To explore the development direction and promotion path of computer teaching in colleges and universities in the new era

With the rapid development of information technology, computer teaching in colleges and universities is also progressing and developing. Nowadays, college computer teaching has formed a relatively perfect teaching system, covering two aspects of theoretical knowledge and practical operation. Teachers need to combine the development needs of students in knowledge, thinking and practice, strengthen basic teaching and practical operation teaching, and promote the combination of curriculum and innovation and entrepreneurship education. Based on this, this paper explores the development direction of computer teaching in colleges and universities in the new era with the author’s practical experience, and puts forward feasible ways to promote it, in order to provide references for colleagues

Research on teaching reform path of Computer Network security course in big data era

With the deepening of education reform, college computer network security teaching should be further optimized, teachers should actively introduce new education concepts, teaching methods, in order to better arouse students’ interest, strengthen their understanding of computer network security knowledge and application level, improve teaching eff ect. Big data technology, as a popular teaching assistant means, plays an important role in enriching the content of computer network security teaching in colleges and universities and broadening the path of education. In view of this, this paper will analyze the teaching reform of computer network security course in the era of big data, and put forward some strategies for your colleagues’ reference

Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. Th goal of this challenge is to upscale RAW Bayer images by 2x, considering unknown degradations such as noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. The performance of the top-5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in RAW Image Super-Resolution.Comment: CVPR 2024 - NTIRE Worksho

On the Importance of Random Education to Preschool Education

Because young children are young, their thinking and attention tend to show random characteristics when they are exposed to new things, and they are very vulnerable to the external environment and their own emotions. When children are attracted, their own thinking and awareness will be more active, their language expression will be more vivid, and the quality of learning can be better improved. Therefore, this article explores the specific ways of random education in early childhood education from multiple perspectives, hoping to enable children to achieve better development.</jats:p

Large Pretrained Foundation Model for Key Performance Indicator Multivariate Time Series Anomaly Detection

In the realm of Key Performance Indicator (KPI) anomaly detection, deep learning has emerged as a pivotal technology. Yet, the development of effective deep learning models is hindered by several challenges: scarce and complex labeled data, noise interference from data handling, the necessity to capture temporal dependencies in time series KPI data, and the complexity of multivariate data analysis. Despite recent progress in large models that show potential for handling complex, multidimensional tasks, the lack of extensive, high-quality datasets presents a significant barrier for directly training these models in KPI anomaly detection. This scarcity limits the models&#x0027; ability to learn and generalize effectively within this specific domain. To overcome this, we propose an innovative approach to adapt fully pretrained large models from other domains to KPI anomaly detection, thereby mitigating data constraints and enhancing detection precision. Our approach involves adapting large models to anomaly detection tasks using patch operations and fine-tuning techniques, which significantly enhances the model&#x0027;s temporal dependency capture capabilities. Furthermore, to address the multivariate challenge, we introduce a novel feature extraction method based on channel independence to optimize information processing across multidimensional features. Additionally, we leverage frequency domain information to design a feature enhancement method, further boosting the model&#x0027;s detection accuracy. By integrating these innovative techniques, we have developed a large-scale KPI anomaly detection model named ViTSD. Empirical evidence from experiments on five benchmark datasets and two additional datasets demonstrates ViTSD&#x0027;s superior performance, outperforming existing models across various evaluation metrics

Self-Supervised Learning-For Underwater Acoustic Signal Classification With Mixup

Underwater acoustic signal classification is a critical task that involves identifying different types of signals in a complex and dynamic underwater environment, which is often contaminated by strong ambient noise. Recent studies have demonstrated that deep learning-based methods can achieve remarkable performance in this task by leveraging large-scale labeled data. However, obtaining labeled data in real-world scenarios can be challenging due to the labor-intensive and expert-dependent nature of the labeling process, especially for underwater scenarios. In this study, we propose a novel self-supervised learning framework combined with mixup-based augmentation that can learn discriminative representations from large-scale unlabeled data, thereby reducing the dependence on labeled data. In addition, we propose a test time augmentation module to further improve the model&#x0027;s robustness. Our proposed approach achieves a classification accuracy of 86.33&#x0025; on the DeepShip dataset, surpassing previous competitive methods by a significant margin. Notably, our method demonstrates excellent generalization performance in few-shot scenarios and low signal-to-noise settings, highlighting its potential for practical applications

Laparoscopic aspirator bracket: A new instrument facilitating the aspiration and exposure of operative field simultaneously in laparoscopic nephron-sparing surgery

Abstract Background Laparoscopic nephron-sparing surgery (NSS) is the standard of care for small renal masses whenever feasible. This study aims to describe a novel laparoscopic aspirator bracket (LAB) and its use in laparoscopic NSS by a simple enucleation technique. Methods Between July 2017 and July 2019, we performed 54 laparoscopic NSS due to renal tumour using an SE technique by either LAB (n = 26) or laparoscopic aspirator (LA, n = 28) in 3 independent medical centres by 6 experienced surgeons. The novel aspirator could aspirate liquid and expose the operative field simultaneously. The details of operative technique are provided in the accompanying video. General characteristics and perioperative data of all patients were collected and retrospectively. Patients were followed up for 12 months after laparoscopic renal tumor simple enucleation (SE) operation. Renal function outcomes and perioperative complications were assessed. Results All 54 patients were successfully operated without conversion to open surgery. The use of the LAB in laparoscopic renal tumor SE operation can shorten the warm ischemia time ([WIT], LAB 12.92 ± 8.38 min vs. LA 17.89 ± 6.73 min) and increase the zero ischemia (ZI) rate (LAB 23.08% vs. LA 3.57%). In the one-year follow-up, the LAB group showed quicker renal function recovery (ipsilateral renal function 3 months after the surgery, LAB 41.67 ± 10.11% vs. LA 36.75 ± 10.30%). The limitations of this study were limited number of patients and retrospective attribute. Conclusion The new LAB could aspirate and expose the operative field with a single instrument. In operations that need to expose and aspirate simultaneously, such as in renal tumor simple enucleation, it could shorten WIT and improve the postoperative renal function recovery.</jats:p

Transurethral plasmakinetic resection of prostate in high-risk benign prostatic hyperplasia patients: a multicenter prospective study

Objective To investigate the clinical efficacy of transurethral plasmakinetic resection of prostate (TUPKP) in treating high-risk benign prostatic hyperplasia (BPH) patients. Methods A prospective multicenter study design was employed. Patients with high-risk BPH treated with TUPKP were enrolled in the urology departments of 20 hospitals nationwide according to the inclusion and exclusion criteria. Relevant data regarding patient baseline, perioperative period, and 3-month postoperative follow-up were analyzed to evaluate efficacy and safety. Results From September 2016 to December 2018, a total of 229 high-risk BPH patients were enrolled. Compared to baseline at the 3-month follow-up, the change in the International Prostate Symptom Score was -17.28[95%CI(-18.02, -16.54)], the change in maximum urinary flow rate was 5.61[95%CI(0.68, 10.54)]mL·s-1, the change in residual urine volume was -84.50 [95%CI(-96.49, -72.51)] mL, and the change in quality of life score was -3.24[95%CI(-3.42, -3.06)],all showing significant differences(