automatic text summarization is the lack of a “gold standard" for summary quality evaluation. ROUGE [9] is the most widely used evaluation metric for summary quality. However, its evaluation merely concentrates on reference summary and overlap features of sentences rather than focusing on more critical semantic features. Some other exiting methods have issues with improper noise handling and high cost. To solve these problems, we propose a lightweight reference-less summary quality evaluation method (SE-tiny), which evaluates the summary from two aspects: the summary’s self-quality and the degree of matching the features of the summary with the key features of the source text. Then, we optimize computational efficiency and space cost. Compared with existing methods, SE-tiny improves the quality of evaluation and reduces the cost. Besides, our method does not rely on reference summaries and can be generalized to evaluation on summarization datasets. For the goal of reproducibility, we make the SE-tiny project’s code and models available.
Exploration of the Basic Path for Cultivating Skilled Talents through Dual Education and Integration of Industry and Education

This study comprehensively reviews and summarizes the theoretical achievements and practical experience of practical teaching research on practical dual education and industry education integration applied talents in domestic and foreign universities on the basis of literature research. By connecting with enterprises, the school designs and develops courses based on the actual needs of enterprises and students, and based on job requirements and abilities, reorganizes and optimizes the course system, thereby constructing a new course system that adapts to production reality. Meantime, effective teaching reform strategies should be formulated to enhance students' professional abilities and comprehensive literacy, and meet the requirements of social development.
Research on Upgrading Path of Major under the Background of New Infrastructure: A Case Study of Intelligent Management of Water Conservancy and Hydropower Projects

The development of new infrastructure and industry has put forward new requirements for the training of traditional water conservancy professionals, and it is an inevitable trend to carry out professional transformation and upgrading with the support of new technologies. According to the new requirements of the new infrastructure, the characteristics of the upgrading and transformation of water conservancy majors are mainly reflected in two aspects: first, the integration of traditional water conservancy engineering majors with new technological development and new industry needs; second, the cultivation of composite high-quality talents with the intelligent application ability of the whole life of water conservancy projects. It is mainly achieved through upgrading the personnel training and innovating the training mode, restructuring the curriculum system, reforming the teaching methods, cultivating high-level teaching teams, building public training bases, enhancing social service capabilities.
Development of Artificial Intelligence as a Breakthrough for Personalization in Marketing

Personalization in marketing has been a buzzword for many years, but the development of artificial intelligence (AI) has opened up a wide range of new possibilities for truly personalized experiences. The concept of personalization involves tailoring marketing messages and experiences to individual customers based on their behavioural patterns. This article discusses the theoretical background of personalization, AI, machine learning, and the GPT model, and examines two examples of AI-powered personalization in marketing: personalized chatbots and personalized e-mailing. The main goal of our paper is to open the topic of new possibilities of personalization in marketing and for it to serve as a starting point for further research, both internal and external. The examples we provide already exist on the market and are possible to implement into the marketing strategy of a company or can be taken as a first step to a greater level of personalization.
PREPARING TO ORGANIZE TECHNICAL UNIVERSITY STUDENTS’ INDEPENDENT WORK USING PROFESSION-ORIENTED BLOGGING

Modern approaches to the organization of students’ independent work based on the use of information and communication technologies are both a promising opportunity and a challenge for education due to its ongoing digitalization. This paper attempts to describe an algorithm for the preparation to such an independent work based on students’ profession-oriented blogging in a foreign language. This algorithm can be replicated by any foreign language teacher interested in developing the professional foreign language competence of future technical university graduates and implies a number of procedures. Starting from the analysis of regulatory documentation and determining the objectives and methods of students’ independent work, it is then suggested to form an expert group for further evaluation of this independent work’s results, the structure, content and levels of the competences to be developed, to organize a pedagogical assessment of the criteria to evaluate these results, as well as to prepare the information content and think through the technical support. The results of experimental work enable us to speak of high effectiveness of the given algorithm, as it implies the focus on professional orientation of students’ independent work which most foreign language teachers find challenging.
THE ECONOMIC EFFECT OF THE INTRODUCTION OF THE “SMART HOME” SYSTEM IN PRESCHOOL INSTITUTIONS

The smart home system is a modern design solution for saving energy resources, which in our time and in the future will not lose its relevance. The concept of “smart house” (smarthouse) radically changes the views on the principles of the organization of everyday life in a modern building. This technology does not just turn devices on and off, it can control the internal environment of the room depending on the time of stay of people and their number. The article discusses the concept of “smart home” in the construction and operation of kindergartens, presents the market of available offers of “smart” systems. The calculation of the economic effect of the introduction of the “smart home” system in preschool institutions is carried out. The calculation algorithm is presented, which provides for the consideration of qualitative parameters. A review of the “smart home” system option packages offered on the market has been conducted. On average, savings on housing and communal services payments after installing the Smart Home system will amount to 10 % of the monthly payment amount. The introduction of the “smart home” system in preschool institutions will reduce utility bills, thereby saving budget funds.
Pengaruh Motivasi dan Pemanfaatan Sarana Belajar Terhadap Prestasi Belajar Sejarah Siswa

Student achievement, especially in cognitive aspects, can be influenced by motivational factors and the use of learning facilities. This study aims to measure the influence of both on the learning achievement of History students of grade XI odd semester at SMA N 6 Bandar Lampung in the 2021/2022 academic year. Using a verifiative descriptive method with an ex post facto approach and survey, this study revealed that the use of learning facilities affected learning achievement by 36.60%, while learning motivation contributed by 30.80%. When the two were combined, its effect on learning achievement increased to 51.30%. Other factors that contributed to learning achievement, but were not studied in this study, contributed 48.70%.
Information Control on Inventory Study at In One of The Garment Companies in Bandung

The objective of this study is to examine the impact of the Finished Goods Inventory Accounting Information System on the internal control of finished goods inventory at PT. Partners Adi Jaya Manunggal in Bandung City. Based on the obtained calculation results, it has been determined that the Accounting Information System (X) for Finished Goods Inventory is deemed to be of satisfactory quality, as indicated by a score of 3.39. The internal control system for managing finished goods inventory (Y) has demonstrated effective performance, as evidenced by a value of 3.37. The optimization and effective utilization of a precise accounting information system for finished goods inventory is expected to enhance internal control measures for such inventory.&#x0D; The research employed a descriptive verification research method at PT. Partners Adi Jaya Manunggal in Bandung City. In order to gather data and information that will substantiate this research, the approach involves the collection of secondary data and the utilization of linear regression hypothesis testing. This methodology aims to offer a comprehensive understanding of the impact of the Finished Goods Inventory Accounting Information System on the Internal Control System for Finished Goods Inventory.&#x0D; According to the findings of the study, the outcomes of the linear regression analysis indicate that the equation Y = 8.006 + 0.817. This finding indicates that a majority of the population in Mitra Adi Jaya Manunggal, Bandung City, accounts for 63.3%, while the remaining 36.7% represents an unexamined variable of influence.
Financial Report Data Processing System Input Vat And Output Vat

Applying the right value-added tax calculation system can satisfy workers and agencies. Calculating input and output value-added tax often experiences problems in its calculation, which is still semi-EDP. The financial data processing system of input VAT and output VAT at Ahass Honda Andrew Motor is web-based with data collection techniques in the form of observation, interviews, and literature studies. It is designed with the OOAD system development method, namely the process of analyzing, designing, and implementing the system based on the object. The design of this system can make it easier for companies to input and calculate value-added tax input and output, facilitate the calculation process because it is done automatically, and facilitate the process of printing the final report to be more effective and efficient.
Employee Payment Data Application Design

At first, employees collect attendance support letters containing attendance sheets and permits if not working as well as overtime certificates to the financial administration section, after which the financial administration will calculate salaries first to be submitted to the accounting department. Because employee payroll is still done manually, errors often occur in absenteeism and in calculating salaries given to employees. Then the frequent occurrence of errors in recording reports is also because the payroll is still done manually.&#x0D; &#x0D; This system is analyzed and designed using descriptive research methods and development techniques using the waterfall method. The programming language used by this system is PHP with XAMPP web server and PHPMyAdmin database. This employee payroll application that has been created can help companies make it easier to record payroll transactions, and archive payroll and attendance reports.
Improved MTAPF method for autonomous vehicles

Path planning has evolved into one of the most crucial critical studies with the introduction of numerous autonomous vehicles. The artificial potential field approach is being used by many intelligent vehicles to plan their paths, and a variety of autonomous vehicles may avoid barriers depending on their own conditions and the movement of the obstacles. The design of dynamic real-time paths frequently makes use of artificial potential field (APF) techniques. The issue of local minima and unattainable targets in the APF approach is addressed in this study with a better solution. The global optimum path is first determined using the heuristic A-STAR path algorithm in this work, which is combined with the artificial potential field approach. The global optimal path is then divided into many sub-goals to form a sequence. The artificial potential field approach is then used to produce these targets' final routes sequentially, considerably lowering the likelihood that many unmanned vehicles may simultaneously enter a local minimum. The method could avoid the local minima problem and plan a viable path is supported by simulation results.
Ancient character recognition with deep learning techniques

Many deep learning models have achieved remarkable results in many areas, such as image classification and image generation. At the same time, with the increasing attention given to the digitization of ancient manuscripts, ancient character recognition has become one of the most fascinating research areas. In this article, we try some CNNs such as ResNet, VGG, AlexNet or simply CNN on the dataset named Oracle-MNIST, an open ancient character dataset. In addition, to improve the accuracy of the models, ensemble learning is also adopted. Compared with the accuracy, the number of model parameters and running time, it was found that one simple CNN model trained as a snapshot performed best, and the recognition accuracy rate reached 97.009%.
Spacecraft environmental path planning method based on improved artificial potential field

Spacecraft is the primary means of transportation for human exploration of outer space. With the advancement of science and technology, existing space exploration can no longer satisfy people's desire to explore, in the future, further and deeper space exploration is the goal of development. And the safety of spacecraft navigation in space requires the development of a path planning system. Although there have been many kinds of methods for path planning at this stage, all of them have their strengths and specialize in planning directions, but they also have defects. Based on this, this study improves the traditional artificial potential field method, by adjusting its repulsive field calculation and combining it with the RRT algorithm. The improved method can avoid the local minimum problem of the traditional artificial potential field and generate paths to meet the requirements of spacecraft. The effectiveness of the algorithm is verified by simulation experimental results.
Convolutional neural network-based modulation recognition technique for communication signals

The last decade has seen breakthroughs in communication technology. The increasingly complex signal transmission environment has placed higher demands on signal modulation recognition. Traditional modulation recognition approaches cannot guarantee satisfactory recognition accuracy. Fortunately, with the continuous advancement of deep learning algorithms, convolutional neural network-based communication signal modulation recognition techniques have become the mainstream of current research. Therefore, this paper first reviews the development history of signal modulation recognition techniques and introduces the concepts of signal modulation theory. It includes ASK, PSK and FSK modulation methods, which are common today. Subsequently, I analyze the principles of signal modulation recognition and the implementation method of CNN in modulation recognition. To further explore the shortcomings of CNNs, I propose two optimized models, the residual network model and the CLDNN model. After comparing the performance, the former has higher performance, but its computational complexity is higher while the latter takes into account the high recognition accuracy while still reducing the network parameters as much as possible to keep the complexity at a low level.
Writing Self-efficacy and Writing Strategies of Non-English Major Students

With 394 college students as subjects, this study used t test, correlation analysis and ANOVA to explore the relationship between self-efficacy and English writing strategies of non-English major college students of different genders and majors. The conclusions are as follows: (1) Non-English major students with high self-efficacy in English writing have a higher level of writing strategies than those with low self-efficacy in English writing, and there is a significant moderate positive correlation between self-efficacy and English writing strategies. (2) Gender and professional differences in English writing strategies are as follows: female college students have significantly higher self-efficacy and English writing strategies than male college students. The use of English writing strategies by liberal arts students is significantly higher than that of science students. There is no significant difference in English writing efficacy among students of different majors. The interaction between gender and major was not significant.
Path planning algorithm based on Improved Artificial Potential Field method

The domain of research and development concerning mobile robot obstacle avoidance continues to remain an active area of interest. Artificial potential fields (APF) are a common and effective method for obstacle avoidance path planning, where the robot is guided to the target location by a simulated environmental potential field. Traditional artificial potential field methods tend to trap robots in local minima, impeding their ability to reach the goal. This research endeavours to introduce a new approach, the Improved Artificial Potential Field (IAPF) algorithm, which incorporates the A-star method in constructing the artificial potential field. This technique more effectively addresses the issue of path planning for mobile robots, thereby avoiding local minimum solutions. Through simulation experiments in different scenarios, the feasibility of the IAPF algorithm of this paper is verified. The results show that, compared with the traditional APF method, the IAPF algorithm can solve problem of local minimum and plan a sensible path.
English text recognition technology and application analysis based on optical character recognition

With the rapid development of artificial intelligence, it is gradually applied to more and more fields. Ais market and technological potential value are unpredictable, which involves interaction between Ai and humans. In addition, optical character recognition is one of the essential roles in the interaction between machines and humans. Recognition of human language from a machine can significantly increase the user's experience and potential efficiency. Hence, this paper will focus on analyzing todays Optical character recognition (OCR) techniques based on feature extraction and neural networks. This article first provides an overview of the important significance of English text recognition, and then selects two typical methods for overview. Then, comparative experiments are conducted on the above methods and their analysis is conducted. By comparing each technique, to decide the restriction and advantage, based on the analysis to conclude and make predictions in OCR optimization.
The classification and application of miniature unmanned aerial vehicle

Miniature unmanned aerial vehicle (MAV) is more and more widely used in daily life. The small size of MAVs brings them many advantages like high portability and manoeuvrability. MAVs are playing important roles in many aspects because of their own catachrestic and advantages. There are many kinds of MAVs such as single-rotor MAV, multi-rotor MAV, fixed-wing MAV and bionic MAV. Each of them has its own advantages and drawbacks. The applications of them are decided by these characteristics. In order to better design MAV, in this paper, the advantages and disadvantages of the MAVs are analysed. The four most common kinds of MAVs are discussed in this paper. They have differences in structure and characteristics. The differences between these aspects are displayed in this paper. The suitable application scenario for each kind of MAV is provided based on its own peformance characteristics. This paper may offer a reference for MAV design.
4-bit absolute value detection for digital signal processing applications

In recent years, as the rapid advancement of the electronic industry and the higher requirements for instruments and circuits, the absolute value circuit has found an increasingly wide utilization in digital signal processing. An absolute value detector is one of the most widely used algorithms used for detecting spikes. Based on traditional designs of absolute value circuits, many optimized designs with better performance have been proposed. Based on previous analysis and research, absolute value circuits can be optimized into different sructures to meet different application requirements. This paper introduces three different absolute value circuits for different uses in digital signal processing by comparing the optimized parts to traditional absolute value circuits and introducing how the optimization can meet the needs of different applications. These optimized absolute value circuit meet the requirements of higher precision and more stable temperature in biological signal processing field, more convenient to apply in the analog-to-digital conversion in ADC field and minimization the latency energy in chip signal processing field. Absolute value circuits can be optimized in different ways to be applied in more fields in the future.
Improved artificial potential field method for local minima

With the development of autonomous driving technology, its applications permeate many aspects of work and life, providing convenience while reducing labor costs. Path planning has always been important for autonomous driving, where APF is widely used thanks to its simplicity of calculation and effectiveness. However, therere still problems existing such as local minima, influenced by initial positions and parameters, and so on. In this study, a better approach to solving the local minimum issue is suggested. Firstly, the odometry method is used for the determination of falling into local minima by saving and computing the relationships between adjacent steps. Subsequently, a variable step length method is designed for escaping local minimum points and bypassing obstacles in front. The feasibility and robustness of the method were verified by simulations, and this method proved capable of solving the local minima and planning a reasonable trajectory.