The problem with previous studies of social One Class Collaborative Ranking (OCCR) algorithms is that they simply integrated the user's social network information into their model, without taking into account the transmission of social trust networks between users. To solve this problem, a new social one class collaborative ranking recommendation algorithm (TrustOCCR) based on CLiMF model and the newest TrustMF model is proposed, which aims to improve the performance of social one class collaborative ranking recommendation by integrating twofold sparse data, i.e., implicit feedback data and the transitive social trust network data. Experimental results on practical dataset show that our proposed TrustOCCR algorithm outperformed existing state-of-the-art OCCF approach over di?erent evaluation metrics, and that the TrustOCCR algorithm possesses good expansibility and is suitable for processing big data in the ?eld of internet information recommendation. 

In the era of big data, it has become a consensus that various business departments can stimulate data value based on the accumulation of existing business data. However, due to the lack of uni?ed data standards across di?erent business systems, disorganized metadata, data silos, and low-quality data problems constantly emerge, hindering the e?ective utilization of data and necessitating necessary governance. Among them, data lineage analysis is one of the key tasks of metadata management, which is of great signi?cance for data traceability and data governance. However, traditional methods for constructing data lineage often face high computational complexity, poor accuracy, and high execution costs. To overcome these issues, a data lineage construction method based on the similarity calculation of data tables is proposed: by text feature representation of the three elements of data table naming, table structure, and data ?elds, using TFIDF to calculate the similarity of data tables, and further constructing the data table lineage relationship through the improved Jaro-Winkler Distances algorithm to verify the ?eld overlap and table name similarity. The results show that the algorithm has a signi?cant e?ect on the construction of data table lineage, facilitating the smooth progress of data governance work.

Power demand response is an important means to maintain the balance of power supply and demand in the peak period, and peak clipping is the main way of smart grid to achieve power demand response. In order to enable enterprises using hybrid ?ow-shop production to better participate in peak clipping demand response(PCDR) and optimize production scheduling, this paper introduces PCDR in the hybrid ?ow-shop scheduling problem, establishes a new problem model, and proposes an improved Sparrow Search Algorithm(ISSA) for model solving. Aiming at the problem that the standard SSA is prone to local optimization, the ISSA improves the local search ability of SSA by adding KMeans clustering replacement strategy. The experimental results show that the proposed model and algorithm can provide a better peak clipping production scheduling scheme, meeting the needs of enterprises to implement PCDR scheduling.

With the popularization of cloud computing technology and the development of containerization technology, Docker and SpringBoot have become indispensable tools for modern software development and deployment. However, this widespread use also comes with security risks. This article provides an in-depth analysis of the unauthorized access risks of Docker API and SpringBoot Actor. When these critical components are exposed to unauthorized access, attackers may exploit these vulnerabilities to perform malicious operations, such as deploying malicious containers, tampering with application con?gurations, or stealing sensitive information. These behaviors may not only lead to service interruption and data leakage, but also cause serious reputation and ?nancial losses to the enterprise. 

SDN adopts a centralized control method, achieving visualization and dynamism of network management, and is currently a new type of network architecture. However, at the beginning of SDN design, security issues were not considered. In the current situation where DDoS attacks are so prevalent, it is necessary to conduct research on attack detection under SDN architecture. After analyzing the SDN architecture mechanism, this article studies the location deployment of attack detection modules and designs a multi-level collaborative detection scheme for DDoS attacks. It designs an independent modeling scheme based on ensemble learning algorithms to address the problem of attack detection modeling. It extracts two sets of samples from the KDDCUP99 dataset without replacement, and uses SVM and adaBoost algorithms for independent modeling and sample combination modeling. The use of the adaBoost algorithm has a certain improvement in the classi?cation performance of samples, and modeling alone has no impact on the classi?cation performance of samples. It also shows higher classi?cation performance than the total samples on teardrop. The experimental results show that independent modeling for collaborative detection of forwarding plane and control plane has certain feasibility. This scheme starts from reducing the burden on the SDN centralized control center, providing a guarantee for e?ective network management, and has certain guiding signi?cance for attack detection of SDN architecture. 

With the large-scale application of various services, protocol adaptation and security issues are becoming increasingly prominent. It has become a focus of attention in related industries to establish an uni?ed mechanism for trust service adaptation, and improve the availability and security of service adaptation. In the construction of industry network trust systems, the combination of di?erent services together forms a complete network trust service system, and there are increasingly more scenarios for forming distributed service systems. The interaction between trust services is frequent, and directly transplanting traditional service adaptation mechanisms to the trust service system will greatly reduce interaction e?ciency and scalability. Therefore, a trust service adaptation method based on secure proxies is proposed, which mainly solves the technical problems of service adaptation usability, scalability, and security. This scheme can meet the adaptive changes in service status, improve the availability of service adaptation, while ensuring the identities of both parties in the interaction and ensuring data security during the interaction process. 

In recent years, network security is facing increasing challenges, among which Distributed Denial of Service (DDoS) attack is a common form of network threats. In order to deal with this challenge, this paper proposes a DDoS attack detection method based on Multi-scale Convolutional Neural Network (MSCNN). The model is trained on the CICDDoS2019day1 dataset, and the model detection performance is tested on the CICDDoS2019day2 dataset. By using MSCNN to predict and classify network tra?c, DDoS attacks can be e?ectively identi?ed and false positive rate can be reduced. Experiments show that the MSCNN method is superior to DNN, CNN and LSTM in terms of accuracy, recall and F1 score performance metrics.

In order to conduct scienti?c research and educational teaching on quantum computing, simulation tools for quantum computing have emerged. Vizard is a virtual reality development platform software that is suitable for building 3D models and can be quickly developed using Python language. A quantum bit visualization simulation model for candy boxes is proposed based on the Vizard platform. This model uses quantum state lollipops to describe the probability amplitude of quantum bits. The candy radius represents the modulus and the angle between the candy stick and the horizontal plane represents the auxiliary angle. Opening the box lid causes the quantum state lollipop to collapse. Using quantum state lollipops, quantum gate components are simulated and quantum circuits are constructed using quantum gate components. The candy box model is easier to describe the superposition state of quantum bits than traditional Bloch balls, and it is interesting and easy to understand, suitable for quantum technology beginners and teenagers to popularize quantum technology education.

This device uses STM32F407 as the core to achieve waveform recognition and parameter measurement of periodic signals. The device consists of a core calculation module, buttons, display, signal conditioning circuit, and zero crossing detection circuit. It can achieve multi-channel gain sampling of periodic signals, measure parameters such as peak to peak value and frequency of waveforms, and identify waveform types such as sine waves, square waves, and triangular waves. This system achieves precise measurement of a wide range of voltage through a multi gain signal conditioning circuit, precise measurement of frequency through a hardware zero crossing circuit, real-time waveform acquisition through frequency calculation of sampling frequency, and recognition of waveform types through model matching. It has the advantages of wide voltage and frequency measurement range, high measurement accuracy, accurate waveform recognition, and fast response speed.

With the rapid development of industrialization and urbanization, water quality safety issues have attracted increasing attention. This study uses a water quality analysis data set containing 7999 data records, covering various chemical substance concentration measurements and safety thresholds, as well as "safe or not" classi?cation variables, and uses the Extra Tree Classi?er model to conduct water quality safety modeling prediction and data analysis. The purpose of this study is to provide a reliable model to help policymakers and relevant departments better monitor and maintain water quality safety, thereby ensuring public health and environmental sustainability.

In order to solve the problem of false information and duplication in the summarizations generated by the T5 PEGASUS model, a text summarization model based on T5 PEGASUS and DeepKE - T5 PEGASUS-DK is proposed. This model combines the T5 PEGASUS model with DeepKE framework. Firstly, the Pkuseg segmentation method is used to improve the segmentation performance. Then, the DeepKE framework is used to extract triads from text. Finally, the word vector set of triads is concatenated with the representation vector of text. By establishing a mapping relationship between text and triads, the model can extract factual knowledge and extract information that is more consistent with the original content as a summary. The experimental results show that the T5 PEGASUS-DK model has the highest ROUGE value, and the generated abstracts are more authentic, coherent, and consistent with the original content.

Due to the in?uence of earthquakes, groundwater levels can undergo abnormal changes. The prediction study of water level change has important theoretical and practical signi?cance for the analysis of possible earthquake precursors and the mitigation of secondary disasters. In this study, the earthquake selected occurred on April 13, 2015, at 10:28 a.m., in Jianshui County, Honghe Hani and Yi Autonomous Prefecture, Yunnan Province. By obtaining groundwater level data from observation wells in the vicinity of the earthquake, the dataset is divided based on the theoretical concepts of earthquake active and non-active periods. Subsequently, an LSTM model is employed for training. The di?erences between the predicted values and the actual values are observed. Experimental results indicate that the model can identify abnormal changes in groundwater levels caused by earthquakes, providing valuable insights into studying groundwater level anomalies as earthquake precursors. 

Aiming at the accidents caused by household gas leakage, such as human poisoning, ?re and explosion, this paper proposes the design and implementation of home kitchen environment remote monitoring system based on cloud platform technology. The system uses STM32 chip as the core processor, analyzes the real-time data collected by the sensor, compares the preset threshold for strategic control, and realizes the intelligent control of the gas electronic valve, alarm and intelligent window in the kitchen. And the data is regularly uploaded to the cloud platform for storage through the ESP8266 communication module, and the storage data of the cloud platform is invoked by the mobile terminal in real time to carry out remote dual control with human intervention on the kitchen state to realize reliable and safe management. 

The service perception experience of high-speed rail network users is signi?cantly impacted by network issues arising from frequency cutting, Doppler e?ect, and carriage penetration loss in the context of a high-speed railway private network. This article ?rstly summarizes the network problems existing in the current high-speed rail private network. Then, starting from the solution strategies for coverage problems, digital twin technology is applied to the planning and construction of high-speed rail private network sites. At the same time, an innovative edge beam collaborative rolling scheme is proposed to enhance the coverage quality of the high-speed rail private network in response to the problem of "dark under the light" at the site. In addition, the paper presents an optimization strategy for achieving super cell overlapping coverage based on CPID, and provides a comprehensive summary of antenna selection schemes tailored to di?erent scenarios along the railway. Finally, this paper presents relevant optimization solutions to address the frequency o?set caused by the Doppler e?ect, mutual interference resulting from di?erences in public-private network coordination, and the perception of low network speed among high-speed rail users. Leveraging the VMAX big data monitoring platform, combined with user tracking and high-speed rail line trajectory maps, precise identi?cation and optimization of high-frequency private network users leaving private network communities have been carried out, e?ectively improving the network perception of high-speed rail users. 

This paper analyzes the focus and relevance of traditional theoretical written exams and emerging online evaluation exams for programming courses in applied universities. Each exam mode focuses on di?erent abilities and can only re?ect one aspect of student capabilities. To adapt to the new engineering education concept and the needs of applied talent training, this paper proposes and practices a blended examination mode and designs a comprehensive exam system for it. Practice shows that this blended examination mode can better re?ect students’ comprehensive mastery of programming course knowledge, and the exam system provides strong support for the implementation of the blended examination mode.

Under the guidance of the concept of engineering education certi?cation, this paper analyzes the current situation and problems in the teaching of Information Theory and Coding, and explores the online and o?ine mixed teaching mode based on “BOPPPS” teaching model from the aspects of clarifying theoretical basis, clarifying teaching objectives, reconstructing teaching content and establishing evaluation system, etc., so as to improve students' autonomous learning ability. At the same time of professional education, we should do a good job in value guidance, and improve the e?ect of course education from professional quality, innovative thinking and comprehensive quality.