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关键词： AI-enabled   Simulation approach   Learning strategies   Innovation breakthrough   Innovation performance   C63   C88   O32   O36  

摘要： With the rapid development of AI technology, individual learning strategies are undergoing profound changes. This study aims to investigate how AI-enabled individual learning strategies impact scientific innovation breakthrough. Addressing the limitations of existing research methods in dealing with this issue, we developed a simulation approach named AI-EEM and validated the theory that the trade-off between exploration and exploitation is crucial for innovation breakthrough using actual publication data from scientists. The results indicate that the AI-EEM model can effectively identify the optimal strategies at different levels of exploration and exploitation. Moreover, this study analyzes the interaction between AI technology and individual learning strategies on the degree of scientific innovation breakthroughs. We find that AI-enabled individual learning strategies may reshape the traditional balance between exploration and exploitation. Specifically, as the level of exploration increases, there is a significant linear positive correlation with the degree of innovation breakthroughs. However, as the level of exploitation increases, the enhancing effect of AI-enabled individual learning strategies on innovation breakthrough diminishes. Therefore, we recommend that scientific researchers prioritize exploratory learning when leveraging AI technology to enhance innovation, in order to fully utilize the opportunities provided by AI and achieve a higher degree of innovation breakthroughs.

关键词： Latches   Inverters   Electronic mail   Feeds   Transistors   Transient analysis   Logic gates   Delays   Very large scale integration   Computer science   Circuit robustness   radiation hardness   single-event transient   soft error   triple node upset  

摘要： In nanoscale CMOS technology, harsh radiations in the environment can now easily cause soft errors, e.g., single-event transients (SETs) and triple node upsets (TNUs), severely affecting the reliability of space applications. In this article, TNUs tolerant and SET-pulses filtered latch (TUTPFL) with low power is proposed, which comprises from four input-stage C-elements (CEs), four inverters, and three output-stage CEs. The CEs' delay differential enables the TUTPFL latch to effectively filter SET-pulse, while the CEs' multilevel error-interception property enables the TUTPFL latch to tolerate any possible TNU. The results of HSPICE-based simulations and FPGA-based emulations demonstrate the TNU tolerance and SET filterability of the TUTPFL latch. Meanwhile, compared to the alternative radiation-hardened latches, the TUTPFL latch reduces power dissipation by roughly 20.43% on average.

关键词： technology   preschool   prediction   computer science   education  

摘要： Featured Application Machine learning algorithms applied to accelerometer data can enhance physical activity and sleep pattern assessment in preschool children, enabling more accurate and automated monitoring for educational and health *** This systematic review evaluated machine learning applications for analysing physical activity and behaviour in preschool children using accelerometer data. Following the PRISMA guidelines, we systematically searched PubMed, FECYT, and ProQuest Central databases. Fourteen studies implementing machine learning approaches for preschool accelerometry data were identified and assessed using the MINORS scale. Studies focused on two primary domains: physical activity analysis (n = 10) and sleep monitoring (n = 4). The ActiGraph GT3X+ was predominantly used, with placement varying between the hip and wrist. Random Forest algorithms proved most effective, achieving accuracy rates up to 86.4% in activity classification and 96.2% in sleep prediction. Sampling frequencies (0.25-100 Hz) and epoch lengths (1-60 s) varied considerably across studies. Machine learning applications show promising results for preschool physical activity assessment. However, small sample sizes and methodological inconsistencies limit generalizability. Future research should prioritise larger cohorts, explore multiple sensor integrations, and develop standardised protocols to enhance practical applications.

关键词： Computer science   Applied computing  

摘要： Spiking neural networks (SNNs) inspired by biological neurons offer energy-efficient and interpretable computation but are limited by the simplistic structure of point neurons. We introduce a multi-compartment spiking neuron model (MCN) with trainable cross-compartment connections that simulate soma-dendrite interactions. Theoretically, we prove that these connections act as spatio-temporal momentum, guiding learning dynamics toward global optima. To leverage this, we propose a Multi-Compartment Spatio-Temporal Backpropagation (MCST-BP) algorithm that enhances gradient flow stability. Experimental results for multiple benchmark datasets, including S-MNIST, CIFAR-10, SHD, and ECG, show that MC-SNNs outperform traditional SNNs in both convergence speed and accuracy. Our work bridges neurobiological structure and computational modeling, providing a theoretical and practical foundation for high-performance, brain-inspired learning systems.

关键词： 大语言模型   地方高校   计算机专业人才  

摘要： 大语言模型技术的发展推动着教育领域的变革。计算机专业与大语言模型技术紧密相连,传统人才培养模式面临严峻考验。地方高校作为服务地方发展的重要力量,如何主动适应大语言模型技术成为一项紧迫课题。文章分析了产业界对计算机专业人才需求的变化,列举了可行对策,为地方高校计算机专业人才培养提供了有益参考。

关键词： 融媒体   课程思政   计算机   双创人才培养  

摘要： 双创人才培养是国家战略发展的需要,课程思政是新时代教育发展对高等学校的根本要求。本研究在马克思关于人的全面发展的理论、加德纳的多元智力理论、创造教育理论及思想政治教育相关理论的指导下,利用融媒体技术,探究将隐性思政教育渗透计算机专业双创人才培养的教学模式和实践策略,以切实提高应用型高校计算机专业学生的创新创业能力,推进思想政治教育的深入开展,促进学生综合素质的全面发展。

关键词： 双体实训   人才培养模式   应用型高校   高质量就业   计算机专业  

摘要： 近10年来,受互联网大潮的影响,高校广泛开设计算机专业,毕业生增多。但随着人工智能普及,行业用人需求波动,且国内院校人才培养模式与市场脱节,致使该专业毕业生就业难。本研究以重庆移通学院计算机专业为例,对比分析近5年国内外高校计算机相关专业人才培养特征,对“双体实训”人才培养模式展开实践探索,旨在借鉴国内外经验,探析就业难题,寻找计算机专业人才高质量就业的有效路径,为推动计算机专业教育发展与提升就业质量提供有益参考。

关键词： AI   人机协同   《生态学》思维   教学改革  

摘要： 针对草业科学专业《生态学》课程教学中存在的学生专注力易分散、知识掌握碎片化及评价方式单一等问题,本研究旨在探索并构建一种基于人工智能(AI)的人机协同教学模式,以提升教学质量与学生综合能力。本研究整合课前、课中、课后全流程,设计了五项核心改革措施:(1)利用AI虚拟仿真实验平台模拟关键生态过程,强化沉浸式学习体验。(2)应用机器学习算法分析学情,支撑个性化学习路径设计,优化知识获取。(3)引入课堂行为监测系统,有效减少学生手机非学习使用时长。(4)设计跨学科开放探究任务,促进学生综合问题解决能力发展。(5)构建“数据-问题”双驱动评价体系,融合标准化测试与实践应用评估。实施该人机协同教学模式后,显著增强了学生在《生态学》领域的系统思维能力与技术应用能力。教学模式为学生能力提升提供了可量化依据,为农林院校相关课程改革提供了有效路径。同时,针对技术依赖性与数据隐私等潜在挑战,研究提出了分层任务设计、明确伦理规范及加强师资培训等应对策略,以确保模式的可持续健康发展。

关键词： Wireless fidelity   Location awareness   Fingerprint recognition   Object recognition   Accuracy   Telecommunications   Internet of Things   Indoor environment   Computer science   User experience   Fingerprinting   indoor localization   media access control (MAC) de-randomization   user identification   Wi-Fi mobile single station (MSS)  

摘要： The utilization of Wi-Fi-based technology for pervasive indoor user identification has gained prominence due to its cost-effective nature and compatibility with user devices. Previous works proposed capturing the media access control (MAC) address emitted from a user's device and using information element (IE)-based MAC de-randomization methods to mitigate the impairment caused by random MAC. However, IE types of different Wi-Fi devices are not consistently differentiated, leading to identification errors in IE-based methods. Additionally, typical Wi-Fi fingerprinting approaches require densely predeployed Wi-Fi stations, contradicting the principle of pervasive localization. To address these challenges, we propose the mobile single-station-based user identification (***) technique, which leverages Wi-Fi mobile single stations for pervasive indoor user identification. *** includes mobile single-station localization (MSL) and MAC de-randomization based on users' spatiotemporal location and IE information (***). MSL can be implemented on a standard mobile Wi-Fi station without extensive predeployment. *** performs MAC de-randomization using the LIC algorithm to identify users with random MAC addresses. Experimental results demonstrate that *** outperforms previous IE-based user identification methods and multistation localization techniques. MSL achieves a localization error of 1.15 m which is better than multistation with 12 APs of 1.40 m. *** demonstrates an identification accuracy of 95.24% which is better than AIMAC of 85.48%.