Reviewerly/exHarmony huggingface dataset:
English teaching resources recommendation based on improved Ant colony optimization algorithms

To achieve the goal of exploiting all the advantages and avoiding the disadvantages, this paper presents a genetic algorithm-based development to integrate the spider colony algorithm for teaching optimization of English data recommendation methods. This algorithm first takes advantage of the genetic algorithm to extract the pheromone required by the ant colony algorithm, and then obtains a good solution through the fast and comprehensive integration mechanism of the global optimization of the ant colony algorithm. Therefore, combining the two algorithms can increase the processing time and efficiency. According to the experimental results, compared to using the genetic algorithm or the ant colony algorithm, the violation of the soft parameters of the training data based on the genetic ant hybrid algorithm is reduced to a minimum, and the feasibility and quality of the training data are good. alone. The power of our algorithm is directly proportional to the number of assignments. The security of the algorithm seems to be stable when the number of scales reaches one. For the same scale, the power of the hybrid genetic colony algorithm is the highest, the ant colony algorithm is the second, and the genetic algorithm is the last. When the class setting is 500, when the motor reaches 300, the algorithm stops working and the test is repeated 10 times. The genetic ant colony hybrid algorithm needs the shortest time to reach the fitness value. Conclusion: this algorithm is much better than genetic algorithm or spider colony algorithm.
Design and Implementation of Intelligent Home Control System Based on Deep Learning in the Internet of Things Environment

The popularization of the Internet of Things has made our daily life more and more convenient. By using the Internet of Things, we can create an intelligent home control system that makes our rooms safer, more comfortable, and can meet our growing demand for items. This article discusses how to use the Internet of Things to improve the efficiency of smart homes, and focuses on how to use wireless sensor networks, embedded systems, and various technologies such as network communication, audio and video to achieve these goals. This can enable these intelligent home control systems to provide efficient services. This article also introduces how to use Internet of Things technology in the design process of home control systems to better handle wiring and other related issues. We will also explore the differences between traditional and smart homes. The results showed that four buyers rated the smart home system, with an average score of 4 before use indicating a lower score, and an average score of 9 after use indicating a higher score.
Analysis of Collaborative Filtering Algorithm in Personalized Push Service of University Library

The personalized push service function of university library is beneficial to the library's role and can help students learn, but whether this function can achieve good results depends largely on the accuracy of push, and collaborative filtering algorithm can help improve the accuracy of push. Therefore, in order to use the collaborative filtering algorithm to do a good job in personalized push service, this paper will carry out relevant research, introduce the basic concepts of personalized push service and collaborative filtering algorithm, and then analyze the use of the algorithm in personalized push service, in order to provide reference help.
Investigation on Innovative Models of Computer Laboratories Based on Cloud Computing

Experimental teaching is an important component of computer practical teaching, and students can better absorb the knowledge learned in class by conducting experiments in computer laboratories. However, there are still some deficiencies in current computer laboratories that need to be supplemented with new technologies. Therefore, this article studied the innovative model of computer laboratories based on cloud computing, aiming to upgrade computer laboratories through cloud computing technology. This article experimentally tested the utilization rate of computer laboratory rooms before and after using cloud computing. Through data analysis, it was found that the utilization rate of computer laboratory rooms after using cloud computing could reach a maximum of 92% and a minimum of 79%. The utilization rate of traditional computer rooms was highest at 68% and lowest at 50%. From this experimental data, it could be seen that cloud computing showed good performance in computer laboratories.
Efficient AI Reasoning Model Based on FPGA

The artificial intelligence inference model is currently the best mathematical model based on artificial neural networks. The intelligent reasoning mode based on artificial intelligence has a large construction scale. In a completed inference, many multiplication and addition operations need to be completed. To establish an effective artificial intelligence inference model, its computational complexity is dozens or even more times higher than traditional artificial intelligence algorithms. This article focused on the research on the isomerization acceleration of FPGA (Field Programmable Gate Array). This article was looking for a method that reduces both system changes and migration, while maximizing the flexibility of FPGA programmability. In order to build on this foundation, a fast interface for fast computation using FPGA was designed. The characteristic of this project was that it could combine the FPGA acceleration platform with ordinary computers or servers, and could effectively utilize traditional computer software and toolchains. This article compared the computational time consumption between the inference model and the CPU. In the scale calculation logic scenario of the inference model designed in this article, it could achieve the same computing power as the CPU when processing 70000 multiplication calculations. The experimental results indicated that the increase in CPU computing power was greater than that of the inference model. This indicated that the larger the amount of computational data, the more significant the acceleration effect of the inference model in this article would be.
The Application of Computer Network Technology in Network Education System

In order to solve the problem of too narrow information in the classroom due to the single content and lack of variability in the form of traditional teaching, an interactive network intelligent education system based on RBF algorithm has been proposed. Firstly, the research status of the interactive teaching intelligent system is analyzed, and then the use of artificial intelligence technology, combined with the interactive teaching method, the establishment of neuron model and RBF algorithm to determine the way of learning, and the design of an autonomous learning platform are discussed. The test results indicate that as students' learning time changes, the teaching efficiency of the system is constantly improving, and it has certain feasibility. The model proposed in this article not only changes traditional teaching methods, but also has been proven to improve students' quality, improve learning efficiency, and encourage children to enjoy learning in the environment.
Intelligent English Translation Model Based on Improved GLR Algorithm

An intelligent English translation model based on the improved GLR algorithm is proposed to solve the imprecise problem of traditional methods. This algorithm is used to create a body of tagged sentences from tens of thousands of English and Chinese words, so that the sentences can be searched. In addition, the knowledge structure translated into English is designed to prepare the knowledge structure based on information collection, processing and production. To use the knowledge of English translation, the English text is written according to the plan, design and negative features are extracted. The clinical trial was based on the standard knowledge of English translation and the trial data was recorded. According to the test results, the accuracy of English translation is 75.1% before modification and 99.1% after using smart text. Syntax and sentence-based knowledge systems have weak nodal point distributions, but the nodal point distributions are compact in the first, fourth, and fifth trials. Conclusion: The English translation knowledge model is highly accurate and can meet the needs of English translation.
Application of AI Intelligent Learning System in Multimedia Demonstration

In order to adapt to the needs of the times, many enterprises have combined AI (Artificial Intelligence) technology with enterprise management to enhance their competitiveness, which is also essential in the field of education. This article attempted to integrate AI technology into multimedia teaching to improve the teaching quality of teachers and students’ understanding of knowledge. The experiment found that AI technology could effectively improve students’ understanding of knowledge points and also improve the clarity of knowledge point explanations during the teaching process. It could be seen that using AI intelligent learning systems for multimedia demonstrations could greatly improve the teaching quality of teachers, as well as the learning interest and effectiveness of students.
An Analysis of Data Mining Techniques in Software Engineering Database Design

With the advent of Internet information technology, data mining technology has been applied in various fields of China's social and industrial development, and has promoted the quality development of the industry [1]. Nowadays, people are widely influenced by Internet computer technology, and the application of computer technology has been indispensable in life, work and study. At the same time, data mining technology arises from Internet communication technology and is used as an important technical means of operation and development by various industries, especially the application of data mining technology in university software engineering teaching is becoming more and more widespread, but there are still many unavoidable problems that require scholars to pay more attention. The article analyzes and discusses the significance of applying data mining technology in software engineering [2], and on this basis, the application path of data mining technology in software engineering database design is proposed, including: mining information, mining loopholes, mining execution records and open source. It is hoped that relevant technology users can efficiently apply data mining technology to make software engineering It is more reasonable and efficient, so that software engineering can be further developed. This paper presents the implementation and performance analysis of the teacher management decision-making system based on data mining, and introduces the implementation process of the system in detail.
Review of Blockchain Tokens Creation and Valuation

Blockchain and tokens are relatively new research areas insufficiently explored from both technical and economic perspectives. Even though tokens provide benefits such as easier market access, increased liquidity, lower transaction costs, and automated transactional process, their valuation and price determination are still challenging due to factors such as a lack of intrinsic value, volatility, and regulation making trading risky. In this paper, we address this knowledge gap by reviewing the existing literature on token creation and valuation to identify and document the factors affecting their valuation, investment, and founding, as well as the most promising domains of applicability. The study follows the PRISMA methodology and uses the Web of Science database, defining clear research questions and objective inclusion criteria for the articles. We discuss token technical development, including creating, issuing, and managing tokens on an Ethereum blockchain using smart contracts. The study revealed several key factors that significantly impact the field of tokenomics: demand and supply, social incentives, market conditions, macroeconomics, collective behavior, speculation, and inclusion in index funds. The most relevant use cases of blockchain and tokens are related to the digitization of virtual and physical assets, accountability, and traceability usual in smart grids or supply chains management, social governance, and art and gamification including metaverse.
Optical Character Recognition Development Using Python

Optical Character Recognition (OCR) is a technology used to convert scanned or digital images into editable text. OCR has become an increasingly important tool in the fields of data extraction and information retrieval, allowing for quick and efficient conversion of scanned documents and digital images into text. In this paper, we explore the use of the Python programming language to implement OCR algorithms and systems. We provide a comprehensive overview of existing Python libraries and packages used for OCR, including Tesseract and pytesseract, along with their strengths and limitations. We also examine the different OCR approaches and techniques, including template matching, feature extraction, and encrypting/decrypting the OCR parsed files and discuss their implementation in Python. Finally, we present a case study of a simple OCR system built using Python and evaluate its performance on a sample dataset. The results of our study highlight the potential of Python for OCR implementation and demonstrate its feasibility for real-world applications.
Optimisation Techniques for Compact CNN on Embedded Systems for Gesture Recognition

Embedded applications are increasingly prevalent in various domains, from consumer electronics to industrial automation and smart cities. With the advances in integrated circuit manufacturing technologies, low-power chips can now execute complex algorithms, including machine learning models. However, the computational constraints of embedded devices require compact and efficient neural network models, as well as software frameworks and optimisation techniques tailored to their hardware resources. This study investigates the implementation of Convolutional Neural Network (CNN) models for gesture recognition on an STM32F4 microcontroller, by exploring the impact of freezing layers, fine-tuning and pruning techniques on pre-trained CNN models. The results demonstrate that fine-tuning and freezing layers improve accuracy by up to 18%. Additionally, freezing layers by 10% and 20% improved the accuracy. Finally, we demonstrate that pruning reduced the model size by 90%, enabling it to perform gesture recognition on small devices. These findings are significant for developing software and optimisation techniques for embedded systems, particularly in the context of the Internet of Things.
Clifford Group and Unitary Designs under Symmetry

We have generalized the well-known statement that the Clifford group is a unitary 3-design into symmetric cases by extending the notion of unitary design. Concretely, we have proven that a symmetric Clifford group is a symmetric unitary 3-design if and only if the symmetry constraint is described by some Pauli subgroup. We have also found a complete and unique construction method of symmetric Clifford groups with simple quantum gates for Pauli symmetries. For the overall understanding, we have also considered physically relevant U(1) and SU(2) symmetry constraints, which cannot be described by a Pauli subgroup, and have proven that the symmetric Clifford group is a symmetric unitary 1-design but not a 2-design under those symmetries. Our findings are numerically verified by computing the frame potentials, which measure the difference in randomness between the uniform ensemble on the symmetric group of interest and the symmetric unitary group. This work will open a new perspective into quantum information processing, such as randomized benchmarking, and give a deep understanding to many-body systems, such as monitored random circuits.Received 18 July 2023Accepted 25 October 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040331Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum circuitsQuantum computationQuantum information processingQuantum information theoryQuantum Information, Science & Technology
Autonomous Drone Navigation

Autonomous drone navigation has evolved significantly as a result of the integration of cutting-edge computer vision and reinforcement learning algorithms. In this study, we propose a comprehensive framework that combines the adaptability of the TD3 neural network, the agility of the Ros2 platform, and the resilience of the YOLOv3 object detection model. Making use of YOLOv3's real-time object detection capabilities, our system proves to be highly adept at recognizing and reacting to changing environmental impediments, which guarantees improved UAV situational awareness. When combined with the TD3 neural network, the Ros2-based navigation framework allows for quick decision- making, which allows the drone to move independently toward predetermined destination points while effectively avoiding obstructions. Our work demonstrates the effectiveness of our suggested strategy through thorough experimentation in simulated situations, highlighting its potential for practical implementation in a variety of applications, from autonomous delivery services to aerial surveillance. The outcomes demonstrate the integrated system's flexibility and resilience, highlighting its capacity to successfully handle challenging navigational situations and guarantee secure and effective aerial operations. Our study adds to the field of autonomous drone navigation by highlighting the vital role that integrated computer vision and machine learning techniques play in enabling smart and flexible unmanned aerial vehicle operations. Keywords— Autonomous drone, YOLOv3, Object detection, Ros2, TD3, Reinforcement learning, Simulated environment, Aerial navigation, obstacle avoidance, Computer vision, Unmanned aerial vehicles (UAVs),Goal-point navigation, Aerial surveillance
Average-value estimation in nonadiabatic holonomic quantum computation

Nonadiabatic holonomic quantum computation has been attracting continuous attention since it was proposed. Until now, various schemes of nonadiabatic holonomic quantum computation have been developed and many of them have been experimentally demonstrated. It is known that at the end of a computation, one usually needs to estimate the average value of an observable. However, computation errors severely disturb the final state of a computation, causing erroneous average value estimation. Thus, for nonadiabatic holonomic quantum computation, an important topic is to investigate how to better give the average value of an observable under the condition of computation errors. While the above topic is important, the previous works in the field of nonadiabatic holonomic quantum computation pay woefully inadequate attention to it. In this paper, we show that rescaling the measurement results can better give the average value of an observable in nonadiabatic holonomic quantum computation when computation errors are considered. Particularly, we show that by rescaling the measurement results, $56.25%$ of the computation errors can be reduced when using the depolarizing noise model, a widely adopted noise model in quantum computation community, to analyze the benefit of our method.
Detecting Adversarial Examples Using Surrogate Models

Deep Learning has enabled significant progress towards more accurate predictions and is increasingly integrated into our everyday lives in real-world applications; this is true especially for Convolutional Neural Networks (CNNs) in the field of image analysis. Nevertheless, it has been shown that Deep Learning is vulnerable against well-crafted, small perturbations to the input, i.e., adversarial examples. Defending against such attacks is therefore crucial to ensure the proper functioning of these models—especially when autonomous decisions are taken in safety-critical applications, such as autonomous vehicles. In this work, shallow machine learning models, such as Logistic Regression and Support Vector Machine, are utilised as surrogates of a CNN based on the assumption that they would be differently affected by the minute modifications crafted for CNNs. We develop three detection strategies for adversarial examples by analysing differences in the prediction of the surrogate and the CNN model: namely, deviation in (i) the prediction, (ii) the distance of the predictions, and (iii) the confidence of the predictions. We consider three different feature spaces: raw images, extracted features, and the activations of the CNN model. Our evaluation shows that our methods achieve state-of-the-art performance compared to other approaches, such as Feature Squeezing, MagNet, PixelDefend, and Subset Scanning, on the MNIST, Fashion-MNIST, and CIFAR-10 datasets while being robust in the sense that they do not entirely fail against selected single attacks. Further, we evaluate our defence against an adaptive attacker in a grey-box setting.
Defending against a laser-seeding attack on continuous-variable quantum key distribution using an improved optical power limiter

Laser-seeding attack enables an attacker to control the source output and eavesdrops on the key information, compromising the security of the continuous-variable (CV) quantum key distribution (QKD) system. In this paper, we suggest an improved optical power limiter (OPL) to defend against a laser-seeding attack on CV QKD systems. The proposed OPL utilizes a temperature monitor to detect Eve's attacks through temperature changes, providing an effective optical component for improving the security and reliability of the CV QKD system in practical environments, which is called an OPL-T scheme. In particular, we consider the security threats of a laser-seeding attack on two common CV QKD systems and prove their theoretical security degradation. Experiments are conducted to verify the effective OPL-T, and the results show that this defense scheme has an advantage in resisting the laser-seeding attack. Numerical simulations demonstrate the security analysis on CV QKD equipped with an OPL-T, which further confirms the effectiveness of our scheme. This work provides a practical solution for the security enhancements of continuous-variable quantum communication in practical applications.
Reverse deconstruction in AI dialogue

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DGU-HAU: A Dataset for 3D Human Action Analysis on Utterances

Constructing diverse and complex multi-modal datasets is crucial for advancing human action analysis research, providing ground truth annotations for training deep learning networks, and enabling the development of robust models across real-world scenarios. Generating natural and contextually appropriate nonverbal gestures is essential for enhancing immersive and effective human–computer interactions in various applications. These applications include video games, embodied virtual assistants, and conversations within a metaverse. However, existing speech-related human datasets are focused on style transfer, so they have limitations that make them unsuitable for 3D human action analysis studies, such as human action recognition and generation. Therefore, we introduce a novel multi-modal dataset, DGU-HAU, a dataset for 3D human action on utterances that commonly occurs during daily life. We validate the dataset using a human action generation model, Action2Motion (A2M), a state-of-the-art 3D human action generation model.