Reform and Practice of the Course“Introduction to Computer Science”in Universities Based on the Cultivation of Computational Thinking and Systematic Abilities

Introduction to Computer Science,as one of the fundamental courses in computer-related majors,plays an important role in the cultivation of computer professionals.However,traditional teaching models and content can no longer fully meet the needs of modern information technology development.In response to these issues,this article introduces the concept of computational creative thinking,optimizes course content,adopts exploratory teaching methods,and innovates course assessment methods,aiming to comprehensively enhance students’computational thinking and innovative abilities.By continuously improving and promoting this teaching model,it will undoubtedly promote computer education in universities to a new level.

Research on the Reform of School-Enterprise Cooperation Teaching and Education Mode for Computer Majors

This paper discusses the innovative methods of school-enterprise cooperation education mode in computer applied talent training.An innovative training model based on school-enterprise cooperation is proposed to promote the cultivation of students’practical and innovative skills,so as to better adapt to the needs of society.By analyzing the key links and influencing factors of the training mode,this paper puts forward some concrete suggestions and measures to provide guidelines for universities and enterprises in personnel training.

Vehicle Abnormal Behavior Detection Based on Dense Block and Soft Thresholding

With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions.Current research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition accuracy.To address this,this research has developed a new model for recognizing abnormal traffic behaviors.This model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature reuse.This approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network structure.Additionally,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at hand.For temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data nuances.This research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test set.Our results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.

Emission and capture characteristics of deep hole trap in n-GaN by optical deep level transient spectroscopy

Emission and capture characteristics of a deep hole trap(H1)in n-GaN Schottky barrier diodes(SBDs)have been investigated by optical deep level transient spectroscopy(ODLTS).Activation energy(Eemi)and capture cross-section(σ_(p))of H1 are determined to be 0.75 eV and 4.67×10^(−15)cm^(2),respectively.Distribution of apparent trap concentration in space charge region is demonstrated.Temperature-enhanced emission process is revealed by decrease of emission time constant.Electricfield-boosted trap emission kinetics are analyzed by the Poole−Frenkel emission(PFE)model.In addition,H1 shows point defect capture properties and temperature-enhanced capture kinetics.Taking both hole capture and emission processes into account during laser beam incidence,H1 features a trap concentration of 2.67×10^(15)cm^(−3).The method and obtained results may facilitate understanding of minority carrier trap properties in wide bandgap semiconductor material and can be applied for device reliability assessment.

A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

Hybrid Chinese Information Retrieval Model Based on the Combination of Keyword and Concept

A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of keywords retrieval and concept retrieval but also can compensate for their shortcomings. Their parameters can be adjusted according to different usage in order to accept the best information retrieval result, and it has been proved by our experiments.

The In-Flight Realtime Trigger and Localization Software of GECAM

Realtime trigger and localization of bursts are the key functions of GECAM,an all-sky gamma-ray monitor launched on 2020 December 10.We developed a multifunctional trigger and localization software operating in the CPU of the GECAM Electronic Box.This onboard software has the following features:high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation,dedicated localization algorithm optimized for both short and long bursts,and low time latency of the trigger information which is downlinked through the Global Short Message Communication service of the global BeiDou navigation system.This paper provides a detailed description of the design and development of the trigger and localization software system for GECAM.It covers the general design,workflow,the main functions,and the algorithms used in the system.The paper also includes on-ground trigger tests using simulated gamma-ray bursts generated by a dedicated X-ray tube,as well as an overview of the performance for real celestial bursts during its in-orbit operation.

Performance and Complexity Trade-Off between Short-Length Regular and Irregular LDPC

In this paper, both the high-complexity near-ML list decoding and the low-complexity belief propagation decoding are tested for some well-known regular and irregular LDPC codes. The complexity and performance trade-off is shown clearly and demonstrated with the paradigm of hybrid decoding. For regular LDPC code, the SNR-threshold performance and error-floor performance could be improved to the optimal level of ML decoding if the decoding complexity is progressively increased, usually corresponding to the near-ML decoding with progressively increased size of list. For irregular LDPC code, the SNR-threshold performance and error-floor performance could only be improved to a bottle-neck even with unlimited decoding complexity. However, with the technique of CRC-aided hybrid decoding, the ML performance could be greatly improved and approached with reasonable complexity thanks to the improved code-weight distribution from the concatenation of CRC and irregular LDPC code. Finally, CRC-aided 5GNR-LDPC code is evaluated and the capacity-approaching capability is shown.

Optimization and Innovation of the Operation Model of Mobile Social E-commerce under AI Empowerment:Taking the Female Community Platform“Little Red Book”as an Example

This article takes the female community platform“Little Red Book”as an example to explore the optimization and innovation of mobile community e-commerce operation mode under Artificial Intelligence(AI)empowerment.Firstly,the relevant concepts were defined,and then the unique attributes of mobile community e-commerce were analyzed.As a typical representative of mobile community e-commerce,Little Red Book introduces the background and characteristics of its platform,analyzes its mobile community operation mode,and focuses on exploring how to establish a mobile community e-commerce platform and effective operation mode under the empowerment of AI technology,to provide some reference and inspiration for the development and operation of Little Red Book and other e-commerce platform enterprises.

Innovation and Practice of Teaching Methods in Digital and Adaptive Learning:Taking Communication Engineering Major as an Example

This paper proposes teaching reforms in communication engineering majors,emphasizing the implementation of digital and adaptive teaching methodologies,integrating emerging technologies,breaking free from the constraints of traditional education,and fostering high-caliber talents.The reform measures encompass fundamental data collection,recognition of individual characteristics,recommendation of adaptive learning resources,process-oriented teaching management,adaptive student guidance and early warning systems,personalized evaluation,and the construction of an integrated service platform.These measures,when combined,form a comprehensive system that is expected to enhance teaching quality and efficiency,and facilitate student development.

Fusion of Absolute and Recursive Information to Overcome Jitter and Occlusion in ARToolKit System

According to the most mature marker based augmented reality system ARToolKit only utilizes absolute information in pose estimation, a novel technique is presented in this paper. The proposed method embeds the recursive information as well to make ARToolKit system smoother by eliminating the jitter and more robust to occlusion conditions. Experiments on the jitter improvement has been performed, the results show that the proposed method is very effective.

Research on the Transformation and Development of Universities under the Combination of Science and Education and the Combination of Production and Education

The research on the transformation and development of colleges and universities in China involves the connotation,causes,target orientation,content and conditions,problems encountered in the transformation and development.Therefore,the author studies the transformation and development of Universities under the integration of education,industry and education.The research shows that the development of integration of general vocational education is the basic feature of Applied Undergraduate education,the development of integration of industry and education is the basic way of Applied Undergraduate education,and the development of integration of science and education is the basic law of Applied Undergraduate education.Colleges and universities must grasp the transformational development path of integration of science and education,integration of production and education,and clarify the orientation of running schools for the local economic development and the development of disciplines to connect regional industries.

Automated detection and identification of white-backed planthoppers in paddy fields using image processing

A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers （WBPHs, Sogatella furcifera （Horvath）） and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient （HOG） features and a support vector machine （SVM） classifier that was trained on Gabor and Local Binary Pattern （LBP） features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.

Characteristics and techniques of GaN-based micro-LEDs for application in next-generation display

Due to the excellent optoelectronic properties,fast response time,outstanding power efficiency and high stability,micro-LED plays an increasingly important role in the new generation of display technology compared with LCD and OLED display.This paper mainly introduces the preparation methods of the GaN-based micro-LED array,the optoelectronic characteristics,and several key technologies to achieve full-color display,such as transfer printing,color conversion by quantum dot and local strain engineering.

Digital Image Scrambling Technology Based on the Symmetry of Arnold Transform

An improved digital image scrambling method based on Arnold transform is proposed. The method can be used for the rectangle image by splitting rectangle image into several square images. Furthermore, a pretreatment is added to speeding up the process and enhancing the scrambling effect. The recovering of the scrambled image depends on the reverse Arnold transform that has the same cycle times with the Arnold transform. The recovering is Iossless and need not calculating the period of the Arnold transform. Finally, experimental results show the robustness of the method.

Fabrication and characterization of one-port surface acoustic wave resonators on semi-insulating GaN substrates

One-port surface acoustic wave resonators(SAWRs) are fabricated on semi-insulating high-quality bulk GaN and GaN film substrates, respectively. The semi-insulating GaN substrates are grown by hydride vapor phase epitaxy(HVPE)and doped with Fe. The anisotropy of Rayleigh propagation and the electromechanical coupling coefficient in Fe-doped GaN are investigated. The difference in resonance frequency between the SAWs between [1120] GaN and [1100] GaN is about 0.25% for the Rayleigh propagation mode, which is smaller than that of non-intentionally doped GaN film(~1%)reported in the literature. The electromechanical coupling coefficient of Fe-doped GaN is about 3.03%, which is higher than that of non-intentionally doped GaN film. The one-port SAWR fabricated on an 8-μm Fe-doped GaN/sapphire substrate has a quality factor of 2050, and that fabricated on Fe-doped bulk GaN has a quality factor as high as 3650. All of our results indicate that high-quality bulk GaN is a very promising material for application in SAW devices.

A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff systems.One of the most effective and critical steps in the fight against COVID-19,is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation imaging.In this paper,five keras-related deep learning models:ResNet50,InceptionResNetV2,Xception,transfer learning and pre-trained VGGNet16 is applied to formulate an classification-detection approaches of COVID-19.Two benchmark methods SVM(Support Vector Machine),CNN(Conventional Neural Networks)are provided to compare with the classification-detection approaches based on the performance indicators,i.e.,precision,recall,F1 scores,confusion matrix,classification accuracy and three types of AUC(Area Under Curve).The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84%and 75%,which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.

Accelerated carbonation and leaching behavior of the slag from iron and steel making industry

Ground granulated blast furnace slag （GGBFS） and steelmaking slag have been used as a raw material for cement production or as an aggregate to make concrete, which contribute aluminum, calcium, iron, and silicon oxides. The suitability of the slag for a particular application depends on its reactivity, cost, availability, and its influence on the properties of the resulting concrete. For the interest of durability studying of concrete in the presence of slag, the accelerated carbonation products and leaching behavior of the slag and Portland cement （PC） were studied. The experimental results confirmed that the slag was more resistant to carbonation compared to PC. The carbonation degree of GGBFS reduced by 17.74%; and the carbonation degrees of steelmaking slags reduced by 9.51%- 11.94%. Carbonation neutralized the alkaline nature of the hydrated pastes and gave rise to the redox potential of the leachate slightly （30-77 mV）. The carbonation also increased the release of most of the elements presented, except for calcium, to the aqueous environment. It is concluded that blend cements （PC plus slag） have economical advantages and better durability compared to PC,

Multi-modal Gesture Recognition using Integrated Model of Motion, Audio and Video

Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. Random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely.

Design and Realization of Phased Array Radar Optical Fiber Transmission System

One optical fiber transmission system is designed. The modularization optical fiber transmission adapters were utilized in the system, so the system structure could be flexibly sealable. The sub-array adapter and signal processor adapter were designed and realized utilizing the new field programmable gate array （FP- GA） which could drive the optical transceiver. The transmission agreement was designed based on the data stream. In order to solve the signal synchronization problem of the optical fiber transmitted phased array radar, a method named synchronous clock was designed. The fiber transmission error code rate of the system was zero with an experimental transmission velocity of 800 Mbit/s. The phased array radar system has detected the airplane target, thus validated the feasibility of the design method.